Scientific Reports (May 2022)

Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity

  • Phrutsamon Wongnak,
  • Séverine Bord,
  • Maude Jacquot,
  • Albert Agoulon,
  • Frédéric Beugnet,
  • Laure Bournez,
  • Nicolas Cèbe,
  • Adélie Chevalier,
  • Jean-François Cosson,
  • Naïma Dambrine,
  • Thierry Hoch,
  • Frédéric Huard,
  • Nathalie Korboulewsky,
  • Isabelle Lebert,
  • Aurélien Madouasse,
  • Anders Mårell,
  • Sara Moutailler,
  • Olivier Plantard,
  • Thomas Pollet,
  • Valérie Poux,
  • Magalie René-Martellet,
  • Muriel Vayssier-Taussat,
  • Hélène Verheyden,
  • Gwenaël Vourc’h,
  • Karine Chalvet-Monfray

DOI
https://doi.org/10.1038/s41598-022-11479-z
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 16

Abstract

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Abstract Ixodes ricinus ticks (Acari: Ixodidae) are the most important vector for Lyme borreliosis in Europe. As climate change might affect their distributions and activities, this study aimed to determine the effects of environmental factors, i.e., meteorological, bioclimatic, and habitat characteristics on host-seeking (questing) activity of I. ricinus nymphs, an important stage in disease transmissions, across diverse climatic types in France over 8 years. Questing activity was observed using a repeated removal sampling with a cloth-dragging technique in 11 sampling sites from 7 tick observatories from 2014 to 2021 at approximately 1-month intervals, involving 631 sampling campaigns. Three phenological patterns were observed, potentially following a climatic gradient. The mixed-effects negative binomial regression revealed that observed nymph counts were driven by different interval-average meteorological variables, including 1-month moving average temperature, previous 3-to-6-month moving average temperature, and 6-month moving average minimum relative humidity. The interaction effects indicated that the phenology in colder climates peaked differently from that of warmer climates. Also, land cover characteristics that support the highest baseline abundance were moderate forest fragmentation with transition borders with agricultural areas. Finally, our model could potentially be used to predict seasonal human-tick exposure risks in France that could contribute to mitigating Lyme borreliosis risk.