Understanding the origin of adaptive genetic variation and its role for the vulnerability of beech forests to climate change

Maladaptation due to rapid climate change is one of the greatest threats to biodiversity and ecosystem functioning. Although most species show substantial amounts of genetic variation, we still do not know how such genetic diversity will contribute to climate resilience. Addressing this question in long-lived species such as forest trees is particularly important, as their rapid adaptation will mainly rely on standing genetic variation. One hypothesis is that spatial genetic adaptations are shaping the genetic diversity found within populations and thereby their evolutionary potential. Forecasts of species extinction risk under climate change commonly ignore such intraspecific diversity, while it will likely play a crucial role for the short- and long-term (mal-)adaptation of populations. This PhD program aims to fill this gap by furthering our understanding of the origin of adaptive genetic diversity and modelling its consequences for the vulnerability of forest tree populations.
The main objectives of the thesis are: (1) the exploration of quantitative trait variation across the species distribution range; (2) the quantification of the impact of environmental heterogeneity on the variation measured within populations; (3) the characterisation of local (mal)adaptations considering potential growth-resistance trade-offs at the intraspecific level; (4) the prediction of population vulnerability to climate change while accounting for intraspecific variation found among- and within-populations.
The project will focus on a major European tree species, common beech (Fagus sylvatica L.), for which many uncertainties exist regarding its capacity to adapt to ongoing environmental changes. To conduct his/her project, the PhD student will (1) benefit from large existing resources for the study species, including data from a network of provenance trials, (2) generate and analyse new phenotypic and genomic data in one established common garden, and (3) develop statistical and mechanistic models to combine genomic, phenotypic and environmental data.

The PhD student will be supervised by Julie Gaüzère and Ivan Scotti, working in evolutionary ecology and forest genetics and with expertise in quantitative and population genetics and genomics. He/she will be based in Avignon, at INRAE’s Mediterranean forest ecology research laboratory (‘URFM’) in the ‘population biology and evolution’ scientific team (‘BioPopEvol’). The PhD student will also benefit from a network of collaborators with expertise in tree ecophysiology and ecological modelling.

PhD starting date: from 1st September 2025

PhD profile and competence:
• Master’s degree or equivalent in evolutionary biology
• Experience with R and statistical analyses (mixed models)
• Strong interest for data analysis
• An interest in forest ecology and data acquisition would be a plus
• Motivated, curious and capable to carry out research with a certain degree of autonomy
• Good writing skills
• Proficiency in English

How to apply: send a CV, a motivation letter and contact information for two references to julie.gauzere[at]inrae.fr and ivan.scotti[at]inrae.fr. The deadline for submitting application is 27 June 2025. Interviews will be done remotely from 30 June to 4 July 2025.