Our ability to understand and mitigate adverse interactions with protected marine species is dependent on direct access to high-quality marine animal data, ocean observations, ecological models, and expert knowledge. The fusion of these diverse information streams into an integrated and spatially explicit decision support system is essential to meet the growing challenges of protected species and marine ecosystem-based management into the future. Through an ongoing collaboration between the Marine Geospatial Ecology Lab at Duke University and NOAA's Southwest Fisheries Science Center (SWFSC) we are actively expanding the use of earth observing data in decision support tools for marine ecosystem and protected species management. This new effort will build on the existing Ocean Biogeographic Information System OBIS-SEAMAP program (Duke University) and the integrated marine mammal modeling and spatial decision support systems programs supported by the Strategic Environmental Research and Development Program (projects SI-1390 Duke University and SI-1391 NOAA-SWFSC). We plan to significantly expand the scope, depth, and integration of remotely sensed data into our modeling and decision support system by: (1) incorporating and evaluating additional oceanographic measurements and indices for species-environment modeling; (2) implementing more robust automated workflows for processing earth observations for marine management use; and (3) expanding data dissemination and decision support functions using web services architectures. Our proposal will directly address priorities 5.1.6 Ecological Forecasting and 5.1.8 Coastal Management specified in the request for proposals. Our project will specifically improve an existing DSS for monitoring and assessing biodiversity at regional, national, and international scales as well as directly involve U.S. government agencies (NOAA, US-NAVY, ONR) and international organizations (OBIS, GBIF) with mandates for biodiversity monitoring and assessment. Our planned expansion of ocean observing data and analysis methods will provide a critical prototype for marine resource management decision systems development for the future.