Estimating fisheries reference points from catch and resilience |
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Authors: | Rainer Froese Nazli Demirel Gianpaolo Coro Kristin M Kleisner Henning Winker |
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Institution: | 1. GEOMAR Helmholtz Centre for Ocean Research Kiel, Kiel, Germany;2. Institute of Marine Sciences and Management, Istanbul University, Istanbul, Turkey;3. Istituto di Scienza e Tecnologie dell'Informazione “A. Faedo”, Consiglio Nazionale delle Ricerche (CNR), Pisa, Italy;4. National Oceanographic and Atmospheric Administration, Northeast Fisheries Science Center, Woods Hole, MA, USA;5. South African National Biodiversity Institute, Kirstenbosch Research Centre, Claremont, South Africa;6. Centre for Statistics in Ecology, Environment and Conservation (SEEC), Department of Statistical Sciences, University of Cape Town, Rondebosch, South Africa |
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Abstract: | This study presents a Monte Carlo method (CMSY) for estimating fisheries reference points from catch, resilience and qualitative stock status information on data‐limited stocks. It also presents a Bayesian state‐space implementation of the Schaefer production model (BSM), fitted to catch and biomass or catch‐per‐unit‐of‐effort (CPUE) data. Special emphasis was given to derive informative priors for productivity, unexploited stock size, catchability and biomass from population dynamics theory. Both models gave good predictions of the maximum intrinsic rate of population increase r, unexploited stock size k and maximum sustainable yield MSY when validated against simulated data with known parameter values. CMSY provided, in addition, reasonable predictions of relative biomass and exploitation rate. Both models were evaluated against 128 real stocks, where estimates of biomass were available from full stock assessments. BSM estimates of r, k and MSY were used as benchmarks for the respective CMSY estimates and were not significantly different in 76% of the stocks. A similar test against 28 data‐limited stocks, where CPUE instead of biomass was available, showed that BSM and CMSY estimates of r, k and MSY were not significantly different in 89% of the stocks. Both CMSY and BSM combine the production model with a simple stock–recruitment model, accounting for reduced recruitment at severely depleted stock sizes. |
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Keywords: | Bayesian state‐space model biomass dynamic model data‐limited stock assessment Monte Carlo method stock– recruitment relationship surplus production model |
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