Predicting the spatio-temporal distribution of Culicoides imicola in Sardinia using a discrete-time population model

Abstract

BackgroundCulicoides imicola KIEFFER, 1913 (Diptera: Ceratopogonidae) is the principal vector of Bluetongue disease in the Mediterranean basin, Africa and Asia. Previous studies have identified a range of eco-climatic variables associated with the distribution of C. imicola, and these relationships have been used to predict the large-scale distribution of the vector. However, these studies are not temporally-explicit and can not be used to predict the seasonality in C. imicola abundances. Between 2001 and 2006, longitudinal entomological surveillance was carried out throughout Italy, and provided a comprehensive spatio-temporal dataset of C. imicola catches in Onderstepoort-type black-light traps, in particular in Sardinia where the species is considered endemic.MethodsWe built a dynamic model that allows describing the effect of eco-climatic indicators on the monthly abundances of C. imicola in Sardinia. Model precision and accuracy were evaluated according to the influence of process and observation errors.ResultsA first-order autoregressive cofactor, a digital elevation model and MODIS Land Surface Temperature (LST)/or temperatures acquired from weather stations explained ~77% of the variability encountered in the samplings carried out in 9 sites during 6 years. Incorporating Normalized Difference Vegetation Index (NDVI) or rainfall did not increase the model's predictive capacity. On average, dynamics simulations showed good accuracy (predicted vs. observed r corr = 0.9). Although the model did not always reproduce the absolute levels of monthly abundances peaks, it succeeded in reproducing the seasonality in population level and allowed identifying the periods of low abundances and with no apparent activity. On that basis, we mapped C. imicola monthly distribution over the entire Sardinian region.ConclusionsThis study demonstrated prospects for modelling data arising from Culicoides longitudinal entomological surveillance. The framework explicitly incorporates the influence of eco-climatic factors on population growth rates and accounts for observation and process errors. Upon validation, such a model could be used to predict monthly population abundances on the basis of environmental conditions, and hence can potentially reduce the amount of entomological surveillance.

Principal climatic and edaphic determinants of Culicoides biting midge abundance during the 2007–2008 bluetongue epidemic in the Netherlands, based on OVI light trap data

“…To obtain the best estimate for potential population size, studies of Culicoides population abundance and distribution have relied largely upon different averaging or ‘maximum level’ measures derived from trap catch data during the peak season of midge activity (Baylis et al , ; Conte et al , ; Purse et al , ; Rigot et al , ). By contrast, in order to avoid the loss of information (i.e.…”

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

“…Using entomological data collected on farms and environmental variables obtained from satellite imagery, it is possible to model and map the abundance of vectors. Culicoides abundance maps for Europe can be found either at a national [15][16][17] or a continental scale for C. imicola [18,19] and for the Obsoletus ensemble [20]. The Culicoides maps available at a continental scale for Europe are usually created with abundance data collected within a limited area of the mapped region.…”

Mechanistic model for predicting the seasonal abundance of Culicoides biting midges and the impacts of insecticide control

“…Contrasting previous studies [22, 25, 33], we have developed a new model framework for predicting the seasonal abundance of the Obsoletus group (defined above), which explicitly links environmental variation with life-history processes, such as development time, fecundity and mortality. These processes are modelled by functional forms that are parameterised by published laboratory and field environmental response experiments (see Additional file 1).…”

“…The two main modelling approaches for understanding seasonal patterns of Culicoides disease vectors are statistical correlative models [22–25] or mechanistic mathematical or simulation models [2, 3, 26–28]. Whilst the statistical models provide accurate descriptions for the datasets they are trained on, they are limited to the datasets and are not readily extendable to new locations without additional data (though this has been done and integrated into transmission models [24]).…”