Disentangling the drivers of diversity gradients can be challenging. The Measurement of Biodiversity (MoB) framework decomposes scale-dependent changes in species diversity into three components of community structure: the species abundance distribution (SAD), the total community abundance, and the within-species spatial aggregation. Here we extend MoB from categorical treatment comparisons to quantify variation along continuous geographic or environmental gradients. Our approach requires sites along a gradient, each consisting of georeferenced plots of abundance-based species composition data. We demonstrate our method using a case study of ants sampled along an elevational gradient of 28 sites in a mixed deciduous forest of the Great Smoky Mountains National Park, USA. MoB analysis revealed that decreases in ant species richness along the elevational gradient were associated with decreasing evenness and total number of species which counteracted the modest increase in richness associated with decreasing spatial aggregation along the gradient. Total community abundance had a negligible effect on richness at all but the finest spatial grains, SAD effects increased in importance with sampling effort, while the aggregation effect had the strongest effect at coarser spatial grains. These results do not support the more-individuals hypothesis, but they are consistent with a hypothesis of stronger environmental filtering at coarser spatial grains. Our extension of MoB has the potential to elucidate how components of community structure contribute to changes in diversity along environmental gradients and should be useful for a variety of assemblage-level data collected along gradients.

中文翻译：

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一个多尺度框架，用于解开均匀度、密度和聚合对多样性梯度的作用

解开多样性梯度的驱动因素可能具有挑战性。生物多样性测量 (MoB) 框架将物种多样性的尺度依赖性变化分解为群落结构的三个组成部分：物种丰度分布 (SAD)、群落总丰度和物种内空间聚集。在这里，我们将 MoB 从分类处理比较扩展到量化沿连续地理或环境梯度的变化。我们的方法需要沿着梯度的站点，每个站点都由基于丰度的物种组成数据的地理参考图组成。我们使用沿美国大烟山国家公园混合落叶林中 28 个地点的海拔梯度采样的蚂蚁案例研究来演示我们的方法。MoB 分析表明，蚂蚁物种丰富度沿海拔梯度的减少与均匀度和物种总数的减少有关，这抵消了与沿梯度空间聚集减少相关的丰富度的适度增加。除了最细的空间颗粒外，总群落丰度对丰富度的影响几乎可以忽略不计，SAD 效应的重要性随着采样努力而增加，而聚集效应对较粗的空间颗粒的影响最强。这些结果不支持更多个体的假设，但它们与在较粗的空间粒度上更强的环境过滤的假设一致。