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New Heuristic Methods for Joint Species Delimitation and Species Tree Inference

Open access
O'Meara, Brian C.·2010·Systematic Biology

Abstract

Species delimitation and species tree inference are difficult problems in cases of recent divergence, especially when different loci have different histories. This paper quantifies the difficulty of jointly finding the division of samples to species and estimating a species tree without constraining the possible assignments a priori. It introduces a parametric and a nonparametric method, including new heuristic search strategies, to do this delimitation and tree inference using individual gene trees as input. The new methods were evaluated using thousands of simulations and 4 empirical data sets. These analyses suggest that the new methods, especially the nonparametric one, may provide useful insights for systematists working at the species level with molecular data. However, they still often return incorrect results.

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Metadata

Authors
O'Meara, B. C.
Venue
Systematic Biology
Published
2009-11-10
Volume
59
Issue
1
Pages
59-73
ISSN
  • 1063-5157 (ISSN-L)
  • 1076-836X
OpenAlex
W2155470232
Cited by
262
References
2
FWCI
12.30
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: New Heuristic Methods for Joint Species Delimitation and Species Tree Inference. https://app.cochat.ai/papers/10.1093/sysbio/syp077
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O'Meara, B. C. (2010). New Heuristic Methods for Joint Species Delimitation and Species Tree Inference. Systematic Biology. https://doi.org/10.1093/sysbio/syp077

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.