The blue whale (Balaenopterus musculus) is a wide-ranging cetacean that can be found in all oceans, inhabiting coastal and oceanic habitats. In the North Atlantic, little is known about blue whale distribution and genetic structure, and if whether animals found in Icelandic waters, the Azores, or Northwest Africa are part of the same population as those from the Northwest Atlantic. In the Northwest Atlantic, seasonal movements of blue whales and habitat use, including the location of breeding and wintering areas, are poorly understood. The behaviour of remotely-monitored animals can be inferred from a time series of location data. This is because animals tend to demonstrate stochasticity in their movement paths as a result of spatial variation in environmental characteristics, such as topography or prey density (Curio 1976; Gardner et al. 1989; Turchin 1991; Wiens et al. 1993). Predators are expected to decrease travel speed and/or increase turning frequency and turning angle when a suitable resource, e.g., food patch, is encountered (Turchin 1991), otherwise known as area-restricted search (ARS). In contrast, animals in transit or travelling tend to move at faster and more regular speeds, with infrequent and smaller turning angles (Kareiva and Odell 1987; Turchin 1998). Based on satellite telemetry to track the seasonal movements of 24 blue whales from eastern Canada in 2002 and from 2010 to 2015, it was possible to estimate trajectories and locations where ARS behaviour of blue whales was inferred at a 4h time interval. To assess blue whale movements and behavior, a Bayesian switching statespace model (SSSM) was applied to Argos-derived telemetry data (Jonsen et al. 2005; Jonsen et al. 2013). An SSSM essentially estimates animal location at fixed time intervals, movement parameters and behavioral patterns. Two important sources of uncertainty can be measured separately: estimation error resulting from inaccurate observations (Argos location error) and process variability linked to the stochasticity of the movement process (behavior mode estimation) (Jonsen et al. 2003; Patterson et al. 2008). The points visible on land are the result of errors in the Argos geographic position calculation. They have been deliberately left unchanged to assess the performance of the model, which was able to clean up some positions, but not all. Lesage, V., Gavrilchuk, K., Andrews, R.D., and Sears, R. 2016. Wintering areas, fall movements and foraging sites of blue whales satellite-tracked in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/078. v + 38 p.

A coastal surficial substrate layer for the coastal Scotian Shelf and Bay of Fundy. To create the layer, previous geological characterizations from NRCan were translated into consistent substrate and habitat characterizations; including surficial grain size and primary habitat type. In areas where no geological description was available, data including digital elevation models and substrate samples from NRCan, CHS and DFO Science were interpreted to produce a regional scale substrate and habitat characterization. Each characterization in the layer was given a ranking of confidence and original data resolution to ensure that decision makers are informed of the quality and scale of data that went into each interpretation. Cite this data as: Greenlaw, M., Harvey, C. Data of: A substrate classification for the Inshore Scotian Shelf and Bay of Fundy, Maritimes Region. Published: March 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/f2c493e4-ceaa-11eb-be59-1860247f53e3

PURPOSE: The focus of this research is on changes in the distribution of killer whales in the Canadian Arctic, which is within the field of marine biogeography and marine megafauna. Our research details change in killer whale presence and ties it to changes in sea ice coverage. These are novel results, presenting trends in the arrival and departure dates of killer whales into the eastern Canadian Arctic for the first time. We go on to discuss the impacts of these changes on other aspects of Arctic ecosystems and how increasing in killer whale presence might affect other species and the management of those species in Canada. Killer whales are a widespread species of interest, especially in the Canadian Arctic as their presence is tied to multiple aspects of a region rapidly changing from the effects of climate change. DESCRIPTION: This study examines 20 years of killer whale (Orcinus orca) sightings in the eastern Canadian Arctic, drawing from a comprehensive sighting database spanning 1850-2023. Despite inherent biases favoring data collection near communities and coastal areas, spatiotemporal analyses reveal significant shifts in killer whale distribution linked to changing sea ice conditions. We developed a clustering metric representing the mean distance to the five nearest sightings and results show that killer whales are progressively moving away from historically high-use areas and that sighting locations are becoming more dispersed over time. A significant year × sea ice interaction indicates observations occur earlier during their arrival period at lower sea ice concentrations over time, suggesting that declining sea ice concentration contributes to earlier arrival. Conversely, for departure periods, killer whales are observed farther south later in the year, likely linked to earlier freeze-up at higher latitudes, and are overall observed later into the year over time. This trend has led to a near doubling of their average presence from 26 days in 2002 to 48 days in 2023 (27 July to 13 September) reflecting an extended open-water season. These findings underscore the prolonged seasonal use of Arctic regions by killer whales, driven by diminishing sea ice and expanding openwater habitat. Such shifts highlight potential implications for Arctic marine ecosystems as killer whales increasingly overlap with endemic species.

Most of the data were collected during aerial surveys carried out at low tides during June and August 1994-1997, 2000 and 2001. June and August are respectively pupping and moulting seasons, when the haulout sites are intensively used by seals. Features in this layer show the Harbour seal distribution and the mean abundance for all aerial surveys (tables 3 and 5, figures 3 and 5 from Robillard et al. 2005). In the estuary, areas of high abundance have more than 30 individuals, areas of medium abundance have between 10 and 30 individuals and areas of low abundance have fewer than 10 individuals. In the Gulf, areas of high abundance have more than 50 individuals and areas of medium to low abundance have fewer than 50 individuals. Unpublished data obtained from Parks Canada and Sepaq were also used to identify important haulout areas in the Saguenay Fjord sector and in Pointe-aux-Vaches tidal flat sectors, which have been categorized in this dataset as high abundance areas. Data are valid only during summer (except for the Pointe-aux-Vaches flats identified as mainly frequented in autumn by Parc Canada), because spring and fall distributions of the Harbour seal are unknown. Data shown in the Estuary and the Gulf of St. Lawrence are a picture of the situation in 2005 because it is the most recent mapping available for this specie. The distribution of the Harbour seal is non-uniform among the different concentration areas but is similar between June and August. However, Harbour seals tend to decrease their presence along the south shore and the Lower Estuary in August to the benefit of the Saguenay River colonies. Abundance classes are arbitrary but fit with the published results of haulout site utilization from Robillard et al. (2005). Data sources : Parks Canada. 2021. Personal communication. Harbor seal monitoring data on the Pointe-aux-Vaches tidal flat. Parks Canada and SÉPAQ, 2020. Données du suivi du phoque commun dans le fjord du Saguenay. Unpublished data. Robillard, A., V. Lesage, and M.O. Hammill. 2005. Distribution and abundance of harbour seals (Phoca vitulina concolor) and grey seals (Halichoerus grypus) in the Estuary and Gulf of St. Lawrence, 1994–2001. Can. Tech. Rep. Fish. Aquat. Sci. 2613: 152 pp.

Significant climate change impacts are highly likely in all Canadian marine and freshwater basins, with effects increasing over time (DFO 2012). Climate models project that ecosystems and fisheries across Canada will be disrupted into the foreseeable future (Lotze et al. 2019; Bryndum-Buchholz et al. 2020; Tittensor et al. 2021; Boyce et al. 2024). Despite its imminence, climate change is infrequently factored into Canada’s primary marine conservation strategies, such as spatial planning (O’Regan et al. 2021) or fisheries management (Boyce et al. 2021; Pepin et al. 2022). The Climate Risk Index for Biodiversity (CRIB) was developed to assess climate risk for marine species in a quantitative, spatially explicit, and scalable manner, supporting climate-informed decision-making. It has been used to evaluate climate risks for marine life globally (Boyce et al. 2022), regionally (Lewis et al. 2023; Boyce et al. 2024; Keen et al. 2023), for fisheries (Boyce et al. 2024), and in support of spatial conservation planning (Keen et al. 2023). This dataset contains climate vulnerability and risk estimates from the CRIB framework adapted to consider warming at both the sea surface and its bottom for 145 marine species of conservation or fisheries interest across Canada’s marine territory. Climate risk is available at a 0.25-degree resolution under two contrasting emission scenarios to 2100. For each species, location, and scenario, 12 climate indexes, three vulnerability dimensions, and an overall vulnerability and risk score are provided. The accompanying report describes the data, methods, and workflow used to calculate risk. This report also guides the interpretation of these data to inform and support climate-informed decision-making in Canada.

The blue shark (Prionace glauca), is a species found in Atlantic Canadian waters which is commonly encountered in commercial and recreational fisheries. Pop-up Satellite Archival Tags (PSAT) and Smart Position and Temperature tag (SPOT) from Wildlife Computers were applied to blue sharks from 2004 to 2008 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial and recreational vessels from mid-August to early October, but mostly in September. A variety of tag models were deployed: PAT 4 (n=16), Mk10 (N=28), and SPOT3 (N=2) and 39 of 46 tags reported. The blue sharks tagged ranged in size from 124 cm to 251 cm Fork Length (curved); 30 were female, 15 were male and 1 was unknown sex. Time at liberty ranged from 4 – 210 days and 16 tags remained on for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

Buffer zone of 300 meters around the railways present on the territory of the city. Used in public safety analyses. **Collection context** Derived from the railway layer in the territory of the city of Saint-Hyacinthe. **Collection method** Buffer zone of 300 meters. Spatial analysis. **Attributes** * `Id` (`long`): Identifier For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Polygon layer of the footprints of buildings on the territory of the city. **Collection context** Collection process based on site plans or location certificates provided by the urban planning department,. Secondary collection based on orthophotographs. **Collection method** Integration from plans or technical drawings using cartographic tools. **Attributes** * `ID_BUILDING` (`integer`): Identifier * `TYPE` (`integer`): Building type * `Usage` (`varchar`): Usage * `MAT10` (`varchar`): Matricule * `varchar`): Matricule * `varchar`): Matricule * `area` (`varchar`): Matricule * `AREA` (`numeric`): Area * `SOURCE` (`varchar`): Source * `DATE_CREATION` (`varchar`): Source * `DATE_CREATION` (`varchar`): Source * `DATE_CREATION` (`varchar`): Source * `DATE_CREATION` datetime`): Date of creation * `DATE_MODIFICATION` (`smalldatetime`): Date of modification * `USER_MODIFICATION` (`varchar`): Modified by * `PLAN_DETAIL` (`varchar`): Location map For more information, consult the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In 1998, Fisheries and Oceans Canada (DFO) published an atlas called "Traditional Fisheries Knowledge for the Southern Gulf of St. Lawrence". The document is composed of a series of maps that contain useful information primarily on nearshore fisheries and fish habitat in the eastern shore of New Brunswick, Prince Edward Island and the Gulf Shore of Nova Scotia. It was used as a working tool to assist in the development of integrated coastal zone management plans, resource management plans, and more. Between 1994 and 1997, data collectors and fishery officers interviewed local fishers and industry representatives. The purpose of these interviews was primarily to gain information on local fishing activities and the location of fisheries' resources and their habitats. The data and information was vetted through a process of verification with scientists, fishers, locals, industry representatives, and government officials. Maps were then compiled for 14 commercially important fish species and made publicly available to consult. These include lobster, rock crab, scallop, snow crab, toad crab, herring, mackerel, American plaice, cod, witch flounder (grey sole), hake, halibut, winter flounder, and unspecified groundfish. This data resource also includes the other 27 species originally not included in the atlas.

Cartography of the vegetation cover of Quebec City. The canopy represents the projection on the ground of the tops (crown) of trees (including leaves, branches, and trunks), which is visible from the sky. The data comes from an automated classification of two satellite images covering Quebec City by the pair of World-View-3 and Pléiades satellites acquired in July 2020 (spatial resolution of 31 cm) and from the 2017 Lidar survey of Quebec City.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**