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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

Ixodes ricinus nymphal densities for different land cover classes. Mean nymphal ticks per 100 m2 collected at sites classified as coniferous forest (C), agricultural area (A), mixed forest (M) and broad-leafed forest (B). While in 2013 nymphal densities of all land cover classes are significant different from those of the default class A (left), in 2014 only nymphal densities of C differs significantly from those of A (right). Lower nymphal densities in 2014 compared to 2013 are mainly related to lower densities in mixed (M) and broad-leafed forests (B).
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Fig7: Ixodes ricinus nymphal densities for different land cover classes. Mean nymphal ticks per 100 m2 collected at sites classified as coniferous forest (C), agricultural area (A), mixed forest (M) and broad-leafed forest (B). While in 2013 nymphal densities of all land cover classes are significant different from those of the default class A (left), in 2014 only nymphal densities of C differs significantly from those of A (right). Lower nymphal densities in 2014 compared to 2013 are mainly related to lower densities in mixed (M) and broad-leafed forests (B).

Mentions: By the choice of the generalized linear model (GLM) it was possible to include the categorical variable land cover. As a result, the performance of the statistical model increased and the tick maps could be calculated with the very high spatial resolution of 0.5 km2. The mean nymphal tick densities of the four land cover (LC) classes are depicted in Fig. 7. For the climatological unremarkable year 2013, a doubling rule may be derived from it. By the transition from one LC class to another, in the order of the classes C-A-M-B, the nymphal density of I. ricinus doubles. As expected, the statistically estimated density of I. ricinus is minimal in coniferous forests and maximal in broad-leafed forests. For the extraordinary hot year 2014, however, the nymphal densities of the LC classes M and B were not significantly different from class A. Only the nymphal densities in LC class C were significantly lower than in class A as depicted by the p-values of the regression coefficients in Table 2. In succession this might be responsible for the lower explained variance of the Poison regression model applied to the data of 2014. In this context it should be noted that A is the default LC in the Poisson model (as described above). Further, the definition of class A is notable, which contains arable land, permanent crops, pastures and heterogeneous agricultural areas. However, for a final statement concerning nymphal densities and LC longer time series of observations are needed. Although it is well known that I. ricinus is present even in city parks [36], no tick densities were estimated for urban areas. Tick habitats in urban areas were treated as sub-scale and not resolved by the maps presented here.Fig. 7


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)

Ixodes ricinus nymphal densities for different land cover classes. Mean nymphal ticks per 100 m2 collected at sites classified as coniferous forest (C), agricultural area (A), mixed forest (M) and broad-leafed forest (B). While in 2013 nymphal densities of all land cover classes are significant different from those of the default class A (left), in 2014 only nymphal densities of C differs significantly from those of A (right). Lower nymphal densities in 2014 compared to 2013 are mainly related to lower densities in mixed (M) and broad-leafed forests (B).
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig7: Ixodes ricinus nymphal densities for different land cover classes. Mean nymphal ticks per 100 m2 collected at sites classified as coniferous forest (C), agricultural area (A), mixed forest (M) and broad-leafed forest (B). While in 2013 nymphal densities of all land cover classes are significant different from those of the default class A (left), in 2014 only nymphal densities of C differs significantly from those of A (right). Lower nymphal densities in 2014 compared to 2013 are mainly related to lower densities in mixed (M) and broad-leafed forests (B).
Mentions: By the choice of the generalized linear model (GLM) it was possible to include the categorical variable land cover. As a result, the performance of the statistical model increased and the tick maps could be calculated with the very high spatial resolution of 0.5 km2. The mean nymphal tick densities of the four land cover (LC) classes are depicted in Fig. 7. For the climatological unremarkable year 2013, a doubling rule may be derived from it. By the transition from one LC class to another, in the order of the classes C-A-M-B, the nymphal density of I. ricinus doubles. As expected, the statistically estimated density of I. ricinus is minimal in coniferous forests and maximal in broad-leafed forests. For the extraordinary hot year 2014, however, the nymphal densities of the LC classes M and B were not significantly different from class A. Only the nymphal densities in LC class C were significantly lower than in class A as depicted by the p-values of the regression coefficients in Table 2. In succession this might be responsible for the lower explained variance of the Poison regression model applied to the data of 2014. In this context it should be noted that A is the default LC in the Poisson model (as described above). Further, the definition of class A is notable, which contains arable land, permanent crops, pastures and heterogeneous agricultural areas. However, for a final statement concerning nymphal densities and LC longer time series of observations are needed. Although it is well known that I. ricinus is present even in city parks [36], no tick densities were estimated for urban areas. Tick habitats in urban areas were treated as sub-scale and not resolved by the maps presented here.Fig. 7

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