17. Mathematical Methods for Identifying Representative Reserve Networks
305
in a dynamic framework, such as stochastic dynamic programming, may be harder
to justify for potential users, because their computational complexity gives them a
“black box” nature.
There are a wide variety of extensions to these problems that are only now
being studied. Complex spatial requirements and reserve design under uncertainty
are only two such avenues. Other areas of interest include problems in which
species or other objectives have different weights (values) and there is a fixed
amount of land to be set aside and problems that attempt to simultaneously
optimize multiple services (e.g., when a species has one value, say, for ecotourism, when it is in a reserve, and a different value, say, for trophy hunting, when it
is outside a reserve). Solutions to these problems—even approximate solutions—
will improve the design of effective reserve networks and, ultimately, will contribute to better protection for biodiversity.
Acknowledgments. This work was conducted as part of the Biological Diversity
Working Group supported by the National Center for Ecological Analysis and
Synthesis, a center funded by NSF (Grant DEB-94-21535), the University of
California—Santa Barbara, the California Resources Agency, and the California
Environmental Protection Agency.
Literature Cited
Andelman SJ, Meir E (2000) Breadth is better than depth: biodiversity data requirements
for adequate reserve networks. Conservation Biology (in press)
Andelman SJ, Fagan W, Davis F, Pressey RL (2000) Tools for conservation planning in an
uncertain world. BioScience (in press)
Bailey RG (1994) Ecoregions of the US. USDA Forest Service, Washington, DC
Ball IR, Smith A, Day JR, Pressey RL, Possingham H (in press) Comparison of mathematical algorithms for the design of a reserve system for nature conservation: an application
of genetic algorithms and simulated annealing. Journal of Environmental Management
Church RL, Stoms DM, Davis FW (1996) Reserve selection as a maximal covering location problem. Biological Conservation 76:105–112
Cocklin C (1989a) Mathematical programming and resources planning I: the limitations of
traditional optimization. Journal of Environmental Management 28:127–141
Cocklin C (1989b) Mathematical programming and resources planning II: new developments in methodology. Journal of Environmental Management 28:143–156
Cocks KD, Baird IA (1989) Using mathematical programming to address the multiple
reserve selection problem: an example from the Eyre Peninsula South Australia. Biological Conservation 49:113–130
Davis FW, Stoms DM, Andelman SJ (2000) Systematic reserve selection in the USA: an
example from the Columbia Plateau ecoregion. Parks (in press)
Fagan WF, Cantrell RS, Cosner C (1999) How habitat edges change species interactions.
American Naturalist 153:165–182
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NPcompleteness. WH Freeman and Company, San Francisco, CA
Golden B, Skiscim C (1986) Using simulated-annealing to solve routing and location
problems. Naval Research Logistics Quarterly 33:261–279
305
in a dynamic framework, such as stochastic dynamic programming, may be harder
to justify for potential users, because their computational complexity gives them a
“black box” nature.
There are a wide variety of extensions to these problems that are only now
being studied. Complex spatial requirements and reserve design under uncertainty
are only two such avenues. Other areas of interest include problems in which
species or other objectives have different weights (values) and there is a fixed
amount of land to be set aside and problems that attempt to simultaneously
optimize multiple services (e.g., when a species has one value, say, for ecotourism, when it is in a reserve, and a different value, say, for trophy hunting, when it
is outside a reserve). Solutions to these problems—even approximate solutions—
will improve the design of effective reserve networks and, ultimately, will contribute to better protection for biodiversity.
Acknowledgments. This work was conducted as part of the Biological Diversity
Working Group supported by the National Center for Ecological Analysis and
Synthesis, a center funded by NSF (Grant DEB-94-21535), the University of
California—Santa Barbara, the California Resources Agency, and the California
Environmental Protection Agency.
Literature Cited
Andelman SJ, Meir E (2000) Breadth is better than depth: biodiversity data requirements
for adequate reserve networks. Conservation Biology (in press)
Andelman SJ, Fagan W, Davis F, Pressey RL (2000) Tools for conservation planning in an
uncertain world. BioScience (in press)
Bailey RG (1994) Ecoregions of the US. USDA Forest Service, Washington, DC
Ball IR, Smith A, Day JR, Pressey RL, Possingham H (in press) Comparison of mathematical algorithms for the design of a reserve system for nature conservation: an application
of genetic algorithms and simulated annealing. Journal of Environmental Management
Church RL, Stoms DM, Davis FW (1996) Reserve selection as a maximal covering location problem. Biological Conservation 76:105–112
Cocklin C (1989a) Mathematical programming and resources planning I: the limitations of
traditional optimization. Journal of Environmental Management 28:127–141
Cocklin C (1989b) Mathematical programming and resources planning II: new developments in methodology. Journal of Environmental Management 28:143–156
Cocks KD, Baird IA (1989) Using mathematical programming to address the multiple
reserve selection problem: an example from the Eyre Peninsula South Australia. Biological Conservation 49:113–130
Davis FW, Stoms DM, Andelman SJ (2000) Systematic reserve selection in the USA: an
example from the Columbia Plateau ecoregion. Parks (in press)
Fagan WF, Cantrell RS, Cosner C (1999) How habitat edges change species interactions.
American Naturalist 153:165–182
Garey MR, Johnson DS (1979) Computers and intractability: a guide to the theory of NPcompleteness. WH Freeman and Company, San Francisco, CA
Golden B, Skiscim C (1986) Using simulated-annealing to solve routing and location
problems. Naval Research Logistics Quarterly 33:261–279