Modelling the Abundances of Two Major [i]Culicoides[/i] (Diptera: Ceratopogonidae) Species in the Niayes Area of Senegal

In Senegal, considerable mortality in the equine population and hence major economic losses were caused by the African horse sickness (AHS) epizootic in 2007. [i]Culicoides oxystoma[/i] and [i]Culicoides imicola[/i], known or suspected of being vectors of bluetongue and AHS viruses are two predominant species in the vicinity of horses and are present all year-round in Niayes area, Senegal. The aim of this study was to better understand the environmental and climatic drivers of the dynamics of these two species. [i]Culicoides[/i] collections were obtained using OVI (Onderstepoort Veterinary Institute) light traps at each of the 5 sites for three nights of consecutive collection per month over one year. Cross Correlation Map analysis was performed to determine the time-lags for which environmental variables and abundance data were the most correlated. [i]C. oxystoma[/i] and [i]C. imicola[/i] count data were highly variable and overdispersed. Despite modelling large [i]Culicoides[/i] counts (over 220,000 [i]Culicoides[/i] captured in 354 night-traps), using on-site climate measures, overdispersion persisted in Poisson, negative binomial, Poisson regression mixed-effect with random effect at the site of capture models. The only model able to take into account overdispersion was the Poisson regression mixed-effect model with nested random effects at the site and date of capture levels. According to this model, meteorological variables that contribute to explaining the dynamics of [i]C. oxystoma[/i] and [i]C. imicola[/i] abundances were: mean temperature and relative humidity of the capture day, mean humidity between 21 and 19 days prior a capture event, density of ruminants, percentage cover of water bodies within a 2 km radius and interaction between temperature and humidity for [i]C. oxysto[/i]ma; mean rainfall and NDVI of the capture day and percentage cover of water bodies for [i]C. imicola[/i]. Other variables such as soil moisture, wind speed, degree days, land cover or landscape metrics could be tested to improve the models. Further work should also assess whether other trapping methods such as host-baited traps help reduce overdispersion.