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

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 Culicoides-borne diseases. Although models have been attempted at the national scale (e.g. Scotland), no investigations have taken place at a finer spatial scale. 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. Local-scale variation in abundance exhibited an almost 500-fold difference (7433720) 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. 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 (6587% variance explained), but species differed in their associations with satellite variables. 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.