Biology subjects

de Villemereuil, P.

Publications and source records attributed to de Villemereuil, P..

6 recordsLinked to original sources

bioRxiv2026

LAVA: a method for identifying local and global adaptation in structured populations

Demonstrating that local adaptation drives phenotypic divergence in quantitative traits requires distinguishing selection from neutral differentiation. Existing methods for detecting selection on quantitative traits, particularly QST - FST comparisons, rely on simplified assumptions about population structure. These methods assume equal relatedness among all subpopulations, which is not necessarily true in a real metapopulation. When assumptions are violated, it has been shown that QST - FST leads to elevated false positive rates. Here we present MONET, a model for the neutral evolution of traits that creates a robust baseline against which hypotheses on trait evolution can be tested. We show how the null model provided by MONET can be the basis of two tests: one based on the log-ratio of ancestral variances (logAV) statistics we previously introduced, and another, based on testing the influence of environmental variables from the populations of origin on the phenotypic traits measured in a common garden. MONET is implemented in an R-package as a Bayesian linear mixed-effect framework modeling population structure through relatedness matrices. We show through extensive simulations across different population structures and selective scenarios how our two hypothesis tests based on MONET maintain proper calibration while maintaining or exceeding the power of alternative methods.

evolutionary biology↗

bioRxiv2026

Salinity exposure in early-life drives genomic adaptation to climate change in Antarctic toothfish (Dissostichus mawsoni)

Antarctic marine ecosystems are undergoing rapid physical change, yet the capacity of long-lived polar fishes to adapt genomically remains poorly understood. Antarctic toothfish (Dissostichus mawsoni) are an exploited top fish predator whose early life stages develop beneath the sea-ice edge, where salinity, temperature and circulation are being reshaped by climate change. Using whole-genome resequencing data, we investigated how environmental exposure, particularly during early life, structures patterns of local adaptation in D. mawsoni. We analysed 2.4 million unlinked SNPs from 24 adults sampled across the circumpolar distribution of D mawsoni, and compared variability in putatively-adaptive loci with variation in environmental parameters from the ORAS5 global ocean reanalysis, including salinity, temperature, mixed-layer depth, sea-ice concentration and thickness, and surface currents. To capture uncertainty in ontogenetic exposure, we constructed three environmental scenarios differing in their spatial and temporal representation of conditions: Point of Capture-Time of Capture (POC-C), Spawning Ground-Time of Birth (SG-B), and Point of Capture-Time of Birth (POC-B). Genotype-environment association (GEA) was performed using redundancy analysis conditioned on fishing pressure and latent-factor mixed models, with high-confidence GEA loci being defined as SNPs jointly detected by both approaches and robust to random-predictor tests. The scenario that best explained genomic variation was SG-B, in which environmental variables were averaged over hypothesised spawning grounds during the egg incubation period of D. mawsoni (August - October). Within this scenario, the significant environmental axis, dominated by salinity, was strongly associated with 854 high-confidence GEA loci. Functional enrichment revealed over-representation of gene ontology terms linked to monoatomic ion transport, ion channel complexes and calcium signalling, consistent with salinity-driven selection on osmoregulatory pathways during early development. Our results provide genomic evidence that early-life salinity exposure is a key driver of local adaptation in D. mawsoni, underscore the importance of correctly representing life-stage-specific environments in climate genomics, and highlight a concrete pathway by which climate-induced freshening and sea-ice change may alter recruitment and stock resilience.

ecology↗

bioRxiv2025

Quantitative genetics of lifetime growth curves in a lizard

Body size is an iconic trait in quantitative genetics. For species with indeterminate growth however, growth curve, a function-valued trait, is the actual trait of interest and requires a specialised framework to study its quantitative genetics. Here, we use such a framework in the common lizard (Zootoca vivipara), using two large datasets (wild and mesocosm populations) with pedigrees, decomposing the total heritability in growth dynamics into two components: the heritability of "intrinsic size" (being small or large across ages) and the heritability of the shape of growth curve. We found slower growth and larger asymptotic sizes in the wild population. We show very low to moderate heritabilities in the parameters of growth curves, varying across populations and sexes. We show a small to moderate heritability in the growth curves, with a stronger importance of the heritability in the shape of growth curves than on average intrinsic sizes. Genetic variation in growth curves differed between the two populations and sexes. Our results highlight the importance of discussing the full dynamics of growth by showing that there is more adaptive potential in the earlier ages for males and in the later ages for females, with differences between the studied populations.

evolutionary biology↗

bioRxiv2025

A method for identifying spatially divergent selection in structured populations.

Species occupy diverse, heterogeneous environments, which expose populations to spatially varied selective pressures. Populations in different environments can diverge due to local adaptation. However, neutral evolution can also drive population divergence. Thus, testing for local adaptation requires a neutral baseline for population differentiation. The classical QST -FST comparison was developed for this purpose. Yet, QST -FST frequently fails to account for the complexities of population structure because the theory underlying this comparison assumes that all subpopulations are equally related, resulting in inflated false positive rates in metapopulations that deviate from the island model. To address this limitation we use estimates of between- and within-population relatedness to model population structure. Using those relatedness matrices, we infer the between- and within-population ancestral additive genetic variances under a mixed-effects model. Under neutrality, these inferred variances are expected to be equal. We propose here a test to detect selection based on the comparison of these two estimates of the ancestral variance and we compare its performance with earlier solutions. We find our method is well calibrated across various population structures and has high power to detect adaptive divergence. Author summaryPopulations of the same species often face different environmental pressures, driving them to adapt locally. However, even in the absence of adaptation, subpopulations can diverge due to random genetic drift and limited migration. Distinguishing between adaptive evolution and random divergence is a central challenge in evolutionary biology. Traditional methods, such as QST -FST comparison, assume equal relatedness among subpopulations--a simplification that rarely holds in complex real-world scenarios, leading to flawed conclusions. To overcome this limitation, we developed a novel method that incorporates genetic relatedness among subpopulations, leveraging quantitative genetic theory to estimate ancestral additive genetic variances. Our approach provides a powerful tool for testing local adaptation, reliably distinguishing adaptive divergence from drift across a variety of population structures.

evolutionary biology↗

bioRxiv2021

Predicting population genetic change in an experimental stochastic environment

Most natural environments exhibit a substantial component of random variation. Such environmental noise is expected to cause random fluctuations in natural selection, affecting the predictability of evolution. But despite a long-standing theoretical interest for understanding the population genetic consequences of stochastic environments, there has been a dearth of empirical validation and estimation of the underlying parameters of this theory. Indeed, tracking the genetics of a large number of replicate lines under a controlled level of environmental stochasticity is particularly challenging. Here, we tackled this problem by resorting to an automated experimental evolution approach. We used a liquid-handling robot to expose over a hundred lines of the micro-alga Dunaliella salina to randomly fluctuating salinity over a continuous range, with controlled mean, variance, and autocorrelation. We then tracked the frequency of one of two competing strains through amplicon sequencing of a nuclear and choloroplastic barcode sequences. We show that the magnitude of environmental fluctuations (variance), but also their predictability (autocorrelation), have large impacts on the average selection coefficient. Furthermore, the stochastic variance in population genetic change is substantially higher in a fluctuating environment. Reaction norms of selection coefficients and growth rates of single strains against the environment captured the mean response accurately, but failed to explain the high variance induced by environmental stochasticity. Overall, our results provide exceptional insights on the prospects for understanding and predicting genetic evolution in randomly fluctuating environments.

evolutionary biology↗

bioRxiv2017

Fixed effect variance and the estimation of the heritability: Issues and solutions

Linear mixed effects models are frequently used for estimating quantitative genetic parameters, including the heritability, of traits of interest. Heritability is an important metric, because it acts as a filter that determines how efficiently phenotypic selection translates into evolutionary change. As a quantity of biological interest, it is important that the denominator, the phenotypic variance, actually reflects the amount of phenotypic variance in the relevant ecological stetting. The current practice of quantifying heritability from mixed effects models frequently deprives the heritability of variance explained by fixed effects (often leading to upward-bias) and it has been suggested to omit fixed effects when estimating heritabilities. We advocate an alternative option of fitting complex models incorporating all relevant effects, while including the variance explained by fixed effects into the estimation of heritabilities. The approach is easily implemented (an example is provided) and allows corrections for the estimation of heritability, such as the exclusion of variance arising from experimental design effects while still including all biologically relevant sources of variation. We explore the complications arising depending on the nature of the covariates included as fixed effects (e.g. biological or experimental origin, characteristics of biological covariates). Furthermore, we discuss fixed effects in non-linear and generalized linear models when fixed effects. In these cases, the variance parameters depend on the location of the intercept and hence on the scaling of the fixed effects. Integration over the biologically relevant range of fixed effects offers a preferred solution in those situations.

evolutionary biology↗