The effects of climate change on Australia’s only endemic Pokémon: Measuring bias in species distribution models
The effects of climate change on Australia’s only endemic Pokémon: Measuring bias in species distribution models
复制标题
气候变化对澳大利亚唯一特有的神奇宝贝的影响：测量物种分布模型中的偏差
DOI：
10.1111/2041-210x.13591
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发表时间：
2021
影响因子：
6.6
通讯作者：
T. Iglesias
中科院分区：
文献类型：
--
作者：
D. Warren;A. Dornburg;Katerina Zapfe;T. Iglesias
物种分布模型（SDM）经常被用来预测气候变化对保护物种的影响。在构建可持续发展模型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理想的保护结果。我们探讨这些问题，并展示了新的方法来估计SDM研究的设计引起的偏见。我们提出这些方法的背景下，估计气候变化对澳大利亚唯一的地方性神奇宝贝的影响。使用公民科学数据集，我们建立物种分布模型的Garura kangaskhani预测气候变化对物种栖息地的适宜性的影响。我们展示了一种新的Monte Carlo方法，用于估计给定研究设计中隐含的偏差，并将神奇宝贝的结果与我们的Monte Carlo测试以及使用模拟和真实的数据在同一地区进行的先前研究的结果进行比较。我们的模型表明，气候变化将影响栖息地的适宜性G。kangaskhani，这可能会加剧威胁的影响，如栖息地丧失和他们在血液运动的使用。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微乎其微。我们表明，气候变化影响的估计偏差的方向和幅度受到建模过程的各个方面的影响，并建议在未来的研究中应包括这种类型的偏差估计。鉴于空间数据模型的广泛使用，系统性偏差可能造成巨大的财政和机会成本。通过展示这些偏差并提出一种新的统计工具来估计它们，我们希望为G。kangaskhani和世界其他地区的生物多样性。
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 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. 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. 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.