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Data for comparison of climate envelope models developed using expert-selected variables versus statistical selection

Dates

Publication Date
Start Date
2012-03-01
End Date
2013-04-01

Citation

Brandt, L.A., Benscoter, A.M., Harvey, R., Speroterra, C., Bucklin, D., 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.

Summary

The data we used for this study include species occurrence data (n=15 species), climate data and predictions, an expert opinion questionnaire, and species masks that represented the model domain for each species. For this data release, we include the results of the expert opinion questionnaire and the species model domains (or masks). We developed an expert opinion questionnaire to gather information on expert opinion regarding the importance of climate variables in determining a species geographic range. The species masks, or model domains, were defined separately for each species using a variation of the “target-group” approach (Phillips et al. 2009), where the domain was determined using convex polygons including occurrence data [...]

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climate_envelope_models_developed_using_expert-selected_variables_versus_statistical_selection.xml
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Purpose

The data were collected 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.

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  • USGS Data Release Products
  • USGS Wetland and Aquatic Research Center

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DOI https://www.sciencebase.gov/vocab/category/item/identifier doi:10.5066/F7J101BT

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