Bias of Equilibrium-Based Estimators under Biological and Fishery Disequilibria

Bias of Equilibrium-Based Estimators under Biological and Fishery Disequilibria

Billy Ernst and Juan L. Valero

Bias of Equilibrium-Based Estimators under Biological and Fishery DisequilibriaThis is part of Fisheries Assessment and Management in Data-Limited Situations
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Description

Steady state or equilibrium assumptions have been recurrent temptations in fishery science to circumvent the problem of limited data and to provide managers with biological parameters and population status estimates in data-poor situations. Historically, fishery scientists and ecologists have used equilibrium approaches to produce estimates of key population parameters (e.g., mortality rates and abundance) with applications ranging from small-scale artisanal to large-scale industrial fisheries. Unfortunately, these methods rely on many restrictive hard-to-test assumptions, especially for those related to the lack of representation of the underlying dynamics. Size (mostly length) frequency data are among the basic statistics collected in most fisheries. Length-based cohort analysis and length-converted catch curve analysis (LCCCA) are part of the methodological toolkit available to modelers to obtain abundance estimates and mortality rates. Ingenious variations and extensions of these methods have been proposed but the uncertainty in model structure has virtually not been addressed. We evaluate the performance of equilibrium-derived estimators using two different methods. We implemented a dynamic operating model to show the lack of robustness of Jones’ length based cohort analysis to trends in fishing mortality rates. We also evaluated the performance of LCCCA in estimating natural mortality rates of the Patagonian scallop (Zygochlamys patagonica) by a cross comparison of these results to the estimates from a fully integrated statistical dynamic model. Substantial biases in parameter estimates suggest the importance of formally incorporating dynamics into the model structure. We recommend that mortality rates and abundance estimates derived from these methods should be cautiously accepted or not used at all. Indeed, scientists and managers should take more proactive measures to collect new data and develop a better understanding of the status and productivity of the stocks.

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