The phylogeny of most of the species in the avian passerine family Locustellidae is inferred using a Bayesian species tree approach (Bayesian Estimation of Species Trees, BEST), as well as a traditional Bayesian gene tree method (MrBayes), based on a dataset comprising one mitochondrial and four nuclear loci. The trees inferred by the different methods agree fairly well in topology, although in a few cases there are marked differences. Some of these discrepancies might be due to convergence problems for BEST (despite up to 1 × 109 iterations). The phylogeny strongly disagrees with the current taxonomy at the generic level, and we propose a revised classification that recognizes four instead of seven genera. These results emphasize the well known but still often neglected problem of basing classifications on non-cladistic evaluations of morphological characters. An analysis of an extended mitochondrial dataset with multiple individuals from most species, including many subspecies, suggest that several taxa presently treated as subspecies or as monotypic species as well as a few taxa recognized as separate species are in need of further taxonomic work.
Journal article
Multilocus analysis of a taxonomically densely sampled dataset reveal extensive non-monophyly in the avian family Locustellidae
Molecular Phylogenetics and Evolution, Vol.58(3), pp.513-526
2011
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Abstract
Details
- Title
- Multilocus analysis of a taxonomically densely sampled dataset reveal extensive non-monophyly in the avian family Locustellidae
- Creators
- Per Alstrom - Swedish University of Agricultural SciencesSilke Fregin - Ernst-Moritz-Arndt-University GreifswaldJanette Ann Norman - University of MelbournePer GP Ericson - Swedish Museum of Natural HistoryLeslie Christidis - Southern Cross UniversityUrban Olsson - University of Gothenburg
- Publication Details
- Molecular Phylogenetics and Evolution, Vol.58(3), pp.513-526
- Identifiers
- 2375; 991012820705102368
- Academic Unit
- School of Environment, Science and Engineering; Faculty of Science and Engineering
- Resource Type
- Journal article