Abstract
There is an overwhelming consensus among conservation experts that an efficient, data-driven, science-based geospatial conservation prioritization tool can help guide or optimize the dollars spent on land conservation. In this work, we developed and implemented a conservation prioritization framework that integrates open-source data and an optimization method based on stochastic multicriteria acceptability analysis (SMAA). SMAA is capable of handling problems with multiple constraints, unknown user preferences, and insufficient or missing data. The data measures were developed from openly available peer-reviewed data from federal and state agencies. These data were used to evaluate projects based on their ecological merit. Since the data that indicate ecological factors tend to be on different scales (geospatial, non-geospatial) and type (raster/vector/table), each of these data measures were converted into a one square km hexagonal grid format over the entire study region (Gulf of Mexico RESTORE Region) for further analysis. SMAA algorithms incorporated over 100,000 user preferences to evaluate the merit of conservation projects by assigning random weights. The increasing use of web applications to provide visualizations of geospatial data has improved access to scientific information. Yet many applications lack capabilities in “on-the-fly” processing and analytics. The framework developed as part of this work was implemented as a web-based, user-friendly, geospatial tool that can prioritize conservation lands “on-the-fly” based on geospatial footprints. From early validations with the stakeholders, the developed framework produces valuable recommendations for conservation agencies based on an easy to use geospatial web interface. This tool will be a part of the Strategic Conservation Assessment project toolkit.
