Phenotypic evolution as an Ornstein-Uhlenbeck process: The effect of environmental variation and phenotypic plasticity

Silva, Clécio C. de Souza; Cirne, Diego; Freitas, Osmar; Campos, Paulo R. A.

doi:10.1103/physreve.107.024417

Cited by 2 publications

References 41 publications

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Understanding evolutionary rescue and parallelism in response to environmental stress

Freitas,

Campos

2024

Evolution

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Evolutionary rescue, the process by which populations facing environmental stress avoid extinction through genetic adaptation, is a critical area of study in evolutionary biology. The order in which mutations arise and get established will be relevant to the population’s rescue. This study investigates the degree of parallel evolution at the genotypic level between independent populations fac- ing environmental stress and subject to diﬀerent demographic regimes. Under density regulation, two regimes exist: in the ﬁrst, the population can restore positive growth rates by adjusting its population size or through adaptive mu- tations, whereas in the second regime, the population is doomed to extinction unless a rescue mutation occurs. Analytical approximations for the likelihood of evolutionary rescue are obtained and contrasted with simulation results. We show that the initial level of maladaptation and the demographic regime signiﬁ- cantly aﬀect the level of parallelism. There is an evident transition between these two regimes. Whereas in the ﬁrst regime, parallelism decreases with the level of maladaptation, it displays the opposite behavior in the rescue/extinction regime. These ﬁndings have important implications for understanding population persis- tence and the degree of parallelism in evolutionary responses as they integrate demographic eﬀects and evolutionary processes.

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Understanding evolutionary rescue and parallelism in response to environmental stress

Freitas,

Campos

2024

Evolution

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Multivariate Trait Evolution: Models for the Evolution of the Quantitative Genetic G-Matrix on Phylogenies

Blomberg,

Muniz,

Bui

et al. 2024

Preprint

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Genetic covariance matrices (G-matrices) are a key focus for research and predictions from quantitative genetic evolutionary models of multiple traits. There is a consensus among quantitative geneticists that the G-matrix can evolve through "deep" time. Yet, quantitative genetic models for the evolution of the G-matrix are conspicuously lacking. In contrast, the field of macroevolution has several stochastic models for univariate traits evolving on phylogenies. However, despite much research into multivariate phylogenetic comparative methods, analytical models of how multivariate trait matrices might evolve on phylogenies have not been considered. Here we show how three analytical models for the evolution of matrices and multivariate traits on phylogenies, based on Lie group theory, Riemannian geometry and stochastic differential (diffusion) equations, can be combined to unify quantitative genetics and macroevolutionary theory in a coherent mathematical framework. The models provide a basis for understanding how G-matrices might evolve on phylogenies, and we show how to fit models to data via simulation using Approximate Bayesian Computation. Such models can be used to generate and test hypotheses about the evolution of genetic variances and covariances, together with the evolution of the traits themselves, and how these might vary across a phylogeny. This unification of macroevolutionary theory and quantitative genetics is an important advance in the study of phenotypes, allowing for the construction of a synthetic quantitative theory of the evolution of species and multivariate traits over "deep" time.

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Phenotypic evolution as an Ornstein-Uhlenbeck process: The effect of environmental variation and phenotypic plasticity

Cited by 2 publications

References 41 publications

Understanding evolutionary rescue and parallelism in response to environmental stress

Understanding evolutionary rescue and parallelism in response to environmental stress

Multivariate Trait Evolution: Models for the Evolution of the Quantitative Genetic G-Matrix on Phylogenies

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