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West Coast Groundfish Harvest Policy

Tools for Estimating Surplus Production and FMSY in Any Stock Assessment Model

, &
Pages 326-338 | Received 14 Mar 2000, Accepted 03 May 2001, Published online: 08 Jan 2011
 

Abstract

Surplus-production calculations can be made in any stock assessment model that includes catch data and that estimates biomass. Maximum sustainable yield (MSY) reference points can also be estimated if there are sufficient data. The “external” method estimates production model parameters after the assessment model is fitted. External calculations are simple and useful; we recommend that they be done routinely. The “internal” method fits a more complicated assessment model and a surplus-production model simultaneously. It includes the external and “all-measurement-error” modeling methods (e.g., ASPIC) as special cases. External MSY estimates for Atlantic surfclam Spisula solidissima off northern New Jersey, cowcod Sebastes levis in the Southern California bight, and yellowtail flounder Pleuronectes ferrugineus on Georges Bank and internal MSY estimates for northern Atlantic surfclam were similar to conventional estimates. Surplus-production calculations and both internal and external modeling approaches are useful for summarizing assessment results in terms of surplus production, which is important to managers, and in identifying plausible modeling scenarios. Internal surplus-production calculations may be most useful when recruitment is highly variable or when there is substantial variability in growth. Unlike some surplus-production modeling techniques, the external and internal approaches use all of the available data (e.g., age and size composition data) to estimate stock status and MSY reference points. They avoid the problems that arise in relating fishing mortality and biomass estimates from one model to reference point calculations from a second. The internal and external methods are examples of Fournier and Warburton's composite approach, which attempts to balance the complimentary strengths and weaknesses of simple and complex models.

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