Collaborative Research on Bayesian Semiparametric Population Dynamics Modeling

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The mechanisms of population regulation are rarely well understood and often hotly debated. In principle, clever experiments could reveal why populations fluctuate but, for most species, such experiments are impractical, if not impossible. Indirect methods for identifying these mechanisms are therefore needed. Statistical models that use semiparametric Bayesian methods will be developed that allow unknown mechanisms to be inferred from observed fluctuations in abundance. Population dynamics models are routinely used to make important decisions in conservation and management. However, all models currently in use have the substantial drawback that they require the specification of all model components whether or not these mechanisms are known. Mistakes in model specification may result in millions of dollars lost to harvesters or the extinction of threatened species. The methods that will be developed under this award will enable the conservation and management communities to generate robust population projections and will greatly increase their chances of success. This work will be a novel synthesis of ideas from ecological modeling and Bayesian nonparametrics. To foster applications of these methods the PIs will develop easy to use software that will be actively distributed through conservation and management meetings and their professional websites.