Access the full text.
Sign up today, get DeepDyve free for 14 days.
R. Vane-Wright, C. Humphries, Paul Williams (1991)
What to protect?—Systematics and the agony of choiceBiological Conservation, 55
Paul Williams, R. Vane-Wright, C. Humphries, J. Lasalle, I. Gauld (1993)
Measuring biodiversity for choosing conservation areas.
A. Land, A. Doig (1960)
An Automatic Method of Solving Discrete Programming ProblemsEconometrica, 28
(1994)
Row reduction in the maximal set covering problem. Working paper QA-1994±005, Department of Quantitative Analysis and Operations Management, University of Cincinnati, Cincinnati, OH
R. Gerrard, R. Church (1995)
A General Construct for the Zonally Constrained p-Median ProblemEnvironment and Planning B: Planning and Design, 22
J. Camm, S. Polasky, A. Solow, B. Csuti (1996)
A note on optimal algorithms for reserve site selectionBiological Conservation, 78
M. Kershaw, P. Williams, G. Mace (1994)
Conservation of Afrotropical antelopes: consequences and efficiency of using different site selection methods and diversity criteriaBiodiversity & Conservation, 3
R. Pressey, C. Humphries, C. Margules, R. Vane-Wright, P. Williams (1993)
Beyond opportunism: Key principles for systematic reserve selection.Trends in ecology & evolution, 8 4
R. Pressey, H. Possingham, C. Margules (1996)
Optimality in reserve selection algorithms: When does it matter and how much?Biological Conservation, 76
(1995)
Bibliography on the Conservation of Biological Diversity: Biological/Ecological, Economic, and Policy Issues
B. Csuti, S. Polasky, P. Williams, R. Pressey, J. Camm, M. Kershaw, A. Kiester, Brian Downs, Richard Hamilton, M. Huso, K. Sahr (1997)
A comparison of reserve selection algorithms using data on terrestrial vertebrates in OregonBiological Conservation, 80
L. Underhill (1994)
Optimal and suboptimal reserve selection algorithmsBiological Conservation, 70
R.S. Garfinkel, G.L. Nemhauser (1972)
Integer programming
(1992)
Optimization Subroutine Library (OSL), Guide and Reference, Release 2
Brian Downs, J. Camm (1996)
An exact algorithm for the maximal covering problemNaval Research Logistics, 43
P. Williams, D. Gibbons, C. Margules, A. Rebelo, C. Humphries, R. Pressey (1996)
A Comparison of Richness Hotspots, Rarity Hotspots, and Complementary Areas for Conserving Diversity of British BirdsConservation Biology, 10
R. Pressey, A. Nicholls (1989)
Application of a Numerical Algorithm to the Selection of Reserves in Semi-arid New South WalesBiological Conservation, 50
C. Margules, A. Nicholls, R. Pressey (1988)
Selecting networks of reserves to maximise biological diversityBiological Conservation, 43
J.D. Camm, D.J. Sweeney (1994)
Row reduction in the maximal set covering problem
A. Nicholls, C. Margules (1993)
An upgraded reserve selection algorithmBiological Conservation, 64
D. White, Jon Kimerling, Scott Overton (1992)
Cartographic and Geometric Components of a Global Sampling Design for Environmental Monitoring, 19
P.H. Williams, R.I. Vane-Wright, C.J. Humphries (1993)
Hymenoptera and Biodiversity
R. Church, D. Stoms, F. Davis (1996)
Reserve selection as a maximal covering location problemBiological Conservation, 76
R. Noss (1987)
From plant communities to landscapes in conservation inventories: A look at the nature conservancy (USA)Biological Conservation, 41
The problem of selecting nature reserves has received increased attention in the literature during the past decade, and a variety of approaches have been promoted for selecting those sites to include in a reserve network. One set of techniques employs heuristic algorithms and thus provides possibly sub-optimal solutions. Another set of models and accompanying algorithms uses an integer programming formulation of the problem, resulting in an optimization problem known as the Maximal Covering Problem, or MCP. Solution of the MCP provides an optimal solution to the reserve site selection problem, and while various algorithms can be employed for solving the MCP they all suffer from the disadvantage of providing a single optimal solution dictating the selection of areas for conservation. In order to provide complete information to decision makers, the determination of all alternate optimal solutions is necessary. This paper explores two procedures for finding all such solutions. We describe the formulation and motivation of each method. A computational analysis on a data set describing native terrestrial vertebrates in the state of Oregon illustrates the effectiveness of each approach.
Environmental and Ecological Statistics – Springer Journals
Published: Sep 28, 2004
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.