Multiple configurations and fluctuating trophic control in the Barents Sea food-web

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.publishedVersio

Combined effects of temperature and fishing mortality on the Barents Sea ecosystem stability

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

A standard protocol for describing the evaluation of ecological models

Numerical models of ecological systems are increasingly used to address complex environmental and resource management questions. One challenge for scientists, managers, and stakeholders is to appraise how well suited these models are to answer questions of scientific or societal relevance, that is, to perform, communicate, or access transparent evaluations of ecological models. While there have been substantial developments to support standardised descriptions of 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 of model evaluation. The protocol is organised in three major parts: the objective(s) of the modelling application, the ecological patterns of relevance and the evaluation methodology proper, and is termed the OPE (objectives, patterns, evaluation) protocol. We present the 25 questions of the OPE protocol which address the many aspects of the evaluation process and then apply them to six case studies based on a diversity of ecological models. In addition to standardising and increasing the transparency of the model evaluation process, we find that going through the OPE protocol helps modellers to think more deeply about the evaluation of their models. From this last point, we suggest that it would be highly beneficial for modellers to consider the OPE early in the modelling process, in addition to using it as a reporting tool and as a reviewing tool.publishedVersio

Investigating the drivers of the Nordic Seas food-web dynamics using Chance and Necessity modelling

Marine ecosystems are under pressures of human activities that have altered their dynamics and structure during the last five decades, but there can also vary due to internal dynamics or chance. Managers focus on human activities and often ignore the contribution of internal dynamics and chance to ecosystem dynamics in the decision-making process. In this thesis, I use food-web modelling based on the principles of Chance and Necessity to explore the possible variability of the Nordic Seas’ food-web. I show that internal dynamics and randomness have a key role in the variability of marine food-webs. I also show that climate change and fisheries affect the variability of the Nordic Seas in a combined manner, which suggest that both stressors should not be considered separately for management. I suggest that Chance and Necessity can used a tool to inform adaptive management, ecosystem-based management, and integrated ecosystem assessment

Combined effects of temperature and fishing mortality on the Barents Sea ecosystem stability

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

Quantification of trophic interactions in the Norwegian Sea pelagic food-web over multiple decades

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 constrained by 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 copepods, krill, amphipods, herring, and blue whiting can primarily be explained by changes in their consumption rather than by predation and fishing. For mackerel, variations in biomass cannot be unambiguously attributed to either consumption or predation and fishing. The model results provide no support for top-down control on planktonic prey biomass and little support for the hypothesised competition for resources between the three small pelagic species, despite partially overlapping diets. This suggests that the lack of explicit accounting for trophic interactions between the three pelagic species likely have had little impact on the robustness of past stock assessments and management in the Norwegian Sea.publishedVersio

A standard protocol for describing the evaluation of ecological models

Numerical models of ecological systems are increasingly used to address complex environmental and resource management questions. One challenge for scientists, managers, and stakeholders is to appraise how well suited these models are to answer questions of scientific or societal relevance, that is, to perform, communicate, or access transparent evaluations of ecological models. While there have been substantial developments to support standardised descriptions of 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 of model evaluation. The protocol is organised in three major parts: the objective(s) of the modelling application, the ecological patterns of relevance and the evaluation methodology proper, and is termed the OPE (objectives, patterns, evaluation) protocol. We present the 25 questions of the OPE protocol which address the many aspects of the evaluation process and then apply them to six case studies based on a diversity of ecological models. In addition to standardising and increasing the transparency of the model evaluation process, we find that going through the OPE protocol helps modellers to think more deeply about the evaluation of their models. From this last point, we suggest that it would be highly beneficial for modellers to consider the OPE early in the modelling process, in addition to using it as a reporting tool and as a reviewing tool

A standard protocol for describing the evaluation of ecological models

Numerical models of ecological systems are increasingly used to address complex environmental and resource management questions. One challenge for scientists, managers, and stakeholders is to appraise how well suited these models are to answer questions of scientific or societal relevance, that is, to perform, communicate, or access transparent evaluations of ecological models. While there have been substantial developments to support standardised descriptions of 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 of model evaluation. The protocol is organised in three major parts: the objective(s) of the modelling application, the ecological patterns of relevance and the evaluation methodology proper, and is termed the OPE (objectives, patterns, evaluation) protocol. We present the 25 questions of the OPE protocol which address the many aspects of the evaluation process and then apply them to six case studies based on a diversity of ecological models. In addition to standardising and increasing the transparency of the model evaluation process, we find that going through the OPE protocol helps modellers to think more deeply about the evaluation of their models. From this last point, we suggest that it would be highly beneficial for modellers to consider the OPE early in the modelling process, in addition to using it as a reporting tool and as a reviewing tool