Improved Adaptation in Exogenously and Endogenously Changing Environments

Abstract

Fitness landscapes are visual metaphors that appeal to our intuition for real-world landscapes to help us understand how populations evolve. The object inspiring the metaphor is better described as a networks composed of all possible genotypes, but they are frequently simplified to a surface where the fitness of each genotype is represented by elevation. Selection drives evolving populations to ascend the landscape until they are dominated by genotypes from which no further beneficial mutations are likely, known as a peak. However, by allowing for environmental change, former peaks can vanish, forcing populations to resume adapting. To explore how changing environments affect adaptation, we used the digital evolution platform, Avida, wherein we could manipulate the organisms’ environment as they are subject to natural evolutionary forces. We found that transient exposure to alternate environments frequently resulted in more fit genotypes. Negative-frequency-dependent environments, in particular, yielded strong fitness benefits after returning to the original environment. Furthermore, we explored how such environmental change could yield adaptive benefits via valley crossing and how such knowledge could be exploited in systems where improving the rate of adaption is beneficial.