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Which randomizations detect convergence and divergence in trait-based community assembly? A test of commonly used null models

机译:哪些随机检测在基于特征的社区集会中发现趋同和分歧?常用空模型的测试

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摘要

Questions: Mechanisms of community assembly are increasingly explored by combining community and species trait data with null models. By investigating if the traits of co-existing species are more similar (trait convergence) or more dissimilar (trait divergence) than expected by chance, these tests relate observed patterns to different co-existence mechanisms. Do null models accurately detect trait convergence and divergence? Are different null models equally good at detecting these two opposing patterns? How important are the species pool and other constraints that are considered by different null models?
机译:问题:通过将社区和物种特征数据与无效模型相结合,越来越多地探索社区组装的机制。通过调查并存物种的特征是否比偶然期望的更相似(特征趋同)或更不相似(特征趋异),这些测试将观察到的模式与不同的共存机制相关联。零模型是否可以准确地检测出特征收敛和差异?不同的空模型是否同样擅长检测这两个相反的模式?不同的空模型考虑的物种库和其他约束有多重要?

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