Using biodiversity databases to assess patterns of endemism at large scales: an example with amphibians of northern South America.

CASAGRANDA M. DOLORES; PADIAL JOSÉ M.; CASTROVIEJO-FISHER SANTIAGO

Trento

Congreso; Hennig XXXIII; 2014

The northern portion of South America includes some of the most diverse regions of the planet, such as the Andes, the Amazon Basin, the Chocó and the Mata Atlántica. Numerous studies have described distribution patterns in this area at local and regional scales, but comprehensive empirical analyses for the whole region remain scarce. Since the last decade, online biodiversity databases have facilitated our access to international baseline data, helping to overcome the data-quantity impediment that limited the scale of past empirical analyses. However, data quality problems persist, such as taxonomic and geographical uncertainty and bias in geographical coverage.In this study we use NDM-VDM programs (Goloboff, 2004) to analyze amphibian?s distribution data from GBIF (http://www.gbif.org/) in order to assess the potential of these data to describe patterns of endemism in northern South America. Our database includes 40.000 specimen/locality records for 1.500 amphibian species inhabiting Bolivia, Brazil, Colombia, Ecuador, French Guiana, Guyana, Peru, Suriname, and Venezuela. Major areas of endemism recovered in our analyses are congruent with patterns previously described, suggesting the effectiveness of this data to characterize areas of endemism at large scales. The Orinoco Basin, the Mata Atlántica (subdivided into north and south areas), the Guiana Shield, and the Araucaria Forests of Southeastern Brazil were recognized as areas of endemism for amphibians, as well as an extended area including the Amazon Basin, the Orinoco Basin, and the Central and Northern Andes. Our results also indicate potential areas of endemism for amphibians not described in the literature.Future analyses including additional data corroborated by specialists will help us to explore the effect of missing data, taxonomic accuracy and geographic bias in the study of distributional patterns of amphibians at large scales.