Evaluation of quota management policies for developing fisheries

Losses can be measured as deviations from a desired reference trajectory of quotas that would be taken if there were no uncertainty and are highly dependent on assessments prior to and during development. Simulations of assessment and quota setting under various quota setting rules indicate that variability in relative abundance indices can cause substantial losses, especially considering cumulative effect of early quota errors on later departures of biomass from that needed to produce the desired quotas, even if optimum fishing mortality rate is known in advance. Conservative assessments (low biomass estimates for which there is only a small probability that biomass is actually lower) are favored during development when loss is measured as the relative departure from the best quota for each year. But if loss is measured as absolute departure from the best quota, it is generally better to base the quota on the biomass estimate for which there is nearly a 50% chance that the stock is smaller. Deliberate ov...

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

[2]  Yong Chen,et al.  Impacts of outliers and mis-specification of priors on Bayesian fisheries-stock assessment , 2000 .

[3]  David A. Fournier,et al.  Impacts of atypical data on Bayesian inference and robust Bayesian approach in fisheries , 1999 .

[4]  Carl J. Walters,et al.  Multispecies spatial assessment models for the British Columbia groundfish trawl fishery , 1999 .

[5]  R. Myers,et al.  A simplified formulation for fish production , 1998 .

[6]  R. Hilborn,et al.  Contested stock assessment: two case studies , 1998 .

[7]  R. Lande,et al.  Harvesting Strategies for Fluctuating Populations Based on Uncertain Population Estimates , 1997 .

[8]  André E. Punt,et al.  The performance of VPA-based management , 1997 .

[9]  J. Hutchings.  Spatial and temporal variation in the density of northern cod and a review of hypotheses for the stock's collapse , 1996 .

[10]  C. Walters,et al.  Fixed exploitation rate strategies for coping with effects of climate change , 1996 .

[11]  Jon T. Schnute,et al.  The influence of error on population estimates from catch-age models , 1995 .

[12]  G. Kirkwood,et al.  Comparative performance of ADAPT and Laurec–Shepherd methods for estimating fish population parameters and in stock management , 1995 .

[13]  Carl J. Walters,et al.  Use of Gaming Procedures in Evaluation of Management Experiments , 1994 .

[14]  Jon T. Schnute,et al.  A General Framework for Developing Sequential Fisheries Models , 1994 .

[15]  André E. Punt,et al.  Placing odds on sustainable catch using virtual population analysis and survey data , 1994 .

[16]  Carl J. Walters,et al.  Calculation of Bayes Posterior Probability Distributions for Key Population Parameters , 1994 .

[17]  André E. Punt,et al.  Fitting Surplus Production Models: Comparing Methods and Measuring Uncertainty , 1993 .

[18]  Carl J. Walters,et al.  Harvesting regulation under quota management systems for ocean fisheries : Decision making in the face of natural variability, weak information, risks and conflicting incentives , 1992 .

[19]  Ray Hilborn,et al.  Adaptive management of developing fisheries , 1988 .

[20]  C. Walters,et al.  Are age-structured models appropriate for catch-effort data? , 1985 .

[21]  P. R. Neal,et al.  Catch-Age Analysis with Auxiliary Information , 1985 .

[22]  David A. Fournier,et al.  A General Theory for Analyzing Catch at Age Data , 1982 .

[23]  Ray Hilborn,et al.  Comparison of Fisheries Control Systems That Utilize Catch and Effort Data , 1979 .