Incorporating species relocation in reserve design models: An example from Ft. Benning GA

• Published in: Ecological modelling 2012-01, Vol.224 (1), p.65-75
• Main Authors: Dissanayake, Sahan T.M., Önal, Hayri, Westervelt, James D., Balbach, Harold E.
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Atmospherics; biogeography; Biological and medical sciences; Climate change; Climatology. Bioclimatology. Climate change; Clustering; Conservation; conservation areas; Earth, ocean, space; Ecology; endangered species; Exact sciences and technology; External geophysics; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Genetic algorithms; geographical distribution; Gopherus polyphemus; keystone species; landscapes; Meteorology; Methods and techniques (sampling, tagging, trapping, modelling...); Military land use; Protected land; Relocation; Reserve design; Reserves; Risk
• ISSN: 0304-3800; 1872-7026
• DOI: 10.1016/j.ecolmodel.2011.07.016

► A linear integer programming model for the relocation of a species to clustered conservation areas. ► The models are applied to relocate Gopher Tortoise, an at-risk keystone species at Ft. Benning GA. ► We analyze the trade-off between compactness and relocation using an efficiency frontier. Given the increasing expansion of human dominated landscapes it often becomes necessary to relocate endangered and at-risk species from existing habitat areas. Further, there is growing research stating that climatic and atmospheric changes attributed to climate change are already affecting species distributions and geographic ranges, requiring endangered species to be relocated. Existing reserve design models mostly focus on choosing the optimal land area given existing species distributions and have not incorporated species relocation as a criterion. This paper introduces linear integer programming formulations for the relocation of multiple populations of a species at risk to clustered conservation areas. We present a basic clustered relocation model and extend the model to minimize the distances of relocation. We apply the models to a dataset related to Gopher Tortoise (GT), a keystone species currently considered ‘at risk’, at Ft. Benning Georgia where expanding military training needs require the relocation of GTs. We present the results and discuss the trade-off between compactness and relocation considerations using an efficiency frontier.