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Combined climate and regional mosquito habitat model based on machine learning

  • Besides invasive mosquito species also several native species are proven or suspected vectors of arboviruses as West Nile or Usutu virus in Western Europe. Habitat models of these native vectors can be a helpful tool for assessing the risk of autochthonous occurrence, outbreaks and spread of diseases caused by such arboviruses. Modelling native mosquitoes is complicated because of the perfect adaptation to the climatic and landscape conditions and their high abundance in contrast to invasive species. Here we present a new approach for such a habitat model for native mosquito species in Germany, which are considered as vectors of West Nile virus (WNV). Epizootic emergence of WNV was registered in Germany since 2018. The models are based on surveillance data of mosquitoes from the German citizen science project “Mückenatlas” complemented by data from systematic trap monitoring in Germany, and on data freely available from the Deutscher Wetterdienst (DWD) and OpenStreetMap (OSM). While climatic factors still play an important role, we could show that habitat suitability is predictable only by the combination of the climate model with a regional model. Both models were based on a machine-learning approach using XGBoost. Evaluation of the accuracy of the models was done by statistical analysis, determining among others feature importances using the SHAP-Library. Final output of the combined climatic and regional models are maps showing the superposed habitat suitability which are generated through a number of steps described in detail. These maps also include the registered cases of WNV infections in the selected region of Germany.

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Metadaten
Author:Ralf WielandORCiD, Katrin KuhlsORCiD, Hartmut H.K. Lentz, Franz Conraths, Helge Kampen, Doreen Werner
URN:urn:nbn:de:kobv:526-opus4-15649
DOI (Version of Record):https://doi.org/10.1016/j.ecolmodel.2021.109594
ISSN:0304-3800
Parent Title (English):Ecological Modelling
Place of publication:Elsevier
Document Type:Article
Language:English
Year of Publication:2021
Publishing Institution:Technische Hochschule Wildau
Release Date:2021/12/17
Tag:Citizen science data; Machine learning; Mosquito habitat modelling; Vector borne diseases; West Nile virus; XGBoost
Volume:452
Article Number:109594
Source:Wieland, R., Kuhls, K., Lentz, H. H. K., Conraths, F., Kampen, H., & Werner, D. (2021). Combined climate and regional mosquito habitat model based on machine learning. Ecological Modelling, 452, 109594. doi:10.1016/j.ecolmodel.2021.109594
Faculties an central facilities:Fachbereich Ingenieur- und Naturwissenschaften
Dewey Decimal Classification:5 Naturwissenschaften und Mathematik / 57 Biowissenschaften; Biologie / 570 Biowissenschaften; Biologie
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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