ArticleExperimental & applied acarology2017
Seasonal cycles of the TBE and Lyme borreliosis vector Ixodes ricinus modelled with time-lagged and interval-averaged predictors.
Article in Experimental & applied acarology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Exploring prediction of tick and tick-borne encephalitis cases in Sweden using citizen science data.iScience · 2026Article
- Carryover Effects of Thermal Conditions on Tick Survival, Behavior, and Simulated Detectability.Ecology and evolution · 2025Article
- Predictive temperature-dependency of tick questing activity in France and relevance to Lyme disease risk in Connecticut.Experimental & applied acarology · 2025Article
- The Role of Environmental Factors in Lyme Disease Transmission in the European Union: A Systematic Review.Tropical medicine and infectious disease · 2024Review
- Article
- Tick paralysis induced byFrontiers in veterinary science · 2024Article
- Correlation between airborne pollen data and the risk of tick-borne encephalitis in northern Italy.Scientific reports · 2023Article
- Revealing the Tick Microbiome: Insights into Midgut and Salivary Gland Microbiota of FemaleInternational journal of molecular sciences · 2023Article
- The Emergence and Dynamics of Tick-Borne Encephalitis Virus in a New Endemic Region in Southern Germany.Microorganisms · 2022Article
- Predicting habitat suitability for Ixodes ricinus and Ixodes persulcatus ticks in Finland.Parasites & vectors · 2022Article
- Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity.Scientific reports · 2022Article
- Model-based extrapolation of ecological systems under future climate scenarios: The example of Ixodes ricinus ticks.PloS one · 2022Article
- Beech tree masting explains the inter-annual variation in the fall and spring peaks of Ixodes ricinus ticks with different time lags.Parasites & vectors · 2021Article
- Article
- Long-term monitoring of the seasonal density of questing ixodid ticks in Vienna (Austria): setup and first results.Experimental & applied acarology · 2020Article
- Tick-borne encephalitis (TBE) cases are not random: explaining trend, low- and high-frequency oscillations based on the Austrian TBE time series.BMC infectious diseases · 2020Article
- Forecasting next season's Ixodes ricinus nymphal density: the example of southern Germany 2018.Experimental & applied acarology · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ticks of the species Ixodes ricinus (L.) are the major vectors for tick-borne diseases in Europe. The aim of this study was to quantify the influence of environmental variables on the seasonal cycle of questing I. ricinus. Therefore, an 8-year time series of nymphal I. ricinus flagged at monthly intervals in Haselmühl (Germany) was compiled. For the first time, cross correlation maps were applied to identify optimal associations between observed nymphal I. ricinus densities and time-lagged as well as temporal averaged explanatory variables. To prove the explanatory power of these associations, two Poisson regression models were generated. The first model simulates the ticks of the entire time series flagged per 100 m[Formula: see text], the second model the mean seasonal cycle. Explanatory variables comprise the temperature of the flagging month, the relative humidity averaged from the flagging month and 1 month prior to flagging, the temperature averaged over 4-6 months prior to the flagging event and the hunting statistics of the European hare from the preceding year. The first model explains 65% of the monthly tick variance and results in a root mean square error (RMSE) of 17 ticks per 100 m[Formula: see text]. The second model explains 96% of the tick variance. Again, the accuracy is expressed by the RMSE, which is 5 ticks per 100 m[Formula: see text]. As a major result, this study demonstrates that tick densities are higher correlated with time-lagged and temporal averaged variables than with contemporaneous explanatory variables, resulting in a better model performance.
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