Brooks, E. N., Powers, J. E., and Cortés, E. 2010. Analytical reference points for age-structured models: application to data-poor fisheries. – ICES Journal of Marine Science, 67: 165–175. Analytical solutions for biological reference points are derived in terms of maximum lifetime reproductive rate. This rate can be calculated directly from biological parameters of maturity, fecundity, and natural mortality or a distribution for this rate can be derived from appropriate metadata. Minimal data needs and assumptions for determining stock status are discussed. The derivations lead to a re-parameterization of the common stock–recruit relationships, Beverton–Holt and Ricker, in terms of spawning potential ratio. Often, parameters in stock–recruit relationships are restricted by tight prior distributions or are fixed based on a hypothesized level of stock resilience. Fixing those parameters is equivalent to specifying the biological reference points. An ability to directly calculate reference points from biological data, or a meta-analysis, without need of a full assessment model or fisheries data, makes the method an attractive option for data-poor fisheries. The derivations reveal an explicit link between the biological characteristics of a species and appropriate management. Predicted stock status for a suite of shark species was compared with recent stock assessment results, and the method successfully identified whether each stock was overfished.
The practice of treating stock assessment model output as data in subsequent modeling efforts is becoming more common, aided in part by the growing availability of online repositories of assessment results (misleadingly referred to as “data” bases). Such modeling exercises frequently overlook the uncertainty in the assessment output, the potential bias in estimates and correlation between estimates, and the structural assumptions of the original assessment model. We provide examples of post hoc analyses and discuss the problems in each case. We suggest alternative approaches that could have avoided using assessment model output altogether or suggest analyses that may have exposed the pitfalls of such methods. Whenever possible, we suggest not using stock assessment model output as data in post hoc analyses. If using assessment model output as data is unavoidable, then to address some aspects of the uncertainties associated with using assessment model estimates, we suggest collaborating with lead assessment scientists, sensitivity analyses, errors-in-variables methods, and cross-validation methods. Such additional work is imperative if research that uses stock assessment output as data is to make robust and meaningful contributions to stock assessment methodology and management decisions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.