Nature Reserve Site Selection to Maximize Expected Species Covered

We analyze the problem of maximizing the expected number of species in a nature reserve network, subject to a constraint on the number of sites in the network, given probabilistic information about species occurrences. The problem is a nonlinear binary integer program that is NP-hard. We develop a linear integer programming approximation that may be solved with standard integer programming software. We compare the approximation with two other approaches, an expected greedy approach and a probability hurdle approach, using probabilistic data on occurrences of terrestrial vertebrates in the state of Oregon. Results of the approximation and an exact algorithm are compared by using samples from the North American Breeding Bird Survey.

[1]  A. O. Nicholls How to make biological surveys go further with generalised linear models , 1989 .

[2]  Jamie B. Kirkpatrick,et al.  An iterative method for establishing priorities for the selection of nature reserves: An example from Tasmania , 1983 .

[3]  D. Dannenbring Procedures for Estimating Optimal Solution Values for Large Combinatorial Problems , 1977 .

[4]  W. Link,et al.  The North American Breeding Bird Survey Results and Analysis , 1997 .

[5]  C. Margules,et al.  Biological Models for Monitoring Species Decline: The Construction and Use of Data Bases , 1994 .

[6]  George L. Nemhauser,et al.  Note--On "Location of Bank Accounts to Optimize Float: An Analytic Study of Exact and Approximate Algorithms" , 1979 .

[7]  RICHARD C. LARSON,et al.  A hypercube queuing model for facility location and redistricting in urban emergency services , 1974, Comput. Oper. Res..

[8]  A. O. Nicholls,et al.  Selecting networks of reserves to maximise biological diversity , 1988 .

[9]  Mark S. Daskin,et al.  A Maximum Expected Covering Location Model: Formulation, Properties and Heuristic Solution , 1983 .

[10]  J. Lawton,et al.  Rare species, the coincidence of diversity hotspots and conservation strategies , 1993, Nature.

[11]  Simon A. Levin,et al.  Fragile Dominion: Complexity and the Commons , 1999 .

[12]  C. Saydam,et al.  Applications and Implementation A SEPARABLE PROGRAMMING APPROACH TO EXPECTED COVERAGE: AN APPLICATION TO AMBULANCE LOCATION , 1985 .

[13]  M. Austin,et al.  New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures , 1984, Vegetatio.

[14]  Mark S. Daskin,et al.  APPLICATION OF AN EXPECTED COVERING MODEL TO EMERGENCY MEDICAL SERVICE SYSTEM DESIGN , 1982 .

[15]  James R. Evans,et al.  A Branch and Bound Algorithm for the List Selection Problem in Direct Mail Advertising , 1981 .

[16]  Amy W. Ando,et al.  Species distributions, land values, and efficient conservation , 1998, Science.

[17]  Robert G. Haight,et al.  An Integer Optimization Approach to a Probabilistic Reserve Site Selection Problem , 2000, Oper. Res..

[18]  Charles S. ReVelle,et al.  The Maximum Availability Location Problem , 1989, Transp. Sci..

[19]  R. Haight,et al.  An optimization approach to selecting research natural areas in National Forests , 1999 .

[20]  K. D. Cocks,et al.  Using mathematical programming to address the multiple reserve selection problem: An example from the Eyre Peninsula, South Australia , 1989 .

[21]  Richard L. Church,et al.  The maximal covering location problem , 1974 .

[22]  Rajan Batta,et al.  The Maximal Expected Covering Location Problem: Revisited , 1989, Transp. Sci..

[23]  Christian A. Vossler,et al.  A comparison of taxonomic distinctness versus richness as criteria for setting conservation priorities for North American birds , 2001 .

[24]  Harry John Betteley Birks,et al.  How to maximize biological diversity in nature reserve selection: Vascular plants and breeding birds in deciduous woodlands, western Norway , 1993 .

[25]  Richard L. Church,et al.  Reserve selection as a maximal covering location problem , 1996 .

[26]  R L Pressey,et al.  Beyond opportunism: Key principles for systematic reserve selection. , 1993, Trends in ecology & evolution.

[27]  Hugh P. Possingham,et al.  Effectiveness of alternative heuristic algorithms for identifying indicative minimum requirements for conservation reserves , 1997 .

[28]  Thomas L. Magnanti,et al.  Applied Mathematical Programming , 1977 .

[29]  Chris Margules,et al.  Patterns in the distributions of species and the selection of nature reserves: An example from Eucalyptus forests in South-eastern New South Wales , 1989 .

[30]  Andrew R. Solow,et al.  Choosing reserve networks with incomplete species information , 2000 .

[31]  Manuela M. P. Huso,et al.  A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon , 1997 .

[32]  G. Nemhauser,et al.  Maximizing Submodular Set Functions: Formulations and Analysis of Algorithms* , 1981 .