These datasets show commercial fisheries catch weight landings of directed fisheries and bycatch from the Scotian Shelf, the Bay of Fundy, and Georges Bank from NAFO Divisions 4VWX and the Canadian portions of 5Y and 5Z. Atlantic Canadian inter-regional maps of four species (Atlantic Halibut, Bluefin Tuna, Redfish and Scallop) are also included from NAFO Divisions 4RST, 3KLMNOP, and 2GHJ. Five-year composite maps (2014–2018) that aggregate catches for each map series are publicly available. The maps aggregate catch weight (kg) per 10 km2 hexagon grid cell for selected species, species groupings and gear types to identify important fishing areas. These maps may be used for decision making in coastal and oceans management, including marine spatial planning, environmental emergency response operations and protocols, Marine Stewardship Council certification processes, marine protected area networks, and ecological risk assessment. These datasets have been filtered to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify NAFO unit areas containing data with less than five vessel IDs, licence IDs or fisher IDs. If this threshold was not met, catch weight locations were withheld from these unit areas to protect the identity or activity of individual vessels or companies. Maps were created for the following species, species groupings and gear types: 1. Groundfish (all species) 2. Groundfish Bottom Trawl 3. Groundfish Gillnet 4. Groundfish Bottom Longline 5. Groundfish (quarterly composites Q1, Q2, Q3, Q4) 6. Atlantic Cod 7. Atlantic Cod, Haddock and Pollock 8. Flatfish 9. Atlantic Halibut 10. Greenland Halibut (Turbot) 11. Hagfish 12. Cusk 13. Dogfish 14. Redfish 15. Red Hake 16. Silver Hake 17. White Hake 18. Monkfish 19. Sculpin 20. Skate 21. Wolffish 22. Squid 23. Herring 24. Mackerel 25. Large Pelagics 26. Bluefin Tuna 27. Other Tuna 28. Swordfish 29. Porbeagle, Mako and Blue Shark 30. Snow Crab 31. Other Crab 32. Scallop 33. Scallop (quarterly composites Q1, Q2, Q3, Q4) 34. Offshore Clam 35. Shrimp 36. Offshore Lobster 37. Disputed Zone Area 38B Lobster 38. Whelk

Points, polylines and polygons where species and features have been found, harvested or detailed. Community Based Coastal Resource Inventory (CCRI) – Fisheries and Oceans Canada in conjunction with several Federal and Provincial agencies created, implemented, and managed a program which set out to develop a coastal resource inventory based on the traditional knowledge of local residents. Through partnerships with the province of Newfoundland and Labrador’s Regional Economic Development (RED) Boards and other community based groups the project assembled a database containing several decade’s worth of local knowledge. The value of the information collected came through individual interviews with people who had extensive knowledge of the immediate geography and resource, having lived, worked and harvested the regions over a lifetime. This project ran from 1996 to 2007.

This dataset is part of Environment and Climate Change Canada’s Shoreline Classification and Pre-Spill database. Shoreline classification data has been developed for use by the Environmental Emergencies Program of Environment and Climate Change Canada for environmental protection purposes. Marine and freshwater shorelines are classified according to the character (substrate and form) of the upper intertidal (foreshore) or upper swash zone (Sergy, 2008). This is the area where oil from a spill usually becomes stranded and where treatment or cleanup activities take place. The basic parameter that defines the shoreline type is the material that is present in the intertidal zone. The presence or absence of sediments is a key factor in determining whether oil is stranded on the surface of a substrate or can penetrate and/or be buried. This dataset contains thousands of linear shoreline segments ranging in length from 200 m and 2 km long. The entities represent the location of the segments and their geomorphological description. There exist further fields in the attribute table for this dataset. We are currently working on standardizing our shoreline segmentation datasets and the updated data will soon be uploaded to the catalog. Sergy, G. (2008). The Shoreline Classification Scheme for SCAT and Oil Spill Response in Canada. Proceedings of the 31stArctic and Marine Oil Spill Program Technical Seminar.Environment Canada, Ottawa, ON, Pp. 811-819.

The Atlantic dataset is part of Environment and Climate Change Canada’s Shoreline Classification and Pre-Spill database. Shoreline classification data has been developed for use by the Environmental Emergencies Program of Environment and Climate Change Canada for environmental protection purposes. Marine and estuarine shorelines are classified according to the character (substrate and form) of the upper intertidal (foreshore) or upper swash zone (Sergy, 2008). This is the area where oil from a spill usually becomes stranded and where treatment or cleanup activities take place. The basic parameter that defines the shoreline type is the material that is present in the intertidal zone. The presence or absence of sediments is a key factor in determining whether oil is stranded on the surface of a substrate or can penetrate and/or be buried. This dataset contains thousands of linear shoreline segments ranging in length from 200 m and 2 km long. The entities represent the location of the segments and their geomorphological description. There exist further fields in the attribute table for this dataset. We are currently working on standardizing our shoreline segmentation datasets and the updated data will soon be uploaded to the catalog. Sergy, G. (2008). The Shoreline Classification Scheme for SCAT and Oil Spill Response in Canada. Proceedings of the 31stArctic and Marine Oil Spill Program Technical Seminar.Environment Canada, Ottawa, ON, Pp. 811-819.

An eelgrass distribution map was classified from remotely sensed imagery in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28th, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence.

In 2021, the Canada Coast Guard (CCG) and Fisheries and Oceans Canada updated its administrative boundaries following the creation a new Arctic region. There are now 4 administrative regions in CCG (Western, Arctic, Central and Atlantic). DFO and Coast Guard Arctic Regions developed these regions in partnership with the people they serve; this important decision will lead to stronger programs and services to better meet the unique needs of our Arctic communities. DFO and CCG operations and research cover Canada's land and waters to the international boundaries (EEZ) and are in no way limited to the boundaries drawn in the map.

The water level data comes from the Provincial Groundwater Monitoring Network (PGMN) of Ontario. Each well in the observation network is equipped with a hydrostatic pressure transducer and a temperature sensor connected to a data logger. A second pressure transducer located above the water surface allows for adjusting the water level according to atmospheric pressure variations. The time series refers to the level below which the soil is saturated with water at the site and at the time indicated. The water level is expressed in meters above sea level (MASL). Groundwater levels are recorded hourly. The dataset consists of a general description of the observation site including; the identifier, the name, the location, the elevation, and a series of numerical values designating the water levels at a defined date and time of measurement.

This dataset contains results from an eelgrass classification for Tracadie Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (https://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 101 sites. Approximately two-thirds of these sites were used to assist in image classification, while the remainder was used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 79.3% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 29 sites that were within the classification area, 18 were correct, 10 were "one-off", and 1 was incorrect [(18 + (10/2))/ 29 = 0.793].

The water level data comes from the groundwater monitoring network of Yukon (Canadian territory). Each well in the observation network is equipped with a hydrostatic pressure transducer and a temperature sensor connected to a data logger. A second pressure transducer located above the water surface allows for adjusting the water level according to atmospheric pressure variations. The time series refers to the level below which the soil is saturated with water at the site and at the time indicated. The water level is expressed in meters above sea level (MASL). The dataset consists of a general description of the observation site including; the identifier, the name, the location, the elevation and a series of numerical values designating the water levels at a defined date and time of measurement.

Description: Data on recreational boating are needed for marine spatial planning initiatives in British Columbia (BC). Vessel traffic data are typically obtained by analyzing automatic identification system (AIS) vessel tracking data, but recreational vessels are often omitted or underrepresented in AIS data because they are not required to carry AIS tracking devices. Transport Canada’s National Aerial Surveillance Program (NASP) conducted aerial surveys to collect information on recreational vessels along several sections of the BC coast between 2018 and 2022. Recreational vessel sightings were modeled against predictor variables (e.g., distance to shore, water depth, distance to, and density of marinas) to predict the number of recreational vessels along coastal waters of BC. The files included here are: --A Geodatabase (‘Recreational_Boating_Data_Model’), which includes: (1) recreational vessel sightings data collected by NASP in BC and used in the recreational vessel traffic model (‘Recreational_Vessels_PointData_BC’); (2) aerial survey effort (or number of aerial surveys) raster dataset (‘surveyeffort’); and (3) a vector grid dataset (2.5 km resolution) containing the predicted number of recreational vessels per cell and predictor variables (‘Recreational_Boating_Model_Results_BC). --Scripts folder which includes R Markdown file with R code to run the modelling analysis (‘Recreational_Boating_Model_R_Script’) and data used to run the code. Methods: Data on recreational vessels were collected by NASP during planned aerial surveys along pre-determined routes along the BC coast from 2018 to 2022. Data on non-AIS recreational vessels were collected using video cameras onboard the aircraft, and data on AIS recreational vessels using an AIS receiver also onboard the aircraft. Recreational boating predictors explored were: water depth, distance to shore, distance to marinas, density of marinas, latitude, and longitude. Recreational vessel traffic models were fitted using Generalized Linear Models (GLM) R packages and libraries used here include: AED (Roman Lustrik, 2021) and MASS (Venables, W. N., Ripley, 2002), pscl package (Zeileis, Kleiber, and Jackman, 2008) for zeroinfl() and hurdle() function. Final model was selected based on the Akaike’s information criterion (AIC) and the Bayes’ information criterion (BIC). An R Markdown file with code use to run this analysis is included in the data package in a folder called Script. Spatial Predictive Model: The selected model, ZINB, consist of two parts: one with a binomial process that predicts the probability of encountering a recreational vessel, and a second part that predicts the number of recreational vessels via a count model. The closer to shore and to marinas, and the higher the density of marinas, the higher the predicted number of recreational vessels. The probability of encountering recreational vessels is driven by water depth and distance to shore. For more information on methodology, consult metadata pdf available with the Open Data record. References: Serra-Sogas, N. et al. 2021. Using aerial surveys to fill gaps in AIS vessel traffic data to inform threat assessments, vessel management and planning. Marine Policy 133: 104765. https://doi.org/10.1016/j.marpol.2021.104765 Data Sources: Recreational vessel sightings and survey effort: Data collected by NASP and analyzed by Norma Serra to extract vessel information and survey effort (more information on how this data was analyzed see SerraSogas et al, 2021). Bathymetry data for the whole BC coast and only waters within the Canadian EEZ was provided by DFO – Science (Selina Agbayani). The data layer was presented as a raster file of 100 meters resolution. Coastline dataset used to estimate distance to shore and to clip grid was provided by DFO – Science (Selina Agbayani), created by David Williams and Yuriko Hashimoto (DFO – Oceans). Marinas dataset was provided by DFO – Science (Selina Agbayani), created by Josie Iacarella (DFO – Science). This dataset includes large and medium size marinas and fishing lodges. The data can be downloaded from here: Floating Structures in the Pacific Northwest - Open Government Portal (https://open.canada.ca/data/en/dataset/049770ef-6cb3-44ee-afc8-5d77d6200a12) Uncertainties: Model results are based on recreational vessels sighted by NASP and their related predictor variables and not always might reflect real-world vessel distributions. Any biases caused by the opportunistic nature of the NASP surveys were minimized by using survey effort as an offset variable.