Selection and the direction of phenotypic evolution
Abstract
Predicting adaptive phenotypic evolution depends on invariable selection gradients and on the stability of the genetic covariances between the component traits of the multivariate phenotype. We describe the evolution of six traits of locomotion behavior and body size in the nematode Caenorhabditis elegans for 50 generations of adaptation to a novel environment. We show that the direction of adaptive multivariate phenotypic evolution can be predicted from the ancestral selection differentials, particularly when the traits were measured in the new environment. Interestingly, the evolution of individual traits does not always occur in the direction of selection, nor are trait responses to selection always homogeneous among replicate populations. These observations are explained because the phenotypic dimension with most of the ancestral standing genetic variation only partially aligns with the phenotypic dimension under directional selection. These findings validate selection theory and suggest that the direction of multivariate adaptive phenotypic evolution is predictable for tens of generations.
Data availability
New data, R code for analysis and modeling results is freely accessible and can be found at https://github.com/ExpEvolWormLab/Mallard_Robertson
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Data from:Partial Selfing Can Reduce Genetic Loads While Maintaining Diversity During Experimental Evolutionfigshare, 10.6084/m9.figshare.8665661.
Article and author information
Author details
Funding
European Research Council (ERC-St-243285)
- Henrique Teotónio
Agence Nationale pour la Recherche (ANR-14-ACHN-0032-01)
- Henrique Teotónio
Agence Nationale pour la Recherche (ANR-17-CE02-0017-01)
- Henrique Teotónio
National Science Foundation (PHY-1748958)
- Henrique Teotónio
Gordon and Betty Moore Foundation (2919.02)
- Henrique Teotónio
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2023, Mallard et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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