Entropy, or Information, Unifies Ecology and Evolution and Beyond

被引:16
作者
Sherwin, William Bruce [1 ]
机构
[1] UNSW Sydney, Sch Biol Earth & Environm Sci, Evolut & Ecol Res Ctr, Sydney, NSW 2052, Australia
关键词
Shannon; diversity-profile; entropy; selection; linkage-disequilibrium; gene-expression; evolutionary algorithms; mutual information; adaptation; artificial intelligence; GENETIC DIVERSITY; STATISTICAL-MECHANICS; NATURAL-SELECTION; DISCOVERY RATES; DIFFERENTIATION; POLYMORPHISM; POPULATIONS; FITNESS;
D O I
10.3390/e20100727
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
This article discusses how entropy/information methods are well-suited to analyzing and forecasting the four processes of innovation, transmission, movement, and adaptation, which are the common basis to ecology and evolution. Macroecologists study assemblages of differing species, whereas micro-evolutionary biologists study variants of heritable information within species, such as DNA and epigenetic modifications. These two different modes of variation are both driven by the same four basic processes, but approaches to these processes sometimes differ considerably. For example, macroecology often documents patterns without modeling underlying processes, with some notable exceptions. On the other hand, evolutionary biologists have a long history of deriving and testing mathematical genetic forecasts, previously focusing on entropies such as heterozygosity. Macroecology calls this Gini-Simpson, and has borrowed the genetic predictions, but sometimes this measure has shortcomings. Therefore it is important to note that predictive equations have now been derived for molecular diversity based on Shannon entropy and mutual information. As a result, we can now forecast all major types of entropy/information, creating a general predictive approach for the four basic processes in ecology and evolution. Additionally, the use of these methods will allow seamless integration with other studies such as the physical environment, and may even extend to assisting with evolutionary algorithms.
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页数:14
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