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Do biological control agents adapt to local pest genotypes? A multiyear test across geographic scales.

Amanda Kyle GibsonFabiane M MundimAbbey L RamirezPatricia Timper
Published in: Evolutionary applications (2024)
Parasite local adaptation has been a major focus of (co)evolutionary research on host-parasite interactions. Studies of wild host-parasite systems frequently find that parasites paired with local, sympatric host genotypes perform better than parasites paired with allopatric host genotypes. In contrast, there are few such tests in biological control systems to establish whether biological control parasites commonly perform better on sympatric pest genotypes. This knowledge gap prevents the optimal design of biological control programs: strong local adaptation could argue for the use of sympatric parasites to achieve consistent pest control. To address this gap, we tested for local adaptation of the biological control bacterium Pasteuria penetrans to the root-knot nematode Meloidogyne arenaria , a global threat to a wide range of crops. We measured the probability and intensity of P. penetrans infection on sympatric and allopatric M. arenaria over the course of 4 years. Our design accounted for variation in adaptation across scales by conducting tests within and across fields, and we isolated the signature of parasite adaptation by comparing parasites collected over the course of the growing season. Our results are largely inconsistent with local adaptation of P. penetrans to M. arenaria : in 3 of 4 years, parasites performed similarly well in sympatric and allopatric combinations. In 1 year, however, infection probability was 28% higher for parasites paired with hosts from their sympatric plot, relative to parasites paired with hosts from other plots within the same field. These mixed results argue for population genetic data to characterize the scale of gene flow and genetic divergence in this system. Overall, our findings do not provide strong support for using P. penetrans from local fields to enhance biological control of Meloidogyne .
Keyphrases
  • plasmodium falciparum
  • magnetic resonance
  • gene expression
  • machine learning
  • magnetic resonance imaging
  • transcription factor
  • high intensity