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Species Delimitation Using a Combined Coalescent and Information-Theoretic Approach: An Example from North American Myotis Bats
Open accessAbstract
Coalescent model-based methods for phylogeny estimation force systematists to confront issues related to the identification of species boundaries. Unlike conventional phylogenetic analysis, where species membership can be assessed qualitatively after the phylogeny is estimated, the phylogenies that are estimated under a coalescent model treat aggregates of individuals as the operational taxonomic units and thus require a priori definition of these sets because the models assume that the alleles in a given lineage are sampled from a single panmictic population. Fortunately, the use of coalescent model-based approaches allows systematists to conduct probabilistic tests of species limits by calculating the probability of competing models of lineage composition. Here, we conduct the first exploration of the issues related to applying such tests to a complex empirical system. Sequence data from multiple loci were used to assess species limits and phylogeny in a clade of North American Myotis bats. After estimating gene trees at each locus, the likelihood of models representing all hierarchical permutations of lineage composition was calculated and Akaike information criterion scores were computed. Metrics borrowed from information theory suggest that there is strong support for several models that include multiple evolutionary lineages within the currently described species Myotis lucifugus and M. evotis. Although these results are preliminary, they illustrate the practical importance of coupled species delimitation and phylogeny estimation.
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Cites
MRBAYES: Bayesian inference of phylogenetic trees.
Huelsenbeck, J. P., & Ronquist, F. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.8.754 · Cited by 21,937
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Evolution of Protein Molecules
JUKES, T. H., & CANTOR, C. R. (1969) · Mammalian Protein Metabolism · doi:10.1016/b978-1-4832-3211-9.50009-7 · Cited by 11,032
Burnham, K. P., & Anderson, D. R. (1998) · doi:10.1007/978-1-4757-2917-7 · Cited by 3,387
Cited by
How to fail at species delimitation
Carstens, B. C., Pelletier, T. A., Reid, N. M., & Satler, J. D. (2013) · Molecular Ecology · doi:10.1111/mec.12413 · Cited by 1,248
Self-citationCoalescent-based species delimitation in an integrative taxonomy
Fujita, M. K., Leaché, A. D., Burbrink, F. T., McGuire, J. A., & Moritz, C. (2012) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2012.04.012 · Cited by 893
Recognition of seven species in the Cryptococcus gattii/Cryptococcus neoformans species complex
Hagen, F., Khayhan, K., Theelen, B., Kolecka, A., Polacheck, I., Sionov, E., Falk, R., Parnmen, S., Lumbsch, H. T., & Boekhout, T. (2015) · Fungal Genetics and Biology · doi:10.1016/j.fgb.2015.02.009 · Cited by 687
Integrative taxonomy, or iterative taxonomy?
YEATES, D. K., SEAGO, A., NELSON, L., CAMERON, S. L., JOSEPH, L., & TRUEMAN, J. W. H. (2011) · Systematic Entomology · doi:10.1111/j.1365-3113.2010.00558.x · Cited by 366
Springer, M. S., & Gatesy, J. (2016) · Molecular Phylogenetics and Evolution · doi:10.1016/j.ympev.2015.07.018 · Cited by 282
Metadata
- Authors
- Carstens, B. C., & Dewey, T. A.
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2010-05-24
- Volume
- 59
- Issue
- 4
- Pages
- 400-414
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W2127670352
- Cited by
- 194
- References
- 4
- FWCI
- 24.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
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