Department of Biology, Stanford University, 371 Serra Mall, Stanford, California, 94305, USA.
Department of Ecology and Evolutionary Biology, University of Toronto, 25 Wilcocks, Toronto, Ontario, M5S 3B2, Canada.
Ecology. 2017 Jan;98(1):198-210. doi: 10.1002/ecy.1631.
Biological communities are structured phylogenetically-closely related species are typically more likely to be found at the same sites. This may be, in part, because they respond similarly to environmental gradients. Accurately surveying biological communities is, however, made difficult by the fact that detection of species is not perfect. In recent years, numerous statistical methods have been developed that aim to overcome deficiencies in the species detection process. However, these methods do not allow investigators to assess phylogenetic community structure. Here, we introduce the phylogenetic occupancy model (POM), which accounts for imperfect species detection while assessing phylogenetic patterns in community structure. Using simulated data sets we show that the POM grants less biased estimates of phylogenetic structure than models without imperfect detection, and can correctly ascertain the effects of species traits on community composition while accounting for evolutionary non-independence of taxa. Integrating phylogenetic methods into widely used occupancy models will help clarify how evolutionary history influences modern day communities.
生物群落是按照系统发生关系组织起来的，通常亲缘关系较近的物种更有可能出现在同一地点。这可能部分是因为它们对环境梯度的反应相似。然而，由于物种的检测并不完美，准确地调查生物群落变得困难。近年来，已经开发了许多统计方法，旨在克服物种检测过程中的缺陷。然而，这些方法并不能让研究人员评估系统发生的群落结构。在这里，我们介绍了系统发生占有率模型（POM），该模型在评估群落结构的系统发生模式时考虑了物种检测的不完美性。使用模拟数据集，我们表明，POM 比没有不完善检测的模型赋予了更无偏差的系统发生结构估计，并且可以在考虑分类单元进化非独立性的情况下，正确确定物种特征对群落组成的影响。将系统发生方法整合到广泛使用的占有率模型中，将有助于阐明进化历史如何影响现代群落。
