
Adaptation in eukaryotes is often assumed to be limited by the waiting time for adaptive mutations. This is because effective population sizes are believed to be relatively small, typically on the order of only a few million reproducing individuals or less. It should therefore take hundreds or even thousands of generations until a particular new mutation emerges. However, several striking examples of rapid adaptation appear inconsistent with this view. In a paper just published by
PloS Genetics, we (co-first authors
Talia Karasov and
Philipp Messer, and
Dmitri) investigate a showpiece case for rapid adaptation, the evolution of pesticide resistance in the classical genetic organism
Drosophila melanogaster. Our analysis reveals distinct population genetic signatures of this adaptation that can only be explained if the number of reproducing flies is, in fact, more than 100-fold larger than commonly believed. We argue that the old estimates, based on standing levels of neutral genetic variation, are misleading in the case of rapid adaptation because levels of standing variation are strongly affected by infrequent population crashes or adaptations taking place in the vicinity of neutral sites.
We suggest that much of the time adaptation in Drosophila takes place in populations that are much larger that a billion meaning that every single-step mutation at every site exists in the population at every given time. This means that soft sweeps should be very common and that complex, multi-step adaptations should fix all at once without intermediate fixations of single-step mutations. We also argue that adaptation should be not mutation-limited in all species with population sizes that exceed a billion (roughly the inverse of mutation rate per site), which is the case for many insects and most marine invertebrates. Nick Barton wrote a
great perspective article and the work was also highlighted in
Nature Review Genetics and
Faculty of 1000. It is currently in the top 10 most viewed articles on
Faculty of 1000 and in PLoS Genetics.
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