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原文連結
論文資訊
- 類型：已發表論文
- 日期：2016-09-20
摘要
Understanding and forecasting 物種' geographic distributions in the face of global change is a central priority in 生物多樣性 science. The existing view is that one must choose between correlative models for many 物種 versus process-based models for few 物種. We suggest that opportunities exist to produce process-based range models for many 物種, by using hierarchical and inverse modeling to borrow strength across 物種, fill data gaps, fuse diverse data sets, and model across 生物 and spatial scales. We review the 統計 生態學 and 族群 and range modeling literature, illustrating these modeling strategies in action. A variety of large, coordinated ecological datasets that can feed into these modeling solutions already exist, and we highlight organisms that seem ripe for the challenge.
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