A heterogeneity test for fine‐scale genetic structure

Abstract

For organisms with limited vagility and/or occupying patchy habitats, we often encounter nonrandom patterns of genetic affinity over relatively small spatial scales, labelled fine-scale genetic structure. Both the extent and decay rate of that pattern can be expected to depend on numerous interesting demographic, ecological, historical, and mating system factors, and it would be useful to be able to compare different situations. There is, however, no heterogeneity test currently available for fine-scale genetic structure that would provide us with any guidance on whether the differences we encounter are statistically credible. Here, we develop a general nonparametric heterogeneity test, elaborating on standard autocorrelation methods for pairs of individuals. We first develop a 'pooled within-population' correlogram, where the distance classes (lags) can be defined as functions of distance. Using that pooled correlogram as our null-hypothesis reference frame, we then develop a heterogeneity test of the autocorrelations among different populations, lag-by-lag. From these single-lag tests, we construct an analogous test of heterogeneity for multilag correlograms. We illustrate with a pair of biological examples, one involving the Australian bush rat, the other involving toadshade trillium. The Australian bush rat has limited vagility, and sometimes occupies patchy habitat. We show that the autocorrelation pattern diverges somewhat between continuous and patchy habitat types. For toadshade trillium, clonal replication in Piedmont populations substantially increases autocorrelation for short lags, but clonal replication is less pronounced in mountain populations. Removal of clonal replicates reduces the autocorrelation for short lags and reverses the sign of the difference between mountain and Piedmont correlograms.

Fine-scale spatial genetic dynamics over the life cycle of the tropical tree Prunus africana

“…We performed a heterogeneity test in order to evaluate the significance of correlograms at a P-level of 0.01 (Banks and Peakall, 2012). Second, we conducted a heterogeneity test to see whether relatedness differed among life stages for each distance interval and across distance intervals (Smouse et al, 2008). The maximum distance interval was set to 1000 m to assure comparability among life stages.…”

Anthropogenic and natural causes influencing population genetic structure of Juniperus procera Hochst. ex Endl. in the Ethiopian highlands

“…In order to Nei's (1978) genetic distance was computed using AFLP-Surv 1.0 from which an UPGMA (Unweighted pair group method using arithmetic averages) dendrogram was generated using the method of SAHN clustering in NTSYSpc 2.0 (Rohlf 1998). In order to estimate the influence of geographic distance on genetic distance between population pairs, a Mantel test (Mantel 1967) was performed using GenAlEx6.3 (Smouse et al 2008;Peakall and Smouse 2006).…”

Characterising the genetic diversity of Pseudomonas syringae pv. syringae isolated from rice and wheat in Iran

“…Nei's estimate of similarity, based on the number of shared RAPD products, was used to generate similarity and distance. The et al 1992), in which the total genetic variance was partitioned into among population and within population component, which were calculated by Genealex program version 6.2 (Beck et al 2008;Smouse et al 2008).…”