title: 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.
authors.affiliation: The Fenner School of Environment and Society, The Australian National University, Canberra, ACT 0200, Australia. sam.banks@anu.edu.au
authors.forename: Sam C
authors.initials: SC
authors.lastname: Banks
authors.forename: Rod
authors.initials: R
authors.lastname: Peakall
mesh_terms: D000818:Animals; D003627:Data Interpretation, Statistical; D017753:Ecosystem; D005260:Female; D014644:Genetic Variation; D005838:Genotype; D008297:Male; D008394:Marsupialia / Q000235:genetics*; D018895:Microsatellite Repeats; D008957:Models, Genetic; D011157:Population Dynamics; D012727:Sex Characteristics