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Environmental variability and fisheries: what can models do?

Profile image of Matthias WolffMatthias Wolff

2008, Reviews in Fish Biology and Fisheries

https://doi.org/10.1007/S11160-007-9075-5
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Abstract

This review is based on 58 climatefisheries models published over the last 28 years that describe the impacts of fishery pressure and environmental variability on populations and ecosystems and include basic principles of population dynamics. It points out that the incorporation of environmental factors in fishery models has already been done and is of great importance for future models used in the assessment of marine resources. The work is guided by the questions to what extent have these models a) enhanced our understanding of the interrelationships between the environment, the fishery and the state of the exploited resources and b) helped to improve the prediction of population dynamics and the assessment of marine resources. For each of the six most commonly used model categories a case study is critically analyzed. The problems of ''breaking relationships'' between environmental factors and the biological response used in models, the trade-off between model complexity (realism) and simplicity (data availability) and the potential of multivariate climate indices for forecasting ecosystem states and for use as proxies for combined models are discussed, as are novel non-linear and spatially explicit modeling approaches. Approaches differ in terms of model complexity, use of linear or non-linear equations, number of parameters, forecast time horizon and type of resource modelled. A majority of the models were constructed for fish and invertebrate stocks of the northeast Pacific and the epicontinental seas of the Atlantic, reflecting the advancement of fisheries science in these regions. New, in parts highly complex models and sophisticated approaches were identified. The reviewed studies demonstrate that the performance of fished stocks can better be described if environmental or climatic variability is incorporated into the fisheries models. We conclude that due to the already available knowledge, the greatly enhanced computer power, new methods and recent findings of large-scale climatic/oceanographic cycles, fisheries modeling should progress greatly in the coming years.

Figures (9)
Fig. 1 Hypothetical biological response to changing intensi- ties of an influencing factor with different ranges of performance of the individual (modified from Townsend et al. 2003) The present work reviewed 58 combined models of 48 scientific papers known to the authors. They were examined and categorized according to model approach used, species and region of implementation. Of these, six case studies each representing a different model type were selected and are presented in the subsequent section for illustration. The review continues with a discussion of the assumptions and limitations of combined models and finishes with a section of pending problems and considerations for future combined models. The reviewed models are listed in the bibliography section that precedes the literature citations.
Fig. 1 Hypothetical biological response to changing intensi- ties of an influencing factor with different ranges of performance of the individual (modified from Townsend et al. 2003) The present work reviewed 58 combined models of 48 scientific papers known to the authors. They were examined and categorized according to model approach used, species and region of implementation. Of these, six case studies each representing a different model type were selected and are presented in the subsequent section for illustration. The review continues with a discussion of the assumptions and limitations of combined models and finishes with a section of pending problems and considerations for future combined models. The reviewed models are listed in the bibliography section that precedes the literature citations.
Fig. 2 Combined fisheries-climate models considered in the present review and their global distribution; H. et al. 1997: Higgins et al. 1997; T. and L. 2005: Tolimieri and Levin 2005; J. et al. 2005: Jacobson et al. 2005; not shown: Klyashtorin (2001) various clupeid stocks in Atlantic and Pacific
Fig. 2 Combined fisheries-climate models considered in the present review and their global distribution; H. et al. 1997: Higgins et al. 1997; T. and L. 2005: Tolimieri and Levin 2005; J. et al. 2005: Jacobson et al. 2005; not shown: Klyashtorin (2001) various clupeid stocks in Atlantic and Pacific
Fig. 3. Results of the SPM model designed for Newfoundland and Labrador cod; dashed line: classic SPM without inclusion of EV; black line: final combined SPM incorporating EV and depletion in r and K (modified after Rose 2004)
Fig. 3. Results of the SPM model designed for Newfoundland and Labrador cod; dashed line: classic SPM without inclusion of EV; black line: final combined SPM incorporating EV and depletion in r and K (modified after Rose 2004)
Fig. 4 Calculated latitudinal distribution ranges of red abalone off the Californian coast in relation to SST changes; 0 is the present mean value at every geographical location (black bars indicate calculated distribution at present temperatures (mod- ified after Hobday and Tegner 2002) population: time to extinction was greatly increased and the overall stock decrease due to fishing was lowered. Very important is the result that the distribution limits of abalone varied strongly with varying SST (Fig. 4). With increasing temperature
Fig. 4 Calculated latitudinal distribution ranges of red abalone off the Californian coast in relation to SST changes; 0 is the present mean value at every geographical location (black bars indicate calculated distribution at present temperatures (mod- ified after Hobday and Tegner 2002) population: time to extinction was greatly increased and the overall stock decrease due to fishing was lowered. Very important is the result that the distribution limits of abalone varied strongly with varying SST (Fig. 4). With increasing temperature
Problems pending and considerations for future combined models “Breaking relationships” between environmental factors and the biological performance
Problems pending and considerations for future combined models “Breaking relationships” between environmental factors and the biological performance
Intensity of the influencing factor, latitudinal distribution It is hardly imaginable that only one environmental factor is relevant for the performance of a biotic entity (e.g., Solomon 1949; Fry 1971; Baumann 1998; Rose 2000; Stenseth et al. 2003). A limiting condition caused by an unobserved factor may thus prevent an observed variable to behave in the expected way. Fry (1971) classified these types of variables as “masking factors” that not only act as
Intensity of the influencing factor, latitudinal distribution It is hardly imaginable that only one environmental factor is relevant for the performance of a biotic entity (e.g., Solomon 1949; Fry 1971; Baumann 1998; Rose 2000; Stenseth et al. 2003). A limiting condition caused by an unobserved factor may thus prevent an observed variable to behave in the expected way. Fry (1971) classified these types of variables as “masking factors” that not only act as
Fig. 7 Mathematical functions used with the different model types; var: various different functions used
Fig. 7 Mathematical functions used with the different model types; var: various different functions used

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K Holsman

Multi-species statistical catch at age models (MSCAA) can quantify interacting effects of climate and fisheries harvest on species populations, and evaluate management trade-offs for fisheries that target several species in a food web. We modified an existing MSCAA model to include temperature-specific growth and predation rates and applied the modified model to three fish species, walleye pollock (Gadus chalcogrammus), Pacific cod (Gadus macrocephalus) and arrowtooth flounder (Atheresthes stomias), from the eastern Bering Sea (USA). We fit the model to data from 1979 through 2012, with and without trophic interactions and temperature effects and use projections to derive single- and multi-species biological reference points (BRP and MBRP, respectively) for fisheries management. The multi-species model achieved a higher over-all goodness of fit to the data (i.e., lower negative log-likelihood) for pollock and Pacific cod. Variability from water temperature typically resulted in 5-15% changes in spawning, survey, and total biomasses, but did not strongly impact recruitment estimates or mortality. Despite this, inclusion of temperature in projections did have a strong effect on BRPs, including recommended yield, which were higher in single species models for Pacific cod and arrowtooth flounder that included temperature compared to the same models without temperature effects. While the temperature-driven multi-species model resulted in higher yield MBPRs for arrowtooth flounder than the same model without temperature, we did not observe the same patterns in multi-species models for pollock and Pacific cod, where variability between harvest scenarios and predation greatly exceed temperature-driven variability in yield MBRPs. Annual predation on juvenile pollock (primarily cannibalism) in the multi-species model was 2-5 times the annual harvest of adult fish in the system, thus predators represent a strong control on population dynamics that exceeds temperature-driven changes to growth and is attenuated through harvest-driven reductions in predator populations. Additionally, although we observed differences in spawning biomasses at the accepted biological catch (ABC) proxy between harvest scenarios and single- and multi-species models, discrepancies in spawning stock biomass estimates did not translate to large differences in yield. We found that multi-species models produced higher estimates of combined yield for aggregate maximum sustainable yield (MSY) targets than single species models, but were more conservative than single-species models when individual MSY targets were used, with the exception of scenarios where minimum biomass thresholds were imposed. Collectively our results suggest that climate and trophic drivers can interact to affect MBRPs, but for prey species with high predation rates, trophic- and management-driven changes may exceed direct effects of temperature on growth and predation. Additionally, MBRPs are not inherently more conservative than single-species BRPs. This framework provides a basis for the application of MSCAA models for tactical ecosystem-based fisheries management decisions under changing climate conditions.

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Can we project changes in fish abundance and distribution in response to climate?
Alastair Grant

Global Change Biology, 2020

Large scale and long-term changes in fish abundance and distribution in response to climate change have been simulated using both statistical and process-based models. However, national and regional fisheries management requires also shorter term projections on smaller spatial scales, and these need to be validated against fisheries data. A 26-year time series of fish surveys with high spatial resolution in the North East Atlantic provides a unique opportunity to assess the ability of models to correctly simulate the changes in fish distribution and abundance that occurred in response to climate variability and change. We use a dynamic bioclimate envelope model forced by physicalbiogeochemical output from eight ocean models to simulate changes in fish abundance and distribution at scales down to a spatial resolution of 0.5°. When comparing with these simulations with annual fish survey data, we found the largest differences at the 0.5° scale. Differences between fishery model runs driven by different biogeochemical models decrease dramatically when results are aggregated to larger scales (e.g. the whole North Sea), to total catches rather than individual species or when the ensemble mean instead of individual simulations are used. Recent improvements in the fidelity of biogeochemical models translate into lower error rates in the fisheries simulations. However, predictions based on different biogeochemical models are often more similar to each other than they are to the survey data, except for some pelagic species. We conclude that model results can be used to guide fisheries management at larger spatial scales, but more caution is needed at smaller scales.

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Related topics

  • Zoology
  • Stock assessment
  • northeast Pacific
  • Spatially Explicit Model
  • Population dynamic

    • Cited by

    Forecasting the dynamics of a coastal fishery species using a coupled climate–population model
    Michael Alexander

    Ecological Applications, 2010

    1 Marine fisheries management strives to maintain sustainable populations while allowing 2 exploitation. However, well-intentioned management plans may not meet this balance 3 since most do not include the effect of climate change. Ocean temperatures are expected 4 to increase through the 21 st century, which will have far-reaching and complex impacts 5 on marine fisheries. To quantify these impacts for one coastal fishery along the east coast 6 of the United States, we develop a coupled climate-population model for Atlantic croaker 7 (Micropogonias undulatus). The model is based on a mechanistic hypothesis: recruitment 8 is determined by temperature-driven, overwinter mortality of juveniles in their estuarine 9 habitats. Temperature forecasts were obtained from two global climate models simulating 10 three standard climate scenarios. The coupled climate-population model demonstrates 11 that both exploitation and climate change will significantly affect abundance and 12 distribution of Atlantic croaker in the future. At current levels of fishing, the average 13 (2010-2100) spawning biomass of the population is forecast to increase by 60-100%.

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    Ecosystem-based fisheries management in the Northwest Atlantic
    Mariano Koen-Alonso

    Fish and Fisheries, 2011

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    What Do Ecological Paradigms Offer to Conservation?
    Henry Howe

    International Journal of Ecology, 2010

    Ecological theory provides applications to biodiversity management-but often falls short of expectations. One possibility is that heuristic theories of a young science are too immature. Logistic growth predicts a carrying capacity, but fisheries managed with the Lotka-Volterra paradigm continue to collapse. A second issue is that general predictions may not be useful. The theory of island biogeography predicts species richness but does not predict community composition. A third possibility is that the theory itself may not have much to do with nature, or that empirical parameterization is too difficult to know. The metapopulation paradigm is relevant to conservation, but metapopulations might not be common in nature. For instance, empirical parameterization within the metapopulation paradigm is usually infeasible. A challenge is to determine why ecology fails to match needs of managers sometimes but helps at other. Managers may expect too much of paradigmatic blueprints, while ecologists believe them too much. Those who implement biodiversity conservation plans need simple, pragmatic guidelines based on science. Is this possible? What is possible? An eclectic review of theory and practice demonstrate the power and weaknesses of the ideas that guide conservation and attempt to identify reasons for prevailing disappointment.

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    Modelling climate change impacts on marine fish populations: process-based integration of ocean warming, acidification and other environmental drivers
    Felix C Mark

    Fish and Fisheries, 2016

    Global climate change affects marine fish through drivers such as warming, acidification and oxygen depletion, causing changes in marine ecosystems and socio-economic impacts. Experimental and observational results inform about anticipated effects of different drivers, but linking between these results and ecosystem level changes requires quantitative integration of physiological and ecological processes into models to advance research and inform management. We give an overview of important physiological and ecological processes affected by environmental drivers. We then provide a review of available modelling approaches for marine fish, analysing their capacities for process-based integration of environmental drivers. Building on this, we propose approaches to advance important research questions. Examples of integration of environmental drivers exist for each model class. Recent extensions of modelling frameworks have a greater potential for including detailed mechanisms to advance model projections. Experimental results on energy allocation, behaviour and physiological limitations will advance the understanding of organism-level trade-offs and

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    What model suits ecosystem-based fisheries management? A plea for a structured modeling process
    Matthias Wolff

    Reviews in Fish Biology and Fisheries, 2012

    As tools within ecosystem-based fisheries management (EBFM), a wide range of Ecosystem Models (EMs) have been designed to represent ecosystem complexity, but it is not always clear how the outputs of these models can be applied. We address this debate in a literature review to illustrate how a better understanding of ecosystem modeling within the EBFM framework could facilitate the use of EMs in the decision-making process. We classify EMs according to their complexity, and qualitatively evaluate their level of success with regard to five general goals of EBFM. In principle, no single EM is found to successfully accomplish all the EBFM goals. Therefore, we suggest that the way in which ecosystem modeling can effectively contribute to EBFM is through a structured modeling process, which should be pursued according to the context of each specific area. Within this planning strategy a range of Ems should be considered, from rather simple ones with few parameters, whose outputs are scientifically robust but possibly of limited use within the EBFM, to those which include a large number of ecosystem elements yet at the expense of increased uncertainty. If multiple EMs, despite their different assumptions, leads to consistent and converging results then robust management decisions will be supported. The present paper appears particularly useful to anyone confronted with the selection of modeling tools for the implementation of fisheries management strategies considering the particular situation of the fishery.

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