IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v230y2012icp1-10.html
   My bibliography  Save this article

Plant Spread Simulator: A model for simulating large-scale directed dispersal processes across heterogeneous environments

Author

Listed:
  • Fennell, Mark
  • Murphy, James E.
  • Armstrong, Cristina
  • Gallagher, Tommy
  • Osborne, Bruce

Abstract

A mechanistic model designed to simulate the spread of invasive plants that primarily propagate via dispersal corridors is described. The model has been parameterised for use with Gunnera tinctoria, an invasive herbaceous plant that is believed to spread via abiotic dispersal corridors, such as roads and rivers. It is an individual based, spatiotemporally explicit, stochastic computer simulation. The model can simulate the influence of habitat type, habitat features (e.g. roads and rivers), propagule pressure, varying climatic conditions, and stochastic long distance dispersal, on plant spread, establishment and survival. A process-based approach, which allows for the non-linear movement of propagules through heterogeneous environments, is used to simulate long distance propagule dispersal. The model is relatively easy to parameterise and provides abundance predictions. An analytical technique for evaluating model accuracy when binned percentage cover data is available for comparison is also presented. To evaluate the model's predictive capabilities, it was seeded at the presumed point of initial invasion on the west coast of Ireland in 1908 and then run for 100 timesteps (timesteps=one year). The simulated distributions were compared to detailed distribution maps of G. tinctoria, which had been recorded in 2008. The 2008 distribution of G. tinctoria was accurately reproduced, as confirmed by all the statistical approaches used (e.g. AUC=0.891, kappa=0.710). Habitat type and abiotic habitat features were shown to play a critical role in determining plant distributions. Predictions on the future spread of G. tinctoria, up to 2031, indicate that this species will substantially increase in abundance (+∼98%) and distribution (+∼59%) unless effective management protocols can be designed and implemented.

Suggested Citation

  • Fennell, Mark & Murphy, James E. & Armstrong, Cristina & Gallagher, Tommy & Osborne, Bruce, 2012. "Plant Spread Simulator: A model for simulating large-scale directed dispersal processes across heterogeneous environments," Ecological Modelling, Elsevier, vol. 230(C), pages 1-10.
  • Handle: RePEc:eee:ecomod:v:230:y:2012:i:c:p:1-10
    DOI: 10.1016/j.ecolmodel.2012.01.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380012000191
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2012.01.008?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. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    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. Martin Ward, 2016. "Action against pest spread—the case for retrospective analysis with a focus on timing," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 8(1), pages 77-81, February.
    2. Liao, Jinbao & Li, Zhenqing & Quets, Jan J. & Nijs, Ivan, 2013. "Effects of space partitioning in a plant species diversity model," Ecological Modelling, Elsevier, vol. 251(C), pages 271-278.
    3. Gagnon, Karine & Peacock, Stephanie J. & Jin, Yu & Lewis, Mark A., 2015. "Modelling the spread of the invasive alga Codium fragile driven by long-distance dispersal of buoyant propagules," Ecological Modelling, Elsevier, vol. 316(C), pages 111-121.
    4. Rougier, Thibaud & Drouineau, Hilaire & Dumoulin, Nicolas & Faure, Thierry & Deffuant, Guillaume & Rochard, Eric & Lambert, Patrick, 2014. "The GR3D model, a tool to explore the Global Repositioning Dynamics of Diadromous fish Distribution," Ecological Modelling, Elsevier, vol. 283(C), pages 31-44.
    5. Martin Ward, 2016. "Action against pest spread—the case for retrospective analysis with a focus on timing," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 8(1), pages 77-81, February.
    6. Costa, Hugo & Ponte, Nuno B. & Azevedo, Eduardo B. & Gil, Artur, 2015. "Fuzzy set theory for predicting the potential distribution and cost-effective monitoring of invasive species," Ecological Modelling, Elsevier, vol. 316(C), pages 122-132.

    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. Hendry, David F. & Clements, Michael P., 2003. "Economic forecasting: some lessons from recent research," Economic Modelling, Elsevier, vol. 20(2), pages 301-329, March.
    2. Madden, Gary & Tan, Joachim, 2007. "Forecasting telecommunications data with linear models," Telecommunications Policy, Elsevier, vol. 31(1), pages 31-44, February.
    3. Tsionas, Mike G., 2021. "Bayesian forecasting with the structural damped trend model," International Journal of Production Economics, Elsevier, vol. 234(C).
    4. Neil R. Ericsson, 2021. "Dynamic Econometrics in Action: A Biography of David F. Hendry," International Finance Discussion Papers 1311, Board of Governors of the Federal Reserve System (U.S.).
    5. Clements, Michael P. & Beatriz Galvao, Ana, 2010. "Real-time Forecasting of Inflation and Output Growth in the Presence of Data Revisions," Economic Research Papers 270771, University of Warwick - Department of Economics.
    6. Armstrong, J. Scott & Green, Kesten C. & Graefe, Andreas, 2015. "Golden rule of forecasting: Be conservative," Journal of Business Research, Elsevier, vol. 68(8), pages 1717-1731.
    7. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    8. Hyndman, Rob J. & Ahmed, Roman A. & Athanasopoulos, George & Shang, Han Lin, 2011. "Optimal combination forecasts for hierarchical time series," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2579-2589, September.
    9. Kourentzes, Nikolaos & Petropoulos, Fotios & Trapero, Juan R., 2014. "Improving forecasting by estimating time series structural components across multiple frequencies," International Journal of Forecasting, Elsevier, vol. 30(2), pages 291-302.
    10. İhsan Erdem Kayral & Tuğba Sarı & Nisa Şansel Tandoğan Aktepe, 2023. "Forecasting the Tourist Arrival Volumes and Tourism Income with Combined ANN Architecture in the Post COVID-19 Period: The Case of Turkey," Sustainability, MDPI, vol. 15(22), pages 1-20, November.
    11. de Silva, Ashton J, 2010. "Forecasting Australian Macroeconomic variables, evaluating innovations state space approaches," MPRA Paper 27411, University Library of Munich, Germany.
    12. Korbinian Dress & Stefan Lessmann & Hans-Jorg von Mettenheim, 2017. "Residual Value Forecasting Using Asymmetric Cost Functions," Papers 1707.02736, arXiv.org.
    13. Kourentzes, Nikolaos & Athanasopoulos, George, 2021. "Elucidate structure in intermittent demand series," European Journal of Operational Research, Elsevier, vol. 288(1), pages 141-152.
    14. Alysha M De Livera, 2010. "Automatic forecasting with a modified exponential smoothing state space framework," Monash Econometrics and Business Statistics Working Papers 10/10, Monash University, Department of Econometrics and Business Statistics.
    15. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    16. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    17. Philippe St-Aubin & Bruno Agard, 2022. "Precision and Reliability of Forecasts Performance Metrics," Forecasting, MDPI, vol. 4(4), pages 1-22, October.
    18. Miroslav Navratil & Andrea Kolkova, 2019. "Decomposition and Forecasting Time Series in the Business Economy Using Prophet Forecasting Model," Central European Business Review, Prague University of Economics and Business, vol. 2019(4), pages 26-39.
    19. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
    20. Spyros Makridakis & Fotios Petropoulos & Yanfei Kang, 2023. "Large Language Models: Their Success and Impact," Forecasting, MDPI, vol. 5(3), pages 1-14, August.

    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:230:y:2012:i:c:p:1-10. 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.