Plasticity-led evolution: evaluating the key prediction of frequency-dependent adaptation
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Updated: Jan 26, 2026

Procedure for Adaptive Laboratory Evolution of Microorganisms Using a Chemostat
Published on: September 20, 2016
Nicholas A Levis1, David W Pfennig1
1Department of Biology, University of North Carolina , CB no. 3280, Chapel Hill, NC 27599 , USA.
Abstract:
Plasticity-led evolution occurs when a change in the environment triggers a change in phenotype via phenotypic plasticity, and this pre-existing plasticity is subsequently refined by selection into an adaptive phenotype. A critical, but largely untested prediction of plasticity-led evolution (and evolution by natural selection generally) is that the rate and magnitude of evolutionary change should be positively associated with a phenotype's frequency of expression in a population. Essentially, the more often a phenotype is expressed and exposed to selection, the greater its opportunity for adaptive refinement. We tested this prediction by competing against each other spadefoot toad tadpoles from different natural populations that vary in how frequently they express a novel, environmentally induced carnivore ecomorph. As expected, laboratory-reared tadpoles whose parents were derived from populations that express the carnivore ecomorph more frequently were superior competitors for the resource for which this ecomorph is specialized-fairy shrimp. These tadpoles were better at using this resource both because they were more efficient at capturing and consuming shrimp and because they produced more exaggerated carnivore traits. Moreover, they exhibited these more carnivore-like features even without experiencing the inducing cue, suggesting that this ecomorph has undergone an extreme form of plasticity-led evolution-genetic assimilation. Thus, our findings provide evidence that the frequency of trait expression drives the magnitude of adaptive refinement, thereby validating a key prediction of plasticity-led evolution specifically and adaptive evolution generally.
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