ICES Journal of Marine Science: Journal du Conseil

Sherman, Kenneth

doi: 10.1093/icesjms/fsv136pmid: N/A

AbstractIn this essay, I review nearly six decades of a career in marine science and fisheries considering scientific contributions, successes, failures, and changes in my field of practice. My body of work has been in plankton research to support fisheries assessments, and in ecosystems programme development and implementation. I describe my early studies on Pacific plankton oceanography in relation to fisheries assessment, and subsequent studies of plankton oceanography and fisheries in relation to coastal ocean fisheries and management. Early in my career, realizing that applications of my published results and those of other fisheries ecologists were generally not included in fish stock assessments, I participated in a national planning group that introduced a system for marine resources monitoring, assessment, and prediction (MARMAP) that included primary productivity, ichthyoplankton, zooplankton, and oceanographic assessments as important components for large-scale fisheries ecology assessment. I joined with European colleagues in ICES to advance fisheries ecology studies in fish stock assessments in the 1970s and 1980s. In 1983, I conceived with Professor Lewis Alexander of the University of Rhode Island a system for assessing and managing marine resources within the spatial domain of ecologically delineated large marine ecosystems (LMEs). On behalf of the National Oceanic and Atmospheric Administration, and in partnership with developing countries, international financial organizations, UN agencies, and NGOs, I am currently contributing scientific and technical advice to a global network of assessment and management projects in 22 LMEs with 110 developing countries and $3.1 billion in financial support. The participating countries are applying a modular framework of natural science and social science indicators for assessing the changing states of LMEs. I conclude the essay with a retrospective viewpoint on my career and changes over half a century of practicing the application of marine science in relation to sustaining the goods and services of the ocean Commons.

Varpe, Øystein; Daase, Malin; Kristiansen, Trond

doi: 10.1093/icesjms/fsv129pmid: N/A

AbstractA gigantic light experiment is taking place in the Arctic. Climate change has led to substantial reductions in sea ice extent and thickness in the Arctic Ocean. Sea ice, particularly when snow covered, acts as a lid hindering light to reach the waters underneath. Less ice will therefore mean more light entering the water column, with profound effects on pelagic and benthic ecosystems. Responses through primary production are so far well acknowledged. Here we argue that there is a need to broaden the view to include light-driven effects on fish, as they depend on light to locate prey. We used the Norwegian Earth System Model estimates of past and future sea ice area and thickness in the Arctic and applied attenuation coefficients for ice and snow to estimate light intensity. The results show a dramatic increase in the amount of light predicted to reach the future Arctic Ocean. We combined this insight with mechanistic understanding of how light modulates visual prey-detection and predict that fish will forage more efficiently as sea ice diminishes and that their populations will expand to higher latitudes, at least seasonally. Poleward shifts of boreal fish species have been predicted by many and to some extent observed, but a changing light environment has so far not been considered a driver. Expanding distributions and greater visual predation may restructure ecological relationships throughout the Arctic foodweb and lead to regime shifts. Research efforts should focus on the dynamics of how less sea ice will affect the feeding ecology and habitat usage of fish, particularly the northern limits of distributions. Mechanistic approaches to these topics offer insights beyond statistical correlations and extrapolations, and will help us understand how changing biophysical dynamics in the Arctic influence complex processes including production, predator–prey interactions, trait-evolution, and fisheries.

Olsson, Jens; Tomczak, Maciej T.; Ojaveer, Henn; Gårdmark, Anna; Põllumäe, Arno; Müller-Karulis, Bärbel; Ustups, Didzis; Dinesen, Grete E.; Peltonen, Heikki; Putnis, Ivars; Szymanek, Lena; Simm, Mart; Heikinheimo, Outi; Gasyukov, Pavel; Axe, Philip;

Lough, R. G.; Kristiansen, T.

doi: 10.1093/icesjms/fsv145pmid: N/A

Abstract Environmental conditions during the pelagic juvenile cod period determine their fitness to survive settlement as demersal juveniles (0-group) and recruitment. This study examines the potential growth of pelagic juvenile cod in five areas of the New England Shelf based on time series of zooplankton and ocean temperature from surveys. An individual-based model was used to estimate the temporal variation in growth of juvenile cod at each survey station based on available prey of appropriate sized copepods of Calanus finmarchicus, Pseudocalanus spp., Centropages typicus, and Centropages hamatus. Mean juvenile cod growth was low (1–7% d−1) during January–February and March–April time series across all areas, Gulf of Maine (GOM), Eastern Georges Bank, Western Georges Bank, southern New England to Middle Atlantic Bight (MAB). Growth increased significantly in May–June with the seasonal increase in copepod density and temperature generally from South to North. The 1990–1999 warm years had the highest growth of 12–14% d−1 compared with the cooler 2000–2006 years and colder 1978–1989 years of similarly lower growth of 8–11% d−1. Growth in the MAB stayed the same 13% d−1 as in 1990–1999, whereas GOM growth decreased significantly to ∼6% d−1. High prey densities during May–June 1990–1999 for Georges Bank and GOM, followed by a strong decrease in 2000–2006 may explain the decrease in growth during the same periods. While all four copepod species contributed to potential growth, C. typicus, a more southern species, could be the more important prey for juveniles in the coastal areas during all months in contrast to Pseudocalanus spp. for the larvae. Centropages typicus also is the most adaptable and likely species able to expand and thrive under warmer climatic conditions, which could be of significance to future recruitment. Age-1 recruitment for Georges Bank cod was found to be related to juvenile growth. Introduction Atlantic cod spawning occurs most months throughout the Northeast US continental shelf peaking from February to April with the highest egg distributions occurring on the Northeast Peak of Georges Bank, around the perimeter of the Gulf of Maine (GOM) and the inner shelf off southern New England (SNE) (Lough, 2004). Generally, peak spawning occurs in winter during mild conditions and in more northerly areas and later in spring when winters are colder and the winds are stronger. Larvae and pelagic juveniles are broadly distributed throughout the region, but by settlement at ∼4–6 cm the demersal 0-group juveniles are located more along the northern edge of Georges Bank, the Nantucket Shoals and along the inner shelf of the GOM (Lough, 2010). A requirement for survival during the important larval and juvenile periods is the availability of prey resources. For feeding, cod larvae at higher latitudes depend primarily on C. finmarchicus, while those along the southern edge (Scotian Shelf, GOM, Georges Bank, North Sea, Irish Sea, Skagerrak and Baltic) depend primarily on Para/Pseudocalanus spp. (Heath and Lough, 2007). The pelagic juveniles continue to prey on these copepods and on more diverse prey such as euphausiids, mysiids, and some other epibenthic invertebrates. The distribution patterns of these four abundant and important copepod species in the region results from the complex interactions of life history traits and the variable biological processes and circulation patterns (Durbin and Casas, 2006; Ji et al., 2009; Stegert et al., 2012). Calanus finmarchicus and Centropages typicus are egg broadcast spawners, whereas Pseudocalanus spp. are egg carrying spawners and Centropages hamatus is a resting egg spawner. Calanus finmarchicus is an oceanic species that requires deep water for diapause in autumn to complete its life cycle. It emerges from diapause in the deep water basins to the surface in the GOM and the offspring are transported onto Georges Bank in early winter reaching maximum abundance in spring. In the Middle Atlantic Bight (MAB) C. fimarchicus also has a similar distribution and abundance pattern likely originating in the GOM (Kane, 2005). Two species of Pseudocalanus found on the northeast shelf are cold water species with different source regions. Pseudocalanus moultoni appears to originate in the inshore regions of the western GOM, whereas P. newmani appear to cross over from the Scotian Shelf (Bucklin et al., 2001). Their abundance is low during fall and winter in the GOM. On Georges Bank, the two species intermix and reach maximum abundance by May–June. Centropages typicus and C. hamatus are warm-water species that tend to be located more southerly on the shelf (Durbin and Kane, 2007). Centropages typicus reaches maximum abundance in the late summer–early fall and has a widespread distribution. In contrast, C. hamatus adults are for the most part restricted to the crest of Georges Bank and have peak abundance in summer. A recent study suggests that decadal changes in nutrient supply and water column stability related to the North Atlantic Oscillations are responsible for changes in phytoplankton phenology and production leading to changes in the copepod species assemblages in the GOM (Ji et al., 2013). In addition, studies using coupled biological–physical modelling for the GOM–Georges Bank region (Ji et al., 2009) suggest that the egg carrying spawning strategy and short generational time of Pseudocalanus spp. in cold water is the major reason for its peak abundance in spring and early summer. The decline during summer has been negatively correlated with warmer temperatures and presence of salps, perhaps indirectly reducing the availability of phytoplankton as food (Kane, 2014). Centropages typicus increases in abundance during summer and is known to prey on the young stages of copepods. A consequence of these species characteristics is that climate warming may likely enhance the seasonal growth of C. typicus and therefore increase their abundance and expand their distribution (Ji et al., 2009). Future warming may also limit the early growth of Pseudocalanus spp. during winter and later in early summer. Shifts in the zooplankton community caused by changes in climatic conditions have been observed in the past. A major shift towards warmer conditions associated with circulation changes occurred on Georges Bank between the 1980s and 1990s had implications for the recruitment of marine fish (Kane, 2007; Kane, 2014; Mountain and Kane, 2010; Friedland et al., 2013; Bi et al., 2014). In fact, changes in prey abundance combined with warmer temperatures promoted higher potential growth and survival rates, enhancing recruitment. The warmer years of the 1990s were associated with earlier spring blooms and higher abundances of zooplankton prey for larval cod. The greater growth of larval cod in 1997–1999, compared with 1995 and 1996, was highly correlated with the increased abundance of their principle prey Pseudocalanus spp. (Buckley and Durbin, 2006). Also, larval mortality estimated from field surveys was lower in 1997–1999, compared with 1995 and 1996, inferring greater survival (Mountain et al., 2008). Higher growth also leads to lower mortality rates as larger larvae and juveniles have less potential predators, such as invertebrates and vertebrates, which combined are likely the critical factor affecting mortality. Buckley et al. (2010) examined the ratio of larval mortality to growth from the 1995–1999 surveys as an index of seasonal changes in cohort biomass. When mortality falls below growth, a window of opportunity occurs which allows the cohort biomass to increase. Variability in the ratio can be related to seasonally increasing photoperiod, temperature, copepod prey, and predators. Buckley et al. (2010) found that larval mortality was positively related to Georges Bank temperature reaching a minimum in March. However, by May, the temperature only increased by a few degrees, whereas there was a large increase in mortality, greater than can be accounted by metabolic processes alone, inferring the importance of predation. The greater larval growth and survivors, especially in 1998 and 1999, could be related to a greater and earlier window of opportunity in the season. The hypothesis is that hatching earlier in the seasonal cycle may allow the larvae to grow faster and beat the increase in predators. An earlier spring bloom can also lead to a longer season allowing for a higher integrated number of larval fish to survive to reach the juvenile stages (Kristiansen et al., 2011). Regardless, the differences in larval growth and survival did not correspond to the age-1 recruitment numbers for those years (Lough and O'Brien, 2012). Thus, Mountain and Kane (2010) concluded that changes in the cod/haddock survivorship between the 1980s and 1990s reflected changes primarily during the juvenile stage, rather than the larval stage. Kane (2007) suggested that the decadal shift in the zooplankton community in 1990s on Georges Bank most likely originated from the greater contribution of Scotian Shelf water and associated plankton. However, the influx of cooler, fresher water, and associated more northern plankton species did not appear to affect the MAB (Kane, 2011). The continued warming trends since the late 1990s (1997–2011) have decreased cold water habitats on the northeast continental shelf necessary for the long-term survival of cold water zooplankton such as Pseudocalanus spp. and C. finmarchicus (Kane, 2007; Hare and Kane, 2012; Friedland et al., 2013). Friedland et al. (2013) hypothesized that the winter spawning cod (February–March) on Georges Bank and Eastern GOM were linked to the decline of Pseudocalanus spp. in these areas, either through changes in advection, primary production and/or predation. While the past focus has been on growth and survival of the larval stage of cod on Georges Bank, we want to extend the scope to the pelagic juveniles and the broader New England Shelf (Figure 1). Pelagic juveniles are still preying on the smaller plankton before bottom settlement and switching to more benthic prey. While settlement is considered a critical period especially in regards to predation, environmental conditions during the pelagic juvenile period can determine their fitness to survive predation (Lough, 2010). The objective of this study is to evaluate the potential growth of pelagic juvenile cod in relation to their zooplankton prey distribution and abundance in five areas of the New England Shelf during the winter–spring time series of plankton surveys, 1978–2006. The hypothesis that juvenile growth is related to recruitment is explored for Georges Bank cod through generalized additive models (GAMs). Figure 1. Open in new tabDownload slide Areas defined for the US Northeast Atlantic continental shelf: GOM, EGB, WGB, SNE, and MAB. Contour labels are in meters. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 1. Open in new tabDownload slide Areas defined for the US Northeast Atlantic continental shelf: GOM, EGB, WGB, SNE, and MAB. Contour labels are in meters. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Methods Zooplankton time series The Marine Resources Monitoring, Assessment, and Prediction (MARMAP) programme and the Ecosystem Monitoring (EcoMon) programme plankton surveys were conducted approximately bimonthly on the US northeast continental shelf since the late 1970s using a stratified random design. At each station, a 61-cm diameter bongo net with 0.333-mm mesh nets sampled the water column to a maximum depth of 200 m. Surface temperatures were made at all stations by bucket samples or vessel thermistor from 1977 to 1999. A conductivity-temperature-depth instrument was attached above the bongo net frame beginning in 1987. Zooplankton was sorted, counted, and identified to the lowest level possible. See Kane (2007) for details of the zooplankton and environmental data. The coarse mesh net captures the larger copepod stages, but still under samples species such as Pseudocalanus spp., Centropages spp., and Oithona spp. completely (Kane, 2007). Nevertheless, those species stages retained by the 0.333-mm mesh nets are most vulnerable to the pelagic juvenile cod based on their gut contents. Stomach contents of pelagic juvenile cod collected by 10-m2 MOCNESS on Georges Bank during June 1984 and 1986 indicated their prey selection was primarily Pseudocalanus spp. and Centropages spp. (Auditore et al., 1988). Within the standard length range of 9–19 mm, Pseudocalanus spp. prey selection ranged 28–53% and Centropages spp. ranged 21–40% of the total prey. This study used the 1978–2006 MARMAP time-series of four potential prey taxa of pelagic juvenile cod that included Pseudocalanus spp., C. finmarchicus, C. typicus, and C. hamatus. The two species of Pseudocalanus (moultoni and newmani) were not identified separately. For each tow, the copepod stages were standardized to mean water column prey density (no. m−3). Also, the stages were converted to dry weight (Lough et al., 2005), summed, and standardized to mean prey biomass (mg m−3). Model simulations A mechanistic individual-based model (IBM) was used to estimate the spatial and temporal variation in growth potential for juvenile cod (Gadus morhua) across the northeast region. The IBM (Figure 2) takes ocean temperature, wind at the surface, modelled light, and prey abundance of four key prey species at different nauplii and copepodite stages as input. The input data were based on bimonthly observations from the MARMAP/EcoMon surveys 1977–2006. The annual, seasonal, and geographical variation in temperature, wind, light, and prey is important environmental characteristics that strongly affect the modelled growth and survival. Here, turbulence at depth was estimated assuming a constant seasonally averaged surface wind, while light was modelled as a function of day of the year, latitude, and depth (Skartveit and Olseth, 1987). The light attenuation coefficient in the water column was assumed constant (k = 0.18) with values typically observed on Georges Bank during May (Lough et al., 2005). For each station observation during MARMAP/EcoMon surveys, temperatures were averaged over the upper 20 m to capture the seasonal surface warming in late spring. Figure 2. Open in new tabDownload slide A schematic of the modelling setup consisting of the observations from the MARMAP/EcoMon surveys, and the IBM. Temperature and prey abundance (four species at six stages each) data were used to force the IBM directly. The prey abundance was used as input to a mechanistic IBM that simulated the feeding ecology and bioenergetics of larval cod. Growth was either food-limited or temperature-limited (growth saturated) depending on how much prey the larva consumed in one time-step and the amount of food in the stomach. Figure 2. Open in new tabDownload slide A schematic of the modelling setup consisting of the observations from the MARMAP/EcoMon surveys, and the IBM. Temperature and prey abundance (four species at six stages each) data were used to force the IBM directly. The prey abundance was used as input to a mechanistic IBM that simulated the feeding ecology and bioenergetics of larval cod. Growth was either food-limited or temperature-limited (growth saturated) depending on how much prey the larva consumed in one time-step and the amount of food in the stomach. The IBM is a composite of important processes that affect feeding, growth, development, metabolism, and stomach fullness of larval and juvenile cod. The mechanistic feeding component of the IBM simulated the highly variable, non-linear interactions between each individual juvenile with the environment. The environment was modelled based on the external forcing mentioned above. For each time step in the model (1 h), the encounter rate, approach and capture success, ingestion and specific growth rates (SGRs) were calculated sequentially (see details in Kristiansen et al., 2009). If the fish was able to encounter and capture enough prey items (biomass) to meet energetic demands limited by upper physiological constraints (Folkvord, 2005), growth was assumed to be temperature limited and not prey limited. Otherwise growth rate was assumed to be food limited and constrained by the amount of food available in the stomach (Kristiansen et al., 2007,, 2009) and temperature limited though its effect on respiration. Behaviourally, the fish in the IBM were limited: each fish was constrained vertically at 20-m depth near the seasonal thermocline, which also is the average depth where most juvenile fish are observed on Georges Bank (Lough and Potter, 1993). Fish were kept at constant depth to allow for easy comparison of growth patterns across regions. The fish were also constrained horizontally and not allowed to move, which allowed us to simulate feeding and growth potential at a fixed position in space over a short period. Also, the predation module was not used in these simulations. SGRs of juvenile fish were averaged over 6 d of simulations. Since the fish were fixed at depth in the simulations, the cost of swimming was not added to the total metabolic cost. Fish were allowed to attempt to capture all copepod stages, although ingestion was limited to prey smaller than their mouth gape, where gape-size depends on size of the fish. Capture success also depended on reaction distance to the prey, light level at depth (visual feeders depend on light to see their prey), water quality (horizontal visibility of prey), prey species and stage-specific escape behaviour, and turbulence (see Kristiansen et al., 2011,, 2009; Fiksen and MacKenzie, 2002 for details). Details of the bioenergetics model can be found in Kristiansen et al. (2011, 2009) except the function for respiration rate for cod, which was taken from Lough et al. (2005). Average daily growth was estimated for each time and place where surveys were conducted and because of this setup, there is no drift of juveniles that affects the spatial distributions; rather the distributions are identical with the grid of stations conducted by the survey for the specific time. Growth patterns were estimated for three sizes of fish (12, 15, and 18 mm SL) during January–February, March–April, and May–June for the each year, 1977–2006. The Northeast US Atlantic continental shelf was divided into five areas for analysis (Figure 1): GOM, Eastern Georges Bank (EGB), Western Georges Bank (WGB), SNE, and MAB. Friedland and Hare (2007) examined the long-term trends in sea surface temperature (SST) on the continental shelf of the northeast United States based on principal component analysis of the observed patterns of SST and circulation. They defined coherent subareas as MAB, SNE, GB, WGOM and EGOM. Our areas generally coincide with their WGOM (GOM), GB (EGB, WGB), SNE, and MAB. The Georges Bank area was selected for additional analysis below because there were more available early life history data from field surveys and experimental studies. Our Georges Bank area was arbitrarily divided into eastern and western areas based on the early life history of cod and seasonal differences in water properties. Cod generally spawn on EGB in winter–early spring and the larvae and pelagic juveniles are transported southwest to WGB by spring. EGB is more under the influence of cooler, fresher Scotian Shelf water and events such as Scotian Shelf overflows (Brink et al. 2009). EGB water is cooler and fresher than WGB in spring as seasonal warming occurs. EGB has deeper bathymetry, whereas WGB is relatively shallow so that water remains well-mixed under strong tides. WGB conditions are more similar to those of SNE than EGB. The SNE area as outlined has relatively uniform near surface conditions alongshelf. The number of stations sampled within each of the five defined areas and three bimonths during the MARMAP time series 1978–2006 is shown in Table 1. A minimum number of positive stations (29) were required to produce a shelf-wide distribution plot and a few year-months of potential cod growth are shown as examples. The derived SGRs were in the reasonable range for the bimonthly periods based on Buckley et al. (2006) RNA/DNA analyses for 12 mm cod: January–February 2–4% d−1, March–April 6–8% d−1, May–June 8–16% d−1. Thus, the simulated SGRs were comparable with the observed growth rates of ∼8–16% d−1 for Georges Bank larval cod at the end of 12 mm when they transition to pelagic juveniles in May–June. Growth trends for all three sizes were more similar than different, although the 12 mm fish had lower and sometimes negative growth in January–February. The 15-mm fish was chosen as the most representative of the pelagic juveniles for further analyses. Time-series plots also were made for total copepod density (no. m−3) and copepod biomass (mg m−3), density of C. finmarchicus, Pseudocalanus spp., C. typicus and C. hamatus density, and for bimonths (January–February, March–April, and May–June) and for the five areas (EGB, WGB, GOM, SNE, and MAB). Table 1. Number of stations sampled within each area and bimonth during the MARMAP time series 1978–2006. Area . EGB . WGB . GOM . SNE . MAB . Total tows . Year . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . . 1978 5 12 11 10 18 18 5 4 9 92 1979 9 11 4 16 19 11 9 9 88 1980 3 11 6 26 24 14 14 17 4 56 30 28 15 248 1981 6 10 9 24 10 11 7 20 32 19 25 13 186 1982 10 11 2 10 12 15 1 10 22 23 13 4 133 1983 11 11 13 13 15 16 16 24 1 12 132 1984 10 10 12 16 16 23 23 15 13 138 1985 12 12 10 12 13 15 6 3 3 28 21 22 14 15 14 200 1986 24 7 12 11 17 13 25 22 14 15 160 1987 24 12 11 13 1 21 24 14 18 138 1988 10 14 24 1989 30 27 21 35 113 1990 18 6 23 10 18 57 3 135 1991 60 49 18 11 95 233 1992 29 10 45 14 30 79 18 1 4 230 1993 31 14 26 11 15 7 64 16 12 2 198 1994 15 18 1 20 14 15 7 46 13 149 1995 8 8 4 14 6 11 16 10 2 9 10 8 106 1996 3 6 3 7 7 11 14 7 8 66 1997 3 15 4 8 4 8 16 15 5 7 85 1998 6 12 15 8 17 5 6 8 15 7 1 9 9 118 1999 5 11 12 3 21 12 10 5 4 10 19 2 10 7 131 2000 7 4 11 2001 5 9 3 2 12 8 8 5 12 12 15 5 3 12 111 2002 8 9 4 7 12 11 8 9 9 16 16 19 7 9 8 152 2003 7 13 8 10 9 9 7 7 3 16 5 9 103 2004 10 14 16 13 2 6 4 6 13 18 7 10 5 124 2005 12 10 13 16 14 12 7 7 12 11 9 8 10 8 149 2006 9 13 12 11 7 8 9 15 11 8 7 110 Area . EGB . WGB . GOM . SNE . MAB . Total tows . Year . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . . 1978 5 12 11 10 18 18 5 4 9 92 1979 9 11 4 16 19 11 9 9 88 1980 3 11 6 26 24 14 14 17 4 56 30 28 15 248 1981 6 10 9 24 10 11 7 20 32 19 25 13 186 1982 10 11 2 10 12 15 1 10 22 23 13 4 133 1983 11 11 13 13 15 16 16 24 1 12 132 1984 10 10 12 16 16 23 23 15 13 138 1985 12 12 10 12 13 15 6 3 3 28 21 22 14 15 14 200 1986 24 7 12 11 17 13 25 22 14 15 160 1987 24 12 11 13 1 21 24 14 18 138 1988 10 14 24 1989 30 27 21 35 113 1990 18 6 23 10 18 57 3 135 1991 60 49 18 11 95 233 1992 29 10 45 14 30 79 18 1 4 230 1993 31 14 26 11 15 7 64 16 12 2 198 1994 15 18 1 20 14 15 7 46 13 149 1995 8 8 4 14 6 11 16 10 2 9 10 8 106 1996 3 6 3 7 7 11 14 7 8 66 1997 3 15 4 8 4 8 16 15 5 7 85 1998 6 12 15 8 17 5 6 8 15 7 1 9 9 118 1999 5 11 12 3 21 12 10 5 4 10 19 2 10 7 131 2000 7 4 11 2001 5 9 3 2 12 8 8 5 12 12 15 5 3 12 111 2002 8 9 4 7 12 11 8 9 9 16 16 19 7 9 8 152 2003 7 13 8 10 9 9 7 7 3 16 5 9 103 2004 10 14 16 13 2 6 4 6 13 18 7 10 5 124 2005 12 10 13 16 14 12 7 7 12 11 9 8 10 8 149 2006 9 13 12 11 7 8 9 15 11 8 7 110 Areas defined for the US Northeast Atlantic continental shelf: GOM, EGB, WGB, SNE, and MAB. Open in new tab Table 1. Number of stations sampled within each area and bimonth during the MARMAP time series 1978–2006. Area . EGB . WGB . GOM . SNE . MAB . Total tows . Year . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . . 1978 5 12 11 10 18 18 5 4 9 92 1979 9 11 4 16 19 11 9 9 88 1980 3 11 6 26 24 14 14 17 4 56 30 28 15 248 1981 6 10 9 24 10 11 7 20 32 19 25 13 186 1982 10 11 2 10 12 15 1 10 22 23 13 4 133 1983 11 11 13 13 15 16 16 24 1 12 132 1984 10 10 12 16 16 23 23 15 13 138 1985 12 12 10 12 13 15 6 3 3 28 21 22 14 15 14 200 1986 24 7 12 11 17 13 25 22 14 15 160 1987 24 12 11 13 1 21 24 14 18 138 1988 10 14 24 1989 30 27 21 35 113 1990 18 6 23 10 18 57 3 135 1991 60 49 18 11 95 233 1992 29 10 45 14 30 79 18 1 4 230 1993 31 14 26 11 15 7 64 16 12 2 198 1994 15 18 1 20 14 15 7 46 13 149 1995 8 8 4 14 6 11 16 10 2 9 10 8 106 1996 3 6 3 7 7 11 14 7 8 66 1997 3 15 4 8 4 8 16 15 5 7 85 1998 6 12 15 8 17 5 6 8 15 7 1 9 9 118 1999 5 11 12 3 21 12 10 5 4 10 19 2 10 7 131 2000 7 4 11 2001 5 9 3 2 12 8 8 5 12 12 15 5 3 12 111 2002 8 9 4 7 12 11 8 9 9 16 16 19 7 9 8 152 2003 7 13 8 10 9 9 7 7 3 16 5 9 103 2004 10 14 16 13 2 6 4 6 13 18 7 10 5 124 2005 12 10 13 16 14 12 7 7 12 11 9 8 10 8 149 2006 9 13 12 11 7 8 9 15 11 8 7 110 Area . EGB . WGB . GOM . SNE . MAB . Total tows . Year . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . January– February . March– April . May– June . . 1978 5 12 11 10 18 18 5 4 9 92 1979 9 11 4 16 19 11 9 9 88 1980 3 11 6 26 24 14 14 17 4 56 30 28 15 248 1981 6 10 9 24 10 11 7 20 32 19 25 13 186 1982 10 11 2 10 12 15 1 10 22 23 13 4 133 1983 11 11 13 13 15 16 16 24 1 12 132 1984 10 10 12 16 16 23 23 15 13 138 1985 12 12 10 12 13 15 6 3 3 28 21 22 14 15 14 200 1986 24 7 12 11 17 13 25 22 14 15 160 1987 24 12 11 13 1 21 24 14 18 138 1988 10 14 24 1989 30 27 21 35 113 1990 18 6 23 10 18 57 3 135 1991 60 49 18 11 95 233 1992 29 10 45 14 30 79 18 1 4 230 1993 31 14 26 11 15 7 64 16 12 2 198 1994 15 18 1 20 14 15 7 46 13 149 1995 8 8 4 14 6 11 16 10 2 9 10 8 106 1996 3 6 3 7 7 11 14 7 8 66 1997 3 15 4 8 4 8 16 15 5 7 85 1998 6 12 15 8 17 5 6 8 15 7 1 9 9 118 1999 5 11 12 3 21 12 10 5 4 10 19 2 10 7 131 2000 7 4 11 2001 5 9 3 2 12 8 8 5 12 12 15 5 3 12 111 2002 8 9 4 7 12 11 8 9 9 16 16 19 7 9 8 152 2003 7 13 8 10 9 9 7 7 3 16 5 9 103 2004 10 14 16 13 2 6 4 6 13 18 7 10 5 124 2005 12 10 13 16 14 12 7 7 12 11 9 8 10 8 149 2006 9 13 12 11 7 8 9 15 11 8 7 110 Areas defined for the US Northeast Atlantic continental shelf: GOM, EGB, WGB, SNE, and MAB. Open in new tab Figure 3. Open in new tabDownload slide Cod (15-mm) area SGR for January–February (blue), March–April (green), May–June (red). GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 3. Open in new tabDownload slide Cod (15-mm) area SGR for January–February (blue), March–April (green), May–June (red). GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Prey preference for the three sizes of pelagic juveniles cod was estimated over the time series as described in Fiksen and MacKenzie (2002): Ps=∑i=1N(Pai⋅PLi⋅Pcai)∑i=1N(Pai⋅Pcai), where Ps, average prey size; PL, prey length; Pa, prey abundance; Pca, prey capture probability, and i is the index of the prey item. In total, we used four species each with seven stages resulting in N = 28. Prey preferences were annually averaged then smoothed with a running average of 5 years. Plots were made over the time series showing the relative size ratio (average-prey-size/average-cod-size). The mechanistic feeding component of the IBM predicts the optimal average size ratio between larvae/juveniles and prey to be ∼0.07 as this will provide them with big prey with a high probability of capture. If large prey is not available, or if the prey is too large (larvae are gape and capture limited), the only option for the larvae is to feed on the smaller prey items which reduces the relative ratio between the size of prey and juveniles. Analyses of juvenile cod growth and recruitment GAMs were used to investigate the possible relationship between Georges Bank cod SSB, juvenile cod growth, length, temperature, copepod density, and the dependent variables recruitment-at-age-1 (age-1 R × 106) and recruitment survival (recruitment/spawning stock biomass, R/SSB). GAMs are more suitable for exploring the data and visualizing the relationship between the dependent variable and the independent variables because they can be non-parametric. An excellent fit can be achieved by GAM when the predictor variables are non-linear and have significant noise. STATISTICA (StatSoft Inc™ StatSoft, 2014 v.12) was used for Generalized Additive Modelling. Independent variables used from the time series were Juvenile (15-mm) May–June mean SGR (%/d), Age-0 mean length (cm), Age-1 mean length (cm), Age-1/0 delta mean length (cm), Temp mean May–June (°C), and Copepod mean density WGB (no/m−3). The cod SSB, age-0 and age-1 lengths, and age-1 R were from the Northeast Fisheries Science Center's (NEFSC) fall groundfish survey GFS) time series (Northeast Fisheries Science Center, 2012, 2013) and updated by L. O'Brien (pers. comm., unpublished data; see Supplementary Table S1). The other variables were derived from this study had significant gaps in time series (Table 1). Due to the limited data available, our objective was to only establish trends and not build a forecasting model. The dependent variables (age-1 R × 106, R/SSB) were modelled separately by GAM using a Poisson distribution with a Log link function. Different combinations of the independent variables were modelled to provide the best predictor for the dependent variable. All degrees of freedom were set to 4. For each continuous predictor variable, STATISTICA computes the cubic spline scatterplot smoother, along with the 95% confidence bands and the observed predictor values plotted against the partial residuals. For computational details, see Hastie and Tibshirani (1990). Results are summarized at the point of convergence and displayed values include the final deviance, the residual degrees of freedom, the number of cases, number of iterations, the estimate of the scale value, and a value of R2 computed as the relative improvement in the overall deviance for the final model, which provided an overall index of the goodness of fit of the model. Results SGR for 12, 15, and 18 mm cod, EGB and WGB, bimonth time series An initial SGR comparison of the three cod size classes only for EGB and WGB, 1978–2006, can be seen in Supplementary Figure S1. During January–February, there were many negative SGR values over the time series for all three sizes of cod, especially for the 12 mm cod; however, SGR was more consistently positive on WGB compared with EGB. The 12 mm fish also had higher SGR during the May–June 1980s on WGB compared with EGB. During March–April, SGR was similar to those during January–February and about the same for all size classes. During May–June, SGR reached its highest values (12–14% d−1) for 15 mm fish. May–June and March–April had relatively high growth rates in the early 1980s decreasing to the time series low in the 1990s, increasing to a high again in the early 2000s. The 2000s period tended to have higher growth rates than the 1980s period. The 15-mm fish were selected to best represent the average potential growth response for the pelagic juvenile period. SGR 15 mm cod, all areas, bimonth time series The annual time series of 15-mm cod growth is presented in bimonth plots for each of the five areas to show the data gaps and variability (Figure 3). The largest data gap was for the May–June 1990s, whereas the March–April data were the most consistent through the time series for all areas. The most consistent growth for all bimonths appeared to be in WGB and SNE. January–February and March–April growth were usually similar; however, January–February growth could be lower and negative, especially for GOM and EGB. Highest growth in all areas was in May–June early 1980s and early 2000s. SGR 15-mm cod, all areas, bimonths, 3 periods The 15-mm cod SGR is grouped into years 1978–1989, 1990–1999, and 2000–2006, each panel showing the bimonth mean values with 95% standard error confidence intervals by each area (Figure 4). Cod SGR were similarly low (1–7% d−1) during January–February and March–April from EGB to MAB and highest during May–June (8–14% d−1). During May–June, SGR increased from EGB to MAB during 1978–1989, the highest during 1990–1999 to ∼12–14% d−1, then decreased during 2000–2006 back to the 1978–1989 levels except for MAB. GOM May–June SGR declined significantly during 2000–2006. Figure 4. Open in new tabDownload slide Cod (15-mm) area SGR for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 4. Open in new tabDownload slide Cod (15-mm) area SGR for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Preferred predator–prey size ratio Based on the most frequently eaten copepod prey in the simulated time series, the prey/fish size ratios for three sizes of fish are shown for each of the five regions in Figure 5. Prey/fish ratios near and above 0.07 are considered optimal for growth since they are eating large prey relative to their size. The prey/fish ratios for the 12-mm fish were all >0.07 for all areas over the time series (Figure 5a). However, prey/fish ratios for a 12-mm fish were highest in the early 1980s and declined to 1990 and increased again in latter half of 1990s, and decline after 2000. The 12-mm fish pattern corresponds to the plankton community changes ∼1980, 1990, and 2000. In contrast to the 12-mm fish, the 15- and 18-mm juveniles had lower prey/fish ratios (smaller prey) that reached a plateau during the 1990s, the 15-mm fish near 0.07 (Figure 5b) and the 18-mm fish near 0.059 (Figure 5c). The WGB area had the highest ratios for the 12-mm fish over the time series. MAB ratios could fluctuate extremely some years compared with the other areas. The MAB had the lowest ratios for 12-mm fish and the highest ratios for the 15- and 18-mm juveniles that may reflect the abundance of larger copepods. Figure 5. Open in new tabDownload slide Time-varying ratio of average preferred prey size and juvenile fish size 12 mm (a), 15 mm (b), and 18 mm (c) for the SNE, WGB, EGB, MAB, GOM, and all regions. Figure 5. Open in new tabDownload slide Time-varying ratio of average preferred prey size and juvenile fish size 12 mm (a), 15 mm (b), and 18 mm (c) for the SNE, WGB, EGB, MAB, GOM, and all regions. Copepod density, biomass and temperature, all areas, bimonth time series The annual time series of copepod density and biomass were examined initially in bimonth plots for each of the five areas (see Supplementary Figure S2). Copepod density and biomass were very low in January–February, increased in March–April and generally reached highest values in May–June across all areas. The March–April densities began to increase in the 1990s continuing into the 2000s. May–June densities were high in the early 1980s and early 2000s. Copepod biomass generally followed the time series trend for density, but high values could be attributed mostly to the larger copepods such as C. finmarchicus. Three-panel figures for copepod density, copepod biomass, and temperature are arranged by the three grouping of years as for cod SGR, each panel showing the bimonth mean values with 95% standard error confidence intervals by area (Figures 6–8). Figure 6. Open in new tabDownload slide Copepod density (no. m−3) for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999 and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 6. Open in new tabDownload slide Copepod density (no. m−3) for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999 and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 7. Open in new tabDownload slide Copepod biomass (mg m−3) for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine, GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 7. Open in new tabDownload slide Copepod biomass (mg m−3) for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine, GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 8. Open in new tabDownload slide Temperature (°C) for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–89, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 8. Open in new tabDownload slide Temperature (°C) for January–February (blue), March–April (green), May–June (red) by areas GOM, Gulf of Maine; GB, Georges Bank; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–89, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Mean copepod density generally increased uniformly from EGB to MAB for all months except for May–June 1990–1999 (Figure 6). Copepod density was lowest during 1978–1989 for all bimonths, increasing slightly from EGB to MAB; highest during 1990–1999. During May–June 1990–1999, copepod density was highest for EGB, WGB, and GOM (1500–2300 m−3), but decreased in 2000–2006 (1000–1300 m−3); however, SNE and MAB increased to their highest values (1600–2000 m−3). Mean copepod biomass increased from January–February to May–June, but the trend varied during the three periods (Figure 7). The 1978–1989 March–April period had the lowest biomass across all areas compared with the more recent periods. The GOM biomass increased significantly during 1990–1999 May–June, whereas the other areas stayed about the same as during 1978–1989. Then in May–June 2000–2006 the SNE and MAB biomass increased significantly to the same high biomass as for the GOM. Mean temperature trends were relatively consistent and low during January–February (5–6°C), increasing slightly in March–April (6–7°C) and warming significantly during May–June (8–13°C) (Figure 8). During January–February and March–April, temperatures were about the same from EGB to SNE and slightly warmer in the MAB. Mean temperatures across areas were slightly warmer (1–2°C) during May–June 1990–1999 compared with the other two periods, which were more similar. Copepod four species density, all areas, bimonth time series The percentage density of the four copepod species presented in Figure 9 was created to show area and year differences on a relative scale. The percentage contribution of the four species to the total copepod density showed considerable variability (Figures 9a–d). Calanus finmarchicus percentage generally was highest in the north (EGB) and decreased uniformly to the south (MAB) for all bimonths and could contribute >50% in many areas (Figure 9a). Percentage abundances were highest for the GOM and higher in all regions during the years 2000–2006. Pseudocalanus spp. percentage contribution was generally between 20 and 40% and similar among bimonths, areas, and years except March–April 1978–1989 where its' contribution rose to 40–60% for GOM, SNE, and MAB (Figure 9b). Centropages typicus generally had increased percentage contribution from north (EGB) to south (MAB), the inverse of C. finmarchicus (Figure 9c). Highest percentages occurred during Jan-Feb (30–70%) in all areas during 1978–1989 and 1990–1999. During 2000–2006 the January–February contribution decreased for EGB, WGB, and GOM to low values, while the SNE and MAB remained high. In March–April, the high percentage contribution was high only for MAB. In May–June, the high percentage contribution was high for EGB, WGB, and MAB only during 1990–1999. Centropages hamatus only made minor contributions and appeared on EGB and WGB in May–June, but amounting to <12% (Figure 9d). Figure 9. Open in new tabDownload slide Calanus (a), Pseudocalanus (b), C. typicus (c), and C. hamatus (d) density (%) for January–February (blue), March–April (green), May–June (red) by areas EGB, Eastern Georges Bank; WGB, Western Georges Bank; GOM, Gulf of Maine; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 9. Open in new tabDownload slide Calanus (a), Pseudocalanus (b), C. typicus (c), and C. hamatus (d) density (%) for January–February (blue), March–April (green), May–June (red) by areas EGB, Eastern Georges Bank; WGB, Western Georges Bank; GOM, Gulf of Maine; SNE, Southern New England; MAB, Middle Atlantic Bight. Each panel is for a different period in the time series: 1978–1989, 1990–1999, and 2000–2006. Vertical bars denote standard error 0.95 confidence intervals. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 9. Open in new tabDownload slide Continued. Figure 9. Open in new tabDownload slide Continued. Juvenile cod growth in relation to age-1 recruitment GAM analysis for the response variable Georges Bank cod recruitment-at-age-1 (cod age-1 R) correlated strongest with juvenile May–June SGR, Age-0 mean length, the change in length Age-1/0, and May–June temperature (Figure 10). The best fit for these variables had an R2 of 87.5% (Table 2). All variables were significantly different (p < 0.05) than the generalized linear model and thus supporting their non-linear importance to the GAM. All observations of the partial residuals are within or close to the 95% confidence band. The juvenile May–June SGR had a dome-shaped response between 8 and 11 before declining to 13% d−1 SGR. The dome response can be considered an optimum growth rate between 8 and 11% d−1 given the conditions at this time and location. Age-0 mean length had a positive trend over the range 7–17 cm. The change in length Age-1/0 also was dome-shaped from 7 to 17 cm with a peak near 13 cm. The May–June temperature was positive from 9 to 11°C. The variable Copepod density was added to two models. The best GAM R2 0f 92.1% added the juvenile May–June SGR, change in length Age1/0, and May–June temperature and copepod density. Copepod density increased from ∼200 to 1000/m3, then decreased after ∼1500/m3 based on two points, which may be outliers. Some of the other models presented did not yield as good a fit. Table 2. Summary statistics for all GAMs for the response variables Georges Bank cod recruitment-at-age-1 (cod age-1 R) and cod recruitment survival (R/SSB). Response variable, models . R2 (%) . Deviance . Cod age-1 R  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 87.5 24.34  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 83.1 33.03  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 70.4 57.86  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 77.5 43.86  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 77.7 44.31  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 92.1 15.53  Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 75.8 48.15 Cod R/SSB  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 80.7 0.34  Age-1 GB mean length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 67.7 0.56  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 56.4 0.76  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 57.1 0.74  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 56.4 0.84  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Copepod density WGB (no. m3) 73.6 0.46  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 61.5 0.67 Response variable, models . R2 (%) . Deviance . Cod age-1 R  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 87.5 24.34  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 83.1 33.03  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 70.4 57.86  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 77.5 43.86  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 77.7 44.31  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 92.1 15.53  Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 75.8 48.15 Cod R/SSB  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 80.7 0.34  Age-1 GB mean length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 67.7 0.56  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 56.4 0.76  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 57.1 0.74  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 56.4 0.84  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Copepod density WGB (no. m3) 73.6 0.46  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 61.5 0.67 An overall goodness of fit of the model for each combination of variables is provided by the final deviance and R2 values. Open in new tab Table 2. Summary statistics for all GAMs for the response variables Georges Bank cod recruitment-at-age-1 (cod age-1 R) and cod recruitment survival (R/SSB). Response variable, models . R2 (%) . Deviance . Cod age-1 R  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 87.5 24.34  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 83.1 33.03  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 70.4 57.86  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 77.5 43.86  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 77.7 44.31  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 92.1 15.53  Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 75.8 48.15 Cod R/SSB  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 80.7 0.34  Age-1 GB mean length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 67.7 0.56  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 56.4 0.76  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 57.1 0.74  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 56.4 0.84  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Copepod density WGB (no. m3) 73.6 0.46  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 61.5 0.67 Response variable, models . R2 (%) . Deviance . Cod age-1 R  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 87.5 24.34  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 83.1 33.03  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 70.4 57.86  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 77.5 43.86  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 77.7 44.31  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 92.1 15.53  Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 75.8 48.15 Cod R/SSB  Age-1/0 GB delta length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 80.7 0.34  Age-1 GB mean length (cm), Age-0 GB mean length (cm), Juv WGB May–June SGR, Temp WGB May–June (°C) 67.7 0.56  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C) 56.4 0.76  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm) 57.1 0.74  Age-0 GB mean length (cm), Age-1 GB mean length (cm), Temp WGB May–June (°C) 56.4 0.84  Juv WGB May–June SGR, Age-0 GB mean length (cm), Age-1 GB mean length (cm), Copepod density WGB (no. m3) 73.6 0.46  Juv WGB May–June SGR, Age-1/0 GB delta length (cm), Temp WGB May–June (°C), Copepod density WGB (no. m3) 61.5 0.67 An overall goodness of fit of the model for each combination of variables is provided by the final deviance and R2 values. Open in new tab Figure 10. Open in new tabDownload slide GAM additive effects for the four variables for the response Cod age-1 Recruitment partial residuals. The spline line is fitted to the partial residuals of the observed values (circles) with 95% confidence band (dashed). See Table 2 for details. Figure 10. Open in new tabDownload slide GAM additive effects for the four variables for the response Cod age-1 Recruitment partial residuals. The spline line is fitted to the partial residuals of the observed values (circles) with 95% confidence band (dashed). See Table 2 for details. From the GAM analysis, we can infer there is some basis for a general non-linear relationship between cod age-1 recruitment and the mean length and potential growth of the pelagic juveniles, May–June temperature and copepod density. Forecast of more recent years did not have measurement of the juvenile May–June SGR data and the same survey coverage of the copepod density, hence simpler models might be more appropriate using Age-0 and Age-1 mean length and May–June temperature. The GAM models included only 17 observations from the years 1978–1983, 1985–1988, and 1999–2006 and may not be representative of the full 29-year time series if data for all years were available. The best fit from the GAM analysis for the response variable Georges Bank cod recruitment survival (R/SSB) had an R2 of 80.7% (Table 2), the same model used for the recruitment-at-age-1 above. Recruitment survival was correlated strongest with the positive linear trend of Age-0 mean length. However, the observed residuals from the models were too scattered to provide any meaningful confidence limits (not shown). Also, none of the variables were significantly different (p > 0.05) than the generalized linear model. Thus, the usefulness of these GAM models for recruitment survival is limited. Discussion This study has shown how juvenile cod growth rate increases significantly on the Northeast US Shelf as spring progresses, where growth is attributed to the seasonal increase in copepod density and temperature, generally propagating from south to north. Also, the 1990–1999 warm years had the highest growth compared with the cooler 2000–2006 years and colder 1978–1989 years of similarly lower growth. Modelled growth for the 12–18 mm cod pelagic juveniles were in the approximate range of expected rates based on late larval projections (Buckley et al., 2006). Since the estimated potential growth rates were in the range of literature values for the 15-mm pelagic juvenile cod, one can conclude that the absolute density of the copepods collected from the field surveys and the functional feeding response in the model are reasonable. Growth optimum for larval and juvenile cod depends on both temperature and prey abundance since an increase in temperature increases metabolism, which also requires more prey until an ingestion capacity is reached (Buckley et al., 2006). The simulations also indicated that the suitable copepod life stages caught by the sampling gear were still not sufficient in many years to support positive growth for the 12-mm fish. While Oithona spp. are not retained by the sampling gear mesh and not included in the simulations, the adults and older copepodite stages can still contributed to the pelagic juvenile cod gut prey biomass (Auditore et al., 1988). Oithona spp. (mostly similis) are commonly found throughout the northeast shelf region, generally low in abundance during winter in the GOM and increasing in abundance during spring on Georges Bank (Durbin and Casas, 2006). Low salinity discharges from the Arctic (Greene and Pershing, 2007), particularly during the 1990s, appeared to have maintained water column stratification of the North Atlantic Shelf Waters. These conditions extended the phytoplankton growth season through autumn and into winter, and it was hypothesized that favourable-feeding conditions led to an increased abundance of the smaller copepod species (see Greene et al., 2013). However, Hare and Kane (2012) found the correlation between salinity anomalies and the copepod community structure did not persist from time series observation after the 2000s in the GOM. In a different study, Ji et al. (2013) assessed the sensitivity of Pseudocalanus spp. and C. typicus populations to bottom-up and top-down forcing in the GOM and Georges Bank regions from the same time series of MARMAP/EcoMon surveys 1977–2006. Results from the coupled model with full population dynamics showed that the populations were more sensitive to changes in mortality rates than phytoplankton bloom magnitude and timing. Total potential predators included chaetognaths, hpyperiids, gammarids, euphausiids, fish larvae, and mysids, but not gelatinous zooplankton. Top-down control of the copepod populations proposed by Frank et al. (2005), the trophic cascading hypothesis, is that overfishing of the large demersal fish populations on the Scotian Shelf led to an increase in planktivorous fish such as herring that feed on large copepods C. finmarchicus. However, herring remained high in the early 2000s on Georges Bank even as the predator population increased. Top-down control may be more important for the GOM populations than those on Georges Bank. Although Friedland et al. (2013) reported no significant trend in C. finmarchicus abundance during the MARMAP time series 1977–2011, our study suggests that the relative contribution of C. finmarchicus increased during 2000–2006 in GOM, EGB, and WGB (Figure 9a). Friedland et al. (2013) did show a consistent downwards trend in spring (February–April) abundance of Pseudocalaus spp. and P. parvus since ∼2000 from Cox Ledge, Georges Bank, and the 43 and 44°N band areas. The authors' downwards trend are consistent with the relative contribution of Pseudocalaus spp. during 2000–2006 in GOM, EGB, and WGB (Figure 9b) found in this study. Note that our GOM area covers 41 30 to 44 00°N, some of Nantucket Shoals and parts of Friedland et al. (2013) bands 42, 43 and 44°N. Friedland et al. (2013) also reported a general increase in warming over the last 15 years on the northeast shelf in spring except for Georges Bank and eastern GOM. The authors used satellite SST, whereas this study used station mean temperature from the upper 20 m of the water column. Our summaries of the surface temperature (Figure 8) did not show a significant change over the time series for January–February and March–April, but an increased warming during May–June 1990–1999 across all areas. The 1978–1989 and 2000–2006 periods had similar lower temperatures. The preferred prey/fish size ratios for 15-mm (and 18-mm) pelagic juveniles reached a plateau during the 1990s (Figure 5), which corresponded to the high May–June cod growth period (Figure 4). There is some correspondence to the May–June copepod density during the same period (Figure 6); however, not for copepod biomass (Figure 7). The ratios suggest that 12-mm cod were able to feed on larger, more energy rich prey in early 1990s, compared with those after 1995, when the relative size ratio declined and the larvae had to feed on smaller prey. The work of Kane (2007) and others using the same zooplankton time series identified four temporal zooplankton communities: pre-1980, 1980–1990, 1991–2000, and post-2000. The 1990s was a period of smaller zooplankton. The 12 mm fish showed smaller prey being eaten during the early 1990s in all areas except for the GOM (Figure 5). However, the 15 and 18 mm pelagic juveniles ate larger prey during this same period in all areas. The 15 and 18 mm patterns were very similar to the 18 mm fish eating slightly larger prey. If large prey were available and large sizes of C. fimmarchicus were more frequent than Pseudocalanus spp., then the larger prey eaten were mostly C. finmarchicus and the smaller prey Pseudocalanus spp. The reason the juveniles fed mostly on smaller prey in the 2000s is probably because these items were more abundant in the water column compared with larger prey. Cold years 1978–1989 Pelagic juvenile cod growth was similarly low and consistent during January–February and March–April 1978–1989 across all areas; however, growth increased significantly during May–June and from EGB to MAB (Figure 4). During 1978–1989, SGR followed the increase in copepod density for all bimonths, but not copepod biomass as closely. The June1986 and 1987 field observations are mentioned here because they are the only surveys where both larvae and pelagic juveniles were collected (Lough, 2010). Abundance estimates for each stage indicated a marked decrease in abundance in the 1986 cohort between the 9–11 mm larvae and the 20–50 mm pelagic juveniles, whereas in 1987 the stage abundances stayed about the same (Lough, 2010). Modelled SGR for 15-mm cod was lower in May–June 1986 than 1987 for both EGB (4.50, 6.16% d−1 resp.) and WGB (7.59, 8.03% d−1 resp.), consistent with recruitment survival (0.35, 0.41 resp.). There was also a decrease in copepod density and biomass in 1986 for May–June EGB and WGB, relative to 1987 and 1985. Pelagic juvenile cod as well as larvae were located more on the flank of EGB in June 1986, whereas in June 1987 they were distributed mostly across the shoal, central part of WGB. The highest simulated growth rates (>6% d−1) generally occurred on the western half of Georges Bank, especially in 1987 (Figure 11). In 1986, the higher growth rates also extended from the Northeast Peak along the southern flank aligning with the larval and pelagic juvenile distributions. The relative contribution of C. typicus increased from 5.2% on EGB in May–June 1986 to 36.4% on WGB in May–June 1987 (Figure 9c). Centropages typicus, a warm-water species, typically has a low relative contribution on EGB (<5%) but higher (>30%) near the coastal areas such as WGB. The higher estimated cod growth may explain the greater survival of the field-caught pelagic juveniles in June 1987. Figure 11. Open in new tabDownload slide Cold years May-June 1986 (left column) and 1987 (right column) daily specific growth rate (SGR) distribution for 12 (e, f), 15 (c, d) and 18 (a, b) mm cod. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 11. Open in new tabDownload slide Cold years May-June 1986 (left column) and 1987 (right column) daily specific growth rate (SGR) distribution for 12 (e, f), 15 (c, d) and 18 (a, b) mm cod. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. The relative contribution of C. typicus was also particularly high, 30–70%, in January–February in all areas during 1978–1989 and 1990–1999, but only for WGB, SNE, and MAB during 2000–2006. If C. typicus is an important potential prey for pelagic juvenile cod in these areas, then spawning earlier in the season may promote better growth and survival and potentially recruitment. This does not rule out other beneficial months since large numbers of C. typicus also occurred for EGB and WGB during 1990–1999. Nevertheless, the highest overall growth of the pelagic juvenile cod occurred in the simulations during May–June, the result of contributions of all four copepod species and the seasonally high temperatures. Higher temperatures promote higher growth as long as there is sufficient prey to meet the increased metabolism. The higher growth is consistent with the higher average prey size during the 1990s (Figure 5). The importance of C. typicus for the pelagic juveniles contrasts the importance of Pseudocalanus spp. for growth during the larval stages (Buckley and Durbin, 2006; Broughton and Lough, 2010). The stages of C. typicus are larger than those of Pseudocalanus spp. and would provide more biomass. Warm years 1990–1999 Mean temperatures during January–February and March–April 1990–1999 were similarly low across the areas with a slight rise in MAB (Figure 8). During May–June temperatures increased significantly from north to south, EGB to MAB, and they were a couple of degrees higher compared with May–June in 1978–1989. Potential juvenile cod growth rates increased significantly in May–June and were more uniform across the areas compared with the 1978–1989 period (Figure 4). The higher growth rates were related to the increased copepod density for EGB, WGB, and GOM (Figure 6), but only an increased biomass for GOM (Figure 7). Potential growth of the larger pelagic juveniles continued to increase with temperature because they were not food limited by capturing the larger potential prey that the smaller larvae cannot. Although the 1990s were relatively warm years, the greater larval and pelagic juvenile growth in 1998 compared with 1995 was related more to an increase in suitable copepod prey. Buckley and Durbin (2006) found that greater growth of Georges Bank larval cod in spring of 1998 and 1999, compared with 1995–1997, was highly correlated with the increased abundance of their principle prey Pseudocalanus spp. Their estimated growth rates approached 8–10% d−1 for an 8 mm larva at 7°C, whereas our simulated growth rates for 12 mm cod were in the 12–14% d−1 range during May–June 1998 and 1999 for EGB and WGB. Growth rates were consistently higher on WGB than EGB due to higher abundance of prey and higher temperatures. Juvenile growth simulation plots were only available for the 1995 March–April period due to a minimum number of stations required. Growth rates for the 12, 15, and 18 mm fish were low (<7% d−1) across the bank for all sizes, but strongly spatially varying for 12 mm fish (Figure 12). Higher growth rates were distributed along the southern flank extending to the western half. The same pattern was observed in 1996 (not shown). Figure 12. Open in new tabDownload slide Warm years March-April 1995 (a, d, g), 1998 (b, e, h), and May-June 1998 (c, f, i) specific growth rate (SGR) distribution for 12 (g, h, i), 15 (d, e, f) and 18 (a, b, c) mm cod. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. Figure 12. Open in new tabDownload slide Warm years March-April 1995 (a, d, g), 1998 (b, e, h), and May-June 1998 (c, f, i) specific growth rate (SGR) distribution for 12 (g, h, i), 15 (d, e, f) and 18 (a, b, c) mm cod. This figure is available in black and white in print and in colour at ICES Journal of Marine Science online. In 1998, growth simulations were available for March–April and May–June (Figure 12). In March–April, the highest growth (4–7% d−1) for the 12 and 15 mm fish extended across the southern flank onto WGB, but <4% d−1 for the 18 mm fish. However, 12 mm fish growth rates showed considerable spatial variation compared with larger sizes. In May–June, very high growth (>7% d−1) was simulated across all of Georges Bank for all three sizes of fish. The 12 and 15 mm fish had very high growth (>8% d−1) on the shoals that appears wrapped around a shelf-slope water intrusion. The greater pelagic juvenile cod growth, especially during May–June 1990–1999, was due to the combined abundance of the four contributing copepods along with the higher temperatures in increased metabolism. Pseudocalanus spp., a preferred prey, occurred in lesser abundance; however, the capture of the larger C. finmarchicus, although not with the same frequency, contributed substantially more biomass for growth. Cool years 2000–2006 Mean temperatures during January–February and March–April 2000–2006 were similar to the previous periods but cooler during May–June across all areas similar to the 1978–1989 period (Figure 8). Pelagic juvenile cod growth during 2000–2006 was lower from the previous period, except for MAB (Figure 4). Mean copepod density and biomass in March–April was similar to the previous period with a slight rise in GOM (Figures 6 and 7). In May–Jun, copepod density was lower in EGB, WGB, and GOM and higher in SNE and MAB than during the 1990–1999 period. Copepod biomass remained high for GOM, SNE, and MAB. A significant decrease in GOM May–June cod SGR occurred from 1990–1999 to 2000–2006, from ∼13% to 6%d−1, coinciding with a slight decrease in copepod density, but not copepod biomass. The low growth for cod in the GOM during May–June 2000–2006 was apparently due to the low abundance of all four copepods, although C. finmarchicus was still the greatest contributor for most bimonths in the GOM. One possible reason for the reduction in growth rates could be that the in the model, very large prey items are difficult to capture, and the handling time increases strongly. Potentially, the lack of smaller prey to feed on is a bigger loss than the positive increase in many very large prey items. Average GOM temperature for May–Jun 2000–2006 was ∼2°C cooler than during 1990–1999. Juvenile cod growth and age-1 recruitment The hypothesis is explored that the higher potential growth of the pelagic cod juveniles together with the increased change in mean length of the age-0 demersal juveniles results in a more recruits. The growth/size hypothesis is supported by the GAM analysis for selected years; however, the limited data preclude its use as a predictive model at this time. A high larval or pelagic juvenile growth rate is not necessarily a predictor of high recruitment since there is still the long duration of the demersal stage when significant predation can occur. Growth and mortality rates derived from field surveys may not be closely related because when prey and temperature are optimal for high growth, more of the slower growing fish may survive. For example, there was no clear relationship between the 12- and 18 mm growth rates in 1986–1987 and age-1 recruitment. Recruitment was only slightly higher in 1987 compared with 1986 (24.5 vs. 16.4 × 106) despite the greater survival of the pelagic juveniles. The estimated demersal juvenile mortality was 2.57% d−1 for 1986 and 2.04% d−1 for 1987, suggesting that predation mortality in the pelagic and demersal juvenile stages may have been the proximate cause (Lough and O'Brien, 2012). In contrast, the 1998 age-1 cod recruitment was higher than in 1995 (12.8 vs. 5.9 × 106), consistent with the larval and juvenile growth differences related to prey abundance. Because of considerable variability in survival during the egg and larval stages, the size of the year class can be more accurately sampled during the juvenile stages closer to recruitment. In a recent study, Stige et al. (2013) attempted to predict recruitment of five fish stocks of four species in three ecosystems from the indices of early life stages and environmental correlates. In some cases, recruitment predictions were better based on the larval stage than the juveniles, most likely due to under sampling. They concluded that incorporating environment correlates into models could improve predictions, but only for certain periods since the relationships were non-stationary. Summary This study estimates potential growth for the pelagic cod juveniles over an extensive regional time series of plankton and temperature observations. The NEFSC's time series of zooplankton on the US Northeast continental shelf was used to estimate the potential growth of cod pelagic juveniles over five areas and three bimonths (January–February, March–April, and May–June). An IBM specifically parameterized for the pelagic stage was used to estimate the spatial and temporal variation in growth potential across the region. Appropriate size potential prey were identified from published, field-caught pelagic cod guts. Seasonally, cod growth increased in May–June coinciding with the increase in zooplankton prey and water temperature. The warm years of 1990–1999 had the highest growth compared with the cooler 2000–2006 years and colder 1978–1989 years of similarly lower growth. Growth generally increased with temperature and was not food limited due to the increase of larger prey available to the larger pelagic juveniles, compared with the smaller larvae. Spatially, mean growth generally increased from north to south, but varied within areas depending on the species abundance over the time series. A significant finding was that C. typicus, a more southern species, could be the more important prey for juveniles in the coastal areas in contrast to the importance of Pseudocalanus spp. for the larvae. Georges Bank cod age-1 recruitment was positively related to juvenile growth for some years. Implications from this study suggest that regional warming may have some potential positive scenarios for cod recruitment. Acknowledgements The authors thank R. Ji for providing the zooplankton series and L. O'Brien for providing the Georges Bank cod age-0 and age-1 data. We are grateful for comments provided by J. Hare and J. Kane on reviewing an early version of this paper. References Auditore P. J. Bolz G. R. Lough R. G. 1988 . Juvenile haddock, Melanogrammus aeglefinus and Atlantic cod, Gadus morhua, stomach contents and morphometric data from four recruitment surveys (1984–1986) in the Georges Bank-Nantucket Shoals area . NMFS/NEFSC Woods Hole Lab. Ref. Doc., No. 88-05 , 105 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bi H. Ji R. Jo Y -H. Hare J. A. 2014 . Decadal changes in zooplankton of the northeast U.S. continental shelf . PLoS ONE , 9 : e87720 . Google Scholar Crossref Search ADS PubMed WorldCat Brink K. H. Beardsley R. C. Limeburner R. Irish J. D. Caruso M. 2009 . Long-term moored array measurements of currents and hydrography over Georges Bank: 1994–1999 . Google Scholar Crossref Search ADS Google Preview WorldCat COPAC Broughton E. A. Lough R. G. 2010 . General trends and interannual variability in prey selection by larval cod and haddock from the southern flank of Georges Bank, May 1993–1999 . NOAA Technical Memorandum, NMFS-NE-217 . 32 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Buckin A. Guarnieri M. McGillicuddy D. J. Hill R. S. 2001 . Spring evolution of Pseudocalanus spp. abundance on Georges Bank based on molecular discrimination of P. moultoni and P. newmani . Deep-Sea Research II , 48 : 589 – 608 . Google Scholar Crossref Search ADS WorldCat Buckley L. J. Caldarone E. Lough R. G. St Onge-Burns J. 2006 . Ontogenetic and seasonal trends in recent growth rates of Atlantic cod and haddock larvae on Georges Bank: effects of photoperiod and temperature . Marine Ecology Progress Series , 325 : 205 – 226 . Google Scholar Crossref Search ADS WorldCat Buckley L. J. Durbin E. G. 2006 . Annual and inter-annual trends in the zooplankton prey and growth rate of Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) larvae on Georges Bank . Deep-Sea Research II , 53 : 2758 – 2770 . Google Scholar Crossref Search ADS WorldCat Buckley L. J. Lough R. G. Mountain D. 2010 . Seasonal trends in mortality and growth of cod and haddock larvae result in an optimal window for survival . Marine Ecology Progress Series , 4054 : 57 – 69 . Google Scholar Crossref Search ADS WorldCat Durbin E. Casas M. C. 2006 . Abundance and spatial distribution of copepods on Georges Bank during the winter/spring period . Deep-Sea Research II , 53 : 2537 – 2569 . Google Scholar Crossref Search ADS WorldCat Durbin E. Kane J. 2007 . Seasonal and spatial dynamics of Centropages typicus and C. hamatus in the western North Atlantic . Progress in Oceanography , 72 : 249 – 258 . Google Scholar Crossref Search ADS WorldCat Fiksen Ø. MacKenzie B. R. 2002 . Process-based models of feeding and prey selection in larval fish . Marine Ecology Progress Series , 243 : 151 – 164 . Google Scholar Crossref Search ADS WorldCat Folkvord A. 2005 . Comparison of size-at-age of larval Atlantic cod (Gadus morhua) from different populations based on the size- and temperature-dependent growth models . Canadian Journal of Fisheries and Aquatic Sciences , 62 : 1037 – 1052 . Google Scholar Crossref Search ADS WorldCat Frank K. T. Petrie B. Choi J. S. Leggett W. C. 2005 . Trophic cascades in a formerly cod-dominated ecosystem . Science 308 : 1621 – 1623 . Google Scholar Crossref Search ADS PubMed WorldCat Friedland K. D. Hare J. A. 2007 . Long-term trends and regime shifts in sea surface temperature on the continental shelf of the northeast United States . Continental Shelf Research , 27 : 2313 – 2328 . Google Scholar Crossref Search ADS WorldCat Friedland K. D. Kane J. Hare J. A. Lough R. G. Fratantoni P. S. Fogarty M. J. Nye J. A. 2013 . Thermal habitat constraints on zooplankton species associated with Atlantic cod (Gadus morhua) on the US Northeast Continental Shelf . Progress in Oceanography , 116 : 1 – 13 . Google Scholar Crossref Search ADS WorldCat Greene C. H. Meyer-Gutbrod E. Monger B. C. McGarry L. P. Pershing A. J. Belkin I. M. Fratantoni P. S. et al. 2013 . Remote forcing of decadal-scale regime shifts in Northwest Atlantic shelf ecosystems . Limnology and Oceanography , 58 : 803 – 816 . Google Scholar Crossref Search ADS WorldCat Greene C. H. Pershing A. J. 2007 . Climate drives sea change . Science , 315 : 1084 – 1085 . Google Scholar Crossref Search ADS PubMed WorldCat Hare J. A. Kane J. 2012 . Zooplankton of the Gulf of Maine—a changing perspective . In Advancing Ecosystem Research for the Future of the Gulf of Maine , pp. 115 – 137 . Ed. by Stephenson R. Annala J. Hall-Arber M. Runge J.. American Fisheries Society, Symposium , Bethesda, MD , 79 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Hastie T. J. Tibshirani R. J. 1990 . Generalized Additive Models . Chapman & Hall , New York . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Heath M. R. Lough R. G. 2007 . A synthesis of large-scale patterns in the planktonic prey of larval and juvenile cod (Gadus morhua) . Fisheries Oceanography , 16 : 169 – 185 . Google Scholar Crossref Search ADS WorldCat Ji R. Davis C. S. Chen C. Beardsley R. C. 2009 . Life history traits and spatiotemporal distributional patterns of copepod populations in the Gulf of Maine-Georges Bank region . Marine Ecology Progress Series , 384 : 187 – 205 . Google Scholar Crossref Search ADS WorldCat Ji R. Stegert C. Davis C. S. 2013 . Sensitivity of copepod populations to bottom-up and top-down forcing: a modeling study in the Gulf of Maine region . Journal of Plankton Research , 35 : 66 – 79 . Google Scholar Crossref Search ADS WorldCat Kane J. 2005 . The demography of Calanus finmarchicus (Copepoda: Calanoida) in the Middle Atlantic Bight, USA, 1977–2001 . Journal of Plankton Research , 27 : 401 – 414 . Google Scholar Crossref Search ADS WorldCat Kane J. 2007 . Zooplankton abundance trends on Georges Bank, 1977–2004 . ICES Journal of Marine Science , 64 : 909 – 919 . Google Scholar Crossref Search ADS WorldCat Kane J. 2011 . Inter-decadal variability of zooplankton abundance in the Middle Atlantic Bight . Journal of Northwest Atlantic Fishery Science , 43 : 81 – 92 . Google Scholar Crossref Search ADS WorldCat Kane J. 2014 . Decadal distribution and abundance trends for the late stage copepodites of Pseudocalanus spp. (Copepoda: Calanoida) in the US Northeast Continental Shelf ecosystem . Journal of Northwest Atlantic Fisheries Science , 46 : 1 – 13 . Google Scholar Crossref Search ADS WorldCat Kristiansen T. Drinkwater K. F. Lough R. G. Sundby S. 2011 . Recruitment variability in the North Atlantic and match-mismatch dynamics . PLoS ONE , 6 : e17456 . Google Scholar Crossref Search ADS PubMed WorldCat Kristiansen T. Fiksen O. Folkvord A. 2007 . Modelling feeding, growth, and habitat selection in larval Atlantic cod (Gadus morhua): observations and model predictions in a macrocosm environment . Canadian Journal of Fisheries and Aquatic Sciences , 64 : 136 – 151 . Google Scholar Crossref Search ADS WorldCat Kristiansen T. Jorgensen C. Lough R. G. Vikebo F. Fiksen O. 2009 . Modeling rule-based behaviour: habitat selection and the growth-survival trade-off in larval cod . Behavioral Ecology , 20 : 490 – 500 . Google Scholar Crossref Search ADS WorldCat Lough R. G. 2004 . Essential fish habitat source document: Atlantic cod, Gadus morhua, life history and habitat characteristics . NOAA Technical Memorandum, NMFS-NE-190 . 93 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Lough R. G. 2010 . Juvenile cod (Gadus morhua) and the importance of bottom sediment type to recruitment on Georges Bank . Fisheries Oceanography , 19 : 159 – 181 . Google Scholar Crossref Search ADS WorldCat Lough R. G. Buckley L. J. Werner F. E. Quinlan J. A. Pehrson Edwards K. 2005 . A general biophysical model of larval cod growth (Gadus morhua) applied to populations on Georges Bank . Fisheries Oceanography , 14 : 241 – 262 . Google Scholar Crossref Search ADS WorldCat Lough R. G. O'Brien L. 2012 . Life-stage recruitment models for Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) on Georges Bank . Fishery Bulletin , 110 : 123 – 140 . Google Scholar OpenURL Placeholder Text WorldCat Mountain D. Green J. Sibunka J. Johnson D. 2008 . Growth and mortality of Atlantic cod Gadus morhua amd haddock Melanogrammus aeglefinus eggs and larvae on Georges Bank, 1995 to 1999 . Marine Ecology Progress Series , 353 : 225 – 242 . Google Scholar Crossref Search ADS WorldCat Mountain D. G. 2003 . Variability in the properties of Shelf Water in the Middle Atlantic Bight, 1977–1999 . Journal of Geophysical Research and Oceans , 108 : C13014 . Google Scholar Crossref Search ADS WorldCat Mountain D. G. Kane J. 2010 . Major changes in the Georges Bank ecosystem, 1980s to the 1990s . Marine Ecology Progress Series , 398 : 81 – 91 . Google Scholar Crossref Search ADS WorldCat Northeast Fisheries Science Center . 2012 . 53rd Northeast Regional Stock Assessment Workshop (53rd SAW) Assessment Report . US Department of Commerce, Northeast Fisheries Science Center Reference Document 12–05 . 559 pp . National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543-1026 or http://nefsc.noaa.gov/publications/ . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Northeast Fisheries Science Center . 2013 . 55rd Northeast Regional Stock Assessment Workshop (53rd SAW) Assessment Report . US Department of Commerce, Northeast Fisheries Science Center Reference Document 13–11 . 845 pp . National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543-1026 or http://nefsc.noaa.gov/publications/ . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Skartveit A. Olseth J. 1987 . A model for the diffuse fraction of hourly global radiation . Solar Energy , 36 : 333 – 343 . Google Scholar Crossref Search ADS WorldCat StatSoft . 2014 . StatSoft, Inc., 2014 . STATISTICA (data analysis software system) , version 12.0 . www.statsoft.com . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Stige L. C. Hunsicker M. E. Bailey K. M. Yaragina N. A. Hunt G. L. Jr . 2013 . Predicting fish recruitment from juvenile abundance and environmental indices . Marine Ecology Progress Series , 480 : 245 – 261 . Google Scholar Crossref Search ADS WorldCat Stegert C. Ji R. Li N. Davis C. S. 2012 . Processes controlling seasonality and spatial distribution of Centropages typicus: a modeling study in the Gulf of Maine/Georges Bank region . Journal of Plankton Research , 34 : 18 – 35 . Google Scholar Crossref Search ADS WorldCat Author notes Handling editor: Francis Juanes Published by Oxford University Press on behalf of International Council for the Exploration of the Sea 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of International Council for the Exploration of the Sea 2015. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Alvarez-Fernandez, S.; Licandro, P.; van Damme, C. J. G.; Hufnagl, M.

doi: 10.1093/icesjms/fsv140pmid: N/A

AbstractDuring the last decade, North Sea autumn spawning herring (Clupea harengus) has gone through consecutive years of low recruitment despite high spawning-stock biomass. Although several mechanisms, such as reduced larval growth and high early larvae mortality, have been identified as co-occurring during these years, the causes behind them have not been identified. In this study, we analyse a long-term dataset of larval distribution, obtained during the International Bottom Trawl Survey, in relation to environmental conditions during winter and zooplankton abundances, obtained from the continuous plankton recorder. These analyses assessed the potential influence of these factors on the reduced survival of larval stages. Generalized additive mixed models on 30 years of data showed the abundance of Pseudocalanus sp. during winter to have a strong relationship with larval distribution and abundance, suggesting that predator–prey processes are behind the low recruitment in recent years. According to our models, the direct effect of temperature on larval abundances was less than the effect of zooplankton abundances.

Espeland, Sigurd Heiberg; Albretsen, Jon; Olsen, Esben Moland; Bodvin, Torjan

doi: 10.1093/icesjms/fsv134pmid: N/A

Abstract Having valid information about the location and dynamics of biological processes is important for coastal management. In this context modelling, the pelagic drift of early life stages has been shown to be an important tool for understanding the spatial scale of population dynamics in marine systems. Often simulated particles are released in hypothetical quantities at assumed spawning grounds with no or few field data to guide the model parametrization. In this study, we combine high-resolution field data and state-of-the-art oceanographic modelling and use a probabilistic approach to construct kernel density distributions of the dispersal of pelagic fish eggs. Specifically, the potential drift of pelagic offspring of cod (Gadus morhua) was investigated in a large, open fjord system in northern Norway by combining field observations of newly spawned cod eggs with simulations of particle movement using a semi-Lagrangian trajectory model with inputs from high-resolution hydrodynamic simulations. The larger part of the distribution of eggs after drift was located in the fjord, suggesting fjord scale recruitment dynamics. Finally, we also examined the drift of eggs released in a uniform distribution and found that lack of egg survey data gave an unrealistically large spread of egg particles within this fjord system. Introduction Sound management requires valid data on location and nature of the biological processes taking place in the coastal zone. For instance, spawning in coastal areas may be subject to different degrees of pelagic drift. This pelagic dispersal of early life stages is a widespread mechanism influencing population structure and dynamics in marine systems (Bohonak, 1999; Cowen et al., 2000; Hastings and Botsford, 2006). Understanding the scale and variability of dispersal from spawning areas is essential for a range of fields, such as, for example, the design of marine protected area networks (Shanks et al., 2003; Botsford et al., 2009) and other trade-offs in conflicts of interest in the coastal zone. The use of oceanographic modelling has gained increased attention in predicting dispersal (Werner et al., 2001; Myksvoll et al., 2011; Bauer et al., 2013; LaCroix et al., 2013; Øresland and Ulmestrand, 2013). However, the fate of the virtual pelagic offspring depends on the resolution and parameterization of the oceanographic model (Lynge et al., 2010; Myksvoll et al., 2012). Model outcomes can also be affected by variations in time, unresolved subgrid turbulence, inclusion of mortality and behaviour (Cowen et al., 2000; Paris et al., 2007), and other discrepancies between models and real life (Watson et al., 2012). In addition, if similar uncertainties apply to the locations of where eggs are released in the model, the results obtained may not be representative of the real dispersal of pelagic offspring. Pelagic eggs may either be dispersed or retained in the area depending on ocean currents in location they are released (Myksvoll et al., 2014a, b), while the overall biological outcome is also dependent on the numbers of eggs released in the different locations. A focus on equal number of trajectories of drift from different locations may give the impression that the endpoints are equally likely unless the model is parametreized with realistic initial data. Dispersal of pelagic offspring has been most extensively studied for teleost fish associated with tropical coral reefs (Cowen et al., 2006; Shanks, 2009; Buston et al., 2012). These are characterized by relatively short pelagic phases, short dispersal distances, and a high degree of self-recruitment (Jones et al., 1999; Cowen et al., 2006; Almany et al., 2007; Planes et al., 2009), but do not represent the global pattern of dispersal and connectivity (Bradbury et al., 2008). Pelagic duration and egg development time increase when moving poleward (increasing latitude) towards colder waters, and comparatively, the genetic differentiation (Fst) has been found to decrease (Bradbury et al., 2008). Thus, there is a negative correlation between pelagic duration and the degree of population differentiation. This decrease in genetic differentiation may be due to the physiology of the species in relation to latitudinal gradients in, for instance, temperature, but oceanography, bottom topography, and other external physical properties without latitudinal gradients may also significantly affect dispersal distances. The Atlantic cod (Gadus morhua) has a development time of 20–24 d from spawning to hatching at 4°C water temperature, and in colder water (near 0°C) the development time may extend beyond 40 d (Westernhagen, 1970). During this long pelagic period, the eggs may potentially be subject to a large variation in oceanographic processes and advected far from the spawning area. When spawning in enclosed fjord basins sheltered from major and persistent current systems, the Atlantic cod can display significant retention of pelagic eggs in the area of release (Knutsen et al., 2007; Ciannelli et al., 2010; Knickle and Rose, 2010; Myksvoll et al., 2012). However, there is less knowledge of the fate of pelagic eggs (from cod and other marine species) spawned in more open fjord and coastal habitats. In this study, we evaluate the dispersal of pelagic offspring of cod in a large open fjord system in northern Norway by a probabilistic interpretation of a dispersal kernel. High-resolution egg sampling surveys of the horizontal and vertical distribution of cod eggs in a spawning area were used to calibrate the number of particles released in variations of an oceanographic model. Simulations of the drift of eggs were also performed on eggs released in a horizontal uniform distribution, with equal amounts of eggs released on all stations, throughout the area. Material and methods Eggs were sampled in two consecutive years, 16 April–20 April in 2009 and 24 and 25 April in 2010. The sampling was performed in the Storfjorden—Lyngen area, Northern Norway (69.25°– 70.10°N, 20.50°E, Figure 1). This fjord system is relatively open, lacking a defined sill or any bottom topography that might promote retention of pelagic early life stages. A grid net of 75 stations was designed to cover both potential spawning areas and the rest of the fjord system. From an interview survey performed by the Directorate of Fisheries, spawning areas for cod were located at several places in the fjord system. However, the spawning areas were not verified scientifically. A mature cod may release eggs due to inflation of the swimbladder when hauled from large depths, even when not spawning, so some reported spawning areas may rather be good fishing areas for mature fish. The average distance between the stations was 1863 m. For logistic reasons, stations were more closely spaced in the inner part of the fjord than in the outer part (Figure 1). Figure 1. Open in new tabDownload slide The study area of the fjords Lyngen branching into Kåfjord and Storfjord. The black dots represent stations where eggs were sampled in both 2009 and 2010. The triangles denote the three RCDP stations. The shaded areas are reported spawning areas according to local fishers. The insert shows where the geographical location of the study area is located in northern Norway. Stations were sampled by vertical hauls (0.5 m s−1) with a WP2 plankton net (diameter = 0.6 m, mesh size = 500 µm). The net could be closed by releasing a weight attached to the rope that would spring a closing mechanism. In 2009, two depth-stratified hauls were taken at each station, where the first haul sampled the deeper 50–20 m depth interval, and the second haul from 20 m depth to the surface. In 2010, a single haul from 50 m depth to the surface was taken at all stations. When analysing the data, the depth-stratified hauls from 2009 were pooled to allow for a direct comparison with the data from 2010. All fish eggs were extracted from the sample and identified to species level. On some occasions, the sample was first sieved through a 2000-µm mesh plankton gauze to remove phytoplankton and copepods, and to ease the visual identification of eggs. Early-stage cod eggs were identified by their size (1.2–1.5 mm) while older stages were identified by their pigmentation (Hiemstra, 1962). Cod eggs were staged according to a five-point scale (Thompson and Riley, 1981). We note that while early-stage eggs of haddock may be misidentified as cod, only a single older stage haddock egg was found in 2010 and none in 2009. In contrast, we found several older stage cod eggs, suggesting that most of the eggs were indeed cod and not haddock. The circulation model used is the Regional Ocean Modeling System (ROMS; Shchepetkin and McWilliams, 2005; Haidvogel et al., 2008). This is a three-dimensional free-surface, hydrostatic, primitive equation ocean model using terrain-following s-coordinates in the vertical. The model was run with a third-order upwind scheme for solving the advection equations, and the generic length scale turbulence closure scheme was used for subgrid-scale mixing (Warner et al., 2005). Due to the varying width of the Storfjord–Lyngen study system, we found it necessary to decrease the horizontal grid resolution to 200 m. The 200-m model covered several of the adjacent fjords and some of the offshore area. A coarser model with a horizontal resolution of 800 m [explained in Albretsen et al. (2011)] provided nesting conditions (hourly fields of sea level, hydrography, and currents) for the fine resolution model. The open boundaries in the 800-m model applied daily averaged fields provided from the operational model system (4 km resolution) at the Norwegian Meteorological Institute. The eight most dominant tidal constituents (four diurnal and four semi-diurnal) were included, based on the global tidal solution TPXO (Egbert and Erofeeva, 2002). High-resolution coastal bathymetric data (originally 50 × 50 m horizontal resolution) were retrieved from the Norwegian Mapping Authority (the Norwegian Hydrographic Service) and merged with offshore data from the ETOPO database (from the National Geophysical Data Centre). An atmospheric model WRF (non-hydrostatic Advanced Weather Research and Forecasting Model AR-WRF version 3.0.1) was run with 3 km horizontal resolution to provide high-resolution wind, air pressure, precipitation, and thermodynamical surface variables for the ocean models. Freshwater discharges from rivers were estimated by a hydrological model (Beldring et al., 2003) and provided by the Norwegian Water Resources and Energy Directorate (NVE). More details on the circulation model can be found in Albretsen et al. (2011). To evaluate the reliability of the numerical circulation model, three current profilers (Recording Doppler Current Profiler 600 from Aanderaa Instruments) were deployed at 50 m depth and recorded current speed and direction for ∼1 month (15 April–14 May 2009). The instruments profiled currents upwards, then recorded currents from 50 to 5 m depth with a vertical spacing of 2 m. The locations of the RDCP sites were all distributed in the Storfjord (Figure 1). In addition, 13 CTD profiles (SAIV SD204, http://www.saivas.no) were taken along the entire fjord at both sides and used for model validation purposes. Modelled hydrography and currents were used at the corresponding observational depths. Currents were extracted every hour, while daily averages of modelled salinity were used in our comparison. The egg drift simulations were based on hourly currents from the finest resolution ROMS simulation (200 m). A standard semi-Lagrangian trajectory model with random-walk diffusion was applied [e.g. Ådlandsvik and Sundby, 1994 or a similar version of the model investigating salmon lice in Norwegian fjords in Asplin et al. (2013)]. To compensate for natural variability in the circulation pattern that was not resolved by the ocean model, a random diffusion term was added to the particles velocity. This random walk was modelled as a Gaussian distributed diffusion with a coefficient of 1 m2 s−1 (corresponding to a velocity limited by −2 and +2 cm s−1). Eggs were initially distributed at several fixed depths between 2 and 50 m according to a Gaussian distribution with an average of 15 m and a standard deviation of 10 m. This gave a depth distribution of eggs that approximately corresponded to the ratio of eggs found in the two different depth intervals examined in 2009. Release of the modelled eggs closely followed the empirical egg sampling dates. Specifically, the sampling in 2009 took place during 16–20 April where the simulations were started at 16, 18, and 20 April. Sampling in 2010 took place during 24 and 25 April, while the simulated eggs were released on 22, 24, and 26 April. The number of simulated eggs released per station was scaled according to the number of first- and second-stage eggs found at each station each year during the field study. Each egg was scaled with 20 particles, implying that a total of 35 020 and 11 740 particles were released in each simulation for 2009 and 2010, respectively. When released in the model, eggs were allowed to drift in their respective predefined depths determined by ocean currents for 30 d. Finally, a simulation where 100 particles were released from all 75 egg survey stations into the 2010 current regime was conducted (7500 particles in total). This was done to evaluate the hypothetical approach in lack of field data where all registered spawning areas were given equal importance. To visualize the dispersal kernels of eggs after drift and to construct a geographic dispersal probability distribution, kernel density distributions (Epanechnikov, 1969, Samiuddin and Elsayyad, 1990) of the eggs at the end of day 30 were created for the total combined drift from both years (all particles released) as well as for each release date, at each of four depth intervals (2–5, 5–10, 10–20, and >20 m) for each year for comparison. A distribution was also created from the uniform release of particles in the fjord. The kernel density distributions were evaluated on a 250 × 250 m matrix. Results Egg surveys In both years, hauls taken in the fjord were dominated by cod eggs, but contained some other fish species as well (Table 1). In 2010, a large number of small gadoid eggs (N = 233, 1.1–1.2 mm in diameter) were found. Based on the known size range of cod eggs (Hiemstra, 1962), these smaller eggs were unlikely to be cod and therefore were not included in the analysis. The total number of cod eggs sampled was considerably higher in 2009 (total of 1868) than in 2010 (total of 703). In 2009, a large proportion of the eggs were in the earliest developmental stage. In 2010, a larger proportion of the eggs were older, and a considerable number of larvae were also found (Table 1). In 2009, the average density of cod eggs was 68.32 eggs m−2 surface (range: 0–2592, s.d.: 303.32). In 2010, the average density was 35.04 eggs m−2 surface (range: 0–293, s.d.: 58.51). The results in 2009 were heavily influenced by one station containing 733 eggs. When omitting this station, the 2009 mean was 34.19 eggs m−2 surface (s.d.: 68.65). Table 1. Results from egg surveys. Stage . Non-cod . Stage I . Stage II . Stage III . Stage IV . Stage V . Larvae . 2009 Number 341 1709 42 103 5 9 0 2009 Ratio 0.91 0.02 0.06 0.00 0.00 2009 0–20 m 177 1328 37 75 3 5 2009 20–50 m 164 381 5 28 2 4 2010 Number 500 502 85 67 12 37 42 2010 Ratio 0.71 0.12 0.10 0.02 0.05 Stage . Non-cod . Stage I . Stage II . Stage III . Stage IV . Stage V . Larvae . 2009 Number 341 1709 42 103 5 9 0 2009 Ratio 0.91 0.02 0.06 0.00 0.00 2009 0–20 m 177 1328 37 75 3 5 2009 20–50 m 164 381 5 28 2 4 2010 Number 500 502 85 67 12 37 42 2010 Ratio 0.71 0.12 0.10 0.02 0.05 Number of eggs and proportion of total according to the stage. Samples from 2009 and 2010 are samples from an identical set of 75 stations. Numbers from 2009 are pooled numbers from an upper and a lower haul given by the depth intervals 2–20 and 20–50 m, respectively. Open in new tab Table 1. Results from egg surveys. Stage . Non-cod . Stage I . Stage II . Stage III . Stage IV . Stage V . Larvae . 2009 Number 341 1709 42 103 5 9 0 2009 Ratio 0.91 0.02 0.06 0.00 0.00 2009 0–20 m 177 1328 37 75 3 5 2009 20–50 m 164 381 5 28 2 4 2010 Number 500 502 85 67 12 37 42 2010 Ratio 0.71 0.12 0.10 0.02 0.05 Stage . Non-cod . Stage I . Stage II . Stage III . Stage IV . Stage V . Larvae . 2009 Number 341 1709 42 103 5 9 0 2009 Ratio 0.91 0.02 0.06 0.00 0.00 2009 0–20 m 177 1328 37 75 3 5 2009 20–50 m 164 381 5 28 2 4 2010 Number 500 502 85 67 12 37 42 2010 Ratio 0.71 0.12 0.10 0.02 0.05 Number of eggs and proportion of total according to the stage. Samples from 2009 and 2010 are samples from an identical set of 75 stations. Numbers from 2009 are pooled numbers from an upper and a lower haul given by the depth intervals 2–20 and 20–50 m, respectively. Open in new tab The highest densities of cod eggs were found in the inner parts of the fjord (Figure 2). To test for spatial autocorrelation, the differences in ln number of eggs were calculated between all pairs of stations. The average difference was calculated for groups based on the average distance between stations (1863 m). The group containing pairs of stations separated by less than the average distance between stations had the lowest average spatial autocorrelation. In 2010, the semi-variance (Rossi et al., 1992; Perry et al., 2002) in this group was 0.7 compared with an average of 1.3 for all other groups (0.9–2.0). There was no linear trend in the semi-variance. In 2009, the spatial autocorrelation was not as apparent, but this was due to the one station containing 42% of the eggs. When omitting this station, the pattern in 2009 resembled the distribution found in 2010. The vertically separated hauls taken in 2009 also showed that most of the eggs (77%) were found in the upper 20 m of the water column. Excluding the single station where 42% of all eggs were found, we found that 36% of the eggs were located between 20 and 50 m depth. Figure 2. Open in new tabDownload slide Distribution of newly spawned cod eggs from the years 2009 and 2010. Filled circles are scaled to the number of cod eggs of stages 1 and 2 at each station sampled. Zero count stations are marked as small dots. Left panel: Distributions from 2009. One circle is left unfilled so to make the other circles visible. Right panel: distributions from 2010. Model validation The modelled currents in the Storfjord showed, in general, a surface current direction dominated by the winds, typically in an along-fjord direction. In periods with calm winds, the modelled surface circulation displayed a complex pattern with small-scale eddies and meanders. The freshwater discharge from rivers was too small to establish a well-defined surface boundary layer during April, implying that the upper 50 m was well mixed with typical salinities at 33.8 and temperatures between 3 and 3.5°C (Figure 3). In May, however, seasonal warming of the fjord and inland melting of snow leading to increased river run-off created a surface layer of 10–15 m thickness (Figure 3). Such dynamical features typical for Norwegian fjords are explained in more detail in, for example, Aure et al. (2007). Figure 3. Open in new tabDownload slide Hydrographic profiles measured 15 April 2009 (a) and (b), and May 14th 2009 (c) and (d) along the entire Storfjord between the surface and 50 m depth. The panels to the left (a) and (c) show salinity and temperatures are displayed to the right (b) and (d). The implication of a well-defined surface layer was that the wind forces had less impact on the ocean currents below this mixed surface layer. Besides the local rivers and the seasonal warming in May, the density stratification in the Storfjord was mainly influenced by exchange of coastal and offshore water through the Lyngenfjord. During winter and spring, water density is mainly determined by salinity in Norwegian waters, and all the salinity measurements have been compared with corresponding model values, separated between 15 April and 14 May (Figure 4). The model indicated no significant bias in salinity, except for the lowest values in mid-May close to the surface. The relatively homogeneous conditions in mid-April are well reproduced in the model, and due to enhanced river run-off initiated by the melting of snow, the model builds up a surface layer in May as seen in the measurements. Figure 4. Open in new tabDownload slide Model validation. Scatter diagrams where observed salinity (along horizontal axis) is plotted against modelled values (vertical axis) for April 15th (a) and May 14th (b) 2009. The grey colours denote depth levels where lighter grey dots represent measurements closer to the surface. The capability of reproducing ocean currents well is vital in our effort of estimating realistic drift patterns for the cod eggs. The drifting phase for the eggs is assumed to be 3–4 weeks, and it must be emphasized to validate the model's ability to reproduce the observed currents, in particular in a statistical sense. The main portion of the particles in the drift model was released between 10 and 20 m, and ocean currents at 20 m depth were used to demonstrate the differences between observed and modelled currents. First, the measured and modelled current speeds are plotted in scatter and quantile diagrams for all stations (Figure 5). In the quantile diagram, first all percentiles from 1 to 99 are found in the observed and modelled time-series individually, then these are compared with each other. A perfect model would then reproduce the diagonal line starting in the origin. As demonstrated by the scatterplots, the model is not able to reproduce the correct current speed at the correct time, but as shown by the quantile diagrams, the statistical values for the observational period of 1 month are more realistic. The model overestimates the strongest currents; however, these represent only a few events. Second, both observed and modelled currents at 20 m depth are presented in a progressive vector diagram (PVD; Figure 6). PVDs alternatively display the particle path based on current vectors from fixed locations. The PVD from RDCP station 1 shows that the main current direction was inward. The model reproduces the direction of the currents well, but the drift speed is overestimated. The RDCP stations 2 and 3 are located east and west, respectively, and measurements show a typical inflow at the western side and an outflow at the opposite side. As for station 1, the model exaggerates the inflow drift speed at station 3. At station 2, the model reproduces the outflow well in the first third of the observational period while observed currents indicate a persistent flow from this location towards land to the east. These onshore currents are not seen in the model results and may be attributed to restrictions and deficiencies due to the spatial resolution, e.g. how well the bathymetry is represented and the resolution of the external forcing. Figure 5. Open in new tabDownload slide Scatter (left panels) and quantile plots (right panels) for current speed at 20 m depth from RDCP locations 1 (a), 2 (b), and 3 (c) based on hourly currents from 15 April to 14 May 2009. Observed and modelled values are displayed along the horizontal and vertical axes, respectively. Figure 6. Open in new tabDownload slide PVD displaying the observed and modelled currents at 20 m depth based on RDCP stations 1, 2, and 3 (see tags in the diagram). All vectors start 15 April 2009 in the origin and end after 30 d. The horizontal and vertical axes denote the hypothetical drift distance (in km) for a passive particle in the West–East and South–North direction, respectively. Particle drift and retention After 30-d drift in the particle-tracking model with input of ocean currents and even if the model domain was extended far from the egg-haul stations, 14.71% of the particles ended up outside the model domain and were excluded in the analysis. The average drift distance for the egg particles was 20.0 and 15.8 km in 2009 and 2010, respectively. The probability distribution of eggs after drift portrayed a concentration of eggs in the inner parts of the Storfjord, with the 75% border of the density distribution on the inside of the Kåfjord, Storfjord branch (Figure 7). Some eggs, shown by the extended 95% border, were transported longer distances eastward (∼100 km) and ended up in the neighbouring fjord (Nordreisa). The overall highest concentrations were found in the innermost part of the Storfjord across all years and release dates (Figure 7). Overall, the eggs located in the deepest levels below 10 m experienced much less dispersal with more concentrated distributions towards the fjord end for both years and for different release dates (Figure 8). The upper 5 m demonstrated the highest degree of dispersal, and the variation in dispersal between different depths was far greater than between different release dates and years (Figure 9). In the simulation, a total of 21% of the eggs were released in the upper 5 m. Releasing egg particles in a uniform distribution, i.e. with an equal number of eggs at each station, gave the overall largest areas and thus the greatest dispersal of eggs (Figure 10). Figure 7. Open in new tabDownload slide Map of where a cod may expect the offspring to end up. The combined probability distribution of all particles released in the 2 years 2009 and 2010. The colour scale of black to white indicates going from a high to low probability of finding an egg after 30 d of drift. The thick line indicates the area where the probability is 50% or higher, the thick dotted line indicates the 75% area, and the thin black line indicates the 95% area. Figure 8. Open in new tabDownload slide Probability density distributions for eggs released at different depths. Upper left: 0–5 m, upper right: 5–10 m, lower left: 10–20 m, and lower right: >20 m. Grey lines are isolines connecting parts of the distribution with equal probability of finding a cod egg 30 d after spawning, so the distribution inside sums up to a given percentage of the entire distribution. One line is drawn for every 5% of the distribution. The thick line indicates the 75% line and the thick dotted line indicates the 50% area. Figure 9. Open in new tabDownload slide Probability distributions for all eggs released at a given day for a given year. Grey lines are isolines connecting parts of the distribution with equal probability of finding a cod egg 30 d after spawning so the distribution inside sums up to a given percentage of the entire distribution. One line is drawn for every 5% of the distribution. The thick line indicates the 75% area and the thick dotted line indicates the 50% area. Every year particles were released on three different dates. The upper row is for two different dates from 2009, with the one showing the least dispersal on the left and the one showing the most dispersal on the right. The lower equals the upper but are for particles released in 2010. Figure 10. Open in new tabDownload slide Areas covered by drift under different release scenarios. Height of bars corresponds to the ln of the area (m2) containing 10, 25, and 50% of the density of distributions. Each colour denotes a different release scenario. The leftmost bars show the areas after drift where eggs were released in the same proportions as they were found in 2009, the next show areas from 2010, and the rightmost bars denote the areas after drift when a uniform distribution of eggs, i.e. same number of particles at every egg station, were released in the 2010 current regime. Discussion In this study, we used a probabilistic approach to investigate the dispersal of pelagic eggs of Atlantic cod spawned within an open Arctic fjord and how these dispersal distributions would be affected by model initialization. Based on empirical observations of the spatial distribution of newly spawned cod eggs, the oceanographic model could be initialized properly, and by applying time-variant ocean currents, we predicted that most offspring will indeed remain within the fjord system throughout the early pelagic phase. In particular, eggs found at depths below 10 m will often be transported up the fjord (away from the open ocean), leading to an aggregation of offspring in the inner parts of the fjord system. In comparison, eggs that were found closer to the surface had a higher probability of being transported out the fjord. Finally, releasing eggs in proportions as found in the field described the initial data more realistically and therefore a more probable retention than releasing eggs in a uniform distribution throughout the fjord, although the latter also described the concentration of eggs in the inner part of the fjord. The main reason for this potential exaggeration of the spread of cod larvae was that eggs were not observed at many of the outermost egg stations, although this part of the fjord system was previously marked as a spawning area based on catches only. Below we discuss the methodological and ecological implications of these findings. Knutsen et al. (2007) reported a possible retention in relation to sills in fjords based on high egg concentrations near sills. Here, we demonstrated that eggs may be retained even in the absence of sills or bottom topographical features limiting dispersal. We showed that ocean currents in the intermediate layer (below the 10–15 m deep surface layer developing in May), and the probable vertical location of the spawned eggs, had a dominating current inflow at the western side of the fjord and mainly current outflow along the eastern side, leaving no clear sign of whether the Storfjord is was a retention area or not. By modelling the release of particles, we demonstrated that the main portion of the spawning products remained in the fjord. Ocean circulation models are certainly not perfect, but the validation results for the Storfjord have shown that ROMS reproduced current statistics and hydrography satisfactorily. The discrepancies shown in the ocean current validation may indicate that particle drift speed was overestimated, implying that larvae end their drifting phase closer to the spawning sites than shown by the model. Releasing 20 times more egg particles than eggs observed allowed statistically significant evaluation of the different particle trajectories within the model, and to some degree, we compensated for deficiencies in the modelled currents by additional random-walk diffusion of each particle. It is then important that our particle drift results are treated in a probabilistic sense, meaning that conclusions based on individual particles are meaningless. Our particle-tracking simulations were conducted by releasing all egg particles at multiple fixed levels below the surface, i.e. between 2 and 50 m with the main portion released between 10 and 20 m depth. In the ocean, cod eggs do not float at a fixed level below the surface, but find their vertical position dependent on their buoyancy (Stenevik et al., 2008). As we pointed out in the results, we found that the hydrography in Storfjord was quite homogeneous during spawning season in April. In addition, we always find discrepancies and biases between observed and modelled hydrography, i.e. model errors in density. We performed initial particle-tracking simulations where the eggs were released at 15 and 30 m depth initially, and were all allowed to drift freely. Measurements of egg density from Stenevik et al. (2008) were applied for the egg particles (denoted with a mean salinity and a standard deviation), and salinity, temperature, and vertical diffusion estimates were used in addition to currents as input from the ocean model to the particle-tracking model. The drift results then showed an upwards drift immediately after release causing an unrealistically high density of particle eggs in the upper 2 m and a subsequent massive drift out of the fjord (as seen in Figure 8, upper left panel). The upwards drift was mainly explained by insertion of eggs in a too dense environment. Due to the homogeneous water in April, very small errors in the modelled density field will have a major impact on the buoyancy of the eggs, either causing them to sink deeper or rise higher than what is realistic. In addition, the applied density of the eggs [from Stenevik et al. (2008)] were retrieved from experiments where adult cod were caught in a fjord representing the area of interest in northern Norway and released in a test cage at the west coast of Norway for spawning. One must assume that cod adjust the internal density of their spawning mass with respect to the oceanic environment. The uncertainties regarding the modelled density fields of the fjord and the density of the cod eggs forced us to simulate drift at fixed vertical levels where we have confidence in the horizontally modelled currents. Our choice of the fixed depths, however, was based on observations of egg density, implying that fjord cod eggs are located below the typical surface mixed layer (Ciannelli et al., 2010; Myksvoll et al., 2011). In addition, choosing deployment of egg particles at all depths at all stations left us with broad drift scenarios where all potential spawning levels were represented. Retention was much clearer in the deep layers than in the surface layers. Field observations showed that about one-third of the eggs were found deeper than 20 m in the water column. Our model showed that if the real vertical distribution of eggs was not taken into account, it overestimated dispersal, implying that retention could easily be affected by the vertical distribution of eggs. This result is in agreement with other studies also demonstrating an important effect of vertical distribution of eggs (Knickle and Rose, 2010; Pacariz et al., 2014). In 2010, we found fewer eggs than the previous year, and in addition a larger proportion of the eggs were located closer to the fjord end. As a result, retention was higher in 2010 than the year before, but combining an observed egg distribution with the drift model simulated a distribution spread over a much smaller area than when eggs were released using a uniform distribution. In conclusion, our study underscores the value of having accurate empirical data on pelagic early life stages to feed into simulation models. By doing this, we revealed that the classical spawning strategy of marine fish might involve retention of early pelagic stages even in fairly open habitats with no obvious barriers to dispersal. Therefore, a more constrained spatial scale of population dynamics may be more widespread than previously thought. Acknowledgements This work was done as a part of national programme of mapping of marine habitats headed by A. B. Storeng (Norwegian Directorate for nature management). The project was partly financed by Fiskeri og Havbruksnæringens Forskningsfond and Landsdelsutvalget. We would further like to thank S. D. Eriksen and P. A. Bjørn for providing local help and support. We thank Cato Hansen for a valuable help with the fieldwork and captaining the boat M/S Havcruise. We will thank two anonymous reviewers for providing helpful comments and also thank Stuart Larson for proofreading the manuscript. References Ådlandsvik B. Sundby S. 1994 . Modelling the transport of cod larvae from the Lofoten area . ICES Journal of Marine Science Symposium , 198 : 379 – 392 . Google Scholar OpenURL Placeholder Text WorldCat Albretsen J. Sperrevik A. K. Staalstrøm A. Sandvik A. D. Vikebø F. Asplin L. 2011 . NorKyst-800 Report No. 1—User Manual and Technical Descriptions . Tech. Rep. Fisken og Havet 2/2011 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Almany G. R. Berumen M. L. Thorrold S. R. Planes S. Jones G. P. 2007 . Local replenishment of coral reef fish populations in a marine reserve . Science , 316 : 742 – 744 . Google Scholar Crossref Search ADS PubMed WorldCat Asplin L. Johnsen I. A. Sandvik A. D. Albretsen J. Sundfjord V. Aure J. Boxaspen K. K. 2013 . Dispersion of salmon lice in the Hardangerfjord . Marine Biological Research , 10 : 216 – 225 . Google Scholar Crossref Search ADS WorldCat Aure J. Asplin L. Sætre R. 2007 . Coast/fjord water exchange . In The Norwegian Coastal Current—Oceanography and Climate , Ed. by Roald Sætre. Tapir Academic Press . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bauer R. K. Stepputtis D. Grawe U. Zimmermann C. Hammer C. 2013 . Wind-induced variability in coastal larval retention areas: a case study on Western Baltic spring-spawning herring . Fisheries Oceanography , 22 : 388 – 399 . Google Scholar Crossref Search ADS WorldCat Beldring S. Engeland K. Roald L. A. Sælthun N. R. Voksø A. 2003 . Estimation of parameters in a distributed precipitation-runoff model for Norway . Hydrology and Earth System Sciences , 7 : 304 – 316 . Google Scholar Crossref Search ADS WorldCat Bohonak A. J. 1999 . Dispersal, gene flow, and population structure . The Quarterly Review of Biology , 74 : 21 – 45 . Google Scholar Crossref Search ADS PubMed WorldCat Botsford L. W. White J. W. Coffroth M. A. Paris C. B. Planes S. Shearer T. L. Thorrold S. R. et al. 2009 . Connectivity and resilience of coral reef metapopulations in marine protected areas: matching empirical efforts to predictive needs . Coral Reefs , 28 : 327 – 337 . Google Scholar Crossref Search ADS PubMed WorldCat Bradbury I. R. Laurel B. Snelgrove P. V. R. Bentzen P. Campana S. E. 2008 . Global patterns in marine dispersal estimates: the influence of geography, taxonomic category and life history . Proceedings of the Royal Society of London Series B-Biological Sciences , 275 : 1803 – 1809 . Google Scholar Crossref Search ADS WorldCat Buston P. M. Jones G. P. Planes S. Thorrold S. R. 2012 . Probability of successful larval dispersal declines fivefold over 1 km in a coral reef fish . Proceedings of the Royal Society of London Series B , 279 : 1883 – 1888 . Google Scholar Crossref Search ADS PubMed WorldCat Ciannelli L. Knutsen H. Olsen E. M. Espeland S. H. Asplin L. Jelmert A. Knutsen J. A. et al. 2010 . Small-scale genetic structure in a marine population in relation to water circulation and egg characteristics . Ecology , 91 : 2918 – 2930 . Google Scholar Crossref Search ADS PubMed WorldCat Cowen R. K. Lwiza K. M. M. Sponaugle S. Paris C. B. Olson D. B. 2000 . Connectivity of marine populations: open or closed? Science , 311 : 522 – 527 . Google Scholar Crossref Search ADS WorldCat Cowen R. K. Paris C. B. Srinivasan A. 2006 . Scaling of connectivity in marine populations . Science , 311 : 522 – 527 . Google Scholar Crossref Search ADS PubMed WorldCat Egbert G. Erofeeva S. 2002 . Efficient inverse modeling of barotropic ocean tides . Journal of Atmospheric and Oceanic Technology , 19 : 183 – 204 . Google Scholar Crossref Search ADS WorldCat Epanechnikov V. A. 1969 . Non-parametric estimation of a multivariate probability density . Teoriya Veroyatnoste\u\i i ee Primeneniya. Izdat, 14 : 156 – 161 . OpenURL Placeholder Text WorldCat Haidvogel D. B. Arango H. G. Budgell W. P. Cornuelle B. D. Curchitser E. Di Lorenzo E. Fennel K. et al. 2008 . Ocean forecasting in terrain-following coordinates: Formulation and skill assessment of the Regional Ocean Modeling System . Journal of Computational Physics , 227 : 3595 – 3624 . Google Scholar Crossref Search ADS WorldCat Hastings A. Botsford L. W. 2006 . Persistence of spatial populations depends on returning home . Proceedings of the National Academy of Sciences of the United States of America , 103 : 6067 – 6072 . Google Scholar Crossref Search ADS PubMed WorldCat Hiemstra W. H. 1962 . A correlation table as an aid for identifying pelagic fish eggs in plankton samples . Journal du Conseil , 27 : 100 – 108 . Google Scholar Crossref Search ADS WorldCat Jones G. P. Milicich M. J. Emslie M. J. Lunow C. 1999 . Self-recruitment in a coral reef fish population . Nature , 402 : 802 – 804 . Google Scholar Crossref Search ADS WorldCat Knickle D. C. Rose G. A. 2010 . Seasonal spawning and wind-regulated retention-dispersal of early life stage Atlantic cod (Gadus morhua) in a Newfoundland fjord . Fisheries Oceanography , 19 : 397 – 411 . Google Scholar Crossref Search ADS WorldCat Knutsen H. Olsen E. M. Ciannelli L. Espeland S. H. Knutsen J. A. Simonsen J. H. Skreslet S. et al. 2007 . Egg distribution, bottom topography and small-scale cod population structure in a coastal marine system . Marine Ecology Progress Series , 333 : 249 – 255 . Google Scholar Crossref Search ADS WorldCat Lacroix G. Maes G. E. Bolle L. J. Volckaert F. A. M. 2013 . Modelling dispersal dynamics of the early life stages of a marine flatfish (Solea solea L.) . Journal of Sea Research , 84 : 13 – 25 . Google Scholar Crossref Search ADS WorldCat Lynge B. K. Berntsen J. Gjevik B. 2010 . Numerical studies of dispersion due to tidal flow through Moskstraumen, northern Norway . Ocean Dynamics , 60 : 907 – 920 . Google Scholar Crossref Search ADS WorldCat Myksvoll M. S. Jung K-M. Albretsen J. Sundby S. 2014b . Modelling dispersal of eggs and quantifying connectivity among Norwegian coastal cod subpopulations . ICES Journal of Marine Science , 71 : 957 – 969 . Google Scholar Crossref Search ADS WorldCat Myksvoll M. S. Sandvik A. D. Asplin L. Sundby S. 2014a . Effects of river regulations on fjord dynamics and retention of coastal cod eggs . ICES Journal of Marine Science , 71 : 943 – 956 . Google Scholar Crossref Search ADS WorldCat Myksvoll M. S. Sandvik A. D. Skarohamar J. Sundby S. 2012 . Importance of high resolution wind forcing on eddy activity and particle dispersion in a Norwegian fjord . Estuarine, Coastal and Shelf Science , 113 : 293 – 304 . Google Scholar Crossref Search ADS WorldCat Myksvoll M. S. Sundby S. Ådlandsvik B. Vikebø F. B. 2011 . Retention of coastal cod eggs in a Fjord caused by interactions between egg buoyancy and circulation pattern . Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science , 3 : 279 – 294 . Google Scholar Crossref Search ADS WorldCat Øresland V. Ulmestrand M. 2013 . European lobster subpopulations from limited adult movements and larval retention . ICES Journal of Marine Science , 70 : 532 – 539 . Google Scholar Crossref Search ADS WorldCat Pacariz S. Bjork G. Jonsson P. Borjesson P. Svedang H. 2014 . A model study of the large-scale transport of fish eggs in the Kattegat in relation to egg density . ICES Journal of Marine Science , 71 : 345 – 355 . Google Scholar Crossref Search ADS WorldCat Paris C. B. Cherubin L. M. Cowen R. K. 2007 . Surfing, spinning, or diving from reef to reef: effects on population connectivity . Marine Ecology Progress Series , 347 : 285 – 300 . Google Scholar Crossref Search ADS WorldCat Perry J. N. Liebhold A. M. Rosenberg M. S. Dungan J. Miriti M. Jakomulska A. Citron-Pousty S. 2002 . Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data . Ecography , 25 : 578 – 600 . Google Scholar Crossref Search ADS WorldCat Planes S. Jones G. P. Thorrold S. R. 2009 . Larval dispersal connects fish populations in a network of marine protected areas . Proceedings of the National Academy of Sciences of the United States of America , 106 : 5693 – 5697 . Google Scholar Crossref Search ADS PubMed WorldCat Rossi R. E. Mulla D. J. Journel A. G. Franz E. H. 1992 . Geostatistical tools for modeling and interpreting ecological spatial dependence . Ecological Monographs , 62 : 277 – 314 . Google Scholar Crossref Search ADS WorldCat Samiuddin M. Elsayyad G. M. 1990 . On nonparametric kernel density estimates . Biometrika , 77 : 865 – 874 . Google Scholar Crossref Search ADS WorldCat Shanks A. L. 2009 . Pelagic larval duration and dispersal distance revisited . Biological Bulletin , 216 : 373 – 385 . Google Scholar Crossref Search ADS PubMed WorldCat Shanks A. L. Grantham B. A. Carr M. H. 2003 . Propagule dispersal distance and the size and spacing of marine reserves . Ecological Applications , 13 : 159 – 169 . Google Scholar Crossref Search ADS WorldCat Shchepetkin A. F. McWilliams J. C. 2005 . The Regional Ocean Modeling System (ROMS): a split-explicit, free-surface, topography following coordinate oceanic model . Ocean Modeling , 9 : 347 – 404 . Google Scholar Crossref Search ADS WorldCat Stenevik E. K. Sundby S. Agnalt A. L. 2008 . Buoyancy and vertical distribution of Norwegian coastal cod (Gadus morhua) eggs from different areas along the coast . ICES Journal of Marine Science , 65 : 1198 – 1202 . Google Scholar Crossref Search ADS WorldCat Thompson B. M. Riley J. D. 1981 . Egg and larval development studies in the North Sea cod (Gadus morhua L.) . Rapports et Proces-verbaux des Réunions. Conseil International pour l’Éxploration de la Mer , 178 : 553 – 559 . Google Scholar OpenURL Placeholder Text WorldCat Warner J. C. Sherwood C. R. Arango H. G. Signell R. P. 2005 . Performance of four turbulence closure models implemented using a generic length scale method . Ocean Modelling , 8 : 81 – 113 . Google Scholar Crossref Search ADS WorldCat Watson J. R. Kendall B. E. Siegel D. A. Mitarai S. 2012 . Changing seascapes, stochastic connectivity, and marine metapopulations dynamics . American Naturalist , 180 : 99 – 112 . Google Scholar Crossref Search ADS PubMed WorldCat Werner F. E. Quinlan J. A. Lough R. G. Lynch D. R. 2001 . Spatially-explicit individual based modeling of marine populations: A review of the advances in the 1990s . Sarsia , 86 : 411 – 421 . Google Scholar Crossref Search ADS WorldCat Westernhagen H. 1970 . Erbrütung der eier vod Dorsch (Gadus morhua), Flunder (Pleuronectes flesus) und Scholle (Pleuronectes platessa) unter kombinierten temperature- und salzgehaltsbedingungen . Helgolander Wissenschaftliche Meeresuntersuchungen , 21 : 21 – 102 . Google Scholar Crossref Search ADS WorldCat Author notes Handling editor: Claire Paris © International Council for the Exploration of the Sea 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © International Council for the Exploration of the Sea 2015

Le Bris, Arnault; Pershing, Andrew J.; Hernandez, Christina M.; Mills, Katherine E.; Sherwood, Graham D.

doi: 10.1093/icesjms/fsv154pmid: N/A

AbstractPreserving larger fish is often advocated as a conservation measure to help fish populations buffer environmental variation and fishing pressure. The rationale is that several size- and age-dependent reproductive traits confer a higher reproductive value to larger fish. The effects of variation in these reproductive traits on the dynamics of populations under various fishing patterns are however seldom evaluated. In this study, we develop a simulation model to evaluate how variation in three reproductive traits (fecundity–mass, hatching probability, and batch spawning) impacts the capacity of a fish population to withstand and recover from high fishing pressure. Biological functions of the model were calibrated based on the Gulf of Maine Atlantic cod stock, which is currently experiencing its lowest biomass level ever estimated. Results showed that variation in the shape of the fecundity–mass relationship had the most substantial impact on population resistance and recovery. Batch spawning and variation in hatching probability had limited impacts. Furthermore, results showed that preserving larger fish by imposing a slot fishery increased the resistance of the population to high fishing pressure, because it helped preserve the population reproductive potential determined by the high fecundity of large fish. The slot fishery, however, impeded population recovery, because it distributed the fishing pressure on intermediate-size classes which potential for biomass growth is maximal. This study underlines the importance of using precise size-dependent fecundity estimates when evaluating the productivity and sustainability of fisheries, as well as the importance of identifying priority among the components of population resilience (e.g. resistance or recovery) before implementing size-selective harvest strategies.

Damalas, Dimitrios; Maravelias, Christos D.; Osio, Giacomo C.; Maynou, Francesc; Sbrana, Mario; Sartor, Paolo; Casey, John

doi: 10.1093/icesjms/fsv141pmid: N/A

Abstract Discarding of commercially important fish species in the bottom trawl fisheries in the northern Mediterranean Sea was investigated by soliciting the long-term recollections of fishers engaged or formerly engaged in such fisheries. The main aim of our investigation was to describe the prevalence of discarding and its evolution over the past 70 years using information gathered through individual questionnaire-based interviews with fishers from ports in Spain, Italy, and Greece, following a standardized sampling protocol. Although it proved impossible to derive absolute estimates of the volume of discarded catches over the period investigated, we conclude that over the past 70 years, discarding as a practice has gradually increased in the northern Mediterranean trawl fisheries and has been accompanied by a shift in the species composition of the discarded catch. While discarding can occur for a number of reasons, our investigations indicate that discarding in the past was mostly driven by market demand, but recent legal and regulatory constraints have led to changes in fishing strategies and became a significant reason for discards. Introduction Discards constitute that portion of the catch, which for whatever reason, is not retained on board during fishing operations and which is returned to the sea. Discards may constitute a large proportion of the total catch (Alverson et al., 1994) and may include species that have or do not have any commercial value. The most recent estimate indicates that worldwide, over the period 1992–2001, ∼7.3 million tonnes of organic material of animal origin were discarded annually, representing ∼8% of the annual global catch (Kelleher, 2005). For the Mediterranean Sea, the most recent estimate of discards is on average around 230 000 tonnes annually or 18.6% of the average annual catches (Tsagarakis et al., 2013). The reasons for discarding are numerous and include legal [e.g. species smaller than the minimum legal size (MLS), catches exceeding quotas, etc.], economic (low market value and highgrading), technical (e.g. characteristics of fishing gears and vessel hold capacity), biological (e.g. species composition and recruitment period), and environmental aspects (e.g. weather conditions affecting sorting practices) (Alverson et al., 1994; Stratoudakis et al., 1998; Rochet and Trenkel, 2005; Tsagarakis et al., 2013). Despite the introduction of technical and tactical measures to reduce unwanted bycatch (STECF, 2013), discarding has remained prevalent in many EU fisheries for decades. While the term discarding may have different interpretations, in this study, we refer to discarding as the unwanted fraction of the catch of species of commercial value discarded from bottom trawl fisheries. In semi-industrial fisheries (purse-seine and otter trawl), discarding is mainly due to market reasons, for example in periods when catches exceed market demand thereby driving down fish prices (Lleonart and Maynou, 2003). In small-scale fisheries (fishing vessels <12 m not using a towed gear—EC, 2006), spoiled and damaged specimens are also routinely discarded. In recent years, increased enforcement of minimum landing size regulations has also led to an increase in discarding of undersized commercially important species, such as hake (Merluccius merluccius) and sardine (Sardina pilchardus). Discarding may have important consequences for the ecological functioning of marine ecosystems. There are indications that discarding has altered the ecosystem functioning of some seabird communities and has negative effects on charismatic and endangered species (Alverson et al., 1994). Discarding has been practised for as long as fishing has taken place. There are early accounts of high discarding in the 18th century Mediterranean sail pair trawl fishery (Osio, 2012). However, for the period before the 1980s, very little information is available (Kelleher, 2005). The absence or scarcity of discard information compromises the stock assessment process, and can result in inaccurate estimates of fishing mortality, which in turn can affect the quality of scientific advice (Diamond and Beukers-Stewart, 2011). In general, reducing or eliminating discards is most complex in mixed-species demersal trawl fisheries where discard mitigation measures are difficult to develop and implement. On average, most of the discards in the Mediterranean Sea (>35% by weight of the total catch) are attributable to such fisheries (Tsagarakis et al., 2013). It has been argued that as a consequence of changes in market demand for smaller sizes of certain species (the so-called poor fish, previously kept for fisher's own consumption) by local consumers and tourists, discarding practices aboard trawlers fishing in the Mediterranean have also changed over time (Sacchi, 2008). Strengthening of monitoring, control, and surveillance to enforce established minimum landing sizes may have also been a significant driver for discarding (Borges, 2015). Sampling programmes to estimate discards are a relatively recent innovation and most cover only relatively short time-series. In the EU Mediterranean waters, monitoring of discards has only been regularly undertaken since 2002 within the framework of the EU data collection programmes (DCF EU Regulation 199/2008; formerly DCR EU Regulation 1543/2000). Consequently, historical data on discarding are scarce. In an attempt to gain an understanding of how discarding in Mediterranean fisheries has evolved over time, we employed techniques that make use of Local and Traditional Knowledge (Neis et al., 1999; Lavides et al., 2010; Thornton and Maciejewski Scheer, 2012) and Traditional Ecological Knowledge (Sáenz-Arroyo et al., 2005). Such approaches make use of fishers' life-long experiences observing the marine system and with appropriate scientific analysis, have the potential to detect changes over several decades. Our approach was to solicit the long-term recollections of fishers from ports in Spain, Italy, and Greece, using information gathered through individual questionnaire-based interviews following a standardized sampling protocol. The main focus of our research was to describe the historical prevalence and evolution of discarding of commercially valuable fish species in the northern Mediterranean bottom trawl fisheries over the past 70 years. It must be emphasized to note that we have not attempted to derive quantitative historical estimates of the absolute volume of fish discarded. While interviewees were able to recollect how much fish they had landed in the past, they were unable to recall the volumes (weight or numbers) of fish that had been discarded. Hence, the trends in discarding we discuss relate to (i) the total number of fishers reporting discards and (ii) the species composition of discards. The geographical and temporal dynamics underlying the resulting trends are also discussed. Material and methods Historical information on discarding was gathered by individual questionnaire-based interviews following a standardized sampling protocol with fishers from ports on the Catalan Sea (Spain), Ligurian, Tyrrhenian and central Adriatic Seas (Italy), and the Ionian and Aegean Seas (Greece; Figure 1). The sampling protocol is the one officially adopted as part of a CIESM international basin-wide monitoring programme (Azzurro et al., 2011). A similar version has been also employed in the FP 6 AFRAME Project (Wilson and Becker-Jacobsen, 2009). Interviews were carried out during 2009 and 2010, and the questionnaire was designed to elicit information relating to vessels, fishing gears deployed, fishing practices (duration of fishing trips, on-board activity, etc.), location of main fishing grounds, main target species, estimation of catches (the usual catch, memories of exceptional captures, sizes of specimen caught, etc.), and species composition of discards. The choice of interview localities in each country was based on: (i) the national importance of the local fisheries in terms of total production, number of crew, and full time equivalent; (ii) the historical context; the existence of experienced/retired skippers to interview, and (iii) the existence of a mutually respectful relationship between the local fishing associations/individual fishers and the researchers. In total, 96 elderly or retired trawler fishers (50 Italians, 23 Spanish, and 23 Greeks) from 26 different fishing ports provided responses covering almost 80 years of observations. Interviewees had a median age of 70 years. The median year in which individuals commenced fishing activities was 1955, although some individuals commenced fishing as early as 1932. All potential interviewees willingly agreed to participate in the survey and several were very enthusiastic. Details of the protocol followed and the structure of the questionnaires are available in Sartor (2011) and Damalas et al. (2015) who carried out an integral study of fishers' perceptions during the 20th century. The results presented here are based on a subset of the information collected therein and which related solely to discarding. Such information relates to: three periods (1940–1959, 1960–1979, and 1980–2008), commercial species/taxon discarded, reasons for discarding, and details of fishing operations. Figure 1. Open in new tabDownload slide Map showing the ports and geographical subareas (GSAs) where the interviews with the fishers were carried out. Spain (GSA 6): 1: Port de la Selva; 2: Roses; 3: Palamos; 4: Blanes; 5: Arenys de Mar; 6: Mataro; 7: Barcelona; 8: Vilanova i la Geltrù; 9: Tarragona; 10: Cambrils; 11: L'Ametlla de Mar; 12: San Carles de la Rapita; Italy (GSAs 9 and 17): 13: Viareggio; 14: Livorno; 15: Elba Island; 16: Castiglione della Pescaia; 17: Porto Santo Stefano; 18: Porto Ercole; 19: Civitavecchia; 20: Fiumicino; 21: Ponza Island; 22: Civitanova Marche; Greece (GSAs 20 and 22): 23: Nea Michaniona; 24: Chalkis; 25: Peireas; 26: Patra. Country maps. Source: European Environmental Agency - Data and Maps. (http://www.eea.europa.eu/data-and-maps/data/elevation-breakdown). Figure 1. Open in new tabDownload slide Map showing the ports and geographical subareas (GSAs) where the interviews with the fishers were carried out. Spain (GSA 6): 1: Port de la Selva; 2: Roses; 3: Palamos; 4: Blanes; 5: Arenys de Mar; 6: Mataro; 7: Barcelona; 8: Vilanova i la Geltrù; 9: Tarragona; 10: Cambrils; 11: L'Ametlla de Mar; 12: San Carles de la Rapita; Italy (GSAs 9 and 17): 13: Viareggio; 14: Livorno; 15: Elba Island; 16: Castiglione della Pescaia; 17: Porto Santo Stefano; 18: Porto Ercole; 19: Civitavecchia; 20: Fiumicino; 21: Ponza Island; 22: Civitanova Marche; Greece (GSAs 20 and 22): 23: Nea Michaniona; 24: Chalkis; 25: Peireas; 26: Patra. Country maps. Source: European Environmental Agency - Data and Maps. (http://www.eea.europa.eu/data-and-maps/data/elevation-breakdown). The accuracy of recollections about past experiences will always be susceptible to the limitations of the individual's memory (Bradburn et al., 1987). Such recollections depending entirely on memory can often be imperfect and thereby unreliable (Hassan, 2006). This systematic error is referred as “Recall bias” and is caused by differences in the accuracy or completeness of the recollections retrieved by study participants regarding events or experiences from the past. To avoid or minimize recall bias, a number of methodological approaches have been suggested (Sabatella and Franquesa, 2003; Hassan, 2006). How we applied some of these methodological approaches is described in Supplementary Appendix. Statistical analyses All taxa reported as discards during each interview were structured in a presence/absence matrix, interviews being the samples (rows), and taxa the variables (columns). Each sample was accompanied by the following corresponding factors: Country, geographical area fished, period, minimum, maximum, and average depths fished. Temporal differences in the presence or absence of discarding of each taxon between regions were investigated by non-parametric multivariate permutational analysis of variance (PERMANOVA; Anderson, 2001). Similarity Percentages Procedure (SIMPER; Clarke, 1993) identified the taxa that characterized discarding practices in each period. Visualization of geographical patterns in discarding was realized through non-metric multi-dimensional scaling (nMDS) ordination. To understand the changes in the presence/absence of species in the reported discards, the species composition of discards in each period was compared with the species composition of the reported landings for the equivalent period. From such a comparison, it is possible to see whether the absence of a species from the reported discards is because that species was not caught at all in the period concerned, or that all catches of that species were landed. After carrying out such a comparison using data from Maynou et al. (2011) and Damalas et al. (2015), it was apparent that one species, Gadiculus argenteus (silvery pout), was discarded in the recent (1980–2008) period but not in earlier periods; this was due to the fact that before 1980, G. argenteus was never caught. Furthermore, since 1980, all catches of G. argenteus have been discarded. Gadiculus argenteus was therefore excluded from the analysis on the grounds that its appearance in the reported discards in the recent period did not signify any change in discarding practice. The comparison also confirmed that other species for which there were no discards reported in any of the periods were caught and landed. Finally, to ensure that “suspicious observations” or “potential outliers” did not influence the results, taxa reported less than three times were eliminated from the statistical analyses (R library vegan—Community Ecology Package ver. 2.0-10). The rationale for doing so is statistically valid and is explained in Barnett and Lewis (1994). Results Table 1 summarizes, by period and country, the proportion of fishers for whom discarding was normal practice. The results indicate an overall increase in the prevalence of discarding expressed as the proportion of fishers reporting discarding over time (Table 1). Such an increase is particularly striking in the responses from Spanish fishers, which indicate that in the 1940s and 1950s, the most trawler skippers (57%) did not discard, whereas in the most recent period (1980–2008) 9 out of 10 did so routinely. The responses from Italian fishers show a similar trend with a marked increase in the prevalence of discarding, especially in the most recent period. The information collected from interviews with Greek fishers indicated that the increase in discarding in the bottom trawl fisheries in the Ionian and Aegean Seas has been much less. In all countries, the main reason for discarding was attributed to “non-marketable” (low value) species (Table 2). The second most important reason for discarding was attributed to “damaged” specimens in all areas for the earlier periods. In the most recent period, discarding of damaged specimens in all fisheries is reported to have decreased and to no longer take place in the Spanish bottom trawl fisheries in the Catalan Sea. Although negligible in the past, discarding for “other” reasons is reported to have increased significantly during the period 1980–2008 in the Italian and Spanish fisheries. Table 1. Proportion (%) of interviewees reporting discarding, as usual practice, by period and country (total numbers of interviewees are given in parentheses). Period . Greece . Italy . Spain . Averagea . 1940–1959 29 (14) 58 (26) 43 (14) 47 1960–1979 36 (22) 55 (47) 70 (23) 54 1980–2008 35 (23) 83 (48) 91 (11) 71 Averagea 34 67 67 58 Period . Greece . Italy . Spain . Averagea . 1940–1959 29 (14) 58 (26) 43 (14) 47 1960–1979 36 (22) 55 (47) 70 (23) 54 1980–2008 35 (23) 83 (48) 91 (11) 71 Averagea 34 67 67 58 aAverage is weighted arithmetic mean. Open in new tab Table 1. Proportion (%) of interviewees reporting discarding, as usual practice, by period and country (total numbers of interviewees are given in parentheses). Period . Greece . Italy . Spain . Averagea . 1940–1959 29 (14) 58 (26) 43 (14) 47 1960–1979 36 (22) 55 (47) 70 (23) 54 1980–2008 35 (23) 83 (48) 91 (11) 71 Averagea 34 67 67 58 Period . Greece . Italy . Spain . Averagea . 1940–1959 29 (14) 58 (26) 43 (14) 47 1960–1979 36 (22) 55 (47) 70 (23) 54 1980–2008 35 (23) 83 (48) 91 (11) 71 Averagea 34 67 67 58 aAverage is weighted arithmetic mean. Open in new tab Table 2. Reasons for discarding by period and country (numbers of interviewees giving this response are given in parentheses). Reason for discarding (in %) . 1940–1959 . 1960–1979 . 1980–2008 . Overall . GRC . ITA . SPN . GRC . ITA . SPN . GRC . ITA . SPN . Damaged specimens 25 (1) 38 (9) 14 (1) 12 (1) 34 (13) 18 (3) 12 (1) 7 (3) 0 (0) 21 Non-marketable species 75 (3) 62 (15) 86 (6) 88 (7) 66 (25) 47 (8) 88 (7) 81 (33) 50 (5) 69 Other (MLS, handling costs) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 35 (6) 0 (0) 12 (5) 50 (5) 10 Reason for discarding (in %) . 1940–1959 . 1960–1979 . 1980–2008 . Overall . GRC . ITA . SPN . GRC . ITA . SPN . GRC . ITA . SPN . Damaged specimens 25 (1) 38 (9) 14 (1) 12 (1) 34 (13) 18 (3) 12 (1) 7 (3) 0 (0) 21 Non-marketable species 75 (3) 62 (15) 86 (6) 88 (7) 66 (25) 47 (8) 88 (7) 81 (33) 50 (5) 69 Other (MLS, handling costs) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 35 (6) 0 (0) 12 (5) 50 (5) 10 MLS: minimum landing size; GRC: Greece; ITA: Italy; SPN: Spain. Open in new tab Table 2. Reasons for discarding by period and country (numbers of interviewees giving this response are given in parentheses). Reason for discarding (in %) . 1940–1959 . 1960–1979 . 1980–2008 . Overall . GRC . ITA . SPN . GRC . ITA . SPN . GRC . ITA . SPN . Damaged specimens 25 (1) 38 (9) 14 (1) 12 (1) 34 (13) 18 (3) 12 (1) 7 (3) 0 (0) 21 Non-marketable species 75 (3) 62 (15) 86 (6) 88 (7) 66 (25) 47 (8) 88 (7) 81 (33) 50 (5) 69 Other (MLS, handling costs) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 35 (6) 0 (0) 12 (5) 50 (5) 10 Reason for discarding (in %) . 1940–1959 . 1960–1979 . 1980–2008 . Overall . GRC . ITA . SPN . GRC . ITA . SPN . GRC . ITA . SPN . Damaged specimens 25 (1) 38 (9) 14 (1) 12 (1) 34 (13) 18 (3) 12 (1) 7 (3) 0 (0) 21 Non-marketable species 75 (3) 62 (15) 86 (6) 88 (7) 66 (25) 47 (8) 88 (7) 81 (33) 50 (5) 69 Other (MLS, handling costs) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 35 (6) 0 (0) 12 (5) 50 (5) 10 MLS: minimum landing size; GRC: Greece; ITA: Italy; SPN: Spain. Open in new tab The responses from fishers in relation to which species have been discarded indicate that over the entire period of the study, a total of 36 species/taxa have been subject to discarding (Figure 2). Eleven species/taxa were mentioned by >10 interviewees; the three most common being European hake (M. merluccius), horse mackerels (Trachurus spp.), and bogue (Boops boops). More than half of the species/taxa reported as discards were discarded throughout all periods (Figure 2). A comparison of the responses from interviewees for the different periods clearly indicates that there was a marked increase in the prevalence of discarding between the early (1940–1959) and subsequent periods (1960–1979 and 1980–2008). In terms of the species composition of the discards in different periods, some notable changes can be identified. For example, it appears that anglerfish were discarded by more fishers in the past compared with the most recent period, whereas the converse is true for red mullet. Sardines are reported to have been discarded by more fishers during the 1960s and 1970s than in the previous or subsequent periods. Figure 2. Open in new tabDownload slide Proportion (%) of fishers discarding by species/taxon and period in the Mediterranean bottom trawl fisheries during 1940–2008. Figure 2. Open in new tabDownload slide Proportion (%) of fishers discarding by species/taxon and period in the Mediterranean bottom trawl fisheries during 1940–2008. The PERMANOVA indicated that discarding practices differed significantly by country and period (Table 3). nMDS ordinations, visualizing the level of similarity of the individual cases in our dataset, demonstrated the geographical structure of discards' species composition during the three periods (1940–1959, 1960–1979, and 1980–2008; Figure 3). The results of the SIMPER analysis (Table 4) classified each period according to the discard species' composition. It also gave the percentage contribution of each species that explains the classifications expressed from highest to lowest. The cumulative percentage contribution indicates which species explain n% of the resulting classifications. Furthermore, the overall percentage dissimilarity between periods was estimated from pairwise comparisons. The results for each period are given below. Table 3. Results of the PERMANOVA. . d.f. . Sums of Sqs . MeanSqs . F model . R2 . Pr (>F) . Country 2 3.521 1.76039 5.7903 0.08264 0.001 Period 2 1.078 0.53916 1.7734 0.02531 0.045 Residuals 125 38.003 0.30402 0.89204 Total 129 42.602 1 . d.f. . Sums of Sqs . MeanSqs . F model . R2 . Pr (>F) . Country 2 3.521 1.76039 5.7903 0.08264 0.001 Period 2 1.078 0.53916 1.7734 0.02531 0.045 Residuals 125 38.003 0.30402 0.89204 Total 129 42.602 1 Sqs, squares; MeanSqs, mean squares. Open in new tab Table 3. Results of the PERMANOVA. . d.f. . Sums of Sqs . MeanSqs . F model . R2 . Pr (>F) . Country 2 3.521 1.76039 5.7903 0.08264 0.001 Period 2 1.078 0.53916 1.7734 0.02531 0.045 Residuals 125 38.003 0.30402 0.89204 Total 129 42.602 1 . d.f. . Sums of Sqs . MeanSqs . F model . R2 . Pr (>F) . Country 2 3.521 1.76039 5.7903 0.08264 0.001 Period 2 1.078 0.53916 1.7734 0.02531 0.045 Residuals 125 38.003 0.30402 0.89204 Total 129 42.602 1 Sqs, squares; MeanSqs, mean squares. Open in new tab Table 4. Discarding: most important species/taxa typifying each period as suggested by SIMPER analysis. Period . Taxon . Contribution (%) . Cumulative (%) . 1940–1959 Nephrops norvegicus 9.44 9.4 Sparisoma cretense 7.93 17.4 Raja spp. 6.51 23.9 Mullus spp. 6.51 30.4 Aristeus antennatus 6.51 36.9 Sardina pilchardus 5.36 42.3 Trisopterus minutus capelanus 5.35 47.6 Cepola rubescens 5.13 52.7 1960–1979 Raja spp. 6.71 6.7 Diplodus spp. 6.71 13.4 A. antennatus 6.71 20.1 Lepidopus caudatus 6.01 26.1 Engraulis encrasicolus 5.36 31.5 Spicara spp. 5.07 36.6 Sparidae 5.07 41.6 Micromesistius poutassou 4.96 46.6 1980–2008 Spicara smaris 8.26 8.26 T. minutus capelanus 6.24 14.50 Mullus barbatus 5.82 20.32 Trachurus spp. 5.49 25.81 Merluccius merluccius 5.39 31.20 M. poutassou 5.32 36.52 C. rubescens 5.02 41.54 Period . Taxon . Contribution (%) . Cumulative (%) . 1940–1959 Nephrops norvegicus 9.44 9.4 Sparisoma cretense 7.93 17.4 Raja spp. 6.51 23.9 Mullus spp. 6.51 30.4 Aristeus antennatus 6.51 36.9 Sardina pilchardus 5.36 42.3 Trisopterus minutus capelanus 5.35 47.6 Cepola rubescens 5.13 52.7 1960–1979 Raja spp. 6.71 6.7 Diplodus spp. 6.71 13.4 A. antennatus 6.71 20.1 Lepidopus caudatus 6.01 26.1 Engraulis encrasicolus 5.36 31.5 Spicara spp. 5.07 36.6 Sparidae 5.07 41.6 Micromesistius poutassou 4.96 46.6 1980–2008 Spicara smaris 8.26 8.26 T. minutus capelanus 6.24 14.50 Mullus barbatus 5.82 20.32 Trachurus spp. 5.49 25.81 Merluccius merluccius 5.39 31.20 M. poutassou 5.32 36.52 C. rubescens 5.02 41.54 Species/taxa characterizing discarding practices in each period are ranked in a decreasing order of importance. Overall between group dissimilarities: “1940–1959” vs. “1960–1979” 79.28; “1940–1959”vs. “1980–2008” 76.24; “1960–1979” vs. “1980–2008” 76.71. Open in new tab Table 4. Discarding: most important species/taxa typifying each period as suggested by SIMPER analysis. Period . Taxon . Contribution (%) . Cumulative (%) . 1940–1959 Nephrops norvegicus 9.44 9.4 Sparisoma cretense 7.93 17.4 Raja spp. 6.51 23.9 Mullus spp. 6.51 30.4 Aristeus antennatus 6.51 36.9 Sardina pilchardus 5.36 42.3 Trisopterus minutus capelanus 5.35 47.6 Cepola rubescens 5.13 52.7 1960–1979 Raja spp. 6.71 6.7 Diplodus spp. 6.71 13.4 A. antennatus 6.71 20.1 Lepidopus caudatus 6.01 26.1 Engraulis encrasicolus 5.36 31.5 Spicara spp. 5.07 36.6 Sparidae 5.07 41.6 Micromesistius poutassou 4.96 46.6 1980–2008 Spicara smaris 8.26 8.26 T. minutus capelanus 6.24 14.50 Mullus barbatus 5.82 20.32 Trachurus spp. 5.49 25.81 Merluccius merluccius 5.39 31.20 M. poutassou 5.32 36.52 C. rubescens 5.02 41.54 Period . Taxon . Contribution (%) . Cumulative (%) . 1940–1959 Nephrops norvegicus 9.44 9.4 Sparisoma cretense 7.93 17.4 Raja spp. 6.51 23.9 Mullus spp. 6.51 30.4 Aristeus antennatus 6.51 36.9 Sardina pilchardus 5.36 42.3 Trisopterus minutus capelanus 5.35 47.6 Cepola rubescens 5.13 52.7 1960–1979 Raja spp. 6.71 6.7 Diplodus spp. 6.71 13.4 A. antennatus 6.71 20.1 Lepidopus caudatus 6.01 26.1 Engraulis encrasicolus 5.36 31.5 Spicara spp. 5.07 36.6 Sparidae 5.07 41.6 Micromesistius poutassou 4.96 46.6 1980–2008 Spicara smaris 8.26 8.26 T. minutus capelanus 6.24 14.50 Mullus barbatus 5.82 20.32 Trachurus spp. 5.49 25.81 Merluccius merluccius 5.39 31.20 M. poutassou 5.32 36.52 C. rubescens 5.02 41.54 Species/taxa characterizing discarding practices in each period are ranked in a decreasing order of importance. Overall between group dissimilarities: “1940–1959” vs. “1960–1979” 79.28; “1940–1959”vs. “1980–2008” 76.24; “1960–1979” vs. “1980–2008” 76.71. Open in new tab Figure 3. Open in new tabDownload slide nMDS ordination comparing discarding by species/taxon across the different regions (country). Figure 3. Open in new tabDownload slide nMDS ordination comparing discarding by species/taxon across the different regions (country). 1940–1959 In total, 21 species/taxa were reported to have been discarded. The most frequently reported included some of the most valuable commercial species, such as European hake. The nMDS ordination plot (Figure 3, top), suggests almost complete separation between the three countries, indicating a very dissimilar composition of discards. The SIMPER analysis (Table 4), when sorting species/taxa in a decreasing order of importance, indicated that discarding practices in the three countries during the 1940s–1950s were characterized largely by Norway lobster, Mediterranean parrotfish, various rays, red mullets, and blue and red shrimps. 1960–1979 Compared with the 1940s–1950s, an increase in the number of species/taxa discarded was reported for the period 1960–1979 (28 species/taxa). The nMDS ordination plot (Figure 3, middle) indicates a partial overlap between countries; however, it is evident that discarding practices in the different countries remained rather distinct. As indicated by SIMPER analysis (Table 4), the species that characterize discards in the three countries during the 1960s and 1970s were various rays, various sea breams, blue and red shrimps, silver scabbardfish, and anchovy. 1980–2008 The results indicate that 29 different species/taxa were discarded during the period 1980–2008. The nMDS ordination plot (Figure 3, bottom) indicates significant overlap between all countries, implying that discarding practices in the fisheries in the different regions in terms of the species, being discarded at that time, were more similar than hitherto. The SIMPER analysis (Table 4) indicates that the five most important species that characterize the three regions during this period were picarel, poor cod, red mullet, horse mackerels, and European hake. As mentioned earlier, silvery pout, a species frequently discarded during 1980–2008, was excluded from the SIMPER analysis since it was not reported as being caught in earlier periods and no evaluation of discarding trend was possible. Silvery pout was the only species in the dataset showing such an inconsistency. Discussion From an ethical point of view, discards can be considered as a waste of natural resources and from an ecological perspective they impact the marine ecosystem in various ways (Diamond and Beukers-Stewart, 2011). Discards are also considered to be inconsistent with responsible fishing (Bellido et al., 2011). Seen from the perspective of fishery managers, discards pose a huge problem to solve and a balance needs to be found between improving fleet selectivity by minimizing unwanted catches and bring an end to a wasteful practice, while ensuring profitability and well-being of the fishing sector (Bellido Millán et al., 2014). From a fishers' standpoint, discards constitute a burden and incur increased costs in terms of both time and money (Macher, 2008). In recent years, the desire to find solutions to reduce or eliminate discards in marine fisheries has become of major concern to conservation organizations and the wider public (Catchpole and Gray, 2010). Such a desire has been instrumental in the adoption of the obligation to land all catches (of species subjected to quota or minimum landing size) enshrined in Article 15 of the 2013 reform of the EU Common Fisheries Policy (EU, 2013), which aspires to gradually phase out discards by 2019. Based on information gathered through structured interviews with individuals with long-term experience, we conclude that discarding in the northern Mediterranean bottom trawl fisheries has increased over the past 70 years, both in terms of the proportion of interviewees reporting that discarding took place and in the number of species/taxa being discarded. There are many potential factors that could have led to such an increase including inter alia increase in fishing power, changes in market demand for different species, changes in environmental conditions affecting fish community structure, progressive introduction of regulatory measures, and changes in the selectivity of fishing gears. Promoted by increases in engine power, during the past century, the range of fishing activities extended from coastal/shelf areas to deeper waters offshore and at the same time improvements to the design of fishing gears took place (Osio, 2012). Such factors have affected catchability and selectivity patterns (Sartor, 2011). The emergence of silvery pout in the catches after the early 1980s reflects the expansion of fishing activity to offshore deep-water areas. Silvery pout is a species that inhabits depths between 100 and 1000 m; in the Mediterranean Sea, its highest abundance is typically at depths greater than 200 m (Damalas et al., 2010). Discarding patterns also seem to be depth-dependent and there are several studies indicating that discarding increases with increasing fishing depth (Machias et al., 2001; D'Onghia et al., 2003). One of the few deep-sea trawl fisheries in the Mediterranean (deep-sea crustacean fishery) is often characterized by high discards (e.g. Castriota et al., 2001). Furthermore, the recent regulation (EU, 2011) specifying the permissible size and geometry of trawlnets was introduced with the specific aim to reduce discards (Bellido Millán et al., 2014). However, Sala et al. (2015) after conducting a series of experimental gear trials argue that more sophisticated alternative gear designs may be needed to achieve the required selectivity changes to reduce discards. In general, in Mediterranean fish markets, the first sale prices are not fixed and fluctuate according to market demand. The high demand for quality fresh fish requires that storage on board needs to be limited to a maximum of 1 d. Moreover, nutritional habits, tastes, and culinary practices have changed in the last half century, largely altering market demand (Essid, 2012). The attitudes of Mediterranean societies have also altered during the past century. In the period immediately following World War II, people would eat virtually anything brought to the market and hence, the prevalence of discarding was low at that time. It has also been demonstrated that increased per capita gross domestic product (GDP) leads to increased discarding. Wealthier societies also seem to be more selective in resource use, whereas in times of poverty more species and possibly wider size ranges are accepted and sold (Tsagarakis et al., 2013). The differences in national GDPs (IMF, 2014) may also explain the generally lower levels of discarding observed in Greek fisheries, compared with fisheries in Italy and Spain. The geographical distribution and timing of fishing in relation to the seasonal distribution of the life history stages of the populations being exploited and the demand for different species and sizes can also influence discarding. Increased discarding has been associated with spawning periods (Tzanatos et al., 2007) and time of recruitment (Sánchez et al., 2004). Periods of low fishing activity have also been associated with an increase in discarding in the Adriatic and Catalan Seas (Sanchez et al., 2007). Adverse weather conditions can also influence discarding. For instance, because of reduced catches as a result of periods of bad weather in the winter and concomitant increases in first sale prices, discarding is usually lower in winter than at other times of the year (Machias et al., 2004). In addition, the availability of different species may also influence discarding and in one reported case, discards of Adriatic sardine were affected by the available size and quantity of anchovy (Santojanni et al., 2005). The introduction of regulatory measures to protect juveniles (MLS), without accompanying measures or changes in fishing tactics to avoid catching undersized individuals, has also meant that fishers must discard at sea, individuals that they formerly brought to the market. Most of the MLS restrictions were imposed with the implementation of the EU Common Fisheries Policy by the Mediterranean Member States and the first so-called Mediterranean Regulations (EC, 1994, 2006b). The list of species therein, regulated under an MLS, includes at least 10 of the species reported by the interviewees. Our results indicate that discarding of European hake, horse mackerel, and red mullet has increased during the most recent period (Figure 2) and there is evidence to indicate a change in fishers' behaviour in recent years. For example, in the northern Tyrrhenian Sea trawl fishery, the size at which 50% of hake specimens were landed increased from 10 to 11 cm total length (TL) in 1995–1998 (Sartor et al., 2001) to ∼18 cm TL in 2013 (De Ranieri, 2014). This may reflect increased compliance with the established minimum landing size (20 cm TL). Abella et al. (2005) demonstrated how the enforcement of control measures in the port of Viareggio (Southern Ligurian Sea—W. Mediterranean) has altered the size composition of landings due to a reduction of fishing pressure on hake nursery grounds. Eliasen et al. (2014), studying the factors affecting discard behaviour, concluded that the extent of control and enforcement exerted over a fishery was shown to influence fishers' behaviour and observed discard levels. On the other hand, while the introduction of MLSs is often stated as a reason for increased discarding, Mediterranean fisheries have a notorious reputation for their “culture of non-compliance”. Recent studies from Greece suggest that MLSs are largely ignored by the trawler skippers (Damalas and Vassilopoulou, 2013). Furthermore, the reformed EU Common Fisheries Policy regulation (EU, 2013) has identified costs of “handling unwanted catches” as a serious impediment; it is even foreseen that, under certain conditions, a fishery may be eligible for an exemption from the rule to land all catches. The technological developments in trawling have revolutionised trawling operations and many hauls can now be undertaken per day. This, together with the development of on-board refrigeration equipment, means that the quantity of daily catch that can be processed (sorted) and stored has increased dramatically; and so have the associated handling costs. This was confirmed by the interviewed fishers since handling costs as a cause of discarding have been reported only during the recent years. The average codend mesh size of trawlnets has increased from less than 20 mm in the 1940s to greater than 40 mm currently. Doubling of mesh size should have resulted in more selective fishing and less discarding. However, from the fishers' responses, it can be deduced that selectivity has not improved. Mediterranean fisheries are currently on the brink of a new era. After introducing almost a hundred technical measures regulations (or amendments) since the 1980s with the aim of improving selectivity and reducing discards (Santurtún et al., 2014), the EU has eventually decided to follow a more aggressive approach in an attempt to solve the discard problem. The landings obligation (or “discard ban”) included under Article 15 of the new CFP basic regulation (EU, 2013) prohibits the discarding of species subject to catch limits (i.e. TAC and quota species) and those subject to minimum size limits in the Mediterranean Sea. Implementation of the landing obligation to different types of fisheries will be undertaken in stages according a timetable (2015–2019) based on “the species that define the fisheries”. Some concerns have already been expressed that due to the absence of catch quotas in the Mediterranean, the landing obligation may lead to an increase in the catch of undersized/juvenile individuals, which will not be counted against any quota (Bellido Millán et al., 2014). Furthermore, transformation of such catches into fishmeal might make them a commercially attractive “target”. García-Rivera et al. (2015) investigated the effectiveness of the landings obligation in a Spanish Mediterranean port, and concluded that the landing obligation regulation has more weaknesses and threats (72.6%) than strengths and opportunities (27.4%). They argue that the measure may prove to be ineffective in the Mediterranean Sea resulting in a failure to reduce discards. In summary, we conclude that over the past 70 years, discarding in the northern Mediterranean bottom trawl fisheries in the Catalan, Ligurian, Tyrrhenian, eastern Ionian, and Aegean Seas has increased. The observed increase has been accompanied by a change in the species composition of the discarded catches. Furthermore, discarding in the past was mostly driven by market demand, but recent legal and regulatory constraints have led to changes in fishing strategies and became a significant reason for discards. Acknowledgements We warmly thank all fishers who dedicated valuable time to help us understand the evolution of Mediterranean fisheries in the second half of the 20th century. Special thanks to the three anonymous reviewers and the Editor for extensive corrections and suggestions that improved the manuscript significantly. Data acquisition was supported by the European Commission through the EU project EVOMED (“The 20th Century evolution of Mediterranean exploited demersal resources under increasing fishing disturbance and environmental change”). European Commission, Directorate-General for Maritime Affairs and Fisheries. Open Call for Tenders n° MARE/2008/11, Proposal for Lot 4, (Contract. N° SI2 539097). References Abella A. Serena F. Ria M. 2005 . Distributional response to variations in abundance over spatial and temporal scales for juveniles of European hake (Merluccius merluccius) in the Western Mediterranean Sea . Fisheries Research , 71 : 295 – 310 . Google Scholar Crossref Search ADS WorldCat Alverson D. L. Freeberg M. H. Murawaski S. A. Pope J. G. 1994 . A Global Assessment of Fisheries Bycatch and Discards . FAO Fisheries Technical Paper, No. 339 . 235 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Anderson M. J. 2001 . A new method for non-parametric multivariate analysis of variance . Australian Journal of Ecology , 26 : 32 – 46 . Google Scholar OpenURL Placeholder Text WorldCat Azzurro E. Moschella P. Maynou F. 2011 . Tracking signals of change in mediterranean fish diversity based on local ecological knowledge . PLoS ONE , 6 : e24885 . Google Scholar Crossref Search ADS PubMed WorldCat Barnett V. Lewis T. , 1994 . Outliers in Statistical Data , 3rd edn. John Wiley & Sons, New York, XVII . 582 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Bellido J. M. Santos M. B. Pennino M. G. Valeiras X. Pierce G. J. 2011 . Fishery discards and bycatch: Solutions for an ecosystem approach to fisheries management? Hydrobiologia , 670 : 317 – 333 . Google Scholar Crossref Search ADS WorldCat Bellido Millán J. Carbonell Quetglas A. Garcia Rodriguez M. Garcia Jimenez T. González Aguilar M. 2014 . The Obligation to Land All Catches—Consequences for the Mediterranean—In-Depth Analysis . IP/B/PECH/IC/2013-168. PE 529.055 . 52 pp . http://www.europarl.europa.eu/RegData/etudes/note/join/2014/529055/IPOL-PECH_NT(2014)529055_EN.pdf ( last accessed 18 October 2014) . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Borges L . 2015 . The evolution of a discard policy in Europe . Fish and Fisheries , 16 : 534 – 540 . Bradburn N. Rips L. Shevell S. 1987 . Answering autobiographical questions: The impact of memory and inference on surveys . Science , 236 : 157 – 161 . Google Scholar Crossref Search ADS PubMed WorldCat Castriota L. Campagnuolo S. Andaloro F. 2001 . Shrimp Trawl Fishery By-catch in the Straits of Sicily (Central Mediterranean Sea) . Scientific Council Research Documents of the Northwest Atlantic Fisheries Organization, Serial No. N4501, No. 01/113 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Catchpole T. L. Gray T. S. 2010 . Reducing discards of fish at sea: A review of European pilot projects . Journal of Environmental Management , 91 : 717 – 723 . Google Scholar Crossref Search ADS PubMed WorldCat Clarke K. R. 1993 . Non-parametric multivariate analyses of changes in community structure . Australian Journal of Ecology , 18 : 117 – 143 . Google Scholar Crossref Search ADS WorldCat Damalas D. Maravelias C. D. Katsanevakis S. Karageorgis A. P. Papaconstantinou C. 2010 . Seasonal abundance of non-commercial demersal fish in the eastern Mediterranean Sea in relation to hydrographic and sediment characteristics . Estuarine, Coastal and Shelf Science , 89 : 107 – 118 . Google Scholar Crossref Search ADS WorldCat Damalas D. Maravelias C. D. Osio G. C. Maynou F. Sbrana M. Sartor P. 2015 . “Once upon a Time in the Mediterranean” long term trends of mediterranean fisheries resources based on fishers’ traditional ecological knowledge . PLoS ONE , 10 : e0119330 . Google Scholar Crossref Search ADS PubMed WorldCat Damalas D. Vassilopoulou V. 2013 . Slack regulation compliance in the Mediterranean fisheries: A paradigm from the Greek Aegean Sea demersal trawl fishery, modelling discard ogives . Fisheries Management and Ecology , 20 : 21 – 33 . Google Scholar Crossref Search ADS WorldCat De Ranieri S. 2014 . Programma nazionale Italiano per la raccolta di dati alieutici 2013 . Campionamento biologico delle catture. Sezioni C ed E. Rapporto finale, Consorzio per il Centro Interuniversitario di Biologia Marina ed Ecologia Applicata “G. Bacci” , Italia . 306 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Diamond B. Beukers-Stewart B. D. 2011 . Fisheries discards in the North Sea: Waste of resources or a necessary evil? Reviews in Fisheries Science , 19 : 231 – 245 . Google Scholar Crossref Search ADS WorldCat D'Onghia G. Carlucci R. Maiorano P. Panza M. 2003 . Discards from deep-water bottom trawling in the eastern-central Mediterranean Sea and effects of mesh size changes . Journal of Northwest Atlantic Fishery Science , 31 : 245 – 261 . Google Scholar Crossref Search ADS WorldCat EC . 1994 . Council Regulation (EC) No. 1626/94 of 27 June 1994 Laying Down Certain Technical Measures for the Conservation of Fishery Resources in the Mediterranean . OJ L 171, 6.7.1994 . p. 1–6 . http://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1433084768019&uri=CELEX:31994R1626 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC EC . 2006 . Council Regulation (EC) No. 1198/2006 of 27 July 2006 on the European Fisheries Fund . OJ L 223, 15.8.2006 . p. 1–44 . http://eur-lex.europa.eu/legal-content/EN/ALL/?uri=CELEX:32006R1198 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC EC . 2006b . Council Regulation (EC) No. 1967/2006 of 21 December 2006 Concerning Management Measures for the Sustainable Exploitation of Fishery Resources in the Mediterranean Sea, Amending Regulation (EEC) No. 2847/93 and Repealing Regulation (EC) No. 1626/94 . OJ L 409, 30.12.2006 . p. 11–85 . http://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1433161780994&uri=CELEX:32006R1967 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Eliasen S. Q. Papadopoulou K. N. Vassilopoulou V. Catchpole T. L. 2014 . Socio-economic and institutional incentives influencing fishers’ behaviour in relation to fishing practices and discard . ICES Journal of Marine Science , 71 : 1298 – 1307 . Google Scholar Crossref Search ADS WorldCat Essid M. Y. 2012 . History of Mediterranean food . In MediTERRA 2012 (English), Presses de Sciences Po , pp. 51 – 69 . www.cairn.info/mediterra-2012-english--9782724612486-page-51.htm . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC EU . 2011 . Regulation (EU) No. 1343/2011 of the European Parliament and of the Council of 13 December 2011 on Certain Provisions for Fishing in the GFCM (General Fisheries Commission for the Mediterranean) Agreement Area and Amending Council Regulation (EC) No. 1967/2006 Concerning Management Measures for the Sustainable Exploitation of Fishery Resources in the Mediterranean Sea . OJ L 347, 30.12.2011 , p. 44–61 . http://eur-lex.europa.eu/legal-content/EN/TXT/?qid=1433084061266&uri=CELEX:32011R1343 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC EU (European Union) . 2013 . Council Regulation (EU) No. 1380/2013 of 11 December 2013 on the Common Fisheries Policy, Amending Council Regulations (EC) No. 1954/2003 and (EC) No. 1224/2009 and Repealing Council Regulations (EC) No. 2371/2002 and (EC) No. 639/2004 and Council Decision 2004/585/EC . Official Journal of the European Union L 354/22 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC García-Rivera S. Lizaso J. L. S. Millán J. M. B. 2015 . A quantitative and qualitative assessment of the discard ban in European Mediterranean waters . Marine Policy , 53 : 149 – 158 . Google Scholar Crossref Search ADS WorldCat Hassan E. 2006 . Recall bias can be a threat to retrospective and prospective research designs . The Internet Journal of Epidemiology , 3.2 : 339 – 412 . Google Scholar OpenURL Placeholder Text WorldCat IMF . 2014 . Report for Selected Countries and Subjects . World Economic Outlook. International Monetary, Fund . http://data.worldbank.org/indicator/NY.GDP.MKTP.CD ( last accessed 18 October 2014 ). Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Kelleher K. 2005 . Discards in the World's Marine Fisheries. An Update . FAO. Rome. No. 470 . 131 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Lavides M. N. Polunin N. V. C. Stead S. M. Tabaranza D. G. Comeros M. T. 2010 . Finfish disappearances around Bohol, Philippines inferred from traditional ecological knowledge . Environmental Conservation , 36 : 235 – 244 . Google Scholar Crossref Search ADS WorldCat Lleonart J. Maynou F. 2003 . Fish stock assessments in the Mediterranean: State of the art . Scientia Marina , 67 ( Suppl. 1 ): 37 – 49 . Google Scholar Crossref Search ADS WorldCat Macher C. 2008 . Joint productions and management measures of mixed fisheries: The case of the Nephrops fishery in the Bay of Biscay . PhD thesis. Economies and Finances. Universite de Bretagne Occidentale - Brest (in French) . http://archimer.ifremer.fr/doc/2008/these-4857.pdf (last accessed 18 October 2014) . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Machias A. Maiorano P. Vasilopoulou V. Papaconstantinou C. Tursi A. Tsimenides N. 2004 . Sizes of discarded commercial species in the eastern-central Mediterranean Sea . Fisheries Research , 66 : 213 – 222 . Google Scholar Crossref Search ADS WorldCat Machias A. Vasilopoulou V. Vatsos D. Bekas P. Kallianotis A. Papaconstantinou C. Tsimenides N. 2001 . Bottom trawl discards in the northeastern Mediterranean Sea . Fisheries Research , 53 : 181 – 195 . Google Scholar Crossref Search ADS WorldCat Maynou F. Sbrana M. Sartor P. Maravelias C. Kavadas S. Damalas D. Cartes J. E. et al. 2011 . Estimating trends of population decline in long-lived marine species in the Mediterranean Sea based on fishers’ perceptions . PLoS One , 6 : e21818 . Google Scholar Crossref Search ADS PubMed WorldCat Neis B. Schneider D. C. Felt L. Haedrich R. L. Fischer J. Hutchings J. A. 1999 . Fisheries assessment: What can be learned from interviewing resource users? Canadian Journal of Fisheries and Aquatic Sciences , 56 : 1949 – 1963 . Google Scholar Crossref Search ADS WorldCat Osio G. C. 2012 . Reconstruction of Mediterranean Trawl Fisheries and Stock Recovery: What are the Perspectives? PhD Thesis, University of New Hampshire , USA . 230 pp . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Rochet M. J. Trenkel V. M. 2005 . Factors for the variability of discards: Assumptions and field evidence . Canadian Journal of Fisheries and Aquatic Sciences , 62 : 224 – 235 . Google Scholar Crossref Search ADS WorldCat Sabatella E. Franquesa R. 2003 . Manual of fisheries sampling surveys: Methodologies for estimations of socio-economic indicators in the Mediterranean Sea . Studies and Reviews. General Fisheries Commission for the Mediterranean. No. 73. Rome, FAO. 2003 . 37 p . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Sacchi J . 2008 . The use of trawling nets in the Mediterranean. Problems and selectivity options . Options Mediterraneennes B , 62 : 87 – 96 . Sáenz–Arroyo A. Roberts C. M. Torre J. Cariño-Olvera M. 2005 . Using fishers’ anecdotes, naturalists’ observations and grey literature to reassess marine species at risk: The case of the Gulf grouper in the Gulf of California, Mexico . Fish and Fisheries , 6 : 121 – 133 . Google Scholar Crossref Search ADS WorldCat Sala A. Lucchetti A. Perdichizzi A. Herrmann B. Rinelli P. 2015 . Is square-mesh better selective than larger mesh? A perspective on the management for Mediterranean trawl fisheries . Fisheries Research , 161 : 182 – 190 . Google Scholar Crossref Search ADS WorldCat Sánchez P. Demestre M. Martín P. 2004 . Characterisation of the discards generated by bottom trawling in the northwestern Mediterranean . Fisheries Research , 67 : 71 – 80 Google Scholar Crossref Search ADS WorldCat Sanchez P. Sartor P. Recasens L. Ligas A. Martin J. De Ranieri S. Demestre M. 2007 . Trawl catch composition during different fishing intensity periods in two Mediterranean demersal fishing grounds . Scientia Marina , 71 : 765 – 773 . Google Scholar Crossref Search ADS WorldCat Santojanni A. Cingolani N. Arneri E. Kirkwood G. Belardinelli A. Giannetti G. Colella S. et al. 2005 . Stock assessment of sardine (Sardina pilchardus, Walb.) in the Adriatic Sea with an estimate of discards . Scientia Marina , 69 : 603 – 617 . Google Scholar Crossref Search ADS WorldCat Santurtún M. Prellezo R. Arregi L. Iriondo A. Aranda M. Korta M. Onaindia I. et al. 2014 . Characteristics of multispecific fisheries in the European Union . IP/B/PECH/IC/2013-088. PE 529.053 . 102 pp . http://www.europarl.europa.eu/RegData/etudes/etudes/join/2014/529053/IPOL-PECH_ET(2014)529053_EN.pdf ( last accessed 15 January 2015 ). Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Sartor P. (coord.) 2011 . The 20th Century Evolution of Mediterranean Exploited Demersal Resources Under Increasing Fishing Disturbance and Environmental Change, EVOMED . Open Call for Tenders No. MARE/2008/11, Proposal for Lot 4 (Contract No. SI2 539097) . 513 pp . http://ec.europa.eu/fisheries/documentation/studies/study_evolution_mediterranean/index_en.htm ( last accessed 18 October 2014 ). Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Sartor P. Sartini M. Reale B. Sbrana M. 2001 . Analysis of the discard practices in the Merluccius merluccius (L. 1758) bottom trawl fishery of the northern Tyrrhenian Sea . Biologia Marina Mediterranea , 8 : 771 – 774 . Google Scholar OpenURL Placeholder Text WorldCat Scientific, Technical and Economic Committee for Fisheries (STECF) . 2013 . Landing Obligation in EU Fisheries (STECF-13-23) . Publications Office of the European Union, Luxembourg, EUR 26330 EN, JRC 86112 . 115 pp . 10.2788/37460 . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Crossref Stratoudakis Y. Fryer R. J. Cook R. M. 1998 . Discarding practices for commercial gadoids in the North Sea . Canadian Journal of Fisheries and Aquatic Sciences , 55 : 1632 – 1644 . Google Scholar Crossref Search ADS WorldCat Thornton T. F. Maciejewski Scheer A. 2012 . Collaborative engagement of local and traditional knowledge and science in marine environments: A review . Ecology and Society , 17 : 8 . Google Scholar Crossref Search ADS WorldCat Tsagarakis K. Palialexis A. Vassilopoulou V. 2013 . Mediterranean fishery discards: Review of the existing knowledge . ICES Journal of Marine Science , 71 : 1219 – 1234 . Google Scholar Crossref Search ADS WorldCat Tzanatos E. Somarakis S. Tserpes G. Koutsikopoulos C. 2007 . Discarding practices in a Mediterranean small-scale fishing fleet (Patraikos Gulf, Greece) . Fisheries Management and Ecology , 14 : 277 – 285 . Google Scholar Crossref Search ADS WorldCat Wilson C. D. Becker-Jacobsen R. 2009 . Governance Issues in Mixed-Fisheries Management: An Analysis of Stakeholder Views . A Framework for Fleet and Area Based Fisheries Management (AFRAME) Project. EU Sixth Framework Programme Contract No. 044168 Deliverable 6.3 . https://dlc.dlib.indiana.edu/dlc/bitstream/handle/10535/5087/200.pdf . Google Scholar Google Preview OpenURL Placeholder Text WorldCat COPAC Author notes Handling editor: Sarah Kraak © International Council for the Exploration of the Sea 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © International Council for the Exploration of the Sea 2015

Gorman, Daniel; Dixon, Cameron

doi: 10.1093/icesjms/fsv147pmid: N/A

AbstractWe present the outcomes of a collaborative research programme tasked with reducing bycatch, and thus discards in a temperate Australian prawn trawl fishery. Sea trials in the Gulf of St Vincent, South Australia, assessed the performance of a modified trawlnet that incorporated a rigid polyethylene grid and a T90-mesh codend. Compared with conventional designs, the modified net yielded marked reductions in bycatch (cumulatively >81% by weight), with pronounced decreases in sponge (92%), elasmobranchs (80%), teleost fish (71%), molluscs (61%), and crustaceans (78%). Using commercial logbook data, we estimate that the use of modified nets could reduce discards by ∼240 tons per year. This outcome was achieved with moderate declines in the catch rate (kg h−1) of the target species, Western King Prawn (mean ∼15%), of which almost all were small adults of low commercial value. Adoption of the modified net by industry was realized in March 2012, because it met environmental objectives (i.e. reducing bycatch and improving public perceptions of sustainability), reduced prawn damage, demonstrated commensurate financial returns, and engaged stakeholders throughout the development process. Overall, the project provides a useful example of bycatch research with demonstrable outcomes of improving the ecological and economic sustainability of prawn harvests.

Cardoso, Inês; Moura, Teresa; Mendes, Hugo; Silva, Cristina; Azevedo, Manuela

doi: 10.1093/icesjms/fsv138pmid: N/A

AbstractThe term “mixed fisheries” refers to fishing activities where more than one species are caught simultaneously and one species may be fished by different gears. Therefore, mixed fisheries present a harder challenge for fisheries management than single-species fisheries and the uncertainty can start at the definition of the target species. In these particular fisheries, we have a large group of species that are caught, being target or not, species with large landing values that are actually not target, and species with a high economic value that can fall in the bycatch category. Although the dynamics of such fisheries is poorly understood, they are known to have a relevant contribution to Portuguese fishers' revenue. The present demand on sustainable fishing activities to ensure marine ecosystem preservation has led towards an ecosystem approach where effort is being made to take into account biological and technical interactions on management measures and advice. In this work, logbooks data of the Portuguese multi-gear fleet were used to identify different fisheries based on catch composition and gears through cluster analysis (CLARA). Two identified fisheries were used to explore the impact of these fishing activities on the ecosystem scale. This approach was achieved by a productivity and susceptibility analysis and through foodweb analysis. The relation between species vulnerability and their functional role in the ecosystem were highlighted. Technical interactions among fishing gears, and species biological interactions, were explored within and among fisheries. We found and illustrated that these interactions go beyond the fleets and fisheries considered in the present work. This approach allows us to identify key elements that can, ultimately, be relevant to an ecosystem-based approach towards mixed fisheries management.