A coastal surficial substrate layer for the coastal Scotian Shelf and Bay of Fundy. To create the layer, previous geological characterizations from NRCan were translated into consistent substrate and habitat characterizations; including surficial grain size and primary habitat type. In areas where no geological description was available, data including digital elevation models and substrate samples from NRCan, CHS and DFO Science were interpreted to produce a regional scale substrate and habitat characterization. Each characterization in the layer was given a ranking of confidence and original data resolution to ensure that decision makers are informed of the quality and scale of data that went into each interpretation.Cite this data as: Greenlaw, M., Harvey, C. Data of: A substrate classification for the Inshore Scotian Shelf and Bay of Fundy, Maritimes Region. Published: March 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/f2c493e4-ceaa-11eb-be59-1860247f53e3

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.

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 objective of benthos monitoring is to know the state of benthic macroinvertebrate communities in rivers according, in particular, to the composition of the substrate and the type of flow. Information on benthic macroinvertebrate samples collected at benthos monitoring stations is classified according to the benthos health index: iSBG for coarse-substrate streams and iSBM for soft-substrate streams. The Benthos Health Index (ISB) is a multimetric index based on benthic macroinvertebrates that assesses the biotic integrity of shallow streams. The benthos monitoring dataset includes a layer of sampling stations sampled between 2003 and 2023 and a layer of drainage areas for each of the types of substrate, either coarse or loose. The drainage area attribute table also provides a compilation of land use by category for the last year available at the time of data production, i.e. the year 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This is a collection of eelgrass (Zostera marina) presence and absence records collected between 2009-2025 in coastal waters in Nova Scotia, Canada. The data collection has been collated by the Coastal Benthic Ecology Lab (CBEL) at the Bedford Institute of Oceanography, Fisheries and Oceans Canada (DFO) under the supervision of Dr. Melisa Wong. Records include those from various research groups at DFO, Nova Scotia provincial departments, academia, and non-profit organizations.Data were collected using various methods including field measurements of eelgrass presence or absence via boat, snorkel and drop camera video transects, surface observations, and various sample collections (including plants, core, sediment, trawl, pop net, seine, etc.). Please see specific record references for associated metadata and detailed descriptions of the methods for eelgrass occurrence data collection. Where available, water depth (m) and substrate type (rock, soft, or mixed) are provided. The substrate type (hard substrate, mixed, mud, rocky, sand, or various combinations) used for the species distribution model published by O’Brien et al. 2022 is also provided where available.The data owner, data source (i.e., reference(s)), and use of the data in the species distribution model of O’Brien et al. 2022 are provided. All records from sources external to DFO are included under data sharing agreements or permission from the data owner.Cite this data as: Wong, M.C., Fraser, M., Thomson, J. A., O’Brien, J. Data of eelgrass (Zostera marina) occurrences in Nova Scotia. Published: April 2026. Coastal Ecosystems Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth NS.

The purpose of this study was to characterize the kelp bed at Batture-aux-Alouettes, a preferred food source for the green sea urchin (Strongylocentrotus droebachiensis). The green urchin is fished commercially in Quebec and the fishing effort is concentrated on the Batture-aux-Alouettes near Tadoussac, at the mouth of the Saguenay Fjord. The study was conducted in two separate phases in 2018 and 2019. The main objective of this study was to determine the abundance and biomass of the kelp bed at Batture-aux-Alouettes. The first phase, using a stratified random sampling design, was conducted from August 21th to August 24th, 2018. Sampling of two 50 x 50 cm quadrats, separated by a distance of approximately 30 m, was conducted at eleven sites during twelve dives in the eastern section of the Batture-aux-Alouettes to collect kelp for biomass estimation and macroalgal species richness assessment. In the second phase, a total of 429 stations were first sampled between July 15 and 18, 2019 with a camera system dropped in two 50 x 50 cm quadrats. The presence or absence of kelp, percent macroalgal cover, and substrate type were assessed for each photo. As a result of this underwater photographic analysis, 129 of these stations were identified as having a presence of kelp and 88 of these stations had a presence of other algal species. To ensure equal representation of the different depth strata, the stations with kelp were divided into three depth categories: shallow (-1.7 m to 0 m), medium (0 m to 2 m) and deep (2 m to 5 m). Dives were conducted from August 13 to 15, 2019, at ten of these stations using a stratified random sampling design, taking care to ensure a balanced spatial distribution as well as an equal distribution of the different depth strata (four in the shallow, three in the medium, and two in the deep). Sampling of the 50 x 50 cm dive quadrat took place at three different distances spaced 5 m apart from a transect, i.e. at the 3 m (_3m), 8 m (_8m) and 13 m (_13m) mark. If there was little or no kelp in the quadrat, the quadrat sampling could be repeated for up to four quadrats per distance for a total area of 1 m². Two additional quadrats were conducted (_x) at two stations. Biomass assessment was also done via "cookie cutter" sampling (_CC). Divers took the same 50 x 50 cm quadrat and placed it on a selected (i.e., non-random) plot with 100% kelp cover.The three files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes generic information about the event, such as date and location. The "additional_information_event_and_occurrence" file includes sample size, protocol and sampling effort. The "taxon_occurrence" file includes the taxonomy of the species observed, identified to the species or lowest possible taxonomic level. To obtain the abundance and biomass assessment of the kelp bed at Batture-aux-Alouettes, contact Rénald Belley (renald.belley@dfo-mpo.gc.ca).For quality control, the organisms were identified in the field fallowing the guide: Chabot, Robert et Anne Rossignol. 2003. Algues et faune du littoral du Saint-Laurent maritime : Guide d'identification. Institut des Sciences de la mer de Rimouski, Rimouski; Pêches et Océans Canada (Institut Maurice-Lamontagne), Mont-Joli. 113 pages. The taxonomy was checked against the World Register of Marine Species (WoRMS) to match recognized standards and using the R obistools and worrms libraries. The WoRMS match was placed in the "scientificNameID" field of the occurrence file. All sample locations were spatially validated. This project was funded by DFO Coastal Environmental Baseline Program under Canada’s Oceans Protection Plan. This initiative aims to acquire environmental baseline data contributing to the characterization of important coastal areas and to support evidence-based assessments and management decisions for preserving marine ecosystems.

These data sets provide information pertaining to epifauna and substrate estimates collected at dockside perimeters of floating docks located in Burrard Inlet and Fraser River Delta, British Columbia, between August and November, 2020. Data sets were compiled and formatted by Meagan Mak.Epifauna diversity was examined along surface perimeters of floating docks in Burrard Inlet and Fraser River Delta in southwestern British Columbia. Diversity estimates were obtained from video surveys collected over three depth-intervals: 1) Splash zone (SZ): depth-interval directly 15-cm above air-water interface; 2) Subsurface zone (SSZ): depth-interval (0-21 cm) below air-water interface; and 3) Deep-water zone (DZ): depth-interval below the SSZ (21-41 cm). Dock substrate consisted of combinations of wood, concrete, tires, plastic-floats, and metal, while epifauna and epiflora included anemones, tunicates, sponge, tube-worms, sea stars, bivalves, crabs, nudibranchs, urchins, barnacles, limpets, chitons, isopods, macroalgae and seagrass. Mussels ranged between 46% and 95% coverage across docks (median: 93%), while frequency of occurrence ranged between 85% and 100% (median: 99%), providing a biological-based substrate for other epifauna. The splash-zone consisted of outcropped mussels, encroached macroalgae from the waterline, and invertebrates above the waterline (limpets, chiton). If present, Ulva spp. typically formed a consistent narrow band (2-3 cm) above the waterline across all docks. Benthic (pipefish, sculpin) and pelagic (perch) fish were associated with epifaunal coverage and pelagic (open-water medium) settings. The Coast Guard Sea Island dock may experience episodic low-salinity intrusions supporting marine organisms at this site (ochre star, sculpin, limpet).

Benthic Marine Ecounits in coastal and offshore British Columbia. Benthic ecounits are intended to describe the sea bed and nearshore. Seven variables were selected to derive benthic ecounits: 1. Depth; 2. Slope; 3. Relief; 4. Temperature; 5. Exposure; 6. Current and 7. Substrate. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.

This point-layer shows the locations of components that make up facilities (Facilities are stored in another layer). Examples of facility components are barbeques, picnic tables, benches, or kiosks. See the Comp domain for a complete list. NOTE: Although some of the items in the domain appear to be activities, they are actually physical entities that appear within a facility. A facility component point would be stored in this layer to show a more precise location of the kayak rental place of business.Data is not necessarily complete - updates will occur weekly.

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7