Detection of areas of endemism on two spatial scales using parsimony analysis of endemicity (PAE): The Neotropical region and the Atlantic Forest

Abstract

An important biological challenge today is the conservation of biodiversity. Biogeography, the study of the distribution patterns of organisms, is an important tool for this challenge. Endemism, the co-occurrence of several species unique to the same area, has important implications for the preservation of biodiversity, since many areas of endemism are also areas with large human impact. More rigorously defined, areas of endemism are historical units of distributional congruence of monophyletic taxa. These areas often assumed to be due to nonrandom historical events that favored conditions associated with high rates of speciation. Thus, understanding endemism and the delimitation of endemic areas has important implications for conservation. Today, most studies delimit areas of endemism by superimposing maps of distribution for various species. This approach suffers from arbitrary delimitations, however, when a great distributional data is used. In this paper we used the method of Parsimony Analysis of Endemicity (PAE) based on georeferenced quadrats in order to delimit areas of endemism. This modality of the method is important due to its testable nature and can also be used to infer area relationships. We applied the method to raw distributional data from 19 unrelated taxa to delimit general patterns of endemism in the Neotropical Region and in the Atlantic forest domain using different grid scales. Neotropical areas found are comprised over the Panama region, northern Andean region and the Atlantic forest. Atlantic forest showed a major division into two distinct components (northern × southern). Endemic areas delimited using smaller scale grids on the Atlantic forest should be considered for conservation priorities once they showed endemism at regional and local scales. The results were also compared to other studies using different taxa and methods. Finally, some considerations on the analysis scale and future perspectives of the method are presented.