Effect of Spatial Resolution, Algorithm and Variable Set on the Estimated Distribution of a Mammal of Concern: The Squirrel Sciurus aberti

Abstract

ABSTRACT Most potential habitat models have been built from WorldClim using low resolution variables, even for areas of high heterogeneity with few weather stations. The resulting models can be too general and lead to erroneous decisions when used for conservation purposes. Sciurus aberti is a tree squirrel inhabiting highlands in the SW US and the Sierra Madre Occidental (SMO) in Mexico, where it is considered a species of low concern. We examined the effect of resolution, variables, and algorithms on the predicted potential habitat of S. aberti in Mexico and compared the resulting models against a previous one created from WorldClim variables using GARP (Genetic Algorithm for Rule Set Production). Our best model, using Maxent, 30 m spatial resolution and topographic variables, predicted a fragmented distribution in pine and pine–oak forests, consistent with what is known about the species' natural history. The area represented only 2% of the SMO (compared to 28% for the GARP model), of which only 0.33% lies within protected areas. The model suggests that the habitat is highly fragmented, which threatens population continuity. Therefore, we propose that the conservation status of Sciurus aberti must be reassessed and that forest management better consider the conservation of arboreal species.