ICES Journal of Marine Science: Journal du Conseil

Couturier, Lydie I, E;Michel, Loïc, N;Amaro,, Teresa;Budge, Suzanne, M;da Costa,, Elisabete;, De Troch, Marleen;, Di Dato, Valeria;Fink,, Patrick;Giraldo,, Carolina;Le Grand,, Fabienne;Loaiza,, Iván;Mathieu-Resuge,, Margaux;Nichols, Peter, D;Parrish, Christopher, C;Sardenne,, Fany;Vagner,, Marie;Pernet,, Fabrice;Soudant,, Philippe

doi: 10.1093/icesjms/fsaa121pmid: N/A

Abstract Determining the lipid content and fatty acid (FA) composition of aquatic organisms has been of major interest in trophic ecology, aquaculture, and nutrition for over half a century. Although protocols for lipid analysis are well-described, their application to aquatic sciences often requires modifications to adapt to field conditions and to sample type. Here, we present the current state of knowledge of methods dedicated to both marine and freshwater lipid analyses, from sampling to data treatment. We review: (i) sample preservation, storage and transport protocols, and their effects on lipids, (ii) lipid extraction, separation of polar and neutral lipids, derivatization, and detection methods, and (iii) available tools for the statistical analysis of FA data. We provide recommendations for best practices in field situations and advocate for protocol standardization and interlaboratory calibration. Introduction Lipids and their fatty acids (FA) are ubiquitous components of all living organisms. They are essential compounds for the formation of cell and tissue membranes, act as major sources of metabolic energy, provide thermal insulation, and serve as chemical messengers. FA generally consist of a linear hydrocarbon chain with a carboxyl group (–COOH) at its end. They are characterized by the length of their carbon chain, generally from 14 to 24 (up to 36) atoms, as well as by the position and the number of double bonds (usually from 0 to 6) that determine their degree of unsaturation. FA are constituents of lipids such as wax and sterol esters (WE, SE), triacylglycerols (TAG), diacylglycerols (DAG), monoacylglycerols (MAG), glycolipids, and phospholipids (PL), which show different levels of regulation from ingestion to incorporation. One or several FA are linked either with an ester (or ether/vinyl ether) bond to an alcohol, mainly glycerol, or with an amide linkage to a long-chain nitrogen base, mainly sphingosine. All of these molecules are categorized as either neutral lipids or polar lipids, with reference to chromatographic methods of lipid class separation but also to their metabolic roles (Christie, 1984). Among neutral lipids, FA-containing compounds include TAG and WE, which are the main storage lipids, whereas DAG, free FA (FFA), and MAG are generally minor constituents. Sterols (both in their free and esterified forms) and fatty alcohols (ALC) are also found in neutral lipids. FFA are rarely present in living organisms, as they may be toxic in high concentrations (Jüttner, 2001). Polar lipids include PL, glycolipids, and sphingolipids. Polar lipids are characterized by a hydrophilic polar head group, which, in contrast to their hydrophobic aliphatic chains, allows for the formation of lipid bilayers found in cell membranes. Polar lipid properties are determined by the nature of the polar head groups, the nature of the linkage between the polar head group and aliphatic chain, the length of the carbon chain, and the position and the number of double bonds within the FA. These different properties influence the functioning of membrane receptors, the fluidity of membranes, and the stability of the bilayer structure (Hazel and Williams, 1990). FA have been analysed in aquatic sciences for over 50 years (Ackman et al., 1968) with the first evidence that FA composition of consumers resembles that of its food (Lee et al., 1971). Over the next 30 years, the concept of FA trophic markers (FATM) gained in popularity in aquatic trophic ecology (Dalsgaard et al., 2003). According to this concept, FA composition of some primary producers is characterized by specific compounds that can be transferred to upper trophic levels (e.g. Kelly and Scheibling, 2012). The FA composition of a consumer is therefore expected to reflect the FA composition of its food sources (“you are what you eat” principle). FA have accordingly been used to study energy flow in food webs in freshwater, estuarine, coastal, and deep-sea environments in all latitudes and to examine functional responses to stressors (Kelly and Scheibling, 2012). More recently, the FATM concept has culminated in quantitative FA-based diet modelling (Iverson et al., 2004; Galloway et al., 2014; Bromaghin et al., 2017), and recent methodological developments allow for accurate estimation of prey contribution to consumer diet (Litmanen et al., 2020). However, diet modelling is based on the fact that aquatic consumers generally lack the ability to synthesize long-chain polyunsaturated FA (LC-PUFA, ≥C20) and have limited capacity for the bioconversion of short-chain PUFA into long-chain PUFA (Castell et al., 1972; Langdon and Waldock, 1981; Sargent et al., 1999; Taipale et al., 2011). This view has recently been challenged (Kabeya et al., 2018). Diet is not the only determinant of the FA composition of aquatic consumers. FA composition varies with intrinsic factors such as phylogeny and developmental/reproductive stages (e.g. Galloway and Winder, 2015) and extrinsic factors such as temperature, salinity, and hydrostatic pressure (e.g. Wodtke, 1981; Cossins and Macdonald, 1989; Hazel and Williams, 1990), thus providing information about physiological condition and habitat type of aquatic organisms (e.g. Meyer et al., 2019). The analysis of the FA composition of aquatic organisms is based on procedures originally developed for applications in food science and medicine. Briefly, once a sample is collected, it is immediately stored in the dark at the lowest possible temperature for the shortest period of time with added antioxidants, and under a nitrogen atmosphere or vacuum. This is to avoid alteration caused by enzymes, heat, oxygen, and light. Lipids are then extracted, and lipid classes may be separated using chromatography. Next, derivatization of FA is performed (methylation in most cases) and FA composition is determined by gas chromatography (GC). Finally, data are treated to obtain the quantitative and qualitative FA profiles of the sample (Figure 1). Figure 1. Open in new tabDownload slide Workflow for the analysis of FA in aquatic samples. The star symbol indicates recommended methods. Dashed arrows point to suggested storage precautions after each analytical step. Transport requirements are colour-coded, and the coldest transport temperature is always the best option regardless of the step. The grey syringe symbol represents the step at which the internal standard for recovery and purity should be added. The black syringe symbol represents the step at which the internal standard for quantification should be added. Figure 1. Open in new tabDownload slide Workflow for the analysis of FA in aquatic samples. The star symbol indicates recommended methods. Dashed arrows point to suggested storage precautions after each analytical step. Transport requirements are colour-coded, and the coldest transport temperature is always the best option regardless of the step. The grey syringe symbol represents the step at which the internal standard for recovery and purity should be added. The black syringe symbol represents the step at which the internal standard for quantification should be added. There are likely to be as many variations in procedures as there are laboratories (Parrish, 1999). Yet, not all may be applicable to aquatic samples. Procedures involved in sample collection, handling, and storage, as well as extraction and transesterification steps, can cause biochemical modifications in FA and differential loss, leading to false or misleading interpretations. Identifying the most appropriate protocols and the best practices in FA analysis of aquatic organisms may prove to be a difficult task for novices, as available literature is widespread and highly diverse, and many studies do not fully describe their methodology (e.g. “modified Folch extraction”). Our objective is to provide an up-to-date review of the procedures used for a large number and variety of aquatic samples and laboratories around the world. This review emerged from technical workshops of the conference “Lipids in the oceans” held in Brest (France) in November 2018. We answer practical questions that are not necessarily formulated in papers and textbooks but essential to obtain a reliable FA profile. For example, which organ/tissue of particular specimens should be analysed to be representative in the context of trophic ecology? How are samples properly collected, transported, or shipped? What are the most appropriate extraction/transesterification methods or solvent systems? How can one ensure reliable FA identification and quantification? How are FA data analysed? This article aims to guide both novice and advanced scientists in identifying the most appropriate protocols and best practices for FA analysis of aquatic samples. We hope that this comparative synthesis will stimulate further international collaborations and facilitate the standardization of lipid analyses. Sample handling and storage before lipid extraction The choice of biological material, its collection, and handling techniques, as well as storage methods, are among the most important steps in lipid analysis (Figure 1). Lipid degradation can occur by hydrolysis (release of FFA from acyl lipids through the enzymatic activity of lipases) or oxidation of carbon–carbon double bonds. In view of the labile nature of lipids, these processes often occur very rapidly, leading to analytical errors and increased sample variability (Christie, 1984). Handling and storage conditions and methods can help minimize sample degradation and analytical bias. Ideally, lipids should be extracted immediately after collection. The second-best option is to flash-freeze samples in liquid nitrogen immediately after collection. As sampling of aquatic organisms is often performed in the field and in remote areas, optimal conditions can rarely be achieved, but samples need to be stored adequately. A broad set of procedures exists, including: (i) sample maintenance on ice, (ii) frozen sample storage from −18°C (standard freezer) to −80°C (laboratory freezers) and/or to −196°C (liquid nitrogen), (iii) freeze drying of samples, or (iv) sample storage in organic solvents for varying storage periods, from a few hours to years prior to analysis. Adequate storage conditions will prevent (or at least limit) the degradation of lipids through hydrolysis and oxidation processes induced by oxygen, temperature, light, and/or enzymatic processes (Rudy et al., 2016). The rate of oxidation and hydrolysis processes (hereafter referred to as “lipid degradation”) is likely to vary greatly across species and tissue types. These processes can be revealed by an increase in FFA and a decrease in glycerolipids and PUFA in samples (Christie, 1984, see section Indicators of sample degradation). Here, we provide an overview of different methods used in the literature and discuss their strengths and limitations. Sample type/sampling strategy Aquatic samples are highly diverse, ranging from bacteria to whales, and sampling procedures need to be adapted to the nature of the studied organisms/functional groups. Samples of seston and phytoplankton are generally collected through water filtration on pre-combusted glass microfiber filters (GF/C or GF/F), while zooplankton can be sorted manually. Such small organisms or functional groups are often directly extracted as whole organisms and stored in solvent. For other organisms such as larger invertebrates, fish, and aquatic mammals, tissue dissection or biopsy is usually required. In the case of multi-purpose samples, it is possible to grind them under liquid nitrogen and subsample the powder (Soudant et al., 1996). In the case of a quantitative study where the mass of lipid or FA is reported per gram of sample, the sample will have to be weighed before or after storage. Sample mass can be measured from fresh or dry tissue. In the latter case, the dry mass may be measured on a dedicated subsample. All material should be lipid-cleaned prior any sampling to avoid cross-contamination (see section Handling cautions). Because it is not possible to describe all sampling strategies here, we highly recommend that any study involving lipid and FA analysis must start with a literature review of available information on targeted organisms or on species that are phylogenetically comparable. Sample handling Ideally, samples should be immediately flash-frozen in liquid N2 in the field and transported to a freezer using a dry shipper. However, maintaining samples on ice is often the first and only practical option available to limit lipid degradation between collection and further processing. Whenever possible, long-lasting dry ice is preferable to water ice. Crushed ice is useful for transportation between locations such as sample transport from the ocean (e.g. small and large vessels) to land, or transport between laboratories. Crushed ice should preferably be used only for a few hours, as this method only slows, but does not stop, lipid and FA degradation (Wood and Hintz, 1971; Chaijan et al., 2006), and samples should therefore be frozen as soon as possible. As an alternative, deep-frozen (i.e. −80°C), long-lasting ice packs are a convenient option for short-term storage in the field (e.g. TechniIce™). These ice packs can maintain temperatures around 0°C for a few hours in a well-insulated container (L. Couturier, pers. obs.). In the particular case of studies on thermal acclimation or adaptation, it may be preferable to maintain samples at environment temperature rather than to cool them before freezing as it may induce a short-term cold response. Freezing/thawing samples more than once should be avoided. Storage Frozen storage (−20°C to −196°C) Storing samples at cryogenic temperature is likely the most common method of preservation before performing FA analyses. Yet, depending on the temperature, storage duration, and type of biological material, this may not be sufficient to limit lipid degradation. It can lead to increased FFA and products of oxidation (e.g. Christie, 1984; Gullian-Klanian et al., 2017; Tenyang et al., 2019). Lower freezing temperatures and short-term storage are generally associated with lower lipid degradation (e.g. Baron et al., 2007; Rudy et al., 2016; Gullian-Klanian et al., 2017). The level of lipid degradation in samples is highly dependent on initial freezing temperature and temperature variation during storage, as well as storage duration prior to analyses (Han and Liston, 1987; Ramalhosa et al., 2012; Romotowska et al., 2017). When long-term storage is required, the safest approach is to freeze samples at −80°C immediately after collection. Although not ideal, long-term storage at −20°C can be suitable for some species and sample/tissue types, in particular when samples are kept frozen within the organic extraction solvent. Levels of n-3 and n-6 PUFA in fish muscle, stored at −30°C and −80°C, did not change for up to 12 months in both TAG and phospholipid fractions (Passi et al., 2005). Shark muscle samples archived for up to 16 years at −20°C retained their FA profiles, while lipid class composition was not analysed (Meyer et al. 2017). Impacts of frozen storage on FA content and composition are species specific and can depend on the total lipid content of the organism and tissue (Rudy et al., 2016). Species with high total lipid content (>10% wet weight) must be treated with increased care (low temperature and short storage period) to avoid changes in FA content during freezing (Rudy et al., 2016; Sardenne et al., 2019). Based on the literature cited, samples should be stored for <6 months prior to analysis at temperature <−20°C when no information is available on the collected biological material. Note that lipid analysis is also possible for samples stored using brine immersion freezing technique (e.g. fisheries-issued material) (Bodin et al., 2014). The sensitivity of lipids to storage varies according to species and tissues. We therefore recommend evaluating the sensitivity of the samples for which there is no information available before the start of the study. For example, a temporal monitoring of the lipid composition of the biological material of interest at the available storage temperatures will make it possible to define the optimal conservation strategy. Freeze drying Freeze drying (or lyophilization) is a low temperature dehydration process of frozen material that is used to reduce enzymatic activity and thus lipid degradation in samples. Freeze-drying samples immediately prior to extraction do not affect lipid class or FA compositions of aquatic animal tissues (e.g. Dunstan et al., 1993; Murphy et al., 2003; Sardenne et al., 2019). This can be a good alternative to frozen storage, as freeze-dried tissues can be easier to grind and to transport than frozen tissues. Rehydration may be required to improve the lipid extraction of TAG (e.g. Dunstan et al., 1993) and to comply with extraction procedures that rely on a particular proportion of water in the sample. Nonetheless, freeze-dried tissues still need to be carefully stored to prevent lipid degradation. Storage duration can have detrimental effects on lipid composition of samples. In fish, crustacean and molluscs, the total duration between freeze drying and lipid extraction should not exceed 1 month at −20°C for quantitative analysis (Sardenne et al., 2019). Similarly, freeze-dried microalgae samples should not be stored for extended period of time. The PUFA proportion of filtered Isochrysis galbana biomass decreased from 36 to 26% after 3 months storage at −76°C (Babarro et al., 2001). Proportions of FFA in freeze-dried Phaeodactylum tricornutum increased from 4 to 6 mg/g DW after 35 days at −20°C while the degree of oxidation was unaffected (Ryckebosch et al., 2011). Variability in freeze-dried sample stability/viability for lipid analysis is likely to reflect differences of biological material and length/duration of the freeze-drying process. Overall, the quality of the freeze-dried storage depends on: (i) the storage temperature (lower temperatures are the best), (ii) the storage duration (shorter durations are better), and (iii) the lipid content of samples (lean samples are conserved better than lipid-rich samples). Lipid oxidation in food products occurs more rapidly when water activity is high (Labuza et al., 1972). Conversely, lowering water activity below 0.2 through freeze-drying process may be a factor promoting lipid degradation through oxidation (Labuza et al., 1972), and specific testing should be considered for aquatic samples. Freeze drying may offer some benefits when used as a short-term preservation method (e.g. during transport), but it can be expensive (necessary equipment) and time-consuming, while potentially shortening the viability of samples for lipid analyses. Solvent and fixative storage Organic solvent. One common storage strategy is to directly immerse small organisms such as phytoplankton collected on filters or small-bodied zooplankton into the lipid extraction solvent (e.g. Marty et al., 1992; Soudant et al., 1998; Windisch and Fink, 2018). If samples in the extraction solvent (generally chloroform CHCl3 and methanol MeOH) are kept in the dark and at low (preferably freezing) temperatures, lipid degradation is minimal. To our knowledge, maximum storage duration of extracted lipids has not been formally investigated. However, samples are likely to remain useable for months to years. For microalgae samples, it is advised to deactivate lipases by a rapid addition of boiling water or isopropanol prior to extraction (Berge et al., 1995; Budge and Parrish, 1999; Ryckebosch et al., 2012). Synthetic antioxidants (0.01% butylated hydroxytoluene, BHT, or 0.1% tertiary butylhydroquinone, TBHQ) can be added to solvent-stored samples to reduce radical formation and oxidative losses of unsaturated lipids (Christie, 2003; Ryckebosch et al., 2012). However, addition of synthetic antioxidant is unnecessary in samples containing large amount of natural antioxidants such as microalgae (Ryckebosch et al., 2012). Note that BHT elute with short-chain FA and when present in large quantities, it can overlap with FA of interest. Fixatives. Fixatives such as ethanol or formalin are commonly used for biodiversity, taxonomy, morphological, or DNA analyses. The use of fixed samples opens new research opportunities, by making it possible to analyse historical samples collected for purposes other than lipid analysis. However, fixatives influence the chemical composition of the samples and should therefore not be used as a routine procedure for lipid analyses. For example, FA composition of crustacean tissues is altered in formalin-preserved samples, but not in ethanol-preserved samples (Phleger et al., 2001). Direct evaluation of fixative effects on lipids is scarce, and preliminary tests on different sample types are highly recommended. Transport and shipping The packaging, transport, and shipping of samples for lipid analysis is a complex process, driven by safety laws, financial costs, stringent storage conditions, and limited duration of storage. Specific legal regulations, including quarantine, must be followed and they can vary, depending on whether the samples are shipped domestically or internationally, and whether there are stopovers during long distance travels. Shipping recommendations to ensure that samples arrive at their destination in good condition include triple packaging with a rigid outer layer and absorbent material, appropriate cooling, accurate labelling (i.e. name, address, phone number, e-mail address, size and weight of the package), and complete legal documentation (e.g. itemized list of content, permits) (see International Air Transport Association IATA recommendations). This section reviews different options of packaging for shipping and travelling and gives an overview of the main flying regulations and key legal information. Shipping samples Long-distance shipping from the sampling site to the laboratory is challenging. Multiple issues need consideration, such as the conservation state of the samples prior to transport (cooled, frozen, deep frozen, freeze dried), the distance, the duration, and the type of transport. As samples are generally prone to degradation, temperatures must be kept low and oxidative conditions reduced as much as possible. Regardless of the method used, we recommend only to ship a part of the original sample and keep a backup sample when possible and to take great care in the packaging (minimum empty space, adapted container to accommodate samples, and cooling methods). When possible, frozen samples should be shipped using dry shippers, which are aluminium containers flooded with liquid nitrogen. They contain adsorbent material that prevents spills of liquid nitrogen during shipment and have attached temperature loggers. Samples can be stored in these conditions for several weeks. However, dry shippers may not be readily available at sampling locations or in laboratories. Restrictions for the transport of liquid nitrogen also need to be considered (e.g. in aeroplanes or closed vehicles). With transporter authorization, frozen samples can also be shipped on dry ice depending on transport duration (about 24 h for 2.5 kg of dry ice). Alternatively, deep-frozen (−80°C) aqueous ice packs or reusable technical ice packs (e.g. TechniIce™, 2019) can be used. A cost-effective option is to freeze-dry samples prior to shipping, as it reduces both the volume to be shipped and the risk of lipid hydrolysis by removing water (see section Sample handling and storage before lipid extraction). When dry ice packaging or deep-frozen ice packs are not available at the sampling location, freeze-dried samples should be kept cool and in an oxygen-free atmosphere by flushing containers with nitrogen gas and then sealing prior to packaging and shipping. It is also possible to ship samples with small amounts of solvents with respect to international regulations. Note, however, that depressurization in aircrafts promotes solvent leakage (F. Pernet, pers. obs.). To limit this phenomenon, tubes and caps can be coated with Teflon. Alternatively, preserved and/or extracted samples in solvent may be evaporated and flushed with nitrogen so that dried extracts can be air-transported as frozen biological material. Dried lipid extracts will have to be re-immersed in solvent as soon as possible upon arrival. We, however, recommend to perform preliminary testing before proceeding with this method. The gold standard for shipping samples are specialized transport companies that maintain samples on dry ice and guarantee express delivery to the final destination. However, these options may not be practical for some laboratories and/or available for all sampling sites and destinations. Care should be taken when selecting the shipping company, as several unfortunate experiences have shown that some may not be reliable in maintaining the cold transportation chain. Flying with samples Although courier is preferable, one might consider putting samples in checked luggage. One of the safest options to transport samples as checked luggage is to use a dry shipper. However, airlines may have different regulations in transporting liquid nitrogen. Non-harmful materials, such as dry ice or technical ice packs, are the best option to meet standard luggage regulations (e.g. <2.5 kg of dry ice per person) and safety (e.g. the package permitting the release of carbon dioxide gas when transporting dry ice). Transporting freeze-dried samples kept in a hermetically closed bag (or a thermos filled with ice) is likely the safest and cheapest option. Upon arrival, samples need to be stored in a freezer (preferentially at −80°C) or immediately put in a solvent solution for lipid extraction (see section Sample handling and storage before lipid extraction for more details on storage duration). Dried lipid extract should be transported as frozen samples and be returned to solvent as soon as possible (see section Shipping samples). Note that preservation methods requiring flammable solvents (e.g. ethanol, MeOH) are not permitted on aircraft within passenger luggage. Recently, the emerging technology of drones opens new opportunities for a rapid and customizable aerial transport of sensitive samples from remote areas to laboratory facilities (e.g. Amukele et al., 2017). Regulations and permits Prior to initiating sample collection and considering transporting them, it is necessary to be well informed on import and export regulations that differ widely across countries and studied organisms’ status. It can be difficult to obtain the necessary information within custom departments and the paperwork involved. Some research institutions may have specialized services available to ensure proper paperwork so that samples are shipped and received safely. To transport samples out of a country, it is often necessary to follow The Nagoya Protocol on Access and Benefit Sharing (United Nations Convention on Biological Diversity, 2011). Although it was initially stated that this only applies to samples for molecular research, all transports of tissue for biochemical screening are now included. This applies only for countries that have signed and ratified the Nagoya Protocol. When samples contain species listed in the Convention on International Trade in Endangered Species of wild fauna and flora (CITES), it is necessary to request an import/export CITES permit from each of the countries involved. This needs to be organized well ahead of sample collection, as it can take up to 3 years to obtain these permits. Additional research permits may be required for the import and export of samples depending on national regulations and target species. Lipid extraction Because of their diverse structures, lipids range from non-polar to highly polar, which makes the selection of a solvent with matching polarity challenging. Methods based on chloroform and methanol (Folch et al., 1957; Bligh and Dyer, 1959) have long been used across a wide range of matrix types with great success. Current alterations in environmental regulations have brought restrictions on the type and amount of solvent usage in laboratories worldwide, encouraging the exploitation of alternative organic solvents and novel eco-friendly methodologies for extraction (e.g. accelerated solvent extraction). Here, we review both the well-established techniques and the newly developed methods; we caution readers that full validation must be carried out with a given biological material before adopting any modified or adapted method. Handling cautions Contamination is an issue that needs to be carefully addressed at every stage of sample handling. It is recommended to; (i) use material that have been cleaned using acetone or extraction solvent mixture to remove any potential lipids (e.g. Teflon-coated needles used for evaporation), (ii) avoid plastic containers, tools or tips, (iii) combust at 450°C for 6 h or solvent-rinse all glassware and glass fibre filters, (iv) use glass tubes fitted with Teflon-lined screw caps, (v) prefer amber glassware (or aluminium foil protected) to limit solvent photo-degradation, (vi) use the highest quality solvents for material/tool cleaning, extraction, or elution, (vii) perform blank and reference samples for each batch of samples, and (viii) wear laboratory gloves to limit exposure to toxic solvents and cross-contamination. Sample preparation Regardless of the technique used, extraction of lipids from aquatic samples requires that the solvent penetrates the sample particles so that it can make contact and solubilize the lipids present. The most efficient extractions ensure that the particle size is as small as possible. The gold standard is to homogenize samples with a ball mill homogenizer (at −196°C using liquid nitrogen with wet samples). With wet plant or animal tissue samples at macro-scales, this may be as simple as chopping or blending. For wet microalgae, grinding of samples after immersion in solvent works well to break open cells. Small organisms (roughly between 1 µm and 1 mm; on a filter or without) can also be directly extracted by direct immersion in the solvent mixture. The use of ultrasound-assisted extraction with a probe or bath may aid in breaking cell walls (Pernet and Tremblay, 2003). When samples have been freeze-dried (section Sample handling and storage before lipid extraction) for preservation, they may be most conveniently ground with mortar and pestle and it may be necessary to rehydrate prior to carrying out the extraction. Any of these techniques, when applied prior to subsampling for analysis, should also serve to homogenize samples effectively, to ensure representative sampling. These diverse techniques also illustrate the need to adapt the preparatory steps and approach to the sample matrix. Traditional extraction methods While many researchers consider Folch et al. (1957) and Bligh and Dyer (1959) extraction methods to be similar, based on their use of chloroform and methanol, the two are quite different and were developed for different objectives. Prior to using those methods, one would do well to read the original publications. Both methods rely on the formation of a biphasic mixture, with a lower layer principally consisting of chloroform and containing the isolated lipids, and an upper phase consisting of methanol and water and containing the non-lipids. The Folch et al. procedure, using CHCl3:MeOH (2:1, v:v) and a solvent to sample ratio of at least 20:1, was designed as a general and robust method for the recovery of lipids from animal tissues (i.e. brain, liver, muscle), now applicable to several tissues and species with a range of lipid content (Iverson et al., 2001). The Bligh and Dyer method uses a more polar solvent mixture of CHCl3:MeOH:H2O (1:2:0.8, v:v:v) with a 4:1 solvent to sample ratio and was originally developed to rapidly extract lipids, principally polar lipids, from lean fish tissues. While the Folch et al. method is more complicated to perform, it is more reliable and applicable to almost all sample types. A key aspect of the original Folch et al. method that is often overlooked is the washing of the bottom phase that contains lipids with a mixture of solvents of the same proportions as originally found in the upper phases. As originally described by Folch et al. (1957), the upper phase consists of CHCl3:MeOH:H2O at 3:48:47 and while some groups do use a 50:50 MeOH:H2O wash as recommended by Christie (1982b), it is exceedingly rare that the correct mixture is used. Thus, almost all Folch et al. procedures are best described as “modified”. The Bligh and Dyer method is easier to carry out but requires greater consideration of sample type and potential modification of the procedure. For instance, Christie (1982b) notes that with high fat samples, an initial pre-extraction with chloroform or diethyl ether is necessary to extract TAG that would otherwise remain unrecovered. The use of the correct proportions of solvents and water is critical for quantitative lipid recovery with both methods. One must take care to adjust the volume of water added when samples with varying moisture contents are analysed (i.e. freeze-dried material vs. fresh tissue) and it is often necessary to determine moisture content before beginning extraction. These basic procedures may require some adjustments depending on lipid matrix before and/or during extraction to increase the yield or specificity of lipids to be analysed. For example, it may include inactivation of lipase, changes in solvent proportions, or cell disruption (e.g. Kumari et al., 2011; Ryckebosch et al., 2012). In a general manner, authors must refer to the available literature on their model organisms or matrices. Given the numerous modified extraction procedures that reflect the diversity of aquatic biological matrices, it is also essential that authors detail their protocol in publications when it differs from original methods. Alternatives to chloroform: towards less toxic solvents Dichloromethane Many laboratories have become increasingly conscious of a range of occupational health and safety areas in recent decades. Lipid studies historically used chloroform as a major solvent in many steps including extraction. The less toxic dichloromethane (CH2Cl2) has regularly been used instead of chloroform for lipid extraction in many aquatic labs. This replacement originated from food chemistry research (e.g. Chen et al., 1981) and was later formally reported for aquatic samples (e.g. Cequier-Sánchez et al., 2008). Regardless of solvent choice, both dichloromethane and chloroform may degrade on storage to produce hydrochloric acid (HCl) and phosgene (Noweir et al., 1972). Phosgene is a much more acutely toxic chemical than either chloroform or dichloromethane, and inhalation can lead to pulmonary oedema (e.g. Snyder et al., 1992). Chloroform degradation or insufficiently stabilized may lead to lipid degradation (i.e. empty chromatograms, including disappearance of the internal standard) (Le Grand and Pernet, pers. obs.). To limit phosgene production, chloroform is stabilized with either ethanol or amylene, with ethanol being the more effective stabilizer. Because dichloromethane is less susceptible to degradation than chloroform, it is typically stabilized with alkenes only. Safe laboratory practices, including restricting solvent use to fume hoods, can effectively avoid risks associated with chlorinated solvents and phosgene formation. In the field, when samples are preserved by direct immersion in solvent, caution is advised. Methyl-tert-butyl ether Methyl-tert-butyl ether (MTBE) was recently used as a less toxic solvent for the extraction of lipids but had lower recovery rates than chlorinated solvents (Sheng et al., 2011; Ryckebosch et al., 2012). However, the MTBE-based extraction protocol appeared especially well adapted to high-throughput lipidomics, as the lipid extract recovered in the upper phase was cleaner with non-extractible material remaining in the lower phase (Matyash et al., 2008). The special case of ethanol-preserved samples The availability of historically preserved samples can often occur for a range of lipid studies (see section Organic solvent in Sample handling and storage before lipid extraction). However, because ethanol extracts some lipids, the sample and all added ethanol must be used in the lipid extraction protocol (Phleger et al., 2001). When using the Bligh and Dyer (1959) method, the extraction should be performed with chloroform (or dichloromethane), ethanol, and water where ethanol replaces methanol. Care is needed when selecting historic ethanol-preserved samples for lipid analysis and in which the ethanol preservative may have been replaced during storage. Other methods Accelerated Solvent Extraction (ASE, Dionex), also known as Pressurized Fluid Extraction, is a widely used method based on a controlled increase in solvent temperature and pressure during extraction. This process maintains the liquid state of solvents near their supercritical region where they have high extraction properties. Several ASE parameters can be adjusted to improve the extraction, such as temperature, pressure, extraction duration (including pre-heating), and number of extraction cycles. All of these parameters affect the final solvent volume and can be adjusted individually to each sample type to optimize the extraction yields and quality (Schäfer, 1998). This sample type-specific adjustment enables high extraction efficiency with a low solvent volume and a short extraction time, which is beneficial from a cost and environmental standpoint. Microscale lipid extraction of fish tissues (100 mg) using ASE is as quantitatively and qualitatively efficient as classical lipid extraction methods (Dodds et al., 2004). Concluding remarks This section did not aim to be exhaustive in describing existing extraction methods. When comparing data from different studies, it is important to consider the lipid extraction techniques used as well as handling and storage methods. When working on new matrices and developing new or optimized extraction protocols, we recommend to compare them with Folch et al. (1957) or Bligh and Dyer (1959) methods, which generally remain the gold standards for lipid recovery. Ensuring complete lipid extraction is critical when applying new protocols. Performing successive lipid extractions on a same sample and measuring its lipid content at each step are the suitable approaches to establish at which stage the sample is totally lipid-depleted. We recommend monitoring extraction efficiency values and ideally reporting them in publications. Alternatively to successive extractions, the fatty acid methyl ester (FAME) content of the lipid extract can be compared to FAME recovered after direct transesterification (section Transesterification). A number of studies have compared the effect of extraction techniques (ASE, Soxhlet, manual) on lipid recovery and FA content (e.g. Ewald et al., 1998; Mulbry et al., 2009; Balasubramanian et al., 2013). While all report differences, there is little agreement as to which may be an optimal method, likely due to substantial differences in the followed protocols. Indicators of sample degradation Sample lipids may degrade through hydrolysis of ester bonds or through oxidation of carbon–carbon double bonds. These are two, largely independent processes leading to different compounds or groups of compounds, which may be used as indicators of sample degradation. Therefore, samples showing indicators of hydrolysis may not have been oxidized and vice versa. Unless necessary for a short time period (e.g. shipping), purified lipid extracts are susceptible to oxidation and should be kept under N2 atmosphere with solvent (Folch mixture is generally preferred). Hydrolysis The most frequently encountered aquatic lipids are acylglycerols (TAG, PL, and glycolipids) in which FA are esterified to a glycerol backbone. There are also ether lipids that replace one ester bond with an ether linkage or a vinyl ether linkage. Esters of carboxylic acids undergo hydrolysis in a reversible reaction, which is catalyzed by hydrolase enzymes called esterases present in biological samples. Lipases, a subclass of esterases, are found in animals, plants, fungi, bacteria, and even in certain viruses (Beisson et al., 2000). Lipid hydrolysis can be assessed from the FFA content, and from the lysophospholipid content when samples are dominated by PL. Standard measurements of oil quality are the acid value (AV) and FFA content, determined in the oil industry by titration and chromatography, respectively. However, both these measurements are rarely used outside of the industrial context. Many chromatographic systems have been used over the past years for the quantitative analysis of major lipid classes including FFA. FFA can be easily separated from extracted lipids by means of well-known methods based on either liquid (LC) or thin-layer chromatography (TLC) (reviewed by Fuchs et al., 2011). For example, high-performance liquid chromatography (HPLC) with electrospray light-scattering detection has been successfully used to detect and quantify FFA in fish with a relatively short processing time and highly reproducible measurements (Christie, 1985; Silversand and Haux, 1997). As for LC, a number of improvements to classic TLC have led to more sophisticated forms, providing better separation efficiencies and quantifications. High-performance TLC coupled with densitometry or TLC coupled with flame ionization detection (TLC-FID, Iatroscan, Shantha, 1992) has also been used to quantify lipid classes and FFA in samples from different origins including marine (Lochmann et al., 1996; Parrish et al., 2009), terrestrial (Zoumpoulakis et al., 2012), and freshwater species (Litz et al., 2017). In food studies, FFA determined by TLC-FID is generally equivalent to AV (Nishiba et al., 2000). TLC is normally less expensive and less time-consuming than HPLC-based methods that can, of course, also be used to provide similar results. The hydrolysis index (HI) of non-polar acyl lipids and the lipolysis index of all acyl lipids (LI) can be used as degradation indicators (Weeks et al., 1993; Parrish et al., 1995) and are calculated as: HI=FFA+ALCTAG+WE+FFA+ALC × 100, LI=FFA+ALCTotal acyl lipids+ALC × 100. LI values are always lower than HI values for any sample because all acyl lipids are considered. Oxidation Lipid oxidation mainly occurs through autoxidation when lipids react spontaneously with atmospheric oxygen. The process is usually autocatalytic, with oxidation products accelerating the reaction so that it increases over time. Other catalysts such as light, heat, enzymes, and metals lead to photo-oxidation, thermal oxidation, and enzymatic oxidation. Fatty acid oxidation is a process in which free radicals cause unsaturated FA to react with molecular oxygen forming acyl hydroperoxides, which can then breakdown into a variety of secondary products. A decrease in PUFA content may indicate oxidation as they are particularly susceptible to degradation (Sasaki and Capuzzo, 1984). Lipid hydrolysis may accelerate lipid oxidation due to the formation of FFA that can be substrates for oxidation reactions, as found in vivo (Saponaro et al., 2015). High levels of PUFA in lipids make them susceptible to oxidation (Koshio et al., 1994). Oxidation of n-3 and n-6 PUFA leads to the formation of highly chemically reactive compounds such as malondialdehyde (MDA), 4-hydroxy-2-hexenal (4-HHE), and 4-hydroxy-2-nonenal (4-HNE). HHE is an aldehyde derived from n-3 PUFA, and HNE from n-6 PUFA. A number of physical and chemical tests, including instrumental analyses, are available to determine primary and secondary products of oxidation (Shahidi and Zhong, 2005). Oil supplement, food, and other industries have a range of standard measurements for determining oil oxidation. These include the internationally recommended peroxide value (PV) and p-anisidine value (pAV) (Nichols et al., 2016). For example, PV can be measured by titration and 2-thiobarbituric acid reactive substances (TBARS) by spectrophotometry and both can be determined using highly precise Fourier transform infrared spectroscopy. Malondialdehyde is commonly monitored using the direct TBARS test (Fernández et al., 1997; Shahidi and Zhong, 2005) and with HPLC (Mendes et al., 2009), while HHE and HNE can be quantified by GC–mass spectrometry (MS) [using chemical ionization (CI)] (Kenmogne-Domguia et al., 2014). Recent analytical developments allowed simultaneous quantification of MDA, HHE, and HNE by LC/APCI–MS and LC–MS/MS (Douny et al., 2016; Tullberg et al., 2016). Together with pAV, MDA, HHE, and HNE are secondary markers of oxidation while PV, hydroperoxides, and conjugated dienes are primary markers. However, it has to be noted that MDA, HHE, and HNE quantifications do not necessarily reflect PV and AVs (Viau et al., 2016). Sterols are also a source of oxygenated lipids named oxysterols, which are sterols bearing a second oxygen functionality. Cholesterol oxidation likely proceeds in conjunction with oxidative decomposition of coexisting PUFA (Ohshima et al., 1993). Cholesterol oxidation products (COP) can be determined by cold saponification of lipids (Ohshima et al., 1993), followed by enrichment of COP by solid-phase extraction (SPE), and then quantification and identification by GC and GC–MS (Pickova and Dutta, 2003). Concluding remarks To conclude, we encourage as a minimum the assessing and reporting of hydrolysis indicators as a quality control criterion of sample handling and extraction procedures. The threshold above which sample/data quality should be questioned depends on the sample matrix, the logistical context, and the study objective. In any case, the extent of either form of degradation should ideally be evaluated and compared with baseline values from fresh and properly extracted samples. Separation of neutral and polar lipids FA are incorporated almost unaltered into the storage lipids such as WE, TAG, and ALC, reflecting the FA profiles of the consumed food (Dalsgaard et al., 2003). In contrast, polar lipids are primarily involved in cell membranes, which regulate and adjust their FA composition in response to environmental factors like temperature, salinity, and hydrostatic pressure (Hazel and Williams, 1990). As such, inference of an organism’s diet based on total lipids can be biased by the ratio of FA originating from storage (mostly neutral lipids) and membrane lipids (mostly polar lipids). This ratio varies widely across species, ontogenetic stages, reproduction, nutritional status, and tissue types. For example, in small pelagic fish from the Mediterranean, the contribution of TAG stored in the muscle in winter was 1.5, 5.2, and 47.6% of total lipids for sardine, anchovy, and sprat, respectively, the remainder being mostly polar lipids (Pethybridge et al., 2014). This demonstrates that bulk FA composition variations among species can be confounded with variations in lipid class composition. However, many studies in trophic ecology have based their trophic interpretation on the FA composition of bulk lipids without considering the FA distribution between storage and structural lipids. When should the lipid fraction be separated? Lipid class composition should be assessed alongside the FA composition to decide whether lipid class separation is necessary for robust dietary inference. Calculating the ratio of storage to structural lipids provides a comprehensive assessment of the cost/benefit of separating neutral and polar lipids fractions. For samples dominated by storage lipids, the FA composition of bulk lipids will mostly mirror the diet, and lipid class fractionation is not mandatory (e.g. fatty fish species, marine mammal blubber, and digestive gland and liver of marine organisms) (e.g. Sardenne et al., 2020). However, for samples rich in structural lipids, fractionation of lipid classes will provide more reliable dietary indicators. The analysis of FA in polar lipids can inform on nutritional requirements of the study organism and on environmental conditions prevailing at the time of sampling. Comparing the relative FA proportions in polar and neutral lipids allows the identification of the dietary FA that were selectively retained/eliminated to form cell membranes (e.g. Delaunay et al., 1993; Plante et al., 2007). The unsaturation level of FA and PUFA incorporated in polar lipids is linked to the temperature in ectothermic organisms (Hazel and Williams, 1990; Hall et al., 2002; Pernet et al., 2007) and can be associated with the basal metabolic rate (Hulbert and Else, 1999; Pernet et al., 2008). Consequently, the separate analysis of neutral and polar lipids is a practical approach to address questions on trophic ecology and on nutrition and comparative physiology. To establish trophic relationships of species for which all potential food sources are known, a good strategy is to analyse the FA composition of total lipids in food sources (e.g. prey) and of neutral lipids in consumers to characterize their diet (e.g. predators). Such approach was implemented by the pioneers of the use of FA as trophic indicators (reviewed in Dalsgaard et al., 2003). Alternatively, when study organisms are both prey and predator, or when predator–prey relationships are not well-known, we suggest at a minimum analysing FA from both neutral and total lipids. Beyond trophic considerations, quantification of lipid classes provides useful information on the physiological condition of organisms (Fraser et al., 1989) and the quality of sample preservation through FFA and lysophospholipid levels (see section Indicators of sample degradation). Commonly used separation methods Storage lipids (SE, WE, TAG, DAG, ALC) and FFA are defined as “neutral” because they are hydrophobic molecules lacking charged groups, whereas PL are defined as “polar” because they have a charged hydrophilic head. Neutral and polar lipids can be separated from each other by TLC or by adsorption chromatography using either laboratory packed or commercial prepared silica columns. A simple and widely used protocol for the separation of neutral and polar lipids consists of a short column of silica prepared in a glass disposable Pasteur pipette plugged with cotton wool (Christie, 1982b). The sample is loaded on the top of the column and washed at low pressure with chloroform (or dichloromethane) often mixed with methanol (e.g. CHCl3:MeOH, 98:2 v/v) to elute neutral lipids. It can then be washed with acetone to obtain acetone mobile polar lipids, generally dominated by pigments and glycolipids. Finally, a last wash is performed with methanol to yield polar lipids dominated by PL. Some PL with lower polarity may elute with acetone, while some glycolipids with higher polarity may elute with methanol. Including an elution step with acetone may not be necessary in non-plant samples as they contain negligible amounts of glycolipids (Christie, 1982b). Yet, the latter step (elution step with acetone) can be useful in deciphering trophic links in deep-sea fishes (Şen Özdemir et al., 2019). Diethyl ether:acetic acid (98:2, v/v) can be used to recover pigments, prior to the elution of glycolipids and PL (Da Costa et al., 2017). Eluent and sample volumes must be adjusted to optimize separation. It is also important to note that hydration of silica changes the efficiency of retention through a partition effect between bound water and polar lipids (Christie, 1982b). The use of silica hydrated with 6% water (m/m) can improve the recovery of polar lipids from marine bivalves (e.g. Marty et al., 1992; Pernet et al., 2006). Commercially pre-packed columns connected to a vacuum manifold are widely used instead of the manually packed Pasteur pipette (Christie, 1992; Ruiz-Gutiérrez and Pérez-Camino, 2000). This procedure, often referred to as SPE, is time effective and provides similar results as classic column chromatography (Juaneda and Rocquelin, 1985). However, it is difficult to achieve quantitative recovery of PL with this approach. Only ∼62% of total lipids were recovered from clam tissue using SPE due to the selective retention of phosphatidylcholine as silica is not rehydrated in these pre-packed columns (Pernet et al., 2006). Commercial SPE columns are available with a wide range of chemically bonded stationary phases. These have great potential for the isolation of specific lipid classes, yet surprisingly few applications have been described to date. For example, an alternative method developed by Kaluzny et al. (1985) using aminopropyl-bonded silica is suitable for separating lipid classes in oyster tissues, though particular care is required to recover the acidic lipids quantitatively (Kim and Salem, 1990; Pernet et al., 2006). Aminopropyl-bonded phases have also been used to separate groups of lipid classes, including neutral lipids such as cholesterol, and non-acidic PL, and they are especially useful for the isolation of the FFA fraction from lipid extracts (Kaluzny et al., 1985; Kim and Salem, 1990). Practical recommendations Before starting to use a separation protocol, we recommend reading handbooks describing the use and applications of SPE for lipid separation including the choice of eluting solvents (Christie, 1982b; Perkins, 1991), and consulting technical resources provided by the SPE manufacturers. Specifically, care must be taken to avoid sample overload and subsequent cross-contamination. This problem can be avoided by knowing the lipid composition of the sample and the carrying capacity of the column prior to SPE. The amount of sample that can be loaded onto a column is governed by the proportion of the retained lipids. For example, 2–5 mg of PL is considered as the maximum load for SPE of neutral and polar lipids on a 100-mg silica column (Christie, 1982b). Although sample overload leads to cross-contamination, an excess of sorbent can also reduce recovery of the retained analytes. It is therefore suggested to experimentally determine the sample load (mass ratio of lipid to sorbent) to optimize purity and recovery (Pernet et al., 2006). To use the SPE system, we suggest replacing the silica originally contained in the pre-conditioned column with rehydrated silica (generally 6% m/m). Purity and recovery analyses after SPE are crucial, particularly when the sample matrix is new to the laboratory and when the stationary phase changes. Different brands or batches of silica gel may vary in their properties, and cross-contamination of fractions may occur (Christie, 1982b). Purity can be evaluated by TLC of lipid classes of each eluted fraction. The simplest way to evaluate recovery is to quantify the lipid class content of samples before and after SPE by TLC and proceed with recovery calculation (i.e. the amount of collected lipid after SPE/the amount of lipids before SPE, as a percent). Alternatively, the recovery purity and efficiency can be estimated by adding an internal standard of known mass before SPE. As such, lyso-phosphatidylcholine 12:0 (22.8 µg of FA) was added to the lipid extract before SPE to assess PL recovery in oyster samples (Pernet et al., 2006). Occurrence of 12:0 was exclusively associated to LPC as this FA is absent in oysters. A full recovery of LPC following SPE on silica indicated that other PL were fully recovered as LPC is the most strongly retained phospholipid on silica (Pernet et al., 2006). To evaluate the purity of each lipid fraction and the absence of cross-contamination, it is necessary to add one standard based on the least neutral lipid, which must remain in the neutral lipid fraction, and another one that corresponds to the least polar lipid, which must be retained in the polar lipid fraction. For example, commercially available 12:0 DAG and 10:0 phosphatidic acid (PA) can be added to the lipid extracts of oysters (F. Pernet, unpublished data). Because these two shorter chain FA are naturally absent in oysters, their occurrence was exclusively associated with DAG and PA standards. The FA 12:0 and 10:0 were expected in neutral and polar lipid fractions, respectively. Note that these standards do not replace the traditional 23:0 used for the mass quantification of FA (see section Analysis of FA). In the case of SPE, 23:0 is generally added directly in the elution tubes to avoid passage in the column as this standard would only be distributed in the neutral fraction. Transesterification Except in highly degraded samples, where FFA proportion can reach high values, FA are generally part of more complex lipid molecules and are linked to glycerol by an ester linkage (e.g. TAG, PL, glycolipids). Therefore, one approach to analyse FA by GC is to hydrolyze the ester linkage and to convert the released non-volatile FA into their volatile derivatives, usually FAME. Saponification followed by methylation is the classical method for preparing FAME, and while laborious, it produces FAME without contamination with unsaponifiable lipid compounds (e.g. non-volatiles, sterols, alcohols, hydrocarbons, phthalates, and pigments). However, most researchers use transesterification. A number of transesterification methods exist, all based on the presence of excess methanol, a catalyst (acid or base) and heat (see Schlechtriem et al., 2008). To be analysed by GC, FAME are then extracted in a GC compatible solvent (often hexane), and the organic phase is washed several times by water to remove acid and glycerol. Produced FAME are stable for months at 4°C in the dark under nitrogen. Transesterification using ethanol instead of methanol is also possible but less used, leading to the formation of FA ethyl esters. Butanol can also be used as a substitute to methanol to quantify very short-chain FA (down to iso5:0) by increasing aliphatic chain length (Koopman et al., 1996, 2003). Although the use of the boron trifluoride (BF3) has been recommended by the AOCS (1989), many catalysts can be used in both acid- and base-catalyzed transesterifications, with each method having their own advantages and limits. The efficiency of these transesterification methods differs as a function of the bound between the aliphatic chains and the glycerol or sphingosine backbones (Christie and Han, 2010). This is particularly important in aquatic samples because, depending on the matrix, a considerable part of these aliphatic chains can be linked to the entire lipid molecule not by an ester linkage, but by a vinyl ether (e.g. plasmalogens), an ether (e.g. alkyl lipids), or an amide (e.g. sphingolipids) linkage. It is thus important to choose the most pertinent transesterification method with respect to the sample nature and the scientific question. For example, the acid-catalyzed transesterification of plasmalogens leads to the formation of FAME (from the acyl lipids) and dimethyl acetals (DMA) (from vinyl ether lipids). Dimethyl acetals are mainly detected in polar lipids but are marginally present in neutral lipids, reflecting the contribution of glyceride ether also named “neutral plasmalogens” (TAG containing at least one vinyl ether bound aliphatic chain). Base-catalyzed transesterification of these molecules only leads to the formation of FAME, leaving vinyl ether bound aliphatic chains unreacted (Crackel et al., 1988; Cruz-Hernandez et al., 2006). However, attention needs to be paid to the particular procedure followed, as base-catalyzed transesterification based on ISO standard methodology incorporates an acid wash to neutralize the catalyst. This acidic wash may lead to DMA formation (Gómez-Cortés et al., 2019). Moreover, neither acid transesterification nor base transesterification has any effect on ether linkages (Christie 1989). This point is important to consider when analysing samples containing large amounts of alkyl ether lipids, such as alkylglyceryl ether lipids in shark liver oil. Transesterification of FA sphingolipids components is acid-catalyzed and is achieved by increasing the reaction time (Eder, 1995; Le Grand et al., 2011). Finally, esterification of FFA is only possible by acid-catalyzed transesterification (Christie, 2003). Depending on the scientific question (e.g. nutritional value, trophic relationships, physiological responses), FA transesterification can be realized directly on the biomass (direct transesterification), on a total lipid extract or on lipid fractions, such as polar or neutral lipids, lipid classes (e.g. TAG, phosphatidylcholine), or lipid subclasses (e.g. phosphatidylethanolamine plasmalogen), isolated beforehand. It can be useful to purify FAME by HPLC or TLC before GC injection (Marty et al., 1992). This prevents the saturation and the degradation of the column through the injection of non-FAME molecules (e.g. non-volatiles, sterols, hydrocarbons, phthalates, pigments) and thus increases its lifespan. Obtained chromatograms are also “cleaner” (no contaminants) and easier to identify. Boron trifluoride The Lewis acid BF3 in methanol (12–14%, w/v) is a powerful acidic catalyst for the transesterification of FA. In general, 800 µl to 1 ml of BF3-MeOH (12–14%, w/v) is added to a dry lipid extract containing a maximum of 1–4 mg of lipids (Liu, 1994; Carrapiso and García, 2000; Le Grand et al., 2011). This method is fast and efficient (Morrison and Smith, 1964; Carrapiso and García, 2000; Cavonius et al., 2014), and it has been successfully used on marine lipid samples where almost all the lipid classes, including FFA and plasmalogens, were transesterified in 10 min at 100°C (Le Grand et al., 2011; Cavonius et al., 2014). For the sphingolipids, such as sphingomyelin and ceramide aminoethylphosphonate, the transesterification should be prolonged and last for 1.5–5 h at 100°C (Eder, 1995; Le Grand et al., 2011). Heating is generally conducted in a dry bath, water bath, or heat block. The higher the temperature used, the shorter the reaction time. An increase in reaction time has no negative effect, provided that no evaporation occurs (Morrison and Smith, 1964). Presence of residual water in the sample limits the acid-catalyzed esterification by BF3 (Sattler et al., 1991). It is thus necessary to dry the sample either by nitrogen evaporation or by the addition of anhydrous sodium sulphate (Molnar-Perl and Pinter-Szakacs, 1986). Because evaporation during the transesterification leads to acid concentration, which degrades FA and especially PUFA (Lough, 1964), it is crucial to prevent vial caps from unscrewing when heating samples (Morrison and Smith, 1964). Poor storage conditions and quality of BF3 cause PUFA degradation (Morrison and Smith, 1964; Christie, 1993). It is recommended to use fresh reagent and to store it at 4°C or colder (−20°C). Despite these issues, the use of BF3-MeOH for transesterification was referenced as an official method (AOCS, 1973; IUPAC, 1979) and Ackman (1998) showed that issues could be avoided following simple rules. Yet, the potential PUFA degradation induced by using BF3 as a transesterification catalyst, the difficulty in purchasing guaranteed anhydrous BF3-MeOH, its high cost, and its high toxicity have led to a decreasing use of this reagent (Liu, 1994; Carrapiso and García, 2000; Cavonius et al., 2014). In samples containing very high levels of storage lipids, such as salmon flesh, copepod oil, and cod liver, the derivatization efficiency can be quite low, leading to small but significant differences in some FA proportions (Schlechtriem et al., 2008). For these reasons, BF3 is no longer recommended as an acidic catalyst in the preparation of FAME. Acidic alternatives to BF3 A number of alternatives to Lewis acids have been employed (Christie, 1982a). Methanolic HCl (3 N) appears to be the most commonly used reagent for transesterification of lipid-bound FA to FAME in lipids from freshwater plankton (von Elert, 2002; Martin-Creuzburg et al., 2012; Windisch and Fink, 2018) and also from marine plankton (e.g. Troedsson et al., 2005). It can easily be produced by adding 24 ml of acetyl chloride to 100 ml of methanol, which leads to the formation of 10% (approximately equivalent to 3 N) methanolic HCl. Sulphuric acid (H2SO4) is often recommended (Christie, 1982a; Budge et al., 2006), as it can easily be prepared by mixing a small amount of concentrated H2SO4 (∼98%) in methanol to achieve a 1% solution (Schlechtriem et al., 2008). The most commonly used acid-catalyzed reagents, such as HCl or H2SO4, are not associated with the issues described for BF3. Caution is, however, needed during heating to prevent evaporation. A number of comparisons of acid catalysts for esterification/transesterification have been made (e.g. Medina et al., 1992; Budge and Parrish, 2003; Thiemann et al., 2004; Carvalho and Malcata, 2005), with no clear consensus on optimal methods. The best advice is likely to select a method and consistently apply it. Basic alternatives Several basic catalysts are available (e.g. NaOH, KOH) to transesterify acyl lipids. While a range of procedures have been described (e.g. Christie, 1982a; Medina et al., 1992; Velasco et al., 2002), most are very similar to the acid-catalyzed method described above with the substitution of the catalyst and the introduction of an additional step after the reaction is quenched to neutralize the remaining base. All base-catalyzed transesterifications proceed very rapidly at room temperature (Christie, 1982a), thereby preserving labile structures (i.e. epoxides, conjugated double bonds) that are often destroyed with harsher acid catalysts. This “gentler” transesterification is the primary motivation for the use of basic catalysts. As noted above, basic catalysts are unable to esterify FFA so these approaches should only be used when one can be confident that FFA are present in very low amounts, or else when the analyst is targeting esterified FA exclusively (e.g. studies on membrane lipids or storage lipids). Finally, there are a number of official and approved methods that employ combined approaches, using both basic and acidic catalysts (e.g. AOCS, 1989). While at least one of these methods (i.e. AOAC 969.33-1969, 1997) describes the procedure as first saponification with NaOH, followed by methylation with BF3 in methanol, the short reaction time with NaOH in methanol is actually an alkali-catalyzed transesterification, followed by acid-catalyzed methylation of any FFA that remain (Ackman, 1998). Although direct acid-catalyzed transesterification is sufficient for a typical aquatic lipid if performed with sufficient time and heat, the alkali-catalyzed step may persist because of its adoption by official bodies (Ackman, 1998). Direct transesterification The standard procedure for determining FA profiles (e.g. extraction to isolate lipids, followed by transesterification to yield FAME), can be labour intensive and increases analytical cost. In some cases, studies are only interested into the FA composition of total lipids in samples and therefore perform a direct one-step transesterification that considerably reduces the analytical time (Lewis et al., 2000). This method is realized directly on the sample and differs from the classical transesterification by the elimination of the lipid extraction step (Lewis et al., 2000; Castro-Gómez et al., 2014; Parrish et al., 2015a). Different methods have been tested and provided reproducible and similar results to the classical extraction–transesterification method (Castro-Gómez et al., 2014; Parrish et al., 2015a). Careful consideration is needed when using this approach as it may not be appropriate to the study goal and/or the matrices analysed. It may also be preferentially applied to small and homogenous (representative of the whole) samples. For example, while it is possible to carry out direct analyses on a small amount of tissue, accurate determination of total blubber FA composition in marine mammals was not possible with this approach due to significant variation in FA composition within the layers of the blubber (Thiemann et al., 2008). Although this rapid direct transesterification approach is less time-consuming and cheaper, once the entire sample is transesterified, it is not possible to redo the analysis, or to analyse the FA composition of different lipid fractions. Alternatives to hexane Hexane, iso-hexane (2-methyl-pentane), or iso-octane are most frequently used to recover FAME after transesterification and before injection. However, hexane is carcinogenic, mutagenic, and reprotoxic and is a hazardous airborne pollutant. Less toxic solvents such as pentane and heptane can be used as alternatives to hexane (Alfonsi et al., 2008). However, recovery of FA from macroalgae is lower with heptane than with hexane, with PUFA tending to be more affected than saturated fatty acid and monounsaturated fatty acid (MUFA) (Le Grand, unpublished data). This preliminary result needs to be verified on other matrices. Pentane rather than hexane may also be used as the solvent in the BF3/MeOH procedure to reduce the reaction temperature from 85–100°C to 80°C to decrease artefact risk (Parrish, 1999). Analysis of FA The full process of FAME analysis consists of injection, separation, identification, and quantification of FAME and each of these steps has to be optimized to achieve high accuracy and precision (see Eder, 1995; Christie, 2003 for review). GC with FID is generally the preferred method for the quantification of FAME in aquatic samples. GC–MS methods for FAME quantification can be advantageous on a routine basis when spectrometric confirmation of compound identity is required. This is especially useful for complex samples containing contaminants, artefacts, or co-eluting compounds. Although these methods compared satisfactorily with GC-FID, they are more complex and tedious to implement and calibrate. Such approaches also require stronger chemistry/spectrometry backgrounds of the user and greater routine maintenance of the instrumentation. Injection Sample injection is a critical step to achieve high accuracy in FAME analysis, and we encourage readers to refer to seminal papers of Konrad Grob for a thorough overview of available techniques (e.g. Grob, 2007). There are currently three injection techniques. Before being transferred into the capillary column, the sample can be (i) evaporated in a permanently hot vaporizing chamber (classical vaporizing injection), (ii) injected into a cool chamber, which is subsequently heated to vaporize the sample (programmed temperature vaporizing injection), or (iii) directly injected into the column inlet (direct injection or on-column). Once vaporized, the sample can be split (i.e. split injection) with only a small part of the vapour entering the column while the rest is vented. This is the most appropriate technique for concentrated samples (e.g. Shantha and Ackman, 1990). Alternatively, the whole vaporized sample is transferred from the injector to the column without splitting of the sample (i.e. splitless injection). This technique is particularly appropriate for trace analysis. The major drawback of the split or splitless injection techniques is the possible discrimination between high- and low-boiling compounds in the sample. FA in aquatic samples have a wide range of boiling points, and quantification may require application of relative response factors (Grob, 2007). On-column injection avoids discrimination of FA and therefore provides the best results. Yet, a large number of aquatic scientists use the vaporizing injection method for suitability and flexibility reasons. High-speed injection, high injector temperature (>220°C) and proper injector inserts all contribute to decreasing discrimination problems. However, we recommend regular checks of FAME discrimination factors using certified reference mixtures (e.g. Supelco 37). Separation Upon injection, FAME are transferred into a wall-coated open tubular capillary column allowing separation according to their carbon number, the number and location of the double bonds, and the cis–trans configuration. The column is located in an oven and exposed to temperature ramps ranging from 60°C to 300°C (e.g. Tang and Row, 2013 for examples of temperature ramping). The carrier gas is generally helium or hydrogen. The resolution capability of the column depends on several factors such as polarity of the stationary phase, column length, internal diameter, and film thickness. In view of its importance, this information should be reported in detail in the method description of any study. Based on the type of FA being investigated, one of the most important steps is the selection of the stationary phase of the column, which influences elution times of FAME, especially PUFA. The polarity of the column ranges from non-polar to extremely polar. On non-polar columns, the elution time generally increases with increasing boiling points of analytes. Therefore, the elution time of FAME increases with chain length and with decreasing unsaturation (e.g. 18:2n-6 elutes first, followed by 18:1n-7 and then by 18:0). The major drawback with non-polar columns is that some FA isomers (cis–trans) or FA pairs may co-elute. Conversely, non-polar columns exhibit low bleed, long lifetime, retention time stability, and high temperature resistance (to 360°C), which makes their use suitable for a large range of compounds. These columns are well-suited for separating neutral compounds such as sterols, alkenones, and hydrocarbons. Commonly used non-polar columns are made of 100% dimethylpolysiloxane or 5% phenyl–95% dimethylpolysiloxane. Many researchers use polar columns, which allow for a better separation of aquatic FA than non-polar phases, with only some rare and minor co-elutions. Ackman (1986) was a strong advocate of the polyethylene glycol (PEG) phase. Columns coated with PEG are easily recognizable because of their commercial name with the suffix “wax”. With this polar phase, FAME continue to elute in order of increasing boiling point, and also roughly in order of increasing numbers of double bonds. However, polar columns are highly sensitive to temperature (max 250°C), water, and oxygen and are thus less stable over time than non-polar columns. Retention times may also vary (decrease) over time, especially for highly unsaturated compounds. Consequently, peak identification (based on elution time) needs to be checked regularly and rigorously. An alternative phase consists of substituting 50% dimethylpolysiloxane (non-polar) with the polar cyanopropylmethyl (e.g. Iverson et al., 2004; Pedro et al., 2019). This phase provides similar resolution as PEG phases, although it is much less sensitive to oxygen, resulting in longer column lifetimes. The chemistry of this phase is more influenced by temperature than other polar columns. Separation of co-eluting peaks can sometimes easily be achieved simply by subtle adjustments to oven temperature ramps. Identification The most common way to identify FAME by GC-FID analysis is to compare their retention times with those of commercially available individual purified standards. Among the most used standards for the FA identification in aquatic samples, there are PUFA No. 3, a complex qualitative standard mixture extracted from Menhaden oil, and Supelco® 37 Component FAME Mix, a comprehensive mixture of 37 FAME ranging from 4:0 to 24:1n-9. Several long-chain and (highly) unsaturated FAME occurring in some samples are not commercially available but can be identified using fully characterized natural products (e.g. the non-methylene interrupted FA typical of marine molluscs in Zhukova and Svetashev, 1986; Ackman, 1989; Kraffe et al., 2004). In addition to these protocols, confirmation of FA identification is best achieved using GC–MS. Peak identification is achieved using commercial spectral libraries (e.g. The LipidWeb http://lipidhome.co.uk), or via personal/laboratory FA mass spectral libraries to assist in identification of often complex environmental FAME samples. Further details on various GC–MS protocols together with specific MS information are available in Christie (1989). Briefly, the base peak at m/z 74 is termed the McLafferty rearrangement ion and is formed by cleavage of the parent FAME molecule beta to the carboxyl group. Where CI is used, the mass spectra have a more prominent quasi-molecular ion (MH+), and minor ions only at [MH−32]+ and [MH−32 − 18]+. Because all FA will have the carboxylic acid (or ester) functionality in common, knowledge of the molecular mass is particularly valuable in determining basic structure with respect to numbers of carbon atoms and double bonds. There are unfortunately generally no characteristic ions that indicate the position or geometry (i.e. cis–trans) of double bonds in positional isomers of monounsaturated and polyunsaturated FAME (Christie, 1989). Where hydroxyl groups are present on the FAME, conversion of the hydroxyl group to an O-trimethyl silyl (O-TMSi) ether or other derivative is generally used. Bistrimethylsilyltrifluoroacetamide is a useful reagent to accomplish this. The high polarity of the free hydroxyl group is reduced by formation of the O-TMSi ether, thereby enhancing the chromatography of the derivative. Fragmentation of the O-TMSi derivative occurs alpha to the carbon containing the hydroxyl group, with the main cleavage on the side adjacent to the carboxyl group. Derivatization protocols have been developed to provide more detailed position of double bonds or geometry information. These include, for example use of pyrrolidine, 3-pyridyl carbinol (picolinyl ester), dimethyl disulphide (DMDS), and 4,4-dimethyloxazolazine (DMOX) derivatives. An advantage of the use of the DMOX protocol is that the FAME and their corresponding DMOX derivatives have the same elution order when run on the same GC column. Polyunsaturated FA DMOX derivatives can be readily formed using the protocols described in Svetashev (2011) and Lee Chang et al. (2016). Interesting examples of application of the DMOX protocol have been the characterization of a novel series of C18 to C22trans n-3 PUFA from a marine haptophyte (Lee Chang et al., 2016) and the 22:4n-9t in scallop (Marty et al., 1999). MonounsaturatedFA double bond position and geometry is readily determined by capillary GC–MS of their DMDS derivatives (Nichols et al., 1986). An example of application of the DMDS protocol has been the characterization of a range of novel C16 and C18 MUFA, including both cis- and trans-isomers, from microbial monocultures and complex soil consortia (Nichols et al., 1986). Other derivatization procedures are also available, although they are not covered here, and are as detailed in Christie (1989). Quantification In GC-FID analyses, the response intensity is based on the number of oxidizable carbon atoms in the analyte; equivalent masses of FA having different carbon chain lengths and/or numbers of double bonds will not produce the same response in the FID (Ackman, 1972). Fatty acid methyl esters are usually quantified by peak areas, and absolute concentrations are determined by adding an internal standard. Discrimination of FAME may be associated with injection technique, and it is often necessary to introduce empirical response factors, determined through careful use of commercially available authentic mixed standards (see section Injection). A way to estimate the importance of bias from different origins (such as injector or detector) is for example to analyse an FA mixture of known theoretical mass composition such as the Supelco® 37 Component FAME Mix. It is also possible to analyse a phospholipid with two different aliphatic chains of different length and saturation (e.g. phosphatidylcholine 16:0/22:6n-3) and confirm that the molar percentage of each FA is 50/50. The internal standard must not coelute with the FAME, its chemical structure must be close to the majority of the FAME, and it must be absent from the sample. A known mass of internal standard should be added either after SPE or before transesterification to account for the derivatization yield and recovery of FAME (see section Separation of neutral and polar lipids). The most widely used standards are the carboxylic form of nonadecanoic acid (19:0), heneicosanoic acid (21:0), and tricosanoic acid (23:0). Data are generally expressed as relative (area %, mass % or mole %) or absolute contents (e.g. mg FA/g sample dry mass). With GC–MS analyses, one approach is to use the total ion chromatogram in a similar fashion as peak area is used with GC-FID, by comparing the peak area of an internal standard with peak areas of compounds of interest, and relative response factors determined individually using authentic standards. Note here that response factors vary with structure (carbon and unsaturation numbers, mostly notably), ionization methods (electronic vs. chemical) and mass detector (e.g. quadrupole vs. ion trap) (Dodds et al., 2005; Quehenberger et al., 2011). Consequently, the range of response factors is greater in MS than with FID. Response factors for FA, where standards are not available, are assumed the same as those of structurally similar and available compounds but this is arguable. For greater sensitivity, calibration can be based on prominent ion(s) using selected ion monitoring (SIM) as a single ion can be scanned many more times in a cycle than the full mass spectrum, giving a higher signal to noise ratio (Dodds et al., 2005; Thurnhofer and Vetter, 2005; Quehenberger et al., 2011). It involves the exclusive acquisition of a group of selected ions during a given period if the identity and retention time of a given analyte have been established through full scan mode. Since fragmentation can vary depending on chain length and degree of unsaturation, careful assignments of selected ions group to analytes and appropriate adjustments of SIM parameters are necessary to achieve accurate quantification and complete discrimination when some FA are co-eluting. Quantification of FA can be further improved by using mixtures of isotopically labelled FA internal standards (e.g. deuterated FA). These offer the advantage of being identical to the analyte of interest in all aspects except mass, so that response, as well as matrix effects, of the labelled and unlabelled components is the same (Quehenberger et al., 2011). The main limitation to this approach is the commercial availability of isotope-labelled FA that could “match” all FA present in aquatic samples. Data treatment As emphasized by the previous sections, obtaining reliable FA data can be a long and complex process. The purpose of this section is therefore to offer guidance in how to valorize these hard-earned data in the best possible way and to use them to shape robust answers to a wide range of scientific questions. As environmental phenomena are inherently complex, a single variable rarely depicts them in a satisfactory way (Buttigieg and Ramette, 2014). Moreover, FA studies typically deal with datasets containing many variables (i.e. individual FA). This section will therefore mostly focus on multivariate analysis. Readers looking for guidance about univariate tests and procedures are encouraged to refer to other publications (e.g. McDonald, 2014). After adequate standardization and/or transformation, FA data can be analysed in different, non-mutually exclusive ways. Unconstrained ordination methods can help visualizing the data, and finding out which samples group together according to their similarity in FA composition. In doing so, they allow determining how similar to one another entities such as sampling locations (Hughes et al., 2005; Parrish et al., 2015b), habitats (Hixson et al., 2015), or species (Sardenne et al., 2016) are. These exploratory methods are adequate to identify patterns between samples, as well as FA markers responsible for these patterns. Conversely, investigators can be interested in assessing the influence of one or several previously identified factors on FA composition, e.g. to explicitly test whether FA composition is different across multiple experimental treatments, ecological conditions, or species. This can be done through hypothesis testing and/or constrained ordination. Regardless of the data analysis approach, FA studies commonly rely on “biomarker compounds”, i.e. FA (or combinations of FA) whose origin can be assigned to a biogeochemical process, an ecosystem compartment, and/or an organism group. When doing so, it is important to remember that there are no ubiquitous FA biomarkers and that their applicability will depend on the studied system or organism (e.g. Feiner et al., 2018). To avoid interpretation biases, biomarker compounds should be chosen carefully with respect to the ecological context and scientific question of interest. When used sensibly, biomarker compounds can be very useful, as proven by their routine use in ecological literature (see Dalsgaard et al., 2003; Kelly and Scheibling, 2012 for review). Standardization and transformation Choosing standardization or a prior transformation method for FA data needs to be based on the research questions, as it can have important impacts on the final results and interpretations (Mocking et al., 2012). Fatty acids can be expressed either as an absolute content or mass fraction, such as mg/g of lipids or of tissue, or as relative proportion (%) of total FA mass or molar concentration. Concentrations are commonly used in nutrition studies and can be useful in controlled feeding experiments (Elsdon, 2010). Proportions of total FA mass are the commonly used standardization method in trophic studies. It avoids occultation of compositional differences in FA profiles by removing the influence of non-FA components from both the lipid fraction (e.g. polar vs. neutral lipids) and the total lipid contents of tissues. Yet, proportion data are generally calculated in relation to identified FA in a sample, which varies across studies and FA analysis methodologies, limiting inter-study comparison. Choice of data standardization is interdependent with the choice of data transformation method (if needed) and of applied statistical test (univariate vs. multivariate tests). FA data transformation can be recommended in some cases (e.g. Kelly and Scheibling, 2012), but the overall effect of the procedure is highly variable (Happel et al., 2017). Deciding on a transformation method depends on the test used and its assumptions (e.g. normal distribution of data, homoscedasticity). Although these assumptions can be met for univariate analyses (that test the effect of one or several factors on individual FA), this is rarely the case for multivariate analyses using large FA datasets, even after transformation (Budge et al., 2006). However, multivariate methods such as unconstrained ordinations (e.g. principal component analysis, see below) give useful and adequate results when the data are properly transformed (e.g. Legendre and Gallagher, 2001). Moreover, proper testing procedures through permutations allow one to circumvent the violation of the multivariate normality assumption in constrained ordination methods such as redundancy analysis (RDA, Legendre and Legendre, 2012; see section Multivariate hypothesis tests and modelling). Kelly and Scheibling (2012) reviewed some of the most common data transformation methods used in FA studies, including log-ratio (Budge et al., 2006), arcsine, and logit transformations. Arcsine transformation has been widely applied to transform proportional FA data, yet its use in biological analysis has been challenged in some studies. Warton and Hui (2011) suggested that applying logistic regression is more appropriate to transform proportion data. It is also important to note that some transformations such as log(x) or log(x + 1) will give more weight to rare (i.e. found in small amounts) FA, while reducing the influence of the abundant FA (Happel et al., 2017). Depending on the study objectives, it is essential to identify clearly whether the scientific question requires emphasizing the presence and the role of rare FA, or if focusing on the dominant FA will give a more global pattern within and across organisms. Exploring FA data through ordination Ordination methods are essential to describe and visualize sample dispersion in a complex multidimensional dataset and to identify groups of co-varying variables. Those are computed based on a matrix of resemblances (distance, dissimilarities, similarities), or derived ranks, rather than raw (or transformed) data. In FA literature, the most commonly used resemblance measure between samples are either the Euclidean distance or percentage difference (also called Bray–Curtis dissimilarity; Legendre and Legendre, 2012). Euclidean distance is generally preferred over Bray–Curtis for FA analysis (Happel et al., 2017). However, just like data transformations, the choice of the resemblance measure can have important impacts on the final results. The choice of matrix will depend on whether a dataset includes null/absent values and if they need to be included in the analysis according to the study question. The percentage difference coefficient is double-zero asymmetric, i.e. the absence of one FA from two individuals is not considered as contributing to their similarity. The Euclidean distance, on the contrary, is double-zero symmetric, i.e. will be affected by double null values (Legendre and Legendre, 2012). Other properties of resemblance measures should also be considered. Choice of the resemblance coefficient has received extensive attention in multivariate community ecology (e.g. Legendre and Legendre, 2012). Commonly used methods to plot ordinations include principal components analysis (PCA), principal coordinates analysis (PCoA), and non-metric multidimensional scaling (nMDS). PCA can only use Euclidean distance, while PCoA and nMDS can use any resemblance measure. PCoA and nMDS use the ranks of similarities or dissimilarities matrix to find a spatial arrangement of individuals and represent relationships among them (Kelly and Scheibling, 2012). NMDS is conceptually simple, generally applicable, and well adapted to ecological data (Clarke and Warwick, 2001). However, it is crucial to take into account the stress coefficient, which is measured from the relationship between ranks of dissimilarities and ranks of sample distances on the plot. A high stress score (>0.05) indicates that data have been highly distorted to fit in the visualization, which may not be representative and should be considered with caution (Clarke and Warwick, 2001; Buttigieg and Ramette, 2014). Principal components analysis also describes relationships among samples, reducing large numbers of variables to a few components that represent most of the variance and that are built by combining correlated variables (Budge et al., 2006; Kelly and Scheibling, 2012). It also helps to visualize variables that contribute the most to the dissimilarity among observed groups and FA that are highly correlated (Budge et al., 2006). After ordination, several procedures (similarity percentage, also called SIMPER, analysis; comparison of PCA loadings) can be applied to identify FA-driving resemblance or dissimilarity among groups (Clarke and Warwick, 2001). Multivariate hypothesis tests and modelling Multivariate analyses are commonly applied in ecological studies to discriminate the influence of different factors on FA composition, such as physiological, spatial, or even temporal variations (Anderson, 2001). Statistical comparison of groups based on FA composition is generally performed through non-parametric or randomization procedures, as FA datasets rarely match the prerequisites of parametric statistical tests (Anderson and Walsh, 2013). Analysis of similarity (ANOSIM; Clarke, 1993) or permutational analysis of variance (PERMANOVA; Anderson, 2001) is among the most commonly used statistical test (e.g. Hughes et al., 2005; Hixson et al., 2015; Parrish et al., 2015a; Sardenne et al., 2016; Mathieu-Resuge et al., 2019). Analysis of similarity uses ranks of pairwise dissimilarities and generates its own statistic by randomization of rank dissimilarities (Clarke and Warwick, 2001). Permutational analysis of variance tests for differences among group centroids (Anderson, 2001). Both methods are affected by dispersion heterogeneity, with ANOSIM being more sensitive than PERMANOVA (Anderson and Walsh, 2013). Permutational analysis of variance is unaffected by differences in correlation structure and seems more powerful to detect changes in FA composition. As such, applying ANOSIM is appropriate for a one-way test (e.g. to compare one species submitted to different diets), but not for more complex study designs (Kelly and Scheibling, 2012). Besides hypothesis testing, constrained ordination (e.g. redundancy analysis, RDA) offers a way to extract and summarize variability from a multivariate dataset (FA data) that can be explained by one or a set of explanatory variables (e.g. locations, species, diets). In other words, it allows regression of multiple response variables (FA) on multiple explanatory variables (chosen according to the scientific question). More details on this technique can be found elsewhere (Legendre and Legendre, 2012). Coupling FA with other ecological markers When tackling elaborate ecological questions, combining FA with other tracers has proven to be very powerful. Tracers commonly combined with FA include stable isotope ratios of light biogenic elements (Kharlamenko et al., 1995, 2001; Nyssen et al., 2005), as well as contaminants (Kainz and Fisk, 2009; Le Croizier et al., 2016). In many cases, those combinations are qualitative. However, modern data analysis frameworks allow quantitative coupling of stable isotope and FA in mixing models (Stock et al., 2018) or ecological niche models (Jackson et al., 2011; Sardenne et al., 2016). Such developments hold great promise for ecological research, due to the complementarity of these two methods. More generally, multiple factor analysis (Pagès, 2014) or co-inertia analysis (Dray et al., 2003) could provide flexible, widely applicable and quantitative ways to couple data from different methods by allowing exploration of the links between multiple multivariate datasets. Conclusion The use of FA analysis in trophic ecology, aquatic nutrition, and aquaculture research and development holds considerable promise to improve our understanding of the natural world from organism taxonomy and physiology to ecosystem functioning. However, the application of this approach needs to be carefully considered when initiating a new study to ensure that interpretations proposed for FA data are relevant and appropriate. Although protocols for lipid analysis are well-described, their application to aquatic sciences often requires modifications to adapt to field conditions and to sample types. Here, we presented the current state of knowledge on methods dedicated to aquatic lipid analyses, from sampling to data treatment. We reviewed sample preservation, storage and transport protocols, and their effects on lipids, lipid extraction methods, separation of polar and neutral lipids, transesterification and quantification techniques, and available tools for the treatment and statistical analysis of FA data. A simplified flowchart of all these steps is represented in Figure 1. Lipids can be degraded by hydrolysis or oxidation at each stage of the lipid analysis process. We thus encourage processing samples at low temperature under nitrogen atmosphere, and reporting hydrolysis indicators. To limit possible deleterious effects, samples should be handled at the lowest suitable temperature before direct extraction, or be immediately frozen for storage, ideally at −20°C for <1 month, −80°C for <6 months or under liquid nitrogen with no time limit. Long distance transport of samples must be planned well ahead of the start of the study to obtain relevant import/export permits and to choose appropriate transportation to maintain sample viability and respect transport regulations and national laws. When possible, we recommend transporting samples freshly frozen under a nitrogen atmosphere. Depending on the objectives of the study, samples can be either directly transesterified or undergo extraction to isolate lipids. Lipid extraction is usually performed following well-established methods using either chloroform or dichloromethane (less toxic), mixed with methanol. We strongly encourage scientists to describe in their publication any modifications from original extraction methods. The lipid extract is either separated into classes or directly transesterified. Separation of polar and neutral lipids prior to transesterification allows for a better interpretation of FA profiles when seeking information on nutritional status and/or physiology of organisms. Special attention should be paid to control for the purity and recovery of each fraction. Non-volatile FA are then transesterified into their volatile derivatives, usually FAME, before being analysed by GC. Acid-catalyzed reagents, such as HCl or H2SO4, are the most commonly used in aquatic science as they present fewer analytical issues than BF3. Hexane is the most widely used solvent to recover FAME after transesterification. Less toxic alternatives are emerging but are not applicable to all sample types. The analysis and quantification of FAME is usually conducted by GC-FID. FAME are identified by comparing their retention times with those of individual purified standards. They are quantified as relative proportions using peak areas or by absolute concentration when an internal standard was added before transesterification. When necessary, peak identification can be confirmed using GC–MS, but this process may require additional derivatization steps. Data treatment and statistical analysis of FA should be carefully applied according to the investigated scientific question. We presented common multivariate statistical procedures that we consider widely relevant for FA-related problems. This list is by no means exhaustive. We encourage analysts to explore their data in multiple ways. A good starting point is usually a simple preliminary visualization of data. This will not only help to check for data quality but also to make informed choices about subsequent procedures. When interpreting data and statistical results, it is crucial to take into account the studied organisms and/or ecosystems and their specificities. We argue that methodological consistency, controlled experimentation, and interlaboratory comparison and calibration can lead to enhanced progress in FA studies and are particularly important for comparative studies. Standardization and optimization of FA methodologies will ultimately improve our understanding on the role of lipids in biological mechanisms and will enable us to use them as ecological or physiological tracers as efficiently as possible. Acknowledgements We thank S. Hervé for his assistance with the figure design and conception and O. Gauthier for conducting the technical workshop “Numerical tools for lipid composition data analysis” and for his feedback and edit on the section Data treatment. We are grateful to H. Browman, the Editor, and three anonymous reviewers for valuable comments that have improved the article. Funding The consortium gathered to conduct this review, emerged from the technical workshops of the conference “Lipids in the Ocean” held from the 17th to 22nd of November 2018 in Brest (France). This conference was sponsored by regional governments (Brest Métropole Ocean, Conseil départemental du Finistère, Région Bretagne), National Research Organizations (Ifremer, CNRS, IRD), the University of Brest and its European Institute for Marine Studies, and Euromarine (European Marine Research Network). This work was also supported by the “Laboratoire d'Excellence” LabexMER (ANR-10-LABX-19) and co-funded by a grant from the French government under the programme “Investissements d'Avenir”. EdC acknowledges FCT/MCTES for the financial support to CESAM (UIDP/50017/2020+UIDB/50017/2020), through national funds. Author contributions LIEC, LNM, FP, and PS equally contributed to the conception of the paper and its figure, and led the writing of the paper. 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Aguzzi,, J;Chatzievangelou,, D;Company, J, B;Thomsen,, L;Marini,, S;Bonofiglio,, F;Juanes,, F;Rountree,, R;Berry,, A;Chumbinho,, R;Lordan,, C;Doyle,, J;, del Rio, J;Navarro,, J;, De Leo, F C;Bahamon,, N;

Doo, Steve S; Kealoha, Andrea; Andersson, Andreas; Cohen, Anne L; Hicks, Tacey L; Johnson, Zackary I; Long, Matthew H; McElhany, Paul; Mollica, Nathaniel; Shamberger, Kathryn E F; Silbiger, Nyssa J; Takeshita, Yuichiro; Busch, D Shallin

doi: 10.1093/icesjms/fsaa094pmid: N/A

Abstract A substantial body of research now exists demonstrating sensitivities of marine organisms to ocean acidification (OA) in laboratory settings. However, corresponding in situ observations of marine species or ecosystem changes that can be unequivocally attributed to anthropogenic OA are limited. Challenges remain in detecting and attributing OA effects in nature, in part because multiple environmental changes are co-occurring with OA, all of which have the potential to influence marine ecosystem responses. Furthermore, the change in ocean pH since the industrial revolution is small relative to the natural variability within many systems, making it difficult to detect, and in some cases, has yet to cross physiological thresholds. The small number of studies that clearly document OA impacts in nature cannot be interpreted as a lack of larger-scale attributable impacts at the present time or in the future but highlights the need for innovative research approaches and analyses. We summarize the general findings in four relatively well-studied marine groups (seagrasses, pteropods, oysters, and coral reefs) and integrate overarching themes to highlight the challenges involved in detecting and attributing the effects of OA in natural environments. We then discuss four potential strategies to better evaluate and attribute OA impacts on species and ecosystems. First, we highlight the need for work quantifying the anthropogenic input of CO2 in coastal and open-ocean waters to understand how this increase in CO2 interacts with other physical and chemical factors to drive organismal conditions. Second, understanding OA-induced changes in population-level demography, potentially increased sensitivities in certain life stages, and how these effects scale to ecosystem-level processes (e.g. community metabolism) will improve our ability to attribute impacts to OA among co-varying parameters. Third, there is a great need to understand the potential modulation of OA impacts through the interplay of ecology and evolution (eco–evo dynamics). Lastly, further research efforts designed to detect, quantify, and project the effects of OA on marine organisms and ecosystems utilizing a comparative approach with long-term data sets will also provide critical information for informing the management of marine ecosystems. Introduction A third of the anthropogenic CO2 released to the atmosphere has been absorbed by the oceans, causing declines in ocean pH and calcium carbonate saturation state (Bindoff et al., 2019; Gruber et al., 2019). These changes are referred to as ocean acidification (OA) (Caldeira and Wickett, 2003; Doney et al., 2009; Le Quéré et al., 2018). Information from the geological record (Hönisch et al., 2012), laboratory experiments (Kroeker et al., 2013), field observations (Keller et al., 2014; Sutton et al., 2016, 2017; Henson et al., 2017; Turk et al., 2019), and numerical modelling (Marshall et al., 2017) strongly suggests that OA has the potential to alter the function of ocean ecosystems, impacting marine biota and ecosystem services (Andersson et al., 2015). However, characterizing current and future effects of OA on marine systems is challenging. While there is a general consensus that OA elicits largely negative effects on calcifying organisms and positive effects on primary producers (Kroeker et al., 2010; Busch and McElhany, 2016; Mostofa et al., 2016), these conclusions are primarily drawn from laboratory experiments in which species sensitivity is evaluated using short-term incubations under elevated CO2 conditions. Controlled experiments have found relationships between organism responses and CO2 conditions (Waldbusser et al., 2014) and have considered how physiological sensitivities scale to predictions of evolutionary responses (Munday et al., 2013). These types of studies offer important insight into mechanistic responses of marine organismal physiology to OA but may provide a limited assessment of population-level impacts due to the complexity of how OA impacts may cascade through ecosystems (e.g. variation in the sensitivity of individuals within a community and subsequent impacts on population dynamics; Busch et al., 2013; Busch and McElhany, 2016). In addition to characterizing existing organismal sensitivities to OA, we must document how ecologically complex in situ conditions (e.g. simulating natural variability of carbonate chemistry, food availability) vary from those observed in the laboratory to understand OA impacts and interpret ecosystem-level responses (Andersson and Mackenzie, 2012). OA sensitivities are also expected to vary widely in natural systems, for example an average 0.1 decline in pH due to OA could be enough to push some species or ecosystems over critical thresholds, or might be unimportant in systems that have natural variability ranging from 0.5 to 1 pH units within a day (Hofmann et al., 2011). Challenges of both spatial and temporal scaling of laboratory results to in situ responses are compounded by the need to understand how OA interacts with other physical, chemical, and biological forcings (Breitburg et al., 2015; Kroeker et al., 2017). While researchers generally agree that a multifaceted approach is necessary, evaluating the benefits and drawbacks of different approaches requires careful consideration (see Andersson et al., 2015; Boyd et al., 2018). For example, free ocean carbon enrichment-type experiments constrain natural variation between specific locations within an ecosystem while only manipulating CO2 (Barry et al., 2014; Gattuso et al., 2014; Doo et al., 2019) but are difficult to scale to ecosystem-level projections of OA impacts. Furthermore, in situ large-scale pelagic mesocosms studies have been performed to document changes in plankton communities, although the community composition and trajectory (e.g. potential phytoplankton blooms in select mesocosms) are difficult to constrain (Bach et al., 2016; Algueró-Muñiz et al., 2017; Riebesell et al., 2017, 2018). Field-based observations are largely gleaned from natural CO2 gradients (from vents/seeps and spatial pH gradients) across ecosystem scales (e.g. Hall-Spencer et al., 2008; Fabricius et al., 2011; Silbiger et al., 2014; Barkley et al., 2015; Mollica et al., 2018). However, these effects are often interwoven with other physical and chemical parameters that are difficult to disentangle (Silbiger et al., 2017). Other methods, including statistical techniques (e.g. Silbiger et al., 2014) and proxies (e.g. Mollica et al., 2018), have been used to gain insight into how ecosystems may respond to OA. Scaling between sensitivity information from laboratory settings to multi-generational and ecosystem-level responses in nature has largely been done using conceptual models (Busch et al., 2015; Edmunds et al., 2016). These models are complemented by laboratory studies that assess potential transgenerational adaptation effects, highlighting the possibility for organisms to rapidly adapt to changing CO2 conditions (Parker et al., 2015; Putnam and Gates, 2015; Wong et al., 2018). Although both natural and laboratory experiments strongly suggest negative biological consequences in response to OA, long-term (multi-decadal) biological and ecological measurements that are unequivocally linked to anthropogenic CO2 accumulation in situ are limited to a handful of studies, mostly on planktonic foraminifera (de Moel et al., 2009; Moy et al., 2009; Fox et al., 2020; Osborne et al., 2020). Disentangling effects of OA on marine species from natural environmental variability and other climate change drivers has been a cornerstone of OA research over the past decade (Breitburg et al., 2015). The quality and abundance of ocean carbonate chemistry measurements have advanced, making progress in attributing ocean chemistry changes to anthropogenic CO2 (Weisberg et al., 2016). Although OA has been unequivocally observed in the open ocean (Bates et al., 2014), this trend is only beginning to be documented in near-shore environments due to high natural variability and limited duration of observations (Duarte et al., 2013; Andersson et al., 2015; Reimer et al., 2017; Sutton et al., 2019). Time of emergence refers to the point at which an anthropogenic signal is detectable outside the bounds of natural variability; it has been applied with success to marine carbonate chemistry and other oceanographic measurements of CO2 increase (Keller et al., 2014; Sutton et al., 2016, 2017; Henson et al., 2017; Turk et al., 2019), but has not been observed in some ocean environments, especially those lacking historical measurements, including many coastal regions (Sutton et al., 2019). With many marine ecosystems lacking time-series measurements of carbonate chemistry and biological indices that are longer than the time of emergence, the extent to which biological responses are attributable to OA in nature remains an open question. The topic of scaling from laboratory-based, single-species studies to understanding OA impacts in situ has been discussed in previous perspectives and syntheses (Hennige et al., 2014; Riebesell and Gattuso, 2015; Hurd et al., 2018). Our aim here is to review our ability to detect and attribute OA impacts for four well-studied groups and to stimulate further discussion and consideration of how to improve detection and attribution as the OA research field continues moving forward. Here, we refer to OA sensitivity as any biological response (physiological change) of an organism to increasing CO2. An impact of OA is defined as a change in an in situ biological measurement that is attributed to in situ changes in seawater chemistry resulting from increasing anthropogenic CO2. We focus on four groups (seagrasses, pteropods, oysters, and coral reefs), selected for their sensitivity to OA and their ecological and/or economic importance. The authors also have expertise in each of these groups. For each, we summarize the results of laboratory and field-based studies on CO2 sensitivity and the current ability to detect and attribute change in the system to OA. The complications discussed here are not meant to criticize existing studies but to highlight the need for a greater understanding of the impacts of OA in natural ecosystems and for an improved ability to attribute and quantify these impacts. Seagrass Seagrasses are commonly considered potential beneficiaries of OA; they are carbon-limited under current CO2 conditions and increase photosynthesis under higher CO2 concentrations (Koch et al., 2013). This is in contrast to most marine autotrophs, which have developed efficient strategies for utilizing bicarbonate (⁠ HCO3− ⁠), and is due to the relatively recent evolution of marine seagrasses under comparatively higher CO2 concentrations (Beer and Koch, 1996; Zimmerman et al., 1997). Results from mesocosm and in situ manipulations of CO2 indicate increased seagrass productivity, shoot density, and biomass under elevated CO2 conditions (Beer and Koch, 1996; Zimmerman et al., 1997; Hall-Spencer et al., 2008; Fabricius et al., 2011; Campbell and Fourqurean, 2014). However, divergent results have been found in volcanic CO2 seep sites. Seagrasses in the Mediterranean show decreases in density and biomass (Apostolaki et al., 2014) and in Papua New Guinea have up to a fivefold biomass increase (Takahashi et al., 2016) with increasing CO2. In addition, seagrass species live in a complex environment; thus, seagrass response to OA will likely be modulated by interactions with other species. For example, a decrease in calcareous epiphytes on seagrasses at CO2 seeps has been shown (Martin et al., 2008), while the potential for an increase in fleshy epiphytes has also been documented (Campbell and Fourqurean, 2014). Globally, seagrass abundance has declined by ∼30%, which has been attributed to coastal urbanization, rising sea surface temperatures, and water quality degradation (Waycott et al., 2009). To our knowledge, no in situ study has attributed positive effects of anthropogenic OA on seagrass growth, while decreases in species density and range have been observed in response to other anthropogenic stress (e.g. pollution, warming; Koch et al., 2013). Furthermore, theoretical OA refugia created by seagrasses have not yet been observed consistently in situ and are likely dependent on site-specific factors (e.g. residence times, autotroph location relative to water advection, community composition) making successful in situ attribution of benefits to adjacent calcifiers difficult (Anthony et al., 2011, 2013; Unsworth et al., 2012; Mongin et al., 2016). In addition, although photosynthesis by seagrasses decreases CO2 during the day, potentially equal or greater night-time respiration may counteract daytime effects by increasing CO2, resulting in a near-zero daily balance that produces negligible effects on the progression of OA (Koweek et al., 2018; Pacella et al., 2018; Kapsenberg and Cyronak, 2019). While the theoretical benefits of OA on seagrass growth have been well documented in the laboratory, it appears that substantial negative impacts from other anthropogenic stressors may counteract any positive effects of increased CO2 and have likely prevented the isolation and attribution of the potential beneficial responses of OA (Koch et al., 2013). Pteropods Pteropods were one of the first taxonomic groups identified as vulnerable to OA (Orr et al., 2005). Numerous laboratory experiments have documented negative effects of exposure to elevated CO2, including shell dissolution, reduced (or absent) calcification, altered respiration rates, decreased sinking rates, differential gene expression, delayed egg development, and increased mortality (Comeau et al., 2009, 2010a, b; Lischka and Riebesell, 2012, 2017; Manno et al., 2012, 2016; Seibel et al., 2012; Busch et al., 2014; Koh et al., 2015; Maas et al., 2015; Thabet et al., 2015; Moya et al., 2016; Johnson and Hofmann, 2017). However, the response of pteropods to high CO2 is not uniformly negative (Maas et al., 2016), and the outer organic layer of the pteropod shell offers some protection from undersaturated waters (Peck et al., 2016, 2018). OA-related pteropod field observations have focused on a variety of time scales and response metrics. Analysis of pteropod shell collections from the past 100 years in the Mediterranean show declines in shell thickness and density for two different species (Howes et al., 2017). Sediment core studies indicate some evidence for a correlation between fossil pteropod shell dissolution during life and atmospheric CO2 (Wall-Palmer et al., 2012, 2013; Manno et al., 2017). Single-season, in situ studies have shown correlations between carbonate chemistry conditions and pteropod shell dissolution, oxidative stress, relative abundance, and vertical distribution (Bednaršek et al., 2012, 2014, 2017, 2018; Bednaršek and Ohman, 2015; Feely et al., 2016; Engström-Öst et al., 2019). Observations of shell dissolution along natural gradients in aragonite saturation state (Ωar) and snapshots of current pteropod distributions correlated with Ωar have been combined with historical reconstructions of carbonate chemistry to provide hypotheses about recent changes in pteropod abundance due to OA (Bednaršek et al., 2017). While spatial gradient studies show correlations with carbonate chemistry that provide strong evidence for a negative effect of OA on pteropod shell condition, they do not necessarily offer direct evidence of modern OA effects because they substitute space for time and make inferences about historical states without direct observations (McElhany, 2017). Available time-series analyses find no significant relationships between pteropod abundance and carbonate chemistry (Howes et al., 2015; Thibodeau et al., 2018). Recent analyses of pteropod abundance time-series from around the globe show that populations vary in trajectories with some declining, some increasing, and others showing no change; this is counter to what would be expected if the negative effects of OA now dominate population processes, suggesting that other local and regional drivers, including ocean warming, currently influence pteropods more than OA (Ohman et al., 2009; Head and Pepin, 2010; Mackas and Galbraith, 2012; Beare et al., 2013; Beaugrand et al., 2013). While both historical and modern samples suggest that pteropods are sensitive to carbonate chemistry conditions, more evidence is needed to link the progress of OA to impacts on the demographics of pteropod populations. It is possible that there are variable responses of pteropods in situ, time-series are not yet long enough to detect a directional change caused by OA, and/or the chemical thresholds at which ocean carbonate chemistry influences pteropods have not yet been crossed at the ecosystem scale. Oysters Impacts of elevated CO2 on oyster larvae were key in raising concerns about the implications of OA for marine ecosystems (Kelly et al., 2014). Laboratory studies have yielded a more complete understanding of the sensitivity of oysters to acidified conditions, documenting effects in the larval stage such as decreased calcification, reduced growth, delayed metamorphosis, and increased mortality (Miller et al., 2009; Talmage and Gobler, 2009; Watson et al., 2009; Parker et al., 2010, 2011; Dickinson et al., 2012; Waldbusser et al., 2013; Barton et al., 2015; Frieder et al., 2017). Laboratory research has also indicated that juvenile and adult oysters are sensitive to OA, though responses are variable. Some species and populations show changes in metabolism, calcification, and shell strength under OA conditions, with effects on juveniles sometimes carried over from larval exposure (Gazeau et al., 2007; Beniash et al., 2010; Welladsen et al., 2010; Parker et al., 2011, 2012; Hettinger et al., 2012; Sanford et al., 2014; Wright et al., 2014). Carbonate chemistry conditions documented in shellfish hatcheries provide an example of how acidification can be linked to declines in larval performance in an artificial system (Barton et al., 2012; Ellis et al., 2017). Many oyster hatcheries now control seawater conditions (modification of carbonate chemistry, abundance of food, decrease in predation) and oyster producers have long practiced selection/breeding for performance (Barton et al., 2012; Ellis et al., 2017). Curiously, Pacific oyster recruitment still occurs in wild populations exposed to Ωar near threshold limits for calcification found in the laboratory (Ruesink et al., 2018). This apparent contradiction suggests that the influence of carbonate chemistry on oyster populations is complex and likely affected by varying and heterogeneous chemical conditions, other environmental factors, adaptation mechanisms, and/or transgenerational effects (Parker et al., 2010, 2012, 2017a, b; Dickinson et al., 2012; Hettinger et al., 2013; Ruesink et al., 2018). There is limited information about the micro-habitat carbonate chemistry conditions that natural oyster populations experience (Hales et al., 2017), though first principles suggest that they persist in a wide range of conditions given the influence of fluctuations in freshwater inputs, other dynamic physical drivers, and biological activity in their habitat. Over the last 130 years, a global decline in oyster populations has been driven by over-harvesting, competition with non-native species, disease, and other anthropogenic factors (Beck et al., 2011). Any role of OA in these changes in situ is still unclear due to the lack of available demographic data and related carbonate chemistry time-series in coastal environments. Tropical coral reefs The expectation that OA will negatively affect tropical coral reef calcification is rooted in thermodynamics (e.g. Plummer and Busenberg, 1987) and early abiogenic CaCO3 precipitation experiments that provided a quantitative framework within which to understand, predict, and interpret biological responses (Burton and Walter, 1987; Morse and Mackenzie, 1990). Subsequent experiments supported the prediction that as Ωar declines, calcification decreases (Langdon et al., 2000; Leclercq et al., 2002; Langdon and Atkinson, 2005) and CaCO3 dissolution increases (Andersson et al., 2007; Andersson and Gledhill, 2013). Field and laboratory-based studies suggest that OA may enhance the bioerosion capabilities of borers, increasing breakdown of the calcium carbonate framework (Tribollet et al., 2009; Wisshak et al., 2012; Silbiger et al., 2014; DeCarlo et al., 2015). Field studies have found correlations between Ωar and net ecosystem calcification (NEC), the net balance of gross ecosystem calcification and dissolution. For example, manipulative short-term, in situ, pulse alkalinization (Albright et al., 2016) and pulse acidification (Albright et al., 2018) experiments across a coral reef flat documented increased and decreased NEC, respectively, providing critical information for how net calcification responds to OA at the ecosystem level. Field observations across natural Ωar gradients report declines in coral skeletal density, coral diversity, colony size, NEC, and increases in bioerosion and dissolution with declining Ωar (Silverman et al., 2007; Manzello et al., 2008; Fabricius et al., 2011; Shamberger et al., 2011; Enochs et al., 2016; Silbiger et al., 2016; Eyre et al., 2018; Mollica et al., 2018). However, there are notable exceptions (e.g. Shamberger et al., 2014; Barkley et al., 2015; DeCarlo et al., 2017; Silbiger et al., 2017). The general expectation, based on theoretical predictions and experimental results, is that OA should have already negatively affected coral reefs (Table 1). However, the current inability to confidently isolate and attribute effects of anthropogenic OA on coral reefs in situ suggests that either the current measurement methods are not sensitive enough to detect expected impacts, or these impacts have been mitigated by other processes or masked by co-varying oceanic changes that have stronger effects. Key insights from the last decade of OA coral reef studies are as follows: Table 1. Summary of marine system responses to OA Marine groups . Summary of experimental findings . Observations of wild populations . Data/analysis that could increase detection in situ . Seagrasses Increased productivity, shoot density, and biomass; changes in community composition No effects attributable directly to OA Improved understanding of the interplay of the factors that drive seagrass abundance and distribution Pteropods Dissolution, reduced calcification, physiological and early life stage impairments, mortality Dissolution in naturally low pH environments; no population effects attributable directly to OA Multi-factor analyses to tease out the role of OA in driving pteropod condition and population dynamics from modern and historical samples Oysters Reduced calcification/growth, physiological effects, and mortality, particularly in larvae and juveniles No effects attributable directly to OA Condition and demography of populations living in different carbonate chemistry environments; studies of the effects of OA throughout the entire life cycle in the context of multiple interacting drivers Coral reef ecosystems Reduced calcification, increased dissolution, and bioerosion Increased bioerosion and dissolution; no effects attributable directly to OA Constrain natural spatiotemporal variability of NEC; understand response to multiple interacting drivers; long-term time-series studies of environmental and reef conditions Marine groups . Summary of experimental findings . Observations of wild populations . Data/analysis that could increase detection in situ . Seagrasses Increased productivity, shoot density, and biomass; changes in community composition No effects attributable directly to OA Improved understanding of the interplay of the factors that drive seagrass abundance and distribution Pteropods Dissolution, reduced calcification, physiological and early life stage impairments, mortality Dissolution in naturally low pH environments; no population effects attributable directly to OA Multi-factor analyses to tease out the role of OA in driving pteropod condition and population dynamics from modern and historical samples Oysters Reduced calcification/growth, physiological effects, and mortality, particularly in larvae and juveniles No effects attributable directly to OA Condition and demography of populations living in different carbonate chemistry environments; studies of the effects of OA throughout the entire life cycle in the context of multiple interacting drivers Coral reef ecosystems Reduced calcification, increased dissolution, and bioerosion Increased bioerosion and dissolution; no effects attributable directly to OA Constrain natural spatiotemporal variability of NEC; understand response to multiple interacting drivers; long-term time-series studies of environmental and reef conditions The expected impacts are based on laboratory/mesocosm CO2 sensitivity experiments, and observations are based on in situ studies (e.g. time-series, natural pH gradients). Data or analyses that may improve the probability of detecting the impacts of OA in situ are suggested. Open in new tab Table 1. Summary of marine system responses to OA Marine groups . Summary of experimental findings . Observations of wild populations . Data/analysis that could increase detection in situ . Seagrasses Increased productivity, shoot density, and biomass; changes in community composition No effects attributable directly to OA Improved understanding of the interplay of the factors that drive seagrass abundance and distribution Pteropods Dissolution, reduced calcification, physiological and early life stage impairments, mortality Dissolution in naturally low pH environments; no population effects attributable directly to OA Multi-factor analyses to tease out the role of OA in driving pteropod condition and population dynamics from modern and historical samples Oysters Reduced calcification/growth, physiological effects, and mortality, particularly in larvae and juveniles No effects attributable directly to OA Condition and demography of populations living in different carbonate chemistry environments; studies of the effects of OA throughout the entire life cycle in the context of multiple interacting drivers Coral reef ecosystems Reduced calcification, increased dissolution, and bioerosion Increased bioerosion and dissolution; no effects attributable directly to OA Constrain natural spatiotemporal variability of NEC; understand response to multiple interacting drivers; long-term time-series studies of environmental and reef conditions Marine groups . Summary of experimental findings . Observations of wild populations . Data/analysis that could increase detection in situ . Seagrasses Increased productivity, shoot density, and biomass; changes in community composition No effects attributable directly to OA Improved understanding of the interplay of the factors that drive seagrass abundance and distribution Pteropods Dissolution, reduced calcification, physiological and early life stage impairments, mortality Dissolution in naturally low pH environments; no population effects attributable directly to OA Multi-factor analyses to tease out the role of OA in driving pteropod condition and population dynamics from modern and historical samples Oysters Reduced calcification/growth, physiological effects, and mortality, particularly in larvae and juveniles No effects attributable directly to OA Condition and demography of populations living in different carbonate chemistry environments; studies of the effects of OA throughout the entire life cycle in the context of multiple interacting drivers Coral reef ecosystems Reduced calcification, increased dissolution, and bioerosion Increased bioerosion and dissolution; no effects attributable directly to OA Constrain natural spatiotemporal variability of NEC; understand response to multiple interacting drivers; long-term time-series studies of environmental and reef conditions The expected impacts are based on laboratory/mesocosm CO2 sensitivity experiments, and observations are based on in situ studies (e.g. time-series, natural pH gradients). Data or analyses that may improve the probability of detecting the impacts of OA in situ are suggested. Open in new tab The metabolism of coral reef organisms strongly affects coral reef seawater chemistry (e.g. Shaw et al., 2012; Cyronak et al., 2014; Shamberger et al., 2014; DeCarlo et al., 2017) and may slow or enhance the acidification of the surrounding open-ocean source water to the reef. Corals and other coral reef organisms modulate the chemistry of their calcifying fluids and may override changes in the chemistry of the seawater source to the site of calcification (Cohen and Holcomb, 2009; Cohen et al., 2009; McCulloch et al., 2012). Coral feeding, availability of dissolved inorganic nutrients, and energetic demands related to reproductive status can mitigate or exacerbate the impact of OA on coral calcification (Langdon and Atkinson, 2005; Cohen and Holcomb, 2009; Holcomb et al., 2010; Edmunds, 2011; Drenkard et al., 2013; Silbiger et al., 2018; Kealoha et al., 2019). Ocean-warming-induced coral bleaching is an important dominant driver of declines in coral growth over the 20th century (Cantin et al., 2010; Courtney et al., 2017; Hughes et al., 2018) that may mask the influence of OA on coral growth histories. Naturally high variability and uncertainty in NEC measurements (Courtney and Andersson, 2019) makes it difficult to determine whether changes in NEC are driven by environmental change or are within the natural variability of the system (Silverman et al., 2014; Shamberger et al., 2018). One consistent response of coral reef organisms and ecosystems across natural gradients in pH, in both laboratory and field experiments and observations, is an increase in bioerosion and sediment dissolution (e.g. Barkley et al., 2015; DeCarlo et al., 2015; Silbiger and Donahue, 2015; Enochs et al., 2016; Silbiger et al., 2016; Eyre et al., 2018). However, these processes are also influenced by factors such as nutrient inputs and organic matter content of sediments, and deconvolving the various contributions remains challenging. Research needs for OA attribution in biological systems Great strides have been made to understand OA impacts. In this perspective, we highlight that laboratory-based studies have identified a variety of ways that a broad taxonomic range of marine species are sensitive to elevated CO2. Informed by these experimental results, progress is also being made on the detection and attribution of anthropogenic OA impacts in wild populations (Table 1). For example, some biological impacts in situ have been correlated with carbonate chemistry and suggest attribution to OA, such as increased shell dissolution of pteropods (Bednaršek et al., 2014) and decreased shell thickness in planktic foraminifera (de Moel et al., 2009; Moy et al., 2009; Fox et al., 2020; Osborne et al., 2020). However, impacts attributable to OA have yet to be detected on ecosystem-level biological parameters such as population density, trophic interactions, or energy transfer through food webs. To improve our detection and attribution ability, research is needed to determine impacts of OA in situ. For some taxa, like oysters, studies are needed to understand how OA may influence the entire life cycle, since OA has different effects across life stages (Pandori and Sorte, 2019). Other groups discussed (seagrasses, oysters, and coral reefs) require efforts to tease out the influence of OA from other co-varying factors that drive physical and chemical conditions (Table 1). Below, we detail four avenues of research that would improve the ability to detect and attribute impacts of OA on marine ecosystems in situ. Quantify the anthropogenic contribution of CO2 in coastal environments: a challenge for attributing change in biological systems to OA is knowledge of the chemical conditions that a species or community inhabits and how OA has altered them. The majority of long-term ocean pH/pCO2 measurements have been made in the open-ocean, which is relatively stable chemically. Coastal oceans tend to have shorter time-series measurements of pH/pCO2, complex biogeochemical and physical processes, and a higher rate of biological activity, causing larger diel, seasonal, and episodic fluctuations in ocean chemistry (e.g. Hofmann et al., 2011; Guadayol et al., 2014; Chan et al., 2017; Silbiger and Sorte, 2018; Lowe et al., 2019). While the chemical signal of OA has already emerged in open oceans, it will take longer to emerge in coastal ecosystems (Sutton et al., 2019). Therefore, we suggest further studies that employ statistical methods to estimate anthropogenic input of CO2 (Gruber et al., 1996; Feely et al., 2016; Carter et al., 2017). These statistical methods will aid in quantifying chemical changes in the oceans due to OA and linking biological impacts. Global coordination of OA monitoring through the Global Ocean Acidification Observing Network will aid robust data collection and synthesis needed for estimating anthropogenic input of CO2 (Newton et al., 2019; Tilbrook et al., 2019). Attribute biological impacts to OA among other co-varying parameters: marine organisms face multiple changing and co-varying physical and chemical parameters associated with climate change (e.g. OA, warming, hypoxia). Identifying specific biological traits that can be measured in situ and empirically linked to OA impacts is of crucial importance in advancing efforts to detect in situ impacts of OA. Such traits of interest to monitor in situ can be physiological (Strader et al., 2019), structural [e.g. coral skeletal density changes in Mollica et al. (2018); foraminifera test thickness changes in Moy et al. (2009)], or components of population fitness (Falkenberg et al., 2018). Importantly, there is a great need to understand how differential sensitivities to OA exist within a species’ life cycle (Byrne and Przeslawski, 2013). In addition, increased efforts to monitor community-level traits of interests (e.g. population density, biomass) are needed to understand ecological alterations in marine ecosystems due to OA. With all research techniques, a holistic approach of detailed characterization of both biological impacts in conjunction with physical and chemical environmental parameters are needed to achieve such an aim. Understand how ecological-evolutionary dynamics alter OA responses in situ: feedbacks between changing conditions in marine environments and organismal adaptation potential have been highlighted with recent efforts to understand the interplay between ecology and evolution (eco–evo dynamics) in driving demographic responses (Parmesan, 2006; Chevin et al., 2013). These eco–evo dynamics on longer time scales have the potential to facilitate intra-generational adaptation to changing ocean conditions through the interplay of ecological processes such as range shifts (Sunday et al., 2012; Vergés et al., 2014; Pecl et al., 2017), alteration in phenotype such as a modification of microbiome (Botté et al., 2019), as well as epigenetic mechanisms (Putnam et al., 2016; Hofmann, 2017). It is crucial to understand how OA has the potential to alter plasticity of phenotypes, which in turn could either constrain adaptive genetic changes through the persistence of diverse genotypes within the population or promote adaptive genetic changes through allowing for persistence in extreme environments (Hendry, 2016). Phytoplankton, in particular, have been used to test the hypothesis that increased phenotypic plasticity over multiple generations will lead to increased evolution in OA conditions (Collins, 2011; Lohbeck et al., 2012; Schaum and Collins, 2014) and have found increased plasticity as a good indicator of adaptation to increasing CO2 conditions (Schaum and Collins, 2014). Future research could expand on current studies that focus on understanding phenotypic plasticity of organismal physiology (Torda et al., 2017; Donelson et al., 2018; Ryu et al., 2018; Willoughby et al., 2018; Catullo et al., 2019) by using modelling efforts that incorporate eco–evo dynamics of both past and future OA conditions. Characterize ecosystem trajectories through long-term monitoring: understanding how and why species are sensitive to OA has vastly improved, but this is just one aspect of understanding population and ecosystem responses in situ. For example, a species’ population dynamics may be influenced more by OA-induced modifications of ecological interactions than by direct sensitivity (Marshall et al., 2017). In some instances, ecological interactions have been hypothesized to mitigate OA impacts through enhancing adaptive capacity or mitigating the effects of elevated CO2 conditions (Kapsenberg and Cyronak, 2019). To attribute changes in species dynamics or ecological processes to OA, more work is needed to describe how OA impacts scale in situ in space and time. Insights into ecosystem environmental changes can be gained using shell geochemistry as paleo-proxies to document OA effects (Foster and Rae, 2016), and potentially how further changes in ocean conditions are linked to mass extinction and declines in biodiversity (Kiessling and Simpson, 2011; Hennige et al., 2014). Modelling exercises can help elucidate ecological processes, but they cannot replace time-series biological data. Of particular importance are long-term observational studies that pair a detectable chemical signal of OA with biological responses that account for ecological processes and patterns (e.g. yearly population growth patterns, NEC). With detailed datasets, broad comparative trends can be used to understand mechanisms of resilience to disturbance events. For example, comparative data indicate that community resilience to changing conditions can develop from various environmental drivers such as indiscriminate disturbance events of crown-of-thorns starfish in Mo’orea, French Polynesia, and repeated thermal stress in Panama, Eastern Tropical Pacific (Edmunds et al., 2019). The variation in environmental drivers has resulted in differences in reproductive strategies of dominant reef-building corals, coral-algal symbiont communities, functional diversity of herbivorous fishes, and the reef framework (Edmunds et al., 2019), highlighting that comparative approaches can be used to understand how differing environmental drivers (such as OA) can alter ecosystem trajectories. Current challenges in attributing large-scale OA effects on marine systems does not mean that there has been no OA effect to date nor that there will not be one in the future. We are beginning efforts to detect and attribute OA impacts in situ, with experimental results informing field campaigns and observational studies approaching the time of emergence for an OA signal in increasingly variable environments. Knowledge accumulated over the last decade puts us in a better position to design an observation system that could detect the emergence of impacts of OA at species and ecosystem levels. Research on species sensitivity to OA that can be scaled into projected ecosystem-level impacts in a multi-stressor ocean and verified with in situ detection is critical to inform the conservation and sustainable use of ocean ecosystems. Acknowledgements This study is a product of the Ocean Acidification Principal Investigators Meeting (17–19 February 2018), organized by the Ocean Carbon and Biogeochemistry Project Office with support from the National Science Foundation. No new data were analyzed or generated in support of this research. Funding SSD was funded by NSF OCE (grant # 1415268). DSB and PM were supported by the NOAA Ocean Acidification Program and Northwest Fisheries Science Center, MHL was supported by NSF OCE (grant # 1633951), ZIJ was supported by NSF OCE (grant # 1416665) and DOE EERE (grant #DE-EE008518), NJS was supported by NSF OCE (grant # 1924281), ALC was supported by NSF OCE (grant # 1737311), and AA was supported by NSF OCE (grant # 1416518). KEFS, AK, and TLH were supported by Texas A&M University. This is CSUN Marine Biology contribution (# 306). Author contributions All authors conceived the idea for this paper in discussion at a workshop and contributed to the writing of the manuscript. SSD and DSB led the group and contributed the most to the text. References Albright R. , Caldeira L. , Hosfelt J. , Kwiatkowski L. , Maclaren J. K. , Mason B. M. , Nebuchina Y. , et al. 2016 . Reversal of ocean acidification enhances net coral reef calcification . Nature , 531 : 362 – 365 . 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Hawkins,, Anthony;Chapman,, Colin

doi: 10.1093/icesjms/fsaa118pmid: N/A

Abstract In the early 1960s, the Marine Laboratory Aberdeen began to examine the behaviour of fish in relation to mobile fishing gears. We were asked to investigate the role of sound in fish behaviour. We decided that our experimental work had to be done in the sea, as under “free-field” conditions the acoustic stimuli could be accurately presented and monitored. We located a suitable site at Loch Torridon and set up a field station there. We carried out unique experiments on the hearing of fishes, their behavioural responses to different sound stimuli, and the sounds made by the fishes themselves. Work was also carried out on the reflection of sounds by fishes, the noise made by fishing vessels and other sources, and the movements and foraging activity rhythms of Atlantic cod. The cod generally showed limited movements within defined home ranges. A large number of scientific papers were written, many of them in collaboration with scientists from other institutes, and other countries. This paper considers the lessons learned from our work, and especially the advantages of observing fish behaviour and carrying out experiments on fishes in the sea. We learned that the sound in the sea was very important to fishes, both the natural sounds, some of which they produce themselves, and sounds made by humans, which could have adverse effects upon them. We hope that this review will encourage a new generation of scientists to carry out field work, similar to ours, in other areas. Since our work, there has been a large increase in anthropogenic noise, particularly from offshore energy sources, but very little work has been done to help regulate and mitigate their effects upon fishes. Introduction In the early 1960s, the Marine Laboratory Aberdeen (MLA), in the north east of Scotland, part of the Department of Agriculture and Fisheries for Scotland, embarked upon fish behaviour studies. There was particular concern by fisheries managers over the performance of fishing gears, and in particular how to reduce the numbers of smaller fish being caught. The UK government had diverted some money from defence spending to the MLA, to help the fishing industry (which had lost distant water fishing grounds off Iceland and elsewhere). The emphasis was on recruiting new gear technologists. Basil Parrish (later the Director of the MLA, and eventually the General Secretary of ICES) began research on the functioning of fishing gears and worked with John Blaxter on the physiology and behaviour of fishes (e.g. Blaxter and Parrish, 1965). Colin Chapman had studied fish physiology as a part-time student at the Fisheries Laboratory in Lowestoft, England, before joining the MLA fish behaviour team in 1962. Anthony Hawkins joined in 1966. He had started studying sound production by fishes at Bristol University, but moved to the MLA because it had excellent aquarium facilities. In 1967, David MacLennan, a physicist, joined the MLA to investigate fishing gear performance and fish detection by sonar. The MLA work carried out on fishing gears has recently been reviewed by MacLennan (2017). An English aircraft manufacturing company (Saunders Roe) had designed a new trawl that was 50% wider and had twice the headline height of the traditional Scottish “Granton” trawl. The two gears were compared in 1963, but results showed little difference in their catch rates. It was realized that trawl performance did not just involve sieving a volume of water. It became apparent that fishes are active animals, responding to sound, visual and other stimuli, and that these responses have to be taken into account when designing or monitoring fishing gears. Consequently, the MLA started a major research programme on fish behaviour. Colin and Anthony were especially interested in the significance to fishes of the underwater sound from fishing gears and the effects of other anthropogenic (human-made) sources of sound. At that time, there was little information about the noise levels generated by fishing vessels and their trawls. In 1964, however, we were able to fill that gap. In a cooperative exercise with the Admiralty and the White Fish Authority, we monitored the sounds produced by the research vessel “Explorer” while it was travelling and towing an “Aberdeen” trawl. The noise trials were conducted on an Admiralty acoustic range on the west coast of Scotland, using calibrated hydrophones placed close to the seabed (Chapman and Hawkins, 1969). The vessel noise dominated until the trawl moved very close to the hydrophone, when the trawl noise dominated. Ship noise was higher when towing a trawl, as a result of the heavier load placed on the propulsion system. Rattling noise was generated by the trawl, with metallic sounds generated by shackles and chains, and lower frequency sounds generated by the ground ropes and trawl otter boards. It became evident that fishes would be able to detect the sounds generated by ships and trawls and might well respond to them. The initial MLA experiments on fish behaviour were carried out in a concrete tank on the shore at Cairnryan on the south west coast of Scotland. The tank was large enough to be able to tow components of trawls, and it was possible to monitor the responses of fish using a high-resolution sector-scanning sonar developed at the University of Birmingham by Welsby and Dunn (1963). Shoals of Atlantic herring (Clupea harengus) showed clear avoidance responses to the playback of trawl noise and also to pure tone sounds (Chapman, 1964; Welsby et al., 1964). It was evident, however, that studies of captive fish in tanks were very limited, and it was realized that the behaviour of wild fishes needed to be studied in the sea. The sonar system that was used had enabled the movements of fish to be examined in detail, and it was later used to study wild fish movements in the sea. While working at Cairnryan, we discovered that the haddock Melanogrammus aeglefinus made underwater sounds (Hawkins and Chapman, 1966). The sounds made during haddock spawning were then studied in the MLA aquarium. Our paper was the lead article in Nature, with a photograph of spawning haddock on the front cover of the printed journal (Hawkins et al., 1967). We then detected haddock sounds in the sea at Loch Ainort, Isle of Skye, on the west coast of Scotland. We caught some of the fish at the loch and supplied them to some physicists for acoustic target strength measurements (McCartney and Stubbs, 1971). Further work on behaviour and sound production by the haddock then took place in Broad Bay, Isle of Lewis, in the Outer Hebrides, in 1965, during a cruise by the Scottish research vessel “Mara.” Bill Hemmings and John Hislop from the MLA examined the survival of haddock after trawl capture by placing them in cages on the seabed. We placed a hydrophone in one of the cages to monitor haddock sounds. Unfortunately, the weather deteriorated and the Mara dragged the hydrophone cable, causing the cages to collapse. It was clear that attempting this type of research from on board a ship was fraught with difficulty, and we decided that further monitoring of fish behaviour in the sea required a marine research facility, situated close to the shore. Setting up our field station In 1966, the MLA allowed us to establish a field station on the west coast of Scotland. Colin travelled along the coast, looking for an area with limited fishing activity, which had relatively deep water close to the shore. He decided that Upper Loch Torridon (Figure 1) was an ideal location. The site chosen was on the Aird Mhor Peninsula on the south side of Upper Loch Torridon (Figure 2). We built a small laboratory there on the shore, comprising two garden sheds, a diesel generator, a seawater pump, and header tank (Figure 2), used in connection with an acoustic range in the sea. Underwater scuba diving took place in the loch, especially by Colin and other members of the MLA diving group, to set up research facilities on the seabed. Diving took place from a small boat with an outboard motor. The diving revealed the presence of lots of scallops and Norway lobsters, which provided us with additional research opportunities. Our initial research work included studies of the hearing abilities of fishes, their responses to anthropogenic noise, and their production of sounds. Figure 1. Open in new tabDownload slide The location of Upper Loch Torridon on the west coast of Scotland. Figure 1. Open in new tabDownload slide The location of Upper Loch Torridon on the west coast of Scotland. Figure 2. Open in new tabDownload slide The location of the Field Station, on the Aird Mhor Peninsula at Upper Loch Torridon, with small sheds close to the sea and an acoustic range within the sea to the left of the photo. The stretch of water is Ob Gorm Mhor (Photo by Peter Shelton). Figure 2. Open in new tabDownload slide The location of the Field Station, on the Aird Mhor Peninsula at Upper Loch Torridon, with small sheds close to the sea and an acoustic range within the sea to the left of the photo. The stretch of water is Ob Gorm Mhor (Photo by Peter Shelton). Loch Torridon is a fjord on the west coast of the Northwest Highlands, surrounded by mountains. The loch was created by glacial action and is about 15 miles (25 km) long with several parts: Upper Loch Torridon is on the landward side, joined by a relatively narrow entrance to Loch Shieldaig and Outer Loch Torridon, on the seaward section of the loch. It proved easy to persuade scientists from other institutes to take part in projects with us there and even to use our field station for their own work. The research carried out expanded considerably, to include work on the acoustic tracking of fishes and crustaceans, observations on fishing gear operations, the calibration of sonar systems, and the biology of Norway lobsters and other shellfish. Work continued there until 1993, when the site was finally closed down. There had been an increase in fishing activities within the loch, especially for shellfish, and the loch was also being used for salmon and mussel farming. Fish hearing studies Fish do not have external ears, but it was thought that the otolith organs, within the head, constituted inner ears (reviewed by Parker, 1903). It was established many years ago that fishes were able to detect sounds. However, hearing experiments carried out in aquarium tanks showed very variable results, even for the same species. Parvulescu (1964) pointed out the pitfalls of carrying out acoustic experiments in small tanks and specifying the sounds solely in terms of sound pressure. Per Enger, from Norway, carried out especially interesting experiments on fish hearing in the sea, and demonstrated that particle motion was an important parameter in determining auditory thresholds for some species (Enger and Andersen, 1967). Our hearing experiments were carried out in mid-water at Loch Torridon, at about 100 m from the shore. An acoustic range was set up at a depth of 21 m. A tower was placed on the seabed, constructed from rigid PVC tubing perforated with holes to admit water and release air. Underwater sound projectors (Dyna-Empire Inc., types J9 and J11), capable of generating frequencies down to about 30 Hz, were moored at different distances and in different angular positions relative to the top of the tower, where the small fish cage was positioned (Figure 3). Calibrated hydrophones were placed beneath the fish cage to measure the sound pressure. In the free sound field, it was possible to estimate the particle motion levels by measuring the sound pressure. This was one of the key reasons for working in the sea. The fish were caught on hand-lines and held in onshore tanks, and cages within the loch, for use in the experiments. They were caught at shallow depths (<10 m) to prevent damage from expansion of the swim bladder. The experiments provided unique information on the hearing abilities of fishes, reviewed by Hawkins (2014). It became evident that it is possible to carry out excellent hearing experiments in the sea, and that for marine species, the results can be far more useful than those carried out in aquarium tanks. Figure 3. Open in new tabDownload slide The acoustic range within the loch consisting of several sound projectors, located at different distances and angular positions, relative to the top of a tower, where a small fish cage was placed by divers and connected to the shore by cables. Figure 3. Open in new tabDownload slide The acoustic range within the loch consisting of several sound projectors, located at different distances and angular positions, relative to the top of a tower, where a small fish cage was placed by divers and connected to the shore by cables. Determination of fish hearing thresholds A cardiac conditioning technique (developed by Otis et al., 1957) was used to determine hearing thresholds. The electrocardiogram of the fish was monitored with a small metal electrode, plugged into a cable running 100 m to the shore. A pure tone stimulus was presented for the duration of four normal heartbeats, followed by a mild electric shock. The conditioned response consisted of a delay in one or more heartbeats, following the onset of the sound and before the electric shock was presented. Once a clear positive conditioned response had been established, the sound level was lowered in 3 dB steps following each positive response and raised by 3 dB following each negative response. von Frisch and Stetter (1932) had developed this “staircase” method for acoustic threshold determination in fish. The lower the threshold the more sensitive the fish was to the sound. Hearing thresholds were determined over a range of pure tone frequencies, providing audiograms for four species from the cod family: the haddock, Atlantic cod Gadus morhua, European pollack, Pollachius pollachius, and ling, Molva molva (Chapman and Hawkins, 1969, 1973; Chapman, 1973). The hearing abilities of other species were also examined. The cod was most sensitive to low-frequency tones in the range of 30–520 Hz, with a sharp cut-off above this range. Interestingly, the thresholds obtained from cod in the sea (Figure 4) were much lower than those obtained previously by Olsen (1969), who had worked with this same species in laboratory tanks. The acoustic conditions in Loch Torridon were much quieter than in most laboratory tanks. Only one small fishing vessel passed by the site occasionally, and the ambient noise levels were quite low. However, some natural variations in the ambient noise level occurred as the wind and wave conditions changed, resulting in changes to the auditory thresholds for the cod. It was evident that the detection of pure tone signals was being masked by the ambient sea noise. These results underlined the importance of performing hearing experiments under quiet conditions, and showed the wisdom of working in the sea, under the soundscape conditions that fish normally experience. Figure 4. Open in new tabDownload slide Hearing thresholds obtained at Loch Torridon for the cod, mainly sensitive to sound pressure, and the salmon and dab, sensitive only to particle motion. The natural level of ambient sea noise can vary, affecting the ability of cod to detect sounds, especially at low frequencies. Figure 4. Open in new tabDownload slide Hearing thresholds obtained at Loch Torridon for the cod, mainly sensitive to sound pressure, and the salmon and dab, sensitive only to particle motion. The natural level of ambient sea noise can vary, affecting the ability of cod to detect sounds, especially at low frequencies. Sounds were presented to the cod from sources at different distances, following the method introduced by Enger and Andersen (1967). Thresholds at frequencies between 60 and 160 Hz were largely independent of sound source distance. At frequencies below 60 Hz, the thresholds were lower when the source was very close to the cod, where particle motion amplitudes were higher for a given sound pressure. It was concluded that the auditory system of the cod was effectively sensitive to sound pressure, but at frequencies below 60 Hz, the ear can respond directly to particle motion when the sound source is close to the fish. Olav Sand, a student of Per Enger, came to Torridon from Norway in 1971. With Colin, Olav investigated the hearing abilities of two species of flatfish, the plaice, Pleuronectes platessa, and the dab, Limanda limanda, both of them lacking a gas-filled swim bladder. The swim bladder acts as a transformer between sound pressure and particle motion in gadoid species. The sound stimuli in the flatfish experiments was varied in two ways (Chapman and Sand, 1974). First, sound projectors were placed at different distances from the fish to vary the ratio between sound pressure and particle motion. Second, the effect of sound radiation from a gas-filled balloon on the auditory sensitivity was examined by placing it close to the head of the dab. Thresholds for the plaice and dab were not as low as they were for gadoid species and their frequency range did not extend as high. There were, however, very clear differences between sound pressure thresholds obtained at different distances, showing that the otolith organs, in the absence of a swim bladder, are sensitive to particle motion rather than sound pressure. However, the presence of a gas-filled balloon close to the head of the dab resulted in much lower thresholds and a more extended frequency range. This experiment provided strong evidence of the role of gas-filled bodies, including the swim bladder, in augmenting hearing in fishes. It verified the importance of carrying out hearing experiments in the sea. Together with Alistair Johnstone from the MLA, Anthony studied the hearing abilities of Atlantic salmon, Salmo salar (Hawkins and Johnstone, 1978). In the sea, salmon responded only to low-frequency pure tones (below 380 Hz). The salmon was relatively insensitive to sounds compared to the cod (Figure 4). Masking of the thresholds did not take place under natural conditions of sea noise but could be imposed by creating higher levels of artificial noise. Again, use was made of the near field effect to expose the salmon to different ratios of sound pressure and particle motion. As with the dab and the plaice, it was confirmed that the salmon was sensitive to particle motion rather than sound pressure. Although the salmon has a swim bladder, this species is a physostome, with an open connection between the swim bladder and the oesophagus. Physostomes can rapidly empty the swim bladder, which they normally do when frightened. Hence, it is uncertain if the salmon had gas in the swim bladder during these experiments. The acoustic characteristics of the swim bladder Olav and Anthony later measured the sound fields re-radiated by the swim bladders of living cod. This required the fish to be moved at up and down to different depths and it was easier to do these experiments in a flooded quarry near Oban, on the west coast of Scotland. A technique for doing this work had previously been applied in experiments carried out beneath a ship at Loch Torridon (McCartney and Stubbs, 1971). In the Oban experiments, the cod was placed inside a large, ring-shaped, piezo-electric sound transducer. The fish was lowered as sounds were presented and changes in the resonance frequency at different depths were monitored (Sand and Hawkins, 1973). It was concluded that the extra auditory gain provided by a swim bladder is mainly in a frequency range below resonance, and that the swim bladder oscillations are heavily damped. From theoretical considerations, it was also apparent that the swim bladder provides no auditory gain below a certain frequency, which depends on both the swim bladder volume and depth. Subsequently, Olav Sand re-joined Per Enger in Norway and they provided further information on the auditory function of the swim bladder in the cod (Sand and Enger, 1973). Masking of sound detection by ambient noise It was evident from our Loch Torridon experiments that the detection of sounds by fishes like the cod and haddock is often masked by natural variations in the levels of ambient sea noise, although no masking occurs under the quietest ambient noise conditions. Masking occurs for species like the plaice, dab, and salmon in the presence of anthropogenic noise. This suggests that the hearing abilities of fishes are closely matched to the levels of background noise in the environment. There is a real need for hearing and fish behaviour experiments to be carried out at acoustic field facilities like those at Loch Torridon, ideally at a location with minimal noise interference from shipping and other human activities. Some additional experiments were carried out in Loch Torridon on masking, involving the presentation of pure tone stimuli in the presence of different noise frequency bands (Hawkins and Chapman, 1975; Hawkins and Johnstone, 1978). We showed that fish, like humans and other mammals, use auditory frequency filters to improve the detection of signals in the presence of ambient noise. Ambient noise levels are now often much higher in the sea, lakes, and rivers because of human activities. Masking by anthropogenic noise can prevent the detection of the sounds made by fish themselves and other sound signals of importance to them. This is likely to have detrimental effects, adversely affecting the ability of fish to find prey, avoid predators, navigate, migrate, and spawn successfully. There is now a need for more research on aquatic soundscapes, and how they may be deteriorating as a result of human activities. Directional hearing Location of the source from which a sound is coming is likely to be important to fishes. It may enable them to seek out prey, avoid predators, find mates, and detect important spatial sound cues. Early sound localization experiments gave negative results, and it was thought unlikely that fishes utilized the same direction-finding mechanisms as terrestrial vertebrates (reviewed by Hawkins and Popper, 2018). However, it was evident from our observations on wild cod in Loch Torridon that fishes could swim towards or away from some underwater sound sources. This led to us wondering whether fish could discriminate between sounds from different directions and distances. Colin showed that the masking effect of noise on the detection of a pure tone by the cod was reduced when the masking noise was transmitted from a different direction (Chapman, 1973). Colin and Alistair did further experiments with cod using four sound projectors, allowing a wider range of angular separation between the signal and noise sources (Chapman and Johnstone, 1974). Experiments were then carried out where cod and haddock were conditioned to a short period of switching of a pulsed tone from one projector to another at different angles of azimuth. The fish readily responded to the switching when the projectors were separated by 20° or more. We later demonstrated that cod are also able to discriminate between sound sources in the median vertical plane (Hawkins and Sand, 1977). We concluded that the otolith organs are involved in directional hearing, through the detection of particle motion. While our experiments were being carried out at Loch Torridon, Arie Schuijf and his colleagues from the University of Utrecht were carrying out experiments on directional hearing by fish in a Norwegian fjord (reviewed by Schuijf, 1975). Arie and his colleagues trained fish to show which of two alternative sound projectors was active by swimming to either of two opposing corners of a cage in return for a food reward. Working with his student Rob Buwalda, Arie showed that the fish could discriminate sound waves travelling towards the head from those travelling towards the tail (Schuijf and Buwalda, 1975). They then came to work with us at Loch Torridon. We showed that cod could discriminate between pure tones emitted alternately from two aligned sound projectors at different distances from the fish (Schuijf and Hawkins, 1983). In a key paper (Schuijf, 1976), Arie had proposed that directional detection might involve two distinct processes: determination of the axis of particle motion by vector weighing and then removal of any remaining 180° ambiguities by analysis of the phase relationship between sound pressure and particle motion. At Torridon, we carried out experiments beneath a raft to test the validity of the phase model in three-dimensional space (Buwalda et al., 1983). The experiments involved a complex configuration of sound projectors around the fish. We showed that cod can discriminate between two sources of low-frequency sound, positioned opposite one another in the median vertical plane. It was evident that most behavioural studies of directional hearing have to be carried out in a free sound field, however, physiological approaches would be possible in a laboratory. During a short visit to the MLA by Per Enger, a discussion took place over conducting laboratory experiments on the mechanisms of directional hearing in fish. We decided to investigate the directional properties of the otolith organs by vibrating a fish in different directions. Microphonic potentials were recorded from the inner ear of a haddock. The fish was mounted in air on a vibration table and artificially respirated with water through a tube. The table consisting of a rotatable metal slab resting upon a foam rubber bed. The slab was driven back and forth by an electromagnetic vibrator. The amplitude of the potentials proved to be a function of both the stimulus strength and the direction of vibration (Enger et al., 1973). Different groups of hair cells within the otolith organs showed different patterns of directional sensitivity when stimulated by the particle motion stimuli. More refined versions of this method were later employed for examining the directional responses of the inner ear of fishes (e.g. Sand, 1974; Hawkins and Horner, 1981; Fay, 1984). It became evident that the directional information conveyed by particle motion can be extracted from the incident sound by comparison of the outputs of differently orientated groups of hair cells. Behavioural responses of fish to sounds During the collaborative work carried out with McCartney and Stubbs (1971) on fish target strengths, we were able to observe the responses of wild shoals of whiting, Merlangius merlangus, a pelagic gadoid species, to a seismic “air gun” sound source. Our research ship, Mara, was above the whiting shoals and the fish observed by means of the ship’s echosounder. The shoals extended from 15 fathoms (27.4 m) down to 30 fathoms (54.9 m) in water that was almost 50 fathoms (91 m) deep, and they had probably entered Loch Torridon to spawn. The air gun was fired intermittently, generating a series of high amplitude, low-frequency sounds. The whiting shoals showed strong downward movements, forming a more compact layer beneath 30 fathoms (Figure 5). The air gun was fired several more times over a period of 1 h, during which the fish habituated to the sounds and steadily ascended. Later on, the sounds were produced again and the fish descended once more (Chapman and Hawkins, 1969). Figure 5. Open in new tabDownload slide The responses of whiting shoals to sounds from a seismic airgun. Figure 5. Open in new tabDownload slide The responses of whiting shoals to sounds from a seismic airgun. We later used the Torridon acoustic range to undertake further experiments on the reactions of wild fishes to sound stimuli. Observations were made using the Birmingham University high-resolution sector-scanning sonar system (Welsby and Dunn, 1963), accompanied by an underwater TV camera. Counts of fish, observed by the sonar and/or TV camera, were made before, during, and after periods of sound transmission, generally lasting 5 or 10 min (Chapman, 1975). The fish observed mainly comprised three gadoid species, the cod, the saithe Pollachius virens, and the pollack, but it was also possible to extend the observations to a flatfish, the dab. Initially, the gadoid fishes showed consistent avoidance reactions to low-frequency narrow band noise stimuli, but as the bandwidth was reduced, the avoidance was less marked and when low-frequency pure tones were transmitted, there was a reversal in response and fish became attracted to the stimuli. In general, the attraction response increased in proportion to sound intensity. When the tone transmission was switched between loudspeakers, the fish always gathered at the active sound source. Later, we found that the gadoid fishes and dabs were strongly attracted to all our low-frequency sounds, both pure tones and narrow band noise. We were able to show that this behaviour was related to our diving activities in the area. In diving to set up apparatus in the sea, we noticed that fish seemed to be attracted towards us, and they appeared to be feeding on the benthic organisms disturbed when we dived close to the seabed. Counts of fish observed by sonar and TV camera were then made before and after a diver was positioned at a particular location. This demonstrated strong fish attraction towards the diver and to the playback of noise recordings of the “scuba” breathing apparatus. We concluded that the fish had learned to associate the noise generated by divers with feeding opportunities (Chapman et al., 1974; Chapman, 1975). Our work also showed that the behaviour of fish may be strongly influenced by other underwater sounds made by humans. Although fish may swim away from mobile fishing gears, they can also move towards such gears, including bottom trawls, seines, and shellfish dredges, all of which cause much disturbance of the seabed. Buerkle (1973) confirmed that the noise produced by trawling can influence their behaviour. Sound production by fishes Anthony’s supervisor at Bristol University, Dr H. P. Whiting, had been a naval officer during the Second World War, and he had been involved in locating submarines by listening for their sounds. He had detected sounds that he believed had been made by fishes. He handed over his naval hydrophone to Anthony and asked him to listen to fishes, both in aquarium tanks and in the sea. Initially, Anthony examined the sounds made by gurnards (Triglidae), both in aquarium tanks and in waters off the coast of Devon. It was evident that sound production is important to some fishes, and he based his PhD on sound production by marine fishes. When Anthony moved to Aberdeen, he and Colin began working together, initially focussing on the sounds made by haddock. At Loch Torridon and elsewhere, it became clear that some other gadoid fishes were vocal, including the Atlantic cod, pollack, ling, tusk Brosme brosme, and the tadpole fish Raniceps raninus. Later, Hawkins and Rasmussen (1978) were able to show that the main differences in the calls of different gadoid fishes were based on their temporal structure, all the calls being made up of low-frequency pulses that were repeated at different rates and in different groupings. The sounds were generated by the repetitive contraction of specialized “drumming muscles” attached to the swim bladder. Examination of a number of other gadoid species showed that they also possessed drumming muscles, although sounds had not been recorded from them. It later became evident that many species of fish are vocal, and that the sounds they produce are used to communicate with one another (Hawkins and Myrberg, 1983). Several research students came to work with us on fish sounds: from Denmark (Knud Just Rasmussen), Portugal (Clara Amorim), and Italy (Licia Casaretto and Marta Picciulin). Their work provided more detailed information on sound production by a range of fishes. Our observations and sound analyses on captive fish at the MLA provided information for locating particular fishes in the sea. Listening was carried out at different locations in Balsfjord, Norway. Long sequences of repeated knocks were heard at one particular location in the fjord, similar to the display sounds recorded during reproductive behaviour by haddock in our aquarium (Casaretto et al., 2014). At night, the sounds merged into a continuous low-frequency rumble, confirming that many vocal haddock were present. Spawning haddock were then found at the same unique location in Balsfjord in several successive years. Our work confirmed that listening for fish sounds provides a reliable, non-invasive way of locating aggregations of spawning fish in the sea, allowing close definition of their spawning grounds. We have recently suggested that it is important to map the spawning grounds of vocal fishes, and especially cod and haddock, to ensure that they are not deleteriously affected by offshore human activities (Hawkins and Picciulin, 2019). Other acoustic work at Loch Torridon On two occasions while we were working at Loch Torridon, we were asked to conduct measurements in relation to two quite different sound sources: one was the sonic boom of a “Concorde” aircraft and the other was the sound from a new purse-seine fishing vessel, the “Semla,” having difficulties in catching fish! The supersonic aircraft Concorde, flown by British Airways and Air France, flew at twice the speed of sound, and because of the sonic booms, it generated many countries would not allow flights over their land. The routes were generally restricted to ocean crossings, although fishermen raised objections to this because of possible effects upon fishes. In 1970, we were asked to measure the underwater sound levels from the sonic booms, and to consider whether they would affect fish behaviour. Concorde’s test flights passed over Loch Torridon and we were able to measure the underwater sounds from the sonic booms. The sounds reaching fishes were composed of two double pulses, one couple passing through the water, and the other generated by substrate transmission. Dramatic slowing of the heart rates of cod revealed that the sounds were being detected, and that they could have adverse effects upon the behaviour and physiological condition of fishes. Anthony was invited to talk to the airlines about the effects of supersonic aircraft upon fishes, and informed them that such aircraft could have detrimental effects upon fishes. Following the crash of an Air France flight, the Concorde was later abandoned. In 1967, the Christian Salvesen Shipping Company launched a new purse-seine fishing vessel the Semla (Registration LH454). Early fishing trials with the vessel on herring shoals were unsuccessful and it was thought that noise from the vessel was scaring the fish. Semla came to Loch Torridon and a number of underwater noise measurements were made as the vessel operated its purse-seine. It was found that tight manoeuvring of the vessel generated rather high noise levels, and it was concluded that it would be necessary to steer the vessel carefully, to avoid sudden changes in the engine noise as the ship approached a fish shoal. Other work on fishes at Loch Torridon The movements of cod Interest in the behaviour of cod within the loch prompted us to develop a fish tracking system, to follow the movements of individual fish. An ultrasonic transmitter, developed by the Fisheries Laboratory at Lowestoft, was placed in the stomach of the cod or surgically implanted within the abdomen. The position of the fish was then determined by comparing the time of arrival of the ultrasonic pulses at an array of hydrophones placed close to the seabed and spaced several hundred metres apart. Cod were tracked continuously for up to 11 days. The tracking system was also used with invertebrates (Chapman et al., 1975), and it was used by others to study the performance of fishing trawls. It was also used to examine the behaviour of cod in Loch Beag, at the western end of Loch Hourn, to the south of Loch Torridon (Hawkins et al., 1974). Such tracking systems are really useful for examining the behaviour of fishes and invertebrates in the sea and could be used more widely. This method of acoustic position fixing is described in detail by Hawkins et al. (1980) and MacLennan and Hawkins (1977). The tracking of juvenile cod showed that they lived near the seabed in and around the edge of Loch Torridon, at depths between 10 and 20 m, moving within restricted home ranges (Hawkins et al., 1980), where they searched for food. The majority of Torridon cod were more active during the day than the night, although a few were nocturnal. In Loch Beag, the cod ranged widely throughout the loch initially, following their release, but later, as in Loch Torridon, they showed movements of only limited extent within home ranges. During the night the cod in Loch Beag moved over a wider area (Figure 6). Indeed, relatively few positions could be plotted during the day because the cod often occupied the same position on successive samplings. The areas occupied by different tracked cod were sometimes adjacent to one another, but they did not overlap to any great extent. The individual cod were occupying separate territories. The timing of their movements, and the areas covered, may perhaps be related to the vulnerability of particular prey. Figure 6. Open in new tabDownload slide Locations of an individual cod within in Loch Beag, monitored at 15-min intervals over two successive days. The positions were followed located at the following times: (a and c) from dusk to dawn (b and d) from dawn to dusk. The cod moved within a home range, covering a wider area at night. Figure 6. Open in new tabDownload slide Locations of an individual cod within in Loch Beag, monitored at 15-min intervals over two successive days. The positions were followed located at the following times: (a and c) from dusk to dawn (b and d) from dawn to dusk. The cod moved within a home range, covering a wider area at night. Invertebrate work During the fish behaviour work, opportunities arose to study various aspects of the biology of certain crustacean species, particularly the Norway lobster Nephrops norvegicus, and to a lesser degree, the brown crab Cancer pagurus, and the velvet swimming crab, Necora puber. It is intended that details of this work should be described in a separate paper (Chapman and Hawkins, in preparation). Much of this work was conducted in collaboration with scientists from other UK institutes. Conclusions Although it can sometimes be difficult to study fish in the sea, such work is often very productive and well worth doing. Much of the work done at Loch Torridon was highly original. Some of the techniques developed there have since been used at other locations, including the fish tracking methods. It is evident that there is a need for similar sea study sites to be developed at other locations, to enable a wide range of work to be carried out on the behaviour of fish and invertebrates, and especially experiments on their hearing. Some experiments on hearing are possible in the laboratory, where small tanks and shaking tables are especially useful for electrophysiological studies, for example on the frequency range of hearing, and on the mechanisms of hearing (e.g. Enger et al., 1973). However, it is generally necessary to control the amplitude, phase, and direction of particle motion and sound pressure at the position of fish and invertebrates, and although this can be achieved in specialized, small, laboratory tanks, the behaviour of fish in such tanks is likely to be rather abnormal, and such experiments, with full stimulus control, are best carried out under free-field conditions in the natural environment. Acoustic field facilities like those at Loch Torridon could readily be established in other areas. The key is finding a suitable location with minimal acoustic interference from human activity. Sadly, Loch Torridon would have to be ruled out now because of increased background noise from fishing, fish-farming, and tourist activity. The remoteness of the Torridon field station, with only a very rough track for access, was essential for security reasons. This enabled the site to gain a licence from the Government Home Office to work with live animals. One of the great advantages of the research facilities that we had available at Loch Torridon was that they attracted eminent and talented scientists, both from the United Kingdom and other countries, to come to Aird Mhor to work with us. A view often expressed was that working there was “like having a paid holiday.” There is currently a strong need to carry out studies on the impact of anthropogenic noise upon fishes and invertebrates (reviewed by Hawkins and Popper, 2017; Popper et al., 2020). There is a particular need for research that will enable aquatic industries and regulators to better understand and mitigate the effects of anthropogenic sounds upon marine animals, and especially those sounds that might impact their population dynamics and affect ecosystems adversely. High-quality scientific data require well-controlled experiments to be carried out on wild fishes and invertebrates within their natural environments. We sincerely hope that this paper will encourage other marine scientists to establish research facilities, like those that we made available at Loch Torridon, at new locations. References Blaxter J. H. S. , Parrish B. B. 1965 . 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Hilborn, Ray

doi: 10.1093/icesjms/fsaa119pmid: N/A

Abstract How do we assess the performance of national and international fisheries management organizations? Many organizations produce measures of the extent of overfishing, typically classifying individual stocks as overfished if they are below some biomass threshold. Most agencies then report their overall status (i.e. percentage overfished, fully exploited, etc.) by giving equal weight to all stocks, regardless of stock size or potential yield. We review the range of indices used to assess overfishing levels and apply them to the data from US fisheries to show how they depict very different performance of fisheries. Given that overfishing is a concept imbedded in the maximization of long-term harvest, we evaluate how well these indices reflect the extent to which fisheries have maximized sustainable yield. Indices that are weighted by the potential yield of the stock much better reflect the regional performance of fisheries but are still limited by the arbitrary use of a threshold abundance. For the United States, weighting by maximum sustainable yield or value suggests that the losses from overfishing are less than existing methods using equal weighting and that underfishing is much more common than overfishing. Introduction Overfishing and the status of fish stocks has been of increasing concern to fisheries managers, fishing industry, and NGOs for several decades (FAO, 2018) but how do we best evaluate the performance of a fisheries management system? There are numerous indices indicating the status of fish stocks and fisheries produced by agencies, NGOs, and academic scientists. Some are targeted for particular purposes, such as the US Endangered Species Act list, or the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) list, which each mandate specific management actions. Some indices provide advice to retailers and consumers such as the Marine Stewardship Council certification or Monterey Bay Aquarium’s Seafood Watch ratings. Perhaps the most cited index is the Food and Agriculture Organization (FAO) calculation of the percentage of stocks that are overfished (FAO, 2018) (see Table 1 for FAO’s definition of the term overfished). While not used in any direct management action, it is commonly used to gauge overall performance of global fisheries. A number of indices like the Ocean Health Index (Afflerbach et al., 2019) are calculated for individual countries with the intention of providing advice as to where improvements are needed. Many countries calculate stock status for individual stocks as part of their management system; stock status calculations are used both as a measure of overall performance and to evaluate if management actions are needed. These indices are typically measures of stock abundance or the fishing mortality relative to targets or limits. Table 1. Different agency definitions for overfished, a term used to report stock status relative to biomass reference points. Agency . Overfished definition . Comments . ICES B < Blim Stocks less than a target biomass and above the limit given a cautious rating US NMFS (Federal Register, 2016) B < 0.5 BMSY Individual management councils can use other definitions New Zealand (MFNZ, 2008) B < 0.5 BMSY or <0.2 B0 Report the percentage of stocks giving equal weighting and weighted by landings Australia (Rayns, 2007) B < Blim Where Blim = 0.2 × B0 Japan B < Blim Canada (Shelton, 2017) SSB < 80% SSBMSY Chile SSB < SSBMSY ICCAT (Anonymous, 2007) SSB < SSBMSY IATTC (Anonymous, 2007) SSB < SSBMSY WCPFC (Anonymous, 2007) SSB < SSBMSY IOTC (Anonymous, 2007) SSB < 0.2 SSBunfished CCSBT (Anonymous, 2007) None FAO B < 0.8 BMSY Agency . Overfished definition . Comments . ICES B < Blim Stocks less than a target biomass and above the limit given a cautious rating US NMFS (Federal Register, 2016) B < 0.5 BMSY Individual management councils can use other definitions New Zealand (MFNZ, 2008) B < 0.5 BMSY or <0.2 B0 Report the percentage of stocks giving equal weighting and weighted by landings Australia (Rayns, 2007) B < Blim Where Blim = 0.2 × B0 Japan B < Blim Canada (Shelton, 2017) SSB < 80% SSBMSY Chile SSB < SSBMSY ICCAT (Anonymous, 2007) SSB < SSBMSY IATTC (Anonymous, 2007) SSB < SSBMSY WCPFC (Anonymous, 2007) SSB < SSBMSY IOTC (Anonymous, 2007) SSB < 0.2 SSBunfished CCSBT (Anonymous, 2007) None FAO B < 0.8 BMSY Typically, the target is where the average stock size should be, often BMSY. The BMSY may be in units of total biomass, mature biomass or spawning stock biomass. The limit (Blim) is the point at which the stock is declared overfished. US NMFS, United States National Marine Fisheries Service; ICCAT, International Commission for the Conservation of Atlantic Tunas; IATTC, Inter-American Tropical Tuna Commission; WCPFC, Western and Central Pacific Fisheries Commission; IOTC, Indian Ocean Tuna Commission; CCSBT, Commission for the Conservation of Southern Bluefin Tunas. Open in new tab Table 1. Different agency definitions for overfished, a term used to report stock status relative to biomass reference points. Agency . Overfished definition . Comments . ICES B < Blim Stocks less than a target biomass and above the limit given a cautious rating US NMFS (Federal Register, 2016) B < 0.5 BMSY Individual management councils can use other definitions New Zealand (MFNZ, 2008) B < 0.5 BMSY or <0.2 B0 Report the percentage of stocks giving equal weighting and weighted by landings Australia (Rayns, 2007) B < Blim Where Blim = 0.2 × B0 Japan B < Blim Canada (Shelton, 2017) SSB < 80% SSBMSY Chile SSB < SSBMSY ICCAT (Anonymous, 2007) SSB < SSBMSY IATTC (Anonymous, 2007) SSB < SSBMSY WCPFC (Anonymous, 2007) SSB < SSBMSY IOTC (Anonymous, 2007) SSB < 0.2 SSBunfished CCSBT (Anonymous, 2007) None FAO B < 0.8 BMSY Agency . Overfished definition . Comments . ICES B < Blim Stocks less than a target biomass and above the limit given a cautious rating US NMFS (Federal Register, 2016) B < 0.5 BMSY Individual management councils can use other definitions New Zealand (MFNZ, 2008) B < 0.5 BMSY or <0.2 B0 Report the percentage of stocks giving equal weighting and weighted by landings Australia (Rayns, 2007) B < Blim Where Blim = 0.2 × B0 Japan B < Blim Canada (Shelton, 2017) SSB < 80% SSBMSY Chile SSB < SSBMSY ICCAT (Anonymous, 2007) SSB < SSBMSY IATTC (Anonymous, 2007) SSB < SSBMSY WCPFC (Anonymous, 2007) SSB < SSBMSY IOTC (Anonymous, 2007) SSB < 0.2 SSBunfished CCSBT (Anonymous, 2007) None FAO B < 0.8 BMSY Typically, the target is where the average stock size should be, often BMSY. The BMSY may be in units of total biomass, mature biomass or spawning stock biomass. The limit (Blim) is the point at which the stock is declared overfished. US NMFS, United States National Marine Fisheries Service; ICCAT, International Commission for the Conservation of Atlantic Tunas; IATTC, Inter-American Tropical Tuna Commission; WCPFC, Western and Central Pacific Fisheries Commission; IOTC, Indian Ocean Tuna Commission; CCSBT, Commission for the Conservation of Southern Bluefin Tunas. Open in new tab To design and calculate any indicator, the purpose and use should be identified. Many of these fisheries performance indices are based on the concept of maximum sustainable yield (MSY) and are designed to indicate the performance at delivering sustainable food. The FAO system of three biomass-based classifications, overfished, maximally sustainably fished, and underfished, could provide a general indication of the performance, and guidance on the extent to which there is potential for increased yield from reducing fishing mortality and rebuilding overexploited stocks, or to increase yield from underexploited stocks. Many alternative indices are designed to measure the performance of fisheries management at a global, national, or regional scale relative to the potential sustainable harvest. In addition to the FAO classification, the National Marine Fisheries Service in the United States reports the proportion of stocks that are overfished (a biomass-based indicator) and subject to overfishing (a fishing mortality measure) (NMFS, 2019). Many other countries and regional fisheries management organizations have similar reports. Overfishing is an assessment based on fishing mortality, usually indicating that fishing mortality is greater than the level that would produce MSY. Overfished is an assessment based on biomass, usually indicating that stock biomass is below some specific level of biomass. Thus, overfishing is a process, and overfished is the consequence. However, overfishing is also used with reference to concern about stocks being in an overfished state as in the phrase “there is an overfishing problem”. In this context, overfishing can refer to both the fishing mortality rate and the status of stocks. In the analysis that follows, I will look at measures of both fishing mortality and stock abundance. Even when these indices are intended to reflect the same quantity, such as the extent of overfishing, they can provide quite conflicting assessments. For instance, FAO uses 80% of the biomass at MSY (BMSY) as the limit below which a stock is deemed overfished; many tuna Regional Fisheries Management Organizations (tRFMOs) use 100% BMSY, and the United States uses as a default 50% BMSY (Table 1). International Council for the Exploration of the Sea (ICES) uses MSY × Btrigger or Blim to reflect when stocks are at low enough abundance to reflect concern, which is essentially the same as other agencies overfishing definitions. The tRFMOs are in the anomalous position of not distinguishing between a target and a limit. A summary of stock status definitions for “overfished” is shown in Table 1, and all agencies that define overfishing use F > FMSY (where F is the rate of fishing mortality and FMSY is the fishing mortality rate that would produce long-term MSY). In addition to indices based on biomass, there are indices based on fishing mortality (e.g. the US overfishing definition), management actions (Melnychuk et al., 2017b), and those that look at broader measures of performance including biological, social, and economic considerations (Anderson et al., 2015). A number of criticisms have been made of both the biomass and fishing mortality rate indices such as those of FAO and other countries (Hilborn and Stokes, 2010; Thorson et al., 2015; Anonymous, 2018; Hilborn, 2019). The key concerns are that (i) the choice of the thresholds is arbitrary and there is no consensus on which is preferred; (ii) well-managed stocks will naturally fluctuate over a wide range of stock sizes and many stocks will be below a specific abundance threshold even if managed to produce MSY; (iii) there is little relationship between the classification and the performance of the fishery relative to producing sustainable food; and (iv) such classifications can be easily misinterpreted by media or the public and have often been intentionally distorted by advocacy groups. The most common distortion has been the use of the FAO classifications to suggest that 90% of fish stocks are in trouble because they are either overfished, typically incorrectly equated with “collapsed”, or fully exploited, often incorrectly equated with being “on the brink of collapse”. This frequent misuse caused FAO to change the term from “fully exploited” to “fully sustainably exploited” for their 2018 assessment (FAO, 2018). In addition to the problem of how to classify individual stocks as “overfished”, there are options for how to present data for global, regional, or national stock status summaries. Most commonly, every stock is given equal weight, so that a stock with 1000 tonnes of long-term yield is given the same weight as a stock with 1 000 000 tonnes of long-term yield. New Zealand (NZ) presents status in two ways: (i) weighting all stocks equally and (ii) weighting them by stock size. NZ reported in 2018 that “84.0% of assessed stocks were above the soft limit” (their overfishing threshold) and “95.3% of the assessed landings was made up of stocks above the soft limit”. NZ also reports some of its statistics based on stock value. For example, they report that “scientifically evaluated stocks account for 68% of the tonnage of landings and 81% of the value of landings” (Fisheries New Zealand, 2020). An alternative to the classification of stocks into discrete categories was proposed by Hilborn (2019). In this method, the expected sustainable yield lost by stock abundance being above or below BMSY or fishing mortality being greater or less than FMSY is calculated as a fraction of the maximum potential yield. This method represented the performance of the fishery relative to the objective of maximizing sustainable food production and is much more effective than current classification systems. The “Goldilocks plot” approach identifies potential to increase fish production by rebuilding stocks that are below MSY levels or by fully exploiting stocks that are above MSY levels (Figure 1). The purpose of this article is to compare a range of criteria for the classification of stock status using US stocks as an example. I look at three schemes for classification of the “overfished” category, using breakpoints for the “overfished” designation of 1, 0.8, and 0.5 of BMSY or a target biomass. I also look at two schemes for classifying the “overfishing” category. I then examine the overall classification outcomes based on three different schemes for weighting stocks: number of stocks (or equal weighting), total potential yield, and economic value (or MSY × price). Each of these classification schemes is compared to lost yield using both B/BMSY and F/FMSY. Methods To examine the frequency of calling a stock overfished when it is, in fact, being managed to produce MSY (a “false positive”), I simulated the frequency of a stock being at different population sizes when annual fishing mortality rate was FMSY. I simulated a fish stock with the following life history characteristics in discrete time: (i) natural mortality rate is 20% per year, (ii) matures and becomes vulnerable to fishing at age 4, and (iii) has a spawner recruitment steepness of 0.8. I used Thorson et al.’s (2015) overall estimate of recruitment standard deviation of 0.736 and time autocorrelation of 0.451. I first estimated FMSY by running the model for 10 000 years at different fishing mortality rates and with random recruitment. I then chose the mortality rate with the highest average catch as FMSY and the average biomass as BMSY. The distribution of the ratio of abundance to BMSY in the final 5000 years of simulation is reported (see Figure 2). Figure 1. Open in new tabDownload slide A Goldilocks plot for ICES European fisheries based on fishing mortality. The gold line is the fraction of stocks that had F/FMSY estimated for each year. The area in red is the proportion of potential yield lost due to F > FMSY, the area in blue is the proportion of yield lost by F < FMSY, and the area in green is the proportion of potential yield achieved by the fishing mortality rate in each year. Figure 1. Open in new tabDownload slide A Goldilocks plot for ICES European fisheries based on fishing mortality. The gold line is the fraction of stocks that had F/FMSY estimated for each year. The area in red is the proportion of potential yield lost due to F > FMSY, the area in blue is the proportion of yield lost by F < FMSY, and the area in green is the proportion of potential yield achieved by the fishing mortality rate in each year. Figure 2. Open in new tabDownload slide The frequency of a stock being at different ratios of B/BMSY when managed at an FMSY policy. The black colour is where B/BMSY is <0.5 (the US and NZ threshold of overfished); the dark grey colour is where 0.5 < B/BMSY < 0.8 (the FAO threshold); the light grey colour is where 0.8 < B/BMSY < 1.0 (the tRFMO threshold); the very light grey colour is where 1.0 < B/BMSY < 1.2 (1.2 is the upper limit of the FAO maximally sustainably exploited), and the white colour is where B/BMSY > 1.2. Figure 2. Open in new tabDownload slide The frequency of a stock being at different ratios of B/BMSY when managed at an FMSY policy. The black colour is where B/BMSY is <0.5 (the US and NZ threshold of overfished); the dark grey colour is where 0.5 < B/BMSY < 0.8 (the FAO threshold); the light grey colour is where 0.8 < B/BMSY < 1.0 (the tRFMO threshold); the very light grey colour is where 1.0 < B/BMSY < 1.2 (1.2 is the upper limit of the FAO maximally sustainably exploited), and the white colour is where B/BMSY > 1.2. To evaluate alternative classification schemes, I calculated both most recent and historic stock status for all US stocks where B/BMSY (either as total, mature, or spawning biomass) and F/FMSY were available in the RAM Legacy Database (www.ramlegacy.org) version 4.491. The number of US stocks for which B/BMSY and F/FMSY were available was between 130 and 167 for the years 1980 to 2010, with fewer stock estimates available before 1980 and after 2010. The lower sample size of estimates available before 1980 may result in more uncertain estimates. The status (B/BMSY and F/FMSY) is based on the most current assessment. Lost yield was calculated using the method of Hilborn (2019) based on both biomass and fishing mortality. Lost yield occurs whenever a stock is not exactly at BMSY for biomass-based lost yield or at FMSY for fishing mortality-based lost yield. For each stock, the yield that is achieved at current biomass or fishing mortality is compared to the MSY, and the difference is the lost yield. For “current” status, I used the most recent assessment available for each stock, some of which would be as late as 2018, but I confine time series analysis to years 1970–2012. Price data were taken from Melnychuk et al. (2017a) and the potential value of a fishery used as one method of stock weighting is simply the MSY times the ex-vessel price. Based on these data, I show current and historical stock status as calculated by three BMSY schemes (i) overfished is <100% BMSY and underfished >150% BMSY, (ii) overfished is <80% BMSY and underfished >120% BMSY, and (iii) overfished is <50% BMSY and underfished >150% BMSY. Scenario 1 uses the tRFMO definition of overfished, scenario 2 uses the FAO definition, and scenario 3 uses the US default definition. Scenario 2 uses the FAO definition of underfished. Agencies other than FAO do not have an underfished definition and I have used 150% as a surrogate. I then compared these indices to the lost yield calculation based on biomass and fishing mortality. Results The results of the simulations are shown in Figure 2. Here, we see that this example stock would below BMSY 59% of the time, below 0.8 BMSY 37% of the time, and below 0.5 BMSY 7% of the time when fished at FMSY. The stock would be above 1.2 BMSY (the FAO definition of underfished) 25% of the time. While this is only illustrative of a single set of life history parameters, it reinforces the point that even if stocks are being managed to produce MSY, we will mistakenly call them overfished and the frequency of this mistake will depend on the threshold used to define overfished. Table 2 summarizes the proportion of US stocks that would be classified as overfished based on different biomass thresholds and Table 3 summarizes the proportion classified as subject to overfishing based on fishing mortality rates. In all cases, the most recent stock status is used for each stock. In both tables, several breakpoints are examined, and weightings based on number of stocks, MSY of stocks, and ex-vessel value of stocks are presented. The lost yield from stocks being below and above BMSY (Table 2) and FMSY (Table 3) is also shown. The overfished thresholds in Table 2 correspond to the tRFMO definition (BMSY), the FAO definition (0.8 BMSY), and the US definition (0.5 BMSY). The underfished threshold of 1.2 BMSY isthe FAO definition. 1.5 BMSY was the author’s choice as an illustration. In Table 3, the overfishing threshold of BMSY is the US standard, and the other values were chosen as illustrations. Table 2. Proportion of US stocks in different abundance status categories using different criteria and weighting methods. Overfished B/BMSY threshold (%) . Underfished B/BMSY threshold (%) . Weighting method . Overfished (%) . Optimum (%) . Underfished (%) . 100 150 All stocks equal 34 22 43 100 150 MSY 15 10 75 100 150 MSY × price 17 24 59 80 120 All stocks equal 23 19 58 80 120 MSY 4 13 83 80 120 MSY × price 11 13 76 50 150 All stocks equal 13 44 43 50 150 MSY 2 22 75 50 150 MSY × price 8 33 59 B < BMSY B > BMSY Lost yield 2 – 47 Overfished B/BMSY threshold (%) . Underfished B/BMSY threshold (%) . Weighting method . Overfished (%) . Optimum (%) . Underfished (%) . 100 150 All stocks equal 34 22 43 100 150 MSY 15 10 75 100 150 MSY × price 17 24 59 80 120 All stocks equal 23 19 58 80 120 MSY 4 13 83 80 120 MSY × price 11 13 76 50 150 All stocks equal 13 44 43 50 150 MSY 2 22 75 50 150 MSY × price 8 33 59 B < BMSY B > BMSY Lost yield 2 – 47 “Lost Yield” is the potential yield lost due to B < BMSY for overfished and B > BMSY for underfished. Open in new tab Table 2. Proportion of US stocks in different abundance status categories using different criteria and weighting methods. Overfished B/BMSY threshold (%) . Underfished B/BMSY threshold (%) . Weighting method . Overfished (%) . Optimum (%) . Underfished (%) . 100 150 All stocks equal 34 22 43 100 150 MSY 15 10 75 100 150 MSY × price 17 24 59 80 120 All stocks equal 23 19 58 80 120 MSY 4 13 83 80 120 MSY × price 11 13 76 50 150 All stocks equal 13 44 43 50 150 MSY 2 22 75 50 150 MSY × price 8 33 59 B < BMSY B > BMSY Lost yield 2 – 47 Overfished B/BMSY threshold (%) . Underfished B/BMSY threshold (%) . Weighting method . Overfished (%) . Optimum (%) . Underfished (%) . 100 150 All stocks equal 34 22 43 100 150 MSY 15 10 75 100 150 MSY × price 17 24 59 80 120 All stocks equal 23 19 58 80 120 MSY 4 13 83 80 120 MSY × price 11 13 76 50 150 All stocks equal 13 44 43 50 150 MSY 2 22 75 50 150 MSY × price 8 33 59 B < BMSY B > BMSY Lost yield 2 – 47 “Lost Yield” is the potential yield lost due to B < BMSY for overfished and B > BMSY for underfished. Open in new tab Table 3. Proportion of US stocks in various status categories based on fishing mortality using different threshold criteria and weighting schemes. Overfishing F/FMSY threshold (%) . Underfishing F/FMSY threshold (%) . Weighting method . Overfishing (%) . Optimum (%) . Underfishing (%) . 120 80 All stocks equal 13 14 73 120 80 MSY 1 6 93 120 80 MSY × price 5 12 83 100 70 All stocks equal 21 8 70 100 70 MSY 6 1 93 100 70 MSY × price 13 4 83 F > FMSY F < FMSY Lost yield 0.4 – 45 Overfishing F/FMSY threshold (%) . Underfishing F/FMSY threshold (%) . Weighting method . Overfishing (%) . Optimum (%) . Underfishing (%) . 120 80 All stocks equal 13 14 73 120 80 MSY 1 6 93 120 80 MSY × price 5 12 83 100 70 All stocks equal 21 8 70 100 70 MSY 6 1 93 100 70 MSY × price 13 4 83 F > FMSY F < FMSY Lost yield 0.4 – 45 “Lost Yield” is the potential yield lost due to F > FMSY for overfishing and F < FMSY for underfishing. Open in new tab Table 3. Proportion of US stocks in various status categories based on fishing mortality using different threshold criteria and weighting schemes. Overfishing F/FMSY threshold (%) . Underfishing F/FMSY threshold (%) . Weighting method . Overfishing (%) . Optimum (%) . Underfishing (%) . 120 80 All stocks equal 13 14 73 120 80 MSY 1 6 93 120 80 MSY × price 5 12 83 100 70 All stocks equal 21 8 70 100 70 MSY 6 1 93 100 70 MSY × price 13 4 83 F > FMSY F < FMSY Lost yield 0.4 – 45 Overfishing F/FMSY threshold (%) . Underfishing F/FMSY threshold (%) . Weighting method . Overfishing (%) . Optimum (%) . Underfishing (%) . 120 80 All stocks equal 13 14 73 120 80 MSY 1 6 93 120 80 MSY × price 5 12 83 100 70 All stocks equal 21 8 70 100 70 MSY 6 1 93 100 70 MSY × price 13 4 83 F > FMSY F < FMSY Lost yield 0.4 – 45 “Lost Yield” is the potential yield lost due to F > FMSY for overfishing and F < FMSY for underfishing. Open in new tab If we look at the time trend of stock status for all US stocks in the RAM Legacy Stock Assessment Database (Figure 3b) as simply the percentage of stocks below criteria of 0.5, 0.8, and 1.0 BMSY, we see similar trends at different scales. However, when we compare that trend to the yield lost based on biomass and fishing mortality, we see quite different patterns. Figure 3. Open in new tabDownload slide Time trend of proportion of potential yield lost (a) and classified as overfished (b). In (a), circles are yield lost based on biomass <BMSY, triangles are yield lost because fishing mortality >FMSY, and squares are yield lost because fishing mortality >FMSY weighted by ex-vessel value. In (b), the proportion of stocks classified as overfished are plotted for a variety of definitions: <BMSY (squares), <0.8 BMSY (triangles), and <0.5 BMSY (circles). Figure 3. Open in new tabDownload slide Time trend of proportion of potential yield lost (a) and classified as overfished (b). In (a), circles are yield lost based on biomass <BMSY, triangles are yield lost because fishing mortality >FMSY, and squares are yield lost because fishing mortality >FMSY weighted by ex-vessel value. In (b), the proportion of stocks classified as overfished are plotted for a variety of definitions: <BMSY (squares), <0.8 BMSY (triangles), and <0.5 BMSY (circles). When we look at the time trend of the proportion of stocks meeting the US overfished definition of B < 0.5 BMSY for the three weighting approaches—number of stocks, MSY, or value—we see strikingly different patterns (Figure 4b). Of particular interest is the very high fraction overfished based on MSY or value in the late 1970s, a period where a number of large stocks were at low abundance. Figure 4. Open in new tabDownload slide Time trend of proportion of potential yield lost (a) and classified as overfished using different weightings (b). In (a), circles are yield lost based on biomass <BMSY, triangles are based on fishing mortality >FMSY, and squares are based on fishing mortality >FMSY weighted by ex-vessel value. In (b), the proportion overfished giving equal weight for all stocks is shown as circles, weighted by value is squares, and weighted by MSY is triangles. Figure 4. Open in new tabDownload slide Time trend of proportion of potential yield lost (a) and classified as overfished using different weightings (b). In (a), circles are yield lost based on biomass <BMSY, triangles are based on fishing mortality >FMSY, and squares are based on fishing mortality >FMSY weighted by ex-vessel value. In (b), the proportion overfished giving equal weight for all stocks is shown as circles, weighted by value is squares, and weighted by MSY is triangles. Discussion The analysis presented is largely dependent upon estimates of the reference points BMSY and FMSY, which are often difficult to estimate. Indeed, in many US fisheries, reference points used are not those that would be calculated from the estimated parameters in a stock assessment. Proxies, such as those based on spawning biomass per recruit, are often used instead. These proxies are often chosen to be precautionary, so in many cases the proxy BMSY used in the assessment and as a management target is higher than the BMSY that is calculated from the stock assessment model. For example, for both widow rockfish (Hicks and Wetzel, 2015) and yellowtail rockfish (Cope et al., 2013), the overfishing threshold is greater than the stock assessment estimate of BMSY and the target BMSY proxy is far above the assessment model estimate of BMSY. For widow rockfish, the spawning biomass target is 32 283 MT, the overfished threshold is 20 177 MT, and the model estimate of SSBMSY is 18 247 MT. A further limitation of our analysis is that we have looked at the United States as a whole, whereas a region by region comparison would perhaps look quite different. By volume, the fisheries of Alaska and the two menhaden stocks (Gulf of Mexico and Atlantic Coast) dominate US fisheries, while by value the Atlantic sea scallop and Maine lobster fisheries have great weight. Our simulations (Figure 2) show that stocks managed at FMSY will be below the FAO criterion of 0.8 BMSY 37% of the time due to natural stock fluctuations. FAO reports that 32% of assessed global stocks are overfished, so one could conclude that none of these global stocks are actually being fished too hard and 32% of stocks classified as overfished are “false positives”. That is, they are below 0.8 BMSY as part of normal fluctuations around BMSY under an FMSY policy. This is clearly not the case; many stocks are at very low abundance, far below the range that would be expected under an FMSY policy. I point out this potential conclusion because it illustrates a major deficiency in the method that FAO and others use to assign stock status—the method fails to distinguish between a stock just below the 0.8 BMSY cut-off, which would be losing very little potential yield, and a stock that is totally collapsed. The US criteria of assigning stocks an “overfished” status when B < 0.5 BMSY appears to be a better indicator in that it allows for some natural fluctuation around BMSY and will produce fewer false positives. However, it also fails to distinguish between a stock just below the cut-off and stocks far below it. Weighting methods also play a large role in the interpretation of stock status. If we look at the US classification system, 13% of stocks are currently classified as overfished when weighted equally, 2% when weighted by potential yield for each stock, and 8% when weighted by potential value of each stock. The Goldilocks approach returns a lost yield estimate of 2%. 13% of stocks below 0.5 BMSY that I calculated differ from the value reported by NMFS in 2018 (NMFS, 2019) of 18% because that report includes a large number of Pacific salmon stocks that are not included in our analysis of marine fisheries and NOAA also considers a number of tuna stocks that are managed by the tRFMOs, which I did not include in my analysis. Using the ratio of B/BMSY as the threshold of declaring stocks overfished does show the trend in stocks over time, but it does not reflect much about the performance of a management system to maximize yield, regardless of what threshold is chosen. All stocks exhibit natural fluctuations that are only partially controlled by fishing regulations. Looking at Figure 4, the percentage of stocks below 1.0, 0.8, and 0.5 BMSY show a similar trend with different scaling. However, those measures fail to show the same trend as the lost yield measures. The lost yield based on fishing mortality has remained very low (average of 1.9%), gradually declining over time. Using the most recent F/FMSY for each stock (Table 3), the lost yield from F > FMSY is <1%. This means that few large stocks had fishing mortality rates >FMSY. This would be reflective of a management system that has been successfully regulating fishing pressure overall. Lost yield based on biomass shows a totally different trend: it is high in the 1970s and then declines considerably from the mid-1970s to the 1990s, at the same time that the proportion of stocks overfished (by any definition) was increasing. The high amount of lost yield based on biomass in the 1970s was predominantly due to several large stocks being well below BMSY, especially Eastern Bering Sea pollock and Atlantic menhaden. It is especially interesting that from the 1970s to the 1990s, the lost yield due to fishing mortality remained low—thus, it was not the fishing mortality rate that caused the decline in those large stocks to the low biomass reflected in the lost yield based on biomass analysis. This suggests that the increase in lost yield based on biomass in the 1970s was not due to fishing too hard, but to random environmental effects affecting several large stocks. The increase in the number of overfished stocks seen from 1970 to 1995 did not include the large stocks that constitute most of the potential catch; if it had, we would have seen the lost yield based on biomass increase rather than decrease during this period. When we consider the yield lost due to F > FMSY weighted by value in Figures 3a and 4a, we see an increase from 1970 to 1980 and then a continuous decline. This is perhaps a better indicator of the overall fisheries performance than lost yield weighted by biomass since biomass weighting is heavily dominated by a few large low-value stocks. So did US fisheries management start to improve around 2000 as indicated by the proportion overfished, or in the mid-1980s as indicated by the lost value? From the perspective of generating catch and value, there can be no doubt that the lost yield calculations reflect this best, but if the national objective is to have no overfished stocks, then the proportion overfished would seem to be the best index. I would argue that, while the goal of MSY underlies US federal fisheries policy, the fraction of stocks classified as “overfished” has been assumed to represent this and become the management objective, a process known as “goal displacement” (Bohte and Meier, 2000). If we compare the different methods of weighting the percentage overfished, we see that the 1970s show the highest percentage of overfished stocks when weighted either by MSY or value, and lost yield based on biomass, but at that time the proportion of all stocks with abundance <0.5 BMSY was quite low. So did fishery management improve or decline between the 1970s and 1990s? Depending on which of these weighting measures we choose, we can interpret the history of fishery performance quite differently. The use of precautionary proxies complicates our interpretation even further. The yield lost by overfishing will be less than calculated in this article when the MSY proxy for BMSY or FMSY is more precautionary than the best model fits. Thus, it seems likely that the performance of US fisheries regarding overfishing is even better than calculated here, and the loss from underfishing is higher. Thus, we need to return to the key question; what are the stock status indices intended to reflect? If the objective is to reflect management performance within the paradigm of sustainable food production, then using MSY weighted indices seems greatly preferred to counting all stocks equally. If the objective is to reflect management performance within the paradigm of economic performance, then using value weighted indices is appropriate. However, since fishery managers can only control the fishing mortality rate and abundance is often strongly influenced by environmental variability, the index reflecting lost yield based on fishing mortality rate better reflects a management objective of achieving MSY. By this measure, US fisheries managers have consistently performed well, although there are undoubtedly regional differences in this measure. This is especially evident when we consider the performance of regional management councils. For example, in New England, overfishing of groundfish stocks was common until 2000 and was a key driver in revision of the national legislation. These stocks are regionally important, but in terms of potential yield or economic value, they are not of much national significance. From an economic perspective, New England is dominated by Maine lobster and Atlantic scallops—so might it not be more appropriate for those stocks to be given more weight in evaluating fisheries performance? In the United States, NOAA has specific instructions on reporting overfished stocks in the Magnuson-Stevens Act Section 304(e)(1). “The Secretary shall report annually to the Congress and the Councils on the status of fisheries within each Council's geographical area of authority and identify those fisheries that are overfished or are approaching a condition of being overfished”. The use of 0.5 BMSY by NOAA is clearly a better reflection of whether a stock is outside the range of variability expected under a policy that would produce MSY than the 0.8 BMSY used by FAO or 1.0 BMSY used by some tRFMOs. Still, it does not reflect the performance of the fisheries management system in producing sustainable yield and does not provide the Councils or Congress with all the information they need to make relevant policy decisions about food production in the United States. When we move beyond the MSY paradigm, we need to consider what would be a good indicator of stock health, not in the context of sustainable yield, but in terms of existence value or role in the ecosystem. The proportion of stocks below 0.5 BMSY could be interpreted as such an indicator, but again, the 0.5 is arbitrary, and from a food web perspective, weighting by stock size would seem much more relevant than counting all stocks equally. Existing endangered species listings in the United States and internationally currently provide indicators of conservation concern that are based primarily on trends in abundance, and absolute abundance. All of the previous discussion has focused on individual species, predicated on the assumption that each stock can be individually managed. In fact, many stocks are caught in highly mixed fisheries, and there is a long list of literature demonstrating that you cannot manage all stocks to simultaneously produce MSY (Ricker, 1958; Paulik et al., 1967; Hilborn, 1976; Worm et al., 2009; Hilborn et al., 2012). Figure 2 in Worm et al. (2009) shows that, in an ecosystem model, maximization of sustainable yield would result in 40% of stocks being collapsed. So if we wish to maximize the sustainable yield from mixed fisheries, we would expect some of the stocks to be fished harder than FMSY and would thus be classified as “subject to overfishing” by US standards. As a result, single-species lost yield calculations, as presented in this paper, would show lost yield by stock and overall even if the mix of species was managed to produce MSY. Evaluation of fisheries performance should evaluate mixed stock fisheries on a different basis than single stock fisheries, but we have found no agencies that have attempted to do this. My analysis and discussion have been narrowly framed around overfishing and the sustainable food production perspective. As mentioned in the Introduction, there are a wide range of other considerations for marine fisheries management, including ecosystem impacts of fishing, subsistence and recreational opportunities, carbon footprint, employment, profit, and maintenance of fishing communities and cultural ties. These are all important dimensions of fisheries that are not captured in any of the indices discussed here and for which indicators of performance should be developed. The Ocean Health Index (Afflerbach et al., 2019) does have a range of sub-indicators that cover some of these concerns I have not addressed. Climate change is a major challenge to stock classification methods as it is expected that some stocks benefit from climate change and others suffer. Reference points used in stock classification (typically BMSY and FMSY) will change as productivity is altered by climatic forcing. Most stock assessments and reference points continue to assume productivity fluctuates around a mean rather than having a trend. This is an active area of research that will require inclusion in future interpretations of stock status and management effectiveness. Given that each country and regional fisheries management organization is reporting on the status of their fisheries in quite different ways (Table 1), and often relying on a single index to represent different goals. I suggest that there is need for an international consensus on “best practices” in reporting the performance of fisheries. Such a “best practice” would clearly define the objective of reporting indices and provide theoretically sound methods for establishing context-appropriate criteria used in defining them. Currently, reporting the proportion of stocks overfished is by far the most common approach and as I demonstrated in this article, such indices do not represent the performance of a region’s fisheries with respect to MSY particularly well. Funding This work was funded by the Seafood Industry Research Fund. Acknowledgements Nicole Baker provided editorial and graphic support. Data availability Stock abundance and status data are available at the RAM Legacy Database website (www.ramlegacy.org) version 4.491. Prices for US stocks are available from NMFS website (https://www.st.nmfs.noaa.gov/commercial-fisheries/commercial-landings/annual-landings/index). References Afflerbach J. 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Google Scholar Crossref Search ADS PubMed WorldCat © International Council for the Exploration of the Sea 2020. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) © International Council for the Exploration of the Sea 2020. All rights reserved. For permissions, please email: [email protected]

Hobday, Alistair, J;Browman, Howard, I;Bograd, Steven, J

doi: 10.1093/icesjms/fsaa151pmid: N/A

Abstract Beginning in February 2020, COVID-19-related stay at home orders and workplace shutdowns worldwide have disrupted personal and professional lives, including those of aquatic scientists. Manuscript submission and peer reviewing data from journals may be indicators of productivity impacts among aquatic scientists. We tested four null hypotheses: the COVID-19 disruption has had no effect on (i) the number of submissions to journals, or (ii) the geographic region in which the corresponding author is based, nor on the peer review process in terms of (iii) acceptance rate of requests to review and (iv) time in review. We used data provided by seven leading aquatic science journals covering the period 2009–2020 and representing 32 756 submissions. Submission differences varied between journals and were lower than expected in March 2020, but due to increases in subsequent months, there was no overall change in the number of submissions during the COVID-19 disruption months of February–June 2020. Geographic patterns in the number of submissions varied more by journal than by region, with both higher and lower numbers of submissions relative to expected numbers. Acceptance rates of requests to review were ∼2% lower overall; however, time in review declined by an average of 5 days relative to earlier years, showing that those scientists undertaking reviews did them more quickly during the COVID-19 disruption. Collectively, these results show that the overall productivity of the aquatic science community, as measured by publications and reviewing rates and times, has thus far only been slightly disrupted, although the impacts will vary greatly among individuals depending on life circumstances. The breadth and longevity of this disruption are unprecedented, making it important to continue to assess the relative impacts across a wide demographic range of aquatic scientists and to consider approaches to allow those differentially affected to recover to pre-COVID-19 levels of productivity. Introduction An extreme event is a dynamic occurrence within a limited timeframe that impedes the normal functioning of a system or systems (Broska et al., 2020). Scientists study the impact of extreme events to help society build resilience and minimize impacts in the future (Altwegg et al., 2017; Solow, 2017) or to better understand the full range of system dynamics (Broska et al., 2020). Scientists are also affected by extreme events yet are not often themselves the subject of study. Extreme events have always affected individuals and are explicitly recognized as mitigating circumstances in grant proposal schemes or in considering “output” relative to opportunity, with allowances provided for family responsibilities in some countries (Malisch et al., 2020; Myers et al., 2020). These individual disruptions (or life choices, in the case of parenting) contrast with the collective extreme event being experienced by societies all around the world due to the COVID-19 pandemic. Scientists are considered one of the more resilient segments of the work force, in terms of maintaining employment, but in many countries have still been affected due to social distancing requirements that have closed or reduced office and laboratory use, curtailed travel for activities such as project work and conferences, and halted research voyages on ships and small boats, and field and laboratory work in general. The extremes of the spectrum of anticipated COVID-19-related impacts for scientists who have retained employment are “this is going to be a productive period” (e.g. Fleming, 2020) vs. “this is going to be a very big disruption to productivity” (e.g. Ling, 2020). Anecdotal evidence of impacts on early career researchers (Pain, 2020), parents (Staniscuaski et al., 2020; 500 Women Scientists, 2020), and women (Minello, 2020; Vincent-Lamarre et al., 2020) suggests declines in productivity as a result of COVID-19 are likely (https://www.thelily.com/women-academics-seem-to-be-submitting-fewer-papers-during-coronavirus-never-seen-anything-like-it-says-one-editor/; https://www.insidehighered.com/news/2020/04/21/early-journal-submission-data-suggest-covid-19-tanking-womens-research-productivity). Part-time volunteer editors may also have additional responsibilities that may affect manuscript handling times (https://publicationethics.org/news/letter-cope-chair-april-2020). Cessation of day care and school in many countries has required parents to increase caring responsibilities while also maintaining various levels of work-related activity (Staniscuaski et al., 2020), which may have been intensified by an increase in the number of virtual meetings. For some researchers, time savings, and increased production of publications, might be a benefit of reduced travel. In one of our organizations, for example a group of 90 marine scientists went from spending >300 person days per month travelling for work to 0 days for the months of March–June 2020. Field work, including for research voyages, has also ceased in many locations. While some of this time could still be directed to completing the same work-related tasks remotely, this represents considerable potential savings in time previously spent travelling. Grant submission deadlines have been delayed in many countries and even cancelled, which might lead to an increased focus on the production of manuscripts and on undertaking review assignments. We evaluate potential COVID-19-related trends in researcher productivity in aquatic science during the first half of 2020. While productivity may take a range of forms, we use the evidence base available from one important area of activity that aquatic scientists undertake—submission of manuscripts to journals and review of manuscripts submitted to these journals—as a proxy for productivity. We test four null hypotheses to assess for changes in these proxies of productivity during the COVID-19 disruption period. There is no change in the number of manuscripts being submitted. There is no change in the geographic pattern of submissions, based on the country of the corresponding author. There is no change in acceptance (or completion) rate for review requests. There is no change in the time taken to complete reviews. While gender differences in scientific publication submissions have been posited (e.g. Vincent-Lamarre et al., 2020; Staniscuaski et al., 2020), with the data collected by journals, and the form in which data were shared with us, it was unfortunately not possible to investigate this aspect. Methods Two datasets were assembled to assess the productivity of aquatic scientists associated with publishing in peer-reviewed journals. The first dataset assembled information on article submission and reviewing rate provided by seven aquatic science journals. We selected journals that had been publishing papers for at least the last decade to have a sufficient reference period. The seven journals were Canadian Journal of Fisheries and Aquatic Sciences, Fisheries Oceanography, Fisheries Research, ICES Journal of Marine Science, Limnology and Oceanography, Marine and Freshwater Research, and Marine Ecology Progress Series. Since the data provided by these journals and their publishers are confidential, they are presented in an anonymized manner. Changes in editorial software systems over time meant that some data were not consistently collected over the entire time period—when that was the case, those data were removed from analyses for that particular journal (e.g. time in review data was inconsistently archived). The second data set was derived from employee submission records for intended peer-reviewed articles from the national Australian science agency (CSIRO) and by a regional division of the United States National Marine Fisheries Service (NOAA/NMFS). Both these government organizations require employees to register all manuscripts prior to submission and, while not specific to any particular journal, these data can be used as another proxy for productivity. Analysis—dataset 1—journal patterns Submissions For each journal, we received data on the number of submissions per month and compared February–June 2020 with data from the same months in previous years (e.g. February–June 2009–2019) (Hypothesis 1). We first checked for trends over time that could confound detection of a COVID-19 effect. In the case of trends (e.g. increase or decrease in submissions over time), we calculated the expected number of submissions based on a linear trend for monthly data prior to 2020 and calculated the difference between the observed and expected values for each of the months February–June 2020. Where there was no trend over time, we subtracted the number of submissions for each 2020 month (February–June) from the average number of submissions in that month prior to 2020 (e.g. February 2020 − the average of February 2009–2019). Negative values indicate fewer than expected submissions, and vice versa. Country of origin for the corresponding author (most often the first author) for submissions was also provided (Hypothesis 2). We assume this was where the author resided. Geographic patterns in submissions were considered at a regional scale by allocating the corresponding author country to a region (North America, Europe, Australia/Pacific, Asia, Africa, and South America), as submissions for individual countries were too few to detect changes for the COVID-19 period. As there were trends over time for most region–journal combinations, we calculated the difference between the observed and expected numbers of submissions for each region in 2020 compared to the expected number based on the linear trend prior to 2020, as for Hypothesis 1. Peer reviewing rates and times In seeking reviewers for a submitted paper, an editor will issue individual requests for each paper (P), which are accepted (A), declined, or ignored at some rate (%A). Sometime later (t2), a percentage of these accepted reviews are completed and returned (%C), and a decision (D) is then made some days later by an editor (t3). Review process=P→%At1→%Ct2→Dt3.(1) Data provided on this process differed by journal, depending on their record-keeping system and were variously based on the number or percentage of reviews accepted or completed (divided by the number of invitations issued), per paper, per month, or per year. We used only data on reviews accepted (A) or completed (C) for original submissions and not revised manuscripts, and hereafter use the general term “review acceptance rate” to describe this proxy. As annual data were provided by some journals, inclusion of January 2020, before most of the isolation measures were implemented around the world, is expected to reduce any COVID-19 effect. Individual or monthly data were aggregated to an annual review acceptance rate to facilitate comparisons by journal, where the year 2020 (i.e. January–June) was assumed to encompass the COVID-19 effect (Hypothesis 3). For two journals that provided monthly data, there were no differences between review acceptance rates for the period January–June in pre-2020 years, and the whole year. For each journal, we calculated the anomaly in review acceptances using the form of the data provided—either the review acceptances (%A) or completions (%C) data for 2020. Several journals also provided data on the “time in review”—based on the time that the first (or all) reviewers took to complete (t2) their review(s) of a submission (Hypothesis 4). One journal also provided data on the time between an editor sending a review request and the acceptance by the reviewer [t1 in (1)]. Data on review acceptance rate and time in review were both inspected for temporal trends and, if necessary, detrended for each journal before calculating differences (anomalies) from expected values based on the trend. Analysis—dataset 2—institutional submission patterns The CSIRO (Australia) requires employees (lead authors and junior authors for submissions not led by CSIRO authors) to register submissions to peer-reviewed journals. These data allow (i) a test of marine science manuscript submissions across a range of journals and (ii) a comparison of the relative productivity change in 450 marine scientists employed by CSIRO with the 4000 scientists employed across other research domains in CSIRO. As for Dataset 1, the number of submissions per month was determined for the Marine Division, and across all other divisions for the period 2015 to May 2020. Data were detrended if necessary and submissions for 2020 were compared to the period 2015–2019. Similarly, the NOAA/NMFS (USA) requires submissions to be approved by a senior manager. Annual data for the period January–May 2015–2020 were provided. The anomaly from the annual trend was calculated for each year, and then 2020 compared to the period 2015–2019. Results Submissions The submission dataset was based on 32 756 submissions to seven journals spanning the period January 2009–June 2020 (Table 1). Four of the submission time series were detrended (see Supplementary material). The number of submissions for the months of February–June 2020 was below expected numbers in February (five of seven journals) and March (four of seven) and above or equal to the expected numbers in April (six of seven), May (four of seven), and June (six of seven) (Figure 1). Overall, only two journals experienced a decline (C: 6%, E: 129%) and five an increase (A: 13%, B: 11%, D: 9%, F: 2%, and G: 16%) in submissions over the COVID-19-affected months of 2020. A total of 68% of the possible 35 journal–month combinations (February–June 2020, 7 journals) had deviations above the expected number of submissions. Across all journals, we calculated that three more papers were submitted to these seven journals during these COVID-19-affected months than would have been expected based on the total observed submissions for the same period in other years, representing a 0.2% increase in submissions. Figure 1. Open in new tabDownload slide Submission patterns per month in 2020 for seven major aquatic science journals, as a difference from the expected value (detrended linearly where there was an increase or decrease in submissions, as noted in Table 1). Figure 1. Open in new tabDownload slide Submission patterns per month in 2020 for seven major aquatic science journals, as a difference from the expected value (detrended linearly where there was an increase or decrease in submissions, as noted in Table 1). Table 1. Summary of data provided by seven aquatic science journals. Journal . Data period . Number of submissions . Geographic data . Review acceptance data . Time in review data . A 2009–2020 (June) 898 Y* Y* Y* B 2009–2020 (June) 5 987* Y* Y* Y C 2012–2020 (June) 3 092 Y* Y* – D 2015–2020 (June) 4 135* Y* – – E 2011–2020 (June) 4 962* Y* Y* Y* F 2010–2020 (June) 10 725* Y* Y* Y* G 2015–2020 (June) 2 957 Y* Y Y* Total – 32 756 – – – Journal . Data period . Number of submissions . Geographic data . Review acceptance data . Time in review data . A 2009–2020 (June) 898 Y* Y* Y* B 2009–2020 (June) 5 987* Y* Y* Y C 2012–2020 (June) 3 092 Y* Y* – D 2015–2020 (June) 4 135* Y* – – E 2011–2020 (June) 4 962* Y* Y* Y* F 2010–2020 (June) 10 725* Y* Y* Y* G 2015–2020 (June) 2 957 Y* Y Y* Total – 32 756 – – – “Y” indicates that the data for the hypotheses regarding geographic patterns, review acceptance, and time in review were usable. * Data were detrended before calculating 2020 values (see Methods). Open in new tab Table 1. Summary of data provided by seven aquatic science journals. Journal . Data period . Number of submissions . Geographic data . Review acceptance data . Time in review data . A 2009–2020 (June) 898 Y* Y* Y* B 2009–2020 (June) 5 987* Y* Y* Y C 2012–2020 (June) 3 092 Y* Y* – D 2015–2020 (June) 4 135* Y* – – E 2011–2020 (June) 4 962* Y* Y* Y* F 2010–2020 (June) 10 725* Y* Y* Y* G 2015–2020 (June) 2 957 Y* Y Y* Total – 32 756 – – – Journal . Data period . Number of submissions . Geographic data . Review acceptance data . Time in review data . A 2009–2020 (June) 898 Y* Y* Y* B 2009–2020 (June) 5 987* Y* Y* Y C 2012–2020 (June) 3 092 Y* Y* – D 2015–2020 (June) 4 135* Y* – – E 2011–2020 (June) 4 962* Y* Y* Y* F 2010–2020 (June) 10 725* Y* Y* Y* G 2015–2020 (June) 2 957 Y* Y Y* Total – 32 756 – – – “Y” indicates that the data for the hypotheses regarding geographic patterns, review acceptance, and time in review were usable. * Data were detrended before calculating 2020 values (see Methods). Open in new tab Geographic data There were fewer than expected submissions from North America (six of seven journals), Australia/Pacific (four of seven), and Asia (four of seven) during the February to June 2020 period (Figure 2). The three other regions had more increases than decreases in submissions—Europe (four of seven journals), Africa (four of seven), and South America (five of seven). Across all journals, there were 30 and 27 more submissions from Asia and Europe, respectively, while North America had 97 fewer submissions, than expected. Figure 2. Open in new tabDownload slide Submission deviation per month by geographic region for seven aquatic science journals for the period February–June 2020, detrended as noted in Table 1. “Total” represents the total deviation in submissions in 2020 for each geographic region across all seven journals. Figure 2. Open in new tabDownload slide Submission deviation per month by geographic region for seven aquatic science journals for the period February–June 2020, detrended as noted in Table 1. “Total” represents the total deviation in submissions in 2020 for each geographic region across all seven journals. Reviewing The acceptance rate for undertaking reviews was lower in 2020 compared to the values prior to 2020 for three of six journals that provided these data, although rates were highly variable among journals (Figure 3). One of the journals had higher than usual acceptance rates (Journal F: +16.7%) and one much lower (Journal A: -23.1%). Averaged across all six journals with these data, the review acceptance rate was only 1.8% lower for the COVID-19 period considered here. Figure 3. Open in new tabDownload slide Reviewer invitation acceptance deviation from expected rates in 2020 for six of seven aquatic science journals (Journal D: no data were provided). Figure 3. Open in new tabDownload slide Reviewer invitation acceptance deviation from expected rates in 2020 for six of seven aquatic science journals (Journal D: no data were provided). Time in review The average time manuscripts were in review could be calculated from data provided by five journals (Table 1) and is reported here annually, which was the most common time scale available (Figure 4). All five of the journals experienced more rapid review times than expected, by between 4 (Journal G) and 7 days (Journal A). These absolute changes corresponded to between 4.6% and 21% faster than expected (Figure 4). At an even finer temporal scale, Journal G provided data showing that the response time to accept an editors’ review request (detrended) was faster by about half a day (0.67) for the 2020 period compared to the expected value of 3.08 days, a reduction of ∼21.7%. Figure 4. Open in new tabDownload slide The time in review deviation from expected in 2020 for four of seven aquatic science journals. For example, Journal A reviews in 2020 were completed 7 days faster than expected, a time reduction of 21%. (Journals C, D, and F: data were not provided or available or were unsuitable). Figure 4. Open in new tabDownload slide The time in review deviation from expected in 2020 for four of seven aquatic science journals. For example, Journal A reviews in 2020 were completed 7 days faster than expected, a time reduction of 21%. (Journals C, D, and F: data were not provided or available or were unsuitable). Institutional submission rates There was no temporal trend in submissions over the period 2015–2019 for the CSIRO’s marine Research Division (Australia), or in total. The number of peer-reviewed journal submissions registered by employees of the marine research division was 13.3% lower in the months February–June 2020 (n = 138) compared to the same months for the period 2015–2020 (n = 159, range 135–411 submissions), and for the whole organization (i.e. including non-marine divisions) was 19.2% below the reference period (831 vs. 1028, range 914–1183). For the NMFS (USA) marine institution, the number of submissions was 17% lower for the months February–May 2020 compared to the 2015–2019 detrended value for the same months. Discussion The worldwide disruption as a result of COVID-19 responses by national governments is unprecedented in the level of impact on society. Beginning in February 2020, measures imposed to slow the spread of the disease dramatically reduced domestic and international travel, shut workplaces and schools, and led to widespread job losses. This disruptive time has required a rethinking of values and work-private priorities at all levels of society (e.g. Corbera et al., 2020) and has been postulated to have an impact on scientific productivity (Ling, 2020; Myers et al., 2020). We tested four hypotheses related to scientific productivity during the initial months of restrictions associated with COVID-19 (February–June 2020). Our analyses of aquatic scientist productivity were based on journal submissions and reviewing patterns, which are only one area of scientist productivity, but given the importance for career progression and funding success, an area that many scientists prioritize (e.g. Peoples et al., 2016). It is also an activity that can be conducted even when travel or fieldwork is interrupted. Submission of manuscripts is a final stage in research, coming after a long series of steps, including grant submission, field, experimental or modelling research, and analysis and synthesis. Preparation of a manuscript may take weeks to months, and disruption in this final stage can often delay submission, resulting in fewer papers received by journals. If the shutdown period allowed scientists to instead focus on this final stage, we expected to see an increase in submission rates. Within this sample of seven journals, some had large declines in submissions and others had moderate increases. The variation among journals may reflect insufficient sample sizes, or differences between the researcher communities that submit to these journals. The monthly data showed that declines were greatest in the first months of the shutdown—February and March 2020, with more typical rates from April onwards. This may reflect scientists settling into new routines. However, not all groups appear to be coping equally (e.g. Minello, 2020; Myers et al., 2020). Overall, we observed no consistent decline and, therefore, cannot reject Hypothesis 1. This pattern varied by journal, which may also indicate differences in the scientific research areas that are most impacted (see Myers et al., 2020). We contend that our sampled journals are representative of the science addressing freshwater, fisheries and marine ecosystem research questions, other marine science specialities such as physical oceanography are submitted to a different set of journals, and different patterns may be found for other research communities. We also cannot exclude the possibility that some scientists have been more productive and that this balanced the productivity decline in negatively impacted scientists. We note that we were unable to examine gender or other demographic differences (e.g. in-country restrictions, career stage, carer responsibilities, e.g. Myers et al., 2020), as such information was not gathered by the journals included in this analysis. Such analyses may be attempted in future based on published articles for which ancillary information such as gender can be added via Internet searches. The geographic patterns in submission rate did not consistently show increases or decreases, which might be expected based on different applications of social isolation policies. Asia, which has had varied responses to COVID-19, showed a relative increase in submissions, as did Europe, where social distancing policies have been implemented less widely and/or commenced later in the year (June–July), while North America had an overall decrease. Thus, Hypothesis 2 cannot be rejected—at the scale we considered the patterns there was no consistent regional shift in submissions. The data for single-government institutions in Australia and the United States had larger decreases in productivity, close to 20%, and this is consistent with early responses to isolation and social distancing in the developed countries such as Australia and the United States. Australia’s COVID-19 outbreak also followed severe societal disruption due to the worst bushfire season on record. It would be useful to examine submission rates from universities and other types of research agencies; however, such data are rarely gathered. The acceptance rate for reviews varied, with some journals showing relatively large declines, and others increases. Thus, Hypothesis 3 that COVID-19 would affect the willingness of scientists to review papers was not rejected. Interestingly, the time to review was quicker for all journals, thus rejecting Hypothesis 4. Scientists who did accept a request to provide a review were completing this task more quickly. This might be considered evidence for additional time allocated to reviewing, or fewer competing tasks. A single journal also provided data that showed the response to a request to review was received faster than in pre-COVID-19 years, indicating a greater willingness to review submissions by some scientists. Our results also provide comfort to editors—if more papers are going to be submitted during the COVID-19 months without an equivalent increase in reviewing, more pressure would be placed on the existing reviewing community. However, we found no decline in submission and only a small decline in reviewing rate. From the data analysed here, we cannot determine whether the individuals who reduced their reviewing load also reduced their submissions, or if those who increased their number of submissions also increased their reviewing rate. It is well known that reviewing activity is not spread evenly among researchers (https://publons.com/blog/spread-of-peer-review-workload/) and COVID-19 may exacerbate this in future. As with many facets of life under COVID-19, the experience so far in 2020 has offered a chance to reflect on effort allocated to writing manuscripts and reviewing tasks that add to the excessive workload already carried by many scientists. While these analyses cannot reveal individual narratives—e.g. some scientists have had higher productivity and others lower—we have shown that the COVID-19 disruption has not dramatically reduced productivity by the aquatic science community as indicated by total manuscript submissions or review acceptance rates to seven journals, and from two large national research institutes, that we assume are representatives of the overall aquatic science community. In the coming years, it will be interesting to track the long-term impact on publishing and other forms of scientific productivity, particularly to quantify this disruption on the career progression for COVID-19-disadvantaged individuals and groups. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Acknowledgements Submission and reviewing data are considered proprietary information by journals, and we are grateful for the access we were granted. We appreciate assistance in obtaining approval to use journal data from Max Finlayson, George Rose, Marisa Spiniello, Yong Chen, K. Dave Hambright, Christine Paetzold, Lia Curtin, Emily Davies, and Georgina Smith. Data on institutional submissions were provided by Meryn Scott (CSIRO library) and Laurie Barak (NOAA SWFSC). Assistance in data preparation was expertly provided by Vicki Walters, as was review by Brett Moloney and Rodrigo Bustamante. 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The decline of women's research production during the coronavirus pandemic. Nature Index. https://www.natureindex.com/news-blog/decline-women-scientist-research-publishing-production-coronavirus-pandemic. 500 Women Scientists. 2020 . Scientist Mothers Face Extra Challenges in the Face of COVID-19. https://blogs.scientificamerican.com/voices/scientist-mothers-face-extra-challenges-in-the-face-of-covid-19/. © International Council for the Exploration of the Sea 2020. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Andersen, K, H

doi: 10.1093/icesjms/fsaa157pmid: N/A

Abstract Fisheries science and management is founded upon the Beverton–Holt theory of fish stock demography. The theory uses age as the structuring variable; however, there are several reasons to use body size as the structuring variable. Most of the processes that affect a fish are determined by its body size rather than its age: consumption, mortality, maturation, fecundity, fish gear selectivity, etc., and measurements of body size are easy, accurate, and abundant. Here, I review size-based theory of a fish stock and compare it to classic age-based theory. I show that size- and age-based demography are equivalent representations of demography. However, size-based theory is axiomatic, which leads to a deeper theory with two advantages: predictions need fewer parameters than age-based theory and the theory connects directly to life-history traits. The connection with traits makes size-based theory particularly useful for data-poor application and facilitates evolutionary calculations. I compare age- and size-based theories for fisheries impact and stock assessments and provide a perspective on the challenges and future of single-stock theory. The increasing exploitation of fish stocks in the 20th century created a need for formal fisheries management. The scientific community responded by developing models of fish stock dynamics (e.g. Graham, 1935). Eventually, a consensus emerged as synthesized by Beverton and Holt (1957). Their age-based demographic description was built upon three assumptions: that growth is described by a von Bertalanffy equation, that natural mortality is constant, and that recruitment happens early in life. The theory has the advantage that it is simple and can easily be implemented numerically, initially in mechanical calculators and later in spreadsheets. The theory was good, and it has supported fisheries science and advice for more than half a century by answering relevant questions: what is the impact of fishing on different age groups in the fish stock? How much should we fish a stock? What are the relevant fisheries reference points (⁠ Fmsy, Flim, Bmsy ⁠, etc.)? Finally, the theory formed the basis for heuristic and statistical fish stock assessment models. Fisheries advice without Beverton–Holt is unthinkable. Today, new questions challenge age-based demography: how do we assess data-poor stocks where the parameters required for age-based assessments are not known? How do we assess the evolutionary side-effects of fishing (Jørgensen et al., 2007)? How do we deal with density-dependent growth or cannibalism? The age-based framework may be adjusted to answer these questions; however, it does so with difficulty because it was only designed to predict demography. We need to reconsider our reliance on age-based demography as the theoretical basis of fisheries advice. A fundamental shortcoming of age-based theory is that age is a weak predictor of many relevant processes. Body size—be it length or weight—has emerged as being more important than age for physiology (Winberg, 1956), mortality of individuals (McGurk, 1986), predator–prey interactions (Ursin, 1973), reproductive investment (Barneche et al., 2018), fisheries impact through gear mesh regulation (Myers and Hoenig, 1997), and usually also market value. From biological, fisheries, and economic perspectives, body size is the natural structuring variable for fish demography. In this Food for Thought, I review size-based theory of a fish stock and compare it to classic age-based demography. Broadly speaking, size-based theory has three advantages: (1) measuring size is easier, cheaper, and more accurate than measuring age; (2) it synthesizes four existing theories: size-based demography, life-history constants, evolutionary ecology and quantitative genetics, and trait-based ecology, into a coherent, axiomatic framework; (3) it opens up for new applications in data-poor stock assessment, assessment of fisheries-induced evolution, cross-species analyses, and assessment of emergent density dependence among adults. I will show how closely related the size- and age-based frameworks actually are; classic age-based Beverton–Holt is essentially a subset of the larger size-based theory. I will not dwell on the mathematical details, which are given elsewhere (Andersen, 2019), though I will compare the parameters between age- and size-based theories. I discuss pros and cons of size-based theory for fisheries impact and stock assessments, open issues, and the potential for future applications. Emergence of size-based theory Size-based demography has been a long time in the making. Jones (1974) developed the fundamental equations to calculate demography in length groups. At the same time, Andersen and Ursin (1977) developed their multi-species theory based on cohorts, but with all rates described by body mass. Beyer (1989) developed the first complete formulation of size-based demography of single fish stock. Despite size-based demography being available it had little impact as fisheries science focused on operationalizing existing age-based theory rather than developing new theory. The relative obscurity of size-based theory has led to confusion about its use and assumptions (Andersen et al., 2016a) and errors in the implementation (Hordyk et al., 2019). Parallel to early size-based theory appeared the concept of life-history constants. Life-history constants are quantities that do not systematically vary between stocks or only have a weak taxonomic relation. The concept was pioneered by Beverton (1992) who found that the strong correlations between central parameters in age-based fish demography could be removed by formulating non-dimensional ratios. Examples are the M/K ratio or the ratio between size at maturation and asymptotic size. Life-history constants are part of general life-history theory developed through evolutionary ecology (Charnov, 1993). Modern size-based theory combines the initial efforts of size-based demography with Beverton’s life-history constants and evolutionary life-history theory into an internally consistent framework (Andersen, 2019). The cost of moving to a size-based framework is the loss of treasured concepts like the von Bertalanffy growth equation with the K and L∞ parameters, abandoning the spreadsheet-friendly life tables, and scrapping the concepts of adult mortality, M and M2. Size-based theory replaces von Bertalanffy growth with physiology, life tables with partial differential equations, and the constant adult mortality with a size-based mortality. The absence of well-known concepts may seem overwhelming and makes the theory appear inaccessible. The reward is a theory that is consistently built upon a few fundamental assumptions and that can perform the same calculations as age-based demography, but that can also be used to estimate the evolutionary rates of life-history traits, be a basis for data-poor stock assessments or consumer-resource dynamics, and which provides general insights into fish life-history theory. What is size-based demography? Demography describes the size and structure of populations and their relation to growth, mortality, reproduction, etc. Classic age-based demography calculates numbers Nα at age α, usually under the assumption that the natural mortality M is constant (see Table A1). Weight-at-age is (usually) described with a von Bertalanffy growth formulation (Figure 1) or supplied from measurements. Multiplying numbers-at-age with weight-at-age gives cohort biomass, and summing all cohorts gives the spawning stock biomass. Reproduction is described by recruitment at an early age, which is calculated from a stock–recruitment relationship or considered constant for yield-per-recruit calculations. Age-based demography is specified by seven parameters: the von Bertalanffy growth parameters, K, L∞ ⁠, and t0 (alternatively a weight-at-age table), the natural mortality M, age at maturation αmat ⁠, and typically two parameters for the recruitment, for example the slope at origin and the maximum recruitment (Table 1). Figure 1. Open in new tabDownload slide Length-at-age of a single female guppy (Poecilia reticulata) fitted to von Bertalanffy growth (black) and bi-phasic growth (grey). Data from Ursin (1967). Figure 1. Open in new tabDownload slide Length-at-age of a single female guppy (Poecilia reticulata) fitted to von Bertalanffy growth (black) and bi-phasic growth (grey). Data from Ursin (1967). Table 1. Parameters in age- and size-based representations. Parameter . Age based . Size based . Asymptotic size Free Free Growth Free A = 5.35 g 0.25 year −1 [1…100] Natural mortalitya Free a=0.42 [0.17…67] Maturation Free wmat=ηmatW∞; ηmat=0.28 [0.1…0.8] Egg weight – 0.001 g [0.0001…0.01] Recruitment slope at originb Free ϵ=0.0066 [0…1] Max. recruitment Free Free Parameter . Age based . Size based . Asymptotic size Free Free Growth Free A = 5.35 g 0.25 year −1 [1…100] Natural mortalitya Free a=0.42 [0.17…67] Maturation Free wmat=ηmatW∞; ηmat=0.28 [0.1…0.8] Egg weight – 0.001 g [0.0001…0.01] Recruitment slope at originb Free ϵ=0.0066 [0…1] Max. recruitment Free Free Ranges are given in brackets. “free” means that the parameter has to be specified on a per-stock basis. Values are from Andersen (2019). a The mortality in size-based demography is specified as the dimensionless ratio between mortality and mass-specific rate of energy acquisition. b The parameter ϵ can conveniently be divided into two parameters, the reproductive efficiency ϵegg and the recruitment efficiency ϵR ⁠, but for simplicity, they are here merged into ϵ=ϵRϵegg ⁠. Open in new tab Table 1. Parameters in age- and size-based representations. Parameter . Age based . Size based . Asymptotic size Free Free Growth Free A = 5.35 g 0.25 year −1 [1…100] Natural mortalitya Free a=0.42 [0.17…67] Maturation Free wmat=ηmatW∞; ηmat=0.28 [0.1…0.8] Egg weight – 0.001 g [0.0001…0.01] Recruitment slope at originb Free ϵ=0.0066 [0…1] Max. recruitment Free Free Parameter . Age based . Size based . Asymptotic size Free Free Growth Free A = 5.35 g 0.25 year −1 [1…100] Natural mortalitya Free a=0.42 [0.17…67] Maturation Free wmat=ηmatW∞; ηmat=0.28 [0.1…0.8] Egg weight – 0.001 g [0.0001…0.01] Recruitment slope at originb Free ϵ=0.0066 [0…1] Max. recruitment Free Free Ranges are given in brackets. “free” means that the parameter has to be specified on a per-stock basis. Values are from Andersen (2019). a The mortality in size-based demography is specified as the dimensionless ratio between mortality and mass-specific rate of energy acquisition. b The parameter ϵ can conveniently be divided into two parameters, the reproductive efficiency ϵegg and the recruitment efficiency ϵR ⁠, but for simplicity, they are here merged into ϵ=ϵRϵegg ⁠. Open in new tab Size-based demography is based on size groups instead of age groups. Mathematically, size-based demography is most conveniently represented as a continuum, but for numerical calculations (and here), it is represented by discrete size groups. Both length and weight groups can be used to represent body size, but weight groups lead to the cleanest theoretical formulation. Conversion between weight and length can be done superficially with the classic cube-law relation w=0.01l3 (with weight w in gram and length l in cm), or a more accurate formulation for the specific group or species (Froese, 2006). We need to decide upon which size groups to use. In age-based demography, 1-year age groups are the natural choice, but in size-based demography there is no such obvious grouping. The size range is typically evenly divided on a log scale from weight at recruitment wR to the asymptotic weight W∞ (Table A1), e.g. 2–4 g, 4–8 g. This procedure results in the complication that numbers-at-size depends on the size range of each group. The narrower the size range, the fewer fish in a size group. To deal with this complication, size-based demography is customarily described with the size spectrum—also referred to as the normalized size spectrum (Sprules and Barth, 2016)—N(w), where w is the body mass (which can be substituted with length with appropriate conversion). The size spectrum is formed by dividing each size group by the size range it represents. The size spectrum is then a density function, similar to the probability density function from statistics, with unit numbers per body weight. While the probability density function is normalized such that the integral over the function is 1, the size spectrum is normalized such that the integral (or sum of all groups each multiplied by its size range) is the total number of individuals in the population. Figure 2 compares age- and size-based representations of demography in terms of numbers and biomass. Age-based demography clearly shows how abundance declines with age and that cohort biomass increases until around age of maturation (Figure 2a and c). The size spectra have a bump around size at maturation (Figure 2b and d). The bump appears because the growth rate declines around maturation and consequently individuals enter size groups at a slower rate than they leave them and pile up in the groups—the same mechanism that creates traffic jams on a highway. It is possible to convert between age- and size-based representations by multiplying with the growth rate: N(α)=N(w)g(w)Δα ⁠, where Δα=1 year is the age span in an age group. Biomass in size-based demography is conveniently represented by the Sheldon spectrum. The Sheldon spectrum is the biomass in logarithmically wide size groups, e.g. from 2 to 4 g, 4 to 8 g, 8 to 16 g. The Sheldon spectrum is found from the number spectrum by multiplying with weight2 (Figure 2d). Each representation, age or size based, numbers, or biomass, has advantages and there is no universally “best” representation. They all represent the same demography. Figure 2. Open in new tabDownload slide Age- and size-based representations of demography of a fish stock with asymptotic mass 5 kg. The first row (a+b) shows abundance as numbers-at-age N(α) (age α in years) and as the abundance size spectrum N(w) (numbers per gram). The second row (b+d) shows cohort biomass B(α) and the Sheldon spectrum BSheldon (biomass in log-width bins; grams). All panels are normalized to 1 at recruitment (1 year; 5.3 g). The arrows show conversions between the four representations with Δα being the width of age groups (units of years) and g(w) (gram per year) being the growth rate. The vertical dotted lines show size and age at maturation. The calculations are done with size-based theory, a bi-phasic growth equation, and parameters from Table 1. Derivation of the conversion between age- and size-based representations is given in Supplementary Appendix A. Figure 2. Open in new tabDownload slide Age- and size-based representations of demography of a fish stock with asymptotic mass 5 kg. The first row (a+b) shows abundance as numbers-at-age N(α) (age α in years) and as the abundance size spectrum N(w) (numbers per gram). The second row (b+d) shows cohort biomass B(α) and the Sheldon spectrum BSheldon (biomass in log-width bins; grams). All panels are normalized to 1 at recruitment (1 year; 5.3 g). The arrows show conversions between the four representations with Δα being the width of age groups (units of years) and g(w) (gram per year) being the growth rate. The vertical dotted lines show size and age at maturation. The calculations are done with size-based theory, a bi-phasic growth equation, and parameters from Table 1. Derivation of the conversion between age- and size-based representations is given in Supplementary Appendix A. What is size-based theory? A mathematical theory is based on a set of assumptions or axioms. Age-based demography is based on assumptions of a growth equation, a mortality (usually constant), and a recruitment formulation. From these assumptions follows predictions of fisheries yield and reference points, Fmsy, Flim ⁠, etc. (Figure 3a). Size-based theory is based on three similar assumptions: Assumption 1: The available energy for growth and reproduction (mass per time) scales with body mass as  Aw3/4 ⁠. This assumption is in line with the standard von Bertalanffy growth equation, though the exponent is usually set to 3/4 instead of 2/3 (see Table 2). The 3/4 exponent lines up with modern metabolic theory (West et al., 1997) and has some empirical support (Essington et al., 2001). However, whether 3/4 or 2/3 is used does not make much of a difference and the assumption that available energy scales with body weight is essentially the same between age- and size-based theories. Assumption 2: Natural mortality declines with size and is proportional to available energy:  μ(w)=aAw−1/4 ⁠. The decline of mortality with a −1/4 exponent with body mass has some empirical support (McGurk, 1986; Hirst and Kiørboe, 2002). The proportionality with available energy means that faster-growing fish also have a higher mortality. In classic life-history theory, this corresponds to a constant “M/K” ratio. The relation between available energy and mortality also has empirical support in relation to growth (e.g. Lankford et al., 2001). Both declining mortality and the relation between available energy and mortality follow from size spectrum theory: if consumption scales as in assumption 1, and bigger organisms eat smaller ones, then mortality scales as assumption 2 (Andersen and Beyer, 2006; Andersen et al., 2009). This means that size-based theory is inherently a metabolic theory at the level of individuals; the only essential assumption is that metabolism and consumption increases as a power-law function with mass (assumption 1). The constant a is called the physiological mortality (Beyer, 1989) and is a central parameter in size-based theory. It represents the growth:mortality ratio in same way as the M/K is in age-based demography, though based on a declining mortality with size. Assumption 3: Density dependence operates early in life, before fishing starts and before maturation. This assumption means that growth and mortality are independent of the abundance of the stock; there is no density-dependent growth and mortality. The assumption works well when the stock is depleted by fishing (Andersen et al., 2016b) but may be less appropriate for unfished or lightly fished stocks where density-dependent reductions in adult growth or cannibalism may occur (Zimmermann et al., 2018)—I will come back to this later. This assumption is the same between age- and size-based theories. From assumption 1 about available energy follows growth and reproductive output as a bi-phasic growth equation (Table 2; Figure 3b). The bi-phasic growth equation is very similar to the von Bertalanffy growth equation and with the quality of extant data practically indistinguishable. Bi-phasic growth has a kink around the size at maturation where energy begins to be allocated from growth towards reproduction. Figure 3. Open in new tabDownload slide Overview of the assumptions, predictions, and applications of age- and size-based theories. Weight-at-age in age-based theory could either be represented with a von Bertalanffy growth curve or with measured weight-at-ages. Note the arrow from the consumption towards mortality, which illustrates the trade-off between available energy and mortality (assumption 2). Figure 3. Open in new tabDownload slide Overview of the assumptions, predictions, and applications of age- and size-based theories. Weight-at-age in age-based theory could either be represented with a von Bertalanffy growth curve or with measured weight-at-ages. Note the arrow from the consumption towards mortality, which illustrates the trade-off between available energy and mortality (assumption 2). Table 2 Bi-phasic growth equations. The von Bertalanffy growth equation is based on an energy budget of an individual fish with weight w: g(w)=Awn−kw, where A is a growth constant and k represents losses. Customarily the exponent n = 2/3 is used (but see Ursin, 1967). The growth constant has fractional units g n−1 per year. Fractional units are uncommon, but are perfectly valid. One should just think of A as the constant that scales the growth rate. Writing the growth rate as g(w)=dw/dt ⁠, the growth model can be solved to yield weight w(α) and length l(α) at age α: w(α)=W∞(1−e−(1−n)AW∞n−1α)1/(1−n) and l(α)=L∞(1−e−Kα). The solution of w(α) is valid for asymptotic sizes W∞≫wegg ⁠. The length-based solution introduces the von Bertalanffy growth constant K, which is derived from A and L∞ (Andersen et al., 2009, see also Table 1). Bi-phasic growth models are based on the same energy budget (Roff, 1983; Lester et al., 2004; Quince et al., 2008). They assume that the losses, kw, represent expenses for reproduction including migration, forgone feeding, and the actual egg or gonad production. These losses therefore mainly occur when the individual is mature. Maturation is described with a maturity function ψmat(w) that switches smoothly between 0 and 1 around the size at maturation: g(w)=Awn−ψmat(w)kw, where n = 3/4 is customarily used. The differences between solutions of the von Bertalanffy growth model (with n = 2/3) and the bi-phasic growth model (with n = 3/4) are fairly small (Fig. 1). This formulation has the advantage that it predicts the number of eggs produced by an individual Rp ⁠: Rp(w)=ϵeggψmat(w)kw/wegg=ϵeggψmat(w)AW∞n−1w/wegg, where ϵegg is the fraction of the reproductive investment that results in eggs. Integrating over the entire stock structure gives the total reproductive output, which predicts the slope at origin in the recruitment function (Andersen and Beyer, 2015, see also Table 1). The von Bertalanffy growth equation is based on an energy budget of an individual fish with weight w: g(w)=Awn−kw, where A is a growth constant and k represents losses. Customarily the exponent n = 2/3 is used (but see Ursin, 1967). The growth constant has fractional units g n−1 per year. Fractional units are uncommon, but are perfectly valid. One should just think of A as the constant that scales the growth rate. Writing the growth rate as g(w)=dw/dt ⁠, the growth model can be solved to yield weight w(α) and length l(α) at age α: w(α)=W∞(1−e−(1−n)AW∞n−1α)1/(1−n) and l(α)=L∞(1−e−Kα). The solution of w(α) is valid for asymptotic sizes W∞≫wegg ⁠. The length-based solution introduces the von Bertalanffy growth constant K, which is derived from A and L∞ (Andersen et al., 2009, see also Table 1). Bi-phasic growth models are based on the same energy budget (Roff, 1983; Lester et al., 2004; Quince et al., 2008). They assume that the losses, kw, represent expenses for reproduction including migration, forgone feeding, and the actual egg or gonad production. These losses therefore mainly occur when the individual is mature. Maturation is described with a maturity function ψmat(w) that switches smoothly between 0 and 1 around the size at maturation: g(w)=Awn−ψmat(w)kw, where n = 3/4 is customarily used. The differences between solutions of the von Bertalanffy growth model (with n = 2/3) and the bi-phasic growth model (with n = 3/4) are fairly small (Fig. 1). This formulation has the advantage that it predicts the number of eggs produced by an individual Rp ⁠: Rp(w)=ϵeggψmat(w)kw/wegg=ϵeggψmat(w)AW∞n−1w/wegg, where ϵegg is the fraction of the reproductive investment that results in eggs. Integrating over the entire stock structure gives the total reproductive output, which predicts the slope at origin in the recruitment function (Andersen and Beyer, 2015, see also Table 1). Open in new tab Table 2 Bi-phasic growth equations. The von Bertalanffy growth equation is based on an energy budget of an individual fish with weight w: g(w)=Awn−kw, where A is a growth constant and k represents losses. Customarily the exponent n = 2/3 is used (but see Ursin, 1967). The growth constant has fractional units g n−1 per year. Fractional units are uncommon, but are perfectly valid. One should just think of A as the constant that scales the growth rate. Writing the growth rate as g(w)=dw/dt ⁠, the growth model can be solved to yield weight w(α) and length l(α) at age α: w(α)=W∞(1−e−(1−n)AW∞n−1α)1/(1−n) and l(α)=L∞(1−e−Kα). The solution of w(α) is valid for asymptotic sizes W∞≫wegg ⁠. The length-based solution introduces the von Bertalanffy growth constant K, which is derived from A and L∞ (Andersen et al., 2009, see also Table 1). Bi-phasic growth models are based on the same energy budget (Roff, 1983; Lester et al., 2004; Quince et al., 2008). They assume that the losses, kw, represent expenses for reproduction including migration, forgone feeding, and the actual egg or gonad production. These losses therefore mainly occur when the individual is mature. Maturation is described with a maturity function ψmat(w) that switches smoothly between 0 and 1 around the size at maturation: g(w)=Awn−ψmat(w)kw, where n = 3/4 is customarily used. The differences between solutions of the von Bertalanffy growth model (with n = 2/3) and the bi-phasic growth model (with n = 3/4) are fairly small (Fig. 1). This formulation has the advantage that it predicts the number of eggs produced by an individual Rp ⁠: Rp(w)=ϵeggψmat(w)kw/wegg=ϵeggψmat(w)AW∞n−1w/wegg, where ϵegg is the fraction of the reproductive investment that results in eggs. Integrating over the entire stock structure gives the total reproductive output, which predicts the slope at origin in the recruitment function (Andersen and Beyer, 2015, see also Table 1). The von Bertalanffy growth equation is based on an energy budget of an individual fish with weight w: g(w)=Awn−kw, where A is a growth constant and k represents losses. Customarily the exponent n = 2/3 is used (but see Ursin, 1967). The growth constant has fractional units g n−1 per year. Fractional units are uncommon, but are perfectly valid. One should just think of A as the constant that scales the growth rate. Writing the growth rate as g(w)=dw/dt ⁠, the growth model can be solved to yield weight w(α) and length l(α) at age α: w(α)=W∞(1−e−(1−n)AW∞n−1α)1/(1−n) and l(α)=L∞(1−e−Kα). The solution of w(α) is valid for asymptotic sizes W∞≫wegg ⁠. The length-based solution introduces the von Bertalanffy growth constant K, which is derived from A and L∞ (Andersen et al., 2009, see also Table 1). Bi-phasic growth models are based on the same energy budget (Roff, 1983; Lester et al., 2004; Quince et al., 2008). They assume that the losses, kw, represent expenses for reproduction including migration, forgone feeding, and the actual egg or gonad production. These losses therefore mainly occur when the individual is mature. Maturation is described with a maturity function ψmat(w) that switches smoothly between 0 and 1 around the size at maturation: g(w)=Awn−ψmat(w)kw, where n = 3/4 is customarily used. The differences between solutions of the von Bertalanffy growth model (with n = 2/3) and the bi-phasic growth model (with n = 3/4) are fairly small (Fig. 1). This formulation has the advantage that it predicts the number of eggs produced by an individual Rp ⁠: Rp(w)=ϵeggψmat(w)kw/wegg=ϵeggψmat(w)AW∞n−1w/wegg, where ϵegg is the fraction of the reproductive investment that results in eggs. Integrating over the entire stock structure gives the total reproductive output, which predicts the slope at origin in the recruitment function (Andersen and Beyer, 2015, see also Table 1). Open in new tab Size-based theory is not strict about any of the assumptions. One can use another growth function than the one following from assumption 1 or another mortality than assumption 2 and still apply the theory—just as age-based theory can use other growth equations than von Bertalanffy and non-constant mortality. Sticking to the formulations in assumptions 1 and 2, however, makes it possible to rely on constant life-history parameters and formulate the trade-offs needed for quantitative genetics. In the final section, I discuss how to relax the third assumption and operate with density-dependent growth and reproduction happening later in life. Size- vs. age-based theory and applications From the three assumptions follows predictions of growth, the slope of the recruitment function, and demography (Figure 3b). As it appears from Table A1, the equations have a fairly similar structure between age- and size-based formulations (at least in the discrete formulation used here). However, the parameters are different. Instead of the von Bertalanffy K parameter, size-based theory uses the growth constant A; instead of the constant natural mortality M, it uses the physiological mortality a; instead of age at maturation αmat ⁠, it specifies the size at maturation as proportional to the asymptotic weight ηmatW∞ ⁠; instead of specifying recruitment slope at origin (or the steepness), it operates with a “recruitment efficiency” ϵ. The age- and size-based parameters are connected and one can convert between them, though not perfectly in all cases (Table A2). There are smaller differences between the demographic theories. For example, size-based theory operates with a size-dependent mortality whereas age-based theory typically operates with a constant mortality. This, however, is a small difference and age-based demography can easily operate with an age-dependent mortality. The growth equations are also slightly different; age-based theory uses a von Bertalanffy growth equation, whereas size-based theory operates with a bi-phasic growth equation. Nevertheless, they are fairly similar, and again, age-based theory could just as well use a bi-phasic growth equation (or vice versa). We can therefore conclude that age- and size-based demography are essentially identical. They are two equally valid representations of fish demography. So, when should one use one framework over the other? In the following, I discuss pros and cons of each framework in terms of the theoretic formulations (1–3), applications to stock assessments and other fisheries advice (4–6), and the use of size/age as a structuring variable (7–9): Size-based theory provides a prediction of egg production. Hence, it does not need an external stock-specific parameter to characterize recruitment (the slope at origin or the steepness). The prediction emerges from the bio-energetic budget (Table 2). This idea has been re-invented many times (Andersen and Ursin, 1977; Roff, 1983; Lester et al., 2004; Mangel et al., 2010; Andersen and Beyer, 2015) and is central in physiologically structured models (de Roos and Persson, 2013) as well as in evolutionary theory, where accounting for the cost of reproduction is essential (Day and Taylor, 1997). The prediction of recruitment from size-based theory can of course be used in age-based demographic calculations. The parameters in size-based theory are for the most part life-history constants. This means that they are not strongly correlated with other parameters and they have an average expected value across all fish stocks (Table 1). Instead of a von Bertalanffy growth constant K that is correlated with asymptotic size, size based uses the growth constant A, which does not, or only weakly, correlate with asymptotic size (Figure 4). The growth parameter A directly indicates somatic growth rate; if A is larger/smaller than about 5 g 0.25 year–1 fish in the stock grows faster/slower than average. Von Bertalanffy K states the maturation rate—how fast maturity is approached—which does not indicate growth rate directly. Similarly, the relative maturation size ηmat can be compared across stocks, while the age at maturation varies with a combination of K and L∞ ⁠. The recruitment parameter—slope at origin—is one of the big unknowns in fish demography, and it varies systematically with asymptotic size (Hall et al., 2006; Thorson, 2020). In contrast, the recruitment efficiency ϵ has a similar value across stocks—even though it is still uncertain with a large variability. Formulating theory in terms of life-history constants is the ultimate realization of Ray Beverton’s project about life-history constants in fish stocks (Beverton, 1992). The relations between the size-based life-history constants and the classic age-based parameters are given in Table A2. Size-based theory can be interpreted as a trait-based theory because of the constant life-history parameters. The “master trait” is the asymptotic size W∞ ⁠, and we can make general predictions across all species just knowing the asymptotic size. Trait-based predictions reveal an important insight: population-level rates, such as fisheries reference points and maximum population growth rates, do not scale metabolically with asymptotic size as W∞−1/4 (Figure 5a and b; Andersen and Beyer, 2015). In other words, smaller species do not necessarily have higher population growth rates than large species. This is a surprising finding because it clashes with metabolic theory, despite size-based theory being based on metabolic assumptions at the level of individuals. The finding emerges because bony fish make eggs with approximately the same size, irrespective of their asymptotic size (Neuheimer et al., 2015). That metabolic scaling rules do not apply for fish has important implications for simple food web calculations or unstructured theory that often rely on metabolic scaling rules for parameterization of P/B rates or population growth rates. While it is clear that asymptotic size is the most important trait, it is less clear which trait to include next. It depends on the application. . For the example with the maximum population growth rate rmax and Fmsy shown here, it is the growth rate A (Figure 4); if the application is an exploration of the importance of big fish for reproduction, the ratio between asymptotic size and size at maturation ηmat is the most relevant trait (Andersen et al., 2019); if the application is stock assessment, it is the growth:mortality ratio a (Kokkalis et al., 2015). As the variation in the life-history parameters is known (Table 1), the uncertainty of prediction based on just one trait—e.g. asymptotic size—can be represented by a Monte Carlo simulation with random values of the other life-history parameters within their known ranges (see for example Andersen and Brander, 2009; Andersen and Beyer, 2015). Alternatively, empirical relations between taxonomy and life-history parameters (Thorson et al., 2017)—e.g. a (M/K) or ηmat—can be used to further refine the estimates. Size-based theory has applications for stock assessments and impact assessments, both in data-rich and data-poor cases, and for evolutionary impact assessments: Fish stock assessment is widely done with age-structured models (or production models). Age-based assessment relies on aging of samples from surveys and catches, a procedure which is costly, only resolves age on a yearly basis, and is prone to error. Size-based assessments use measurements of length or weight that are cheap, precise, and with little scope for error of interpretation. A size assessment essentially estimates the log–log slope of the size distribution (Figure 2a), which is directly related to the mortality: growth ratio a; the steeper the slope, the higher the mortality or the slower the growth. However, to know the mortality—and then the fishing mortality—we need to multiply the slope with the growth rate constant A. In other words, we need to know the growth rate, which requires aging. Some of the advantages of the size-based assessment therefore disappears because if we have to do aging we could just as well do age-based assessments. Some information of growth rate can be found by following cohorts in size (e.g. Fournier et al., 1998), but this is fairly unreliable in many cases. However, if we are satisfied with only knowing the status of the stock, i.e. the ratio between fishing mortality and the reference point, for example F/Fmsy ⁠, then we do not need to know growth. The reason being that most reference points are also proportional to the growth rate A, and therefore, the ratio F/Fmsy becomes independent of the growth rate (Kokkalis et al., 2015). If we are willing to sacrifice the accurate estimation of F and just estimate F/Fmsy ⁠, then size-based assessment will be as accurate as age-based assessment, even without knowledge of growth (Kokkalis et al., 2017). As many central management decisions are made with reference to the stock status F/Fmsy (or similar ratios with other reference points), knowledge of the absolute fishing mortality is of secondary relevance and there is a big untapped potential for size-based assessment of stock status. The central role of life-history constants and traits makes the theory the natural starting point for data-poor stock assessments and impact assessments. Size-based data-poor stock assessment methods are actively being developed (e.g. Le Quesne and Jennings, 2012; Hordyk et al., 2015; Kokkalis et al., 2017) (though at times they fail to properly account for the difference between age- and size-based demography; Hordyk et al., 2019). They all rely on some variant of life-history constants as a “Robin Hood” method of using knowledge from other stocks to determine parameter values. In the extreme case, knowing just the asymptotic size we can estimate the fisheries reference points, even accounting for density-dependent recruitment. As explained above, the main unknown, the growth rate A, disappears if we form the ratio of observed fishing mortality and the fisheries reference point. This means that we can predict the status of the fishery, whether it is over- or under-exploited with respect to Fmsy (Kokkalis et al., 2015). We can even account for uncertainty by varying the life-history parameters within their known ranges (Kokkalis et al., 2017). Knowledge of some parameters, for example the growth parameter A, the size at maturation, the growth:mortality ratio, can be used to refine the predictions, e.g. from taxonomically informed methods (Thorson, 2020). Size-based theory should be the starting point for size-based data-poor stock assessment. The full bio-energetic formulation with a trade-off between consumption and mortality (assumption 1) and between somatic growth and reproduction (Table 2) opens the door to quantitative genetics-based predictions of fisheries-induced evolution (Figure 5c; Andersen and Brander, 2009). Size-based theory can therefore be used to make evolutionary impact assessment of fishing. The example given here shows how size (and age) of maturation declines with increased fishing mortality. A similar pattern emerges if natural mortality is increased, in accordance with observations of a correlation between M/K and size of maturation (Thorson et al., 2017). There are also some advantages of an age-based formulation of demography: Age-based demography is easier to implement quickly (compare the equations in Table A1). However, as age-based demography is done with age groups of 1 year we need to compensate for the mortality during the year with Baranov’s catch equation when calculating yield. This correction is not needed in size-based theory, which typically operates with many (100s) of size groups. Age-based representations are also more intuitive; from Figure 2a and c, we immediately see the number and the biomass of fish with a certain age. The size-based representation shows the number density, or the Sheldon biomass spectrum, which are more abstract representations of demography. There is a trade-off between the simplicity of age-based and the higher resolution of size-based demography. Age-based theory naturally incorporates the annual schedule that governs many aspects of fish demography, in particular, annual reproduction. Resolving an annual schedule in size-based theory does not come naturally. While most relevant physiological processes and gear selectivity depend more on size than on age, one effect does depend on age: senescence. There is little evidence for senescent drop in reproduction, but some evidence for senescent mortality (Beverton et al., 2004). However, senescence is unlikely to be relevant in fished populations, where older individuals are very rare, so it is safe to ignore senescence for most applications to fished populations. If needed, age-based senescence can be resolved in size-based theory with a size-age relation, just like size-based mortality can be incorporated in age-based theory. Figure 4. Open in new tabDownload slide The trait space of bony fish (Actinopterygii) as defined by their asymptotic weight, W∞ ⁠, and their growth rate constant, A. The area of each point is proportional to the maximum population growth rate as estimated from W∞ ⁠, A, and the default life-history parameters in Table 1 (calculation in Supplementary Appendix C). The grey patch to the right shows the distribution of A with a geometric mean of 5.15 g 1/4 year−1. Data are from FishBase (Froese and Pauly, 2013) selected with the criterion that t0 is in the range ±1. Note that because there is no correction for taxonomy the figure has an over-representation of species like cod, rockfish, etc.A more proper representation could be found by applying FishLife (Thorson et al, 2020). Figure 4. Open in new tabDownload slide The trait space of bony fish (Actinopterygii) as defined by their asymptotic weight, W∞ ⁠, and their growth rate constant, A. The area of each point is proportional to the maximum population growth rate as estimated from W∞ ⁠, A, and the default life-history parameters in Table 1 (calculation in Supplementary Appendix C). The grey patch to the right shows the distribution of A with a geometric mean of 5.15 g 1/4 year−1. Data are from FishBase (Froese and Pauly, 2013) selected with the criterion that t0 is in the range ±1. Note that because there is no correction for taxonomy the figure has an over-representation of species like cod, rockfish, etc.A more proper representation could be found by applying FishLife (Thorson et al, 2020). Figure 5. Open in new tabDownload slide Trait-based predictions from size-based theory of (a) Fmsy ⁠, (b) maximum population growth rate rmax ⁠, and (c) fisheries-induced evolution of size at maturation relative to weight at maturation. Each prediction is made for slow- to fast-growing species (thin/thick grey lines correspond to half/double the average growth rate). The prediction of rmax is based on the approximation in Supplementary Appendix 7, which underestimates rmax for small species. The predictions of Fmsy and evolutionary responses are made with a trawl selectivity with a 50% selectivity at 5% of the maximum weight and a fishing mortality of 0.3 year-1. Figure 5. Open in new tabDownload slide Trait-based predictions from size-based theory of (a) Fmsy ⁠, (b) maximum population growth rate rmax ⁠, and (c) fisheries-induced evolution of size at maturation relative to weight at maturation. Each prediction is made for slow- to fast-growing species (thin/thick grey lines correspond to half/double the average growth rate). The prediction of rmax is based on the approximation in Supplementary Appendix 7, which underestimates rmax for small species. The predictions of Fmsy and evolutionary responses are made with a trawl selectivity with a 50% selectivity at 5% of the maximum weight and a fishing mortality of 0.3 year-1. Overall, the difference between age- and size-based theories is in the scope of the theoretical framework. Size-based theory is a comprehensive axiomatic framework within which age-based demography is embedded as a special case. Predictions from size-based theory of parameters, recruitment, etc., can be used for age-based demography. The choice between age- and size-based demography is therefore often one of the conveniences: if annual schedules are central, an age-based formulation is to be preferred; if precision of resolution is important, or data-poor or fisheries-induced evolution applications is needed, then size based is preferred. For stock assessments, in particular, in data-poor situations, there are potential big gains in moving to a size-based formulation because of the easy access to cheap and accurate size measurements. Challenges and future perspectives Size-based methods have maturated to be useful for practical fisheries advice, and it is already actively used to develop data-poor stock assessments (e.g. Hordyk et al., 2015; Kokkalis et al., 2017), in ecosystem models (Scott et al., 2014; Spence et al., 2020), and for assessments of fisheries-induced evolution (Andersen and Brander, 2009). This situation should not keep us from testing and developing the theory further. Three areas warrant attention: the bio-energetic basis, predictions of recruitment, and applications in consumer-resource modelling for modelling-dependent growth and mortality. The core of size-based theory is the bio-energetic budget used to calculate growth and reproduction (Table 2). Fairly little experimental work on bio-energetic growth models has been done since Ursin (1967), probably due to the difficulty and expense of working experimentally with long-lived species like fish. The consequence is that we have an incomplete understanding of exactly which processes determine investment in reproduction, metabolism, and asymptotic size, and how they are related. There are evident trade-offs between these three processes, for example higher metabolic costs and activity leads to smaller asymptotic size everything else being equal. However, everything else is not equal; reproduction also plays a role and how the organism balances this three-way trade-off is unknown. Furthermore, the reproductive output is often not just proportional to mass but in many cases increases faster (Barneche et al., 2018). Is this process due to ecology—size-based variability in available food and body condition as seen for Icelandic cod (Marteinsdottir and Begg, 2002)—or is it an evolutionary adaptation? Knowing whether more than proportional investment in reproduction is due to the environment or is a species trait matters for statistical estimation, which often assume a taxonomic signal. A similar problem is how bio-energetic processes depend on temperature, where our current understanding has been thrown into doubt (Lefevre et al., 2017; Jutfelt et al., 2018). What is missing is not more theory but long-term experiments on individual fish, carefully designed to inform about the trade-offs between growth, metabolism, activity, reproduction, maturation, and asymptotic size, and how they all respond to temperature, both for short-term acclimation, longer-term adaptation, within and across species of different asymptotic sizes. The prediction that the recruitment slope at origin declines with asymptotic size and increases with growth rate (Table A1) is crucial because it determines how fisheries reference points scale with asymptotic size (Andersen and Beyer, 2015). The prediction that slope decrease with asymptotic size conforms qualitatively with analyses of the RAM database (Hall et al., 2006; Thorson, 2020). However, the observed slope declines faster than predicted (roughly an exponent −0.76 vs. predicted −0.25). The predicted scale of the slope as W∞−1/4 leads to fisheries references points that only differ slightly between small and large species (Figure 5b). In contrast, if the slope is ≈−0.93 ⁠, close to the observed −0.76, then fisheries reference points scale metabolically. That means that the Fmsy of a 5 g forage fish is 10 times higher than that of a 50-kg large demersal species (see Supplementary Appendix C). However, observations of Fmsy show that Fmsy is roughly similar between small and large species (Andersen and Beyer, 2015; Jacobsen, 2015). The importance of the prediction of the recruitment slope for fisheries reference points makes it an important question to resolve. Working with recruitment data is difficult because of the very noisy nature of the observations and the difficulty in assessing the slope at origin or steepness from the observations. Furthermore, the data need to be corrected for age/size at recruitment, which implies further assumptions. Finally, there is an increasing realization of the importance of accounting for density-dependent growth (Lorenzen and Enberg, 2002; Zimmermann et al., 2018) and cannibalism in the impact assessments of fisheries (van Gemert and Andersen, 2018a). Density-dependent processes that occur later in life break assumption 3, which is a foundation of both age- and size-based demography. Density-dependent growth and cannibalism can potentially change reference points fundamentally (Andersen et al., 2016b) and induce Allee effects that hinder recovery (Van Leeuwen et al., 2008). Even though it is still unclear how important such effects are for reference points (van Gemert and Andersen, 2018b), it is likely that fisheries science and management will increasingly be asked to assess density-dependent processes besides stock–recruitment. Resolving density-dependent growth and mortality can be done by formulating heuristic relations between growth (A) and mortality (a) and stock biomass, such that growth declines (or mortality increases) as the stock increases (Lorenzen, 2008; van Gemert and Andersen, 2018b). A deeper understanding requires a consumer-resource perspective, such as is commonly taken in ecology. Physiologically structured modelling is the natural approach to combine classic consumer-resource theory with structured populations in a fisheries context (Persson et al., 2014). In physiologically structured models, all density dependence emerges from competition for a resource or from cannibalism (de Roos and Persson, 2013). Size-based theory is naturally extended to a physiologically structured model, however, with the slight difference that it also incorporates a stock–recruitment relation to account for early-life density dependence due to habitat limitation or larvae survival (Andersen, 2019, chap. 10) or spatial population dynamics (Andersen et al., 2016b; see Figure 6 for an example of the mechanism in a size-based consumer-resource model and the implications for Fmsy ⁠). It is an open question whether these more advanced consumer-resource models can be integrated into practical fisheries advice: do we have the knowledge to parameterize them, and the ability to apply the predictions for management? Figure 6. Open in new tabDownload slide Comparison between a full consumer-resource calculation (solid lines) and classic theory with early-life density dependence (dashed lines). The resource represents an even size distribution of prey (a). Competition for food by adults reduces the resource. The reduced resource leads to slower growth (c) and a reduced adult abundance (b). The density dependence due to competition may change the reference points (d). Here, the Fmsy is increased but the change depends on details of the size selection by fishing (Andersen et al., 2016b) and the relative strength of early and competition density dependence (van Gemert and Andersen, 2018b). The model is described in Andersen (2019, chap. 10). Figure 6. Open in new tabDownload slide Comparison between a full consumer-resource calculation (solid lines) and classic theory with early-life density dependence (dashed lines). The resource represents an even size distribution of prey (a). Competition for food by adults reduces the resource. The reduced resource leads to slower growth (c) and a reduced adult abundance (b). The density dependence due to competition may change the reference points (d). Here, the Fmsy is increased but the change depends on details of the size selection by fishing (Andersen et al., 2016b) and the relative strength of early and competition density dependence (van Gemert and Andersen, 2018b). The model is described in Andersen (2019, chap. 10). Conclusion The emergence of size-based theory does not replace existing age-based theory. Rather, it embraces and expands it. Size-based theory offers a solid axiomatic framework with more applications than age-based theory and is directly linked to modern ecological and evolutionary theories. Instead of having to develop disjoint frameworks for data-poor assessments, fisheries-induced evolution, density dependent processes, etc., size-based theory offers a unified framework. The theory is formulated with specific assumptions about the growth equation, about mortality, and about reproductive investments. It is important to remember that size-based theory is not dogmatic with regards to these formulations; another growth equation, mortality, or formulation of reproductive investment can be used if it is more appropriate—as can also be done in age-based demography. However, the simple formulation offers a route to make impact assessment with little data and to make sweeping cross-taxa prediction. Finally, size-based demography links to directly to size-based models of the entire fish community that are being increasingly used as models of intermediate complexity (Scott et al., 2014; Spence et al., 2020). Despite the advantages of size-based theory, age-based demography will continue to form the basis of much fisheries advice because it is well known and well established. However, where we develop new advice for emerging fish stocks or in countries with poorly developed management, it should be size based. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Data availability All calculations are done with the R package FishSizeSpectrum https://github.com/Kenhasteandersen/FishSizeSpectrum. Data and code for figures are available on GitHub: https://github.com/Kenhasteandersen/FishDemography. Acknowledgements Many thanks to Jan Beyer and Keith Brander for comments on a draft of the manuscript. The work is supported by the VKR Foundation through the Ocean Life Center of Excellence and by the PANDORA EU H2020 project. References Andersen K. H. 2019 . Fish Ecology, Evolution, and Exploitation . Princeton University Press , Princeton, USA . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Andersen K. H. , Beyer J. E. 2006 . Asymptotic size determines species abundance in the marine size spectrum . The American Naturalist , 168 : 54 – 61 . Google Scholar Crossref Search ADS PubMed WorldCat Andersen K. H. , Beyer J. 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Moore, Marianne, V

doi: 10.1093/icesjms/fsaa188pmid: N/A

Abstract From landlocked Iowa to the outback of Australia and the interior of Siberia, I found water and opportunities to build bridges between disciplines and among scientists from different countries and cultures. What I have learned is that preparation, persistence when challenged, daring to follow the path of opportunity, and generously sustaining collaborations can lead to a successful, adventure-filled, and satisfying career. I was on a lake at 2 a.m. during a full lunar eclipse and I wanted to do science. Enter the dead body. It was 1982 in the middle of the night, and I was using an echo sounder on a New Hampshire lake to track the behavioural response of a diel vertical migrator (Chaoborus, the planktonic larvae of the lake fly) to a full lunar eclipse. Suddenly, an enormous black signal—bigger than any fish or anything I had ever seen before—appeared on the printout of the echosounder, floating at the depth of the thermocline. Doug, the undergraduate student working with me, pleaded for permission to lower my net (30-cm diameter) down into the water column to try and nab this large mysterious object. I replied emphatically, “No! We’re not going to risk damaging my net on whatever that is”. I was using the net to collect samples to determine whether the signals from my echo sounder were Chaoborus. I had purchased the expensive net using monies from a research award from the International Women’s Fishing Association (which is still assisting graduate students). So, I was reluctant to sacrifice this hard won piece of equipment to the unknown. Finally, the object drifted off screen. Meanwhile, the moon had become the eponymous “Blood Moon”, shrouded by the earth’s shadow but with an eerie red tinge. At about this moment, a banshee wail sounded across the lake from onshore. To many, the combination of a Blood Moon and banshee howls would have been unnerving, but my attention was fixed on the echo sounder, which showed that Chaoborus was doing exactly what I had expected. The insect had stayed deep below the thermocline while the full moon shone, but during the eclipse it ascended upwards through the thermocline. Once the moon was fully occluded, all Chaoborus moved into the epilimnion, despite the presence of fish. The insect was responding to subtle changes in exceedingly low levels of light, and I was thrilled! But this thrill was soon replaced by a different kind of excitement, one mixed with apprehension. The next morning, I discovered that a drowning had occurred in the lake ∼2 h before Doug and I began sampling. I immediately called the local police who put me in touch with the dive team that was searching for the body. They urged me to meet them at the lake where I showed them the printout and where we had been sampling. Their reply: “You spotted the body”. A man had drowned while fishing in the middle of the lake the previous night. Badly inebriated, he had gotten into an argument with his fishing buddy and stepped out of the boat intending to walk to shore. The banshee-like howl? A family member’s response upon first learning of the man’s demise. Although the dive team and I searched for the body using my echo sounder, we had no luck. By then, it had apparently sunk to the lake bottom and was no longer detectable using acoustics. This true story is still told among ecology graduate students at Dartmouth College where I completed my PhD research (e.g. Moore, 1986, 1988), and it conveys one of the many adventures I have had in the field during my career. But there’s more than a story here. The sampling I did on that Blood Moon night was not part of my dissertation research; it was a side project that I was pursuing for fun. This research diversion later led to a publication with marine and freshwater scientists about the usefulness of acoustic techniques for observing zooplankton (Smith et al., 1992), and it led to research that my students and I pursued 15 years later regarding effects of artificial light at night on the vertical distribution of aquatic organisms in lakes across an urban to rural gradient (Moore et al., 2001, 2006). Despite the challenges that arose during that full lunar eclipse, the science was fun and worthwhile because of the insights gained. Seek people whose judgement you trust Not all challenges I encountered during my career, however, surfaced from unexpected situations in the field. Being a young woman in the world of science in the mid-1970s and 1980s had its burdens as it still can today. I worked in all-male laboratories during my MS and PhD training, which was quite normal at the time. Fortunately, I had acquired water skills that prepared me well for field research on lakes and rivers because I had worked as a public life guard, a swimming instructor at a camp, and a canoe guide in the Boundary Waters of the United States and Canada prior to entering graduate school. In fact, the men in my first laboratory group respected me for those skills, especially after I insisted on occupying the stern position of canoes whenever we ventured out onto the middle of windy lakes for sampling. None of the men knew how to steer a canoe; yet, canoes were our vessel of choice because they were more easily transported to distant lakes than motor boats. But, my water skills did not shield me from sexual harassment. For example, a graduate student intentionally flashed his private parts while we worked together alone in the field in a remote location. This happened more than once. I was frightened as to what might occur next, so I pretended to ignore the graduate student while silently planning multiple escape routes in case that became necessary. Thankfully, it did not. Later in my career, I experienced additional difficulties because of my gender, but here the discrimination was more subtle, yet still hurtful and consequential. Among these experiences, the ones that were perhaps more pernicious in terms of my career development involved exclusion from informal opportunities to discuss science that were available only to men. I remember feeling isolated, confused, and very much alone in dealing with these experiences because there was no Human Resources Office or ombudsperson to turn to. So, I stumbled forward by focusing on the science. In hindsight, the difficulties I experienced as a woman in science pale in comparison to the struggles of other female scientists. But, I also believe that enormous progress towards gender equality has occurred globally for women in science during the last 40–50 years, resulting in a more welcoming work environment, both in the field and laboratory. In many countries, women now lead our scientific research societies, journal editorial boards, funding agencies, and nominating committees for national and international awards. But there is more to be done, particularly at the level of institutional policy (Greider et al., 2019), and we must continually guard against new barriers of inequity creeping into our workplaces while watching for old, hidden ones that can make people of an underrepresented gender or ethnicity uncomfortable. Importantly, if you encounter such barriers, do not go it alone like I did. Seek out people whose judgement you trust for guidance in how to address these situations promptly. “Take math courses until you get a C” Barriers to personal progress in science can also arise from undeveloped skills. “Take math courses until you get a C” is what G. Evelyn Hutchinson, the eminent ecologist (Hutchinson, 2011), told me and other graduate students at Dartmouth College when he visited our small group in the mid-1980s. I have recalled his wise advice numerous times throughout my career, and I passed it on to my students. Thankfully, I received solid statistical training, both in parametric and nonparametric statistics, as a graduate student, and I was introduced to mathematical modelling while taking a simulation modelling course during a sabbatical leave. But, I always wished that I had had more exposure to quantitative techniques, for example to multivariate statistics, the topic of a course I avoided during my graduate training to my perpetual regret. Ours is a number-driven world, and knowledge of statistics, mathematical modelling, data visualization techniques, machine learning, computer programming, etc., are absolutely essential for today’s aquatic scientists. So, throw out any concerns about maths courses tarnishing your grade point average and stomp out all self-doubt about your ability to do maths. Instead, grab every opportunity to expand, upgrade, and hone your quantitative skills. You will never regret this. This tip, however, comes with a caveat. You also need a “feeling for the organism” (Keller, 1983), community or ecosystem described by the data. Without the intimate hands-on knowledge that comes from field and laboratory work with the focal organisms or ecosystems, spotting spurious values in the data or false trends becomes difficult and reaching false conclusions all the more likely. So, if you become a data geek, in love with meta-analyses, for example include a collaborator or a friendly reviewer who has direct experience collecting those data. Seize opportunities As my earlier story of the “Blood Moon” night illustrated, seizing unexpected opportunities will likely be rewarding and adventure rich, but your career path may not be a direct one or result in numerous papers published on a single scientific topic. Publishing is unquestionably very important, but snapping up alternative opportunities such as field work and public science outreach can also be professionally enriching, providing opportunity for travel and connections with scientists and nonscientists. Here, I provide two examples from my experience. For my MS research at Iowa State University in 1976–1977, I investigated effects of a nitrogen-rich sewage effluent on benthic macroinvertebrate diversity in a Midwestern river. Although this project provided me with a solid foundation in applied aquatic ecology, I must admit that I spent many evenings imagining alternative research projects in more picturesque locations, e.g. an alpine lake or a tumbling mountain stream. This resulted in me applying for a Fulbright fellowship and 15 glorious months in New Zealand where I explored patterns of zooplankton vertical migration in mountain lakes with and without fish. Choosing my lakes strategically, I maximized travel throughout this spectacular country. Although I did not publish my New Zealand research, this first international experience bolstered my self-confidence and independence, later convincing me to pursue a PhD in aquatic ecology. Also, while there, I interacted with an Australian expert on saline lakes who invited me 4 years later to participate in a 7-week zooplankton survey that spanned the Australian outback, an adventure and honeymoon for me and my husband (a terrestrial ecologist) who accompanied us as an auto mechanic! Upon returning to the United States, my husband and I wrote and published an article, describing our outback experiences, for the Des Moines Register newspaper, the only article for which either one of us has ever been paid! A public science outreach project arrived shortly after I had received tenure at Wellesley College when I felt free to pursue alternative adventures. Jane Goldman, an artist from the local Boston area, who sometimes taught at the college, inquired if I would be interested in helping her compete for a commission from the Massachusetts Port Authority (Massport), which operates Boston’s Logan International Airport. The challenge was to design an art installation for the floor of a new long walkway linking the two most distant terminals at the airport. Jane, a Texas native who had recently moved to Boston, chose a marine theme because the airport is located on the edge of the Atlantic Ocean. She needed guidance in choosing which organisms to feature and how to organize them. My husband, one of the world’s best idea generators, suggested that the organisms in the long walkway depict a depth gradient extending from shore to the depths beyond the continental shelf. So my students and I had great fun showing Jane various local marine organisms from which she chose those that could best be displayed in a terrazzo (mosaic) floor. Jane not only won the commission, but the two of us were subsequently asked to design two more walkways in which we featured mesopelagic organisms from the Northwest Atlantic in one walkway and benthic deep sea organisms in the other. So, a Texan and an Iowan with her students designed the marine walkways that now catch the eye of travellers from around the world. But the story does not end here. Collaborating with a computer scientist and the artist, we subsequently applied to the US National Science Foundation (US NSF) for funding to develop interactive computer programmes to be installed in kiosks within the airport that would allow the public to explore the organisms within the airport walkways, their behaviour, and conservation status. Although our application was not funded, I still take pride in knowing that two of my favourite marine organisms—the diatom Chaetoceros and a pteropod (sea butterfly)—are displayed in a public space that will outlast me. Engaging in public science outreach is not only fun, but it can hone personal skills (e.g. grant writing and public communication) and benefit society, e.g. by enhancing scientific literacy. Build bridges between subdisciplines About 7 years ago, some members of the Association for the Sciences of Limnology and Oceanography expressed concern about the “the salty divide”, a term referring to the self-segregation of freshwater and marine scientists (Kavanaugh et al., 2013; Cole, 2013; Marra, 2014). Limnologists and oceanographers were seldom citing each other’s papers; they were dividing themselves by attending separate meetings focusing on their respective subdisciplines (another divide, more aptly described as a chasm, occurs between aquatic and terrestrial ecologists; Stergiou and Browman, 2005). Discussion of “the salty divide” caught my attention largely because I felt I had crossed it, under duress, 25 years earlier. I was trained as a limnologist, something I hesitate to admit, especially in an article in this particular journal. However, I was hired by Wellesley College (an undergraduate, liberal arts college for women) to teach marine biology, despite my lack of any formal training in this discipline. This unusual hire probably happened because the Wellesley College biology faculty were hiring new faculty, in part, on the basis of their research achievements, and it is not unusual for faculty to be asked to teach courses outside their area of expertise. So, as a new assistant professor, I suddenly needed to become salty—a marine scientist. I worked like the devil to familiarize myself with the discipline—by reading scientific papers and multiple textbooks, attending US NSF-sponsored workshops for marine educators, spending my summer vacations at the seashore or at marine field stations, and inviting leading marine scientists to come to Wellesley College to deliver lectures and to speak and meet with my marine biology students. I learned an enormous amount from this sudden immersion into marine ecology, and it enriched my freshwater research and teaching, especially when I began taking American students and research colleagues to Lake Baikal in Siberia (see below for explanation of Lake Baikal opportunity). For example, the role of large crustaceans (isopods, crabs) as benthic scavengers on the deep sea floor is well known, but few people in the West realize that something similar occurs in L. Baikal, the largest (volumetrically), and deepest lake in the world that functions more like an ocean than even the largest Laurentian Great Lakes (but see Janssen et al., 2014; Pritt et al., 2014 for how larval fish and fish recruitment in L. Baikal, the LGL, and the ocean are similar). At L. Baikal, there is a local legend that the Russian mafia prefer to dispose of dead bodies in the lake because nearly nothing is left after 48 h. So, when my Wellesley College students became intrigued with this tale, I encouraged them to test it using food fall experiments (baited with fish; not human remains) patterned after those performed in the ocean. The result? The mafia’s alleged disposal site for dead bodies is a good idea! In slightly <48 h, hundreds of mostly endemic gammarids and snails devoured 60–100% of the fish bait in our traps deployed along a water depth gradient (Hughes et al., 2008). Another example of bridging the salty divide comes from my work with colleagues and students on the microbial loop and its potential influence on carbon cycling in Lake Baikal. Although now recognized to be important in oligotrophic lakes, the microbial loop was first described in the ocean (Azam et al., 1983). I had emphasized this concept annually in my marine biology course at Wellesley College, and it became so embedded in my mind that I began wondering if and how the microbial loop was functioning in the pelagic waters of L. Baikal. Although a stable isotope investigation had previously proclaimed that the lake had a simple pelagic food web (i.e. diatoms–copepods–fish–seal), I suspected otherwise. Results of in situ feeding experiments done with a colleague and undergraduates confirmed my hunch that most of the carbon ingested by the dominant zooplankter in L. Baikal comes from mixotrophic ciliates within the lake’s well-developed microbial food web (Moore et al., 2019). In other words, the lake’s summer food web and the biogeochemical cycling of carbon are much more complex—and oceanic—than previously thought. The larger lesson here for young scientists is that you may resist teaching courses in areas outside your expertise, but doing so can invigorate your research and engage talented undergraduates while expanding and diversifying your knowledge (Figure 1). Figure 1. Open in new tabDownload slide From left to right, Marianne Moore and Wellesley College students, Kristin Huizenga (now a PhD student at the University of Rhode Island Graduate School of Oceanography) and Bella Nikom (now a software engineer), catching live Epischura baikalensis, an endemic copepod, for an in situ experiment performed in Lake Baikal, Siberia, in August 2015 (photo by Dr Bart DeStasio, Jr). Figure 1. Open in new tabDownload slide From left to right, Marianne Moore and Wellesley College students, Kristin Huizenga (now a PhD student at the University of Rhode Island Graduate School of Oceanography) and Bella Nikom (now a software engineer), catching live Epischura baikalensis, an endemic copepod, for an in situ experiment performed in Lake Baikal, Siberia, in August 2015 (photo by Dr Bart DeStasio, Jr). Build interdisciplinary bridges Now I must explain how this remarkable opportunity to teach and conduct research at L. Baikal came about, because this vignette illustrates well the rich rewards of building interdisciplinary bridges plus the power of random conversations! During my third year as an assistant professor at WC, a new professor in the Russian Department, and one of the world’s most avid fishers, invited me to lunch because he wanted to know where the good spots for trout fishing were in the local Boston area (there are none!). After the trout conversation concluded abruptly, we immediately began discussing Lake Baikal. Why? Whenever you bring a Russianist and a limnologist together, the conversation will beeline to this lake because it looms large in Russian culture and in the field of limnology. For Russians, the lake represents the unspoiled beauty of the Russian motherland, and all Russians either know or recognize the lake’s anthem—“Glorious Sea, Sacred Baikal”. For limnologists and evolutionary biologists, Lake Baikal is a treasure chest of biodiversity harbouring more endemic plant and animal species than any other lake in the world (Moore et al., 2009). So, the Wellesley College Russian professor and I ended our lunch vowing that once we both had tenure we would seek funding to finance a reconnaissance trip to the lake to determine if we could teach an interdisciplinary field course there that would bring together students interested in Russian language and culture with students of the aquatic sciences. After making that reconnaissance trip in 2000, we taught our first interdisciplinary course titled, Lake Baikal: the Soul of Siberia, to Wellesley College students on-site in 2001 and nearly every other year since then. Importantly, the Lake Baikal course could never have happened without the language skills and knowledge of Russian culture that my faculty colleague possessed. Although I had taken a year of undergraduate college Russian at Colorado College, and I can speak it with the fluency of a 3-year old, my language skills were sorely inadequate for the task! Unknown to both of us at the time, the trust and friendships established with our Russian scientific colleagues during the early years of this course would later spawn multiple research investigations involving scientists from five different countries (see below). Be daring The Lake Baikal course provided a way for me to integrate teaching with my research, which is essential at an institution with equally high teaching and research expectations of its faculty. Of course, there are obvious ways of combining research and teaching such as offering an advanced course in which students are encouraged to pursue independent research projects in the faculty member’s research area and illustrating general principles in introductory courses using the published research of the faculty member’s laboratory group. But, be more daring than this! Develop a new course for students, a faculty development seminar, or a special symposium at a research conference on a new topic, forcing you to explore novel ways of thinking and doing (e.g. new, emerging data visualization techniques) that will benefit your research and teaching. If you do not create a mutualistic relationship between your research and teaching, you will pursue two full time careers simultaneously and drive yourself nuts. I speak from experience because I kept my teaching (marine biology, tropical ecology, and introductory organismal biology) and research (freshwater ecology; e.g. Moore et al., 1994, 1997) separate for my first 16 years at Wellesley College. It took the simultaneous arrival of a cancer diagnosis and my first opportunity to teach at L. Baikal for me to realize the necessity and benefits of combining teaching with research. After a year of personal leave, and with this new approach, I was able to continue and even expand my research during the last two decades of my college career via new collaborations. Collaboration also brings support, and the efforts of my medical team and the unwavering support of my husband, who accompanied me as my “teaching assistant” on many teaching trips to L. Baikal, were essential during this transition period. Bridge scientific cultures Collaborations that bridge scientific cultures can also lead to scientific breakthroughs. Although aquatic scientists from different countries may share a common sub-discipline such as oceanography or limnology, their approaches to research will likely differ due to historical differences in training and funding within their countries. For example, many of my Russian colleagues are superb taxonomists, capable of identifying nearly anything aquatic, and often terrestrial, to the species level, whereas I am lousy at this as are most of my US colleagues. Aquatic scientists in the United States often receive cursory training in taxonomy, if at all, because this discipline fell out of favour decades ago as experimentation, quantitative analyses, and molecular techniques were emphasized. So, when my Russian scientific colleagues first invited me to work with them, I immediately suggested that we apply for funding to investigate their 60-year data set describing the biological and physical characteristics of Lake Baikal’s pelagic zone. They brought the high-resolution taxonomic data; myself and a US colleague organized a team of faculty and students to help with meetings and the needed quantitative analyses. Together we wrote a proposal to secure funding from the National Center for Ecological Analysis and Synthesis (NCEAS) at the University of California, Santa Barbara. This funding brought our Russian colleagues to the United States for two working group meetings, and our final meeting occurred on site at L. Baikal. Although NCEAS does not normally fund working groups that focus on a single data set from a single sampling location, the uniqueness and quality of the Russians’ data overcame these funding criteria. These data, collected by three generations of limnologists, from the same Russian family, their students, and staff, have impressively high taxonomic and temporal resolution of plankton dynamics, with nearly all organisms identified to species (and often to life stage), as well as water temperature and transparency records. Thanks to my Russian colleagues’ amazing taxonomic work and diligent sampling through all seasons of the year, plus the quantitative skills of one of my Western aquatic colleagues, we were able to show that this lake was responding both physically and biologically to contemporary climate change (Hampton et al., 2008; Izmest’eva et al., 2016). At the time, this was surprising news, because it was thought that a lake as deep, cold, and large as Lake Baikal, and especially one with an unusual deep-water renewal process (Schmid et al., 2008; Tsimitri et al., 2015), would be relatively resistant to climate change. I come from a sharing culture in rural Iowa, so it was readily apparent that the first act of great generosity by my Russian colleagues in sharing their data was based on trust and the knowledge that we would be partners in the work that followed. Sharing meals, celebrating birthdays, contributing resources (e.g. to refurbish a laboratory at Lake Baikal), and sharing authorship on papers and presentations, i.e. supporting each other, have helped sustain this collaboration and made it a pleasure (see also Popper, 2020). Subsequently, our collaboration expanded and led to substantially more research funding from the US National Science Foundation and other sources, enabling myself, additional Western scientists from multiple countries (United States, Canada, Norway) as well as Russian and Japanese scientists to explore not only effects of contemporary climate change on plankton genetic, taxonomic, and functional biodiversity (e.g. Figure 2; Hampton et al., 2014; Katz et al., 2015; Izmest’eva et al., 2016; O’Donnell et al., 2017;,Bowman et al., 2018, 2019; Moore et al., 2019; Ozersky et al., 2020; Wilburn et al., 2020) but also contaminant burdens in the Baikal seal (Ozersky et al., 2017; Poste et al., 2018) and effects of coastal eutrophication on this unique aquatic ecosystem (Timoshkin et al., 2018). So, my first, adventurous teaching trip in 2001 was followed by more than a dozen additional trips to L. Baikal where I now have many dear Russian friends and colleagues. Bridging disciplines between research and teaching and among scientists from different countries and cultures has been and continues to be hugely rewarding and fun. Figure 2. Open in new tabDownload slide Part of our Russian-American research team at Lake Baikal in August 2012. Our t-shirts say “I love plankton” in Cyrillic. We are standing on the cutter Kozhov, named after Professor Mikhail Kozhov of Irkutsk State University who initiated in 1946 a plankton monitoring programme that continues to this day (photo by Dr Ted Ozersky). Figure 2. Open in new tabDownload slide Part of our Russian-American research team at Lake Baikal in August 2012. Our t-shirts say “I love plankton” in Cyrillic. We are standing on the cutter Kozhov, named after Professor Mikhail Kozhov of Irkutsk State University who initiated in 1946 a plankton monitoring programme that continues to this day (photo by Dr Ted Ozersky). So my advice to young scientists is that you take advantage of every opportunity to develop your skills, particularly in quantitative analysis and taxonomy; persist when challenged physically, intellectually and with inequities; dare to follow paths of opportunity even when the destination is not clear; and be generous in sustaining collaborations, especially those that bridge disciplinary and cultural divides. This approach can lead to an adventure-filled and satisfying career, as my experience attests. Career opportunities and my choices have taken me on adventures around the world, but my greatest successes have come from building bridges between disciplines and among scientists and cultures. Data availability statement No new data were generated or analysed for this essay. Food for Thought articles are essays in which the author provides their perspective on a research area, topic, or issue. They are intended to provide contributors with a forum through which to air their own views and experiences, with few of the constraints that govern standard research articles. This Food for Thought article is one in a series solicited from leading figures in the fisheries and aquatic sciences community. The objective is to offer lessons and insights from their careers in an accessible and pedagogical form from which the community, and particularly early career scientists, will benefit. The International Council for the Exploration of the Sea (ICES) and Oxford University Press are pleased to make these Food for Thought articles immediately available as free access documents. Acknowledgements I thank Howard Browman for the invitation to write this essay and for encouraging me to share personal stories previously buried under many layers of sediment. Martina Königer, Amanda Gardner, Nicholas Rodenhouse, and an anonymous reviewer provided numerous helpful suggestions. 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Groundwater contamination by sewage causes benthic algal outbreaks in the littoral zone of Lake Baikal (East Siberia) . Journal of Great Lakes Research , 44 : 230 – 244 . Google Scholar Crossref Search ADS WorldCat Tsimitri C. , Rockel B. , Wüest A. , Budnev N. M. , Sturm M. , Schmid M. 2015 . Drivers of deep-water renewal events observed over 13 years in the South Basin of Lake Baikal . Journal of Geophysical Research: Oceans , 120 : 1508 – 1526 . Google Scholar Crossref Search ADS WorldCat Wilburn P. , Shchapov K. , Theriot E. C. , Litchman E. 2020 . Environmental drivers define contrasting microbial habitats, diversity, and community structure in Lake Baikal, Siberia, bioRxiv, https://doi.org/10.1101/605899, 20 May 2020, preprint: not peer reviewed. © International Council for the Exploration of the Sea 2020. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Hammer,, Cornelius

doi: 10.1093/icesjms/fsaa091pmid: N/A

Abstract A career does not follow a straight path. Determination, decision-making, and focus are essential ingredients, as well as a fair amount of flexibility, especially when one is struggling with contradictory signals. Career planning and the necessary decision-making must be learned however, and this may be particularly difficult when negative outcomes are likely and encouragement is rare. Under such circumstances, finding a job that makes one happy could be considered a noteworthy measure of success. However, even after attaining such a position, many tend to compare their own performance and career development with those of the celebrities in the field. This can only result in frustration and insecurity. Furthermore, success in marine science is generally characterized by metrics, together with the manner in which one’s career has advanced through a series of positions occupied in the zig-zag from student life to retirement. For me, a more personal kind of success has been to overcome the fear of failure that arises through constant comparison of my own performance and achievements with those who are perceived as the best in the field. This might be viewed more as social anxiety than fear as I will explain in this article. Preamble One day while snow piled up outside, I emailed Howard Browman about Milton Love’s (2020) brilliant article that I had just read and that cheered me up for days. Howard answered swiftly, thanking me for this and added the question: Perhaps you would consider writing one of these essays?—I laughed and my initial (and over decades well-trained) reflex was: No way! What on earth would I have to say? I am in no way a celebrity in our field—but then again, it was Howard asking me. Although I know that Howard normally is pretty serious in such matters, I was sure he was joking. However, he was not. Of course, I am serious he wrote back and put a smiley next to it. I needed a moment to comprehend what the smiley implied. Then, I realized that he laughed in sympathy since he knew exactly into what kind of abyss he had kicked me and that my only way out was to comply. That was precisely what had caused my reaction and made me feel uneasy about his request, since it implied looking back and, in retrospect, asking myself as a freshly retired scientist: Is there anything that remains important enough to be told? Reflecting on my scientific life, I instantly see failures and even embarrassing ones too, although quite a few successes as well. But is it worth telling? On the other hand, as Howard asked me, there should be something worth telling, at least in his judgement. So, let us find out by doing it, I thought, and writing this is way better than writing detective stories that no one wants to publish anyway. Norwegian mountains and normal distributions Adding to this, and reflecting on my initial reaction, I asked myself, why this reflex of coy pretence and understatement, which, I think, runs these days under the banner of the “imposter syndrome”, instead of feeling honoured by being asked? Easy answer: because there are many scientists with outstanding lists of publications who can look back at an assembly of impressive metrics and achievements (I mean h > 50 and citations in the tens of thousands) who I would expect to be asked to write essays in the first place. Those metrics are, however, something that never interested me very much, and this for two reasons. When I started scientific life in the early-1980s in Hamburg, Germany, metrics were less important than personality and one’s overall perceived potential as well as reputation and character for being accepted for a job, and impact factors were not taken all too seriously. Hardly anyone had any proper metrics to promote themselves anyway and the overall discussion at the time was not about the impact factors but rather why (on earth) we should publish in English and not in German. I had to defend myself to colleagues for writing in English when I wrote my first papers. Later, I was fortunate that metrics remained fairly unimportant as I zig-zagged along the path of my career. We will come back to that. At this point, dear reader, I hear you sighing: Well, OK, but today … Yes, I know. There are jobs given strictly to those who have the best numbers and indices, which I consider a serious mistake and may lead, as we all know, to that what Smaldino and McElreath (2016) called natural selection of bad science. A good publication record is fine. Good papers that have an impact are even better. However, when it comes to giving someone a job, personality should come first. This is at least how I did it, when I conducted job interviews and sat on the other side of the table while applicants were nervously eyeing me. I am getting ahead of myself though, since when I was a young scientist long publication lists in pretty good journals never failed to impress me. And later on, during my self-critical 5-min year−1, I could even acknowledge that my overall lack of interest in metrics had something to do with my own lack of having them. Fortunately, those 5 min passed quickly. Conversely, however, this implies that I always had a deep respect for and was always greatly impressed by those having long publication records. In addition, what impressed me even more than the metrics, the number of citations, the H-Indices, and what made me feel uneasy in the presence of celebrities in our field, was their sparkling intelligence, phenomenal memory, and apparently unlimited working capacity driven by a boiling reservoir of enthusiasm and energy (who needs sleep anyway?), not to mention their self-confidence that seems to be as unshakable as Norwegian mountains. You certainly have met them as well: when they appear at a conference, they behave like celebrities on the red carpet before the Oscars. Sometimes they do not appear to be walking but rather to be floating on a cushion of success, always surrounded by a group of (younger) peers, all of them devoted to the motto: success breeds success. But I know better now (which is an advantage of age), since once in a while I would indeed see the Norwegian mountains shaking. I have seen one such celebrity before her talk, nervously pacing up and down the room practicing her talk, red spots of high stress visible in her face. I have seen one giving a PPT presentation filled with unbelievable gimmicks and no content, who I later encountered at the bar, pretty drunk. I have seen another one after his talk, soaked as if he was on his way to a wet T-shirt contest. I have seen one with nails bitten down to the point of bleeding. I have met others, however, who were none of that at all but were simply perfect, stunningly brilliant and for who I have only one word: Chapeau. But they were rare and mind you, they were the humble ones. Always. Why is it that way? I would be tempted to leave it to you to find out, but my best guess is that, when true competence and insight into the complexity is accompanied by the willingness to put yourself and your accomplishments into perspective, then the realization swiftly follows that what you really know is, after all, very little. Once you come to recognize and really grasp the magnitude of the discrepancy between what is in your papers and what is actually out there, yet to be understood, you inevitably become humbler. At least, I think, you should. It should not be surprising that I always felt that these folks play in a completely different league than I did, deliberately leaving aside what the measure of comparison is and leaving the question aside as well of what and who after all will make an impact on the development of science and society. I leave this aside because this is theory and it is a rational approach to comprehend what success is. In practice, I could never help but putting myself constantly into perspective with the best, which is not so unusual, I suppose, but not very constructive either. This simply because if you are sitting on the top of the normal distribution you exclusively look at the tail end to the few very best, where the curve approaches zero. That is, I think, what many of us (and managers and directors) like to do and it helps little that Daniel Pauly so correctly stated (I forgot when and where) that it is detrimental to your mental health to compare yourself with Einstein. Along this line: comparing yourself with the improbable is a splendid recipe for disaster, neurosis, or worse. And to push this thought even a bit further: outliers are usually omitted, aren't they? Not so, apparently, when it is about us and “them”. The Jacques Cousteau syndrome But here I am, an officially retired scientist, looking back, asking myself: what is success? Was I successful? Time to go back to the beginning. Who wants to be a scientist? asked Rothwell (2002), who gave a few serious answers in 166 pages. Did I want to be a scientist? Well, at the beginning, probably yes. Since I, along with many, watched Jacques Cousteau once or twice as a boy in the early-1960s in Hamburg at the home of a friend whose parents already had a television. That was enough to develop, what I called later, a serious Jacques Cousteau syndrome (JCS). Many at my age suffered from it, and others followed, all trapped by the false deduction that what Jacques was doing on the Calypso (and countless others like him on Discovery Channel and the like) was science. Thus, for me as a young lad science was diving, watching, cruising, adventure. Great, that is what I wanted to do as well. So, I decided on science at the age of 14, I think. And it was not only me. The infection rate was and is high. I even know some who have never recovered from the JCS and still run around with it, even after years in science, although a therapy is simple: just realize that Jacques Cousteau never was a scientist but rather was an army diver and later an entrepreneur and inventor of scuba equipment, who successfully sold films to TV stations. This is true for many others who embarked in this business as well. It would be interesting to investigate whether, with regard to the JCS, there is a bias in marine scientists having gravitated towards certain fields, possibly as a function of age and gender, etc. I have the strong feeling that there is indeed such a relationship. But, so what? Why should I be critical about cruising around on the Calypso, going diving, and admiring what I see? This, to develop passion and enthusiasm in the sense so well described by Love (2020). Maybe a certain form of JCS is even necessary to generate the near-fanaticism that is required to be able to deal with all that comes later? Maybe even more so, if the newcomers had read Nancy Rothwell before enrolling, which, with greatest likelihood, they have not. At least I never met anyone who had. This seems to be as rare as a young fisher and their bank manager coming to the research station to talk with those who know a bit about the stocks and the prospects for the trade, before buying a cutter and a quota. In such a way, assessing whether it is sensible to make a huge investment and plunge into heavy indebtedness would be the most logical, reasonable, and sensible thing to do. This has, however, never happened throughout my 25 years in stock assessment and I have never heard of it either. Neither have I met a student who had read a book like that by Nancy Rothwell before making a serious decision about pursuing science as a career, although it should likewise be the most rational and logical thing to do. Well, those who have, probably never showed up at university in the first place and thus are likely to have dropped out of my internal statistics before they could be registered. Based on this, it occurs to me that young science students have more in common with the young fishers and bank managers than you might think: none of them want to know what they are going to face, but reality hits pretty quickly. For the fishers, this probably happens when they learn about the quota for next year, and for the students when they enter mathematics class and realize that this is something utterly different than standing on the deck of a fancy yacht with binoculars in hand, looking for whales, with an ultra-cool and tanned skipper at the wheel in his muscle shirt and skull cap. Plenty of discouragement In my case, reality hit me the very first day I arrived at Hamburg university in 1973 and was greeted by the dismissive comment of a zoology professor who asked us (rhetorically) what we wanted (for goodness sake) at university, since we would all inevitably end up being unemployed, unless we were not only excellent in everything, but rather stunningly brilliant. So, if we did not think that that was what we were (he meant IQ > 135), we should leave and become taxi drivers right away, because that is where we would end up anyway. And that was it. The mark was set. Reflecting on my (in those days) still very recent poor performance at high school in Latin, physics, and mathematics, I made the first big mistake in my career that had not even begun: I believed him. From then on it was clear to me that over there, on the other side of the room, where the few hot shots were seated, that is where the welcomed ones were, the ones who were really entitled to attend the class, whereas the rest of us on the other side were, well … not really so welcomed. No need to say that that is exactly where I was seated. We were, however, tolerated, since there are financial considerations for the university, you know. Years later I completed a very good diploma in hydrobiology and fishery science, but this did not fundamentally change my perception of who I was and what I was capable of and I never stopped putting myself into perspective with the few on the other side of the classroom who projected the aura of Einstein. Despite this discouraging welcome, I stayed at the university and continued studying out of a mixture of hating to abandon something that I had started, a lack of alternatives and, as I now know, a constant underlying suppressed anxiety and depressed mood, which, in hindsight, does not surprise me at all. Rainer Weiss (Nobel Prize laureate 2017 in physics) said that his mentor once told him that he was not as stupid as he looked and that it was enormously important for him at the time to be told that. He added that it is important for everyone to have someone who gives him or her such encouragement. Indeed, a pat on my shoulders would have helped enormously in those days. There was very little that helped me to develop enthusiasm and devotion, and it fits the picture that I took a job in the zoological museum and worked on the storage of crustaceans, where I identified species, filled jars with alcohol, arranged them nicely on the shelves, and learned what a holotype and a syntype is. The room (built above an atomic bunker) had no windows, and I was alone with thousands of jars filled with crustaceans, all of them being quiet and very, very dead. One day a professor came to me and acknowledged my interest in those beautiful creatures by asking me if I wanted to become a decapod taxonomist and perhaps he could offer me a small contract after my diploma. I contemplated it. Friends teased me about this, expecting me to soon start running sideways like a brachyure, but there was no money in it, no adventure, no JCS, no proper job, not even bigger projects. And many of the taxonomists I had met lived year-to-year badly paid from small projects and all kinds of soft money, surrounded by too many very, very dead animals. In short, the creatures were fascinating but the prospects were not really sexy or sustainable. A decision, finally! So, I declined the offer. Jacques had saved me from this path to disaster. Instead, I went into fisheries science. My rationale for this was simple: I thought it would bring me out of the bunker and much closer to Jacques. In fisheries, I thought, there was always money for research, since stocks went down and fish (including crustaceans in this context) are a very valuable commodity and commercial fishing is very prominent in the public and political conscience, all qualities which the taxonomy of decapods does not have. The future would show that I was right in this assessment. Though fishing vessels were quite different from the Calypso, I came out of the bunker into the air, the sea, fishing vessels with sea-life on deck and booze with the fishermen. I loved it. Thus, it was an easy choice: I wanted to become a fishery scientist. Talking to one of them first would have helped, but it goes without saying that I did not. I had no idea what this choice implied. It did not take long to dawn on me that fishery science had a lot to do with mathematics and statistics. I decided, better not think about it too hard, and so I proceeded. When the studies came to an end, and when I was eventually expelled from paradise, I had mastered my way over the hurdles and had made far-reaching decisions based on very little rational evaluation. In hindsight, it is interesting to see what had happened so far: I came into fishery science, based on romantic (JCS), very materialistic considerations (money in research), free of enthusiasm for something in particular (no genuine curiosity), and void of any preparation for a life in science (the University felt that was not their job to provide). If anyone at the time had asked me about my career planning, I would have laughed out loud (although, unaware of it, I had already made a career decision by leaving the bunker). On the one hand, I could not really relate to the concept of career planning and felt it was simply arrogant to plan my career and, on the other hand, I had never believed that there was anything for me in particular to plan. I felt I should be happy if there was anything I could grab or any train I could hop onto. What I thought was needed most was flexibility rather than developing a plan. Such thinking was the mantra of the time but seems still to be pretty common, since even in the past couple of years teaching as a professor at the university of Rostock I have met many students at the end of their master courses who were exactly the same as I was then. In other words, there was and there is something deeply wrong with the messages we convey to our students. This is definitely not what I call motivation and encouragement for students to give them confidence in their potential and to develop devotion and to encourage them to plan actively for their career. However, it should not be implied by this that a fair amount of flexibility is not needed. By contrast, when reality kicks in, flexibility is essential, but there is a great difference between flexibility in achieving a desired goal and the flexibility to accept and take advantage of whatever comes. When reality kicks in The reality of scientific life hit me for the first time as a nearly graduated student when I had to give a 30-min presentation on the results of my diploma thesis that was originally supposed to be on the recruitment of roach (Rutilus rutilus) in a lake in Northern Germany and that failed terribly since, as it turned out, I did not have the appropriate means to collect an even barely representative sample of young fish. Initially, panic struck me but then I improvised, tried to make the best out of it, and turned the topic into larval ecology without further ado, not to mention without background or knowledge. My supervising professor reacted to the change of topic with only modest interest, shrugging and commenting with a “well, if you think so …” statement. I subsequently investigated the feeding of the roach larvae, starting with the earliest larvae and their very first feeding and then followed them one developmental step after the other, comparing what they had in their stomachs with what was present in the water in what I thought was their near-field environment, without having the faintest clue about the underlying metrics and statistical requirements, since quantitative ecology had not existed in our curriculum. Even good old Mr Ivlev I came to know only later. A few years later I published the results (Hammer 1985) and to my great surprise it became a very well-cited paper for many years, since it turned out that, to my utter ignorance, no one had yet investigated the very first feeding of cyprinids. In retrospect, I think this is a good example of what Campana (2018) meant when he so correctly stated that there is almost always opportunity in catastrophe. In a way, this would become my guiding principle throughout my professional life, although I would only realize this much later. The only thing I was aware of at the time was that I had to give a presentation on my diploma research and that I was scared to death. I thought that such anxiety was normal, because for me it was. Being in the audience and asking a question was impossible, my heart would have been pounding too loud, pumping adrenalin through my veins, so much that it would narrow my vision, making me forget my question the moment I opened my mouth. Only years later would I learn the name of it: a fully grown social anxiety disorder. Today, we are all much cleverer and know how to deal with it, but in those days, we did not even have a name for it. Well, I made it through the presentation and people liked it. Probably they all noticed how nervous I was but the audience (most of them my fellow students) smiled at me in sympathy. That I found astonishing. I had expected very critical looks and frowns. More in line with that, all the old professors who were sitting in the first two rows did indeed look critical and asked me why I had used the mean instead of the median. That I did not find astonishing. Brewing beer in Australia? A couple of days later I walked off the campus and expected it to happen: the big unemployment to come, the instant drop into social disaster. Therefore, it was time for hard decisions, which I instantly took. I sold everything I could not pack into two suitcases or gave it away (mostly books that would be little treasures today) and prepared to start a new life in Australia. However, when I was already under way making concrete plans for the move, someone informed me about a job opportunity and encouraged me to apply, which I did. It was a job at a company for Technical Cooperation, which was at the time something like sophisticated development aide. At the University of Diliman in the Philippines near Manila was a College of Fisheries to be supported with equipment, a research vessel, lectures, and research. I came there as a youngster and started learning. I worked in an international team and learned what no one had even mentioned before: focus, enthusiasm, and ambition. I had colleagues and friends from all over the world with enormous devotion and ambition. This, not in a negative sense, not in the sense of ambition against someone or in the attempt to outcompete, but ambition in the sense of a will to discover and a will to accomplish a task and reach a target. A new world opened up in front of me. It is interesting to note at this point that I always perceived my first employment as pure chance and the fact that someone encouraged me to apply for this job was simply, as Waples (2020) put it, Lady Luck having grabbed me by the throat and I was flexible enough to jump at it. However, this was only my self-perception, whereas in reality my professors had carefully evaluated my potential and promoted me secretly. So, in my perception, I remained on the top of the normal distribution, whereas in fact I was already gradually gliding down towards success, whereas others dropped out and became taxi drivers. As Waples (2020) points out, when an opportunity like this arises, you should be able to step up and make the most of it. That is what I did, at least I tried, but in my recollection, it remained pure luck, whereas, in fact, it was not. Three years later I came back to Hamburg university and was beginning my doctorate since it was offered to me (without payment though) and I took it, although I was still planning for Australia. I thought, however, that a doctor’s degree could only be an advantage, no matter what I would be doing eventually in Australia, which was symptomatic of my lack of career planning. I applied for a research grant and received the answer that money was granted for some equipment and consumables but not for subsistence, since they were not utterly convinced by the project. They wanted me to first show some results before granting subsistence (do not ask me how they thought I should accomplish this). When 2 years later I did so with a couple of papers, they acknowledged my progress and added that now they were convinced but I would finish soon anyway and thus would not need the subsistence any longer. The topic was on the question of whether one could use the accumulation of age pigments (Lipofuscins) of different tissues to determine the age of fish. The results in short: you cannot, the variability is far too high. The whole thing was not such a brilliant idea from the start but initially I found the question interesting. Apart from that, I was able to do the research with an absolute minimum of equipment and it left me enough time for working during the night in a moonlit garage in the backyard, fixing cars to make some money. So, you want to become a scientist?—From failure to success I had just begun my doctorate when someone from another university called me and asked me to participate in a panel discussion. He asked me to contribute with my experiences of the past years in the Philippines. They wanted a conversation, no talks, it was supposed to be very relaxed and cool, just a chat with others on the podium. I believed him, so I agreed. Big mistake. When I turned up, there was a big audience in a lecture hall and everyone was giving proper and well-elaborated talks. They had all known better and were prepared. I was trapped, since, of course, I was not prepared. I felt like Bridget Jones as a Bunny at the party. Being called up to the podium and asked to give a reflection and analysis on the success of development aide in science, but not having prepared a talk, leads to a lot of social anxiety and is a good recipe for disaster. And that is what it was. I drove home, shattered. So, you want to become a scientist? Slowly it dawned on me that I was further away from Calypso-cruising than ever and that life in science was not only about mathematics and statistics but was also a lot about giving talks and presentations. Jacques, my old friend, where had you gone? I had only a little time to lick my wounds since the next event was already scheduled. This time, I was invited by the University of Bremen to give a 15-min talk, which was supposed to be about management and impact of developmental aide. I thought hard about it. I prepared a talk, wrote it down word for word, prepared acetate sheets (since we had already moved past the good old Geppe glass-mounted slides). I changed the talk repeatedly and I practiced it over and over again. The dreaded day finally came; no need to tell you how I felt. Russel Crowe once said in one of his early films that many people are less afraid of death than giving public talks. And this was meant pretty literally. I could feel it at that particular moment as well. I had not eaten for a day and when the university came closer, I stopped the car and looked at the building. There are always alternatives, I told myself after a couple of minutes gazing at the concrete. You do not need to go there. You can turn around now, simply disappear, go to Australia, brew German beer, and never come back. That was tempting. Or, I thought, instead, you go there and give the talk. In case you make a fool of yourself again, you can still drive away, go to Australia, brew German beer, never come back, see none of them ever again, and never give a public talk again. The difference is nothing more than a couple of hours, and at least you have given it a chance. If, at that moment, someone had told me that 35 years later I would end my career as President of the International Council for the Exploration of the Sea (ICES) , I would have turned the car around immediately. But I did not know and decided to give it a try. Last chance. I came into a huge and packed lecture hall. I had expected a small audience since I considered the topic rather exotic, but there were hundreds. Down there in the distance, the speaker had a microphone around his neck, one of those who seem to be sensitive not only to your voice but rather to the trembling in it. I eventually walked up to the podium. I was not scared so much at that particular moment, since it had become clear to me in the meantime: my future was in brewing beer anyway. I gave the talk, and the success was enormous. They said it was the best talk of the entire symposium. They said it was the only talk that really had addressed the issue and the only one of relevance. Hence, I postponed the beer brewing and stayed, since I had learnt one important lesson: I can do it. All it required was excellent preparation and taking the issue very seriously. I had learnt to make a virtue out of necessity or, as Campana (2018) put it: turned adversity into opportunity, in which he paraphrases Benjamin Franklin, who first stated this. And that would become my guiding principle and eventually the key to the success of my career. I developed strength out of my weakness. Thus, I came to the conclusion that for the time being the Aussies could survive with the beer that they had. Instead, I finished the doctorate and, after a year in project management for the Technical Cooperation in Eschborn, near Frankfurt, I applied for and was selected for a 6-year period of what was then called Habilitation but is now called Assistant Professorship at the University of Hamburg. Having had a number of papers to put forward in the selection process was important but personality and the overall perception of those doing the interview was equally important, if not more. I came to this realization by reading people’s body language during the interview and I could feel that there were key persons who wanted me and that that counted more than the number of papers I had. Over the next 6 years I worked in the field of bioenergetics of fish locomotion, which is a topic that still fascinates me. The more talks and lectures I gave the easier it became. The nervousness gradually declined but never really disappeared. I learnt to accept it and to see it as a part of myself and to use the nervousness as activation energy for good preparation. But still: when the 6 years of Habilitation came to an end, I felt that I did not want to spend my entire professional life at a university. The decision came on the one hand out of deep admiration for the really competent and gifted colleagues at the university and out of the overall tiredness of competing with them. I felt that I should be far more competitive to become a respected and acknowledged member of the university and science community. Together with the enormous energy drained from me to lecture and give talks, it became clear that in the long run, staying at the university went against my nature. It was time for a change. Finally, it was a conscious career-oriented decision, based on how I felt and an honest evaluation of my potential. In retrospect, it was the best decision I could have made. It was (finally!) a real decision, and in each decision, there is a will and the will is the foundation of focus, and focus is the basis of success. Zig-zagging upwards In the mid-1990s, precisely when my contract at the university came to an end, a job came up at the Federal Research Centre for Fisheries in Hamburg and I found myself in charge of the sub-department for pelagic fishes in the North Sea and the North Atlantic. As a result, I moved into stock assessment. In the selection process my publication record was not unimportant but was not of primary importance either. The question whether I would fit into the team and would be able to develop the sub-department was far more important. Managerial competence was not an issue during the job interview either and I was put in charge of the job without any preparation for leadership. I was simply thrown into this particular kind of water and had to swim. So I did. Being in charge of a sub-department sounds great, although in the beginning the unit was basically me, a few older scientists on the way to retirement and a couple of technicians. I loved the work at sea and in the laboratory. I often could not wait to go back to work in the morning. I became a member of different ICES working groups and loved the international collaboration. Luck struck, when 1 day a young man was hired and came to join me in the pelagic section. Christopher Zimmermann and I became friends instantly and we decided to rock the boat. And that is what we did. What followed was a time of expansion and rapid development, both at the Research Centre and within ICES. However, slowly approaching my 50s, I felt again that it was time for a change. The choice was either to stay in my position for the remaining 15+ years of my professional life, or to switch. I applied for the directorship of one of our research institutes, today the “Thünen Institute of Baltic Sea Fisheries” in Rostock, Germany, and was selected for the job. This time my publication record was of secondary importance as well, since my managerial potential counted more. However, my managerial potential was assessed only on the basis of the talk that I gave for the interview, which was about my assessment of the overall situation in the fishery and how I thought it could be improved. Whether I would be a competent manager was not assessed directly; maybe indirectly or by overall perception, but I do not know. I was leading a small pelagic section and I had been cruise leader of research cruises, which is all I could present as credentials for my managerial competence, which obviously was enough, since I got the job. To my own surprise, I soon realized that I had found the place where I felt even more comfortable than was the case in the previous position, since I did not deal with analyses any longer, but rather with people. My social anxiety did not hamper me too much in this context, since it came into play only when I was standing in front of a large crowd. I was now in fisheries management and, thereby, very much into what Hare (2020) described as the “wicked problem” of fishery science and management, being neatly interwoven into a scientific, social, economic, and political fabric. I did not do much research any longer. Rather, I organized it. I had found my true vocation. For a time, management of the institute came relatively easy in relation to my struggles at the ICES working groups or at the university. Still, I had to give a lot of talks, but that became slightly easier with time. Amazing discovery There were, however, still embarrassing moments, when out of the blue my old social anxiety caught up with me, always in bigger and more formal meetings; I had better spare you these stories. Nevertheless, there are a couple of very important lessons that I learnt from these nightmarish events. I had always assumed that in such moments of nervousness the audience would secretly or openly laugh about me and would, from then on, be contemptuous in one way or another. Indeed, sometimes some of them were, but only very rarely. To my great surprise and amazement, most people reacted with sympathy instead of contempt. What I received were rather smiles and friendship instead of a cool face. Based on this, I took it a step further one day and instead of making small talk during a reception I talked to my neighbour at the table about my nervousness before giving a talk. My colleague laughed and told me that she felt exactly the same way. We laughed together to tears telling the secrets of our failures and embarrassing little events here and there, which maybe only we had noticed, and this changed everything. With this I had broken the spell and it became instantly less important, less threatening. The pressure went out, like the air out of a tire. So, what had happened in my mind? For me, this colleague had come down from the upper level, from the far-right end of the bell curve, closer to me, closer to the middle. She had moved from where I had put her (and all the other top-notch scientists around me) and had come closer to me by sharing her own fears, successes, and failures. She was a bit like me. In dry science, we had found a personal bond. Since that day we are good friends. Again, I had made the astonishing observation that, after all, scientists are human beings breathing oxygen and are made of carbon as well—OK, most of them at least. In showing my vulnerability and imperfection, I gained more sympathy and friendship than I had ever experienced. Even more, by doing so, I opened a door to give myself the opportunity to overcome the demon of social anxiety that I had carried from childhood, since the simple message I learnt was: most people are nice. And the rest who are not? Forget about them and try to keep a distance. And, on top of this: maybe I had opened a way for the nice ones to reach out to me as well. What a new perspective! Ovenden (2019) so nicely described how uneven the path to success in the life of a scientist can be. Careers do not always follow a straight path. Right. I can only confirm this. I have had my fair share of unevenness but ended up—at first glance mysteriously—at the upper end of what might be perceived as a career ladder. From roach larvae to age pigments and bioenergetics of fish locomotion into stock assessment and fishery management, I had eventually made my way into institutional management. Allow me the vanity of listing that path: I was Director of a research institute. For a couple of years, I was Director General of the Federal Research Centre. I became an Associate Professor at the University, I became German Delegate to ICES, I became Vice President of the Thünen Institute in Germany, and finally President of ICES. The small things matter Viewed from the outside, I had a good career, which, as a matter of fact, is the contrary of sitting on top of a normal distribution. So far so good. But is this success? Objectively yes, but for me it never felt like that. Well, if it is not the metrics and the positions, what else is the measure of success for me? My wife said, well, look at the list. At least for a moment you could acknowledge that you have achieved quite something. OK, yes. But is it exactly what I perceive as success? To some degree certainly yes, though for me real success feels different and in my perception what I have achieved seems not to be enough for telling a story and is nothing I am particularly proud of. It just happened and is nothing exceptional. And I am all too aware of the mistakes that I have made during my time as a science and institute manager. For instance, that I did not take a number of politicians seriously enough (big mistake) and they could read this in my face (even bigger mistake). Or, that I thought naively that all other scientists must be open to rational arguments, which they often by no means are. Or, for instance, when I tried to force people stubbornly to do something that they were not doing and I did not realize that even with best intentions they simply could not and what I had asked for was beyond their capacity. So, what should I be proud of? My 100+ papers cannot be it either, though my best-seller on fatigue and exercise tests in fish (Hammer, 1995) has been cited I do not know how many hundred times and is still often referred to. Likewise, the “Hammer rule” which someone came up with, based on a paper (Hammer, 1994) and according to which, as a rule of thumb, fish in the field swim about 1 body length s−1; that cannot be the reason either, I consider this (flattering though) rather a joke. Thus, if you do not have impressive metrics to be proud of, that make you truly happy, and your zig-zag career does not do it either, then it must be something else. In my case, it was the small things in life, and that is what comes to my mind instantly when I think back. It was, for instance, when students came to me for counselling and showed me their deepest heart-felt concerns and all the despair they were running around with, trusting in me and my advice. It was when students came to me and told me that my lecture was the most important one during their entire studies and that it had an impact on their life. It was, when someone became my friend because, in dire times, I stood by him. It was, when an employee told a representative of the ministry that he would walk through fire for his boss. It was, when the ministry told me I could have all the money I had requested since they knew it was well-invested in my institute. It was, when a colleague and friend from another institute who I deeply admire told me that he could not have done what I did, which I seriously doubt. You see? For me, it was the small things that people told me. That is what matters. It is what I remember and why it was all worth doing. To corroborate this, an anecdote: a couple of years after I had joined the Federal Research Center to work on stock assessment of pelagic fish, it must have been in the late-1990s, I was standing in the queue for the buffet at the reception of the conference dinner at the ICES Annual Science Conference in Reykjavik, when someone read the name on my badge and said in surprise and astonishment: “You’re the Cornelius Hammer, are you really!!!???”. I was totally caught by surprise and stuttered something like “ehrr … yes … eh”, thinking, what have I done wrong? The guy continued: “I am proud to meet you and make your acquaintance”. Now I was sure he was kidding me and I was wondering what the joke was directed at. But it turned out that he was serious. In their laboratory in the United States they had been talking about the drawings that I had made for the covers of our ICES working group reports because I thought that the dry matter inside could well deserve some humour, at least on the outside. I did this for a couple of years until someone from the upper spheres of ICES called it to a halt since he thought that it might ridicule the content. Unfortunately, I cannot recall who approached me 25 years ago in Reykjavik, but I was proud for a moment. A couple of years later I took up the pencil again and made a cartoon of the Advisory Committee for Fisheries Management (ACFM) at ICES (Figure 1). This was a farewell present to Jean-Jacques Maguire who was our excellent ACFM-Chair for many years. Figure 1. Open in new tabDownload slide Impression of the ICES Advisory Committee for Fisheries Management (ACFM) at the end of the 1990s. Figure 1. Open in new tabDownload slide Impression of the ICES Advisory Committee for Fisheries Management (ACFM) at the end of the 1990s. Again, the drawing was enjoyed by many and resulted in a couple of pats on the back. These little signs of appreciation that I have received, they are what I recall most. However, is it all that remains and the only thing that I consider as success? In a way, yes, but not entirely. There is something more important and that came almost at the end of my career and sounds equally unimportant as the funny event in Reykjavik but never the less had great importance for me. It was again at the ICES Annual Science Conference in Riga in 2016 where I had to stand up at the Conference Dinner as President of ICES and make a clown of myself, which is sort of an ICES tradition. Telling a funny story, giving a talk with good humour or singing a song, something like this was expected of me and it was exactly that, what I had dreaded most of all for so long and there was no way to sneak out of it. It was the most difficult thing in my life to do. Chairing a Council meeting for 2 days was nothing compared to that. The idea of standing alone in front of many hundred people, singing a song (to make it worse, I cannot sing at all), entertaining them, inviting them to fall into the chorus, was the ultimate nightmare. I had rewritten the text of Puff the Magic Dragon … and I had turned it into Puff the ICES Dragon is under your seat and if you don’t take care enough, it’s fancy you to eat … and so on (full text in the Supplementary Material). I was scared to death when it was time to stand up and to take the mic but it went well and turned out to be a great success. It has been a long path, starting from not being able to ask a question in a lecture hall to eventually singing a song at the ICES conference dinner in front of hundreds of people, but afterwards, I looked back and said to myself: Yes, I did it. The most difficult thing for me to do had come at the end of my career, and I made it. I had conquered my demons. I had never given up. I had not run away, and the Aussies do well even without my beer. I remember that I took a deep sigh and said: For me, yes, that is success. So, what is the message? Forget about all metrics and just stand by your shortcomings, and everything will work out fine? Surely not. Our endeavour is to produce science. This materializes in papers and presentations. These are our products. This must always be what we are striving for. That is what we are paid for and what it is all about: preferably good papers in good journals. These are counted and weighed, we should not be surprised by that, nor should we oppose the fact that it ends up in metrics. The point is how to deal with them, what relevance we allow them to attain in our judgements, and what weight we allow them to have in concert with character, personality, attitude, and behaviour with colleagues and in the team. Maybe it has become clear by now that I consider the latter at least as important as the former. It is important to keep the balance, finely tuned. More in a personal view it is likewise important to keep balance and to keep in mind that one should focus more on his or her own potential rather than comparing one’s self constantly with the best. The more focus and attention is put on the super-achievers at the outlier end of the normal distribution (in times of more-more-faster-faster) the more pressure is automatically generated and put on the others (the great majority) to the left of the distribution and tends to automatically reduce their achievements. And by the way: there is a good reason why a normal distribution is called a normal distribution. And even if you gradually slip down from the slope of normality and approach those at the far-right end of the distribution, bear in mind that this is based only on what is measured, and I hope that I have convinced you that there is far more to success. Finally, here is my 5-cents worth of opinion and advice for young scientists If you decide to take this path you have to take it seriously. However, never forget that there is far more of relevance in your life than just science. (You think that goes without saying? It does not). Do not believe in everything they say to discourage you (especially not on the first day of university). We need you. Society needs you more than ever. And this despite the impression that many politicians and their administrations make. Do not always compare yourself with the best. That sucks too much energy. Rather, use this energy for yourself. You are not perfect—laugh about it. Try to find the opportunity in it. Maybe it is not really straight forward to find, but it is there. On the way to finding opportunity, you will encounter problems. First of all, you will encounter problems with yourself. It is human to hide them, since we all strive to be perfect. That is wrong. Admit your imperfections. Learn to laugh about your imperfections and accept yourself with them. Once you can laugh about yourself, you are ready to talk to others about it (but do not be a drip). And that might be the first step to success. A career is not a straight path so you need to be flexible. This does however not mean that you have to or should accept everything that is laid out in front of you. Otherwise you might lose your enthusiasm, which is something you cannot afford to do, since life in science is too demanding. In other words: stay focused on your goal and what you truly love. You have to weigh carefully between focus and flexibility. And if you feel that you have deviated from this path, then be honest with yourself once in a while and ask yourself what your true potential is. In doing so, admit to yourself that you have potential, and explore it. This will not spare you occasional disasters and you will undoubtedly experience setbacks. However, bear in mind, that on the one hand, you are not alone. And on the other hand, that in each catastrophe and set-back there is opportunity and this might (eventually) even be the key to success. But then again, who am I to tell you?—Which brings us back to the beginning. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Food for Thought articles are essays in which the author provides their perspective on a research area, topic, or issue. They are intended to provide contributors with a forum through which to air their own views and experiences, with few of the constraints that govern standard research articles. This Food for Thought article is one in a series solicited from leading figures in the fisheries and aquatic sciences community. The objective is to offer lessons and insights from their careers in an accessible and pedagogical form from which the community, and particularly early career scientists, will benefit. The International Council for the Exploration of the Sea (ICES) and Oxford University Press are pleased to make these Food for Thought articles immediately available as free access documents References Campana S. E. 2018 . 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Google Scholar OpenURL Placeholder Text WorldCat Love M. S. 2020 . A 45-year career in marine science—better than a sharp stick in the eye . ICES Journal of Marine Science , 77 . Google Scholar OpenURL Placeholder Text WorldCat Ovenden J. R. 2019 . Breaking the myths (or how to have a successful career in science) . ICES Journal of Marine Science , 76 : 23 – 27 . Google Scholar Crossref Search ADS WorldCat Rothwell N. 2002 Who Wants to be a Scientist?—Choosing Science as a Career . Cambridge University Press . 166 pp. Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Smaldino P. E. , McElreath R. 2016 . The natural selection of bad science . Royal Society of Open Science , 3 : 160384 . Google Scholar Crossref Search ADS WorldCat Waples R. S. 2020 Serendipity and me. ICESJMS-2020-109.R1, in press. © International Council for the Exploration of the Sea 2020. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Turner, Nancy, J

doi: 10.1093/icesjms/fsaa095pmid: N/A

Abstract Indigenous peoples have occupied the northwestern North American coast for at least 15 000 years—a time when much of the land was covered by a kilometre or more of ice and only patches of land were glacier free. Over the millennia, through difficult times and seasons of plenty, they have built up an immense body of local knowledge, practice, and belief—Indigenous, or Traditional Ecological Knowledge—enabling them to live well, learning about the plants and animals of terrestrial, aquatic, and marine environments on which they have depended, and how to harvest and process them into nutritious foods, healing medicines, and useful materials. Although it has been commonly assumed that these people, as so-called “hunter-gatherers”, were simply helping themselves to nature’s provisions, over decades of learning from Indigenous plant specialists and other knowledge holders as an ethnobotanist, I have come to see First Peoples as resource tenders and managers over countless generations. Their traditional land and resource management systems provide many lessons on how we humans can work with natural processes to ensure the well-being not only of ourselves but also of the species and habitats on which we rely. Introduction Indigenous peoples have lived on the Pacific Northwest Coast of North America since “time immemorial”: at least 15 000 years ago. At this time, much of the land was covered by a kilometre or more of ice with very limited refugia—patches of unglaciated land where plants, animals, and people were able to survive. One can only imagine the difficulties these early peoples on the coast faced—with cold, icy conditions, changing sea levels in many places, undoubted food shortages, few places of refuge from storms, and limited access to plant and animal resources. For example, there would have been no western red-cedar, since these trees were absent from most of the coast until around 5000 years ago. Yet, the people survived, thanks in large part to the marine and coastal resources they were able to access: the kelp beds, intertidal areas, beaches, lagoons, and river estuaries up and down the coast. From their ancestral home in northeastern Asia, these early people would have arrived with at least some knowledge of survival in maritime environments, but this knowledge would have been adapted and enriched over the millennia in their new homeland, through difficult times and seasons of plenty. Learning about the plants and animals of terrestrial, aquatic, and marine environments available to them, and developing methods of harvesting and processing them into nutritious foods, healing medicines, and useful materials, would have been a huge collective enterprise. Then, as the human populations grew, learning how to use these resources sustainably, without depleting them, would have been critically important. Acquiring this knowledge was obviously successful, since at the time just before Europeans arrived in the area, there were dense, thriving, diverse populations of people living all along the coast who were generally as healthy, if not healthier, than their European counterparts of the day. The systems of knowledge, practice, and belief—Indigenous, or Traditional Ecological Knowledge—they developed are still present today, despite the disruptions of colonialism and all that it has entailed (Brown et al., 2009). As an aspiring ethnobotanist on the west coast of Canada in the late-1960s and early-1970s, I was fascinated by the diversity of plants that the First Peoples of our region were using, and had set out, first as an undergraduate student in biology at the University of Victoria and then as a graduate student in botany at the University of British Columbia, to learn as much as I could about the ways in which plants were used and named in the different First Nations’ territories. I was so fortunate to learn from many knowledgeable and kind elders of the day, working in collaboration with linguists and supported by my supervisors and university departments, to document this information (cf. Turner and Bell, 1971, 1973; Turner, 1973, 1974). My first teacher, Christopher Paul, a Saanich (WSÁNEĆ) elder from Tsartlip, taught me about many plants, especially blue camas (Camassia spp.), and about the importance of salmon, herring, and other marine life as well. At the time I was told by almost everyone that this type of cultural information was rapidly disappearing, that it would be “gone within 10 years”. Today, looking back over 50 years later, I have to say that this observation was inaccurate. I came to learn over several decades that documenting the names and cultural uses of plants was not sufficient to understand the human–plant relationships that existed, or, more broadly, the underlying relationships between First Peoples and their homelands, of which plants were one, but not the only element. I came to the realization that these relationships were important in their own right and were key to the way the First Peoples thought about the world: their “worldview”. Furthermore, worldview, I learned, determined in large measure how people have valued and cared for the lands, waters, and other species on which they depended. The study of the cultural relationships people have with the ecosystems in which they live is known as “ethnoecology” (Nazarea, 1999), and the systems Indigenous peoples and others living in one place for many generations have developed to care for and sustain their resources are called traditional land and resource management (TLRM) systems (Berkes, 1999 and subsequent editions). This article reflects a personal journey of learning about the immense richness and complexity of Indigenous peoples’ Traditional Ecological Knowledge and TLRM systems on the coast of British Columbia, focusing on a few of the key knowledge holders who have been my teachers and guides, as well as some of the insights I have gained from personal observation and experience of being out on the lands and waters of the coast. Looking back over the decades, I see that my knowledge and understanding have grown incrementally, with a few sudden “ah-ha!” moments when pieces of the puzzle came together and my comprehension took a leap forward. My work with Interior First Nations has been a part of this learning journey as well, and there are many parallels and similarities in peoples’ worldviews and approaches to TLRM, but here I focus on what I and my colleagues have learned about Indigenous peoples’ relationships with coastal ecosystems and environments, mainly along the coast of British Columbia, and also including adjacent Alaska and Washington. I start with a brief overview of the First Peoples of the Northwest Coast and the coastal ecosystems on which they depend. Northwest Coast peoples and their environments Much has been written about the Northwest Coast Culture Area and the Indigenous peoples who have made the Northwest Coast their home for countless generations (see Suttles, 1990; Moss, 2011). These peoples—the Tlingit, Haida, Nisga’a, Ts’msyen, Haisla, Heiltsuk, Kwakwaka’wakw, Nuu-chah-nulth, Ditidaht, Makah, and Quileute, as well as the Nuxalk Salish of the Central Coast and numerous Coast Salish peoples of southern British Columbia and Washington—are known for their diverse maritime economies, based on fishing, sea mammal hunting, and harvesting quantities of different types of shellfish. Not only the marine habitats but also the associated rivers and lakes and the vast coastal rainforests and other diverse terrestrial environments were, and still are, essential to peoples’ lifeways. The coastal climate, though changing over time, has always been milder than in the Interior, due to the oceanic influence, with cool moist winters and warm, relatively moist summers. At least within the past few millennia, the Northwest Coast peoples mostly travelled by dugout canoes made from western red-cedar (Thuja plicata) and most have lived during the winter months in large, multi-family houses constructed of red-cedar posts, beams, and planks, while travelling seasonally to different harvest sites within their overall territory in a regular harvest cycle. Most groups have marked hierarchical social organization with hereditary leadership and defined roles, based on inheritance and aptitude, around food procurement and other cultural activities. Individuals are often trained from birth to become experts in particular areas, including various types of land and resource management. Salmon, halibut, rockfish, herring, and other fish have been a staple food for most Northwest Coast people, as have been herring eggs and the nutritious fat rendered from a small oily fish, the eulachon, which runs up some of the major rivers to spawn along their shores. Seals, sea lions, sea otters, porpoise, and on the west coast of Vancouver Island, whales are hunted, or have been in the past. Clams, cockles, mussels, sea urchins, chitons, abalone, and other inshore shellfish have also been major food sources. People also eat some types of seaweed, the rhizomes of eelgrass (Zostera marina), a number of beach and tidal marsh root vegetables, various greens, inner bark of some trees, and a range of fruits from crab apples (Malus fusca) to salal berries (Gaultheria shallon), blueberries and huckleberries (Vaccinium spp.), and salmonberries (Rubus spectabilis). Altogether, the coastal peoples have enjoyed nutritious, balanced diets high in diversity and essential nutrients (Kuhnlein et al., 2009). Glimpses towards a different understanding “Hunter-gatherer” is the standard term applied to the Northwest Coast First Peoples by anthropologists and archaeologists, and in starting my own research, this is how I referred to them as well, often adding “Fisher” to the description. Along with all the other researchers, I made the assumption that, although people of this region recognized and used many different plants and animals for food, materials, and medicines, they did not really have much influence over the populations of these species, or on their quality or productivity. I soon became aware that plants and their applications were under-represented in descriptions of Northwest Coast cultures and later came to realize that this was at least partly due to the gender imbalance of researchers, who tended to be men and who tended to talk with the males of the various First Nations. Since women were mainly the ones who harvested and processed plant foods and plant fibres, as well as many of the plant-based medicines, their work, and the training they provided for the next generations of plant harvesters, was often overlooked or at least considered less important (cf. Bruchac, 2014; Turner and Spalding, 2018). Early on in my ethnobotanical studies I started contemplating the role of women, as well as the way people were not only harvesting plants but also tending them. In my university Northwest Coast anthropology class, I read some of the descriptions and found maps of the tidal marsh root gardens of the “Southern Kwakiutl” (Kwakwaka’wakw) river estuaries in books by Franz Boas and George Hunt. These gave me my first glimpses into a different portrayal of Northwest Coast peoples and their resources. For example, in his Geographical Names of the Kwakiutl Indians, Boas (1934, p. 37) described some of the “gardens”: Map 21 represents the approximate lay-out of the clover gardens at the mouth of Nimkish (Nimpkish) River. These garden beds are made by removing pebbles from the soil and piling them up along the boundaries of each plot. In many cases the boundaries are marked by boards put up on edge and held between stakes. Each bed is the property of a family within the EnEEmē’m. I did not suceed (sic) in getting any detailed information regarding the transmission of property rights, nor regarding the permanence of ownership. In the summer of 1969, newly married, my husband Bob and I had the opportunity to travel to the Kwakwaka’wakw village of Alert Bay, where I learned a little more about the clover and other culturally important plants of coastal environments. I had my first taste of red laver seaweed (Pyropia sp.; formerly Porphyra) there, at the home of Daisy Roberts. She had a large jar of beautifully dried and chopped seaweed and noted that it was a “valuable food” (Turner and Bell, 1973, p. 262). During that same time, I learned from another elder, Agnes Cranmer, about the bulbs of northern riceroot (xúkwem, Fritillaria camschatcensis), as a common coastal food of the “old days”, and also about another traditional and valued food: herring eggs deposited on the blades of giant kelp (k’áxk’elis; Macrocystis pyrifera) (Turner and Bell 1973, p. 271, 261). Jimmy King, a Kwakwaka’wakw elder from Kingcome Inlet, described how eelgrass, called ts’áts’ayem, was harvested in the spring with long twisting poles at low tide and the long, fleshy roots (rhizomes) and tender youngest leaves were a “favourite food” (Turner and Bell 1973, p. 274). A little over a year later, starting my doctoral field work, I travelled to Bella Coola, where I was taken down to the estuary of the Bella Coola River by Nuxalk plant specialist Margaret Siwallace, to see first-hand the extensive patches of springbank clover (Trifolium wormskioldii), Pacific silverweed (Potentilla egedii), and riceroot that were special root vegetables for the Nuxalk as well as for the Kwakwaka’wakw and other coastal groups. I also got to taste oulachen grease, fermented salmon eggs, special hard flat sticks of smoked-dried salmon called slaq, and herring eggs deposited on hemlock boughs (Tsuga heterophylla) anchored out in the bays for the specific purpose of providing a substrate on which the herring could spawn. Unlike the giant kelp fronds that were eaten together with the herring eggs coating them, the tree boughs were cooked with the spawn, but then it was peeled off and the boughs and needles discarded. Around that time I also spent time on Haida Gwaii, learning about plants and their roles from many other knowledgeable elders, including Florence Davidson, Sol and Emma Wilson, and George Young. They, too, introduced me to many valued marine and coastal foods, including the edible seaweed, riceroot, springbank clover, coastal lupine (Lupinus littoralis), and herring eggs on giant kelp, as well as salmon, halibut, black cod, cockles, and razor clams. When I showed Skidegate elder George Young a sample of Pacific silverweed, called ts’ii’aal, he teased me, asking if I had paid the owner of the silverweed patch before I harvested the plant, since, at least formerly, these plants were so highly valued that productive patches of them were owned by individuals and families, just as the Kwakwak’awakw, Nuu-chah-nulth, Nuxalk, Heiltsuk, and other families, and lineages all along the coast held proprietorship of key resource areas (Turner et al., 2005; Turner, 2020, p. 140). Over the next couple of decades, as well as revisiting Bella Coola and the other communities, I had the opportunity to travel to other coastal centres, including Hesquiaht (Nuu-chah-nulth) and Ditidaht (Nitinaht), both on the west coast of Vancouver Island, and Sechelt and Squamish on the Lower Mainland. The plant experts of those communities highlighted the same coastal plants, animals, and habitats I had been learning about from the beginning, and little by little, my perception of people’s relationships with these species was changing. I learned about the First Salmon Ceremony, held in many communities to honour and thank the first runs of Pacific salmon coming up the rivers to spawn. I also learned that some of the key former fishing methods—the use of stone fish traps and weirs, and the reefnet fishery of the WSÁNEĆ and other Straits Salish peoples—had been banned by the Canadian government (Harris, 2001). The “seeds” were there: I was learning how important these various coastal resources were, and how much people valued them. Further insights with time, observation, and instruction One of the highlights in the greater recognition of Indigenous peoples as stewards and managers of their homelands was the publication of Our Common Future, the Report of the United Nations Commission on Environment and Development, led by Gro Harlem Brundtland, which stated: … These (Indigenous) communities (of the world) are the repositories of vast accumulations of traditional knowledge and experience that link humanity with its ancient origins. Their disappearance is a loss for the larger society which could learn a great deal from their traditional skills in sustainably managing very complex ecological systems (United Nations Commission on Environment and Development 1987, p. 115). Ethnobiologists in various parts of the world were also starting to recognize Indigenous peoples as conscientious managers of their lands and resources (Williams and Hunn, 1982; Ford, 1985; International Society of Ethnobiology, 1988). Then, in 1993, I was invited to serve on the Scientific Panel for Sustainable Forest Practices in Clayoquot Sound, together with a dozen or so other scientists and four members of Nuu-chah-nulth Nation communities in the region, including Dr. Richard Atleo, Umeek, of Ahousaht, who became co-chair of the Panel. The Panel worked together for over 2 years, and in my role as ethnobotanist, I spent much of my time working with the “cultural” committee: the Nuu-chah-nulth members and myself. Together, we produced one of the Panel’s five reports: First Nations’ Perspectives of Forest Practices in Clayoquot Sound (Scientific Panel … 1995). Working with the Nuu-chah-nulth panel members was a privilege and a deep learning experience for me. I learned about not only how logging had impacted the salmon populations because of destruction of their spawning beds but also how the herring no longer spawned in areas where they used to be abundant because of logging debris on the ocean floor and around the shoreline. Impacts to river estuaries in particular, were discussed, in the context of the deep responsibilities the Nuu-chah-nulth held for the care and protection of their lands and the species on which they depended. I came to realize that the system of stewardship that the Nuu-chah-nulth and other coastal peoples followed was generations-deep, was embedded in their stories, ceremonies, and cultural protocols and involved decades of training and experience, beginning at birth, with knowledge passed on out on the lands and waters and during more formal settings such as feasts and potlatches. Chiefs and their designates took on immense responsibilities for sustaining the forests and other habitats, as well as the people of their community, present and future. Richard Atleo has written about the overriding philosophy of “Tsawalk” or “One-ness” that binds all of us together—human and non-human beings alike—with our environments, and the obligations that we humans carry to use our resources with care and consideration, not to wantonly destroy or waste them (Atleo, 2004, 2011). This understanding has underlain Nuu-chah-nulth law and environmental relationships, and it is very similar for other Coastal First Peoples. Around the same time, in the early-1990s, I had the good fortune to meet Kwakwaka’wakw scholar and cultural expert Kim Recalma-Clutesi (Oqwilogwa) of the Qualicum First Nation (Kwagiulh/Pentlatch) (see Ecotrust, 2010). She, in turn, introduced me to Clan Chief Adam Dick (Kwaxsistalla) and Daisy Sewid-Smith (Mayanilth) (granddaughter of Daisy Roberts and Agnes Alfred, with whom I had worked and learned from in Alert Bay all those years previously). These three ninogad—trained experts in resource management and cultural protocols—have been crucial in helping me to understand traditional ecological practices, and the key role of leadership and intergenerational continuity in resource management and its associated knowledge. Kwaxsistalla passed away in 2018, but his teachings continue to have a profound influence on me and many others of the group we affectionately called “Adam’s School” (see Ninogad Knowledge Keepers, 2020). In an interview with Kwaxsistalla in 1996, he talked about the tidal marsh gardens in the estuary of the Kingcome River where he grew up. He would go with his mother and grandmother to work in these gardens, called t’Əkilakw, to tend and harvest the edible roots there: springbank clover (t’Əxwsús), Pacific silverweed (dlƏksƏm), Nookta lupine (qw’anniy), and northern riceroot (xukwem) (Figure 1). He described how the plots of roots, owned by different families, were marked off and pegged, weeded, or “cleaned” in the springtime, and harvested in the fall, when the leaves had started to die down. During the harvesting, he was taught to remove the small sprouts growing from the base of some of the riceroot bulbs and put them back into the ground. He recalled: “Yes, well that was my job … to pick them off … it’s on the bottom, called the gagemp (meaning ‘grandfather’ in Kwak’wala) (Figure 2). Then they told me to throw it back in the (root garden plot) …. It’s like a cup and that xukwem sits in there … that was my job as a kid, when I was with the old people ….” (Kwaxsistalla Clan Chief Adam Dick, pers. comm. 1996). When I asked, “So, Adam, is that gagempgoing to grow into a new plant?” he explained, patiently, “Yes! That’s why we did it!” I was amazed to hear about this. This was not “gathering”, or even harvesting; it was tending, planting, and cultivation. Kwaxsistalla himself used that word in describing the t’Əkilakw: It was all important…. See, when they go down the flats, they use little pegs. ‘This is my area.’ You got your own pegs, in the flats. And then you continue on that, digging the soft ground… so it will grow better every year. Well, I guess, fertilizing, cultivating, I guess that’s… the word for it. Every family had pegs, owned their little plots in the flats (pers. comm. 1996). Figure 1. Open in new tabDownload slide Tidal marsh garden “roots”: northern riceroot (Fritillaria camschatcensis) bulbs, springbank clover (Trifolium wormskioldii) rhizomes, and roots of Pacific silverweed (Potentilla egedii) and Nootka lupine (Lupinus nootkatensis), prepared for pit-cooking at Kingcome village, under the guidance of Clan Chief Adam Dick (Kwaxsistalla). Figure 1. Open in new tabDownload slide Tidal marsh garden “roots”: northern riceroot (Fritillaria camschatcensis) bulbs, springbank clover (Trifolium wormskioldii) rhizomes, and roots of Pacific silverweed (Potentilla egedii) and Nootka lupine (Lupinus nootkatensis), prepared for pit-cooking at Kingcome village, under the guidance of Clan Chief Adam Dick (Kwaxsistalla). Figure 2. Open in new tabDownload slide Riceroot bulb sprouts that were replanted when the larger bulbs were harvested. Figure 2. Open in new tabDownload slide Riceroot bulb sprouts that were replanted when the larger bulbs were harvested. The realization had been staring me in the face all along: Indigenous Peoples of northwestern North America have been long-time cultivators. Subsequently, I learned about similar practices of systematically replanting propagules of harvested “roots” of many different kinds, but this information shared by Kwaxsistalla was a critically important step for me in shifting my assumptions and started me along a new trajectory of thought and understanding. From “hunter-gatherers” to “cultivators” In 2001, I joined a team of researchers—from science, social science, and humanities—in a collaborative federally funded research project called Coasts under Stress (CUS), led by Dr. Rosemary Ommer. The team was looking at the combined social ecological impacts of environmental impacts for coastal communities and ecosystems on both Atlantic and Pacific Coasts of Canada (Ommer, 2007). For me, this project presented a major opportunity to continue working and learning in coastal communities. One of these was the Gitga’at Ts’msyen community of Hartley Bay, at the mouth of Douglas Channel on the north coast of British Columbia. I was able to attend and participate in the Gitga’at spring harvest camp at K’yel on Princess Royal Island several years in succession (Hood and Fox, 2003). I learned from Helen Clifton and many other elders about her mother-in-law Lucille Clifton who had been a major source of knowledge throughout the entire community at the time when the current generation of elders was children. These elders, in turn, shared this knowledge passed along to them, with myself and colleagues, in particular about the traditions of sustainable harvesting and careful ways of processing their seaweed (Figure 3) (Turner and Clifton, 2009; Turner et al., 2012). They also talked about, as well as processed and served on various occasions, crabs, halibut, clams, cockles, abalone, chitons, and many other seafoods, as well as northern riceroot, crab apples, highbush cranberries, and other plant foods of the coastal forest and estuarine ecosystems (Turner and Thompson, 2006; Turner and Clifton, 2006; Turner et al., 2012). With these experiences, my understanding grew, through first-hand observation and careful teachings, of the complexities and intricacies of peoples’ relationships with their resources and habitats within their territories, confirming what I had already learned from Kwaxsistalla, Mayanilth, Umeek, and many others up and down the coast. Figure 3. Open in new tabDownload slide Picking red laver seaweed (Pyropia abbottiae) on Campania Island, Gitga’at territory. Figure 3. Open in new tabDownload slide Picking red laver seaweed (Pyropia abbottiae) on Campania Island, Gitga’at territory. I was not alone in this learning journey. I have many colleagues who have inspired me and who have also been learning about Indigenous management systems, in some cases from the same knowledge holders. My students also participated and undertook various projects that focused on learning more about some of these management practices. Douglas Deur, who started as a graduate student looking at “Native American Plant Cultivation on the Northwest Coast of North America” (Deur, 2000), was also a student of Kwaxsistalla and other knowledgeable elders of the region, learning in particular about the t’Əkilakwestuarine root gardens as management systems (Figure 4). Together, we learned from Kwaxsistalla the concept called qw’aqw’ala7owkw (literally, “keeping it living” in Kwak’wala) that underlay many of the management practices relating to plants. Inspired by what we were learning, at Doug Deur’s initiative, we co-edited a book, “Keeping it Living”: Traditions of Plant Use and Management on the Northwest Coast (Deur and Turner, 2005), with chapters by ourselves and our colleagues and students, exploring particular aspects of Indigenous plant management. Other researchers in North America and beyond were also documenting Indigenous systems of resource management. Published books and articles from around the same time describing Indigenous Management systems include, to name just a few: Berkes (1999), Boyd (1999), Baker et al. (2001), Minnis and Elisens (2000), Anderson (2005), Menzies (2006), Lepofsky and Lertzman (2008), Lepofsky (2009), Campbell and Butler (2010), and Fowler and Lepofsky (2011). Figure 4. Open in new tabDownload slide Kwaxsistalla (Clan Chief Adam Dick) showing how the boundaries of the t’Əkilakw estuarine root garden plots at Kingcome Inlet were located by sighting from the adjacent mountain peaks. Figure 4. Open in new tabDownload slide Kwaxsistalla (Clan Chief Adam Dick) showing how the boundaries of the t’Əkilakw estuarine root garden plots at Kingcome Inlet were located by sighting from the adjacent mountain peaks. Meanwhile, one of the CUS researchers, geomorphologist John Harper and his wife Mary Morris, a marine biologist, had undertaken an aerial survey of the BC Coastline, mapping coastal habitat for B.C.’s Ministry of Sustainable Development in 1995. On Tracey Island, they noticed a distinctive line of stones running across the mouth of a small bay, exposed by the very low tide. No one they talked with originally knew what this stone formation was. When John joined the CUS team, he was still seeking answers about its origin. He met one of the local fishermen in the area, who told him it was a “clam garden”, one of many in the area. Then, the pieces of his puzzle finally came together when he met Kwaxsistalla in August 2003. Kwaxsistalla immediately identified these as lúxwxiwey (rolled rocks to clear an area), which had been built by long ago ancestors and maintained by succeeding generations, including Kwaxsistalla’s own grandfather and himself (Figure 5). Kwaxsistalla knew stories and even a traditional song about the lúxwxiweys. This information, which became widely shared (e.g. Recalma-Clutesi, 2005; Williams, 2006), started a whole new line of research and understanding of beach cultivation, with archaeologists, marine biologists, and Indigenous knowledge holders, on the construction and use of “clam gardens”, and other forms of “mariculture”, including the discovery of earlier documentation (Boas and Hunt, 1906; Stern, 1934). Figure 5. Open in new tabDownload slide Kwaxsistalla’s family “clam garden” at Deep Harbour, Broughton Archipelago. Figure 5. Open in new tabDownload slide Kwaxsistalla’s family “clam garden” at Deep Harbour, Broughton Archipelago. Figure 6. Open in new tabDownload slide Eelgrass (Zostera marina) showing the orange-coloured edible rhizome. Figure 6. Open in new tabDownload slide Eelgrass (Zostera marina) showing the orange-coloured edible rhizome. Tending coastal habitats and species Table 1 summarizes some of the ways I have learned over the decades about how TLRM approaches have been applied in relation to coastal resources on the Northwest Coast, starting with the subtidal zone and moving through the intertidal area and inland to coastal terrestrial habitats. There are also documented management practices associated with harvesting and sustaining populations of halibut, seals and sea lions, crabs, rockfish, kelp, sea urchins, sea cucumbers, octopus, mussels, limpets, chitons, ducks, and geese, among other resources (Mathews and Turner, 2017). Table 1. Examples of traditional land and resource management approaches in marine and coastal environments on the Northwest Coast, which I have personally learned about and observed (general references: Deur and Turner, 2005; Lepofsky and Lertzman, 2008; Campbell and Butler, 2010; Lepofsky and Caldwell, 2013; Turner et al., 2013b; Thornton et al., 2015; Mathews and Turner, 2017; Lepofsky and Armstrong, 2018). Habitat/species . Notes . References . Reefnet salmon fishing (Straits Salish); sockeye (Oncorhynchus nerka) and other salmon species Willowbark nets are strung between two canoes positioned where salmon runs are passing en route to the Fraser River and other rivers; the fish are selectively and carefully harvested, with a hole at the far end ensuring that some will always pass through; associated with a First Salmon Ceremony; overseen by owner of reefnet site Pers. comm.: Elsie Claxton (1990), Elsie Claxton (2002); cf. also Claxton and Elliott (1994), Claxton (2015), Turner and Berkes (2006) Herring (Clupea pallasi), roe on kelp (Macrocystis pyrifera) and hemlock (Tsuga heterophylla) boughs Naturally growing giant kelp fronds or small hemlock trees or bundles of boughs anchored in spawning areas collect the spawn from herring which continue to live, returning year after year; fertilized herring eggs sometimes released in new locales to extend the range of herring; large-scale commercial herring roe fishery in which the fish are caught and killed for their roe has caused severe decline in herring in many areas along the coast. Pers. comm.: George Young (1971), Stanley Sam (1995), Cyril Carpenter (2002), Barbara Wilson (Kii’iljuus) (2004); cf. also Turner (2010), Thornton (2015), Thornton et al. (2015) Eelgrass (Zostera marina) meadows Eelgrass plants (Figure 6) are harvested in spring at low tide using long twisting poles of western hemlock (Tsuga heterophylla); the rhizomes with the youngest leaf wrapped around them, are eaten raw, dipped in oulachen grease, and are a favourite food; the harvesting process creates moderate disturbance that helps thin the plants so that they regenerate more rapidly. Brants and other geese also benefit from this harvesting process. Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla), Daisy Sewid-Smith (Mayanilth) (2003); cf. also Cullis-Suzuki et al. (2015) Clam gardens: littleneck clams (Protothaca staminea), butter clams (Saxidomus gigantea), razor clams (Siliqua patula), cockles (Clinocardium nuttallii), and other spp. Large stones were rolled down to the lowest tide line to form a wall or terrace that helped to build up the beach, creating the maximum ideal habitat for various clams: “clam gardens”. The shellfish are selectively harvested, leaving the smaller “seed” clams to continue to grow, and the aeration and loosening of the sand during the digging process promotes the growth and vitality of the clams. The stone walls are habitat for myriad other sea life: crabs, octopus, sea cucumbers, small fish, and marine algae. They need to be monitored, tended, and used to maintain healthy habitat Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla) (2011); cf. also Recalma-Clutesi (2005), Williams (2006), Cannon and Burchell (2009), Groesbeck et al. (2014), Deur et al. (2015), Lepofsky et al. (2015), Toniello et al. (2019) Seaweed (Pyropia abbottiae and other spp.) Seaweed harvested in May from specific places owned by families/clans; pulled off rocks by hand, leaving the meristematic tissues at the base to regenerate; sometimes two “crops” of seaweed harvested from the same place in a single season; harvesting said to maintain the populations of seaweed; many taboos and protocols around picking seaweed. Pers. comm.: Helen and Chief Johnny Clifton (2003), Kwaxsistalla (2011); cf. also Turner and Thompson (2006), Deveau (2010) Northern abalone (Haliotis kamtschatkana) Harvested only at lowest tides of the year; only largest abalone selected; formerly piled 3–4 deep in harvest areas; protected against excessive noise: “very sensitive animal”; commercial abalone fishing in the 1970s caused the abalone populations to collapse, and Northern abalone is now protected under the Canadian Species at Risk Act (SARA) as well as the federal Fisheries Act, and all harvesting is prohibited. Pers. comm.: Helen and Chief Johnny Clifton (2002); cf. also Fisheries and Oceans Canada (2018) Salmon fishing at shoreline, river estuaries: Stone fish traps, weirs and holding pools (Oncorhynchus spp.) Traps and weirs allowed selective harvesting of all types of salmon; the largest, especially females, were allowed to proceed upriver to spawn; fishing overseen by knowledgeable individuals (river guardians); sites owned by families and individuals and inherited; catches and populations monitored year to year; salmon eggs transplanted if runs were diminished, and pools maintained to support the salmon moving upstream; rivers were carefully watched to ensure there were no pollutants Pers. comm.: Cyril Carpenter (2002), Luschiim Arvid Charlie (1999), Earl Maquinna George (1995); cf. also Anderson (1996), Carpenter et al. (2000), Jones (2002), Xanius White (2006), Langdon (2006), Moss (2013), Thornton and Deur (2015), Thornton et al. (2015) Oulachen, or eulachon (Thaleichthys pacificus) Individuals assigned to monitor the oulachen coming up the river, ensuring that they were able to spawn before any were caught; fishing spots were owned and populations monitored Pers. comm.: Margaret Siwallace (1972, 1984), Kwaxsistalla (1996); cf. also Ban et al. (2008), Moody (2008), Thornton et al. (2015) Seabird eggs Eggs of gulls, oystercatchers, and other birds were harvested selectively in season, while the female birds were still actively laying; only one or two eggs removed from a nest, and care used not to alarm the birds in the nesting colonies Pers. comm.: Elsie Claxton (1990), Ernie Hill Jr and Helen Clifton (2003); cf. also Hunn et al. (2003) Tidal marsh root gardens (Camassia quamash; Fritillaria camscatchensis; Lupinus nootkatensis; Potentilla egedii; Trifolium wormskioldii) Tidal marsh gardens were set out in plots, owned by particular families, who weeded them in springtime, selectively harvested them in the fall and winter, with propagules being replanted to regrow in successive years Pers. comm.: Margaret Siwallace (1972), Kwaxsistalla (2009); cf. also Turner et al. (1983), Deur (2005), Lloyd (2011), Joseph (2012) Crab apple (Malus fusca) “orchards” Crab apple “orchards” were maintained along river estuaries, shorelines, and wetlands; trees were sometimes pruned or lopped; owned by individuals or families; the apples also attracted grouse, bears, and other wildlife. Marjorie Hill and Ernie Hill Jr. (2003), Kwaxsistalla (2011); cf. also Wyllie de Echeverria (2013) Habitat/species . Notes . References . Reefnet salmon fishing (Straits Salish); sockeye (Oncorhynchus nerka) and other salmon species Willowbark nets are strung between two canoes positioned where salmon runs are passing en route to the Fraser River and other rivers; the fish are selectively and carefully harvested, with a hole at the far end ensuring that some will always pass through; associated with a First Salmon Ceremony; overseen by owner of reefnet site Pers. comm.: Elsie Claxton (1990), Elsie Claxton (2002); cf. also Claxton and Elliott (1994), Claxton (2015), Turner and Berkes (2006) Herring (Clupea pallasi), roe on kelp (Macrocystis pyrifera) and hemlock (Tsuga heterophylla) boughs Naturally growing giant kelp fronds or small hemlock trees or bundles of boughs anchored in spawning areas collect the spawn from herring which continue to live, returning year after year; fertilized herring eggs sometimes released in new locales to extend the range of herring; large-scale commercial herring roe fishery in which the fish are caught and killed for their roe has caused severe decline in herring in many areas along the coast. Pers. comm.: George Young (1971), Stanley Sam (1995), Cyril Carpenter (2002), Barbara Wilson (Kii’iljuus) (2004); cf. also Turner (2010), Thornton (2015), Thornton et al. (2015) Eelgrass (Zostera marina) meadows Eelgrass plants (Figure 6) are harvested in spring at low tide using long twisting poles of western hemlock (Tsuga heterophylla); the rhizomes with the youngest leaf wrapped around them, are eaten raw, dipped in oulachen grease, and are a favourite food; the harvesting process creates moderate disturbance that helps thin the plants so that they regenerate more rapidly. Brants and other geese also benefit from this harvesting process. Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla), Daisy Sewid-Smith (Mayanilth) (2003); cf. also Cullis-Suzuki et al. (2015) Clam gardens: littleneck clams (Protothaca staminea), butter clams (Saxidomus gigantea), razor clams (Siliqua patula), cockles (Clinocardium nuttallii), and other spp. Large stones were rolled down to the lowest tide line to form a wall or terrace that helped to build up the beach, creating the maximum ideal habitat for various clams: “clam gardens”. The shellfish are selectively harvested, leaving the smaller “seed” clams to continue to grow, and the aeration and loosening of the sand during the digging process promotes the growth and vitality of the clams. The stone walls are habitat for myriad other sea life: crabs, octopus, sea cucumbers, small fish, and marine algae. They need to be monitored, tended, and used to maintain healthy habitat Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla) (2011); cf. also Recalma-Clutesi (2005), Williams (2006), Cannon and Burchell (2009), Groesbeck et al. (2014), Deur et al. (2015), Lepofsky et al. (2015), Toniello et al. (2019) Seaweed (Pyropia abbottiae and other spp.) Seaweed harvested in May from specific places owned by families/clans; pulled off rocks by hand, leaving the meristematic tissues at the base to regenerate; sometimes two “crops” of seaweed harvested from the same place in a single season; harvesting said to maintain the populations of seaweed; many taboos and protocols around picking seaweed. Pers. comm.: Helen and Chief Johnny Clifton (2003), Kwaxsistalla (2011); cf. also Turner and Thompson (2006), Deveau (2010) Northern abalone (Haliotis kamtschatkana) Harvested only at lowest tides of the year; only largest abalone selected; formerly piled 3–4 deep in harvest areas; protected against excessive noise: “very sensitive animal”; commercial abalone fishing in the 1970s caused the abalone populations to collapse, and Northern abalone is now protected under the Canadian Species at Risk Act (SARA) as well as the federal Fisheries Act, and all harvesting is prohibited. Pers. comm.: Helen and Chief Johnny Clifton (2002); cf. also Fisheries and Oceans Canada (2018) Salmon fishing at shoreline, river estuaries: Stone fish traps, weirs and holding pools (Oncorhynchus spp.) Traps and weirs allowed selective harvesting of all types of salmon; the largest, especially females, were allowed to proceed upriver to spawn; fishing overseen by knowledgeable individuals (river guardians); sites owned by families and individuals and inherited; catches and populations monitored year to year; salmon eggs transplanted if runs were diminished, and pools maintained to support the salmon moving upstream; rivers were carefully watched to ensure there were no pollutants Pers. comm.: Cyril Carpenter (2002), Luschiim Arvid Charlie (1999), Earl Maquinna George (1995); cf. also Anderson (1996), Carpenter et al. (2000), Jones (2002), Xanius White (2006), Langdon (2006), Moss (2013), Thornton and Deur (2015), Thornton et al. (2015) Oulachen, or eulachon (Thaleichthys pacificus) Individuals assigned to monitor the oulachen coming up the river, ensuring that they were able to spawn before any were caught; fishing spots were owned and populations monitored Pers. comm.: Margaret Siwallace (1972, 1984), Kwaxsistalla (1996); cf. also Ban et al. (2008), Moody (2008), Thornton et al. (2015) Seabird eggs Eggs of gulls, oystercatchers, and other birds were harvested selectively in season, while the female birds were still actively laying; only one or two eggs removed from a nest, and care used not to alarm the birds in the nesting colonies Pers. comm.: Elsie Claxton (1990), Ernie Hill Jr and Helen Clifton (2003); cf. also Hunn et al. (2003) Tidal marsh root gardens (Camassia quamash; Fritillaria camscatchensis; Lupinus nootkatensis; Potentilla egedii; Trifolium wormskioldii) Tidal marsh gardens were set out in plots, owned by particular families, who weeded them in springtime, selectively harvested them in the fall and winter, with propagules being replanted to regrow in successive years Pers. comm.: Margaret Siwallace (1972), Kwaxsistalla (2009); cf. also Turner et al. (1983), Deur (2005), Lloyd (2011), Joseph (2012) Crab apple (Malus fusca) “orchards” Crab apple “orchards” were maintained along river estuaries, shorelines, and wetlands; trees were sometimes pruned or lopped; owned by individuals or families; the apples also attracted grouse, bears, and other wildlife. Marjorie Hill and Ernie Hill Jr. (2003), Kwaxsistalla (2011); cf. also Wyllie de Echeverria (2013) Open in new tab Table 1. Examples of traditional land and resource management approaches in marine and coastal environments on the Northwest Coast, which I have personally learned about and observed (general references: Deur and Turner, 2005; Lepofsky and Lertzman, 2008; Campbell and Butler, 2010; Lepofsky and Caldwell, 2013; Turner et al., 2013b; Thornton et al., 2015; Mathews and Turner, 2017; Lepofsky and Armstrong, 2018). Habitat/species . Notes . References . Reefnet salmon fishing (Straits Salish); sockeye (Oncorhynchus nerka) and other salmon species Willowbark nets are strung between two canoes positioned where salmon runs are passing en route to the Fraser River and other rivers; the fish are selectively and carefully harvested, with a hole at the far end ensuring that some will always pass through; associated with a First Salmon Ceremony; overseen by owner of reefnet site Pers. comm.: Elsie Claxton (1990), Elsie Claxton (2002); cf. also Claxton and Elliott (1994), Claxton (2015), Turner and Berkes (2006) Herring (Clupea pallasi), roe on kelp (Macrocystis pyrifera) and hemlock (Tsuga heterophylla) boughs Naturally growing giant kelp fronds or small hemlock trees or bundles of boughs anchored in spawning areas collect the spawn from herring which continue to live, returning year after year; fertilized herring eggs sometimes released in new locales to extend the range of herring; large-scale commercial herring roe fishery in which the fish are caught and killed for their roe has caused severe decline in herring in many areas along the coast. Pers. comm.: George Young (1971), Stanley Sam (1995), Cyril Carpenter (2002), Barbara Wilson (Kii’iljuus) (2004); cf. also Turner (2010), Thornton (2015), Thornton et al. (2015) Eelgrass (Zostera marina) meadows Eelgrass plants (Figure 6) are harvested in spring at low tide using long twisting poles of western hemlock (Tsuga heterophylla); the rhizomes with the youngest leaf wrapped around them, are eaten raw, dipped in oulachen grease, and are a favourite food; the harvesting process creates moderate disturbance that helps thin the plants so that they regenerate more rapidly. Brants and other geese also benefit from this harvesting process. Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla), Daisy Sewid-Smith (Mayanilth) (2003); cf. also Cullis-Suzuki et al. (2015) Clam gardens: littleneck clams (Protothaca staminea), butter clams (Saxidomus gigantea), razor clams (Siliqua patula), cockles (Clinocardium nuttallii), and other spp. Large stones were rolled down to the lowest tide line to form a wall or terrace that helped to build up the beach, creating the maximum ideal habitat for various clams: “clam gardens”. The shellfish are selectively harvested, leaving the smaller “seed” clams to continue to grow, and the aeration and loosening of the sand during the digging process promotes the growth and vitality of the clams. The stone walls are habitat for myriad other sea life: crabs, octopus, sea cucumbers, small fish, and marine algae. They need to be monitored, tended, and used to maintain healthy habitat Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla) (2011); cf. also Recalma-Clutesi (2005), Williams (2006), Cannon and Burchell (2009), Groesbeck et al. (2014), Deur et al. (2015), Lepofsky et al. (2015), Toniello et al. (2019) Seaweed (Pyropia abbottiae and other spp.) Seaweed harvested in May from specific places owned by families/clans; pulled off rocks by hand, leaving the meristematic tissues at the base to regenerate; sometimes two “crops” of seaweed harvested from the same place in a single season; harvesting said to maintain the populations of seaweed; many taboos and protocols around picking seaweed. Pers. comm.: Helen and Chief Johnny Clifton (2003), Kwaxsistalla (2011); cf. also Turner and Thompson (2006), Deveau (2010) Northern abalone (Haliotis kamtschatkana) Harvested only at lowest tides of the year; only largest abalone selected; formerly piled 3–4 deep in harvest areas; protected against excessive noise: “very sensitive animal”; commercial abalone fishing in the 1970s caused the abalone populations to collapse, and Northern abalone is now protected under the Canadian Species at Risk Act (SARA) as well as the federal Fisheries Act, and all harvesting is prohibited. Pers. comm.: Helen and Chief Johnny Clifton (2002); cf. also Fisheries and Oceans Canada (2018) Salmon fishing at shoreline, river estuaries: Stone fish traps, weirs and holding pools (Oncorhynchus spp.) Traps and weirs allowed selective harvesting of all types of salmon; the largest, especially females, were allowed to proceed upriver to spawn; fishing overseen by knowledgeable individuals (river guardians); sites owned by families and individuals and inherited; catches and populations monitored year to year; salmon eggs transplanted if runs were diminished, and pools maintained to support the salmon moving upstream; rivers were carefully watched to ensure there were no pollutants Pers. comm.: Cyril Carpenter (2002), Luschiim Arvid Charlie (1999), Earl Maquinna George (1995); cf. also Anderson (1996), Carpenter et al. (2000), Jones (2002), Xanius White (2006), Langdon (2006), Moss (2013), Thornton and Deur (2015), Thornton et al. (2015) Oulachen, or eulachon (Thaleichthys pacificus) Individuals assigned to monitor the oulachen coming up the river, ensuring that they were able to spawn before any were caught; fishing spots were owned and populations monitored Pers. comm.: Margaret Siwallace (1972, 1984), Kwaxsistalla (1996); cf. also Ban et al. (2008), Moody (2008), Thornton et al. (2015) Seabird eggs Eggs of gulls, oystercatchers, and other birds were harvested selectively in season, while the female birds were still actively laying; only one or two eggs removed from a nest, and care used not to alarm the birds in the nesting colonies Pers. comm.: Elsie Claxton (1990), Ernie Hill Jr and Helen Clifton (2003); cf. also Hunn et al. (2003) Tidal marsh root gardens (Camassia quamash; Fritillaria camscatchensis; Lupinus nootkatensis; Potentilla egedii; Trifolium wormskioldii) Tidal marsh gardens were set out in plots, owned by particular families, who weeded them in springtime, selectively harvested them in the fall and winter, with propagules being replanted to regrow in successive years Pers. comm.: Margaret Siwallace (1972), Kwaxsistalla (2009); cf. also Turner et al. (1983), Deur (2005), Lloyd (2011), Joseph (2012) Crab apple (Malus fusca) “orchards” Crab apple “orchards” were maintained along river estuaries, shorelines, and wetlands; trees were sometimes pruned or lopped; owned by individuals or families; the apples also attracted grouse, bears, and other wildlife. Marjorie Hill and Ernie Hill Jr. (2003), Kwaxsistalla (2011); cf. also Wyllie de Echeverria (2013) Habitat/species . Notes . References . Reefnet salmon fishing (Straits Salish); sockeye (Oncorhynchus nerka) and other salmon species Willowbark nets are strung between two canoes positioned where salmon runs are passing en route to the Fraser River and other rivers; the fish are selectively and carefully harvested, with a hole at the far end ensuring that some will always pass through; associated with a First Salmon Ceremony; overseen by owner of reefnet site Pers. comm.: Elsie Claxton (1990), Elsie Claxton (2002); cf. also Claxton and Elliott (1994), Claxton (2015), Turner and Berkes (2006) Herring (Clupea pallasi), roe on kelp (Macrocystis pyrifera) and hemlock (Tsuga heterophylla) boughs Naturally growing giant kelp fronds or small hemlock trees or bundles of boughs anchored in spawning areas collect the spawn from herring which continue to live, returning year after year; fertilized herring eggs sometimes released in new locales to extend the range of herring; large-scale commercial herring roe fishery in which the fish are caught and killed for their roe has caused severe decline in herring in many areas along the coast. Pers. comm.: George Young (1971), Stanley Sam (1995), Cyril Carpenter (2002), Barbara Wilson (Kii’iljuus) (2004); cf. also Turner (2010), Thornton (2015), Thornton et al. (2015) Eelgrass (Zostera marina) meadows Eelgrass plants (Figure 6) are harvested in spring at low tide using long twisting poles of western hemlock (Tsuga heterophylla); the rhizomes with the youngest leaf wrapped around them, are eaten raw, dipped in oulachen grease, and are a favourite food; the harvesting process creates moderate disturbance that helps thin the plants so that they regenerate more rapidly. Brants and other geese also benefit from this harvesting process. Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla), Daisy Sewid-Smith (Mayanilth) (2003); cf. also Cullis-Suzuki et al. (2015) Clam gardens: littleneck clams (Protothaca staminea), butter clams (Saxidomus gigantea), razor clams (Siliqua patula), cockles (Clinocardium nuttallii), and other spp. Large stones were rolled down to the lowest tide line to form a wall or terrace that helped to build up the beach, creating the maximum ideal habitat for various clams: “clam gardens”. The shellfish are selectively harvested, leaving the smaller “seed” clams to continue to grow, and the aeration and loosening of the sand during the digging process promotes the growth and vitality of the clams. The stone walls are habitat for myriad other sea life: crabs, octopus, sea cucumbers, small fish, and marine algae. They need to be monitored, tended, and used to maintain healthy habitat Pers. comm.: Clan Chief Adam Dick (Kwaxsistalla) (2011); cf. also Recalma-Clutesi (2005), Williams (2006), Cannon and Burchell (2009), Groesbeck et al. (2014), Deur et al. (2015), Lepofsky et al. (2015), Toniello et al. (2019) Seaweed (Pyropia abbottiae and other spp.) Seaweed harvested in May from specific places owned by families/clans; pulled off rocks by hand, leaving the meristematic tissues at the base to regenerate; sometimes two “crops” of seaweed harvested from the same place in a single season; harvesting said to maintain the populations of seaweed; many taboos and protocols around picking seaweed. Pers. comm.: Helen and Chief Johnny Clifton (2003), Kwaxsistalla (2011); cf. also Turner and Thompson (2006), Deveau (2010) Northern abalone (Haliotis kamtschatkana) Harvested only at lowest tides of the year; only largest abalone selected; formerly piled 3–4 deep in harvest areas; protected against excessive noise: “very sensitive animal”; commercial abalone fishing in the 1970s caused the abalone populations to collapse, and Northern abalone is now protected under the Canadian Species at Risk Act (SARA) as well as the federal Fisheries Act, and all harvesting is prohibited. Pers. comm.: Helen and Chief Johnny Clifton (2002); cf. also Fisheries and Oceans Canada (2018) Salmon fishing at shoreline, river estuaries: Stone fish traps, weirs and holding pools (Oncorhynchus spp.) Traps and weirs allowed selective harvesting of all types of salmon; the largest, especially females, were allowed to proceed upriver to spawn; fishing overseen by knowledgeable individuals (river guardians); sites owned by families and individuals and inherited; catches and populations monitored year to year; salmon eggs transplanted if runs were diminished, and pools maintained to support the salmon moving upstream; rivers were carefully watched to ensure there were no pollutants Pers. comm.: Cyril Carpenter (2002), Luschiim Arvid Charlie (1999), Earl Maquinna George (1995); cf. also Anderson (1996), Carpenter et al. (2000), Jones (2002), Xanius White (2006), Langdon (2006), Moss (2013), Thornton and Deur (2015), Thornton et al. (2015) Oulachen, or eulachon (Thaleichthys pacificus) Individuals assigned to monitor the oulachen coming up the river, ensuring that they were able to spawn before any were caught; fishing spots were owned and populations monitored Pers. comm.: Margaret Siwallace (1972, 1984), Kwaxsistalla (1996); cf. also Ban et al. (2008), Moody (2008), Thornton et al. (2015) Seabird eggs Eggs of gulls, oystercatchers, and other birds were harvested selectively in season, while the female birds were still actively laying; only one or two eggs removed from a nest, and care used not to alarm the birds in the nesting colonies Pers. comm.: Elsie Claxton (1990), Ernie Hill Jr and Helen Clifton (2003); cf. also Hunn et al. (2003) Tidal marsh root gardens (Camassia quamash; Fritillaria camscatchensis; Lupinus nootkatensis; Potentilla egedii; Trifolium wormskioldii) Tidal marsh gardens were set out in plots, owned by particular families, who weeded them in springtime, selectively harvested them in the fall and winter, with propagules being replanted to regrow in successive years Pers. comm.: Margaret Siwallace (1972), Kwaxsistalla (2009); cf. also Turner et al. (1983), Deur (2005), Lloyd (2011), Joseph (2012) Crab apple (Malus fusca) “orchards” Crab apple “orchards” were maintained along river estuaries, shorelines, and wetlands; trees were sometimes pruned or lopped; owned by individuals or families; the apples also attracted grouse, bears, and other wildlife. Marjorie Hill and Ernie Hill Jr. (2003), Kwaxsistalla (2011); cf. also Wyllie de Echeverria (2013) Open in new tab At least some of these systems and practices extend far back into the past (Lepofsky and Lertzman, 2008; Campbell and Butler, 2010). They are interconnected and complex, reflecting ecological, technological, social, and spiritual practices that, together, have resulted in long-term productivity, greater abundance, and in some cases higher quality, of the resources, as well as their increased availability, diversity, and equitable distribution within communities. Associated practices, often applied in combination, relating to these features include: clearing or “cleaning” away rocks or driftwood; habitat creation or extension; establishing boundaries; tilling and aerating substrate; dissemination or translocation of propagules; selective harvesting; nutrient channelling; systems of ownership; short-term and long-term monitoring; conservation; division of labour; ceremonial honouring; distributed access; sharing resources; and knowledge transmission (Mathews and Turner, 2017). Discussion Today, the traditional tending of our coast and its resources by Indigenous peoples seems obvious. Coming from a different kind of farming, however, the European newcomers and colonial officials did not recognize this kind of in situ maintenance and propagation of species and habitats and the nuances of ownership and proprietorship coupled with responsibility and intergenerational sustainability. The newcomers’ lack of insight led to profound injustices, including the exclusion of people from vast parts of their traditional territories, and the loss not only of access to many traditional foods but also of opportunities to pass on the knowledge and practices relating to these management systems to the younger generations. Together with the impacts of disease, residential schools, decades-long banning of the Potlatch, language suppression, and other losses, it has been one of many discriminatory actions that have, in my view, not only hurt Indigenous peoples but also have resulted in degraded and damaged habitats and a loss of other species, which has been detrimental to all of us. We can still learn from the whole array of approaches and protocols that helped people not only sustain but also enhance the quality and productivity of the plant and animal resources on which they relied (Brown et al., 2009). Some of these have not been widely practiced for some time, but there are still individuals who know about them, and ways of learning about them through archaeological, paleoecological, and ethnographic research (cf. Campbell and Butler, 2010; Moss, 2013; Deur et al., 2015; Trant et al., 2016; Toniello et al., 2019). Indigenous harvesting practices have special status under Canadian law—there is an Aboriginal right to fish for food and ceremonial purposes—but this is often not recognized, and since there have been generations of government-sanctioned overharvesting of salmon, herring, rockfish, and oulachen (as by-catch), restoring the original management practices for these resources, even for those who still know about them, is only part of the answer. We are starting a societal learning process, and a journey of reconciliation through which we might correct some of the previous injustices. Furthermore, a profound respect for other species that has been a part of these traditional management systems, needs to be instilled in all of us. We need to feel an accountability and responsibility, not just to other humans but to our non-human relatives. For example, Adam Olsen of the Tsartlip Nation and member of the British Columbia Legislative Assembly described the profound responsibility of the owner of a Straits Salish reef net and noted that the wealth of a reef netter was not only seen through the number of fish he could catch, but by “… the long-term quality and abundance of the fishing grounds he owned” [2014, cited in Mathews and Turner (2017)]. This is in great contrast with the type of mass-scale commercial fishing that has been common since the days of the canneries, resulting in massive declines in salmon and other fish populations, and tremendous waste and by-catch impact on species like oulachens, as witnessed by many of those I have learned from over the years. Combined with other impacts—pollution, hydro-electric development, draining, filling and dyking of estuarine wetlands and lakes for agriculture and municipal use, road construction, urbanization, and damage of spawning beds and waterways from destructive logging practices—it has been a painful and shameful story of resource mismanagement for short-term gains at the expense of long-term sustainability (Ommer, 2007; Trosper, 2009; Turner et al., 2013a). Similar stories can be told for abalone, rockfish, halibut, herring, kelp, eelgrass, and many other coastal resources. If we are able to reinstate and emulate some of these stewardship and tending practices, with permission, guidance, and collaboration from those whose cultural knowledge they represent, we may be able to reverse some of the damage that we have done to our coastal ecosystems. We might restore some of the diversity and productivity and increase the populations of various marine and coastal species. This could also help ameliorate some of the impacts of climate change that we have been witnessing. Working with natural processes to restore and maximize the health, regeneration, and productivity of our coastal species and ecosystems is, to me, one of the most desirable steps we can take for the health of all of us, including future generations. Finding ways, as in First Nations’ traditions, to oversee these resources and to make informed decisions about protecting and restoring them over the long term is one of the most important governance steps that we can take. Conclusions It has been commonly assumed that Indigenous peoples of the Northwest Coast of North America, as so-called “hunter-gatherers”, were simply helping themselves to what nature provided. However, over decades of learning from Indigenous plant specialists and practitioners as an ethnobotanist, I and my colleagues have come to realize that the First Peoples were tending and managing their resources in many different ways and have been doing so for countless generations. Their TLRM systems provide many lessons on how humans can work with natural processes to ensure the well-being not only of ourselves, but also of the species and habitats on which we rely. In this article, I have focused on the Indigenous management of marine and coastal ecosystems. Comparable management systems, centuries and perhaps millennia old, have also been in place for terrestrial ecosystems and species, in the form of berry “gardens”, managed prairielands, and tended forests (Deur and Turner, 2005; Turner et al., 2013b). We have seen places where all of these different ways of tending the lands, waters, and ocean ecosystems are reflected in special habitation sites today, in places such as Húỳat in Heiltsuk territory on North Hunter Island and “Old Town” (Laxgalts’ap in Gitga’ata territory) (Lepofsky et al., 2017). We know that the lands, waters, and oceans are interconnected in many ways, so that by understanding some of these processes and practices as they pertain to one type of habitat, one area, and one region, we can learn about how they might apply more broadly, even in other parts of the world. Western scientific knowledge and research are critically important, but even with the sophistication and understanding science has given us, humans still have not managed very well to sustain the integrity, productivity, or diversity of the environments on which we rely. We need, perhaps, to embrace other values and perspectives, other ways of decision-making, to guide us in how we put our scientific knowledge to use in ways that promote sustainability and that show reciprocity to the other species that support us. I am so grateful to those who have taught me to value the world in this way, and I hope that a collective focus on Indigenous management systems will continue to inform society at large, and particularly decision-makers, present and future, as part of national commitments to Reconciliation with Indigenous Peoples. Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. 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