Red‐shifts and red herrings in geographical ecology

Red‐shifts and red herrings in geographical ecology I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated ‐ the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables “significant” much more frequently than it should. The chances of mistakenly identifying a “significant” slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red‐shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecography Wiley

Red‐shifts and red herrings in geographical ecology

Ecography, Volume 23 (1) – Feb 1, 2000

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Publisher
Wiley
Copyright
Copyright © 2000 Wiley Subscription Services, Inc., A Wiley Company
ISSN
0906-7590
eISSN
1600-0587
D.O.I.
10.1111/j.1600-0587.2000.tb00265.x
Publisher site
See Article on Publisher Site

Abstract

I draw attention to the need for ecologists to take spatial structure into account more seriously in hypothesis testing. If spatial autocorrelation is ignored, as it usually is, then analyses of ecological patterns in terms of environmental factors can produce very misleading results. This is demonstrated using synthetic but realistic spatial patterns with known spatial properties which are subjected to classical correlation and multiple regression analyses. Correlation between an autocorrelated response variable and each of a set of explanatory variables is strongly biased in favour of those explanatory variables that are highly autocorrelated ‐ the expected magnitude of the correlation coefficient increases with autocorrelation even if the spatial patterns are completely independent. Similarly, multiple regression analysis finds highly autocorrelated explanatory variables “significant” much more frequently than it should. The chances of mistakenly identifying a “significant” slope across an autocorrelated pattern is very high if classical regression is used. Consequently, under these circumstances strongly autocorrelated environmental factors reported in the literature as associated with ecological patterns may not actually be significant. It is likely that these factors wrongly described as important constitute a red‐shifted subset of the set of potential explanations, and that more spatially discontinuous factors (those with bluer spectra) are actually relatively more important than their present status suggests. There is much that ecologists can do to improve on this situation. I discuss various approaches to the problem of spatial autocorrelation from the literature and present a randomisation test for the association of two spatial patterns which has advantages over currently available methods.

Journal

EcographyWiley

Published: Feb 1, 2000

References

  • Partialling out the spatial component of ecological variation
    Borcard, Borcard; Legendre, Legendre; Drapeau, Drapeau
  • Spatial autocorrelation in California land birds
    Koenig, Koenig
  • Forest pattern, climate and vulcanism in central North Island, New Zealand
    Leathwick, Leathwick; Mitchell, Mitchell
  • Study of spatial components of forest cover using partial Mantel tests and path analysis
    Leduc, Leduc
  • Climate and woody plant diversity in southern Africa: relationships at species, genus and family levels
    O'Brien, O'Brien; Whittaker, Whittaker; Field, Field
  • Bark beetle diversity at different spatial scales
    Peltonen, Peltonen

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