This kangaskhan_readme.txt file was generated on 2021-03-18 by Dan Warren GENERAL INFORMATION 1. Title of Dataset: Data and code for analysis of effects of climate change on kangaskhan and summary of simulations from Warren et al. 2020 2. Author Information A. Principal Investigator Contact Information Name: Dan Warren Institution: Okinawa Institute of Science and Technology Graduate University Address: 1919-1 Tancha, Onna, Kunigami District, Okinawa 904-0495 Email: dan.l.warren@gmail.com 3. Date of data collection: 2018-10-7 4. Geographic location of data collection: Australia 5. Information about funding sources that supported the collection of the data: None SHARING/ACCESS INFORMATION 1. Licenses/restrictions placed on the data: CCBY 2. Links to publications that cite or use the data: doi://10.1111/2041-210X.13591 3. Links to other publicly accessible locations of the data: None 4. Links/relationships to ancillary data sets: None 5. Was data derived from another source? yes/no A. If yes, list source(s): Warren, Dan L., Nicholas J. Matzke, and Teresa L. Iglesias. 2020. “Evaluating Presence‐only Species Distribution Models with Discrimination Accuracy Is Uninformative for Many Applications.” Journal of Biogeography 47 (1): 167–80. 6. Recommended citation for this dataset: Warren, Dan L., Alex Dornburg, Katerina Zapfe, and Teresa L. Iglesias. 2021b. Data and code for analysis of effects of climate change on kangaskhan and summary of simulations from Warren et al. 2020, Dryad digital repository, https://doi.org/10.5061/dryad.p8cz8w9px DATA & FILE OVERVIEW 1. File List: kangaskhan_points.csv Occurrence points for kangaskhan mx.mc.csv Summary stats for Monte Carlo replicates, Maxent models dm.mc.csv Summary stats for Monte Carlo replicates, Domain models rf.mc.csv Summary stats for Monte Carlo replicates, Random Forests models gam.mc.csv Summary stats for Monte Carlo replicates, Generalized Additive models glm.mc.csv Summary stats for Monte Carlo replicates, Generalized Linear models bc.mc.csv Summary stats for Monte Carlo replicates, Bioclim models prediction_summary.csv Summary stats for empirical models, all methods regback.aggregated_projections.csv Summary statistics for projected models and true niches from Warren et al. 2020 Appendix_S2.pdf Appendix_S3.pdf Code for building models and conducting Monte Carlo tests 2. Relationship between files, if important: Code in Appendix_S3.pdf and Appendix_S2.pdf uses the .csv files as input. 3. Additional related data collected that was not included in the current data package: None 4. Are there multiple versions of the dataset? No METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: Kangaskhan points were collected from websites dedicated to Pokemon Go. Simulation results were based on projections of models retained from Warren et al. 2020. 2. Methods for processing the data: ENM/SDM models were built using the kangaskhan data, and projected to scenarios of climate change. Results were compared to the results from Monte Carlo tests where identical models were built from randomly chosen occurrence data. 3. Instrument- or software-specific information needed to interpret the data: None. 4. Standards and calibration information, if appropriate: None. 5. Environmental/experimental conditions: None. 6. Describe any quality-assurance procedures performed on the data: Duplicate occurrence points were removed. 7. People involved with sample collection, processing, analysis and/or submission: Dan Warren, Teresa Iglesias, Alex Dornburg, and Katerina Zapfe. DATA-SPECIFIC INFORMATION FOR: kangaskhan_points.csv 1. Number of variables: 3 2. Number of cases/rows: 37 3. Variable List: Latitude Longitude Classification 4. Missing data codes: NA 5. Specialized formats or other abbreviations used: None DATA-SPECIFIC INFORMATION FOR: files named *.mc.csv 1. Number of variables: 14 2. Number of cases/rows: 2400 3. Variable List: "" name pred.cells.declining.all pred.change.all pred.cells.declining.occ pred.change.occ pred.pa.declining pred.pa.increasing pred.pa.declining.occ pred.pa.increasing.occ model.type rep year scenario climate.model 4. Missing data codes: NA 5. Specialized formats or other abbreviations used: None DATA-SPECIFIC INFORMATION FOR: prediction_summary.csv 1. Number of variables: 9 2. Number of cases/rows: 144 3. Variable List: "" name pred.cells.declining.all pred.change.all pred.cells.declining.occ pred.change.occ pred.pa.declining pred.pa.increasing pred.pa.declining.occ pred.pa.increasing.occ 4. Missing data codes: NA 5. Specialized formats or other abbreviations used: None DATA-SPECIFIC INFORMATION FOR: regback.aggregated.projections.csv 1. Number of variables: 24 2. Number of cases/rows: 36960 3. Variable List: model projection.layers true.change.all pred.change.all pred.error.all pred.cor.all true.change.occ pred.change.occ pred.error.occ pred.cor.occ true.cells.declining.all pred.cells.declining.all prop.agreed.declining.all true.cells.declining.occ pred.cells.declining.occ prop.agreed.declining.occ spearman.occ spearman.all model.type bias.level rep year scenario climate.model 4. Missing data codes: NA 5. Specialized formats or other abbreviations used: None