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.

This entry provides access to surficial geology maps that have been published by the Geological survey of Canada. Two series of maps are available: "A Series" maps, published from 1909 to 2010 and "Canadian Geoscience Maps", published since 2010. Three types of CGM-series maps are available: 1)Surficial Geology: based on expert-knowledge full air photo interpretation (may include interpretive satellite imagery, Digital Elevation Models (DEM)), incorporating field data and ground truthing resulting from extensive, systematic fieldwork across the entire map area. Air photo interpretation includes map unit/deposit genesis, texture, thickness, structure, morphology, depositional or erosional environment, ice flow or meltwater direction, age/cross-cutting relationships, landscape evolution and associated geological features, complemented by additional overlay modifiers, points and linear features, selected from over 275 different geological elements in the Surficial Data Model. Wherever possible, legacy data is also added to the map. 2)Reconnaissance Surficial Geology: based on expert-knowledge full air photo interpretation (may include interpretive satellite imagery, DEMs), with limited or no fieldwork. Air photo interpretation includes map unit/deposit genesis, texture, thickness, structure, morphology, depositional or erosional environment, ice flow or meltwater direction, age/cross-cutting relationships, landscape evolution and associated geological features, complemented by additional overlay modifiers, points and linear features, selected from over 275 different geological elements in the Surficial Data Model. Wherever possible, legacy data is also added to the map. 3)Predictive Surficial Geology: derived from one or more methods of remote predictive mapping (RPM) using different satellite imagery, spectral characteristics of vegetation and surface moisture, machine processing, algorithms etc., DEMs, where raster data are converted to vector, with some expert-knowledge air photo interpretation (training areas or post-verification areas), varying degrees of non-systematic fieldwork, and the addition of any legacy data available. Each map is based on a version of the Geological Survey of Canada's Surficial Data Model (https://doi.org/10.4095/315021), thus providing an easily accessible national surficial geological framework and context in a standardized format to all users. "A series" maps were introduced in 1909 and replaced by CGM maps in 2010. The symbols and vocabulary used on those maps was not as standardized as they are in the CGM maps. Some "A series" maps were converted into, or redone, as CGM maps, Both versions are available whenever that is the case. In addition to CGM and "A series" maps, some surficial geology maps are published in the Open File series. Those maps are not displayed in this entry, but can be found and accessed using the NRCan publications website, GEOSCAN:(https://geoscan.nrcan.gc.ca).

This dataset provides geospatial polygon boundaries for marine bivalve shellfish harvest area classification in New Brunswick, Canada. 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 on the basis of sanitary 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 when in Open Status. The classification 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. This dataset is 'Deprecated'. Please use updated source here. https://open.canada.ca/data/en/dataset/7aef69b5-3aaf-4d50-bb86-083031e6dc47

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.

Three marine spatial planning areas are delineated in Eastern Canada to define the spatial extents of marine spatial plans being led by Fisheries and Oceans Canada (DFO): the Estuary and Gulf of St. Lawrence (EGSL), the Newfoundland and Labrador (NL) Shelves, and the Scotian Shelf and Bay of Fundy. The EGSL planning area includes the St. Lawrence River estuary from northeast of Île d’Orléans, Quebec, the Saguenay River estuary, and the entire Gulf of St. Lawrence as far north as the Strait of Belle Isle (NAFO Divisions 4RST). The NL Shelves planning area includes areas off southern, eastern and northern Newfoundland, part of the Churchill River and Lake Melville, as well as off the Labrador coast to the extent of the exclusive economic zone (EEZ) (NAFO Divisions 2GHJ and 3KLNOP). The Scotian Shelf and Bay of Fundy planning area includes DFO Maritimes’ administrative region off the Atlantic coast of Nova Scotia to the extent of the EEZ, the Bay of Fundy and the Canadian portion of the Gulf of Maine (NAFO Divisions 4VWX, 5Ze, and the Canadian portion of 5Y). The French EEZ for St. Pierre et Miquelon is excluded from the three planning areas. These planning areas are derived from Federal Marine Bioregions (https://open.canada.ca/data/en/dataset/23eb8b56-dac8-4efc-be7c-b8fa11ba62e9) that were developed by a Canadian Science Advisory Secretariat process using ecosystem-based management principles to define 13 ecological bioregions that have informed but not directed DFO implementation of marine spatial planning.

Proxied dataset of inshore lobster commercial fishing for 2012 - 2021 in the Newfoundland and Labrador region. Only lobster harvested from the Newfoundland and Labrador region are included, based on species sought. Commercial data for the inshore lobster fishery does not require a set of coordinates be provided for catch records. With zero georeferenced inshore lobster records, the inshore lobster fishery leaves a major data gap in one of Newfoundland and Labradors largest fisheries. The Gulf region created a lobster proxy mapping tool, which associated each commercial lobster record with the most likely 10km2 hexagon grid cell based on a number of weighted variables. The tool was adopted by the Newfoundland and Labrador region and altered to work with its own variables which include human use, habitat, accessibility, area/location, home port distance, traditional ecological knowledge and depth. Each hexagon represents the summed total weight of all records associated with a particular hexagon. The best available commercial data used in this model is derived from landings data and may not include catches that have resulted in cash/wharf sales. As a result, there are some areas of Newfoundland and Labrador that may be under represented in this dataset where wharf sales may be high. Therefore, this dataset should be viewed as a general estimation on lobster harvesting patterns within Newfoundland and Labrador.

The Water Survey of Canada (WSC) is the national authority responsible for the collection, interpretation and dissemination of standardized water resource data and information in Canada. In partnership with the provinces, territories and other agencies, WSC operates over 2800 active hydrometric gauges across the country. WSC maintains and provides real-time and historic hydrometric data for some 8000 active and discontinued stations. This dataset consists of a set of polygons that represent the drainage areas of both active and discontinued discharge stations. Users are encouraged to report any errors using the “Contact Us” webpage at: https://weather.gc.ca/mainmenu/contact_us_e.html?site=water

Past wind directions are mapped from stabilized sand dunes in Canada and the northern United States. The map shows the near-surface wind directions responsible for transporting sand when the dunes were active. The directions were mapped by interpreting the orientation of parabolic dunes from open-sourced Lidar (light detection and ranging) derived digital terrain models. The map also shows new dune areas that add to the existing knowledge of dune fields in North America. The interpreted wind directions provide insight into the past atmospheric circulation patterns that occurred during the deglaciation of North America and the transition to modern circulation patterns that occur today.

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

The Quebec—Saint-Lawrence River 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 the 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.