Aim To analyse the distributional patterns of the Baja California Peninsula's resident avifauna, and to generate a regionalization based on a method that uses a parsimony analysis (parsimony analysis of endemicity, PAE) of point data and modelled potential distributions.
Location The Baja California Peninsula, Mexico.
Methods A data base was constructed containing records of 113 species of resident terrestrial birds present in the Baja California Peninsula. Records and localities were obtained from the literature and from specimens housed in scientific collections world-wide. Raw data points and potential distribution maps obtained using the software Genetic Algorithms for Rule-set Prediction (GARP), were analysed with PAE.
Results The data base consisted of 4164 unique records (only one combination of species/locality) belonging to 113 terrestrial resident bird species, in a total of 809 localities. From the point distribution matrix, the analysis generated 500 equally parsimonious trees, from which a strict consensus cladogram with 967 steps was obtained. The cladogram shows a basal polytomy and some geographical correspondence of a few resolved groups obtained in the analysis. These results do not allow the recognition of areas defined by avifaunistic associations. From the potential distribution matrix, the analysis generated 501 equally parsimonious trees, and a strict consensus cladogram of 516 steps was obtained. The cladogram shows a higher resolution because of the number of resolved groups with better geographical correspondence and therefore regions are well-defined.
Main conclusions The correspondence of some groupings of species suggest their validity as areas with biogeographical (historical and/or ecological) meaning. This regionalization in the Baja California avifauna seems to be consistent with previous regionalizations for other groups. Hence, PAE is a useful tool for area categorization if reliable point records and prediction tools are available. Our results suggest that the geographical definition is much better using potential data generated by GARP, particularly when they are contrasted with the results from point data. Thus, this is an excellent alternative for developing biogeographical studies, as well as for improving the use of data from scientific collections and other sources of biodiversity information.
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