Environmental Fluctuations and Their Consequences for the Evolution of Phenotypic Diversity

Fuentes, Miguel; Ferrada, Evandro

doi:10.3389/fphy.2017.00016

Cited by 9 publications

(11 citation statements)

References 27 publications

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“…Fluctuating environmental conditions are hypothesized to play a major role in the evolution of complex adaptive traits (Pál and Papp, 2017). The spatially heterogeneous and dynamic nature of the environment, encompassing both biotic and abiotic factors, such as temperature, is thought to be one of the evolutionary drivers behind the high diversity of species that have populated our biosphere over time (Fuentes and Ferrada, 2017). Environmental complexity, besides the connatural potential of living systems (Ruiz-Mirazo et al, 2008), is thought to help explain the seemingly open ended nature of the evolutionary process.…”

Section: Introductionmentioning

confidence: 99%

Emerging Adaptive Strategies Under Temperature Fluctuations in a Laboratory Evolution Experiment of Escherichia Coli

et al. 2021

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Generalists and specialists are types of strategies individuals can employ that can evolve in fluctuating environments depending on the extremity and periodicity of the fluctuation. To evaluate whether the evolution of specialists or generalists occurs under environmental fluctuation regimes with different levels of periodicity, 24 populations of Escherichia coli underwent laboratory evolution with temperatures alternating between 15 and 43°C in three fluctuation regimes: two periodic regimes dependent on culture's cell density and one random (non-periodic) regime with no such dependency, serving as a control. To investigate contingencies on the genetic background, we seeded our experiment with two different strains. After the experiment, growth rate measurements at the two temperatures showed that the evolution of specialists was favored in the random regime, while generalists were favored in the periodic regimes. Whole genome sequencing demonstrated that several gene mutations were selected in parallel in the evolving populations with some dependency on the starting genetic background. Given the genes mutated, we hypothesized that the driving force behind the observed adaptations is the restoration of the internal physiology of the starting strains' unstressed states at 37°C, which may be a means of improving fitness in the new environments. Phenotypic array measurements supported our hypothesis by demonstrating a tendency of the phenotypic response of the evolved strains to move closer to the starting strains' response at the optimum of 37°C, especially for strains classified as generalists.

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Section: Introductionmentioning

confidence: 99%

Emerging Adaptive Strategies Under Temperature Fluctuations in a Laboratory Evolution Experiment of Escherichia Coli

et al. 2021

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“…To investigate this further, we compare numerical simulations of the IB model with numerical solutions of the continuum model in the setting of Figure 2 (i.e. defining the term I h via (12) and considering different values of θ) but using lower values of the parameters λ H and λ L . The results, summarised in Figure 5, demonstrate that while excellent quantitative agreement between numerical simulations of the IB model and numerical solutions of the continuum model is obtained for relatively large values of θ (see Figure 5(b)), significant differences in the behaviour of the two models can be observed for relatively low values of θ (see Figure 5(a)).…”

Section: B Sensitivity Analysis Of the Probabilities Of Phenotypic Va...mentioning

confidence: 99%

“…Mathematical modelling of evolutionary dynamics in time-varying environments has received considerable attention from mathematicians and physicists over the past fifty years -see, for instance, [9][10][11][12][13][14][15][16][17][18][19][20] and references therein. Recently, deterministic continuum models formulated in terms of non-local partial differential equa-tions (PDEs) for the evolutionary dynamics of populations, structured by phenotypic traits, have been used to address open questions concerning the adaptation of asexual species to periodically fluctuating environments [21][22][23][24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments

Ardaševa,

Gatenby,

Anderson

et al. 2020

Preprint

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Deterministic continuum models formulated in terms of non-local partial differential equations for the evolutionary dynamics of populations structured by phenotypic traits have been used recently to address open questions concerning the adaptation of asexual species to periodically fluctuating environmental conditions. These deterministic continuum models are usually defined on the basis of population-scale phenomenological assumptions and cannot capture adaptive phenomena that are driven by stochastic variability in the evolutionary paths of single individuals. In light of these considerations, in this paper we develop a stochastic individual-based model for the coevolution between two competing phenotype-structured cell populations that are exposed to time-varying nutrient levels and undergo spontaneous, heritable phenotypic variations with different probabilities.Here, the evolution of every cell is described by a set of rules that result in a discrete-time branching random walk on the space of phenotypic states, and nutrient levels are governed by a difference equation in which a sink term models nutrient consumption by the cells. We formally show that the deterministic continuum counterpart of this model comprises a system of non-local partial differential equations for the cell population density functions coupled with an ordinary differential equation for the nutrient concentration. We compare the individual-based model and its continuum analogue, focussing on scenarios whereby the predictions of the two models differ. The results obtained clarify the conditions under which significant differences between the two models can emerge due to stochastic effects associated with small population levels. In particular, these differences arise in the presence of low probabilities of phenotypic variation, and become more apparent when the two populations are characterised by less fit initial mean phenotypes and smaller initial levels of phenotypic heterogeneity. The agreement between the two modelling approaches is also dependent on the initial proportions of the two populations.

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“…Many life‐history traits were also found to constrain intraspecific phenotypic variation such as individual generalization in three‐spine sticklebacks (Bolnick et al., 2010), choice of breeding resources in burying beetles (Nicrophorinae; Hopwood, Moore, Tregenza, & Royle, 2016) or life cycle in eriogonoids (Kostikova, Silvestro, Pearman, & Salamin, 2016). Finally, strong selective pressures such as predation (Pontarp & Petchey, 2018) or environmental instability (Fuentes & Ferrada, 2017) are expected to limit niche expansion and thus constrain intraspecific phenotypic variation. Thus, exploring the dynamics of intraspecific phenotypic variation at the macroevolutionary scale by including it in the model could be both relevant due to its heritability, and particularly insightful to tackle long‐standing evolutionary questions.…”

Section: Introductionmentioning

confidence: 99%

A multi‐platform package for the analysis of intra‐ and interspecific trait evolution

et al. 2020

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1. Evolutionary forces affect the distribution of phenotypes both within and among species. Yet, at the macro-evolutionary scale, the evolution of intraspecific variance is rarely considered. Here, we present an r and a BEAST 2 implementation that extends the JIVE (Joint inter-and Intraspecific Variance Evolution) model aimed at the analysis of continuous trait evolution at both inter-and intraspecific level. 2. Using a hierarchical Bayesian approach, we implemented a range of models for continuous trait evolution that operate independently on species means and variances along a phylogeny. The package uses Markov chain Monte Carlo for the inference of parameters and the evaluation of model fit. JIVE is available in the bite (Bayesian Integrative models of Trait Evolution) r package, as well as in BEAST 2. The two implementations offer the same continuous trait evolutionary models, but differ in their use and types of analyses. The r implementation allows for faster analyses by taking the phylogeny as data, while providing graphical and statistical functions as part of tools for model comparison, result parsing and summary, and plotting. In the BEAST 2 implementation, the species tree is a parameter, and both its topology and divergence times are jointly estimated with trait model parameters. 3. The bite package and the BEAST 2 implementation introduce new frameworks within comparative phylogenetics that explicitly model intraspecific variance. These tools allow users to tackle long-standing questions in evolutionary biology, such as the identification of key evolutionary processes determining niche conservatism, niche partitioning, and life-history strategies.

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Environmental Fluctuations and Their Consequences for the Evolution of Phenotypic Diversity

Cited by 9 publications

References 27 publications

Emerging Adaptive Strategies Under Temperature Fluctuations in a Laboratory Evolution Experiment of Escherichia Coli

Emerging Adaptive Strategies Under Temperature Fluctuations in a Laboratory Evolution Experiment of Escherichia Coli

A comparative study between discrete and continuum models for the evolution of competing phenotype-structured cell populations in dynamical environments

A multi‐platform package for the analysis of intra‐ and interspecific trait evolution

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