title: Phylogenetic measures of biodiversity.
abstract: We developed a theoretical framework based on phylogenetic comparative methods to integrate phylogeny into three measures of biodiversity: species variability, richness, and evenness. These metrics can be used in conjunction with permutation procedures to test for phylogenetic community structure. As an illustration, we analyzed data on the composition of 58 lake fish communities in Wisconsin. The fish communities showed phylogenetic underdispersion, with communities more likely to contain closely related species. Using information about differences in environmental characteristics among lakes, we demonstrated that phylogenetic underdispersion in fish communities was associated with environmental factors. For example, lakes with low pH were more likely to contain species in the same clade of acid-tolerant species. Our metrics differ from existing metrics used to calculate phylogenetic community structure, such as net relatedness index and Faith's phylogenetic diversity. Our metrics have the advantage of providing an integrated and easy-to-understand package of phylogenetic measures of species variability, richness, and evenness with well-defined statistical properties. Furthermore, they allow the easy evaluation of contributions of individual species to different aspects of the phylogenetic organization of communities. Therefore, these metrics should aid with the incorporation of phylogenetic information into strategies for understanding biodiversity and its conservation.
authors.affiliation: Department of Zoology, University of Wisconsin, Madison, Wisconsin 53706, USA. mrhelmus@wisc.edu
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Helmus
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Bland
authors.forename: Christopher K
authors.initials: CK
authors.lastname: Williams
authors.forename: Anthony R
authors.initials: AR
authors.lastname: Ives
mesh_terms: D000818:Animals; D044822:Biodiversity; D005399:Fishes; D060106:Lakes; D008954:Models, Biological; D015233:Models, Statistical; D010802:Phylogeny; D012044:Regression Analysis; D014922:Wisconsin