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Applying search theory to determine the feasibility of eradicating an invasive population in natural environments

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  • Cacho, Oscar J.
  • Hester, Susan M.
  • Spring, Daniel

Abstract

The detectability of invasive organisms influences the feasibility of eradicating an infestation. Search theory offers a framework for defining and measuring detectability, taking account of searcher ability, biological factors and the search environment. In this paper, search theory concepts are incorporated into a population model, and the costs of search and control are calculated as functions of the amount of search effort (the decision variable). Simulations are performed on a set of weed scenarios in a natural environment, involving different combinations of plant longevity, seed longevity and plant fecundity. Results provide preliminary estimates of the cost and duration of eradication programs to assist in prioritising weeds for control. The analysis shows that the success of an eradication program depends critically on the detectability of the target plant, the effectiveness of the control method, the labour requirements for search and control, and the germination rate of the plant.

Suggested Citation

  • Cacho, Oscar J. & Hester, Susan M. & Spring, Daniel, 2007. "Applying search theory to determine the feasibility of eradicating an invasive population in natural environments," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 51(4), pages 1-19.
    • Handle: RePEc:ags:aareaj:118522
    DOI: 10.22004/ag.econ.118522
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    1. Odom, Doreen I. S. & Cacho, Oscar J. & Sinden, J. A. & Griffith, Garry R., 2003. "Policies for the management of weeds in natural ecosystems: the case of scotch broom (Cytisus scoparius, L.) in an Australian national park," Ecological Economics, Elsevier, vol. 44(1), pages 119-135, February.
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    Cited by:

    1. Cacho, Oscar J. & Hester, Susan M., 2022. "Modelling biocontrol of invasive insects: An application to European Wasp (Vespula germanica) in Australia," Ecological Modelling, Elsevier, vol. 467(C).
    2. Cacho, Oscar J. & Hester, Susan M., 2011. "Deriving efficient frontiers for effort allocation in the management of invasive species," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(1), pages 1-18.
    3. Lavallée, François & Smadi, Charline & Alvarez, Isabelle & Reineking, Björn & Martin, François-Marie & Dommanget, Fanny & Martin, Sophie, 2019. "A stochastic individual-based model for the growth of a stand of Japanese knotweed including mowing as a management technique," Ecological Modelling, Elsevier, vol. 413(C).
    4. Morteza Chalak & David J. Pannell, 2015. "Optimal Integrated Strategies to Control an Invasive Weed," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 63(3), pages 381-407, September.
    5. Kompas, Tom & Chu, Long & Nguyen, Hoa Thi Minh, 2016. "A practical optimal surveillance policy for invasive weeds: An application to Hawkweed in Australia," Ecological Economics, Elsevier, vol. 130(C), pages 156-165.
    6. Chalak, Morteza & Pannell, David J., 2012. "Optimal control of a stochastic biological invasion," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124373, Agricultural and Applied Economics Association.
    7. Florec, Veronique & Sadler, Rohan J. & White, Ben & Dominiak, Bernie C., 2013. "Choosing the battles: The economics of area wide pest management for Queensland fruit fly," Food Policy, Elsevier, vol. 38(C), pages 203-213.
    8. İ. Esra Büyüktahtakın & Robert G. Haight, 2018. "A review of operations research models in invasive species management: state of the art, challenges, and future directions," Annals of Operations Research, Springer, vol. 271(2), pages 357-403, December.

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