A General Model for Estimating Macroevolutionary Landscapes

Boucher, Florian C.; Démery, Vincent; Conti, Elena; Harmon, Luke J.; Uyeda, Josef C.

doi:10.1093/sysbio/syx075

Cited by 52 publications

(61 citation statements)

References 57 publications

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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%

Functional biogeography of angiosperms: life at the extremes

et al. 2018

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Nonlinear relationships between species and their environments are believed common in ecology and evolution, including during angiosperms' rise to dominance. Early angiosperms are thought of as woody evergreens restricted to warm, wet habitats. They have since expanded into numerous cold and dry places. This expansion may have included transitions across important environmental thresholds. To understand linear and nonlinear relationships between angiosperm structure and biogeographic distributions, we integrated large datasets of growth habits, conduit sizes, leaf phenologies, evolutionary histories, and environmental limits. We consider current-day patterns and develop a new evolutionary model to investigate processes that created them. The macroecological pattern was clear: herbs had lower minimum temperature and precipitation limits. In woody species, conduit sizes were smaller in evergreens and related to species' minimum temperatures. Across evolutionary timescales, our new modeling approach found conduit sizes in deciduous species decreased linearly with minimum temperature limits. By contrast, evergreen species had a sigmoidal relationship with minimum temperature limits and an inflection overlapping freezing. These results suggest freezing represented an important threshold for evergreen but not deciduous woody angiosperms. Global success of angiosperms appears tied to a small set of alternative solutions when faced with a novel environmental threshold.

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Section: Discussionmentioning

confidence: 97%

Functional biogeography of angiosperms: life at the extremes

et al. 2018

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“…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%

On the effect of asymmetrical trait inheritance on models of trait evolution

Duchen

Alfaro

Rolland

et al. 2019

Preprint

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1Current phylogenetic comparative methods modeling quantitative trait evolution 2 generally assume that, during speciation, phenotypes are inherited identically 3 between the two daughter species. This, however, neglects the fact that species 4 consist of a set of individuals, each bearing its own trait value. Indeed, because 5 descendent populations after speciation are samples of a parent population, we can 6 expect their mean phenotypes to randomly differ from one another potentially 7 generating a "jump" of mean phenotypes due to asymmetrical trait inheritance at 8 cladogenesis. Here, we aim to clarify the effect of asymmetrical trait inheritance at 9 speciation on macroevolutionary analyses, focusing on model testing and parameter 10 estimation using some of the most common models of quantitative trait evolution. We 11 developed an individual-based simulation framework in which the evolution of species 12 phenotypes is determined by trait changes at the individual level accumulating across 13 generations and cladogenesis occurs then by separation of subsets of the individuals 14 into new lineages. Through simulations, we assess the magnitude of phenotypic jumps 15 at cladogenesis under different modes of trait inheritance at speciation. We show that 16 even small jumps can strongly alter both the results of model selection and parameter 17 estimations, potentially affecting the biological interpretation of the estimated mode 18 of evolution of a trait. Our results call for caution interpreting analyses of trait 19 evolution, while highlighting the importance of testing a wide range of alternative 20 models. In the light of our findings, we propose that future methodological advances 21 in comparative methods should more explicitly model the intra-specific variability 22 around species mean phenotypes and how it is inherited at speciation. 23 Understanding the mechanisms underlying the evolution of phenotypic traits is 25 fundamental when addressing questions about functional diversity, ecological 26 interactions, and the overall build-up of biodiversity (Futuyma and Agrawal, 2009; 27 Katzourakis et al., 2009; Campbell and Kessler, 2013). Inferences about the processes 28 governing phenotypic changes are facilitated by the use of macroevolutionary models, 29 which deal with the evolution of phenotypic traits among related species (Stanley, 30 1979; Lande, 1980; Felsenstein, 1985 Felsenstein, , 2004. These macroevolutionary models are 31 able to infer the evolution of a phenotype under a variety of scenarios including 32 neutral or adaptive evolution (Lande, 1976; Beaulieu et al., 2012), as well as early 33 and rapid phenotypic differentiation (Harmon et al., 2010; O'Meara, 2012). These 34 models have been used to address a variety of evolutionary processes, helping us 35 understand, for instance, body size evolution in mammals (Venditti et al., 2011; 36 Slater, 2013), adaptation in Anolis lizards (Losos, 2011), evolutionary processes in 37 extinct and extant vertebrates (Wagner, 2017; Si...

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“…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%

“…While the evolutionary potential can in theory take any shape, it is necessary to specify a parametric function for it so that it can be fitted to empirical datasets. 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). The model thus has five free parameters: three describing the shape of the potential (a, b, and c), the rate of evolution σ, and the value of the trait at the root of the phylogeny x 0 .…”

Section: Approachmentioning

confidence: 99%

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BBMV: an R package for the estimation of macroevolutionary landscapes

Boucher

2018

Ecography

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The distribution of traits along phylogenies bears signatures of how ecological and evolutionary processes have interacted to influence phenotypic evolution, which can be deciphered using macroevolutionary models. BBMV implements a model for the evolution of continuous characters on phylogenies that generalizes existing ones, like Brownian motion and the Ornstein-Uhlenbeck model. In this model quantitative characters evolve under both random diffusion and a deterministic force that can be of any possible shape and strength. The model can be used to infer evolutionary scenarios that remained inaccessible so far, like directional trends, disruptive selection, and even bounded evolution. With this new tool at hand, researchers will be able to test complex hypothesis-driven scenarios regarding trait evolution, but they will also have the possibility to estimate the shape of the adaptive landscapes in which traits evolved. Ultimately, this will provide a way to infer how ecological processes have influenced phenotypic evolution over long timescales. The BBMV package is implemented in the R statistical language and is freely available on the CRAN repository . All source code can also be found on < https://github.com/fcboucher/BBMV >, along with a detailed tutorial.

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A General Model for Estimating Macroevolutionary Landscapes

Cited by 52 publications

References 57 publications

Functional biogeography of angiosperms: life at the extremes

Functional biogeography of angiosperms: life at the extremes

On the effect of asymmetrical trait inheritance on models of trait evolution

BBMV: an R package for the estimation of macroevolutionary landscapes

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