title: The probability of evolutionary rescue: towards a quantitative comparison between theory and evolution experiments.
abstract: Evolutionary rescue occurs when a population genetically adapts to a new stressful environment that would otherwise cause its extinction. Forecasting the probability of persistence under stress, including emergence of drug resistance as a special case of interest, requires experimentally validated quantitative predictions. Here, we propose general analytical predictions, based on diffusion approximations, for the probability of evolutionary rescue. We assume a narrow genetic basis for adaptation to stress, as is often the case for drug resistance. First, we extend the rescue model of Orr & Unckless (Am. Nat. 2008 172, 160-169) to a broader demographic and genetic context, allowing the model to apply to empirical systems with variation among mutation effects on demography, overlapping generations and bottlenecks, all common features of microbial populations. Second, we confront our predictions of rescue probability with two datasets from experiments with Saccharomyces cerevisiae (yeast) and Pseudomonas fluorescens (bacterium). The tests show the qualitative agreement between the model and observed patterns, and illustrate how biologically relevant quantities, such as the per capita rate of rescue, can be estimated from fits of empirical data. Finally, we use the results of the model to suggest further, more quantitative, tests of evolutionary rescue theory.
authors.affiliation: Université Montpellier 2, CC 065, Place Eugène Bataillon, 34095 Montpellier Cedex 05, France. guillaume.martin@univ-montp2.fr
authors.forename: Guillaume
authors.initials: G
authors.lastname: Martin
authors.forename: Robin
authors.initials: R
authors.lastname: Aguilée
authors.forename: Johan
authors.initials: J
authors.lastname: Ramsayer
authors.forename: Oliver
authors.initials: O
authors.lastname: Kaltz
authors.forename: Ophélie
authors.initials: O
authors.lastname: Ronce
mesh_terms: D000220:Adaptation, Biological; D000818:Animals; D005075:Biological Evolution; D058448:Biota; D003198:Computer Simulation; D036262:Empirical Research; D053476:Extinction, Biological; D008954:Models, Biological; D009154:Mutation; D011336:Probability; D011551:Pseudomonas fluorescens / Q000235:genetics / Q000254:growth & development*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000254:growth & development*; D012641:Selection, Genetic; D013269:Stochastic Processes; D013312:Stress, Physiological; D013997:Time Factors