Camille Lepoittevin

Forestry industry requires high-quantity and quality seeds for afforestation and assisted migration programs. Finding reliable non-destructive methods to characterize seeds would significantly enhance efforts to identify climate-adapted populations. This study presents near-infrared (NIR) spectroscopy models to classify seed origin and predict germination characteristics at different temperatures non-destructively. We focus on Abies alba Mill., a key European forest tree with genetic variation along climatic gradients and seeds with shallow physiological dormancy. Seeds from six populations were analyzed using NIR spectroscopy, and germination was tested at 15°C, 20°C, and 25°C after stratification treatments at 4°C (0 or 3 weeks). Population classification accuracy using Partial Least Squares Discriminant Analysis was 69%, with significant NIR peaks at 1712, 1929, and 2111 nm, linked to moisture content and storage compounds. NIR spectra explained 51% and 65% of the variation in germination probability and timing using Partial Least Squares Regression, with significant peaks at 1712, 1929, 2111, 1632, and 2073 nm. General Linear Mixed-Effects Models showed that a NIR predictor contributed to 39% of the germination probability variance explained by fixed-effects, and the stratification treatment was the most important driver explaining germination time. Our results proved the utility of NIR-based tools to effectively classify bulked seeds and predict germination, opening new perspectives to nursery and forestry sectors and populations’ adaptation and adjustments to warming climate. This study will facilitate further investigations on the physiological processes that occur during dormancy, a critical process for forest regeneration given the expected impact of shorter and warmer winters on seed behavior.

Key message: By monitoring a field experiment over nine years, we investigated the impacts of the two main pathogen species Erysiphe quercicola S. Takam. and U. Braun and Erysiphe alphitoides (Griffon and Maubl.) U. Braun and S. Takam causing powdery mildew on a young cohort of pedunculate oak (Quercus robur L.), both from a demographic and genetic point of view using SNP markers. We show that survival rate is affected by mean disease severity. But while the growth-related tolerance to infection of the oak individual seems to be more determinant than resistance against infection, no equalizing effect of the disease could be detected. Context: Studies on the effects of pathogens on the survival and population dynamics of forest trees are scarce. Yet a better understanding of these interactions could prove strategic in the challenging context of climate change. Aims: Our general objective was to characterize the demographic and genetic impact of the two main pathogen species Erysiphe quercicola S. Takam. and U. Braun and Erysiphe alphitoides (Griffon and Maubl.) U. Braun and S. Takam causing powdery mildew in the early stages of a Quercus robur L. population. Methods: An ad hoc field design with two disease exposures, natural and protected, was surveyed over nine years. This enabled a detailed phenotypic monitoring of 1733 emerging individuals from 15 progenies, and the genotyping of 68% of them. Results: The pathogen induced high levels of seedling mortality several years after sowing, associated with reduced growth and capacity to overwinter. Fast-growing families showed the highest survival rate under both natural and protected disease exposure. Contrary to a possible trade-off hypothesis between growth and defense, family height potential was not negatively related to disease resistance across the studied oak mother trees. While supporting a deleterious effect of very low individual heterozygosity on the probability of survival, average genomic diversity was not significantly affected by mortality associated with powdery mildew. Our study also points to a few candidate genes for several fitness-related traits. Conclusion: Overall, our results suggest that in oak natural populations, infection levels (related to resistance sensu stricto) may be less determinant than growth-related tolerance to infection for the fate of seedlings. However, an equalizing effect of powdery mildew on relative oak genotype performances cannot be excluded at later stages.

The demographic and genetic impacts of powdery mildew on the early stages of an oak population were studied in an ad hoc field design with two disease exposures. This enabled a detailed phenotypic monitoring of 1,733 emerging individuals from 15 progenies over nine years, and the genotyping of 68% of them. The pathogen induced high levels of seedling mortality several years after sowing, associated with reduced growth and capacity to overwinter. The probability of juvenile survival could be predicted from mean disease severity in early years and acorn weight. Fast-growing families showed the highest survival rate under both natural and protected disease exposure. Correlatively, no equalizing effect of increased powdery mildew pressure on the relative contribution of mother trees to the next generation could be detected. Contrary to a possible trade-off hypothesis between growth and defense, family height potential was not negatively related to disease resistance across the studied oak mother trees. Overall, our results suggest that in Quercus robur natural populations, infection levels (related to resistance sensu stricto ) may be less determinant than growth-related tolerance to infection for the fate of seedlings. However, an equalizing effect of powdery mildew on relative oak genotype performances cannot be excluded at later stages since such an effect was already visible on height. Average genomic diversity was not significantly affected by mortality associated with powdery mildew. However, our study brings support to a deleterious effect of very low individual heterozygosity on the probability of survival across the different families. Finally, our study points to a few candidate genes for several fitness-related traits.

An Illumina Infinium SNP genotyping array was constructed for European white oaks. Six individuals of Quercus petraea and Q. robur were considered for SNP discovery using both previously obtained Sanger sequences across 676 gene regions (1371 in vitro SNPs) and Roche 454 technology sequences from 5112 contigs (6542 putative in silico SNPs). The 7913 SNPs were genotyped across the six parental individuals, full‐sib progenies (one within each species and two interspecific crosses between Q. petraea and Q. robur) and three natural populations from south‐western France that included two additional interfertile white oak species (Q. pubescens and Q. pyrenaica). The genotyping success rate in mapping populations was 80.4% overall and 72.4% for polymorphic SNPs. In natural populations, these figures were lower (54.8% and 51.9%, respectively). Illumina genotype clusters with compression (shift of clusters on the normalized x‐axis) were detected in ~25% of the successfully genotyped SNPs and may be due to the presence of paralogues. Compressed clusters were significantly more frequent for SNPs showing a priori incorrect Illumina genotypes, suggesting that they should be considered with caution or discarded. Altogether, these results show a high experimental error rate for the Infinium array (between 15% and 20% of SNPs potentially unreliable and 10% when excluding all compressed clusters), and recommendations are proposed when applying this type of high‐throughput technique. Finally, results on diversity levels and shared polymorphisms across targeted white oaks and more distant species of the Quercus genus are discussed, and perspectives for future comparative studies are proposed.

Understanding adaptive genetic responses to climate change is a main challenge for preserving biological diversity. Successful predictive models for climate-driven range shifts of species depend on the integration of information on adaptation, including that derived from genomic studies. Long-lived forest trees can experience substantial environmental change across generations, which results in a much more prominent adaptation lag than in annual species. Here, we show that candidate-gene SNPs (Single Nucleotide Polymorphisms) can be used as predictors of maladaptation to climate in maritime pine (Pinus pinaster Aiton), an outcrossing long-lived keystone tree. A set of 18 SNPs potentially associated with climate, five of them involving amino acid-changing variants, were retained after performing logistic regression, latent factor mixed models and Bayesian analyses of SNP-climate correlations. These relationships identified temperature as an important adaptive driver in maritime pine and highlighted that selective forces are operating differentially in geographically discrete gene pools. The frequency of the locally advantageous alleles at these selected loci was strongly correlated with survival in a common garden under extreme (hot and dry) climate conditions, which suggests that candidate-gene SNPs can be used to forecast the likely destiny of natural forest ecosystems under climate change scenarios. Differential levels of forest decline are anticipated for distinct maritime pine gene pools. Geographically-defined molecular proxies for climate adaptation will thus critically enhance the predictive power of range-shift models and help establishing mitigation measures for long-lived keystone forest trees in the face of impending climate change.

Single nucleotide polymorphisms (SNPs) are the most abundant source of genetic variation among individuals of a species. New genotyping technologies allow examining hundreds to thousands of SNPs in a single reaction for a wide range of applications such as genetic diversity analysis, linkage mapping, fine QTL mapping, association studies, marker-assisted or genome-wide selection. In this paper, we evaluated the potential of highly-multiplexed SNP genotyping for genetic mapping in maritime pine (Pinus pinaster Ait.), the main conifer used for commercial plantation in southwestern Europe.