Liquid biofuel production will likely have its greatest impact on biodiversity through the large-scale changes in land use that will be required to meet the production of this energy source. In this study, we develop a framework that integrates species distribution models, land cover, land capability, and various biodiversity conservation data to identify natural areas with (i) a potentially high risk of transformation for biofuel production and (ii) potential impact on biodiversity conservation areas. The framework was tested in the Eastern Cape of South Africa, a region that has been earmarked for the cultivation of biofuels. We expressly highlight the importance of biodiversity conservation data that enhance the protected area network to limit potential losses by comparing the overlap of areas likely to become cultivated with (i) protected areas; (ii) biodiversity hot spots not currently protected; and (iii) ‘ecological corridors’ (areas deemed important for the migration of species and linkages between important biodiversity areas). Results indicate that the introduction of spatial filters reduced available land from 54% to 45%. Including all biodiversity scenarios reduced available land to 15% of the Eastern Cape should avoiding conflict with biodiversity conservation areas be prioritized. The assumption that agriculturally marginal land offers a unique opportunity to be converted to biofuel crops does not consider the biodiversity value attached to these areas. We highlight that decisions relating to large-scale transformation and changes in land cover need to take account of broader ecological processes. Determining the spatial extent of threats to biodiversity facilitates the analysis of spatial conflict. This article demonstrates a proactive approach for anticipating likely habitat transformation and provides an objective means of mitigating potential conflict with existing land use and biodiversity.
Anticipating potential biodiversity conflicts for future biofuel crops in south Africa: Incorporating spatial filters with species distribution models
Year: 2015
