Crown Game Preserves were established to prohibit or regulate the hunting and trapping of wildlife in specific areas to restore local populations.

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).

To support the surveillance of key macroalgae and non-indigenous species in Nova Scotia and New Brunswick, five quantitative PCR (qPCR) assays were designed and tested at 111 sites in 2022-2023 targeting the following non-indigenous macroalgal species: Antithamnion sparsum, Bonnemaisonia hamifera, Codium fragile, Dasysiphonia japonica, Fucus serratus. All assays were developed in 2022 by the Center for Environmental Genomics Applications (CEGA, Newfoundland, Canada) except Antithamnion sparsum, for which an assay was developed in 2023 by the Aquatic Biotechnology Laboratory (ABL) at the Bedford Institute of Oceanography. All amplification was performed by the ABL in 2022-2023. The assay developed for Fucus serratus was later determined to be non-specific, and amplifies both F. serratus and Fucus distichus.Cite this data as: Krumhansl K, Brooks C, Lowen B, DiBacco C, (2025). Quantitative PCR (qPCR) of Key Macroalgal Non-Indigenous Species in Nova Scotia and New Brunswick Waters. Version 1.5. Fisheries and Oceans Canada. Samplingevent dataset. https://ipt.iobis.org/obiscanada/resource?r=quantitative_qpcr_macroalgal_nonindigenous_species_novascotia_newbrunswick_2022_2023&v=1.5For additional information please see:LeBlanc F., Belliveau V., Watson E., Coomber C., Simard N., DiBacco C., Bernier R., Gagné N. 2020. Environment DNA (eDNA) detection of marine aquatic invasive species (AIS) in Eastern Canada using a targeted species-specific qPCR approach. Management of Biological Invasions 11(2):201-217Krumhansl K.A., Brooks C.M., Lowen B., O’Brien J., Wong M., DiBacco C. Loss, resilience and recovery of kelp forests in a region of rapid ocean warming. Annals of Botany 2024 Mar 8; 133(1):73-92Brooks C.M., Krumhansl K.A. 2023. First record of the Asian Antithamnion sparsum Tokida, 1932 (Ceramiales, Rhodophyta) in Nova Scotia, Canada. BioInvasions Records 12(3):745-725.

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

Timing windows are the period(s) during the year when work may be carried out in and about water bodies with the lowest risk to fish and wildlife species and habitat. Timing windows and terms and conditions vary based on regional differences in fish and wildlife species and habitat, and geography. The timing window of least risk to fish and fish habitat must be applied to all activities in water bodies, as well as tributaries that have a risk of depositing sediment into water bodies. Windows of least risk are designed to protect all fish species known to occur in a water body.

As part of the Pan-Canadian approach to transforming Species at Risk conservation in Canada, a total of 11 Priority Places were affirmed by federal, provincial, and territorial governments in December 2018. One additional priority place was affirmed in 2024. The places selected have significant biodiversity, concentrations of species at risk, and opportunities to advance conservation efforts. In each Priority Place, the federal and provincial or territorial governments are working with Indigenous Peoples, partners, and stakeholders to develop conservation action implementation plans. Using a defined planning approach (such as the Open Standards for the Practice of Conservation), these implementation plans identify key actions to address the greatest threats to species. Conservation implementation plans provide the foundation for collaborative action on the ground.The federal government, in collaboration with the provinces and territories, has agreed to the implementation of the Pan-Canadian Approach to Transforming Species at Risk Conservation in Canada. This new approach shifts from a single-species approach to conservation to one that focuses on multiple species and ecosystems. This enables conservation partners to work together to achieve better outcomes for Species at Risk. These 12 Priority Places are complemented by a suite of Community-Nominated Priority Places (CNPP), identified through an open call for applications.To learn more about the Priority Places initiative and the work undertaken by our partners to recover Species at Risk within these Priority Places, please visit our interactive website https://environmental-maps.canada.ca/CWS_Storylines/index-ca-en.html#/en/priority_places-lieux_prioritaires

Marine Protected Areas require comprehensive monitoring to ensure objectives are achieved; however, monitoring natural ecosystems at scale is challenged by the biodiversity it aims to measure. Environmental DNA (eDNA) metabarcoding holds promise to address this monitoring challenge. We conducted paired sampling at 54 sites for fish and invertebrate assemblages in the Northwest Atlantic using groundfish trawls and eDNA metabarcoding of benthic seawater using four genetic markers (12S rRNA, 16S rRNA, 18S rRNA, and CO1). Compared to trawling, eDNA detected similar patterns of species turnover, larger estimates of gamma diversity, and smaller estimates of alpha diversity. A total of 63.6% (42/66) of fish species captured by trawling were detected by eDNA, along with an additional 26 species. Of the 24 missed detections by eDNA, 12 were inevitable as they lacked reference sequences. Excluding taxa assigned to higher than species level and those without a species name, 23.6% (17/72) of invertebrate species captured by trawling were detected by CO1, which detected an additional 98 species. We demonstrate that eDNA is capable of detecting patterns of community assemblage and species turnover in an offshore environment, emphasizing its strong potential for a non-invasive, comprehensive, and scalable tool for biodiversity monitoring supporting marine conservation programmes.Cite this data as: Jeffery, N., Rubidge, E., Abbott, C., Westfall, K., Stanley, R. (2024): Data of: eDNA metabarcoding enriches traditional trawl survey data for monitoring biodiversity in the marine environment.Published: August 2024. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/43a91ba7-8025-4330-88db-db14022d729d

The Ecological Land Mass (ELM) classification was established through the 2020 National Interest Land Mass (NILM) Update. ELM lands describe ecological corridors that have inherent natural values and that protect Species at Risk (SAR) and their habitats. The classification identifies lands to protect in perpetuity through planning and partnership efforts. ELM was derived from two separate analyses - the Ontario side from the AECOM natural linkages analysis (2012) and the Quebec side from Del Degan, Masse (DDM) ecological corridors analysis (2012). Adjustments were made as appropriate.

The Species at Risk (SAR) Program is responsible for carrying out DFO’s mandate under the Species at Risk Act (SARA) to protect, recover and conserve all listed aquatic SAR in Canada. As part of this mandate, this spatial database has been developed to identify areas in which aquatic species listed under SARA may be found.Distribution and range information are identified for species listed as Endangered, Threatened or Special Concern under SARA.Distribution (range) polygons and lines were assembled by regional SARA biologists using the best available information, including COSEWIC status reports, recovery potential assessments, academic literature, and expert opinion. These spatial data support the protection, recovery and conservation of species listed as Endangered, Threatened or Special Concern under SARA. Species distributions are also described and displayed in Recovery Strategies, Action Plans and/or Management Plans. Discrepancies may exist between the distribution data shown in a species’ SARA recovery document and the current spatial data. Please contact DFO for more information on any data discrepancies.

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.