The SITES reserve selection system: A critical review

Numerous models have been put forth to help with the growing demand for the establishment of biodiversity reserves. One site selection model that has been used in several recent studies is SITES [S.J. Andelman, I. Ball, F.W. Davis and D.M. Stoms, SITES V 1.0: an analytical toolbox for designing ecoregional conservation portfolios, Unpublished manual prepared for the nature conservancy, 1999, 1–43. (available at )]. SITES includes two heuristic solvers: based on Greedy and Simulated Annealing. We discuss the formulation of the SITES model, present a new formulation for that problem, and solve a number of test problems optimally using off-the-shelf software. We compared our optimal results with the SITES Simulated Annealing heuristic and found that SITES frequently returns significantly suboptimal solutions. Our results add further support to the argument, started by Underhill [L.G. Underhill, Optimal and suboptimal reserve selection algorithms, Biol. Conserv. 70 (1994) 85–87], continuing through Rodrigues and Gaston [A.S.L. Rodrigues and K.J. Gaston, Optimization in reserve selection procedures – why not?, Biol. Conserv. 107 (2002) 123–129], for greater integration of optimal methods in the reserve design/selection literature.     

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.     

Counterpart Models in Facility Location Science and Reserve Selection Science

Five classes of zero–one programming models for discrete facility location problems are compared to counterpart models for the selection of conservation reserves. The basic problem of siting facilities to cover demand for services is analogous to the problem of selecting reserves to support species diversity. The classes of models include the set covering and maximal covering models, as well as models for backup and redundant coverage. Issues of reliability and uncertainty are addressed by chance constrained covering models and maximal expected covering models. Exact and heuristic solution approaches are discussed. Multi-objective and economic issues are considered.     

Novel operations research methods for efficiently determining irreplaceable sites for conservation

We examine the irreplaceability of sites in the context of the species set covering problem and the maximal covering species problem. We show that a succession of 0-1 programming problems can be solved to quickly determine the set of irreplaceable sites. For the widely available Oregon data set, we find a general lack of trend in the number of irreplaceable sites with the number of sites available for selection used. Moreover, we observe that irreplaceability at one level of resource may not be a predictor of irreplaceability at a higher or a lower level of resource. Finally, we investigate the economic value of irreplaceable sites by trial removals of those sites. This paper is dedicated to the memory of Dr. Charles S. ReVelle.     

A comparison of fixed-site and non-fixed-site approaches for species protection

The efficacy of simultaneously advancing two distinct conceptual designs (referred to here as fixed-site and non-fixed-site) for species conservation and protection is addressed. In the literature, numerous models can be found that typically stem from a particular design, but rarely are comparisons made between approaches. This paper presents a more integrated optimization framework that models landowner behavior and species viabilities at a landscape scale. Regional demand for resource extraction is used as the economic driver, a variant of simulated annealing is used to solve the model under different species protection approaches, and a detailed species population simulator is utilized to measure biological responses. When directly comparing the outcomes of different species protection strategies from a case study in Oregon (USA), it was found that neither approach was universally superior in terms of financial value or degree of protection for two late seral forest dependent species.     

Working backwards to move forwards: Graph-based connectivity metrics for reserve network selection

In this paper we apply graph theory in a reserve selection exercise to explore the tradeoffs between maintaining connectivity and minimizing the total area of a protected area network. Rather than focus on a single organism, we used a multi-species approach and looked at the tradeoff curves for organisms with varying dispersal abilities. We first generated the tradeoff curves using a graph-based metric to determine the importance of individual patches for maintaining connectivity. We then performed an analogous set of analyses using patch size as a surrogate measure of importance.     

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.     

Waste management modeling by MARKAL model: A case study for Basilicata Region

Air pollution is one of the most pressing environmental problems which affects likewise urban, industrial and rural areas. Environmental planners, regulators and decision makers need reliable, scientifically based tools to find out strategies for controlling air pollution in a cost-effective way, taking into account the whole productive system. In this framework the basic elements of energy-environmental planning have to be extended to include also waste processing technologies amongst the usually considered pollution sources. Bottom-up optimizing models, based on linear programming techniques and customized for specific cases, represent a powerful tool in energy-environmental management. This paper focuses on the integrated modeling of material flows and energy system performed on a local scale case study (Basilicata Region, Southern Italy) using the linear programming model IEA-MARKAL. We have evaluated the feasibility of the model in representing the waste management system to estimate the environmental impact of the waste processing technologies in the context of the whole productive system. A sensitivity analysis has been carried out to emphasize the connections between tariffs, waste disposal technologies assessment and atmospheric emissions. This revised version was published online in July 2006 with corrections to the Cover Date.