Increased Taxon Sampling Greatly Reduces Phylogenetic Error
Abstract
Several authors have argued recently that extensive taxon sampling has a positive and important effect on the accuracy of phylogenetic estimates. However, other authors have argued that there is little benefit of extensive taxon sampling, and so phylogenetic problems can or should be reduced to a few exemplar taxa as a means of reducing the computational complexity of the phylogenetic analysis. In this paper we examined five aspects of study design that may have led to these different perspectives. First, we considered the measurement of phylogenetic error across a wide range of taxon sample sizes, and conclude that the expected error based on randomly selecting trees (which varies by taxon sample size) must be considered in evaluating error in studies of the effects of taxon sampling. Second, we addressed the scope of the phylogenetic problems defined by different samples of taxa, and argue that phylogenetic scope needs to be considered in evaluating the importance of taxon-sampling strategies. Third, we examined the claim that fast and simple tree searches are as effective as more thorough searches at finding near-optimal trees that minimize error. We show that a more complete search of tree space reduces phylogenetic error, especially as the taxon sample size increases. Fourth, we examined the effects of simple versus complex simulation models on taxonomic sampling studies. Although benefits of taxon sampling are apparent for all models, data generated under more complex models of evolution produce higher overall levels of error and show greater positive effects of increased taxon sampling. Fifth, we asked if different phylogenetic optimality criteria show different effects of taxon sampling. Although we found strong differences in effectiveness of different optimality criteria as a function of taxon sample size, increased taxon sampling improved the results from all the common optimality criteria. Nonetheless, the method that showed the lowest overall performance (minimum evolution) also showed the least improvement from increased taxon sampling. Taking each of these results into account re-enforces the conclusion that increased sampling of taxa is one of the most important ways to increase overall phylogenetic accuracy.
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Historically, simulation has been an effective strategy for empirically testing the performance of phylogenetic approaches.
“Historically, simulation has been an effective strategy for empirically testing the performance of phylogenetic approaches (Kuhner and Felsenstein 1994; Swofford et al. 2001; Zwickl et al. 2002; Kubatko and Degnan 2007; Huang and Knowles 2016).”
From Six-state amino acid recoding is not an effective strategy to offset the effects of compositional heterogeneity and saturation in phylogenetic analyses (2019) · cites this paper as Zwickl et al. 2002
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- Authors
- Zwickl, D. J., & Hillis, D. M.
- Affiliations
- The University of Texas at Austin (US)
- ORCID
- Hillis, David M.
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-07-01
- Volume
- 51
- Issue
- 4
- Pages
- 588-598
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W2150815933
- Cited by
- 939
- References
- 2
- Citations by year
- 2026: 15 · 2025: 24 · 2024: 31 · 2023: 28 · 2022: 32 · 2021: 37 · 2020: 37 · 2019: 46 · 2018: 45 · 2017: 40 · 2016: 24 · 2015: 35 · 2014: 45 · 2013: 55 · 2012: 50
- FWCI
- 13.21
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Systematic Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Plant Diversity and Evolution
- Keywords
- taxon sampling, phylogenetic accuracy, tree search, optimality criteria, minimum evolution, phylogenetic inference
- MeSH terms
- Phylogeny, Research Design, Likelihood Functions
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