Maximum entropy, logistic regression, and species abundance
There is considerable debate about the utility of statistical mechanics in predicting diversity patterns in terms of life history traits. Here, I reflect on this debate and show that a community is controlled by the balance of two opposite forces: the entropic part (the natural tendency of the syste...
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Published in | Oikos Vol. 119; no. 4; pp. 578 - 582 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Oxford, UK
Oxford, UK : Blackwell Publishing Ltd
01.04.2010
Blackwell Publishing Ltd Blackwell Publishing |
Subjects | |
Online Access | Get full text |
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Summary: | There is considerable debate about the utility of statistical mechanics in predicting diversity patterns in terms of life history traits. Here, I reflect on this debate and show that a community is controlled by the balance of two opposite forces: the entropic part (the natural tendency of the system to be in the configuration with the highest possible entropy) and environmental, ecological and evolutionary constraints maintaining order (reducing entropy). The Boltzmann distribution law that can be derived from the maximum entropy formalism provides a fundamental model for linking species abundance to life history traits and environmental constraining factors. This model predicts a global pattern of diversity evenness along a latitudinal gradient. Although the Boltzmann distribution and the logistic regression models represent two fundamentally different approaches, the two models have an identical mathematical form. Their identical formalisms facilitate the interpretation of logistic regression models with statistical mechanics, and reveal several limitations of the maximum entropy formalism. I argued that although maximum entropy formalism is a promising tool for modeling species abundances and for linking microscopic quantities of individual life history traits to macroscopic patterns of diversity, it is necessary to revise the Boltzmann distribution law for successful prediction of species abundance. |
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Bibliography: | http://dx.doi.org/10.1111/j.1600-0706.2009.17113.x istex:8986DFD30D0C2EE917A4B77D2582DEE3FC5085B8 ArticleID:OIK17113 ark:/67375/WNG-P54MV3XH-7 ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0030-1299 1600-0706 |
DOI: | 10.1111/j.1600-0706.2009.17113.x |