Maximizing return on conservation investment in the conterminous U.S

Background/Question/Methods

Traditional conservation priority-setting approaches have sought to identify areas with the greatest value for conservation, e.g. biodiversity hotspots, areas of high endemism, and/or the most threatened ecoregions and habitats.  These approaches take into account some measure of biodiversity, and threats to that biodiversity.  Conservation planners have increasingly included the costs of conservation, typically land acquisition, in priority-setting analyses.  The expectation is that by explicitly considering conservation costs and benefits, we can maximize the "return on investment" (ROI) or the conservation benefit per dollar spent. Here we present an approach to calculating ROI for the conterminous United States at the county level that incorporates a conservation target (vertebrate species richness), a measure of diminishing returns (percentage of the county already protected), a measure of habitat diversity (Simpson’s diversity index for “natural” habitats), a measure of threat (habitat loss from 1992-2001), and land costs (in $/acre, which takes into account per-acre net returns of lands in crops, pasture, forest, range, and development).  We compare calculations of ROI using three methods: optimization, a greedy heuristic, and the conservation planning software Marxan.

Results/Conclusions

Incorporating measures of diminishing returns, habitat diversity and threat yielded results different than just considering how to maximize species protected for the least cost.  The three methods we compared yielded similar sets of counties selected, especially if required to account for all species on our final list (1066 in the conterminous U.S.). Setting budget constraints that allowed for less than complete ‘coverage’ of species protected showed that counties with especially low land costs (e.g. parts of the Great Basin and Great Plains) have the potential for high return on investment, even if they are not typically identified as having high conservation value. Moving from simple biodiversity-based reserve-selection analyses to more formal ROI analyses will provide a more efficient use of scarce conservation dollars.