This project proposes development of a spatial decision support system (DSS) designed to address an identified major conservation goal of the Eastern Tallgrass Prairie and Big Rivers Landscape Conservation Cooperative (ETPBR LCC), in collaboration with adjacent LCCs in the Midwestern U.S. Specifically, the DSS will be designed to identify select geographic areas (watersheds) within the Mississippi River Basin (MRB) where the application of conservation practices (e.g., planting perennial grasses, drainage management systems) can simultaneously (1) reduce nutrient export to the Gulf of Mexico hypoxia zone and (2) enhance habitat and conservation for grassland birds and riparian species (also avian migration corridors), and where (3) landowners are amenable and have the capacity to effectively implement these conservation practices. The DSS will be used to identify appropriate conservation practices to be implemented and to quantify potential benefits for both nutrient export and riparian and grassland bird habitat as a result of implementing these practices. This project has support and includes contributions from LCCs and participating agencies throughout the MRB, including federal and state conservation agencies and universities. The project is intended to be a pilot for a larger future effort, and seeks to help move opportunistic conservation to a more strategic approach that identifies where to locate projects in critical watersheds for the greatest overall conservation benefit. This pilot project will focus specifically on identifying the spatial intersection of important grassland/riparian bird assets, potential nutrient reduction sites, and amenable and capable landowners/managers with access to policy and economic support. The DSS will maximize regional portability and species flexibility via well-defined programming interfaces, modular design, and emphasis on universally available data. The DSS will consist of sub-models (e.g., species habitat, surface hydrology, landscape consolidation, stewardship/landowner participation, best management practices) that encapsulate processes and generalize input/output to facilitate integration. The DSS and sub-models will be written in Python and implemented within ESRI Model-builder. This allows for localized processing on desktop computers, while leaving open the possibility of distributed processing over the internet as a geo-processing service. It also enables extension to other areas or species.
