Higher immunocompetence is associated with higher genetic diversity in feral honey bee colonies (Apis mellifera)
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Honey bees are the most important managed pollinators as they provide key ecosystem services for crop production worldwide. Recent losses of honey bee colonies in North America and Europe have demonstrated a need to develop strategies to improve their health and conserve their populations. Previously, we showed that feral honey bees—colonies that live in the wild without human assistance—exhibit higher levels of immunocompetence than managed colonies in North Carolina (USA). In a first attempt to investigate the underlying mechanisms of this difference in immune response, here we characterize the genetic composition of feral and managed honey bees using microsatellite markers. Our results reveal significant but small genetic differentiation between feral and managed honey bee colonies (ϕCT = 0.047, P = 0.03) indicating admixture between these two groups. Higher genetic diversity was correlated with higher immune response in feral (P MANOVA = 0.011) but not managed bees, despite the fact that the latter group showed significantly higher average genetic diversity (P ANCOVA < 0.001). These findings suggest that genetic diversity is positively associated with immunocompetence in feral honey bee colonies, and that the benefits of genetic diversity are obscured in managed bees, perhaps as a result of artificial selection. We hypothesize that high genetic variability provides the raw material upon which natural selection acts and generates adaptive genotypes in unmanaged populations. Feral populations could be useful sources of genetic variation to use in breeding programs that aim to improve honey bee health.
KeywordsMicrosatellites Antimicrobial peptides Defensin Hymenoptaecin Management
We would like to thank Mark Jandricic for assistance with microsatellite genotyping, Michael Simone-Finstrom, editor Antonella Soro and two anonymous reviewers for valuable comments on the manuscript, and Zhian Kamvar for support with the R code for population genetic analyses.
This study was funded by the CALS Dean’s Enrichment Grant from North Carolina State University (to DRR, SDF, and RRD) and a National Science Foundation (NSF) Postdoctoral Fellowship (1523817 to MMLU).
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