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Maximizing a new quantity in sequential reserve selection

Published online by Cambridge University Press:  19 December 2013

ADAM W. SCHAPAUGH  and
ANDREW J. TYRE
ADAM W. SCHAPAUGH*
Affiliation:
School of Natural Resources, Hardin Hall, 3310 Holdrege Street, University of Nebraska-Lincoln, Lincoln, Nebraska 68510, USA
ANDREW J. TYRE
Affiliation:
School of Natural Resources, Hardin Hall, 3310 Holdrege Street, University of Nebraska-Lincoln, Lincoln, Nebraska 68510, USA
*
*Correspondence: Dr Adam Schapaugh Tel: +1 785 317-2571 e-mail: [email protected]
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Summary

The fundamental goal of conservation planning is biodiversity persistence, yet most reserve selection methods prioritize sites using occurrence data. Numerous empirical studies support the notion that defining and measuring objectives in terms of species richness (where the value of a site is equal to the number of species it contains, or contributes to an existing reserve network) can be inadequate for maintaining biodiversity in the long-term. An existing site-assessment framework that implicitly maximized the persistence probability of multiple species was integrated with a dynamic optimization model. The problem of sequential reserve selection as a Markov decision process was combined with stochastic dynamic programming to find the optimal solution. The approach represents a compromise between representation-based approaches (maximizing occurrences) and more complex tools, like spatially-explicit population models. The method, the inherent problems and interesting conclusions are illustrated with a land acquisition case study on the central Platte River.

Keywords

Bayesian networkMarkov decision processreserve adequacyreserve selectionstochastic dynamic programming
Type
THEMATIC SECTION: Spatial Simulation Models in Planning for Resilience
Information
Environmental Conservation , Volume 41 , Issue 2 , June 2014 , pp. 198 - 205
Copyright
Copyright © Foundation for Environmental Conservation 2013 

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References

Araujo, M.B., Williams, P.H. & Fuller, R.J. (2002) Dynamics of extinction and the selection of nature reserves. Proceedings of the Royal Society London, Biological Series 269: 1971–1980.Google Scholar
Barto, A.S., Bradtke, S. & Singh, S. (1995) Learning to act using real-time dynamic programming. Artificial Intelligence 72: 81–138.Google Scholar
Beissinger, S. & Westphal, M. (1998) On the use of demographic models of population viability in endangered species management. Journal of Wildlife Management 62: 821–841.Google Scholar
Bellman, R. (1957) Dynamic Programming. Princeton, NJ, USA: Princeton University Press.Google Scholar
Bellman, R. (1961) Adaptive Control Processes: a Guided Tour. Princeton, NJ, USA: Princeton University Press.CrossRefGoogle Scholar
Boutilier, C., Dean, T. & Hanks, S. (1999) Decision theoretic planning: structural assumptions and computational leverage. Journal of Artificial Intelligence Research 11: 1–94.Google Scholar
Calkin, D., Montgomery, C., Schumaker, N., Polasky, S., Arthur, J. & Nalle, D. (2002) Developing a production possibility set of wildlife species persistence and timber harvest value. Canadian Journal of Forest Research 32: 1329–1343.Google Scholar
Costello, C. & Polasky, S. (2004) Dynamic reserve site selection. Resources and Energy Economics 26: 157–174.Google Scholar
Cowling, R.M., Pressey, R.L., Rouget, M. & Lombard, A.T. (2003) A conservation plan for a global biodiversity hotspot: the Cape Floristic Region, South Africa. Biological Conservation 112: 191–216.Google Scholar
Emerton, L., Bishop, J. & Thomas, L. (2006) Sustainable Financing of Protected Areas: a Global Review of Challenges and Options. Gland, Switzerland: The World Conservation Union (IUCN).Google Scholar
Faith, D.P., Carter, G., Cassis, G., Ferrier, S. & Wilkie, L. (2003) Complementarity, biodiversity viability analysis, and policy-based algorithms for conservation. Environmental Science and Policy 6: 311–328.Google Scholar
Fuller, R.A., McDonald-Madden, E., Wilson, K., Carwardine, J., Grantham, H., Watson, J., Klein, C., Green, D. & Possingham, H. (2010) Replacing underperforming protected areas achieves better conservation outcomes. Nature 466: 365–367.Google Scholar
Haight, R.G., Snyder, S.A. & ReVelle, C.S. (2005) Metropolitan open-space protection with uncertain site availability. Conservation Biology 19: 327–337.Google Scholar
Kearns, M., Mansour, Y. & Ng, A.Y. (2002) A sparse sampling algorithm for near-optimal planning in large Markov decision processes. Machine Learning 49: 193–208.Google Scholar
Kirkpatrick, J.B. (1983) An iterative method for establishing priorities for the selection of nature reserves: an example for Tasmania. Biological Conservation 25: 127–134.Google Scholar
Kirkpatrick, J.B. & Harwood, C. (1983) Conservation of Tasmanian macrophytic wetland vegetation. Proceedings of the Royal Society of Tasmania 117: 5–20.Google Scholar
Knight, A., Cowling, R. & Campbell, B. (2006) An operational model for implementing conservation action. Conservation Biology 20: 408–419.Google Scholar
Mangel, M. & Clark, C.W. (2000) Dynamic State Variable Models in Ecology: Methods and Applications. Oxford Series in Ecology and Evolution. New York, NY, USA: Oxford University Press.Google Scholar
Mangel, C.R. & Tier, C. (1993) A simple direct method for finding persistence times of populations and applications to conservation problems. Proceedings of the National Academy of Sciences USA 90: 1083–1086.Google Scholar
Margules, C.R., Nicholls, A.O. & Pressey, R.L. (1988) Selecting networks of reserves to maximize biological diversity. Biological Conservation 43: 63–76.Google Scholar
Margules, C.R., Nicholls, A.O. & Usher, M. (1994) Apparent species turnover, probability of extinction and the selection of nature reserves: a case study on the Ingelborough limestone pavements. Conservation Biology 8: 398–409.Google Scholar
Markowitz, H.M. (1952) Portfolio selection. The Journal of Finance 7: 77–91.Google Scholar
McBride, M.F., Wilson, K.A., Bode, M. & Possingham, H.P. (2005) Incorporating the effects of socioeconomic uncertainty into priority setting for conservation investment. Conservation Biology 21: 1463–1474.Google Scholar
McDonald-Madden, E., Bode, M., Game, E., Grantham, H. & Possingham, H. (2008) The need for speed: informed land acquisitions for conservation in a dynamic property market. Ecology Letters 11: 1169–1177.Google Scholar
Moilanen, A., Possingham, H. & Polasky, S. (2009) A mathematical classification of conservation prioritization problems. In: Spatial Conservation Prioritization: Quantitative Methods and Computational Tools, ed. Moilanen, A., Wilson, K. & Possingham, H., pp. 28–42. New York, NY, USA: Oxford University Press Inc.Google Scholar
Nicol, S., Chades, I., Linke, S. & Possingham, H. (2010) Conservation decision-making in large state spaces. Ecological Modelling 221: 2531–2536.Google Scholar
Nicholson, E., Westphal, M., Frank, K., Rochester, W., Pressey, R., Lindenmayer, D. & Possingham, H. (2006) A new method for conservation planning for the persistence of multiple species. Ecology Letters 9: 1049–1069.Google Scholar
Platte River Recovery Information Program (1997) Cooperative agreement for Platte River research and other efforts relating to endangered species habitats along the Central Platte River, Nebraska [www document]. URL https://www.platteriverprogram.org/PubsAndData/Pages/ProgramLibrary.aspx Google Scholar
Possingham, H., Day, J., Goldfinch, M. & Salzborn, F. (1993) The mathematics of designing a network of protected areas for conservation, In: Proceedings of the 12th Australian Operation Research Conference, ed. Pearce, D., pp. 536–545. Adelaide, Australia: Adelaide University.Google Scholar
Possingham, H., Ball, I. & Andelman, S. (2000) Mathematical methods for identifying representative reserve networks. In: Quantitative Methods for Conservation Biology, ed. Ferson, S. & Burgman, M., pp. 291–309. New York, NY, USA: Springer-Verlag.Google Scholar
Possingham, H., Moilanen, A. & Wilson, K. (2009) Accounting for habitat dynamics in conservation planning. In: Spatial Conservation Prioritization: Quantitative Methods and Computational Tools, ed. Moilanen, A., Wilson, K. & Possingham, H., pp. 135–144. New York, NY, USA: Oxford University Press Inc.Google Scholar
Pressey, R. & Nicholls, A. (1989) Application of a numerical algorithm to the selection of reserves in semi-arid New South Wales. Biological Conservation 50: 263–278.CrossRefGoogle Scholar
Pressey, R. & Tully, S. (1994) The cost of ad hoc reservation: a case study in the Western Division of New South Wales. Australian Journal of Ecology 19: 375–384.Google Scholar
Pressey, R., Cabeza, M., Watts, M., Cowling, R. & Wilson, K. (2007) Conservation planning in a changing world. Trends in Ecology and Evolution 22: 583–592.Google Scholar
Putterman, M. (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York, NY, USA: Wiley.Google Scholar
Root, K., Ackakaya, H. & Ginsberg, L. (2003) A multispecies approach to ecological valuation and conservation. Conservation Biology 17: 196–206.Google Scholar
Sabbadin, R., Spring, D. & Rabier, C. (2007) Dynamic reserve site selection under contagion risk of deforestation. Ecological Modelling 210: 75–81.Google Scholar
Schapaugh, A.W. & Tyre, A.J. (2012) Bayesian networks and the quest for reserve adequacy. Biological Conservation 152: 178–186.Google Scholar
Snyder, S.A., Haight, R.G. & ReVelle, C.S. (2004) A scenario optimization model for dynamic reserve site selection. Environmental Modeling and Assessment 9: 179–187.Google Scholar
Soule, M. (1991) Conservation: tactics for a constant crisis. Science 253: 744–750.Google Scholar
Toth, S., Haight, R.G. & Rogers, L. (2011) Dynamic reserve selection: optimal land retention with price feedbacks. Operations Research 59: 1059–1078.Google Scholar