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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper explores the stochastic dynamics of a simple foodweb system using a network model that mimics interacting species in a biosystem. It is shown that the system can be described by a set of ordinary differential equations with real-valued uncertain parameters, which satisfy a set of linear inequality constraints. The constraints restrict the solution space to a bounded convex polytope. We present results from numerical experiments to show how the stochasticity and uncertainty characterizing the system can be captured by sampling the interior of the polytope with a prescribed probability rule, using the Hit-and-Run algorithm. The examples illustrate a parsimonious approach to modeling complex biosystems under vague knowledge. [ABSTRACT FROM AUTHOR]

A food web is an ecological network and its topological description consists of the list of nodes, i.e., trophospecies, the list of links, i.e., trophic interactions, and the direction of interactions (who is the prey and who is the predator). Food web topologies are widely used in ecology to describe structural properties of communities or ecosystems. The selection of trophospecies and trophic interactions can be realized in different manners so that many different food webs may be constructed for the same community. In the Barents Sea, many simple food webs have been constructed. We present a comprehensive food web topology for the Barents Sea ecosystem, from plankton to marine mammals. The protocol used to compile the data set includes rules for the selection of taxa and for the selection and documentation of the trophic links. The resulting topology, which includes 244 taxa and 1589 trophic links, can serve as a basis for topological analyses, comparison with other marine ecosystems, or as a basis to build simulation models of the Barents Sea ecosystem. The data set consists of three related tables: (1) the hst of taxa, (2) the list of pairwise interactions, and (3) the list of bibliographical references. [ABSTRACT FROM AUTHOR]