Estimating reference fishing mortality rates from noisy spawnerrecruit data

We review and evaluate methods of estimating reference fishing mortality rates from spawner–recruit (SR) data to obtain maximum sustainable yield. Using Monte Carlo simulations, we found that a reference fishing mortality rate derived from the maximum likelihood estimates of the SR parameters was less biased than reference fishing mortality rates obtained using the mode of the marginal probability distribution for the maximum rate that spawners produce recruits or by finding the fishing mortality rate that maximizes the expected yield. However, the maximum likelihood method produced the most variable estimates, at times leading to substantial under- or over-exploitation of the population. In contrast, the decision theoretic method of maximizing the expected yield exhibited less variability, produced higher yields, and substantially reduced the risk of overexploiting the population. We show how these methods can be extended to include information from other populations. Bayesian priors for the SR parameter...

[1]  R. Hilborn,et al.  Fisheries stock assessment and decision analysis: the Bayesian approach , 1997, Reviews in Fish Biology and Fisheries.

[2]  Carl Walters,et al.  Stock information requirements for quota management systems in commercial fisheries , 1996, Reviews in Fish Biology and Fisheries.

[3]  R. Beverton,et al.  On the dynamics of exploited fish populations , 1993, Reviews in Fish Biology and Fisheries.

[4]  J. J. Hunt,et al.  Workshop on risk evaluation and biological reference points for fisheries management , 2004, Reviews in Fish Biology and Fisheries.

[5]  C. Walters,et al.  Quantitative fisheries stock assessment: Choice, dynamics and uncertainty , 2004, Reviews in Fish Biology and Fisheries.

[6]  Ding-Geng Chen,et al.  A regional meta-model for stock-recruitment analysis using an empirical Bayesian approach , 2002 .

[7]  Ray Hilborn,et al.  Inferring Bayesian Priors with Limited Direct Data: Applications to Risk Analysis , 2002 .

[8]  J. Brodziak.  In Search of Optimal Harvest Rates for West Coast Groundfish , 2002 .

[9]  W. Clark.  F35% Revisited Ten Years Later , 2002 .

[10]  M. Dorn,et al.  Advice on West Coast Rockfish Harvest Rates from Bayesian Meta-Analysis of Stock−Recruit Relationships , 2002 .

[11]  Anders Nielsen,et al.  Comparison of the frequentist properties of Bayes and the maximum likelihood estimators in an age-structured fish stock assessment model , 2002 .

[12]  Ransom A. Myers,et al.  Hierarchical Bayesian models of length-specific catchability of research trawl surveys , 2001 .

[13]  Ransom A. Myers,et al.  What is the carrying capacity for fish in the ocean? A meta-analysis of population dynamics of North Atlantic cod , 2001 .

[14]  P. Mace.  a new role for MSY in single-species and ecosystem approaches to fisheries stock assessment and management , 2001 .

[15]  Debashis Kushary,et al.  Bootstrap Methods and Their Application , 2000, Technometrics.

[16]  Ransom A. Myers,et al.  Still more spawner-recruitment curves: the hockey stick and its generalizations , 2000 .

[17]  Ransom A. Myers,et al.  Maximum reproductive rate of fish at low population sizes , 1999 .

[18]  Precision and uses of biological reference points calculated from stock recruitment data , 1999 .

[19]  Terrance J. Quinn,et al.  Quantitative Fish Dynamics , 1999 .

[20]  Ransom A. Myers,et al.  Reducing uncertainty in the biological basis of fisheries management by meta-analysis of data from many populations: a synthesis , 1998 .

[21]  J. Maguire,et al.  Recent international agreements and the precautionary approach: new directions for fisheries management science , 1998 .

[22]  A. Rukhin.  Bayes and Empirical Bayes Methods for Data Analysis , 1997 .

[23]  Bradley P. Carlin,et al.  BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..

[24]  J. Caddy,et al.  Targets or Limits for Management of Fisheries , 1996 .

[25]  B. Efron.  Empirical Bayes Methods for Combining Likelihoods , 1996 .

[26]  J. Ianelli,et al.  Decision analysis of alternative harvest policies for the Gulf of Alaska Pacific ocean perch fishery , 1995 .

[27]  S. Frederick,et al.  Choosing fisheries harvest policies : when does uncertainty matter? , 1995 .

[28]  J. F. Caddy,et al.  Reference Points for Fisheries Management , 1995 .

[29]  P. Mace,et al.  Relationships between Common Biological Reference Points Used as Thresholds and Targets of Fisheries Management Strategies , 1994 .

[30]  P. Mace,et al.  How much spawning per recruit is enough , 1993 .

[31]  William G. Clark,et al.  Groundfish Exploitation Rates Based on Life History Parameters , 1991 .

[32]  G. Swartzman,et al.  On Managing Variable Marine Fisheries , 1987 .

[33]  John Shepherd,et al.  A versatile new stock-recruitment relationship for fisheries, and the construction of sustainable yield curves , 1982 .

[34]  C. Walters,et al.  Optimal harvesting with imprecise parameter estimates , 1982 .

[35]  C. Goodyear.  Assessing the Impact of Power Plant Mortality on the Compensatory Reserve of Fish Populations , 1977 .

[36]  P. A. Larkin.  An Epitaph for the Concept of Maximum Sustained Yield , 1977 .

[37]  J. A. Gulland,et al.  SCIENTIFIC ADVICE ON CATCH LEVELS , 1973 .