Maximizing Diversity in Biology and Beyond

Entropy

Mark W. Meckes¹  Tom Leinster² 

[1] Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH 44106, USA;School of Mathematics, University of Edinburgh, Edinburgh EH9 3FD, UK;

关键词: diversity;    biodiversity;    species similarity;    entropy;    Rényi entropy;    maximum entropy;    metric entropy;    Hill number;    maximum clique;   

DOI  :  10.3390/e18030088

来源: DOAJ

【 摘 要 】

Entropy, under a variety of names, has long been used as a measure of diversity in ecology, as well as in genetics, economics and other fields. There is a spectrum of viewpoints on diversity, indexed by a real parameter q giving greater or lesser importance to rare species. Leinster and Cobbold (2012) proposed a one-parameter family of diversity measures taking into account both this variation and the varying similarities between species. Because of this latter feature, diversity is not maximized by the uniform distribution on species. So it is natural to ask: which distributions maximize diversity, and what is its maximum value? In principle, both answers depend on q, but our main theorem is that neither does. Thus, there is a single distribution that maximizes diversity from all viewpoints simultaneously, and any list of species has an unambiguous maximum diversity value. Furthermore, the maximizing distribution(s) can be computed in finite time, and any distribution maximizing diversity from some particular viewpointq > 0actually maximizes diversity for all q. Although we phrase our results in ecological terms, they apply very widely, with applications in graph theory and metric geometry.

【 授权许可】

Unknown