Genetic spatial autocorrelation can readily detect sex‐biased dispersal

Abstract

Sex-biased dispersal is expected to generate differences in the fine-scale genetic structure of males and females. Therefore, spatial analyses of multilocus genotypes may offer a powerful approach for detecting sex-biased dispersal in natural populations. However, the effects of sex-biased dispersal on fine-scale genetic structure have not been explored. We used simulations and multilocus spatial autocorrelation analysis to investigate how sex-biased dispersal influences fine-scale genetic structure. We evaluated three statistical tests for detecting sex-biased dispersal: bootstrap confidence intervals about autocorrelation r values and recently developed heterogeneity tests at the distance class and whole correlogram levels. Even modest sex bias in dispersal resulted in significantly different fine-scale spatial autocorrelation patterns between the sexes. This was particularly evident when dispersal was strongly restricted in the less-dispersing sex (mean distance <200 m), when differences between the sexes were readily detected over short distances. All tests had high power to detect sex-biased dispersal with large sample sizes (n ≥ 250). However, there was variation in type I error rates among the tests, for which we offer specific recommendations. We found congruence between simulation predictions and empirical data from the agile antechinus, a species that exhibits male-biased dispersal, confirming the power of individual-based genetic analysis to provide insights into asymmetries in male and female dispersal. Our key recommendations for using multilocus spatial autocorrelation analyses to test for sex-biased dispersal are: (i) maximize sample size, not locus number; (ii) concentrate sampling within the scale of positive structure; (iii) evaluate several distance class sizes; (iv) use appropriate methods when combining data from multiple populations; (v) compare the appropriate groups of individuals.

Genetic diversity and spatial structure of the Rufous‐throated Antbird (Gymnopithys rufigula), an Amazonian obligate army‐ant follower

“…Under a monogamic mating system, as seen in G. rufigula , males and females will disperse equally, as they share the same costs of parental care and dispersal; that is, both sexes are subjected to the same competition processes and have same variance in reproductive success (Brom, Massot, Legendre, & Laloi, 2016). Nonetheless, these results need to be interpreted with caution, as differences in dispersal between males and females need to be intense in order to be detected using microsatellite data (Goudet et al., 2002), and the power of detecting sex‐biased dispersal in spatial autocorrelation analysis might also be affected by the sample size (Banks & Peakall, 2012). …”

“…Additionally, we used spatial autocorrelation analyses to test whether different dispersal by males and females results in differences in spatial structure between sexes within DFR (Banks & Peakall, 2012). Firstly, we conducted the analysis separately for males and females, using distance classes of 400 m and 1 km.…”

“…Firstly, we conducted the analysis separately for males and females, using distance classes of 400 m and 1 km. Then, we compared the patterns of genetic structure between sexes by determining the 95% bootstrap confidence intervals (CI) for the autocorrelation r values for each sex (Banks & Peakall, 2012). If differences in dispersal existed, we expected CIs between sexes not to overlap, with r values significantly greater in the dispersal‐restricted sex.…”

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

“…Maximum distance intervals were 1100 for SDFRT and SLYNG, 1600 for SLMID and SLOLD, and 1700 for ADLTS (for the mean number of pairs per distance interval see Table 1). 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).…”

Localized extinction of an arboreal desert lizard caused by habitat fragmentation

“…Statistical significance was achieved with 1000 permutations and 1000 bootstraps to estimate 95% confidence intervals. The method has high power for detecting genetic structure and sex-biased dispersal particularly over small spatial scales (Banks and Peakall, 2012;Epperson, 2010).…”