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Using stochastic dynamic programming to determine optimal fire management for Banksia ornata

  1. M.A. McCarthy1,2,3,*,
  2. H.P. Possingham1,2,
  3. A.M. Gill4

Article first published online: 21 DEC 2001

DOI: 10.1046/j.1365-2664.2001.00617.x

Issue

Journal of Applied Ecology

Journal of Applied Ecology

Volume 38, Issue 3, pages 585–592, June 2001

Additional Information

How to Cite

McCarthy, M.A., Possingham, H.P. and Gill, A.M. (2001), Using stochastic dynamic programming to determine optimal fire management for Banksia ornata. Journal of Applied Ecology, 38: 585–592. doi: 10.1046/j.1365-2664.2001.00617.x

Author Information

  1. 1

    National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, CA 93101–3351,USA;

  2. 2

    Department of Applied and Molecular Ecology, The University of Adelaide, Adelaide, SA 5005, Australia;

  3. 3

    School of Botany, University of Melbourne, Parkville, VIC 3010, Australia; and

  4. 4

    Center for Plant Biodiversity Research, CSIRO Division of Plant Industry, GPO Box 1600, Canberra, ACT 2601, Australia

*‡Correspondence: Michael A. McCarthy, School of Botany, University of Melbourne, Parkville, VIC 3010, Australia (fax 61 3 9347 5460; e-mailmamcca@unimelb.edu.au).

Publication History

  1. Issue published online: 21 DEC 2001
  2. Article first published online: 21 DEC 2001

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Keywords:

  • conservation;
  • decision theory;
  • disturbance;
  • extinction;
  • wildfire

Summary

  • 1
     A model of the population dynamics of Banksia ornata was developed, using stochastic dynamic programming (a state-dependent decision-making tool), to determine optimal fire management strategies that incorporate trade-offs between biodiversity conservation and fuel reduction.
  • 2
     The modelled population of B. ornata was described by its age and density, and was exposed to the risk of unplanned fires and stochastic variation in germination success.
  • 3
     For a given population in each year, three management strategies were considered: (i) lighting a prescribed fire; (ii) controlling the incidence of unplanned fire; (iii) doing nothing.
  • 4
     The optimal management strategy depended on the state of the B. ornata population, with the time since the last fire (age of the population) being the most important variable. Lighting a prescribed fire at an age of less than 30 years was only optimal when the density of seedlings after a fire was low (< 100 plants ha−1) or when there were benefits of maintaining a low fuel load by using more frequent fire.
  • 5
     Because the cost of management was assumed to be negligible (relative to the value of the persistence of the population), the do-nothing option was never the optimal strategy, although lighting prescribed fires had only marginal benefits when the mean interval between unplanned fires was less than 20–30 years.
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