期刊
FISH AND FISHERIES
卷 18, 期 2, 页码 226-243出版社
WILEY
DOI: 10.1111/faf.12174
关键词
Data-limited methods; fisheries management; maximum sustainable yield; Pella-Tomlinson model; seasonal population dynamics; stock assessment
类别
资金
- Norden Top-level Research Initiative subprogramme 'Effect Studies and Adaptation to Climate Change'
- Danish Ministry of Environment and Food
- AZTI Technalia, Spain
Surplus production modelling has a long history as a method for managing datalimited fish stocks. Recent advancements have cast surplus production models as state-space models that separate random variability of stock dynamics from error in observed indices of biomass. We present a stochastic surplus production model in continuous time (SPiCT), which in addition to stock dynamics also models the dynamics of the fisheries. This enables error in the catch process to be reflected in the uncertainty of estimated model parameters and management quantities. Benefits of the continuous-time state-space model formulation include the ability to provide estimates of exploitable biomass and fishing mortality at any point in time from data sampled at arbitrary and possibly irregular intervals. We show in a simulation that the ability to analyse subannual data can increase the effective sample size and improve estimation of reference points relative to discrete-time analysis of aggregated annual data. Finally, subannual data from five North Sea stocks are analysed with particular focus on using residual analysis to diagnose model insufficiencies and identify necessary model extensions such as robust estimation and incorporation of seasonality. We argue that including all known sources of uncertainty, propagation of that uncertainty to reference points and checking of model assumptions using residuals are critical prerequisites to rigorous fish stock management based on surplus production models.
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