A Test of Species Distribution Model Transferability Across Environmental and Geographic Space for 108 Western North American Tree Species
A Test of Species Distribution Model Transferability Across Environmental and Geographic Space for 108 Western North American Tree Species
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DOI：
10.3389/fevo.2021.689295
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发表时间：
2021-07-01
影响因子：
3
通讯作者：
Enquist, Brian J.
中科院分区：
文献类型：
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作者：
Charney, Noah D.;Record, Sydne;Enquist, Brian J.
物种分布模型（SDM）的预测通常用于支持环境决策，以探索气候变化对生物多样性的潜在影响。然而，由于未来的气候可能与当前的气候不同，人们一直有兴趣了解SDM在新条件下预测物种反应的能力（即，模型可转移性）。在这里，我们探讨了空间和环境的限制，外推在SDM使用森林资源清查数据从11个模型算法，108个树种在美国西部。算法在预测发生在同一地理区域的地块中的发生率方面表现良好。然而，在预测算法不适用的地理区域时，相当一部分模型的表现比随机模型差。我们的研究结果表明，对于地理空间中的传输，没有特定的算法优于另一种算法，因为不同算法的预测性能没有显着差异。在环境空间中传输的算法的预测性能有显着差异，GAM表现最好。然而，GAM的预测性能急剧下降，增加外推在环境空间相对于其他算法。这项研究的结果表明，SDMs可能是有限的，在他们的能力，预测超出环境数据用于模型拟合的物种范围。在预测气候驱动的分布范围变化时，外推法也可能没有反映物种分布范围的重要生物和非生物驱动因素，从而进一步歪曲了已实现的分布范围变化。未来的研究调查可转让的过程为基础的SDMS或地理多样性和生物多样性之间的关系可能有希望。
Predictions from species distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of climate change on biodiversity. However, because future climates are likely to differ from current climates, there has been ongoing interest in understanding the ability of SDMs to predict species responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using forest inventory data from 11 model algorithms for 108 tree species across the western United States. Algorithms performed well in predicting occurrence for plots that occurred in the same geographic region in which they were fitted. However, a substantial portion of models performed worse than random when predicting for geographic regions in which algorithms were not fitted. Our results suggest that for transfers in geographic space, no specific algorithm was better than another as there were no significant differences in predictive performance across algorithms. There were significant differences in predictive performance for algorithms transferred in environmental space with GAM performing best. However, the predictive performance of GAM declined steeply with increasing extrapolation in environmental space relative to other algorithms. The results of this study suggest that SDMs may be limited in their ability to predict species ranges beyond the environmental data used for model fitting. When predicting climate-driven range shifts, extrapolation may also not reflect important biotic and abiotic drivers of species ranges, and thus further misrepresent the realized shift in range. Future studies investigating transferability of process based SDMs or relationships between geodiversity and biodiversity may hold promise.