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Data from: Demographic inferences after a range expansion can be biased: the test case of the blacktip reef shark (Carcharhinus melanopterus)

Citation

Maisano Delser, Pierpaolo et al. (2018), Data from: Demographic inferences after a range expansion can be biased: the test case of the blacktip reef shark (Carcharhinus melanopterus), Dryad, Dataset, https://doi.org/10.5061/dryad.553cm8g

Abstract

The evolutionary history of species is a dynamic process as they modify, expand and contract their spatial distributions over time. Range expansions (REs) occur through a series of founder events that are followed by migration among neighbouring demes. The process usually results in structured metapopulations and leaves a distinct signature in the genetic variability of species. Explicitly modeling the consequences of complex demographic events such as REs is computationally very intensive. Here we propose an an alternative approach that requires less computational effort than a comprehensive RE model, but that can recover the demography of species undergoing a RE, by combining spatially explicit modelling with simplified but realistic metapopulation models. We examine the demographic and colonization history of Carcharhinus melanopterus, an abundant reef-associated shark, as a test case. We first used a population genomics approach to statistically confirm the occurrence of a RE in C. melanopterus and identify its origin in the Indo-Australian Archipelago. Spatial genetic modelling identified two waves of stepping-stone colonization: an eastward wave moving through the Pacific and a westward one moving through the Indian Ocean. We show that metapopulation models best describe the demographic history of this species and that not accounting for this may lead to incorrectly interpreting the observed genetic variation as signals of widespread population bottlenecks. Our study highlights insights that can be gained about demography by coupling metapopulation models with spatial modeling and underscores the need for cautious interpretation of population genetic data when advancing conservation priorities.

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Funding

National Science Foundation, Award: NSF Award DEB-01132229