Analyzing beta diversity: Partitioning the spatial variation of community composition data
Analyzing beta diversity: Partitioning the spatial variation of community composition data
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DOI：
10.1890/05-0549
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发表时间：
2005-11-01
影响因子：
6.1
通讯作者：
Peres-Neto, PR
中科院分区：
文献类型：
--
作者：
Legendre, P;Borcard, D;Peres-Neto, PR
Robert H.惠特克将贝塔多样性定义为一个地理区域内不同地点之间物种组成的变化。β多样性是理解生态系统功能、保护生物多样性和生态系统管理的关键概念。本文解释了如何假设的起源β多样性可以通过分区的空间变化的社区组成数据（存在或不存在或丰富的数据）相对于环境变量和空间基函数进行测试。我们比较了两种统计方法来实现这一点。平方和的社区组成数据表，这是一个可能的措施β多样性，正确划分的规范排序，因此，规范分区产生正确的估计不同部分的社区组成的变化。近年来，几位作者感兴趣的网站之间的社区组成的变化（β多样性）使用另一种方法，距离矩阵的变化分割（曼特尔方法）。他们的结果使我们使用在有关地点之间群落组成变化的假设下生成的模拟数据来比较两种划分方法。理论的发展和模拟结果导致以下观察：（1）社区组成表的方差是β多样性的度量。(2)地点之间的相异度矩阵的方差不是群落组成表的方差，也不是β多样性的度量;因此，不应使用距离矩阵的划分来研究地点之间群落组成的变化。(3)在我们所有的模拟中，距离矩阵的划分低估了原始数据方法解释的群落组成的变化量，（4）显著性检验的效力低于典型排序检验。因此，适当的统计程序划分的空间变化的社区组成数据之间的环境和空间的组成部分，并测试假说的起源和维护的变化，在社区组成网站之间，是典型的分区。Mantel方法适用于检验其他假设，例如不同地点之间的β多样性变化。距离矩阵的回归也适用于将模型拟合到相似性衰减图。
Robert H. Whittaker defined beta diversity as the variation in species composition among sites in a geographic area. Beta diversity is a key concept for understanding the functioning of ecosystems, for the conservation of biodiversity, and for ecosystem management. This paper explains how hypotheses about the origin of beta diversity can be tested by partitioning the spatial variation of community composition data (presence-absence or abundance data) with respect to environmental variables and spatial base functions. We compare two statistical methods to accomplish that. The sum-of-squares of a community composition data table, which is one possible measure of beta diversity, is correctly partitioned by canonical ordination; hence, canonical partitioning produces correct estimates of the different portions of community composition variation. In recent years, several authors interested in the variation in community composition among sites (beta diversity) have used another method, variation partitioning on distance matrices (Mantel approach). Their results led us to compare the two partitioning approaches, using simulated data generated under hypotheses about the variation of community composition among sites. The theoretical developments and simulation results led to the following observations: (1) the variance of a community composition table is a measure of beta diversity. (2) The variance of a dissimilarity matrix among sites is not the variance of the community composition table nor a measure of beta diversity; hence, partitioning on distance matrices should not be used to study the variation in community composition among sites. (3) In all of our simulations, partitioning on distance matrices underestimated the amount of variation in community composition explained by the raw-data approach, and (4) the tests of significance had less power than the tests of canonical ordination. Hence, the proper statistical procedure for partitioning the spatial variation of community composition data among environmental and spatial components, and for testing hypotheses about the origin and maintenance of variation in community composition among sites, is canonical partitioning. The Mantel approach is appropriate for testing other hypotheses, such as the variation in beta diversity among groups of sites. Regression on distance matrices is also appropriate for fitting models to similarity decay plots.