How do species adapt to changing environments? How does genetic variation map onto phenotypic variation, and ultimately fitness? How does the structure of the genotype-phenotype map affect the process of adaptation? These questions are at the center of our research on the process of adaptation to changing environments. To answer them, we utilize the evolutionary quantitative genetics framework to model the evolution of phenotypic traits under natural selection for local adaptation on environmental gradient that may shift in time and space. We look for the joint effects of major evolutionary forces and of the patterning of gene-gene and gene-trait interactions on the rate of contemporary adaptation.
Genes often affect multiple traits, either because they are expressed in different tissues, their product catalyses different reactions or they are translated into different products. Genes that affect multiple traits are said to be pleiotropic. When a mutation appears in a pleiotropic gene, its effects will likely show in different phenotypes. For instance, the Toll gene has been shown to be involved in both the immune system and the developmental segmentation process of Drosophila melanogaster. Non-synonymous mutations in Toll may thus affect both the immune and the developmental systems, generating genetic correlation between the affected traits. The evolutionary consequences of pleiotropic mutations are expected to be negative on average because a mutation is more likely to be beneficial to a subset of the affected traits only rather than to all of them. A pleiotropic mutation thus carries deleterious side effects that may nullify the beneficial effects on some of the traits. This is why pleiotropy and the resulting genetic correlations among phenotypic traits are often seen as constraints on adaptation, with possible long-lasting effects on species divergence.
Pleiotropy, and genetic correlations need not always be constraining adaptation. This is the case when the affected traits are co-selected and mutational effects are aligned with the direction on selection on the traits. This alignement of the correlational effects of selection and mutations can potentially be achieved when the pleiotropic effects of the genes are limited to a module of co-selected traits. It is expected that such modular organization of the traits may appear when there are strong evolutionary constraints between the traits caused by divergent selection between traits, and genetic variation for variable pleiotropic effects. Natural selection can then break constraining pleiotropic effects and lead to a more modular g-p map.
Genetic covariance between phenotypic traits can stem from pleiotropy of the underlying genes or from linkage between non-pleiotropic genes affecting different traits. Until recently, it wasn't clear how strongly linked should the genes be to maintain genetic covariance under correlational selection. We have shown in Chebib & Guillaume (2021) that mutations at linked loci can only cause genetic correlation of a much lower magnitude when compared to pleiotropic mutations. Linked mutations must appear at an unrealistically high rate to be able to generate high genetic correlation under correlational selection.
G-P map and the evolution of genetic constraints:
Guillaume, F. and Whitlock, M. C. 2007. Effects of migration on the genetic covariance matrix. Evolution 61(10):2398-2409; https://doi.org/10.1111/j.1558-5646.2007.00193.x.
Guillaume, F. 2011. Migration-induced phenotypic divergence: the migration-selection balance of correlated traits. Evolution 65(6):1723-1738; http://dx.doi.org/10.1111/j.1558-5646.2011.01248.x.
Chebib, J. and Guillaume, F. 2017. What affects the predictability of evolutionary constraints using a G-matrix? The relative effects of modular pleiotropy and mutational correlation. Evolution 71(10):2298-2312; http://onlinelibrary.wiley.com/doi/10.1111/evo.13320/abstract.
Chebib, J. and Guillaume, F. 2021. Pleiotropy or linkage? Their relative contributions to the genetic correlation of quantitative traits and detection by multitrait GWA studies. Genetics 219(4):iyab159; https://academic.oup.com/genetics/article/219/4/iyab159/6375447.
Evolution of pleiotropy:
Guillaume, F. and Otto, S. P. 2012. Gene functional trade-offs and the evolution of pleiotropy. Genetics 192:1389-1409; https://doi.org/10.1534/genetics.112.143214.
Chebib, J. and Guillaume, F. 2022. The relative impact of evolving pleiotropy and mutational correlation on trait divergence. Genetics 220(1):iyab205; https://doi.org/10.1093/genetics/iyab205.
Genetic architecture of adaptation:
Yeaman, S. and Guillaume, F. 2009. Predicting adaptation under migration load: the role of genetic skew. Evolution 63(11):2926-2938; http://dx.doi.org/10.1111/j.1558-5646.2009.00773.x.
Débarre, F., Yeaman, S. and Guillaume, F. 2015. Evolution of quantitative traits under a migration-selection balance: when does skew matter? The American Naturalist. 186:S37-S47; http://www.jstor.org/stable/info/10.1086/681717.
Csilléry, K., Rodríguez-Verdugo, A., Rellstab, C. and Guillaume, F. 2018. Detecting the genomic signal of polygenic adaptation and the role of epistasis in evolution. Molecular Ecology 27:606-612; https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.14499.
We use Tribolium castaneum, the red flour beetle, to study adaptation to contrasted environments. Our goals are to understand the relative importance of plastic versus genetic changes in gene expression, estimate the evolutionary potential of evolving lines and assess the genome-wide signals of selection during adaptation to novel environments. To this end, in 2014 we started an evolution experiment in three different test environments: Hot (H) 37°C, 70% rH, Dry (D) 33°C, 30% rH, and Hot-Dry (HD) 37°C, 30% rH, and a control environment: (CT) 33°C, 70% rH. A total of 10 replicated lines where maintained in those four conditions for over 20 generations.
Our approach is to mix genomics, transcriptomics (RNA-seq) and quantitative genetics to understand the evolutionary changes occurring within genomes at the individual level. We have estimated the evolutionary potential (total selection) of gene expression by linking observed variation in gene expression and fitness (number of surviving offspring) in the different environments utilizing a large half-sib/full-sib cross at generation 1 (147 sires, 3 dams/sire). Our experimental design allows us to test whether evolutionary changes of gene expression occurred at generation 21 as predicted at generation 1 (Koch, Rocabert and Guillaume, in prep.)
The next steps will be to add gene flow between pre-adapted lines on an environmental gradient to study the genetic architecture of adaptation. One key question we aim to answer is under which conditions do we reach polygenic versus oligogenic adaptation. We will implement a whole-genome re-sequencing approach with an Evolve & Resequence design using a mix of pool-sequencing and individual WGS. Our aim is to understand the role of gene flow, selection and shifting environmental conditions in shaping the genetic architecture of adaptation.
Koch, E. L. & Guillaume, F. 2020. Additive and mostly adaptive plastic responses of gene expression to multiple stress in Tribolium castaneum. PLoS Genetics 16:e1008768; https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1008768.
Koch, E. L. & Guillaume, F. 2020. Restoring ancestral phenotypes is a general pattern in gene expression evolution during adaptation to new environments in Tribolium castaneum. Molecular Ecology 29:3938-3953; https://onlinelibrary.wiley.com/doi/10.1111/mec.15607.
Koch, E. L., Sbilordo, S. H. & Guillaume, F. 2020. Genetic variance in fitness and its cross-sex covariance predict adaptation during experimental evolution. Evolution 74:2725-2740; https://doi.org/10.1111/evo.14119.
How will species cope with the high pace of global changes, in particular current climate warming? Which species are most at risk of extinction? What are the main determinants of population persistence? Answering those questions means understanding the ecological and evolutionary factors affecting growth in a changing environment. While the risks of species extinction have been evaluated mostly from predicted shifts of species ranges under climate warming scenarios in species distribution models (SDMs), few attempts at incorporating ecological and evolutionary processes have been made. SDMs are static phenomenological models associating species presence/absence data with climate variables, and thus do not consider the effects of migration or adaptation to local conditions on species' range evolution. We think that a more global, process-based approach can provide more accurate evaluations of species' persistence in face of fast global changes, and help pinpoint the key eco-evolutionary processes on which we should focus our attention to preserve biodiversity on our planet.
Dynamic eco-evolutionary niche modelling is a process-based approach to predict shifts in species' ranges at regional scales. It is a computer simulation approach that seeks to model local population demography and evolutionary dynamics with individual-based simulations. We have developed our simulation tool Nemo to perform such simulations on landscapes up to 100km2 incorporating millions of interacting individuals. We have been able to model the eco-evolutionary dynamics of four alpine plant species on 15 different landscapes set across the Alps for about a century into the future applying IPCC climate change scenarios. The simulation models are structured populations set to model the complex life histories of perennial alpine plants undergoing vegetative growth, random mating, seed dormancy and dispersal, all life-history parameters being set from empirical estimates. We showed that for species with the narrowest niches, their long life span acts as a strong constraint against population turnover and adaptation to new conditions, strongly jeopardising their persistence to future conditions.
Cotto, O., Wessely, J., Georges, D., Klonner, G., Schmid, M., Dullinger, S., Thuiller, W. and Guillaume, F. 2017 . A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nature Communications 8 15399; https://www.nature.com/articles/ncomms15399.
Our next steps are to incorporate species interactions into our models. We have started by modelling demographic interactions of two sedge species in the Alps, developing models of resource competition (eg. competition for light) and density dependence interactions (competition for space). We will further develop models for trophic interactions (food webs) and host-parasites interactions. A further line of research is to move away from computationally demanding individual-based simulations and develop more efficient population-based models.
What makes a species successfully persist in face of environmental fluctuations? How do different life-history strategies compare relative to their adaptive rate to fluctuating conditions? Is there a trade-off between evolvability and demographic persistence? We address these questions with a mix of mathematical and computational approaches. We are here specifically interested in how variation in local conditions translate into demographic rate variation (survival and fecundity) as a function of life-history strategies (e.g. long-/short-lived, itero-/semel-parous species). The impact of rate variation on population growth depends also on the evolutionary response of the species to shifts in local conditions. We find that short-lived species (annuals) have fast rates of adaptation to shifting conditions but at the price of increased sensitivity (reduced robustness) of the demographic rates (Schmid et al. 2022).
Schmid, M., Paniw, M., Postuma, M., Ozgul, A. and Guillaume, F. 2022. A tradeoff between robustness to environmental fluctuations and speed of evolution. The American Naturalist 200(1): E16-E35; https://www.journals.uchicago.edu/doi/10.1086/719654.
Postuma, M., Schmid, M., Guillaume, F., Ozgul, A. and Paniw, M. 2020. The effect of temporal environmental autocorrelation on eco-evolutionary dynamics across life histories. Ecosphere 11(2): e03029; https://esajournals.onlinelibrary.wiley.com/doi/abs/10.1002/ecs2.3029.
Eco-evolutionary dynamics also depend on the genetic architecture of the phenotypic traits mediating local adaptation. Phenotypic plasticity may here play an important role in dampening the environmental fluctuation. By evolving with increased fluctuations, phenotypic plasticity may also increase persistence and the rate of species' range evolution. We have shown these effects with computer simulations using our Nemo software.
Schmid, M. and Guillaume, F. 2017. The role of phenotypic plasticity on population differentiation. Heredity 119:214-225; https://www.nature.com/articles/hdy201736.
Schmid, M., Dallo, R. and Guillaume, F. 2019. Species' range dynamics affect the evolution of spatial variation in plasticity under environmental change. The American Naturalist 193(6):798-813; https://www.journals.uchicago.edu/doi/10.1086/703171.
We develop and use the individual-based forward-time population simulators Nemo and Nemo-age.
Nemo is a feature rich simulator capable of simulating many different types of populations, from simple Wright-Fisher populations of constant size with random mating to complex landscapes composed of thousands of patches connected by any form of sex-specific migration and holding age/stage-structured populations. Nemo is very modular and allows for the simulation of any kind of life cycle.
Population dynamics result from the balance between reproduction, migration, survival, and random extirpation. Fecundity and survival can be function of individual fitness determined from the genetic elements the individuals carry. Different models of density dependent regulation are implemented, allowing for complex dynamics in stage-structured populations with overlapping generations.
Evolutionary dynamics depend on all evolutionary forces present: drift, mutation, migration, selection and recombination. Selection can be indirect and act on life-history traits like dispersal. More often, selection directly acts on the different types of trait available whose genetics can be set explicitly. For instance, an individual's fecundity or survival can depend on the number of deleterious mutations it carries and on the value of its phenotypic traits relative to a local optimum value.
Inheritance, mutation and recombination of the genetic elements can be set precisely according to different mating systems, mutation types and recombination maps, from large QTL down to the nucleotide level.
With these features, Nemo allows for the study of evolutionary dynamics of simple to complex genetic architectures, from population genetics of simple elements under direct selection to quantitative genetics of complex traits determined by the joint effects of hundreds of quantitative loci with pleiotropic and epistatic effects.
Guillaume, F. and Rougemont, J. 2006. Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22(20):2556-2557; http://bioinformatics.oxfordjournals.org/cgi/content/abstract/22/20/2556.
Cotto, O., Schmid, M. and Guillaume, F. 2020. Nemo-age: spatially explicit simulations of eco-evolutionary dynamics in stage-structured populations under changing environments. Methods in Ecology and Evolution 11(10):1227-1236; https://doi.org/10.1111/2041-210X.13460
Because we run large numbers of simulations on computer clusters, we developed a utility to ease the process of submitting embarrassingly large numbers of jobs to cluster schedulers. This tool is called nemosub. Like Nemo, it takes an input parameter file containing the set of parameter values to explore across all parameters of a simulation campaign and starts one simulation per each combination of parameter value. This utility greatly helps with streamlining the simulation process with large projects involving thousands of simulation runs.