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Ecological studies often attempt to link observed effects to multiple causal factors which may be operating simultaneously. Although in situ randomized experiments in which factor levels are controlled may be a powerful means for disentangling causal relationships, an experimental approach is not always feasible or even a desirable first step in the analysis, particularly when there is insufficient background knowledge of the system. In such cases, analysis of survey data, reflecting natural (co)variation in the putative causal factors and their direct and indirect effects, can be a practical and useful alternative to experiments. When set in the proper statistical framework, survey data can be used to assess whether a given factor has a detectable effect once the effect of other factors has been accounted for statistically (by partialling), and to estimate what proportion of the effect can be attributed to each factor (by variance decomposition). This analysis can help establish whether a particular causal model is consistent with the data at hand, and should be viewed as preliminary to a mechanistic approach, providing support and guidance for the investigation of more realistic variables. Here, we use three examples based on survey data from fish and invertebrate lacustrine communities to illustrate the application of partialling and variance decomposition in a multivariate setting. The first example shows that variation in the abundance and size structure of cladoceran taxa is still associated with fish species composition when potentially confounding effects of abiotic variables are accounted for by partialling. In the second and third examples, variance decomposition is used to determine the relative contribution of the environmental and spatial components to variation in the community structure of littoral zoobenthos and in the diet of a freshwater fish species.Contribution of the Group for Interuniversitary Research in Limnology (G.R.I.L.). 相似文献
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