Estimating Ixodes ricinus densities on the landscape scale

Abstract

BackgroundThe study describes the estimation of the spatial distribution of questing nymphal tick densities by investigating Ixodes ricinus in Southwest Germany as an example. The production of high-resolution maps of questing tick densities is an important key to quantify the risk of tick-borne diseases. Previous I. ricinus maps were based on quantitative as well as semi-quantitative categorisations of the tick density observed at study sites with different vegetation types or indices, all compiled on local scales. Here, a quantitative approach on the landscape scale is introduced.MethodsDuring 2 years, 2013 and 2014, host-seeking ticks were collected each month at 25 sampling sites by flagging an area of 100 square meters. All tick stages were identified to species level to select nymphal ticks of I. ricinus, which were used to develop and calibrate Poisson regression models. The environmental variables height above sea level, temperature, relative humidity, saturation deficit and land cover classification were used as explanatory variables.ResultsThe number of flagged nymphal tick densities range from zero (mountain site) to more than 1,000 nymphs/100 m2. Calibrating the Poisson regression models with these nymphal densities results in an explained variance of 72 % and a prediction error of 110 nymphs/100 m2 in 2013. Generally, nymphal densities (maximum 374 nymphs/100 m2), explained variance (46 %) and prediction error (61 nymphs/100 m2) were lower in 2014. The models were used to compile high-resolution maps with 0.5 km2 grid size for the study region of the German federal state Baden-Württemberg. The accuracy of the mapped tick densities was investigated by leave-one-out cross-validation resulting in root-mean-square-errors of 227 nymphs/100 m2 for 2013 and 104 nymphs/100 m2 for 2014.ConclusionsThe methodology introduced here may be applied to further tick species or extended to other study regions. Finally, the study is a first step towards the spatial estimation of tick-borne diseases in Central Europe.Electronic supplementary materialThe online version of this article (doi:10.1186/s12942-015-0015-7) contains supplementary material, which is available to authorized users.

Prevalence of type I sensitization to alpha‐gal in forest service employees and hunters

“…In addition, a density survey conducted in the observation area for Ixodes ricinus reported 53 nymphs/100 m² in 2013 and 25 nymphs/100 m² in 2014 …”

“…In autumn 2013, a year with a high tick population, such local allergic reactions were regularly observed and documented during examinations of sensitized participants by allergologists . In the following year, which had a low tick population, this phenomenon was rare and only reported for a single tick bite. Therefore, we assume that in Europe this phenomenon only occurs in years with high tick populations and more tick bites occurring due to favorable seasonal conditions.…”

“…The tick species Dermacentor reticulatus and Ixodes ricinus are found in this area and in the surrounding forests. In this region, tick populations are monitored by a tick control program …”

Research paper on abiotic factors and their influence on Ixodes ricinus activity—observations over a two-year period at several tick collection sites in Germany

“…In recent studies, nymph density was investigated as a potential indicator for the risk of tick-borne diseases and for producing high-resolution risk maps for Baden-Wuerttemberg (Boehnke et al 2015) and Germany (Brugger et al 2016). These investigations emphasise that nymphs may be the most important tick stage for the transmission of pathogens.…”

Spatiotemporal cluster and incidence analysis of cattle mortality caused by bovine babesiosis in Styria, Austria, between 1998 and 2016

“…Furthermore, there are limited data on the density of Ixodes ricinus, the vector of Babesia spp. Based on recent studies in Germany, forest or forest-like habitats offer better survival conditions for ticks than meadows or clear areas, where lower population densities are observed (Böhnke et al 2015). In addition, there were also lower densities reported in higher altitudes with coniferous woods or an alpine climate.…”