Multiple factors affecting <i>Ixodes ricinus</i> ticks and associated pathogens in European temperate ecosystems (northeastern France)

TitleMultiple factors affecting Ixodes ricinus ticks and associated pathogens in European temperate ecosystems (northeastern France)
Publication TypeJournal Article
Year of Publication2024
AuthorsBoulanger N., Aran D., Maul A., Camara B.I, Barthel C., Zaffino M., Lett M.C, Schnitzler A., Bauda P.
JournalScientific Reports
Volume14
Pagination17
Date PublishedApr
Type of ArticleArticle
ISBN Number2045-2322
Accession NumberWOS:001207900600082
KeywordsABUNDANCE, borrelia-burgdorferi, burgdorferi sensu-lato, disease, ecology, forest, infection, lyme borreliosis, prevalence, scapularis, Science & Technology - Other Topics
Abstract

In Europe, the main vector of tick-borne zoonoses is Ixodes ricinus, which has three life stages. During their development cycle, ticks take three separate blood meals from a wide variety of vertebrate hosts, during which they can acquire and transmit human pathogens such as Borrelia burgdorferi sensu lato, the causative agent of Lyme borreliosis. In this study conducted in Northeastern France, we studied the importance of soil type, land use, forest stand type, and temporal dynamics on the abundance of ticks and their associated pathogens. Negative binomial regression modeling of the results indicated that limestone-based soils were more favorable to ticks than sandstone-based soils. The highest tick abundance was observed in forests, particularly among coniferous and mixed stands. We identified an effect of habitat time dynamics in forests and in wetlands: recent forests and current wetlands supported more ticks than stable forests and former wetlands, respectively. We observed a close association between tick abundance and the abundance of Cervidae, Leporidae, and birds. The tick-borne pathogens responsible for Lyme borreliosis, anaplasmosis, and hard tick relapsing fever showed specific habitat preferences and associations with specific animal families. Machine learning algorithms identified soil related variables as the best predictors of tick and pathogen abundance.

Short TitleSci RepSci Rep
Alternate JournalSci Rep
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Times Cited: 0
Cited Reference Count: 69
Boulanger, Nathalie Aran, Delphine Maul, Armand Camara, Baba Issa Barthel, Cathy Zaffino, Marie Lett, Marie-Claire Schnitzler, Annick Bauda, Pascale
Camara, Baba Issa/0000-0003-0921-7938
DRIIHM; French programme "Investissements d'Avenir" [ANR-11-LABX-0010]
This work was co-funded by the DRIIHM and the French programme "Investissements d'Avenir" (ANR-11-LABX-0010) which is managed by the ANR. The authors thank Brieuc Conan-Guez for his help in developing the graphical representations, Xavier Rochel for his expertise in local historic geography, and Sandrine Signoret for her help with SIG tools. Sampling campaigns were performed with the help of students Elodie Lindauer, Charlotte Boumans, Quentin Araujo da Costa, Christelle Stegmann, and Marie-Oceane Fayet.

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Boulanger, N (corresponding author), Univ Strasbourg, PHAVI Grp Borrelia, UR3073, F-67000 Strasbourg, France.; Boulanger, N (corresponding author), Ctr Hosp Reg Univ, Ctr Natl Reference Borrelia, Strasbourg, France.; Bauda, P (corresponding author), Univ Lorraine, CNRS, LIEC, F-57000 Metz, France.
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