Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 4206
Title: Biomass targets for exploited marine fishes, incorporating taxonomic and life history information
Author: James T. Thorson, J. M. Cope, Trevor A. Branch, Olaf Jensen
Publication Year: 2012
Journal: Canadian Journal of Fisheries and Aquatic Sciences
Volume: 69
Issue: 9
Pages: 1556-1568
DOI: 10.1139/f2012-077
Keywords: Schaeffer surplus production model, Fox surplus production model, Pella-Thomlinson shape parameter, target depletion, meta-analysis,
Abstract:

Surplus production represents the processes that affect sustainable fishery harvest and is central to the ecology and management of marine fishes. Taxonomy and life history influence the ratio of spawning biomass at maximum sustainable yield to average unfished spawning biomass (SBMSY/SB0), and estimating this ratio for individual stocks is notoriously difficult. We use a database of published landings data and stock assessment biomass estimates and determine that process errors predominate in this data set by fitting a state–space model to data from each stock individually. We then fit multispecies process-error models while treating SBMSY/SB0 as a random effect that varies by taxonomic order and maximum length. The estimated SBMSY/SB0 = 0.40 for all 147 stocks is intermediate between the values assumed by the Fox and the Schaefer models, although Clupeiformes and Perciformes have lower and Gadiformes and Scorpaeniformes have higher SBMSY/SB0 values. Model selection supports the hypothesis that large-bodied fishes for a given taxonomic order have relatively higher SBMSY/SB0. Results can be used to define reference points for data-poor fisheries or as input in emerging assessment methods.

Theme: Recovery, Rebuilding and Sustainability of Marine and Anadromous Species
Foci: Describe the relationship among human activities and species stock status, recovery, rebuilding and sustainability.
Characterize vital rates and other demographic parameters for key species, and develop and improve methods for predicting risk and viability/sustainability from population dynamics and demographic information.