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.2021.104765Data 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.

This service provides routeing measures. These include established (mandatory) direction of traffic flow, recommended direction of traffic flow, separation lines, separation zones, limits of restricted routeing measure, limits of routeing measures, precautionary areas, archipelagic sea lanes (axis line and limit beyond which vessels shall not navigate) and fairways designated by regulatory authority.

DFO’s Oceans and Coastal Management Division (OCMD) in the Maritimes Region has updated its fisheries landings maps for 2010–2014. These maps will be used for decision making in coastal and oceans management, including mitigating human use conflicts, informing environmental emergency response operations and protocols, informing Marine Stewardship Council certification processes, planning marine protected area networks, assessing ecological risks, and monitoring compliance and threats in coral and sponge closures and Marine Protected Areas. Fisheries maps were created to identify important fishing areas using aggregate landed weight (kg) per 2 x 2-minute grid cell for selected species/gear types.This dataset has 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, license IDs and 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/gear types: 1. Atlantic Halibut2. Bluefin Tuna3. Bottom Longline Groundfish4. Bottom Trawl Groundfish5. Cod6. Cod, Haddock, Pollock7. Cusk8. Dogfish9. Flatfish10. Gillnet Groundfish11. Greenland Halibut12. Groundfish 13. Groundfish (quarterly composites Q1, Q2, Q3, Q4)14. Hagfish15. Herring16. Large Pelagics17. Mackerel18. Monkfish19. Offshore Clam20. Offshore Lobster21. Grey Zone Lobster22. Other Crab23. Other Tuna24. Pollock25. Porbeagle, Mako and Blue Shark26. Red Hake27. Redfish28. Scallop29. Scallop (quarterly composites Q1, Q2, Q3, Q4)30. Sculpin31. Sea Urchin32. Shrimp33. Silver Hake34. Skate35. Snow Crab36. Squid37. Swordfish38. White Hake39. Wolffish

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 Trawl3. Groundfish Gillnet4. Groundfish Bottom Longline5. Groundfish (quarterly composites Q1, Q2, Q3, Q4)6. Atlantic Cod7. Atlantic Cod, Haddock and Pollock8. Flatfish9. Atlantic Halibut10. Greenland Halibut (Turbot)11. Hagfish12. Cusk13. Dogfish14. Redfish15. Red Hake16. Silver Hake17. White Hake18. Monkfish19. Sculpin20. Skate21. Wolffish22. Squid23. Herring24. Mackerel25. Large Pelagics26. Bluefin Tuna27. Other Tuna28. Swordfish29. Porbeagle, Mako and Blue Shark30. Snow Crab31. Other Crab32. Scallop33. Scallop (quarterly composites Q1, Q2, Q3, Q4)34. Offshore Clam35. Shrimp36. Offshore Lobster37. Disputed Zone Area 38B Lobster38. Whelk

Facilities are an oil and gas activity, defined in the Energy Resources Activities Act as a system of vessels, piping, valves, tanks and other equipment used to gather, process, measure, store or dispose of petroleum, natural gas, water or a substance referred to in paragraph (d) or (e) of the definition of pipeline. This dataset contains point features for proposed applications collected through the BC Energy Regulator's Application Management System (AMS). This dataset is updated nightly.

Dataset of species/gear type commercial fisheries from 2014 to 2023 in the Eastern Canada Regions. Only fish harvested from the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions are included (Species Sought).The data was obtained from Statistical Services, Fisheries and Oceans Canada (DFO) and consists of commercial species/gear type landings data from 2014 to 2023 taken from Northwest Atlantic Fisheries Organization (NAFO) Subareas 0, 2, 3, 4 and 5 and fished in the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions. The layer was created by overlaying a 2 minute hexagonal grid (approx. 10km2 cell) on species/gear type commercial fisheries point data and summing the total landings by weight reported for each cell over the ten year period. Therefore, the value of each grid cell is equal to the total species/gear type landings in kg from 2014 to 2023 for the area, and may represent many fishing events from several vessels over the ten year period. All landings are from Canadian vessels and does not include information pertaining to international fishing vessels (i.e., St. Pierre). Individuals should exercise caution when interpreting this data. Data has not been altered and is mapped from the original logbook entry for each record prior to amalgamation. Data may contain errors such as inaccurate or nonviable coordinates, landed weights and/or species identification. For example, cases of fishing events reported in a NAFO Division with corresponding coordinates falling outside that particular NAFO Division or fishing events which appear to be located on a land mass due to rounding errors in the original entries. Such cases were excluded from the dataset. Only one location is given for each fishing event; therefore, a fishing activity that would normally cover a large area (i.e., trawling) is only shown in a single location. Some species may not include all records or locations where activity is taking place due to regional differences in permissions for mapping, or because the fishery is only partially georeferenced (e.g. Lobster). The locations/areas shown should only be used as an estimation of fishing intensity and a general guide of where particular species/gear type fishing occurs. This dataset has been privacy screened 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, license IDs and fisher IDs. If this threshold was not met, catch weight locations have been withheld from these statistical areas to protect the identity or activity of individual vessels or companies. In some instances, permissions were obtained to map species or gears with a limited number of vessels, licenses, or fisher ids. The withheld areas are indicated by the unit area that has been removed and given a weight of -9999.

To develop a database of high quality CTD observations at key locations in DFO’s Pacific Region, 22 stations have been selected for sampling as often as possible. Chief Scientists of DFO vessels with CTD equipment on board are asked to acquire a CTD profile at as many of these stations as possible. There may be circumstances that will prevent conducting a CTD cast but the intent is to collect as many as possible such that over time useful time series of CTD profiles will be available at these locations.

This data is from the Newfoundland and Labrador Region Multispecies Trawl survey, specific to Snow Crab from 2018-25, with only successful tows included in the database. Details of annual survey coverage and an overview of the survey program can be found in Rideout et al. (2025) and references therein. This trawl survey follows a depth-stratified random design, designated by type = 1 (Survey). From 2021-23, a comparative fishing program (i.e., direct side-by-side paired comparison between old and new vessels) was undertaken to determine differences in catchability due to vessel changes and associated modifications to the trawl gear with the retirement of the CCGS Alfred Needler and CCGS Teleost (called ‘old vessels’) and the introduction of the CCGS Capt. Jacques Cartier and CCGS John Cabot (called ‘new vessels’). During this period other set types are in the dataset (6 and 7) from paired tows and all station allocation may not have been stratified random. The DFO NL Region comparative fishing success varied by year, NAFO division, and vessel combinations and resulted in conversion factors to adjust the catches of Snow Crab in the old vessels time series into the same units as the new vessels. Methodologies and conversion factors are described in Wheeland et al. (2024), Trueman et al. (2025), Trueman et al. (2026), and Pantin et al. (in prep). Conversion factors have been applied to the numbers of Snow Crab caught, where conversion factors exist, and are in the converted_number column. An NA in the converted_number column indicates that conversion factors do not exist for this vessel/area combination and therefore direct comparisons cannot be made between old vessels and new vessels. Weights are those recorded at sea and do not have conversion factors applied. Analyses by the DFO NL Snow Crab group apply a length-weight relationship to the converted numbers to determine converted weights, which is not in this dataset. All reasonable attempts are made to standardize fishing time during trawl surveys, however there is always some variation in the actual time on bottom. The species weights and numbers in this dataset are not standardized for tow distance/duration. Spatial coverage of this survey has varied over time, therefore, not all divisions, or depths within divisions, are sampled every year or season. Biological measurements are described in Pantin et al. (2025). Tows that caught zero Snow Crab are denoted in the dataset by a ‘number’ of 0.References:McCallum, B. R., and S. J. Walsh. 1996. Groundfish survey trawls used at the Northwest Atlantic Fisheries Centre, 1971-present. NAFO Sci. Coun. Studies, 29: 93-104.Pantin, J., D. Mullowney, K. Baker, K. Lefort, W. Coffey, F. Cyr, H. Munro, and Koen-Alonso, M. 2025. An Assessment of Newfoundland and Labrador Snow Crab (Chionoecetes opilio) in 2023. DFO. Can. Sci. Advis. Sec. Res. Doc. 2025/080. iv + 152 p.Pantin, J., Charmley, K., Mullowney, D., Baker, K., Lefort, K., Coyne, J., Munro, H., and Koen-Alonso, M. In prep. An Assessment of Newfoundland and Labrador Snow Crab (Chionoecetes opilio) in 2024. DFO Can. Sci. Advis. Sec. Res. Doc. year/nnn.Rideout, R.M., Wheeland, L., Perreault, A., Regular, P., Skanes, K., Simms, L., and Makrides, J. 2025. Canadian (Newfoundland and Labrador Region) Multi-Species Research vessel Bottom Trawl Survey Report for Surveys Conducted in 2024. NAFO SCR Doc. 25/028: 1-68.Trueman, S., Wheeland, L., Benoît, H., Munro, H., Nguyen, T., Novaczek, E., Skanes, K., and Yin, Y. 2025. Results of Comparative Fishing Between the CCGS Teleost and CCGS Alfred Needler with the CCGS John Cabot and CCGS Capt. Jacques Cartier in the Newfoundland and Labrador Region in 2021 and 2022. DFO Can. Sci. Advis. Sec. Res. Doc. 2025/021. v + 237 p. Trueman, S., Wheeland, L., Pantin, J., Baker, K., and Mullowney, D. 2026. On the relative catchability of Snow Crab in the Newfoundland and Labrador Multispecies trawl surveys. DFO Can. Sci. Advis. Sec. Res. Doc. 2026/008. iv + 41 p. Wheeland, L., Skanes, K. and Trueman, S. 2024. Summary of Comparative Fishing Data Collected in Newfoundland & Labrador from 2021-2022. Can. Tech. Rep. Fish. Aquat. Sci. 3579: iv + 132 p.

Description:These commercial whale watching data are comprised of two datasets. First, the ‘whale_watching_trips_jun_sep_british_columbia’ data layer summarizes commercial whale watching trips that took place in 2019, 2020 and 2021 during the summer months (June to September). The second data layer, ‘wildlife_viewing_events_jun_sep_british_columbia’ contains estimated wildlife viewing events carried out by commercial whale watching vessels for the same years (2019, 2020 and 2021) and months (June to September). Commercial whale watching trips and wildlife viewing events are summarized using the same grid, and they can be related using the unique cell identifier field ‘cell_id’. The bulk of this work was carried out at University of Victoria and was funded by the Marine Environmental Observation, Prediction and Response (MEOPAR) Network under the ‘Whale watching AIS Vessel movement Evaluation’ or WAVE project (2018 – 2022). The aim of the WAVE project was to increase the understanding of whale watching activities in Canada’s Pacific region using vessel traffic data derived from AIS (Automatic Identification System). The work was finalized by DFO Science in the Pacific Region. These spatial data products of commercial whale watching operations can be used to inform Marine Spatial Planning, conservation planning activities, and threat assessments involving vessel activities in British Columbia.Methods:A list of commercial whale watching vessels based in British Columbia and Washington State and their corresponding MMSIs (Maritime Mobile Service Identity) was compiled from the whale watching companies and Marine Traffic (www.marinetraffic.com). This list was used to query cleaned CCG AIS data to extract AIS positions corresponding to commercial whale watching vessels. A commercial whale watching trip was defined as a set of consecutive AIS points belonging to the same vessel departing and ending in one of the previously identified whale watching home ports. A classification model (unsupervised Hidden Markov Model) using vessel speed as the main variable was developed to classify AIS vessel positions into wildlife-viewing and non wildlife viewing events. Commercial whale watching trips in the south and north-east of Vancouver Island were limited to a duration of minimum 1 hour and maximum 3.5 hours. For trips in the west coast of Vancouver island the maximum duration was set to 6 hours. Wildlife-viewing events duration was set to minimum of 10 minutes to a maximum of 1 hour duration. For more information on methodology, consult metadata pdf available with the Open Data record.References:Nesdoly, A. 2021. Modelling marine vessels engaged in wildlife-viewing behaviour using Automatic Identification Systems (AIS). Available from: https://dspace.library.uvic.ca/handle/1828/13300.Data Sources:Oceans Network Canada (ONC) provided encoded AIS data for years 2019, 2020 and 2021, within a bounding box including Vancouver Island and Puget Sound used to generate these products. This AIS data was in turn provided by the Canadian Coast Guard (CCG) via a licensing agreement between the CCG and ONC for the non-commercial use of CCG AIS Data. More information here: https://www.oceannetworks.ca/science/community-based-monitoring/marine-domain-awareness-program/ Molly Fraser provided marine mammal sightings data collected on board a whale watching vessels to develop wildlife-viewing events classification models. More information about this dataset here: https://www.sciencedirect.com/science/article/pii/S0308597X20306709?via%3DihubUncertainties:The main source of uncertainty is with the conversion of AIS point locations into track segments, specifically when the distance between positions is large (e.g., greater than 1000 meters).

Map of Canada showing locations of facilities selling nautical charts in both paper and digital formats.