Li Daijiang, Ives Anthony R, Waller Donald M
Department of Botany, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA.
Department of Zoology, University of Wisconsin, 430 Lincoln Drive, Madison, WI, 53706, USA.
New Phytol. 2017 Apr;214(2):607-618. doi: 10.1111/nph.14397. Epub 2017 Jan 3.
Phylogenetic and functional trait-based analyses inform our understanding of community composition, yet methods for quantifying the overlap in information derived from functional traits and phylogenies remain underdeveloped. Does adding traits to analyses of community composition reduce the phylogenetic signal in the residual variation? If not, then measured functional traits alone may be insufficient to explain community assembly. We propose a general statistical framework to quantify the proportion of phylogenetic pattern in community composition that remains after including measured functional traits. We then illustrate the framework with applications to two empirical data sets. Both data sets showed strong phylogenetic attraction, with related species likely to co-occur in the same communities. In one data set, including traits eliminated all phylogenetic signals in the residual variation of both abundance and presence/absence patterns. In the second data set, including traits reduced phylogenetic signal in residuals by 25% and 98% for abundance and presence/absence data, respectively. Our framework provides an explicit way to estimate how much phylogenetic community pattern remains in the residual variation after including measured functional traits. Knowing that functional traits account for most of the phylogenetic pattern should provide confidence that important traits for phylogenetic community structure have been identified. Conversely, knowing that there is unexplained residual phylogenetic information should spur the search for additional functional traits or other processes underlying community assembly.
基于系统发育和功能性状的分析有助于我们理解群落组成，但量化从功能性状和系统发育中获得的信息重叠的方法仍不完善。在群落组成分析中加入性状是否会降低残差变异中的系统发育信号？如果不会，那么仅测量功能性状可能不足以解释群落组装。我们提出了一个通用的统计框架，以量化在纳入测量的功能性状后群落组成中仍然存在的系统发育模式的比例。然后，我们通过应用于两个实证数据集来说明该框架。两个数据集都显示出强烈的系统发育吸引力，相关物种可能在同一群落中共存。在一个数据集中，纳入性状消除了丰度和存在/缺失模式的残差变异中的所有系统发育信号。在第二个数据集中，纳入性状分别使丰度和存在/缺失数据的残差中的系统发育信号降低了25%和98%。我们的框架提供了一种明确的方法，来估计在纳入测量的功能性状后，残差变异中仍存在多少系统发育群落模式。知道功能性状解释了大部分系统发育模式，应该会让人相信已经识别出了系统发育群落结构的重要性状。相反，知道存在无法解释的残差系统发育信息，应该会促使人们寻找其他功能性状或群落组装背后的其他过程。
