Jérôme Goudet

Understanding how wild populations undergo rapid phenotypic evolution requires linking phenotypic change to its underlying genomic architecture over contemporary timescales. Here, we leverage a 30-year study of Swiss barn owls ( Tyto alba ) to examine long-term changes in two melanin-based traits (plumage coloration and spottiness) that have previously been linked to multiple fitness-related traits. We document a consistent temporal shift toward darker and more heavily spotted individuals and show, using whole-genome sequencing of 3,102 individuals, that these traits are controlled by a small number of loci of major effect with partially overlapping architectures. Temporal allele frequency analyses reveal subtle but consistent changes at these loci. By comparing empirical trajectories to simulations, we show that genetic drift alone is insufficient to explain the observed patterns at one major locus, indicating a role for selection. Our results demonstrate how selection acting on a small number of loci can contribute to rapid phenotypic evolution of continuous traits in the wild and highlight the value of long-term genomic and phenotypic monitoring for disentangling evolutionary processes.

Introgression following hybridization is increasingly recognized as a major driver of evolution. However, its importance depends on its frequency in nature, which remains to be quantified. To address this, we provide a snapshot of ongoing introgression in a whole species assemblage (4126 ant colonies). 23% of all 82 local species show signs of introgression, which is more than twice previous estimates. Introgression is typically subtle, yet contributes measurably to genetic diversity. Species divergence, rather than classical prezygotic reproductive barriers (mating phenology, ecological niche, fine-scale habitat use) constrains introgression, suggesting that the main reproductive barriers are postzygotic at this stage of divergence. Our results indicate that introgression may be a common but often overlooked feature of natural communities.

The rate of new mutations is the ultimate source of genetic variation, and thus, a fundamental quantity in evolution. Mutation rates vary across the tree of life, influenced by selection pressures and life-history traits. Within species, mutation rates and types show differences between sexes as well as a positive association with parental age. Most of the direct estimates come from a few mammal species, often primates, and their generality has not been fully realized. Using 33 parent–offspring trios, we investigate the mutation rate in the barn owl (Tyto alba) and show that the species has a mutation rate of 4.96 × 10−9 per site per generation, matching rates of passerine species with a similar generation time of 2 to 3 years. Using read- and trio-based phasing methods we find that fathers pass on twice as many mutations as mothers and identify subtle differences in the sex-specific spectra of mutations. We also show that the number of mutations increases with paternal age, bringing direct evidence for germline senescence in birds. While across-species mutation rates and types appear conserved, the potential for within species variation due to age is substantial.

Although local adaptation influences species distributions, its role in driving evolutionary resilience under climate change remains unclear. Current predictive models focus on genetic adaptation to present climates, providing limited insight into future adaptive capacity. We hypothesise that historical responses to climatic shifts can reveal candidate loci for local adaptation in the future. Combining ecological niche modelling and genomic analyses, we investigate spatiotemporal patterns and mechanisms of local adaptation of the Western Palearctic barn owl ( Tyto alba ). Ecological modelling reveals that barn owls now occupy a broader climatic niche than during the Last Glacial Maximum. Genomic analyses indicate ongoing adaptation, with regions under selection linked to environmental factors across all populations. We find that local adaptation drives evolutionary changes across populations, enabling colonisation of new habitats and shaping responses to climate change in resident populations. We show that standing genetic diversity plays a crucial role in adaptation to past, present, and future environmental shifts.

Despite its detrimental effects, inbreeding depression in wild populations is relatively under-explored. Here, we use whole-genome sequence data from more than 2700 wild barn owls (Tyto alba) to investigate the presence, severity and genetic architecture of inbreeding depression in three morphological traits. Using linear models (accounting for age) and nonlinear models (measuring growth effects) we show evidence for inbreeding depression. Moreover, by partitioning developmental stages we improve our ability to detect it, as in some traits we find that it manifests during juvenile growth, and in others during adulthood. These trait-specific patterns may reflect changes in environmental influences, as we also find that juvenile heritability is lower. We additionally use two genomic inbreeding coefficients: FROH and FUniW, and while both showed the same direction of effect, the strength of evidence for inbreeding depression differs. This discrepancy offers some insight into the frequency distribution of responsible variants, as the coefficients weight variants differently based on their frequencies. Finally, an assessment of local genomic inbreeding effects highlights a handful of genomic regions with significantly deleterious effects when homozygous, alongside many regions with a smaller contribution. Overall, we provide a detailed examination of inbreeding depression affecting morphological and growth traits in this wild population.

We offer our perspective on the rich literature on the estimation of measures of inbreeding, relatedness, and population structure, beginning with Sewall Wright’s F-statistics and moving onto the descriptive statistics of Masatoshi Nei and Clark Cockerham before discussing recent papers. The current availability of genome-level single nucleotide variant data is allowing for sophisticated treatments of inferred identity by descent segments and inferred ancestral recombination graphs. Underlying such disparate methods is an emphasis of characterizing the descent status of alleles within and between individuals and populations, and we have found allele-sharing statistics a convenient framework for examining the differences and similarities among different estimators. We have been able to resolve some long-standing reported differences among estimators, especially those involving the work of Nei. In the course of our algebraic and empirical treatment of descent measure estimation, we have been able to formulate a set of five recommendations. Following the early work of Sewall Wright, we recommend (1) State that descent measures for pairs of alleles are relative to values in a reference set of allele pairs. With this view, we recommend (2) Use estimators that preserve descent measure rankings over different reference sets. Allele-sharing estimators satisfy this recommendation. Reducing genotypic data to allelic data has the benefit of reducing dimensionality, but we recommend (3) If genotypic data are available, avoid having to assume Hardy–Weinberg equilibrium by not reducing them to allelic data. Partly as a consequence of working with genotypic data, we recommend (4) Recognize that allele frequencies do not need to be estimated. Not estimating allele frequencies prevents the confounding of descent estimates for target pairs of alleles by the status of all pairs in a reference set. On the basis of both theoretical and empirical results, finally, we recommend (5) Consider both inbreeding and kinship when estimating either one. It is difficult to envisage a natural population that does not have at least some level of both relatedness and inbreeding.

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 Q _ST – F _ST 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 Q _ST – F _ST leads to elevated false positive rates. Here we present LAVA, an R implementation of the Log _{???? V} test: a method for detecting signatures of local and global adaptation in spatially structured populations. LAVA compares two estimates of ancestral additive genetic variance derived from different levels of the demographic history of the metapopulation. Under neutrality, these two estimates should be equal as both reflect the same ancestral additive genetic variance. LAVA additionally allows for the incorporation of environmental covariates to test specific hypotheses about ecological drivers of divergence. LAVA uses a Bayesian linear mixed-effect framework to model population structure through relatedness matrices. We show through extensive simulations across different population structures and selective scenarios how LAVA maintains proper calibration while maintaining or exceeding the power of alternative methods.

Whole genome sequencing (WGS) of a large number of samples is costly. Solutions include targeting a proportion of the genome (eg SNP arrays) or lowering the sequencing depth (low-coverage WGS, lcWGS) but both approaches suffer from either genotype missingness or uncertainty. Genomic imputation addresses this problem by inferring missing or uncertain genotypes using a collection of high quality genomic data (reference panel). However, certain methods can impute a lcWGS dataset without a reference panel. Because generating a reference panel can be prohibitively expensive in nonmodel species, a benchmarking of the accuracy of these alternative methods of imputation can help inform study design. Here, we imputed a dataset of 2,800 barn owls (Tyto alba) sequenced in lcWGS in the presence and absence of a reference panel of 502 samples. We used 32 individuals sequenced at both high and low coverage to estimate the accuracy of each method and explored the limitations of lcWGS sample size and reference panel size. Although the best results were achieved with a large reference panel, using only a lcWGS dataset showed very accurate imputation, when over 500 samples were used and we account for missing data and low frequency alleles. In addition, imputation with or without a reference panel returned similar results in a GWAS of a polygenic trait but caution was required when comparing identified homozygous-by-descent segments. Thus, while using a reference panel remains the ideal approach, imputation in suitably large lcWGS datasets can provide sufficient accuracy given proper quality control.

Recent advances in long-read sequencing have enabled near telomere-to-telomere (T2T) assemblies across diverse taxa. However, avian genomes remain challenging due to numerous microchromosomes, small, typically < 20Mb, elements that are gene-, GC-, and repeat-rich. As a consequence, microchromosomes are often missing from genome assemblies. Here, we present a chromosome-level, haplotype-resolved genome assembly for the Western barn owl ( Tyto alba ). Using a trio-binning strategy with Illumina parental reads combined with PacBio HiFi and Oxford Nanopore Technologies data, we generated two phased contig sets. These were scaffolded into 40 linkage groups using a linkage map. Comparative analyses identified unplaced HiFi scaffolds corresponding to microchromosomes, which we integrated into six additional microchromosomes using long reads information. The two assemblies present 46 chromosomes, matching the karyotype of the species. They exhibit strong synteny between parental haplotypes, except for a ∼38 Mb complex region on chromosome 7 containing nested inversions. This high-quality reference provides the first haplotype-resolved and chromosome-level genome for Strigiformes , enabling fine-scale studies of structural variation and avian genome evolution.

Understanding factors influencing the dynamics and distribution of parasites is essential to decipher the mechanism behind their spread and the identification of populations with elevated risks of infection. Bats-together with the diverse parasites they host and the influence of their social behaviour on parasitism-offer a suitable system. We investigated the extent to which differences in life history traits between parasite species found on the same host influence their dispersal dynamics across bat metapopulations. To do so, we compared the population genetic structure of two obligate ectoparasites of the same bat, the Daubenton's bat (Myotis daubentonii): the specialist wing mite Spinturnix andegavinus and the more generalist bat fly Nycteribia kolenatii, and we expected the bat fly to exhibit a higher connectivity than the wing mites. Using double-digest restriction site-associated DNA sequencing (ddRADseq), we genotyped 426 bat flies and 171 wing mites across 13 maternity colonies and foraging sites and 1 putative swarming site in Switzerland. We found high genetic homogeneity across the metapopulations for both species, highlighting the dispersal capacity of N. kolenatii and S. andegavinus, probably driven by the high mobility of their bat hosts. The positive population-specific F_IS and the excess of low-frequency alleles in both species suggest within-site expansion and provide insights into the life cycle of these ectoparasites. Altogether, these findings enhance our understanding of the interplay between nycteribiid flies and spinturnicid mites and their host movement in shaping population structure in Europe, offering broader insights into their potential role in pathogen transmission across bat populations.