The Elevational Gradient of Bird Beta Diversity in the Meili Snow Mountains, Yunnan Province, China

Abstract

Understanding the elevational patterns of beta diversity in mountain regions is a long-standing problem in biogeography and ecology. Previous research has generally focused on the taxonomy facet on a large scale, but was limited with regard to multi-facet beta diversity. Accordingly, we constructed a multi-dimensional (taxonomic/phylogenetic/functional) framework to analyze the underlying mechanisms of beta diversity. Within an approximately 2000 m altitudinal range (from 2027 m to 3944 m) along the eastern slope of the Meili Snow Mountains in Deqin County, Yunnan Province, China, we performed field surveys of breeding and non-breeding birds in September/2011 and May/2012, respectively. In total, 132 bird species were recorded during the fieldwork. The results indicated that taxonomic beta diversity contributed 56% of the bird species diversity, and its turnover process dominated the altitudinal pattern of taxon beta diversity; beta phylogenetic diversity contributed 42% of the bird phylogenetic diversity, and its turnover process also appeared to be stronger than the nestedness. For both taxonomy and phylogeny, the null models standardized measures (SES.βsim/SES.βsne/SES.βsor) of paired dissimilarities between elevation zones all showed statistically significant differences (p ≤ 0.05) and were higher than expected (SES.β > 0). However, standardized functional beta diversity showed convergence along the elevational gradient with no significant change. Moreover, the functional beta diversity contributed 50% of the bird functional diversity; there was no significant difference between the turnover and the nestedness-resultant component. Based on these results, we discerned that taxonomic and phylogenetic beta diversity patterns among the elevational zone were overdispersed, which indicated that limiting similarity dominated the turnover process among the bird species and phylogenetic communities in the Meili Snow Mountains.