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 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.

物种分布模型（Species Distribution Models，SDMs）常被用于预测气候变化对受保护关注物种的影响。在构建SDMs并将其外推至新气候情景的过程中所固有的偏差，可能会产生不尽如人意的保护结局。本研究针对这些问题展开探讨，并提出了新方法，用以估算由SDM研究设计所引发的偏差。我们以评估气候变化对澳大利亚唯一特有宝可梦的影响为研究场景，阐述了上述方法。本研究依托市民科学数据集，为G. kangaskhani构建物种分布模型，以预测气候变化对该物种栖息地适宜性的影响。我们提出了一种全新的蒙特卡洛（Monte Carlo）流程，用以估算特定研究设计中隐含的偏差；并将宝可梦的研究结果与蒙特卡洛测试结果，以及该区域此前采用模拟数据与真实数据开展的同类研究结果进行对比。我们的模型结果显示，气候变化将对G. kangaskhani的栖息地适宜性产生负面影响，这可能会加剧栖息地丧失以及其被用于血腥运动等威胁带来的影响。不过我们也发现，利用SDMs估算气候变化影响时，可能伴随极强的偏差，以至于数据集本身对建模结果的影响微乎其微。我们证实，气候变化影响评估中的偏差方向与幅度，会受到建模流程各环节的影响；并建议此类未来研究应纳入偏差估算环节。鉴于SDMs的应用范围广泛，系统性偏差可能会带来可观的经济成本与机会成本。本研究通过阐明这些偏差并提出一种全新的统计工具用以估算偏差，希望能为G. kangaskhani以及全球其他生物多样性提供更稳固的未来。