Abstracts (first author)


Can we predict adaptation trajectories on simple fitness landscapes?

Author(s): Martin G


The dynamics of adaptation to a new environment is inherently complex, even in the simplest situations such as encountered in experimental evolution). Over the past decades, several empirical studies have measured the long-term dynamics of adaptation in different model species (mostly micro-organisms). Yet, existing theory does not, to our knowledge, provide quantitative predictions to which such trajectories could be compared. Indeed, the speed with which a population adapts (i.e. the speed of the mean fitness increase) depends on the rate, fitness effects, and fate of beneficial mutations. While tremendous progress has been made in modelling these processes in steady state regimes, the key parameters are still difficult to measure, and more important, they vary over time in observed adaptive trajectories, because of background dependence effects (epistasis). The net result is that populations do not simply adapt linearly over time (i.e. at some steady state rate of increase), and the form of this non-linearity is not predicted by any widely accepted model. Our goal here will be to show that some tools exist that provide testable predictions in this context, and check on a few examples if these predictions are accurate. We will first present some old and new results on Fisher’s model and how they allow to predict the change in rate and effect of beneficial mutations over adaptive trajectories, from empirically measurable deleterious mutation effects and rate. Then we will discuss several alternative tools that can be used to model adaptation trajectories in these types of landscapes, accounting for non - stationary distributions of mutation effects and rates. We will illustrate the use of this approach on some empirical trajectories in model species.



Chairman: Octávio S. Paulo
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XIV Congress of the European Society for Evolutionary Biology

Organization Team
Department of Animal Biology (DBA)
Faculty of Sciences of the University of Lisbon
P-1749-016 Lisbon


Computational Biology & Population Genomics Group