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In the Norwegian Sea, assessment of diet composition relies on annual cruise activity combined with visual identification and weight determination of prey from fish stomachs. This weight-based method is labour intensive, and suggestions to reduce cost include moving from the weight-based to the occurrence-based method and/or reducing sampling effort. Studies have suggested that the occurrence-based method may be more robust while providing similar results as the weight-based method. Here we re-analyse data from >14.000 stomachs for herring, mackerel, and blue whiting. We compare diet composition estimates and quantify the uncertainty using both methods. We also quantify the impact of reducing sampling effort (number of trawl stations and fish per station) on the diet uncertainties. Our results confirm that occurrence-based estimates are more precise than those based on weight. In addition, they are better at capturing year-to-year fluctuations. The occurrence-based method provides similar results to the weight-based method. Differences between methods arise primarily from disparities in the mean weight of prey in stomachs. Decreasing the number of stations sampled leads to increased uncertainty, while reducing the sampling effort from 10 to 5 fish per station has little impact on uncertainty estimates. These results provide quantified insights to guide future diet monitoring programmes.

Climate change impacts are pronounced at high latitudes, where warming, reduced sea-ice-cover, and ocean acidification affect marine ecosystems. We review climate change impacts on two major gateways into the Arctic: the Bering and Chukchi seas in the Pacific and the Barents Sea and Fram Strait in the Atlantic. We present scenarios of how changes in the physical environment and prey resources may affect commercial fish populations and fisheries in these high-latitude systems to help managers and stakeholders think about possible futures. Predicted impacts include shifts in the spatial distribution of boreal species, a shift from larger, lipid-rich zooplankton to smaller, less nutritious prey, with detrimental effects on fishes that depend on high-lipid prey for overwinter survival, shifts from benthic- to pelagic-dominated food webs with implications for upper trophic levels, and reduced survival of commercially important shellfish in waters that are increasingly acidic. Predicted changes are expected to result in disruptions to existing fisheries, the emergence of new fisheries, new challenges for managing transboundary stocks, and possible conflicts among resource users. Some impacts may be irreversible, more severe, or occur more frequently under anthropogenic climate change than impacts associated with natural variability, posing additional management challenges.

Many marine species are shifting their distribution poleward in response to climate change. The Barents Sea, as a doorstep to the fast-warming Arctic, is experiencing large scale changes in its environment and its communities. This paper aims at understanding what environmental predictors limit fish species habitats in the Barents Sea and discuss their possible evolution in response to the warming of the Arctic.Species distribution models usually aim at predicting the probability of presence or the average abundance of a species, conditional on environmental drivers. A complementary approach is to determine suitable habitats by modelling the upper limit of a species’ response to environmental factors. Using quantile regressions, we model the upper limit of biomass for 33 fish species in the Barents Sea in response to 10 environmental predictors. Boreal species are mainly limited by temperatures and most of them are expected to be able to expand their distribution in the Barents Sea when new thermally suitable habitats become available, in the limit of bathymetric constraints. Artic species are often limited by several predictors, mainly depth, bottom and surface temperature and ice cover, and future habitats are hard to predict qualitatively. Widespread species like the Atlantic cod are not strongly limited by the selected variables at the scale of the study, and current and future suitable habitats are harder to predict. These models can be used as input to integrative tools like end-to-end models on the habitat preference and tolerance at the species scale to inform resource management and conservation.

Temporal variability in abundance and composition of species in marine ecosystems results from a combination of internal processes, external drivers, and stochasticity. One way to explore the temporal variability in an ecosystem is through temporal stability, measured using the inverse of the coefficient of variation for biomass of single species. The effect of temperature and fisheries on the variability of the Barents Sea food web is still poorly understood. To address this question, we simulate the possible dynamics of Barents Sea food web under different temperature and fishery scenarios using a simple food-web model (Non-Deterministic Network Dynamic [NDND]). The NDND model, which is based on chance and necessity (CaN), defines the state space of the ecosystem using its structural constraints (necessity) and explores it stochastically (chance). The effects of temperature and fisheries on stability are explored both separately and combined. The simulation results suggest that increasing temperature has a negative effect on species biomass and increasing fisheries triggers compensatory dynamics of fish species. There is a major intra-scenario variability in temporal stability, while individual scenarios of temperature and fisheries display a weak negative impact and no effect on stability, respectively. However, combined scenarios indicate that fisheries amplify the effects of temperature on stability, while increasing temperature leads to a shift from synergistic to antagonistic effects between these two drivers.

Numerical models o ecological systems are increasingly used to address complex environmental and resource management questions. One challenge or scientists, managers, and stakeholders is to appraise how well suited these models are to answer questions o scientifc or societal relevance, that is, to perorm, communicate, or access transparent evaluations o ecological models. While there have been substantial developments to support standardised descriptions o ecological models, less has been done to standardise and to report model evaluation practices. We present here a general protocol designed to guide the reporting o model evaluation. The protocol is organised in three major parts: the objective(s) o the modelling application, the ecological patterns o relevance and the evaluation methodology proper, and is termed the OPE (objectives, patterns, evaluation) protocol. We present the 25 questions o the OPE protocol which address the many aspects o the evaluation process and then apply them to six case studies based on a diversity o ecological models. In addition to standardising and increasing the transparency o the model evaluation process, we fnd that going through the OPE protocol helps modellers to think more deeply about the evaluation o their models. From this last point, we suggest that it would be highly benefcial or modellers to consider the OPE early in the modelling process, in addition to using it as a reporting tool and as a reviewing tool. Standardisation Best practice Ecological patterns Skill assessment Transparency

While ecosystem-based fisheries management calls for explicit accounting for interactions between exploited populations and their environment, moving from single species to ecosystem-level assessment is a significant challenge. For many ecologically significant groups, data may be lacking, collected at inappropriate scales or be highly uncertain. In this study, we aim to reconstruct trophic interactions in the Norwegian Sea pelagic food-web during the last three decades. For this purpose, we develop a food-web assessment model compatible with existing observations and knowledge. The model is based on inverse modelling and is designed to handle input observations and knowledge that are uncertain. We analyse if the reconstructed food-web dynamics are supportive of top-down or bottom-up controls on zooplankton and small pelagic fish and of competition for resources between the three small pelagic species. Despite high uncertainties in the reconstructed dynamics, the model results highlight that interannual variations in the biomass of herring, mackerel and blue whiting can primarily be explained by changes in consumption rather than by predation or fishing. Variations in the biomass of copepods and krill were also linked to variations in consumption, while the past dynamics of amphipods can be explained by both consumption and predation. The model results provide little support for the hypothesised competition for resources between the three small pelagic species, despite their overlapping diets. We conclude that it is unlikely that the assessment and management of these commercial stocks during the last 30 years would have benefited from explicit incorporation of trophic interactions., This article is published and available at: https://doi.org/10.1093/icesjms/fsac111

Nearly 30 years ago, emerged the concept of deterministic chaos. With it came sensitivity to initial conditions, nonlinearities, and strange attractors. This constituted a paradigm shift that profoundly altered how numerical modellers approached dynamic systems. It also provided an opportunity to resolve a situation of mutual misunderstanding between scientists and non-scientists about uncertainties and predictability in natural systems. Our proposition is that this issue can be addressed in an original way which involves modelling based on the principles of chance and necessity (CaN). We outline the conceptual and mathematical principles of CaN models and present an application of the model to the Barents Sea food-web. Because CaN models rely on concepts easily grasped by all actors, because they are explicit about knowns and unknowns and because the interpretation of their results is simple without being prescriptive, they can be used in a context of participatory management. We propose that, three decades after the emergence of chaos theories, CaN can be a practical step to reconcile scientists and non-scientists around the modelling of structurally and dynamically complex natural systems, and significantly contribute to ecosystem-based fisheries management.

The Barents Sea is a subarctic shelf sea which has experienced major changes during the past decades. From ecological time-series, three different food-web configurations, reflecting successive shifts of dominance of pelagic fish, demersal fish, and zooplankton, as well as varying trophic control have been identified in the last decades. This covers a relatively short time-period as available ecological time-series are often relatively short. As we lack information for prior time-periods, we use a chance and necessity model to investigate if there are other possible configurations of the Barents Sea food-web than those observed in the ecological time-series, and if this food-web is characterized by a persistent trophic control. We perform food-web simulations using the Non-Deterministic Network Dynamic model (NDND) for the Barents Sea, identify food-web configurations and compare those to historical reconstructions of food-web dynamics. Biomass configurations fall into four major types and three trophic pathways. Reconstructed data match one of the major biomass configurations but is characterized by a different trophic pathway than most of the simulated configurations. The simulated biomass displays fluctuations between bottom-up and top-down trophic control over time rather than persistent trophic control. Our results show that the configurations we have reconstructed are strongly overlapping with our simulated configurations, though they represent only a subset of the possible configurations of the Barents Sea food-web.

The Nansen Legacy workshop on best practices for ecological model evaluation, chaired by Benjamin Planque (IMR) was held in Tromsø on the 6-7th November 2018. The objective of the workshop was to develop recommendations for best practice in evaluation of the performance of food-web simulation models (deliverable 4-4.1.1 of the Nansen Legacy project).

A joint workshop by the Nansen Legacy and Barents Risk projects, to explore the possible states of the Barents Sea by the horizon 2050, the associated risks and the possible ways to mitigate or adapt to them.

The second Nansen Legacy workshop on best practices for ecological model evaluation, chaired by Benjamin Planque (IMR) was held at the University of Oslo on the 23-25th September 2019. The objective of the workshop was to develop a protocol for describing the evaluation of ecological models.

We propose a trend estimation and classification (TREC) approach to estimating dominant common trends among multivariate time series observations. Our methods are based on two statistical procedures that includes trend modelling and discriminant analysis for classifying similar trend (common trend) classes. We use simulations to evaluate the proposed approach and compare it with a relevant dynamic factor analysis in the time domain, which was recently proposed to estimate common trends in fisheries time series. We apply the TREC approach to the multivariate short time series datasets investigated by the ICES integrated assessment working groups for the Norwegian Sea and the Barents Sea. The proposed approach is robust for application to short time series, and it directly identifies and classifies the dominant trends underlying observations. Based on the classified trend classes, we suggest that communication among stakeholders like marine managers, industry representatives, non-governmental organizations, and governmental agencies can be enhanced by finding the common tendency between a biological community in a marine ecosystem and the environmental factors, as well as by the icons produced by generalizing common trend patterns.

Source at: http://doi.org/10.1111/ecog.03443 Large-scale patterns in species diversity and community composition are associated with environmental gradients, but the implications of these patterns for food-web structure are still unclear. Here, we investigated how spatial patterns in food-web structure are associated with environmental gradients in the Barents Sea, a highly productive shelf sea of the Arctic Ocean. We compared food webs from 25 subregions in the Barents Sea and examined spatial correlations among food-web metrics, and between metrics and spatial variability in seawater temperature, bottom depth and number of days with ice cover. Several food-web metrics were positively associated with seawater temperature: connectance, level of omnivory, clustering, cannibalism, and high variability in generalism, while other food-web metrics such as modularity and vulnerability were positively associated with sea ice and negatively with temperature. Food-web metrics positively associated with habitat heterogeneity were: number of species, link density, omnivory, path length, and trophic level. This finding suggests that habitat heterogeneity promotes food-web complexity in terms of number of species and link density. Our analyses reveal that spatial variation in food-web structure along the environmental gradients is partly related to species turnover. However, the higher interaction turnover compared to species turnover along these gradients indicates a consistent modification of food-web structure, implying that interacting species may co-vary in space. In conclusion, our study shows how environmental heterogeneity, via environmental filtering, influences not only turnover in species composition, but also the structure of food webs over large spatial scales.

Identification of cryptic species can have profound implications in fishery management, conservation and biodiversity contexts. In the North Atlantic, the genus Sebastes is currently represented by four species, although additional cryptic species have been assumed. The connectivity of the gene-pools within the genus in Greenland waters, in particular, remains largely unexplored. Using a panel of 13 microsatellite markers for 720 fish, we explored the species complex of Sebastes norvegicus in Greenland waters. Genetic analyses provided evidence for three cryptic species in samples that were morphologically identified as S. norvegicus. They were termed S. norvegicus-A, S. norvegicus-B, and S. norvegicus-giants. A few phenotypic features exist to identify adult S. norvegicus giants, but no characteristics have been identified for the two other cryptic species. The proposed cryptic species should be recognized in the management regime to ensure sustainable exploitation and conservation of Sebastes species in Greenland waters.

A food web topology describes the diversity of species and their trophic interactions, i.e. who eats whom, and structural analysis of food web topologies can provide insight into ecosystem structure and function. It appears simple, at first sight, to list all species and their trophic interactions. However, the very large number of species at low trophic levels and the impossibility to monitor all trophic interactions in the ocean makes it impossible to construct complete food web topologies. In practice, food web topologies are simplified by aggregating species into groups termed trophospecies. It is not clear though, how much simplified versions of food webs retain the structural properties of more detailed networks. Using the most comprehensive Barents Sea food web to date, we investigate the performance of methods to construct simplified food webs using three approaches: taxonomic, structural and regular clustering. We then evaluate how topological properties vary with the level of network simplification. Results show that alteration of food web structural properties due to aggregation are highly sensitive to the methodology used for grouping species and trophic links. In the specific case of the Barents Sea, we show that it is possible to preserve key structural properties of the original complex food web in simplified versions when using taxonomic or structural clustering combined with intermediate 25% linkage for trophic aggregation.

Golden redfish (Sebastes norvegicus) are a long-living (>50 years), late maturing (>10 years) species of commercial interest, distributed along the coast, shelves and continental slope of Norway, down to approximately 400 m depth in the water column. In recent years, analyses of size-at-age data have revealed variable growth trajectories for this species. Whilst some individuals appear to grow slowly after sexual maturity (37cm at ~ 15 years), others continue to grow throughout their lifetime up to 100 cm or more. To investigate how depth and latitude affect golden redfish growth patterns, we developed non-linear mixed effects statistical models. Alongside this, small scale experiments were also conducted to assess the quality of age-determination. The results showed that individuals found in deeper, northern waters present a higher growth potential, even when uncertainty in age determination and species identification were considered. The proximal causes for variations in the growth potential of S. norvegicus are still unresolved and the existence of a possible cryptic species remains a fundamental issue that will need to be addressed, in order to understand the causes behind observed growth variations.

Source at https://doi.org/10.1111/faf.12356. Anticipating future changes in marine social‐ecological systems (MSES) several decades into the future is essential in the context of accelerating global change. This is challenging in situations where actors do not share common understandings, practices, or visions about the future. We introduce a dedicated scenario method for the development of MSES scenarios in a participatory context. The objective is to allow different actors to jointly develop scenarios which contain their multiple visions of the future. The method starts from four perspectives: “fisheries management,” “ecosystem,” “ocean climate,” and “global context and governance” for which current status and recent trends are summarized. Contrasted scenarios about possible futures are elaborated for each of the four single perspectives before being integrated into multiple‐perspective scenarios. Selected scenarios are then developed into storylines. Focusing on individual perspectives until near the end allows actors with diverse cultures, interests and horizons to confront their own notions of the future. We illustrate the method with the exploration of the futures of the Barents Sea MSES by 2050. We emphasize the following lessons learned: first, many actors are not familiar with scenario building and attention must be paid to explaining the purpose, methodology, and benefits of scenarios exercises. Second, although the Barents Sea MSES is relatively well understood, uncertainties about its future are significant. Third, it is important to focus on unlikely events. Fourth, all perspectives should be treated equally. Fifth, as MSES are continuously changing, we can only be prepared for future changes if we collectively keep preparing.

Understanding and predicting patterns arising from the dynamics of marine food webs is central to trophic and community ecology and numerical models of food webs constitute a primary tool to simulate these dynamics. Food web simulation models are often highly complex while at the same time often too constrained to reproduce the level of variability observed in real systems. The recently developed non-deterministic network dynamics (NDND) modelling framework has been suggested as a simulation alternative, which can generate multiple patterns of food web variability despite great structural simplicity. Two important aspects of the NDND modelling framework remain unexplored: first the derivation of model input parameters from empirical or theoretical studies and second the evaluation of the model simulations against observations. We provide a methodology for the derivation of model parameters based on empirical observations, the metabolic theory of ecology and life-history theory and apply it to the specific case of the Barents Sea food web. We then evaluate the ability of the NDND simulations to reproduce a wide range of patterns of food web dynamics against observations collected in the Barents Sea during 28 years. Patterns emerging from the simulations include trends and cycles in biomass, trophic levels and transfer efficiency, density-dependent growth, top-down vs bottom-up oscillations, ecosystem level stability and synchrony and trophic functional responses. The ability of the NDND to generate so many patterns observed empirically in the Barents Sea is remarkable given that it is based only on random trophic interactions operating within few constraints set by ecological rules. Our results show that investigations of food web dynamics in marine ecosystems, including the definition of reference states and responses to climate and exploitation pressures, may be achieved with models that are structurally simple and based on few well-established assumptions.

The identification of isolated populations in widely distributed marine species is often impeded by low levels of genetic differentiation. However, modern genetic approaches now allow for the efficient detection of potentially large numbers of novel genetic variations, thereby improving the power to identify biologically meaningful management units. To investigate the genetic structuring of saithe (Pollachius virens L.), we applied seascape genetic approaches to 131 single nucleotide polymorphism loci genotyped in 584 fish collected from 11 locations across the North Atlantic. Wombling analysis and the Monmonier algorithm revealed four genetic clusters (Barents Sea, Central Northeast Atlantic, Rockall, and Canada) across the species distribution range. These results imply that genetic structuring in saithe may be influenced by abiotic factors such as geographical distance, and bathymetry as well as biotic factors such as sex-biased migration, and natal homing. The results suggest a potential mismatch between management and biological units across the Northeast Atlantic, which may have implications for sustainable exploitation of the species.

The development of ecosystem-based fisheries management over the last two decades has increased attention on the protection of vulnerable resources that are of little or no economic significance including bycatch of benthos in bottom trawling. Current knowledge on the response of benthic communities to the impact of trawling is still rudimentary. In the present study, we used data collected in the Barents Sea during 2011 to assess the vulnerability of benthic species to trawling, based on the risk of being caught or damaged by a bottom trawl. Using trait table analysis, we identified 23 “high-risk” benthic species, which include “large weight and upraised” taxa as “easily caught” by a bottom trawl. We further identified a “low-risk” category containing 245 taxa/species and a “medium-risk” category with 80 species. A clear decline in biomass was noted for all three categories when comparing trawled vs. untrawled areas. This suggests that trawling significantly affects the biomass of all species, but predominantly the “high-risk” taxa. Some Barents Sea regions were particularly susceptible to trawling due to the dominance of the “high-risk” species, including Geodia sponges in the southwestern Barents Sea, basket stars ( Gorgonocephalus ) in the northern Barents Sea, sea pen ( Umbellula encrinus ) on the shelf facing the Arctic Ocean, and sea cucumber ( Cucumaria frondosa ) in shallow southern areas. These findings can guide management decisions to meet targets set by the United Nations Convention of Biological Diversity and the OSPAR Commission (“Protecting and Conserving the North-East Atlantic and its Resources”). We specifically recommend management action in the southwestern and the northwestern Barents Sea and on the Arctic shelf facing the Arctic Ocean.

Biodiversity is an increasingly important issue for the management of marine ecosystems. However, the proliferation of biodiversity indices and difficulties associated with their interpretation have resulted in a lack of clearly defined framework for quantifying biodiversity and biodiversity changes in marine ecosystems for assessment purpose. Recent theoretical and numerical developments in biodiversity statistics have established clear algebraic relationships between most of the diversity measures commonly used, and have highlighted those that most directly relates to the concept of biological diversity, terming them “true” diversity measures. In this study, we implement the calculation of these “true” diversity measures at the scale of a large-marine ecosystem, the Barents Sea. We applied hierarchical partitioning of biodiversity to an extensive dataset encompassing 10 years of trawl-surveys for both pelagic and demersal fish community. We quantify biodiversity and biodiversity changes for these two communities across the whole continental shelf of the Barents Sea at various spatial and temporal scales, explicitly identifying areas where fish communities are stable and variable. The method is used to disentangle areas where community composition is subject to random fluctuations from areas where the fish community is drifting over time. We discuss how our results can serve as a spatio-temporal biodiversity baseline against which new biodiversity estimates, derived from sea surveys, can be evaluated.

Genetic population structure is often used to identify management units in exploited species, but the extent of genetic differentiation may be inflated by geographic variation in the level of hybridization between species. We identify the genetic population structure of Sebastes mentella and investigate possible introgression within the genus by analyzing 13 microsatellites in 2562 redfish specimens sampled throughout the North Atlantic. The data support an historical divergence between the ‘shallow’ and ‘deep’ groups, beyond the Irminger Sea where they were described previously. A third group, ‘slope’, has an extended distribution on the East Greenland Shelf, in addition to earlier findings on the Icelandic Slope. Furthermore, S. mentella from the Northeast Arctic and Northwest Atlantic waters are genetically different populations. In both areas, interspecific introgression may influence allele frequency differences among populations. Evidence of introgression was found for almost all the identified Sebastes gene pools, but to a much lower extent than suggested earlier. Greenland waters appear to be a sympatric zone for many of the genetically independent Sebastes groups. This study illustrates that the identified groups maintain their genetic integrity in this region despite introgression. This article is protected by copyright. All rights reserved.

Aim This study focuses on the influence of methodological choices on the predictive performance of macroecological models (MEMs), i.e. statistical models designed to predict patterns of biodiversity using environmental predictors. We emphasize the influence of three methodological choices: (1) the choice of the currency in which the abundance of each species is measured, i.e. numbers of individuals or biomass; (2) the rules used to define the species assemblage under focus, i.e. broad communities or refined ecological guilds; and (3) the influence of rare over common species in the biodiversity measure. Location The effects of these choices are investigated using an exhaustive dataset on the fish fauna of the continental shelf of the Barents Sea. Methods We conducted an analysis of 220 models resulting from all possible combinations of the three methodological choices. For each, we evaluated the predictive performance through an iterative cross-validation process. Results All methodological choices we investigated strongly affected the predictive performance of MEMs. High predictive performances were obtained when using biomass instead of numbers of individuals, when focusing on narrow ecological guilds composed of species sharing the same ecological traits and when using diversity measures that give high weight to rare species. Main conclusions We recommend that future projections of biodiversity pay more attention to abundance currency, ecological homogeneity of focal species assemblages and the diversity metric used, and systematically investigate these methodological choices prior to producing biodiversity forecasts. Splitting a whole set of species into ecological guilds appears to be a promising practice, leading to a selected set of MEMs with high predictive performances and more detailed forecasts on the fate of diversity.

Beaked redfish inhabits North Atlantic waters in the depth range 100–950 m, over the continental shelf, slope and the open ocean. Individuals can live demersal or pelagic, at various stages of their life cycle. The geographical distribution of the species extends to most of the Atlantic waters from Newfoundland and the Labrador basin in the west to the Barents Sea in the east. Monitoring beaked redfish is challenging because of the species wide geographical distribution and large scale migrations; deep distribution, which complicates trawling and hydroacoustic surveys; difficulties with tagging; and persistent difficulties in taxonomic identification. These challenges make it a particularly problematic species to observe with conventional research methods. We review these key challenges and provide recommendations for the coordinated observation of Sebastes mentella in the North Atlantic that would best contribute to the assessment and ecological research on this species.

An assessment of the potential for 17 fish or shellfish stocks or stock groups to move from the sub-Arctic areas into the Arctic Ocean was conducted. A panel of 34 experts was convened to assess the impact of climate change on the potential movement of the 17 stocks or stock groups. The panel considered the exposure of species to climate change, the sensitivity of species to these changes and the adaptive capacity of each stock or stock group. Based on expert opinions, the potential for expansion or movement into the Arctic was qualitatively ranked (low potential, potential, high potential). It is projected that the Arctic Ocean will become ice-free during the summer season, and when this happens new areas will open up for plankton production, which may lead to new feeding areas for fish stocks. Five stocks had a low potential to move to, or expand in, the high Arctic. Six species are considered as potential candidate species to move to, or expand in, the high Arctic. Six stocks had a high potential of establishing viable resident populations in the region. These six stocks exhibit life history characteristics that allow them to survive challenging environmental conditions that will continue to prevail in the north. This study suggests that several life history factors should be considered when assessing the potentiality of a species moving in response to changing climate conditions.

Genetic population structure is often used to identify management units in exploited species, but the extent of genetic differentiation may be inflated by geographic variation in the level of hybridization between species. We identify the genetic population structure of Sebastes mentella and investigate possible introgression within the genus by analyzing 13 microsatellites in 2,562 redfish specimens sampled throughout the North Atlantic. The data support an historical divergence between the “shallow” and “deep” groups, beyond the Irminger Sea where they were described previously. A third group, “slope,” has an extended distribution on the East Greenland Shelf, in addition to earlier findings on the Icelandic slope. Furthermore, S. mentella from the Northeast Arctic and Northwest Atlantic waters are genetically different populations. In both areas, interspecific introgression may influence allele frequency differences among populations. Evidence of introgression was found for almost all the identified Sebastes gene pools, but to a much lower extent than suggested earlier. Greenland waters appear to be a sympatric zone for many of the genetically independent Sebastes groups. This study illustrates that the identified groups maintain their genetic integrity in this region despite introgression.

The presence of a dense layer of organisms in the mesopelagic zone is a ubiquitous feature of the world oceans, and these organisms may constitute a major component of marine biomass worldwide. Many mesopelagic organisms perform light-dependent diel vertical migration. It has been hypothesised that extreme light regimes encountered at high latitudes may disturb these migration patterns and thereby limit the northern expansion of mesopelagic fauna into the Arctic. Using hydroacoustic data collected during 4 surveys conducted in the open Norwegian Sea during the summer season, we evaluated if the key features of mesopelagic fauna reported worldwide (high density and diel vertical migration) are also observed in the high latitudes of the Northeast Atlantic. The results confirm that the high-latitude Northeast Atlantic hosts a high density of mesopelagic fauna which performs daily migration patterns similar to those reported in other regions. They also support the limiting effect of photoperiod on its potential biomass. These results stress the need for thorough studies on the abundance, biodiversity and trophic ecology of the mesopelagic fauna in this region.

Understanding and predicting the distribution of organisms in heterogeneous environments lies at the heart of ecology. The spatial distribution of fish populations observed in the wild results from the complex interactions of multiple controls both external or internal to the fish populations. Whilst species distribution models (SDMs) have been mostly concerned with static description of species distribution as a function of environmental constraints, models of animal movements (MAMs) have focussed on the dynamic nature of spatial distribution of groups of individuals under a number of constraints external and internal to the population. Besides SDMs and MAMs, modelling the spatial distribution of fish populations can be achieved by models that are fundamentally static (like SDMs) but can also incorporate many hypotheses on the control of fish spatial distribution (like MAMs). The hypotheses underlying these models need to make sense at the population level - rather than at the individual or species level –we term these ‘population distribution models’ (PDMs). PDMs are statistical models that rely on several hypotheses, which include: (i) control through geographical attachment, (ii) environmental conditions, (iii) density-dependent habitat selection, (iv) spatial dependency, (v) population demographic structure, (vi) species interactions and (vii) population memory. We review the basis behind each of these conceptual models and we examine corresponding numerical applications. We argue that the conceptual models are complementary rather than competing, that existing numerical applications are still rarely compared and combined, and that PDMs can offer a statistical framework to achieve this. We recommend that the numerical models associated with different hypotheses be constructed within such a common general framework. This will permit evaluation, comparison and combination of the multiple hypotheses on fish spatial distribution. It will ultimately lead to a more comprehensive understanding of the factors controlling the spatial distribution of fish populations and to more accurate predictions in which model uncertainty is accounted for.

The processes that control the spatial distribution of North Sea whiting (Merlangius merlangus) spawning adults are investigated using a statistical multi-model approach. Models of external and internal controls on the population, such as environmental conditions, spatial constraints, present or past spatial distribution, and demographic state of the population, are evaluated, compared and ranked to select those that are the best able to predict the observed distribution of spawning adults. Model selection is greatly influenced by the selection method, either based on data fitting or prediction, as well as by the threshold value used to stop the selection. Model selection based on prediction tends to select simpler models than selection based on data fitting. The hypotheses underlying the selected models are inferred to play a significant role in controlling North Sea whiting spatial distribution. The multi-model inference approach developed in this study enables comparison of several theoretical concepts and hypotheses and the results provide important clues on the processes involved in the control of the spatial distribution of whiting. We conclude that whiting has a high spatial fidelity to spawning site which can be linked to either geographical attachment or year-to-year persistence of the spatial distribution of the population. Environmental factors – temperature and salinity – appear to influence the geographical extent of spawning whiting distribution, whereas local abundance levels are primarily controlled by internal factors, i.e., population size and spatial segregation between ages.

Modelling habitat of species necessitates robust identification of relevant environmental variables linked to species dis tribution. To achieve this, we connect hierarchical patch theory and habitat modelling at multiple scales. We suggest discriminating between ‘circumstancial variables’ and ‘process variables’ on the basis of temporal evolution of the spatial links between species distribution and their environment at different scales. ‘Process variables’ are informative of the eco logical processes driving the distribution of organisms at multiple scales. By opposition, ‘circumstantial variable’ provide little insight because their relationship with animal spatial distribution is subject to great variations through time. As a real case study, we investigate the relationships between auk distribution (mainly Uria aalge) and oceanographic landscapes over two scales (i.e. large ~ 200 km and medium ~ 50 km) during the wintering season in the Bay of Biscay. Surface salinity, mixed layer depth and chlorophyll a are identified as ‘process variables’ as they are invariably correlated with the spatial distribution of auks, whereas bottom temperature can be viewed as a ‘circumstantial variable’ since the correlation is nonconstant through time at large scale. The process variables at large scale are used to model the potential habitat of auks in the Bay of Biscay during the wintering season. At medium scale, only the chlorophyll a is identified as ‘process variable’ and used to model preferential habitat of wintering auks in the Bay of Biscay. The analytical approach proposed here (i.e. mul tivariate factorial kriging in a temporal context) is an effective framework to model the potential and preferential habitat of a species and can be related to the ecological niche concept and by focusing explicitly on scale dependence, the distinction between the variables that can be used as niche descriptors into species distribution models. Then our method lead to the identification of variables that should be used to define the Grinnellian niche which is defined by environmental conditions on broad scales and the Eltonian niche which focuses on biotic interactions and resource–consumer dynamics that can be measured principally at local scales.

Summary Estimates of fish biomass, derived from hydroacoustic registrations are generally dependent on biological information from trawl catches. Catch composition is necessary for accurate quantitative allocation of acoustic energy to the different species present in the water column. For this purpose, the species composition in the trawl catch must be representative of the true species composition in the ocean, which requires that the catchability of the trawl for individual species is known. The catchability is expected to be trawl- and species-specific as a result of variations in fish behaviour in front of the trawl (fright reactions, swimming speeds and other factors) which can affect the chance of fish being caught or escaping. We show that catchability estimates for several species can be simultaneously derived from combined trawl-acoustic samples through a simple and straight- forward optimization method. We apply the method to estimate the catchability of two important pelagic species in the Norwegian Sea, beaked redfish (Sebastes mentella) and blue whiting (Micromesistius poutassou) when fished with a large pelagic trawl, the Gloria 2048.

Modern fisheries research and management must understand and take account of the interactions between climate and fishing, rather than try to disentangle their effects and address each separately. These interactions are significant drivers of change in exploited marine systems and have ramifications for ecosystems and those who depend on the services they provide. We discuss how fishing and climate forcing interact on individual fish, marine populations, marine communities, and ecosystems to bring these levels into states that are more sensitive to (i.e. more strongly related with) climate forcing. Fishing is unlikely to alter the sensitivities of individual finfish and invertebrates to climate forcing. It will remove individuals with specific characteristics from the gene pool, thereby affecting structure and function at higher levels of organisation. Fishing leads to a loss of older age classes, spatial contraction, loss of sub-units, and alteration of life history traits in populations, making them more sensitive to climate variability at interannual to interdecadal scales. Fishing reduces the mean size of individuals and mean trophic level of communities, decreasing their turnover time leading them to track environmental variability more closely. Marine ecosystems under intense exploitation evolve towards stronger bottom-up control and greater sensitivity to climate forcing. Because climate change occurs slowly, its effects are not likely to have immediate impacts on marine systems but will be manifest as the accumulation of the interactions between fishing and climate variability - unless threshold limits are exceeded. Marine resource managers need to develop approaches which maintain the resilience of individuals, populations, communities and ecosystems to the combined and interacting effects of climate and fishing. Overall, a less-heavily fished marine system, and one which shifts the focus from individual species to functional groups and fish communities, is likely to provide more stable catches with climate variability and change than would a heavily fished system. Crown Copyright (C) 2009 Published by Elsevier B.V. All rights reserved.

Overexploitation and climate change are increasingly causing unanticipated changes in marine ecosystems, such as higher variability in fish recruitment and shifts in species dominance. An ecosystem-based approach to fisheries attempts to address these effects by integrating populations, food webs and fish habitats at different scales. Ecosystem models represent indispensable tools to achieve this objective. However, a balanced research strategy is needed to avoid overly complex models. Ecosystem oceanography represents such a balanced strategy that relates ecosystem components and their interactions to climate change and exploitation. It aims at developing realistic and robust models at different levels of organisation and addressing specific questions in a global change context while systematically exploring the ever-increasing amount of biological and environmental data.

Understanding and modelling the environmental control of fish recruitment has been a central question in fish population ecology for the last century. Most environment-recruitment models have primarily been developed to model mean recruitment using conventional regression techniques which assume that all environmental parameters are included and that the residual unexplained variability is unstructured. However, the complexity of environmental controls and the empirical evidence that many relationships have failed when retested suggest that these assumptions are generally not met. Most environmental controls may be considered as limiting factors to recruit- ment and act in interaction with other factors (often not measured or not known). We used quantile regression modelling, which is specifically designed to model limiting relationships, to reanalyse environment-recruitment relationships that have been published for 4 fish stocks: (1) Northeast Arctic cod (Barents Sea), (2) Atlanto-Scandian herring, (3) Bay of Biscay anchovy and (4) Pacific sar- dine. The method was adapted to the specific case of autocorrelated time series, a common feature of most environmental signals. The results from quantile regression were not straightforward exten- sions of conventional regressions. For Northeast Arctic cod and Pacific sardine, the original relation- ships with temperature were not statistically significant in the quantile model. For Atlanto-Scandian herring the relationship was confirmed and temperature clearly appeared as a limiting factor to recruitment. The published relationship for the Bay of Biscay anchovy with upwelling was not con- firmed, but the previously undetected relationship with river runoff was established. In this specific case, it was only by using a quantile model that the relationship could be detected as statistically sig- nificant. These results confirm the ability of quantile regression models to provide robust interpreta- tion of environment-recruitment relationships and to produce environmentally based advance warning when recruitment is expected to be low.