scholarly journals The evolution of trait correlations constrains phenotypic adaptation to high CO 2 in a eukaryotic alga

2021 ◽  
Vol 288 (1953) ◽  
pp. 20210940
Author(s):  
Nathan G. Walworth ◽  
Jana Hinners ◽  
Phoebe A. Argyle ◽  
Suzana G. Leles ◽  
Martina A. Doblin ◽  
...  
Keyword(s):  
Food Webs ◽  
Predictive Model ◽  
Microbial Evolution ◽  
Fitness Landscapes ◽  
Significant Challenge ◽  
Eukaryotic Alga ◽  
Elemental Cycling ◽  
Adaptive Walks ◽  
Sheer Number ◽  
Trait Correlations

Microbes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here, we present an approach for integrating multivariate trait data into a predictive model of trait evolution. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO 2 . We found that the direction of historical bias (existing trait correlations) influenced both the rate of adaptation and the evolved phenotypes (trait combinations). Critically, we use fitness landscapes derived directly from empirical trait values to capture known evolutionary phenomena. This work demonstrates that ecological models need to represent both changes in traits and changes in the correlation between traits in order to accurately capture phytoplankton evolution and predict future shifts in elemental cycling.

scholarly journals The evolution of trait correlations constrains phenotypic adaptation to high CO2 in a eukaryotic alga

2020 ◽  
Author(s):  
Nathan G. Walworth ◽  
Jana Hinners ◽  
Phoebe A. Argyle ◽  
Suzana G. Leles ◽  
Martina A. Doblin ◽  
...  
Keyword(s):  
De Novo ◽  
Principal Component ◽  
Biogeochemical Cycling ◽  
Microbial Evolution ◽  
Interactive Effects ◽  
Ecosystem Models ◽  
Evolutionary Trajectories ◽  
Adaptive Walks ◽  
Sheer Number ◽  
Trait Correlations

AbstractMicrobes form the base of food webs and drive biogeochemical cycling. Predicting the effects of microbial evolution on global elemental cycles remains a significant challenge due to the sheer number of interacting environmental and trait combinations. Here we present an approach for modeling the interactive effects of de novo biological change and multivariate trait correlation evolution using principal component axes. We investigated the outcome of thousands of possible adaptive walks parameterized using empirical evolution data from the alga Chlamydomonas exposed to high CO2. We found that only a limited number of phenotypes emerged. Applying adaptive trait correlations to the starting population (historical bias) accelerated adaptation while highly convergent, nonrandom phenotypic solutions emerged irrespective of bias. These findings are consistent with a limited set of evolutionary trajectories underlying the vast amount of possible trait combinations (phenotypes). Critically, we demonstrate that these dynamics emerge in an empirically defined multidimensional trait space and show that trait correlations, in addition to trait values, must evolve to explain multi-trait adaptation. Identifying high probability high-fitness outcomes based on trait correlations is necessary in order to connect microbial evolutionary responses to biogeochemical cycling, thereby enabling the incorporation of these dynamics into global ecosystem models.

scholarly journals Adaptive walks on time-dependent fitness landscapes

1999 ◽  
Vol 60 (2) ◽  
pp. 2154-2159 ◽  
Author(s):  
Claus O. Wilke ◽  
Thomas Martinetz
Keyword(s):  
Fitness Landscapes ◽  
Time Dependent ◽  
Adaptive Walks

scholarly journals Adaptive walks on high-dimensional fitness landscapes and seascapes with distance-dependent statistics

2018 ◽  
Author(s):  
Atish Agarwala ◽  
Daniel S. Fisher
Keyword(s):  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Broad Class ◽  
Fitness Landscapes ◽  
High Dimensional ◽  
General Function ◽  
Long Distance ◽  
Future Evolution ◽  
Adaptive Mutations ◽  
Adaptive Walks

AbstractThe dynamics of evolution is intimately shaped by epistasis — interactions between genetic elements which cause the fitness-effect of combinations of mutations to be non-additive. Analyzing evolutionary dynamics that involves large numbers of epistatic mutations is intrinsically difficult. A crucial feature is that the fitness landscape in the vicinity of the current genome depends on the evolutionary history. A key step is thus developing models that enable study of the effects of past evolution on future evolution. In this work, we introduce a broad class of high-dimensional random fitness landscapes for which the correlations between fitnesses of genomes are a general function of genetic distance. Their Gaussian character allows for tractable computational as well as analytic understanding. We study the properties of these landscapes focusing on the simplest evolutionary process: random adaptive (uphill) walks. Conventional measures of “ruggedness” are shown to not much affect such adaptive walks. Instead, the long-distance statistics of epistasis cause all properties to be highly conditional on past evolution, determining the statistics of the local landscape (the distribution of fitness-effects of available mutations and combinations of these), as well as the global geometry of evolutionary trajectories. In order to further explore the effects of conditioning on past evolution, we model the effects of slowly changing environments. At long times, such fitness “seascapes” cause a statistical steady state with highly intermittent evolutionary dynamics: populations undergo bursts of rapid adaptation, interspersed with periods in which adaptive mutations are rare and the population waits for more new directions to be opened up by changes in the environment. Finally, we discuss prospects for studying more complex evolutionary dynamics and on broader classes of high-dimensional landscapes and seascapes.

Adaptive walks on correlated fitness landscapes with heterogeneous connectivities

2012 ◽  
Vol 2012 (02) ◽  
pp. P02014 ◽  
Author(s):  
J A de Lima Filho ◽  
F G B Moreira ◽  
P R A Campos ◽  
Viviane M de Oliveira
Keyword(s):  
Fitness Landscapes ◽  
Adaptive Walks

scholarly journals On the deformability of an empirical fitness landscape by microbial evolution

2018 ◽  
Vol 115 (44) ◽  
pp. 11286-11291 ◽  
Author(s):  
Djordje Bajić ◽  
Jean C. C. Vila ◽  
Zachary D. Blount ◽  
Alvaro Sánchez
Keyword(s):  
Environmental Effects ◽  
Evolutionary Dynamics ◽  
Fitness Landscape ◽  
Adaptive Dynamics ◽  
Microbial Evolution ◽  
Fitness Landscapes ◽  
Complex Genetics ◽  
Evolutionary Innovation ◽  
Genotype Space ◽  
Evolutionary Trajectories

A fitness landscape is a map between the genotype and its reproductive success in a given environment. The topography of fitness landscapes largely governs adaptive dynamics, constraining evolutionary trajectories and the predictability of evolution. Theory suggests that this topography can be deformed by mutations that produce substantial changes to the environment. Despite its importance, the deformability of fitness landscapes has not been systematically studied beyond abstract models, and little is known about its reach and consequences in empirical systems. Here we have systematically characterized the deformability of the genome-wide metabolic fitness landscape of the bacterium Escherichia coli. Deformability is quantified by the noncommutativity of epistatic interactions, which we experimentally demonstrate in mutant strains on the path to an evolutionary innovation. Our analysis shows that the deformation of fitness landscapes by metabolic mutations rarely affects evolutionary trajectories in the short range. However, mutations with large environmental effects produce long-range landscape deformations in distant regions of the genotype space that affect the fitness of later descendants. Our results therefore suggest that, even in situations in which mutations have strong environmental effects, fitness landscapes may retain their power to forecast evolution over small mutational distances despite the potential attenuation of that power over longer evolutionary trajectories. Our methods and results provide an avenue for integrating adaptive and eco-evolutionary dynamics with complex genetics and genomics.

scholarly journals Analysis of statistical correlations between properties of adaptive walks in fitness landscapes

2020 ◽  
Vol 7 (1) ◽  
pp. 192118
Author(s):  
Sandro M. Reia ◽  
Paulo R. A. Campos
Keyword(s):  
Experimental Evolution ◽  
Fitness Landscape ◽  
Shortest Paths ◽  
Fitness Landscapes ◽  
The Other ◽  
Attraction Basin ◽  
Statistical Correlations ◽  
Adaptive Walks ◽  
Nk Landscapes ◽  
The Relationship

The fitness landscape metaphor has been central in our way of thinking about adaptation. In this scenario, adaptive walks are idealized dynamics that mimic the uphill movement of an evolving population towards a fitness peak of the landscape. Recent works in experimental evolution have demonstrated that the constraints imposed by epistasis are responsible for reducing the number of accessible mutational pathways towards fitness peaks. Here, we exhaustively analyse the statistical properties of adaptive walks for two empirical fitness landscapes and theoretical NK landscapes. Some general conclusions can be drawn from our simulation study. Regardless of the dynamics, we observe that the shortest paths are more regularly used. Although the accessibility of a given fitness peak is reasonably correlated to the number of monotonic pathways towards it, the two quantities are not exactly proportional. A negative correlation between predictability and mean path divergence is established, and so the decrease of the number of effective mutational pathways ensures the convergence of the attraction basin of fitness peaks. On the other hand, other features are not conserved among fitness landscapes, such as the relationship between accessibility and predictability.

scholarly journals Phase transition in random adaptive walks on correlated fitness landscapes

2015 ◽  
Vol 91 (4) ◽  
Author(s):  
Su-Chan Park ◽  
Ivan G. Szendro ◽  
Johannes Neidhart ◽  
Joachim Krug
Keyword(s):  
Phase Transition ◽  
Fitness Landscapes ◽  
Adaptive Walks
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