Pelagic Marine Ecounits are intended to describe the sea surface and water column. Two variables were selected to derive pelagic ecounits:1. Salinity and 2. Stratification. The British Columbia Marine Ecological Classification (BCMEC) is a hierarchical classification that delineates Provincial marine areas into Ecozones, Ecoprovinces, Ecoregions and Ecosections. The classification was developed from previous Federal and Provincial marine ecological classifications which were based on 1:2,000,000 scale information. The BCMEC has been developed for marine and coastal planning, resource management and a Provincial marine protected areas strategy. A new, smaller level of classification termed ecounits developed using 1:250,000 scale depth, current, exposure, subsurface relief and substrate was created to verify the larger ecosections, and to delineate their boundaries. 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.

Marine Ecosection classification for coastal and offshore British Columbia. The Marine Ecosections are: Johnstone Strait; Continental Slope; Dixon Entrance; Hecate Strait; Queen Charlotte Strait; Juan de Fuca Strait; North Coast Fjords; Queen Charlotte Sound; Strait of Georgia; Subarctic Pacific; Transitional Pacific; and Vancouver Island Shelf. The British Columbia Marine Ecological Classification (BCMEC) is a hierarchical classification that delineates Provincial marine areas into Ecozones, Ecoprovinces, Ecoregions and Ecosections. The classification was developed from previous Federal and Provincial marine ecological classifications which were based on 1:2,000,000 scale information. The BCMEC has been developed for marine and coastal planning, resource management and a Provincial marine protected areas strategy. A new, smaller level of classification termed ecounits developed using 1:250,000 scale depth, current, exposure, subsurface relief and substrate was created to verify the larger ecosections, and to delineate their boundaries. 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.

In 2009, DFO defined 12 marine bioregions across the three oceans bordering Canada to support its marine planning efforts, such as the establishment of networks of marine protected areas. However, these bioregions cover vast areas and exhibit significant ecological heterogeneity, especially along the coasts. Yet, this heterogeneity in coastal ecosystems often needs to be considered at the local scale, particularly for management and conservation purposes.The objective of this exercise is to subdivide the Estuary and Gulf of St. Lawrence (EGSL) bioregion for the province of Quebec into coastal sub-bioregions to better reflect local and regional coastal characteristics. The coastal classification presented in this report is based on the integration of four existing classification systems for the EGSL, which were not specifically designed for classifying coastal ecosystems. Integrating these classification systems into a single approach allowed us to define 13 coastal sub-bioregions for the EGSL. Data presented here are the limit of the 13 ecoregions made from this work. A technical report is available for more details. See supporting documents:Gendreau, Y., Narancic, B. et Bourassa, M-N. 2025. Classification écologique du territoire côtier de l’estuaire et du golfe du Saint-Laurent au Québec. Rapp. tech. can. sci. halieut. aquat. 0000 :v + 22p.

Description:Biophysical Units: Under the Pacific Marine Ecological Classification System (PMECS; DFO 2016; Rubidge et al. 2016), biophysical units are areas of distinct physiographic and oceanographic conditions and processes that shape species composition at spatial extents of 1000s of km. Geomorphic units:Geomorphic units or geozones are discrete geomorphological structures at the scale of 100s of km that are assumed to have distinctive biological assemblages (e.g., plateaus, ridges, seamounts, canyons). Although the spatial scale of geomorphic units is nested within biophysical units, a single geomorphic unit such as a trough may span more than one biophysical unit. The following 5 layers are included in this geodatabase:1. Biophysical_Units_L4A - Predicted PMECS Biophysical Units (Level 4A) output from the random forest analysis2. Biophysical_Units_L4B - Predicted PMECS Biophysical Units (Level 4B) output from the random forest analysis3. Biophysical_Units_ProbAssign_L4AB - Layer showing the probability that a grid cell was assigned to a given biophysical unit in the final random forest predictive modelling step4. Cluster_L4AB - Layer showing the output of species assemblage cluster analysis5. Geomorphic_Units - Geomorphic units for the BC coast that combines geomorphic units produced by Rubidge et al. 2016) and Proudfoot and Robb (2022).Methods:Biophysical Units:Rubidge et al. (2016) used a two-step process to identify biophysical units in British Columbia. First, a cluster analysis based on the similarity of species composition was used to group sites with similar species into distinct biological assemblages. Second, a random forest analysis was used to identify environmental correlates of the biological assemblages identified by the cluster analysis and to predict and assign the biological assemblage present in areas with too few biological data. Two different similarity thresholds were used to identify two levels (4A, 4B) of biophysical units; see Rubidge et al. (2016) for details. Indicator species for each assemblage (biophysical unit) were also identified.Geomorphic units:Rubidge et al. (2016) used the benthic terrain modeller (BTM) tool with broad and fine-scale benthic positioning index (BPI) parameters to define geomorphic units on the continental shelf in the Northern Shelf Bioregion and the continental slope in both the Northern Shelf Bioregion and Southern Shelf Bioregion. In 2022, geomorphic units were produced for the Strait of Georgia and Southern Shelf Bioregions following the same methods as Rubidge et al. (2016) (Proudfoot and Robb 2022). The geomorphic units produced as part of the PMECS process were merged with the geomorphic units produced for the Strait of Georgia and Southern Shelf bioregions to produce a continuous spatial data product representing geomorphic units for the Canadian Pacific continental shelf and slope. After merging, the geomorphic units produced in 2016 were unchanged (i.e., they are consistent with the original geomorphic units described in Rubidge et al. 2016).Data Sources:From Rubidge et al. (2016): Species data was taken from Fisheries and Oceans Canada (DFO) standardized fisheries-independent research surveys: groundfish trawl and long-line (2003-2013), Tanner Crab trawl and trap (2000–2006), and Dungeness Crab trap (2000–2014). Environmental data came from NASA, the Canadian Hydrographic Service, Fisheries and Oceans Canada, Bio-ORACLE, and elsewhere (details in Rubidge et al. 2016). From Proudfoot and Robb (2022): bathymetry data came from Natural Resources Canada (details in Proudfoot and Robb 2022).Uncertainties:The data is intended for use at the bioregional scale, and caution should be used for finer-scale analyses.

This dataset provides marine bacteriological water quality data for bivalve shellfish harvest areas in Canada (British Columbia, New Brunswick, Newfoundland and Labrador, Nova Scotia, Prince Edward Island and Quebec). Shellfish harvest area water temperature and salinity data are also provided as adjuncts to the interpretation of fecal coliform concentration data. The latter is the indicator of fecal contamination monitored by Environment and Climate Change Canada (ECCC) within the framework of the Canadian Shellfish Sanitation Program (CSSP). The geospatial positions of the sampling sites are also provided.These data are collected by ECCC for the purpose of making recommendations on the classification of shellfish harvest area waters. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO).

This dataset provides marine bacteriological water quality data for bivalve shellfish harvest areas in Prince Edward Island, Canada. Shellfish harvest area water temperature and salinity data are also provided as adjuncts to the interpretation of fecal coliform density data. The latter is the indicator of fecal matter contamination monitored annually by Environment and Climate Change Canada (ECCC) within the framework of the Canadian Shellfish Sanitation Program (CSSP). The geospatial positions of the sampling sites are also provided. These data are collected by ECCC for the purpose of making recommendations on the classification of shellfish harvest area waters. ECCC recommendations are reviewed and adopted by Regional Interdepartmental Shellfish Committees prior to regulatory implementation by Fisheries and Oceans Canada (DFO).This dataset is 'Deprecated'. Please use updated source here.https://open.canada.ca/data/en/dataset/6417332a-7f37-49bd-8be9-ce0402deed2a

This dataset contains results from an eelgrass classification in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence.

An eelgrass distribution map was classified from remotely sensed imagery in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28th, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence.

Marine classification schemes based on abiotic surrogates often inform regional marine conservation planning in lieu of detailed biological data. However, theses chemes may poorly represent ecologically relevant biological patterns required for effective design and management strategies. We used a community-level modeling approach to characterize and delineate representative mesoscale (tens to thousands of kilometers) assemblages of demersal fish and benthic invertebrates in the North-west Atlantic. Hierarchical clustering of species occurrence data from four regional annual multispecies trawl surveys revealed three to six groupings (predominant assemblage types) in each survey region, broadly associated with geomorphic and oceanographic features. Indicator analyses identified 3–34 emblematic taxa of each assemblage type. Random forest classifications accurately predicted assemblage dis-tributions from environmental covariates (AUC > 0.95) and identified thermal limits (annual minimum and maximum bottom temperatures) as important pre-dictors of distribution in each region. Using forecasted oceanographic conditions for the year 2075 and a regional classification model, we projected assemblage dis-tributions in the southernmost bioregion (Scotian Shelf-Bay of Fundy) under ahigh emissions climate scenario (RCP 8.5). Range expansions to the north eastare projected for assemblages associated with warmer and shallower waters of the Western Scotian Shelf over the 21st century as thermal habitat on the rela-tively cooler Eastern Scotian Shelf becomes more favorable. Community-level modeling provides a biotic-informed approach for identifying broadscale ecolog-ical structure required for the design and management of ecologically coherent, representative, well-connected networks of Marine Protected Areas. When com-bined with oceanographic forecasts, this modeling approach provides a spatial tool for assessing sensitivity and resilience to climate change, which can improve conservation planning, monitoring, and adaptive management.Cite this data as: O'Brien, J.M., Stanley, R.R.E., Jeffery, N.W., Heaslip, S.W., DiBacco, C., and Wang, Z. Demersal fish and benthic invertebrate assemblages in the Northwest Atlantic.Published: December 2024. Coastal Ecosystems Science Division, Maritimes region, Fisheries and Oceans Canada, Dartmouth NS.https://open.canada.ca/data/en/dataset/14d55ea5-b17d-478c-b9ee-6a7c04439d2b

Bay and Channel (fresh and marine) features and associated names