Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity
Unpartitioned dissimilarity indices such as the Sørensen index (βsor) tend to categorize areas according to species number. The use of turnover indices, such as the Simpson index (βsimp), may lead to the loss of important information represented by the nestedness component (βnest). Recent studies have suggested the importance of integrating nestedness and turnover information. We evaluated this proposition by comparing biogeographical patterns obtained by unpartitioned (βsor) and partitioned indices (βsimp and βnest) on presence data of western Mediterranean butterflies.
We assessed the regionalization of 81 mainland and island faunas according to partitioned and unpartitioned dissimilarity by using cluster analyses with the unweighted pair-group method using arithmetic averages (UPGMA) combined with non-metric multidimensional scaling (NMDS). We also carried out dissimilarity interpolation for βsor, βsimp, βnest and the βnest/βsor ratio, to identify geographical patterns of variation in faunal dissimilarity.
When the unpartitioned βsor index was used, the clustering of sites allowed a clear distinction between insular and mainland species assemblages. Most islands were grouped together, irrespective of their mainland source, because of the dominant effect of their shared low richness. βsimp was the most effective index for clustering islands with their respective mainland source. βsimp clustered mainland sites into broader regions than clusters obtained using βsor. A comparison of regionalization and interpolation provided complementary information and revealed that, in different regions, the patterns highlighted by βsor could largely be determined either by nestedness or turnover.
Partitioned and unpartitioned indices convey complementary information, and are able to reveal the influence of historical and ecological processes in structuring species assemblages. When the effect of nestedness is strong, the exclusive use of turnover indices can generate geographically coherent groupings, but can also result in the loss of important information. Indeed, various factors, such as colonization–extinction events, climatic parameters and the peninsular effect, may determine dissimilarity patterns expressed by the nestedness component.