Modelling the Spatial Distribution of Culicoides imicola: Climatic versus Remote Sensing Data

Abstract

Culicoides imicola is the main vector of the bluetongue virus in the Mediterranean Basin. Spatial distribution models for this species traditionally employ either climatic data or remotely sensed data, or a combination of both. Until now, however, no studies compared the accuracies of C. imicola distribution models based on climatic versus remote sensing data, even though remotely sensed datasets may offer advantages over climatic datasets with respect to spatial and temporal resolution. This study performs such an analysis for datasets over the peninsula of Calabria, Italy. Spatial distribution modelling based on climatic data using the random forests machine learning technique resulted in a percentage of correctly classified C. imicola trapping sites of nearly 88%, thereby outperforming the linear discriminant analysis and logistic regression modelling techniques. When replacing climatic data by remote sensing data, random forests modelling accuracies decreased only slightly. Assessment of the different variables' importance showed that precipitation during late spring was the most important amongst 48 climatic variables. The dominant remotely sensed variables could be linked to climatic variables. Notwithstanding the slight decrease in predictive performance in this study, remotely sensed datasets could be preferred over climatic datasets for the modelling of C. imicola. Unlike climatic observations, remote Remote Sens. 2014, 6 6605 sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species' presence and changing environment.

Spatial distribution and habitat selection of culicoides imicola: The potential vector of bluetongue virus in Tunisia

“…To determine which environmental variables most influence the distribution of C. imicola , the main vector of BT in Tunisia, we included 12 bioclimatic variables in our model ( Table 1 ). The selection of environmental and climatic predictors used in the analysis was based on the literature review and association already proved with the disease (Calvete et al 2009 ; Ciss et al 2019 ; Van Doninck et al 2014 ). The bioclimatic data, with a spatial resolution of 30 arc seconds (~1 kilometres [km]), were downloaded from the World Climate website ( http://www.worldclim.org/current ) and averaged over a 50 year period between 1950 and 2000 at the same spatial resolution.…”

“…The selection of environmental and climatic predictors used in the analysis was based on the literature review and association already proved with the disease (Calvete et al 2009;Ciss et al 2019;Van Doninck et al 2014…”

Predicting and mapping human risk of exposure to Ixodes ricinus nymphs using climatic and environmental data, Denmark, Norway and Sweden, 2016

“…Machine learning techniques combined with environmental predictors have been used in modelling biting midges ( Culicoides sp.) [30-33], and mosquitoes [28,34,35], and studies on ticks include modelling tick distribution or abundance [36-38] as well as the distribution of tick-borne human diseases [15,39].…”

Role of Eco-Climatic Factors in the Distribution of Bluetongue in Endemic Areas in Tunisia

“…The distribution of C. imicola is highly dependent on-air temperature, a major limiting factor. However, the precise mechanism of this dependency is not yet known ( Van Doninck et al ., 2014 ). Previously conducted laboratory studies revealed that vector competence varies with temperature and infecting BTV serotype.…”