Identifying conservation-priority areas in a fragmented minnesota landscape based on the umbrella species concept and selection of large patches of natural vegetation

Two common but relatively untested approaches to prioritizing conservation areas involve identification of umbrella-species habitat and selection of large blocks of remaining natural vegetation. We tested an umbrella-species approach to identification of conservation areas in the Agassiz Beach Ridges landscape of Minnesota based on the habitat needs of the Greater Prairie Chicken (Tympanuchus cupido pinnatus). All natural vegetation patches that fell within 1.6 km of prairie chicken booming grounds were selected as conservation priorities and were compared with randomly selected vegetation patches. Patches selected by the umbrella-species approach encompassed slightly more biologically important land (59%) than selection of the largest patches (56%), and both these sets contained more biologically important land than randomly selected patches (47%). The set of largest natural vegetation patches encompassed the greatest number of natural community types (12 of 16 possible). Patches selected by the umbrella-species approach encompassed more occurrence (individual locations) of species and communities than both other approaches. Using the habitat requirements of prairie chicken to identify conservation area adequately addressed other grassland species and communities but not uncommon forested habitats. In many cases, the large-patch approach did equally well or better at protecting various biodiversity components, indicating that the umbrella-species and large-patch approaches are complementary in this landscape. Because we had long-term information on our umbrella species, we were able to explore the setting of conservation priorities using different levels and types of data. As expected, increasing the number of booming grounds by combing years of data (n = 25 to n = 337) generally increased the number and area of selected vegetation patches and the number of occurrences and amount of biologically important land contained within them. We also found that data sets with the fewest booming grounds often produced the most efficient results, such as most biologically important land per unit area selected. We observed that stable booming grounds were more efficient in incorporating other components of biodiversity per unit area of selected habitat than were temporary booming grounds. These results demonstrate that the quantity and quality of data on umbrella species will greatly affect planning results.