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Gewählte Publikation:

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Publikationstyp: Zeitschriftenaufsatz
Dokumentart: Originalarbeit

Publikationsjahr: 2017

AutorInnen: Brugger, K; Walter, M; Chitimia-Dobler, L; Dobler, G; Rubel, F

Titel: Seasonal cycles of the TBE and Lyme borreliosis vector Ixodes ricinus modelled with time-lagged and interval-averaged predictors.

Quelle: Exp Appl Acarol. 2017; 73(3-4):439-450



Autor/innen der Vetmeduni Vienna:

Brugger Katharina
Rubel Franz
Walter Melanie

Beteiligte Vetmed-Organisationseinheiten
Abteilung für Öffentliches Veterinärwesen und Epidemiologie


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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