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.

Planting activities reported on openings into RESULTS. More than one planting activitiy may occur within an opening. Planting activiites may also overlap. Planting attribute reporting is mandatory requirement while planting maps are optional. Planting reports includes species and seedlot information. This is a part of Silviculture and Land Status dataset, which includes tracking achievement of silviculture obligations on Crown land

Description:This dataset contains layers of predicted occurrence for 65 groundfish species as well as overall species richness (i.e., the total number of species present) in Canadian Pacific waters, and the median standard error per grid cell across all species. They cover all seafloor habitat depths between 10 and 1400 m that have a mean summer salinity above 28 PSU. Two layers are provided for each species: 1) predicted species occurrence (prob_occur) and 2) the probability that a grid cell is an occurrence hotspot for that species (hotspot_prob; defined as being in the lower of: 1) 0.8, or 2) the 80th percentile of the predicted probability of occurrence values across all grid cells that had a probability of occurrence greater than 0.05.). The first measure provides an overall prediction of the distribution of the species while the second metric identifies areas where that species is most likely to be found, accounting for uncertainty within our model. All layers are provided at a 1 km resolution.Methods:These layers were developed using a species distribution model described in Thompson et al. 2023. This model integrates data from three fisheries-independent surveys: the Fisheries and Oceans Canada (DFO) Groundfish Synoptic Bottom Trawl Surveys (Sinclair et al. 2003; Anderson et al. 2019), the DFO Groundfish Hard Bottom Longline Surveys (Lochead and Yamanaka 2006, 2007; Doherty et al. 2019), and the International Pacific Halibut Commission Fisheries Independent Setline Survey (IPHC 2021). Further details on the methods are found in the metadata PDF available with the dataset.Abstract from Thompson et al. 2023:Predictions of the distribution of groundfish species are needed to support ongoing marine spatial planning initiatives in Canadian Pacific waters. Data to inform species distribution models are available from several fisheries-independent surveys. However, no single survey covers the entire region and different gear types are required to survey the range of habitats that are occupied by groundfish. Bottom trawl gear is used to sample soft bottom habitat, predominantly on the continental shelf and slope, whereas longline gear often focuses on nearshore and hardbottom habitats where trawling is not possible. Because data from these two gear types are not directly comparable, previous species distribution models in this region have been limited to using data from one survey at a time, restricting their spatial extent and usefulness at a regional scale. Here we demonstrate a method for integrating presence-absence data across surveys and gear types that allows us to predict the coastwide distributions of 66 groundfish species in British Columbia. Our model leverages the use of available data from multiple surveys to estimate how species respond to environmental gradients while accounting for differences in catchability by the different surveys. Overall, we find that this integrated method has two main benefits: 1) it increases the accuracy of predictions in data-limited surveys and regions while having negligible impacts on the accuracy when data are already sufficient to make predictions, 2) it reduces uncertainty, resulting in tighter confidence intervals on predicted species occurrences. These benefits are particularly relevant in areas of our coast where our understanding of habitat suitability is limited due to a lack of spatially comprehensive long-term groundfish research surveys.Data Sources:Research data was provided by Pacific Science’s Groundfish Data Unit for research surveys from the GFBio database between 2003 and 2020 for all species which had at least 150 observations, across all gear type and survey datasets available.Uncertainties:These are modeled results based on species observations at sea and their related environmental covariate predictions that may not always accurately reflect real-world groundfish distributions though methods that integrate different data types/sources have been demonstrated to improve model inference by increasing the accuracy of the predictions and reducing uncertainty.

A Guardrail is a barrier fastened to the end of a bridge abutment, along the shoulder of a road or between travel lanes of opposing traffic. It is a Linear feature

A research survey on snow crab (Chionoecetes opilio) was conducted from July 7 to July 26, 2019 in the Estuary St. Lawrence River between Forestville, Baie-Comeau and Matane. The main objective of this survey was to assess the abundance of snow crab and benthic species associated with snow crab habitat. Only data for benthic species associated with snow crab habitat are presented in this dataset.Data were collected according to a fixed station sampling design consisting of 66 stations, between 31 and 279 meters depth. Specimens were collected using a beam trawl with a total width of 2.8 meters and a total height of 0.76 meters. The codend was lined with a 16 millimeter stretched mesh net in order to harvest the small individuals. The hauls were made at a target speed of 2 knots and a target duration of 15 minutes. Start and end positions were recorded to calculate the distance traveled on each tow using the geosphere library in R. The average tow distance was approximately 25 m. The area covered at each tow was the product of the trawl opening and the distance traveled.The two files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "Activity_Information" file includes generic activity information, including date and location. The "occurrence_taxon" file includes the taxonomy of the species observed, identified to the species or lowest possible taxonomic level. To obtain the abundance and biomass assessment, contact Cedric Juillet (cedric.juillet@dfo-mpo.gc.ca).For quality controls, all taxonomic names were checked against the World Register of Marine Species (WoRMS) to match recognized standards. The WoRMS match was placed in the "ScientificnameID" field of the occurrence file. Data quality checks were performed using the R obistools and worrms libraries. All sampling locations were spatially validated.

The Coastal Biodiversity Trawl Survey for the Passamaquoddy Bay was conducted annually between July to October from 2009 to 2019. This survey was intended to monitor long-term change in local species presence, habitat utilization, and health. The sampling activities support coastal research in fisheries, aquaculture, marine protected areas, and ecosystem change. Data collected prior to 2013 are generally not recommended for comparative analysis due to changes in vessel, sampling effort, and protocols.

Zooplankton samples were collected at Ocean Station "P" (50.0000, -145.0000) from 1956 to 1980, and were analyzed to various levels of taxonomic resolution over the years. Although summaries of these data have been previously published ((LeBrasseur 1965) and (Fulton 1978, 1983)) the detailed species data have never been published. This detailed dataset contains total zooplankton wet weights/m3 for the whole period of 1956 to 1980, as well as densities (numbers/m3) for five major taxa (copepods, chaetognaths, euphausiids, amphipods, and Aglantha) from 1964 to 1967, species identifications, counts and lengths for many samples collected between 1968 to 1980. The attached supporting document (Ocean Station "Papa" detailed zooplankton data: 1956 – 1980) contains information on the methods used to collect and process the data along with descriptions of a number of fairly minor points about the data that were not resolved. It also describes, in detail, the format of the original data files, the corrections/changes that were made to these files in creating this version, and how these errors affect what was published in Fulton (1983).The purpose of this record is to make the detailed data available to the scientific community in an electronic format and to provide a convenient reference for citing the detailed data. Waddell, Brenda J., and Skip McKinnell. 1995. Ocean Station "Papa" detailed zooplankton data:1956 - 1980. Can. Tech. Rep. Fish. Aquat. Sci. 2056: 21 p.

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.

A derivative of DFO’s benthic species survey for the Strategic Program for Ecosystem-based Research and Advice (SPERA) (open data record ID: e736c0f0-b19e-4842-903d-28bfc756d48a), this benthic survey funded through the Canadian Healthy Oceans Network (CHONeII) looks at the presence/absence and abundance of two biogenic habitat-forming species that are listed as vulnerable to disturbance in a subset of 50 drift camera transects in the ‘Head Harbour/West Isles Archipelago/The Passages’ Ecologically and Biologically Significant Area (EBSA) in the Bay of Fundy, New Brunswick, Canada (~113km2). Presence/absence and abundance data of the stalked sea squirt (Boltenia ovifera) and horse mussel (Modiolus modiolus) were derived from the use of high-resolution Nikon D800 36.1 megapixel still images (n=2576, see link to parent record for more descriptive survey information) to be used in species distribution modelling. Image field of view (FOV) was estimated using a 10 cm-wide trigger weight for scale,and standardized across images using the average FOV estimate (0.75 x 0.5 m) across a subset of 200 images. Species counts were then converted to abundance estimates (number of individuals per square-meter) by dividing counts by 0.375m2. Boltenia ovifera was observed at densities reaching 456 ind./m2, while Modiolus modiolus density reached a maximum of 240 ind./m2.Cite this data as: Mireault C.A., Lawton P., Devillers R. and Teed L. Presence/absence and abundance of vulnerable marine ecosystem species Boltenia ovifera and Modiolus modiolus in the lower Bay of Fundy derived from high resolution still imagery. Published September 2023. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/152ae3f1-d2b9-43d9-a7b4-d769d9e9fc41

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