Representational success: A new paradigm for achieving species protection by reserve site selection

Models for designing habitat reserve networks have focused on minimizing the number of sites necessary to cover each species one or more times. A solution to this problem is usually one from among a large number of alternative optimal configurations of sites. This paper develops an iterative method for building reserve networks that produces an optimal solution to the species set covering problem (SSCP) and also maximizes the number of species covered two or more times, three or more times, and so on, conditional on the solution to the previous iteration. We refer to this as representational success. Thus, a pareto optimal species set covering is achieved that is preferable to an arbitrary optimal solution to the SSCP.     

Designing a conservation reserve network with minimal fragmentation: A linear integer programming approach

In the biological conservation literature, the optimum reserve site selection problem has often been addressed by using the prototype set covering and maximal covering formulations, assuming that representation of species is the only criterion in site selection. This approach usually results in a small but highly fragmented reserve, which is not useful for practical conservation planning. To improve the chances of species' persistence, it may be desirable to reduce habitat fragmentation. This paper presents a linear integer programming formulation to minimize spatial gaps between selected sites in a reserve network, which is applied to a data set on breeding birds. The authors express their willingness to share the database used in this study. Those readers who wish to have access to the data may contact Robert A. Briers at r.briers@napier.ac.uk.     

A Scenario Optimization Model for Dynamic Reserve Site Selection

Conservation planners are called upon to make choices and trade-offs about the preservation of natural areas for the protection of species in the face of development pressures. We addressed the problem of selecting sites for protection over time with the objective of maximizing species representation, with uncertainty about future site development, and with periodic constraints on the number of sites that can be selected. We developed a 0–1, linear optimization model with 2 periods to select the sites that maximize expected species coverage subject to budget constraints. The model is based on the idea that development uncertainty can be characterized by a set of scenarios, each of which is a possible second-period development outcome for the set of sites. We also suggest that our 2-period model can be used in a sequential fashion that is consistent with adaptive planning. Results are presented for the Fox River watershed in Chicago.