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Maximum Entropy and the Inference of Pattern and Dynamics in Ecology

November 17, 2015 - 4:00pm
John Harte

Constrained maximization of information entropy yields least biased probability distributions. From physics to economics, from forensics to medicine, this powerful inference method has enriched science. Here we apply this method to ecology, using constraints derived from ratios of ecological state variables, and infer functional forms for the ecological metrics describing patterns in the abundance, distribution, and energetics of species. I show that a static version of the theory describes remarkably well essentially all observed patterns in quasi-steady-state systems but fails for systems undergoing rapid change. A promising stochastic-dynamic extension of the theory will also be discussed.

John Harte is Professor of Ecosystem Sciences at the University of California, Berkeley. His degrees in physics, are from Harvard and U. Wisconsin. He was formerly a physics professor at Yale and is currently an External Faculty Member of the Santa Fe Institute and a senior researcher at the Rocky Mountain Biological Laboratory. His research includes experimental field investigations of ecosystem-climate feedbacks and theoretical studies in macroecology. He is a Fellow of the American Physical Society and the AAAS, and in 1990 was awarded a Pew Scholars Prize in Conservation and the Environment. In 1993 he was awarded a Guggenheim Fellowship and in 1998 he was appointed a Phi Beta Kappa Distinguished Lecturer. He is the 2001 recipient of the Leo Szilard prize from the American Physical Society, a recipient of a George Polk award in journalism, and has served on six National Academy of Sciences Committees. He has authored 220 scientific publications, including eight books.

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