Do bioclimate variables improve performance of climate envelope models?

Authors

James I. Watling, University of FloridaFollow
Stephanie S. Romanach, U.S. Geological Survey
David N. Bucklin, University of Florida
Carolina Speroterra, University of Florida
Laura A. Brandt, U.S. Fish and Wildlife Service
Leonard G. Pearlstine, South Florida Natural Resources CenterFollow
Frank J. Mazzotti, University of Florida

Document Type

Article

Date of this Version

2012

Citation

Ecological Modelling 246 (2012) 79– 85; http://dx.doi.org/10.1016/j.ecolmodel.2012.07.018

Abstract

Climate envelope models are widely used to forecast potential effects of climate change on species distributions. A key issue in climate envelope modeling is the selection of predictor variables that most directly influence species. To determine whether model performance and spatial predictions were related to the selection of predictor variables, we compared models using bioclimate variables with models constructed from monthly climate data for twelve terrestrial vertebrate species in the southeastern USA using two different algorithms (random forests or generalized linear models), and two model selection techniques (using uncorrelated predictors or a subset of user-defined biologically relevant predictor variables). There were no differences in performance between models created with bioclimate or monthly variables, but one metric of model performance was significantly greater using the random forest algorithm compared with generalized linear models. Spatial predictions between maps using bioclimate and monthly variables were very consistent using the random forest algorithm with uncorrelated predictors, whereas we observed greater variability in predictions using generalized linear models.