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Pollinator loss causes rapid adaptive evolution of selfing and dramatically reduces genome-wide genetic variability

Cite this dataset

Busch, Jeremiah; Bodbyl-Roels, Sarah; Tusuubira, Sharif; Kelly, John (2022). Pollinator loss causes rapid adaptive evolution of selfing and dramatically reduces genome-wide genetic variability [Dataset]. Dryad.


While selfing populations harbor little genetic variation limiting evolutionary potential, the causes are unclear. We experimentally evolved large, replicate populations of Mimulus guttatus for nine generations in greenhouses with or without pollinating bees and studied DNA polymorphism in descendants. Populations without bees adapted to produce more selfed seed yet exhibited striking reductions in DNA polymorphism despite large population sizes. Importantly, the genome-wide pattern of variation cannot be explained by a simple reduction in effective population size, but instead reflects the complicated interaction between selection, linkage, and inbreeding. Simulations demonstrate that the spread of favored alleles at few loci depresses neutral variation genome-wide in large populations containing fully selfing lineages. It also generates greater heterogeneity among chromosomes than expected with neutral evolution in small populations. Genome-wide deviations from neutrality were documented in populations with bees, suggesting widespread influences of background selection. After applying outlier tests to detect loci under selection, two genome regions were found in populations with bees, yet no adaptive loci were otherwise mapped. Large amounts of stochastic change in selfing populations compromise evolutionary potential and undermine outlier tests for selection. This occurs because genetic draft in highly selfing populations makes even the largest changes in allele frequency unremarkable.


Replicate plant populations were experimentally evolved for nine generations in a greenhouse with or without pollinating bees. Descendants from these populations had their DNA extracted in pools, which were then sequenced. We examined the whole genome response of populations and interpreted these patterns in light of simple, chromosome-level simulations with and without natural selection. We also identified targets of natural selection using SNP outlier methods.

Usage notes

The programs we share here require a working knowledge of Python3.


National Science Foundation, Award: DEB-1911313

National Science Foundation, Award: MCB-1940785