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Cited by 72Evolution and Paleontology StudiesEvolution and Genetic DynamicsMorphological variations and asymmetry

Inferring ancestral states without assuming neutrality or gradualism using a stable model of continuous character evolution

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Elliot, Michael G, Mooers, Arne Ø·2014·BMC Evolutionary Biology
DOI 10.1186/s12862-014-0226-8PMC PMC4266906
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Abstract

BACKGROUND: The value of a continuous character evolving on a phylogenetic tree is commonly modelled as the location of a particle moving under one-dimensional Brownian motion with constant rate. The Brownian motion model is best suited to characters evolving under neutral drift or tracking an optimum that drifts neutrally. We present a generalization of the Brownian motion model which relaxes assumptions of neutrality and gradualism by considering increments to evolving characters to be drawn from a heavy-tailed stable distribution (of which the normal distribution is a specialized form). RESULTS: We describe Markov chain Monte Carlo methods for fitting the model to biological data paying special attention to ancestral state reconstruction, and study the performance of the model in comparison with a selection of existing comparative methods, using both simulated data and a database of body mass in 1,679 mammalian species. We discuss hypothesis testing and model selection. The stable model outperforms Brownian and Ornstein-Uhlenbeck approaches under simulations in which traits evolve with occasional large "jumps" in their value, but does not perform markedly worse for traits evolving under a truly Brownian process. CONCLUSIONS: The stable model is well suited to a stochastic process with a volatile rate of change in which biological characters undergo a mixture of neutral drift and occasional evolutionary events of large magnitude.

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  • Modeling the errors using fat-tailed distributions may mitigate the impact of singular evolutionary events on the estimation of the slope in PGLS and other linear modeling approaches.

    “For PGLS and other linear modeling approaches, modeling the errors using fat-tailed distributions (Blomberg et al. 2012;Landis et al. 2012;Elliot and Mooers 2014;Duchen et al. 2017) may mitigate the impact of singular evolutionary events on the estimation of the slope (also see Slater and Pennell 2013, for an alternative approach using robust regression).”

    From Rethinking phylogenetic comparative methods (2018) · cites this paper as Elliot and Mooers 2014

    The Background toy example shows that a heavy-tailed stable model is less affected than a Brownian model by an unusually large change on one branch, but its outcome is ancestral-state reconstruction, not a regression slope. The Discussion proposes heavy-tailed methods for future studies of correlated evolution and notes that stable distributions lack the covariance matrix used in phylogenetic regression. It does not show that fat-tailed errors mitigate the effect of singular events on slope estimates in PGLS or other linear models; page numbers were not provided in our copy. (LLM, conf=0.85)

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Authors
Elliot, M. G., & Mooers, A. Ø.
Affiliations
  • University of Cambridge (GB)
  • Simon Fraser University (CA)
Corresponding author
Elliot, Michael G
ORCID
Venue
BMC Evolutionary Biology
Publisher
Springer Science and Business Media LLC
Published
2014-11-28
Volume
14
Issue
1
Pages
226-226
ISSN
1471-2148 (ISSN-L)
DOI
10.1186/s12862-014-0226-8
PMC
PMC4266906
OpenAlex
W1985570840
Cited by
72
References
8
Citations by year
2026: 3 · 2025: 5 · 2024: 3 · 2023: 8 · 2022: 7 · 2021: 5 · 2020: 11 · 2019: 2 · 2018: 8 · 2017: 6 · 2016: 5 · 2015: 5 · 2014: 3 · 2013: 1
FWCI
4.24
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Evolutionary Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • Summit (Simon Fraser University) (submittedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
  • arXiv (Cornell University)
Funders
  • Compute Canada
  • Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
  • Physical Sciences › Mathematics › Geometry and Topology › Morphological variations and asymmetry
Keywords
ancestral state reconstruction, stable distributions, heavy-tailed distributions, Brownian motion model, Ornstein-Uhlenbeck process, non-neutral evolution, body mass, mammals, model selection, hypothesis testing, Markov chain Monte Carlo
MeSH terms
Animals, Biological Evolution, Mammals, Markov Chains, Models, Genetic, Monte Carlo Method, Phylogeny

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