Theoretical Ecology and Biodiversity, and Infraestruturas de Portugal Biodiversity Chair, CIBIO/InBio, Centro de Investigação em Biodiversidade e Recursos Genéticos, Laboratório Associado, Universidade do Porto. Campus Agrário de Vairão, 4485-661, Vairão, Portugal.
CEABN/InBio, Centro de Ecologia Aplicada "Professor Baeta Neves", Instituto Superior de Agronomia, Universidade de Lisboa, Tapada da Ajuda, 1349-017, Lisboa, Portugal.
Sci Rep. 2017 Jun 20;7(1):3899. doi: 10.1038/s41598-017-04126-5.
Species abundance distributions (SAD) are central to the description of diversity and have played a major role in the development of theories of biodiversity and biogeography. However, most work on species abundance distributions has focused on one single spatial scale. Here we used data on arthropods to test predictions obtained with computer simulations on whether dispersal ability influences the rate of change of SADs as a function of sample size. To characterize the change of the shape of the SADs we use the moments of the distributions: the skewness and the raw moments. In agreement with computer simulations, low dispersal ability species generate a hump for intermediate abundance classes earlier than the distributions of high dispersal ability species. Importantly, when plotted as function of sample size, the raw moments of the SADs of arthropods have a power law pattern similar to that observed for the SAD of tropical tree species, thus we conjecture that this might be a general pattern in ecology. The existence of this pattern allows us to extrapolate the moments and thus reconstruct the SAD for larger sample sizes using a procedure borrowed from the field of image analysis based on scaled discrete Tchebichef moments and polynomials.
物种丰度分布（SAD）是描述多样性的核心，在生物多样性和生物地理学理论的发展中发挥了重要作用。然而，大多数关于物种丰度分布的研究都集中在单一的空间尺度上。在这里，我们使用节肢动物的数据来检验计算机模拟得到的预测，即扩散能力是否会影响 SAD 随样本量变化的速率。为了描述 SAD 形状的变化，我们使用分布的矩：偏度和原始矩。与计算机模拟一致，低扩散能力的物种比高扩散能力的物种更早地产生中间丰度类的驼峰。重要的是，当作为样本量的函数绘制时，节肢动物 SAD 的原始矩具有类似于热带树种 SAD 观察到的幂律模式，因此我们推测这可能是生态学中的一般模式。这种模式的存在使得我们能够使用基于缩放离散切贝谢夫矩和多项式的图像分析领域中的一种方法来外推矩，从而使用更大的样本量重建 SAD。
