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.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

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

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.

Monitoring of Unit 2 redfish by Fisheries and Oceans Canada (DFO) ceased in 2002. Since then, the Atlantic Groundfish Council (AGC, formerly the Groundfish Enterprise Allocation Council [GEAC]) has funded surveys approximately every two years in the area, in collaboration with DFO. Over the years, various vessels and gear types have been used. In 2024, a comparative survey was conducted using the CCGS John Cabot and a fishing industry vessel (the Léry Charles) to develop conversion factors that allow data from the 2020, 2022, and 2024 Unit 2 redfish surveys to be standardized to CCGS John Cabot equivalents. The survey covered the waters off southwestern Newfoundland and eastern Cape Breton, corresponding to redfish management Unit 2, which includes NAFO subdivisions 3Pn, 3Ps, 4Vn, 4Vs, and 4W. Here, data collected aboard the CCGS John Cabot are presented. Objectives• Assess the abundance and condition of groundfish and invertebrates• Assess environmental conditions• Inventory the biodiversity of benthic and demersal megafauna• Monitor the pelagic ecosystem• Collect samples for various research projectsData The CCGS John Cabot employed a four-sided modified Campelen 1800 shrimp trawl, fitted with a Rockhopper ("bicycle") footgear. The trawl extension and codend were lined with 12.7 mm knotless nylon mesh. Standard trawl tows were 15 minutes in duration, timed from bottom contact, with a target towing speed of 3 knots.For each fishing tow, the catch is sorted and weighed by taxa; individuals are then counted and biological data are collected on a subsample. For fish, crab and squid, size and weight are measured by individual and, for some species, sex, gonad maturity, and the weight of certain organs (stomach, liver, gonads) are also evaluated. The soft rays of the anal fin are counted for redfish, and the otoliths are sampled for several species such as Atlantic cod, Atlantic halibut, and Greenland halibut. Roughly 2-kg of shrimp are sorted and weighed by species. The other invertebrates are counted (no individual measurements) and photographed.The biological data are divided into 4 files: a “Stations” file containing set information, a “Catches” file containing catches per set for fish taxa, a “Carbio” file containing biological and morphometric measurements per individual and a “Shrimps” file containing information on shrimp catches. The columns source_info, no_survey, nbpc and set_number serve as a common key linking the four datasets.It is important to note that this is raw data. Only sets considered successful are retained. In each set, all species are kept, with a few exceptions. Data is available from 1997-2022 but please contact the data management team (gddaiss-dmsaisb@dfo-mpo.gc.ca) for access and further details. For any other information please also contact the data management team.

Projects submitted to Transport Canada’s Navigation Protection Program. Please note that where appropriate, the content is displayed in the language of the original submission and has not been altered.

Dataset of species/gear type commercial fisheries from 2012 to 2021 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 2012 to 2021 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 2012 to 2021 for the area, and may represent many fishing events from several vessels over the ten year period. All landings are from Canadian vessels greater than 35-ft, 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.

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).