This framework is designed to provide resource managers and decision makers with tools to guide the information discovery process for questions regarding the risk and health of Great Lakes coastal ecosystems. It is based on information collected through two complementary efforts Great Lakes Environmental Indicators (GLEI) and the Great Lakes Environmental Assessment and Mapping (GLEAM) projects. GLEI incorporates research conducted between 2000 and 2015, including field sampling of coastal ecosystems and landscape analyses to quantify watershed-based environmental stressors. GLEAM mapped stressors within the lakes themselves, integrating within-lake and watershed based stressors. Together, these two projects developed a comprehensive map of environmental stress for the entire Great Lakes basin.
To sift through the many layers of spatial data collected in these projects, the user provides information on fundamental decision points, include geographic extent (e.g. Great Lakes Basin, Ecoprovince, Lake) and ecosystem type of interest (pelagic, coastal, nearshore). Once the extent and ecosystem type is selected, an interactive map will show the degree of risk to particular watersheds – either land-based stressors, lake-based stressors, or a combination of both. End users can explore the various components of risk using an interactive graphing tool. The tool also includes the option to download spatial and tabular data for further analysis.
In addition to environmental risk, the web application displays and summarizes sample data from the GLEI I and GLEI II projects. This include biotic indicators from a range of taxa (birds, fish diatoms, macroinvertebrates), as well as descriptions of what those indicators mean, answering question such as "Is this particular site in better or worse condition than adjacent watersheds? How does it compare to the lake as a whole?" If, for example, a user is interested in riverine wetland, the application will retrieve data from all the sites in the area of interest, and display the mean and range of response variables.