Abstract
Species distribution models (SDMs) yield reliable and needed predictions to identify regions that have similar environmental conditions and were used here to predict potential ranges of rare species to identify new localities were they might occur based on their occurrence probability (i.e. niche suitability). We modeled the potential distribution ranges of ten endangered or rare birds from the South American Cerrado biome, using four temperature- and four precipitation-related bioclimatic variables, three topographical variables, and nine different niche modeling methods for each species. We used an ensemble-forecasting approach to reach a consensus scenario to obtain the average distribution for each species based on the five best models generating a distribution map of each species. Model efficiency was related to sample size and not appropriate below ten independent spatial occurrences. The potential distributions of seven species revealed that their occurrence ranges might go beyond their known ranges, but that most of them seem to occur near the regions where they have already been reported. The models of only three species were considered unsatisfactory in helping identify their potential distribution. Models created maps with higher occurrence probability regions where rare Cerrado birds might occur. These range projections can potentially decrease the costs and improve the efficiency of future field searches. On methodological terms, the application of SDMs to predict species ranges should compare different modeling methods and evaluate the effect of sample size on their performance.
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Acknowledgments
M.Â.M. was supported by a Post-doctorate fellowship from CAPES, and L.E.L. by a doctorate fellowship from FAPEMIG. We thank W. Thuiller for providing the updated version of BIOMOD and A. T. Peterson for providing the topographical layers.
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Marini, M.Â., Barbet-Massin, M., Lopes, L.E. et al. Predicting the occurrence of rare Brazilian birds with species distribution models. J Ornithol 151, 857–866 (2010). https://doi.org/10.1007/s10336-010-0523-y
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DOI: https://doi.org/10.1007/s10336-010-0523-y