Description of the thesis topic

Forests host a large part of terrestrial biodiversity, which is now increasingly threatened by the magnitude of global change. For instance, forest plant species have been shown to partially compensate for recent temperature increases by shifting upslope, while the combined effects of climate change and bark beetle outbreaks have led to widespread dieback of Norway spruce stands in the Vosges Mountains. These dynamics are driving major community reshuffling, whose consequences for forest biodiversity and ecosystem functioning remain poorly understood and difficult to anticipate. Predicting the spatio-temporal dynamics of these changes generally relies on empirical models, most of which project correlational relationships between species occurrences and environmental conditions under future scenarios. Although informative, such approaches reflect changes in potential biodiversity and fail to account for many key processes involved in community assembly and responses to environmental change, such as competition, growth, reproduction, dispersal, adaptation, or microclimate regulation.

The objective of this PhD project is to simulate spatio-temporal changes in biodiversity in response to global change while explicitly accounting for interactions between forest microclimate, forest stand dynamics, and biodiversity redistribution. The PhD candidate will build on the PhorEau model (Postic et al. 2025), a semi-mechanistic forest stand dynamics model resulting from the coupling of a gap model (ForCEEPS; Morin et al. 2021) with a phenology-based species distribution model (PHENOFIT; Chuine & Beaubien 2001), a tree hydraulics model (SurEAU; Cochard et al. 2021), and a microclimate model (Gril et al. 2023).

During the PhD, the candidate will be expected to:
(1) Scale up forest dynamics predictions from stand to landscape level.
As the PhorEau model cannot be run in a fully spatially explicit manner at large spatial scales, the PhD candidate will interpolate point-based PhorEau projections using a machine-learning model predicting tree species richness as a function of spatially explicit abiotic and biotic covariates, including satellite-derived data providing nationwide coverage. National Forest Inventory (NFI) data will be used both to initialize PhorEau simulations and for spatial validation.

(2) Simulate the future of tree species diversity.
The PhD candidate will conduct simulations under multiple environmental change scenarios (accounting for climate change at least) and produce maps of projected changes in tree species diversity. The impacts of environmental changes on diversity will be quantified and interpreted in relation to associated changes in forest productivity and microclimate conditions.

(3) Begin extending simulations to forest ecosystem biodiversity.
Tree diversity represents only part of forest biodiversity, although it underpins forest habitats hosting many plant and animal species. Based on forest states predicted by the PhorEau model, the PhD candidate will either model the redistribution of a focal animal or herbaceous plant species, or simulate changes in potential animal and plant diversity at the ecosystem level.

References:
Chuine I. & Beaubien E.G. (2001). Phenology is a major determinant of tree species range. Ecology Letters, 4(5), 500-510.

Cochard H. et al. (2021). SurEau: a mechanistic model of plant water relations under extreme drought. Annals of Forest Science, 78(2), 55.

Gril E. et al. (2023). Slope and equilibrium: A parsimonious and flexible approach to model microclimate. Methods in Ecology and Evolution, 14(3), 885-897.

Morin X. et al. (2021). Beyond forest succession: A gap model to study ecosystem functioning and tree community composition under climate change. Functional Ecology, 35(4), 955-975.

Postic T. et al. (2025). PHOREAU v1. 0: a new process-based model to predict forest functioning, from tree ecophysiology to forest dynamics and biogeography. Geoscientific Model Development, 18(20), 7603-7679.

Work Context

Academic context and supervision
The proposed PhD project is part of the PEPR FORESTT, a national interdisciplinary research program aimed at studying the socio-ecological transition of forest systems. The targeted MONITOR project within this PEPR, to which this PhD is attached, aims to monitor and study French forest productivity and biodiversity, in particular to simulate their future trajectories.

The PhD candidate will benefit from strong interactions with the working group dedicated to forest dynamics modelling within the MONITOR project, which brings together field ecologists and modelers.
The PhD will be supervised by Romain Bertrand (CRBE, CNRS Toulouse), Marion Jourdan (SILVA, INRAE Nancy), and Xavier Morin (CEFE, CNRS Montpellier).

The candidate will be hosted at the Centre for Research on Biodiversity and the Environment (CRBE) in Toulouse, located on the campus of Université Toulouse III – Paul Sabatier. The PhD candidate will be affiliated with the SEVAB doctoral school of the University of Toulouse.

Research stays and visits to other involved research units, such as SILVA, CEFE, LESSEM, and DYNAFOR, will be possible (and encouraged) in order to benefit from their expertise in functional ecology, landscape and conservation ecology, and forest management.

Required skills and qualifications
- Applicants must hold a Master's degree or an engineering degree.
- The candidate should have strong skills in programming (e.g. Java, Bash, R/Julia, C++, or similar) and in modelling natural systems, as well as a strong motivation to learn new programming languages relevant to this field.
- A solid understanding of the concepts involved in eco-evolutionary responses of species to environmental change is also expected.
- Previous experience in forest ecology or forest-related studies will be considered an asset.

How to apply?
Applications must include:
• a detailed CV (maximum 2 pages),
• a cover letter (maximum 1 page) explaining the candidate's motivation and suitability for the PhD project,
• Master's degree transcripts (M1 and M2),
• at least two referees (persons who may be contacted),
• the Master's thesis report.

All application documents must be submitted on the emploi.cnrs.fr website.

The application deadline is March 1st, 2026. A first selection will be based on the evaluation of the submitted documents, followed by an interview with a selection committee during which the candidate will present their background and explain how it prepares them to successfully carry out the PhD project.

The position is located in a sector under the protection of scientific and technical potential (PPST), and therefore requires, in accordance with the regulations, that your arrival is authorized by the competent authority of the MESR.