Testing length-based reference points in a management strategy evaluation for cuckoo ray (Leucoraja naevus) and thornback ray (Raja clavata)
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Abstract Elasmobranchs grow relatively slowly and mature at a relatively high age, leading to longer generation time. Due to low fecundity of these stocks, sufficient numbers of mature individuals are important to ensure a viable recruitment level and sustainable management. Length-based indicators (LBIs), such as the mean length and the mean length of the largest 5% in the catch, can be used to characterize the length distribution of exploited stocks and aid a data-limited assessment. Reference points for these indicators are calculated using basic life history parameters. Using cuckoo ray, Leucoraja naevus, and thornback ray, Raja clavata, as example species, we apply management strategy evaluations to test the performance of LBI-based harvest control rules (HCRs) in their ability to recover overexploited stocks. We illustrate the importance of the stock–recruitment relationship for the management outcome. If immature individuals are targeted by the fishery, HCRs perform better in terms of stock recovery when coupled with reference points, which account for the maturation schedule of the stock. The sensitivity of reference points to parameter misspecification means that elasmobranchs stocks in which immature individuals are exploited by the fishery may require more precautionary reference points, with consideration of the trade-off between biomass recovery and yield.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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Abstract Observations of fecundity from the 2001 western horse mackerel spawning-stock biomass survey suggest that the species is an indeterminate spawner. Therefore, estimates of fecundity based on biological analyses and until recently used in the calibration of the stock assessment are now questioned. The stock is assessed by fitting a linked Separable and ADAPT VPA-based model to the catch-at-age data and to the egg production estimates. Currently, the assumption is that egg production and spawning-stock biomass are linked by a constant but unknown fecundity parameter, estimated within the model. In this study, the effects of introducing relationships linking biological indicators of fecundity, such as lipid content or feeding intensity during the spawning season, to actual fecundity are examined within a simulation framework. Simulations suggest that when the underlying relationships between fecundity and the proxy are poorly described, weak, or based on a relatively short time-series of data, the assumption of constant fecundity will result in better management advice than using the proxy.
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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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