Incorporating a distance cost in systematic reserve design

The selection of parcels of land to incorporate into reserve systems necessitates trade-offs among biodiversity targets, costs such as land area and spatial compactness. There are well-established systematic reserve design algorithms that incorporate these trade-offs to assist decision-makers in this process. One cost that has received little attention is the proximity of new land parcels to the existing reserve network: the ability of environmental managers to effectively maintain and protect additional land units is often constrained by their proximity to existing reserve networks. The selection of parcels of land close to existing reserves makes them logistically easier to deploy infrastructure to and can also improve the spatial contiguity of the existing reserve network. Previous research has been limited to using distance from the centroids of existing reserves, which significantly biases algorithms when reserves are irregularly shaped. Here we describe a new approach that overcomes this limitation by using the existing reserve boundary to determine proximity. We provide an example of this approach by implementing it as an additional constraint in an analysis of biodiversity targets within the Greater Blue Mountains World Heritage Area, Australia, via the Marxan reserve design software. The incorporation of the distance cost in the analysis was effective in selecting parcels near to the existing reserve system and can be combined with other variables in the algorithm to improve spatial compactness while meeting biodiversity and other targets. It provides alternative solutions for use by reserve planners when extending reserve systems.

[1]  Peter J. Auster,et al.  Use of Simulated Annealing for Identifying Essential Fish Habitat in a Multispecies Context , 2005 .

[2]  Matthew E. Watts,et al.  Marxan and relatives: Software for spatial conservation prioritization , 2009 .

[3]  Charles S. ReVelle,et al.  Spatial attributes and reserve design models: A review , 2005 .

[4]  Mark D. McDonnell,et al.  Mathematical Methods for Spatially Cohesive Reserve Design , 2002 .

[5]  Jane Elith,et al.  Sensitivity of conservation planning to different approaches to using predicted species distribution data , 2005 .

[6]  D. Tilman,et al.  The Importance of Land-Use Legacies to Ecology and Conservation , 2003 .

[7]  S. Andelman,et al.  Mathematical Methods for Identifying Representative Reserve Networks , 2000 .

[8]  Robert L. Pressey,et al.  Ad Hoc Reservations: Forward or Backward Steps in Developing Representative Reserve Systems? , 1994 .

[9]  J. Lobo,et al.  Threshold criteria for conversion of probability of species presence to either–or presence–absence , 2007 .

[10]  John Sessions,et al.  Designing Compact and Contiguous Reserve Networks with a Hybrid Heuristic Algorithm , 2002, Forest Science.

[11]  Hugh P. Possingham,et al.  Efficiency, costs and trade-offs in marine reserve system design , 2005 .

[12]  J. Lawton,et al.  The Gaps between Theory and Practice in Selecting Nature Reserves , 1999 .

[13]  Richard L. Church,et al.  Clustering and Compactness in Reserve Site Selection: An Extension of the Biodiversity Management Area Selection Model , 2003, Forest Science.