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Combining phenotypic and genomic approaches reveals no evidence for adaptation to the local mutualist in Medicago lupulina

T. L. Harrison,C. Wood,I. L. Borges,J. Stinchcombe

Published 2016 in bioRxiv

ABSTRACT

Local adaptation is a common but not ubiquitous feature of species interactions, and understanding the circumstances under which it evolves illuminates the factors that influence adaptive population divergence. Antagonistic species interactions dominate the local adaptation literature relative to mutualistic ones, preventing an overall assessment of adaptation within interspecific interactions. Here, we tested whether the legume Medicago lupulina is locally adapted to two species of mutualistic nitrogen-fixing rhizobial bacteria that vary in frequency across its eastern North American range. We reciprocally inoculated northern and southern M. lupulina genotypes with the northern (Ensifer medicae) or southern bacterium (E. meliloti) in a greenhouse experiment, and performed a genome scan for loci that showed elevated differentiation between field-collected plants that hosted different bacteria. Despite producing different numbers of root nodules (the structures in which the plants house the bacteria), neither northern nor southern plants produced more seeds, flowered earlier, or were more likely to flower when inoculated with their local rhizobia. None of the loci identified in our genomic analysis belonged to the well-characterized suite of legume-rhizobia symbiosis genes, suggesting that the rhizobia do not drive genetic divergence between M. lupulina populations. Our results demonstrate that local adaptation has not evolved in this mutualism despite large-scale geographic variation in the identity of the interacting species.

PUBLICATION RECORD

  • Publication year

    2016

  • Venue

    bioRxiv

  • Publication date

    2016-09-30

  • Fields of study

    Biology, Environmental Science

  • Identifiers
    DOI 10.1101/078675
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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