PURPOSE:This study is part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake. The main objectives are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality and primary productivity are monitored to evaluate potential effects of change on fisheries. DESCRIPTION:Great Bear Lake, one of the largest lakes in North America, contains culturally and recreationally important fish species. Great Bear Lake is located in the sub-Arctic and Arctic Circle. As part of a two-decade series of research aimed to provide a comprehensive synthesis of the effects of harvest and environmental change on fisheries in Great Bear Lake, the main objectives of this study are to assess demographic traits and the current status of harvested species, with a focus on evaluating sustainable harvest levels of lake trout, a cold-adapted species with a relatively narrow thermal niche. As part of this research, trends in water quality and primary productivity are monitored to evaluate potential effects of change on fisheries. From 2021 to 2024, surface water temperature data was collected at depths of 0.1 to 1.0 meters using an RBR Maestro3 through partnered community-led and community/Fisheries and Oceans Canada/university partner collaborative sampling. The project has strong community involvement, including youth through the Guardian Program, to facilitate capacity building and community leadership in the long-term monitoring of Great Bear Lake fisheries and the aquatic ecosystem. This data is an extension of baseline data sets on water quality on the lake. These data will contribute to a better understanding cumulative impacts of climate change on the functioning of large northern lake ecosystems and provide a benchmark for monitoring further change. This data will be important for developing effective strategies for maintaining community-led aquatic monitoring and managing natural resources, particularly fish, which are expected to be increasingly important to communities with declines in other country foods such as caribou.We acknowledge the data were collected in the Sahtú Settlement Area and are made publicly available with the agreement of the Délı̨nę Renewable Resources Council (Délı̨nę Ɂehdzo Got’ı̨nę (Renewable Resources Council)). Collaborators include: the Community of Délı̨nę partners (data collection), Délı̨nę Renewable Resource Council, Sahtú Renewable Resource Board, and University of Manitoba. Community of Délı̨nę partners and field workers that participated in data collection include Chris Yukon, Archie Vital, Ted Mackienzo, Daniel Baton, Lloyd Baton, Simon Neyelle, and Stanley Ferdanan.Funding and logistical support was provided by: Northwest Territories Cumulative Impact Monitoring, Sahtú Renewable Resource Board, the Polar Continental Shelf Program and Fisheries and Oceans Canada.

This dataset displays the Canadian geographic ranges of the priority species identified under the Pan-Canadian Approach for Transforming Species at Risk Conservation in Canada (“Pan-Canadian Approach”). These species include Barren-ground Caribou (including the Dolphin and Union population); Greater Sage-Grouse; Peary Caribou; Wood Bison; Caribou, Boreal population (“Boreal Caribou”); and Woodland Caribou, Southern Mountain population (“Southern Mountain Caribou”). The priority species were chosen following a number of criteria and considerations in collaboration with federal, provincial, and territorial partners. These include, but were not limited to, the species' ecological role on a regional or national scale, their conservation status and achievability of conservation outcomes, their social and cultural value (particularly to Indigenous peoples), and the leadership/partnership opportunities that they present. Delivering conservation outcomes for targeted priority species can have significant co-benefits for other species at risk, and wildlife in general. For more information on the Pan-Canadian Approach and the priority species, see https://www.canada.ca/en/services/environment/wildlife-plants-species/species-risk/pan-canadian-approach.html.This dataset includes: 1) the range for the Boreal Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/2253); 2) the local populations for the Southern Mountain Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/1309); 3) the range for the Greater Sage-Grouse (see https://species-registry.canada.ca/index-en.html#/consultations/1458); 4) local populations for the Peary Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/3657); 5) range for the Barren-ground Caribou (see https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 6) range for the Barren-ground Caribou, Dolphin and Union population (https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 7) range for the Wood Bison (see https://species-registry.canada.ca/index-en.html#/consultations/2914).

This national dataset contains geographic range data for 488 Species at risk based on NatureServe data, SAR recovery strategies, Environment Canada resources and COSEWIC status reports.

This map, created in 2002 using ArcGIS, describes the number of animal and plant species that are at risk in Alberta. 'Species at risk' is a term used by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) that includes the following categories of plants and animals:Extirpated species - no longer in the wild in Canada.Endangered species - species facing imminent extirpation or extinction.Threatened species - likely to become an endangered species if nothing is done to reverse factors leading to its extirpation or extinction.Species of special concern - species that may become threatened or endangered due to biological characteristics or identified threats.

This table contains information about 11 animal types associated with assigned cases in the Manitoba Animal Welfare Program.This table contains information about animal types, grouped into 11 categories, associated with assigned cases in the Manitoba Animal Welfare Program for each year, starting in 2016, to the most recent quarter. This data is populated by the Provincial Animal Welfare Database for the Manitoba Animal Welfare Program. It is displayed in the Manitoba Animal Welfare Program – Animal Types chart. The table is updated on a quarterly basis. Fields included [Alias (Field Name): Field description] SpeciesStatsGrouping (SpeciesStatsGrouping): Includes one of the possible 11 animal type groups associated with each assigned case (e.g., Avian, Bison, Bovine) Year (Year): Includes the year, beginning in 2016, to the current year (e.g., 2016, 2017, 2018) Month (Month): Includes the numeric value of all months in a calendar year (e.g., 1, 2, 3) Quarter (Quarter): Includes the numeric values of all quarters in a calendar year (e.g., 1, 2, 3, 4), where quarter 1 corresponds with January, February and March, quarter 2 corresponds with April, May and June, quarter 3 corresponds with July, August and September and quarter 4 corresponds with October, November and December YQ (YQ): Includes the year and quarter of the most recent 12 quarters (e.g., 2021 Q1, 2021 Q2 )

Monitoring data from DFO invasive species monitoring programs, along with occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict present-day and project future distributions of 24 non-indigenous species (NIS) on North America`s east coast, and 31 NIS on its west coast. Future distributions were predicted for 2100, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) have been estimated by summing the occurrence probabilities of NIS. This data set includes the present-day and year 2100 species distribution modeling results for each species, and the estimated species richness.Cite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Updated species distribution models for marine invasive species hotspot identification. Published: November 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1439dcb3-82a6-40fd-a9a4-8f045b20ff5b

Since 2005, Fisheries and Oceans Canada has been collecting monitoring data for aquatic invasive species (e.g. https://open.canada.ca/data/en/dataset/8d87f574-0661-40a0-822f-e9eabc35780d, https://open.canada.ca/data/en/dataset/503a957e-7d6b-11e9-aef3-f48c505b2a29, https://open.canada.ca/data/en/dataset/8661edcf-f525-4758-a051-cb3fc8c74423). This monitoring data, as well additional occurrence information from online databases and the scientific literature, have been paired with high resolution environmental data and oceanographic models in species distribution models that predict the present-day and future potential distributions of 12 moderate to high risk invasive species on Canada’s east and west coasts. Future distributions were predicted for 2075, under Representative Concentration Pathway 8.5 from the Intergovernmental Panel on Climate Change’s fifth Assessment Report. Present-day and future richness of these species (i.e., hotspots) has also been estimated by summing their occurrence probabilities. This data set includes the occurrence locations of each species, the present-day and future species distribution modeling results for each species, and the estimated species richness. This research has been published in the scientific literature(Lyons et al. 2020).Lyons DA, Lowen JB, Therriault TW, Brickman D, Guo L, Moore AM, Peña MA, Wang Z, DiBacco C. (In Press) Identifying Marine Invasion Hotspots Using Stacked Species Distribution Models. Biological InvasionsCite this data as: Lyons DA., Lowen JB, Therriault TW., Brickman D., Guo L., Moore AM., Peña MA., Wang Z., DiBacco C. Data of: Species distribution models and occurrence data for marine invasive species hotspot identification. Published: November 2020. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/1bbd5131-8b34-4245-b999-3b4c4259d74f

The dataset consists of maps detailing the boundaries of the designatable units for conservation considerations as defined by the Committee on the Status of Endangered Wildlife in Canada (COSEWIC) for species of Pacific Salmon in British Columbia and the Yukon. Designatable units represent geographical areas that support groups of individuals with a unique genetic heritage makes them discrete and evolutionarily significant units of the taxonomic species, where “significant” means that the unit is important to the evolutionary legacy of the species as a whole and if lost is unlikely to be replaced through natural dispersion.

DescriptionConservation of marine biodiversity requires understanding the joint influence of ongoing environmental change and fishing pressure. Addressing this challenge requires robust biodiversity monitoring and analyses that jointly account for potential drivers of change. Here, we ask how demersal fish biodiversity in Canadian Pacific waters has changed since 2003 and assess the degree to which these changes can be explained by environmental change and commercial fishing. Using a spatiotemporal multispecies model based on fisheries independent data, we find that species density (number of species per area) and community biomass have increased during this period. Environmental changes during this period were associated with temporal fluctuations in the biomass of species and the community as a whole. However, environmental changes were less associated with changes in species’ occurrence. Thus, the estimated increases in species density are not likely to be due to environmental change. Instead, our results are consistent with an ongoing recovery of the demersal fish community from a reduction in commercial fishing intensity from historical levels. These findings provide key insight into the drivers of biodiversity change that can inform ecosystem-based management.The layers provided represent three community metrics: 1) species density (i.e., species richness), 2) Hill-Shannon diversity, and 3) community biomass. All layers are provided at a 3 km resolution across the study domain for the period of 2003 to 2019. For each metric, we provide layers for three summary statistics: 1) the mean value in each grid cell over the temporal range, 2) the probability that the grid cell is a hotspot for that metric, and 3) the temporal coefficient of variation (i.e., standard deviation/mean) across all years.Methods:The analysis that produced these layers is presented in Thompson et al. (2022). The analysis uses data from the Groundfish Synoptic Bottom Trawl Research surveys in Queen Charlotte Sound (QCS), Hecate Strait (HS), West Coast Vancouver Island (WCVI), and West Coast Haida Gwaii (WCHG) from 2003 to 2019. Cartilaginous and bony fish species caught in DFO groundfish surveys that were present in at least 15% of all trawls over the depth range in which they were caught were included. This depth range was defined as that which included 95% of all trawls in which that species was present. The final dataset used in our analysis consisted of 57 species (Table S1 in Thompson et al. 2022).The spatiotemporal dynamics of the demersal fish community were modeled using the Hierarchical Modeling of Species Communities (HMSC) framework and package (Tikhonov et al. 2021) in R. This framework uses Bayesian inference to fit a multivariate hierarchical generalized mixed model. We modeled community dynamics using a hurdle model, which consists of two sub models: a presence-absence model and a biomass model that is conditional on presence. Our list of environmental covariates included bottom depth, bathymetric position index (BPI), mean summer tidal speed, substrate muddiness, substrate rockiness, whether the trawl was inside or outside of the ecosystem-based trawling footprint, and survey region (QCS & HS vs. WCVI & WCHG)), mean summer near-bottom temperature deviation, mean summer near-bottom dissolved oxygen deviation, mean summer cross-shore and along-shore current velocities near the seafloor, mean summer depth-integrated primary production, and local-scale commercial fishing effort.Layers are provided for three community metrics. All metrics should be interpreted as the value that would be expected in the catch from an average tow in the Groundfish Synoptic Bottom Trawl Research Surveys taken in a given 3 km grid cell. Species density (sometimes called species richness) should be interpreted as the number of the 57 species that would be caught in a trawl. Hill-Shannon diversity is a measure of diversity that gives greater weight to communities where biomass is spread equally across species. Community biomass is the total biomass across all 57 species that would be expected to be caught per square km in an average tow. Data Sources:Research data was provided by Pacific Science's Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2019 that occurred in four regions: Queen Charlotte Sound, Hecate Strait, West Coast Haida Gwaii, and West Coast Vancouver Island. Our analysis excludes species that are rarely caught in the research trawls and so our estimates would not include the occurrence or biomass of these rare species.Commercial fishing data was accessed through a DFO R script detailed here: https://github.com/pbsassess/gfdata. Local scale commercial fishing effort was calculated from this data. The substrate layers were obtained from a substrate model (Gregr et al. 2021). The oceanographic layers (bottom temperature, dissolved oxygen, tidal and circulation speeds, primary production) were obtained from a hindcast simulation of the British Columbia continental margin (BCCM) model (Peña et al. 2019).Uncertainties:Species that are not well sampled by the trawl surveys may not be accurately estimated by our model. The model did not include spatiotemporal random effects, which likely underestimates spatiotemporal variability in the region. It is also important to underline covariate uncertainty and model uncertainty. The hotspot estimates provide one measure of model uncertainty/certainty.

This theme presents observations of invasive exotic species (IAS)transmitted and validated using the Sentinelle tool, an EEE detection system.An invasive exotic species is a plant, animal or microorganism (virus,bacteria or fungi) that are introduced outside of their natural range. Sonestablishment or its spread may pose a threat to the environment,the economy or society. The species listed are species of fauna and floraconcerning (or potentially worrying) for Quebec's biodiversity. Ellesinclude EEE present in Quebec and EEE not listed in Quebec atmonitor.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**