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.

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)

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 data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

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 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 water chemistry data collected as part of this project including suspended nitrogen, dissolved nitrogen, suspended phosphorus, dissolved phosphorus, dissolved organic carbon, suspended carbon, chlorophyll a, and suspended silicon.

The Ontario Road Network (ORN) Composite product is a segmented derivative of the ORN Road Net Element (ORNELEM) data class. You can use it for mapping and general spatial analysis. Road segment information includes: * addressing * full street name * alternate street name * speed limit * number of lanes * pavement status * road class * jurisdiction * route number * direction of traffic flow * shield type information The ORN is a provincewide geographic database of over 250,000 km of: * municipal roads * provincial highways * resource and recreational roads The ORN is the authoritative source of roads data for the Government of Ontario. This product is derived from the [ORN Road Net Element](https://geohub.lio.gov.on.ca/datasets/mnrf::ontario-road-network-orn-road-net-element/about) data class. It combines three types of geometry: * road elements * ferry connections * virtual roads This product also includes additional road feature layers including: * blocked passages * underpasses * toll points * structures

The food desert is a sector offering poor access to food stores (grocery stores, supermarkets and public markets), i.e. a distribution area (AD) in which residences are located on average more than 1 kilometer from a food store in urban areas and more than 16 kilometers in rural areas, and characterized by very high material deprivation (quintile 5). Accessibility to food stores is a quantitative measure used to assess the geographic ease of access with which individuals or populations can access food stores (grocery stores, supermarkets, and public markets) from where they live.For more information on the food desert index, you can consult * [Identifying Food Deserts] (https://www.inspq.qc.ca/boite-a-outils-cooperative-alimentaire/identification-deserts) *.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The HealthLink BC Mental Health and Substance Use (MHSU) data set includes the following: Programs that offer early intervention, transitional care or other services that supplement and facilitate primary and adjunctive therapies; which offer community mental health education programs; or which link people who are in need of treatment with appropriate providers. Programs that provide preventive, diagnostic and treatment services in a variety of community and hospital-based settings to help people achieve, maintain and enhance a state of emotional well-being, personal empowerment and the skills to cope with everyday demands without excessive stress or reliance on alcohol or other drugs. Treatment may include emotional support, introspection and problem-solving assistance using a variety of modalities and approaches, and medication, as needed, for individuals who have a substance use disorder involving alcohol and/or other drugs or for people who range from experiencing difficult life transitions or problems in coping with daily living to those with severe, chronic mental illnesses that seriously impact their lives. Multidisciplinary programs, often offered on an inpatient basis with post-discharge outpatient therapy, that provide comprehensive diagnostic and treatment services for individuals who have anorexia nervosa, binge-eating disorder, bulimia or a related eating disorder. Treatment depends on the specific type of eating disorder involved but typically involves psychotherapy, nutrition education, family counseling, medication and hospitalization, if required, to stabilize the patient's health. Alliance of Information & Referral Systems (AIRS) / 211 LA County taxonomy is the data classification used for all HealthLink BC directory data, including this MHSU data set (https://www.airs.org/i4a/pages/index.cfm?pageid=1). AIRS taxonomy and data definitions are protected by Copyright by Information and Referral Federal of Los Angeles County, Inc (https://211taxonomy.org/subscriptions/#agreement)

Under the Regulation Respecting the Mandatory Reporting of Certain Contaminant Emissions into the Atmosphere (RDOECA), the Ministère de l'Environnement et de la Lutte contre les Changements Climates de la Faune et des Parcs collects data on air contaminants emitted by Quebec businesses in particular. Thus, any person or municipality operating an establishment that emits air contaminants above thresholds into the atmosphere is required to report its emissions no later than June 1 of each year. This dataset includes emissions of air contaminants reported under RDOECA or voluntarily expressed in metric tons, with the exception of dioxin and furan emissions, which are expressed in metric tons of toxic equivalents.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**