Peatlands include information relating to peatlands defined as a wetland, colonized by vegetation allowing the formation of a soil made of peat that is the result of the fossilization of organic matter.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

A towfish containing sidescan and video hardware was used to map eelgrass in two shallow northern New Brunswick estuaries. The sidescan and video data were useful in documenting suspected impacts of oyster aquaculture gear and eutrophication on eelgrass. With one boat and a crew of three, the mapping was accomplished at a rate of almost 10 km2 per day. That rate far exceeds what could be accomplished by a SCUBA based survey with the same crew. Moreover, the towfish survey applied with a complementary echosounder survey is potentially a more cost effective mapping method than satellite based remote sensing. Cite this data as: Vandermeulen H. Data of: Bay Scale Assessment of Eelgrass Beds Using Sidescan and Video - Richibucto 2007. Published: October 2017. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/ca7af8ba-8810-4de5-aa91-473613b0b38d

A global decline in seagrass populations has led to renewed calls for their conservation as important providers of biogenic and foraging habitat, shoreline stabilization, and carbon storage. Eelgrass (Zostera marina) occupies the largest geographic range among seagrass species spanning a commensurately broad spectrum of environmental conditions. In Canada, eelgrass is managed as a single phylogroup despite occurring across three oceans and a range of ocean temperatures and salinity gradients. Previous research has focused on applying relatively few markers to reveal population structure of eelgrass, whereas a whole genome approach is warranted to investigate cryptic structure among populations inhabiting different ocean basins and localized environmental conditions. We used a pooled whole-genome re-sequencing approach to characterize population structure, gene flow, and environmental associations of 23 eelgrass populations ranging from the Northeast United States, to Atlantic, subarctic, and Pacific Canada. We identified over 500,000 SNPs, which when mapped to a chromosome-level genome assembly revealed six broad clades of eelgrass across the study area, with pairwise FST ranging from 0 among neighbouring populations to 0.54 between Pacific and Atlantic coasts. Genetic diversity was highest in the Pacific and lowest in the subarctic, consistent with colonization of the Arctic and Atlantic oceans from the Pacific less than 300 kya. Using redundancy analyses and two climate change projection scenarios, we found that subarctic populations are predicted to be more vulnerable to climate change through genomic offset predictions. Conservation planning in Canada should thus ensure that representative populations from each identified clade are included within a national network so that latent genetic diversity is protected, and gene flow is maintained. Northern populations, in particular, may require additional mitigation measures given their potential susceptibility to a rapidly changing climate. Cite this data as: Jeffery, Nicholas et al. (2024). Data from: Variation in genomic vulnerability to climate change across temperate populations of eelgrass (Zostera marina) [Dataset]. Dryad. https://doi.org/10.5061/dryad.xpnvx0kp2

PURPOSE: An aerial survey of the Western Hudson Bay beluga whale (Delphinapterus leucas) population was conducted on August 19th, 2015 to provide a population estimate. Surveys were flown in a DeHavilland Twin Otter (DH-6) fitted with four bubble windows and an optical glass-covered camera hatch at the rear underbelly of the plane. A Global Positioning System (GPS) unit logged the position, altitude, speed, and heading of the aircraft each second. Surveys were initially flown at a target ground speed of 100 knots (185 km/h), and target altitudes of 1,000 ft (305 m) for visual surveys and 2,000 ft (610 m) for photographic surveys. After the second day of flying, the target ground speed was adjusted to 110 knots (204 km/h). Complete coverage of the two photographic strata was achieved using a Nikon D810 camera fitted with a 25 mm lens. The camera was mounted at the rear of the aircraft and directed straight down with the longest side perpendicular to the track line. The camera was connected to a GPS unit to geo-reference photographs, and to a laptop computer to control exposure settings and photo interval. At an altitude of 2,000 ft (610 m), the 25 mm lens captured a ground area of approximately 875 m x 585 m. The photograph interval was set to maintain an overlap of 20 to 40 % between consecutive photos, and with a transect spacing of 600 m, the lateral overlap between photos from adjacent transects was approximately 30 %. DESCRIPTION: Aerial surveys of summering Western Hudson Bay (WHB) beluga (Delphinapterus leucas) were conducted on August 19th, 2015 to update the previous population abundance. The survey area comprised five strata (three visual and two photographic) encompassing high use areas around three river estuaries where recurring aggregations of WHB beluga are found during the summer months. This metadata covers the photographic data related to the survey. The photographic surveys completely covered high density aggregations in the Churchill River and near the mouth of the Seal River.

The National Ecological Framework for Canada's "Surficial Geology by Ecoprovince” dataset contains tables that provide surficial geology information with the ecoprovince framework polygons. It provides codes that characterize surficial geology (unconsolidated geologic materials) and their English and French-language descriptions as well as information about the area and percentage of the polygon that the material occupies.

Effective conservation planning relies on understanding population connectivity which can be informed by genomic data. This is particularly important for sessile species like the horse mussel (Modiolus modiolus), a key habitat-forming species and conservation priority in Atlantic Canada), yet little genomic information is available to describe horse mussel connectivity patterns. We used more than 8000 restriction-site associated DNA sequencing-derived single nucleotide polymorphisms and a panel of 8 microsatellites to examine genomic connectivity among horse mussel populations in the Bay of Fundy, along the Scotian Shelf, and in the broader northwestern Atlantic extending to Newfoundland. Despite phenotypic differences between sampling locations, we found an overall lack of genetic diversity and population structure in horse mussels in the Northwest Atlantic Ocean. All sampled locations had low heterozygosity, very low FST, elevated inbreeding coefficients, and deviated from Hardy-Weinberg Equilibrium, highlighting generally low genetic diversity across all metrics. Principal components analysis, Admixture analysis, pairwise FST calculations, and analysis of outlier loci (potentially under selection) all showed no independent genomic clusters within the data, and an analysis of molecular variance showed that less than 1% of the variation within the SNP dataset was found between sampling locations. Our results suggest that connectivity is high among horse mussel populations in the Northwest Atlantic, and coupled with large effective population sizes, this has resulted in minimal genomic divergence across the region. These results can inform conservation design considerations in the Bay of Fundy and support further integration into the broader regional conservation network. Cite this data as: Van Wyngaarden, Mallory et al. (2024). Widespread genetic similarity between Northwest Atlantic populations of the horse mussel, Modiolus modiolus. Published: May 2025. Coastal Ecosystem Science Division, Maritimes Region, Fisheries and Oceans Canada, Dartmouth, NS.

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.

Results of the visual inspection carried out in 2019 of the condition of all alleys located on the territory of the City of Montreal.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Electoral division of the 2017 election. **Collection context** Creation of districts in collaboration with the legal services and the electoral data of the Director General of Elections (DGE). Balancing of districts according to anthropogenic constraints and number of voters. **Collection method** Computer-aided mapping. **Attributes** * `ID_SEC_DIS` (`long`): Identifier * `NAME_DISTRI` (`varchar`): District name * `NO` (`long`): District number * `AREA` (`varchar`): Area * `ADVISORY_NAME` (`varchar`): Name of the advisor * `SOURCE` (`varchar`): Source * `DATE_CREAT` (`date`): Creation date * `DATE_MODIF` (`date`): Date of modification * `USER_MODIF` (`varchar`): Modified by 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).**

Major assignment layer. **Collection context** Taken from the zoning by-law. **Collection method** Computer-aided mapping. **Attributes** * `ID_ASSIGNATION` (`integer`): Identifier * `ASSIGNMENT` (`varchar`): Assignment * `CODE_ASSIGNATION` (`varchar`): Assignment code * `ZON_AREA` (`numeric`): Area * `SECTOR` (`varchar`): Sector * `NOTE` (`varchar`): Note * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by * `SOURCE` (`varchar`): Source 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).**