Modelling the spatial distribution of Culicoides biting midges at the local scale

Abstract

Summary1. Culicoides midges (Diptera: Ceratopogonidae) are ubiquitous on farms in the United Kingdom (UK), but little research has explored their abundance, an important determinant of disease risk. Models to explain and predict variation in their abundance are needed for effective targeting of control methods against bluetongue virus (BTV) and other Culicoidesborne diseases. Although models have been attempted at the national scale (e.g. Scotland), no investigations have taken place at a finer spatial scale. 2. Midge abundances were estimated using light traps on 35 farms in Bala, north Wales. Culicoides catches were combined with remotely sensed ecological correlates, and on-farm host and environmental data, within a GLM model. Drivers of local-scale variation were determined at the 1-km resolution. 3. Local-scale variation in abundance exhibited an almost 500-fold difference (74-33 720) between farms in maximum Obsoletus Group catches. The Obsoletus Group model explained 81% of this variance and was dominated by normalized difference vegetation index (NDVI). This is consistent with previous studies suggesting strong impacts of forest cover and vegetation activity on distribution, as well as shaded breeding site requirements. 4. The variance explained was consistently high for the Pulicaris Group, C. pulicaris and C. punctatus (80%, 73% and 74%), the other probable BTV vector species in the United Kingdom. The abundance of all vector species increased with the number of sheep on farms, but this relationship was missing from any of the non-vector models. This is particularly interesting given that none of the species concerned are known to utilize sheep-associated larval development sites. Performance of the non-vector models was also high (65-87% variance explained), but species differed in their associations with satellite variables. 5. Synthesis and application. At a large spatial scale, there is significant variation in Culicoides Obsoletus Group abundance, which undermines attempts to record their nationwide distribution in larger-scale models. Satellite data can be used to explain a high proportion of this variation and, if shown to be generalizable, they may produce effective predictive models of disease vector abundance. We recommend undertaking a prior survey for farms with high Culicoides catches within the sampling area and checking stability in catch size between seasons and years.

The threat of midge‐borne equine disease: investigation of Culicoides species on UK equine premises

“…In the UK, members of these groups have been shown to comprise 93.5–97 per cent of Culicoides caught on farms using light-­vacuum traps (Purse and others 2012, Kluiters and others 2013). These investigations were carried out on rural properties housing mostly ­cattle and sheep.…”

Comparison of single- and multi-scale models for the prediction of the Culicoides biting midge distribution in Germany

“…Since the availability of digital datasets of land cover, temperature, or potential hosts is continuously increasing, several studies have also used this kind of data to predict the prevalence of biting midge species, e.g. the normalised difference vegetation index (NDVI) (Purse et al, 2004;Calvete et al, 2008;Kluiters et al, 2013) or the CORINE land cover data (Kirkeby et al, 2009;Purse et al, 2011). These data are available or used on different scales raising the question, which spatial scale, or scales, should be chosen to reach the best possible predictions for different biting midge species.…”

“…Although there is huge lack of knowledge about the causal connection between environmental variables and the distribution of biting midges, several studies modelled biting midge distribution and phenology using different sets of environmental data (e.g. Purse et al, 2004Purse et al, , 2011Calvete et al, 2008;Kluiters et al, 2013). These modelling approaches used environmental data from various scales, e.g.…”

“…all environmental data at one scale (e.g. 1 km, Kluiters et al, 2013) or at different scales (e.g. between 1 and 8 km, Calvete et al, 2008;Purse et al, 2011).…”

The impact of temperature changes on vector-borne disease transmission: Culicoides midges and bluetongue virus

“…When seasonal variation in midge activity is also considered, for example by imputing the duration and peak timing of the midge season, additional climatic predictors become important, such as mean temperature during spring [25]. Different midge species and midge species groups have been reported as having significantly different responses to climatic variables [26,27]. This differential response to climate continues even when distinguishing between morphologically similar species within a single Culicoides species group [28].…”