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.

Identification of significant concentrations of sea pens 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 sea pens. 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.

11 tagged Blue whales (Balaenoptera musculus) were tracked during the daytime movements as well as the feeding behaviour in the St. Lawrence River estuary. Kernel density was applied to derminate the high density feeding areas of all individuals combined (30, 40, 50, 60, 75, 95 %).Doniol-Valcroze T, Lesage V, Giard J, Michaud R, 2012. Challenges in marine mammal habitat modelling: evidence of multiple foraging habitats from the identification of feeding events in blue whales. Endang Species Res, Vol. 17 : 255–268, doi : 10.3354/esr00427(English version only)

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.

BLKPASSAGE_OFFICIAL is one of the important layers for Saskatchewan Upgraded Road Network (SURN) and National Road Network (NRN). The BLKPASSAGE_OFFICIAL provides the information of road blocked passage to clients that require accurate, relatively up-to-date and detailed description of Saskatchewan Road Network.BLKPASSAGE_OFFICIAL, This layer contains data for a structure indicating a physical barrier on a Road Element built to prevent or control further access. BLKPASSAGE_OFFICIAL contains all the blocked passage points in the Saskatchewan. BLKPASSAGE_OFFICIAL is an important part of the Saskatchewan road network dataset. Each point geometry " BLKPASSAGE" has unique Identifiers (NID). "NID" National Identifier is used to manage the updates between data producer and data users.

This map service provides access to the Cratonic Elements dataset shown on the GeoAtlas application.**Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse. They are therefore identical and share the same refresh schedule. This dataset shows the Cratonic Elements of the province of Saskatchewan at 1:1 million scale. This data was compiled using years of bedrock mapping, compiled into a file geodatabase feature class and output for public distribution.

ROADSEG is one of the important layers for Saskatchewan Upgraded Road Network (SURN) and National Road Network (NRN). The ROADSEG is created to serve a diverse group of clients that require relatively up-to-date and accurate description of Saskatchewan Road Network.ROADSEG contains all the features from HIGHWAY_OFFICIAL, RURAL_ROAD_OFFICIAL and OTHER_ROAD_OFFICIAL. ROADSEG is the Saskatchewan road network data that is being used by NRCAN as National Road Network (NRN). ROADSED data layer has been generated by merging HIGHWAY_OFFICIAL, RURAL_ROAD_OFFICIAL and OTHER_ROAD_OFFICIAL. Unique Identifiers (NID) is associated with each line segment. "NID" National Identifier is used to manage the updates between data producer and data users. ROADSEG data layer is one of the important data table for SURN, NRN, SASK911, CAR, SGI-iHaul and other applications.

This dataset shows the location of the distribution of structure form lines for the Province of Saskatchewan.This dataset shows the structure form lines for the Province of Saskatchewan and is designed for the Resource Map of Saskatchewan. Mapping in support of the dataset includes years of field observations which were then compiled and integrated into this dataset. The data was created as a file geodatabase feature class and output for public distribution. **Please Note – All published Saskatchewan Geological Survey datasets, including those available through the Saskatchewan Mining and Petroleum GeoAtlas, are sourced from the Enterprise GIS Data Warehouse.  They are therefore identical and share the same refresh schedule. 

TOLLPOINT _OFFICIAL is one of the important layers for Saskatchewan Upgraded Road Network (SURN) and National Road Network (NRN). The TOLLPOINT _OFFICIAL provides the information of road Toll Points to clients that require accurate, relatively up-to-date and detailed description of Saskatchewan Road Network.TOLLPOINT _OFFICIAL, This layer contains data about a place where a right-of-way is charged to gain access to a motorway, a bridge, etc. TOLLPOINT _OFFICIAL contains all the Toll Point locations in the Saskatchewan. TOLLPOINT _OFFICIAL is an important part of the Saskatchewan road network dataset. Each point geometry " TOLLPOINT" has unique Identifiers (NID). "NID" National Identifier is used to manage the updates between data producer and data users.

Find information about irrigation equipment suppliers in Ontario who offer specialized equipment or services. These lists are maintained by the Ministry of Agriculture, Food and Agribusiness (OMAFA) and include: * business names * telephone numbers * website addresses (where available) * description of equipment and services These lists do not imply any endorsement or recommendation by the ministry. If you would like to nominate a supplier for inclusion on this page, contact the Agricultural Information Contact Centre at 1-877-424-1300 or [ag.info.omafa@ontario.ca](ag.info.omafa@ontario.ca) for additional support.