Robust conservation planning for biodiversity under climate change uncertainty – example dataset

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GENERAL INFORMATION 1. Title of Dataset: Robust Conservation Planning for Biodiversity Under Climate Change Uncertainty – SPCA example dataset 2. Author Information A. Principal Investigator Contact Information Name: C. PARMESAN Institution: SETE Address: 09200, Moulis, FR Email: parmesan@austin.utexas.edu B. Alternate Contact Information Name: A. Rutschmann Institution: SORBONNE UNIVERSITE, UMR 7618, CNRS Address: CNRS, Sorbonne Université Email: alexis.rutschmann@gmail.com 3. Date of data generation: 2023 to 2024 4. Information about funding sources that supported the collection of the data: Make Our Planet Great Again award’ CCISS ANR-17-MPGA-0007 (AR, CP); NSF/EaSM grant # 1049208 (RL, CP, LM); DoE / NICCR grant # DE-FC02-06ER64156 / 09-NICCR-1077 (CP); US DoE RGCM program award DOE DE-SC0016605 (SM); (LABEX) TULIP ANR-10-LABX-41 SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: Please contact the author before using the dataset or to obtain data for other species. 2. Links to publications that cite or use the data: A. Rutschmann et al., (in review). Robust Conservation Planning for Biodiversity Under Climate Change Uncertainty. Global Change biology. 3. Recommended citation for this dataset: A. Rutschmann et al., (in review). Robust Conservation Planning for Biodiversity Under Climate Change Uncertainty. Global Change biology. METHODOLOGICAL INFORMATION 1. Description of methods used for generation of data: For each species used in the manuscript, numerous SDM models were run by systematically sampling combinations of modelling approaches. The ‘SPCA_futures’ file countains the 176 SDMs for Speyeria carolae (the Carole's fritillary; SPCA), the species we used to illustrate our approach. Files’ names specify which of the 4 statistical algorithms was used in the modelling process (GBM, GLM, MARS, RF). The name also indicates which of the 4 non-redundant subsets of bioclimatic predictors was used. Note that for SPCA, only one subset was used (Sub0). Similary, the name details the spatial regions (mp10, mp25, sq10, sq25) and the combinations of global and regional climate models used in the SDM. 11 combinations of GCM and RCM were used for each speceis(e.g., RCM3_gfdl, WRFG_ccsm, WRFM_cgcm3, …). See main text and supplementary files for more details. The ‘SPCA_Ensemble_Data’ file countains the modelling ensembles used to create the different conservation strategies tested in our approach. These strategies are based on a combination of ensembles that have been run at different temporal (present or future) and geographical scales (Local, Minimum Convex Polygon or 250 km buffer). They also account for land already owned by govenmental agencies. See main text and supplementary files for details. DATA-SPECIFIC INFORMATION: 1. Variable List for the SPCA files: X: Latitude. Y: Longitude. Score: Habitate suitability as predicted by the SDM (from 0 to 1). 2. Variable List for other files: X: Latitude. Y: Longitude. ScoreT50: 0 or 1. Habitat is suitable (1) if at least 50 % of the SDM project the location (X,Y) to be suitable. ScoreT95: 0 or 1. Habitat is suitable (1) if at leaste 95% of the SDM project the location (X,Y) to be suitable. 3.Parks: land owned by american gouvernmental agencies (e.g., National Parks or Forest): X: Latitude. Y: Longitude.

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External Organisations
Sorbonne Paris Cité University; University of Texas at Austin; Rand Corporation; Santa Monica, CA, USA; University of Wyoming; NSF National Center for Atmospheric Research; CNRS; The University of Texas at Austin

Associated Persons
Alexis Rutschmann (Creator); Matthew P. Moskwik (Creator); Robert J. Lempert (Creator); Melissa S. Bukovsky (Creator); Seth McGinnis (Creator); Linda O. Mearns (Creator); Camille Parmesan (Creator)