Journal cover Journal topic
Web Ecology An open-access peer-reviewed journal
Journal topic
Volume 9, issue 1
Web Ecol., 9, 77-81, 2009
https://doi.org/10.5194/we-9-77-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
Web Ecol., 9, 77-81, 2009
https://doi.org/10.5194/we-9-77-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.

  21 Dec 2009

21 Dec 2009

The potential for misleading correlations in single-factor analysis of complex gradients

W.-M. He1 and R. M. Callaway2 W.-M. He and R. M. Callaway
  • 1State Key Lab of Vegetation and Environmental Change, Inst. of Botany, Chinese Academy of Sciences, 100093 Beijing, China
  • 2Division of Biological Sciences, Univ. of Montana, Missoula, 59812-1002, USA

Abstract. Gradient analysis is an important tool for describing patterns in ecology. Natural environmental gradients are complex combinations of factors, suggesting that gradientsshould, when possible, be analyzed in multi-factorial ways. We searched papers published in Ecology, Global Change Biology, Journal of Ecology, Oecologia, Oikos, and Journal of Vegetation Science from January 2001 to December 2005, and found 133 papers matching two keywords: “gradient analysis” and “environmental gradient”. Of these, 86 utilized single-factor correlation analyses between ecological entities and natural environmental gradients. Thus the use of single-factor correlations in studies of natural environmental gradients is widespread despite the potential of this approach to overemphasize the importance of the particular factor chosen. We reanalyzed a data set from the literature, provided a example of contrasting analyses, and analyzed our own data with both single- and multiple-factor analyses to demonstrate how single-factor correlation can result in correlations that provide incomplete analysis. Integrated multi-factor approaches to studying natural environmental gradients cannot solve all analytical problems when two or more important variables are correlated, but are likely to better test the relative importance of factors driving ecological patterns.

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