Effects of climate change on ticks and tick-borne diseases in europe

Abstract

Zoonotic tick-borne diseases are an increasing health burden in Europe and there is speculation that this is partly due to climate change affecting vector biology and disease transmission. Data on the vector tick Ixodes ricinus suggest that an extension of its northern and altitude range has been accompanied by an increased prevalence of tick-borne encephalitis. Climate change may also be partly responsible for the change in distribution of Dermacentor reticulatus. Increased winter activity of I. ricinus is probably due to warmer winters and a retrospective study suggests that hotter summers will change the dynamics and pattern of seasonal activity, resulting in the bulk of the tick population becoming active in the latter part of the year. Climate suitability models predict that eight important tick species are likely to establish more northern permanent populations in a climate-warming scenario. However, the complex ecology and epidemiology of such tick-borne diseases as Lyme borreliosis and tick-borne encephalitis make it difficult to implicate climate change as the main cause of their increasing prevalence. Climate change models are required that take account of the dynamic biological processes involved in vector abundance and pathogen transmission in order to predict future tick-borne disease scenarios.

Figure 1

Changes in tick distribution in northern and central Sweden. White dots illustrate districts in Sweden where ticks were reported to be present before 1980 (a) and in 1994-1995 (b). The study region is within the black line (Lindgren et al. 2000, [12] with permission from Environmental Health Perspectives).

Figure 2

Dorsally marked adult female Ixodes ricinus questing on a wooden rod placed in a field plot for observation of I. ricinus questing activity in a Germany (Berlin) forest (Dautel et al. 2008 [37], with permission from International Journal of Medical Microbiology).

Figure 3

The vegetation-derived clusters as recognized over the western Palaearctic. The image was obtained from a yearly series of monthly satellite images, capturing the Normalized Difference Vegetation Index (NDVI): a measure of the photosynthetic activity of the vegetation. These images were subjected to a cluster analysis according to the monthly NDVI features to obtain 10 categories (category 1 is water and not displayed in the picture). (Estrada-Peña et al. 2006 [48], with permission from Medical and Veterinary Entomology).

Figure 4

An analysis of the long-term changes in climate suitability for the tick Ixodes ricinus in Europe (1900–1999). A temporally extensively gridded dataset was subjected to a temporal analysis to understand how climate has changed in 100 years and how this trend affected the climate suitability for the tick. Areas are divided into suitable and unsuitable (the last, without colors in the figure). In the panel “A,” the area marked as suitable and stable means no changes in suitability for the tick. Deterministic increase or decrease means a continued trend towards increasing or decreasing climate suitability. Panel B shows the areas where random walk trend has been observed. These areas are subjected to periodic cycles of climate, thus allowing cycles of increasing or decreasing climate suitability for the tick. (Estrada-Peña and Venzal 2006 [50], with permission from Ecohealth).

Figure 5

Predicted geographic impact (habitat suitability turnover) of different climate change scenarios. The maps shows the forecasted changes in habitat suitability for different tick species, with changes in temperature (left column) or rainfall (right column) analyzed by consensus analysis (a statistical method of classification using multiple input variables) to show the most coherent response to a range of changes in predictor variables. Dark shades of grey indicate increased climate suitability following a decrease in the predictor variable scenario (temperature or rainfall). Light shades of grey indicate increased climate suitability following an increase in the predictor variable. A and B: B. annulatus; C and D: D. marginatus; E and F: H. excavatum; G and H: H. marginatum; I and J: R. bursa; K and L: R. turanicus. (Estrada-Peña and Venzal 2007 [52], with permission from Journal of Medical Entomology).