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

Show simple item record Cuéllar, Ana Carolina Jung Kjær, Lene Baum, Andreas Stockmarr, Anders Skovgard, Henrik Achim Nielsen, Søren Gunnar Andersson, Mats Lindström, Anders Chirico, Jan Lühken, Renke Steinke, Sonja Kiel, Ellen Gethmann, Jörn Conraths, Franz J. Larska, Magdalena Smreczak, Marcin Orłowska, Anna Venail, Roger Hamnes, Inger Sviland, Ståle Hopp, Petter Brugger, Katharina Rubel, Franz Balenghien, Thomas Garros, Claire Rakotoarivony, Ignace Allène, Xavier Lhoir, Jonathan Chavernac, David Delécolle, Jean-Claude Mathieu, Bruno Delécolle, Delphine Setier-Rio, Marie-Laure Scheid, Bethsabée Miranda Chueca, Miguel Ángel Barceló, Carlos Lucientes, Javier Estrada, Rosa Mathis, Alexander 2020-04-20T07:11:50Z 2020-04-20T07:11:50Z
dc.description.abstract [eng] Background: Culicoides biting midges transmit viruses resulting in disease in ruminants and equids such as bluetongue, Schmallenberg disease and African horse sickness. In the past decades, these diseases have led to important economic losses for farmers in Europe. Vector abundance is a key factor in determining the risk of vector-borne disease spread and it is, therefore, important to predict the abundance of Culicoides species involved in the transmission of these pathogens. The objectives of this study were to model and map the monthly abundances of Culicoides in Europe. Methods: We obtained entomological data from 904 farms in nine European countries (Spain, France, Germany, Switzerland, Austria, Poland, Denmark, Sweden and Norway) from 2007 to 2013. Using environmental and climatic predictors from satellite imagery and the machine learning technique Random Forests, we predicted the monthly average abundance at a 1 km2 resolution. We used independent test sets for validation and to assess model performance. Results: The predictive power of the resulting models varied according to month and the Culicoides species/ensembles predicted. Model performance was lower for winter months. Performance was higher for the Obsoletus ensemble, followed by the Pulicaris ensemble, while the model for Culicoides imicola showed a poor performance. Distribution and abundance patterns corresponded well with the known distributions in Europe. The Random Forests model approach was able to distinguish diferences in abundance between countries but was not able to predict vector abundance at individual farm level.
dc.format application/pdf
dc.relation.ispartof Parasites & Vectors, 2020, vol. 13, num. 194, p. 1-18
dc.rights , 2020
dc.subject.classification 57 - Biologia
dc.subject.other 57 - Biological sciences in general
dc.title Modelling the monthly abundance of Culicoides biting midges in nine European countries using Random Forests machine learning
dc.type info:eu-repo/semantics/article 2020-04-20T07:11:51Z
dc.rights.accessRights info:eu-repo/semantics/openAccess

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