The article "Spatial Autocorrelation and Red Herrings in Geographical Ecology" by Diniz-Filho, Bini, and Hawkins explores the impact of spatial autocorrelation on statistical analyses in ecological studies. The authors analyze bird species richness in western Europe, North Africa, and the Middle East to illustrate the issues involved. They use Moran's I coefficients to generate spatial correlograms and identify the strongest predictors of species richness using ordinary least-squares (OLS) and generalized least squares (GLS) regression models. The results show that bird richness is characterized by a quadratic north-south gradient, with positive autocorrelation up to about 1600 km. Including environmental variables in the OLS model reduces spatial autocorrelation in the residuals to non-detectable levels, indicating that these variables explain all spatial structure in the data. The authors conclude that while spatial autocorrelation should be investigated, it does not necessarily generate bias. Claims that analyses not accounting for spatial autocorrelation are flawed are unfounded. The study emphasizes the importance of considering spatial scale when interpreting ecological mechanisms and suggests that GLS models can provide a more accurate representation of the spatial patterns at different scales.The article "Spatial Autocorrelation and Red Herrings in Geographical Ecology" by Diniz-Filho, Bini, and Hawkins explores the impact of spatial autocorrelation on statistical analyses in ecological studies. The authors analyze bird species richness in western Europe, North Africa, and the Middle East to illustrate the issues involved. They use Moran's I coefficients to generate spatial correlograms and identify the strongest predictors of species richness using ordinary least-squares (OLS) and generalized least squares (GLS) regression models. The results show that bird richness is characterized by a quadratic north-south gradient, with positive autocorrelation up to about 1600 km. Including environmental variables in the OLS model reduces spatial autocorrelation in the residuals to non-detectable levels, indicating that these variables explain all spatial structure in the data. The authors conclude that while spatial autocorrelation should be investigated, it does not necessarily generate bias. Claims that analyses not accounting for spatial autocorrelation are flawed are unfounded. The study emphasizes the importance of considering spatial scale when interpreting ecological mechanisms and suggests that GLS models can provide a more accurate representation of the spatial patterns at different scales.