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

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...

物种分布模型（SDMs）常用于预测气候变化对受保护物种的影响。构建SDMs并将其迁移至新气候情景的过程中存在固有偏差，这些偏差可能导致不理想的保护结果。我们探讨了这些问题，并展示了估算由SDM研究设计引发的偏差的新方法。我们在估算气候变化对澳大利亚唯一特有宝可梦（Pokémon）影响的背景下，介绍了这些方法。利用公民科学数据集，我们构建了G. kangaskhani的物种分布模型，以预测气候变化对该物种栖息地适宜性的影响。我们展示了一种新颖的蒙特卡洛（Monte Carlo）程序，用于估算特定研究设计中隐含的偏差，并将宝可梦的相关结果与我们的蒙特卡洛测试结果以及同一区域内使用模拟数据和真实数据的先前研究结果进行了比较。我们的模型表明，气候变化将...