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Estimating Ixodes ricinus densities on the landscape scale.

Boehnke D, Brugger K, Pfäffle M, Sebastian P, Norra S, Petney T, Oehme R, Littwin N, Lebl K, Raith J, Walter M, Gebhardt R, Rubel F - Int J Health Geogr (2015)

Bottom Line: Calibrating the Poisson regression models with these nymphal densities results in an explained variance of 72 % and a prediction error of 110 nymphs/100 m(2) in 2013.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 m(2) for 2013 and 104 nymphs/100 m(2) for 2014.The methodology introduced here may be applied to further tick species or extended to other study regions.

View Article: PubMed Central - PubMed

Affiliation: Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Reinhard-Baumeister-Platz 1, 76131, Karlsruhe, Germany. denise.boehnke@kit.edu.

ABSTRACT

Background: The 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.

Methods: During 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.

Results: The number of flagged nymphal tick densities range from zero (mountain site) to more than 1,000 nymphs/100 m(2). Calibrating the Poisson regression models with these nymphal densities results in an explained variance of 72 % and a prediction error of 110 nymphs/100 m(2) in 2013. Generally, nymphal densities (maximum 374 nymphs/100 m(2)), explained variance (46 %) and prediction error (61 nymphs/100 m(2)) were lower in 2014. The models were used to compile high-resolution maps with 0.5 km(2) 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 m(2) for 2013 and 104 nymphs/100 m(2) for 2014.

Conclusions: The 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.

No MeSH data available.


Related in: MedlinePlus

Environmental variables affecting tick densities at different spatial scales. Scales are characterized by the model domain in km2, adapted from [13].
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Fig1: Environmental variables affecting tick densities at different spatial scales. Scales are characterized by the model domain in km2, adapted from [13].

Mentions: Calculations of the spatial distribution of tick densities, however, are rare. The few papers available comprise the mapping of the nymphal density of I. pacificus in California using GIS and satellite derived data [6], the mapping of the density of I. scapularis in the eastern United States using geo-statistical interpolation [7] and a zero-inflated negative binominal model [8]. In Europe, the spatial distribution of tick densities was exclusively estimated on local scales. Studies comprise the mapping of I. ricinus densities at two sites in the Czech Republic [9] and in a German nature reserve [10]. The two latter, however, are simply based on semi-quantitative categories of tick densities classified according to the number of ticks collected at different study sites, which were characterised by typical vegetation cover. No statistical models were developed for these density maps [9, 10]. In a further local scale analysis [11], generalized estimating equations were applied for mapping of I. ricinus densities in an Italian nature reserve (model domain: 5 km2). Although other statistical models for I. ricinus were developed, these were not used to compile density maps [12]. In contrast, the study presented here, is a contribution to the quantitative spatial density estimation of ixodid ticks on the landscape scale (35,750 km2). Here, the landscape scale is defined for model domains of 100–50,000 km2 (Fig. 1), where the environmental variables land-use, topography and climate are affecting the distribution of ticks. Thus, biotic interactions expressed for example by vector-to-host ratios and other local phenomena were not considered.Fig. 1


Estimating Ixodes ricinus densities on the landscape scale.

Boehnke D, Brugger K, Pfäffle M, Sebastian P, Norra S, Petney T, Oehme R, Littwin N, Lebl K, Raith J, Walter M, Gebhardt R, Rubel F - Int J Health Geogr (2015)

Environmental variables affecting tick densities at different spatial scales. Scales are characterized by the model domain in km2, adapted from [13].
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4536605&req=5

Fig1: Environmental variables affecting tick densities at different spatial scales. Scales are characterized by the model domain in km2, adapted from [13].
Mentions: Calculations of the spatial distribution of tick densities, however, are rare. The few papers available comprise the mapping of the nymphal density of I. pacificus in California using GIS and satellite derived data [6], the mapping of the density of I. scapularis in the eastern United States using geo-statistical interpolation [7] and a zero-inflated negative binominal model [8]. In Europe, the spatial distribution of tick densities was exclusively estimated on local scales. Studies comprise the mapping of I. ricinus densities at two sites in the Czech Republic [9] and in a German nature reserve [10]. The two latter, however, are simply based on semi-quantitative categories of tick densities classified according to the number of ticks collected at different study sites, which were characterised by typical vegetation cover. No statistical models were developed for these density maps [9, 10]. In a further local scale analysis [11], generalized estimating equations were applied for mapping of I. ricinus densities in an Italian nature reserve (model domain: 5 km2). Although other statistical models for I. ricinus were developed, these were not used to compile density maps [12]. In contrast, the study presented here, is a contribution to the quantitative spatial density estimation of ixodid ticks on the landscape scale (35,750 km2). Here, the landscape scale is defined for model domains of 100–50,000 km2 (Fig. 1), where the environmental variables land-use, topography and climate are affecting the distribution of ticks. Thus, biotic interactions expressed for example by vector-to-host ratios and other local phenomena were not considered.Fig. 1

Bottom Line: Calibrating the Poisson regression models with these nymphal densities results in an explained variance of 72 % and a prediction error of 110 nymphs/100 m(2) in 2013.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 m(2) for 2013 and 104 nymphs/100 m(2) for 2014.The methodology introduced here may be applied to further tick species or extended to other study regions.

View Article: PubMed Central - PubMed

Affiliation: Institute of Geography and Geoecology, Karlsruhe Institute of Technology, Reinhard-Baumeister-Platz 1, 76131, Karlsruhe, Germany. denise.boehnke@kit.edu.

ABSTRACT

Background: The 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.

Methods: During 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.

Results: The number of flagged nymphal tick densities range from zero (mountain site) to more than 1,000 nymphs/100 m(2). Calibrating the Poisson regression models with these nymphal densities results in an explained variance of 72 % and a prediction error of 110 nymphs/100 m(2) in 2013. Generally, nymphal densities (maximum 374 nymphs/100 m(2)), explained variance (46 %) and prediction error (61 nymphs/100 m(2)) were lower in 2014. The models were used to compile high-resolution maps with 0.5 km(2) 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 m(2) for 2013 and 104 nymphs/100 m(2) for 2014.

Conclusions: The 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.

No MeSH data available.


Related in: MedlinePlus