Skip to main content
Dryad logo

Data and code for analysis of effects of climate change on kangaskhan and summary of simulations from Warren et al. 2020

Citation

Warren, Dan; Dornburg, Alex; Zapfe, Katerina; Iglesias, Teresa (2021), Data and code for analysis of effects of climate change on kangaskhan and summary of simulations from Warren et al. 2020, Dryad, Dataset, https://doi.org/10.5061/dryad.p8cz8w9px

Abstract

Species distribution models (SDMs) are frequently used to predict the effects of climate change on species of conservation concern. Biases inherent in the process of constructing SDMs and transferring them to new climate scenarios may result in undesirable conservation outcomes. We explore these issues and demonstrate new methods to estimate biases induced by the design of SDM studies. We present these methods in the context of estimating the effects of climate change on Australia’s only endemic Pokémon. Using a citizen science data set, we build species distribution models for G. kangaskhani to predict the effects of climate change on the suitability of habitat for the species. We demonstrate a novel Monte Carlo procedure for estimating the biases implicit in a given study design, and compare the results seen for Pokémon to those seen from our Monte Carlo tests as well as previous studies in the same region using both simulated and real data. Our models suggest that climate change will impact the suitability of habitat for G. kangaskhani, which may compound the effects of threats such as habitat loss and their use in blood sport. However, we also find that using SDMs to estimate the effects of climate change can be accompanied by biases so strong that the data itself has minimal impact on modeling outcomes.  We show that the direction and magnitude of bias in estimates of climate change impacts are affected by every aspect of the modeling process, and suggest that bias estimates should be included in future studies of this type. Given the widespread use of SDMs, systemic biases could have substantial financial and opportunity costs. By demonstrating these biases and presenting a novel statistical tool to estimate them, we hope to provide a more secure future for G. kangaskhani and the rest of the world’s biodiversity.

Methods

Data was collected from websites for the video game Pokemon Go, as well as reanalysis of simulations and models from Warren et al. 2020.

Warren, D.L, N. Matzke, and T.L. Iglesias. 2020. Evaluating presence-only species distribution models with discrimination accuracy is uninformative for many applications. Journal of Biogeography 47:167-180.

Usage Notes

Data in "kangaskhan points.csv" was collected from online websites dedicated to the Pokémon Go video game. Duplicate occurrences were removed. Appendix S2 contains code for summarizing results of the simulations from Warren et al. 2020 when projected to future climate scenarios, and "regback.aggregated projections.csv" contains the data being summarized. Appendix S3 contains results from Monte Carlo simulations.

Files ending in "mc.csv" are the summary statistics from the Monte Carlo models, while "prediction_summary.csv" contains summary statistics from the projections of the empirical models for kangaskhan.

Funding

National Science Foundation, Award: 0000-0002-5159-641X