Main Article Content
In this paper we present a novel methodology applied in Spain to model spatial abundance patterns of potential vectors of disease at a medium spatial resolution of 5 x 5 km using a countrywide database with abundance data for five Culicoides species, random regression Forest modelling and a spatial dataset of ground measured and remotely sensed eco-climatic and environmental predictor variables. First the probability of occurrence was computed. In a second step a direct regression between the probability of occurrence and trap abundance was established to verify the linearity of the relationship. Finally the probability of occurrence was used in combination with the set of predictor variables to model abundance. In each case the variable importance of the predictors was used to biologically interpret results and to compare both model outputs, and model performance was assessed using four different accuracy measures. Results are shown for C. imicola, C. newsteadii, C. pulicaris group, C. punctatus and C. obsoletus group. In each case the probability of occurrence is a good predictor of abundance at the used spatial resolution of 5 x 5 km. In addition, the C. imicola and C. obsoletus group are highly driven by summer rainfall. The spatial pattern is inverse between the two species, indicating that the lower and upper thresholds are different. C. pulicaris group is mainly driven by temperature. The patterns for C. newsteadii and C. punctatus are less clear. It is concluded that the proposed methodology can be used as an input to transmission-infection-recovery (TIR) models and R0 models. The methodology will become available to the general public as part of the VECMAPTM software.
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