PhD student (M/F): Theoretical and statistical modeling of eco-evolutionary feedback loops from abundance-phenotype time series

Job Description

Thesis Subject

### ***Scientific Context***

Eco-evolutionary feedback loops describe reciprocal interactions between the genetic adaptation of organisms and the biotic or abiotic environment in which they evolve. While environmental components such as temperature or competition impose natural selection on morphology, physiology, and behavior, organisms in turn modify the environment through their abundance and traits, influencing ecosystem functioning and stability.

Despite their theoretical and applied relevance, eco-evolutionary feedback loops remain poorly understood empirically. Few theoretical or statistical tools currently exist to detect them from observational data in the wild and to predict their consequences on population dynamics and trait evolution.

### ***Project overview***

This project focuses on a potentially major feedback loop between body size and population density. Body size is a key ecological trait that may further influence individual reproductive output. In turn, population density also affects body size, either via plastic responses or by modifying the evolutionary optimum through competition intensity.

The project has both a fundamental and a methodological purpose : (i) from a fundamental point of view, determining the conditions under which eco-evo feedback loops emerge under natural settings, and (ii) from a methodological point of view, assess the sampling strategy and modeling tools making it possible to detect such loops.

The PhD student will develop a theoretical framework and exploit advanced statistical tools to detect eco-evolutionary feedbacks from abundance-body size time series. Specifically, the student will extend a base eco-evo model recently developed by our consortium to account for increasing biological complexity (e.g, overlapping generations, population structure) or specific sampling effects, such as a limited capture rate.

### Skills

We are looking for a a student with a Master degree in applied mathematics, theoretical biology, or ecology, attracted by fundamental questions in ecology and evolution. Solid training in programming (R, Python), and statistics (Bayesian and frequentist approaches, knowledge in machine learning would be appreciated) is required.

Your Work Environment

This PhD is already funded as part of the collaborative BackOut ANR project (eco-evolutionary feedback loops out from the laboratory) running from 2025 to 2029, involving 6 national and international partners. The PhD student will be co-supervised by Arnaud Le Rouzic (CNRS researcher, EGCE, IDEEV, Université Paris-Saclay) and Eric Edeline (INRAe Research Director, DECOD, Rennes), and will benefit from interactions with other project partners (6 virtual meetings/year), including Julien Papaïx (INRAe BioSP) and Willem Bonnafé (Oxford University).

The student will be based at the EGCE research unit in Gif-sur-Yvette, ~ 35 km south of Paris, on the new Paris-Saclay campus. EGCE is part of the Institute for Diversity, Ecology, and Evolution; Le Rouzic's "Genomes and Evolution" group hosts 8 researchers and ~ 15 international students and postdocs. Frequent physical meetings with the co-supervisor will be organised.

Compensation and benefits

Compensation

2300 € gross monthly

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

The research professions