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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^2, 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^2. The second model explains 96% of the tick variance. Again, the accuracy is expressed by the RMSE, which is 5 ticks per 100 m^2. 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.
Highlights
It is undeniable that ticks and their ability to transmit medically relevant pathogens play an important role for public health
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
To contribute to an adequate tick-borne encephalitis (TBE) and Lyme borreliosis (LB) risk assessment, which should incorporate the phenology of the vectors involved (Norman et al 2016), an enhanced method to determine variables explaining the seasonal cycles of ticks is introduced
Summary
It is undeniable that ticks and their ability to transmit medically relevant pathogens play an important role for public health. Brugger et al (2016) compiled a dataset of 69 German sites from which monthly I. ricinus time series were collected. Most of these time series are only 1–2 years long and so unsuitable to depict inter-annual tick fluctuations. The longest time series in Haselmuhl (Germany) lasting eight consecutive years without data gaps, was used in this study. As all the other tick time series compiled by Brugger et al (2016), the number of ticks (abundance) was related to the same flagging area of 100 m2. Some time-series last over more than two centuries, e.g. in Germany the stations Berlin or Hohenpeißenberg
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