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原文連結
論文資訊
- 類型：已發表論文
- 日期：2021-07-01
摘要
Predictions from 物種 distribution models (SDMs) are commonly used in support of environmental decision-making to explore potential impacts of 氣候 change on 生物多樣性. However, because future 氣候s are likely to differ from current 氣候s, there has been ongoing interest in understanding the ability of SDMs to predict 物種 responses under novel conditions (i.e., model transferability). Here, we explore the spatial and environmental limits to extrapolation in SDMs using 森林 inventory data from 11 model algorithms for 108 tree 物種 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 whic
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