Abstract
We document a time–density model for in-season assessment of salmon stocks that integrates both relative and absolute indicators of abundance and incorporates preseason information on run size and migration timing using a Bayesian framework. We evaluate different data collection programs for Fraser River sockeye salmon (Oncorhynchus nerka) by examining the precision, bias, and timeliness of resulting run-size estimates with a retrospective analysis of 20 years of data. We quantify the run-size bias if migration was early versus late and evaluate the impact of run-size uncertainty on the ability to reach management objectives. In-season assessments greatly improve the accuracy and precision of run-size estimates compared with preseason forecasts. For the in-season assessment of Fraser River sockeye, catch-per-unit-effort (CPUE) data from seaward marine test fisheries, although less precise, were more informative at the peak marine migration than more precise terminal, in-river hydroacoustic data obtained on the same date due to the time lag associated with migration from the marine test fishing locations to the lower river. Throughout the season, the best fisheries management results were obtained by relying on in-season assessments using both marine CPUE data as well as marine reconstructed abundance estimates derived from in-river hydroacoustic estimates, thereby taking advantage of the benefits of both sources of information.
Highlights
Introduction aftManagement of fisheries typically involves the assessment of the abundance of stocks using different types of information collected over varying periods of time
In this paper we describe a general time-density model developed in a Bayesian framework that can be used to provide in-season assessments of total run size and timing for anadromous species that migrate past one or more assessment locations
We retrospectively evaluate the gains in the accuracy, precision and timeliness of the run-size estimates when using relative and/or absolute indices of abundance within the in-season assessment as compared to https://mc06.manuscriptcentral.com/cjfas-pubs relying on pre-season forecast estimates
Summary
Management of fisheries typically involves the assessment of the abundance of stocks using different types of information collected over varying periods of time. This information typically includes fishery-dependent as well as fishery-independent data. More recently Bayesian methodology has been employed allowing for objective approaches to combine results from multiple models and sources of information and to provide estimates of uncertainty of abundance The assessment of uncertainty about stock abundance has become an important part of fisheries management, allowing for an objective decision process to evaluate the outcomes of various management actions (Rosenberg and Restrepo 1994). The quantification of uncertainty can be used by managers to assess the contribution or value of scientific programs and fishery dependent data
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