Target groups masks for climate envelope models for 15 threatened and endangered species in Florida

Publication Date

2017

Time Period

2016-12-14

Brandt, L.A., Benscoter, A.M., Harvey, Rebecca, Speroterra, Carolina, Bucklin, David, Romañach, S.S., Watling, J.I., and Mazzotti, F.J., 2017, Data for comparison of climate envelope models developed using expert-selected variables versus statistical selection: U.S. Geological Survey data release, https://doi.org/10.5066/F7J101BT.

The spatial domain, or masks, were created as part of a larger project to compare climate envelope models outputs that were generated using two types of predictor variables: expert opinion and statistical method. Climate envelope models are increasingly used to characterize potential future distribution of species under climate change scenarios. It is acknowledged that the use of climate envelope models comes with both strengths and limitations, and that results are sensitive to modeling assumptions, inputs, and specific methods. The selection of predictor variables, an integral modeling step, is one factor that can affect the modeling outcome. The selection of climate predictors if frequently achieved using statistical methods that ascertain correlations between species occurrence and climate data; this approach has been critiqued because it depends on statistical properties of the data, and does not directly implement biological information about how species respond to temperature or precipitation. In this study, we compared models and prediction maps for 15 threatened or endangered species in Florida created using two variable selection techniques: expert opinion and a statistical method. We compared model performance for contemporary predictions, and also compared the spatial correlation, spatial overlap, and area predicted for contemporary and future climate predictions between these two approaches. These spatial data masks for each species could be used to delimit potential areas where these species, or closely related species, may potentially live.

• USGS Wetland and Aquatic Research Center

Data source

Input directly