Future climate change is expected to result in tree species shifting their geographic distributions in ways that could reorganize species into assemblages with no contemporary analog. These projected no‐analog forests raise concern as their ecological function could similarly shift, which may challenge established conservation and management efforts. Here, we implement a community‐level modelling approach to identify the key climatic and topographic drivers of forest composition in North America, and then use these models to predict the distribution of “disappearing” and “novel” forest assemblages in future climate. We applied this modelling technique to both the taxonomic and phylogenetic composition of forest trees, to identify where species turnover may be greatest, and whether species are likely to be replaced with close relatives. Our work shows that approximately 11.9% of contemporary North American forests have low predicted similarity to future forests, and 26.2% of future forests could be compositionally novel compared with contemporary forests, but there was substantial geographic variability in the magnitude of these metrics across the continent. High‐elevation regions in the west tend to be nearest to their closest compositional analog, suggesting these regions may be most likely to realize the future predicted composition. This work provides a new approach to understanding how forest composition may shift in future climates in a way that avoids the need for individual species predictions and extends climate‐matching approaches with meaningful biological data.

中文翻译：

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气候变化和北美非模拟森林组合的出现


预计未来的气候变化将导致树种改变其地理分布，从而将物种重组为没有当代类似物的组合。这些预计的无模拟森林引起了人们的关注，因为它们的生态功能可能发生类似变化，这可能会对既定的保护和管理工作构成挑战。在这里，我们实施了社区层面的建模方法，以确定北美森林组成的关键气候和地形驱动因素，然后使用这些模型来预测未来气候中“消失”和“新”森林组合的分布。我们将这种建模技术应用于森林树木的分类学和系统发育组成，以确定物种更替可能最大的地方，以及物种是否有可能被近亲取代。我们的研究表明，大约 11.9% 的当代北美森林与未来森林的预测相似性较低，与当代森林相比，26.2% 的未来森林在成分上可能是新颖的，但这些指标的大小在整个大陆存在很大的地理差异。西部的高海拔地区往往最接近其最接近的成分类似物，这表明这些地区最有可能实现未来预测的成分。这项工作提供了一种新方法来了解森林组成如何在未来气候中发生变化，从而避免对单个物种进行预测，并将气候匹配方法扩展到有意义的生物数据。