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预测芬兰璃眼蜱和全沟硬蜱的栖息地适宜性。

Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland.

机构信息

Department of Geosciences and Geography, University of Helsinki, P.O. Box 64, 00014, Helsinki, Finland.

Department of Virology, University of Helsinki, P.O. Box 21, 00014, Helsinki, Finland.

出版信息

Parasit Vectors. 2022 Aug 30;15(1):310. doi: 10.1186/s13071-022-05410-8.

Abstract

BACKGROUND

Ticks are responsible for transmitting several notable pathogens worldwide. Finland lies in a zone where two human-biting tick species co-occur: Ixodes ricinus and Ixodes persulcatus. Tick densities have increased in boreal regions worldwide during past decades, and tick-borne pathogens have been identified as one of the major threats to public health in the face of climate change.

METHODS

We used species distribution modelling techniques to predict the distributions of I. ricinus and I. persulcatus, using aggregated historical data from 2014 to 2020 and new tick occurrence data from 2021. By aiming to fill the gaps in tick occurrence data, we created a new sampling strategy across Finland. We also screened for tick-borne encephalitis virus (TBEV) and Borrelia from the newly collected ticks. Climate, land use and vegetation data, and population densities of the tick hosts were used in various combinations on four data sets to estimate tick species' distributions across mainland Finland with a 1-km resolution.

RESULTS

In the 2021 survey, 89 new locations were sampled of which 25 new presences and 63 absences were found for I. ricinus and one new presence and 88 absences for I. persulcatus. A total of 502 ticks were collected and analysed; no ticks were positive for TBEV, while 56 (47%) of the 120 pools, including adult, nymph, and larva pools, were positive for Borrelia (minimum infection rate 11.2%, respectively). Our prediction results demonstrate that two combined predictor data sets based on ensemble mean models yielded the highest predictive accuracy for both I. ricinus (AUC = 0.91, 0.94) and I. persulcatus (AUC = 0.93, 0.96). The suitable habitats for I. ricinus were determined by higher relative humidity, air temperature, precipitation sum, and middle-infrared reflectance levels and higher densities of white-tailed deer, European hare, and red fox. For I. persulcatus, locations with greater precipitation and air temperature and higher white-tailed deer, roe deer, and mountain hare densities were associated with higher occurrence probabilities. Suitable habitats for I. ricinus ranged from southern Finland up to Central Ostrobothnia and North Karelia, excluding areas in Ostrobothnia and Pirkanmaa. For I. persulcatus, suitable areas were located along the western coast from Ostrobothnia to southern Lapland, in North Karelia, North Savo, Kainuu, and areas in Pirkanmaa and Päijät-Häme.

CONCLUSIONS

This is the first study conducted in Finland that estimates potential tick species distributions using environmental and host data. Our results can be utilized in vector control strategies, as supporting material in recommendations issued by public health authorities, and as predictor data for modelling the risk for tick-borne diseases.

摘要

背景

蜱虫是全球多种重要病原体的传播媒介。芬兰位于两种人咬蜱虫共存的地区:蓖子硬蜱和太平洋革蜱。在过去几十年中,全球北方地区的蜱虫密度有所增加,蜱传病原体已被确定为气候变化下公共卫生的主要威胁之一。

方法

我们使用物种分布模型技术,结合 2014 年至 2020 年的聚合历史数据和 2021 年的新蜱虫发生数据,预测了蓖子硬蜱和太平洋革蜱的分布。为了填补蜱虫发生数据的空白,我们在芬兰各地创建了一个新的采样策略。我们还从新收集的蜱虫中筛选了 tick-borne encephalitis virus (TBEV) 和 Borrelia。气候、土地利用和植被数据以及蜱虫宿主的种群密度在四个数据集上以不同组合使用,以 1 公里分辨率估计芬兰大陆主要蜱种的分布。

结果

在 2021 年的调查中,我们在 89 个新地点进行了采样,其中发现了 25 个蓖子硬蜱的新出现地点和 63 个缺失地点,以及一个太平洋革蜱的新出现地点和 88 个缺失地点。共采集和分析了 502 只蜱虫;没有蜱虫检测到 tick-borne encephalitis virus (TBEV),但 120 个池中包括成虫、若虫和幼虫池的 56 个(47%)检测到 Borrelia(最小感染率分别为 11.2%)。我们的预测结果表明,基于集成均值模型的两个组合预测因子数据集对蓖子硬蜱(AUC=0.91,0.94)和太平洋革蜱(AUC=0.93,0.96)的预测准确性最高。蓖子硬蜱的适宜栖息地由较高的相对湿度、空气温度、降水总量和中红外反射率水平以及白尾鹿、欧洲野兔和赤狐的较高密度决定。对于太平洋革蜱,降水和空气温度较高以及白尾鹿、梅花鹿和山兔密度较高的地区与较高的出现概率相关。蓖子硬蜱的适宜栖息地范围从芬兰南部延伸到中奥斯特博尼亚和北卡累利阿,不包括奥斯特博尼亚和皮尔坎马地区。对于太平洋革蜱,适宜的地区位于奥斯特博尼亚西部沿海地区到拉普兰南部,在北卡累利阿、北萨沃、卡累利阿、凯努和皮尔坎马和皮海梅地区。

结论

这是在芬兰进行的首次使用环境和宿主数据估计潜在蜱种分布的研究。我们的研究结果可用于蚊虫控制策略,作为公共卫生当局发布的建议的辅助材料,并作为预测 tick-borne diseases 风险的预测数据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3332/9429443/3ea48d072ae6/13071_2022_5410_Fig1_HTML.jpg

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