ArticleProceedings of the National Academy of Sciences of the United States of America2019
Automatic generation of evolutionary hypotheses using mixed Gaussian phylogenetic models.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Rates of passerine body plan evolution in time and space.Nature ecology & evolution · 2026Article
- Bayesian inference of mixed Gaussian phylogenetic models.BMC bioinformatics · 2026Article
- Phylogenetic comparative methods for studying adaptation: the adaptation-inertia framework.Journal of evolutionary biology · 2026Review
- Multi-response phylogenetic mixed models: concepts and application.Biological reviews of the Cambridge Philosophical Society · 2025Review
- Leveraging graphical model techniques to study evolution on phylogenetic networks.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2025Review
- Genome and life-history evolution link bird diversification to the end-Cretaceous mass extinction.Science advances · 2024Article
- Robust Phylogenetic Regression.Systematic biology · 2024Article
- phytools 2.0: an updated R ecosystem for phylogenetic comparative methods (and other things).PeerJ · 2024Article
- Rules of teeth development align microevolution with macroevolution in extant and extinct primates.Nature ecology & evolution · 2023Article
- Model Selection Performance in Phylogenetic Comparative Methods Under Multivariate Ornstein-Uhlenbeck Models of Trait Evolution.Systematic biology · 2023Article
- The evolution of fast-growing coral reef fishes.Nature · 2023Article
- Consilience of methods for phylogenetic analysis of variance.Evolution; international journal of organic evolution · 2022Article
- General statistical model shows that macroevolutionary patterns and processes are consistent with Darwinian gradualism.Nature communications · 2022Article
- Inferring Phenotypic Trait Evolution on Large Trees With Many Incomplete Measurements.Journal of the American Statistical Association · 2022Article
- Of Traits and Trees: Probabilistic Distances under Continuous Trait Models for Dissecting the Interplay among Phylogeny, Model, and Data.Systematic biology · 2021Article
- A Multitype Birth-Death Model for Bayesian Inference of Lineage-Specific Birth and Death Rates.Systematic biology · 2020Article
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3 authors.
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Abstract
Phylogenetic comparative methods are widely used to understand and quantify the evolution of phenotypic traits, based on phylogenetic trees and trait measurements of extant species. Such analyses depend crucially on the underlying model. Gaussian phylogenetic models like Brownian motion and Ornstein-Uhlenbeck processes are the workhorses of modeling continuous-trait evolution. However, these models fit poorly to big trees, because they neglect the heterogeneity of the evolutionary process in different lineages of the tree. Previous works have addressed this issue by introducing shifts in the evolutionary model occurring at inferred points in the tree. However, for computational reasons, in all current implementations, these shifts are "intramodel," meaning that they allow jumps in 1 or 2 model parameters, keeping all other parameters "global" for the entire tree. There is no biological reason to restrict a shift to a single model parameter or, even, to a single type of model. Mixed Gaussian phylogenetic models (MGPMs) incorporate the idea of jointly inferring different types of Gaussian models associated with different parts of the tree. Here, we propose an approximate maximum-likelihood method for fitting MGPMs to comparative data comprising possibly incomplete measurements for several traits from extant and extinct phylogenetically linked species. We applied the method to the largest published tree of mammal species with body- and brain-mass measurements, showing strong statistical support for an MGPM with 12 distinct evolutionary regimes. Based on this result, we state a hypothesis for the evolution of the brain-body-mass allometry over the past 160 million y.
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