Global crustacean stock assessment modelling: Reconciling available data and complexity
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Abstract Despite their growing socio‐economic importance globally, relatively little is understood about how crustacean stocks are assessed, which has potential to compromise fishery sustainability, especially under heavy exploitation and environmental changes. To inform stock assessment model application for emergent fisheries, we evaluated model use for crustacean stocks available in the RAM Legacy Database (RAMLDB) and the evolution of model use for four case‐study fisheries, emphasizing the relationship between data availability and model complexity. Differences in model use between FAO fishing regions and crustacean species sub‐groups were identified. Only 60.9% of crustacean stocks in the RAMLDB identified the model used for assessment. For the remaining stocks, we collected ancillary data to fill the information gaps, amounting to 92.5% of crustacean stocks in RAMLDB. Of these, model complexity varied from count‐based to environmentally explicit statistical catch‐at‐length methods, but tended to be data intensive, likely due to biases towards regions with more developed fishery management programmes. Furthermore, regional comparisons indicated that crustaceans are only well‐assessed in a few geographical hotspots. The progression of model use over time was inconsistent between case‐study fisheries, being driven by myriad factors including data availability, confidence in biological processes and ecological considerations. Our findings can be used as a resource to help inform model choice for fisheries management. Towards the goal of seeking global best practices for crustacean stock assessments, future work should address knowledge gaps in regional stock assessment model use and conduct comparative studies to evaluate stock‐specific costs and benefits relating to model complexity.Keywords:
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Estimating fish stock status is very challenging given the many sources and high levels of uncertainty surrounding the biological processes (e.g. natural variability in the demographic rates), model selection (e.g. choosing growth or stock assessment models) and parameter estimation. Incorporating multiple sources of uncertainty in a stock assessment allows advice to better account for the risks associated with proposed management options, promoting decisions that are more robust to such uncertainty. However, a typical assessment only reports the model fit and variance of estimated parameters, thereby underreporting the overall uncertainty. Additionally, although multiple candidate models may be considered, only one is selected as the 'best' result, effectively rejecting the plausible assumptions behind the other models. We present an applied framework to integrate multiple sources of uncertainty in the stock assessment process. The first step is the generation and conditioning of a suite of stock assessment models that contain different assumptions about the stock and the fishery. The second step is the estimation of parameters, including fitting of the stock assessment models. The final step integrates across all of the results to reconcile the multi-model outcome. The framework is flexible enough to be tailored to particular stocks and fisheries and can draw on information from multiple sources to implement a broad variety of assumptions, making it applicable to stocks with varying levels of data availability The Iberian hake stock in International Council for the Exploration of the Sea (ICES) Divisions VIIIc and IXa is used to demonstrate the framework, starting from length-based stock and indices data. Process and model uncertainty are considered through the growth, natural mortality, fishing mortality, survey catchability and stock-recruitment relationship. Estimation uncertainty is included as part of the fitting process. Simple model averaging is used to integrate across the results and produce a single assessment that considers the multiple sources of uncertainty.
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Crustaceans, such as crab, lobster, prawn, and Antarctic krill, have formed a vast and commercially valuable fishery globally. Although the importance and scale of these crustacean fishe-ries are increasing, the suitable and effective methods for stock assessment and management of crustacean fisheries are urgent to be improved compared to other fisheries. We reviewed and evaluated four kinds of stock assessment methods for assessing crustacean fishery, including the surplus production model, delay-difference model, the depletion model, and size-structured model. We described the application of those models in stock assessment of crustacean fishery, and briefly summarized the assumptions and data needed in these models. We further compared the advantages and disadvantages of those models. In addition, the assumptions of the models, the estimation method of the parameters, and the general solution of uncertainty were analyzed. Finally, the future direction and prospect of crustacean stock assessment were discussed.目前甲壳类生物资源,如蟹、龙虾、对虾及南极磷虾等组成了全球庞大且极具商业价值的渔业.虽然这些渔业的重要性逐步提升,规模也在扩大,但相对于其他渔业,适合且有效的海洋甲壳类资源评估与管理方法仍需进一步发展.本文回顾和评价了各种用于甲壳类生物资源评估的方法与模型,对剩余产量模型、时滞差分模型、损耗模型及体长结构模型等应用到甲壳类生物资源评估的4种主要模型进行了归纳和分析,简要地总结了这几种模型在应用时所需要的假设前提以及对所需数据的要求等,并对比分析了几种模型的优、缺点.此外,本文还列举了关于资源评估方法中模型的假设要求.参数的估算方法、不确定性来源及一般性解决办法等.最后,本文对甲壳类资源评估方法的发展方向和前景进行了展望.
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Development of rational management plans and correct management are essential to maintain the sustainability of fisheries resources. Fisheries management plans should be based on stock assessment and so stock assessment models are important tools for this. Over the last thirty years, fishery stock assessment models experienced a golden age and the number of models grew exponentially with improved computer technology and the integration of multidisciplinary research. At the same time, the complexity and diversity of the models makes choosing the correct one increasingly difficult for researchers and abuse of fisheries models may lead to stock collapse. In this paper, we reviewed fisheries stock assessment model structure, type, and estimators, i.e. fixed effect,random effect, and hierarchical Bayes to identify the typical models currently used in fisheries stock assessment and track their evolution and development. Meanwhile, the present paper discusses the problems with these models and presents prospects for their future development.
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The inclusion of biological and ecological aspects in the assessment of fish population status is one of the bases for an ecosystem-based fisheries management. During the past two decades the Eastern Baltic cod has experienced a drastic reduction in growth and body condition that may have affected its survival. We used results from published experimental literature linking cod condition to starvation and mortality, to estimate the annual proportion of cod close to the lethal condition level in the Eastern Baltic cod stock. Thereafter we applied these results to adjust the natural mortality (M) assumed in the analytical stock assessment model. The results in terms of Spawning Stock Biomass (SSB), Fishing mortality (F) and Recruitment (R) in the final year from the stock assessment using M values adjusted for low condition were up to 40% different compared with the assessment assuming a constant M = 0.2. This method could be used for adjusting natural mortalities for other cod stocks where changes in condition are observed.
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The accurate assessment of fish stocks is crucial for sustainable fisheries management. However, existing statistical stock assessment models can have low forecast performance of relevant stock parameters like recruitment or spawning stock biomass, especially in ecosystems that are changing due to global warming and other anthropogenic stressors. In this paper, we investigate the use of machine learning models to improve the estimation and forecast of such stock parameters. We propose a hybrid model that combines classical statistical stock assessment models with supervised ML, specifically gradient boosted trees. Our hybrid model leverages the initial estimate provided by the classical model and uses the ML model to make a post-hoc correction to improve accuracy. We experiment with five different stocks and find that the forecast accuracy of recruitment and spawning stock biomass improves considerably in most cases.
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This document provides guidelines for fish stock assessment and fishery management using the software tools and other outputs developed by the United Kingdom's Department for International Development's Fisheries Management Science Programme (FMSP) from 1992 to 2004. It explains some key elements of the precautionary approach to fisheries management and outlines a range of alternative stock assessment approaches that can provide the information needed for such precautionary management. Four FMSP software tools, LFDA (Length Frequency Data Analysis), CEDA (Catch Effort Data Analysis), YIELD and ParFish (Participatory Fisheries Stock Assessment), are described with which intermediary parameters, performance indicators and reference points may be estimated. The document also contains examples of the assessment and management of multispecies fisheries, the use of Bayesian methodologies, the use of empirical modelling approaches for estimating yields and in analysing fishery systems, and the assessment and management of inland fisheries. It also provides a comparison of length- and age-based stock assessment methods. A CD-ROM with the FMSP software packages CEDA, LFDA, YIELD and ParFish is included.
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Many methods exist to assess the fishing status of data-limited stocks; however, little is known about the accuracy or the uncertainty of such assessments. Here we evaluate a new size-based data-limited stock assessment method by applying it to well-assessed, data-rich fish stocks treated as data-limited. Particular emphasis is put on providing uncertainty estimates of the data-limited assessment. We assess four cod stocks in the North-East Atlantic and compare our estimates of stock status (F/Fmsy) with the official assessments. The estimated stock status of all four cod stocks followed the established stock assessments remarkably well and the official assessments fell well within the uncertainty bounds. The estimation of spawning stock biomass followed the same trends as the official assessment, but not the same levels. We conclude that the data-limited assessment method can be used for stock assessment and that the uncertainty estimates are reliable. Further work is needed to quantify the spawning biomass of the stock.
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