Historical legacies in world amphibian diversity revealed by the turnover and nestedness components of Beta diversity

PLoS One. 2012;7(2):e32341. doi: 10.1371/journal.pone.0032341. Epub 2012 Feb 23.

Abstract

Historic processes are expected to influence present diversity patterns in combination with contemporary environmental factors. We hypothesise that the joint use of beta diversity partitioning methods and a threshold-based approach may help reveal the effect of large-scale historic processes on present biodiversity. We partitioned intra-regional beta diversity into its turnover (differences in composition caused by species replacements) and nestedness-resultant (differences in species composition caused by species losses) components. We used piecewise regressions to show that, for amphibian beta diversity, two different world regions can be distinguished. Below parallel 37, beta diversity is dominated by turnover, while above parallel 37, beta diversity is dominated by nestedness. Notably, these regions are revealed when the piecewise regression method is applied to the relationship between latitude and the difference between the Last Glacial Maximum (LGM) and the present temperature but not when present energy-water factors are analysed. When this threshold effect of historic climatic change is partialled out, current energy-water variables become more relevant to the nestedness-resultant dissimilarity patterns, while mountainous areas are associated with higher spatial turnover. This result suggests that nested patterns are caused by species losses that are determined by physiological constraints, whereas turnover is associated with speciation and/or Pleistocene refugia. Thus, the new threshold-based view may help reveal the role of historic factors in shaping present amphibian beta diversity patterns.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Amphibians
  • Animals
  • Biodiversity*
  • Climate
  • Climate Change
  • Ecology
  • Geography
  • Models, Statistical
  • Regression Analysis
  • Time Factors