IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v337y2016icp310-329.html
   My bibliography  Save this article

Geospatial pest-parasitoid agent based model for optimizing biological control of forest insect infestation

Author

Listed:
  • Anderson, Taylor M.
  • Dragićević, Suzana

Abstract

Forest insect infestations behave as complex systems and can be represented using agent-based modeling (ABM) approaches to explore and optimize eradication strategies such as biological control. This study develops novel geospatial agent-based EAB-BioCon model for the interactions of emerald ash borer (EAB) with the parasitoid Tetrastichus planipennisi (TP) wasp in order to evaluate the spread of forest infestations. The model is implemented on geospatial data from City of Oakville, Canada and is composed of: (1) EAB-Baseline model, representing EAB geospatial dynamics; and (2) EAB-TP model that employs scenarios to measure EAB response to variations in TP-based biological control strategies. The EAB-BioCon model simulation results indicate that variations of TP densities, timing of TP release, and number of TP release points are important considerations in the EAB biological control and thus providing useful conclusions in decision making and management.

Suggested Citation

  • Anderson, Taylor M. & Dragićević, Suzana, 2016. "Geospatial pest-parasitoid agent based model for optimizing biological control of forest insect infestation," Ecological Modelling, Elsevier, vol. 337(C), pages 310-329.
  • Handle: RePEc:eee:ecomod:v:337:y:2016:i:c:p:310-329
    DOI: 10.1016/j.ecolmodel.2016.07.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380016302617
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2016.07.017?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. Christophe Deissenberg & Sander van Der Hoog & Herbert Dawid, 2008. "EURACE: A Massively Parallel Agent-Based Model of the European Economy," Working Papers halshs-00339756, HAL.
    2. Bone, Christopher & Altaweel, Mark, 2014. "Modeling micro-scale ecological processes and emergent patterns of mountain pine beetle epidemics," Ecological Modelling, Elsevier, vol. 289(C), pages 45-58.
    3. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    4. Epanchin-Niell, Rebecca S. & Liebhold, Andrew M., 2015. "Benefits of invasion prevention: Effect of time lags, spread rates, and damage persistence," Ecological Economics, Elsevier, vol. 116(C), pages 146-153.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anderson, Taylor M. & Dragićević, Suzana, 2018. "Network-agent based model for simulating the dynamic spatial network structure of complex ecological systems," Ecological Modelling, Elsevier, vol. 389(C), pages 19-32.

    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. Furtado, Bernardo Alves & Eberhardt, Isaque Daniel Rocha, 2015. "Modelo espacial simples da economia: uma proposta teórico-metodológica [A simple spatial economic model: a proposal]," MPRA Paper 67005, University Library of Munich, Germany.
    2. J. Farmer & Cameron Hepburn & Penny Mealy & Alexander Teytelboym, 2015. "A Third Wave in the Economics of Climate Change," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 62(2), pages 329-357, October.
    3. Patrick Mellacher, 2021. "Growth, Inequality and Declining Business Dynamism in a Unified Schumpeter Mark I + II Model," Papers 2111.09407, arXiv.org, revised Nov 2023.
    4. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    5. Luca Riccetti & Alberto Russo & Mauro Gallegati, 2015. "An agent based decentralized matching macroeconomic model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 10(2), pages 305-332, October.
    6. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    7. Roos, Michael W. M., 2015. "The macroeconomics of radical uncertainty," Ruhr Economic Papers 592, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    8. Yao, Richard T. & Wallace, Lisa, 2024. "A systematic review of non-market ecosystem service values for biosecurity protection," Ecosystem Services, Elsevier, vol. 67(C).
    9. Jagadish, Arundhati & Dwivedi, Puneet & McEntire, Kira D. & Chandar, Mamta, 2019. "Agent-based modeling of “cleaner” cookstove adoption and woodfuel use: An integrative empirical approach," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    10. Roberto Veneziani & Luca Zamparelli & Michalis Nikiforos & Gennaro Zezza, 2017. "Stock-Flow Consistent Macroeconomic Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 31(5), pages 1204-1239, December.
    11. Cincotti, Silvano & Raberto, Marco & Teglio, Andrea, 2010. "Credit money and macroeconomic instability in the agent-based model and simulator Eurace," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 4, pages 1-32.
    12. repec:spo:wpmain:info:hdl:2441/50jd34uldo9jioklc7b0dpu4ej is not listed on IDEAS
    13. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2016. "How Inflation Affects Macroeconomic Performance: An Agent-Based Computational Investigation," Macroeconomic Dynamics, Cambridge University Press, vol. 20(2), pages 558-581, March.
    14. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    15. Gianluca Fabiani & Nikolaos Evangelou & Tianqi Cui & Juan M. Bello-Rivas & Cristina P. Martin-Linares & Constantinos Siettos & Ioannis G. Kevrekidis, 2024. "Task-oriented machine learning surrogates for tipping points of agent-based models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    16. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    17. Jascha-Alexander Koch & Jens Lausen & Moritz Kohlhase, 2021. "Internalizing the externalities of overfunding: an agent-based model approach for analyzing the market dynamics on crowdfunding platforms," Journal of Business Economics, Springer, vol. 91(9), pages 1387-1430, November.
    18. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    19. Antoine Mandel & Carlo Jaeger & Steffen Fürst & Wiebke Lass & Daniel Lincke & Frank Meissner & Federico Pablo-Marti & Sarah Wolf, 2010. "Agent-based dynamics in disaggregated growth models," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00542442, HAL.
    20. 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.
    21. Ashraf, Quamrul & Gershman, Boris & Howitt, Peter, 2017. "Banks, market organization, and macroeconomic performance: An agent-based computational analysis," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 143-180.

    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:337:y:2016:i:c:p:310-329. 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.