fishStan: Hierarchical Bayesian models in support of fisheries management for fisheries
Hierarchical Bayesian models allow for using shared statistical distributions to inform parameter estimates.. The fishStan statistical programming package in R has been used to evaluate common fisheries models using the probabilistic programming language Stan language. The software package also includes documentation about how to use the models.
Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above examples also demonstrate how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river navigation pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and require manual programming. Scientists at USGS UMESC and the State University of New York -Oneonta created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.
fishStan Package Version 2
Hierarchical Bayesian models allow for using shared statistical distributions to inform parameter estimates.. The fishStan statistical programming package in R has been used to evaluate common fisheries models using the probabilistic programming language Stan language. The software package also includes documentation about how to use the models.
Fisheries managers and ecologists use statistical models to estimate population-level relations and demographic rates (e.g., length-maturity curves, growth curves, and mortality rates). These relations and rates provide insight into populations and inputs for other models. For example, growth curves may vary across lakes showing fish populations differ due to management actions or underlying environmental conditions. A fisheries manager could use this information to set lake-specific harvest limits or an ecologist could use this information to test scientific hypotheses about fish populations. The above examples also demonstrate how populations exist within hierarchical structures where sub-populations may be nested within a meta-population. More generally, these hierarchical structures may be both biological (e.g., different lakes or river navigation pools) and statistical (e.g., correlated error structures). Currently, limited options exist for fitting these hierarchical models and require manual programming. Scientists at USGS UMESC and the State University of New York -Oneonta created fishStan to share hierarchical models for fisheries and ecology in an easy-to-use R package.