Confounding of Gear Selectivity and the Natural Mortality Rate in Cases where the Former is a Nonmonotone Function of Age

An "exponential–logistic" selectivity function is presented in which a single parameter (γ) determines whether gear selectivity is asymptotic (γ = 0) or reaches a maximum at finite age (γ > 0). The function is used to develop a model in which both γ and the natural mortality rate M are formally indeterminate and in which the coming year's catch limit can be viewed as a response function of either estimated γ or estimated M. Decision theory is then used to derive the optimal catch. The optimal catch is shown to increase with the degree of uncertainty surrounding M, although this conclusion may depend on the short managerial time frame assumed. Three "suboptimal" strategies are also considered: (1) setting catch at the level corresponding to the expected value of M, (2) setting catch at the minimum of the response function, and (3) setting catch at the level corresponding to γ = 0. The first suboptimal strategy never results in a catch greater than the optimum and always results in a lower expected loss tha...

[1]  H. Lassen,et al.  Error in the Virtual Population Analysis: the effect of uncertainties in the natural mortality coefficient , 1973 .

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

[3]  C. Goodyear,et al.  Spawning stock biomass per recruit in fisheries management: foundation and current use , 1993 .

[4]  G. Degani,et al.  Effects of Dietary 17α-Methyltestosterone and Bovine Growth Hormone on Growth and Food Conversion of Slow- and Normally-Growing American Elvers (Anguilla rostrata) , 1985 .

[5]  Mikael Hildén,et al.  Errors of perception in stock and recruitment studies due to wrong choices of natural mortality rate in Virtual Population Analysis , 1988 .

[6]  Colin W. Clark,et al.  Mathematical Bioeconomics: The Optimal Management of Renewable Resources. , 1993 .

[7]  Ø. Ulltang Sources of errors in and limitations of Virtual Population Analysis (Cohort analysis) , 1977 .

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

[9]  J. E. Paloheimo,et al.  Estimation of Mortality Rates in Fish Populations , 1980 .

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

[11]  Robert V. Hogg,et al.  Introduction to Mathematical Statistics. , 1966 .

[12]  S. E. Sims An analysis of the effect of errors in the natural mortality rate on stock-size estimates using Virtual Population Analysis (Cohort Analysis) , 1984 .

[13]  R. Peterman,et al.  Trends in Fishing Mortality Rate along with Errors in Natural Mortality Rate can cause Spurious Time Trends in Fish Stock Abundances Estimated by Virtual Population Analysis (VPA) , 1989 .

[14]  B. Megrey,et al.  Recent Developments in the Quantitative Analysis of Fisheries Data , 1994 .

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

[16]  G. Kesteven Population Studies in Fisheries Biology , 1947, Nature.

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

[18]  James R. Bence,et al.  Influence of Age-Selective Surveys on the Reliability of Stock Synthesis Assessments , 1993 .

[19]  Carl J. Walters,et al.  Adaptive Management of Renewable Resources , 1986 .

[20]  David B. Sampson,et al.  Variance estimators for Virtual Population Analysis , 1987 .

[21]  M. Degroot Optimal Statistical Decisions , 1970 .

[22]  G. Thompson Management advice from a simple dynamic pool model , 1992 .

[23]  D. A. Fournier An Analysis of the Hecate Strait Pacific Cod Fishery Using an Age-structured Model Incorporating Density-dependent Effects , 1983 .