1. 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.
2. Using a citizen science dataset, we build species distribution models for Garura 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.
3. 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 themselves have minimal impact on modelling outcomes.
4. We show that the direction and magnitude of bias in estimates of climate change impacts are affected by every aspect of the modelling 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.

中文翻译：

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气候变化对澳大利亚唯一地方性神奇宝贝的影响：测量物种分布模型的偏差

1. 物种分布模型 (SDM) 经常用于预测气候变化对保护关注物种的影响。在构建 SDM 并将其转移到新的气候情景的过程中固有的偏见可能会导致不良的保护结果。我们探讨了这些问题并展示了估计由 SDM 研究设计引起的偏差的新方法。我们在估计气候变化对澳大利亚唯一的地方性神奇宝贝的影响的背景下介绍了这些方法。
2. 我们使用公民科学数据集为Garura kangaskhani构建物种分布模型，以预测气候变化对物种栖息地适宜性的影响。我们展示了一种新颖的蒙特卡罗程序，用于估计给定研究设计中隐含的偏差，并将 Pokémon 的结果与我们的蒙特卡罗测试以及使用模拟和真实数据在同一地区的先前研究中看到的结果进行比较。
3. 我们的模型表明，气候变化将影响G. kangaskhani栖息地的适宜性，这可能会加剧威胁的影响，例如栖息地丧失及其在血液运动中的使用。然而，我们还发现使用 SDM 来估计气候变化的影响可能伴随着如此强烈的偏差，以至于数据本身对建模结果的影响最小。
4. 我们表明，气候变化影响估计中偏差的方向和大小受建模过程的各个方面的影响，并建议偏差估计应包括在未来的此类研究中。鉴于 SDM 的广泛使用，系统性偏见可能会产生巨大的财务和机会成本。通过证明这些偏差并提供一种新的统计工具来估计它们，我们希望为G. kangaskhani和世界其他生物多样性提供一个更安全的未来。