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Numerous datasets are available to public land management agencies for prioritizing aquatic restoration efforts, but their use has been limited by inadequate data access and/or a lack of guidance of how to integrate the data into a decision support framework.� The objective of this work was to use the Escalante River Watershed (ERW) in the Grand Staircase-Escalante National Monument as a case study to show how public land managers can use multi-scaled information to prioritize restoration and conservation actions.� �The Colorado Plateau Rapid Ecoregional Assessment (REA) estimated ecosystem intactness at the subwatershed scale, as well as anthropogenic drivers for observed departures. ��Aquatic Assessment Inventory and Monitoring (AIM) was then used to characterize drivers of aquatic condition at specific locations within these subwatersheds.� This case study provided insights about the limitations of the scale of data in land management assessments.� The REA data can show drivers of aquatic intactness (e.g., hydrologic alteration and habitat quality) at the subwatershed level, but cannot be used to determine causes of departures from reference conditions at specific locations within a subwatershed and therefore aid in deciding what restoration actions are appropriate at the project scale.� Aquatic AIM data can help to identify physical, biological, and chemical conditions at specific locations and provides some insight into the cause of departure from reference conditions so that land managers can prioritize restoration and conservation projects.� Local knowledge from resource specialists and data gathered from watershed partner groups was also important for determining causes of departure.� This study was an important first-step in developing guidance for assessing aquatic condition using multi-scaled data and for setting up a functional decision support framework that will aid public land managers in aquatic restoration project prioritization.� Decision support tools will be developed that show how to incorporate these data into management decisions.