Population viability analysis based on combining Bayesian, integrated, and hierarchical analyses

Abstract

Abstract Several methods used in fisheries stock assessment models that can be applied to population viability analysis are presented. (1) Integrated analysis allows the use of all information on a particular population, and ensures that all model assumptions and parameter are consistent throughout the analysis, that uncertainty is propagated throughout the analysis, and that the correlation among parameters is preserved. (2) Bayesian analysis allows for the inclusion of prior information, and is a convenient way to represent uncertainty. (3) Random-effects models based on hierarchical modeling allow information to be shared among parameter estimates and allow the separation of process error from estimation error. (4) Non-parametric representation of parameters allows for a more flexible relationship among the parameters. (5) Robust likelihood functions provide an automatic method to reduce the influence of outliers when the data sets are large. These methods are applied to artificial data sets provided by the Extinction Risk Working Group of the National Center for Ecological Analysis and Synthesis (NCEAS) using AD Model Builder software (Otter Research™).