Testing Inferences about Microevolutionary Processes by Means of Spatial Autocorrelation Analysis

Abstract

We generated numerous simulated gene-frequency surfaces subjected to 200 generations of isolation by distance with, in some cases, added migration or selection. From these surfaces we assembled six data sets comprising from 12 to 15 independent allele-frequency surfaces, to simulate biologically plausible population samples. The purpose of the study was to investigate whether spatial autocorrelation analysis will correctly infer the microevolutionary processes involved in each data set. The correspondence between the simulated processes and the inferences made concerning them is close for five of the six data sets. Errors in inference occurred when the effect of migration was weak, due to low gene frequency differential or low migration strength; when selection was weak and against a background with a complex pattern; and when a random process-isolation by distance-was the only one acting. Spatial correlograms proved more sensitive to detecting trends than inspection of gene-frequency surfaces by the human eye. Joint interpretation of the correlograms and their clusters proved most reliable in leading to the correct inference. The inspection and clustering of surfaces were useful for determining directional components. Because this method relies on common patterns across loci, as many gene frequencies as feasible should be used. We recommend spatial autocorrelation analysis for the detection of microevolutionary processes in natural populations.

Dispersal, Philopatry, and Infidelity: Dissecting Local Genetic Swtructure in Superb Fairy-Wrens (Malurs Cyaneus)

“…A similar pattern of genetic structure was predicted from computer simulations of populations with restricted gene flow (e.g., Sokal and Jacquez 1991;Epperson and Li 1997;Sokal et al 1997;Epperson et al 1999) and has also been reported in empirical studies of plants (e.g., Smouse and Peakall 1999) and mammals (Peakall et al 2003). Similarly, the autocorrelation analysis of female fairy-wrens alone detected significant positive genetic correlation but only in the 40-and 80-m distance classes.…”

Spatial structure of genetic variability in natural stands of Fagus sylvatica L. (beech) in Italy

“…To gain insight into the relationship between autocorrelation and heterozygosity, computer simulations of an isolation-by-distance process were carried out following Sokal & Wartenberg (1983) and Sokal & Jacquez (1991 It should be noted that in these simulations the same reproduction event involves all loci of a tree independent of their heterozygosities, so that similar patterns of genetic spatial structuring should be produced for all loci.…”

Phylogenetic correlograms and the evolution of body size in South American owls (Strigiformes)

“…Further investigation of this topic is necessary, but there is a possibility that the increasing rate of evolution, associated with selection, may produce higher slopes in the correlogram. Another possibility, following the inferential procedures in spatial autocorrelation, is that multivariate (or multivariable) analysis can be useful to distinguish between different processes (Sokal and Jacquez, 1991). Other methods have been proposed to distinguish between Brownian motion and O-U processes, but their application in this study would be difficult, because of limitations in the data set.…”