Cartographic representations of Fisheries Management Areas (FMA)s in the Atlantic and Arctic Regions. Currently Published Fisheries Management Areas: Capelin Crab Herring Mackerel Salmon, Atlantic Scallop Shrimp Snow Crab Squid Each polygon feature class delineates the coordinates of a different series of FMAs. Shapes have been drafted based on a combination of sources including: the Atlantic Fisheries Regulations, Integrated Fisheries Management Plans, indigenous treaties, the bounds of the Territorial Sea, and other information made public on Fisheries and Oceans websites. Information from Variation orders and Conditions of License were also incorporated. The specific sources used to construct each feature class is listed in its metadata and direct links to public sources are included. The original documentation uses a diverse combination datums, or include coordinates with no listed datum. This data series has been projected into NAD83. Vertices in this dataset may differ from the original source documents to fix slivers, make areas congruent with coastlines, or align with other administrative boundaries. Changes made to the original areas in order to make drafting possible have been highlighted in the comments field in the attribute tables. Lines were first drafted as geodesics and vertices were added to approximate loxodromes using the Construct Geodesic Tool in ArcGIS Pro 2.9.8. As documentation is drafted, additional FMAs will be added to the dataset. Currently drafted FMAs my change and expand into currently unmapped areas as new information is incorporated. The feature classes produced as a part of this data series are cartographic representations of legal documents and are meant to be used for general reference in support of marine planning. Whenever there is a difference between the original written source documentation and this digital representation, the originals should be considered authoritative. Every effort has been made to ensure that these files are as accurate as possible but these feature classes are not intended to be used for navigation, legal interpretation or enforcement.

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).

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.

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].

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.

GIS compilation of data used to perform the stacked cumulative chance of success (resource potential map) in Open file 8556. Natural Resources Canada (NRCan) has been tasked, under the Marine Conservation Targets (MCT) initiative announced in Budget 2016, with evaluating the petroleum resource potential for areas identified for possible protection as part of the Government of Canada's commitment to conserve 10% of its marine areas by 2020. As part of this initiative, NRCan's Geological Survey of Canada (GSC) conducted a broad regional study of the petroleum potential over the majority of the Magdalen Basin, which is the principal geological basin in the southern Gulf of St. Lawrence. The GSC resource assessment is visually represented by a qualitative petroleum potential map. Disclaimer: A simplified colored version of the map is displayed on the Web Mapping Service (WMS). The correct version is available for download through the Federal Geospatial Platform (FGP) and GEOSCAN.

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

This dataset provides marine bacteriological water quality data for bivalve shellfish harvest areas in Newfoundland and Labrador, 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

This dataset provides marine bacteriological water quality data for bivalve shellfish harvest areas in Quebec, 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

A Priority Place is an area of high biodiversity value that is seen as a distinct place with a common ecological theme by the people who live and work there. As part of the Pan-Canadian approach to transforming Species at Risk conservation in Canada, a total of 11 Priority Places were affirmed by federal, provincial, and territorial governments. One additional priority place was affirmed in 2024. The places selected have significant biodiversity, concentrations of species at risk, and opportunities to advance conservation efforts. In each Priority Place, the federal and provincial or territorial governments are working with Indigenous Peoples, partners, and stakeholders to develop conservation implementation plans. This dataset displays the geographic area covered by each of the 12 Priority Places using the best available information from the Canadian Wildlife Service (CWS). Boundary information for each Priority Place was provided by its respective CWS regional office. The federal government, in collaboration with the provinces and territories, has agreed to the implementation of the Pan-Canadian Approach to Transforming Species at Risk Conservation in Canada. This new approach shifts from a single-species approach to conservation to one that focuses on multiple species and ecosystems. This enables conservation partners to work together to achieve better outcomes for species at risk. These 12 Priority Places are complemented by a suite of Community-Nominated Priority Places (CNPP), identified through an open call for applications.