Electoral division of the 2017 election. **Collection context** Creation of districts in collaboration with the legal services and the electoral data of the Chief Electoral Officer (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).**

Catch, effort, location (latitude and longitude), and associated biological data from the Eulachon Migration Study Bottom Trawl surveys - North on the coast of British Columbia. Introduction: The Eulachon Migration Study Bottom Trawl survey - North (Eul-N) is part of the in the Eulachon Migration Study Bottom Trawl survey series and took place on the coast of British Columbia. The other survey in this series is the Eulachon Migration Study Bottom Trawl survey –South (Eul-S). The Eulachon Migration Study Bottom Trawl survey - North (Eul-N) was conducted monthly from July 2018 to March 2019 and was funded by the Fisheries and Oceans Canada (DFO) National Rotational Survey Fund. The objective of this survey was to learn about the distribution, ecology, and migration times of Eulachon into the Nass and Skeena rivers by observing their spatial and temporal occurrence and biological condition over a wide survey region and over several months. This survey follows a random block design in a targeted depth range of 80 – 300 metres. The sampling units were 2 km by 2 km blocks. Fishing was conducted using the Canadian Coast Guard Research Vessel Neocaligus to tow an American shrimp trawl net (Cantrawl Nets Ltd., Richmond, BC). The horizontal opening of the polypropylene net was estimated to be 34 to 37 feet (10 to 11 m), while the center of the opening had a vertical height of approximately 7 to 9 feet (2 to 3 m). A 0.4” (10 mm) liner was used in the codend. The net was configured with roller gear and 72” (1.8 m) Thyboron Type 2 trawl doors. Tow duration was typically 5 minutes. The standard hours of fishing were 0800 to 1700 hours, depending on sunrise and sunset in winter months. The Eulachon Migration Study Bottom Trawl survey – North was conducted by the Department of Fisheries and Oceans Canada (DFO). This survey fished mainly in Chatham Sound with sets in Hecate Strait and Portland Inlet including Pacific Fishery Management areas (PFMA’s) 3, 4, and 104. Effort: This table contains information about the survey trips and fishing events (trawl tows/sets) that are part of this survey series. Trip-level information includes the year the survey took place, a unique trip identifier, the vessel that conducted the survey, and the trip start and end dates (the dates the vessel was away from the dock conducting the survey). Set-level information includes the date, time, location, and depth that fishing took place, as well as information that can be used to calculate fishing effort (duration) and swept area. All successful fishing events are included, regardless of what was caught. Catch: This table contains the catch information from successful fishing events. Catches are identified to species or to the lowest taxonomic level possible. Most catches are weighed, but some are too small (“trace” amounts) or too large (e.g. very large Big Skate). The unique trip identifier and set number are included so that catches can be related to the fishing event information (including capture location). Biology: This table contains Eulachon biological data including length, sex, and weight. Information is provided on whether stomachs or teeth were examined, and whether genetics (DNA) samples were collected. Eulachon maturity data, diet data, and teeth presence data are available on request from the data contacts. Additional analyses are ongoing, including histology, fatty acid profiling, and genetic analysis; frozen heads are also available for a future aging project. In addition to the Eulachon biological data, lengths and weights were collected from American Shad.The unique trip identifier and set number are included so that samples can be related to the fishing event and catch information.

Hunting districts as presented in the Compendium of Migratory Bird Hunting Regulations: Quebec https://www.canada.ca/fr/environnement-changement-climatique/services/chasse-oiseaux-migrateurs-gibier/reglementation-resumes-provinciaux-territoriaux/quebec.html These boundaries are presented for information purposes only and have no legal value.

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.

Average grid cell density is a polygon feature class containing the average density value for each grid cell per species/groups and season.

Survey transects is a line feature class containing transects completed in 2011.

The dataset represents known concentration areas of harvested or unharvested Arctic wedge clam (Mesodesma arctatum) in the Estuary and the Gulf of St. Lawrence, Quebec region. The dataset was created for the National Environmental Emergencies Centre (NEEC) for preparation and response purposes in case of an oil spill. Concentration areas were delimited using Fisheries and Oceans Canada (DFO) inventories conducted between 2000 and 2020 and data from various DFO research projects. For more information on how the data layer was built, see the metadata included in its shapefile (.shp), particularly the “Lineage” section. This layer is dependent on the inventories carried out and thus only represents the known concentration areas of the Arctic wedge clam. It does not represent the general distribution of the species nor the extent to which fishing is allowed. Most of the information comes from inventories that did not necessarily target this species, therefore its distribution is undoubtedly wider than what is recorded in this layer. In addition, the extent of shellfish beds can change over time in response to, among others, harvesting and recruitment rates. Some beds were mapped based on DFO research project data which were compiled in a benthic biodiversity Access database. Polygons drawn around these data are not precise and may be reviewed. The polygons delimited based on inventory data are more precise but might underestimate the concentration areas because sampling was made where the target resource was known to be more abundant without necessarily sampling the entire bed. Nonetheless, the precision is sufficient for resource protection and management needs in case of an environmental incident. Data sources and references: Bourdages, H., P. Goudreau, J. Lambert, L. Landry et C. Nozères. 2012. Distribution des bivalves et gastéropodes benthiques dans les zones infralittorale et circalittorale des côtes de l’estuaire et du nord du golfe du Saint-Laurent. Rapp. tech. can. sci. halieut. aquat. 3004: iv + 103 p. Brulotte, S. Données non-publiées. Pêches et Océans Canada. Brulotte, S. 2011. Évaluation des stocks de mye commune des eaux côtières du Québec. Secr. can. de consult. sci. du MPO. Doc. de rech. 2011/44: x + 53 p. Brulotte, S. 2012. Évaluation des stocks de buccin des eaux côtières du Québec. Secr. can. de consult. sci. du MPO. Doc. de rech. 2012/058: xi + 106 p. Brulotte, S. et M. Giguère. 2003. Évaluation d'un gisement de mye commune (Mya arenaria) de l'embouchure de la rivière Mingan, Québec, Rapp. can. ind. sci. halieut. aquat. No. 2511: xi + 58. Gendreau, Y. 2018. MS Access database on benthic biodiversity. Fisheries and Oceans Canada. Giguère, M., S. Brulotte et F. Hartog.2007. Évaluation de quelques gisements de mye commune (Mya arenaria) de la rive sud de l'estuaire du Saint-Laurent en 2005 et 2006. Rapp. can. ind. sci. halieut. aquat. No. 2738: xi + 107. Giguère, M., S. Brulotte, M. Boudreau et M.-F. Dréan. 2008. Évaluation de huit gisements de mye commune (Mya arenaria) de la rive nord de l’estuaire du Saint-Laurent de 2002 à 2008. Rapp. tech. can. sci. halieut. aquat. 2821 : x + 91 p. Provencher, L. Unpublished data. Fisheries and Oceans Canada. Provencher, L. et C. Nozères. 2011. Protocole de suivi des communautés benthiques de la zone de protection marine Manicouagan. Secr. can. de consult. sci. du MPO. Doc. de rech. 2011/051:iv +25 p.

Eelgrass (Zostera marina) is important to waterfowl such as Atlantic Brant (Branta bernicla hrota), Canada Goose (Branta canadensis), American Black Duck (Anas rubripes), Common Goldeneye (Bucephala clangula) and Barrow's Goldeneye (Bucephala islandica). In New Brunswick eelgrass can be found along the Gulf of St. Lawrence, in protected harbours. Within this dataset are the results of eelgrass land-cover classifications using either satellite or aerial photography for seven harbours: Bouctouche (46 30’N, 64 39’W); Miscou (47.90 N, -64.55 W); Neguac (47.25 N, -65.03 W); Richibucto (46.70 N, -64.80 W); Saint-Simon (47.77 N, -64.76 W); Tracadie (47.55 N, -64.88 W); and Cocagne (46.370 N, -64.600 W). Information on each dataset is provided: 1. Bouctouche This dataset contains results from an eelgrass classification for Bouctouche Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 688 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 83.7% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 187 sites that were within the classification area, 131 were correct, 51 were "one-off", and 5 were incorrect [(131 + (51/2))/ 187 = 0.837]. 2. Miscou True colour aerial photography at 57 centimetre resolution was collected on August 20th and 24th, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 103 sites. From these sites 70% were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 96.7% of the time (30/31 = 0.967). 3. Neguac This dataset contains results from an eelgrass classification for Neguac Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 126 sites. Two-thirds of these sites were used to assist in image classification, while the remainder were used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 81% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 39 sites that were within the classification area, 27 were correct, 9 were "one-off", and 3 were incorrect [(27 + (9/2))/ 39 = 0.81]. 4. Richibucto Eelgrass classification in Richibucto Harbour, New Brunswick. Derived from a Quickbird satellite image collected on August 28, 2007 at as close to low-tide as possible. Quickbird's ground resolution is 2.4 m. Classification was objected-oriented using Definiens software. Accuracy was 81.5%. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 5. Saint-Simon An eelgrass distribution map was classified from remotely sensed imagery in Shippagan Harbour, New Brunswick. Derived from a Quickbird satellite image collected on July 27, 2007 at as close to low-tide as possible. Classification was objected-oriented using Definiens software. Data used for accuracy and training was collected along transects using a differential GPS positioned towfish holding sidescan sonar, and a video camera that was later transcribed as XY points to describe eel-grass presence. 6. Tracadie This dataset contains results from an eelgrass classification for Tracadie Bay, New Brunswick. True colour aerial photography at 57 centimetre resolution was collected on September 2, 2009 by Nortek Resources of Thorburn, Nova Scotia (http://www.nortekresources.com/). Image classification was conducted using eCognition Developer v. 8 Software, which first segments the image into spectrally similar units, which were then classified manually. Additionally, the Department of Fisheries and Oceans (Gulf Region, Moncton, NB) conducted a visual field survey in the same field season at 101 sites. Approximately two-thirds of these sites were used to assist in image classification, while the remainder was used to assess accuracy. Three classes were identified: i. Good Quality Eelgrass: relatively dense, clean, green blades with minimal epiphytes or algal growth. ii. Medium Quality Eelgrass: predominately green blades that may have some epiphyte or algal growth. These stands can be less or equally dense as Good Quality Eelgrass, but the best grasses are certainly not as abundant. iii. Eelgrass Absent/Poor Quality: eelgrass is absent, or if it is present it is typically covered with epiphytes or other algae or dying or dead. Eelgrass was classified correctly 79.3% of the time in a fuzzy accuracy assessment technique, whereby those classes that were ‘off’ by one class, e.g. Good Quality eelgrass classed as Medium Quality, were given half credit towards the overall accuracy. Of 29 sites that were within the classification area, 18 were correct, 10 were "one-off", and 1 was incorrect [(18 + (10/2))/ 29 = 0.793]. 7. Cocagne Visible orthorectified aerial photography was used to classify polygons containing eelgrass in Cocagne Harbour. Field data for image training and validation were collected along transects in summer 2008 using a dGPS positioned towfish holding sidescan sonar and a video camera that was later transcribed as XY geographic points to describe eelgrass presence and a qualitative description of density. The area was flown for photography on September 24, 2008. eCognition Developer 8 software was used to segment the imagery, essentially polygons. Polygons were then classified manually for the presence of eelgrass. Using field data revealed eelgrass presence to be mapped correctly 87.2% of the time.

Sectoral division of leisure districts. **Collection context** Historical breakdown provided by the recreation department. **Collection method** Computer-aided mapping. **Attributes** * `ID_SEC_LOISIR` (`integer`): Identifier * `SECTOR_NUM` (`varchar`): Sector number * `SECTOR_NAME` (`varchar`): Sector name * `SOURCE` (`varchar`): Source * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by * `NEIGHBORHOOD` (`varchar`): Neighborhood 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).**

Polygonal layer of electoral districts for the 2025 election. New balancing of voters increases the number of districts from 11 to 10 for the 2025 election. **Collection context** Review committee to balance the districts according to the data of the Chief Electoral Officer. **Collection method** Analysis of voters by address using cartographic analysis software. Update by computer-aided mapping. **Attributes** * `ID_SEC_DISTRICT_ELEC` (`integer`): Identifier * `DISTRICT_NAME` (`varchar`): District name * `NO` (`integer`): Number * `AREA` (`varchar`): Area * `ADVISOR_NAME` (`varchar`): Recommended * `SOURCE` (`varchar`): Source * `DATE_CREATION` (`smalldatetime`): Created on * `DATE_MODIFICATION` (`smalldatetime`): Modified on * `USER_MODIFICATION` (`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).**