Explaining species richness patterns is a central issue in ecology, but a general explanation remains elusive. Environmental conditions have been proposed to be important drivers of these patterns, but we still need to better understand the relative contribution of environmental factors. Here, we aim at testing two environmental hypotheses for richness gradients: energy availability and environmental seasonality using diversity patterns of the family Leguminosae across Mexico. We compiled a data base of 502 species and 32,962 records. After dividing Mexico into 100 × 100 km grid cells, we constructed a map of variation in species richness that accounts for heterogeneity in sampling effort. We found the cells with the highest species richness of legumes are in the Neotropical region of Pacific coastal and southern Mexico, where the legume family dominates the tropical rain forests and seasonally dry tropical forests. Regression models show that energy and seasonality predictors can explain 25% and 49% of the variation in richness, respectively. Spatial autocorrelation analysis showed that richness has a strong spatial structure, but that most of this structure disappears when both energy and seasonality are used to account for richness gradient. Our study demonstrates multiple environmental conditions contribute complementarily to explain diversity gradients. Moreover, it shows that in some regions, environmental seasonality can be more important than energy availability, contradicting studies at coarser spatial scales. More basic taxonomic and floristic work is needed to help describe patterns of diversity for many groups to allow for testing the underlying mechanisms responsible for diversity gradients.

中文翻译：

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墨西哥豆科植物物种丰富度与环境的关系：量化能源和环境季节性的贡献

解释物种丰富度模式是生态学中的中心问题，但一般的解释仍然难以捉摸。已经提出环境条件是这些模式的重要驱动因素，但是我们仍然需要更好地了解环境因素的相对贡献。在这里，我们旨在测试两个环境假说中的丰富度梯度：使用墨西哥各地豆科的多样性模式的能源可利用性和环境季节性。我们建立了502种和32,962条记录的数据库。将墨西哥划分为100×100 km的网格单元后，我们构建了物种丰富度变化图，该图说明了采样工作的异质性。我们发现豆类物种丰富度最高的细胞位于太平洋沿岸和墨西哥南部的新热带地区，豆科植物在热带雨林和季节性干燥的热带森林中占主导地位。回归模型显示，能量和季节预测因子可以分别解释丰富度变化的25％和49％。空间自相关分析表明，丰富度具有很强的空间结构，但是当同时使用能量和季节性来解释丰富度梯度时，这种结构的大部分会消失。我们的研究表明，多种环境条件可以互补地解释多样性梯度。此外，它表明，在某些地区，环境季节性可能比能源可利用性更为重要，这与在较粗的空间尺度上进行的研究相矛盾。