Spatial autocorrelation and red herrings in geographical ecology
Article first published online: 31 JAN 2003
Global Ecology and Biogeography
Volume 12, Issue 1, pages 53–64, January 2003
How to Cite
Diniz-Filho, J. A. F., Bini, L. M. and Hawkins, B. A. (2003), Spatial autocorrelation and red herrings in geographical ecology. Global Ecology and Biogeography, 12: 53–64. doi: 10.1046/j.1466-822X.2003.00322.x
- Issue published online: 31 JAN 2003
- Article first published online: 31 JAN 2003
- generalized least squares;
- latitudinal gradients;
- multiple regression;
- spatial autocorrelation;
- species richness
Aim Spatial autocorrelation in ecological data can inflate Type I errors in statistical analyses. There has also been a recent claim that spatial autocorrelation generates ‘red herrings’, such that virtually all past analyses are flawed. We consider the origins of this phenomenon, the implications of spatial autocorrelation for macro-scale patterns of species diversity and set out a clarification of the statistical problems generated by its presence.
Location To illustrate the issues involved, we analyse the species richness of the birds of western/central Europe, north Africa and the Middle East.
Methods Spatial correlograms for richness and five environmental variables were generated using Moran's I coefficients. Multiple regression, using both ordinary least-squares (OLS) and generalized least squares (GLS) assuming a spatial structure in the residuals, were used to identify the strongest predictors of richness. Autocorrelation analyses of the residuals obtained after stepwise OLS regression were undertaken, and the ranks of variables in the full OLS and GLS models were compared.
Results Bird richness is characterized by a quadratic north–south gradient. Spatial correlograms usually had positive autocorrelation up to c. 1600 km. Including the environmental variables successively in the OLS model reduced spatial autocorrelation in the residuals to non-detectable levels, indicating that the variables explained all spatial structure in the data. In principle, if residuals are not autocorrelated then OLS is a special case of GLS. However, our comparison between OLS and GLS models including all environmental variables revealed that GLS de-emphasized predictors with strong autocorrelation and long-distance clinal structures, giving more importance to variables acting at smaller geographical scales.
Conclusion Although spatial autocorrelation should always be investigated, it does not necessarily generate bias. Rather, it can be a useful tool to investigate mechanisms operating on richness at different spatial scales. Claims that analyses that do not take into account spatial autocorrelation are flawed are without foundation.