Estimating fisheries reference points from catch and resilience

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Froese R., Demirel N., Coro G., Kleisner K. M., Winker H.

FISH AND FISHERIES, vol.18, pp.506-526, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 18
  • Publication Date: 2017
  • Doi Number: 10.1111/faf.12190
  • Journal Name: FISH AND FISHERIES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.506-526
  • Keywords: Bayesian state-space model, biomass dynamic model, data-limited stock assessment, Monte Carlo method, stock-recruitment relationship, surplus production model, LIFE-HISTORY, NATURAL MORTALITY, STOCK ASSESSMENT, MODEL, FISH, STEEPNESS, LIMITS
  • Istanbul University Affiliated: Yes


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.