This dataset contains the Great Lakes Shoreline Ecosystem (GLSE) inventory, which is a geospatial digital inventory of: * ecosite polygon mapping * field survey calibration points * associated ground level ecological data It replaces Great Lakes Shoreline Ecosystem mapping products versions 1.0 and 2.0. It is an update to those products and includes the data and information contained in them but now includes data and information for the entire Canadian side of the Great Lakes shoreline including connecting channels, from the land to water or wetland interface to two kilometers inland. Mapping and ground sampling adheres to the Great Lakes Shoreline Ecosystem classification system, (Lee et. al., in prep.) which is a detailed ecological classification and surveying method.

Water quality and ecosystem health data used to conduct a cumulative effects assessment of Canadian Great Lakes nearshore waters in support of the Great Lakes Water Quality Agreement are included in this dataset. The data was collected by various government and non-government agencies and organizations and integrated into this dataset to allow the assessment to be conducted. By conducting a regular, systematic assessment of cumulative effects in the nearshore waters of the Great Lakes Environment and Climate Change Canada (ECCC) is able to identify areas of high quality and areas under stress. Knowledge of ecological thresholds, other Great Lakes assessments, stressor information, indicators and local and traditional ecological knowledge will be used to aid in: 1) the identification and mapping of high quality nearshore areas and areas that are or may become subject to high stress and; 2) the determination of factors and cumulative effects that are causing stress or threats. Cumulative effects impacting the nearshore and future threats to areas of high ecological value will be better understood and the knowledge shared will assist in priority setting for science and management at a meaningful and practical spatial scale within each Great Lake and connecting channel.

The Great Lakes Fish Biodiversity Science Database is a compilation of fish community and habitat data from DFO Science surveys, primarily related to freshwater fishes of conservation concern in the Great Lakes basin. Data include: sampling site location, date, fish species and counts, and associated habitat information. Project-specific details including purpose/objectives and study methodology are often reported in the DFO Canadian data report of fisheries and aquatic sciences series.

This historic dataset delineates valley segments based on a number of different natural features. The data applies to valley segments on the Ontario side of the Great Lakes. Aquatic Landscape Inventory System (ALIS) has also been incorporated into the [Aquatic Ecosystem Classification: Great Lakes Basin and Wetlands Data Class](/dataset/aquatic-ecosystems-in-the-great-lakes-basin) and the Great Lakes Conservation Blueprint for Aquatic Biodiversity datasets. We are no longer updating this data. It is best suited for historical research and analysis.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

The Regional Deterministic Wave Prediction System (RDWPS) produces wave forecasts out to 48 hours in the future using the third generation spectral wave forecast model WaveWatch III® (WW3). The model is forced by the 10 meters winds from the High Resolution Deterministic Prediction System (HRDPS). Over the Great Lakes, an ice forecast from the Water Cycle Prediction System of the Great Lakes (WCPS) is used by the model to attenuate or suppress wave growth in areas covered by 25% to 75% and more than 75% ice, respectively. Over the ocean, an ice forecast from the Regional Ice Ocean Prediction System (RIOPS) is used: in the Northeast Pacific, waves propagate freely for ice concentrations below 50%, above this threshold there is no propagation; in the Northwest Atlantic the same logic is used as in the Great Lakes. Forecast elements include significant wave height, peak period, partitioned parameters and others. This system includes several domains: Lake Superior, Lake Huron-Michigan, Lake Erie, Lake Ontario, Atlantic North-West and Pacific North-East.

PURPOSE:This study is part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake. The main objectives are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality are monitored to evaluate potential effects of change on fisheries. DESCRIPTION:Great Bear Lake, one of the largest lakes in North America, contains culturally and recreationally important fish species. Great Bear Lake is located in the sub-Arctic and Arctic Circle. As part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake, the main objectives of this study are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality are monitored to evaluate potential effects of change on fisheries. From 2000 to 2011, surface water temperature data was collected at depths of approximately 0.1 to 1.0 meters using a handheld digital thermometer through partnered community-led and community/Fisheries and Oceans Canada/ university partners collaborative sampling. The project has strong community involvement, including youth through the Guardian Program, to facilitate capacity building and community leadership in the long-term monitoring of Great Bear Lake fisheries and the aquatic ecosystem. This data is the beginning in a series of baseline data sets on water quality from this lake. These data will contribute to a better understanding of cumulative impacts of climate change on the functioning of large northern lake ecosystems, and provide a benchmark for monitoring further change. This data will be important for developing effective strategies for maintaining community-led aquatic monitoring and managing natural resources, particularly fish, which are expected to be increasingly important to communities with declines in other country foods such as caribou. We acknowledge the data were collected in the Sahtú Settlement Area and are made publicly available with the agreement of the Délı̨nę Renewable Resources Council (Délı̨nę Ɂehdzo Got’ı̨nę (Renewable Resources Council)).Collaborators include: the Community of Délı̨nę partners (data collection), Délı̨nę Renewable Resource Council, Sahtú Renewable Resource Board, Great Lakes Fisheries Commission, and University of Alberta. Community of Délı̨nę partners and field workers that participated in data collection include Jane Baptiste, Doug Baton, Moise Beyonnie, Gloria Gaudette, Bruce Kenny, Greg Kenny, Hughie Kenny, Jonas Kenny, Morris Lennie, George Menacho, Morris Modeste, Nathan Modeste, Isreal Neyelle, Lyle Neyelle, Clyde Takazo, Lucy Ann Takazo, Freddie Vital, Cameron Yukon, Cyre Yukon, Tyrone Yukon, Charity Yukon, Chris Yukon, Archie Vital, John Betsidea, and Barbara Yukon.Funding and logistical support was provided by: Northwest Territories Cumulative Impact Monitoring, Sahtú Renewable Resource Board, the Polar Continental Shelf Program, Canadian Circumpolar Institute, Natural Sciences and Engineering Research Council of Canada, Government of Northwest Territories Renewable Resources Délı̨nę, and Fisheries and Oceans Canada.