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量化基于局部小生境和随机过程对热带树木群落组装的重要性。

Quantifying the importance of local niche-based and stochastic processes to tropical tree community assembly.

机构信息

Département de biologie, Université de Sherbrooke, Sherbrooke, Quebec J1K 2R1, Canada.

出版信息

Ecology. 2012 Apr;93(4):760-9. doi: 10.1890/11-0944.1.

Abstract

Although niche-based and stochastic processes, including dispersal limitation and demographic stochasticity, can each contribute to community assembly, it is difficult to quantify the relative importance of each process in natural vegetation. Here, we extend Shipley's maxent model (Community Assembly by Trait Selection, CATS) for the prediction of relative abundances to incorporate both trait-based filtering and dispersal limitation from the larger landscape and develop a statistical decomposition of the proportions of the total information content of relative abundances in local communities that are attributable to trait-based filtering, dispersal limitation, and demographic stochasticity. We apply the method to tree communities in a mature, species-rich, tropical forest in French Guiana at 1-, 0.25- and 0.04-ha scales. Trait data consisted of species' means of 17 functional traits measured over both the entire meta-community and separately in each of nine 1-ha plots. Trait means calculated separately for each site always gave better predictions. There was clear evidence of trait-based filtering at all spatial scales. Trait-based filtering was the most important process at the 1-ha scale (34%), whereas demographic stochasticity was the most important at smaller scales (37-53%). Dispersal limitation from the meta-community was less important and approximately constant across scales (-9%), and there was also an unresolved association between site-specific traits and meta-community relative abundances. Our method allows one to quantify the relative importance of local niche-based and meta-community processes and demographic stochasticity during community assembly across spatial and temporal scales.

摘要

虽然基于小生境和随机过程(包括扩散限制和种群统计学)都可以促进群落组装,但很难量化每个过程在自然植被中的相对重要性。在这里,我们扩展了 Shipley 的最大熵模型(通过特征选择进行群落组装,CATS),用于预测相对丰度,以纳入来自更大景观的基于特征的过滤和扩散限制,并开发了一种统计分解方法,用于将局部群落中相对丰度的总信息量的比例归因于基于特征的过滤、扩散限制和种群统计学。我们将该方法应用于法属圭亚那一个成熟、物种丰富的热带森林中的树木群落,尺度分别为 1 公顷、0.25 公顷和 0.04 公顷。特征数据由在整个元群落和在每个 1 公顷的 9 个小区中分别测量的 17 个功能特征的物种平均值组成。在每个站点分别计算的特征平均值总是给出更好的预测结果。在所有空间尺度上都有明显的基于特征的过滤证据。基于特征的过滤在 1 公顷的尺度上是最重要的过程(34%),而在较小的尺度上(37-53%)种群统计学是最重要的过程。来自元群落的扩散限制不太重要,并且在不同尺度上大致保持不变(-9%),而且站点特异性特征与元群落相对丰度之间也存在未解决的关联。我们的方法允许在空间和时间尺度上量化群落组装过程中局部基于小生境和元群落过程以及种群统计学的相对重要性。

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