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An understanding of the role of factors intrinsic to a species’ life history in structuring contemporary genetic variation is a fundamental, but understudied, aspect of evolutionary biology. Here, we assessed the influence of the propensity to outbreak in shaping worldwide genetic variation in Locusta migratoria, a cosmopolitan pest well known for its expression of density-dependent phase polyphenism. We scored 14 microsatellites in nine subspecies from 25 populations distributed over most of the species’ range in regions that vary in the historical frequency and extent of their outbreaks. We rejected the hypothesis that L. migratoria consists of two genetically distinct clusters adapted to habitats either rarely (nonoutbreaking) or cyclically (outbreaking) favourable to increases in population density. We also invalidated the current subspecific taxonomic classification based on morphometrics. Bayesian inferences indicated evidence of a homogenizing effect of outbreaks on L. migratoria population structure. Geographical and ecological barriers to gene flow in conjunction with historical events can also explain the observed patterns. By systematically assessing the effects of null alleles using computer simulations, we also provide a template for the analysis of microsatellite data sets characterized by a high prevalence of null alleles. [ABSTRACT FROM AUTHOR]

We propose a general formulation of the Bayesian method for assigning individuals to a population among a predetermined set of reference populations using molecular marker information. Compared to previously published methods, ours allows us to consider different types of prior information about allele frequencies by using a Dirichlet prior probability distribution. It also makes it possible to assign a set of individuals assumed to belong to the same population with increased accuracy using their pooled genotype data. The efficiency of the method is illustrated by application to a group of closely related coconut populations. An interesting feature of the Bayesian procedure is the way it handles imprecise information. With a poor or even incomplete dataset, assignment is still be possible and gives valid results: poor data quality is reflected in an ambiguous result rather than in a false conclusion. [ABSTRACT FROM AUTHOR]