Predicted bulk density (g/cm3) at defined depth ranges (0–5 cm, 5–15 cm, 15–30 cm, 30–60 cm, 60–100 cm). The mass of dry soil per unit bulk volume.

The Automatic Identification System (AIS) is a global, satellite-based and terrestrial-based ship tracking system that uses shipborne equipment to remotely track vessel identification and positional information and is typically required on vessels of 300 gross tonnage or more on an international voyage, of 500 gross tonnage or more not on an international voyage, and passenger ships of all sizes. AIS tracking technologies are primarily used in support of real-time maritime domain awareness and for maritime security and safety of life at sea. This report describes a geographic information system (GIS) analysis of 2019 AIS data to produce yearly and monthly vessel density maps of all vessel classes combined and yearly density maps of each vessel class. The year 2019 was selected to portray shipping densities in a pre-COVID 19 pandemic depiction of the maritime transport sector in the Northwest Atlantic. Vessel density map applications include use in spatial analysis and decision support for marine spatial planning.In 2023 the process was applied to the years 2013 through to 2022 and were made available using the same processes that were applied to the original 2019 datasets.

This layer represents the relative summer density of belugas in the St. Lawrence Estuary based on 35 aerial surveys carried out from 1990 to 2009. The boundaries of the areas were determined by combining the highest densities until the desired proportion of the population was obtained using kernel density estimation in order to obtain a smooth and continuous density distribution.Within Fisheries and Oceans Canada (DFO), the ecosystem approach is considered a tool for operational planning, project implementation and preparation of advisory reports. In response to this strategic direction, the DFO science division is committed to implement the ecosystem approach in its activities as Ecosystem Research Initiatives (ERI) in each of the six administrative regions of DFO. In the Quebec region, two pilot projects were implemented, of which one aimed to define and characterize the habitat of the St. Lawrence beluga (Delphinapterus leucas).Data sources and references:DFO. 2016. Ecosystem Research Initiative (ERI): Integrated Advice on the Summer Habitat of the St. Lawrence Estuary Beluga (Delphinapterus leucas). DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2016/030. Mosnier, A., R. Larocque, M. Lebeuf, J.-F. Gosselin, S. Dubé, V. Lapointe, V. Lesage, V., H. Bourdages, D. Lefaivre, S. Senneville and C. Chion. 2016. Définition et caractérisation de l'habitat du béluga (Delphinapterus leucas) de l'estuaire du Saint-Laurent selon une approche écosystémique. Secr. can. de consult. sci. du MPO. Doc. de rech. 2016/052. vi + 93 p.

This layer represents the relative summer density of beluga calves in the St. Lawrence Estuary based on 35 aerial surveys carried out from 1990 to 2009. The boundaries of the areas were determined by combining the highest densities until the desired proportion of the population was obtained using kernel density estimation in order to obtain a smooth and continuous density distribution.Within Fisheries and Oceans Canada (DFO), the ecosystem approach is considered as a tool for operational planning, project implementation and preparation of advisory reports. In response to this strategic direction, the DFO science division is committed to implement the ecosystem approach in its activities as Ecosystem Research Initiatives (ERI) in each of the six administrative regions of DFO. In the Quebec region, two pilot projects were implemented, of which one aimed to define and characterize the habitat of the St. Lawrence beluga (Delphinapterus leucas).Data sources and references:DFO. 2016. Ecosystem Research Initiative (ERI): Integrated Advice on the Summer Habitat of the St. Lawrence Estuary Beluga (Delphinapterus leucas). DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2016/030. Mosnier, A., R. Larocque, M. Lebeuf, J.-F. Gosselin, S. Dubé, V. Lapointe, V. Lesage, V., H. Bourdages, D. Lefaivre, S. Senneville and C. Chion. 2016. Définition et caractérisation de l'habitat du béluga (Delphinapterus leucas) de l'estuaire du Saint-Laurent selon une approche écosystémique. Secr. can. de consult. sci. du MPO. Doc. de rech. 2016/052. vi + 93 p.

Kernel density estimation (KDE) utilizes spatially explicit data to model the distribution of a variable of interest. It is a simple non-parametric neighbour-based smoothing function that relies on few assumptions about the structure of the observed data. It has been used in ecology to identify hotspots, that is, areas of relatively high biomass/abundance, and in 2010 was used by Fisheries and Oceans Canada to delineate significant concentrations of corals and sponges. The same approach has been used successfully in the Northwest Atlantic Fisheries Organization (NAFO) Regulatory Area. Here, we update the previous analyses with the catch records from up to 5 additional years of trawl survey data from Eastern Canada, including the Gulf of Saint Lawrence. We applied kernel density estimation to create a modelled biomass surface for each of sponges, small and large gorgonian corals, and sea pens, and applied an aerial expansion method to identify significant concentrations of these taxa. We compared our results to those obtained previously and provided maps of significant concentrations as well as point data co-ordinates for catches above the threshold values used to construct the significant area polygons. The borders of the polygons can be refined using knowledge of null catches and species distribution models of species presence/absence and/or biomass.

Production of a layer that includes the known information on seaweed medium to high density areas in the Chaleur Bay, the Estuary and the Gulf of St. Lawrence according to a literature review of documents produced between 1995 and 1999.Additional InformationSeaweed density areas were produced according to a literature review of the following documents:Mariculture de Percé inc. 1995. Essai d'augmentation de la biomasse du homard "Récifs artificiels", Rapport no 95, Programme d'essai et d'expérimentation halieutiques et aquicoles.Lemieux, C. 1995. Acquisition de connaissances des habitats côtiers dans la région de Rimouski (1995). Rapport du Groupe-Conseil GENIVAR présenté au Ministère des Pêches et des Océans du Canada, Division de la Gestion de l’Habitat du Poisson, 52 pages + 2 annexes.Belzile, L., Lalumière, R., Cloutier, O. et J.F. Martel. 1997. Inventaire des laminaires dans la Baie des Chaleurs entre Miguasha et Bonaventure. Rapport conjoint Groupe-conseil Génivar inc. et Regroupement des pêcheurs professionnels du sud de la Gaspésie pour le compte de Pêches et Océans Canada, Québec. 13 pagesVaillancourt, M.-A. et C. Lafontaine. 1999. Caractérisation de la Baie Mitis. Jardins de Métis et Pêches et Océans Canada. Grand-Métis. 185 p.Calderón, I. 1996. Caractérisation de la végétation et de la faune ichtyenne de la baie de Sept-Îles. Document réalisé par la Corporation de protection de l'environnement de Sept-Îles pour Pêches et Océans Canada. 26p. + 5 annexes.Calderón, I. 1996. Caractérisation des habitats du poisson de la baie de Sept-Îles - Phase II. Corporation de protection de l'environnement de Sept-Îles. 37 pages.

Average grid cell density is a polygon feature class containing the average density value for each grid cell per species/groups and season.

This data shows spatial density of Soybean cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Soybeans are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have Soybeans based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of sunflower cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which sunflower is more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have sunflower based on analysis of the 2009 to 2021 AAFC annual crop inventory data.

This data shows spatial density of rye cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which rye are more expected. Results are provided as rasters with numerical values for each pixel indicating the spatial density calculated for that location. Higher spatial density values represent higher likelihood to have rye based on analysis of the 2009 to 2021 AAFC annual crop inventory data.