The maximum entropy method of inference and its application to ecology

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Lecture Abstract: Constrained maximization of information entropy yields least biased probability distributions. In statistical physics, this powerful inference method yields classical thermodynamics under the constraints implied by conservation laws. Applied to ecology, with constraints derived from ratios of ecological state variables, this method yields realistic abundance distributions, species-area relationships, spatial aggregation patterns, and body-size distributions. I first review this theory and compare its predictions to census data over a wide range of taxonomic groups, habitats and spatial scales. Then I discuss three areas of current research: extending the theory to allow prediction of ecosystem network structure and trophic flows, deriving the consequences of constraints arising from knowledge of higher taxonomic levels, and predicting the dynamics of macroecological patterns in systems undergoing rapid change.

Slides: Harte Slides