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).

PURPOSE:The purpose of this work was to determine (1) how the population genomic structure relates to gut microbiota composition of White Hake (Urophycis tenuis), and (2) whether microbiota community variation provides complementary insights into population structure of this species in eastern Canada.DESCRIPTION:Integrating host-associated microbiota with genomic approaches offers an opportunity to better understand the multiple biological dimensions shaping population structure in marine fishes. A clear understanding of population structure and dynamics is essential for informed fisheries management and conservation decisions; however, while genomic approaches have greatly improved our ability to delineate biological populations, they provide only a partial representation of biological structure, as patterns of differentiation reflect both historical divergence and contemporary ecological conditions. Host-associated microbiota can influence population-level ecological processes by contributing functional, potentially heritable variation that shapes host phenotype and fitness.In this study, we combined genotyping-by-sequencing with 16S rRNA gene amplicon sequencing to examine how population genomic structure relates to gut microbiota composition and to evaluate whether microbiota community variation provides complementary insights into population structure in White Hake (Urophycis tenuis) in eastern Canada. Genomic analyses identified two populations with greater spatial overlap than previously reported. Variation partitioning revealed that host genetics explained a negligible proportion of microbiota variation compared to environmental factors and fish length, suggesting that ontogenetic shifts in habitat use and resource acquisition influence gut microbiota composition. Several taxa were differentially abundant among fish length categories used as a proxy for diet, including taxa with chitin-degrading potential such as Photobacterium and Lachnospirales, which were enriched in smaller fish known to consume a crustacean-dominated diet. Together, these results indicate that gut microbiota composition in White Hake primarily reflects ecological and life-history processes rather than host population structure. PARAMETERS COLLECTED:Environmental parameters were also collected at most sampling sites, including depth, water temperature, oxygen and salinity levels.PHYSICAL SAMPLE DETAILS:Fin samples were collected to characterize the fish genotypes. Intestine (rectum) samples were collected to study fish gut microbiome.SAMPLING METHODS:In 2022 and 2023, White Hake were sampled during Fisheries and Oceans Canada (DFO) annual bottom trawl ecosystem monitoring surveys.USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

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.

Polygon geometry for an invasive species observation. The polygon represents the area of the observation. An observation of an invasive species can be either positive or negative. Positive indicates the species was present at the time of the observation. Negative indicates the species was not present at the time of the observation. Dataset currently only includes plant species.

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 )

An exploratory fishery was carried out in the Mackenzie River Delta between 1989 and 1993 at the request of the Inuvik Hunters and Trappers Committee to assess the commercial fishery potential in this area. Data collected during this exploratory fishery was used in two technical reports. Broad whitefish were the target species of this fishery (DFO Technical Report 2180), however other species such as northern pike, inconnu, and lake whitefish were also harvested (DFO Technical Report 2330).Biological data from broad whitefish (fork length, age, gonadosomatic index and instantaneous mortality) were analyzed to assess the impact of an exploratory fishery in the Mackenzie River Delta. The data were collected using variable mesh experimental gill nets and commercial harvesters' 139 mm (5.5”) mesh gill nets. The data suggest that the broad whitefish population might be separated, with larger mature spawners gathering in the main channels prior to spawning and smaller, immature or resting fish staying in side channels away from strong currents. From this analysis we conclude that the size and structure of the broad whitefish population(s) found in this area are stable at the current level of total harvest (commercial and subsistence combined) There may be room for increased harvests but to what level is uncertain.Biological data from northern pike, inconnu, and lake whitefish were analyzed to assess the impact of the fishery on population abundance and structure. All three by catch species support subsistence fisheries in the Mackenzie Delta. lnconnu and lake whitefish migrate substantial distances, crossing land claim borders and are likely fished by a variety of user groups. Northern pike, on the other hand tend to be non-migratory with localized populations that are mainly fished by people living in the near vicinity. A concern was that commercial fishing pressure might reduce the numbers of fish available to subsistence users. Based on trends in size and age frequency, age at maturity, sex ratio, growth rates, and mortality rates, we conclude that inconnu and lake whitefish populations in the Mackenzie Delta have remained healthy and stable at the current harvest levels, however northern pike populations showed a decrease in the proportion of older fish, possibly indicating over-fishing of local stocks. lnconnu and lake whitefish may be able to withstand increased harvest, but to what extent is unknown, given that little reliable information is available on subsistence harvest levels in this region. Increasing harvest levels of pike may be risky and we suggest that a reduction of current commercial harvest quotas be considered.

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

The Aquatic Invasive Species Surveillance Database is a compilation of fish community and habitat data from DFO’s Aquatic Invasive Species and Invasive Carp Program early detection surveillance efforts in Canadian waters of the Great Lakes basin. Data includes: sampling site location, date, fish species and counts, and associated habitat information. Annual project-specific details including purpose/objectives and study methodology are often reported in the DFO Canadian manuscript report of fisheries and aquatic sciences series.

Boundaries identifying similar behavioural ecotypes and sub-populations of Grizzly bears. This dataset contains versions from multiple years. From 2018 on, NatureServe conservation concern ranking categories (e.g., Very Low, Low, Moderate, High, Extreme Concern) supersede the pre-2018 population status categories (e.g., Viable, Threatened, Extirpated) contained in the field STATUS. NatureServe conservation concern ranking categories reflect population size and trend, genetic and demographic isolation, as well as threats to bears and their habitats. The NatureServe conservation concern ranking fields are named CONSERVATION_CONCERN_RANK and CONSERVATION_CONCERN_DESC. Please view the attached PDF file for a summary of changes to this dataset from 2012 onward. To download only the 2018 units, in the link below, select the "Export" tab, then select the "Provincial Layer Download" button: https://maps.gov.bc.ca/ess/hm/imap4m/?catalogLayers=7744,7745 Grizzly Bear Conservation Ranking results table is available here: https://catalogue.data.gov.bc.ca/dataset/e08876a1-3f9c-46bf-b69a-3d88de1da725 Grizzly Bear population estimates from various years are available here: https://catalogue.data.gov.bc.ca/dataset/2bf91935-9158-4f77-9c2c-4310480e6c29 Grizzly Bear reports are available here: https://www2.gov.bc.ca/gov/content/environment/plants-animals-ecosystems/wildlife/wildlife-conservation/grizzly-bear

Bay-scale empirical demonstrations of how bivalve aquaculture alters plankton composition, and subsequently ecological functioning and higher trophic levels, are lacking. Temporal, inter- and within-bay variation in hydrodynamic, environmental, and aquaculture pressure limit efficient plankton monitoring design to detect bay-scale changes and inform aquaculture ecosystem interactions. Here, we used flow cytometry to investigate spatio-temporal variations in bacteria and phytoplankton (< 20 µm) composition in four bivalve aquaculture embayments. We observed higher abundances of bacteria and phytoplankton in shallow embayments that experienced greater freshwater and nutrient inputs. Depleted nutrient conditions may have led to the dominance of picophytoplankton cells, which showed strong within-bay variation as a function of riverine vs freshwater influence and nutrient availability. Although environmental forcings appeared to be a strong driver of spatio-temporal trends, results showed that bivalve aquaculture may reduce near-lease phytoplankton abundance and favor bacterial growth. We discuss aquaculture pathways of effects such as grazing, benthic-pelagic coupling processes, and microbial biogeochemical cycling. Conclusions provide guidance on optimal sampling considerations using flow cytometry in aquaculture sites based on embayment geomorphology and hydrodynamics.Cite this data as: Sharpe H, Lacoursière-Roussel A, Barrell J (2024). Monitoring bay-scale bivalve aquaculture ecosystem interactions using flow cytometry. Version 1.2. Fisheries and Oceans Canada. Samplingevent dataset. https://ipt.iobis.org/obiscanada/resource?r=monitoring_bay-scale_bivalve_aquaculture_ecosystem_interactions_using_flow_cytometry&v=1.2