The Atlantic Canadian Seabird Viewer is a collaborative project by Bird Studies Canada, Mount Allison University, and World Wildlife Fund Canada to share information about seabirds and the risks they face from human activity in the marine environment. The data underlying this project was compiled as part of a three-year study funded by the Atlantic Ecosystem Initiative of Environment and Climate Change Canada (2015-18) and consists of: (1) locations of known breeding colonies, for 13 species/groups of species, surveyed at least once between 1996-2016; (2) predictions of the at-sea distribution of 13 species / groups of species of seabirds based on machine-learning models constructed using tracking data for n= 520 individual birds contributed by project partners; (3) relative intensity (scaled from 0 to 1) of a range of human activities: light pollution (derived from NOAA’s 1993-2003 DMSP OLS composite), marine traffic (derived from Automated Identification System, or AIS data for Atlantic Canada from January 2014-March 2015), and ship-source oil pollution threat (a weighted-composite based on average fuel carrying capacity for different classes of vessel). The information on species distribution and the threat posed by human activity (for which data was available) was combined to produce a single seabird cumulative risk score, which integrates both the overall “exposure” of the seabird community as well as its sensitivity to the human activities present in a particular grid cell. You can query cumulative risk at any grid cell location, or draw or upload polygons outlining areas of interest to you in order to assess cumulative risk for grid cells overlapping with your drawing / uploaded polygon. ** Intended Purpose ** This data is intended to support strategic decision making, and is best suited to “first-pass” assessments with a goal to identify at-risk areas. The results of assessments based on this data can be used to help make broad-scale decisions (e.g., identify candidate marine protected areas), or pursue finer-scale analysis using more localized data, e.g., aid in the deployment of fishery observers to monitor bycatch. It can also be used to identify candidate locations for implementation of specific management decisions. This data, within the confines of the study area boundary, is a powerful tool for risk assessment, and conservation and management decision making.