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

Eelgrass (Zostera marina) is important to waterfowl such as Atlantic Brant (Branta bernicla hrota), Canada Goose (Branta canadensis), American Black Duck (Anas rubripes), Common Goldeneye (Bucephala clangula) and Barrow's Goldeneye (Bucephala islandica). In New Brunswick eelgrass can be found along the Gulf of St. Lawrence, in protected harbours. Within this dataset are the results of eelgrass land-cover classifications using either satellite or aerial photography for seven harbours: Bouctouche (46 30’N, 64 39’W); Miscou (47.90 N, -64.55 W); Neguac (47.25 N, -65.03 W); Richibucto (46.70 N, -64.80 W); Saint-Simon (47.77 N, -64.76 W); Tracadie (47.55 N, -64.88 W); and Cocagne (46.370 N, -64.600 W). Information on each dataset is provided: 1. Bouctouche This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://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 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were 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 83.7% 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 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837]. 2. Miscou True colour aerial photography at 57 centimetre resolution was collected on August 20th and 24th, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://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 103 sites. From these sites 70% were used to assist in image classification, while the remainder were 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 96.7% of the time (30/31 = 0.967). 3. Neguac This dataset contains results from an eelgrass classification for Neguac Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://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 126 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were 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 81% 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 39 sites that were within the classification area, 27 were correct, 9 were "one-off", and 3 were incorrect [(27 + (9/2))/ 39 = 0.81]. 4. Richibucto Eelgrass classification in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28, 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. 5. Saint-Simon An eelgrass distribution map was classified from remotely sensed imagery in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. 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. 6. Tracadie 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 (http://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]. 7. Cocagne Visible orthorectified aerial photography was used to classify polygons containing eelgrass in Cocagne Harbour. Field data for image training and validation were collected along transects in summer 2008 using a dGPS positioned towfish holding sidescan sonar and a video camera that was later transcribed as XY geographic points to describe eelgrass presence and a qualitative description of density. The area was flown for photography on September 24, 2008. eCognition Developer 8 software was used to segment the imagery, essentially polygons. Polygons were then classified manually for the presence of eelgrass. Using field data revealed eelgrass presence to be mapped correctly 87.2% of the time.

This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://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 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were 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 83.7% 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 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837].

This dataset provides marine bacteriological water quality data for bivalve shellfish harvest areas in New Brunswick, Canada. Shellfish harvest area water temperature and salinity data are also provided as adjuncts to the interpretation of fecal coliform density data. The latter is the indicator of fecal matter contamination monitored annually by Environment and Climate Change Canada (ECCC) within the framework of the Canadian Shellfish Sanitation Program (CSSP). The geospatial positions of the sampling sites are also provided. These data are collected by ECCC for the purpose of making recommendations on the classification of shellfish harvest area waters. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). This dataset is 'Deprecated'. Please use updated source here. https://open.canada.ca/data/en/dataset/6417332a-7f37-49bd-8be9-ce0402deed2a

Fetch is a proxy for wind-wave action and exposure. Estimates of fetch over a total of 39,938 km of the BC coastline were calculated at 50 m intervals, yielding 799,220 near shore fetch points. Fetch was calculated for five regions in Pacific Canada: Haida Gwaii (HG), North and Central Coast (NCC), Queen Charlotte and Johnstone Straits (QCS), Salish Sea (SoG), and West Coast Vancouver Island (WCVI). For all regions, a bearing interval of 5 degrees was used to generate fetch lines for each point along the shoreline, resulting in 72 fetch lines per point. A maximum fetch distance of 200 km was used to ensure the barrier effect of Haida Gwaii was captured. Supplementary information provided includes the fetch geometry calculator script and user guide (Gregr 2014) and a report on the fetch processing objectives, process, and results (Gregr 2015).

These datasets show the general spatial distribution of commercial fishing harvest and landed values by fishery on a 1km x 1km planning grid. They aggregate key statistics around fleet specific fishing activity and catch in British Columbia (BC) within the exclusive economic zone (EEZ). These gridded data describe the annual average landed weight (Rounded KGs), and landed catch values (CAD $2016) of the subject fishery over the period. The data represented were created from logbook records and matched to prices from fish slips submitted to DFO by participants of BC’s commercial fishing fleets. The dataset is comprised of an aggregate of all species over 10, 9, or 5 years of fishing seasons, depending on the fishery. To preserve potentially proprietary information, a privacy filtering Rule of Five has been applied to each planning unit (each 1km x 1km planning unit). If any planning units do not meet this minimum of 5 unique vessels/unique identifiers during the time span then they are flagged as being filtered and an average of all filtered planning units is applied. The accompanying GeoDB contains two data layers, “all_fisheries_filtered_gridded “, which includes all of the commercial fisheries data in 1km x 1km grids, and “DFO_marine_bioregions_NSB_subregions”, which includes polygon feature boundaries for the federal marine bioregions and Northern Shelf bioregion sub-regions. This dataset contains data for the following fisheries: - Bottom trawl (2012-2016) - Midwater trawl (2012-2016) - Shrimp by trawl (2007-2016) - Prawn trap (2007-2016) - Rockfish (2012-2016) - Sablefish (2007-2016) - Halibut (2007-2016) - Combo trips - halibut/sablefish (2007-2016) - Lingcod (2007-2016) - Green sea urchin (2006-2015) - Red sea urchin (2007-2015) - Sea cucumber (2008-2016) - Geoduck (2007-2015)

This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in Canada (British Columbia, New Brunswick, Newfoundland and Labrador, Nova Scotia, Prince Edward Island and Quebec). These data represent the five classification categories of marine bivalve shellfish harvest areas (Approved; Conditionally Approved; Restricted; Conditionally Restricted; and Prohibited) under the Canadian Shellfish Sanitation Program (CSSP). Data are collected by Environment and Climate Change Canada (ECCC) for the purpose of making applicable classification recommendations based on pollution source assessment and water quality survey results. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO). These geographic data are for illustrative purposes only; they show shellfish harvest area classifications that may be superseded at any time by regulatory orders issued by DFO, which place areas in Closed Status, due to conditions such as sewage overflows or elevated biotoxin levels. For further information about the current status and boundary coordinates for areas under Prohibition Order, please contact your local DFO office.

As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping, across known caribou ranges, as of 2010. The attached dataset comprises the second 5-year update (first one in 2015) bringing the data up to 2020. The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008-2010. Since then, anthropogenic disturbances within 51 caribou ranges across Canada were remapped every five years to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The ranges were defined by individual provinces and territories across Canada. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for a caribou resource selection function (habitat modeling) and to assess overall disturbance levels on each caribou ranges. As with the 2010 mapping project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat 30-metre multi-band imagery at a viewing scale of 1:50,000. The same concept was followed for the 2015 and 2020 disturbance mapping and any additional disturbance features that were observed since the original mapping date, were added. The 2015 database was used as a starting point for the 2020 database. Unlike the previous iteration, features were not removed in the mapping process which was a decision made in the name of time. Interpretation was carried out based on the most recent cloud free imagery available up to mid fall for a given year. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. Polygonal disturbances included: cutblocks, harvest (added in 2020), mines, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. For each type of anthropogenic disturbance, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Features were only digitized if they were clearly visible in the Landsat imagery at the prescribed viewing scale. In comparison to the previous mapping protocol, one enhancement to the mapping process in 2020 was the addition of CFS harvest polygons (Ref: NRCan-CFS NTEMS; Wulder 2020) into the database prior to interpretation. This considerably reduced the digitizing time for polygons and accelerated the data collection process. The CFS harvest polygons were checked before inclusion, removing some which had been generated erroneously in their process. A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. Subsequently, the vector data of individual linear and polygonal disturbances were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons for the past forty years (CNFDB 1981-2020) were merged into the buffered anthropogenic footprint in order to create an overall disturbance footprint. These buffered datasets were used in the calculation of range disturbance levels and for integrated risk assessment analysis.

As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping, across known caribou ranges, as of 2015. This data comprises a 5-year update to the mapping of 2008-2010 disturbances, and allows researchers to better understand the attributes that have a known effect on caribou population persistence. The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008 -2010. The mapping process used in 2010 was repeated using 2015 Landsat imagery to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for a caribou resource selection function (habitat modeling) and to assess overall disturbance levels on each caribou ranges. Anthropogenic disturbances within 51 caribou ranges across Canada were mapped. The ranges were defined by individual provinces and territories across Canada. Disturbances were remapped across these ranges using 2015 Landsat-8 satellite imagery to provide the most up-to-date data possible. As with the 2010 mapping project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat imagery with 30-metre multi-band imagery at a viewing scale of 1:50,000. A minimum mapping unit MMU of 2 ha (approximately 22 contiguous 30-metre pixels) was selected. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Polygonal disturbances included: cutblocks, mines, reservoirs, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. For each type of anthropogenic disturbance, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Features were only digitized if they were visible in the Landsat imagery at the prescribed viewing scale. A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. For this 2015 update an additional, separate higher-resolution database was created by repeating the process using 15-metre panchromatic imagery. For the 30-metre database only, the line and poly data were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons were merged into the anthropogenic footprint in order to create an overall disturbance footprint. These buffered datasets were used in the calculation of range disturbance levels and for integrated risk assessment analysis.