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Phylogenetic skew: an index of community diversity
Author(s) -
Chen Hungyen,
Shao KwangTsao,
Kishino Hirohisa
Publication year - 2015
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.13064
Subject(s) - biology , species richness , community structure , species diversity , sampling (signal processing) , diversity index , divergence (linguistics) , phylogenetic diversity , phylogenetic tree , ecology , community , statistics , habitat , mathematics , linguistics , philosophy , biochemistry , filter (signal processing) , computer science , gene , computer vision
The distribution of divergence times between member species of a community reflects the pattern of species composition. In this study, we contrast the species composition of a community against the meta‐community, which we define as the species composition of a set of target communities. We regard the collection of species that comprise a community as a sample from the set of member species of the meta‐community, and interpret the pattern of the community species composition in terms of the type of species sampled from the meta‐community. A newly defined effective species sampling proportion explains the amount of the difference between the divergence time distributions of the community and that of the meta‐community, assuming random sampling. We propose a new index of phylogenetic skew ( PS ), as the ratio of the maximum‐likelihood estimate of the effective species sampling proportion to the observed sampling proportion. A PS value of 1 is interpreted as random sampling. If the value is >1, the sampling is suspected to be phylogenetically skewed. If it is <1, systematic thinning of species is likely. Unlike other indices, the PS does not depend on species richness as long as the community has more than a few members of a species. Because it is possible to compare partially observed communities, the index may be effectively used in exploratory analysis to detect candidate communities with unique species compositions from a large number of communities.