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Derivations of the Core Functions of the Maximum Entropy Theory of Ecology
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Information Across the Ecological Hierarchy

Department of Biology, University of Florida, Gainesville, FL 32611-8525, USA
Center for Environmental Science, University of Maryland, Solomons, MD 20688-0038, USA
Entropy 2019, 21(10), 949;
Received: 17 July 2019 / Revised: 23 September 2019 / Accepted: 25 September 2019 / Published: 27 September 2019
(This article belongs to the Special Issue Information Theory Applications in Biology)
The ecosystem is a theatre upon which is presented, in various degrees and at differing scales, a drama of constraint and information vs. disorganization and entropy. Concerning biology, most think immediately of genomic information. It strongly constrains the form and behavior of individual species, but its influence upon community structure is indeterminate. At the community level, information acts as a formal cause behind regular patterns of development. Community structure is an amalgam of information and entropy, and the Gibbs–Boltzmann formula departs from the thermodynamic sense of entropy. It measures only the extreme that entropy might reach if the elements of the system were completely independent. A closer analogy to physical entropy in systems with interactions is the conditional entropy—the amount by which the Shannon measure is reduced after the information in the constraints among elements has been subtracted. Finally, at the whole ecosystem level, in communities that inhabit mostly fixed physical environments (e.g., landscapes or seabeds), the distributions of plants and animals appear to be independent both of causal mechanisms and trophic controls, and assume instead forms that maximize the overall entropy of dispersal. View Full-Text
Keywords: centripetality; conditional entropy; entropy; information; MAXENT; mutual information; network analysis centripetality; conditional entropy; entropy; information; MAXENT; mutual information; network analysis
MDPI and ACS Style

Ulanowicz, R.E. Information Across the Ecological Hierarchy. Entropy 2019, 21, 949.

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