Dissecting the spatial structure of ecological data at multiple scales
Dissecting the spatial structure of ecological data at multiple scales
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DOI：
10.1890/03-3111
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发表时间：
2004-07-01
期刊：
影响因子：
4.8
通讯作者：
Tuomisto, H
中科院分区：
文献类型：
--
作者：
Borcard, D;Legendre, P;Tuomisto, H
空间结构可能不仅是生态相互作用的结果，它们还可能在组织相互作用中发挥重要的功能作用。因此，在多个空间和时间尺度上对空间格局进行建模是理解生态群落功能的关键一步。PCNM(相邻矩阵的主坐标)分析实现了采样点之间的空间关系的频谱分解，创建了与给定数据集中可以感知的所有空间尺度相对应的变量。然后，分析找出感兴趣的数据表所对应的标度。重要的PCNM变量可以直接用空间尺度来解释，或者包括在关于空间和环境组成部分的变化分解过程中。本文介绍了PCNM分析在生态数据中的四种应用：断面或表面数据、规则或非规则抽样方案、单变量或多变量数据的组合。这些数据集包括亚马逊蕨类植物、热带海洋浮游动物、海洋泻湖中的叶绿素以及泥炭沼泽中的甲螨类。在每个案例中，通过PCNM分析获得了新的生态知识。
Spatial structures may not only result from ecological interactions, they may also play an essential functional role in organizing the interactions. Modeling spatial patterns at multiple spatial and temporal scales is thus a crucial step to understand the functioning of ecological communities. PCNM (principal coordinates of neighbor matrices) analysis achieves a spectral decomposition of the spatial relationships among the sampling sites, creating variables that correspond to all the spatial scales that can be perceived in a given data set. The analysis then finds the scales to which a data table of interest responds. The significant PCNM variables can be directly interpreted in terms of spatial scales, or included in a procedure of variation decomposition with respect to spatial and environmental components. This paper presents four applications of PCNM analysis to ecological data representing combinations of: transect or surface data, regular or irregular sampling schemes, univariate or multivariate data. The data sets include Amazonian ferns, tropical marine zooplankton, chlorophyll in a marine lagoon, and oribatid mites in a peat bog. In each case, new ecological knowledge was obtained through PCNM analysis.