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Modelling the spatial distribution of Culicoides imicola : climatic versus remote sensing data

(2014) REMOTE SENSING. 6(7). p.6604-6619
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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 sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species' presence and changing environment.
Keywords
bluetongue, MODIS, species distribution modelling, WorldClim, random forests, variable importance, BLUETONGUE VIRUS VECTORS, ENTOMOLOGICAL SURVEILLANCE, SATELLITE IMAGERY, RANDOM FORESTS, BITING MIDGES, BREEDING SITES, ABUNDANCE, CERATOPOGONIDAE, UNCERTAINTY, PREDICTION

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Citation

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Chicago
Van doninckJasper, Bernard De Baets, Jan Peters, Guy Hendrickx, Els Ducheyne, and Niko Verhoest. 2014. “Modelling the Spatial Distribution of Culicoides Imicola : Climatic Versus Remote Sensing Data.” Remote Sensing 6 (7): 6604–6619.
APA
Van doninckJasper, De Baets, B., Peters, J., Hendrickx, G., Ducheyne, E., & Verhoest, N. (2014). Modelling the spatial distribution of Culicoides imicola : climatic versus remote sensing data. REMOTE SENSING, 6(7), 6604–6619.
Vancouver
1.
Van doninckJasper, De Baets B, Peters J, Hendrickx G, Ducheyne E, Verhoest N. Modelling the spatial distribution of Culicoides imicola : climatic versus remote sensing data. REMOTE SENSING. 2014;6(7):6604–19.
MLA
Van doninckJasper, Bernard De Baets, Jan Peters, et al. “Modelling the Spatial Distribution of Culicoides Imicola : Climatic Versus Remote Sensing Data.” REMOTE SENSING 6.7 (2014): 6604–6619. Print.
@article{5796706,
  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 sensing provides an equally high spatial resolution globally. Additionally, its high temporal resolution allows for investigating changes in species' presence and changing environment.},
  author       = {Van doninck, Jasper and De Baets, Bernard and Peters, Jan and Hendrickx, Guy and Ducheyne, Els and Verhoest, Niko},
  issn         = {2072-4292},
  journal      = {REMOTE SENSING},
  keyword      = {bluetongue,MODIS,species distribution modelling,WorldClim,random forests,variable importance,BLUETONGUE VIRUS VECTORS,ENTOMOLOGICAL SURVEILLANCE,SATELLITE IMAGERY,RANDOM FORESTS,BITING MIDGES,BREEDING SITES,ABUNDANCE,CERATOPOGONIDAE,UNCERTAINTY,PREDICTION},
  language     = {eng},
  number       = {7},
  pages        = {6604--6619},
  title        = {Modelling the spatial distribution of Culicoides imicola : climatic versus remote sensing data},
  url          = {http://dx.doi.org/10.3390/rs6076604},
  volume       = {6},
  year         = {2014},
}

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