Speciation in chestnut-shouldered fairy-wrens (Malurus spp.) and rapid phenotypic divergence in variegated fairy-wrens (Malurus lamberti): a multilocus approach.
ABSTRACT The chestnut-shouldered fairy-wrens comprise a subgroup of four species in the genus Malurus (Passeriformes: Maluridae). Collectively, they are widespread across the Australian continent but phenotypic variation is strongly structured geographically in just one species, M. lamberti. Earlier phylogenetic analyses of this group have been limited to one or two individuals for each species and have not represented all currently recognised subspecies of M. lamberti. Historically, the taxonomy and nomenclature of the M. lamberti complex has been debated, in part because of morphological similarities among its subspecies and another member of the group, M. amabilis. We reconstructed the phylogeny of all four species of chestnut-shouldered fairy-wrens including all four subspecies of M. lamberti using a mitochondrial gene (ND2), five anonymous nuclear loci and three nuclear introns. Phylogenetic analysis of the mitochondrial ND2 gene nests M. amabilis within M. lamberti rendering the latter paraphyletic. Individual nuclear gene trees failed to reliably resolve each of the species boundaries or the phylogenetic relationships found in the mtDNA tree. When combined, however, a strongly supported overall topology was resolved supporting the monophyly of M. lamberti and its sister species relationship to M. amabilis. Current subspecific taxonomy of M. lamberti was not concordant with all evolutionary lineages of M. lamberti, nominotypical M. l. lamberti being the only subspecies recovered as a monophyletic group from mtDNA. Some genetic structuring is evident and potential barriers to gene flow are discussed.
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ABSTRACT: Methods that integrate population-level sampling from multiple taxa into a single community-level analysis are an essential addition to the comparative phylogeographic toolkit. Detecting how species within communities have demographically tracked each other in space and time is important for understanding the effects of future climate and landscape changes and the resulting acceleration of extinctions, biological invasions and potential surges in adaptive evolution. Here we present a statistical framework for such an analysis based on hierarchical approximate Bayesian computation (hABC) with the goal of detecting concerted demographic histories across an ecological assemblage. Our method combines population genetic datasets from multiple taxa into a single analysis to estimate: 1) the proportion of a community sample that demographically expanded in a temporally clustered pulse; and 2) when the pulse occurred. To validate the accuracy and utility of this new approach, we use simulation cross-validation experiments and subsequently analyze an empirical dataset of 32 avian populations from Australia that are hypothesized to have expanded from smaller refugia populations in the late Pleistocene. The method can accommodate dataset heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and exploits the statistical strength from the simultaneous analysis of multiple species. This hABC framework used in a multi-taxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently, and can be used with a wide variety of comparative phylogeographic datasets as biota-wide DNA barcoding data sets accumulate.Molecular Biology and Evolution 06/2014; · 10.35 Impact Factor