Identification of significant concentrations of sponges in the Gulf of St. Lawrence biogeographic unit using Kernel density estimation (KDE).This method was applied to create a modelled biomass surface for each taxa and an aerial expansion method was permitted to identify significant concentrations. Only geo-referenced biomass data have been used to identify the “hot spots”. The borders of the areas were refined using knowledge of null catches and species distribution models. Predictive models were produced using a random forest machine-learning technique. For more details, please refer to this report: Kenchington, E., L. Beazley, C. Lirette, F.J. Murillo, J. Guijarro, V. Wareham, K. Gilkinson, M. Koen Alonso, H. Benoît, H. Bourdages, B. Sainte-Marie, M. Treble, and T. Siferd. 2016. Delineation of Coral and Sponge Significant Benthic Areas in Eastern Canada Using Kernel Density Analyses and Species Distribution Models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/093. vi + 178 p.http://waves-vagues.dfo-mpo.gc.ca/Library/40577806.pdfThe present layer only contains the analysis results for sponges. Purpose:As part of the Canada's commitment to the identification and protection of sensitive benthic marine ecosystems, maps of the location of significant concentrations of corals and sponges on the east coast of Canada were produced through quantitative analyses of research vessel trawl survey data, supplemented with other data sources where available. The taxa analyzed are sponges (Porifera), large and small gorgonian corals (Alcyonacea), and sea pens (Pennatulacea). However, only the sponges (Porifera) and sea pens (Pennatulacea) have been considered in the analysis concerning the Gulf of St. Lawrence biogeographic unit.

This dataset contains the outputs for Bute Inlet from two simulations shown in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). The Finite Volume Community Ocean Model (FVCOM v4.1) was run with two different sets of initial conditions for the Discovery Islands region of British Columbia, Canada, from May 24 to June 27, 2019. The "Baseline" simulation used observed initial conditions, while the "Sensitivity" simulation removed the observed cold subsurface water mass from the initial profiles. Here in this dataset, we provide 29-day averages of the following variables in a transect along Bute Inlet: potential temperature, density, along-inlet velocity, and Brunt-Väisälä frequency (N^2). The averaging properly removes the tidal effects.

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.

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.

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 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.

This data shows spatial density of Canola cultivation in Canada. Regions with higher calculated spatial densities represent agricultural regions of Canada in which Canola 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 Canola based on analysis of the 2009 to 2021 AAFC annual crop inventory data.