Stochastic Evolutionary Demography under a Fluctuating Optimum Phenotype

Chevin, Luis‐Miguel; Cotto, Olivier; Ashander, Jaime

doi:10.1086/694121

Cited by 47 publications

(91 citation statements)

References 71 publications

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“…To model possibly correlated fluctuations in the joint optimum as in Chevin () as well as autocorrelation across time (as in Lande and Shannon ; Lande ; Tufto ; Chevin et al. ), the random effects representing yearly variation in overall survival

u_{t}

and variation in the optimal laying date

ζ_{t}

are assumed to follow a first‐order vector autoregressive VAR(1) process

[\begin{matrix} u_{t} \\ ζ_{t} \end{matrix}] = Φ [\begin{matrix} u_{t - 1} \\ ζ_{t - 1} \end{matrix}] + {boldw}_{t},

where

normalΦ

is a 2 × 2 matrix of autoregressive coefficients and

w_{t}

is bivariate normal

N (0, Σ)

white noise. This only specifies the autocorrelation matrix function (see Wei , ch.…”

Section: Methodsmentioning

confidence: 99%

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Environmental drivers of varying selective optima in a small passerine: A multivariate, multiepisodic approach

et al. 2018

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In changing environments, phenotypic traits are shaped by numerous agents of selection. The optimal phenotypic value maximizing the fitness of an individual thus varies through time and space with various environmental covariates. Selection may differ between different life‐cycle stages and act on correlated traits inducing changes in the distribution of several traits simultaneously. Despite increasing interests in environmental sensitivity of phenotypic selection, estimating varying selective optima on various traits throughout the life cycle, while considering (a)biotic factors as potential selective agents has remained challenging. Here, we provide a statistical model to measure varying selective optima from longitudinal data. We apply our approach to analyze environmental sensitivity of phenotypic selection on egg‐laying date and clutch size throughout the life cycle of a white‐throated dipper population. We show the presence of a joint optimal phenotype that varies over the 35‐year period, being dependent on altitude and temperature. We also find that optimal laying date is density‐dependent, with high population density favoring earlier laying dates. By providing a flexible approach, widely applicable to free‐ranging populations for which long‐term data on individual phenotypes, fitness, and environmental factors are available, our study improves the understanding of phenotypic selection in varying environments.

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u_{t}

and variation in the optimal laying date

ζ_{t}

are assumed to follow a first‐order vector autoregressive VAR(1) process

[\begin{matrix} u_{t} \\ ζ_{t} \end{matrix}] = Φ [\begin{matrix} u_{t - 1} \\ ζ_{t - 1} \end{matrix}] + {boldw}_{t},

where

normalΦ

is a 2 × 2 matrix of autoregressive coefficients and

w_{t}

is bivariate normal

N (0, Σ)

white noise. This only specifies the autocorrelation matrix function (see Wei , ch.…”

Section: Methodsmentioning

confidence: 99%

“…; Chevin et al. ). This was recognized more than thirty years ago by Arnold and Wade () who highlighted the need to measure selection through separate episodes of selection across the life cycle.…”

mentioning

confidence: 98%

Environmental drivers of varying selective optima in a small passerine: A multivariate, multiepisodic approach

et al. 2018

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“…, Chevin et al. ), especially in populations with weak density feedbacks (Morris and Doak ). Under global environmental change, which is expected to alter temporal autocorrelation in natural environments (Boulton and Lenton , Lenton et al.…”

Section: Introductionmentioning

confidence: 99%

“…For positive autocorrelation, a good year is more likely to be followed by another, and vice versa, leading to larger stretches of population growth and shrinkage. Long stretches of adverse conditions can lead to higher risk of population extinction (Petchey 2000, Laakso et al 2003, Pike et al 2004, Tuljapurkar and Haridas 2006, Engen et al 2013, Chevin et al 2017, especially in populations with weak density feedbacks (Morris and Doak 2002). Under global environmental change, which is expected to alter temporal autocorrelation in natural environments (Boulton and Lenton 2015, Lenton et al 2017, extinction risk may be exacerbated (van der Bolt et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…This may lead to maladaptation when individuals are exposed to another environment (Frank et al 2017). This (mal)adaptation can, in turn, affect vital rates such as survival and reproduction and therefore lead to changes in the mean population structure and growth (Chevin et al 2017). At the same time, it is not well known to which degree traitmediated demographic effects of autocorrelated environmental conditions are determined by the life-history strategy of a species.…”

Section: Introductionmentioning

confidence: 99%

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The effect of temporal environmental autocorrelation on eco‐evolutionary dynamics across life histories

et al. 2020

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One of the largest causes of fluctuations in the size and structure of populations is changes in the environment. In nature, these changes are often temporally autocorrelated and the strength and direction of the autocorrelation can affect population dynamics. These effects are mediated by complex, simultaneously occurring ecological and evolutionary processes, such as phenotypic plasticity and selection. Determining how these processes interact to affect responses of different life histories to autocorrelated environmental fluctuations is of paramount importance to infer which taxa are likely to go extinct or become invasive under global change. Here, we assessed the effect of autocorrelation in environmental states on the trait and population dynamics of different life histories using an evolutionary explicit individual‐based modeling approach. We found that, in general, higher positive temporal autocorrelation caused more variation in population size. Fast life histories were more affected than slow ones as they were able to adapt more quickly to varying environmental optima and therefore experienced larger initial decreases in population size when optima changed. Including adaptive phenotypic plasticity buffered the effects of autocorrelation on population dynamics while nonadaptive plasticity amplified them, especially for slow life histories, which recovered less from maladaptation. This study highlights that integration of phenotypic plasticity, selection, and population dynamics is important to improve our understanding of how different life histories deal with autocorrelated climate variability.

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Experimental evolution of environmental tolerance, acclimation, and physiological plasticity in a randomly fluctuating environment

et al. 2022

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Environmental tolerance curves, representing absolute fitness against the environment, are an empirical assessment of the fundamental niche, and emerge from the phenotypic plasticity of underlying phenotypic traits. Dynamic plastic responses of these traits can lead to acclimation effects, whereby recent past environments impact current fitness. Theory predicts that higher levels of phenotypic plasticity should evolve in environments that fluctuate more predictably, but there have been few experimental tests of these predictions. Specifically, we still lack experimental evidence for the evolution of acclimation effects in response to environmental predictability. Here, we exposed 25 genetically diverse populations of the halotolerant microalgae Dunaliella salina to different constant salinities, or to randomly fluctuating salinities, for over 200 generations. The fluctuating treatments differed in their autocorrelation, which determines the similarity of subsequent values, and thus environmental predictability. We then measured acclimated tolerance surfaces, mapping population growth rate against past (acclimation) and current (assay) environments. We found that experimental mean and variance in salinity caused the evolution of niche position (optimal salinity) and breadth, with respect to not only current but also past (acclimation) salinity. We also detected weak but significant evidence for evolutionary changes in response to environmental predictability, with higher predictability leading notably to lower optimal salinities and stronger acclimation effect of past environment on current fitness. We further showed that these responses are related to the evolution of plasticity for intracellular glycerol, the major osmoregulatory mechanism in this species. However, the direction of plasticity evolution did not match simple theoretical predictions. Our results underline the need for a more explicit consideration of the dynamics of environmental tolerance and its underlying plastic traits to reach a better understanding of ecology and evolution in fluctuating environments.

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Stochastic Evolutionary Demography under a Fluctuating Optimum Phenotype

Cited by 47 publications

References 71 publications

Environmental drivers of varying selective optima in a small passerine: A multivariate, multiepisodic approach

Environmental drivers of varying selective optima in a small passerine: A multivariate, multiepisodic approach

The effect of temporal environmental autocorrelation on eco‐evolutionary dynamics across life histories

Experimental evolution of environmental tolerance, acclimation, and physiological plasticity in a randomly fluctuating environment

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