Improving Fish Stock Assessments (1998) / Chapter Skim
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3 Assessment Methods
3 Assessment Methods
Pages 27-36
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From page 27... ...
and parental spawning biomass and can occur as unpredictable variations in the age-specific fishing mortality rates of an exploited population from year to year. In contrast, observational errors arise in the process of obtaining samples from a fishery or by independent surveys.
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From page 28... ...
These models have simple productivity parameters embedded and require no age or length data (Schaefer, 1954; Fletcher, 1978; Prager,1994~. Estimation is accomplished by fitting nonlinear model predictions of exploitable biomass to some indices of exploitable population abundance (usually standardized catch per unit effort, CPUE)
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From page 29... ...
Stock Synthesis is an age-structured assessment technique based on maximum likelihood methods, but with more flexibility to include auxiliary information and fitting criteria. Additional details about ADAPT and Stock Synthesis are provided in Chapter 5.
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From page 30... ...
Instead of fitting the model to age-specific abundance indices, the Stock Synthesis model treats the indices of overall stock abundance separately from the age composition of the survey catches. Thus, year effects affect only year-specific abundance indices and do not introduce correlations among the age-specific observations.
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From page 31... ...
inferences about parameters and other unobserved quantities of interest are based exclusively on the probability of those quantities given the observed data and the prior probability distributions. In a fully Bayesian model, unknown parameters for a system are replaced by known distributions for those parameters observed previously, usually called priors.
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From page 32... ...
This provides a natural hybrid modeling method that could have fishery applications. Advantages of Bayesian Models and Methods A number of features of Bayesian modeling make it particularly useful for fish stock assessments: In a complex model, if a key parameter is treated as totally unknown, the set of parameters of the model Such priors are sometimes C`improper,, in that the specified prior density is not a true density because it does not integrate to 1.
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From page 33... ...
To do a fully Bayesian analysis in a complex setting, with no reasonable prior distributions available from scientific information, requires a careful construction whose effect on the final analysis is not clear without sensitivity analysis. If some priors are available but not for all parameters, a hybrid methodology, which does not yet fully exist, is preferred.
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From page 34... ...
Formally speaking, a Bayesian model is a closed system of undeniable truth, lacking an exterior viewpoint to make a rational model assessment or to construct estimators that are robust to the model building process. To do so, and retain the Bayesian structure, requires constructing a yet more complex Bayesian model that includes all reasonable alternatives to the model in question, and then assessing the posterior probability of the original model within this setting.
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From page 35... ...
For example, species of high economic value or large population size are more likely to have been studied in the past, but they could differ in key biological characteristics from the new species of interest. RETROSPECTIVE ANALYSIS IN STOCK ASSESSMENTS The reliance of most stock assessment models on time-series data implies not only that each successive assessment characterizes current stock status and other parameters used for management, but also that the complete time series of past abundance estimates is updated.
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From page 36... ...
This issue will be more broadly explored at the 1997 Lowell Wakefield Symposium sponsored by the Alaska Sea Grant College Program. UNCERTAINTY IN STOCK ASSESSMENT METHODS AND MODELS Stock assessments are intrinsically uncertain.
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