Document Detail


The probability of evolutionary rescue: towards a quantitative comparison between theory and evolution experiments.
MedLine Citation:
PMID:  23209169     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
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:
Guillaume Martin; Robin Aguilée; Johan Ramsayer; Oliver Kaltz; Ophélie Ronce
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Publication Detail:
Type:  Comparative Study; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Philosophical transactions of the Royal Society of London. Series B, Biological sciences     Volume:  368     ISSN:  1471-2970     ISO Abbreviation:  Philos. Trans. R. Soc. Lond., B, Biol. Sci.     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2012-12-04     Completed Date:  2013-05-07     Revised Date:  2014-01-23    
Medline Journal Info:
Nlm Unique ID:  7503623     Medline TA:  Philos Trans R Soc Lond B Biol Sci     Country:  England    
Other Details:
Languages:  eng     Pagination:  20120088     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Adaptation, Biological*
Animals
Biological Evolution*
Biota
Computer Simulation
Empirical Research
Extinction, Biological
Models, Biological*
Mutation
Probability
Pseudomonas fluorescens / genetics,  growth & development*
Saccharomyces cerevisiae / genetics,  growth & development*
Selection, Genetic
Stochastic Processes
Stress, Physiological
Time Factors
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