Building capacity in Bayesian analysis for practicing ecologists

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Traditionally, ecologists used designed experiments and associated statistical procedures to learn about the environment. Although this approach provided valuable insight, it has become clear that many pressing problems in ecology cannot be solved using classical methods for design and analysis. In particular, ecological researchers need new methods to honestly represent uncertainty, to use data from multiple sources, to estimate quantities that cannot be observed, and to make forecasts about the environment. Hierarchical Bayesian methods meet these needs, allowing investigators to address questions that heretofore were seen as too complex to answer. Many ecologists at universities and management agencies recognize the promise of these methods but lack an efficient way to learn them. The research team will offer intensive training on hierarchical Bayesian methods to post-doctoral researchers, academic faculty, and scientists at agencies and non-governmental organizations. Participation by groups under-represented in science will be enhanced by holding one of the workshops at the University of Puerto Rico in Rio Piedras. A listserv on the application of Bayesian methods in ecology will be developed. A website with open access to training materials will be maintained. The impact of the project will be amplified several fold as newly trained scientists apply Bayesian methods in their research, teaching, and decision-support.