IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v452y2021ics0304380021001563.html
   My bibliography  Save this article

Combined climate and regional mosquito habitat model based on machine learning

Author

Listed:
  • Wieland, Ralf
  • Kuhls, Katrin
  • Lentz, Hartmut H.K.
  • Conraths, Franz
  • Kampen, Helge
  • Werner, Doreen

Abstract

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.

Suggested Citation

  • Wieland, Ralf & Kuhls, Katrin & Lentz, Hartmut H.K. & Conraths, Franz & Kampen, Helge & Werner, Doreen, 2021. "Combined climate and regional mosquito habitat model based on machine learning," Ecological Modelling, Elsevier, vol. 452(C).
    • Handle: RePEc:eee:ecomod:v:452:y:2021:i:c:s0304380021001563
    DOI: 10.1016/j.ecolmodel.2021.109594
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380021001563
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2021.109594?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Wieland, Ralf & Kerkow, Antje & Früh, Linus & Kampen, Helge & Walther, Doreen, 2017. "Automated feature selection for a machine learning approach toward modeling a mosquito distribution," Ecological Modelling, Elsevier, vol. 352(C), pages 108-112.
    2. LonÄ arić, Željka & K. Hackenberger, Branimir, 2013. "Stage and age structured Aedes vexans and Culex pipiens (Diptera: Culicidae) climate-dependent matrix population model," Theoretical Population Biology, Elsevier, vol. 83(C), pages 82-94.
    3. Valdez, L.D. & Sibona, G.J. & Condat, C.A., 2018. "Impact of rainfall on Aedes aegypti populations," Ecological Modelling, Elsevier, vol. 385(C), pages 96-105.
    4. Jácome, Gabriel & Vilela, Paulina & Yoo, ChangKyoo, 2019. "Present and future incidence of dengue fever in Ecuador nationwide and coast region scale using species distribution modeling for climate variability’s effect," Ecological Modelling, Elsevier, vol. 400(C), pages 60-72.
    5. Ulfia A. Lenfers & Julius Weyl & Thomas Clemen, 2018. "Firewood Collection in South Africa: Adaptive Behavior in Social-Ecological Models," Land, MDPI, vol. 7(3), pages 1-17, August.
    6. Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Kerkow, Antje & Wieland, Ralf & Gethmann, Jörn M. & Hölker, Franz & Lentz, Hartmut H.K., 2022. "Linking a compartment model for West Nile virus with a flight simulator for vector mosquitoes," Ecological Modelling, Elsevier, vol. 464(C).
    2. Früh, Linus & Kampen, Helge & Kerkow, Antje & Schaub, Günter A. & Walther, Doreen & Wieland, Ralf, 2018. "Modelling the potential distribution of an invasive mosquito species: comparative evaluation of four machine learning methods and their combinations," Ecological Modelling, Elsevier, vol. 388(C), pages 136-144.
    3. Ulfia A. Lenfers & Nima Ahmady-Moghaddam & Daniel Glake & Florian Ocker & Daniel Osterholz & Jonathan Ströbele & Thomas Clemen, 2021. "Improving Model Predictions—Integration of Real-Time Sensor Data into a Running Simulation of an Agent-Based Model," Sustainability, MDPI, vol. 13(13), pages 1-14, June.
    4. Schratz, Patrick & Muenchow, Jannes & Iturritxa, Eugenia & Richter, Jakob & Brenning, Alexander, 2019. "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data," Ecological Modelling, Elsevier, vol. 406(C), pages 109-120.
    5. Tshidzumba, Ratsodo Phillip & Chirwa, Paxie Wanangwa, 2022. "Forest-based land reform partnerships in rural development and the sustenance of timber markets. Learning from two South African cases," Forest Policy and Economics, Elsevier, vol. 140(C).
    6. Wang, Yan & Li, Yazhi & Liu, Lili & Liu, Xianning, 2022. "A periodic Chikungunya model with virus mutation and transovarial transmission," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    7. Paulina Vilela & Gabriel Jácome & Wladimir Moya & Pouya Ifaei & Sungku Heo & Changkyoo Yoo, 2023. "A Brief Insight into the Toxicity Conundrum: Modeling, Measuring, Monitoring and Evaluating Ecotoxicity for Water Quality towards Environmental Sustainability," Sustainability, MDPI, vol. 15(11), pages 1-28, May.
    8. Giovanni Marini & Piero Poletti & Mario Giacobini & Andrea Pugliese & Stefano Merler & Roberto Rosà, 2016. "The Role of Climatic and Density Dependent Factors in Shaping Mosquito Population Dynamics: The Case of Culex pipiens in Northwestern Italy," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-15, April.
    9. Benkendorf, Donald J. & Schwartz, Samuel D. & Cutler, D. Richard & Hawkins, Charles P., 2023. "Correcting for the effects of class imbalance improves the performance of machine-learning based species distribution models," Ecological Modelling, Elsevier, vol. 483(C).
    10. Ulfia A. Lenfers & Nima Ahmady-Moghaddam & Daniel Glake & Florian Ocker & Julius Weyl & Thomas Clemen, 2022. "Modeling the Future Tree Distribution in a South African Savanna Ecosystem: An Agent-Based Model Approach," Land, MDPI, vol. 11(5), pages 1-24, April.
    11. Damiana Ravasi & Francesca Mangili & David Huber & Laura Azzimonti & Lukas Engeler & Nicola Vermes & Giacomo Del Rio & Valeria Guidi & Mauro Tonolla & Eleonora Flacio, 2022. "Risk-Based Mapping Tools for Surveillance and Control of the Invasive Mosquito Aedes albopictus in Switzerland," IJERPH, MDPI, vol. 19(6), pages 1-22, March.
    12. Gerrit Günther & Thomas Clemen & Rainer Duttmann & Brigitta Schütt & Daniel Knitter, 2021. "Of Animal Husbandry and Food Production—A First Step towards a Modular Agent-Based Modelling Platform for Socio-Ecological Dynamics," Land, MDPI, vol. 10(12), pages 1-25, December.
    13. Christian Berger & Mari Bieri & Karen Bradshaw & Christian Brümmer & Thomas Clemen & Thomas Hickler & Werner Leo Kutsch & Ulfia A. Lenfers & Carola Martens & Guy F. Midgley & Kanisios Mukwashi & Victo, 2019. "Linking scales and disciplines: an interdisciplinary cross-scale approach to supporting climate-relevant ecosystem management," Climatic Change, Springer, vol. 156(1), pages 139-150, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecomod:v:452:y:2021:i:c:s0304380021001563. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/ecological-modelling .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.