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)

A Linear Safety Feature is one of a number of various appliances/appurtenances that have been installed or constructed either alongside or as an integral part of the road infrastructure to reduce the severity or potential of accidents. It is a Linear feature

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.

A Safety Feature is one of a number of various appliances/appurtenances that have been installed or constructed either alongside or as an integral part of the road infrastructure to reduce the severity or potential of accidents. It is a Point feature

The objective of this project was to gather data to develop a model of the food web of the lower trophic levels of the nearshore area of the Beaufort Sea. Sampling took place from 2005 to 2008 using the CCGS Nahidik. The multidisciplinary character of the Nahidik program produced measurements of biology/ecology (primary production, phytoplankton, zooplankton, benthos, fish), chemical and physical oceanography, contaminants, geology and hydro acoustics. The data were collected in July and August of each year. The Nahidik program provided data to provide a baseline for future studies as well as an information source for environmental assessment.

Public safety incidents that occurred on the territory of the City of Sherbrooke over the past three years. The location of incidents other than road accidents is reported to the intersection of the road network closest to the scene of the incident.attributs:IncidentType - Incident Type (coded value) DESCRIPTION - Description of the type of incidentDateIncident - Incident Date - Incident Date - Incident Date - Incident Date - Incident Date - Incident Date Text - Incident Date in text format (YYYY-MM-DD))**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

PURPOSE:Given the paucity of information on Arctic char along western Hudson Bay, in 2018, Fisheries and Oceans Canada (DFO) hosted an Arctic char workshop in Rankin Inlet, Nunavut, bringing together local resource users, knowledge holders, and co-management groups (e.g., Hunters and Trappers Organizations, Regional Wildlife Organization) to identify and discuss community-based Arctic char research priorities across the Kivalliq region of Nunavut. Communities were especially interested in examining “what Arctic char were eating” and “why the colour of their muscle is different” along the western Hudson Bay coastline, and in the summer of 2018, a regional community-based Arctic char monitoring program was implemented across the region. DESCRIPTION:Climate-induced alterations to Arctic sea ice dynamics are influencing the availability and distribution of resources, and in turn, the nutrient and energy intake of opportunistic predators across the food web. These temporal changes in local prey communities likely influence the availability of carotenoid-rich prey types, as well as the foraging ecology of opportunistic predators that forage in the marine environment, such as anadromous Arctic char (Salvelinus alpinus). Despite its socioeconomic importance across its range, anadromous Arctic char foraging ecology and its influence on muscle pigmentation, particularly in relation to sea ice dynamics, remains understudied. Here, over two years (2021, 2022) with contrasting sea ice dynamics, we investigated the foraging ecology of anadromous Arctic char and its influence on their muscle pigmentation at a southern (Rankin Inlet) and northern (Naujaat) location along western Hudson Bay using a combination of stomach contents, stable isotopes (δ¹³C and δ¹⁵N), highly branched isoprenoids, carotenoid spectrophotometry, and a standard muscle colour scale (DSM SalmoFan). Spatiotemporal variation in Arctic char diet occurred, where Rankin Inlet Arctic char generally consumed more fish and phytoplankton-based carbon sources, occupied a higher trophic position, and displayed a similar isotopic niche breadth compared to Arctic char in Naujaat. Invertebrates were higher in carotenoid concentration than fishes, and in association with a more invertebrate-based diet, Arctic char in Naujaat contained higher muscle carotenoid concentrations (e.g., astaxanthin) compared to Rankin Inlet Arctic char in 2021. In 2022, however, muscle carotenoid concentrations in Naujaat and Rankin Inlet Arctic char were more similar, as the diet of Arctic char in both locations was largely fish-based despite muscle colour remaining redder in Naujaat Arctic char. Overall, the observed plastic foraging ecology of Arctic char highlights this species' ability to adjust to inter-annual variability in environmental changes, which then impacts their muscle carotenoid concentration. Such inter-annual variation in Arctic char foraging ecology is anticipated to increase with unpredictable climate-driven environmental changes in the region, which could therefore negatively affect local resource users over the long term, resulting in socioeconomic impacts across the Arctic.Collection/sampling methodology:Arctic char were collected by angling and gillnetting (5.5” mesh, regularly checked) between June and August in the estuarine and marine environments near the communities of Rankin Inlet and Naujaat, Nunavut. In 2021, Naujaat Arctic char were collected by community fishers as part of a community-based sampling program. Concurrently, invertebrate prey types were opportunistically collected in the vicinity of Arctic char sampling sites using a conical zooplankton net (200-μm mesh; 10-minute tows) or obtained fresh from Arctic char stomachs. Additionally, marine fishes were opportunistically collected by angling or obtained fresh from Arctic char stomachs over both years in Rankin Inlet, while samples from the Naujaat area were collected in 2018 and 2019.The Kivalliq Wildlife Board (Rankin Inlet, NU) and Arviq Hunters and Trappers Association (Naujaat, NU) each supported this community-formulated research project and assisted with sample collections throughout the duration of the project. We would like to recognize and thank Sonny Ittinuar (Kivalliq Wildlife Board/Rankin Inlet Local Resource User), Clayton Tartak (Kivalliq Wildlife Board), Vincent L’Herault (ArctiConnexion), and Gail Davoren (University of Manitoba MSc co-supervisor) for their participation in the project. We would also like to thank Sonny Ittinuar, Poisey (Adam) Alogut, John-El, Peter, Quassa, and Goretti Tinashlu, who assisted in field work. USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The objective of this project was to gather data to develop a model of the food web of the lower trophic levels of the nearshore area of the Beaufort Sea. Sampling took place from 2005 to 2008 using the CCGS Nahidik. The multidisciplinary character of the Nahidik program produced measurements of biology/ecology (primary production, phytoplankton, zooplankton, benthos, fish), chemical and physical oceanography, contaminants, geology and hydro acoustics. The data were collected in July and August of each year. The Nahidik program provided data to provide a baseline for future studies as well as an information source for environmental assessment. This record contains the geographic coordinates and station names from 2005 to 2008.

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.

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.