Hello everyone. I received requests for the answers to my negative spatial autocorrelation question so below I have summarised them. I thank everyone (Suzanne Sadedin, Craig Streatfield, Filipe Alberto, Kathryn Elmer, Heribert Hofer, Paul Sunnucks, Ross Croizer and Xavier Turon) very much for their kind attention to it. It seems I had simply gotten mixed up in a jumble of words (more similar, less dissimilar, more genetic distance: more difference, less distance..). The term negative spatial autocorrelation refers to individuals being less similar (more different) than expected, not the other way around as I said in the past email. Now things do make biological sense to me! I am glad I sent the question not only because this has been made clear to me but because some of the answers, including one from an email sent directly to Genalex authors Peter Smouse and Rod Peakall, have turned my attention to the fact that because of the way calculations are made, a see-saw effect comes into play: if you push up on one side (positive autocorrelation) the opposite side goes down (negative autocorrelation). No pushing on either side makes for a flat see-saw (no autocorrelation, no restrictions to gene flow at the scale that is being measured). I incorporated the see-saw analogy myself and it's not without its imperfections so please see Smouse's explanation below. Thanks again, Gisselle Gisselle Perdomo Laboratorio de Ecolog�a del Comportamiento Departamento de Estudios Ambientales Divisi�n de Ciencias Biol?gicas Universidad Sim�n Bol�var Apdo. 89.000, Caracas 1080-A, Venezuela Telf: (58-212) 906 3043 Fax: (58-212) 906 3039 email: gisselle_p@yahoo.com --- Peter Smouse quote: The correlation of two individuals is defined, relative to what would happen for the average pair in the study, which average is (by virtue of the way the autocorrelation is computed from the covariance matrix) precisely zero. If the average autocorrelation (over the whole data set) is centered at zero (and it is), then if some of the N(N-1)/2 pairs are positively autocorrelated, others must be negatively autocorrelated. Otherwise, the whole set would not average out to zero! If the close neighbors are positively autocorrelated, and they typically are, then those that are more distant will be negatively autocorrelated. As I said, not profound, just a matter of having the algebra add up properly. Another way to say it, in less algebraic terms, is that if some individuals are more correlated (related) than average, others must be less correlated (related) than average, since the average is defined to be "uncorrelated". gisselle_p@yahoo.com