Introduction

Ecological informatics is concerned with the use of advanced computational technology to: (1) further our understanding of ecosystems at all levels of detail and (2) support rational and transparent decision making concerning ecological management. Distinct features of ecological informatics include: integration across scales and levels of complexity, translation of patterns in data to ecological processes, and adaptive methods of model revision and prediction under uncertainty. One approach for pursuing these goals is Bayesian network (BN) modeling. By succinctly and effectively translating causal assertions between variables into patterns of probabilistic dependence, Bayesian networks facilitate logical and holistic reasoning under uncertainty in complex systems. Such reasoning is necessary for accurate analysis, synthesis, prediction, inference, and decision making.

The first section of this article defines BNs and introduces a simple ecological example that will be used throughout to illustrate basic concepts. Methods for constructing BNs will then be described, including specification of model structure and conditional probabilities. This will be followed by a description of BN use for prediction, inference, explanation, intervention, and decision. Finally, special cases of BNs will be presented including hierarchical, dynamic, and integrated modeling. In this relatively brief overview there is not enough space for detailed theoretical development, algorithms, or examples. Rather, the goal is to provide an introduction to the basic concepts and rely on the sources listed for further reading to fill in the details.

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