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Optimal management of non-Markovian biological populations

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  • Williams, Byron K.

Abstract

Wildlife populations typically are described by Markovian models, with population dynamics influenced at each point in time by current but not previous population levels. Considerable work has been done on identifying optimal management strategies under the Markovian assumption. In this paper we generalize this work to non-Markovian systems, for which population responses to management are influenced by lagged as well as current status and/or controls. We use the maximum principle of optimal control theory to derive conditions for the optimal management such a system, and illustrate the effects of lags on the structure of optimal habitat strategies for a predator–prey system.

Suggested Citation

  • Williams, Byron K., 2007. "Optimal management of non-Markovian biological populations," Ecological Modelling, Elsevier, vol. 200(1), pages 234-242.
    • Handle: RePEc:eee:ecomod:v:200:y:2007:i:1:p:234-242
    DOI: 10.1016/j.ecolmodel.2006.07.039
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    References listed on IDEAS

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    1. Masami Fujiwara & Hal Caswell, 2001. "Demography of the endangered North Atlantic right whale," Nature, Nature, vol. 414(6863), pages 537-541, November.
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    Cited by:

    1. David Breininger & Brean Duncan & Mitchell Eaton & Fred Johnson & James Nichols, 2014. "Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk," Land, MDPI, vol. 3(3), pages 1-24, July.
    2. Williams, Byron K. & Eaton, Mitchell J. & Breininger, David R., 2011. "Adaptive resource management and the value of information," Ecological Modelling, Elsevier, vol. 222(18), pages 3429-3436.

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