Heredity

Whole genome duplication is a common mutation in eukaryotes with far-reaching phenotypic effects. The resulting morphological, physiological, and fitness consequences and how they affect the survival probability of newly polyploid lineages are intensively studied, but very little is known about the effect of genome doubling on the short-term evolvability of populations. Understanding the effect of polyploidization on the adaptive potential of populations is of crucial importance to predict the future of polyploid populations. In this paper, I investigate the immediate consequences of genome doubling on the genetic variance of populations. To do so, I performed numerical iterations and simulations of how the genetic variance of a quantitative trait changes after polyploidization, under different genetic architectures (additivity, dominance, and epistasis). I found that genetic variance generally decreases after genome doubling. Non-additive gene actions can make autotetraploid populations genetically more diverse than their diploid progenitors in rare cases, notably with overdominance and directional epistasis. By collecting estimates from the agronomic literature, I found that both dominance and epistatic variance contribute to the genetic variance of polyploid populations. These results bring new insights into the adaptive potential of newly formed tetraploid populations, and call for further experimental investigations of how polyploidization is associated with a short-term decrease in evolvability.

Interest in quantifying linkage disequilibrium (LD, non-random associations of alleles at different loci) has skyrocketed in recent years as researchers have focused on use of LD in genome-wide association studies (GWAS), for studying historical demography, and for estimating effective population size (Ne). The most widely used LD metric is r2 = the squared correlation of alleles at a pair of loci. Despite a half century of efforts, developing an unbiased expectation of r2 as a function of the many factors that can affect it (physical linkage, genetic drift, selection, migration, mutation, mating systems) remains elusive. Furthermore, even when all of these other factors are absent, empirical estimates of r2 are upwardly biased by sampling a finite number (S) of individuals, and that must be accounted for if one wants to focus on the desired signal of LD. Previous approaches to estimate [Formula] have been shown to be biased to greater or lesser degrees. The purpose of this short paper is to demonstrate that a simple and apparently exact expression for [Formula] does exist for the special case where sampling error is the only factor contributing to r2, in which case [Formula] = 1/(S - 1). When other factors contribute heavily to LD, [Formula] shrinks toward 0 as empirical r2 [->] 1. However, for estimating contemporary Ne with unlinked markers, empirical r2 will generally be small and 1/(S - 1) will provide a robust estimate of [Formula].

The fungal crop pathogen Blumeria hordei, causal agent of powdery mildew on barley, presents life-history and epidemiological characteristics, as well as and selective pressures due to modern agriculture leading to expected sweepstakes reproduction, that is highly skewed offspring distributions. Using genome-wide polymorphism data and population genomics inferences, we aim to 1) infer the past demographic history and the strength of sweepstakes reproduction in B. hordei, and 2) quantify the contributions of these selective and neutral processes in the genome. An new inference method based on Neural Posterior Estimation and diversity and linkage disequilibrium statistics was developed and tested on simulated and B. hordei genomic data. We confirm that B. hordei exhibits a moderate sweepstakes reproduction (-parameter of 1.6). We highlight that the Site Frequency Spectrum (SFS) appears sensitive to the joint occurrence of sweepstakes and recent demographic changes, which may caution on the reliability of the SFS to infer sweepstakes reproduction. We then scan the genome for selective sweeps, adjusting the significance thresholds of the methods for demographic history and sweepstakes reproduction, thereby yielding a counterintuitive result. When conditioning the significance threshold for sweep detection on simulations under sweepstakes and demography, a very large number of putatively selected regions is found (11.6% of the genome). We suggest that sweepstakes reproduction in B. hordei is due to 1) neutrality (clonal/sexual phases and Boom-and-Bust cycles) generating a genome-wide level of background noise in the coalescent genealogies, and 2) selective sweepstakes due to pervasive positive selection. Our findings have important implications for both population genomic methodology and our understanding of pathogen evolution.

Longitudinal data on disease susceptibility in forest trees are rare but essential for understanding host-pathogen dynamics and genetic variation in susceptibility traits. We present a long-term multisite common garden dataset quantifying susceptibility of Scots pine (Pinus sylvestris) to Dothistroma needle blight. The dataset comprises annual disease assessments collected from the same trees across 11 years, spanning 168 families and 21 Scottish provenances. This design enables partitioning of genetic and environmental sources of variation, evaluation of temporal stability in host response, and estimation of variance components and narrow-sense heritability of susceptibility. The data support analyses of phenotypic plasticity, provenance-level responses, and interactions between disease susceptibility and other adaptive traits. This resource will facilitate predictive modelling of host susceptibility under current and future environmental conditions.

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

Sex ratio distorters (SRDs) are heritable elements that bias offspring sex ratios to enhance their transmission. In the terrestrial isopod Armadillidium vulgare, feminization of genetic males can occur through vertical transmission of the sex ratio distorter known as the f-element, as well as through infection by Wolbachia, a maternally inherited bacterial endosymbiont that can alter host reproduction. Previous studies have focused on the distribution of SRDs and their associations with mitochondrial haplotypes in native European populations, but these patterns are poorly understood in the United States. In this study, we sampled A. vulgare in 12 states, screening individuals for Wolbachia infection, the presence of the f-element, and mitochondrial haplotypes. We found that Wolbachia shows a heterogeneous distribution across populations and haplotypes, in contrast with stronger associations in Europe. The f-element occurred in lower overall frequencies but showed a strong association with mitochondrial haplotype VI. These results indicate that patterns associated with SRD differ from those observed in Europe and suggest that multiple introductions and population mixing have shaped these distributions.

Sexual selection arises from individual differences in reproductive success, which can drive the maintenance of genetic polymorphisms in genes subject to balancing selection by the pleiotropic effects that trade-off between survival and reproduction. However, the extent to which sexual selection maintains genetic polymorphisms in wild populations remains unclear. Here, we explored on genomic signatures of balancing selection and selective sweep in the northern medaka, Oryzias sakaizumii in Japan by performing whole-genome resequencing of wild individuals. In addition, we re-evaluated the population genetic structure and admixture of Oryzias latipes and O. sakaizumii across the Japanese archipelago and detected genomic regions affected by introgression. Regions with signatures of selection from multiple statistics were located on eleven chromosomes. In particular, a region spanning 4.25 to 6.80 Mb on chromosome 18 showed high genetic diversity that could not be explained by sex differentiation or introgression from O. latipes in Eastern Japan. This pattern suggests that balancing selection maintains genetic polymorphisms in O. sakaizumii. Specifically, because a previously reported quantitative trait locus associated with female mating behavior overlaps with this region, we infer that sexual selection contributes to the maintenance of genetic polymorphism at this locus.

Genetically identical cells grown in the same environment show variation in gene expression known as expression noise. Expression noise can be heritable and impact fitness, making it subject to natural selection. Increasing expression noise for the Saccharomyces cerevisiae TDH3 gene was shown to be beneficial in glucose-based media when mean TDH3 expression was far from the fitness optimum but deleterious when it was close to this optimum. Here, we show that growth on different carbon sources alters the effects of new mutations on TDH3 expression noise and examine the fitness effects of changing expression noise. In galactose-based media, we observed the same relationship between expression noise and fitness seen in glucose-based media, but in glycerol- and ethanol-based media, we observed the opposite relationship or no significant relationship, respectively. Using simulations of single-cell organisms, we found that these differences were most likely explained by environment-specific relationships between gene expression and fitness. We also found that, far from the optimum, the fitness effects of noise were greatest when expression was highly heritable between mother and daughter cells. The empirical observations and simulations reported in this study show how environments influence both the production of expression noise and its impacts on fitness.

Social interactions are ubiquitous in nature and have the potential to affect trait evolution, particularly in group-living animals such as cooperative breeders. Interactions among conspecific individuals can affect the amount of additive genetic variation for a trait when the phenotype of an individual is also affected by the genotype of its social partner(s) via indirect genetic effects. Thus, quantifying both direct and indirect genetic effects of social partners is critical for understanding and predicting evolutionary trajectories. While much is known about maternal indirect genetic effects, empirical estimates of indirect genetic effects from other social partners remain limited, particularly in wild populations. Here, we use animal models to assess the contribution of indirect genetic effects from all social partners in a family group (mothers, fathers, and helpers) on juvenile morphometric traits across ontogeny in the cooperatively-breeding Florida scrub-jay (Aphelocoma coerulescens). We found indirect genetic effects of helpers and fathers on nestling weight, but no indirect genetic effect of mothers. Across ontogeny, we found increasing additive genetic variation in both weight and tarsus length. Our study provides a comprehensive assessment of within-group indirect genetic effects in a cooperative breeder and highlights the importance of considering indirect genetic effects beyond maternal effects.

Most species are geographically structured, leaving characteristic signatures in neutral regions of the genome. These signatures can be distorted when neutral regions are linked to deleterious mutations. In such regions, purifying selection can reduce genetic diversity through Background Selection (BGS) or, for recessive mutations, increase diversity through Associative Overdominance (AOD). While the effect of BGS and AOD are well characterized in panmictic populations, their effects remain largely unexplored in structured populations. Here, we investigated an Isolation with Migration model using forward simulations across a range of migration, selection, dominance, and recombination parameters. We first used a genotype-based approach to quantify the effects of deleterious mutations on standard summary statistics ({pi}, dxy, FST, DAFi). We then showed that an Ancestral Recombination Graph-based (ARG) approach, tracking tree sequences from a sample of one diploid per deme, recovers the same patterns while directly relating genetic variation to the underlying coalescent processes. When recombination is sufficiently low, we found a BGS-driven regime for weakly codominant mutations, characterized by lower diversity and increased genetic differentiation (FST). For recessive mutations, we first identified an AOD-driven regime, characterized by increased diversity and lower FST values followed by a transition to a subsequent BGS-driven regime. Genealogies were similarly impacted by deleterious mutations: BGS shrunk coalescent times and produced a shift towards lineage sorting topologies, while AOD stretched coalescent times and produced a shift toward incomplete lineage-sorting topologies. These patterns were weakened by gene flow, with FST and topologies remaining close to expected under neutrality, while diversity and coalescence times remained robust to demography. Our results provide clear evidence of BGS, AOD, and of their transition in a structured model with gene flow. Importantly, these processes leave distinct and interpretable signatures on gene trees, highlighting the potential of ARG-based approaches for inferring linked selection and dominance in structured populations. Author summaryCharacterizing how demography and selection jointly shape genomic variation is a central question in population genetics. As deleterious mutations reduce fitness, they are continuously removed from populations by purifying selection. Through linkage, this affects nearby regions of the genome, leaving signatures of selection on linked neutral genetic diversity. While these effects are well understood in random mating populations, much less is known in structured populations. Specifically, the occurrence of Background Selection (BGS), which reduces diversity, and Associative Overdominance (AOD), which increases diversity, remains underexplored. Here, we used simulations to investigate how deleterious mutations shape genomic variation in a structured two-population isolation with migration model. By combining standard population genetic analyses with a genealogical approach based on Ancestral Recombination Graphs (ARGs), we showed that BGS and AOD leave distinct and interpretable signatures on common summary statistics and the underlying genealogies. We identified clear signatures of BGS and AOD when recombination was low and revealed a transition from AOD to BGS for recessive mutations, as the strength of selection increased. Our results highlight the importance of jointly considering demography and linked selection when interpreting genomic data and demonstrate the potential of ARGs to jointly infer demography, selection, and dominance from genomic data.

Maximum utilization of existing genetic variability in a breeding program depends on the efficient classification of the inbred lines into heterotic groups, particularly under stress conditions. This study applied practical breeding approaches to determine the mode of genetic inheritance for Striga resistance and proposes a weighted heterotic grouping method based on the general combining ability of multiple traits (WHGCAMT) and compares its effectiveness with other existing methods in classifying the inbred lines into heterotic groups in Striga-infested and optimum environments. Using Diallel design IV, 300 crosses were generated from 21 inbred lines and 4 standard testers. The crosses, along with six checks, were evaluated in an 18 x 17 alpha lattice design with two replications at two locations, in both artificial Striga-infested and Striga-free environments. The inbred lines were genotyped using DArTtag SNP markers. Phenotypic and genotypic data were analyzed using R. Analysis of variance revealed significant mean squares for hybrid, general combining ability (GCA), specific combining ability (SCA) and their interactions with environment. Significant positive and negative GCA and SCA effects were detected for grain yield and other measured traits. However, a larger proportion of additive gene action than non-additive gene action was observed for grain yield and most measured traits. The analysis of molecular variance also showed substantial genetic differences within and between clusters. Except for HSCA, the mean grain yield between the inter-group and intra-group hybrids was significant for each method. Pairwise comparison of the inter- and intra-group hybrids of all the methods showed significant differences between the WHGCAMT and all other methods in most cases. WHGCAMT consistently produced higher-yielding inter-group hybrids and lower-yielding intra-group hybrids, achieving breeding efficiency improvements of 55.8%, 4.3%, 15.7%, and 11.4% over the HSCA, HSGCA, HGCAMT and molecular marker methods, respectively, under Striga infestation. Thus, WHGCAMT offers more precise, reliable and biologically meaningful heterotic groups among early-maturing maize inbred lines.

The fitness of immigrants and their descendants determines the effectiveness of gene flow. Genetic incompatibilities or outbreeding depression can limit the spread of novel alleles, while highly fit immigrant lineages can hasten introgression. These fitness effects of gene flow can also differ between generations as immigrant and resident haplotypes recombine. Understanding the genetic factors that shape immigrant fitness over multiple generations is increasingly important as habitat fragmentation threatens populations by reducing genetic variation and leading to increased levels of inbreeding. Few studies have measured the multigenerational fitness of immigrant lineages, especially within populations that had histories of high gene flow. We used 33 years of life history and pedigree data on a population of Florida scrub-jays (Aphelocoma coerulescens) with historically high immigration to quantify the fitness of immigrants and their descendants. We compared the fitness of immigrants and residents as well as their resulting descendants (F1, F2, etc.) to determine the composite genetic effects responsible for fitness differences. We found evidence of additive benefits of immigrant ancestry and heterosis driven by non-additive effects that persists for multiple generations. These results are promising for conservation efforts aiming to increase connectivity and illustrate the complex dynamics that determine the rates of introgression in natural populations.

In most species, unmated individuals run the risk of dying with zero fitness. This strong selection on virgin females to mate may also explain why females subsequently remate, despite fitness costs; all that is required is a genetic correlation between virgin and non-virgin mating propensity. Despite being the null model for the evolution and maintenance of polyandry, this hypothesis has received no empirical test. We performed separate quantitative genetic and artificial selection experiments to test the presence of this cross-context genetic correlation in the cow-pea weevil, Callosobruchus maculatus. A quantitative genetic experiment did not find evidence of the hypothesised genetic correlation. However, after 13 generations of artificial selection on virgin mating latency, we found strong evidence for evolutionary divergence in remating latency. Females from lines selected for longer virgin mating latency took approximately twice as long to remate and, were less polyandrous if their virgin mating latency was longer. There was no evidence that females could mate indiscriminately and then trade-up, rather, trading up could only occur if virgin discrimination was present. Selection against virgin death will thus constrain both the evolution of non-virgin discrimination and trading up, increasing rates of polyandry. These findings reveal a genetic correlation between virgin and non-virgin latency to mate suggesting that polyandry may be maintained because of the need to breed.

The Red Queen Hypothesis proposes that genetic variation is maintained in populations through antagonistic coevolution of hosts and parasites. A major assumption of the Red Queen Hypothesis is tight genetic specificity for infection. However, it has been argued that this genetic interaction of host and parasite (GHxGP) is sensitive to environmental context (GHxGPxE). Environmental change could accordingly disrupt coevolutionary oscillations on relevant time scales, calling into question antagonistic coevolution as a general and robust explanation for the maintenance of genetic diversity. To evaluate this critique, we used the plant-parasitic nematode Meloidogyne arenaria and its natural bacterial parasite Pasteuria penetrans to determine if specificity is altered by temperature. We exposed six isofemale host lines to five parasite sources at three ecologically relevant temperatures. We found that, at two of three temperatures, susceptibility to infection depended on the specific combination of host line and parasite source (GHxGP). This specificity varied across temperatures, consistent with a GHxGPxE effect. This three-way interaction was driven both by quantitative changes in the strength of specificity across temperatures and shifts in the susceptibility rankings of host-parasite combinations. Our study contributes a rare experimental test of a proposed challenge to the Red Queen Hypothesis and suggests the potential for environmental context to change host-parasite specificity.

The evolution of alternative male reproductive strategies represents an intriguing evolutionary phenomenon. Divergent strategies are persistently at risk of local extinction or invasion, depending on the suites of traits expressed within and between morphs; hence, understanding the correlational selection that aligns reproductive strategies with behaviour, morphology and physiology is key to understanding the origin and maintenance of genetic polymorphisms. In the polychromatic painted dragon, Ctenophorus pictus, yellow, orange and red morphs are well characterised, but the blue morph has been historically absent from studied populations. Here we document the local distribution, morphology and male-contest interactions in a population where blue males are relatively common. We find that blue males express head colouration after a reaching a threshold body size, and that small blue males can reside in close proximity to other males; patterns consistent with a novel size-dependent conditional tactic within the suite of genetic strategies seen in this species. Condition-dependent, positively allometric throat bibs were non-randomly distributed among male morphs, implicating variation in correlational selection and the genetic architecture of the polymorphism. We were unable to definitively assign a morph that was superior in male competition but found that within morphs, male size was the determinant of competitive success, whilst between morphs it was not. Furthermore, contests between morphs were resolved with less aggression than contests within morphs, supporting the idea that badges resolve conflict, and that the invasion of new colour morphs may be facilitated by negative frequency dependent benefits to novel colour variants. These findings highlight the divergent phenotypic, genetic and selective environments that lead to the diversity of colour morphs.

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

The field of genetics of bird migration advances, driven by exponential refinements of sequencing and tracking technologies. In willow warblers (Phylloscopus trochilus), a complex repeat-rich region named MARB (Migration Associated Repeat Block) has recently been found to correlate with the routes taken by individual birds from Europe to their African wintering grounds. However, the genomic location of this region remains unknown. Here, we characterized MARB using a combination of approaches to understand how it evolved. We describe the region using long-read genome assemblies of two willow warbler subspecies (P. t. trochilus and P. t. acredula), two related species, the common chiffchaff (P. collybita) and the greenish warbler (P. trochiloides), and whole genome sequencing data from 76 willow warblers. Finally, we applied karyotyping and fluorescent in situ hybridization techniques on willow warbler spermatocytes to cytogenetically locate MARB. Due to the many repeats, we cannot order scaffolds in silico, but probe hybridization on the karyotype shows that MARB constitutes a single locus (~27.5 Mb) spanning most of the 11th largest chromosome in the willow warbler genome. Interestingly, the MARB regions of all species share several characteristics such as relatively high GC content (50%), a high density of specific repeat families and notably, more than 800 olfactory receptor sequences. Regions homologous to MARB may exist in several migrant bird genomes, though currently unassembled due to their complexity. Resolving these in species with similar migratory polymorphisms to willow warblers will be essential to determine whether MARB influences migratory behaviour across species.

O_LIHost specialization is a major driver of genetic structure in fungal plant pathogens, but it remains unclear whether specialization on different cereal hosts prevents sexual recombination when mating-type compatibility is retained. We addressed this question in stripe rust, caused by Puccinia striiformis, by crossing wheat-adapted P. striiformis f. sp. tritici and barley-adapted P. striiformis f. sp. hordei, two divergent host-adapted forms that share common barberry (Berberis vulgaris) as a sexual host. C_LIO_LIControlled reciprocal crosses on barberry produced 18 aeciospore-derived progeny, demonstrating that wheat- and barley-adapted Puccinia striiformis can undergo sexual recombination despite strong host specialization during asexual infection. Chromosome-scale parental assemblies placed the homeodomain (HD) mating-type locus, containing bW-HD1 and bE-HD2, on chromosome 2 and the pheromone receptor (PR) mating-type locus, containing STE3 and mfa genes, on chromosome 6. HD restriction genotyping showed biparental inheritance in all progeny, with each progeny carrying one HD haplotype from each parent. Together with conservation of PR-associated coding sequences and amplification of STE3-associated markers in progeny, these results are consistent with retention of tetrapolar mating across the two host-adapted lineages. C_LIO_LIHost interaction phenotypes were assessed across wheat and barley differentials, near-isogenic lines and wild relatives. The parental isolates retained contrasting wheat- and barley-restricted profiles, whereas progeny did not reproduce either parental virulence profile, but instead showed recombinant infection patterns, including compatibility with both wheat and barley genotypes. C_LIO_LIThese findings indicate that host specialization in Puccinia striiformis does not necessarily prevent sexual compatibility on a shared alternate host. Together with retention of tetrapolar mating, alternate-host sexual reproduction may provide a route for genetic exchange between host-specialized pathogen populations, enabling recombination to generate new combinations of host-interaction traits when divergent pathogen lineages mate on a shared alternate host. C_LI

Climatic and biogeographic variables are often used as a proxy for tree genetic diversity, but local factors can also influence it. We propose that woodland age, presence of ancient trees, and population size could impact genetic diversity. Using the RBG Kew UK National Tree Seed Project as a study case, we examined how these factors are accounted for during seed collection. We found 42% of tree seed collections come from ancient woodlands and that 8.4% overlap with ancient trees. Sampled forest patches size ranges from few individuals to several thousand. We then carried out a pilot to examine the role of population size on functional traits variation, testing the relationship between population size and seed germination and seedling thermal stress sensitivity in three populations of the Betula pubescens Ehrh. complex. We found that seeds and seedlings from larger populations showed higher fitness and stress resistance. Our results highlight the importance of local factors to predict variation in functional traits, relevant for tree resilience. Existing seed collections of native species stored in conservation seed banks offer a valuable resource to explore these factors and improve our understanding of genetic diversity in tree populations, with implications for biodiversity conservation and forestry production.