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Evidence for rapid evolution in a grassland biodiversity experiment.

Sofia A W MoorselMarc W SchmidNiels C A M WagemakerThomas van GurpBernhard SchmidPhilippine Vergeer
Published in: Molecular ecology (2019)
In long-term grassland experiments, positive biodiversity effects on plant productivity commonly increase with time. Subsequent glasshouse experiments showed that these strengthened positive biodiversity effects persist not only in the local environment but also when plants are transferred into a common environment. Thus, we hypothesized that community diversity had acted as a selective agent, resulting in the emergence of plant monoculture and mixture types with differing genetic composition. To test our hypothesis, we grew offspring from plants that were grown for eleven years in monoculture or mixture environments in a biodiversity experiment (Jena Experiment) under controlled glasshouse conditions in monocultures or two-species mixtures. We used epiGBS, a genotyping-by-sequencing approach combined with bisulphite conversion, to provide integrative genetic and epigenetic (i.e., DNA methylation) data. We observed significant divergence in genetic and DNA methylation data according to selection history in three out of five perennial grassland species, namely Galium mollugo, Prunella vulgaris and Veronica chamaedrys, with DNA methylation differences mostly reflecting the genetic differences. In addition, current diversity levels in the glasshouse had weak effects on epigenetic variation. However, given the limited genome coverage of the reference-free bisulphite method epiGBS, it remains unclear how much of the differences in DNA methylation was independent of underlying genetic differences. Our results thus suggest that selection of genetic variants, and possibly epigenetic variants, caused the rapid emergence of monoculture and mixture types within plant species in the Jena Experiment.
Keyphrases
  • dna methylation
  • genome wide
  • copy number
  • gene expression
  • healthcare
  • climate change
  • big data
  • machine learning
  • metabolic syndrome
  • electronic health record
  • type diabetes
  • genetic diversity
  • data analysis