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 layer details Important Areas (IAs) relevant to key cetacean species in the West Coast Vancouver Island (WCVI) ecoregion. This data was mapped to inform the selection of marine Ecologically and Biologically Significant Areas (EBSA). Experts have indicated that these areas are relevant based upon their high ranking in one or more of three criteria (Uniqueness, Aggregation, and Fitness Consequences). The distribution of IAs within ecoregions is used in the designation of EBSAs. Canada’s Oceans Act provides the legislative framework for an integrated ecosystem approach to management in Canadian oceans, particularly in areas considered ecologically or biologically significant. DFO has developed general guidance for the identification of ecologically or biologically significant areas. The criteria for defining such areas include uniqueness, aggregation, fitness consequences, resilience, and naturalness. This science advisory process identifies proposed EBSAs in Canadian Pacific marine waters, specifically in the Strait of Georgia (SOG), along the west coast of Vancouver Island (WCVI, southern shelf ecoregion), and in the Pacific North Coast Integrated Management Area (PNCIMA, northern shelf ecoregion). Initial assessment of IAs in PNCIMA was carried out in September 2004 to March 2005 with spatial data collection coordinated by Cathryn Clarke. Subsequent efforts in WCVI and SOG were conducted in 2009, and may have used different scientific advisors, temporal extents, data, and assessment methods. WCVI and SOG IA assessment in some cases revisits data collected for PNCIMA, but should be treated as a separate effort. Other datasets in this series detail IAs for birds, coral and sponges, fish, geographic features, invertebrates, and other vertebrates. Though data collection is considered complete, the emergence of significant new data may merit revisiting of IAs on a case by case basis.

This dataset contains the modelled and observed data used in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). An application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations.

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.

The shallow substrate bottom type model was created to support near shore habitat modelling. Data sources include both available observations of bottom type and environmental predictor layers including oceanographic layers, fetch, and bathymetry and its derivatives. Using weighted random forest classification from the ranger R package, the relationship between observed bottom type and predictor layers can be determined, allowing bottom type to be classified across the study areas. The predicted raster files are classified as follows: 1) Rock, 2) Mixed, 3) Sand, 4) Mud The categorical substrate model domains are restricted to the extent of the input bathymetry layers (see data sources) which is 5 km from the 50 m depth contour.

Fetch is a useful proxy for exposure (an important driver of nearshore species distributions) in the absence of a wind-wave model. We derived the first-ever coast-wide estimates of fetch for Pacific Canadian waters. Fetch was calculated for over 41,720 km of the British Columbia coastline, at 50 m intervals, yielding 811,727 nearshore fetch points. Fetch was calculated for five regions in Pacific Canada: Haida Gwaii (HG), Queen Charlotte Strait (QCS), Strait of Georgia (SOG), West Coast Vancouver Island (WCVI), and the North Central Coast (NCC; Prince Rupert to Cape Caution). 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).

Megafauna distribution of biomass (g/15 min trawling) in the Barents Sea in 2007, 2011 and 2015. The green circles show the distribution of the snow crab as it spreads from east to west, and the blue triangles show the invasion of king crab along the coast of the southern Barents Sea. Data from Institute of Marine Research, Norway and the Polar Research Institute of Marine Fisheries and Oceanography, Murmansk, Russia. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/benthos" target="_blank">Chapter 3</a> - Page 95 - Figure 3.3.2 The annual joint Norwegian–Russian Ecosystem Survey provides from more than 400 stations and during extensive cruise tracks covering more or less the whole Barents Sea in August– September. The sampling is based on a regular grid spanning about 1.5 millionkm2 with fixed positions of stations which make it possible to measure changes in spatial distribution over time. The trawl is a Campelen 1800 bottom trawl rigged with rock-hopper groundgear and towed on double Warps. The mesh size is 80 mm (stretched) in the front and 16–22 mmin the cod end, allowing the capture and retention of smaller fish and the largest benthos from the seabed (benthic megafauna). The horizontal opening was 11.7 m, and the vertical opening 4–5 m (Teigsmark and Øynes, 1982). The trawl configuration and bottom contact was monitored remotely by SCANMAR trawl sensors. The standard distance between trawl stations was 35 nautical miles (65 km), except north and west of Svalbard where a stratified sampling was adapted to the steep continental shelve. The standard procedure was to tow 15 min after the trawl had made contact with the bottom, but the actual tow duration ranged between 5 min and 1 h and data were subsequently standardized to 15 min trawl time. Towing speed was 3 knots, equivalent to a towing distance of 0.75 nautical miles (1.4 km) during a 15 min tow. The trawl catches were recorded using the same procedures on the Russian and the Norwegian Research vessels to ensure comparability across Barents Sea regions. The benthic megafauna was separated from the fish and shrimp catch, washed, and sorted to lowest possible taxonomic level, in most cases to species, on-Board the vessel. Species identification was standardized between the researcher teams by annually exchanging the benthic expert’s among the vessels and taxon names were fixed each year according toWORMSwhen possible.This resulted in an Electronic identification manual and photo-compendium as a tool to standardize taxon identifications, in addition to various sources of identification literature. Difficult taxa were photographed and, in some cases, brought back as preserved voucher specimens for further identification. Wet-weight biomass was recorded with electronic scales in the ship laboratories for each taxon.The biomass determination included all fragments.

Routes used for hunting polar bear in Ittoqqoortoormiit, East Greenland before 1999 (red line), and in 2012 (yellow), 2013 (blue) and 2014 (green). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/findings/marine-mammals" target="_blank">Chapter 3</a> - Page 159 - Box figure 3.6.1

This visualization product displays beaches locations where the Marine Strategy Framework Directive (MSFD) monitoring protocol has been applied to collate data on macrolitter (> 2.5 cm). Reference lists associated with these protocols have been indicated with different colors in the map. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale. Preliminary processing were necessary to harmonize all the data: - Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring; - Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations); - Exclusion of beaches without coordinates; - Some categories & some litter types like organic litter, small fragments (paraffin and wax; items > 2.5cm) and pollutants have been removed. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines and EU Threshold Value for Macro Litter on Coastlines from JRC (these two documents are attached to this metadata). More information is available in the attached documents. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

Average September sea ice extent in 1979 (blue) compared with 2016 (white) and the median sea ice extent (yellow line) from 1981 to 2010 (Data: NSDIC 2016). STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/marine" target="_blank">Chapter 2</a> - Page 27 - Figure 2.4