A novel Bayesian method for inferring and interpreting the dynamics of adaptive landscapes from phylogenetic comparative data

By Josef C. Uyeda, Luke J. Harmon. bioRxiv, 2014

320 citations recorded by the source corpus.

Our understanding of macroevolutionary patterns of adaptive evolution has greatly increased with the advent of large-scale phylogenetic comparative methods. Widely used Ornstein-Uhlenbeck (OU) models can describe an adaptive process of divergence and selection. However, inference of the dynamics of adaptive landscapes from comparative data is complicated by interpretational difficulties, lack of identifiability among parameter values and the common requirement that adaptive hypotheses must be assigned a priori. Here we develop a reversible-jump Bayesian method of fitting multi-optima OU models to phylogenetic comparative data that estimates the placement and magnitude of adaptive shifts directly from the data. We show how biologically informed hypotheses can be tested against this inferred posterior of shift locations using Bayes Factors to establish whether our a priori models adequately describe the dynamics of adaptive peak shifts. Furthermore, we show how the inclusion of informative priors can be used to restrict models to biologically realistic parameter space and test particular biological interpretations of evolutionary models. We argue that Bayesian model-fitting of OU models to comparative data provides a framework for integrating of multiple sources of biological data—such as microevolutionary estimates of selection parameters and paleontological timeseries—allowing inference of adaptive landscape dynamics with explicit, process-based biological interpretations.

Subfields: Geometry and Topology, Molecular Biology, Paleontology

View Paper: https://www.semanticscholar.org/paper/d7849cd5d4bde54e25b6df4042bb3b6d5a9ba6d2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Geometry and Topology

1. Karl Rohe
2. Cécile Ané
3. Mohammad Hassan Khabbazian
4. R. Kriebel

In Molecular Biology

1. R. Kriebel
2. Karl Rohe
3. Mohammad Hassan Khabbazian
4. Cécile Ané

In Paleontology

1. Karl Rohe
2. Mohammad Hassan Khabbazian
3. R. Kriebel
4. Cécile Ané

Citations in the corpus

• Fast and accurate detection of evolutionary shifts in Ornstein–Uhlenbeck models (2016, methodology): “In particular, even if the ‘optimal value’ is estimated to be constant within a given clade, this value may only reflect a broad adaptive zone, around which the true optimal value constantly fluctuates (Uyeda & Harmon 2014).”