BibTeX
@ARTICLE{
         Maunder2004PVA,
       title = "Population Viability Analysis, Based on Combining Integrated, Bayesian, and
         Hierarchical Analyses",
       author = "M.N. Maunder",
       year = "2004",
       journal = "Acta Oecologica",
       volume = "26",
       pages = "85--94",
       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 RiskWorking Group of the
         National Center for Ecological Analysis and Synthesis (NCEAS) using AD Model Builder software (Otter
         Research™).",
       ad_area = "Biology",
       ad_tools = "AD Model Builder"
}