Boulanger Nathalie, Aran Delphine, Maul Armand, Camara Baba Issa, Barthel Cathy, Zaffino Marie, Lett Marie-Claire, Schnitzler Annick, Bauda Pascale
Université de Strasbourg UR3073: PHAVI: Groupe Borrelia, 67000, Strasbourg, France.
Centre National de Référence Borrelia, Centre Hospitalier Régional Universitaire, Strasbourg, France.
Sci Rep. 2024 Apr 24;14(1):9391. doi: 10.1038/s41598-024-59867-x.
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.
在欧洲,蜱传人畜共患病的主要传播媒介是蓖麻硬蜱,它有三个生命阶段。在其发育周期中,蜱从各种各样的脊椎动物宿主身上分三次吸食血液,在此期间它们能够获取并传播人类病原体,如莱姆病螺旋体狭义种,即莱姆病的病原体。在法国东北部进行的这项研究中,我们研究了土壤类型、土地利用、林分类型和时间动态对蜱及其相关病原体数量的影响。对结果进行的负二项回归建模表明,石灰岩基土壤比砂岩基土壤更有利于蜱生存。在森林中观察到蜱的数量最多,尤其是在针叶林和混交林中。我们确定了森林和湿地栖息地时间动态的影响:与稳定森林和 former wetlands 相比,新形成的森林和当前的湿地分别支持更多的蜱。我们观察到蜱的数量与鹿科、兔科和鸟类的数量密切相关。导致莱姆病、无形体病和硬蜱复发性发热的蜱传病原体表现出特定的栖息地偏好,并与特定的动物科有关联。机器学习算法将与土壤相关的变量确定为蜱和病原体数量的最佳预测指标。