We compare the number of species represented and the spatial pattern of reserve networks derived using five types of reserve selection algorithms on a set of vertebrate distribution data for the State of Oregon (USA). The algorithms compared are: richness-based heuristic algorithms (four variations), weighted rarity-based heuristic algorithms (two variations), progressive rarity-based heuristic algorithms (11 variations), simulated annealing, and o linear programming-based branch-and-bound algorithm. The linear programming algorithm provided optimal solutions to the reserve selection problem, finding either the maximum number of species for a given number of sites or the minimum number of sites needed to represent all species. Where practical, we recommend the use of linear programming algorithms for reserve network selection. However, several simple heuristic algorithms provided near-optimal solutions for these data. The near-optimality, speed and simplicity of heuristic algorithms suggests that they are acceptable alternatives for many reserve selection problems, especially when dealing with large data sets or complicated analyses.
Bibliographical noteFunding Information:
Data compilation and some analyses reported here are a part of the Oregon Gap Analysis Program, US National Biological Service. This work was supported by the Biodiversity Research Consortium (BRC), a group of US government agencies, academic and non-governmental institutions performing coordinated research on biodiversity assessment and management methods. The BRC acknowledges the support of Cooperative Research Agreement PNW 92-0283 between the USDA Forest Service and Oregon State University, Interagency Agreement DW12935631 between the US EPA and the USDA Forest Service, and the USDA Forest Service, Pacific Northwest Research Station. We thank Brian Garber-Yonts and Michael Jaspin for research assistance, and Eleanor Gaines for directing development of the species distribution database. Oregon State Agricultural Experiment Station Technical Paper No. 10997.