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.

This dataset contains the outputs for Bute Inlet from two simulations shown 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). The Finite Volume Community Ocean Model (FVCOM v4.1) was run with two different sets of initial conditions for the Discovery Islands region of British Columbia, Canada, from May 24 to June 27, 2019. The "Baseline" simulation used observed initial conditions, while the "Sensitivity" simulation removed the observed cold subsurface water mass from the initial profiles. Here in this dataset, we provide 29-day averages of the following variables in a transect along Bute Inlet: potential temperature, density, along-inlet velocity, and Brunt-Väisälä frequency (N^2). The averaging properly removes the tidal effects.

The assessment of the status of eelgrass (Zostera marina) beds at the bay-scale in turbid, shallow estuaries is problematic. The bay-scale assessment (i.e., tens of km) of eelgrass beds usually involves remote sensing methods such as aerial photography or satellite imagery. These methods can fail if the water column is turbid, as is the case for many shallow estuaries on Canada’s eastern seaboard. A novel towfish package was developed for the bay-scale assessment of eelgrass beds irrespective of water column turbidity. The towfish consisted of an underwater video camera with scaling lasers, sidescan sonar and a transponder-based positioning system. The towfish was deployed along predetermined transects in three northern New Brunswick estuaries. Maps were created of eelgrass cover and health (epiphyte load) and ancillary bottom features such as benthic algal growth, bacterial mats (Beggiatoa) and oysters. All three estuaries had accumulations of material reminiscent of the oomycete Leptomitus, although it was not positively identified in our study. Tabusintac held the most extensive eelgrass beds of the best health. Cocagne had the lowest scores for eelgrass health, while Bouctouche was slightly better. The towfish method proved to be cost effective and useful for the bay-scale assessment of eelgrass beds to sub-meter precision in real time. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Beds Using Sidescan and Video - Bouctouche. Published: November 2017. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b4c83cd2-20f2-47d8-8614-08c1c44c9d8c

This dataset provides projected 30-year, 50-year, and 100-year return levels for harbours in British Columbia by 2050 and 2100 under a high emission scenario SSP585, relative to the mean sea level over 1993-2020. The return levels are a combination of estimated present extreme sea levels and projected mean sea level rise. The present extreme sea levels are derived from hourly coastal sea levels for the period from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM). The projected mean sea level rise is derived from the regional mean sea level rise data of the IPCC 6th Assessment Report under SSP585, adjusted for the local vertical land motion.

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%3Dihub Uncertainties: 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).

Arctic Ecologically and Biologically Significant Areas (EBSAs) and Arctic Marine Areas of Heightened Ecological and Cultural Significance as identified in the Arctic Marine Shipping Assessment (AMSA) IIC report. STATE OF THE ARCTIC MARINE BIODIVERSITY REPORT - <a href="https://arcticbiodiversity.is/marine" target="_blank">Chapter 1</a> - Page 16 - Box Figure 1.1

This dataset provides projected 30-year, 50-year, and 100-year return levels for harbours in British Columbia by 2050 and 2100 under a low emission scenario SSP126, relative to the mean sea level over 1993-2020. The return levels are a combination of estimated present extreme sea levels and projected mean sea level rise. The present extreme sea levels are derived from hourly coastal sea levels for the period from 1993 to 2020, simulated using a high-resolution Northeast Pacific Ocean Model (NEPOM). The projected mean sea level rise is derived from the regional mean sea level rise data of the IPCC 6th Assessment Report under SSP126, adjusted for the local vertical land motion

Bay Scale Assessment of Nearshore Habitat Bras dOr Lake - Malagawash 2007 2008 data is part of the publication Bay Scale Assessment of Nearshore Habitat Bras d'Or Lakes. A history of nearshore benthic surveys of Bras d’Or Lake from 2005 – 2011 is presented. Early work utilized drop camera and fixed mount sidescan. The next phase was one of towfish development, where camera and sidescan were placed on one platform with transponder-based positioning. From 2009 to 2011 the new towfish was used to ground truth an echosounder. The surveys were performed primarily in the northern half of the lake; from 10 m depth right into the shallows at less than 1 m. Different shorelines could be distinguished from others based upon the relative proportions of substrate types and macrophyte canopy. The vast majority of macrophytes occurred within the first 3 m of depth. This zone was dominated by a thin but consistent cover of eelgrass (Zostera marina L.) on almost all shores with a current or wave regime conducive to the growth of this plant. However, the eelgrass beds were frequently in poor shape and the negative impacts of commonly occurring water column turbidity, siltation, or possible localized eutrophication, are suspected. All survey data were placed into a Geographic Information System, and this document is a guide to that package. The Geographic Information System could be used to answer management questions such as the placement and character of habitat compensation projects, the selection of nearshore protected areas or as a baseline to determine long term changes. Vandermeulen, H. 2016. Video-sidescan and echosounder surveys of nearshore Bras d’Or Lake. Can. Tech. Rep. Fish. Aquat. Sci. 3183: viii + 39 p. Cite this data as: Vandermeulen H. Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake - Malagawash 2007 - 2008. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

An annual trawl survey is conducted in Southwestern Nova Scotia and the Bay of Fundy to assess the lobster stocks in the area. The survey is conducted with the Northeast Fisheries Science Center Ecosystem Survey Trawl (NEST), a small mesh trawl with a cod end liner, which ensures the capture of various sizes of lobster. The dimensions and location of the trawl are monitored and recorded throughout the tow using an electronic trawl mensuration system. In addition, water temperature and depth are also monitored. The target tow length is 1 kilometer which is tracked using an Olex marine charting system. Vessel crew, DFO science staff and a contracted at sea observer work together to perform required tasks and collect all relevant data. Catch from each tow is separated by species, weighed and counted. Length frequency data is collected on select groundfish and crab species and detailed morphometric data is collected on each lobster. PARAMETERS COLLECTED: Set information and profile - includes set date, time depth and location Catch summary - weight and number caught of each species Length Freqeuncies - completed for up to 100 fish for selected species Lobster Morphological Data - detailed data collected on each lobster Bottom Temperature Trawl Metrics NOTES ON QUALITY CONTROL: Data is visually verified and double keypunched. On loading to Oracle, data is run through rigorous automated checks to verify data accuracy and integrity. SAMPLING METHODS: The target tow length is 1 kilometer which is tracked using an Olex marine charting system. Vessel crew, DFO science staff and a contracted at sea observer work together to perform required tasks and collect all relevant data. Catch from each tow is separated by species, weighed and counted. Length frequency data is collected on select groundfish and crab species, detailed morphometric data is collected on each lobster. Bottom temperature and trawl metrics are collected for each tow. CITATION LIST: Denton, Cheryl M.. 2020. Maritimes Region Inshore Lobster Trawl Survey Technical Description. Canadian technical reports of fisheries and aquatic sciences (DFO) 3376.

A history of nearshore benthic surveys of Bras d’Or Lake from 2005 – 2011 is presented. Early work utilized drop camera and fixed mount sidescan. The next phase was one of towfish development, where camera and sidescan were placed on one platform with transponder-based positioning. From 2009 to 2011 the new towfish was used to ground truth an echosounder. The surveys were performed primarily in the northern half of the lake; from 10 m depth right into the shallows at less than 1 m. Different shorelines could be distinguished from others based upon the relative proportions of substrate types and macrophyte canopy. The vast majority of macrophytes occurred within the first 3 m of depth. This zone was dominated by a thin but consistent cover of eelgrass (Zostera marina L.) on almost all shores with a current or wave regime conducive to the growth of this plant. However, the eelgrass beds were frequently in poor shape and the negative impacts of commonly occurring water column turbidity, siltation, or possible localized eutrophication, are suspected. All survey data were placed into a Geographic Information System, and this document is a guide to that package. The Geographic Information System could be used to answer management questions such as the placement and character of habitat compensation projects, the selection of nearshore protected areas or as a baseline to determine long term changes. Vandermeulen, H. 2016. Video-sidescan and echosounder surveys of nearshore Bras d’Or Lake. Can. Tech. Rep. Fish. Aquat. Sci. 3183: viii + 39 p. Cite this data as: Vandermeulen H. Bay Scale Assessment of Nearshore Habitat Bras d'Or Lake. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.