Species discovery and validation in a cryptic radiation of endangered primates: coalescent-based species delimitation in Madagascar's mouse lemurs.

Journal Article (Journal Article)

Implementation of the coalescent model in a Bayesian framework is an emerging strength in genetically based species delimitation studies. By providing an objective measure of species diagnosis, these methods represent a quantitative enhancement to the analysis of multilocus data, and complement more traditional methods based on phenotypic and ecological characteristics. Recognized as two species 20 years ago, mouse lemurs (genus Microcebus) now comprise more than 20 species, largely diagnosed from mtDNA sequence data. With each new species description, enthusiasm has been tempered with scientific scepticism. Here, we present a statistically justified and unbiased Bayesian approach towards mouse lemur species delimitation. We perform validation tests using multilocus sequence data and two methodologies: (i) reverse-jump Markov chain Monte Carlo sampling to assess the likelihood of different models defined a priori by a guide tree, and (ii) a Bayes factor delimitation test that compares different species-tree models without a guide tree. We assess the sensitivity of these methods using randomized individual assignments, which has been used in bpp studies, but not with Bayes factor delimitation tests. Our results validate previously diagnosed taxa, as well as new species hypotheses, resulting in support for three new mouse lemur species. As the challenge of multiple researchers using differing criteria to describe diversity is not unique to Microcebus, the methods used here have significant potential for clarifying diversity in other taxonomic groups. We echo previous studies in advocating that multiple lines of evidence, including use of the coalescent model, should be trusted to delimit new species.

Full Text

Duke Authors

Cited Authors

  • Hotaling, S; Foley, ME; Lawrence, NM; Bocanegra, J; Blanco, MB; Rasoloarison, R; Kappeler, PM; Barrett, MA; Yoder, AD; Weisrock, DW

Published Date

  • May 2016

Published In

Volume / Issue

  • 25 / 9

Start / End Page

  • 2029 - 2045

PubMed ID

  • 26946180

Electronic International Standard Serial Number (EISSN)

  • 1365-294X

International Standard Serial Number (ISSN)

  • 0962-1083

Digital Object Identifier (DOI)

  • 10.1111/mec.13604


  • eng