ICES Journal of Marine Science: Journal du Conseil

Beraud, Claire;van der Molen, Johan;Armstrong, Mike;Hunter, Ewan;Fonseca, Leila;Hyder, Kieran;Kaplan, Handling editor: David

doi: 10.1093/icesjms/fsx195pmid: N/A

Abstract The European Seabass (Dicentrarchus labrax) is a slow-growing late maturing fish. The northern stock has been declining since 2010 and is thought to be caused by a combination of fishing and weak year classes. Large inter-annual variation in settlement has been observed, so a better understanding of the mechanisms driving settlement success will aid interpretation of the variation between years, and help to improve the stock assessment models and management strategies. In this study, an individual-based model (IBM) was developed to investigate the factors affecting sea bass settlement on nursery grounds of the northern sea bass stock. The IBM was coupled with hydrodynamics to track particles, whereas egg and larval development, and vertical migration behaviour are fully incorporated. The IBM successfully predicted inter-annual differences in settlement regardless of larval behaviour. The highest settlement success was predicted with neutrally buoyant eggs, hatchlings, and larval stages, in combination with tidal migration at the final larval stage. Dispersal was driven mainly by the influence of wind on residual currents and water temperature, with warmer temperatures reducing the duration of the pelagic phase and stronger current increasing the potential to drift further. Eggs spawned in the central western English Channel settled in both England and France, with movement from the central to the eastern English Channel occurring only in warm years. Larval duration was driven by water temperature and showed an increase in duration from the southwest to northeast areas of the northern stock. The results are discussed in the context of sea bass management and conservation strategies. Introduction Understanding the relationship between the adult stock and number of young fish recruiting to that stock (the stock–recruitment relationship) has been studied for many years (e.g. Beverton and Holt, 1954). Predicting this relationship remains one of the foremost challenges in fisheries science (Houde, 2008; Subbey et al., 2014) as it underpins reference points for sustainable fishing, but is often obscured by large inter-annual variability and autocorrelation between the environmental factors that drive recruitment. The pelagic egg and larval phases (hereafter termed pelagic phase) has been well studied in many marine systems including coral reefs (Munday et al., 2009), intertidal rocky shores (e.g. Gaines and Roughgarden, 1985; Caley et al., 1996), and fully marine environments (Bolle et al., 2009). Marine systems described as “open” (Roughgarden et al., 1985; Hyder et al., 2001) often have the potential for protracted larval dispersal (Gaines et al., 2007; van der Molen et al., 2007). Consequently, the pelagic phase is important in determining the variability in year class strength of many fish (van der Veer et al., 2000), but the underlying influencing factors are poorly understood (Houde, 2008; Subbey et al., 2014). To improve understanding of the pelagic larval phase of marine fish, mathematical models have been developed including simple statistical approaches (Subbey et al., 2014), non-spatial structured population models (Hyder and Nash, 1998), and spatially explicit particle tracking approaches combining both hydrodynamics and individual dynamics (van der Veer et al., 1998; van der Molen et al. 2007; Savina et al., 2010; Rochette et al., 2012; Lacroix et al., 2013; Tiessen et al. 2014). Individual-based models (IBMs) are popular tools to aid interpretation of ecological and evolutionary processes (DeAngelis and Grimm, 2014). IBMs have been used to model fish populations (DeAngelis and Mooij, 2005), and incorporate behaviours including shoaling (Jeon et al., 2013; Accolla et al., 2015) and migration (Okunishi et al., 2009). For example, the movement towards nursery areas has been modelled using IBMs that include vertical migration for European anchovy (Ospina-Alvarez et al., 2012) and sole (Savina et al., 2010; Lacroix et al., 2013), and consistent directional movement along a river towards nursery grounds (Baetens et al., 2013). IBMs coupled with hydrodynamic models have been used to assess the temporal distribution of larvae (van der Molen et al., 2007), the importance of passive drift for variation in year class strength (van der Veer et al., 1998; Tiessen et al., 2014), the supply of larvae to coastal nursery grounds (Rochette et al., 2012), and link larval supply and habitat models (Rochette et al., 2013). The European sea bass, Dicentrarchus labrax, is distributed across the North-East Atlantic from northwest Africa to southern Scandinavia, with individuals present in the Mediterranean and Black Seas (Pickett and Pawson, 1994). Sea bass in the northern stock are relatively slow growing fish that can reach up to 30 years of age and take between 4 and 7 years to reach maturity (Pawson and Pickett, 1996). Mature sea bass aggregate to spawn between February and April from the Celtic Sea to the southern North Sea (Dando and Demir, 1985; Sabriye et al., 1988; Jennings, 1990). The geographic extent of spawning is thought to be bounded by the 9 °C isotherm and can expand both as the season progresses and in warmer years (Pickett and Pawson, 1994). Spawning involves the release of ripe ova in two to three batches over a two- to three-week period (Mayer et al., 1990). The pelagic phase of sea bass lasts between 50 and 70 days (Jennings and Ellis, 2015) and dispersal brings a proportion of the larvae to the vicinity of nursery grounds in estuaries, salt marshes, and other sheltered coastal sites. From around 4 years of age, the juveniles become widely distributed in coastal waters before joining the adult population once mature (Pawson et al., 2007). Settlement in the northern stock is highly correlated with temperature with poor settlement in cold years (ICES, 2012). Temperature may act as a direct stressor affecting survival of juveniles in nursery areas and could also be correlated with meteorological processes driving egg and larval drift patterns. Genetic studies show limited distinction between stocks (Fritsch et al., 2007), and tagging studies have shown large migrations of bass (Pawson et al., 2007; Quayle et al., 2009) with some evidence of philopatry, where adults return to the same coastal site after spawning each year (Pawson et al., 2008). Sea bass is a high value fish that is exploited by commercial fisheries (ICES, 2012) and is an important species for recreational anglers with removals constituting around 25% of the total harvest in 2012 for the northern stock (Armstrong et al., 2013). Bass are currently managed in four discrete regions: (i) Iberian Coast; (ii) Bay of Biscay; (iii) west of Scotland and south and west of Ireland; and (iv) North Sea, English Channel, Celtic Sea, and Irish Sea (ICES, 2012). Scientific assessments of the northern stock have shown a rapid decline in the spawning stock biomass (SSB) since 2010 attributed to a succession of weak year classes from 2008 to 2012 and increased fishing mortality (ICES, 2015). The stock exhibits very large inter-annual variability in settlement, most probably driven by environmental factors. To conserve the stock, significant reductions in the harvest of sea bass have been implemented by the European Commission through seasonal and area closures, increasing the Minimum Conservation Reference Size to 42 cm, monthly boat limits or bycatch limits for commercial fishers, and bag limits for recreational anglers (Council Regulation (EU) 2107/127). Similar patterns were observed in the late 1980s that led to a number of conservation measures including the designation of bass nursery areas (BNAs) around England and Wales to protect aggregations of fish below the minimum landing size (Pickett and Pawson, 1994). In this study, factors affecting settlement of juvenile sea bass were investigated on nursery grounds in the North Sea, English Channel, Celtic Sea, and Irish Sea (the “northern stock”—ICES Areas IVb&c, VIIa, d–h). An IBM was developed that coupled hydrodynamics and particle tracking with a pelagic phase model that included egg and larval development and vertical migration behaviour. Potential effects of spawning stock and the spatial and temporal distributions of spawning were excluded to assess the effect of the physical environment on settlement levels, through adoption of a standardized method of particle release. Model predictions were assessed in terms of settlement density, spatial patterns, pelagic phase duration, and inter-annual variation, and were used to select the larval behaviour that maximized settlement in known nursery areas. The sensitivity of the settlement patterns to variation in the physical environment and the connectivity between spawning and nursery grounds were assessed. The implications of these findings are discussed in the context of management of sea bass. Material and methods The particle tracking model combines active and passive transport of a time-evolving “particle.” The aim was to simulate pelagic migration from spawning ground to nursery area, by defining growth and behaviour that was dependent on the physical environment. The selection of the most appropriate behavioural scenario was based on field observation in river estuaries and other coastal sites defined as BNAs [Bass (Specified Sea Areas) (Prohibition of Fishing) Order 1990: SI1990 No. 1156] that are now sampled in support of the Water Framework Directive (Coates et al., 2007). The hydrodynamic and particle tracking models A three-dimensional implementation of the Eulerian General Estuarine Transport Model (GETM—www.getm.eu, Burchard & Bolding, 2002) was used to derive current vectors and water temperatures for the particle tracking model [General Individuals Transport Model (GITM)] (Wolk, 2003; Tiessen et al., 2014; van der Molen et al., 2015). Here, a version of GITM was developed that simulated sea bass development depending on environmental parameters (e.g. temperature) and drift using current fields mediated by position in the water column. The north-west European shelf setup GETM was run using a time step of 10 s at a spatial resolution of 0.08 × 0.05°, with 25 vertical sigma layers. The model was forced at open-boundaries by tidal elevations from Topex-Poseidon satellite altimetry (Le Provost et al., 1998) and by winds, temperature, and humidity derived from the European Centre for Medium-Ranged Weather Forecast reanalyses (ECMWF, 2006a, b). The relative contributions of warm water pockets were not included (e.g. outflow pipes from industrial developments such as power stations). A full description of the model setup and forcing can be found in van der Molen et al. (2015). The GITM model allows the pelagic phase to be split into different stages that are representative of a type of development and/or behaviour. For larval stages, a variety of vertical behaviours have been implemented including diurnal and tidal migration. Movement of particles related to buoyancy can also be incorporated, and settlement was simulated by freezing particle motion on reaching a certain size and/or appropriate physical conditions (e.g. water depth, temperature, salinity, and sea-bed composition) (see Tiessen et al., 2014; van der Molen et al., 2015 for a full description). This study focused on the northern sea bass stock in the North Sea, English Channel, Celtic Sea, and Irish Sea (ICES Fishing Areas IVb-c and VIIa, d–h) (ICES, 2012). The domain of the particle tracking model was defined as the area from 48°N to 54.5°N and 8°W to 8.5°E (Figure 1). The domain was based on sea bass data from 28 sampled estuaries (Kelley, 1988) and 37 BNAs in England and Wales [The Bass (Specified Sea Areas) (Prohibition of Fishing) Order 1990: SI1990 No. 1156; The Bass (Specified Areas) (Prohibition of Fishing) (Variation) Order 1999: SI 1999 No. 75]. The model domain covers most sea BNAs as sampling of 64 UK waterbodies had not provided evidence of populations of juvenile sea bass above 54°N (see Coates et al., 2007 for a description of sampling). Figure 1. View largeDownload slide Known sea BNAs (purple/grey) in England and Wales (The Bass (Specified Areas) (Prohibition of Fishing) (Variation) Order 1999, SI 1999, No. 75). Contour lines indicate bathymetry (see online version for colours). Figure 1. View largeDownload slide Known sea BNAs (purple/grey) in England and Wales (The Bass (Specified Areas) (Prohibition of Fishing) (Variation) Order 1999, SI 1999, No. 75). Contour lines indicate bathymetry (see online version for colours). Spawning A total of 46548 individual sea bass eggs (particles) were released at the surface over the whole model domain (Figure 2); with this number of particle dictated by available computational resource. One particle was released in every three longitudinal grid cells and every second latitudinal grid cell. A total of 1724 particles were released every 3 days between February and April, with the 3-day interval giving a reasonable representation of simulated environmental conditions. This release pattern covered the known offshore spawning grounds and time period of interest (Pickett and Pawson, 1994), and allowed for uncertainty in observed spawning areas and water temperature preferences. In addition, it provided a good balance between computation effort and numbers of particles. Although such a release scheme did not replicate the real spawning distribution, it allowed assessment of the effects of environmental conditions on connectivity between spawning locations and coastal nursery grounds. More realistic spatial egg production scenarios could be developed, but this was not done as it would make it difficult to assess the effects of environmental conditions on settlement, and there would be large uncertainties surrounding the spatial and temporal variation in spawning. To test the impact of different spawning parameters, post-processing scripts were used to select particles spawned within certain environmental conditions at particular locations. The 9 °C isotherm has been postulated as the threshold above which bass spawning occurs (Pickett and Pawson, 1994), and was used to define spawning areas in the model. Figure 2. View largeDownload slide Coastal ICES rectangles (1 × 0.5° subdivisions of ICES Area) (dashed line) containing known BNAs (bold solid line) for the model domain. Dots indicate potential spawning locations where eggs were released. Figure 2. View largeDownload slide Coastal ICES rectangles (1 × 0.5° subdivisions of ICES Area) (dashed line) containing known BNAs (bold solid line) for the model domain. Dots indicate potential spawning locations where eggs were released. Nursery areas In the United Kingdom, all non-polluted estuaries from the Ribble Estuary in the North–West to the Blackwater in the South–East England are likely to be nursery habitats (Kelley, 1988). In England and Wales, 37 rivers, estuaries, and other coastal sites have been defined as BNAs for juvenile bass, where additional restrictions on commercial and recreational fishing are imposed for all or part of the year [Bass (Specified Sea Areas) (Prohibition of Fishing) Order 1990: SI1990 No. 1156] (Figure 1). It is very likely that nursery areas occur in other countries within the northern stock area, with some identified (e.g. Wadden Sea—Cardoso et al., 2015) and studies underway in some countries to map these areas (e.g. France). Sea bass development and vertical behaviour The pelagic phase of sea bass was split into egg and larval stages (see Jennings, 1990). One egg and three larval stages (hatchling, larva, and fry) were defined with distinct sizes, rates of development, and behavioural characteristics (Table 1). Sea bass eggs have been found at or near to the surface (Pickett and Pawson, 1994; van Damme et al., 2011a, b), so were assumed positively buoyant in the model with an upward velocity of 0.002 m s−1 (Edwards et al., 2008). The resulting vertical positions were driven by this buoyancy and physical mixing. The development of eggs was dependent on temperature using an existing relationship (Jennings and Pawson, 1991):   ln⁡(age)=a+bT, (1) where T was the temperature and a and b were defined for each of the 13 egg sub-stages (Jennings and Pawson, 1991). Jennings and Pawson (1991) also estimated the average hatching age, where half of the eggs have spawned, as 6.47 and –0.129 for a and b coefficient, respectively. Assuming a suitable temperatures range from 8 to 20 °C, the average duration of the egg phase ranged from 3 to 7.5days, which was consistent with reported duration in other studies (Olivier et al., 2013). Once hatched, the hatchling carries a buoyant yolk sac and has limited swimming ability, so it was assumed that hatchlings were positively buoyant with an upward velocity of 0.003 m s−1 (Edwards et al., 2008). Hatchlings were initially 1.5 mm in length and the duration of this stage (D) depended on temperature, using the following equation:   D=10a/10(bT) (2) with a = 1.89 and b = 0.077 (Jennings, 1990). The duration of the first larval stage was between 1 and 7 days and transition to the second larval stage occurred at a length of 5.5 mm that corresponded to absorption of the yolk sac (Jennings, 1990). Table 1. Predicted duration and size of sea bass at different pelagic developmental stages, parameters used to model growth, and behaviours. Phase  Egg  Larvae   Stage  Egg  Hatchling  Larva  Fry  Development  Egg  Yolk sac  Swim bladder and development of fins  Swim bladder and active swimming  Duration (days)  3–7.5  1–7  25  22.5  Size (mm)  1.3  1.5–5.5  5.5–10.5  10.5–18  Growth  Temperature dependent  Temperature dependent  0.2 mm day–1  0.2 mm day–1  Exponential (base e) increase with temperature  Exponential (base 10) increase with temperature  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1991)  (Jennings, 1990b)  Behaviour  Drift + float  Drift + float  Vertical migration  Vertical migration at 0.02 m s−1 Settles in shallow coastal water (<20 m) if large enough (15–20 mm)  (0.002 m s−1)  (0.003 m s−1)  (0.01 m s−1)  Behavioural regime          Scenario 1  Float  Float  Diurnal  Float when ready to settle  Scenario 2  Float  Float  Diurnal  Tidal when ready to settle  Scenario 3  Float  Float  Float  Float when ready to settle  Scenario 4  Float  Float  Float  Tidal when ready to settle  Phase  Egg  Larvae   Stage  Egg  Hatchling  Larva  Fry  Development  Egg  Yolk sac  Swim bladder and development of fins  Swim bladder and active swimming  Duration (days)  3–7.5  1–7  25  22.5  Size (mm)  1.3  1.5–5.5  5.5–10.5  10.5–18  Growth  Temperature dependent  Temperature dependent  0.2 mm day–1  0.2 mm day–1  Exponential (base e) increase with temperature  Exponential (base 10) increase with temperature  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1991)  (Jennings, 1990b)  Behaviour  Drift + float  Drift + float  Vertical migration  Vertical migration at 0.02 m s−1 Settles in shallow coastal water (<20 m) if large enough (15–20 mm)  (0.002 m s−1)  (0.003 m s−1)  (0.01 m s−1)  Behavioural regime          Scenario 1  Float  Float  Diurnal  Float when ready to settle  Scenario 2  Float  Float  Diurnal  Tidal when ready to settle  Scenario 3  Float  Float  Float  Float when ready to settle  Scenario 4  Float  Float  Float  Tidal when ready to settle  Table 1. Predicted duration and size of sea bass at different pelagic developmental stages, parameters used to model growth, and behaviours. Phase  Egg  Larvae   Stage  Egg  Hatchling  Larva  Fry  Development  Egg  Yolk sac  Swim bladder and development of fins  Swim bladder and active swimming  Duration (days)  3–7.5  1–7  25  22.5  Size (mm)  1.3  1.5–5.5  5.5–10.5  10.5–18  Growth  Temperature dependent  Temperature dependent  0.2 mm day–1  0.2 mm day–1  Exponential (base e) increase with temperature  Exponential (base 10) increase with temperature  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1991)  (Jennings, 1990b)  Behaviour  Drift + float  Drift + float  Vertical migration  Vertical migration at 0.02 m s−1 Settles in shallow coastal water (<20 m) if large enough (15–20 mm)  (0.002 m s−1)  (0.003 m s−1)  (0.01 m s−1)  Behavioural regime          Scenario 1  Float  Float  Diurnal  Float when ready to settle  Scenario 2  Float  Float  Diurnal  Tidal when ready to settle  Scenario 3  Float  Float  Float  Float when ready to settle  Scenario 4  Float  Float  Float  Tidal when ready to settle  Phase  Egg  Larvae   Stage  Egg  Hatchling  Larva  Fry  Development  Egg  Yolk sac  Swim bladder and development of fins  Swim bladder and active swimming  Duration (days)  3–7.5  1–7  25  22.5  Size (mm)  1.3  1.5–5.5  5.5–10.5  10.5–18  Growth  Temperature dependent  Temperature dependent  0.2 mm day–1  0.2 mm day–1  Exponential (base e) increase with temperature  Exponential (base 10) increase with temperature  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1992)  (Jennings and Pawson, 1991)  (Jennings, 1990b)  Behaviour  Drift + float  Drift + float  Vertical migration  Vertical migration at 0.02 m s−1 Settles in shallow coastal water (<20 m) if large enough (15–20 mm)  (0.002 m s−1)  (0.003 m s−1)  (0.01 m s−1)  Behavioural regime          Scenario 1  Float  Float  Diurnal  Float when ready to settle  Scenario 2  Float  Float  Diurnal  Tidal when ready to settle  Scenario 3  Float  Float  Float  Float when ready to settle  Scenario 4  Float  Float  Float  Tidal when ready to settle  During the second larval stage, the “larva,” sea bass have a swim bladder, develop fins, and range from 5.5 to 10.5 mm in length (http://www.fao.org/docrep/field/003/ac230e/AC230E02.htm#ch2.11). Sea bass larvae have some swimming ability, but with few data on the swimming velocity or direction, the vertical swimming velocity measured for black sea bass of 0.01 m s−1 was used (Edwards et al., 2008). No information about the impact of temperature on growth at this stage was available, so a constant larval growth rate of 0.2 mm day−1 was assumed (Jennings and Pawson, 1992). This gave a stage duration of 25 days that was consistent with other studies (http://www.fao.org/docrep/field/003/ac230e/AC230E02.htm#ch2.10). The final larval stage, the “fry,” has good swimming ability and is ready to settle on a nursery ground, and was assumed to occur between lengths of 10.5 and 15 mm. A vertical swimming velocity based on black sea bass of 0.02 m s−1 was used (Edwards et al., 2008). A constant growth rate of 0.2 mm day−1 (Jennings and Pawson, 1992) gave a duration of 22.5 days that was consistent with other studies (http://www.fao.org/docrep/field/003/ac230e/AC230E02.htm#ch2.10). When larvae are smaller than 15 mm, they are not able to swim sufficiently fast for long enough to influence dispersal (Leis et al., 2012), so swimming was assumed not to influence settlement location significantly. Settlement was considered to have been successful when a fry of length of 15 mm or above (Jennings and Ellis, 2015) arrived in a coastal area at a depth of <20 m. The total pelagic phase duration based on this parameterization ranged between 49.5 and 61.5 days, and was consistent with the observed field settlement time of 50–70 days (Jennings and Ellis, 2015). Mortality Mortality studies generally focus on adult farmed fish (El-Shebly, 2009), but studies have provided constant daily instantaneous mortality rates for the larvae of some fish (Houde, 1989) or derived instantaneous mortality rates from other life history characteristics (McGurk, 1986). Daily instantaneous mortality has been implemented in some IBMs for egg and larval stages, but were generally not related to temperature or food availability (Rochette et al., 2012). A variety of approaches have been used to model mortality including temperature-dependent mortality for egg and first larval stages (Lacroix et al., 2013), paritioning instanteous mortality into baseline and predation effects (Hyder and Nash, 1998), and exclusion of mortality (van der Molen et al., 2007). However, as no mortality rates have been reported for sea bass eggs or larvae in field conditions (El-Shebly, 2009), the stage duration was used as a proxy for instantaneous mortality in the model, and was equivalent as assuming a constant daily instantaneous mortality rate. Considering the lack of information on sea bass mortality, this assumption was appropriate because only constant mortality rates could be applied, and was the most flexible approach to maximize the potential for use of post-processing. Model behaviour selection Behaviour was selected from simulated scenarios. First, the spatial distribution of larvae settling into coastal areas was compared with known nursery areas. Hence, when larvae did not reach a coastline encompassing reported nursery areas, larval behaviour was not considered relevant. Second, relevant behaviour was selected when inter-annual differences in settlement rate between good and poor settlement years were properly reproduced, with the same method used to setup model forcing for the relevant period. Successful settlement was defined as larvae reaching coastal areas containing known nursery grounds (Figure 1). Simulating the pelagic phase Evaluating the effects of inter-annual variation and larval behaviour on settlement Sea bass settlement has high inter-annual variation that is strongly associated with temperature (ICES, 2012). Hydrodynamics were simulated using a hindcast GETM model over the period 1995–2009, and predictions of monthly averaged salinity and temperature were assessed. In addition, a visual comparison between model outputs and yearly averaged SSB measurements from ICES was done over the same period. Environmental conditions were general similar between years, except for the two successive years, 1996 and 1997, which represented the minimum and maximum reported settlement for the modelled time period. Computational limitations meant that the number of years modelled was highly constrained, so two contrasting years were chosen based on a settlement index for year class strength derived from juvenile sea bass surveys conducted in the Solent since 1977 (Brown, 2013). These were 1996, a poor settlement year (hereafter PSY), and 1997, a good settlement year (hereafter GSY). The effects of the physical environment, life history characteristics, and vertical migration behaviours on settlement of sea bass were assessed for both years. Eulerian GETM runs were used to force GITM simulations covering the pelagic phase period from February to September. Environmental parameters and current velocities were extracted hourly for both years and were used to drive the particle tracking model GITM. Hydrodynamics and temperature drove the particle passive motion, development, and growth over the pelagic phase. The number of particles released depended on the local temperature distribution. The number of particles settling in known nursery areas and the percentage of successful settlement were assessed and compared for both years. Sea bass eggs have been reported as buoyant, but little is known about larval behaviour. To establish vertical behaviour(s) leading to successful settlement, several biologically plausible combinations of vertical migration behaviours (floating, diurnal, and tidal vertical migration) were implemented for each larval stage (Table 1). Float was defined as a positively buoyant passive particle with a constant upward vertical velocity. Diel migration was defined as a vertical movement up to the surface during daylight hours and descent to depth at night, and is a commonly observed behaviour of planktonic organisms (Enright and Hamner, 1967). Tidal migration is a tactic used by many organisms to selectively achieve directional movement, where individuals move up into mid-water during transporting tides (Gibson, 2003). For both vertical diurnal and tidal migrations, movements occur at the vertical swimming velocity of the larvae and horizontal movement is passive (advection by ambient currents). Particles were assigned one of four possible behavioural regimes composed of combinations of behaviours at different larval stages, and successful settlement was assessed for each behavioural regime (Table 1), through the number of settlers reaching nursery areas, the associated number particles settling, and replicating the difference between the PSY and GSY. The behavioural regime that replicated these criteria most accurately was selected and used to assess the importance of physical parameters in successful settlement and connectivity between spawning and nursery areas. Assessing the environmental drivers for success of settlement and pelagic duration The main environmental drivers in the model are current velocities, air temperature and wind direction and strength, with air temperature affecting water temperature and varying with wind. The differences in residual current velocities between years are also driven mainly by wind and will dominate the inter-annual variability in passive dispersal of particles. Wind velocity variability among the PSY and GSY was assessed by averaging the wind strength and direction over the pelagic phase. The differences between years were used to assess the importance of wind in driving settlement and pelagic phase duration (proxy for instantaneous constant mortality) in PSY (1996) and GSY (1997). Water temperature defines the spawning area and affects growth, so will influence the location of release, settlement, and duration of the pelagic phase. Sea bass eggs are sensitive to water temperature and have been shown to develop at temperatures between 8.7 and 18.6 °C (Jennings and Pawson, 1991). As a result, the 9 °C isotherm has been postulated as the threshold at which bass spawning occurs (Pickett and Pawson, 1994). Post-processing scripts were used to assess the sensitivity of settlement to the spawning temperature threshold using the 8, 9, and 10 °C isotherms, with larger spawning areas at a lower than a higher temperature threshold. The impact on number of particles released, numbers settling, and percentage and duration of successful settlement was assessed for the PSY and GSY. As the spawning extent varies with temperature, only the ratio of the number settling to number released was compared for both years. Connectivity between spawning areas and nursery ground The connectivity between spawning and nursery areas was assessed using model outputs of the locations of spawned particles that settled successfully across the whole model domain. A more detailed spatial analysis was carried out for three regions representing differing environmental conditions: North Sea, English Channel, Celtic Sea and Bristol Channel, and Irish Sea. Analysis was performed to link the settlement of particles to spawning at an ICES area scale that included both transport (released in offshore water and settlement in coastal water) and self-seeding (particles released in a particular coastal area that settle in the same area) processes. The first of these processes linked different ICES areas and the second process implied local spawning population that supply nursery areas in the same ICES area. Results Environmental characteristics over 1996 and 1997 During the spawning period, the area delimited by 9 °C isotherm in the GSY (1997) gradually expanded with time, whereas in the PSY (1996), this area contracted slightly over the first two months and then expanded in April (Figure 3). Temperature gradients shown by distances among isotherms were much larger in the GSY (1997) than the PSY (1996) (Figure 3). The averaged wind field over the period simulated was markedly different in the PSY (1996) and GSY (1997) (Figure 4). In the GSY (1997) winds were relatively strong and homogeneous westerlies, but in the PSY (1996) average winds were more variable with no particularly clear directionality or strength (Figure 4). Figure 3. View largeDownload slide Monthly averaged temperature for February (a, d), March (b, e), and April (c, f) for poor (1996, a–c) and good (1997, d–f) settlement years. Contour lines represent the 8 °C (dashed), 9 °C (solid), and 10 °C (dotted) isotherms. Axes are longitude and latitude. Figure 3. View largeDownload slide Monthly averaged temperature for February (a, d), March (b, e), and April (c, f) for poor (1996, a–c) and good (1997, d–f) settlement years. Contour lines represent the 8 °C (dashed), 9 °C (solid), and 10 °C (dotted) isotherms. Axes are longitude and latitude. Figure 4. View largeDownload slide Wind velocity averaged over the sea bass pelagic phase duration (from February to September) for poor (1996—a) and good (1997—b) settlement years. Figure 4. View largeDownload slide Wind velocity averaged over the sea bass pelagic phase duration (from February to September) for poor (1996—a) and good (1997—b) settlement years. The effects of inter-annual variation More eggs were spawned in the GSY (Table 2) as warmer sea temperatures increased the area available for spawning (Figure 5). Settlement in the known BNAs (indicated in Figures 1 and 2) was predicted to be higher in GSY than in PSY, the only exception was areas with very low settlement in North Wales (Rivers Conwy and Dee) and the Teifi Estuary (Table 3). However, the difference in predicted settlement between the two years was much smaller than estimated in the stock assessment (ICES 2016) (Table 2). Overall, more larvae and higher levels of settlement were observed in GSY than PSY irrespective of behaviour. Table 2. ICES stock assessments estimated in 1997 and 1996 (ICES, 2016), number of particles released in poor (1996) and good (1997) settlement year, and the predicted numbers and percentage of released particles settling (reaching coastal areas) for spawning at water temperatures of above 9 °C and behavioural scenarios 1–4 (see Table 1). Year  ICES stock assessments  Released particles  Number settling   Percentage settling   Scenario 1  Scenario 2  Scenario 3  Scenario 4  Scenario 1  Scenario 2  Scenario 3  Scenario 4  1996  1 024  15 957  1 919  3 105  2 112  3 882  12.0  19.5  13.2  24.3  1997  23 272  18 241  4 011  5 717  4 282  6 591  22.0  31.3  23.5  36.1  Ratio  22.7  1.14  2.09  1.84  2.03  1.70  1.83  1.61  1.77  1.49  Year  ICES stock assessments  Released particles  Number settling   Percentage settling   Scenario 1  Scenario 2  Scenario 3  Scenario 4  Scenario 1  Scenario 2  Scenario 3  Scenario 4  1996  1 024  15 957  1 919  3 105  2 112  3 882  12.0  19.5  13.2  24.3  1997  23 272  18 241  4 011  5 717  4 282  6 591  22.0  31.3  23.5  36.1  Ratio  22.7  1.14  2.09  1.84  2.03  1.70  1.83  1.61  1.77  1.49  Ratio represents the ratio between good (1997) and poor (1996) settlement years. Mortality is not included in those simulations and is investigated later as a post-process. Table 2. ICES stock assessments estimated in 1997 and 1996 (ICES, 2016), number of particles released in poor (1996) and good (1997) settlement year, and the predicted numbers and percentage of released particles settling (reaching coastal areas) for spawning at water temperatures of above 9 °C and behavioural scenarios 1–4 (see Table 1). Year  ICES stock assessments  Released particles  Number settling   Percentage settling   Scenario 1  Scenario 2  Scenario 3  Scenario 4  Scenario 1  Scenario 2  Scenario 3  Scenario 4  1996  1 024  15 957  1 919  3 105  2 112  3 882  12.0  19.5  13.2  24.3  1997  23 272  18 241  4 011  5 717  4 282  6 591  22.0  31.3  23.5  36.1  Ratio  22.7  1.14  2.09  1.84  2.03  1.70  1.83  1.61  1.77  1.49  Year  ICES stock assessments  Released particles  Number settling   Percentage settling   Scenario 1  Scenario 2  Scenario 3  Scenario 4  Scenario 1  Scenario 2  Scenario 3  Scenario 4  1996  1 024  15 957  1 919  3 105  2 112  3 882  12.0  19.5  13.2  24.3  1997  23 272  18 241  4 011  5 717  4 282  6 591  22.0  31.3  23.5  36.1  Ratio  22.7  1.14  2.09  1.84  2.03  1.70  1.83  1.61  1.77  1.49  Ratio represents the ratio between good (1997) and poor (1996) settlement years. Mortality is not included in those simulations and is investigated later as a post-process. Table 3. Locations of known BNAs by ICES rectangle, latitude and longitude, and percentage of released particles settling (reaching coastal areas) for spawning at water temperatures of above 9 °C and behavioural Scenario 4 (see Table 1). BNAs  ICES rectangle  Latitude  Longitude  1996  1997  Ratio  Fal Estuary, Percuil River, Helford River  VIIf10  50 to 50.5°  –6 to –5°  2.71  3.76  1.39  Milford Haven  VIIg05  51.5 to 52°  –6 to –5°  1.12  3.49  3.13  River Yealm, Plymouth Rivers—Plym, Tamar, Tavyand Lynher, River Fowey  VIIe03  50 to 50.5°  –5 to –4°  2.30  3.08  1.34  River Camel  VIIf08  50.5 to 51°  –5 to –4°  0.76  1.14  1.50  River Torridge, River Taw  VIIf04  51 to 51.5°  –5 to –4°  0.71  1.32  1.84  Burry Inlet, The Three Rivers—Taf, Tywi and Gwendraeth  VIIf01  51.5 to 52°  –5 to –4°  1.47  2.04  1.39  Teifi estuary  VIIa22  52 to 52.5°  –5 to –4°  0.04  0.07  1.62  River Dyfi, River Mawddach, Dwyryd and Glaslyn Estuary  VIIa19  52.5 to 53°  –5 to –4°  0.32  0.29  0.89  River Dart, Salcombe Harbour, River Avon  VIIe04  50 to 50.5°  –4 to –3°  0.66  1.53  2.30  River Exe, River Teign  VIIe01  50.5 to 51°  –4 to –3°  0.11  0.30  2.67  Aberthaw Power Station Outfall  VIIf05  51 to 51.5°  –4 to –3°  0.28  0.35  1.25  River Conwy, River Dee  VIIa16  53 to 53.5°  –4 to –3°  0.16  0.01  0.07  The Fleet  VIIe02  50.5 to 51°  –3 to –2°  0.13  0.40  3.19  Langstone Harbour, Portsmouth Harbour, Southampton water, Fawley Power Station Outfall—Stanwood Bay, Poole Harbour  VIId01  50.5 to 51°  –2 to –1°  0.06  0.21  3.79  Chichester Harbour  VIId02  50.5 to 51°  –1 to 0°  0.00  0.07  –  Dungeness Power Station  VIId03  50.5 to 51°  0 to 1°  0.19  0.52  2.65  Grain Power Station Outfall, Kingsnorth Power Station Outfall  IVc25  51 to 51.5°  0 to 1°  0.00  0.01  –  Bradwell power station  IVc21  51.5 to 52°  0 to 1°  0.01  0.05  7.87  BNAs  ICES rectangle  Latitude  Longitude  1996  1997  Ratio  Fal Estuary, Percuil River, Helford River  VIIf10  50 to 50.5°  –6 to –5°  2.71  3.76  1.39  Milford Haven  VIIg05  51.5 to 52°  –6 to –5°  1.12  3.49  3.13  River Yealm, Plymouth Rivers—Plym, Tamar, Tavyand Lynher, River Fowey  VIIe03  50 to 50.5°  –5 to –4°  2.30  3.08  1.34  River Camel  VIIf08  50.5 to 51°  –5 to –4°  0.76  1.14  1.50  River Torridge, River Taw  VIIf04  51 to 51.5°  –5 to –4°  0.71  1.32  1.84  Burry Inlet, The Three Rivers—Taf, Tywi and Gwendraeth  VIIf01  51.5 to 52°  –5 to –4°  1.47  2.04  1.39  Teifi estuary  VIIa22  52 to 52.5°  –5 to –4°  0.04  0.07  1.62  River Dyfi, River Mawddach, Dwyryd and Glaslyn Estuary  VIIa19  52.5 to 53°  –5 to –4°  0.32  0.29  0.89  River Dart, Salcombe Harbour, River Avon  VIIe04  50 to 50.5°  –4 to –3°  0.66  1.53  2.30  River Exe, River Teign  VIIe01  50.5 to 51°  –4 to –3°  0.11  0.30  2.67  Aberthaw Power Station Outfall  VIIf05  51 to 51.5°  –4 to –3°  0.28  0.35  1.25  River Conwy, River Dee  VIIa16  53 to 53.5°  –4 to –3°  0.16  0.01  0.07  The Fleet  VIIe02  50.5 to 51°  –3 to –2°  0.13  0.40  3.19  Langstone Harbour, Portsmouth Harbour, Southampton water, Fawley Power Station Outfall—Stanwood Bay, Poole Harbour  VIId01  50.5 to 51°  –2 to –1°  0.06  0.21  3.79  Chichester Harbour  VIId02  50.5 to 51°  –1 to 0°  0.00  0.07  –  Dungeness Power Station  VIId03  50.5 to 51°  0 to 1°  0.19  0.52  2.65  Grain Power Station Outfall, Kingsnorth Power Station Outfall  IVc25  51 to 51.5°  0 to 1°  0.00  0.01  –  Bradwell power station  IVc21  51.5 to 52°  0 to 1°  0.01  0.05  7.87  Ratio represents the ratio between good (1997) and poor (1996) settlement years. Map of ICES rectangle delimiting BNA is presented in Figure 1. Table 3. Locations of known BNAs by ICES rectangle, latitude and longitude, and percentage of released particles settling (reaching coastal areas) for spawning at water temperatures of above 9 °C and behavioural Scenario 4 (see Table 1). BNAs  ICES rectangle  Latitude  Longitude  1996  1997  Ratio  Fal Estuary, Percuil River, Helford River  VIIf10  50 to 50.5°  –6 to –5°  2.71  3.76  1.39  Milford Haven  VIIg05  51.5 to 52°  –6 to –5°  1.12  3.49  3.13  River Yealm, Plymouth Rivers—Plym, Tamar, Tavyand Lynher, River Fowey  VIIe03  50 to 50.5°  –5 to –4°  2.30  3.08  1.34  River Camel  VIIf08  50.5 to 51°  –5 to –4°  0.76  1.14  1.50  River Torridge, River Taw  VIIf04  51 to 51.5°  –5 to –4°  0.71  1.32  1.84  Burry Inlet, The Three Rivers—Taf, Tywi and Gwendraeth  VIIf01  51.5 to 52°  –5 to –4°  1.47  2.04  1.39  Teifi estuary  VIIa22  52 to 52.5°  –5 to –4°  0.04  0.07  1.62  River Dyfi, River Mawddach, Dwyryd and Glaslyn Estuary  VIIa19  52.5 to 53°  –5 to –4°  0.32  0.29  0.89  River Dart, Salcombe Harbour, River Avon  VIIe04  50 to 50.5°  –4 to –3°  0.66  1.53  2.30  River Exe, River Teign  VIIe01  50.5 to 51°  –4 to –3°  0.11  0.30  2.67  Aberthaw Power Station Outfall  VIIf05  51 to 51.5°  –4 to –3°  0.28  0.35  1.25  River Conwy, River Dee  VIIa16  53 to 53.5°  –4 to –3°  0.16  0.01  0.07  The Fleet  VIIe02  50.5 to 51°  –3 to –2°  0.13  0.40  3.19  Langstone Harbour, Portsmouth Harbour, Southampton water, Fawley Power Station Outfall—Stanwood Bay, Poole Harbour  VIId01  50.5 to 51°  –2 to –1°  0.06  0.21  3.79  Chichester Harbour  VIId02  50.5 to 51°  –1 to 0°  0.00  0.07  –  Dungeness Power Station  VIId03  50.5 to 51°  0 to 1°  0.19  0.52  2.65  Grain Power Station Outfall, Kingsnorth Power Station Outfall  IVc25  51 to 51.5°  0 to 1°  0.00  0.01  –  Bradwell power station  IVc21  51.5 to 52°  0 to 1°  0.01  0.05  7.87  BNAs  ICES rectangle  Latitude  Longitude  1996  1997  Ratio  Fal Estuary, Percuil River, Helford River  VIIf10  50 to 50.5°  –6 to –5°  2.71  3.76  1.39  Milford Haven  VIIg05  51.5 to 52°  –6 to –5°  1.12  3.49  3.13  River Yealm, Plymouth Rivers—Plym, Tamar, Tavyand Lynher, River Fowey  VIIe03  50 to 50.5°  –5 to –4°  2.30  3.08  1.34  River Camel  VIIf08  50.5 to 51°  –5 to –4°  0.76  1.14  1.50  River Torridge, River Taw  VIIf04  51 to 51.5°  –5 to –4°  0.71  1.32  1.84  Burry Inlet, The Three Rivers—Taf, Tywi and Gwendraeth  VIIf01  51.5 to 52°  –5 to –4°  1.47  2.04  1.39  Teifi estuary  VIIa22  52 to 52.5°  –5 to –4°  0.04  0.07  1.62  River Dyfi, River Mawddach, Dwyryd and Glaslyn Estuary  VIIa19  52.5 to 53°  –5 to –4°  0.32  0.29  0.89  River Dart, Salcombe Harbour, River Avon  VIIe04  50 to 50.5°  –4 to –3°  0.66  1.53  2.30  River Exe, River Teign  VIIe01  50.5 to 51°  –4 to –3°  0.11  0.30  2.67  Aberthaw Power Station Outfall  VIIf05  51 to 51.5°  –4 to –3°  0.28  0.35  1.25  River Conwy, River Dee  VIIa16  53 to 53.5°  –4 to –3°  0.16  0.01  0.07  The Fleet  VIIe02  50.5 to 51°  –3 to –2°  0.13  0.40  3.19  Langstone Harbour, Portsmouth Harbour, Southampton water, Fawley Power Station Outfall—Stanwood Bay, Poole Harbour  VIId01  50.5 to 51°  –2 to –1°  0.06  0.21  3.79  Chichester Harbour  VIId02  50.5 to 51°  –1 to 0°  0.00  0.07  –  Dungeness Power Station  VIId03  50.5 to 51°  0 to 1°  0.19  0.52  2.65  Grain Power Station Outfall, Kingsnorth Power Station Outfall  IVc25  51 to 51.5°  0 to 1°  0.00  0.01  –  Bradwell power station  IVc21  51.5 to 52°  0 to 1°  0.01  0.05  7.87  Ratio represents the ratio between good (1997) and poor (1996) settlement years. Map of ICES rectangle delimiting BNA is presented in Figure 1. Figure 5. View largeDownload slide Number of eggs spawned between February and April for different temperature thresholds (8, 9, and 10 °C) in poor (1996—a) and good (1997—b) settlement years. Figure 5. View largeDownload slide Number of eggs spawned between February and April for different temperature thresholds (8, 9, and 10 °C) in poor (1996—a) and good (1997—b) settlement years. The effect of larval behaviour on settlement Settlement in the Wadden Sea and Morecambe Bay only occurred in the model when tidal migration occurred (Scenarios 2 and 4), so Scenarios 1 and 3 were excluded from further analysis. Diurnal migration in the larva stage (Table 1) did not affect the number settling or nursery areas reached, but stronger larval dispersal was found with floating behaviour. Similar trends in connectivity were found for the scenarios including diurnal migration in Stage 3 (Figure 6). In this case, connectivity in the English Channel was split between the east and west, and was northward in the Irish Sea and the North Sea. However, connectivity depended on the behavioural scenario, with the maximum settlement obtained in Scenario 4 (float, float, float, and tidal) (Table 2). As a result, Scenario 4 was considered to be the most appropriate, as it produced the highest settlement and reproduced the PSY and GSY, so it was used for all subsequent model simulations. The spatial distribution of settlers in Scenario 4 showed that settlement occurred in most known bass nursery grounds and the highest levels were found in the southwest United Kingdom (Figure 7a and b). There was also settlement predicted in the more northerly regions of the Irish Sea and more broadly across the North Sea (Figure 7a and b). Figure 6. View largeDownload slide Connectivity matrices between spawning and settlement by ICES areas for the whole model domain including Bristol Channel and Celtic Sea (VIIf, g&h), English Channel (VIId&e), Irish Sea (VIIa), and North Sea (IVb&c) for poor (1996—a) and good (1997—b) settlement years. Connectivity (number of particles) is coloured according to its strength, with white for no connectivity (0 particle), green for weak connectivity (1 to 100 particles), orange for medium connectivity (100 to 500 particles), and red for strong connectivity (more than 500 particles). ICES areas with self-seeding (the number of particles that are released in a particular coastal area that settle in the same area) have bold borders. Figure 6. View largeDownload slide Connectivity matrices between spawning and settlement by ICES areas for the whole model domain including Bristol Channel and Celtic Sea (VIIf, g&h), English Channel (VIId&e), Irish Sea (VIIa), and North Sea (IVb&c) for poor (1996—a) and good (1997—b) settlement years. Connectivity (number of particles) is coloured according to its strength, with white for no connectivity (0 particle), green for weak connectivity (1 to 100 particles), orange for medium connectivity (100 to 500 particles), and red for strong connectivity (more than 500 particles). ICES areas with self-seeding (the number of particles that are released in a particular coastal area that settle in the same area) have bold borders. Figure 7. View largeDownload slide Number of particles settling in coastal areas in Scenario 4 for poor (1996—a) and good (1997—b) settlement years. Coastal ICES rectangles (dashed line) that contain known BNAs (bold solid line). Figure 7. View largeDownload slide Number of particles settling in coastal areas in Scenario 4 for poor (1996—a) and good (1997—b) settlement years. Coastal ICES rectangles (dashed line) that contain known BNAs (bold solid line). Assessing the environmental drivers for success of settlement and pelagic duration Lowering the spawning temperature threshold led to more eggs spawned, with larger numbers of settlers in the GSY than PSY (Figures 3 and 5). More particles were release in PSY than in GSY at a threshold of 10°C, but the opposite was observed at 8 and 9°C (Table 4). However, number settling was always larger in GSY than PSY, regardless of the spawning temperature threshold (Table 4). Settlement was lower and dispersal was over a smaller area in the PSY than GSY, because of weaker average wind speed in northerly or westerly directions (Figure 4a). However, in the warm year (1997), settlement was higher and extended further north in the Irish and North Seas, with prevailing westerly winds of on average of 2 m s−1 influenced dispersal (Figure 4b). Table 4. Number of particles released, numbers and percentage of released particles settling (reaching coastal areas) for different spawning temperatures thresholds and behavioural Scenario 4 (see Table 1).   Number released   Number settling   Percentage settling   Year  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  1996  21 458  15 957  10 810  6 282  3 105  1 069  29.3  19.5  9.9  1997  25 941  18 241  10 611  10 068  5 717  2 127  38.8  31.3  20.1  Ratio  1.21  1.14  0.98  1.60  1.84  1.99  1.33  1.61  2.03    Number released   Number settling   Percentage settling   Year  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  1996  21 458  15 957  10 810  6 282  3 105  1 069  29.3  19.5  9.9  1997  25 941  18 241  10 611  10 068  5 717  2 127  38.8  31.3  20.1  Ratio  1.21  1.14  0.98  1.60  1.84  1.99  1.33  1.61  2.03  Ratio represents the ratio between good (1997) and poor (1996) settlement years. Mortality is not included in those simulations and is investigated later as a post-process. Table 4. Number of particles released, numbers and percentage of released particles settling (reaching coastal areas) for different spawning temperatures thresholds and behavioural Scenario 4 (see Table 1).   Number released   Number settling   Percentage settling   Year  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  1996  21 458  15 957  10 810  6 282  3 105  1 069  29.3  19.5  9.9  1997  25 941  18 241  10 611  10 068  5 717  2 127  38.8  31.3  20.1  Ratio  1.21  1.14  0.98  1.60  1.84  1.99  1.33  1.61  2.03    Number released   Number settling   Percentage settling   Year  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  8 °C  9 °C  10 °C  1996  21 458  15 957  10 810  6 282  3 105  1 069  29.3  19.5  9.9  1997  25 941  18 241  10 611  10 068  5 717  2 127  38.8  31.3  20.1  Ratio  1.21  1.14  0.98  1.60  1.84  1.99  1.33  1.61  2.03  Ratio represents the ratio between good (1997) and poor (1996) settlement years. Mortality is not included in those simulations and is investigated later as a post-process. The average pelagic phase duration for successful settlement was only very slightly longer in the PSY (1996 ∼ 75 ± 5.5 days) than in the GSY (1997 ∼ 73 ± 5.5 days), but the spatial distribution varied between the years (Figure 8a and b). The longest pelagic stage durations in GSY were located at the extremities of the model domain (northern Celtic and North Seas), with shorter durations in southwest England (Figure 8b). In the PSY, longer pelagic duration was seen in coastal areas, notably on southern and western coasts in the PSY compared with GSY (Figure 8a and b). Limited larval dispersal in the PSY meant that only the southern North Sea was reached in the PSY, but settlement occurred in the northern North Sea nursery areas in GSY, even with moderate pelagic phase duration. Figure 8. View largeDownload slide Average duration of pelagic phase for bass settling in different ICES rectangles for poor (1996—a) and good (1997—b) settlement years. Figure 8. View largeDownload slide Average duration of pelagic phase for bass settling in different ICES rectangles for poor (1996—a) and good (1997—b) settlement years. Connectivity between spawning areas and nursery grounds The spawning location of particles that settled successfully showed a broader distribution in GSY than in PSY (Figure 9). Large numbers of particles were spawned in deep areas settled successful, especially in the southern Irish Sea in the GSY and on the Cornwall promontory in both years (Figure 9). A detailed analysis of the mean connectivity patterns between spawning and nursery grounds was performed separately for the Irish Sea, North Sea, and English Channel. Self-seeding was more common in GSY than in PSY (Figure 6), with higher levels of self-seeding in the Irish Sea in GSY driven by sufficiently high sea temperatures for spawning close to nursery grounds (Figure 6e). Less self-seeding occurred in the central Irish sea in PSY (Figure 6b), yet successful migration towards the northern Irish Sea was driven by prevailing south-westerly winds (Figure 6a). The model also showed strong connections between north Cornwall, Devon, and Bristol Channel with the Irish nursery areas in both years, with spawning grounds extending westward in GSY (Figure 6b and e). Eggs spawned in the central western English Channel could settle both in England and France, with movement from the central to the eastern English Channel only found in GSY (Figure 6a and d). Figure 9. View largeDownload slide Number of particles originating from an ICES rectangle (spawning location) that settle in a nursery area for poor (1996—a) and good (1997—b) settlement years. Figure 9. View largeDownload slide Number of particles originating from an ICES rectangle (spawning location) that settle in a nursery area for poor (1996—a) and good (1997—b) settlement years. Discussion Model performance Model performance in relation to the larval behaviour scenario had no effect on inter-annual settlement variability. The vertical behaviour strategy was selected to make larger numbers of particles reach nursery areas, and a combination of floating behaviour for the egg and early-larval stages in which no active vertical migration occurs, resulted in particles dispersion driven by wind. Coastward migration was only achieved in the model by implementing tidally synchronized vertical migration, with strong tidal currents occurring throughout the domain. The importance of active migration in the final larval stage has been reported for plaice (Fox et al., 2006) and supports the patterns observed from our model. The predicted areas of highest successful settlement showed good agreement with the main known spawning grounds located in the English Channel, Celtic Sea, Bristol Channel, and North Sea (Thompson and Harrop, 1987; Jennings and Pawson, 1992; Pickett and Pawson, 1994; Fritsch et al., 2007); the annual variability between the GSY and the PSY was also reproduced. The model also predicted successful migration to known nursery grounds in the Wadden Sea (Cardoso et al., 2015) and the Western Scheldt estuary although at lower abundance in other estuaries and lagoons surveyed (ICES, 2014). Sea BNAs occur in estuaries in France and southern Ireland, although at relatively low density in most years compared with similar habitats in England (Fahy et al., 2000). No information on BNAs was available for the Belgian coast, although the short coastline has few potential habitats for young sea bass. The observed differences in settlement between the GSY and the PSY could be driven by a combination of many factors including: spawning stock, spawning distribution and success, predation, food availability, disease, environmental conditions, and the larval dispersal patterns that we focus on here. The model experiments were set up to focus on the effects of larval dispersal patterns in response to inter-annual differences in environmental forcing, so only included a subset of these factors and was likely to be the reason for differences in the magnitude of the ratio between PSY and GSY. The correspondence between model predictions and observations of higher settlement in the GSY was a strong indication that hydrodynamic conditions are an important factor in settlement success of sea bass. Adult spawning in coastal waters led to higher settlement in the GSY than in the PSY, highlighting importance of appropriate spawning conditions in the coastal water for settlement success. The spatial density of spawning was the same for both the PSY and the GSY, and only occurred where water temperature was above a threshold, leading to more particles being spawned in the GSY or warm year (1997) than for the PSY or cold year (1996). In reality, the spawning output of the stock will vary between years and also be a highest at the centre of spawning aggregations (Fahy et al., 2000), with this centre changing throughout the spawning season moving eastward in the English Channel as sea temperatures increase. The present model can be used to predict the settlement pattern for larvae at nursery habitats [Kelley, 1988; Bass (Specified Sea Areas) (Prohibition of Fishing) Order 1999: SI1999 No. 75], based on any plausible time-dependent density distribution of eggs should such data be available for a given year. Currently, spawning distribution data for sea bass in the northern stock are limited to egg distribution maps from the early 1980s and one area in 1990 (Pickett and Pawson 1994). The IBM results presented here were based on uniform egg distribution, therefore provided a biased prediction of the numbers of sea bass larvae reaching nursery habitats relative to numbers of eggs spawned. However, the model predicted the dispersal patterns from individual spawning locations, so indicates physically plausible connectivity between spawning locations and nursery areas and how this varied between years. Moreover, using the same spawning pattern for both years allows for closer identification of the contribution of environmental factors influencing larval dispersal on settlement (see later). For the years examined, the results showed that there was a greater incidence of shorter distance linkages between spawning location and nursery areas in the PSY than in the GSY. As similar trends in main connectivity directions and settlement success based on spawning locations were found for different migration strategies, a similar connectivity pattern would be found with a different spawning distributions despite differences in numbers of settlers. Role of hydrodynamics in determining year class strength The two environmental factors represented in the model forcing with potential to cause a difference in the results for the two years were air temperature and wind stress, leading to differences in water temperature and residual circulation between the years. In this area, wind and temperature tend to be related. Westerly winds associated with Atlantic depressions bring temperate air from the Atlantic Ocean while enhancing the southwest to northeast residual circulation. In contrast, northerly and easterly winds, associated with high-pressure systems over the continent, bring arctic and/or continental air and stall the residual circulation (Furnes, 1980; Pingree and Griffiths, 1980). The differences in (surface) water temperature between the years affected the size of the spawning area through shifts in the position of the spawning temperature threshold, and affected both development and growth rates. Hence, environmental changes related to increasing sea temperature could lead to earlier spawning and broader spawning area (Politikos et al., 2015), but may also be mediated by latitudinal variations in day length (Vinagre et al., 2009). Hydrodynamics drive the variation of water temperature, so the spatial extent of an area with a temperature above a threshold varied. For example, in the GSY (1997), the 10 °C isotherm encompassed a smaller spawning area than in the PSY (1996). With fewer eggs released at higher spawning temperatures threshold, lower settlement was observed, although the success rate was quite high in PSY compared with GSY. The effect of temperature on egg and larval stage duration, through growth and development rates, was probably under-estimated in the model. This was due to the assumption of constant growth rates for the last two larval stages, but a lack of information on temperature-dependant growth led to this approach. Nevertheless, slightly longer larval durations were simulated in the cold year (1996), resulting in a longer time to reach the settlement size. Mortality was not included explicitly because of lack of data, with stage duration used instead as a proxy for mortality (equivalent to assuming a constant daily mortality). Hence, longer stage durations would also result in higher cumulative mortality. It is not possible to infer which of these effects would dominate, but it is likely that the effect was overshadowed in the model by the difference in wind-driven circulation. The effects of temperature and wind could be separated further with additional model runs using winds from one year and air temperatures from another, but was beyond the scope of the present study. The difference in wind stress between the two years led to a reduction in north-eastward transport in the English Channel in the PSY (1996) that decreased the number transported successfully to UK nursery grounds. In the GSY (1997), westerly winds predominated, advecting warm oceanic water further eastwards into the English Channel. This caused more short-distance connections between spawning sites and nursery areas, and led to a higher proportion reaching nursery areas. At the local level of individual nursery areas, there were variations on this general theme, most likely related to the complex topography of the area, which could not be investigated within this study. The difference of settlement in the Morecambe Bay for both years was probably driven by the north-easterly wind, with a stronger wind for the GSY, even in the case where spawning in offshore waters was higher in the PSY because of local water temperature. Pelagic duration was negatively correlated with temperature and wind, and was proportional to the migration distance from the spawning ground to coastal areas. As a result, it may be possible to use averaged wind direction, influencing residual current and water temperature, as a proxy for successful settlement. This was not possible within the scope of the current study, but further investigation of the links between annual settlement variability and key drivers (i.e. wind and temperature) would enable a more thorough quantification of their effect, and help to develop a tool for forecasting sea bass settlement. Implications of connectivity on sea bass management The definition of biological stocks of sea bass in the NE Atlantic has proved elusive (ICES, 2012). Adult sea bass show strong site fidelity in the non-spawning period (Pawson et al., 2008) and then undertake annual migrations of widely differing distances depending on their location to reach water of appropriate temperature for spawning. The IBM scenarios explored in this paper show that known nursery areas around southwest England, southwest Wales and coastal sites in northwest Brittany and southeast Ireland, are likely to have high settlement rates with relatively short larval transport connections with the main spawning sites in the western English Channel and Celtic Sea. As the spawning season progresses, and particularly in years with stronger westerly winds and warmer conditions, spawning is likely to penetrate farther east in the Channel and into the North Sea. The model shows that in years with stronger penetration of warmer water into the southern North Sea, spawning there can lead to advection of larvae into nursery areas such as in the Wadden Sea and estuaries in the Netherlands, or in the Thames. From a fisheries management perspective, the results of this study suggest that there is considerable potential for genetic mixing because of larval dispersal leading to weak stock differentiation. Despite the wide and variable dispersal of larvae from spawning sites indicated by the model, spatial management measures to reduce targeting of spawning aggregations in only some areas could have a disproportionate benefit on settlement of young sea bass in nursery areas, with the strongest transport connectivity with the spawning sites being protected. For example, protection of spawning aggregations in the northeast Celtic Sea and off the Bristol Channel might have greatest benefit for settlement to sea BNAs in the Bristol Channel, southwest Wales, parts of southeast Ireland, and in the Irish Sea. Conversely, protection of aggregations only in the Western Channel would mainly benefit nursery areas in both sides of the English Channel depending on the wind conditions and residual current patterns driving the larval transport. Finally, protection of spawning aggregations farther east in the English Channel and in the southern North Sea would have greatest benefits for nursery areas in the eastern Channel and North Sea. Ultimately, consideration of spatial management measures will need an understanding of where the fish spawning at any location have migrated from. Acknowledgements All authors were supported by Defra projects MF1230 “Population studies in support of the conservation of the European sea bass,” MI001 “Recreational Sea Angling Surveys,” and MF1223 “Citizen Science Investigations.” The hydrodynamic model setup was developed under Cefas Seedcorn project DP315. References Accolla C., Nerini D., Maury O., Poggiale J. C. 2015. Analysis of functional response in presence of schooling phenomena: an IBM approach. Progress in Oceanography , 134: 232– 243. Google Scholar CrossRef Search ADS   Armstrong M., Brown A., Hargreaves J., Hyder K., Munday M., Proctor S., Roberts A., et al.   2013. 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Briseño-Avena, Christian; Franks, Peter J S; Roberts, Paul L D; Jaffe, Jules S

doi: 10.1093/icesjms/fsx171pmid: N/A

Abstract In 1980, Holliday and Pieper stated: “Most sound scattering in the ocean volume can be traced to a biotic origin.” However, most of the bioacoustics research in the past three decades has focused on only a few groups of organisms. Targets such as small gelatinous organisms, marine snow, and phytoplankton, e.g. have been generally to be considered relatively transparent to acoustic waves due to their sizes and relatively low sound speed and density contrasts relative to seawater. However, using a broadband system (ZOOPS-O2) we found that these targets contributed significantly to acoustic returns in the 1.5–2.5 MHz frequency range. Given that phytoplankton and marine snow layers are ubiquitous features of coastal regions; this works suggests that they should be considered as potential sources of backscatter in biological acoustic surveys. Introduction There is a continued interest in understanding the sources of oceanic backscatter and the use of echosounders to estimate the abundances and distributions of marine organisms. Using acoustic methods to understand the distributions of biological acoustic scatterers in the water column requires knowledge of the acoustic properties of the targets. Although several ground-truthing experiments (i.e. inter-method comparisons) and models have concentrated on understanding of the acoustic properties and detection of fishes, e.g. relatively little effort has been focused on planktonic organisms. Plankton-oriented studies have been carried out in laboratory settings, through in situ observations, modelling efforts, or combinations of these (e.g. Greenlaw, 1977; Holiday and Pieper, 1980; Richter, 1985; Demer and Martin, 1995; Martin et al., 1996; Stanton et al., 1996; Jaffe et al., 1988; Lawson et al., 2004; Briseño-Avena et al., 2015). However, there are still significant gaps in our knowledge. Data from the latest generation of broadband sonars present new opportunities for acoustical investigations of plankton (Lavery et al., 2010; Trenkel et al., 2016). Such investigations require a thorough understanding of the capabilities and limitations of such systems. Though water is relatively transparent to many acoustic wavelengths, it is relatively opaque to most of the electromagnetic spectrum. Aquatic and fisheries ecologists have therefore favored acoustical methods over optical imaging to conduct large-scale biological surveys of zooplankton and fish assemblages (Fernandes et al., 2002). Though the sensing capability, fast acquisition, and almost real-time processing of acoustical data give acoustical methods an advantage over optical technologies, classification of acoustic target returns can be ambiguous. In contrast to sound, light is strongly attenuated by water, limiting the working range of underwater optical imaging. However, while optical imaging methods lack long-range capabilities (with the exception of oligotrophic waters with low turbidity) they are capable of generating detailed images of targets, commonly allowing identification down to genus and often to species; coarse taxonomic identification (e.g. copepod, hydromedusa, ctenophore, chaetognath, appendicularian, etc.) is almost always possible. Historically, narrowband acoustic systems working at discrete frequencies have been at the forefront of acoustical methods (Stanton, 2012 and references therein; Fornshell and Tesei, 2013). However, determining the identity of the targets insonified by such tools has been a major challenge. New developments in broadband acoustic sensors and signal processing have begun to give us new insights into the acoustical properties of planktonic organisms. With a few exceptions in higher latitudes, most marine ecosystems are species rich (e.g. De Monte et al., 2013), with a high diversity of species, body shapes, sizes, compositions, and behaviours. In such regions acoustical methods are at a disadvantage when compared with optical ones: while it is possible to visually distinguish among planktonic taxa, it is difficult to acoustically differentiate even the most basic forms with current signal-processing methods (Fielding et al., 2004) and the use of narrowband systems. Ground-truthing exercises are by far the best and most direct method to aid in the interpretation of acoustic data. Comparing acoustic data to net-derived and optic-derived estimates of abundance and/or biomass is the most common approach (e.g. Wiebe et al., 1996; Benfield et al., 1998; Sutor et al., 2005; Lara-Lopez and Neira, 2008; Powell and Ohman, 2012). However, fragile organisms such as jellyfish (e.g. hydromedusae, narcomedusae, medusae), ctenophores, siphonophores, doliolids, and appendicularians are often severely damaged by nets, making it difficult to fully account for them during acoustic-net comparisons. Furthermore, particles (i.e. marine snow) and organisms such as phytoplankton are not sampled by nets targeting zooplankton. Because the goal has generally been to sample zooplankton, other organisms and particles are typically unaccounted for during traditional ground-truthing experiments, despite their conspicuous presence in rich, productive regions of the ocean. One possible justification for ignoring such targets is that they have been thought to make relatively small contributions to acoustic backscatter at commonly used frequencies. To explore the validity of this assumption, this work investigates the acoustic reflectivity of these often-ignored organisms and particles at the ultrasonic frequencies of 1.5–2.5 MHz. The idea of phytoplankton and marine snow contributing to acoustic returns is not new; references as far back as the 1950s (Cushing et al., 1956) mention this possibility. After a gap of almost four decades, the idea of such targets contributing to acoustic returns re-surfaced in a report by Anoshkin and Goncharov (1993). Since then, there have been a few efforts to quantify the acoustic reflectance of phytoplankton (Selivanovsky et al., 1996; Bok et al., 2010,, 2013), and large (9.6–61 cm umbrella diameter) gelatinous organisms (Mutlu, 1996; Brierley et al., 2005; Warren and Smith, 2007, Weibe et al., 2010) in laboratory settings. However, no attempts have focused on the potential acoustic reflectance of phytoplankton and marine snow in the field. Because of their microscopic size, fragile composition, and patchy distribution, these targets have not been studied in underwater acoustical research and perhaps more importantly, in the interpretation of high-frequency acoustic survey data. Thin phytoplankton layers are ubiquitous features of coastal regions, extending over kilometers and persisting from hours to several days. Their thickness ranges from tens of centimeters to a few meters as documented in a variety of marine environments (e.g. Cowles et al., 1998; McManus et al., 2003). Phytoplanktons have usually been considered transparent to acoustic waves, and thus ignored as potential acoustic reflectors. Recently, however, Timmerman et al. (2014) suggested that a layer of large diatom flocculates (∼2 cm in diameter) was detected with a narrowband sonar (200 kHz). The authors, however, could not refute the hypothesis that the high acoustic returns were caused by the presence of oxygen bubbles. Marine snow layers are common features of coastal waters (Alldredge and Silver, 1988; Alldredge et al., 2002), occurring in both surface waters, and—unlike phytoplankton—at depths well below the euphotic zone (e.g. Ransom et al., 1998). However, little work has been conducted on acoustic estimates of these abundant and densely aggregated particles, though there are anecdotal references in the acoustical literature (e.g. Anoshkin and Goncharov, 1993). One approach to understanding the sources of reflected sound is to combine optical imaging and acoustical methods, orienting the sensors so that a common volume is observed by both modalities, such as the ZOOPS-O2 system that measures the acoustic reflectivity of targets in situ with coincident optical determination of taxa, size, and orientation (Briseño-Avena et al., 2015). Here, we use data acquired by that system to estimate the acoustic reflectivity of phytoplankton, marine snow, and small gelatinous organisms (350 μm–24 mm), as well as the widely accepted strong scatterers such as crustaceans. Material and methods ZOOPS-O2 system description ZOOPS-O2 is a broadband, ultra high-frequency (1.5–2.5 MHz) system that combines an instrument to measure the in situ acoustic reflectivity of individual targets (ZOOPlankton Sonar: ZOOPS) with two cameras (O-Cam 1 and O-Cam 2: O2) giving concurrent stereo visual images (later referred to as “image pairs”) of the targets reflecting the sound. We refer the reader to Briseño-Avena et al. (2015) for a full system description. In the following sections we give a brief explanation of the platform to add information relevant to this work that has not been discussed in detail in previous publications. Acoustic target strength (TS) values for the ZOOPS-O2 system are given as broadband target strength (BTS), and reported for each individual target. Unlike the common TS measurement used for discrete-frequency, narrowband systems, the BTS represents the intensity of the returned energy weighted over the 1 MHz bandwidth of the chirp: BTS=10log10(ybsycal)−58.1 dB re 1 m,(1) where ybs is the magnitude-squared of the compressed pulse of the echo from a target, and ycal is the magnitude-squared of the first reflection from the echo of a calibration sphere (Briseño-Avena et al., 2015). Five volumes are sampled by individual and combined components of ZOOPS-O2 (Figure 1). The transducer acoustic beam insonifies 1 L of water over a range of 0.65–1.22 m (tall polygon in Figure 1a, and long, thin cylinder in Figure 1b), with an ability to resolve targets that are farther than 1.5 mm apart in range. This range resolution results from the fact that we use an envelope on the chirp waveform that reduces the usable bandwidth of the system to roughly 500 kHz. Each camera has an individual imaging volume of 106 mL (dashed rectangles in Figure 1a, and small cylinders crossing the acoustic beam in Figure 1b). The stereoscopic volume—the intersection of the two camera imaging volumes (black diamond in Figure 1a)—has an effective sampling volume of 20.03 mL. The entire stereoscopically imaged volume is contained within the acoustic beam at acoustic ranges between 0.84 and 0.88 m. Finally, the acoustic beam (side-view inset pointing to Figures 1a and b) intersecting the stereoscopic volumes at ranges 0.84–0.88 m (bean diameter ranging from 4 to 4.2 mm), insonifies a volume of 53.74 mL. Figure 1. Open in new tabDownload slide Schematic representation of ZOOPS-O2 showing the relative position of the stereoscopic camera system, transducer, and ancillary sensors (CTD, Fluorometer). Modified version of Figure 5 from Briseño-Avena et al. (2015) to highlight components relevant to this work—the three different volume estimates used in the present analysis: individual camera imaging volumes, stereoscopic volume, and insonified (acoustic beam) volume. Figure 1. Open in new tabDownload slide Schematic representation of ZOOPS-O2 showing the relative position of the stereoscopic camera system, transducer, and ancillary sensors (CTD, Fluorometer). Modified version of Figure 5 from Briseño-Avena et al. (2015) to highlight components relevant to this work—the three different volume estimates used in the present analysis: individual camera imaging volumes, stereoscopic volume, and insonified (acoustic beam) volume. Field work Data were extracted from 7 vertical profiles (18 347 image pairs) out of a total of 19 (42 779 total image pairs) profiles acquired during a cruise aboard R/V New Horizon on 28–29 March 2012 in the Southern California Bight at inshore (Scripps Canyon) and offshore (San Diego Trough) locations. A Conductivity-Temperature-Depth sensor (SBE 911 plus) equipped with a fluorometer (Seapoint Chlorophyll a Fluorometer) was mounted on the ZOOPS-O2 platform for this fieldwork (Figure 1a). Maximum cast depths ranged between 40 and 500 m, the maximum operational depth of the package. Optical data analysis Manual counting and identification of zooplankton and marine snow particles The ZOOPS-O2 samples at 1 Hz, generating two still images and one acoustic record every second. To estimate the concentration of zooplankton and marine snow particles, we only used images from O-Cam 1. Using only one camera increases the likelihood of finding zooplankton and marine snow in the already small sampling volume of each camera (106 mL). The images from O-Cam 1 were manually processed by visually identifying and counting zooplankton and marine snow particles in each frame. For ease of processing and to save the data automatically to a disk, we used a custom-made point-and-click graphical user interface in MATLAB. Particles were sorted into 22 categories (e.g. euphausiids, calanoid copepods, jellyfish, marine snow, etc.). Concentration estimates (individuals per mL) for each category were obtained by dividing the number of organisms or particles per image by the camera’s imaged volume. Diatom-like particle semi-automatic image identification and quantification The ZOOPS-O2 images were processed to identify the number of diatom-like particles and their 3D locations. The 3D location of each particle in the stereoscopic volume was estimated based on a careful stereo calibration of the cameras (see details in Briseño-Avena et al., 2015). First, a subset of the images was used to construct a training set of diatom-like particles by manually selecting the objects identified as centric diatoms. In the images the centric diatoms were clearly distinguishable as circular opaque objects when viewed end-on, and in side view both thecae (Petri dish-like shape) were obvious. Then, some descriptors of the manually selected objects were obtained using an automated image-processing algorithm. The algorithm automatically detects connected pixels [i.e. the object, or region of interest (ROI)] with a high contrast against the image background and extracts object descriptors such as location on the object in the image (centroid), size measurements (i.e. length or major axis, width or minor axis, area occupied by the pixels), and aspect ratio. This was implemented using “regionprops” in MATLAB’s Image Processing Toolbox, but similar methods are available in other programming languages with similar image-processing toolboxes. Next, the descriptors for each ROI were used to automatically detect diatom-like particles in images from the O-Cam 1 and O-Cam 2 (Figure 2a and b). Visual inspection of at least 100 randomly selected image pairs corroborated that the automatic processing was accurate in identifying the centric diatoms. Finally, using the stereoscopic calibration described in Briseño-Avena et al. (2015), corresponding diatom-like particles found within the stereoscopic volume were identified from the image pairs (Figure 2a, open circles). That is, when particles in O-Cam 1 images (crosses) are overlapped by open circles, the algorithm has found the exact particles imaged by O-Cam 2 (Figure 2b, stars). The open circle also carries information of the predicted 3D coordinate of the particle imaged simultaneously by both cameras (Figure 2c). The remaining particles (black dots not marked in Figure 2a and b) were outside of this common stereoscopic volume and hence not quantified. Diatom-like particle concentration was then estimated by dividing the total diatom-like particle counts (Figure 2c) by the estimated stereoscopic imaged volume (∼20.0 mL). Figure 2. Open in new tabDownload slide Result of the diatom-like particle detection. Images from the two O-Cams showing the automatic detection of diatom-like particles [crosses in (a) and stars in (b)]. Open circles that overlap the crosses in (a) indicate the diatom-like particles that have a corresponding particle in (b). Counts in (a) and (b) are the total numbers of diatom-like particles identified by the automatic detection algorithm. (c) Total diatom-like particles and their 3D locations in the stereoscopic volume. Figure 2. Open in new tabDownload slide Result of the diatom-like particle detection. Images from the two O-Cams showing the automatic detection of diatom-like particles [crosses in (a) and stars in (b)]. Open circles that overlap the crosses in (a) indicate the diatom-like particles that have a corresponding particle in (b). Counts in (a) and (b) are the total numbers of diatom-like particles identified by the automatic detection algorithm. (c) Total diatom-like particles and their 3D locations in the stereoscopic volume. Acoustic data analysis Individual zooplankton and marine snow in situ acoustic reflections The 18 347 image pairs and their accompanying acoustic records were processed using the ‘echo-locator’ algorithm reported in Briseño-Avena et al. (2015). Briefly, the ZOOPS-O2 stereoscopic volume intersected the acoustic beam at 0.84 and 0.88 m range; the acoustic records containing echoes (visualized as peaks in the echogram) within that range were extracted for further processing. These features mostly appeared as distinct, isolated peaks with BTS values at or above −120 dB. Next, the image pairs were viewed side-by-side along with their corresponding echogram. Once the identified acoustic target was confirmed to be in the stereoscopic volume, its acoustic range was estimated from the images. The projected acoustic range was then compared with that of the echogram: when the projected (optical) range of the echo matched the actual (acoustic) range of the location of the echo peak, we assumed that the echo originated from the stereoscopically imaged target. The echo properties (range, BTS, raw echo signal, and echo envelope) were saved for further analysis. The matching particles were then measured and their length was recorded. Table 1 summarizes the basic statistics. Table 1. In situ BTS measured from individual targets using ZOOPS-O2. Group . Sample size (n) . L (mm) . W (mm) . a (mm) . BTS (dB) . Copepodsa 224 0.35–4.5 0.02–1.2 0.04–0.8 −113.9 to − 100.2 Marine snow 86 0.05–25 0.18–7 0.27–7.6 −113.9 to − 105.5 Euphausiidsb 2 4.6–4.7 0.48–0.52 0.95–1.1 −113.0 to − 108.8 Mysids 4 1.6–6.5 0.35–0.55 0.31–0.63 −108.3 to − 104.8 Appendiculariansc 22 1.5–11.4 0.11–1.2 0.24–1.4 −113.7 to − 105.0 Chaetognaths 3 7.8–15 0.66–0.76 0.75–1.0 −114.5 to − 106.3 Siphonulaed 3 0.6–1.15 0.37–0.75 0.21–0.43 −106.1 to − 105.0 Hydromedusae 8 0.7–3.7 2.1–19.6 0.95–3.2 −113.9 to − 104.6 Doliolids 12 4.3–10.4 2.2–6.5 1.4–3.8 −112.9 to − 105.5 Ctenophore 1 3.7 5.9 2.5 −110.9 Radiolarian 1 1.8 1.9 0.95 −111.1 Ostracod 1 1.3 0.44 0.31 −110.0 Others 6 0.7–30 0.13–1.7 0.19–2.2 −113.0 to − 104.3 Group . Sample size (n) . L (mm) . W (mm) . a (mm) . BTS (dB) . Copepodsa 224 0.35–4.5 0.02–1.2 0.04–0.8 −113.9 to − 100.2 Marine snow 86 0.05–25 0.18–7 0.27–7.6 −113.9 to − 105.5 Euphausiidsb 2 4.6–4.7 0.48–0.52 0.95–1.1 −113.0 to − 108.8 Mysids 4 1.6–6.5 0.35–0.55 0.31–0.63 −108.3 to − 104.8 Appendiculariansc 22 1.5–11.4 0.11–1.2 0.24–1.4 −113.7 to − 105.0 Chaetognaths 3 7.8–15 0.66–0.76 0.75–1.0 −114.5 to − 106.3 Siphonulaed 3 0.6–1.15 0.37–0.75 0.21–0.43 −106.1 to − 105.0 Hydromedusae 8 0.7–3.7 2.1–19.6 0.95–3.2 −113.9 to − 104.6 Doliolids 12 4.3–10.4 2.2–6.5 1.4–3.8 −112.9 to − 105.5 Ctenophore 1 3.7 5.9 2.5 −110.9 Radiolarian 1 1.8 1.9 0.95 −111.1 Ostracod 1 1.3 0.44 0.31 −110.0 Others 6 0.7–30 0.13–1.7 0.19–2.2 −113.0 to − 104.3 Lengths (L) and widths (W) (mm) are stereoscopically derived measurements; the equivalent spherical radius (a) was estimated from the volume of a prolate spheroid of major (L/2) and minor (W/2) semi-axes. a Mostly calanoid and poecilostomatoid copepods. It may include early developmental stages. Length of copepods is the estimated prosome length. b Small euphausiids (probably juvenile stages). c Including individuals with and without their “house”. d An early developmental stage of siphonophores. Open in new tab Table 1. In situ BTS measured from individual targets using ZOOPS-O2. Group . Sample size (n) . L (mm) . W (mm) . a (mm) . BTS (dB) . Copepodsa 224 0.35–4.5 0.02–1.2 0.04–0.8 −113.9 to − 100.2 Marine snow 86 0.05–25 0.18–7 0.27–7.6 −113.9 to − 105.5 Euphausiidsb 2 4.6–4.7 0.48–0.52 0.95–1.1 −113.0 to − 108.8 Mysids 4 1.6–6.5 0.35–0.55 0.31–0.63 −108.3 to − 104.8 Appendiculariansc 22 1.5–11.4 0.11–1.2 0.24–1.4 −113.7 to − 105.0 Chaetognaths 3 7.8–15 0.66–0.76 0.75–1.0 −114.5 to − 106.3 Siphonulaed 3 0.6–1.15 0.37–0.75 0.21–0.43 −106.1 to − 105.0 Hydromedusae 8 0.7–3.7 2.1–19.6 0.95–3.2 −113.9 to − 104.6 Doliolids 12 4.3–10.4 2.2–6.5 1.4–3.8 −112.9 to − 105.5 Ctenophore 1 3.7 5.9 2.5 −110.9 Radiolarian 1 1.8 1.9 0.95 −111.1 Ostracod 1 1.3 0.44 0.31 −110.0 Others 6 0.7–30 0.13–1.7 0.19–2.2 −113.0 to − 104.3 Group . Sample size (n) . L (mm) . W (mm) . a (mm) . BTS (dB) . Copepodsa 224 0.35–4.5 0.02–1.2 0.04–0.8 −113.9 to − 100.2 Marine snow 86 0.05–25 0.18–7 0.27–7.6 −113.9 to − 105.5 Euphausiidsb 2 4.6–4.7 0.48–0.52 0.95–1.1 −113.0 to − 108.8 Mysids 4 1.6–6.5 0.35–0.55 0.31–0.63 −108.3 to − 104.8 Appendiculariansc 22 1.5–11.4 0.11–1.2 0.24–1.4 −113.7 to − 105.0 Chaetognaths 3 7.8–15 0.66–0.76 0.75–1.0 −114.5 to − 106.3 Siphonulaed 3 0.6–1.15 0.37–0.75 0.21–0.43 −106.1 to − 105.0 Hydromedusae 8 0.7–3.7 2.1–19.6 0.95–3.2 −113.9 to − 104.6 Doliolids 12 4.3–10.4 2.2–6.5 1.4–3.8 −112.9 to − 105.5 Ctenophore 1 3.7 5.9 2.5 −110.9 Radiolarian 1 1.8 1.9 0.95 −111.1 Ostracod 1 1.3 0.44 0.31 −110.0 Others 6 0.7–30 0.13–1.7 0.19–2.2 −113.0 to − 104.3 Lengths (L) and widths (W) (mm) are stereoscopically derived measurements; the equivalent spherical radius (a) was estimated from the volume of a prolate spheroid of major (L/2) and minor (W/2) semi-axes. a Mostly calanoid and poecilostomatoid copepods. It may include early developmental stages. Length of copepods is the estimated prosome length. b Small euphausiids (probably juvenile stages). c Including individuals with and without their “house”. d An early developmental stage of siphonophores. Open in new tab Comparing in situ BTS spectra to distorted wave born approximation spectra estimates We computed the estimated spectra B(f) from individual targets using the equation: B(f)=10log10|CPbs(f)CPsphere(f)|2−58.1 dB re 1 m,(2) where CPsphere(f) is the Fourier transform of the windowed and zero-padded first reflection of the calibration sphere, and CPbs(f) the Fourier transform of the windowed and zero-padded echo from the target. The DWBA (distorted wave born approximation) model (Chu and Ye, 1999; Briseño-Avena et al., 2015) was applied to all targets using a prolate spheroid approximation after estimating the length (L), width (W), and orientation (θ) of a target with respect to the transducer. This model requires values for density contrast (g) and sound-speed contrast (h). Because several previously poorly studied groups were included, values were taken from the literature (Greenlaw and Johnson, 1982; Chu and Wiebe, 2005; Smith et al., 2010). Values of g = 1.05 and h = 1.05 were used for all developmental stages of copepods. For other taxa, the (g, h) values used were: (1.04, 1.022) for hydromedusae, (1.04, 1.07) for mysids, and (1.028, 1.022) for euphausiids. Groups for which no values were available in the literature (i.e. marine snow, appendicularians, siphonulae, doliolids, ctenophores, radiolarians, ostracods, and “others”) were assigned values similar to these, based on our best judgment. Acoustic concentration estimates of zooplankton and marine snow Field studies show that ZOOPS-O2 is able to detect echoes from individual targets ranging in size from 360 μm to just under 2.5 cm (Table 1). Many of these echoes corresponded to visually identified individual zooplankton and marine snow particles. Particle/organism concentrations were estimated by counting the number of echoes (echo counting) recorded from ranges 0.65 to 1.22 m (full operational range of the system) and, dividing the total target counts by the effective acoustic beam volume (1 L). Acoustic concentration estimates of diatom-like particles To test the ability of ZOOPS-O2 to detect phytoplankton aggregated in high densities, the concentration estimates obtained from the automated diatom-like particle detection algorithm described above were compared with the acoustic reflectivity profile. One profile, in which bulk fluorescence was dominated by centric diatoms (determined to be Coscinodiscus sp. from a water sample taken during the cruise), is used for this comparison. We assumed that diatoms had a reflectivity between −130 and −123 dB. Since no other zooplankton or marine snow particle returned such low BTS values, we interpreted any echoes within this 7 dB range as originating from the diatoms. The number of peaks (assumed to be individual diatoms) detected in this range interval was divided by the estimated acoustic volume (53.74 mL) of the beam between ranges 0.84 and 0.88 m (the range intercepted by the stereoscopic volume) to obtain acoustic estimates of diatom-like target concentration, reported here as targets per mL. Results and discussion After rigorous examination and selection, only 373 image pairs (out of 18 347) clearly showed a single planktonic particle/organism common to both images, whose position also yielded an unambiguous echo in the acoustic record. From these image pairs we found a diverse group of echogenic targets (Table 1). Values of ka (the product of the acoustic wave number k and the equivalent spherical radius a; Demer and Martin, 1995) at the centre frequency of ZOOPS-O2 (2 MHz) were computed for all the targets identified in Table 1 (Figure 3). With the exception of two targets (small copepods; Figure 3a), all the individual targets were found to be located in the geometric scattering region (ka > 1). In no case was there a clear linear relationship between ka and BTS, suggesting that size (a) is not a good predictor of BTS at the centre frequency of the system. It is likely that other body properties (e.g. density, body shape, internal organs) have a stronger influence than size on the acoustic reflectivity of the organisms and marine snow studied in this work. Figure 3. Open in new tabDownload slide Summary of ka (wavenumber x equivalent spherical radius) vs. BTS for all targets reported in Table 1. Panel (a) copepods and marine snow. Panel (b) euphausiids, mysids, appendicularians, chaetognaths, siphonulae, hydromedusae, doliolids, ctenophore, radiolarian, ostracod, and other. The wavenumber was estimated at the system centre frequency of 2 MHz. Figure 3. Open in new tabDownload slide Summary of ka (wavenumber x equivalent spherical radius) vs. BTS for all targets reported in Table 1. Panel (a) copepods and marine snow. Panel (b) euphausiids, mysids, appendicularians, chaetognaths, siphonulae, hydromedusae, doliolids, ctenophore, radiolarian, ostracod, and other. The wavenumber was estimated at the system centre frequency of 2 MHz. Zooplankton and marine snow acoustic reflectivity We obtained echoes from individual crustacean zooplankton (e.g. copepods, euphausiids, and mysids; Figure 4), but were surprised to find small gelatinous organisms (e.g. hydromedusae, doliolids, chaetognaths, appendicularians; Figure 5) and marine snow particles (Figure 6) in the acoustic records. Phytoplanktons were also present in the acoustic and stereoscopic records; we discuss this group separately below. Interestingly, we found that, despite the size range of the targets, there was a general overlap of BTS values for all categories (Table 1). These observations suggest that at frequencies of 1.5–2.5 MHz, and using only BTS, a large marine snow particle (Figure 6b) can return as much acoustic energy as—and be confounded with—small crustacean zooplankton (Figure 4). Although the size of the target has an impact on the scattered sound, the body or particle composition is also a large factor affecting the acoustic return. For instance, while the marine snow particle in Figure 6b is larger than the measured copepods, the marine snow is much more porous than the body of the crustacean. Thus its density and sound speed contrast properties would be much smaller, producing a weaker return than if it were a more compact, dense object. Although object orientation could also impact the acoustic return, at least for copepods, BTS was found to depend only weakly on orientation, with a mean difference between side and head-on views of only 7 dB (Briseño-Avena et al., 2015). Figure 4. Open in new tabDownload slide Examples of individual crustacean zooplankton whose BTS (in dB) was measured in situ with ZOOPS-O2. For each triplet: left image = O-Cam 1, right image = O-Cam 2, overlapping plot = echo envelope (solid line). Scale bars are given for each image pair. The arrows indicate the direction of the incident acoustic waves. (a) Calanoid copepod; (b) Eucalanid copepod; (c) Mysid; (d) Euphausiid. Figure 4. Open in new tabDownload slide Examples of individual crustacean zooplankton whose BTS (in dB) was measured in situ with ZOOPS-O2. For each triplet: left image = O-Cam 1, right image = O-Cam 2, overlapping plot = echo envelope (solid line). Scale bars are given for each image pair. The arrows indicate the direction of the incident acoustic waves. (a) Calanoid copepod; (b) Eucalanid copepod; (c) Mysid; (d) Euphausiid. Figure 5. Open in new tabDownload slide Examples of individual gelatinous and other fragile zooplankton whose BTS (in dB) was measured in situ with ZOOPS-O2. Legend same as Figure 4. (a) Hydromedusa; (b) Doliolid; (c) Appendicularian; (d) Chaetognath. Figure 5. Open in new tabDownload slide Examples of individual gelatinous and other fragile zooplankton whose BTS (in dB) was measured in situ with ZOOPS-O2. Legend same as Figure 4. (a) Hydromedusa; (b) Doliolid; (c) Appendicularian; (d) Chaetognath. Figure 6. Open in new tabDownload slide Examples of small (a) and large (b) marine snow particles whose BTS (in dB) was measured in situ with ZOOPS-O2. Legend same as Figure 4. Figure 6. Open in new tabDownload slide Examples of small (a) and large (b) marine snow particles whose BTS (in dB) was measured in situ with ZOOPS-O2. Legend same as Figure 4. Marine snow layers are common features in coastal oceans, yet their contribution to acoustic returns in field surveys seems to be virtually unexplored. Importantly, these layers can have intense biological activity. For example, the coincidence of marine snow layers, copepods, and their predators has been documented in the Baltic Sea using underwater optics (Möller et al., 2012). In light of the present results, the collocation of such targets can complicate acoustic processing and interpretation, potentially leading to an over-estimate, e.g. of small crustacean abundance when acoustic returns from marine snow particles are mistaken for organisms. Though the TS (dB) of some gelatinous organisms has been measured using narrowband, single frequency systems, most studies have focused on physonect siphonophores (a colonial pelagic cnidarian with a pneumatophore or “float”, filled mostly with carbon monoxide): the air-filled pneumatophore strongly scatters sound. The TS of large jellyfish has also been measured using narrowband frequencies centered at 120 and 200 kHz (Aurelia aurita, umbrella diameter: 9.5–15.5 cm; Mutlu, 1996), and at 18, 38, and 120 kHz (Chrysaora hysoscella (5–8.5 cm), Aequorea aequorea (10–61 cm); Brierley et al., 2005, and Chrysaora melanaster (21–31 cm); De Robertis and Taylor, 2014). The hydromedusae measured in the present study were much smaller, ranging from 0.2 to 2.4 cm bell diameter. Notice that at frequencies of 1.5-2.5 MHz, the BTS of a large gelatinous organism (Figure 5a) could be misinterpreted as a medium-sized copepod (Figure 4a). However, the shape of the echo envelope (Figures 4 and 5, red lines on overlapping plots) is qualitatively different for these two groups, although the orientation of a target in relation to the direction of the incident acoustic wave may affect the echo envelope shape. At present, however, only measurements related to the TS (or BTS as in this work) of targets are generally utilized in the acoustic community. The properties of the echo envelope related to each individual target should be investigated in future work, such as has been done with fish (e.g. Reeder and Stanton, 2004). This work is a cautionary tale for interpreting acoustic data from emerging broadband, high frequency technologies—and even more significantly, single, narrowband technologies—that rely on echo-integrating principles. As we have shown, many unexpected taxa have the potential to contribute to acoustic signals. DWBA model vs. BTS spectra measurements As part of our research effort we also sought to examine the agreement of a well-known model, the DWBA, with our collected data. The comparison of measured [B(f)] and modelled (DWBA) spectra yielded interesting results (Figure 7) for a copepod, euphausiid, mysid, large crustacean carcass, doliolid, hydromedusa, and large and small marine snow particles. Our DWBA model yielded spectra comparable to the measured spectra; though the nulls do not always match up, they are in close agreement. The peak BTS values (Figure 7, stars) for each individual are plotted (arbitrarily centered on the x axis): a single TS value does not vary among groups as much as the spectra do. Notice that the euphausiid’s measured and modelled spectra (Figure 7b) had the greatest discrepancy in magnitude; one likely factor is that the g and h values available in the literature were often for animals larger than the ones we observed. Another factor is the simplicity of our model compared with the actual shape of the organism or relative to the bent cylinder and higher resolution models of Stanton and Chu (2000) where orientation, as well as material properties (i.e. g and h) are identified as important factors affecting the TS from individual euphausiids. Figure 7. Open in new tabDownload slide Measured (B(f), solid lines) and modelled (DWBA, dotted lines) spectra, and target BTS (stars) from eight targets at different orientations with respect to the sonar. Different values of g and h where used for different groups, and they are noted in each panel. The values of θ are given in degrees. L, length (major axis a); W, width (minor axis b). Figure 7. Open in new tabDownload slide Measured (B(f), solid lines) and modelled (DWBA, dotted lines) spectra, and target BTS (stars) from eight targets at different orientations with respect to the sonar. Different values of g and h where used for different groups, and they are noted in each panel. The values of θ are given in degrees. L, length (major axis a); W, width (minor axis b). To explore how the results from the high-frequencies used by ZOOPS-O2 would apply to more commonly used frequencies in acoustic surveys we extended our DWBA model to include frequencies from 38 kHz to 2.5 MHz. For each measured and modelled target we then located the frequency of the transition from geometric to Rayleigh (G-R) scattering (Figure 8). For most organisms, including the appendicularian shown in Figure 8a, the mean G-R transition frequency is located within the range of commonly used lower frequencies (shaded area and downward-pointing triangles in Figure 8b). Most systems using lower frequencies than ZOOPS-O2 measure volume backscatter strength rather than individual echoes and direct comparisons between measurements are not recommended. However, the fact that the G-R transitions for the particles and organisms reported here occur at frequencies between 38 and 420 kHz suggests that the results from this high-frequency, broadband system might apply to the more commonly used lower-frequency narrowband systems when such particles and organisms are found in high concentrations or dense aggregations. Figure 8. Open in new tabDownload slide G-R transition location for modelled (DWBA) spectra. (a) Example of the G-R location (downward-pointing arrow) on the modelled spectrum (dotted line) of an appendicularian (inside its house) using a fluid-like prolate spheroid of L = 3.7 mm, W = 0.8 mm, θ = 120°, g = 1.05, and h = 1.05. The corresponding in situ measured spectrum is superimposed (solid line). Inset (b): average (solid circles) ± 1 SD (horizontal lines) of the estimated G-R location for each group reported in Table 1. Shaded area encompasses the range of frequencies most commonly used by narrowband systems (downward-pointing triangles). Figure 8. Open in new tabDownload slide G-R transition location for modelled (DWBA) spectra. (a) Example of the G-R location (downward-pointing arrow) on the modelled spectrum (dotted line) of an appendicularian (inside its house) using a fluid-like prolate spheroid of L = 3.7 mm, W = 0.8 mm, θ = 120°, g = 1.05, and h = 1.05. The corresponding in situ measured spectrum is superimposed (solid line). Inset (b): average (solid circles) ± 1 SD (horizontal lines) of the estimated G-R location for each group reported in Table 1. Shaded area encompasses the range of frequencies most commonly used by narrowband systems (downward-pointing triangles). Phytoplankton acoustic reflectivity To explore whether diatoms found at the deep chlorophyll a maximum in high densities (e.g. those observed in thin layers) reflect sound we used a profile with a well-defined peak as shown by the bulk fluorescence signal (Figure 9a and b). A water sample taken at the depth of the chlorophyll maximum and later inspected under a microscope confirmed that the phytoplankton assemblage was comprised of the centric diatom Coscinodiscus sp.: one of the largest solitary marine planktonic genera, measuring up to 500 μm in diameter in 250 μm in width, similar in size to the ones imaged by the O-cams. At the centre frequency of the system (2 MHz) such large diatoms have a ka = 1.5 (i.e. ka > 1), suggesting that they are detectable by the ZOOPS if their sound speed and density contrast are high enough. We refer to these imaged centric diatoms as “diatom-like particles” due to limitations of the image processing method utilized. However, no other phytoplankton types were apparent in the image records; suggesting the layer was dominated by a single species. The maxima in concentration of these diatom-like particles peaked in the same depth interval as the fluorescence, suggesting that these diatoms dominated the fluorescence signal (Figure 9b). Stereoscopically derived concentration estimates of the centric diatoms (Figure 9c, thick dashed line) fall within the range observed in the Southern California Bight region (e.g. Venrick, 2012). We found an increase in the acoustic-derived concentration of targets with BTS values between −130 and −123 dB coincident with both the diatom-like concentration peaks and the layer of high-intensity bulk fluorescence. This observation provides support for the hypothesis that these large diatoms were the source of the acoustic signal. Figure 9. Open in new tabDownload slide Acoustic reflections from two peaks of large centric diatoms sampled on 28 March 2012. (a) Fluorescence intensity profile; (b) images from O-Cam 1; (c) ZOOPS-O2 profiles comparing optic (broken lines) and acoustic (solid lines) data; thin solid line represents targets in the range of zooplankton and marine snow (BTS > −123 dB); dotted line represents zooplankton and marine snow counts from one O-Cam; thick solid line indicates concentration estimates from acoustic data from targets whose BTS ranged between −130 and −123 dB (diatom-like targets); thick dashed line represents diatom-like particle concentration obtained with the semi-automatic quantification method. Inset in (c) shows the relationship between optically and acoustically derived estimates of diatom concentration. Figure 9. Open in new tabDownload slide Acoustic reflections from two peaks of large centric diatoms sampled on 28 March 2012. (a) Fluorescence intensity profile; (b) images from O-Cam 1; (c) ZOOPS-O2 profiles comparing optic (broken lines) and acoustic (solid lines) data; thin solid line represents targets in the range of zooplankton and marine snow (BTS > −123 dB); dotted line represents zooplankton and marine snow counts from one O-Cam; thick solid line indicates concentration estimates from acoustic data from targets whose BTS ranged between −130 and −123 dB (diatom-like targets); thick dashed line represents diatom-like particle concentration obtained with the semi-automatic quantification method. Inset in (c) shows the relationship between optically and acoustically derived estimates of diatom concentration. Dense phytoplankton aggregations are often regions of intense grazing, and increased abundances of zooplankton within or near the observed diatom peaks might be expected. However, acoustic- and optic-derived concentration estimates of zooplankton (targets whose BTS > −120 dB; Table 1) and marine snow are comparable in magnitude through the entire profile (Figure 9c, thin solid line and dotted line, respectively), and their concentrations did not peak with the diatoms concentrations. This suggested that neither zooplankton nor marine snow were the cause of the increased acoustic signals found within the diatom peaks (Figure 9c, thick solid line). This further supported the hypothesis that the high concentration of diatoms was responsible for the broadband, high-frequency (1.5–2.5 MHz) signal detected by the ZOOPS-O2 system. One other potential source of the acoustic signal in the layers is thermal microstructure. Acoustic scattering associated with the thermocline, e.g. was reported as early as Weston (1958). It has also been found that thermal structures are sensed by broadband systems at high and ultra-high frequencies (Holliday and Pieper, 1980; Lavery et al., 2010). The coincidence of biological and thermal structures has been recognized since early acoustic underwater research (Gessner, 1948; Cushing et al., 1956; Cushing and Richardson, 1956; Tveite, 1969; Derenbach et al., 1980). Given that ZOOPS-O2 is a broadband system operating at 1.5–2.5 MHz, we wanted to rule out the possibility that the acoustic signal in the profile in Figure 9c (thick solid line) was due to thermal microstructure. To test this, temperature, acoustic and stereoscopically derived diatom-like concentration data were binned over 1 meter, and temperature gradients estimated by measuring the change in temperature (dT) over 1 m depth (dZ) bins (Figure 10, thin black solid line). Three main sharp temperature gradients were apparent. The first (Figure 10, feature labelled “1”) was found above the diatom peaks, and the two subsequent ones (Figure 10, features labelled “2” and “3”) at the top and bottom of the two diatom-like peaks. If the increase in the acoustic signal (Figures 9c and 10, thick solid line) was solely a response to the sharp temperature gradients, one would expect to see three corresponding acoustic peaks. However, the acoustic signal did not drop where there were no sharp temperature gradients. Instead, high concentrations of diatom-like particles were observed in that depth interval. This suggested that temperature microstructure was not the main source of the observed acoustic return in the −130 to −123 dB signal. Furthermore, a simple regression showed that variations in the acoustic data were better explained by the stereoscopic (optically derived) diatom-like concentration (r2 = 0.85) than by thermal gradients (r2 < 0.07). Although this does not rule out the possibility of thermal microstructure contributing to some degree to the acoustic signal, our analyses suggest that the increase in the acoustic returns is primarily due to the presence of centric diatoms. Figure 10. Open in new tabDownload slide Optically and acoustically derived diatom-like concentrations (dashed line and thick solid line, respectively) and temperature gradients (dT/dZ; thin gray solid line) for the same profile shown in Figure 9. The three sharpest gradients in temperature are indicated with circled numbers 1, 2, and 3. Figure 10. Open in new tabDownload slide Optically and acoustically derived diatom-like concentrations (dashed line and thick solid line, respectively) and temperature gradients (dT/dZ; thin gray solid line) for the same profile shown in Figure 9. The three sharpest gradients in temperature are indicated with circled numbers 1, 2, and 3. Interestingly, the relationship between the optic- and acoustic-derived densities of diatom-like particles was not linear (Figure 9c, inset). This suggests that at high diatom concentrations the acoustic signal became saturated, no longer reflecting the echoes of individual diatoms, but rather integrating over multiple targets. Because the diatoms were smaller than the range resolution (1.5 mm) of the sonar system, and multiple cells occurred in the volume at the same acoustic ranges, our system was not able to resolve echoes from individual phytoplankton cells at high concentrations. We did not pursue volume backscatter relationships in this article, and they remain a topic for further study. It is also apparent that the acoustically derived concentrations of diatom-like particles (Figure 10, thick solid line) are over estimated at lower concentrations (from camera estimates; dashed line). There are two explanations for why this might be happening. The first has to do with a signal to noise ratio (SNR) issue: because our estimates were near the lower threshold of our system for detecting individual echoes, it is possible that a low SNR might be preventing us from accurately estimating diatom abundance with the sonar. The second reason is the potential variability in diatom size through the profile. Coscinodiscus (the imaged diatoms reported in this work) range in diameter from 300 to 500 μm in diameter and 150–250 μm in width. At the lower size range (a = 0.108 mm) these diatoms would have ka = 0.9, effectively putting them in the Rayleigh scattering region (ka < 1). At the other end of the size range (and similar to the ones we encountered), Coscinodiscus has an equivalent spherical diameter of a = 0.1802 and ka = 1.5 (i.e. ka > 1; geometric region). Such variability in size might have also contributed to missing echoes from individual cells. It is not clear how much acoustic energy several small diatoms would reflect when in high concentrations. However, this work is not intended to show that the sonar is able to quantify diatom concentrations with the same accuracy as the optical method, but rather, to show that our sonar can record a measurable echo originating from locally high concentrations of diatom cells. Conclusions In this work we have shown that individual gelatinous zooplankton and marine snow targets are capable of reflecting broadband, ultra high-frequency (1.5–2.5 MHz) acoustic energy. We also showed that layers of locally high diatom concentrations were capable of reflecting sound at these high frequencies. Knowing that diatom and marine snow layers are conspicuous and recurrent features in coastal areas, this work suggests that such particles should be taken into account during acoustic surveys, especially when high frequencies are used and the relative concentration of such particles is significantly higher than co-occurring zooplankton. Furthermore, gelatinous organisms (e.g. jellyfish, siphonophores, doliolids, and salps—although no salps where observed in this work) can dominate zooplankton communities at times (e.g. Richardson et al., 2009; Everett et al., 2011; Alvarez Colombo et al., 2009) and can become significant contributors to acoustic signals, as it has been shown for the large jellyfish C. melanaster (De Robertis and Taylor, 2014). It is hard to know whether the observations made with this broadband, high-frequency system are relevant to the more commonly used lower-frequency systems without having empirical data. However, Figure 8b shows that the location for the transition frequency between the geometric and Rayleigh scattering in the modelled spectra for the type and size of targets observed here fall within the frequency range 38–420 kHz, and are therefore likely to be picked up by systems using these lower frequencies. These organisms could include large phytoplankton cells and phytoplankton chains; it is advisable, therefore, to exercise caution when interpreting narrowband acoustic data acquired in regions where large aggregations of phytoplankton or marine snow occur. For example, Timmerman et al. (2014) suggested that a layer composed of diatom flocculates was detected with a narrowband sonar using 200 kHz. Furthermore, it has been shown that phytoplankton-derived flocculates can carry bubbles (Riebesell, 1992), which can have a large resonance at lower frequencies than those considered in this work. The emergence of commercially available broadband technologies creates new opportunities for the use of processing methods for interpreting acoustic data. Although not fully considered in this work, the properties of acoustic echoes (e.g. the echo envelope; lines in Figures 4–6) can add more information for acoustic target classification. The results presented here underscore the need to exercise caution when interpreting acoustic data based solely on TS measurements. This issue is particularly relevant when encountering a diverse group of echogenic particles that range widely in size but return overlapping BTS values (Table 1). Take for instance the case of copepods and doliolids: the tiny crustaceans can be an order of magnitude smaller than the pelagic tunicates, yet the measured BTS values overlapped significantly, despite of having no overlap in their ka values (Figure 3a and b). Simple modelled spectra for organisms such as copepods, euphausiids, mysids, doliolids, and hydromedusae, as well as marine snow showed good agreement with in situ measurements. These results could be particular useful for models considering more complex body shapes, and organisms of different composition. The DWBA model using a prolate spheroid of dimensions similar to the organisms studied here could be used initially to aid in differentiation among targets. However, the relatively scarcity of g and h values for zooplankton hinders our ability to obtain more accurate comparisons. Given the sensitivity of the DWBA model to g and h (data not shown), we should strive to get better values for zooplankton to produce more accurate model results. In theory, one could diagnose g and h values by fitting model spectra to the measured spectra by varying g and h. The work presented here may prove useful in biological field surveys; when possible, incorporating a suite of technologies including acoustic, optical imaging, and net systems will generate a more complete picture of planktonic distributions and possible insights to processes occurring at fine scales. In light of the present observations, it is advisable that zooplankton surveys using high-frequency acoustics take into account phytoplankton and marine snow, if such features are known to be conspicuous in the region studied. Although nets target a finite size-range of planktonic organisms—retaining large particles and destroying fragile organisms and marine snow particles—optical cameras are an important complement because of their non-invasive, non-destructive capabilities. Furthermore, nets cannot resolve the fine spatial scales at which phytoplankton and marine snow layers occur. Optical tools, on the other hand, can be used to detect such ubiquitous features and provide accurate ground truthing information for acoustic studies that might be affected by phytoplankton, marine snow and fragile, gelatinous taxa. Acknowledgements We would like to thank the National Science Foundation (0728305) for funding the development of the ZOOPS-O2 system. Christian Briseño-Avena would like to thank UC MEXUS (USA) and CONACyT (Mexico) for their support to fund his PhD studies at UC San Diego, Scripps Institution of Oceanography. We would also like to thank the captain and crew of the R/V New Horizon for their assistance during fieldwork. The authors thank three reviewers for their constructive criticism, which they found very helpful and resulted in an improved document. References Alldredge A. L. , Cowles T. J., MacIntyre S., Rines J. E. B., Donaghay P. L., Greenlaw C. F., Holliday D. V. et al. 2002 . 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Morales-Nin, Beatriz;Browman, Handling editor: Howard

doi: 10.1093/icesjms/fsx210pmid: N/A

Abstract In this essay, I review my career in fish and fisheries, describing my work in the context of the ideas of the period and how they have changed with time. My experience may be interesting especially for other women from social backgrounds that do not promote competitive careers. The main lessons that I have learned are that to be persistent and ambitious, to associate with top scientist, and good persons too! is rewarding at several levels. Not to try to be the one in the spotlight but to recognize other people merits gives good results in the long run. Moreover, to take risks from time to time and explore new territories, like science administration in my case, helps to reinvent yourself and keep the intellectual interest alive. I would like to encourage young scientists to persevere, be resilient and take, from time to time, risky decisions. Maybe one does not become rich, but the mental challenges, the networking, the travel and the exceptional places and life experiences make you rich in many worthy ways! One does never get bored. Introduction When I was invited to contribute to the Food for Thought series I doubted that my career was relevant enough, but I finally decided that my experience could be inspiring for other people like myself, who began with poor prospects in a career in science. A woman of my time and culture, a non-native English speaker with a large family, was a person with few opportunities to develop a career in fisheries. However, I have been lucky, and taking advantage of the opportunities that presented themselves, I have been able to contribute to science, viewing it as the pyramidal structure described by Pauly (2016): not as the Pharaoh but as one of many workers carrying stone blocks. The bulk and conductor of my work as fishery biologist has been the aging of fish, otolith structure, and the determination of fish growth and mortality rates. The presence of rings on fish vertebrae and the possibility of aging fish by counting these rings was described as early as the XVIII century (see review in Meunier and Panfili, 2002). Otolith age-based determination is still a very important part of routine fisheries management, with more than 1 million otoliths read each year (Campana and Thorrold, 2001). A logic development in my career was to get involved in fisheries studies and fisheries management. In the 40 years that I have been active in these fields, they have evolved significantly; age determination was considered an art and is now a technique, and the study of otoliths has flourished in new ways; fishery studies have expanded beyond considering only main industrial fisheries to include small-scale and recreational fisheries. Moreover, working in marine science has changed from a somewhat narrow specialization (in my case age determination) to a wider holistic approach with the ecosystem and the global change as a background. Consequently, there is a strong interest in reaching the society to involve them in issues that are relevant for everybody. In the last part of my working life, I have contributed to the administration of marine science in Spain, a responsibility that I performed under conservative and social-democrat governments with different agendas. This has undoubtedly been the most challenging part, requiring integration of administrative and sociological skills in which a fishery biologist, such as myself is not typically trained. Background: marine sciences in Spain Spain is a medium-sized country, surface 505 992 km2, with a modest economy, the fifth in Europe in nominal terms, based mainly on services like tourism. Education, science and technology have not been traditionally major issues for our politicians, as Spanish investment in science and technology is one of the lowest in Europe (1.22% GDP on 2015). After the major emigration of the best Spanish scientists due to the Civil War (1936–1939) the science scenario in Spain was quite bleak. However, the situation improved progressively with the economic opening of the 60s and the dramatic changes that followed the arrival of democracy in the 80s. The first social-democrat government created the Science Law and the National Research and Innovation Plan in 1986, aiming to improve scientific research quality and offer opportunities for scientists (Muñoz and Ornia, 1986; Sánz Menéndez, 1997). The development of the Science Law allowed the Science Council of Spain (Consejo Superior de Investigaciones Científicas, CSIC) Institutes to elect Directors by a vote of their scientific members, creating a more democratic organization. In parallel, the Science Law promoted competitive science with funding dependent on the individual scientist’s capacity to obtain external funds (Fernández-Esquinas et al., 2009). The Inter-ministerial Commission of Science and Technology was in place to fund research for different research areas; after 2000 this role was taken consecutively by different Ministries. Marine Science was a part of the Natural Resources science and technology area; only in 1995 a specific programme was created. Spain joined the European Union (EU) in 1985 when I was in my 30s. I remember how proud I felt the first time that I passed a police control at an airport as an EU citizen. In addition to my personal history, the possibility of obtaining EU funds was a very important contribution to the development of science in Spain. The EU framework programmes (FPs) resulted in an increase in funding from 1% of foreign funding in 1985 to 8% foreign funding, representing 100 million Euros in 2015 (Instituto Nacional de Estadistica). We evolved with the different FPs and are now involved in the most integrative and innovative projects with stakeholders from industry, management and social organizations through Horizon2020 (COM 2010). Therefore, the marine science landscape has changed for better in Spain regarding funding, integration and possibilities for networking, opening new opportunities for innovation and collaboration. Overall, lacking a direct economic evaluation of the services and economic revenues related to coastal and marine areas, Europe must address the challenges that emerge from growing competing uses of the sea, ranging from maritime transport, fishing, aquaculture, leisure activities, off-shore energy production and other forms of seabed exploitation. By helping to develop a more sophisticated understanding of the impact of human activities on marine systems, scientific research and technology may provide the key to obtain a sustainable use of the seas. From rings to increments: a time development I started my biology studies in 1970 at Barcelona University the same year that I married. I started volunteer work at the “Instituto de Investigaciones Pesqueras de Barcelona” (IIP, now “Instituto Ciencias del Mar” ICM-CSIC) in 1975. This collaboration opened me the doors to the development of my career. My mentor, Dr. Carles Bas, suggested I develop the technique to observe the daily growth increments (DGIs) in the otoliths of Mediterranean fishes, following the discovery of Pannella (1971), which opened up new fields of investigation. Luckily, we had a scanning electron microscope (SEM) at the Institute. Learning from the beginning and based on the then recent work of Rannou and Thiriot-Quievreux (1975), I pioneered the technique in Spain for observing DGI using optical and SEM microscopy. This was the subject of my Master’s thesis, defended at Barcelona University in 1978. Then, there were not word processors and I did all my writing with an electric typing machine. I was really angry when my Director corrected my text; I had to type so much afterwards! In this period I had my first two children, I managed to do my work through the economic support of my husband the help of my mother and sister baby-sitting! This help was fundamental in my early working years; family networking is typical in a Mediterranean country like Spain. In 1978, I was part time contracted to collaborate in the project on Spanish Namibian fisheries that was then run by IIP. The fishery was managed since 1967 by the International Commission for the Southeast Atlantic Fisheries (ICSEAF), which was responsible for studying the environment, fisheries and ecosystem to assess and provide management for setting quotas of major fish stocks. As part of this project, I was assigned to read Cape hake (Merluccius capensis) and Merluccius paradoxus otoliths, and I earned my first money as a biologist. I started travelling as part of my work, something that is one of the pleasures of my life. I was enticed by the opportunity of participating in research cruises in Namibian and South African waters on board the RV Africana. It was also very interesting from the sociological point of view, these were my first travels outside Europe and these waters, overflowing with life, were a strong contrast to the Mediterranean. Additionally, I started a somewhat turbulent and life-long relationship with Merluccius otoliths and their fractal structure, with rings inside rings. My participation in the annual ICSEAF scientific meetings was very instructive. I still remember when I participated the first time in 1979, pregnant of my third child and with a poor English level, and I was requested to express my opinion as representing Spain because all other delegates were not present. After that, everything was quite easy! My age-length data were used in virtual population analysis for the assessment (Lleonart and Morales-Nin, 1985). I felt the responsibility towards the management of the fishery, and due to the inherent subjectivity of otolith age reading I wanted to get support in my interpretations, thus I actively contributed to establish an “ICSEAF ad hoc working group on age interpretation” where the international team working on several species could standardize procedures and develop common interpretation criteria. The working group was active, with annual meetings and workshops, and produced two special publications for the interpretation of hake otoliths (M. capensis, M. paradoxus) and horse mackerel (Trachurus capensis) (Morales-Nin 1983, 1986a, b, c). Contributions of age, growth and biology were published as ICSEAF Research Documents. My involvement in ICSEAF finished in 1988. Getting degrees I was puzzled by the opaque and translucent growth structure of otoliths and how the structure of the otolith interacted to create the DGI and rings. Hickling (1933) had already postulated that the growth structures in European hake otoliths (M. merluccius) were composed of organic and inorganic constituents and that their relative variation caused changes in opacity. Based on the works published in the 60s and 80s (Irie,1960; Carlstrom, 1963; Christensen, 1964; Degens et al., 1969; Dunkelberger et al.,1980) I studied the microstructure of the increments considering the organic matrix and its interaction with the aragonite microcrystals in Dicentrarchus labrax from the Mediterranean and M. capensis, M. paradoxus, and Genypterus capensis from the SE Atlantic. I described the aragonite microstructure and ontogenic differences in the organic matrix, with acidic and hydrophoric amino acids increasing with age, while basic amino acids decreased, and polar amino acids and glycine remaining stable (Morales-Nin, 1986a, b, c). In parallel, the influence of the environment and of inner physiological rhythms in the formation of daily and subdaily DGI were discussed (Morales-Nin, 1987a). I published a model of the interaction of crystalline and matrix ultrastructure in the Fish Age and Growth Iowa proceedings Symposium of 1986 (Morales-Nin, 1987b). This was my first International Symposium dedicated to the research that has been so important to my career. These works constituted my PhD thesis, defended at Barcelona University in 1984. I required 6 years to complete my PhD because besides my family duties I was working as technician and did my research on spare time. My first publication on the microstructure of M. paradoxus otoliths was in Spanish, in a Spanish publication with small distribution (Morales-Nin, 1980a). At the same time, I was able to present my work at the “Commission Internationale pour l’exploration scientifique de la mer Mediterranee” dealing with otolith microstructure (Morales-Nin, 1980b) and a work analysing the mercury content in the flesh, vertebrae and otoliths of Bluefin tuna (Morales-Nin and Cruzado, 1991). My results showed that heavy elements like mercury do not easily pass the inner barriers from the sacculus, and thus otoliths are not a good register for heavy metal contamination. Much later, in experiments with turbot juveniles and a diet contaminated with Prestige oil, we showed that Na, Mg, and Sr did register in the otolith, revealing the possibility of using otoliths for trace contamination (Morales-Nin et al., 2007). Again publishing locally, which was at that time encouraged in Spain, I published a work on the crystalline G. capensis otoliths (Morales-Nin, 1985). This early contribution to biomineralization could have had greater impact if published in an international Journal. I learned that it is important to project your work in international phora, and I have encouraged scientists in developing countries to do so. Sometimes local rules or lack of confidence, result in limiting the impact of sound work and knowledge contribution. Trying to contribute to otolith growth modelling, as a part of my PhD, I contacted a dendrocronologist, Dr Emilia Gutiérrez (Barcelona University), and doing an interdisciplinary work we showed that otolith growth is a conservative process, as growth on a given day depends on fish growth on the previous ones. The effect of water temperature was registered on the following 3 days of growth (Gutiérrez and Morales-Nin, 1986a, b, c). During this time, I was working as technician with a temporal contract (1979) and afterwards with a permanent contract (1983). In 1986, I secured a permanent position as a tenured scientist at the “CSIC.” Therefore I could breathe more freely and expand my interests in different fields not having to do routine otolith reading for assessment. I believe that to be a good technician you have to study and with an extra effort it is possible to develop a scientific career and promote professionally. I have seen many similar cases in Spain, mostly women. From the tropics to Antarctica Again my mentor, Dr C. Bas, encouraged me to apply for a FAO André Mayer Fellowship (1988–1989). The aim was to determine whether the DGI could be applied to ageing tropical fishes, which were supposed to not have seasonal growth increments. To my surprise I was successful and I got a contract for 18 months and the possibility of being a visitor in a foreign laboratory. I chose Dr Richard Radtke’s laboratory at the University of Hawaii at Manoa to develop my work because he was one of the few scientists that worked with tropical fishes and DGI. This was a huge opportunity for me; as well as a personal challenge due to my family. I remember crying during my briefing in Rome, because I never was separated from my children for long periods. I put a photo of myself on top of the TV and hoped for the best! The fellowship provided salary and travel money but also allowed me to visit different laboratories and meet relevant people. I visited Dr D. Pauly at the International Center for Living Aquatic Resources Management and improved my length frequency analysis, and in a 2-week visit I was also involved in writing a contribution to a book on Peruvian fisheries analysing anchovy length frequency and otolith microstructure to determine age and growth (Morales-Nin, 1989). I was able to prove that at least some tropical fish like Lutjanus kasmira indeed had seasonal increments, as validated by marginal increment analysis, DGI enumeration and length frequency analysis (Morales-Nin and Ralston, 1990). The final product was a FAO Fishing Technical Document (No. 322, English and Spanish versions, Morales-Nin, 1992) that reviewed otolith extraction, preparation, age determination and age validation methods for tropical fishes. The challenging situation of a somewhat open opportunity to develop and manage my work, albeit having to provide results in a short period of time, gave me confidence in myself. I could see that I was able to adapt to a new country and way of living (the United States), to be separated from my family and to manage my own research, not being in the framework of any other project. This experience of being on my own has been useful all my life. Thus more confident in my capabilities, I returned to my Barcelona Institute for a short period of time; I was invited to be part of a new Mediterranean Advanced Studies Institute (IMEDEA) a joint venture of CSIC and the University of the Balearic Islands (UIB). As I had fallen in love with Islands and my family accepted, we moved to Mallorca in 1990 and we still are there. To be part of a new Institute has offered me the opportunity of developing new research lines (like small scale and recreational fisheries) and to consolidate a team. Besides the Balearic Islands are in a privileged geographical situation allowing to be a very suitable place for Mediterranean studies. Initially, I spent 10 fruitful years at the “Centre Oceanografic de Baleares-IEO,” thanks to an agreement between CSIC and “Instituto Español de Oceanografía” (IEO). In 2000, IMEDEA moved to a new installation and I was transferred to a new building where I could develop a schlerocronological laboratory and consolidate my research. Through my collaboration with Dr R. Radtke I was invited to participate in Antarctic cruise fishing for planktonic fish eggs on 1990. This experience resulted in being nominated as the chief scientist in the maiden Spanish Antarctic cruise of the RV Hespérides in 1991. This was a big challenge, because the ship is manned by the Spanish Army, and there was not much previous experience in scientists–military collaboration. Moreover, the ship and the equipment were not previously tested in freezing temperatures. Thus, as the late Captain José Carlos Manzano said, we began a science-military relationship that has proved to be successful in following years. It was amazing that in agreement with the ship name “Hespérides”—from the mythological Greek nymphs taking care of an apple garden in an island—the scientist team was from the Canary and Mallorca Islands and the Captain name was “Manzano” meaning apple tree! I became 40 during the cruise and it was my most amazing birthday! Thereafter I participate in several Antarctic cruises publishing in fish distribution, biology and physiology (Morales-Nin, 1994; Morales-Nin et al., 1995, 1998, 2002; Balguerias and Morales-Nin, 1997; Morla et al., 2003). I think that Antarctica was an extraordinary experience from the point of view of the biology of its inhabitants and their adaptations to this unique environment and also due to my feeling of being in the last uncontaminated land and sea. From a black box to a white one Otoliths are like flight data recorders. However, these black boxes are devices that can be viewed in terms of its inputs and outputs without any knowledge of its internal workings. This initial view is changing thanks to intensive work to understand how an otolith is formed, how it works and which are their fundamental properties. In fact, otoliths are becoming the opposite, transparent boxes in which the system’s inner components are available for inspection. I have had the opportunity to contribute participating in several initiatives, as well as with my personal work. It had now become clear that with suitable techniques, the information encoded in most otoliths can be deciphered. I found the same increments in deep-sea fish (Morales-Nin, 1990), tropical fish (Morales-Nin and Panfili, 2005), Antarctic fish (Morales-Nin et al., 2000), and Mediterranean fish (Morales-Nin and Aldebert, 1997). In my single investigation into fossil otoliths, I was amazed by how the inner structure of otoliths endures with time, with similar DGI and seasonal increments in the Oligo-Miocene and today (Woydack and Morales-Nin, 2001). Age determination and standardization The recognized need of standardization of procedures and to change from an “art” to common protocols for age-length keys and growth curves, promoted the European Fish Aging Network (EFAN) (1997–2000), an EU-funded project coordinated by Dr Erlend Moksness, that was followed by Towards Accreditation and Certification of Age Determination of Aquatic Resources (TACADAR, 2002–2006). Networking established permanent trust, friendship and collaborations all across Europe, that have allowed competition for EU funding in an environment of major projects and increasing competence (Appelberg et al., 2005). I coordinated IBACS: integrated approach to the biological basis of age estimation in commercial important fish species (QQLRT-2001-01610, 2002–2005), which aimed to understand the mechanisms underlying otolith growth. Validation of growth structures in otoliths and microchemical and optical studies allowed developing a bio-energetic model (Hussy and Mosegaard, 2004). A very valuable and timely development was the edition of the Manual of Fish Sclerochronology (Panfili et al., 2002). This manual, using in part the work of EFAN and TACADAR, thoroughly revised guidelines on calcified tissues applications (including videos of methodology). I had the honour to contribute with six chapters. I consider it the otolith “bible” that compiles the state of the art in calcified tissues, establishing the research background for a long time to come. By 1993, the first of a new generation otolith symposia was born, namely, the First International Symposium on Fish Otoliths: Research and Applications (Secor et al., 1995). In 2004, the name was shortened to the International Otolith Symposium (IOS). The series was designed to encourage the exchange of information and expertise and to promote the development of new techniques and applications for otolith-based analysis in ecology, management, and conservation, creating a sound background. IOS are not directly supported by any organization, the age scientists organize them themselves, taking turns to host the Symposium with a 4-year periodicity. The relevance for the community is proved by the IOS long life. I was honoured to be selected to organize the IOS2014. I asked Prof. Audrey J. Geffen (Bergen University, UoB) to be my co-convener, and thus this symposium was the first convened by women. The IOS2014 was in my home island, Mallorca, and we organized it into four main topics and two workshops with support from the International Council for the Exploration of the Sea (ICES) and local institutions. Two special journal issues, in the ICES Journal of Marine Sciences and Marine and Freshwater Research, respectively, reflect the main contributions (Morales-Nin and Geffen, 2015; Geffen et al., 2016). This was the culmination of my “otolith career” and it was a joy to balance science, gender equality, a good atmosphere and cultural events (like the fire Mallorca demons) that I hope that the participants will remember. Otolith morphology Otoliths have been viewed as magic objects, amply used in folklore as medicine or charms. Since Aristotle described the presence of stones in fish heads, and Cuvier recognized the specific nature of the otolith morphology, these external features have been used to reconstruct fossil paleo faunas, for taxonomy and for the identification of stomach contents (see Tuset et al., 2008 for a historical perspective). Following my Merluccius studies, we explored the otolith morphology of nine species to define their evolutionary trends with my first PhD student, Dr Antoni Lombarte (ICM, CSIC). Moreover, the influence of environmental factors such as fish vertical distribution on otolith shape and microstructure (Lombarte and Morales-Nin, 1995) were assessed. In 1993, we developed semiautomatic otolith reading using a Fourier analysis of the luminous signal after filtering high frequencies by means of a moving threshold and additional spatio-frequential wavelet analysis. Thereafter we obtained a combined age determination and vector of increment widths that was included in an Optimas application (Morales-Nin et al., 1998). Otolith morphology and shape are a good example of how the technology might open new opportunities; e.g. extended use of shape analysis is used for species and stock determination, as on-line tools are developed and even allow statistical shape analysis like AFORO (http://www.cmima.csic.es/aforo/) (Lombarte et al., 2006). Otolith geotags Otoliths are relatively pure CaCO3 compounds with inorganic impurities amounting to <1% of otolith weight, although many elements are present, mainly derived from surrounding water. As reviewed by De Pontual and Geffen (2002), there are many mechanisms, causes and applications of element and isotope incorporation into otoliths. Chemical assays of fish otoliths have increased notably to give insight into migration patterns, life history strategies, and mixed stock dynamics (Campana, 1999). We successfully used trace elements in the otoliths of deep water Nezumia aequalis, a small macrourid that is widely distributed throughout the Atlantic and Mediterranean, with significant differences between populations of the elements Li and Sr (Swan et al., 2003). Solution-based inductively coupled plasma mass spectrometry of whole otoliths used in this work, combined with LA-ICPMS of the otolith nucleus, was applied to differentiate two exploited continental slope species, Helicolenus dactylopterus and M. merluccius, from five to seven locations. The results showed that Sr, Ba and Cu could separate H. dactylopterus populations, while Mg and Pb differentiate M. merluccius in the Atlantic and Ba, Sr, and Pb in the Mediterranean (Swan et al., 2006). In these papers, we showed that otolith trace elements were good classification tools for stocks and produced results that were as sound as the morphometric and other methods (i.e. parasites) used at the time. The rapid development of trace element studies (or geotags) requires standardization, covering procedures and processes from postmortem contamination of otoliths (i.e. handling and storage methods (Swan et al., 2006), cleaning procedures (Davies et al., 2011); otolith preparation to detection limits and accuracy and precision of the analytical equipment (Campana et al., 1997). Due to the fact of the measurement of otolith elements may vary with methodology, it is imperative to consider the sensitivity, accuracy and precision for each application as the suite of elements of interest may differ. The development of reference materials has been the first step in standardization of the procedures, but there is a need for wider ranging calibration and comparison studies (Geffen et al., 2013). One geotag application is the differentiation between stocks of widely distributed species (Elston and Gillanders, 2003). However, they can also give insight into individual fish life strategies. An example is Argyrosomus regius, which uses different water masses during its life history; it is associated with estuaries for reproduction, and the habitat use of wild fish suggested that most fish spend the first 2–4 years in offshore waters and begin moving between water masses after age 6 (Morales-Nin et al., 2012). We used oxygen (δ18O) and carbon (δ13C) isotope ratios in sagittal otoliths of recruits and juveniles of the European hake Merluccius merluccius to show that fish from the early stages in June 2003 encountered less trophic resources, resulting in poor fish conditions and subsequent a decrease of recruitment. The estimated temperature from the core area of otoliths showed lower temperature regimes in the 2002 hatching season compared with 2003, which could be a possible explanation for the observed differences in success of subsequent recruitment (Hidalgo et al., 2008,a, b, 2009). An interesting article used different information encoded in the otoliths of a cod (Gadus morhua) found in the catch of a Mallorcan trawler, despite being a cold-water species (Morey et al., 2012). Our results showed that this surprising migrant was a female 4-year-old which entered the Mediterranean in her first year of life. This cod followed the millions of tourists visiting the Balearic Islands, albeit afterwards not being in a good condition index and with atresic gonads. So, facts not always support theory. The funny part was that for a paper based on a single fish there were eight authors! Another application is trace temporal exposure to metals. Recent evidence suggests that the chemical composition (mainly metals) of ingested hooks can potentially be absorbed and retained within the soft tissues of fish and can subsequently have adverse effects on their health. We positively tested the hypothesis that otoliths should also show a chemical signal from the hook, using the mulloway (Argyrosomus japonicus) as a case study (Alós et al., 2016). Despite the wide use of geotags, many issues are not yet solved (Campana, 1999; Elsdon et al., 2008). There are age related trends in M. merluccius otoliths (Morales-Nin et al., 2005a) as well a large overlap in composition between six different locations (Morales-Nin et a.l, 2014). The ontogenic effects in otolith trace element composition and the possible permeability of otoliths to certain elements (Gauldie et al., 1998) merit more in-depth studies. From industrial to recreational fisheries During my career, the fisheries concept has changed from one of resource exploitation that has to expand and maintain a Maximum Sustainable Yield to a more integrated ecological perspective, including sustainability and human and sociologic factors. I became interested in locally relevant fisheries with my transfer to the Island of Mallorca (W Mediterranean). There, I learned about the seasonal small-scale fisheries typical of the Mediterranean and realized that they are more important socially and economically than previously estimated. To study small-scale fisheries in 1990 was to go against the main current; there were few data, and I could, therefore, be instrumental in increasing awareness of their relevance. In the Mediterranean, small-scale fleets play an important socio-economic role and have a long-lasting tradition (Morales-Nin et al., 2010). This fleet represents 80% (42 000 boats) of the EU Mediterranean fishing vessels and contributes to 12% of EU catches and provide ∼100 000 jobs and represent 42% of EU fishing sector employment (C.O.M., 2002; Morales-Nin et al., 2010; Maynou et al., 2013). The majority of the small-scale fleet in Majorca exploits different “métiers” depending on seasonal species abundance (Iglesias et al., 1994; Palmer et al., 2017). However, the biology of species and fishery characteristics were poorly understood. I believe that it is basic to know the life history of the exploited species, thus working in collaboration with the other research institutions in the Island, we have described growth and reproduction traits (Massutí and Morales-Nin, 1995; Iglesias et al., 1997; Massutí et al., 1998, 1999a, b; Iglesias and Morales-Nin, 2001; Alós et al., 2010; Grau et al., 2016), mobility (Alós et al.,2011) and population connectivity (Morales-Nin et al., 2014). Regarding small-scale fisheries, we have described the characteristics of some fisheries across the Mediterranean (Morales-Nin et al., 1999; and their relative economic relevance in the Balearic Islands (Merino et al., 2008; Reglero and Morales-Nin, 2008; Maynou et al., 2013). Although these are data-poor fisheries, due to the lack of long term registers and un-reporting, there are approaches to solve management issues (Pilling et al., 2008). For instance, the crystal goby (Aphia minuta) fishery is the second case of co-management in the Mediterranean (Morales-Nin et al., 2017). The implication of stakeholders in the fishery management implies shared responsibilities, transparency and contributes to a dynamic flow of information. My experience has been positive; the fishermen appreciate the direct contact with scientists. It is important to consider that they are gaining their livelihood (not very well), therefore the solutions based solely on cutting effort, might be considered with precaution. The small-scale fisheries in Europe face an uncertain situation, with various pressures that will undermine their social and economic viability in the near future. We investigate the issue of sustainability and options for future development by determining socio-economic status; the economic profiability of this activity is low, and under the current economic situation, small-scale fishing has limited attractiveness. However, as producers of high-quality fish, the activity should create opportunities in the future. The factors identified as responsible for the low profitability are excessive pressure on resources and competition with other fisheries operating in the same area, including recreational activity. We suggested diversifying activity by combining professional fishing with tourism fishing, even though this is less attractive to professional fisherman in Majorca. This model of development is acceptable to the stakeholders involved in the fishery and is also in line with current European policies (Marine Strategy Framework directive) that favour spatial planning as fisheries management measures (Maynou et al., 2013).This work has been greatly dependent on good collaboration with the regional fisheries government, “Direcció General de Pesca i Medi Mari.” We also established a cooperative venture with the “Laboratori d’Investigacions Marines i Aqüicultura” (Balearic Government). The result of this collaboration has been a long-term aim of providing basic knowledge for managing coastal fisheries (Morales-Nin et al., 2010). It was evident that in the coastal area, there was also a very important recreational fishery. Although in the US recreation fisheries are well studied, in Europe and in the Mediterranean, marine recreation activity was almost unknown. We performed a comprehensive study of this very popular and extended activity using various census methods, concluding that almost 10% of the Mallorca population fished at least once a year (Morales-Nin et al., 2005b). We have explored angler motivations, the importance for tourism, and how households with anglers make different consumer choices (Cardona et al., 2010; Cardona and Morales-Nin, 2013; Morales-Nin et al., 2013, 2015). The effect of the environmental conditions on the fishers behaviour was also explored (Cabanellas-Reboredo et al., 2012). We noted that specifically in the case of recreational fisheries the biomass extracted depended on the spatial and temporal heterogeneity of the fishing effort, environmental effects on the capture per unit of effort and the variability in anglers skills (Cabanellas-Reboredo et al., 2017). We also studied the impact of the fishery on the fish, showing how the recreational fishing pressure can affect fish growth (Alós et al., 2014) and behaviour (Alós et al., 2012). This information has been in part used to manage the recreational fisheries in Mallorca: closed seasons, protected areas, gear regulations, and obligatory licencing are applied. The awareness of the need to consider the recreational fishery in the management of coastal resources has increased in the recent years, resulting in different international initiatives. Like the creation of the Working Group on Recreational Fisheries Surveys (WGRFS); this is the ICES forum for planning and coordination of marine recreational fishery data collection for stock assessment purposes. WGRFS is an offshoot of the 2009 Workshop on Sampling Methods for Recreational Fisheries, I participated in these activities and hosted the WGRFS in Mallorca on 2013. Moreover, this was also felt in the Mediterranean, with a General Fisheries Commission for the Mediterranean (GFCM) Transversal Workshop on the Monitoring of recreational fisheries in the GFCM area that I hosted in Mallorca in 2010. From these activities it was felt that it is necessary to develop and promote a more structured approach towards recreational fisheries management by responsible institutions, taking into account the importance of the activity to local and regional economies and the positive contributions recreational fisheries organizations can make to the management of the resources. Recreational fisheries management must be backed by reliable monitoring and scientific conclusions, independently verified, and include all fishing modalities. The science needed to determine the best methods, data treatment, biological and environmental data gathering, model implementation, risk analyses, among others, must be taken into account by managers. As far as I know, data gathering is only performed for few species in the ICES areas and in general there is a lack of information. There is still a need for bespoke, regular, and statistically sound data collection to underpin European fisheries management. Albeit many regulations on catch, with minimum lengths and bag limits, effort, with licencing systems in some areas, are implemented, future progress is still required (Hyder et al., 2017). Contributing to marine sciences management In 2002, I had the honour to be invited to be the Marine Sciences Manager (MSM) (the first woman to occupy such a role), a position which I held until 2008. But how I arrived to be a manager? Probably I was chosen as MSM because my previous activities as a scientist in the Antarctic showed my capacity for coordinating teams and facing challenges. What I learned during this period was that with some common sense, diplomacy, being not afraid or shy to ask for relevant expert collaborators, most hard challenges can be overcome. In the process, I have become stronger, and I have learned to put my problems in a wider context, thus being more realistic. Also, to get out of my comfort area (research and personal) has been a refreshing period shaking me deeply. Besides my many duties as manager of different annual competitive calls for research technology and science projects, I reviewed the progress in marine sciences and technology Spanish-funded projects since the beginning of the programme in 1995 (Morales-Nin and Sánchez, 2004), and identified the research and strategic areas not covered (Morales-Nin and Ramos, 2009). The relevance of marine-related services and economic revenue for Spain requires the development of a strategy to address the challenges that emerge from growing competing uses of the sea, ranging from tourism, maritime transport, fishing, aquaculture, leisure activities, off-shore energy production, and other forms of seabed exploitation (Morales-Nin and Ramos, 2009). A successful example of a targeted programme assessing a specific Spanish problem was the case of the Prestige. Just a few months after my nomination as MSM, on November 2002 the oil tanker Prestige was shipwrecked. This major oil spill affected the NW Spanish Atlantic coast from Galicia to the Basque country and the south of the Bay of Biscay, producing one of the worst oil spill events ever in the region. The Prestige oil spill attracted an exceptionally high level of public attention and concern due to the large area affected and its high ecological and socioeconomic impacts (Albaigés et al., 2006). In January 2003, the Ministry designed and approved and Urgent Strategic Action and a mid-term Scientific Response Plan to manage ample funds earmarked for a research response. As MSM, I was nominated President of the Scientific Commission in charge of both activities. In the beginning, this was tense work due to the urgent situation, but I felt that this work was important, resulting in the adoption of best operational practices, and thus into value for society. The Plan was organized in six main subject areas, encompassing the oil behavior in the sunken vessel tanks; seismic risks for the wreck; the fate of the oil in the environment; biological effects; socio-economic impacts; and definition and implementation of contingency plans, including operational oceanography systems for the prediction of oil trajectories in the open sea. We organized two International Symposia on Marine Spills that were highly attended (Vertimar 2005, 2007). Many special issues, publications and workshops were produced around this event. Today, thanks at least in part to the Prestige accident, Spain has a solid system for oil pollution prevention and response (De la Torre and Albaigés, 2016). The experience was quite extreme for me, oil spills were not my expertise field and due to the initial bad political management the social situation was difficult; but I could associate with the best experts, and acting as a facilitator and providing the required visibility to all partnerships, we could reach timely results as well as provide the fundaments for a structured response to future spills. I had the opportunity to participate in the construction of the European common space for research (since the Lisbon agreement, COM 2000) through the ERA-Nets, which aims to coordinate the national funding agencies to build up common procedures for research funding in EU countries. I first contributed to the drafting of the Era-Net AMPERA (related to accidental oil-spills) and MarinERA projects, funded by the EU FP6 ERA-NET Scheme and coordinated by Spain. Also I served as coordinator of MarinERA for 2 years (2006–2008). MarinERA was a partnership of the leading Marine RTD Funding Organizations in 13 European Member States, aiming to coordinate national and regional RTD activities dealing with the Marine Sciences in general. One of the objectives was to identify research priorities in marine science at European level (Morales-Nin and Albaigés, 2015). The ERA-Nets are associations of funding organizations; we realized how many administrative rules and legislation are active in each country, most notably in southern EU countries, that were barriers for joint funding. However, common calls were organized and the bases for more integrated cooperation were established. JPI Oceans was the following step with the aim to increase the value of national R&D investments in marine and maritime research across Europe (http://www.jpi-oceans.eu/). With the end of my responsibilities with MarinERA I finished my collaboration with the Ministry in 2009. Another management experience, at a smaller scale, was to be elected as Director of my Institute IMEDEA (2008–2016). My bad luck was that the worst recent economic crisis erupted on 2008, so I had to cope with these difficult years and with the cuts in funding. Due to the shortage in Spanish and regional investments, we had to increase our search for European and private funding. We survived through the joint effort of Institute staff, which was not a minor feat. My primary tasks were to develop and support the management lines of the Institute, to facilitate relationships with our institutions (CSIC and the UIB) and other regional organizations and to make the Institute more visible to society. This opportunity to serve during two terms was educational and showed me how in reality possibilities are limited within a structured burocratic organization with many administrative constrains. I learned that in opinion of my Institute staff if anything was positive, it was due to good luck, their own scientiphic merits, etc.; if anything went bad it was my fault. This sobering experience helped me to realize how you change your vision according to your position. It also showed me how the continuous need to show your value in the competitive calls (i.e. for promotions, for projects) influences the perception of your own value and result in a self-centered vision. Final remarks As in other science fields, technology has evolved geometrically; in my lifetime I have passed from a single computer in the Institute using perforated cards to laptops for everybody. The evolution of otolith studies and applications has been related to technological development as well. For me, the most interesting development has been the image analysis systems that offer a way to register and analyse otolith images for shape analysis or for incremental semi-automatic enumeration. In 1980s, I was enumerating DGI using a microscope and an eyelash glued to the objective as a pointer! Moreover, the possibility to exchange images and enhance them was a powerful tool for technical improvements in aging. The semi-automatization of increment identification is a great help that surely will enhance research in the future. In addition to technical developments, the openness of the social panorama and free interchange with other institutions and colleagues was a breath of fresh air. Moreover, the opening of the management to include the stakeholders’ vision and opinion provides new insights that may result in a sustainable exploitation of the stocks. Also, when I started working the need to open science to society was not considered, and not considering that we must justify our work to the tax payers, whom at the end are our funders. Maybe the need to justify policies to society has a double result; I have participated in many commissions and workshops as well as in developing policy documents at different organizational levels. However, the work and recommendations were somewhat lost in the main organizations political agenda, where decisions where at much higher management levels. I feel that the work of many experts is commonly used as a justification and marketing (“we have consulted experts”), whereas the decisions are based on higher level agendas like the economic situation. Gender issues have also changed, with instructions for gender equality initiatives in both Spanish and EU proposals. In reality, however, I am not sure that things have changed so much (Shen, 2013; de la Rica et al., 2008). In Spain, I have not found direct opposition to my work, I was the first female MSM, ERA-Net coordinator, and one of the few female CSIC Institute Directors, but the social pressure was there and I am afraid that it continues to be. I have travelled quite a bit, participating in many research cruises and in workshops, working at home during weekends and holidays. I tried to give my family “quality time,” in place of being 100% at home. The traditional stereotypes of female roles, as nurturer and family oriented, are internalized. Despite contributing to our income significantly, I had a sense of responsibility that at the same time gave me satisfaction (I was important) and was a burden. For instance, when my children failed in an exam I felt guilty; wondering if having been more with them could have helped their success. However, my male colleagues never felt or commented that they were responsible. So, I, besides the society, was creating stress for myself. At the time, there were not social policies to help, so I had to rely in my family network, typical of Latin countries! Now most of our PhD students are female, but the employment pyramid in science and education, which is based on permanent positions gained with competitive exams, remains very biased, with very few female in top positions. Probably because family choices seem to weigh more heavily on the career goals of women; besides the persistence of an inbreeding system operates to the disadvantage of women (Doherty et al., 2006). I would like to see this change in the future because probably the system would improve being less competitive and more collaborative. I hope that time will change things. I reject the idea that to be an ambitious professional woman means not having a good family life; it can be difficult, but why we have to choose between both? Interestingly, being a female has helped me to have a good career, possibly because I am not trying to copy α-male attitudes. I have tried to give credit to people, apply meritocracy and to act democratically, as well as to place myself in the other’s position. Although to be compassionate doesn’t mean to be weak! I have been considered a consensus person able to work in different political scenarios under conservative and progressive administrations. I would like to conclude that if you are not afraid of hard work and are open to challenges and taking risks, even if you start as a technician and in unfavourable conditions, marine sciences offer an ample field for development. I also think that to approach work with a balanced attitude, not only based on competition, with persistency and openness would give rewards. Work has been to me a very satisfactory experience that has made me grow as a person. I have appreciated it to the last minute. I am sorry that only few working years are left for me; I would like to start doing research cruises again and enjoying the sea and its life! Acknowledgements My gratitude to all the people that in different capacities have contributed to my work, especially my colleagues at ICM and IMEDEA. First to my mentor Carles Bas Peired (CSIC), who gave me several opportunities and new insights; to Josefina Castellví (CSIC) for her trust in me. To Pere Oliver (IEO) for the opportunity to cooperate and change my life by moving to Mallorca; to Enric Tortosa (CSIC), who has worked infatigably for IMEDEA; to Antoni Lombarte (CSIC) for his resilience and tenacious work; to my friends and colleagues Isabel Moreno (UIB) and Audrey J. Geffen (Bergen University) for stimulating and sisterly cooperation; to Miquel Palmer, Ignacio Catalan and Silvia Pérez-Mayol (IMEDEA) for many years of continuous collaboration; to Joan Albaigés (CSIC), Javier Ruiz (CSIC), and Guillermo Morales (MINECO) for the stimulating years working for the Ministry; last, but not least, to my family; thanks for your patience and love and for sharing the adventure of life! Thanks are due to my late husband José Tena, my mother Dolores Nin and sister Rosa María, and specially to my children: Miguel, Elena and Beatriz and grandchildren: Naiara, Nil, Adriá, Elena, and Blanca. This contribution has been written during a MAEC “Salvador de Madariaga” stay at Bergen University (Norway). References Albaigés J., Morales-Nin B., Vilas F. 2006. The Prestige oil spill: a scientific response. Marine Pollution Bulletin , 53: 205– 207. Google Scholar CrossRef Search ADS   Alós J., March D., Palmer M., Grau A., Morales-Nin B. 2011. Spatial and temporal patterns in Serranus cabrilla habitat use in the NW Mediterranean revealed by acoustic telemetry. Marine Ecology Progress Series , 427: 173– 186. Google Scholar CrossRef Search ADS   Alós J., McGrath S., Pérez-Mayol S., Morales-Nin B., Butcher P. A. 2016. The chemical signature of retained hooks in mulloway (Argyrosomus japonicus) revealed by otolith microchemistry. Fisheries Research , 186: 659– 664. 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Paffenhöfer, Gustav-Adolf;Browman, Handling editor: Howard

doi: 10.1093/icesjms/fsx185pmid: N/A

Abstract The idea behind the various papers called Food for Thought was to show how advanced researchers developed their careers, informing about successes and misfortunes. This presentation reports not only on the experiences made by the author; it includes those researchers who provided ideas and support for the author which then led to progress. It often occurs that cooperative efforts are actually needed to advance. The interdisciplinary oceanographic studies reported here were made possible by truly cooperative planning and data-sharing efforts of several individuals which then led to our pioneering advances. Similarly, the successes on obtaining the actual feeding behaviour data of calanoid copepods, after decades of guesswork, could only be achieved through cooperation. Much of the credit goes to my colleagues at the Food Chain Research Group at Scripps Institution of Oceanography who pioneered the combination of field and laboratory efforts to arrive at an understanding of biological processes in the ocean. Overall, not so much my initiative-taking but the repeated encouragement by and feed-back from my colleagues and friends both at Scripps, at Skidaway and other institutes made advances possible. Introduction and early studies The author encountered the ocean as an undergraduate student in 1962 during a field trip to the island of Sylt, eastern North Sea. The multitude and activity of organisms from a zooplankton tow led to the author’s assumption that many scientific questions concerning marine invertebrates remained unanswered. Many students in zoology at our university focused on marine themes as the department heads for decades had been marine oriented. A range of processes determined the evolution of the scientific career of the author. Following the suggestion of the department head of zoology (Wulf Emmo Ankel) at the Justus-Liebig-Universität in Giessen, Germany, to look for a thesis project, the author met in 1963 the new director of the Biologische Anstalt Helgoland (BAH), Otto Kinne, who was looking for doctorate students. The author followed Otto’s suggestion to publish aside of the thesis project papers on related themes in order to enhance getting a position. The main move towards the author’s future came from the microbiologist Wilfried Gunkel who had been a post-doc with Claude Zobell at Scripps Institution of Oceanography (SIO) in 1959. SIO at that time was considered the Mecca of oceanography. Wilfried guided me towards John Strickland, head of the Food Chain Research Group (FCRG), who was at that time one of the leading scientists in Biological Oceanography. Wilfried had been a recipient of the annual reports of the FCRG, providing detailed information of the Group’s activities. Strickland had six researchers in his FCRG financed by the Atomic Energy Commission [later Department of Energy (DOE)] whose major grant supported all of the nearly 25 employees of the FCRG including eight graduate students (Figure 1); John’s goal was to obtain a comprehensive understanding of processes in the water column, publishing in 1970 an issue on the ecology of the plankton off La Jolla, with papers of all of FCRG’s researchers (Strickland, 1970). Figure 1. View largeDownload slide The FCRG in 1969, Dr Strickland at centre up front. Michael Mullin is the uppermost on the far right (with glasses); the author is the uppermost on the far left (black sweater). Figure 1. View largeDownload slide The FCRG in 1969, Dr Strickland at centre up front. Michael Mullin is the uppermost on the far right (with glasses); the author is the uppermost on the far left (black sweater). I was hired by Strickland on a 1-year postdoc position to work on planktonic copepods including developing models; my early knowledge about planktonic copepods then was close to zero. I knew that planktonic copepods had been rarely studied experimentally partly because it was so difficult to keep them alive. Two questions crossed my mind: How can planktonic copepods persist in the ocean where food levels (phytoplankton) are so low (Conover, 1968)? How can they persist as they serve as a major food source for abundant species of fish and their juveniles i.e. feed and avoid predation? Among the FCRG staff was Michael Mullin, at that time having produced the most advanced papers on processes of planktonic copepods, including nauplii. He had started at Woods Hole there being advised by Robert Conover, then a leading expert on calanoid copepods; Mullin studied on both coasts and developed a detailed knowledge on population dynamics of the genus Calanus; his papers guided me initially concerning feeding studies with Calanus helgolandicus/Calanus pacificus (Mullin and Brooks, 1967, 1970) Strickland and his colleagues felt that only a combination of oceanographic studies combined with focused laboratory experiments would allow arriving at an understanding of the functioning of a plankton community. Strickland wanted to include modeling but that expertise was in its infancy. Upon my arrival at SIO Strickland showed me the Deep Tank, 10 m height, 3 m diameter, which was filled with seawater with phytoplankton and the planktonic copepod C. helgolandicus/C. pacificus; Through portholes we observed the copepods which intermittently jumped for 10 to over 50 cm t; this observation was probably the main message for my post-doc research = these animals needed space when running laboratory experiments. Following that observation: within 3 months I had designed a tumbler which kept phytoplankton and copepods continuously suspended having a volume between 4 and 8 l in which the copepods appeared not affected by walls as they cruised slowly while feeding. Now Calanus grew with low to zero mortality from Nauplius to adult, grazed heavily at environmental food levels and newly fertilized females reproduced continuously for several weeks. After a few months of successful research, and unbeknown to me, Sytrickland had my salary augmented by 5%: Unbelievable, having lived in a financially-inflexible environment in Germany. This enhanced the author’s scientific efforts and his relation to Strickland. Even nowadays this behaviour of John’s is uncommon. He wanted to be kept informed about any progress at least weekly. Nine months after arriving at Scripps the first manuscript was completed (Paffenhöfer, 1970). Now calanoid copepods could be readily studied in the laboratory providing the right experimental conditions. Strickland was very happy about this unexpected progress and offered me a second post-doc year; also, my attitude towards research, working intensively for about 12 or more hours per day, including weekends, probably led to that decision. During those months Strickland became my mentor/scientific father; we communicated frequently about results, myself often receiving encouragements from him. He suggested some novel experiments, offering detritus to Calanus, some of which I happily ran for him; at that time there was much talk in the scientific community about the significance of detritus as food for zooplankton. The author had not been aware of this topic. Within days John had written a short manuscript having me as first author; despite my suggestion to list me just in Acknowledgements he insisted me being first author because of alphabetical order of authors! (Paffenhöfer and Strickland, 1970). I had contributed at best only 10% of this article! Could there be any better way to motivate a young scientist? This behaviour of his continues to be rarely occurring. This prompted me to place, on occasion, a younger scientist as first author, despite myself having the research idea and conducting at least 50% of the research. There was an obvious pioneering spirit in the FCRG as he was a major motivator, being fully familiar what each of his researchers was working on; he communicated about copepods as if he had run the experiments. He often had new ideas as e.g. using holography to record these animals operating in 3D. At the same time my other colleagues at FCRG i.e. Angelo Carlucci, Richard Eppley, Michael Mullin and Peter Williams repeatedly provided encouragements. All researchers of the FCRG showed up on Saturdays at work for at least half a day, despite the fact that most of them had a family; there was indeed a pioneering spirit. In 1969 Dr Sheina Marshall joined the FCRG for one year, following an invitation of Dr Strickland. Dr Marshall was considered the world’s expert on marine planktonic copepods about which she had regularly published since 1924 working at the Marine Station, Millport, Isle of Cumbrae, Scotland. We developed a harmonious relationship, exchanging findings and ideas regularly (Figure 2). Dr Marshall was a thoughtful, inquisitive and equally dynamic personality. I considered her the Queen of copepods. Figure 2. View largeDownload slide Dr Sheina Marshall and the author at the Deep Tank at SIO, Autumn 1969. The Deep Tank (3 m diameter, 10 m deep) was originally designed to test equipment by physical oceanographers; later the FCRG used it for phyto- and zooplankton studies. Figure 2. View largeDownload slide Dr Sheina Marshall and the author at the Deep Tank at SIO, Autumn 1969. The Deep Tank (3 m diameter, 10 m deep) was originally designed to test equipment by physical oceanographers; later the FCRG used it for phyto- and zooplankton studies. On our floor of Sverdrup Hall at SIO was the Department of Biochemistry led by Prof. Andrew Benson who also was a big-time motivator, with an absolutely positive attitude. I did not know at that time that he had done about 50% of the research for which Melvin Calvin had received in 1961 the Nobel Prize in chemistry for what was called the Benson-Calvin-Bassham Cycle. The publication records showed that Andy, as we were asked by him to call him, between 1948 and 1952 was first author on nearly half of the respective papers and co-author on the others. In essence he should have been co-recipient of the Nobel Prize, as pointed out by Timothy Walker, Director of the University of Oxford Botanical Garden, years later! Andy agreed to have me work with his graduate student Richard Lee, resulting in several papers on lipid content and composition of Calanus. He called this young post-doc a world expert in copepods! It was an extraordinary experience to work with Richard Lee, Judd Nevenzel, a visiting researcher from Los Angeles, and him. It was my first experience of interdisciplinary research. Dr Strickland and I had agreed that I would return to the FCRG after a year’s absence (US Visa renewal required an absence of 1 year). However, John passed away in late 1970 because of problems concerning his encysted kidneys (hereditary). I returned in 1971 for 5 months to continue with the FCRG on copepod research arriving at results on the feeding of nauplii, copepodids, and adult females at rates far higher than previously found (Paffenhöfer, 1971, 1976a), allowing this species to persist and grow at rather low environmental food levels. Intermittent studies Returning to Germany in 1971 led me to the Litoralstation of the BAH on the island of Sylt in the North Sea in order to continue zooplankton studies, this time on planktonic copepods and appendicularia, the latter developing as follows:. Watching then graduatebstudent Harald Rosenthal (Gotthilf Hempel, advisor) 1965–1966 rearing herring from hatching to beyond metamorphosis to adult had raised my interest in fish larvae. Coming upon two papers by Shelbourne (1964) and Ryland (1966) who showed that plaice larvae depended on feeding on the appendicularian Oikopleura dioica, I turned my interest to the latter’s population dynamics of which nothing was known. Within several months numerous generations of this species had been reared in the laboratory (Paffenhöfer, 1973). Offering natural particulate matter from the Frisian Wadden Sea, generation times and reproduction rates of O. dioica were obtained at environmental temperatures. Using environmental abundances of plaice larvae and O. dioica from the southwestern North Sea, the daily larval feeding rates and O. dioica population reproduction and growth rates matched well i.e. the plaice larvae obtained in situ sufficient O. dioica without decreasing the latter’s stock significantly (Paffenhöfer 1976b). This experimental methodology allowed for numerous future studies on appendicularia after Robert Fenaux, then the leading expert on appendicularia, visited me on Sylt and received all the details to run appendicularian experiments at Villefranche-sur-Mer in conjunction with his students. The author was happy to be recognized for his scientific advances and equally happy to share his knowledge, much in the spirit of John Strickland’s. Similarly, Harold Edgerton, Massachusetts Institute of Technology, and also Rudi Strickler, University of Ottawa, shared their experiences with the author. In the meantime I had been contacted by Dr Eric Corner from the Plymouth Laboratory inquiring about Dr Roger Harris joining me to become familiar with planktonic copepod experiments. Roger Harris joined me from February through August 1974. We ran population dynamics experiments with the temperate copepods Temora longicornis and Pseudocalanus elongatus at a range of environmental phytoplankton abundances. The resulting papers provided information on rates of feeding, growth, reproduction and mortality as those two species were reared from hatching to adulthood (e.g. Harris and Paffenhöfer, 1976). At that time few similar studies of this type had been conducted elsewhere (e.g. Mullin and Brooks, 1970). Our cooperation was the beginning of Roger Harris’ fabulous career in studies on planktonic copepods. In 1973, I realized that the probability to conduct advanced larger-scale studies would most likely not be possible in the Federal Republic of Germany, including interdisciplinary field studies, and developing large mesocosms. The institute director had decided not to have two Deep Tanks built (Like at Scripps = 10 m height, 3 m diameter) despite the fact that the money for building them had been secured; and I had achieved cooperation from colleagues from the Institute of Marine Sciences, Kiel University. So what to do? Upon inquiring about possibilities of open jobs in the United States, my SIO colleague Angelo Carlucci directed me to the recently founded Skidaway Institute of Oceanography (SkIO) where David Menzel had become director in 1970. As the response of Menzel’s to my job application in August 1973 was somewhat lukewarm but the position there still open, I decided to visit Savannah in December of 1973. After a very short interview with Menzel and three of his scientists on a Sunday he offered me the position of Assistant Professor to which I responded one month later after having spoken with Carlucci and Mullin about getting started there. At that time I had still been hoping to get back to SIO. However, with Strickland having passed away, there was no real leader within the FCRG. Introduction to oceanography Little did I know about the opportunities which would arise at SkIO including the oceanographic environment off the coast of the southeastern US Menzel who had contributed major papers in various journals differed from Strickland in being a low-key person. Both personalities had visions for future oceanographic research. As Menzel had wideranging oceanographic experience he was aiming in developing a team of researchers who could conduct interdisciplinary oceanography which was then at its infancy. He was an initiative-taker in the sense that he visited intermittently some of us in our offices to talk science i.e. we did not necessarily have to visit him. He was often more of a colleague than a director which strengthened our oceanographic cooperation. At the same time the CUEA programme (Coastal Upwelling Ecosystem Dynamics) was evolving being supported within IDOE (International Decade of Ocean Exploration). Our plan had the advantage of earlier studies on the U.S. southeastern shelf revealing upwellings of Gulf Stream water (Blanton, 1971) which supported earlier anecdotal observations on cold Gulf Stream water off Daytona by Green (1944) and Taylor and Stewart (1959). We also had the good fortune of the DOE starting to support a long-term study describing the circulation on the US southeastern shelf. This effort supported well four physical plus three biological and one chemical oceanographer. After several years of exploring and describing intrusions (upwelled water masses) from the Gulf Stream during summer with intermittent short-term studies we then ran in summer of 1981 a two and a half month oceanographic time series with a total of three research vessels on the SE shelf. The goal was to quantify frequency, development, size, and longevity of such intrusions which were thought to originate from weekly occurring Gulf Stream eddies. Current metres and ship observations, guided by truly cooperative efforts, allowed us to make interdisciplinary history in oceanography: We were able to observe and follow the development of phyto- and zooplankton patches over weeks, guided by hydrography (Atkinson et al., 1987). This issue in Progress in Oceanography was organized by the author and contained nine papers covering our time-series. The author had decided not to be mentioned as a guest editor. How could that be achieved? Not only considerable competence but also altruistic behaviour of several of our colleagues led to success: Especially Larry Atkinson (chemical oceanographer) who was the initiator of the entire more than decadal effort, and Tom Lee (physical oceanographer, University of Miami) through detailed planning, data collection and distribution led the way. Also, Jim Yoder (phytoplankton) and Larry Pomeroy (microbiology, University of Georgia) contributed. The backbone for interpreting the weekly collected 3D samples (onshore-offshore transects at about 15 nautical miles distance from each other north-south) of zooplankton, particle and phytoplankton distributions, were the current metre and hydrographic data of Larry and Tom. This enabled us to quantify from those weekly samples how the neritic copepod Temora turbinata completed its life cycle within 20 days on the SE shelf in a defined body of water i.e. an intrusion. The various short-term studies during the previous years, lasting from 10 days to weekly single transects (e.g. Atkinson et al., 1984; Paffenhöfer et al., 1984) provided us with the experience to carry out a 2½-month time-series in summer of 1981. Past efforts had shown that it was difficult to follow a water mass und its contents unless you had accompanying hydrographic variables well quantified. Previously, one of the initial and best-known efforts of a long-term time series was the one by David Cushing following a patch of the copepod genus Calanus in the southern North Sea (Cushing, 1963). We ought to point out that not all our oceanographic efforts were readily accomplished: In 1976, 1977, and 1981 our studies could not be run smoothly and continuously as we experienced drive-shaft breaks on our research vessel Blue Fin which each time had limited our scientific efforts severely. Although much of the attention during the previous decades had focused on planktonic copepods as they were considered of major significance for juvenile fish, and various adults like herring, we encountered blooms of gelatinous zooplankton, in particular thaliacea, first in April 1975 (Atkinson et al., 1978). When we made our initial observations Hamner et al. (1975) at the west coast drew attention to the potential significance of gelatinous zooplankton which resulted in numerous papers of theirs. We almost regularly found doliolids (mainly the circumglobally occurring Dolioletta gegenbauri) in and near upwellings on the US SE shelf. Our field studies, particularly in summer 1981, revealed the almost explosive rapidity of developing blooms of this species (Paffenhöfer et al., 1987). They could colonize a wider continental shelf within two weeks thanks to their asexual reproduction (Deibel, 1982, Paffenhöfer and Gibson, 1999). Also, they would be able to suppress the development of egg-spawning calanoids as long as such doliolids occurred at least as one large gonozooid (≥7 mm length) L−1 which could clear about 1000 ml d−1 (Paffenhöfer et al., 1995). We found eggs of the calanoid Paracalanus sp. in ocean-produced fecal pellets of large doliolids; from these numbers and the abundance of that copepod taxon over time we could show statistically that D. gegenbauri suppressed the population increase of that copepod by preying on that copepod’s eggs. In essence, doliolid blooms can control the appearance of calanoids on continental shelves. Blooms exceeding ours have been observed in the Inland Sea of Japan (Nakamura 1998) and at the confluence of the Oyashio and Kuroshio (Takahashi et al., 2015) We later showed that the appearance of D. gegenbauri on the US east coast from Florida to Block Island/Long Island appeared to be facilitated by the Gulf Stream and adjacent waters (Deibel and Paffenhöfer, 2009). Boero et al. (2008) had stated that thaliacea were unpredictable in relation to time and location. We showed that circulation in ocean margins of the east coast of the United States. from the Florida Straits to off New England led to predictable occurrences of doliolids in those regions. Towards zooplankton behaviour As the oceanographic studies were initiated in 1975, I realized that to understand the small-scale plankton processes in those intrusions specific laboratory studies on zooplankton population dynamics ought to be run. As that got underway I was contacted by Rudi Strickler who then was at Johns Hopkins University in Baltimore. Rudi had graduated in 1970 from the Eidgenössische Technische Hochschule, Zürich, Switzerland, working on freshwater copepods at the Kastanienbaum laboratory, Luzern, Switzerland. Our interest in understanding how calanoid copepods were feeding culminated in June 1978 when he had set up a laboratory at the University of Ottawa, Canada. Similar to the Austrian limnologist Storch (1928) he had decided on high-speed cinematography. Until then zooplanktologists thought that calanoid copepods would retain phytoplankton cells by filtering using for cell retention the second maxillae of their head appendages. We knew that such filtering process would not work to keep the copepod alive = that process would be too slow to obtain sufficient food to remain alive. Also, the feeding processes were so fast that capture and ingestion could not be followed. Rudi had set up a high-speed camera, at 500 frames s−1 and optics to observe a calanoid which Miguel Alcaraz (Barcelona, Spain) had fastened with a glued hair to a positioned forceps so the copepod could not reposition itself. I had provided the respective species Eucalanus pileatus or Eucalanus crassus adult females. On the first day we made two 16 mm—movies of a feeding female. After watching the movies, developed the same day at a local TV station, it became obvious how calanoids captured and ingested larger phytoplankton cells: It took a female a total of 50 ms from starting to capture a cell with its maxillipeds to moving it to the second maxillae, then to the mouth and immediate ingestion. This behaviour is characteristic of calanoid copepods using a feeding current to displace food particles towards the copepod (e.g. Paffenhöfer et al., 1982; Paffenhöfer and Lewis, 1989, 1990). The combination of different expertises among the three of us, Miguel, Rudi, and the author, resulted in significant scientific advances. As we had shown the significance of behaviour in the life of planktonic copepods we asked ourselves where we would go from here? Those early and following observations led to our decision to organize a symposium on zooplankton behaviour in Savannah. The goal was to introduce Zooplankton Behaviour to a wide group of marine researchers. After NSF had declined our proposal (graduate student Holly Price and myself) our director David Menzel supported generously with institute funds our effort to organize and run this Zooplankton Behaviour Symposium (Paffenhöfer and Price, 1988). With this approach the discipline Zooplankton Behaviour became Centre Stage, triggering numerous studies in the future decades. The oceanographic community started to recognize that zooplankton from protists to large scyphozoans were able to perceive signals and react to them individually mainly in order to survive in a continuously dangerous environment. Zooplankton behaviour was now much more than vertical migration! Building on our oceanographic observations (e.g. Paffenhöfer, 1983; Paffenhöfer et al., 1984, 1987) several laboratory/experimental studies were undertaken to answer specific questions, or address different ecologically-oriented goals. One of the keys to forming those questions was longer-term laboratory observations of feeding and moving copepods, registering 3D behaviour of such copepods over minutes to parts of an hour. Similarly, Rudi Strickler operated as a graduate student at the Kastanienbaum laboratory of Lake Luzern, Switzerland, looking at copepod behaviour in large aquaria. First, how will planktonic copepods behave when encountering several species of phytoplankton simultaneously, like in the ocean, as compared with most earlier experimental studies just offering one species at a time? We decided to quantify how a common calanoid was feeding on a phytoplankton spectrum simulating intrusion waters i.e. offering to Paracalanus sp., from nauplii to adult female, in a time series simultaneously three phytoplankton species of different sizes, representing a developing upwelling (Paffenhöfer, 1984). That implied that we ran experiments from nauplius to adult at low, medium, and high phytoplankton concentrations, resembling phytoplankton abundances in a developing intrusion of upwelled water (e.g. Paffenhöfer, 1983). An example of a developing Paracalanus cohort feeding simultaneously on three species of phytoplankton is given for phytoplankton concentrations in an advanced intrusion (Figure 3). At the same time we ran for comparison parallel experiments as we did earlier i.e. just offering one phytoplankton species (Figure 4). This abundant zooplankton taxon utilizes simultaneously all three offered food species from copepodid IV on (Figure 3). Copepods in the multialgal experiment hardly ingested more than in the parallel unialgal experiment (3 × 0.3 vs. 0.3 mm3 l −1 of Thalassiosira weissflogii); ingestion was expressed as µg nitrogen ingested copepod−1 d−1 (Paffenhöfer, 1984, its Figure 5 not shown here). The ecological implication was that calanoids would not be able to use a multialgal diet as well as a unialgal diet; or, would not exhaust a multialgal source as fast as a unialgal source; the latter is encountered occasionally in nature. Late copepodids and adult females ingested all 3 phytoplankton species simultaneously: Isochrysis galbana contributed near 10%, Rhizosolenia alata near 15% and T. weissflogii near 75% of the ingested nitrogen (Paffenhöfer, 1984, its Figure 9). Figure 3. View largeDownload slide Ingestion rates of nauplii to adult females of Paracalanus sp. being offered simultaneously 3 species of phytoplankton (I. galbana, T. weissflogii, and R. alata) each at an average concentration of 0.3 mm3 l−1 (modified from Paffenhöfer, 1984). Figure 3. View largeDownload slide Ingestion rates of nauplii to adult females of Paracalanus sp. being offered simultaneously 3 species of phytoplankton (I. galbana, T. weissflogii, and R. alata) each at an average concentration of 0.3 mm3 l−1 (modified from Paffenhöfer, 1984). Figure 4. View largeDownload slide Ingestion rates of juveniles to adult females of Paracalanus sp. on I. galbana, and T. weissflogii at different concentrations (modified from Paffenhöfer, 1984). Figure 4. View largeDownload slide Ingestion rates of juveniles to adult females of Paracalanus sp. on I. galbana, and T. weissflogii at different concentrations (modified from Paffenhöfer, 1984). A second goal was based on the fact that small copepods were hardly paid any attention because they were vastly undersampled with the commonly used mesh of 200 µm width, and therefore appeared to be hardly abundant in the ocean. Following the suggestions of Michael Mullin and John Beers (Beers and Stewart, 1970) we used 100 and 30 µm mesh when sampling vertical profiles with a pump for zooplankton on the SE shelf in our initial studies, to determine the abundance of shelf metazooplankton quantitatively (Paffenhöfer, 1983; Paffenhöfer et al., 1984). That included not only all copepodid stages but also nearly all nauplii. This methodology revealed the actual high abundances of Oithonidae and Oncaeidae, leading to a paper emphasizing the significance of those genera in the world’s oceans (Paffenhöfer, 1993): The genus Oithona occurs in every geographical region, from estuaries to the high ocean; the genus Oncaea everywhere except estuaries and then through much of the water column. We determined that while Calanoida usually reproduce at a high rate for relatively short periods of 1–3 weeks, similarly sized Oithonidae and Oncaeidae stretch their reproduction for up to 10 weeks in subtropical waters (Paffenhöfer, 1993). Bottom line: In order to meet a study’s goal the methodology, here mesh sizes and sampling each has to meet the goals. When sampling in the North Atlantic Subtropical Gyre we used 64 µm mesh to collect all copepodid stages of planktonic copepods (Paffenhöfer and Mazzocchi, 2003), and sampled for comparison with 200 µm mesh which has been traditionally used for decades to quantify metazooplankton. Aside of taxon-specific vertical distributions our results showed that copepods were ten times more abundant with the 64 µm mesh than the 200 µm mesh. A third goal was to determine the feeding rates of calanoid females at a range of environmental food concentrations via high speed movie and video i.e. seeing is believing (Paffenhöfer and Lewis, 1990). That approach revealed that phytoplankton species like T. weissflogii of 10–12 µm ESD were already perceived by E. pileatus females before they reached, in the feeding current, the setae tips of the collecting cephalic appendages. This was observed at very low food levels of 8 µg of C l−1 or lower. It appeared to indicate that chemical compounds from the phytoplankton cells provided the females with an early signal that food was approaching (chemosensory). This idea which had been put forward by Strickler (1982), based on a single observation, has been challenged recently (Tiselius et al., 2013; Gonçalves et al., 2014). However, we have shown, presenting our data in detail, that chemosensory is the means by which calanoids with a feeding current can perceive a phytoplankton cell at a distance (Paffenhöfer and Jiang, 2016). Also, our study revealed why clearance rates of planktonic copepods increased with decreasing food levels: As food levels decrease a calanoid with its feeding current tries to compensate for the decreasing abundance by enhancing its perceiving and selection performance. Although ingesting only a small percentage of the encountered cells at a high food level (80 µg C l−1 of T. weissflogii) it perceived and ingested all encountered cells at a very low food level i.e. 8 µg C l−1 (Paffenhöfer and Lewis 1990). Similar visual observations were made when offering the large diatom Thalassiosira eccentrica to adult females of the calanoid Paracalanus aculeatus at different food levels (Paffenhöfer et al., 1995). At the AGU Ocean Sciences Meeting in New Orleans January 1987 Cabell Davis, Woods Hole Oceanographic Institution, and Mark Huntley, SIO, approached me, after a suggestion from Sharon Smith, University of Miami, whether we could organize a meeting towards developing ideas for future marine zooplankton studies. We started communicating with colleagues at that meeting and were able within weeks to organize the First Marine Zooplankton Colloquium for March 1987 (Marine Zooplankton Colloquium 1, 1988). Almost all of the about 45 international participants who came to Lake Arrowhead, California, paid their travel expenses out of their own pocket. The directors of Scripps, Woods Hole and Skidaway provided some travel funds, which served e.g. one of our French colleagues (Serge Poulet) well as he arrived late at Los Angeles and had a taxi cab (le Taxi) to Lake Arrowhead to participate in our colloquium! Following the numerous citations of our published colloquium 1, one may conclude that this meeting provided quite a few ideas for future research. Such colloquia were repeated in Savannah (Marine Zooplankton Colloquium 2, 2001) and Ischia, Italy (Paffenhöfer et al., 2005), and subsequently published after thorough reviews in Marine Ecology Progress Series, also thanks to the open-mindedness of Otto Kinne’s, the editor-in-chief of Marine Ecology Progress Series. Our idea was that instead of having reports on the proceedings loosely distributed in the community, to have our proceedings published, i.e. openly available to everyone in our oceanographic community. Conclusion Scientifically, observations in both, the ocean on larger scales, and at smaller scales in the laboratory, proved to be fruitful. For example, utilizing small meshes, as suggested by Beers and Mullin, revealed the abundances of Cyclopoida and Poecilostomatoida (e.g. Paffenhöfer, 1983), leading to numerous future studies to explore their performances. On the human side it became clear that a true cooperative spirit, as initially encountered in Strickland’s Group, was indeed rewarding. The author encountered this also in our field studies with Larry Atkinson and Thomas Lee who were not hesitating to be givers, as John Strickland did. Similar cooperative efforts occurred with my graduate students, and Miguel Alcaraz and Rudi Strickler. Despite individual successes the cooperative approach was the most rewarding. The author retired officially in 2003. However, he continued laboratory and field research, partly supported by two grants from the National Science Foundation. It appears that increasing age does not prevent producing new scientific questions. 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Markus, Till;Hillebrand, Helmut;Hornidge, Anna-Katharina;Krause, Gesche;Schlüter, Achim;Browman, Handling editor: Howard

doi: 10.1093/icesjms/fsx201pmid: N/A

Abstract Recent events and trends in international relations are making it necessary for scientists to design their projects in ways that can integrate disciplinary perspectives and learn how to communicate their results in governance processes. Some examples of settings in which such skills would be needed are the debates about the political and legal relevance of the “Anthropocene” as a concept, the establishment and implementation of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), the recent International Court of Justice’s decision on what constitutes “scientific purpose” under the Whaling Convention, and the ongoing international efforts to regulate deep seabed mining activities. These events reveal an acceleration of growing environmental, distributional, and geostrategic conflicts over ocean resources which are changing the character of marine research. For some time now marine sciences have recognized the interdependence of social and ecological systems and the cumulative effects of multiple environmental pressures. In addition, we observe that the relationship between science and policy-making is rapidly changing in a process which we refer to here as the internationalization of knowledge, and that scientific research activities and results are progressively being internationally contested. Altogether these developments constitute extrinsic constraints that render transcending disciplinary boundaries a conditio sine qua non for future marine research. Better comprehension of these trends and their implications may help us to understand marine science’s functioning in the near future, particularly the relationship between disciplines involved. Introduction Developments over the last five to six decades have substantially changed the amount, scope, and purpose of marine research, as well as the relative weight and role of disciplines involved. Marine research has come to a point where there are many diverse branches which are highly specialized, while at the same time there is a growing awareness of the interaction between social and ecological systems, as well as cumulating multiple pressures on ecological systems that create complex environmental problems. Two more significant and yet largely unexplored trends are the internationalization of knowledge and growing international conflict over marine research activities and results, both of which are causing the various branches of marine research to undergo dramatic changes. We argue that these developments constitute strong extrinsic motivators for marine researchers to communicate and integrate their highly specialized disciplinary knowledge into different scientific, political, legal, or practical contexts. Though these trends were already set in during the late 20th century, they have become even more relevant in several recent political, legal and institutional actions such as the establishment of the IPBES, the inclusion of an explicitly ocean-focussed sustainable development goal into the UN 2030 Agenda, the International Court of Justice’s decision on what constitutes “scientific purposes” under the Whaling Convention, the international regulation of ocean fertilization experiments, the establishment of the world’s largest marine protected area in Antarctic waters, the United Nation’s efforts to protect marine biodiversity in areas beyond national jurisdiction, the regulatory actions regarding deep seabed mining, and the adoption of several national and regional integrated ocean policies. Such actions indicate a growing urgency to develop tools and skills to communicate and integrate disciplinary knowledge. Better understanding these trends and their implications may help us to comprehend marine science’s functioning in the near future, particularly the relationship between disciplines involved. Accordingly, this article traces the change in the functionality and disciplinary composition of marine research, explains why marine scientists increasingly have to be able to communicate disciplinary knowledge as well as integrate it into other disciplines, and finally outlines important mechanisms and strategies that foster coherence and the integration of research agendas, results, and expertise. Marine research in flux Present social and ecological developments demand different scientific expertise concerning the seas and coasts than they did half a century ago. Traditionally, marine-related research was mainly carried out by biologists, chemists, geologists, and physicists (as well as non-scientific occupations such as naval officers, colonial seafarers, merchants and fishers) whose various goals included the documentation and better understanding of natural processes in the sea and of marine fauna and flora, as well as gaining an edge over competitors with improved navigation and military and commercial technologies (Deacon, 1997; Reed, 2009). Developments over the last five to six decades, however, have substantially changed marine research’s overall character and functionality. A growing world population, technological advances, and globalising markets’ increasing demands for raw materials have led to what Hance Smith in 2000 called “the industrialization of the oceans” (Smith, 2000). Along these lines, competition over watercourses, fisheries, mineral and biological resources, and large marine areas have escalated and led to environmental, distributional, and geostrategic conflicts—a trend which is likely to continue in the future (WBGU, 2013). One example among many such conflicts over securing marine resources is the current political dispute between China and Vietnam with reference to several reefs and small islands in the South China Sea claimed by Beijing as Chinese territory (Roszko, 2015). In response to the growing number of such conflicts, national and international regulations, institutions, and juridical decisions of regional and global courts have proliferated (Harrison, 2011; Oxman, 2015). Altogether, these developments have intensified existing research in the marine natural sciences and ignited new economic, sociological, anthropological, political, and legal investigations which analyse actors’ interests and strategies, their behavioural patterns and negotiation practices, as well as the design, structure and functioning of political, and judicial institutions governing these issues (Hoagland and Ticco, 2010; Hallwood, 2014; Zacharias, 2014). In particular, marine management and conservation-related research activities have advanced significantly in both the natural and social sciences. As for the former, biological, ecological, physical, and chemical research increasingly analyses the quality and the extent of the effects of single and multiple pressures on the marine environment (Norse and Crowder, 2005; Halpern et al., 2008; Levin et al., 2009; Long et al., 2015). As for the latter, sociology, political and legal sciences, and economics systematically evaluate social drivers behind exploitation and conservation, approaches to distribution, and the often contentious effects of depleting resources and deteriorating ecosystems on societies as well as possible mitigation strategies (Hornidge and Scholtes, 2011; Markus and Salomon, 2012; Schlüter et al., 2013). The urgent need for integrating research The growing demands, competition, and conflicts regarding marine resources in addition to increasing environmental issues have not only intensified both natural and social science research but have also created the need for researchers to integrate their highly specialized disciplinary knowledge into different scientific, political, legal, or practical contexts. To be able to orient, communicate, justify, and legitimize research activities is at the core of these efforts. This holds particularly true where scientific research is supposed to directly support economic, governance, or judicial endeavours, and where its expenses and potential effects need to be justified to the satisfaction of funding agencies or the public. The increasing recognition of interdependence of social and ecological systems The imprint of human activities on the environment, including oceans, has become substantial. This is evident in the prominent discourses on the “limits to growth,” “sustainability and precaution,” “the planetary boundaries,” “shifting baselines,” and lately the “Anthropocene” epoch (Steffen et al., 2011; Rockström and Klum, 2014). Besides the fundamental assumption that nature’s capacity to provide for resources and recover from human interventions is limited, discussions around these concepts have made clear that social and natural processes are nowadays recognized as inextricably linked to one another and that neither of them can be fully appreciated without understanding the other (Berkes et al., 2000; Berkes et al., 2003; Ostrom, 2009). Scientists have become increasingly aware over the last decade that most ecosystems and resources are embedded in complex social–ecological systems and that effective research, governance and conservation activities urgently require the integration and communication of scientific knowledge between different actors, disciplines, and governance processes (Cash et al., 2003; Schellnhuber et al., 2004; Glaser et al., 2012). The increasing recognition of cumulating environmental pressures Another development necessitating the integration and communication of disciplinary knowledge is the pressing need to manage multiple activities and cumulating environmental pressures as a whole to maintain or restore good environmental status in marine environments (Underdahl, 1980; Halpern et al., 2008; Markus et al., 2011; Long et al., 2015). Not least, this development has significantly changed the scientific underpinnings of nature conservation and ecosystem management, particularly concerning motives and purposes (see generally Mace, 2014; Abelson et al., 2016). The focus has shifted from merely analysing and considering the effects of single damaging events on specific habitats or species in order to understand how to protect or maximize their use value, to instead acknowledging that there are multiple, sometimes cumulative or overlapping pressures, and that specific species and habitats form parts of more complex ecosystems. In response, various international conventions, programmes and scientific reports throughout the last decades have highlighted the need to consider the interplay between different exploitation and use activities and their effects on the marine environment. The recent establishment of the world’s largest marine protected area in Antarctic waters as well as the UN’s effort to develop an instrument to protect biodiversity in areas beyond national jurisdiction constitute two important examples in this regard (Ardron et al., 2014; Freestone et al., 2014; CCAMLR, 2016). In addition, governments have increasingly adopted national or regional programmes and instruments that acknowledge the importance of holistic policies that allow for a comprehensive and coordinated governance of the different activities and interests related to the seas. For example, in the 1990s, Brazil, the United States, Australia, and Canada began to develop comprehensive national maritime policies, in 2007 Japan introduced its Basic Act on Ocean Policy, and in 2008 the European Union adopted its Marine Strategy Framework Directive (Juda, 2003; Markus et al., 2011). Such policies and instruments have included the adoption of measures aimed at sustainable use and conservation of marine biodiversity, in particular the establishment of marine spatial plans and marine protected areas. The underlying rationale of all of these initiatives is that use and conservation conflicts in the seas cannot be solved by measures addressing single activities, sectors or species. The internationalization of marine knowledge Another trend demanding the integration and communication of disciplinary knowledge is the changing relationship between research and policy-making. Research has always been guided and prioritized to some extent by policy-making and its resultant budgetary incentives (Longino, 1990, 2002). But scientific research and knowledge, in turn, has also influenced, catalysed, and framed political discourses and legal processes (Haas, 1992; Bocking, 2004; Pielke, 2007; Campbell Keller, 2009). It is argued here that this reciprocal relationship is presently changing in a process which we refer to here as the internationalization of knowledge. The process may best be described as one in which the identification, framing, assessment, and valuation of contemporary marine research results and demands is increasingly carried out in a partly scientific, partly political process at the international level. We argue that this process is particularly obvious in the area of marine research (see e.g. Haas, 1990; Walsh, 2004; Markus, 2013). Given that marine ecosystem services and the effects of anthropogenic impacts on the marine environment often extend beyond national borders, the exploitation, management, and conservation of marine ecosystems and resources demand internationally coordinated approaches. To this end, different regional or global legal regimes and organizations are recognizing the importance of a shared scientific knowledge base for cooperation (see already Livingston, 1968). To inform political decision-making processes, some regimes require: (i) the ad-hoc analysis and assessment of specific issues, (ii) the identification of further research demands, (iii) the exchange of scientific information and data between states and other actors, (iv) the development of a common scientific understanding based on aligned scientific criteria and methodological standards, and (v) providing a continuous flow of the desired information. All of these activities require the establishment of some form of governance arrangement. The institutional design of such arrangements usually depends on their functions and tasks. Accordingly, the many existing arrangements vary quite substantially. The international organizations listed in Box 1 on the next page are examples of some of the major fora in which scientific expertise intersects with policy-making. View largeDownload slide View largeDownload slide Most of these arrangements are not purely scientific. They are often composed of scientists as well as experts from national governments and their usual tasks are to prepare scientific information for political decision-making, identify needs for further political action, and make specific recommendations to governments for decision-taking. Accordingly, these governance arrangements can be understood as fora or social arenas in which scientific expertise intersects with policy-making. For example, the aforementioned Subsidiary Body on Scientific, Technical and Technological Advice (SBSTTA) prepares regulatory options and decision-making for the CBD’s Conference of the Parties (COPs). It has been estimated that ∼90% of all its proposals are later adopted by the COP with only few minor modifications or none at all. The SBSTTA thus strongly influences decision-making-practices of the CBD-parties. Accordingly, political negotiations have largely shifted to the SBSTTA-meetings which also have been referred to as “pre-COP-exercises” (Johnston, 1997). But these kinds of governance arrangements do not only directly influence political decision-making. By analysing and assessing specific issues, identifying research demands, and aligning scientific criteria and methodological standards they shape the generation and use of scientific knowledge and thus contribute to the establishment of a standardized regional or even global understanding, perception, and valuation of specific topics and issues (Stokke and Coffey, 2004; Walsh, 2004; Gillespie, 2006; Markus, 2013; Hornidge, 2014). Where experts and scientists participate in deciding which type of knowledge is generated (particularly which research is being funded), which results matter, and which scientific insights and expertise will be considered in policies (and which are left out), they direct and influence the thoughts and actions of those actors engaging with the respective knowledge (Kitcher, 2011; Barker and Kitcher, 2014). The increasing contestation of marine research activities In recent years, marine research activities have more and more frequently been contested and have increasingly come into the focus of international politics and law (Gorina-Ysern, 2004; Stephens and Rothwell, 2015). The increased role of marine scientific research in international regulatory and judicial decisions is evidence that scientists are more often required to communicate, justify, and legitimize their research to the public, to funding agencies, to political institutions, and even to courts. Two recent events illustrate the necessity of several states to take political and legal action in order to define and defend legitimate scientific research. In the first case, the fear of unilaterally authorized commercial ocean fertilization as well as adverse and irreversible environmental impacts of scientific fertilization experiments compelled the international community under the London Convention and Protocol on Dumping to regulate the issue during the years between 2007 and 2012. To ban fertilizing for commercial purposes and yet allow environmentally sound research, states had to establish criteria and procedures which: (i) distinguished between what has been termed “legitimate scientific research” and “commercial activities” and (ii) ensured that experiments would not negatively affect the marine environment. In essence, they created an assessment framework which requires a scientific quality check to ensure that experiments have “proper scientific attributes” and an environmental impact assessment (Markus and Ginzky, 2011). The second example concerns Japan’s practice of catching whales “for scientific purposes.” The meaning of what actually constitutes scientific research was disputed between Australia and Japan before the International Court of Justice between 2010 and 2015. Basically, Australia argued that Japan’s practice of hunting whales neither constituted “science” nor was it carried out “for scientific purposes” as understood under the International Convention for the Regulation of Whaling. In abstaining from giving a general definition as to what constitutes scientific research, the Court decided that Japan’s whaling programme was not reasonable in relation to its officially claimed scientific objectives and therefore was not carried out “for the purpose of scientific research” (International Court of Justice, 2014). In addition, it has been pointed out that marine scientific research activities are increasingly met with scepticism for their environmental effects and trade-offs (e.g. Verlaan, 2007; Hubert, 2015). Though marine environmental threats posed by scientific activities are generally deemed low compared with industrial ones, some research experiments—including seismic surveying, ocean fertilization, the introduction of genetically modified organisms into marine waters, and the killing and study of large animals such as whales or tunas, as well as all other kinds of invasive research in sensitive areas (e.g. seabed areas in which hydrothermal vents exist)—have been subject to public criticism. Accordingly, marine scientific research activities often have to be justified against public concerns and criticism and are increasingly subjected to national and international environmental regulation (Hubert, 2015). Strategies and techniques for marine researchers under new conditions Against the background of these drivers and trends that are changing marine research we argue that it would be in the interest of scientists involved in marine research to develop the necessary skills to be able to solve complex ecological problems, and to communicate, justify, and legitimize research activities to scientists from other disciplines, policy makers, funding agencies, and the public. A concrete example highlighting the importance of integrating disciplines and communicating between them, and also between science and governance, is the concept of “ecological stability.” Natural scientists have developed multiple loosely defined measures of stability to capture the ability of ecosystems to absorb or withstand environmental change, making stability a cornerstone of ecological research, especially in a global change context (Pimm, 1984; Ives and Carpenter, 2007; Timpane-Padgham et al., 2017). Donohue et al. (2016) conducted a systematic literature review and detected a tendency within ecology towards reductionist approaches where each study focused on a single driver of change and a single aspect of stability in isolation. Even more striking, however, is that the different stability aspects used in ecology were not at all congruent with the stability concepts used in major environmental policy documents addressing conservation, ecosystem management, and services. Thus, because the different scientific communities have not yet been able to develop a complex and generalizable approach to measure stability, the scientific and regulatory communities are not guided by a common analytical framework. Several steps may be undertaken to promote the objective of communicating between and integrating disciplines. First, scientists may consider formulating research questions and designing experiments in ways that allow and encourage collaboration with different disciplines. Different disciplinary perspectives should be able to work together to provide integrated ‘answers’ to larger umbrella questions. Second, researchers may strive toward gaining a basic understanding of governance processes and developing techniques to connect their results to these processes (Boesch, 1999; Cash, 2003; Ostrom, 2009). For both, a basic understanding about other disciplines, their epistemological perspectives, and problem foci constitutes a substantial asset for all involved researchers. On a basic level, this requires a continuous and institutionally supported interdisciplinary exchange, the clarification of terminologies, research practices and methods, and the various disciplines’ concrete contributions to the joint research question, as well as jointly developed models (Kohler, 2002; Cash, 2003 here speak of conscious and systematic “boundary work” for crossing disciplinary gaps). This can best be achieved if researchers have been socialized into a community where they have the opportunity to develop the required interdisciplinary language, conceptual, and methodological skills, and thus can interact in a mutually respectful and productive manner. Building such a community includes measures reaching from educational programmes to establishing institutions that continuously engage in knowledge integration and interdisciplinary work (Lentsch and Weingart, 2011). It also requires education in the theory of science, introduction to rudiments and basic ideas, terms, and concepts of other disciplines related to one’s own research field, and the teaching of analytical and methodological approaches for integrating research results from different disciplines (Lang et al., 2012; Neßhöver et al., 2013; Ciannelli et al., 2014; Pohl et al., 2017). On a scientific level, disciplinary knowledge integration demands an analytical or classificatory framework that allows the organization of research results from different disciplines (Ostrom, 2009). Future marine research and conservation efforts may thus gradually be designed in a transdisciplinary and synthesising way, acknowledging the causal links between societies and ecosystems as well as the complexity of ecosystems and the different types of cumulative and overlapping pressures from different sea-, air-, and land-based sources (Levin et al., 2009; Tallis et al., 2010; Long et al., 2015). With respect to marine conservation, this suggests in practical terms the development of ecological, economic, and social indicators and environmental scientific criteria based on current marine environmental and socio-economic statuses, and an ideal status and management strategy (ibid). It also argues in favour of identifying and quantifying ecosystem services, evaluating social demands and interests of different actors from different sectors, and identifying and evaluating trade-offs among management options (ibid). In this regard, bridging different spatial, ecosystem, and administrative scales and sectors is often necessary (Krause, 2014; Schwerdtner Máñez et al., 2014). It is important to stress in this context that interdisciplinary work must not in any way reduce the quality of specific disciplinary research results but must instead find ways to link and accumulate it. Scientists must become more aware that their work is necessary to solve the problems of our time, that it is a pertinent part of complex political and economic processes, that it may have substantial impacts on the marine environment, and that it may influence future scientific knowledge generation and use. These aspects make scientific research more than ever subject to contestation, both in national and international public, political, and even legal contexts. Scientists should prepare to communicate and legitimize their work in these contexts and in light of these kinds of demands and concerns. The following is a general list of strategies that may help scientists to communicate and justify research activities within public, political, regulatory or even judicial processes: making the purpose of scientific endeavours transparent to allow a clear distinction between scientific ends and commercial interests (who benefits?); clearly articulating the potential environmental, socio-economic, and socio-cultural effects of the research; and participating constructively in the regulation of scientific endeavours where negative effects on the environment or society cannot be ruled out. outlining the underlying rationale and the data used in decision making processes, and disclosing value judgments and uncertainties; including distinguished and independent experts to guarantee that research results and arguments are based on well-founded expertise; including representatives from different cultures and scientific backgrounds; consider making research methods and results publicly available (if commercial interests in carrying out experiments should be ruled out); granting permission to those affected by the research projects to voice their interests. Conclusion Changing research demands have redefined contemporary marine research. Natural scientific research has become more specialized and is increasingly complemented by research in the social sciences and the humanities. Greater recognition of the interdependence of social and ecological systems, rising demands for enhancing marine environmental conservation, the internationalization of knowledge, and the growing conflicts over marine scientific research are making it essential for marine scientists to design their research questions in such a way that they may be integrated with other disciplinary perspectives in order to achieve a more holistic understanding of the interdependences between the ocean and the social, economic, and political world. Recent incidents indicate an acceleration of these trends and issues as well as a growing urgency to be able to communicate and integrate disciplinary knowledge. These trends require mechanisms that foster coherence and integration of research agendas, results and expertise. Such mechanisms may be either of a more procedural type or more of a methodological type. Interdisciplinary integration and boundary crossing, however, must not in any way reduce the quality of specific disciplinary research results but instead must find ways to link and systematically synthesize. This can only be achieved if there is a community of researchers which has been socialized into and developed the required skills for interdisciplinary work, and thus can meaningfully interact accordingly. Building that community requires educational programmes and institutions that continuously engage in interdisciplinary work; it furthermore encourages scientists to become more aware of the theory of science, to become more literate in the rudiments and basic ideas, terms, and concepts of other disciplines related to one’s own research field, and to acquire an analytical and classificatory framework for integrating research results from different disciplines. The benefits of a basic integration of increasingly specialized research activities are hard to estimate, though it is possible that inconsistencies and inefficiencies may for the most part be avoided while synergetic and mutual benefits may be reaped. Interdisciplinary research has for a long time been carried out by intrinsically motivated scientists and it has been seen as a necessary addendum, adding some “extra flavour” to disciplinarily designed projects in order to systematically diminish the blind spots created by the disciplinary boundaries guiding our thoughts. We have entered a new era of interdisciplinarity, where extrinsic constraints make transcending disciplinary boundaries a conditio sine qua non for future marine research. Acknowledgements Authors have been funded by the University of Bremen, the University of Oldenburg, the Helmholtz-Institute for Functional Marine Biodiversity at the University Oldenburg (HIFMB), the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, and the Leibniz Centre for Tropical Marine Research. This article has also been inspired by the discussions taking place under the Oceans Past Platform (OPP) COST Action network (IS1403) as well as the Ocean Governance for Sustainability (OceanGov) COST Action network (CA 15217), both of which have been supported by COST (European Cooperation in Science and Technology). It also draws inspiration from work and courses carried out at the Bremen International Graduate School for Marine Sciences - GLOMAR, MARUM - Center for Marine Environmental Sciences, University of Bremen, as well as in the context of The International Research Training Group INTERCOAST – Integrated Coastal Zone and Shelf-Sea Research. We would like to thank three anonymous reviewers as well as Michael Flitner (University of Bremen), Victor Smetazceck (Alfred Wegener Institute), Simon Lohse (University of Hanover), and Ellen Sabo for their valuable ideas and comments on early drafts of this article. 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Pine, Matthew, K;Wang,, Ding;Porter,, Lindsay;Wang,, Kexiong

doi: 10.1093/icesjms/fsx197pmid: N/A

Abstract Given the common physical overlapping between coastal developments and important marine mammal habitats, there is a need to identify potentially important foraging grounds for dolphins when informing marine spatial planning and management of underwater noise. Hydrophones were deployed at four locations either side of the mainland China–Hong Kong Special Administrative Region border to monitor the presence of soniferous fishes; a key prey item for Indo-Pacific humpback dolphins. Five distinct chorus-types were identified; each showing spatiotemporal variability. Each chorus-type was assumed to represent a separate species. Chorus-type diversity also differed between sites, with SP4 and SP5 types only being detected within Hong Kong waters where bottom trawling is illegal. Chorus-type SP1 was only detected at the recording sites in mainland Chinese waters. Call rates and chorus duration were highest during the spring and summer months. Given these dolphins show a predator-prey relationship, these data provide new information on the local fish communities at a much finer-scale than fish landing records and a baseline of fish activity in an environment that is challenging to explore. Overlaid with acoustic detections of foraging dolphins, these data form a basis for identifying potentially important foraging habitats that should be afforded the highest priority for protection. Introduction The biological impact from underwater noise on ecologically significantly regions is an internationally recognized issue (Williams et al., 2015). This is particularly relevant where large human populations overlap important marine mammal habitats, such as key foraging areas or nursery habitat. The Pearl River Estuary (PRE) in China is a good example of this whereby nine major cities, encompassing three separate reporting and management jurisdictions, share a single population of Indo-Pacific humpback dolphins (Sousa chinensis). Due to the economic importance of the PRE, the scale of coastal developments, and thus the increasing noise levels, within the estuary has been extraordinary and the potential noise impacts on the resident dolphin population is a significant conservation concern. The Indo-Pacific humpback dolphin ranges from somewhere east of India to the Indo-Malay Archipelago, north along the East Asian coasts and south to northern Australian waters. The boundaries of the genus are poorly understood and it was only recently that Australian humpback dolphins were designated as a separate species. Populations are fragmented and usually associated with major estuaries. Little or no information exists regarding the overall number of this population; however, some areas have been better studied than others, e.g. the PRE and the Eastern Taiwan Straits. The Indo-Pacific humpback dolphin is regarded as Near Threatened by the International Union for Conservation of Nature. Under Chinese legislation, this species is considered a Grade 1 National Key Protected Species and is afforded the highest protection (Xu et al., 2015). Within the PRE, it is believed that the resident population may be over 2000 individuals (Jefferson and Smith, 2016), and it is decreasing at a rate of 2.46% per annum (Huang et al., 2012). Considering the mounting evidence for the adverse effects of noise on marine mammals (see Weilgart, 2007; Williams et al., 2015), it is possible that increasing levels of underwater noise may be a contributing factor (SCU, 2012). Although the population of dolphins that reside within the PRE is better studied than most, relatively little is understood of their estuary wide range and the distribution of prey within this range. Recent research has shown detection rates of Indo-Pacific humpback dolphins correlating with fish call rates near the Sanjiao islands in the PRE, with no such correlation to the ambient noise levels from proximate vessel traffic (Pine et al., 2016). These data suggest that dolphins are often exposed to acoustic stress in order to forage in areas where prey and anthropogenic noise sources overlap (Pine et al., 2016). These issues have been recognized in mainland China and Hong Kong, with local governments requiring impact assessments, including noise mitigation strategies, as part of the environmental permitting process. Notwithstanding however, many developments (such as the construction of the Hong Kong-Zhuhai-Macau Bridge, the Third Runway development at the Hong Kong International Airport and the Guishan offshore wind farm), all overlap with important marine mammal habitats; a scenario that is sometimes discovered after construction has commenced. Understanding where these important foraging habitats may be before or during the environmental impact assessment phase of future developments is thus fundamental for the conservation of these dolphins within the PRE. Similar to marine mammals, the sensitivity of fish to underwater sound has also been well documented and dolphins eavesdrop on their potential prey during foraging (Gannon et al., 2005). Like marine mammals, fish use underwater sounds to sense their environment, as well as coordinate certain behaviours such as reproduction or territorial defence (Hawkins, 1986; Rountree et al 2006). They typically produce low-frequency sounds (<1 kHz), including grunts, croaks, clicks, knocks, and snaps, through stridulation (the rubbing of skeletal components), drumming (rapid contraction of the sonic muscles on or near the swim bladder) or hydrodynamics (quickly altering swimming direction and speed) (Ladich and Fine, 2006; Fine and Parmentier, 2015). These mechanisms for producing sounds often depend on the situation. For example, stridulation often occurs during feeding when the teeth or pharyngeal teeth and jaws are engaged, during territorial displays or as a fright response (Hawkins, 1986). Often the swim bladder amplifies stridulation-generated sounds (Hawkins, 1986). However, drumming may be used to produce mating calls, with the most well known sounds classified as croaks and hums; a common acoustic feature of members of the Sciaenidae family, as well as toadfishes (of the family Batrachoididae) (Mann, 1998; Mann et al., 2006). Consequently, a lot of vocal behaviour in fishes is associated with spawning and thus shows seasonal variability. Furthermore, the size and condition of the sonic muscles also show seasonality that is related to the species’ reproduction period (Kasumyan, 2008; Lagardére and Mariani, 2006). For example, seasonal periodicity in the sonic organs have been described in the weakfish, Cynoscion regalis, haddock, Melamogrammus eaglepinus (Templeman and Hodder, 1958, Hawkins et al., 1967, Connaughton and Taylor, 1994, 1996) and the plainfin midshipman, Porichthys notatus (Kasumyan, 2008). Members of the Sciaenidae family, also known as the croakers and drummers, are the largest known family of soniferous fishes (Ramcharitar et al., 2006). Based on local fisheries landing records and dietary studies of the Indo-Pacific humpback dolphins within the PRE (Barros et al., 2004; Fish and Mowbray, 1970; Banner, 1972; Whitehead and Blaxter, 1989; Ren et al., 2007) there are at least three sciaenid species within the PRE: the lionhead (Collichthys lucida), the Belanger’s croaker (Johnius belangerii) and the big-snout croaker (Johnius macrorhynus). However, anchovies (Thryssa sp.) have also been found within the stomach contents of stranded Indo-Pacific humpback dolphins within Hong Kong (Barros et al., 2004) and could be soniferous (Whitehead and Blaxter, 1989). Sciaenid fishes typically produce a series of rapid pulses commonly described as croaking knocking, clucking, or purring (Ramcharitar et al., 2006). Relying on a diet almost exclusively of fish, Indo-Pacific humpback dolphins within the PRE feed primarily on these soniferous croakers (Barros et al., 2004) and dolphin detection rates have been linked to soniferous fish activity in certain areas of the PRE (Pine et al., 2017). However, very little is understood on the distribution and seasonality of these ecologically important fishes. Notwithstanding, there are some data that provide insight into potential seasonal and lunar interactions in soniferous fishes in the western waters of Hong Kong. For example, passive acoustic monitoring on dolphins north of Lantau Island indirectly revealed higher fish calling activity during the spring months (Munger et al., 2016). Although incomplete, other data from within the PRE indirectly suggest seasonality in fish calling rates, with higher call rates during the summer months compared with late autumn (Pine et al., 2017). However, while these data do suggest seasonality, no comprehensive effort that directly investigates the spatiotemporal distribution of vocal fishes in the area has been done. Given these vocal fishes are an important prey item for the Indo-Pacific humpback dolphins within mainland PRE and Hong Kong waters (Barros et al., 2004), and that positive correlations between short-term vocal activity in fish and dolphin presence have been demonstrated (Pine et al., 2017), understanding the spatiotemporal distributions of these fish would assist in the identification of important foraging habitats for dolphins. The current study is the first to establish a baseline for fish detections across several sites in both mainland Chinese and Hong Kong waters of the PRE. Material and methods Study sites Four passive acoustic listening stations were set up at four locations within the PRE (Figure 1). Each listening station were maintained between August 2015 and September 2016 and where specifically located off Sanjiao Island (referred to as the Sanjiao site (N 22.118 E 113.716), Qi’An island (Qi’An site; N 22.420 E 113.679), Lamma Island (Lamma site; N 22.189 E 114.116) and Lung Kwu Chau (A5 site; N 22.373 E 113.898). The Sanjiao and Qi'’n sites were located within mainland Chinese waters, while the Lamma and A5 sites were within Hong Kong SAR waters. Due to the high fishing pressure in mainland Chinese waters, acoustic loggers at the Sanjiao and Qi’An sites were deployed at least 5 m from a disused radio tower and offshore lighthouse, respectively. By doing so, the recorders were safe from bottom trawlers whilst being distant enough for minimal noise contamination from waves against the pillar (determined during a series of pilot studies in 2015). Since bottom trawling does not occur within the Hong Kong SAR, the acoustic loggers could be deployed in isolation with fewer restrictions. Figure 1. Open in new tabDownload slide Study site in the PRE along the southern coast of China. Solid black circles represent each recording site while *represents the locations of each weather station. Figure 1. Open in new tabDownload slide Study site in the PRE along the southern coast of China. Solid black circles represent each recording site while *represents the locations of each weather station. Listening stations and acoustic loggers The listening stations consisted of the acoustic logger (a SoundTrap 300 HF acoustic recorder from Ocean Instruments New Zealand) secured to an iron rod set in a 30 cm deep, 50 kg concrete base that sat directly on the seafloor. When attached, the hydrophone component of the SoundTrap recorder was 1 m off the seabed. Each armed acoustic logger was set to operate on a duty cycle (2-min recording every 15-min at a sampling rate of 288 kHz and logged temperature every 60 s) to economize on battery life and available data card storage whilst improving data handling efficiency (Pine et al., 2017). Electronic calibration checks were completed at the start of each recording and checked at the start and middle of each recording day for each season. Environmental conditions during recording At higher wind speeds, noise from the oscillating air bubbles entrained at the surface from wind can peak around 500 Hz (Erbe et al., 2015) and therefore has the potential to mask fish calls. Therefore, to quickly and easily identify periods when this may be the case, wind speed, direction and rainfall were continuously monitored from the closest three weather stations (Fan Lau Shan, Hong Kong International Airport and Taipa in Macau) when the listening stations were active. Data analysis Recordings were uploaded into Adobe Audition 3.0 software for automated batch processing and editing before the customized fish detectors in Raven Pro 1.5 and Matlab were run and daily spectrograms were created for each deployment day. All statistical tests were carried out using SPSS 17.0 and SigmaPlot 11.0 and a p-value ≤ 0.05 was considered significant. Recordings were analysed for each deployment day over the study period and visually inspected after a 24 h period power spectral density spectrogram was generated in Matlab (using a 1 s Hanning window, 50% overlap and 10 s time-averaging). Recordings that did contain fish choruses were set aside and those PSD spectrograms were later correlated with the fish detector results. Weather station data were used to identify periods of high wind speeds (>10 km h−1) and rainfall and were excluded from the fish detector data after confirming any acoustic contamination. In order to understand the spatiotemporal distributions of the key fish species producing evening and night choruses within the PRE, as well as the temporal variation between seasons, the duration (mins) of each chorus (of each dominate call-type) recorded from all four listening stations was calculated. Choruses consisting of a single call-type were referred to as chorus-types. Call-types were first identified based on the received spectral and temporal characteristics in Raven Pro 1.5: the centre and peak frequencies (Hz), 90% bandwidth (Hz), 90% duration (s), overall call duration (s) and the burst rate per call. A total of 150 samples of each call-type were then randomly selected for statistical testing, whereby specific call-types were confirmed by statistically significant differences between either the spectral and/or temporal characteristics using a one-way ANOVA, after confirming the required assumptions. Where appropriate, Holm-Sidak pairwise comparisons were used to distinguish which call-types significantly differed from others within each acoustic characteristic and referred to as chorus-types SP1–SP5. This statistical testing application was done due to the inability to identify the species of fish each call type was coming from. An important assumption; however, is that each fish species within the PRE produce a chorus composed of a single dominant call-type and that each different chorus-type represents the presence of a different species. This assumption was considered appropriate within the current study due to the observation that choruses were of specific spectral bandwidths lasting several hours and because of the consistent temporal partitioning of chorus-types throughout a 24-h period. Following the identification of each chorus-type, the durations of each chorus from each recording site was calculated and plotted. Call rates for the most prevalent chorus-type was also calculated using the same band-limited energy detectors and plotted. To contextualize the spatiotemporal distributions around the PRE, the proportion of time that each chorus-type was detected for each season were calculated by totalling the duration of all choruses recorded and plotted to form a spatiotemporal map of fish activity. Results A total of 103 104 recordings, equating to 3436.8 recording hours, were successfully obtained. Five distinct call-types making up evening and/or night choruses were identified across all recording sites (Figure 2). Key acoustic parameters of each call-type that made up a chorus are provided in Table 1. Peak and centre frequencies across all call-types were below 1 kHz and the 90% bandwidths were below 1800 Hz. Call-types SP2 and SP4 shared similar peak (Holm-Sidak test, p = 0.961) and centre (Holm-Sidak test, p = 0.099) frequencies but were distinguished from one another based on the differing 90% bandwidth, and temporal characteristics (90% duration, total duration and pulse rate). Similarly, call-types SP1 and SP3 where of similar peak frequencies (Holm-Sidak test, p = 0.416), however differed in centre frequency (Holm-Sidak test, p < 0.001), 90% bandwidth (Holm-Sidak test, p < 0.001), 90% duration (Holm-Sidak test, p < 0.001), call-burst duration and pulse rate (Table 1). Table 1. Summary of the acoustic parameters used to confirm the presence of a new call-type chorus (mean ± SD). Parameter Chorus-type Sp1 Sp2 Sp3 Sp4 Sp5 Peak frequency (Hz) 863.3 ± 111.8a 668.0 ± 173.3b 853.2 ± 291a 665.9 ± 165.3b 144.1 ± 14.8c Centre frequency (Hz) 875.3 ± 84.3a 719.1 ± 159.4b 948.5 ± 148.3c 674.2 ± 96.6b 144.1 ± 8.8d 90% bandwidth (Hz) 1129.8 ± 160a 437.9 ± 186.6b 1723.7 ± 174.7c 572.8 ± 57.7d 107.4 ± 30.2e Burst-duration (ms) 161 ± 31.8a 136 ± 52a 342 ± 51b 230 ± 37.1c 1011 ± 225.3d 90% duration (ms) 140 ± 49.1a 95.5 ± 49b 300 ± 49.5c 206 ± 23.9d 860 ± 199.3e Pulse rate per call 9-11 13-16 36-42 12-13 11-14 Parameter Chorus-type Sp1 Sp2 Sp3 Sp4 Sp5 Peak frequency (Hz) 863.3 ± 111.8a 668.0 ± 173.3b 853.2 ± 291a 665.9 ± 165.3b 144.1 ± 14.8c Centre frequency (Hz) 875.3 ± 84.3a 719.1 ± 159.4b 948.5 ± 148.3c 674.2 ± 96.6b 144.1 ± 8.8d 90% bandwidth (Hz) 1129.8 ± 160a 437.9 ± 186.6b 1723.7 ± 174.7c 572.8 ± 57.7d 107.4 ± 30.2e Burst-duration (ms) 161 ± 31.8a 136 ± 52a 342 ± 51b 230 ± 37.1c 1011 ± 225.3d 90% duration (ms) 140 ± 49.1a 95.5 ± 49b 300 ± 49.5c 206 ± 23.9d 860 ± 199.3e Pulse rate per call 9-11 13-16 36-42 12-13 11-14 Note these are received levels averaged over 150 samples. Superscripts identify which call-types statistically differ within each parameter (Holm-Sidak test, p < 0.05). Table 1. Summary of the acoustic parameters used to confirm the presence of a new call-type chorus (mean ± SD). Parameter Chorus-type Sp1 Sp2 Sp3 Sp4 Sp5 Peak frequency (Hz) 863.3 ± 111.8a 668.0 ± 173.3b 853.2 ± 291a 665.9 ± 165.3b 144.1 ± 14.8c Centre frequency (Hz) 875.3 ± 84.3a 719.1 ± 159.4b 948.5 ± 148.3c 674.2 ± 96.6b 144.1 ± 8.8d 90% bandwidth (Hz) 1129.8 ± 160a 437.9 ± 186.6b 1723.7 ± 174.7c 572.8 ± 57.7d 107.4 ± 30.2e Burst-duration (ms) 161 ± 31.8a 136 ± 52a 342 ± 51b 230 ± 37.1c 1011 ± 225.3d 90% duration (ms) 140 ± 49.1a 95.5 ± 49b 300 ± 49.5c 206 ± 23.9d 860 ± 199.3e Pulse rate per call 9-11 13-16 36-42 12-13 11-14 Parameter Chorus-type Sp1 Sp2 Sp3 Sp4 Sp5 Peak frequency (Hz) 863.3 ± 111.8a 668.0 ± 173.3b 853.2 ± 291a 665.9 ± 165.3b 144.1 ± 14.8c Centre frequency (Hz) 875.3 ± 84.3a 719.1 ± 159.4b 948.5 ± 148.3c 674.2 ± 96.6b 144.1 ± 8.8d 90% bandwidth (Hz) 1129.8 ± 160a 437.9 ± 186.6b 1723.7 ± 174.7c 572.8 ± 57.7d 107.4 ± 30.2e Burst-duration (ms) 161 ± 31.8a 136 ± 52a 342 ± 51b 230 ± 37.1c 1011 ± 225.3d 90% duration (ms) 140 ± 49.1a 95.5 ± 49b 300 ± 49.5c 206 ± 23.9d 860 ± 199.3e Pulse rate per call 9-11 13-16 36-42 12-13 11-14 Note these are received levels averaged over 150 samples. Superscripts identify which call-types statistically differ within each parameter (Holm-Sidak test, p < 0.05). Figure 2. Open in new tabDownload slide Representative amplitude and spectrograms of the dominate call-type making up each of the five chorus-types. Spectrograms generated using a 2048 sample Hann window, 50% overlap, 1024 Hop Size and 202 Hz 3 dB filter bandwidth. Figure 2. Open in new tabDownload slide Representative amplitude and spectrograms of the dominate call-type making up each of the five chorus-types. Spectrograms generated using a 2048 sample Hann window, 50% overlap, 1024 Hop Size and 202 Hz 3 dB filter bandwidth. The greatest diversity of chorus-types was detected at the Sanjiao and A5 recording sites, with three distinct chorus-types (SP1–SP3 at the Sanjiao site an SP2–SP4 at the A5 site), followed by Lamma (SP2 and SP5) and Qi’An (SP1 only). Interestingly, chorus-type SP1 was only detected within the Sanjiao and Qi’An recording sites, while chorus-type SP4 was only detected at A5 and SP5 at the Lamma recording site (Figure 3). The duration of each chorus-type varied over the year, with the longest durations occurring between spring and summer months (Figure 4). Over a 24-h period, chorus-type SP1 was the longest-lasting, with a maximum duration of 660 mins (during April at the Sanjiao recording site), while the shortest-lasting chorus-type was SP4, with a maximum duration of 150 min (during May, July, and August at the A5 site). All chorus-types followed the general trend of heightened activity (represented by longer chorus durations) between spring and summer months that gradually decreased through autumn to minimal activity over the winter. Figure 3. Open in new tabDownload slide Map showing the spatial variation in chorus-types at each of the recording sites. Percentages were calculated based on the total duration of each chorus after combining the durations of all chorus-types for each recording site. Basemap is the World Water Bodies map, dated 12 May 2017, downloaded from the ArcGIS website. Figure 3. Open in new tabDownload slide Map showing the spatial variation in chorus-types at each of the recording sites. Percentages were calculated based on the total duration of each chorus after combining the durations of all chorus-types for each recording site. Basemap is the World Water Bodies map, dated 12 May 2017, downloaded from the ArcGIS website. Figure 4. Open in new tabDownload slide Chorus duration (minutes) of each chorus-type per each night between August 2015 and September 2016 at each recording site (SJD, Sanjiao site; QAD, Qi’An site; LAM, Lamma site; and A5, A5 site). The black segments respresent the periods when no data were collected. Figure 4. Open in new tabDownload slide Chorus duration (minutes) of each chorus-type per each night between August 2015 and September 2016 at each recording site (SJD, Sanjiao site; QAD, Qi’An site; LAM, Lamma site; and A5, A5 site). The black segments respresent the periods when no data were collected. When all chorus-type durations (referred to as chorus-mins) were totalled over the survey period, chorus-type SP1 made up 64% of the total chorus-mins at the Sanjiao recording site (31 596 mins over a total of 49 584 chorus-mins), SP2 accounted for 19% (total mins of 9648 over the survey period) and SP3 made up the remaining 17% (8340 min) (Figure 3). Broken down into seasons, chorus-type SP2 was the more common-occurring chorus-type during the summer, making up 42% of choruses at the Sanjiao recording site (Figure 5) while chorus-types SP1 and SP3 contributed 32 and 26% of the total chorus duration, respectively. Over at the A5 recording site, chorus-type SP2 made up the second largest proportion across all sites of 85% (14 700 min of a total 17 520 min), followed by 85% of total chorus-mins at the Lamma recording site (Figure 4). During the autumn, chorus-type SP2 decreased in activity within both the Sanjiao and A5 recording sites, making 15 and 61% within either site, respectively (Figure 5), while chorus-type SP1 was the more common-occurring chorus-type at Sanjiao. At the Lamma and A5 recording sites; however, chorus-type SP2 was the most common-occurring during the autumn months, making up 53% of all the total chorus-mins, while SP5 contributed to 47% at the Lamma recording site. Winter showed the lowest diversity of chorus-types among sites, with only SP1 being detected at the Sanjiao site and SP2 at Lamma, before rising again during the spring months (Figure 5). Figure 5. Open in new tabDownload slide Map showing the spatiotemporal variation in chorus-types at each of the recording sites between seasons. Percentages were calculated based the total duration of each chorus after combining the durations of all chorus-types over each season for each recording site. Basemap is the World Water Bodies map, dated 12 May 2017, downloaded from the ArcGIS website. Figure 5. Open in new tabDownload slide Map showing the spatiotemporal variation in chorus-types at each of the recording sites between seasons. Percentages were calculated based the total duration of each chorus after combining the durations of all chorus-types over each season for each recording site. Basemap is the World Water Bodies map, dated 12 May 2017, downloaded from the ArcGIS website. As a further indication of the temporal variability in fish activity within the proposed area, call rates during each SP1 chorus showed the highest rates (approximately 222.7 ± 12.4 calls per 2-min recording) during April and lowest rates during the winter months December and January (∼37.3 ± 20.9 and 45.6 ± 8.2 calls per 2-min recording, respectively) (Figure 6). Although call rates throughout the summer, autumn and winter months showed a unimodal distribution between 17:00 and 02:00 h, a bimodal distribution formed during the spring months, starting in March and by April the call rates showed two clear peaks of ∼127.1 ± 10.1 and 222.7 ± 12.4 calls per 2-min recording at around 16:00 and 22:00 h, respectively. Figure 6. Open in new tabDownload slide Mean call rates (number of calls per 2 min recording) between 12:00 and 06:00 h (the following day), averaged over each month at the Sanjiao recording site. Figure 6. Open in new tabDownload slide Mean call rates (number of calls per 2 min recording) between 12:00 and 06:00 h (the following day), averaged over each month at the Sanjiao recording site. Discussion Identifying potentially important foraging habitat for threatened dolphin populations is a fundamental step towards informing management and formulating mitigating strategies to minimize anthropogenic noise impacts. This was the principal rationale for the current study which provides preliminary information on the spatiotemporal distribution of soniferous fishes; a key prey item for the Indo-Pacific humpback dolphins within the PRE (Barros et al., 2004) and to which dolphin presence is linked (Pine et al., 2017). Although a complete and comprehensive survey is not possible using passive acoustics alone, by “eavesdropping” on the soundscape an understanding of the seasonal and temporal distributions of vocal fishes can be mapped. This study therefore aimed to investigate the fish choruses detected during each season to better understand the spatiotemporal variability of a key factor influencing the current habitat use of dolphins within the PRE. The data presented in this paper provides the foundation for the eventual process of relating the spatiotemporal variation in fish activity with dolphin movements. Analyses reveal the presence of at least five different chorus-types within the PRE, each showing seasonal patterns in activity. For the purposes of investigating the distribution of fishes, the duration (mins) of each chorus-type was analysed. An important consideration within this study is that each chorus-type was assumed to be representative of a different species and was based on the temporal and spectral dynamics of a chorus’s most dominant call-type. However, fish sometimes produce different sounds during a same chorus. For example, the sea catfish (Arius maculatus), as well as the croakers Johnius sp. which occur within the PRE, often produce strumming and stridulation sounds which vary both temporally (i.e. duration and pulse-rates) and spectrally (Mok et al., 2011). However, by focussing on each chorus as a whole and characterizing it by the most dominate call-type, the probability of the same species being counted as separate chorus-types is controlled for. The consistent bandwidth and temporal partitioning between chorus-types is further evidence that each chorus-type is likely of a separate species. Certain chorus-types were also recorded at only certain recording sites. For example, chorus-type SP2 peaked during early evening, followed by chorus-type SP1 between ∼20:00 and 23:00 h, then chorus-type SP3 with peak activity occurring between midnight and 02:00 h. Resource partitioning is a fundamental ecological concept for reducing competition between co-occurring species (Schoener, 1974; Hastings and Sirovic, 2015). Fish have appeared to evolve bandwidth and/or temporal partitioning to likely prevent species from confusing conspecific calls from interspecific ones (Luczkovich and Sprague, 2011; Wilkins et al., 2013; Parsons et al., 2016; Staaterman et al., 2013; Hastings and Sirovic, 2015), while other families that compete for acoustic bandwidth may show temporal partitioning (Luczkovich and Sprague, 2011; Hastings and Sirovic, 2015; Ruppé et al., 2015; Parsons et al., 2016). This was observed within the current study with each chorus-type being of varying bandwidths and rarely showed temporal overlap. By avoiding temporal overlap between chorus-types of similar peak-frequencies and burst-rates, such as chorus type SP2 and SP4, each fish species appears to be displaying a degree of resource partitioning, as seen in other regions (Hastings and Sirovic, 2015). Further to the temporal partitioning in chorus-types, the current study has also outlined seasonal patterns in choruses. Previous studies characterizing the diversity and variation in fish choruses have shown diurnal, lunar and seasonal patterns among choruses; thereby emphasizing the use of PAM for temporal and spatial predictive modelling (Parsons et al., 2016). At least three sciaenid species are known to exist within the PRE (the lionhead, Belanger’s croakers, and the big-snout croaker) and are most likely the source of at least three of the chorus-types. It was not possible to identify the species of fish from which the received signals were emitted, due to the limited information on species-specific vocalisations, as well as due to the nature of the data collection. However, the dominate call-types within each chorus were largely commensurate with the acoustic characteristics of strumming and drumming. Although no published investigations on the sound production of fishes within the PRE were found, the predominate call-type making up the SP1 chorus-type showed overlapping frequency and temporal characteristics with the Belanger’s croaker (J. belangerii) (Pilleri et al., 1982). For example, calls from the Belagner’s croaker range between 500 and 1250 Hz (dominant frequencies), with bursts lasting ∼140–160 ms with 4–14 pulses per call (Ramcharitar et al., 2006). However, the Belanger’s croaker has also been known to produce calls lasting between 140 and 260 ms (Pilleri et al., 1982). Therefore, while unable to confirm this, it may be that SP1 chorus-type is predominately that of the Belanger’s croaker. Similarly, chorus-type SP3 shows a degree of overlap with calls from the big-snout croaker, J. macrorhynus (Lin et al., 2007). Notwithstanding the challenges in species identification, there was considerable spatiotemporal variation among chorus-types. During the dry season (cooler sea surface temperatures), there was a considerable decrease in chorus-type diversity and activity. For example, a single chorus-type was recorded over only 11 nights during the dry season and the average call rates were significantly lower compared with the wet season. This dramatically contrasted to the wet season where all five chorus-types were detected, durations remained for hours at a time over most nights and call rates peaked. Croakers (Johnius sp.) and other potential prey species of the dolphins are known to move into the shallower waters during the wet season, while moving to deeper waters during the dry seasons (Chen and Liu, 1982; Munger et al., 2016). Given the previous finding that dolphin occurrence correlates with fish activity between August and November (the wet season), dolphin detections are expected to reach the lowest rates of the year over the dry season. Although not directly investigated to date, observations around north Lantau Island, near the A5 recording site within this study, have shown fewer acoustic detections of dolphins during early spring, despite higher fish activity (Munger et al., 2016). Although this could be due to lower acoustic activity in the dolphins, evidence suggests that dolphin densities are lower coming into the wet season off the northern shores of Lantau Island (Jefferson, 2000; Hung, 2008) with the dolphins’ possibly feeding on other fish species further offshore (Munger et al., 2016). Although data from the A5 recording site were first collected during mid-autumn, only chorus-type SP2 was present until June; after which chorus-types SP3 and SP4 were then detected. Based on information as recent as 2014, this finding may be unusual as fish activity in the area is thought to be most intense during the early spring (Munger et al., 2016). With the development of the Hong Kong International Airport and increased dredging within the PRE, this finding is particularly interesting in that it provides potential insight into new methods for assessing possible impacts from developments on fish communities. Although these data do not support this as a conclusion, it does warrant further research whereby quantified comparisons between acoustic detections of fishes and dolphins, as well as anthropogenic sources, are tested. Such comparisons would provide critical information on the extent of possible ecological changes within the PRE over a very small time-frame between 2014 and 2016 due to coastal development projects. Unique to the Lamma recording site was chorus-type SP5. Occurring mostly during the spring and autumn months, chorus-type SP5 was of considerably lower peak frequencies, shorter bandwidths and longer durations compared with the other four chorus-types. It may be possible that this call-type is also from Johnius sp., however the times during which chorus-type SP5 were detected appeared independent from chorus-type SP2 and neither chorus-types SP1, SP3, or SP4 were detected at the Lamma site; thereby suggesting a different species. Although this cannot be confirmed and care should be taken when assuming a chorus-type does in fact represent a new species, this preliminary finding may suggest chorus-type SP5 to be relatively restricted in its distribution. If chorus-type SP5 is confirmed to be produced from a different fish species, further research into the distributions of this chorus-type would be very interesting given that it was only detected at the one site and over a relatively short period of time. Acknowledgements We would like to thank all members of the Conservation Biology of Aquatic Animals research group at the Institute of Hydrobiology, Chinese Academy of Sciences. In particular, I thank Zhaolong Cheng, Dr Lijun Dong, Dr Liang Fang, and Changqun Zhang for their valued assistance in the field. I also sincerely thank Director Xichun Gu, Vice-director Huantong Wan and technician Xi Chen from the PRE Chinese White Dolphin National Nature Reserve for their assistance in the field. 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Ballesteros, Marta;Chapela, Rosa;Ramírez-Monsalve, Paulina;Raakjaer, Jesper;Hegland, Troels J;Nielsen, Kåre N;Laksá, Unn;Degnbol, Poul;Link, Handling editor: Jason

doi: 10.1093/icesjms/fsx181pmid: N/A

Abstract The International Council for the Exploration of the Sea (ICES) occupies a central role in the advice system to support the implementation of an ecosystem approach to fisheries management (EAFM) in the European Union (EU). Despite improvements, its capacity to deliver ecosystem advice seems to be far from a fully functional operational framework. To what extent availability of appropriate scientific advice is a barrier for a more widespread use of an EAFM in Europe remains an open question. Building on the findings of a large research project, this article explores what advice ICES can provide. The article concludes that: (i) ICES has taken a leading role in generating an EAFM framework in which management decisions can operate; (ii) the advice “suppliers” and the advice “users” agree on the feasibility of using existing knowledge to “do EAFM now”; (iii) ICES can address a range of shortcomings, but some of the present bottlenecks demand concerted action between the advisory system and the political realm. The implementation of an EAFM requires consistency between science and management. ICES appears as well-suited to facilitate the dialogue on applying an EAFM in the EU, but it is unrealistic to expect ICES to produce all the answers. Introduction In recent years, the debate about the ecosystem approach to fisheries management (EAFM) has shifted from the definition of the concept towards implementation (Link and Browman, 2014). Researchers have analysed the challenges and barriers related to the scientific knowledge base (Frid et al., 2006; Symes and Hoefnagel, 2010; Österblom, et al., 2011) and the institutional framework in the European Union (EU) (Jennings and Rice, 2011; Ramírez-Monsalve, et al., 2016b). However, challenges associated with how the advice system has embedded an EAFM have proven more difficult to address. This article explores the advisory process developed by the International Council for the Exploration of the Sea (ICES) to support an EAFM in EU policies. By analysing the ICES mandate, implementation strategy, and users’ demands, we aim to investigate to what extent the scientific advice is either slowing down or facilitating progress in the application of an EAFM. ICES is a global network organization, enabling the coordination and research efforts of near 700 marine institutes in its twenty member countries, five affiliates and multiple international initiatives. The organization facilitates the framework within which more than 5000 individual scientists work together to provide the scientific basis for advice (figures from ICES’ website, July 2017). ICES provides biological advice on fisheries resources and ecosystems of the North-East Atlantic to diverse clients, including nation states and international bodies. Besides the EU, several major clients are pursuing an ecosystem approach (EA) in different ways (see O’Boyle and Jamieson, 2006; Olsen et al., 2007; Sígurjónsson, 2008; Pitcher et al., 2009; Link et al., 2011; Levin et al., 2014; Dolan et al., 2016; Gullestad et al., 2017; Harvey et al., 2017; ICES, 2016a). Although beyond the scope of this article, it is therefore clear that ICES needs procedures that do not only accommodate the EU. For most nation state clients, legal responsibilities are historically divided between a body in charge of the marine environment and another responsible for fisheries. This division is also apparent in the EU Commission with the Directorate General for Maritime Affairs and Fisheries (DG MARE), responsible for the Common Fisheries Policy (CFP), and the Directorate General for Environment (DG-ENV), responsible for the Marine Strategy Framework Directive (MSFD). A similar divide is present among clients in the shape of international organizations. In the fisheries domain, it includes the North East Atlantic Fisheries Commission (NEAFC), which is the regional fisheries management organization (RFMO) for the area, and the Joint Russian-Norwegian Fisheries Commission, responsible for management of fisheries on joint stocks in the Barents Sea. And, in the marine environment domain, the Convention for the Protection of the Marine Environment of the North East Atlantic (OSPAR) and the Baltic Marine Environment Protection Commission–Helsinki Commission (HELCOM), which are cooperation bodies for governments regarding marine environment matters in the North East Atlantic and the Baltic respectively. Concerning the EU, ICES occupies a central role by providing scientific advice to primarily the CFP through the Commission/DG MARE, which has the power to initiate fisheries policies, propose new legislation to be considered by decision-makers, implement decisions, oversee member states’ (MS) implementation, etc. In addition, ICES provides advice to DG-ENV and EU MS regarding the MSFD, which aims at securing the protection of the marine environment, setting a goal of achieving good environmental status (GES) by 2020. The relationship between ICES and the EU constitutes part of an advisory landscape comprising other bodies. Formally, the Commission acts on input from its Scientific, Technical and Economic Committee for Fisheries (STECF), a group of commissioned external experts. Therefore the “external” scientific advice received by the Commission is reviewed by STECF. As shown in Figure 1, this relates to scientific advice received from ICES, but also to advice from the General Fisheries Commission for the Mediterranean and from science committees of RFMOs to which the EU is contracting party. Figure 1. View largeDownload slide Main aspects of the advisory system of the CFP. “?” indicates request for advice. “!” indicates delivery of advice. The figure draws on Hegland (2006), Wilson (2009), and ICES (2016c). SCs RMFOS are the scientific committees of the RFMO; Reg. MS Groups are the Regional Groups of the MS. Note: Not depicted in Figure 1 is the regular commissioning and use by the Commission of tenders and research projects, and—later in the legislative process—the European Parliament’s commissioning of reports and use of parliamentary hearings to strengthen the knowledge base ahead of decisions. Figure 1. View largeDownload slide Main aspects of the advisory system of the CFP. “?” indicates request for advice. “!” indicates delivery of advice. The figure draws on Hegland (2006), Wilson (2009), and ICES (2016c). SCs RMFOS are the scientific committees of the RFMO; Reg. MS Groups are the Regional Groups of the MS. Note: Not depicted in Figure 1 is the regular commissioning and use by the Commission of tenders and research projects, and—later in the legislative process—the European Parliament’s commissioning of reports and use of parliamentary hearings to strengthen the knowledge base ahead of decisions. The Advisory Councils (ACs), in contrast, provide experience-based knowledge and stakeholders opinions (fisheries sector organizations, environmental organizations, and others) in the form of advice on particular fisheries or specific regional seas, often drawing on science. The ACs are discussion arenas designed for favouring consensus among multiple interests from different stakeholders, and their roles in the advisory process have been intensively studied (see e.g. Coers et al., 2012; Hatchard and Gray, 2014; Linke and Jentoft, 2014). Internally in the Commission, DG MARE draws on support from the Joint Research Centre, a supplier of various services related to science to the different directorates. Finally, following the latest CFP reform (EU, 2013), cooperative regional (organized by sea basin) member state groups have been established to submit joint recommendations for inter alia multiannual management plans. Recommendations from these groups shall draw on advice from the ACs, though the approach for doing so appears underdeveloped or unclear in some regions (Eliasen et al., 2015; WEAF, 2016). Over the past 15 years, ICES has gone through major reforms, driven by a need to improve efficiency and respond to the evolution of policy and science (Stange et al., 2012). The result is an organization focused on delivering science and advice to meet sector-specific requirements while considering cross-cutting issues such as the EA, maritime spatial planning and climate change. ICES progress to the EA has been analysed in Wilson (2009) and Stange et al., (2012) while this article discusses the most recent developments related to the EU. In a context of knowledge politicization and scientification of politics (Carter, 2013), ICES advice has evolved from an exploratory role towards a normative one through the implementation of the precautionary approach (Hoydal, 2007). Considering the main changes in the form of advice provided by the Advisory Committee (ACOM; see Table 1) to recipients of ICES advice, the EA has posed the biggest challenge (see Wilson, 2009 for an overview of ICES past advice and the tensions between comprehensive vs. particularistic approaches to an EAFM). For the EU, an initial core dilemma in this process was the lack of legal frameworks, which could trigger requests for EA advice. Subsequently, after both the CFP and the MSFD included relevant law, the absence of a formalized process for an integrated advice into management provided a challenge. Table 1. Dominant forms of ICES advice in the ACOMa Period  Policy basis  Dominant forms of advice  Assumptions  1980s  Long-term optimization of harvest  Stock advice  Predictability  2000s  Precautionary approach regarding harvest from single stocks  Stock advice Mixed-species advice  Uncertainty  2010s  Precautionary and integrated approach regarding fish stocks in the ecosystem  Stock advice Fishery Advice Ecosystem Advice  Interdependency  Period  Policy basis  Dominant forms of advice  Assumptions  1980s  Long-term optimization of harvest  Stock advice  Predictability  2000s  Precautionary approach regarding harvest from single stocks  Stock advice Mixed-species advice  Uncertainty  2010s  Precautionary and integrated approach regarding fish stocks in the ecosystem  Stock advice Fishery Advice Ecosystem Advice  Interdependency  a See Wilson (2009), Lassen et al. (2014), and Wenzel (2017) for an overview of ICES advice. Table 1. Dominant forms of ICES advice in the ACOMa Period  Policy basis  Dominant forms of advice  Assumptions  1980s  Long-term optimization of harvest  Stock advice  Predictability  2000s  Precautionary approach regarding harvest from single stocks  Stock advice Mixed-species advice  Uncertainty  2010s  Precautionary and integrated approach regarding fish stocks in the ecosystem  Stock advice Fishery Advice Ecosystem Advice  Interdependency  Period  Policy basis  Dominant forms of advice  Assumptions  1980s  Long-term optimization of harvest  Stock advice  Predictability  2000s  Precautionary approach regarding harvest from single stocks  Stock advice Mixed-species advice  Uncertainty  2010s  Precautionary and integrated approach regarding fish stocks in the ecosystem  Stock advice Fishery Advice Ecosystem Advice  Interdependency  a See Wilson (2009), Lassen et al. (2014), and Wenzel (2017) for an overview of ICES advice. Despite the improvement in the ICES capacity to deliver ecosystem advice (see Rice and Rogers, 2006) it appears far from a fully functional operational framework for fisheries management. This article intends to increase the understanding on what advice ICES can, in fact, provide to the EU. With this objective in mind, we review the developments in ICES towards providing ecosystem advice for fisheries to the EU. First, we summarize the policy context that shapes the EU demands for advice. Second, we analyse the scope of the advice framework agreed between the EU and ICES for 2016 and what ICES is currently delivering. Third, based on qualitative research at EU level (Table 2), we outline what actors and stakeholders expect from ecosystem advice. Finally, we identify main shortcomings and discuss how ICES could address them. Table 2. Description of the methods used for the collection of primary data for this article. Method  Research focus  Topics  Participants  Focus Group, June 2014  Gain information from the interaction between participants, which discuss issues in their terms, revealing normative as well as substantive perceptions  To identify the advice generated within the ACs.To explore alternatives for ACs to provide EAFM advice.  ACs secretariats from the North Sea AC (NSAC), the North Western Waters AC (NWWAC), Pelagic AC (PAC), and Mediterranean AC (MEDAC)  Informant interviews, July to October 2014  In depth-interviews with selected (non-random) group of experts.  To identify ACs constraints in terms of resources, scope and processes.  NSAC, NWWAC, PAC, MEDAC, and the secretariats from the South Western Waters and the Baltic (BSAC)  Round Table Discussion, January 2015.  Panel of participants covering a particular subject.  To analyse the challenges regarding the EAF in the CFP.To explore operational options on how to set-up the EAF process  DG MARE, ICES, STECF, and The European Fisheries and Aquaculture Research Organizations (EFARO)  Case study meetings (19) in seven case studies in all EU sea basins  Multi-stakeholders platform to develop a decision-support framework for implementing an EAFM  To define a fisheries management problem, to identify alternative management scenarios, and to advance in providing support for the development of a management plan.  NSAC, PELAC, MEDAC, NWWAC, BSAC, policy-makers at regional and national level, research organizations  Online surveya, October–December 2015  Standard set of questions  To gather structured information on stakeholders views about EAFM advice and participation  Members of the seven ACs (Industry, NGOs, other organizations)  Workshop on the EAF Advice in the EU October 2016  Interactive session combining qualitative research, brainstorming and problem-solving.  To review the current advisory process and discuss possible changes in scoping processes and regionalization  DG-MARE, ACs, STECF, ICES, Fishing industry representatives, NGOs  Method  Research focus  Topics  Participants  Focus Group, June 2014  Gain information from the interaction between participants, which discuss issues in their terms, revealing normative as well as substantive perceptions  To identify the advice generated within the ACs.To explore alternatives for ACs to provide EAFM advice.  ACs secretariats from the North Sea AC (NSAC), the North Western Waters AC (NWWAC), Pelagic AC (PAC), and Mediterranean AC (MEDAC)  Informant interviews, July to October 2014  In depth-interviews with selected (non-random) group of experts.  To identify ACs constraints in terms of resources, scope and processes.  NSAC, NWWAC, PAC, MEDAC, and the secretariats from the South Western Waters and the Baltic (BSAC)  Round Table Discussion, January 2015.  Panel of participants covering a particular subject.  To analyse the challenges regarding the EAF in the CFP.To explore operational options on how to set-up the EAF process  DG MARE, ICES, STECF, and The European Fisheries and Aquaculture Research Organizations (EFARO)  Case study meetings (19) in seven case studies in all EU sea basins  Multi-stakeholders platform to develop a decision-support framework for implementing an EAFM  To define a fisheries management problem, to identify alternative management scenarios, and to advance in providing support for the development of a management plan.  NSAC, PELAC, MEDAC, NWWAC, BSAC, policy-makers at regional and national level, research organizations  Online surveya, October–December 2015  Standard set of questions  To gather structured information on stakeholders views about EAFM advice and participation  Members of the seven ACs (Industry, NGOs, other organizations)  Workshop on the EAF Advice in the EU October 2016  Interactive session combining qualitative research, brainstorming and problem-solving.  To review the current advisory process and discuss possible changes in scoping processes and regionalization  DG-MARE, ACs, STECF, ICES, Fishing industry representatives, NGOs  a The response rate to the survey was low (11%). The survey data were used for qualitative analysis. Table 2. Description of the methods used for the collection of primary data for this article. Method  Research focus  Topics  Participants  Focus Group, June 2014  Gain information from the interaction between participants, which discuss issues in their terms, revealing normative as well as substantive perceptions  To identify the advice generated within the ACs.To explore alternatives for ACs to provide EAFM advice.  ACs secretariats from the North Sea AC (NSAC), the North Western Waters AC (NWWAC), Pelagic AC (PAC), and Mediterranean AC (MEDAC)  Informant interviews, July to October 2014  In depth-interviews with selected (non-random) group of experts.  To identify ACs constraints in terms of resources, scope and processes.  NSAC, NWWAC, PAC, MEDAC, and the secretariats from the South Western Waters and the Baltic (BSAC)  Round Table Discussion, January 2015.  Panel of participants covering a particular subject.  To analyse the challenges regarding the EAF in the CFP.To explore operational options on how to set-up the EAF process  DG MARE, ICES, STECF, and The European Fisheries and Aquaculture Research Organizations (EFARO)  Case study meetings (19) in seven case studies in all EU sea basins  Multi-stakeholders platform to develop a decision-support framework for implementing an EAFM  To define a fisheries management problem, to identify alternative management scenarios, and to advance in providing support for the development of a management plan.  NSAC, PELAC, MEDAC, NWWAC, BSAC, policy-makers at regional and national level, research organizations  Online surveya, October–December 2015  Standard set of questions  To gather structured information on stakeholders views about EAFM advice and participation  Members of the seven ACs (Industry, NGOs, other organizations)  Workshop on the EAF Advice in the EU October 2016  Interactive session combining qualitative research, brainstorming and problem-solving.  To review the current advisory process and discuss possible changes in scoping processes and regionalization  DG-MARE, ACs, STECF, ICES, Fishing industry representatives, NGOs  Method  Research focus  Topics  Participants  Focus Group, June 2014  Gain information from the interaction between participants, which discuss issues in their terms, revealing normative as well as substantive perceptions  To identify the advice generated within the ACs.To explore alternatives for ACs to provide EAFM advice.  ACs secretariats from the North Sea AC (NSAC), the North Western Waters AC (NWWAC), Pelagic AC (PAC), and Mediterranean AC (MEDAC)  Informant interviews, July to October 2014  In depth-interviews with selected (non-random) group of experts.  To identify ACs constraints in terms of resources, scope and processes.  NSAC, NWWAC, PAC, MEDAC, and the secretariats from the South Western Waters and the Baltic (BSAC)  Round Table Discussion, January 2015.  Panel of participants covering a particular subject.  To analyse the challenges regarding the EAF in the CFP.To explore operational options on how to set-up the EAF process  DG MARE, ICES, STECF, and The European Fisheries and Aquaculture Research Organizations (EFARO)  Case study meetings (19) in seven case studies in all EU sea basins  Multi-stakeholders platform to develop a decision-support framework for implementing an EAFM  To define a fisheries management problem, to identify alternative management scenarios, and to advance in providing support for the development of a management plan.  NSAC, PELAC, MEDAC, NWWAC, BSAC, policy-makers at regional and national level, research organizations  Online surveya, October–December 2015  Standard set of questions  To gather structured information on stakeholders views about EAFM advice and participation  Members of the seven ACs (Industry, NGOs, other organizations)  Workshop on the EAF Advice in the EU October 2016  Interactive session combining qualitative research, brainstorming and problem-solving.  To review the current advisory process and discuss possible changes in scoping processes and regionalization  DG-MARE, ACs, STECF, ICES, Fishing industry representatives, NGOs  a The response rate to the survey was low (11%). The survey data were used for qualitative analysis. The policy context in the EU: between healthy fish stocks and healthy marine ecosystems The delivery of science-based advice from ICES to the EU has since 1987 been managed through a Memorandum of Understanding (MoU) (Since 2017 the official designation is “Administrative Arrangement.”) between the two organizations. Notably, ICES was ahead of EU policies—and the policies of other ICES clients—in pointing out the need for scientific advice on the ecosystem context (see Rice, 2005). Since 1992 the ICES Working Group on Ecosystem Effects of Fishing Activities has considered the framework and application of the EA, providing leadership in the development of major concepts, such as those underlying the MSFD and the use of indicators to inform assessment and management actions. The ICES’ Strategic plans included the approach (e.g. in 1999 and 2001) promoting the adaptation of the organizational structure (e.g. creating the Advisory Committee on Ecosystems on Ecosystems in 2001). The reform of the CFP in 2002 called for a progressive implementation of an EAFM, but it was not until the approval of the MSFD (2008) and the latest CFP reform (2013) that the approach can be considered embedded in the policy framework. The EU understands the MSFD as the general basis for implementing an EA to the marine environment “which fully benefit sustainable fisheries by ensuring integrative management of all human, environmental and economic interactions in the maritime field” (EC, 2008b: 4). The CFP narrows the scope of the EA to the fisheries realm. A review of the implications of the different focus and locus in both policies has been presented by Ramírez-Monsalve et al. (2016b), whereas broader governance issues are addressed in Garcia et al. (2014). The conceptual debate on the EA has been lively and can within the ICES community be traced through workshops and strategic plans (ICES, 2000, 2004a; Wenzel, 2017). At a certain stage, there was a conceptual distinction between ecosystem-based and EA, where “based” was considered to be about ecosystem engineering and “approach” was interpreted to mean precautionary. Currently, the terms ecosystem-based management and EA have been generalized (as in e.g. Fanning et al., 2011; Link and Browman, 2017). A proposed differentiation between EAFM and EBFM is based on whether the focus is on single stocks or multiple fisheries within an ecosystem (see Dolan et al., 2016). But the European policy has somehow blurred the lines by using ecosystem-based approach in the MSFD and EAFM in the fisheries policy (Prellezo and Curtin, 2015). In this article the term EAFM will be used, and the ICES’ EBFM should be read as a synonym. However, the artificial boundary around the EAFM concept—used for analytical purposes—should recognize the fundamental need for an appropriate level of integration among all the sectors operating in the marine ecosystem (see ICES, 2004b; Cowan et al., 2012). Formally, ICES’ advice is developed within a specific organizational structure responding to concrete client demands within a set advisory process. Hence, in the EU the ICES’ advisory process is caught in a dilemma between the limitation of the single-sector scope of EA within the CFP and an understanding that EA by its very nature must be cross-sectoral. In practice, the inclusion of ecosystem considerations in fisheries advice has been stated in general terms in several MoUs and described as an incremental inclusion of knowledge: “The advice shall be based on an ecosystem approach. This will be implemented incrementally so that any information on the interactions between fisheries, the fish stocks and the marine ecosystem is considered and incorporated in the advice as it becomes available” (MoU, 2007). Other fisheries related clients used a similar language. However, the CFP (as many fisheries policies) still had its main focus on the single stock total allowable catch. Serving this need, the advisory focus remained on single stock fishing opportunities. The integration of ecosystem considerations into the final stock advice was rather small (e.g. referring to escapement biomass to support predators) due to limitations of the underlying science and management framework. Other pieces of advice, which may be regarded as elements of an EAFM, included advice on avoidance of bycatches of mammals and on area closures to protect bottom habitats. These illustrate the tension between the extremes of, at one end, managing fisheries with the aim to restore or create specific properties of marine ecosystems, and at the other end to manage fisheries with a general aim of a minimal unwanted impact, such as expressed in the precautionary approach. The former may emphasize the use of various measures regarding how fishing is performed concerning location, timing and gear properties such as mesh size and bottom contact, while the latter may emphasize the overall extent of the impact such as the total outtake relative to populations in the ecosystem. In practice, both sets of tools are relevant in both cases. A more formalized and policy-linked inclusion of EAFM in the EU was introduced in 2007 (see Wilson, 2009 for previous stages) with the preparation of the MSFD and ICES started to receive requests for fisheries advice including considerations of this part of EU policy. The policy landscape for ICES advice also includes the Water Framework Directive (2000), the Birds Directive (2009), the Habitat Directive (1992), the EU Research Programmes and the overarching framework of the Integrated Maritime Policy (2007), which directly addresses the EA as a principle to guide EU action. A consequence of the formalized introduction of EAFM in the EU was that the formal requirements for integrating ecosystem considerations on fisheries advice became different for ICES clients. For the EU it was critical to ensure that there was no conflict between the advice given regarding fisheries and regarding marine environmental policy as specified in the MSFD and other directives. For other fisheries clients, the linkage was different. For instance, for NEAFC reference was made to various United Nations (UN) agreements regarding the environmental impact of high sea fisheries while for the Joint Russian-Norwegian Fisheries Commission there was no parallel joint marine environmental policy to refer to. When advice could not be given separately to these clients, such as for stocks which are relevant to more than one client, it was necessary to ensure that the clients concerned would have a common understanding of how an EAFM was to be understood in each case. In practice, this took place in discussions on the annual meeting between ICES and the advice clients. Another problem was that clients have different interpretations of the UN Stocks Agreement (1995) requirement regarding Maximum Sustainable Yield (MSY). Although MSY was introduced at a time and in a context when the focus was entirely on single stocks, it shall be reinterpreted in an ecosystem context to address issues such as food web integrity. From a science perspective, this requires incorporation of biological species interactions in the ecosystem as well as the issue that most fisheries catch several species simultaneously. Mixed fisheries advice became a priority (see Wilson 2009: 161–166; Reeves et al., 2009), but it shows the impossibility of the objective of MSY for all stocks. This issue is interpreted differently by diverse clients and has, specifically in the EU context, caused a policy tension regarding how to deal with the trade-off between fishing opportunities on various stocks. In the following, the specific remits for ICES advice are explored by analysing the EU-ICES MoU for 2016. The EU aims for a long-term ideal of managing marine ecosystems with a full cross-sectoral integration. However, the institutional structures for doing this are far from being in place (see Ramirez et al., 2016a, b; Patrick and Link, 2015b). A marine EA across EU policies (CFP, MSFD, and the other directives above) appears currently unfeasible and politically unmanageable (RTD, 2015). The scope of EAFM advice in the MoU The ICES-EU MoU provides the framework for the provision of services and scientific advisory deliverables in support of the management of activities affecting the marine ecosystem. Recurring advice has a direct financial agreement on a budget and covers: (i) marine ecosystems and human impacts; (ii) fisheries and their impact on the ecosystem; and (iii) fish stocks. The MoU also enables specific agreements, including payment for cost, to be set up between the EU and ICES about other issues –the so-called non-recurring advice. In the 2016 MoU, this includes inter alia advice about other aspects of the MSFD. The MoU without the detailed budget is publicly available on the ICES website (http://www.ices.dk/explore-us/Documents/Cooperation agreements/EU/2016_MoU_EC_ICES_web.pdf). The ecosystem advice requested by the EU Currently, the science aspect of an EA to fisheries in the MoU appears to be defined as (i) the consistency between the CFP and the MSFD and (ii) that fish stocks advice should consider biological interactions between fish stocks. The request for consistency is relaxed for ecosystem deliverables by setting an incremental implementation “as information on the interactions between fish stocks and the marine ecosystem becomes available” (MoU, 2016: 12). Therefore “The recurring advisory deliverables shall be based on an ecosystem approach consistent with the targets and objectives of Good Environmental Status where these have been fixed under the MSFD. This will be implemented incrementally…” The demands from the MSFD include proposals for reference points for descriptors and assessments of fisheries disturbance of marine ecosystems where reference levels have been established. Reference levels are set by EU MS in an interactive process with the Commission within the MSFD; individual MS may choose to act on scientific advice from national sources or advice obtained through intergovernmental bodies such as OSPAR, HELCOM, or ICES (see ICES, 2012b). In addition “ICES will inform the EU of any notable impact of other factors on and imbalances in ecosystem structure that may prejudice the stocks of commercially valuable species and their long term exploitation” (MoU, 2016: 12). Regarding fish stocks “The advice should be prepared taking into account the biological interactions between the fish stocks, such as predation or competition” (MoU, 2016:14). The process of an EAFM in the MoU The MoU does not specify a process for conducting an EAFM. Although it includes requirements for transparency to stakeholders and to the EU itself, it does not identify dialogues or processes by which interaction with stakeholders and policy makers may help scientist to identify societal choices regarding an EAFM in more detail than what is stated in policy documents. The process is thus in the MoU largely seen as a one-way information stream from science to stakeholders where stakeholders may be observers to the process and documentation and data are made public. Commission officials have generally held the view that stakeholders’ engagement, beyond transparency in the scientific process, is the sole responsibility and competence of government bodies, through which information on societal choices is mediated to scientists. However, for many issues concerning an EAFM, it is not feasible to specify beforehand—in sufficient detail—which choices of risks and trade-offs are most pertinent for scientific analysis. In the absence of a clear delineation between stakeholders’ knowledge and stakeholders’ preferences, ICES has two options: (i) to make its best guess, which may result in advice being seen as less relevant for subsequent policy discussions and may be perceived as scientists overstepping their role by making policy choices; or (ii) set up its own process to engage with stakeholders and policy makers as required for each specific request for advice. ICES has in practice chosen to do the latter (e.g. ICES, 2017a). The Commission has not opposed this, and direct costs for organizing such processes can normally be financially covered under the MoU. Resourcing EAFM through the MoU The MoU includes a financial agreement where the EU pays ICES to provide recurrent advice on annual fishing opportunities through a set budget and non-recurrent advice on an advice-by-advice basis. In both cases, the managing process, including costs of meetings and supporting staff in the ICES Secretariat is covered, whereas the core resource—the time of the scientists doing the analysis and developing the advice– is not covered by the MoU. Thus, the availability of scientists depends on either governments’ funding for scientists to directly participate in the ICES advisory work; or those individual scientists and the organizations for which they work agree to support their participation. This has created resource problems for an EAFM as the government funding of expertise targeted to support ICES advice tends to be aligned to the policies of yesteryear. In most EU (and ICES non-EU) countries there are specific institutes funded to support annual fish stock assessments. These may either be institutes receiving core funding or university departments, which have a contract to deliver advice support to fisheries policy. Other institutes typically receive some financial support to participate in the advisory process through the Data Collection Framework Directive (EC, 2008a). There are generally no similar arrangements for marine ecologists or social scientists, both of which are in high demand whenever processes for EAFM are set up. The result is a bias in the availability of the ICES process to develop and implement an EAFM, including in some cases that relevant and necessary expertise regarding specific subjects may not be available to ICES. It could be argued that this represents a resourcing issue at the national institutes’ level. However, if an EAFM is a priority, resources to support it should be allocated across all levels. What is ICES currently delivering? ICES provides the scientific basis for ecosystem-based decision making for the management of fisheries and other sectors in the ICES area. It delivers knowledge to explore trade-offs and uses its network, data centre, and advisory role to provide the scientific basis for operational management. As the process is incremental, ICES hopes to respond appropriately to the changing demands of a developing policy landscape and dynamic ecosystem. Notwithstanding the formal provisions of the MoU, the consolidated framework to support DG-MARE for three decades contrasts with the absence of a similar set-up to provide advice to DG-ENV (but see e.g. ICES, 2004b; Wilson, 2009). Proactively, ICES has instigated an expert group to report out on the potential rationale for ICES ecosystem/environmental advice. In the meantime, the advice is developed in an ad hoc manner with different processes based on specific users’ demands. ICES provides advice on criteria and methodological standards for GES descriptors of the MSFD (EC, 2008c; Annex I), which describe GES in more detail. These include: D1 (Biodiversity, e.g. ICES, 2016b), D3: Populations of commercially exploited fish and shellfish (e.g. ICES, 2017b), D4 (Food webs, e.g. ICES, 2015a), D6 (Seafloor integrity, e.g. ICES, 2015b), and D11 (Energy, including water noise, e.g. ICES, 2014). In practice, ICES generates operational information products to underpin the exploration of what can be called the safe-operational space for trade-offs (constrained optima). Some spatial management and regional priorities are addressed through the advice being given by ecoregion, which reflects both biogeography and the management of the area by national and regional authorities. Three main outputs support an EAFM: Advice on fishing opportunities (see e.g. ICES, 2016c) can be viewed as the “traditional” ICES product. The specific advice text is based on agreed management plans, where such exist and are recognized by ICES as having been evaluated as sustainable. Where such plans do not exist the advice text is based on MSY, an approach which has been agreed between ICES and the fisheries advice clients. However, it has evolved from a focus on single species catch options to include an assessment of the stock status, the exploitation rate concerning MSY and projections of the consequences of fisheries actions for each stock impacted by fisheries in the European area. The assessments are a mixture of analytical and knowledge-limited (proxy) approaches which encompass target species, bycatch species, and deep sea and elasmobranch fisheries. Researchers are encouraged to consider the evidence of productivity changes in the ecosystem or fish stocks, and their implications for management. Advice on fishing opportunities is based on rules, with associated reference points, which reflect policy objectives. Mixed fisheries considerations address the consequences of technical interactions in multi-stock, multi-gear fisheries. The advice is currently available for the North Sea and is under development for the other ecoregions. ICES’ approach is to integrate the EA in the reference points whenever there is sufficient information to do so. Such information includes knowledge about the current state of the ecosystem and any effects of the ecosystem on stock dynamics. Where appropriate, estimates of the temporal variation of natural mortality are built into the stock assessments to consider the implications for fish for top predators or density effects on stock dynamics. ICES builds precautionarity into its advice by estimating buffers on biomass limit reference points (lower limits of stocks). For short-lived species, an “escapement” approach is used, that accounts for the need to maintain a certain biomass for sustainability and ecosystem functioning. The fisheries overviews (e.g. ICES, 2017c), of which first examples were released in 2017, include: a summary of the activities and impacts of the fleets fishing in the ICES area; a regional assessment of the performance of fisheries management regarding targets, as well as an assessment of GES for MSFD descriptor 3 (ICES, 2012a); a description of the fleets and their interactions with the ecosystem; a description of the consequences and options for management of mixed fisheries; maps of the distributions of fishing by gear type and maps of the impact on the seabed of trawled fishing gear; and a risk assessment by gear of the impact of bycatch on endangered, protected, or threatened species. The ecosystems overviews (e.g. ICES, 2017d; see Wilson, 2009: 184) place fishing into a broader context alongside other activities that exert pressure on the marine system, as well as the trends and status of the marine ecosystem as a whole. ICES may regard this as a way to highlight the need for a cross-sectoral perspective, but the actual use by policy makers remains to be seen. Using qualitative methods, the overviews identify and focus on the top five priority human activities and resulting pressures that can be locally managed within each ecoregion. Quantitative methods to further assess these pressures are currently being developed. In many ecoregions, ICES considers that fishing contributes to major anthropogenic pressures on the ecosystem. The approach of assessing activities, pressures, and state of the ecosystem provides the flexibility to monitor for cumulative effects of the pressures on the ecosystem and to accommodate impacts of climate change as they become apparent. In addition to the above, ICES is regularly asked to provide tailored advice on issues relating to the EA (e.g. ICES, 2017e). In recent years, methods have been devised to assess the status of “information poor” stocks, monitor recreational fishing, and explore MSY as a range of catch rather than as a point estimate. The latter is, however, more triggered by the institutional requirements within the EU than by a genuine wish by policy makers to fully address the problems inherent in the use of the MSY in legal text and implementation. ICES advice relating to MSY therefore similarly still falls short of transgressing a concept like single-stock MSY. The EU has also requested ICES advice on distributional shifts of fish stocks (ICES, 2017g). Likewise, advice has been issued on the impact of aquaculture (ICES, 2016d). The ICES data centre also hosts and maintains the OSPAR and HELCOM impulsive noise register (http://www.ices.dk/marine-data/data-portals/Pages/underwater-noise.aspx), marine litter datasets (collected in conjunction with other coordinated surveys), a biodiversity portal (aimed at seal and bird populations; http://www.ices.dk/marine-data/data-portals/Pages/Biodiversity.aspx) and the North Atlantic vulnerable marine ecosystem (VME) portal (http://www.ices.dk/marine-data/data-portals/Pages/vulnerable-marine-ecosystems.aspx). Transparency, adaptation, and inclusiveness are guiding principles for ICES (2013a). Transparency is at the core of science and means that ICES science processes, documentation, and products must be open to observation and scrutiny for the users of the science and advice. The evidence base and methodologies used to provide knowledge products are openly accessible in the highest resolution that the underlying data sources allow. Adaptation—a suitable response to changes in the marine science and policy landscape—and inclusiveness are as well essential to an EA. ICES engages with the users of its science and advice to define the issues of concern, understand interests, bring in other sources of knowledge, and ensure that advice relates to societal choices. Inclusiveness is implemented through multiple pathways, from scoping exercises to benchmark processes. In fact, the benchmark process to decide on the most appropriate assessment methodology (see ICES, 2013b) is now widely used throughout ICES to enable stakeholder input into method development and knowledge acquisition. The Industry-science partnerships also feed information through to ICES products. ICES works hard to ensure the legitimacy and credibility of its advice, an effort widely recognized by the ACs (ACs, 2014). Nonetheless, the use of experience-based knowledge in ICES advice appears to be rather limited and anecdotic (see Mackinson et al., 2011: 21), calling for both further research and strategic actions. Notwithstanding the advances in the knowledge foundations to support an EAFM for EU policies, there are some gaps regarding the content and processes of what ICES is currently delivering: Lack of consideration of limits to the carrying capacity of the system. Such approaches are applied elsewhere e.g. in Alaskan fisheries (Dicosimo et al., 2010) when setting the total catch limit, and for New England Fisheries where the newly developed Fishery Ecosystem Plans for the George Banks Ecological Productivity Units is being proposed for mixed fisheries management (NEFMC, 2016). A framework for the provision of advice on ecosystem aspects is missing (but see Wilson, 2009), except for catch opportunities. Nevertheless, ICES has provided advice for the Ecologically or Biologically Sensible Areas process (Rice et al., 2014) and has created a tool to show how assessments are made when delineating VMEs in the NEAFC area (see ICES Data Portals). Lack of a suite of indicators (except precautionary and MSY reference points) to quantitatively assess the state of the marine system and the effectiveness of management action. The national scientists that form the so-called “ICES community” have advanced research on the use of indicators (ICES, 2005; Link, 2005; Samhouri et al., 2009; Rochet and Trenkel, 2009; Shin et al., 2010; Thorpe et al., 2016) but validation issues hamper their implementation. Fisheries and ecosystem overviews would benefit from an increase in the use of quantitative methods, although this is not necessarily a task for ICES. Any exploration of the trade-offs required for the management of marine activities will probably require intersections with knowledge providers on fishing activity and the impact of fisheries. The ecosystem overviews have been perceived to be descriptive with limited capability to inform actual short-term decision-making. However, they have deliberatively kept an informative nature after a thoughtful analysis of their role in the advisory process (ICES, 2013c). Advice on management options and trade-offs when meeting targets for the state of the environment are excluded from the overviews. The design aims to “highlight ICES capacity to provide integrated advice that is expected to meet the future needs of client commissions” (ICES, 2013c: 2). Using the ecosystem overviews in the policy process would imply the EU to request a tailored analysis of management options linked to the overview outputs. The formulation of concrete operational management objectives so far is policy linked (CFP and MSFD respectively). Advances in scientific knowledge are focused on exploring tool gaps (models, indicators, etc.) and potential applications. However, before such applications can be applied in the ICES advisory process, they need to be discussed and agreed to be found useful. Within the EU there is a debate on whether the scope of ICES is enough to provide EAFM advice or if there are some components that should be placed elsewhere in the advisory system (e.g. in STECF, see Figure 1). Undoubtedly, issues related to societal choices, as well as to the economic and social considerations are critical to an EAFM process. The move towards regional management poses a challenging for the advisory system, as it requires a broader scope. To accommodate this challenge, a first attempt to establish a suitable process was presented to the ICES ACOM in September 2016, which is currently being reformulated (ICES, 2016, pers. comm.). If successfully designed and carried out, this would allow for science and knowledge to build up using a participatory approach. Nevertheless, the output of any scoping exercise would have still a long way to be linked to policy implementation. What do the EU stakeholders expect? Throughout the MareFrame project (Co-creating Ecosystem-based Fisheries management solutions) a total of 22 stakeholders events were organized at EU (3) and case study level (19) covering all the EU sea basins from the Baltic to the Black Sea (www.mareframe-fp7.org). The participants included decision-makers, scientists and representatives from fisheries organizations, e-NGOs, and EU advisory bodies (ICES, STECF, ACs). Qualitative research techniques were used to support structured dialogue on an EAFM and are summarized in Table 2. The stakeholders’ insights provide valuable information on how they perceive fisheries advice framed within the EA as well as how they understand their role in the process. Rather than seeking to understand everything to implement an EAFM, the stakeholder community favours a more focused dialogue. The debate could start by addressing a list of “big issues”: trade-offs for mixed fisheries, the impact of fisheries on the seafloor, biodiversity and the food web as well as climate change and its impact on ecosystem resilience. These aspects not only need to be addressed because of impacts associated with fishing but also because they may affect the viability of fishing activities (RTD, 2015). However, this list does not always match the list of “acute problems” perceived by some stakeholders, particularly concerning resource allocation and the multiple uses of the marine space (AC, 2014). The stakeholders commonly agreed that EAFM advice is primarily seen as an element for policy dialogue for the mid-term (e.g. to support a possible coming reform of the CFP likely in 2022/23) rather than as a basis for immediate decisions (e.g. to set fishing opportunities). Other players than those of scientific advisory bodies could lead such dialogues (WEAF, 2016). Hence, underlying tensions in the science-policy interface become explicit (RTD, 2015): On the one hand, science tends to provide a form of advice that policy makers are not fully prepared to utilize, confronting them with complicated policy processes by making explicit the trade-offs and consequences of their objectives. Although transparency is central to inclusive democracy, fundamental changes in the processes are viewed as threatening by many policy makers. On the other hand, science often assumes that policy makers have decided what is socially desirable; policy makers aggregate social preferences and those may or may not be addressing the perceived critical issues from an ecosystem perspective (e.g. protection of iconic species). Furthermore, we know from a political science perspective that this assumption is flawed and that the social system is considerably more complex. In this light, it might be more relevant if EAFM advice respond to “what if” questions as part of a focused process supported by risk assessments (see e.g. Fletcher 2005; Williams et al., 2011; Cormier et al., 2013; Piet et al., 2015), defining the potentially most significant disturbance for a given ecosystem, as well as testing social objectives and acceptability (RTD, 2015). The use of qualitative approaches by ICES for the ecosystem overviews addresses such issues. Apparently, suppliers and receivers of the advice agree that neither the constant demand for more research and development of new methods nor the identification of knowledge gaps should restrain actors from “doing EAFM now”. However reasonable these demands may seem, they should never postpone the provision of the best possible advice here and now, based on existing knowledge (RTD, 2015; Patrick and Link, 2015a; see e.g. STECF, 2010; ICES, 2016a). Moreover, an open attitude towards any advance should be taken, instead of focusing on whether the target has been reached or not (WEAF, 2016). What ICES is currently delivering to support the implementation of an EAFM could be used selectively. For example, single-stock advice used to set the baseline, fisheries advice to integrate at a metiér level, and ecosystem advice to assess the impact. These approaches are complementary, not contradictory. Therefore, ecosystem advice may provide the broader framework and limits within which fisheries and single stock advice are necessary for day-to-day management (RTD, 2015). Regarding the process, ICES’ engagement with stakeholders is perceived positively by all stakeholder profiles (ACs, 2014; RTD, 2015; WEAF, 2016). Some ideas for improvements were, however, brought to the table, for instance on the best way of involving stakeholders in identifying the problems to be tackled (WEAF, 2016). Scoping processes appear as a way to structure meaningful participation, ensuring inclusiveness through a flexible approach in the setting of objectives, a course of action and scientific methodology; inclusiveness refers from the outset to both participants and scientific disciplines (natural and social science). Stakeholders indicate their preference for an ongoing process—rather than a one-time scoping exercise—attached to a work plan. This could help to remove scepticism regarding the nature of advice and to build more trust between the scientists, their ecosystem models, and the fisheries sector (ACs, 2014). Participation within the ICES advisory process and through the advisory system requires an organizational structure that enables stakeholders to participate at “the right scale”. Regionalization, to be consistent with an EAFM, involves not only regional (sea basin) but also sub-regional and supra-regional approaches. Specific topics benefit from an integrated approach rather than an artificial sub-division at regional levels to adjust them to a specific management structure (WEAF, 2016). Furthermore, ACs (2014) highlighted the lack of regional frameworks and forums for managing marine ecosystems. The Regional Groups under the CFP (see Figure 1) may have been a step in that direction. However, there is a need for further transparency (WEAF, 2016) and they are to some extent resorting to informal advice generated by national scientists, which could jeopardize the overall system (RTD, 2015; WEAF, 2016). Stakeholders supported the independent advisory system already in place to avoid redundancy in scientific advice and to ensure independence from decision makers (WEAF, 2016). Setting reasonable expectations Ironically, EAFM becomes the almighty panacea (Cardinale and Svedäng, 2008: 245) that ultimately avoids uniform recipes for success in fisheries management. Already in 2004 an ICES Dialogue meeting addressed the provision of scientific advice for an EAFM, considering three aspects: (i) making it coherent across management of human activities that impact on marine ecosystems; (ii) making it operational; and (iii) making it more credible (which involves research resources, transparency, clear and effective communication, quality assurance, and inclusiveness in the decision-making process) (ICES, 2004a). At the meeting, it was agreed that “the scientific, administrative and institutional capacity was insufficient to implement the complex ecosystem approach” (ICES, 2004a: 14). Our analysis shows that ICES has been guided by an incremental perspective, including knowledge gradually as it has been generated by the scientific community. This has led to a reformulation of the type of advice delivered and of how this advice has been communicated through processes and procedures to foster integrity and transparency. There are underlying tensions owed to the range of advice clients that keep shaping ICES’ developments. In the context of the EU, the policy framework has aggravated the tension between an EAFM focus on the process and the aim to make it operational in the actual management measures, placing ICES between a rock and a hard place. An EAFM focus on the process should have prompted the Commission to request for non-recurrent, long-term (vs. case-by-case) advice for setting policy objectives. A focus on a functional operational framework should have provided consistency between the EU policies and integrate biological interactions between fish stocks in the fish stock advice. For a constructive debate, it should be noted that the tendency to equate EAFM implementation to the setting of actual management measures (particularly annual fishing opportunities) is misleading; as it would be to read any stand-alone ecosystem component consideration in the advice process as the implementation of an EAFM. In a framework characterized by policy tensions, plural actors and institutional vacuum at the regional level, ICES has taken a leading role aiming to get the best out of the process. In doing so, ICES has somehow contributed to generating an EAFM framework for its clients, as it has been done previously by ICES in the environmental realm (see ICES, 2004b). The underlying debate about the attributes of the advice needed for an EAFM is far from being closed. Pathways have been explored (Rice, 2005; Wilson, 2009; Stange et al., 2012; Lassen et al., 2014; Dickey-Collas, 2014; Link and Browman, 2014; Marshak et al., 2017) and the rationale for the alternatives discussed within the ICES community (see e.g. ICES, 2008). Our review of what ICES is delivering to an EAFM has identified three types of shortcomings, of which ICES itself can remedy some: Shortcomings that can be addressed by ICES within the current set-up include: (a) to integrate the ecosystem carrying capacity in the advice, which will better delineate the sustainability space within which stakeholders can explore options; (b) to integrate ecosystem status on stock dynamics (presently limited to a minority of stocks), which will provide evidence of the effectiveness of management measures and the need for management intervention; (c) to elaborate the dialogue with policy makers and stakeholders through iterative scoping exercises, which should be integrative both in terms of participants’ profiles and the scientific disciplines involved. Shortcomings that can be partially addressed by ICES: The allocation of funds for research needed for ecosystem advice is a major constraint for how integrative EAFM becomes. As discussed, all the streams depend heavily on national funding. ICES might press for the MoU with the Commission to be better balanced between short term demands for fisheries advice and the longer term research needs for EAFM advice. It is also feasible to broaden the scope of science processes, optimizing resources from research programs and agreements (e.g. H2020 or the Galway Statement on Atlantic Ocean Cooperation; see ICES, 2016a). Additionally, EAFM advice requires further efforts in the integration of the fisheries advice system (Figure 1), namely between ICES and STECF to avoid piecemeal advice on individual sustainability dimensions. Although there are complementarities, the presence of overlaps raises the question of whether there is a need for two separate advisory bodies. The answer is far from straightforward due to the nature of the STECF (a body within the Commission) and its geographical scope (covering all EU waters, including the Mediterranean and the Black Sea.) As a starting point, coordination should ensure the optimization of resources between the two bodies (RTD, 2015). As stated by ICES (2004a), advice will not necessarily come from a single source, increasing the need for interaction between the science and management process and a solid dialogue among the necessary disciplines. Shortcomings that are beyond the control of ICES: Currently there is an inconsistency between the CFP and the MSFD, and—at present—the advice assessing the GES of a given fish stock for the environmental policy does not feed management decisions for the fisheries policy and vice versa. This reflects the difficult integration between the two policies, in part due to the multi-level competence (EU for fisheries and the MS for environmental policy), the policy focus, and the absence of a regional level that could make the advice operational (AC, 2014; RTD, 2015, Van Hoof, 2015). Similar problems emerge when policies differ between external clients for ICES advice. No matter how creative ICES has become, addressing this gap calls for concerted actions from its clients in the political realm. In summary, in the EU context ICES is struggling to develop a structured process within which EAFM advice can fit in appropriately. The actions promoted at the macro level (e.g. regional scoping exercises) are essential but perceived as cumbersome and distant from operational needs; the actions carried out at the micro level (e.g. exploring tools and indicators) are pertinent but driven by science challenges rather than by the management challenges. At a global level, the discussion of the extent to which current ICES advisory policies and practices provide the right foundation for an EA should continue within and beyond the ICES community. The focus on the advances may have concealed a more critical overview of the involutions derived from political processes (e.g. sectoral vs. integrated advice). Moving forward will also require a better understanding of the interconnections among the multiple players involved in a cross-sectoral approach. To succeed in an inherently chaotic system (Dickey-Collas, 2014) ICES pushes not only to increase its capacity to produce and evaluate knowledge; in the process, it is generating answers to the questions of “for whom” the EAFM advice is generated and “for what purposes”. However, operating under a full cost recovery system, the capability to provide advice above and beyond what clients are asking is rather limited. The proactive role of ICES in filling the gaps has raised unreasonable expectations of what it could and should deliver and somehow increased the responsibilities placed on scientists to generate a fully functional operational framework. ICES is most likely a suitable organization to facilitate advances for an operational EAFM in the EU, enabling interaction and dialogue platforms between the sciences, but it is unrealistic to expect ICES also to produce all the answers. Acknowledgements We remain grateful to all organizations and stakeholders who made this research possible through their active involvement. We are likewise thankful to all project partners, especially the Case Study leaders and the Advisory Councils. The ICES availability for iterative dialogue and consultation during the development of this research is highly appreciated. The authors express a particular gratitude to the editor and the three reviewers for improving and enriching earlier versions of the article. The research leading to these results has received funding from the European Union’s Seventh Framework Programme Project MareFrame: Co-creating Ecosystem-based Fisheries Management Solutions under Grant Agreement no. 613571. 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Zhang, Fan;Reid, Kevin B;Nudds, Thomas D;Hidalgo, Handling editor: Manuel

doi: 10.1093/icesjms/fsx188pmid: N/A

Abstract Fish stock–recruitment relationships (SRRs) may vary in response to ecosystem change, increasing uncertainty for fisheries management. We defined three periods between 1975 and 2015 over which Lake Erie, a Laurentian Great Lake, underwent significant ecosystem changes: before zebra mussel (Dreissena polymorpha) establishment, after zebra mussel establishment and before re-eutrophication, and after re-eutrophication. To examine the extent to which SRRs of Lake Erie yellow perch (Perca flavescens) also varied over these periods, we compared the performance of Baseline (constant recruitment), Ricker (constant SRR), Periodic Ricker (different SRRs among three periods) and Random-walk Ricker (annually varying SRRs) models fitted to data for yellow perch stocks corresponding to three lake basins. Periodic and Random-walk Ricker models performed better for stocks in the western and eastern basins, but the Baseline model performed best in the central basin. Annual variation in the SRRs coincided with the timing of zebra mussel establishment and re-eutrophication in the shallower western basin, but not in the deeper eastern basin, where quagga mussels (Dreissena bugensis) established later and conditions are less eutrophic. These results underscore that temporally and spatially varying SRRs associated with ecosystem change should be taken into account in models of fish population dynamics. Introduction The stock–recruitment relationship (SRR) is a core concept in fisheries science (Hilborn and Walters, 1992) that describes how recruitment is a function of the spawning stock biomass (SSB). Classical forms of SRRs include the Ricker and Beverton–Holt models and their various extensions (Quinn and Deriso, 1999). The existence of SRRs is widely assumed in modern fisheries stock assessment (Hilborn and Walters, 1992; Quinn and Deriso, 1999) as they are often central to predicting population trends, setting management reference points, and guiding management strategies. For example, SRRs provide a theoretical justification for harvest regulation to maintain spawning stocks at sizes such that future recruitment is not severely impaired, i.e. preventing “recruitment overfishing” (Sissenwine and Shepherd, 1987). Furthermore, SRRs are closely related to many commonly used biological and management reference points, e.g. SSB0 (unfished SSB), Bmsy (biomass leading to maximum sustainable yield) and Fmsy (the rate of fishing mortality leading to maximum sustainable yield) (Mangel et al., 2013). Nevertheless, observation and process errors often reduce detectability of SRRs (Walters and Ludwig, 1981; Subbey et al., 2014) and SRRs are used less to attempt to understand ecological mechanisms affecting fish recruitment (Mangel et al., 2013; Subbey et al., 2014). SSB is one among numerous ecological factors that affect fish recruitment (Houde, 2008; Pepin, 2016), and pioneering research about fish recruitment actually focused on ecological factors affecting fish survival during early life-history stages (Hjort, 1914). Temporal variation in ecological factors that affect fish recruitment may lead to non-stationary SRRs, especially when there are systemic changes in the environment (Walters, 1987). Non-stationary SRRs were observed among Pacific salmon (Oncorhynchus spp.) stocks along the west coast of North America, as reflected by strong temporal variation in the productivity parameter of Ricker stock–recruitment models (Dorner et al., 2008; Peterman and Dorner, 2012). Similarly, productivity of Atlantic cod (Gadus morhua) stocks in the northwest and northeast Atlantic Ocean also varied over time (Minto et al., 2014). Spatially synchronized variation in productivity across large spatial scales was detected among Pacific salmon stocks, as well as among Atlantic cod stocks, suggesting large scale oceanographic regime shifts may be an important cause of non-stationary SRRs (Dorner et al., 2008; Minto et al., 2014). Non-stationary SRRs were also detected among European hake (Merluccius merluccius) stocks in the Northeast Atlantic, and were largely attributed to fisheries induced demographic changes (Hidalgo et al., 2014). Empirical studies on effects of ecosystem change on SRRs for freshwater fish, however, are relatively rare (Feiner et al., 2015). Lake Erie, the shallowest of the Laurentian Great Lakes in North America, experienced large ecosystem changes over several decades. Extensive industrial and agricultural nutrient loading caused eutrophication in the 1960s (Davis, 1964; Beeton, 1965). Following nutrient regulation in the early-1970s, the lake gradually became mesotrophic over the 1970s and 1980s (Makarewicz and Bertram, 1991), accompanied by changes in fish community structure (Ludsin et al., 2001). Around 1990, several invasive species, e.g. zebra (Dreissena polymorpha) and quagga (Dreissena bugensis) mussels, invaded Lake Erie and rapidly expanded across all three basins within a few years (Griffiths et al., 1991; Mills et al., 1993). Through the early-1990s, changes in water chemistry, nutrient cycles, and food web structure were documented (Vanderploeg et al., 2002; Hecky et al., 2004). Since the mid-1990s, re-eutrophication occurred with increasing phytoplankton biomass and hypoxia, followed by frequent algal blooms in the 2000s (Michalak et al., 2013; Scavia et al., 2014). Lake Erie comprises three basins (Figure 1) with apparently different physical and environmental conditions (Bunnell et al., 2014) to test whether heterogeneous conditions among the basins relate to variation in responses by stocks of the same species of fish to ecological change, such as eutrophication and species invasion. We explored whether and how SRRs of yellow perch (Perca flavescens), an economically valuable species to commercial and recreational fisheries (Belore et al., 2016; Jones et al., 2016), changed between 1975 and 2015 within and among the three basins of Lake Erie, and whether variation in SSRs corresponded with ecosystem changes over those four decades. Figure 1. View largeDownload slide Distribution of yellow perch MUs in the three basins of Lake Erie. Figure 1. View largeDownload slide Distribution of yellow perch MUs in the three basins of Lake Erie. Methods Study area and data collection Lake Erie comprises western, central, and eastern basins (Figure 1). The shallow eutrophic western basin is distinguished from the deeper mesotrophic central basin, which, in turn, is separated by the Pennsylvania Ridge from the even deeper oligotrophic eastern basin. The yellow perch fishery is assessed and administered in four management units (MUs), i.e. MU1 (western basin), MU2 (west-central basin), MU3 (east-central basin), and MU4 (eastern basin) (Figure 1). Despite both phenotypic and genetic evidence that there are multiple yellow perch stocks in Lake Erie (Sepulveda-villet and Stepien, 2011; Kocovsky and Knight, 2012), there is no consensus regarding yellow perch stock structure; we assumed that each basin comprised a single stock of yellow perch. The Yellow Perch Task Group of the Lake Erie Committee estimated age-specific biomass of yellow perch with a statistical catch-at-age model (compiled with Automatic Differentiation Model Builder) in each MU from 1975 to 2015 (Belore et al., 2016). We used the biomass estimates from MUs 1 and 4 to represent stocks in the western and eastern basins, respectively, and pooled the biomass estimates from MUs 2 and 3 to represent the stock in the central basin. In Lake Erie, age-2 yellow perch grow to the minimum size vulnerable to the commercial gillnet fishery, so knife-edged recruitment of fish aged 2 was assumed, as widely adopted by the fisheries biologists and management agencies around the lake (Belore et al., 2016). The SSB was the summed biomass of mature fish two years prior to recruitment,   St=∑a=2npa*Ba,t where St is the SSB in year t, pa is the mean percentage of maturation of age a class, and Ba,t is the biomass of age a class in year t. The mean percentages of maturation for age 2 and older females in each MU were calculated on basis of the lake-wide fisheries-independent annual gillnet index surveys between 1989 and 2013 (D. Gíslason, unpublished data). Sex ratios of each cohort were assumed to be 1:1 over the study period. Effects of ecosystem change on SRRs We partitioned the time series of data from 1975 to 2015 into three periods on basis of the timing of zebra mussel establishment and re-eutrophication in each of the three basins of Lake Erie (Table 1), i.e. before zebra mussel establishment (Period_1), after zebra mussel establishment and before re-eutrophication (Period_2), and after re-eutrophication (Period_3). Zebra mussels invaded Lake Erie from the western end, fully occupied the western basin by 1987, dispersed over the central basin in 1988, and established in the eastern basin in 1989 (Griffiths et al., 1991). Re-eutrophication was mainly detected in the western and central basins, signalled by increasing phytoplankton biomass and hypoxia beginning in the mid-1990s (Conroy et al., 2005b; Zhou et al., 2013; Scavia et al., 2014) and algal blooms (Michalak et al., 2013). Table 1. Definition of three periods between 1975 and 2015 in the western, central and eastern basins of Lake Erie: before zebra mussel establishment (Period_1), after zebra mussel establishment and before re-eutrophication (Period_2), and after re-eutrophication (Period_3).   Period_1  Period_2  Period_3  Western basin  1975–1986  1987–1994  1995–2013  Central basin  1975–1987  1988–1994  1995–2013  Eastern basin  1975–1988  1989–1994  1995–2013    Period_1  Period_2  Period_3  Western basin  1975–1986  1987–1994  1995–2013  Central basin  1975–1987  1988–1994  1995–2013  Eastern basin  1975–1988  1989–1994  1995–2013  Table 1. Definition of three periods between 1975 and 2015 in the western, central and eastern basins of Lake Erie: before zebra mussel establishment (Period_1), after zebra mussel establishment and before re-eutrophication (Period_2), and after re-eutrophication (Period_3).   Period_1  Period_2  Period_3  Western basin  1975–1986  1987–1994  1995–2013  Central basin  1975–1987  1988–1994  1995–2013  Eastern basin  1975–1988  1989–1994  1995–2013    Period_1  Period_2  Period_3  Western basin  1975–1986  1987–1994  1995–2013  Central basin  1975–1987  1988–1994  1995–2013  Eastern basin  1975–1988  1989–1994  1995–2013  A Baseline model (BM; assuming constant recruitment over time), a Ricker model (RM; assuming constant SRR with time-invariant parameters), a Periodic RM (PRM; time-varying SRR with different parameters among three periods), and a Random-walk RM (RRM; assuming annually varying SRR) were fitted to time series data of SSB and recruitment for yellow perch stocks in each of the three basins.   Rt=μeɛ1 (BM)  Rt+2=αSte-βSteɛ2 (RM)  Rt+2=αtSte-βtSteɛ3; log⁡(αt)=a1(t in Period_1)a2(t in Period_2)a3(t in Period_3); βt=b1(t in Period_1)b2(t in Period_2)b3(t in Period_3) (PRM)  Rt+2=αtSte-βtSteɛ4; ln⁡αt=ln⁡αt-1+wt; βt=βt-1+vt (RRM) where Rt is the recruitment biomass in year t, St is the SSB in year t, α is the productivity parameter, β is the density-dependence parameter, a1, a2, and a3 are the logarithmized productivity parameters in three periods, b1, b2, and b3 are the density dependence parameters in three periods, αt is the productivity parameter in year t, βt is the density-dependence parameter in year t, and ɛ1, ɛ2, ɛ3, ɛ4, wt, and vt are error terms following normal distributions with mean 0 and variance of σ12, σ22, σ32, σ42, σw2, and σv2. Model parameters were estimated using Bayesian methods in R package R2OpenBUGS with R 3.3.2 (Sturtz et al., 2005; R Core Team, 2016). Informative priors, i.e. bounded uniform distributions, were assumed on basis of the biological assumptions of each model and the stock information in each basin (Supplementary Material). Variances were uniformly distributed between 0.0001 and 10 (Jiao et al., 2009). For BM, μ was uniformly distributed between the min(Rt) and max(Rt). For RM and RRM, α and α1 were uniformly distributed between min(Rt+2/St) and max(Rt+2/St), and β and β1 were uniformly distributed between 0.0001 and max((ln(max(Rt+2/St)) + ln(St)− ln(Rt+2))/St) (Jiao et al., 2009). For PRM, a1, a2, and a3 were uniformly distributed between min(ln(Rt+2/St)) and max(ln(Rt+2/St)), b1, b2, and b3 were uniformly distributed between 0.0001 and max((ln(max(Rt+2/St)) + ln(St)− ln(Rt+2))/St). Stock-recruitment data vary among three basins, so the priors were different among the three yellow perch stocks (Supplementary Material). Each model was run for three chains. Depending on model complexity, BM was run for 50 000 iterations, RM and PRM were run for 100 000 iterations, and RRM was run for 200 000 iterations. The first 10 000 iterations (the burn in period) of each model were abandoned, and the following iterations were analysed with thinning of 1. The convergence of each model was checked using trace plots and potential scale reduction factor (PSRF) developed by Gelman and Rubin (1992). Details are in Supplementary Material. The deviance information criterion (DIC) was used to compare model performance (Burnham and Anderson, 2002; Spiegelhalter et al., 2002),   DIC=D-+PD  PD=D--D^ where D- is the posterior mean of deviance, D^ is the point estimate of deviance using the posterior mean of parameters, and PD is the effective number of parameters. As a rule of thumb, a model with lower DIC outperformed a model with higher DIC when ΔDIC > 5 (Burnham and Anderson, 2002). If recruitment was relatively constant overtime, without strong effects of SSB and/or other ecological factors, BM should perform best. If recruitment was mainly affected by SSB, RM should perform best. If recruitment was mainly driven by ecosystem changes associated with zebra mussel invasion and re-eutrophication, PRM should perform best. If recruitment was mainly affected by ecosystem changes not closely linked to zebra mussel invasion and re-eutrophication, RRM should perform best. Results All parameter estimations reached MCMC convergence on basis of visual checks of trace plots and statistical tests using PSRF (Gelman and Rubin, 1992; Supplementary Material). Yellow perch SSB and recruitment exhibited strong temporal variation over 40 years in each of the three basins of Lake Erie, and population dynamics were mainly driven by variation in recruitment (Figure 2). No strong effects of SSB on recruitment were detected. BM and RM performed similarly (ΔDIC < 5) across yellow perch stocks in all three basins, and RM had greater DIC than BM for yellow perch stocks in the western and central basins (Table 2). Table 2. DICs of the BM (constant recruitment), RM (constant SRR), PRM (different SRRs among three periods), and RRM (annually varying SRRs) fitted to yellow perch stock–recruitment data in the western, central, and eastern basins of Lake Erie between 1975 and 2015. Basin  Model  No. of parameters  DIC  ΔDIC  Western Basin  BM  2  113.6  0    RM  3  115.0  1.4    PRM  7  107.4  −6.2a    RRM  81  109.2  −4.4  Central Basin  BM  2  186.6  0    RM  3  189.7  3.1    PRM  7  190.3  3.7    RRM  81  189.2  2.6  Eastern Basin  BM  2  168.4  0    RM  3  165.1  −3.3    PRM  7  150.6  −17.8a    RRM  81  132.0  −36.4a  Basin  Model  No. of parameters  DIC  ΔDIC  Western Basin  BM  2  113.6  0    RM  3  115.0  1.4    PRM  7  107.4  −6.2a    RRM  81  109.2  −4.4  Central Basin  BM  2  186.6  0    RM  3  189.7  3.1    PRM  7  190.3  3.7    RRM  81  189.2  2.6  Eastern Basin  BM  2  168.4  0    RM  3  165.1  −3.3    PRM  7  150.6  −17.8a    RRM  81  132.0  −36.4a  The lowest DIC of each basin is bolded. ΔDIC is the difference of DICs between each model and BM. a The models had lower DIC than BM and ΔDIC > 5. Table 2. DICs of the BM (constant recruitment), RM (constant SRR), PRM (different SRRs among three periods), and RRM (annually varying SRRs) fitted to yellow perch stock–recruitment data in the western, central, and eastern basins of Lake Erie between 1975 and 2015. Basin  Model  No. of parameters  DIC  ΔDIC  Western Basin  BM  2  113.6  0    RM  3  115.0  1.4    PRM  7  107.4  −6.2a    RRM  81  109.2  −4.4  Central Basin  BM  2  186.6  0    RM  3  189.7  3.1    PRM  7  190.3  3.7    RRM  81  189.2  2.6  Eastern Basin  BM  2  168.4  0    RM  3  165.1  −3.3    PRM  7  150.6  −17.8a    RRM  81  132.0  −36.4a  Basin  Model  No. of parameters  DIC  ΔDIC  Western Basin  BM  2  113.6  0    RM  3  115.0  1.4    PRM  7  107.4  −6.2a    RRM  81  109.2  −4.4  Central Basin  BM  2  186.6  0    RM  3  189.7  3.1    PRM  7  190.3  3.7    RRM  81  189.2  2.6  Eastern Basin  BM  2  168.4  0    RM  3  165.1  −3.3    PRM  7  150.6  −17.8a    RRM  81  132.0  −36.4a  The lowest DIC of each basin is bolded. ΔDIC is the difference of DICs between each model and BM. a The models had lower DIC than BM and ΔDIC > 5. Figure 2. View largeDownload slide Temporal variation in recruitment and SSB of yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie between 1975 and 2015. Figure 2. View largeDownload slide Temporal variation in recruitment and SSB of yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie between 1975 and 2015. Strong variation in SRRs of Lake Erie yellow perch was detected in the western and eastern basins, but not in the central basin. In the western basin, PRM had the lowest DIC, and outperformed BM (ΔDIC > 5) (Table 2). In the central basin, the SRR was relatively stable over time. The BM had the lowest DIC, though all four models performed similarly (ΔDIC < 5) (Table 2). In the eastern basin, the RRM had the lowest DIC, and both the RRM and the PRM outperformed BM (ΔDIC > 5) (Table 2). In each basin, the selected model (with the lowest DIC) had relatively low precision (Figure 3). Figure 3. View largeDownload slide Observed and predicted recruitment dynamics of yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie between 1977 and 2015. Predictions in WB, CB, and EB were generated by the PRM, the BM, and the RRM (selected models on basis of DIC comparison), respectively. Grey areas are the 95% confidence intervals of recruitment predictions. Figure 3. View largeDownload slide Observed and predicted recruitment dynamics of yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie between 1977 and 2015. Predictions in WB, CB, and EB were generated by the PRM, the BM, and the RRM (selected models on basis of DIC comparison), respectively. Grey areas are the 95% confidence intervals of recruitment predictions. In the western basin, variation in the SRR was mainly owing to changes in density dependence. Density dependence was greatest in Period_2, followed by Period_3 and Period_1; productivity was similar among the three periods (Figure 4). Variation in the SRR among the three periods was mainly reflected as changes in the curvature at greater SSB, i.e. degree of density dependence, rather than the slope nearer the origin, i.e. productivity (Figure 5). Density dependence increased in Period_1, peaked at the time of zebra mussel establishment, decreased through Period_2, and then increased again during Period_3 (Figure 6). Figure 4. View largeDownload slide Shifts in productivity (alpha) and density dependence (beta) of SRRs among three pre-defined periods between 1975 and 2015 for yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie. Thin solid and dashed lines are the means and 95% confidence intervals, respectively, of parameters estimated by the BM. Thick solid lines and grey areas are, respectively, the means and 95% confidence intervals of parameters estimated by the PRM. Figure 4. View largeDownload slide Shifts in productivity (alpha) and density dependence (beta) of SRRs among three pre-defined periods between 1975 and 2015 for yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie. Thin solid and dashed lines are the means and 95% confidence intervals, respectively, of parameters estimated by the BM. Thick solid lines and grey areas are, respectively, the means and 95% confidence intervals of parameters estimated by the PRM. Figure 5. View largeDownload slide Stock–recruitment relationships (solid dots) and fitted stock-recruitment curves (on basis of estimated means of parameters by the PRM) among three pre-defined periods between 1975 and 2015 for yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie. Period_1 is before zebra mussel establishment (solid lines); period_2 is after zebra mussel establishment and before re-eutrophication (dashed lines); and period_3 is after re-eutrophication (dotted lines). Figure 5. View largeDownload slide Stock–recruitment relationships (solid dots) and fitted stock-recruitment curves (on basis of estimated means of parameters by the PRM) among three pre-defined periods between 1975 and 2015 for yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie. Period_1 is before zebra mussel establishment (solid lines); period_2 is after zebra mussel establishment and before re-eutrophication (dashed lines); and period_3 is after re-eutrophication (dotted lines). Figure 6. View largeDownload slide Annual variation in productivity (alpha) and density dependence (beta) of SRRs for yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie between 1975 and 2015. Grey areas are the 95% confidence interval. Left vertical dashed lines are the year of zebra mussel establishment, and right vertical lines are the year of re-eutrophication. Figure 6. View largeDownload slide Annual variation in productivity (alpha) and density dependence (beta) of SRRs for yellow perch stocks in the western (WB), central (CB), and eastern (EB) basins of Lake Erie between 1975 and 2015. Grey areas are the 95% confidence interval. Left vertical dashed lines are the year of zebra mussel establishment, and right vertical lines are the year of re-eutrophication. In the eastern basin, variation in the SSR was caused by changes in both productivity and density dependence. Yellow perch experienced lower productivity and greater density dependence in Period_2 than in Periods_1 and _3 (Figure 4). The SRR exhibited strong variation in both the slope nearer the origin and curvature at greater SSB (Figure 5). Annual variation in SRR was more strongly affected by variation in productivity than density dependence (Figure 6). Productivity decreased over Period_1, rebounded 1 year after zebra mussel establishment and further increased through Period_2 and the beginning of Period_3, and then stabilized through Period_3 (Figure 6). Density dependence exhibited opposite and weaker annual variation over the three periods (Figure 6). Discussion Strong variation in yellow perch SRRs coinciding with large ecosystem changes were detected in the western and eastern basins, but not the central basin, of Lake Erie. In the western and eastern basins, recruitment appeared to be strongly associated with ecological processes linked to establishment of invasive mussels and eutrophication. In the central basin, on the other hand, the mechanisms driving yellow perch recruitment appeared to be more complicated, obscuring ecological effects associated with invasive mussels and eutrophication. These differential responses of yellow perch to ecosystem change among the three basins may, in turn, be attributed to variation in other environmental conditions and/or yellow perch stock structure (Kocovsky and Knight, 2012; Bunnell et al., 2014). There is strong evidence that the western and eastern basins comprise separate yellow perch stocks (Sepulveda-villet and Stepien, 2011; Kocovsky and Knight, 2012), but stock structure in the central basin is not well understood. Movement of yellow perch across adjacent basins may complicate stock structure in the central basin, decreasing the detectability of non-stationary SRRs. Response of yellow perch to ecosystem change in the western basin Strong variation in the SRR of yellow perch was detected in the western basin of Lake Erie, which coincided with ecosystem changes associated with zebra mussel invasion and re-eutrophication. Increasing density dependence between 1975 and 1986 suggested degraded environmental conditions for yellow perch recruitment, perhaps as a consequence of eutrophication in 1960s and 1970s (Davis, 1964; Beeton, 1965). Control of phosphorus loading began in 1972, and brought about signs of ecosystem recovery (Makarewicz and Bertram, 1991; Ludsin et al., 2001), but yellow perch response may have lagged. Density dependence did not decrease until 1987, coincident with the establishment of zebra mussels in the western basin (Griffiths et al., 1991). Density dependence further decreased through zebra mussel invasion, suggesting that zebra mussel establishment may have positively affected environmental conditions for yellow perch recruitment. Zebra mussels may affect yellow perch by altering the structure of the benthic community. Large invertebrates, e.g. Hexagenia and amphipods, were the main prey of yellow perch before the 1950s, and were severely reduced, or had even disappeared, by the 1960s (Tyson and Knight, 2001; Burlakova et al., 2014). The benthic community was thereafter dominated by small invertebrates tolerant of organic enrichment, e.g. oligochaetes and chironomids (Burlakova et al., 2014), which may have negatively affected yellow perch growth and recruitment (Tyson and Knight, 2001). After zebra mussel establishment, however, the abundance of macro-invertebrates increased (Dermott and Kerec, 1997; Tyson and Knight, 2001), and Hexagenia recolonized Lake Erie (Krieger et al., 1996; Burlakova et al., 2014). Zebra mussels can facilitate the growth and expansion of macro-invertebrates by increasing food availability through organic-rich feces and pseudofeces, and/or providing protection from predation by increasing habitat complexity and heterogeneity (Stewart et al., 1998; Beekey et al., 2004; DeVanna et al., 2011). In the western basin, the increasing proportion of macroinvertebrates in yellow perch diets may have improved adult condition, facilitated yellow perch growth, and increased abundance of age-0 juveniles (Thayer et al., 1997; Trometer and Busch, 1999; Tyson and Knight, 2001). After the mid-1990s, density dependence increased, coinciding with a period of re-eutrophication in the western basin, characterized by greater plankton biomass (Conroy et al., 2005b), stronger extent of hypoxia (Zhou et al., 2013), and more frequent algal blooms (Michalak et al., 2013; Scavia et al., 2014). Re-eutrophication in the western basin may be associated with an increase in bioavailable phosphorus from agricultural nonpoint sources, internal phosphorus loading from the central basin, as well as the fertilizing effects of zebra mussels (Zhang et al., 2011; Michalak et al., 2013). Zebra mussels may also more positively affect phytoplankton growth, by releasing bioavailable phosphorus through nutrient excretion, than they negatively affect it by grazing (Zhang et al., 2011). Additionally, selective grazing by zebra mussels on micro-zooplankton, in turn, a major predator of cyanobacteria, may have further exacerbated algal blooms by facilitating the growth of cyanobacteria (Vanderploeg et al., 2002; Davis et al., 2012). Regardless of the mechanism, similar to the effects of eutrophication in 1960s and 1970s, the re-eutrophication in the western part of Lake Erie appears also to have degraded the environment for yellow perch recruitment, as evidenced once again by increasing strength of density-dependence exhibited by SSRs since the mid-1990s. Response of yellow perch to ecosystem change in the eastern basin The SRR of the yellow perch stock in the eastern basin also varied strongly, but the annual variation matched neither the timing of zebra mussel establishment nor re-eutrophication in Lake Erie on the whole, implying that ecosystem changes associated with other ecological processes may drive the variation in the SRR in the eastern basin. Similar to the western basin, decreasing productivity and increasing density dependence before zebra mussel establishment suggested a prolonged negative effect of eutrophication in the 1960s and 1970s (Davis, 1964; Beeton, 1965) on yellow perch recruitment. Around 1990, the lagged response of yellow perch to zebra mussel establishment in the eastern basin may be related to strong effects of quagga mussel. The first sightings of quagga mussels were traced to 1989 in the northeastern part of the eastern basin (Mills et al., 1993), which was a few years later than the invasion of zebra mussels (Griffiths et al., 1991). Quagga mussels are better adapted to deeper water and lower water temperature than zebra mussels, making it a superior competitor in the deeper and cooler eastern basin (Mills et al., 1993; Vanderploeg et al., 2002). Consequently, the eastern basin was mainly colonized by quagga mussels, whereas the distribution of zebra mussel was largely restricted in the western basin (Patterson et al., 2005). Similar to zebra mussel, however, the establishment of quagga mussel in the eastern basin may have facilitated the growth of macro-invertebrates (Dermott and Kerec, 1997), which lead eventually to positive effects on yellow perch recruitment similar to the western basin. Re-eutrophication does not appear as evident in the eastern as western basin. No consistent increase in plankton biomass or extent of hypoxia was reported in the eastern basin (Scavia et al., 2014), and no strong algal blooms have been observed since the mid-1990s (Michalak et al., 2013). On the one hand, the fertilizing effects of dreissenids may be weaker in the eastern than western basin of the lake. Phosphorus excretion by quagga mussels was lower than zebra mussels (Conroy et al., 2005a), and a high P:N ratio is critical to cyanobacteria growth (Smith, 1983). Hence, the fertilizing effect of quagga mussel on cyanobacteria growth may be weaker than zebra mussel, leading to lower probability of algal blooms in the eastern basin dominated by quagga mussels than in the western basin where zebra mussels are abundant (Patterson et al., 2005). On the other hand, following the establishment of zebra and quagga mussels, water clarity in the eastern basin increased more than that in the western and central basins (Bunnell et al., 2014; Binding et al., 2015), suggesting strong filtering effects of invasive mussels in the eastern basin. Regardless of the mechanisms, less eutrophic conditions in the eastern basin of Lake Erie may have contributed to the relatively stable SRRs since the mid-1990s. Non-stationary SRRs and ecosystem change Large ecosystem change has attracted much attention in many marine ecosystems, e.g. North Sea, Bering Sea, North Pacific (Hare and Mantua, 2000; Beaugrand, 2004), which are usually characterized by sudden, low frequency, but large amplitude, changes in multiple aspects of biological and physical ecosystem components across large spatial scales (Lees et al., 2006). Such variation in ecological conditions may cause persistent changes in recruitment dynamics, leading to non-stationary SRRs of marine fish stocks that are mainly driven by oceanographic and climatic processes (Dorner et al., 2008; Minto et al., 2014) as well as large scale marine fisheries (Hidalgo et al., 2014). In contrast, SRRs of freshwater fish stocks may be more strongly affected by relatively smaller scale ecosystem processes, e.g. pollution and introduction of exotic species (Feiner et al., 2015). The SRR of Lake Erie yellow perch exhibited persistent, directional variation following ecosystem changes associated with invasive mussels and eutrophication, especially in the western and eastern basins. This is consistent with previous findings that recruitment by Lake Erie yellow perch was mainly affected by ecological factors during early life-history stages, rather than by SSB (Zhang et al., 2017). Also, consistent with the idea that strongly density-dependent ecological factors can drive recruitment (Walters and Korman, 1999), we detected stronger variation in density dependence than in productivity on yellow perch SRR in the western basin (Figure 6), as opposed to marine systems where non-stationary SRRs appear to be mainly caused by variation in productivity (Dorner et al., 2008; Minto et al., 2014). Density dependent regulation tended to occur at earlier life history stages for fish residing in larger habitats (Andersen et al., 2017). As one of the Laurentian Great Lakes, Lake Erie approaches, in many biophysical aspects, those of large, open marine ecosystems (Pritt et al., 2014). Consistently, recruitment by Lake Erie yellow perch was largely determined during the first few month after spawning (Farmer et al., 2015; Zhang et al., 2017), implying early density dependent regulation. More work is needed to better understand the explicit mechanisms that affect the extent of density dependent regulation linked to non-stationary SRRs of Lake Erie yellow perch stocks. Implications for fisheries management Non-stationary SRRs imply that fish recruitment varies in response to regime shifts quite independent of variation in SSB (Gilbert, 1997; Vert-pre et al., 2013; Szuwalski et al., 2015), posing challenges to the ways that traditional SSRs are typically used for fisheries management which assume SSB is the main driver of fish recruitment (Hilborn and Walters, 1992; Myers and Barrowman, 1996). Additionally, spatial complexity in both the biophysical aspects of ecosystems, as well as fish stock structure, may interact to generate differential effects of ecological factors on recruitment among adjacent fish stocks (Hidalgo et al., 2014), leading to spatially varying SRRs. In such cases, it is difficult to characterize appropriate SRRs for fisheries management, especially when there is a mismatch between management-defined and biological stock structures (Kerr et al., 2017; Goethel and Berger, 2017). Hence, incorporating ecological processes into fisheries management has been recognized among management objectives of many national and international organizations (Marasco et al., 2007). Despite general acknowledgement of ecosystem based management paradigms among managers and policy makers (Link and Browman, 2014), empirical examples of ecosystem processes taken explicitly into account in fisheries management are rare (Skern-Mauritzen et al., 2016). This may be caused by tension between, on the one hand, emerging mandates to adopt these new paradigms and, on the other, the lack of capacity in research and management to implement them (Rice and Browman, 2014), e.g. lack of ecological data, poor mechanistic understanding of ecosystem processes, and limited management tools. Actually, many fisheries around the world, especially those in developing countries and regions, are still too data limited to implement even traditional, single-species fisheries management (Costello et al., 2012, 2016; Lorenzen et al., 2016), let alone more complicated and demanding management paradigms (Skern-Mauritzen et al., 2016). The transition from traditional fisheries management to ecosystem-based management paradigms may be better served by evolution than revolution (Marasco et al., 2007), and the linkage between temporally and spatially varying SRRs and ecosystem processes offers a natural and intuitive way to integrate ecosystem processes into the framework of traditional fisheries management. In the case of Lake Erie yellow perch, population dynamics are largely driven by recruitment variation (Figure 2), indicating the importance of understanding recruitment mechanisms in fisheries management. However, no strong effects of SSB on yellow perch recruitment were detected, and SRRs varied in response to ecosystem changes. This demonstrates a bottom-level need to incorporate ecological factors into recruitment models, so that management uncertainty caused by non-stationary SRRs can be reduced. On the other hand, traditional fisheries management has been well developed for Lake Erie yellow perch (Belore et al., 2016), and fisheries and ecological data are relatively abundant owing to extensive ecological surveys in Lake Erie (Thomas et al., 2014; Belore et al., 2016). The well-developed management system and data-rich condition enabled greater capacity for research and management of Lake Erie percid fisheries (Jones et al., 2016). Therefore, it is both necessary and feasible for Lake Erie fisheries management to move towards ecosystem-based management paradigms, and recruitment research in the context of ecological processes may provide a potential way to facilitate this transition (Rice and Browman, 2014). Supplementary data Supplementary material is available at the ICESJMS online version of the manuscript. Acknowledgements We thank D. Gíslason (University of Guelph) for providing data on percentage maturation at age of Lake Erie yellow perch. We thank the editor M. 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Bornarel, Virginie;Lambert, Patrick;Briand, Cédric;Antunes, Carlos;Belpaire, Claude;Ciccotti, Eleonora;Diaz, Estibaliz;Diserud, Ola;Doherty, Denis;Domingos, Isabel;Evans, Derek;de Graaf, Martin;O’Leary, Ciara;Pedersen, Michael;Poole, Russell;

Henderson, Christopher J;Stevens, Tim;Gilby, Ben L;Lee, Shing Y;Secor, Handling editor: David

doi: 10.1093/icesjms/fsx192pmid: N/A

Abstract The positioning of habitats interacts with variability in abiotic factors (e.g. seasonal changes in temperature and extreme weather events) to change how animals use a land or seascape. Marine reserves can regulate how human activities alter fish communities and increase the abundance of targeted species, but the combined influence of reserves and seascape context on species habitat use remains uncertain in many ecosystems. Further, marine reserve effectiveness might be low for mobile species if the size of the reserve is less than a species usual range, reducing the overall time a individual may be protected. In this study, we tracked 19 giant shovelnose rays (Glaucostegus typus), an IUCN listed vulnerable species within the Moreton Bay Marine Park in eastern Australia. We used an array of 28 acoustic receivers within a complex mosaic of seagrass patches, bare sand, mangrove forests and deep-water channels and used regression tree analyses to determine which spatial, temporal and protection factors contributed most to G. typus habitat use. Overall, 50% of the total detections in the study occurred inside marine reserves containing large seagrass beds (>7.09 m2) and in close proximity to mangroves (<7.47 km). During winter (<20.2 °C), G. typus centre of activity increased significantly (p < 0.001), and greater than 50% of the detections occurred in reserves in winter. Conversely, during the rest of the year (water temperature > 20.2 °C), the habitat use of individuals is contained in smaller centres of activity compared with winter, however, protection effects varied. Our results show that seascape context and marine reserves combine to provide the optimal areas for G. typus habitat selection. Limited food resources likely caused larger centres of activity during winter. Identifying priority habitats for vulnerable species is critical for ongoing protection and maintaining effective conservation initiatives. We have shown here that incorporating spatial features into the design of marine reserves can improve conservation outcomes for mobile benthic predators such as G. typus and other species that use such seascapes. Introduction Habitat heterogeneity affects species habitat utilization, and the movement of organisms and energy across land or seascapes (Loreau et al., 2003; Hyndes et al., 2014). Further, movement patterns can be influenced by variable prey availability, predator avoidance, temperature changes, and reproductive movements at different times of the year (Bond et al., 2012, Jewell et al., 2013, Heupel and Simpfendorfer, 2014). Understanding patterns in habitat utilization by different species across both space and time is a fundamental goal of ecologists, as these movements can significantly affect the composition and function of ecosystems, and therefore how they are managed (Speed et al., 2010; Andrews and Harvey, 2013). In order to further understand where and when individuals are moving, it is critical to incorporate spatial and temporal factors that will drive the movement of animals (Nathan et al., 2008). Therefore, determining the main external factors driving the distribution and movement of different species, particularly species of conservation (i.e. listed species) or economic concern (i.e. harvested species), across multiple spatio-temporal scales is important for managers (Nathan et al., 2008; White et al., 2014). No-take marine reserves seek to maintain biodiversity, improve resilience and help re-establish species that have been overharvested (Babcock et al., 2010; Edgar et al., 2014). The optimal spatial arrangement of reserves in heterogeneous seascape and their size must, however, be optimized for individual areas and objectives (Olds et al., 2016). For example, large reserves are most effective in protecting fish over time (Halpern, 2003), but this comes at a cost in terms of reducing fishing areas (Stewart and Possingham, 2005). Although it is well established that the seascape context of an area (e.g. depth, proximity to other habitats, habitat size) can influence the effectiveness of marine reserves (Olds et al., 2016), robust metrics for incorporating connectivity into reserve design are difficult to establish (Kearney et al., 2013; Olds et al., 2016). Notionally, sedentary species should benefit more from marine reserve implementation than more wide-ranging species (Zeller, 1997, Palumbi, 2004, Bryars et al., 2012). Therefore the protection value of reserves for individual species might be reduced if the species range beyond the boundaries of the reserve at different times of the year when they may be susceptible to harvest (Chapman and Kramer, 2000; Claudet et al., 2010; Gaines et al., 2010). Some studies have, however, shown that marine reserves can be beneficial for species that have a large home range or activity space, possibly due to increased habitat quality inside reserves resulting in less movement outside of reserves (Moffitt et al., 2009; Claudet et al., 2010). Further, most studies assessing such an effects are conducted on coral reefs (Olds et al., 2012c; Pittman and Olds, 2015), meaning that the effect of seascape context on such metrics remains untested for many important habitats (Olds et al., 2016). Consequently, it is vital that studies continue to assess such effects to enable marine reserve placement to be optimized in different seascape arrangements and for different habitats. Seagrasses play an integral role in coastal seascapes, providing nursery areas for numerous harvested fish species that play critical roles in the functions of these habitats in the wider seascape (Bell et al., 1988; Nagelkerken et al., 2001). Seagrasses are highly productive, providing foraging areas for many species, meaning the size of seagrass meadows is a key determinant for the supply of these resources (Bowden et al., 2001; Boström et al., 2006). Seagrasses are of high ecological and economic significance for threatened and harvested species, but are also being reduced in extent globally due to coastal development, sedimentation and eutrophication (Heck et al., 2003; Orth et al., 2006; Waycott et al., 2009; Nagelkerken et al., 2015). Seagrass beds are often positioned within heterogeneous seascapes, making them a suitable habitat to assess the influence of seascape context in driving the movement of individuals within an ecosystem (Connolly and Hindell, 2006; Boström et al., 2011). Early studies in seascape ecology used seagrass habitats to focus on the implications of habitat size, edge effects and distance to the nearest seagrass patch on fishes and invertebrates in seagrass meadows (Robbins and Bell, 1994; Irlandi and Crawford, 1997; Micheli and Peterson, 1999). However, despite the well established importance of highly inter-connected seagrass meadows (Unsworth et al., 2015), how the size of seagrass meadows and connectivity with other habitats influence the movement of individuals within a seascape and the implications of this for marine reserve effectiveness remains under-researched (Connolly and Hindell, 2006). This is particularly important for species that use these habitats, whether it is to forage in or move across from one area to another. It therefore is critical to understand how such species use these areas and provide improved conservation initiatives for them. The giant shovelnose ray, Glaucostegus typus, is a shark-like batoid that inhabits coastal tropical and sub-tropical waters of eastern Australia and Southeast Asia. G. typus is a benthic predator that feeds on invertebrates and small fish on seagrass meadows, mangrove forest fringes and sandy bottoms (Vaudo and Heithaus, 2011; Bessey and Heithaus, 2013; White et al., 2014). Previous studies into the movement of this species have found that G. typus generally stay in inshore areas for extended periods of time, before moving further away from the coast (White et al., 2014). However, this study was located well within the latitudinal range of the species, suggesting that their movement patterns may alter relative to where in the species range a population is located (Last and Stevens, 2009). Reproductive movements can also alter how this species uses a habitat with G. typus expected to pup from September to November, however this wont occur until individuals are above 200 cm in length (White et al., 2014). The ecological characteristics of this species, its reliance on multiple, diverse habitats, and its status as a threatened species on the IUCN Red List (White and McAuley, 2003; White et al., 2014) make it an ideal species to assess for the effectiveness of spatial conservation measures. In this study, we use acoustic telemetry to determine which spatial (e.g. habitat positioning), temporal (e.g. changes in temperature and rainfall throughout the year) and protection (i.e. within a marine reserve or a fished area) factors contribute most to the habitat use of G. typus within the Moreton Bay Marine Park in central eastern Australia. While the Moreton Bay Marine Park was specifically designed to be representative of all different habitats within the bay, there were also important considerations to protect species of conservation concern and to take an adaptive management approach, allowing assessment to determine the effectiveness of zoning for a range of species and habitats. Approximately 16% of the marine park is located within marine reserves, where no extraction of resources of any form is allowed. Moreton Bay therefore offers a suitable location to test these questions, as (1) there is an extensive acoustic array situated in across both fished and reserve sites; (2) G. typus are under threat as a result of habitat degradation (Orth et al., 2006; Maxwell et al., 2015), being targeted by fishers (Webley et al., 2015) and being by-catch in broader fisheries (Pogonoski et al., 2002); and (3) it offers a heterogeneous seascape comprised of shallow seagrass habitats interspersed with deep channels, coral reefs and mangrove forests at varying distances to the open ocean. Acoustic telemetry offers a unique way of determining how individuals within a population use a range of habitats and whether they may be spending time within reserves (Kramer and Chapman, 1999; Speed et al., 2010; Bond et al., 2012). Methods Study site and acoustic receiver array Glaucostegus typus were tracked in the Moreton Bay Marine Park from January 2015 to March 2016. G. typus are a zoobenthivore, feeding predominantly on invertebrates located in the sediment, however, they have also been found to have a small amount of fish in their diet (Froese and Pauly, 2000; Elliott et al., 2007). This study therefore started in the austral summer (December, January, and February) and went through the austral winter (June, July, and August) and end after a second austral summer. Moreton Bay is a sub-tropical embayment in southeast Queensland, Australia (27°S, 153°E; 1582 km2). Moreton Bay is bordered by three large sand islands to the east and to the west by Brisbane, the third largest city in Australia (Figure 1). Oceanic water is exchanged between the eastern sand islands, and three large rivers discharge into the western regions of the bay (Gibbes et al., 2014). The study was conducted in the central eastern sector of the bay; a heterogeneous seascape close to the open ocean that is dominated by shallow seagrass habitats and interspersed with deep channels and mangrove forests (Figure 1). Figure 1. View largeDownload slide The acoustic array in the Eastern Banks region of Moreton Bay, illustrating the main habitat types and the 5 m isobath. Crosshatched areas represent marine reserves. Figure 1. View largeDownload slide The acoustic array in the Eastern Banks region of Moreton Bay, illustrating the main habitat types and the 5 m isobath. Crosshatched areas represent marine reserves. The acoustic receiver array in Moreton Bay comprised 28 VR2W monitoring receivers (VEMCO ltd, Halifax, Canada) positioned on shallow seagrass beds (between 1.5 and 5 m depth) or in adjacent channels (in 5–25 m water depth). Five receivers were positioned as a gate at the nearest large opening to the open ocean to record individuals entering and leaving the area. The array covers a total area (minimum convex polygon) of approximately 180 km2, but detection range is limited to a radius of 500 m around each receiver, resulting in a total detection area of approximately 44 km2 (Zeh et al., 2015). The array also covers portions of five separate no-take marine reserves, which make up approximately 31% of the total array area (∼59 km2). Of the 28 receivers in the array, 11 (39%) are positioned either within marine reserves or on the edge. This array was not set up for this project, but the receiver locations provided adequate coverage to evaluate habitat use of G. typus in the study area. Animal capture, handling, and tracking G. typus were caught on 25 m bottom-set setlines using ten 10/0 circle hooks on a 2 m wire tracer baited with mullet (Mugil spp.). Once individuals were caught, they were manoeuvred to the side of the boat, where they were turned onto their back and into a state of tonic immobility before being sexed and measured (stretch total length). Individuals in the study ranged from 97 cm to 175 cm, with all individuals fitting into the sub-adult life stage (Last and Stevens, 2009). While this life stage was not specifically targeted, it does suggest that sub-adults may dominate individuals within Moreton Bay. Transmitters were surgically inserted into the abdominal cavity through a small incision on the underside of the individual to reduce tag loss and biofouling (White et al., 2014). VEMCO V13 (13 x 36 mm) acoustic transmitters on a 60–120 s random interval at 69 kHz, which minimized simultaneous detections on the receiver array. Therefore, a single detection refers to any recorded detection on a receiver, by any transmitter this transmits on the 60–120 s random interval. Receiver detections were downloaded on three separate occasions; May 2015, October 2015, and April 2016. The final receiver download coincided with the removal of the array, ending the project after 1 year and 3 months. Environmental attributes We used GIS to extract a range of spatial attributes associated with individual receivers (and therefore individual detections). These attributes were; total area of the seagrass meadow in which the receiver was located; the proximity of the receiver to the open ocean and mangrove forests; the deepest water depth within a 1 km radius of the receiver; and whether the receiver was positioned within a no-take marine reserve or in a fished area. Average monthly rainfall [taken from daily recordings at the nearest weather station (BOM, 2016)]; and average monthly water temperature [recorded by the Healthy Waterways Monitoring Program (HWMP, 2015)] were assigned to each detection depending on date of detection. A description, justification, and underlying ecological hypothesis for each variable are given in Table 1. Table 1. Explanatory variables to be used in the regression tree analysis. Variable  Description  Method/Source  Underlying ecological hypothesis  Distance to mangroves  The distance from each receiver to the nearest mangrove forest  (Herbarium, 2015)  Mangroves are important for prey species (White et al., 2014)  Distance to ocean  The distance from each receiver to the ocean  GIS  Proximity to the ocean is important for prey availability and reproductive movements  Seagrass patch area  The area of the seagrass patch that each receiver is present in  (HWMP, 2015)  Larger seagrass meadows provide more resources than small meadows  Protection  Whether a receiver is located in a marine reserve or fished zone  (Queensland Government, 2007)  Higher abundance of prey items (Kramer and Chapman, 1999; Marshell et al., 2011).  Depth contour  The deepest water within a 1 km radius of a receiver  (HWMP, 2015)  Nearby deep water is important for feeding, movement and as a predator refuge  Water temperature  The monthly water temperature  (HWMP, 2015)  Temperature changes will alter prey availability  Rainfall  The monthly rainfall  (BOM, 2016)  Inundation of freshwater and increased sediment may cause individuals to shift movement  Variable  Description  Method/Source  Underlying ecological hypothesis  Distance to mangroves  The distance from each receiver to the nearest mangrove forest  (Herbarium, 2015)  Mangroves are important for prey species (White et al., 2014)  Distance to ocean  The distance from each receiver to the ocean  GIS  Proximity to the ocean is important for prey availability and reproductive movements  Seagrass patch area  The area of the seagrass patch that each receiver is present in  (HWMP, 2015)  Larger seagrass meadows provide more resources than small meadows  Protection  Whether a receiver is located in a marine reserve or fished zone  (Queensland Government, 2007)  Higher abundance of prey items (Kramer and Chapman, 1999; Marshell et al., 2011).  Depth contour  The deepest water within a 1 km radius of a receiver  (HWMP, 2015)  Nearby deep water is important for feeding, movement and as a predator refuge  Water temperature  The monthly water temperature  (HWMP, 2015)  Temperature changes will alter prey availability  Rainfall  The monthly rainfall  (BOM, 2016)  Inundation of freshwater and increased sediment may cause individuals to shift movement  Each variable used is listed, along with a description, method attained, and the underlying ecological hypothesis. Table 1. Explanatory variables to be used in the regression tree analysis. Variable  Description  Method/Source  Underlying ecological hypothesis  Distance to mangroves  The distance from each receiver to the nearest mangrove forest  (Herbarium, 2015)  Mangroves are important for prey species (White et al., 2014)  Distance to ocean  The distance from each receiver to the ocean  GIS  Proximity to the ocean is important for prey availability and reproductive movements  Seagrass patch area  The area of the seagrass patch that each receiver is present in  (HWMP, 2015)  Larger seagrass meadows provide more resources than small meadows  Protection  Whether a receiver is located in a marine reserve or fished zone  (Queensland Government, 2007)  Higher abundance of prey items (Kramer and Chapman, 1999; Marshell et al., 2011).  Depth contour  The deepest water within a 1 km radius of a receiver  (HWMP, 2015)  Nearby deep water is important for feeding, movement and as a predator refuge  Water temperature  The monthly water temperature  (HWMP, 2015)  Temperature changes will alter prey availability  Rainfall  The monthly rainfall  (BOM, 2016)  Inundation of freshwater and increased sediment may cause individuals to shift movement  Variable  Description  Method/Source  Underlying ecological hypothesis  Distance to mangroves  The distance from each receiver to the nearest mangrove forest  (Herbarium, 2015)  Mangroves are important for prey species (White et al., 2014)  Distance to ocean  The distance from each receiver to the ocean  GIS  Proximity to the ocean is important for prey availability and reproductive movements  Seagrass patch area  The area of the seagrass patch that each receiver is present in  (HWMP, 2015)  Larger seagrass meadows provide more resources than small meadows  Protection  Whether a receiver is located in a marine reserve or fished zone  (Queensland Government, 2007)  Higher abundance of prey items (Kramer and Chapman, 1999; Marshell et al., 2011).  Depth contour  The deepest water within a 1 km radius of a receiver  (HWMP, 2015)  Nearby deep water is important for feeding, movement and as a predator refuge  Water temperature  The monthly water temperature  (HWMP, 2015)  Temperature changes will alter prey availability  Rainfall  The monthly rainfall  (BOM, 2016)  Inundation of freshwater and increased sediment may cause individuals to shift movement  Each variable used is listed, along with a description, method attained, and the underlying ecological hypothesis. Data analyses We used conditional inference regression tree analyses in the party package of R (Hothorn et al., 2006) to determine which environmental attributes correlated most with habitat use by G. typus. Branches in the tree were permitted only for significant (p < 0.05) splits. Regression trees were conducted on the presence/absence of detections for each week of the study for each receiver. Due to this a binomial distribution was applied to the regression tree analysis. If an individual was found to be present at a receiver during a week, it was marked as present; those not detected at that receiver were marked absent. Centres of activity were plotted as minimum convex polygons (MCPs) for individuals in GIS (ArcGIS v10.0 ESRI, Redlands, CA, USA) to visualize the area used over different time periods. Kernel densities were not used in this study, as many individuals were not recorded on enough receivers to be sufficient. MCPs represent the extent of an individual’s movement within the acoustic array (Marshell et al., 2011). A residency index (% days detected from first to last detection) was calculated for each individual to show the amount of time individuals are spending inside the area of the array. As some individuals were only detected on two receivers in some seasons, MCPs could not be constructed for all individuals. Therefore, when MCPs were unable to be used we used the number of receiver which each individual was detected on instead, as a proxy for home range size (Garla et al., 2006). Differences in centres of activity between different times of the year were determined using paired t-tests. Similarly, marine reserve use across seasons was compared using a paired t-test on the number of receivers each individual is detected on within and outside of reserves for the seasons of interest. Results Spatial and temporal drivers of habitat use During this study, detections were recorded for 19 out of 20 tagged individuals, with 22 of 28 receivers detecting individuals. Three individuals were recorded on 7 days or less and so were excluded from analyses. The following analyses are therefore based on 16 individuals. Detections on individuals was highly variable with some individuals being detected <50 times, while others were detected >3500 times (Supplementary Table S1). Individuals were detected on average across a 280-day period (the time between the first and last detection) (Supplementary Table S1). Detections only occurred on ∼15% of the total number of tagged days (number of days all individuals were tagged, 4202 tagged days), with ∼58% of days detected having a detection inside a reserve. Of these, individuals were detected across an average of 280 days (min. 121, max. 441) (Supplementary Table S1) and residency index (% days detected from first to last detection) averaged 7.56 +/- 1.8 SE (minimum 0, maximum 28.73; Supplementary Table S1). The area that each individual used varied greatly, with an average minimum convex polygon size of 24.7 km2 +/- 5.6 (minimum 2 km2, maximum 76 km2; Supplementary Figure S1). Size (t = 1.165, p = 0.262) or gender (f = 0.003, p = 0.956) of individuals did not result in a significant difference in the size of minimum convex polygons. The regression tree analysing the presence or absence of individuals at individual receivers showed that monthly average water temperature was the most influential factor, causing the first split in the tree (Figure 2). When water temperature was below 20.2°C (June–August), the seascape factors distance to mangrove, seagrass patch area and distance to ocean all caused further splits in the tree. Therefore, during winter, individuals were most likely to be found at sites in larger seagrass beds (> 25.84 km2) close to mangroves (< 7.47 km). Conversely, when temperature was above 20.2°C (April–May and September–April), individuals were most often detected on receivers surrounded by shallow water and on smaller seagrass patches below 25.84 km2. Figure 2. View largeDownload slide A conditional inference tree on (a) the presence and absence of individuals at different receivers throughout the study period. Interpolated maps of the acoustic array highlight when water temperature was (b) above 20.2 °C and (c) below 20.2 °C. The colour scale of purple-white-orange (black-white-grey) indicates a high-medium-low presence of individuals. Figure 2. View largeDownload slide A conditional inference tree on (a) the presence and absence of individuals at different receivers throughout the study period. Interpolated maps of the acoustic array highlight when water temperature was (b) above 20.2 °C and (c) below 20.2 °C. The colour scale of purple-white-orange (black-white-grey) indicates a high-medium-low presence of individuals. Centres of activity (shown as MCPs for individuals that appeared on three or more receivers) changed dramatically for some individuals for times when water temperature was below 20.2°C compared with above, with some MCPs being as small as 2.64 km2 in summer and changing to 56 km2 (Figure 3). After the initial split (temperature < or > 20.2°C) in the regression tree assessing the number of detections, a paired t-test showed that individuals were recorded on more receivers when water temperature was below 20.2°C compared with above (p = 0.006). Figure 3. View largeDownload slide A minimum convex polygon (e.g. grey shaded area) for three individuals during summer (> 20.2°C, top) and winter (< 20.2°C, bottom). Marine reserves overlapping with the minimum convex polygon are highlighted in white. Minimum convex polygons varied through seasons: (a) 4.95 km2, (b) 2.35 km2, (c) 6.6 km2, (d) 14.75 km2, (e) 54.99 km2, and (f) 63.2 km2. Figure 3. View largeDownload slide A minimum convex polygon (e.g. grey shaded area) for three individuals during summer (> 20.2°C, top) and winter (< 20.2°C, bottom). Marine reserves overlapping with the minimum convex polygon are highlighted in white. Minimum convex polygons varied through seasons: (a) 4.95 km2, (b) 2.35 km2, (c) 6.6 km2, (d) 14.75 km2, (e) 54.99 km2, and (f) 63.2 km2. Marine reserve effectiveness Acoustic receivers within reserves represent 39% of the total array in Moreton Bay, suggesting that when the proportion of detections was above this value, there was greater use of the reserves than outside for individual within the study. Fifty-three percent of the total detections in this study were recorded in reserves during winter, with only 23% in reserves in the remainder of the year. Receivers that were located within protected areas had more detections when water temperature was below 20.2°C. Overall the number of days detected inside marine reserves for all individuals was 58% when compared to number of days detected. With a further 58% (∼34% of the total number of days detected) of the days individuals were detected inside marine reserves occurring when water temperature was below 20.2°C. Individuals were also recorded on a greater number of receivers inside reserves when temperature was below 20.2°C (p = 0.003, Figure 3). Discussion Seascape context interacts with marine reserves to alter fish abundance, community composition, and ecological processes globally (Berkström et al., 2012; Pittman and Olds, 2015; Olds et al., 2016). Understanding species movement and habitat use across both space and time is important for management, as these movements can significantly affect the composition and function of ecosystems (Speed et al., 2010). Our results show that the seascape context of habitats is critical in the use of marine reserves by G. typus, with marine reserves located in large seagrass meadows and near deep water being used more by individuals. Our findings concur with other studies that have reported positive seascape effects on marine reserve performance (Huntington et al., 2010; Olds et al., 2012b; Martin et al., 2015), however, these studies focused primarily on fish abundance and fish community composition. Here, we focus on how seascape factors influence habitat use and the success of marine reserves on a highly mobile species. We show that beyond these priority effects of seascape, habitat use and marine reserve use by G. typus varies according to temperature. Approximately 70% of detections occurred within marine reserves during winter, while less than 25% were inside marine reserves during the remaining periods of study. Approximately, 58% of the days individuals were detected inside reserves occurred during winter. Our results suggest that the current placement and amount of no-take marine reserves in Moreton Bay provides periodic protection to the vulnerable G. typus, and that the success of these marine reserves in protecting such species also depends on the seascape context, especially seagrass bed size. Spatial use by animals in heterogeneous seascapes is governed by the habitat size and positioning relative to other habitats and habitat patches (Dorenbosch et al., 2005; Almany et al., 2009; Berkström et al., 2012). In this study, seascape context played a pivotal role in the presence of individuals and the amount of time spent in different positions in the seascape. Larger seagrass beds provide a more productive ecosystem to meet resource needs of individuals (Prado et al., 2008; Smith et al., 2010; Espinoza et al., 2015), including a more stable food supply (Bowden et al., 2001; Boström et al., 2006). While seagrass bed size played a key role in the amount of detections, the availability of nearby deep water was also critical in the space use of individual, therefore suggesting that the inclusion of this important refuge in spatial conservation plans would be suitable (Jankowski et al., 2015; Papastamatiou et al., 2015). As many minimum convex polygons were generally limited by the western margin of the acoustic array and detections throughout the study were low, it is likely that deep water habitats to the west play a more integral part in the movement of this species than this array allows to determine. Deep-water habitats are critical for many species, they offer a refuge from predation and fish and alternate food resources (Jankowski et al., 2015). Re-designing reserves to include multiple habitats or deep-water refuges, in particular allowing reserve boundaries to not be constrained by habitat boundaries (Knip et al., 2012; Graham et al., 2016), may further increase the effectiveness of reserves in this heterogeneous seascape. This would also likely benefit a range of other species within the Moreton Bay Marine Park, with previous work in seagrass meadows and coral reefs supporting the need for better connected reserve sites that include multiple habitat types (Olds et al., 2012a; Gilby et al., 2016; Henderson et al., 2017). In order for marine reserves to provide adequate protection, a significant proportion of the life cycle of an animal needs to be protected (Hooker et al., 2011; Graham et al., 2016). Marine reserves within the Moreton Bay Marine Park were important for the protection of G. typus; however, the strength of the effect was increased in winter, was dependent on depth, seagrass patch size and proximity to mangrove forests. Therefore, reserves within Moreton Bay only periodically protect these individuals, which is crucial at these early life stages (Heupel et al., 2007). While it is critical that these early life stages are protected, management also needs to focus on incorporating all life stages into management plans (Kinney and Simpfendorfer, 2009). Most current marine reserves within MBMP, however, only cover small shallow seagrass meadows or sand flats that do not provide optimal habitat for feeding for G. typus (White and McAuley, 2003; Queensland Government, 2007). Most of the reserves in the network are defined by the edge of these habitats, and may be missing crucial parts of the species range, especially deep water or connected mangroves (White et al., 2014; Dance and Rooker, 2015). Temporal variation in the movement of species may be because of physiological temperature limits (Reid et al., 1991; Heupel and Simpfendorfer, 2014), spawning/breeding migrations (Costa et al., 2012; Taylor and Mills, 2013) or resource availability (Kramer and Chapman, 1999; Marshell et al., 2011). However, due to the presence of individuals year round, the latter two are most likely the factors causing the largest changes in movement patterns in this region. In this study, individuals extended their centre of activity to react to what could be a likely reduction in available resources, as has been seen in many marine (Green et al., 2015) and terrestrial ecosystems (Moorcroft et al., 2006). Given that the majority of detections during this study are inside a marine reserve, we expect that marine reserves, when designed to incorporate important seascape properties, may be capable of managing fluctuations in resource availability throughout a year (Dell et al., 2015). Alternatively, this species has been shown to increase its centre of activity or migrate during different parts of the year (White et al., 2014), however this species was present year round in Moreton Bay, only increasing detections during winter. In other locations, this species and other shovelnose ray like species use inshore embayment’s for pupping and mating resulting in highly variable home ranges and core activity areas throughout different seasons (Talent, 1985). Whether the changes in habitat use by G. typus are influenced predominantly by resource availability or a need to migrate for spawning or breeding, marine reserves still need to be designed to provide protection for a species throughout the entire year. Here, we have seen that while these factors are highly influential, incorporating seascape properties further into the design of marine reserves can provide protection across these temporal scales. This can also benefit a range of other species in Moreton Bay; with connectivity between habitats being well known to improve reserves, it would be beneficial to improve reserves in the MBMP by crossing habitat boundaries, increasing the size of reserves and having better connected reserves (Olds et al., 2012a; Henderson et al., 2017). In this study, we show that the protection effects of marine reserves for G. typus are greater when the reserves are positioned in large seagrass beds, near other important seascape features. Further, this study provides proof-of-concept that acoustic telemetry and seascape context factors can be combined to determine the effectiveness of conservation measures and find what factors can be used to improve reserves. The use of acoustic telemetry in marine reserve assessment is suitable as it provides long-term data on the use of an area across multiple spatial and temporal scales (Meyer et al., 2007; Marshell et al., 2011). Incorporating the knowledge that is gained from studies such as this one and others like it, managers can assess how re-designing marine reserves to include a range of seascape properties to improve reserve performance for this species and others (Knip et al., 2012). Consequentially, understanding the movement and habitat use of harvested species, such as G. typus, a species that is representative of a large proportion of the community, is vital to the adequate design of successful marine reserves (Schofield et al., 2013). While this study has only been conducted on a single species in a location at the southern end of its range. The results are still critical for providing further understanding of this species, but this also serves as an important case study on determining important seascape factors that drive species movement and habitat use and can be beneficial in reserve design. Therefore, reserve effectiveness across multiple scales needs to be investigated further and results incorporated into the improved design and monitoring of marine reserves. Acknowledgements Funding for this project was provided by the Margaret Middleton Fund as part of the Australian Academy of Science. We thank the staff of the Moreton Bay Research Station and K. Finlayson, C. March, B. Hodgson, S. Brodie, N. Harcla-Goody and V. Thomson for field assistance. Animal ethics protocols were followed as per Griffith University Ethics approval ENV/07/13/AEC. 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