CE45 - Mathématique, informatique, automatique, traitement du signal pour répondre aux défis de la biologie et de la santé

Evolutionary rescue, stochastic effects and interaction with environmental stress – RESISTE

Submission summary

A key challenge of 21st century biology, arguably, is to merge concepts and findings from ecology and evolution into a predictive science. These two ‘sister disciplines’ have seen much progress during the last century, but an integrated, empirically validated, predictive framework has yet to emerge. There is, to our knowledge, no predictive theory that has been shown to make sense of the wealth of empirical observations across species, ecosystems and environments.

Evolutionary Rescue (ER) is an emerging and somewhat central example of such processes where eco-evolutionary thinking is required, and where critical applied issues are at stake. ER occurs when a population, initially declining because of exposure to an environment outside of its ecological niche, avoids extinction via genetic adaptation restoring population growth. This phenomenon underlies a range of biological contexts of fundamental and applied importance: range expansions/contractions, host shifts in pathogens, and the emergence of resistance to chemical treatment in various agronomic and medical contexts. The development of general models of ER is a typical example where an eco-evolutionary framework is required. Their empirical test is also a challenge, as it requires fine-scale studies of demographic and evolutionary dynamics, at high replication, under well-controlled or at least well-quantified stresses. Better management strategies of resistance emergence is a critical demand for health and agronomy. Such strategies would benefit from an empirically established and general framework to understand and predict ER, in more or less complex situations. This is the aim of this project.

To capture the dependence of mutational parameters on stress levels for different demographic rates, we will use Fisher’s geometrical model. We will assume that these rates depend on some underlying multivariate phenotype, with a given optimum per environment and rate considered. We will assume that, among other effects, increasing stress levels (e.g. antibiotic concentration) increasingly shift this optimum along a given direction, for a given stress and the rate considered (e.g. birth vs. death rate).

We will predict adaptation trajectories and resulting stochastic demographic dynamics in this landscape, to compute the probability of ER vs. extinction, in a given pattern of stress (level, combinations, time dynamics and spatial pattern). The coordinators have already successfully applied this approach to the case of a single component (growth rate) under an abrupt change. Various extensions will be considered: abrupt stress with multiple components, combinations of stresses (synergism/antagonism), arbitrarily changing stress over time, coupling with ecological dynamics for a biotic stress (predation, epidemiology), source-sink connected by migration, gradual change in stress level over space.

We will test our predictions quantitatively on in vitro experiments in Escherichia coli (in an original high-throughput experimental system). Various stresses will be explored: copper ions (as used in agronomy), antibiotics (single or in combinations), predation by a unicellular eukaryote or infection by one or several lytic phage species (phagotherapy). Some predictions will also be tested in the field and in natura on a virus – plant pathosystem near Avignon.

A collaborative website will be developed where authors can contribute their own ER data, to expand the tests and parameter estimates across species and stresses. Associated online toolboxes will be produced to facilitate the use and test of the models and a summer school will be organized at the end of the project.

This is a collaboration between an evolutionary biologist (PI) and a mathematician (coPI), and their respective colleagues, forming a very multidisciplinary consortium with various skills (theoretical evolution/ecology, statistics, mathematics, virology, microbial experimental evolution).

Project coordination

GUILLAUME MARTIN (Institut des Sciences de l'Evolution de Montpellier)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


BioSP Biostatistique et Processus Spatiaux
ISEM Institut des Sciences de l'Evolution de Montpellier

Help of the ANR 396,593 euros
Beginning and duration of the scientific project: January 2019 - 48 Months

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