Description du sujet de thèse

Conservation faces a fundamental tension: we must decide how best to allocate limited resources—should we protect species that maximize ecological function now, or species whose lineages hold promise for resilience, adaptation, and future ecosystem services? In other words, should we prioritize functional diversity (FD) or phylogenetic diversity (PD)?
Existing approaches often treat conservation as static, ignoring both temporal trade-offs (short-term ecosystem performance vs. long-term evolutionary potential) and uncertainty (in species interactions, ecological responses, and future conditions).
EcoDisco aims to fill this gap by developing a dynamic, discounting-based framework that:
● embeds economic discounting theory into biodiversity prioritization (i.e. weighing benefits
over time);
● models how functional and phylogenetic diversity may diverge or align under species loss and environmental change;
● tackles uncertainty via decision‐theoretic methods
● links conservation decisions to climate change mitigation and economic risk (e.g. by treating loss of ecosystem functions as economic shock).
The project will apply the framework to real data (e.g. from GBIF, IUCN, trait databases), test trade‐off scenarios, and compare alternative discounting schemes (constant vs declining discount rates) to see how they influence conservation prioritization.
By explicitly combining ecology, economics, and decision theory, EcoDisco seeks to produce methods that are robust, policy-relevant, and sensitive to the deep uncertainties inherent in conservation.

Contexte de travail

The candidate will be a central actor in EcoDisco, responsible for:
● Designing and implementing models that integrate ecological dynamics, species traits, phylogenetic trees, and economic discounting;
● Devising Bayesian or POMDP frameworks to handle uncertainty about species
interactions, extinctions, and environmental change;
● Running simulations and scenario analyses to explore how different discounting rules or time preferences shift optimal conservation choices;
● Fitting models to empirical biodiversity, trait, or phylogenetic data sets; validating and comparing predictions;
● Collaborating with ecologists and economists across ENS, SETE, and partner
institutions to interpret results and guide policy insights;
● Publishing methodological and applied findings, and presenting at international
conferences.
Your PhD will thus lie at the interface: you'll not only build mathematical/decision models, but anchor them in real-world biodiversity systems and economic trade-offs.

Contraintes et risques

without constraints

Informations complémentaires

Candidate profile
We seek someone who:
● Has a strong quantitative or computational base (probability, optimization, dynamic
systems, decision theory, Bayesian inference)
● Is motivated to apply these methods to ecological, evolutionary, and conservation
systems;
● Is comfortable with uncertainty, modeling of stochastic systems, and possibly
reinforcement learning / POMDPs;
● Has, or will soon acquire, skills in Python or R (or equivalent);
● Is willing and able to move between ENS in the Paris region and SETE in Moulis (for
field, lab, or collaboration work);
● Thrives in interdisciplinary settings, bridging ecology, economics, and theory.
● Is fluent in English.