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).**

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.

This dataset reports on lobster abundance and individual biological characteristics (size, sex, shell hardness, egg status), along with seabed substrate information, collected at various coastal sites in the Bay of Fundy, Canada. Surveys were conducted over a 40-year period between 1982 and 2021. Survey areas and SCUBA dive sites were located around Grand Manan Island, Deer Island, Campobello Island, and along the Bay of Fundy’s New Brunswick shore stretching from Passamaquoddy Bay, east to Maces Bay. One survey area was located on the Bay of Fundy’s southern shore (Nova Scotia) in the Annapolis Basin (Lawton et al. 1995). The data represent a compilation of SCUBA diving surveys (1003 belt transects) conducted directly by Fisheries and Oceans Canada (DFO) scientific SCUBA divers (1982-2019), or by contracted commercial divers funded in association with outside collaborating organizations; Department of Fisheries and Agriculture (DFA; 1990 – 1993), the Grand Manan Fishermen’s Association (GMFA; 2013-2015), and the University of New Brunswick (UNB; 2019-2021).Cite this data as: Lawton P, Dinning K, Rochette R, Teed L. American lobster (Homarus americanus) abundance and biological characteristics collected from SCUBA dive surveys in the Bay of Fundy from 1982-2021. Published August 2024. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B.For additional information please see:Campbell, A. 1990. Aggregations of berried lobsters (Homarus americanus) in shallow waters off Grand Manan, eastern Canada. DFO Can. J. Fish. Aquat. Sci. 47: 520-523.Denton, C.M. 2020. Maritimes Region Inshore Lobster Trawl Survey Technical Description. DFO Can. Tech. Rep. Fish. Aquat. Sci. 3376: v + 52 p.Lawton, P. 1993. Salmon aquaculture and the traditional invertebrate fisheries of the Fundy Isles region: habitat mapping and impact definition: Cooperation Agreement on Fisheries and Aquaculture Development. Submitted by Peter Lawton to the New Brunswick Department of Fisheries and Aquaculture, 84 p. Unpublished monograph. Available from Fisheries and Oceans Canada Library, Dartmouth, NS (Monographs: SH 380.2 .C2 .L39 1992).https://science-catalogue.canada.ca/record=3943769~S6Lawton, P., Robichaud, D.A., and Moisan, M. 1995. Characteristics of the Annapolis Basin, Nova Scotia, lobster fishery in relation to proposed marine aquaculture development. DFO Can. Tech. Rep. Fish. Aquat. Sci. 2035: iii + 26 p.Lawton, P., Robichaud, D.A., Rangeley, R.W., and Strong, M.B. 2001. American Lobster, Homarus americanus, population characteristics in the lower Bay of Fundy (Lobster Fishing Areas 36 and 38) based on fishery independent sampling. DFO Can. Sci. Advis. Sec. Res. Doc. 2001/093.Wentworth, C.K. 1922. A Scale of Grade and Class Terms for Clastic Sediments. The Journal of Geology 30(5): 377-392.Dinning, K.M., Lawton, P., and Rochette, R. 2025. Increased use of mud bottom by juvenile American lobsters (Homarus americanus) in Maces Bay and Seal Cove, Bay of Fundy, after three decades of population increases and predator declines. Canadian Journal of Fisheries & Aquatic Sciences 82; https://doi.org/10.1139/cjfas-2023-0312

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.

PURPOSE:In this study, we examined the structure and function of the Southampton Island marine food web across 149 species of benthic and pelagic invertebrates, fishes, marine mammals and seabirds collected from 2016 to 2019, to provide a baseline for future studies that aim to quantify temporal changes in food web structuring. More specifically,we used a multi-biomarker approach combining stable isotopes and HBIs to: (i) determine the vertical trophic structure of the marine food web, (ii) investigate the contribution of benthic and pelagic-derived prey to the higher trophic level species of the Arctic food web, and (iii) determine the role of ice algae and phytoplankton carbon source use across different trophic levels and compartments (pelagic and benthic). By shedding new light on the functioning of the Southampton Island food web and specifically how the contribution of ice algae and benthic habitat shapes its structure, these results will be relevant to adaptive management and conservation initiatives implemented in response to anthropogenic stressors and climate change. DESCRIPTION:Climate-driven alterations of the marine environment are most rapid in Arctic and subarctic regions, including Hudson Bay in northern Canada, where declining sea ice, warming surface waters and ocean acidification are occurring at alarming rates. These changes are altering primary production patterns that will ultimately cascade up through the food web. Here, we investigated (i) the vertical trophic structure of the Southampton Island marine ecosystem in northern Hudson Bay, (ii) the contribution of benthic and pelagic-derived prey to the higher trophic level species, and (iii) the relative contribution of ice algae and phytoplankton derived carbon in sustaining this ecosystem. For this purpose, we measured bulk stable carbon, nitrogen and sulfur isotope ratios as well as highly branched isoprenoids in samples belonging to 149 taxa, including invertebrates, fishes, seabirds and marine mammals. We found that the benthic invertebrates occupied 4 trophic levels and that the overall trophic system went up to an average trophic position of 4.8. The average δ34S signature of pelagic organisms indicated that they exploit both benthic and pelagic food sources, suggesting there are many interconnections between these compartments in this coastal area. The relatively high sympagic carbon dependence of Arctic marine mammals (53.3 ± 22.2 %) through their consumption of benthic invertebrate prey, confirms the important role of the benthic subweb for sustaining higher trophic level consumers in the coastal pelagic environment. Therefore, a potential decrease in the productivity of ice algae could lead to a profound alteration of the benthic food web and a cascading effect on this Arctic ecosystem.Collaborators:Centre for Earth Observation Science, University of Manitoba, Winnipeg, Manitoba, Canada - R´emi Amiraux, C.J. Mundy, Jens K. Ehn, Z.A. Kuzyk.Quebec-Ocean, Sentinel North and Takuvik, Biology Department, Laval University, Quebec, Quebec, Canada - Marie Pierrejean.Scottish Association for Marine Science, Oban, UK - Thomas A. Brown.Department of Natural Resource Sciences, McGill University, Ste. Anne de Bellevue, Quebec, Canada - Kyle H. Elliott.Department of Biological Sciences, University of Manitoba, Winnipeg, Manitoba, Canada - Steven H. Ferguson, Cory J.D. Matthews, Cortney A. Watt, David J. Yurkowski.School of the Environment, University of Windsor, Windsor, Ontario, Canada - Aaron T. Fisk.Science and Technology Branch, Environment and Climate Change Canada, Ottawa, Ontario, Canada - Grant Gilchrist.College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Fairbanks, AK, USA - Katrin Iken.Department of Earth Sciences, University of New Brunswick, Fredericton, NB, Canada - Audrey Limoges.Department of Integrative Biology, University of Windsor, Windsor, Ontario, Canada - Oliver P. Love, Wesley R. Ogloff.Department of Arctic Biology, The University Centre in Svalbard, Longyearbyen, Norway - Janne E. Søreide.