<< Vers l'annuaire

François Rousset

CNRS research director Team Evolutionary genetics
Phone +33 (0)4 67 14 46 30
Localization bâtiment 22, 1er étage



My research interests include evolutionary genetics, behavioural ecology, and host-parasite interactions. A large part of my work deals with theoretical and statistical issues raised by these topics, and has led to the development of several softwares (listed below). I am very lazy, so I try to use concepts applying accross a large diversity of organisms and biological processes. In my former life as wet-biologist, I worked on Wolbachia bacteria and their diverse hosts. Afterwards, I have written or collaborated on birds (not enough), humans (and their bugs), black truffles, malaria vectors, damselflies, snails, kangaroo-rats, ascidians, moths, frogs, lizards, a leaf-pathogen ascomycete, gammarids (and their trematodes), acari, and more to come.

Some recent works :

The sex life of the black truffle Tuber melanosporum uncovered by Taschen et al.;

the summary likelihood method implemented in the Infusion R package for simulation-based inference, and its performance investigated by Rousset et al.;

the effects of pollen dispersal on the response to climate change by Aguilée et al.;

Leblois et al. (2014) Maximum likelihood inference of population size contractions from microsatellite data Mol. Biol. Evol. 31 : 2805-2823 (and the Migraine software).

Recent works on social evolution include an extension of the original verbal interpretation of inclusive fitness to provide the two relationships required to determine the two coefficients –c and b: Regression, least squares, and the general version of inclusive fitness by Rousset F (2015) in Evolution 69: 2963-2970. This work confirms the validity of using regression concepts in general formulations of inclusive fitness. The genetical theory of social behaviour is an in-depth survey of insights from population genetics and game theory by Lehmann & Rousset (2014) in Phil. Trans. R. Soc. B 369: 1471-2970.

Broad interest for statistical inference from spatial data has led to the development of the spaMM R package for analyzing spatial data using generalized linear mixed model (GLMM), and its application for testing environmental and genetic effects in the presence of spatial autocorrelation by Rousset & Ferdy (2014) in Ecography 37: 781-790. This followed a criticism of some popular alternative (Guillot, G., Rousset, F. (2013) Dismantling the Mantel tests, Methods in Ecology and Evolution 4 : 336-344).

and also…

Lehmann, L. Rousset, F. (2010) How life-history and demography promote or inhibit the evolution of helping behaviors. Phil. Trans. Roy. Soc. London 365: 2599–2617

Building models of evolution in spatially structured populations requires some knowledge of what is a “realistic” spatial structure. This has motivated my works defining and validating (along with several collaborators) a simple but robust set of methods of estimation of dispersal rates under isolation by distance. For an entry point to this line of work see Watts et al. 2007. These methods, and various more traditional ones, have been implemented in the Genepop software. We have also performed a similar validation program for maximum likelihood inferences based on the coalescent algorithms developed by R.C. Griffiths and collaborators (see Rousset and Leblois 2012 and the Migraine software). In Robledo-Arnuncio, J.J., Rousset, F. (2010) we further derive the effective parameters for populations in continuous space from first principles (without any pain in the torus), and quantifies them in simulations.


Infusion R package

A standard R package for likelihood inference from summary statistics whose distributions are simulated. Go to the reference page for more information.

blackbox R package

A standard R package for use with the Migraine software, or independently for optimization of functions which are avaluated with some error.

spaMM R package

A standard R package for fitting mixed models, including spatial mixed models. Go to the reference page for more information.

IsoriX R package

by Courtiol et al., a standard R package for building geographical maps of isotopic variation (isoscapes) using mixed models and inferring the geographic origin of organisms based on their isotopic ratios. Go to the reference page for more information.

Genepop R package and stand-alone software

You will find there links to all files for the latest version of Genepop (Rousset 2008). Also available is a recompiled version of the linkdos program (Garnier-Géré and Dillmann 1992), which was distributed with previous versions of Genepop.


(written in collaboration with Raphaël Leblois) implements some coalescent methods for likelihood inferences in population genetics. Go to the distribution page for more information.


(mostly written by Raphaël) simulates samples under models of isolation by distance. Go to Raphaël Leblois’s page for more information.


I teach a bit on social evolution and a moderately bigger bit on statistical genetics.


Head of the Department of Ecology, Evolution, Environment, Earth Sciences & Hydrology (B3ESTE) of the University of Montpellier; representing our University in the board of directors of the Fondation pour la Recherche sur la Biodiversité; member of the managing board of the Institute of Computational Biology; and a few other duties understandable only by those initiated to the secrets of French administration.


Links to some past and current collaborators :


Here is a publication list with links, updated when summer torpor keeps my colleagues unable to disturb me. I also have a ResearchGate profile but I check it almost as unfrequently. See also the links and post-scriptum for the book Genetic structure and selection in subdivided populations.

An example of genetic kin-recognition: the zoning of rotting wood by fungi. The mycelia of different individuals are separated by melanized borders (in black). Photo : Marc-André Selosse. See Rousset, F., Roze, D. (2007) Constraints on the origin and maintenance of genetic kin recognition. Evolution 61: 2320–2330.