The abundance of Ixodes ricinus ticks depends on tree species composition and shrub cover

The mainstream forestry policy in many European countries is to convert coniferous plantations into (semi-natural) deciduous woodlands. However, woodlands are the main habitat for Ixodes ricinus ticks. Therefore, assessing to what extent tick abundance and infection with Borrelia spirochetes are affected by forest composition and structure is a prerequisite for effective prevention of Lyme borreliosis. We selected a total of 25 pine and oak stands, both with and without an abundant shrub layer, in northern Belgium and estimated tick abundance between April and October 2008-2010. Additionally, the presence of deer beds was used as an indicator of relative deer habitat use. Borrelia infections in questing nymphs were determined by polymerase chain reactions. The abundance of larvae, nymphs, and adults was higher in oak stands compared to pine stands and increased with increasing shrub cover, most likely due to differences in habitat use by the ticks' main hosts. Whereas tick abundance was markedly higher in structure-rich oak stands compared to homogeneous pine stands, the Borrelia infection rates in nymphs did not differ significantly. Our results indicate that conversion towards structure-rich deciduous forests might create more suitable tick habitats, but we were unable to detect an effect on the infection rate

Circulation of four Anaplasma phagocytophilum ecotypes in Europe.

BACKGROUND Anaplasma phagocytophilum is the etiological agent of granulocytic anaplasmosis in humans and animals. Wild animals and ticks play key roles in the enzootic cycles of the pathogen. Potential ecotypes of A. phagocytophilum have been characterized genetically, but their host range, zoonotic potential and transmission dynamics has only incompletely been resolved. METHODS The presence of A. phagocytophilum DNA was determined in more than 6000 ixodid ticks collected from the vegetation and wildlife, in 289 tissue samples from wild and domestic animals, and 69 keds collected from deer, originating from various geographic locations in The Netherlands and Belgium. From the qPCR-positive lysates, a fragment of the groEL-gene was amplified and sequenced. Additional groEL sequences from ticks and animals from Europe were obtained from GenBank, and sequences from human cases were obtained through literature searches. Statistical analyses were performed to identify A. phagocytophilum ecotypes, to assess their host range and their zoonotic potential. The population dynamics of A. phagocytophilum ecotypes was investigated using population genetic analyses. RESULTS DNA of A. phagocytophilum was present in all stages of questing and feeding Ixodes ricinus, feeding I. hexagonus, I. frontalis, I. trianguliceps, and deer keds, but was absent in questing I. arboricola and Dermacentor reticulatus. DNA of A. phagocytophilum was present in feeding ticks and tissues from many vertebrates, including roe deer, mouflon, red foxes, wild boar, sheep and hedgehogs but was rarely found in rodents and birds and was absent in badgers and lizards. Four geographically dispersed A. phagocytophilum ecotypes were identified, that had significantly different host ranges. All sequences from human cases belonged to only one of these ecotypes. Based on population genetic parameters, the potentially zoonotic ecotype showed significant expansion. CONCLUSION Four ecotypes of A. phagocytophilum with differential enzootic cycles were identified. So far, all human cases clustered in only one of these ecotypes. The zoonotic ecotype has the broadest range of wildlife hosts. The expansion of the zoonotic A. phagocytophilum ecotype indicates a recent increase of the acarological risk of exposure of humans and animals

Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning

Background: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. Methods: We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance. Results: The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish differences in abundance between countries but was not able to predict vector abundance at individual farm level. Conclusions: The models and maps presented here represent an initial attempt to capture large scale geographical and temporal variations in Culicoides abundance. The models are a first step towards producing abundance inputs for R0 modelling of Culicoides-borne infections at a continental scale

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance

Background: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. Methods : We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. Results: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. Conclusions: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain