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Humphreys JM, Elsner JB, Jagger TH, Steppan SJ.2019. Integrated modeling of phylogenies, species traits, and environmental gradients to better predict biogeographic distributions. PeerJ Preprints7:e28001v1https://doi.org/10.7287/peerj.preprints.28001v1
There is an acknowledged need to combine species distribution and macro-ecological models with phylogenetic information, particularly when biogeographic research incorporates multiple species, explores phenotypic traits, or is spatially dynamic. Our aim is to present a new approach to multi-species joint modeling that applies spatially explicit phylogenetic regression to simultaneously predict species occurrence probability and the geographic distribution of interspecific continuous morphological traits. We developed a multi-tiered Bayesian geostatistical model that incorporates a species phylogeny, morphometric traits, and environmental variables to jointly estimate traits and geographic distributions for six species of South American leaf-eared mice (genus: Phyllotis). Covariates are included with the model to control for genetic relatedness, specimen age, specimen sex, and repeated measures errors. To help gauge model performance, we compared our approach to predictions made using several other species distribution modeling applications. Our integrated modeling framework demonstrated improved accuracy over alternative species distribution modeling techniques as judged by model sensitivity, specificity, and the true skill statistic. The inclusion of trait-based covariates and model terms to account for genetic relatedness, repeated measures, and spatial error were determined important as judged by credible intervals and parsimony metrics. Species distribution models and trait-based approaches that do not account for spatial dependencies, phylogenetic relationships, or repeated measures sampling errors may produce parameter estimates with smaller uncertainty than is warranted and produce predictions with significant error. Our study offers tools to address spatially and phylogenetically structured species data and presents an approach to integrating biological comparative methods in biogeographic research.