AREAS OF ENDEMISM OF MEXICAN TERRESTRIAL MAMMALS: A CASE STUDY USING SPECIES' ECOLOGICAL NICHE MODELING, PARSIMONY ANALYSIS OF ENDEMICITY AND GOLOBOFF FIT

摘要

Se identificaron áreas de endemismo de mamíferos terrestres de México, usando modelos de nicho ecológico proyectados como distribuciones potenciales de especies, con el fin de comparar su desempeño respecto a análisis previos que usan datos puntuales de ocurrencia, incorporando el ajuste de Goloboff al Análisis de Parsimonia de Endemismos (PAE) para mejorar la identificación de áreas de endemismo. Se desarrollaron seis PAE, combinados o no con el ajuste de Goloboff (k=0 y 2) usando distribuciones potenciales de especies de 429 mamíferos terrestres sobrepuestas a 248 y 232 cuadros de 1° de latitud-longitud a lo largo del país. Se utilizaron los índices de consistencia (CI) y retención (RI) para identificar especies endémicas, posiblemente endémicas y características.%Areas of endemism of Mexican terrestrial mammals using ecological niche modeling projected as species' potential distributions were identified to compare its performance with a previous analysis that used point occurrence data, and to incorporate Goloboff fit to Parsimony Analysis of Endemic-ity (PAE) for improving identification of areas of endemism. Six PAE were performed, combined or not, with Goloboff fit (k=0 and 2) using species' potential distributions of 429 terrestrial mammals overlaid on 248 by 232 quadrats of 1° latitude-longitude countrywide. Consistency (CI) and retention (RI) indices were used for identifying endemic, characteristic, and possibly endemic species. Based on the strict consensus cladogram with k=0, seven areas of endemism defined by two or more species were identified: the Mexican Plateau, the Baja California Peninsula (with a nested pattern of endemism in the south and north), Chiapas (with a nested pattern of endemism in the south and north), the Mexican Pacific Coast, the Isthmus of Tehuantepec, the Sierra Madre Occidental, and the Yucatan Peninsula. PAE cladograms using species' potential distributions showed a better resolution than those produced using point occurrence data, showing consensus with fewer number of steps and higher number of synapomorphies. Goloboff fit (k) allowed individual down-weighting of "noisy" species, thus increasing the number of synapomorphies in the cladograms, and even identifying more areas of endemism. Cladograms with k= 0 had the largest number of synapomorphies, whereas k=2 allowed to obtain a smaller number of cladograms.