Beals Monica L
Department of Ecology and Evolutionary Biology, University of Tennessee, 569 Dabney Hall, Knoxville, TN 37996, USA.
Oecologia. 2006 Dec;150(3):484-95. doi: 10.1007/s00442-006-0551-8. Epub 2006 Sep 19.
Habitat is known to influence community structure yet, because these effects are complex, elucidating these relationships has proven difficult. Multiple aspects of vegetation architecture or plant species composition, for example, may simultaneously affect animal communities and their constituent species. Many traditional statistical approaches (e.g., regression) have difficulty in handling large numbers of collinear variables. On the other hand, multivariate methods, such as ordination, are well suited to handle these large datasets, but they have primarily been used in ecology as descriptive techniques, and less frequently as a data reduction tool for predictor variables in regression. Here, I employ a multivariate approach for variable reduction of both the predictor and response variables to investigate the influences of vegetation architecture and plant species on community composition in spiders using multiple regression. This allows retention of the information in the original dataset while producing statistically tractable variables for use in further analyses. I used nonmetric multidimensional scaling to reduce the number of variables for predictor (habitat architecture and plant species) and response (spider species) data matrices, and used these new variables in multiple regression analyses. These axes can be interpreted based on their correlations with the original variables, allowing for recovery of biologically meaningful information from regressions. Consequently, the important variables are determined by the data themselves, rather than by a priori assumptions of the researcher. Contrary to expectations based on previous work in spiders and other animals, plant species composition explained more variation in spider communities than did habitat architecture, and was also a stronger predictor of other community structure variables (overall abundance, species richness, and species diversity). I discuss possible ecological explanations for these results, and the advantages of the proposed method.
已知栖息地会影响群落结构，然而，由于这些影响很复杂，要阐明这些关系已被证明很困难。例如，植被结构或植物物种组成的多个方面可能会同时影响动物群落及其组成物种。许多传统的统计方法（如回归）在处理大量共线变量时存在困难。另一方面，多变量方法，如排序，非常适合处理这些大型数据集，但它们主要在生态学中用作描述性技术，较少用作回归中预测变量的数据简化工具。在这里，我采用多变量方法对预测变量和响应变量进行变量简化，以使用多元回归研究植被结构和植物物种对蜘蛛群落组成的影响。这既能保留原始数据集中的信息，又能生成便于进行进一步分析的统计上易于处理的变量。我使用非度量多维标度法来减少预测变量（栖息地结构和植物物种）和响应变量（蜘蛛物种）数据矩阵的变量数量，并在多元回归分析中使用这些新变量。这些轴可以根据它们与原始变量的相关性来解释，从而从回归中恢复具有生物学意义的信息。因此，重要变量由数据本身决定，而不是由研究人员的先验假设决定。与基于之前对蜘蛛和其他动物的研究得出的预期相反，植物物种组成比栖息地结构能解释蜘蛛群落中更多的变异，并且也是其他群落结构变量（总丰度、物种丰富度和物种多样性）的更强预测指标。我讨论了这些结果可能的生态学解释以及所提出方法的优点。
