A COMPARATIVE METHOD FOR STUDYING ADAPTATION TO A RANDOMLY EVOLVING ENVIRONMENT
Abstract
Most phylogenetic comparative methods used for testing adaptive hypotheses make evolutionary assumptions that are not compatible with evolution toward an optimal state. As a consequence they do not correct for maladaptation. The "evolutionary regression" that is returned is more shallow than the optimal relationship between the trait and environment. We show how both evolutionary and optimal regressions, as well as phylogenetic inertia, can be estimated jointly by a comparative method built around an Ornstein-Uhlenbeck model of adaptive evolution. The method considers a single trait adapting to an optimum that is influenced by one or more continuous, randomly changing predictor variables.
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Depending on the Bayesian network structure, the appropriate method of analysis can range from a nonphylogenetic regression, to commonly used comparative methods such as PGLS, to methods that require modeling both the evolutionary history of interaction of both trait X and trait Y.
“Depending on the Bayesian network structure, the appropriate method of analysis can range from a nonphylogenetic regression (Fig. 7A), to commonly used comparative methods such as PGLS (Fig. 7B), to methods that require modeling both the evolutionary history of interaction of both trait X and trait Y (Fig. 7C) (Hansen 1997;Butler and King 2004;Hansen et al. 2008;Revell 2010;Hansen and Bartoszek 20”
From Rethinking phylogenetic comparative methods (2018) · cites this paper as Hansen et al. 2008
Cites
Phylogenies and the Comparative Method
Felsenstein, J. (1985) · The American Naturalist · doi:10.1086/284325 · Cited by 9,793 · see citing works
Brain Size Allometry Ontogeny and Phylogeny
Martin, R. D., & Harvey, P. H. (1985) · Size and Scaling in Primate Biology · doi:10.1007/978-1-4899-3647-9_8 · Cited by 119 · see citing works
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The seven deadly sins of comparative analysis
FRECKLETON, R. P. (2009) · Journal of Evolutionary Biology · doi:10.1111/j.1420-9101.2009.01757.x · Cited by 339 · see citing works
Rethinking phylogenetic comparative methods
Uyeda, J. C., Zenil-Ferguson, R., & Pennell, M. W. (2018) · Systematic Biology · doi:10.1093/sysbio/syy031 · Cited by 313 · see citing works
Grabowski, M., Voje, K. L., & Hansen, T. F. (2016) · Journal of Human Evolution · doi:10.1016/j.jhevol.2016.03.001 · Cited by 55 · see citing works
Self-citation
- Authors
- Hansen, T. F., Pienaar, J., & Orzack, S. H.
- Affiliations
- Florida State University (US)
- Fresh Pond Research Institute (US)
- Corresponding author
- Hansen, Thomas F.
- Pienaar, Jason
- Venue
- Evolution
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-06
- Volume
- 62
- Issue
- 8
- Pages
- ???-???
- ISSN
- 0014-3820 (ISSN-L)
- 1558-5646
- OpenAlex
- W2070073898
- Cited by
- 336
- References
- 2
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 18 · 2023: 18 · 2022: 17 · 2021: 15 · 2020: 16 · 2019: 24 · 2018: 27 · 2017: 20 · 2016: 25 · 2015: 24 · 2014: 26 · 2013: 18 · 2012: 22
- FWCI
- 10.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Evolution (publishedVersion)
- PubMed (publishedVersion)
- BioOne Complete (BioOne) (submittedVersion)
- Funders
- National Institute on Aging (P01 AG 0225000-01)
- National Institute on Drug Abuse (R01DA015789)
- Division of Environmental Biology
- Topics
- Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
- Physical Sciences › Mathematics › Geometry and Topology › Morphological variations and asymmetry
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- phylogenetic comparative analysis, adaptive evolution, maladaptation, phylogenetic inertia, Ornstein-Uhlenbeck process, adaptive hypothesis testing
- MeSH terms
- Acclimatization, Adaptation, Physiological, Algorithms, Animals, Environment, Biological Evolution, Lizards, Models, Biological, Models, Genetic, Models, Theoretical, Phylogeny, Regression Analysis, Software, Stochastic Processes, Models, Statistical
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