Département de Sciences Biologiques, Université de Montréal, , C.P. 6128, Succursale Centre-ville, Montréal, Québec, Canada , H3C 3J7, Laboratoire des Sciences de l'Environnement Marin (LEMAR), UMR CNRS 6539, Institut Universitaire Européen de la Mer, Université de Bretagne Occidentale, , rue Dumont d'Urville, Plouzané 29280, France.
Proc Biol Sci. 2014 Jan 15;281(1778):20132728. doi: 10.1098/rspb.2013.2728. Print 2014 Mar 7.
This review focuses on the analysis of temporal beta diversity, which is the variation in community composition along time in a study area. Temporal beta diversity is measured by the variance of the multivariate community composition time series and that variance can be partitioned using appropriate statistical methods. Some of these methods are classical, such as simple or canonical ordination, whereas others are recent, including the methods of temporal eigenfunction analysis developed for multiscale exploration (i.e. addressing several scales of variation) of univariate or multivariate response data, reviewed, to our knowledge for the first time in this review. These methods are illustrated with ecological data from 13 years of benthic surveys in Chesapeake Bay, USA. The following methods are applied to the Chesapeake data: distance-based Moran's eigenvector maps, asymmetric eigenvector maps, scalogram, variation partitioning, multivariate correlogram, multivariate regression tree, and two-way MANOVA to study temporal and space-time variability. Local (temporal) contributions to beta diversity (LCBD indices) are computed and analysed graphically and by regression against environmental variables, and the role of species in determining the LCBD values is analysed by correlation analysis. A tutorial detailing the analyses in the R language is provided in an appendix.
本综述重点分析时间β多样性，即在研究区域内随时间推移群落组成的变化。时间β多样性通过多元群落组成时间序列的方差来衡量，并且可以使用适当的统计方法对该方差进行划分。其中一些方法是经典的，例如简单或典范排序，而另一些则是最近才出现的，包括为探索单变量或多变量响应数据的多尺度变化（即解决多个变化尺度）而开发的时间特征函数分析方法，据我们所知，这些方法首次在本综述中得到了回顾。这些方法用来自美国切萨皮克湾的 13 年底栖调查的生态数据进行了说明。以下方法应用于切萨皮克湾数据：基于距离的 Moran 特征向量图、非对称特征向量图、标度图、变分分析、多元相关图、多元回归树和双向 MANOVA 来研究时间和时空变异性。通过回归分析和环境变量计算和图形分析局部（时间）对β多样性的贡献（LCBD 指数），并通过相关分析分析物种在确定 LCBD 值方面的作用。附录中提供了详细说明 R 语言中分析过程的教程。
