2017
DOI: 10.1093/sysbio/syx075
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A General Model for Estimating Macroevolutionary Landscapes

Abstract: The evolution of quantitative characters over long timescales is often studied using stochastic diffusion models. The current toolbox available to students of macroevolution is however limited to two main models: Brownian motion and the Ornstein-Uhlenbeck process, plus some of their extensions. Here, we present a very general model for inferring the dynamics of quantitative characters evolving under both random diffusion and deterministic forces of any possible shape and strength, which can accommodate interes… Show more

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Cited by 51 publications
(61 citation statements)
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“…() modeled predictors as evolving continuously by Brownian motion, we discretize predictors into bins, modeling them using discrete Markov models. This coarser treatment of predictors combined with limits on computational efficiency may cause some lost power (Boucher et al ., ). However, we still detected differences in overall fit and parameter estimates among different models.…”
Section: Discussionmentioning
confidence: 97%
“…() modeled predictors as evolving continuously by Brownian motion, we discretize predictors into bins, modeling them using discrete Markov models. This coarser treatment of predictors combined with limits on computational efficiency may cause some lost power (Boucher et al ., ). However, we still detected differences in overall fit and parameter estimates among different models.…”
Section: Discussionmentioning
confidence: 97%
“…We used a fitness landscape to describe the fitness of individuals as a function of their phenotypic trait value (Simpson, 1944; Arnold et al, 2001; Svensson and Calsbeek, 2012; Boucher et al, 2017). The shape of the fitness landscape defines the survival probability of an individual and, as explained at the beginning of the Methods, we used two types of landscapes.…”
Section: Methodsmentioning
confidence: 99%
“…BBMV implements a very flexible form with only three parameters, V(x) = ax 4 + bx 2 + cx, which can model a variety of scenarios (Boucher et al 2018). BBMV implements a very flexible form with only three parameters, V(x) = ax 4 + bx 2 + cx, which can model a variety of scenarios (Boucher et al 2018).…”
Section: Approachmentioning
confidence: 99%
“…The process has a stationary distribution that is proportional to exp (-V(x)) and can be interpreted as the average macroevolutionary landscape over which the trait evolved (Simpson 1944). The likelihood of the FPK model is computed by discretizing trait values into a set of points equally spaced between two bounds (Boucher andDémery 2016, Boucher et al 2018). In the general form of the FPK model, bounds are placed far apart from the observed trait interval so that they do not influence the process: in such a situation bounds do not exist in practice.…”
Section: Approachmentioning
confidence: 99%
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