IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v354y2017icp140-171.html
   My bibliography  Save this article

An improved simulation model to describe the temperature-dependent population dynamics of the grain aphid, Sitobion avenae

Author

Listed:
  • Duffy, Catriona
  • Fealy, Rowan
  • Fealy, Reamonn M.

Abstract

Effective decision support tools are required in order to provide agricultural practitioners with advice regarding appropriate and economic pest management strategies. Process-based simulation models could enhance farmer’s abilities to make knowledge-based decisions regarding both the timing and extent to which chemical management is relied upon in an agronomical context. This work describes a new simulation model that quantifies the size and timing of grain aphid populations in response to temperature. The simulation model is comprised of compartmentalised aspects of the aphid’s life cycle, which interact with one another to produce the population dynamics. The model is subjected to an independent evaluation in order to assess its descriptive capacity in comparison with previous simulation models, and is shown to constitute an improved tool to describe the seasonal population dynamics of S. avenae.

Suggested Citation

  • Duffy, Catriona & Fealy, Rowan & Fealy, Reamonn M., 2017. "An improved simulation model to describe the temperature-dependent population dynamics of the grain aphid, Sitobion avenae," Ecological Modelling, Elsevier, vol. 354(C), pages 140-171.
    • Handle: RePEc:eee:ecomod:v:354:y:2017:i:c:p:140-171
    DOI: 10.1016/j.ecolmodel.2017.03.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380017302132
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2017.03.011?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. Cariboni, J. & Gatelli, D. & Liska, R. & Saltelli, A., 2007. "The role of sensitivity analysis in ecological modelling," Ecological Modelling, Elsevier, vol. 203(1), pages 167-182.
    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. Seokil Lee & Jeffrey Vitale & Dayton Lambert & Pilja Vitale & Norman Elliot & Kristopher Giles, 2023. "Effects of Weather on Sugarcane Aphid Infestation and Movement in Oklahoma," Agriculture, MDPI, vol. 13(3), pages 1-17, March.
    2. Neta, Ayana & Gafni, Roni & Elias, Hilit & Bar-Shmuel, Nitsan & Shaltiel-Harpaz, Liora & Morin, Efrat & Morin, Shai, 2021. "Decision support for pest management: Using field data for optimizing temperature-dependent population dynamics models," Ecological Modelling, Elsevier, vol. 440(C).

    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. Imron, Muhammad Ali & Gergs, Andre & Berger, Uta, 2012. "Structure and sensitivity analysis of individual-based predator–prey models," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 71-81.
    2. Frank H. Koch & Denys Yemshanov & Daniel W. McKenney & William D. Smith, 2009. "Evaluating Critical Uncertainty Thresholds in a Spatial Model of Forest Pest Invasion Risk," Risk Analysis, John Wiley & Sons, vol. 29(9), pages 1227-1241, September.
    3. Chu-Agor, M.L. & Muñoz-Carpena, R. & Kiker, G.A. & Aiello-Lammens, M.E. & Akçakaya, H.R. & Convertino, M. & Linkov, I., 2012. "Simulating the fate of Florida Snowy Plovers with sea-level rise: Exploring research and management priorities with a global uncertainty and sensitivity analysis perspective," Ecological Modelling, Elsevier, vol. 224(1), pages 33-47.
    4. Petropoulos, G. & Wooster, M.J. & Carlson, T.N. & Kennedy, M.C. & Scholze, M., 2009. "A global Bayesian sensitivity analysis of the 1d SimSphere soil–vegetation–atmospheric transfer (SVAT) model using Gaussian model emulation," Ecological Modelling, Elsevier, vol. 220(19), pages 2427-2440.
    5. Lola Gilbert & Tiphaine Jeanniard-du-Dot & Matthieu Authier & Tiphaine Chouvelon & Jérôme Spitz, 2023. "Composition of cetacean communities worldwide shapes their contribution to ocean nutrient cycling," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    6. Wu, Qiong-Li & Cournède, Paul-Henry & Mathieu, Amélie, 2012. "An efficient computational method for global sensitivity analysis and its application to tree growth modelling," Reliability Engineering and System Safety, Elsevier, vol. 107(C), pages 35-43.
    7. Song, Xiaodong & Bryan, Brett A. & Almeida, Auro C. & Paul, Keryn I. & Zhao, Gang & Ren, Yin, 2013. "Time-dependent sensitivity of a process-based ecological model," Ecological Modelling, Elsevier, vol. 265(C), pages 114-123.
    8. Link, Jason S. & Pranovi, Fabio & Libralato, Simone, 2022. "Simulations and interpretations of cumulative trophic theory," Ecological Modelling, Elsevier, vol. 463(C).
    9. Carus, Jana & Heuner, Maike & Paul, Maike & Schröder, Boris, 2017. "Which factors and processes drive the spatio-temporal dynamics of brackish marshes?—Insights from development and parameterisation of a mechanistic vegetation model," Ecological Modelling, Elsevier, vol. 363(C), pages 122-136.
    10. Pal, Saheb & Ghosh, Indrajit, 2023. "Dynamics of a coupled socio-environmental model: An application to global CO2 emissions," Ecological Modelling, Elsevier, vol. 478(C).
    11. Amir Mokhtari & Jane M. Van Doren, 2019. "An Agent‐Based Model for Pathogen Persistence and Cross‐Contamination Dynamics in a Food Facility," Risk Analysis, John Wiley & Sons, vol. 39(5), pages 992-1021, May.
    12. Dueri, Sibylle & Faugeras, Blaise & Maury, Olivier, 2012. "Modelling the skipjack tuna dynamics in the Indian Ocean with APECOSM-E – Part 2: Parameter estimation and sensitivity analysis," Ecological Modelling, Elsevier, vol. 245(C), pages 55-64.
    13. Vinatier, Fabrice & Tixier, Philippe & Le Page, Christophe & Duyck, Pierre-François & Lescourret, Françoise, 2009. "COSMOS, a spatially explicit model to simulate the epidemiology of Cosmopolites sordidus in banana fields," Ecological Modelling, Elsevier, vol. 220(18), pages 2244-2254.
    14. Huber, Nica & Bugmann, Harald & Lafond, Valentine, 2018. "Global sensitivity analysis of a dynamic vegetation model: Model sensitivity depends on successional time, climate and competitive interactions," Ecological Modelling, Elsevier, vol. 368(C), pages 377-390.
    15. Baey, Charlotte & Didier, Anne & Lemaire, Sébastien & Maupas, Fabienne & Cournède, Paul-Henry, 2014. "Parametrization of five classical plant growth models applied to sugar beet and comparison of their predictive capacity on root yield and total biomass," Ecological Modelling, Elsevier, vol. 290(C), pages 11-20.
    16. Gilardelli, Carlo & Confalonieri, Roberto & Cappelli, Giovanni Alessandro & Bellocchi, Gianni, 2018. "Sensitivity of WOFOST-based modelling solutions to crop parameters under climate change," Ecological Modelling, Elsevier, vol. 368(C), pages 1-14.
    17. Priyadarshi, Anupam & Chandra, Ram & Kishi, Michio J. & Smith, S.Lan & Yamazaki, Hidekatsu, 2022. "Understanding plankton ecosystem dynamics under realistic micro-scale variability requires modeling at least three trophic levels," Ecological Modelling, Elsevier, vol. 467(C).
    18. Cadero, A. & Aubry, A. & Brun, F. & Dourmad, J.Y. & Salaün, Y. & Garcia-Launay, F., 2018. "Global sensitivity analysis of a pig fattening unit model simulating technico-economic performance and environmental impacts," Agricultural Systems, Elsevier, vol. 165(C), pages 221-229.
    19. Larocque, Guy R. & Bhatti, Jagtar & Arsenault, André, 2014. "Integrated modelling software platform development for effective use of ecosystem models," Ecological Modelling, Elsevier, vol. 288(C), pages 195-202.
    20. Yeste, Antonio & Seely, Brad & Imbert, J. Bosco & Blanco, Juan A., 2024. "Sensitivity of long-term productivity estimations in mixed forests to uncertain parameters related to fine roots," Ecological Modelling, Elsevier, vol. 490(C).

    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:354:y:2017:i:c:p:140-171. 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.