Abstract

Understanding the drivers of species’ geographic distribution and assembly of communities is one of the most intriguing questions in ecology and has become extremely important in face of global changes. This study aims to assess broad-scale patterns of tree species diversity and compare the importance of environmental correlates relative to purely spatial factors in structuring communities in the Brazilian Atlantic Forest. We compiled abundance data from 2948 species in 117 localities across the entire biome and obtained climatic and soil variables that we hypothesized to be environmental filters. We constructed correlograms for species richness and composition and assessed the relative importance of correlates using spatially explicit generalized additive models coupled with information-theoretic analyses. Variation partitioning was used to infer the relative importance of environmental- and spatial-based hypotheses. Species richness presented positive spatial correlation of ~ 435 km beyond which it became negative and again positive at the furthest distances, whereas compositional turnover showed positive correlation of ~ 435–555 km with decreasing similarity in increasing distances. Climatic variables were important factors correlating with richness patterns, with soil being less important. Species composition was significantly correlated with environmental variables and spatial constraints. The spatial component showed similar amount of explanation in statistical models compared to the environmental component. Our results suggest a combined contribution of environment, stochasticity, and historical-dispersion processes in patterns of biodiversity. More interestingly, a similar set of variables was the better correlates of species richness and composition, indicating convergence of driving factors for both descriptors of plant communities.

中文翻译：

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解释树木物种丰富度和组成的机制在一个巨大的热点中趋于一致

摘要

了解物种的地理分布和社区聚集的动因是生态学中最引人入胜的问题之一，并且在面对全球变化时已变得极为重要。这项研究旨在评估树木物种多样性的大范围模式，并比较在建立巴西大西洋森林社区中与环境因素相关的相对于纯粹空间因素的重要性。我们汇总了整个生物群落中117个地区的2948个物种的丰度数据，并获得了我们假设为环境过滤器的气候和土壤变量。我们构造了物种丰富度和组成的相关图，并使用空间明确的广义加性模型以及信息理论分析来评估相关性的相对重要性。变异划分被用来推断基于环境和空间的假设的相对重要性。物种丰富度呈现出〜435 km的正空间相关性，超过该距离时它呈负相关，并且在最远距离处又呈正相关，而组成更新显示〜435–555 km呈正相关性，且随着距离的增加而相似性减小。气候变量是与丰富度模式相关的重要因素，而土壤则不那么重要。物种组成与环境变量和空间限制显着相关。与环境成分相比，空间成分在统计模型中显示出相似的解释量。我们的结果表明，环境，随机性，和生物多样性模式中的历史传播过程。更有趣的是，一组相似的变量是物种丰富度和组成之间的更好关联，表明两个植物群落描述因素的驱动因素都趋于一致。