An Integer Optimization Approach to a Probabilistic Reserve Site Selection Problem

• Published in: Operations research Vol. 48; no. 5; pp. 697 - 708
• Main Authors: Haight, Robert G, Revelle, Charles S, Snyder, Stephanie A
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.09.2000; Operations Research Society of America; Institute for Operations Research and the Management Sciences
• Subjects: Algorithms; Biodiversity; Biodiversity conservation; Community ecology; Community structure; Coniferous forests; Conservation; Conservation biology; Ecosystem biology; Ecosystems; Endangered & extinct species; Environment: protected natural reserves on national forests; Forests; Heuristic; Heuristics; Integer programming; Linear programming; National forests; Operations research; Optimization; Plant communities; Probability; Probability, applications: presence-absence data of species in reserves; Programming, integer: discrete 0 1 by exact solution methods; Protected areas; RNA; Site selection; Species; Studies; Vegetation; Wildlife conservation; United States > US; Minnesota
• ISSN: 0030-364X; 1526-5463
• DOI: 10.1287/opre.48.5.697.12411

Interest in protecting natural areas is increasing as development pressures and conflicting land uses threaten and fragment ecosystems. A variety of quantitative approaches have been developed to help managers select sites for biodiversity protection. The problem is often formulated to select the set of reserve sites that maximizes the number of species or ecological communities that are represented, subject to an upper bound on the number or area of selected sites. Most formulations assume that information about the presence or absence of species in the candidate sites is known with certainty. Because complete information typically is lacking, we developed a reserve selection formulation that incorporates probabilistic presence-absence data. The formulation was a discrete 0/1 optimization model that maximized the number of represented vegetation communities subject to a budget constraint, where a community was considered represented if its probability of occurrence in the set of selected sites exceeded a specified minimum reliability threshold. Although the formulation was nonlinear, a log transformation allowed us to represent the problem in a linear format that could be solved using exact optimization methods. The formulation was tested using a moderately sized reserve selection problem based on data from the Superior National Forest in Minnesota.