Grain size is the most fundamental physical property of sediment, and these data are widely used in a variety of applications in science. Marine expeditions of the Geological Survey of Canada have been collecting grain size information on seabed and sub-seabed samples for over 50 years. Results have been recorded at 5th phi midpoints since the early 1990's in contrast to the earlier full, half or quarter phi interval end point values. Users of high resolution data must note that the sum of %Silt and %Clay equals the total %Mud makeup and that %Gravel, %Sand, %Silt and %Clay sum to 100%. Summary statistics include percentages of gravel, sand, silt, clay and mud as well as mean, kurtosis, skewness and standard deviation. The quality of these data varies. Results should be used with some caution as they may not be fully representative of seabed grainsize, particularly in areas of sandy and coarser sediment (e.g., sand and mud can leak out of the sampler during recovery). Canada makes no representation or warranty of any kind with respect to the accuracy, usefulness, novelty, validity, scope, completeness or currency of the data and expressly disclaims any implied warranty of merchantability or fitness for a particular purpose of the data. For the purpose of the web mapping service, grain size data are sorted by the expedition id. Coarse and detailed grain size distribution plots are shown when a point is chosen. If the sample contains more that one sub-sample ( e.g., as with a piston core sequence), the grain size plots are stacked in the display window from the top of the core downwards.

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.

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 shallow, coastal regions of the world’s oceans are highly productive ecosystems providing important habitat for commercial, forage, endangered, and iconic species. Given the diversity of ecosystem services produced or supported by this ecosystem, a better understanding of its structure and function is central to developing an ecosystem-based approach to management. However this region termed the ‘white strip’ by marine geologists because of the general lack of high-resolution bathymetric data - is dynamic, highly variable, and difficult to access making data collection challenging and expensive. Since substrate is a key indicator of habitat in this important ecosystem, we created a continuous substrate map of Bottom Patches (BoPs) from the best available bottom type data using an approach that is simple, quantitative, and transparent making it amenable to iterative improvement as data quality and availability improve. To provide subsequent analyses (such as habitat models) with some confidence in the defined bottom type values, we developed a corresponding confidence surface based on the agreement of, and distance between observations. Such data are critical to assessments of species distributions and anthropogenic risk. Bottom patches (BoPs) have been created to represent bottom type for the entire Pacific Canadian coast from the high high water line to a depth of 50 metres (m). As a polygon representation, the BoPs describe patches of similar substrate prescribed by depth classes and the available field observations. In the areas where no observations are available, predicted bottom type values are used. The approach is described in Gregr et al. (2013), as a spatial framework for representing nearshore ecosystems. Accuracy of the bottom type depends on a multitude of factors but primarily the reliability and density of the bottom type observations. The horizontal accuracy of these data likely ranges from metres to 10s of metres because of the source data or data processing required. Areas with a higher data density, where the data show strong coherence, are understood to have higher accuracy. The BoPs use depth ribbons (polygons describing bathymetric ecozones) as an input. Depth ribbons for Pacific Canada were created from a high resolution (20 x 20 m2) bathymetry. Given the resolution of these data, processing was facilitated by dividing the Pacific Coast into 5 regions.The West Coast of Vancouver Island, extending from Cape Sutil in the North past Port San Juan to the South, includes a total of 110,313 BoP polygons. Bottom Patches for Queen Charlotte Strait and Strait of Georgia regions were combined for a total of 235,754 BoP polygons. The North Central Coast region, extending from the Alaskan border in the North to Cape Caution in the South, includes a total of 431,639 BoP polygons. The Haida Gwaii region includes a total of 86,825 BoP polygons.These data are intended for scientific research only. The developers (Fisheries and Oceans Canada, SciTech Environmental Consulting) are not responsible for damages resulting from any omissions or errors that may be contained in this dataset and expressly disclaims any warranty of fitness for any particular purpose. Developers shall not be liable for any losses, financial or otherwise, due to the use of these data. The user assumes the entire risk as to the suitability, results and performance of the dataset for their proposed use. Please credit SciTech and Fisheries and Oceans Canada as the source of the data in any maps, reports, or articles that are printed or published on paper or the Internet.

Geochemistry includes information related to rock samples and sediment samples.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This dataset was designed for Environment and Climate Change Canada's (ECCC) National Environmental Emergencies Center (NEEC) for oil spill preparedness and response. The polygons from this layer come from the coastal ecosystems geodatabase as part of the Mapping of coastal ecosystems of the Estuary and Gulf of St. Lawrence project. This layer represents semi-vegetated (25-75%) and vegetated (75-100%) zones of which marsh vegetation is the dominant. The study area includes all of the estuarine and maritime coasts of Quebec, with the exception of certain sectors, including most of the Lower North Shore and Anticosti Island, with the exception of villages of Kegaska, la Romaine, Chevery, Blanc-Sablon and Port-Menier. Some islands off the estuary and gulf coasts are part of the region covered, such as Île d'Orléans, Isle-aux-Coudres, Île Verte and Île Bonaventure.The mapping of coastal ecosystems was carried out jointly by the Laboratory for Dynamics and Integrated Coastal Zone Management (LDGIZC) of the University of Quebec at Rimouski as part of the Coastal Resilience Project (https: //ldgizc.uqar.ca/Web/projets/projet-resilience-cotiere) funded by the MELCC; and by the Fisheries and Oceans Canada team, as part of its Integrated marine response planning (IMRP) component of the Oceans Protection Plan (OPP), with the objective of updating the Marine Oil Spill Preparedness and Response Regime of Canada. The master geodatabase of coastal ecosystems is hosted and distributed by UQAR on their SIGEC-Web mapping platform: https://ldgizc.uqar.ca/Web/sigecwebThe characterization of marshes was mainly carried out using photo-interpretation of RVBI aerial photos acquired by DFO (2015-2020) and oblique photos taken by helicopter acquired by UQAR in 2017. This dataset also includes the information from validation stations visited by UQAR (2018-2020), used to validate and refine the photo-interpretation.

Sealion rafting areas. 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.

California and Steller sealion haulout areas. Coastal Resource Information Management System (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.

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