Expert opinion questionnaire regarding the importance of climate in determining species distribution for 15 threatened and endangered species in Florida
Dates
Publication Date
2017
Start Date
2012-03-01
End Date
2013-04-01
Citation
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.
Summary
We developed an expert opinion questionnaire to gather information regarding expert opinion regarding the importance of climate variables in determining a species geographic range (Brandt et al. 2017). The data on the Survey_Results tab represent the raw survey questions and responses. Each column in the spreadsheet (except the first four columns, described below) represents a survey question, which is written in the first cell of that column. Each survey response for that question is listed below. Some questions have multi-part answers, and are listed in multiple columns, and appended with letters (e.g., Q8A, Q8B, Q8C, etc.). The first four columns of the spreadhseet represent unique information for that survey response, and are described [...]
Summary
We developed an expert opinion questionnaire to gather information regarding expert opinion regarding the importance of climate variables in determining a species geographic range (Brandt et al. 2017). The data on the Survey_Results tab represent the raw survey questions and responses. Each column in the spreadsheet (except the first four columns, described below) represents a survey question, which is written in the first cell of that column. Each survey response for that question is listed below. Some questions have multi-part answers, and are listed in multiple columns, and appended with letters (e.g., Q8A, Q8B, Q8C, etc.). The first four columns of the spreadhseet represent unique information for that survey response, and are described here. Respondent_ID: The Respondent_ID represents a unique identifier for each individual survey response. Start_Date: The Start_Date represents the beginning day of the survey response for each survey. End_Date: The End_Date represents the last day of the survey response for each survey. Custom_Data: The Custom_Data column represents unique information filled out by the survey respondent for that survey, which typically includes the species name for which the survey applies to, as written by the survey respondent. Reference:Brandt, L.A., Benscoter, A.M., Harvey, R., Speroterra, C., Bucklin, D., Romañach, S.S., Watling, J.I., Mazzotti, F.J., 2017, Comparison of climate envelope models developed using expert-selected variables versus statistical selection, Ecological Modelling 345:10-20.
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Expert_Questionnaire_FGDC.xml Original FGDC Metadata
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Expert_Questionnaire.csv
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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.
