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On costs and decisions in population management

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  • Ross, J.V.
  • Pollett, P.K.

Abstract

Many populations have a negative impact on their habitat, or upon other species in the environment, if their numbers become too large. For this reason they are often managed using some form of control. The objective is to keep numbers at a sustainable level, while ensuring survival of the population. Here we present models that allow population management programs to be assessed. Two common control regimes will be considered: reduction and suppression. Under the suppression regime the population is maintained close to a particular threshold through near continuous control, while under the reduction regime, control begins once the population reaches a certain threshold and continues until it falls below a lower pre-defined level. We discuss how to best choose the control parameters, and we provide tools that allow population managers to select reduction levels and control rates. Additional tools will be provided to assess the effect of different control regimes, in terms of population persistence and cost. In particular we consider the effects of each regime on the probability of extinction and the expected time to extinction, and compare the control methods in terms of the expected total cost of each regime over the life of the population. The usefulness of our results will be illustrated with reference to the control of a koala population inhabiting Kangaroo Island, Australia.

Suggested Citation

  • Ross, J.V. & Pollett, P.K., 2007. "On costs and decisions in population management," Ecological Modelling, Elsevier, vol. 201(1), pages 60-66.
    • Handle: RePEc:eee:ecomod:v:201:y:2007:i:1:p:60-66
    DOI: 10.1016/j.ecolmodel.2006.07.021
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    References listed on IDEAS

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    1. P. D. O’Neill & G. O. Roberts, 1999. "Bayesian inference for partially observed stochastic epidemics," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(1), pages 121-129.
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    Cited by:

    1. Keeling, M.J. & Ross, J.V., 2009. "Efficient methods for studying stochastic disease and population dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 133-141.

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