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Adaptation is a central topic in theoretical biology, of practical importancefor analyzing drug resistance mutations. Several authors have used argumentsbased on extreme value theory in their work on adaptation. There arecomplications with these approaches if fitness is additive (meaning thatfitness effects of mutations sum), or whenever there is more additivity thanwhat one would expect in an uncorrelated fitness landscape. However, theapproaches have been used in published work, even in situations withsubstantial amounts of additivity. In particular, extreme value theory has beenused in discussions on evolutionary predictability. We say that evolution ispredictable if the use of a particular drug at different locations tends leadto the same resistance mutations. Evolutionary predictability depends on theprobabilities of mutational trajectories. Arguments about probabilities basedon extreme value theory can be misleading. Additivity may cause errors inestimates of the probabilities of some mutational trajectories by a factor 20even for rather small examples. We show that additivity gives systematic errorsso as to exaggerate the differences between the most and the least likelytrajectory. As a result of this bias, evolution may appear more predictablethan it is. From a broader perspective, our results suggest that approacheswhich depend on the Orr-Gillespie theory are likely to give misleading resultsfor realistic fitness landscapes whenever one considers adaptation in severalsteps.

Evolutionary Predictability and Complications with Additivity