Powell Craig R, McKane Alan J
Theoretical Physics Group, School of Physics and Astronomy, University of Manchester, Manchester M13 9PL, UK.
J Theor Biol. 2008 Dec 21;255(4):387-95. doi: 10.1016/j.jtbi.2008.09.005. Epub 2008 Sep 18.
A large number of models of the species abundance distribution (SAD) have been proposed, many of which are generically similar to the log-normal distribution, from which they are often indistinguishable when describing a given data set. Ecological data sets are necessarily incomplete samples of an ecosystem, subject to statistical noise, and cannot readily be combined to yield a closer approximation to the underlying distribution. In this paper, we adopt the Webworld ecosystem model to study the predicted SAD in detail. The Webworld model is complex, and does not allow analytic examination of such features; rather, we use simulation data and an approach similar to that of ecologists analysing empirical data. By examining large sets of fully described data we are able to resolve features which can distinguish between models but which have not been investigated in detail in field data. We find that the power-law normal distribution is superior to both the log-normal and logit-normal distributions, and that the data can improve on even this at the high-population cut-off.
已经提出了大量物种丰度分布（SAD）模型，其中许多在一般情况下与对数正态分布相似，在描述给定数据集时，它们往往难以区分。生态数据集必然是生态系统的不完整样本，会受到统计噪声的影响，并且不能轻易合并以更接近地逼近潜在分布。在本文中，我们采用网络世界生态系统模型来详细研究预测的物种丰度分布。网络世界模型很复杂，不允许对这类特征进行解析研究；相反，我们使用模拟数据以及一种类似于生态学家分析经验数据的方法。通过检查大量完整描述的数据，我们能够分辨出可以区分不同模型但在实地数据中尚未详细研究的特征。我们发现幂律正态分布优于对数正态分布和对数几率正态分布，并且在高种群截断值处，数据甚至可以在此基础上进一步优化。
