This dataset contains observations of species occurrences from seafloor imagery collected by the autonomous underwater vehicle (AUV) during the 2012 Expedition to Cobb Seamount. The National Oceanographic and Atmospheric Administration-operated SeaBED-class AUV which collected photographic images from 4 transects ranging from 436 m to 1154 m in depth.

This dataset contains observations of species occurrences from seafloor imagery collected by the remotely operated underwater vehicle (ROV) during the 2012 Expedition to Cobb Seamount. The ROV operated by Fisheries and Oceans Canada was a customized Deep Ocean Engineering Phantom HD2+2 which collected photographic images from 12 transects ranging from 35 m to 211 m in depth.

The data layer (.shp) presented is the result of an unsupervised classification method for classifying seafloor habitat in the Bay of Fundy (Northwest Atlantic, Canada). This method involves separating environmental variables derived from multibeam bathymetry (slope, bathymetric position index), backscatter, and oceanographic information (wave-shear current velocity) into spatial units (i.e. image objects) and classifying the acoustically and oceanographically separated units into 7 habitat classes (Bedrock and Boulders, Mixed Sediments, Gravelly Sand, Sand, Silty Gravel with Anemones, Silt, and Tidal Scoured Mixed Sediments) using in-situ data (imagery). Benthoscape classes (synonymous to landscape classifications in terrestrial ecology) describe the geomorphology and biology of the seafloor and are derived from elements of the seafloor that were acoustically and oceanographically distinguishable. Reference:Wilson, B.R., Brown, C.J., Sameoto, J.A., Lacharite, M., Redden, A. (2021). Mapping seafloor habitats in the Bay of Fundy to assess macrofaunal assemblages associated with Modiolus modiolus beds. Estuarine, Coastal and Shelf Science, 252. https://doi.org/10.1016/j.ecss.2021.107294Cite this data as: Wilson, B.R., Brown, C.J., Sameoto, J.A., Lacharite, M., Redden, A. Bay of Fundy Benthoscape. Published May 2023. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/dbabd17a-a2c7-4b3f-9bd8-a77a9c7f9c1c

PURPOSE:Eastern Beaufort Sea beluga whales form one of the largest summering aggregations of the species in the Mackenzie Estuary. In 2010, the Tarium Niryutait Marine Protected Area (TNMPA) was designated to protect beluga whales and their habitats As a part of ongoing ecological monitoring efforts in the TN MPA, passive acoustic monitoring (PAM) was implemented in 2011 to act as continuous monitoring method, filling the temporal gaps associated with historical aerial surveys. Beginning in 2014, PAM effort increased each year, and oceanographic sensors were added to moorings to (1) better understand oceanographic conditions within the TN MPA and (2) examine the environmental parameters that drive beluga movement and habitat use patterns within the estuary. Several studies using this dataset have been completed, and others are ongoing. However, much more can be done with the acoustic and environmental data. The purpose of this report is to outline deployment methods and instrument settings for moorings to support the full use of the data collected. DESCRIPTION:Each summer, Eastern Beaufort Sea beluga whales form one of the largest aggregations of the species in the Mackenzie Estuary. In 2010, the Tarium Niryutait Marine Protected Area (TNMPA) was designated in the estuary to protect beluga whales and their habitats. As a part of ongoing ecological monitoring efforts in the TN MPA, passive acoustic monitoring (PAM) was implemented in 2011 to act as continuous monitoring method, filling the temporal gaps associated with historical aerial surveys. Beginning in 2014, PAM effort increased each year, and oceanographic sensors were added to each PAM mooring to (1) better understand oceanographic conditions (i.e., temperature, salinity, turbidity, and wave conditions) within the TN MPA and (2) to examine the environmental parameters that drive beluga movement and habitat use patterns within the estuary. Moorings have been deployed with varying configurations of oceanographic sensors in Kugmallit Bay since 2015, but typically record water temperature, salinity, depth, and wave conditions. In 2018, the program was expanded to the Niaqunnaq parcel of the MPA (Shallow Bay), and in 2021 it was expanded again to the Okeevik parcel of the MPA. These observatories have provided new knowledge about drivers of beluga habitat use in the TN MPA, in particular in Kittigaryuit, but more recently in Niaqunnaq and Okeevik.

The marks left in the seabed by the commercial anchoring process can be seen as linear features in high-resolution multibeam bathymetry data. These features have been digitized to polylines for individual marks and polygons for anchor scour zones for British Columbia's (BC) commercial anchorages. They are made available via the Federal Geospatial Platform (FGP) for use in a Geographical Information System (GIS). This feature dataset is complete for published BC commercial anchorages and the multibeam bathymetry data available in 2021. It does not represent features produced since the collection of each multibeam bathymetry survey nor any features infilled since. The data are intended to be used for scientific research to better understand the cumulative impacts to the seabed from commercial anchoring at a 1:5000 scale or greater.

This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

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) MudThe 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.

Funded through DFO's Strategic Program for Ecosystem-based Research and Advice (SPERA), this benthic survey covers several seabed areas adjacent to Deer Island and Campobello Island, the Wolves Islands, and Grand Manan (NB) over a two-year study period (2016-2017). One hundred and fifty drift camera transects were completed within the ~91 sq-km study region collecting continuous high-definition video with periodic 4K resolution video (provided by a downward facing Blackmagic Production Camera 4K equipped with video lights). A Nikon D800 36.1 megapixel digital still imagery camera (equipped with a studio strobe light) captured seafloor images at ~30s intervals over a maximum 25-minute drift survey period. The camera was triggered by lowering the camera frame within 1 m of the seabed, releasing tension on a trigger weight suspended below the frame. Camera location was tracked using an ultra-short baseline acoustic positioning system (Tracklink 1500HA transceiver with 1505B transponder on the camera frame). Species presence/absence, abundance, and bottom type was recorded manually using PhotoQuad v1.4 software. An average field of view of 0.7 x 0.5 m was determined from a subset of digital still images within which the 10 cm diameter trigger weight was fully in view. Thirty-eight key and common species were described using explicit taxonomic identifiers, while other species were recorded within broader general categories (e.g. unidentified Cnidaria). Identification was made to the lowest possible taxonomic level. Primary bottom-type was defined as the grain size with the most percent coverage for each image/video interval. Grain size limits were determined using the Wentworth scale.Cite this data as: Lawton P. Benthic Species Presence/Absence in the Lower Bay of Fundy Derived From High Resolution Video and Still Imagery. Published May 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.

The data layer (.shp) presented is the result of an unsupervised classification method for classifying seafloor habitat on German Bank (off South West Nova Scotia, Canada). This method involves separating environmental variables derived from multibeam bathymetry (Slope, Curvature) and backscatter (principal components: Q1, Q2, and Q3) into spatial units (i.e. pixels) and classifying the acoustically separated units into 5 habitat classes (Reef, Glacial Till, Silt, Silt with Bedforms, and Sand with Bedforms) using in situ data (imagery). Benthoscape classes (synonymous to landscape classifications in terrestrial ecology) describe the geomorphology and biology of the seafloor and are derived from elements of the seafloor that were acoustically distinguishable.Unsupervised classifications (acoustic classifications) optimized at 15 classes using Idrisi CLUSTER method (pixel based)Number representing the benthoscape classes (CLASS) derived from in situ imagery and video (See Brown et al., 2012, Figure 3, Table 1).Benthoscape classes (See Brown et al., 2012, Figure 3).Reference:Brown, C. J., Sameoto, J. A., & Smith, S. J. (2012). Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research, 72, 1–13. https://doi.org/10.1016/j.seares.2012.04.009Cite this data as: Brown, C. J., Sameoto, J. A., & Smith, S. J. Data of: Benthoscape Map of German Bank. Published: February 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b7f81d4a-2cb6-4393-b35b-e536ec63e834

The annual summer scallop surveys on the principal grounds in the Bay of Fundy follow stratified-random designs. The gear comprises a ‘Digby scallop drag’ with four ‘buckets’, each of 760 mm inside width, their bags being made of 74 mm steel-wire rings linked by rubber washers. A comparative data set of three scallop grounds (Digby, Lurcher Shoal and Grand Manan) was produced comprised of 190 stations sampled in 1997 and 213 from 2007–08. Presence/absence of a common suite of 68 benthic invertebrate taxa were recorded: 43 individual species, 20 additional genera and five higher taxa, all drawn from nine phyla. Each taxon was coded for each of seven biological traits (each with associated modalities), selected for their assumed relevance to environmental drivers. A score between 0 and 3 was assigned based on the literature for the taxon’s affinity to each modality, using ‘fuzzy coding’. Non-zero scores were assigned to as many modalities as required to represent the traits of the taxon’s adult stage. The resulting taxa x traits matrix, of 68 taxa by 27 modalities, is provided here along with the metadata for each station sampled. In addition, fourteen environmental variables, deemed relevant to benthic epifauna and representing both seabed sediments and the water column, were quantified for each survey station. Seabed depth, mean grain size, mean significant wave height, mean seabed shear stress, root mean square tidal current speed 1 m above the seabed and combined averaged wave-current shear velocity were each extracted from a sediment transport model for the Bay of Fundy prepared by Li et al. (2015). Mean values for current velocities, salinity and temperature for both surface and bottom layers, plus maximum mixed layer depth and bottom shear were each drawn from the Bedford Institute of Oceanography North Atlantic Model (BNAM: Wang et al., 2018). BNAM values averaged across 1990–2015 were used when examining faunal differences among survey areas, but explorations of temporal change used annual values for 1997 and 2007 individually. The variable nomenclature in the attached spreadsheet follows those of Li et al. (2015) and Wang et al. (2018). Results of the spatial and temporal analyses of these data are found in Staniforth et al. (2023). The values for each of the environmental variables are provided in the spreadsheet below. Their interpolated surfaces are also provided.Cite this data as: MacDonald, Barry; Staniforth, Calisa; Lirette, Camille; Murillo, Francisco; Kenchington, Ellen; Kenchington, Trevor (2023). Benthic Megafaunal Assemblages on Scallop Fishing Grounds in the Bay of Fundy (1997 and 2007). Published May 2024. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/935836da-a565-4f1e-806e-d354d8db252c