Quantifying the Community: Using Bayesian Learning Networks to find Structure and Conduct Inference in Invasions Biology

One of the key obstacles to better understanding, anticipating, and managing biological invasions is the difficulty researchers face when trying to quantify the many important aspects of the communities that affect and are affected by non-indigenous species (NIS). Bayesian Learning Networks (BLNs) combine graphical models with multivariate Bayesian statistics to provide an analytical tool for the quantification of communities. BLNs can determine which components of a natural system influence which others, quantify this influence, and provide inferential analysis of parameter changes when changes in network variables are hypothesized or observed. After a brief explanation of these three functions of BLNs, a simulated network is analyzed for structure, parameter estimation, and inference. Discussion of this approach to invasions biology is explored and expanded applications for BLNs are then offered.

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