The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

Westphal, Michael I., Pickett, Marcus, Getz, Wayne M. and Possingham, Hugh P. (2003) The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations. Ecological Applications, 13 2: 543-555. doi:10.1890/1051-0761(2003)013[0543:TUOSDP]2.0.CO;2

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Author Westphal, Michael I.
Pickett, Marcus
Getz, Wayne M.
Possingham, Hugh P.
Title The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations
Journal name Ecological Applications   Check publisher's open access policy
ISSN 1051-0761
Publication date 2003-04-02
Sub-type Article (original research)
DOI 10.1890/1051-0761(2003)013[0543:TUOSDP]2.0.CO;2
Open Access Status File (Publisher version)
Volume 13
Issue 2
Start page 543
End page 555
Total pages 13
Place of publication Washington
Publisher Ecological Society of America
Collection year 2003
Language eng
Abstract A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of management actions is critical, and this can only be effectively derived from stochastic dynamic programming. The model illustrates the underlying difficulty in determining simple rules of thumb for the sequence of management actions for a metapopulation. This use of a decision theory framework extends the capacity of population viability analysis (PVA) to manage threatened species.
Keyword Ecology
Decision Theory
Optimal Landscape Reconstruction
Southern Emu-wren
Stipiturus Malachurus Intermedius
Stochastic Dynamic Programming
Threatened Species
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