This dataset includes five ESRI ArcMap shapefiles to provide metadata for those shapefiles. Additional, within the attribute table of each shapefile is a data field titled "SHP_Source" that provides a brief description of the public data sources used to create the shapefile. These data include aquatic non-native species presence in waters of the Laurentian Great Lakes, US and Canadian population for urban areas around the Great Lakes, locations of marinas and ports in the Great Lakes, and data on maritime commerce within the Great Lakes. This dataset is associated with the following publication: O'Malia, E., L. Johnson, and J. Hoffman. Pathways and places associated with nonindigenous aquatic species introductions in the Laurentian Great Lakes. HYDROBIOLOGIA. Springer, New York, NY, USA, 817(1): 23-40, (2018).

The data provided in the file are measurements (and calculations) of various morphological characteristics made on individual larval fish specimens. This dataset is associated with the following publication: Peterson, G., and J. Lietz. Identification of Ruffe larvae (Gymnocephalus cernuus) in the St. Louis River, Lake Superior: Clarification and guidance regarding morphological descriptions. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 205-210, (2017).

The data provided in the file are measurements (and calculations) of various morphological characteristics made on individual larval fish specimens. This dataset is associated with the following publication: Peterson, G., and J. Lietz. Identification of Ruffe larvae (Gymnocephalus cernuus) in the St. Louis River, Lake Superior: Clarification and guidance regarding morphological descriptions. JOURNAL OF GREAT LAKES RESEARCH. International Association for Great Lakes Research, Ann Arbor, MI, USA, 43(1): 205-210, (2017).

The USGS developed the second in a series of informative spatial distribution datasets of submersed aquatic vegetation (SAV) in the western basin of Lake Erie. The second dataset was developed by object-based image analysis of high-resolution imagery (US waters < 6 meters deep) collected during peak biomass in 2018 to allow assessments of changes in SAV distribution. Assessing SAV abundance may contribute to inform the long-term impacts of Grass Carp, Common Carp, eutrophication, wind fetch and sedimentation on vegetation communities throughout Lake Erie and the impact these stressors may have on other organisms in the ecosystem. These data may also help inform the deployment of toxic bait deployments targeting Grass Carp. Bait placement can be strategically aligned with the spatial distribution and diet preferences of Grass carp to maximize control efforts while minimizing impacts to native species. These data provide a good baseline of SAV at an early point in the invasion/population growth curve for grass carp from which later assessments/models might project from, and also considered valuable for bioenergetic modeling efforts to project grass carp biomass or other species dependent on SAV.

The USGS developed the second in a series of informative spatial distribution datasets of submersed aquatic vegetation (SAV) in the western basin of Lake Erie. The second dataset was developed by object-based image analysis of high-resolution imagery (US waters < 6 meters deep) collected during peak biomass in 2018 to allow assessments of changes in SAV distribution. Assessing SAV abundance may contribute to inform the long-term impacts of Grass Carp, Common Carp, eutrophication, wind fetch and sedimentation on vegetation communities throughout Lake Erie and the impact these stressors may have on other organisms in the ecosystem. These data may also help inform the deployment of toxic bait deployments targeting Grass Carp. Bait placement can be strategically aligned with the spatial distribution and diet preferences of Grass carp to maximize control efforts while minimizing impacts to native species. These data provide a good baseline of SAV at an early point in the invasion/population growth curve for grass carp from which later assessments/models might project from, and also considered valuable for bioenergetic modeling efforts to project grass carp biomass or other species dependent on SAV.

In the Great Lakes basin, there are numerous organizations undertaking scientific monitoring and research efforts with the goal of identifying threats and evaluating management strategies that will protect and restore the Great Lakes ecosystem. Coordination among all these stakeholders is a challenge, and having a centralized location where researchers and managers can identify relevant scientific activities and access fundamental information about these activities is crucial for efficient management. The Science in the Great Lakes (SiGL) Mapper was a map-based discovery tool that spatially displayed basin-wide multidisciplinary monitoring and research activities conducted by both USGS and partners from all five Great Lakes. It was designed to help Great Lakes researchers and managers strategically plan, implement, and analyze monitoring and restoration activities by providing easy access to historical and on-going project metadata while allowing them to identify gaps (spatially and topically) that have been underrepresented in previous efforts or need further study. SiGL provided a user-friendly and efficient way to explore Great Lakes projects and data through robust search options while also providing a critical spatial perspective through its interactive mapping interface.

In the Great Lakes basin, there are numerous organizations undertaking scientific monitoring and research efforts with the goal of identifying threats and evaluating management strategies that will protect and restore the Great Lakes ecosystem. Coordination among all these stakeholders is a challenge, and having a centralized location where researchers and managers can identify relevant scientific activities and access fundamental information about these activities is crucial for efficient management. The Science in the Great Lakes (SiGL) Mapper was a map-based discovery tool that spatially displayed basin-wide multidisciplinary monitoring and research activities conducted by both USGS and partners from all five Great Lakes. It was designed to help Great Lakes researchers and managers strategically plan, implement, and analyze monitoring and restoration activities by providing easy access to historical and on-going project metadata while allowing them to identify gaps (spatially and topically) that have been underrepresented in previous efforts or need further study. SiGL provided a user-friendly and efficient way to explore Great Lakes projects and data through robust search options while also providing a critical spatial perspective through its interactive mapping interface.

The USGS developed the second in a series of informative spatial distribution datasets of submersed aquatic vegetation (SAV) in the eastern basin of Lake Erie. The second dataset was developed by object-based image analysis of high-resolution imagery (US waters < 6 meters deep) collected during peak biomass in 2018 to allow assessments of changes in SAV distribution. Assessing SAV abundance may contribute to inform the long-term impacts of Grass Carp, Common Carp, eutrophication, wind fetch and sedimentation on vegetation communities throughout Lake Erie and the impact these stressors may have on other organisms in the ecosystem. These data may also help inform the deployment of toxic bait deployments targeting Grass Carp. Bait placement can be strategically aligned with the spatial distribution and diet preferences of Grass carp to maximize control efforts while minimizing impacts to native species. These data provide a good baseline of SAV at an early point in the invasion/population growth curve for grass carp from which later assessments/models might project from, and are valuable for bioenergetic modeling efforts to project grass carp biomass or other species dependent on SAV.

All available bathymetry and related information for Pierre Greys Lakes were collected and hard copy maps digitized where necessary. The data were validated against more recent data (Shuttle Radar Topography Mission 'SRTM' imagery and Indian Remote Sensing 'IRS' imagery) and corrected where necessary. The published data set contains the lake bathymetry formatted as an Arc ascii grid. Bathymetric contours and the boundary polygon are available as shapefiles.

Image and biometric data were collected for 22 species of fish from Great Lakes Tributaries in Michigan and Ohio, and the Illinois River for the purpose of developing a fish identification classifier. Data consists of a comma delimited spreadsheet that identifies image file names and associated fish identification number, common name, species code, family name, genus, and species, date collected, river from which each fish was collected, location of sampling, fish fork length in millimeters, girth in millimeters, weight in kilograms, and personnel involved with image collection. Biometric data are saved as .csv comma delimited format and image files are saved as .png file type.