An exploratory project on the taxonomic and genetic diversity of decapods in three ocean subregions (Northeast Pacific, Canadian Arctic, and Northwest Atlantic), which were sampled in 2022, was undertaken by the Arctic Working Group under the Canada-U.S. Fisheries and Climate Collaboration between Fisheries and Oceans Canada (DFO) and the National Marine Fisheries Service (NMFS) of the National Oceanic and Atmospheric Administration (NOAA). This collaboration framework aims to pool Canadian and U.S. data to explore the impacts of broad-scale climate change on marine biodiversity. In early summer 2022, a sampling protocol with the selection of targeted decapods was provided to DFO and NOAA collaborators. Targeted genera were collected from a total of 10 research programs across three ocean subregions and four marine regions. The Northeast Pacific samples were collected in the Bering Sea during the Northern Bering Sea Ecosystem and Surface Trawl Survey, and the Eastern and Northern Bering Sea Continental Shelf Bottom Trawl Survey of Groundfish and Invertebrate Fauna onboard the F/V Northwest Explorer, F/V Alaska Knight and F/V Vesteraalen. In the Western Canadian Arctic (mainly from Beaufort Sea and Amundsen Gulf), specimens were collected during DFO’s Canadian Beaufort Sea – Marine Ecosystem Assessment (CBS-MEA) survey onboard the F/V Frosti. In Eastern Canadian Arctic (mainly from Baffin Bay and Davis Strait), specimens were collected during DFO’s Knowledge and Ecosystem-Based Approach in Baffin Bay (KEBABB) survey onboard the CCGS Amundsen and DFO’s North Atlantic Fisheries Organization (NAFO) Subarea 0B survey onboard the R/V Tarajoq. In the Estuary and Gulf of St. Lawrence (EGSL), specimens were collected from coastal surveys (scallops, sea cucumber, snow crab, and whelk surveys) onboard the CCGS Leim and offshore during the Ecosystemic Survey onboard the CCGS Teleost. Decapods were collected from various sampling gears (benthic beam trawl, modified Atlantic Western IIA otter trawl, Bacalao trawl, shrimp trawl, Digby scallop dredge, or modified sea cucumber dredge) and identified to the lowest possible taxonomic level and photographed, when possible. All specimens were frozen at sea (n = 995). In the lab, the identifications were validated or refined with the photos and the frozen specimens. DNA was extracted for 87 specimens and a section of COI gene was amplified in order to be sequenced using Sanger method. Sequences were compared with existing data using The Basic Local Alignment Search Tool (BLAST) in the National Center for Bio-technology Information Nucleotide database (NCBI-nt, including the GenBank database) to compare scientific names, where available.The present dataset includes 391 decapod species occurrences. DNA was extracted for a subset of 87 specimens (COI gene); sequences are publicly available on BOLD data portal under project code DDAO (see supporting document "citations_references.csv" for more information).The data are presented in Darwin Core format and are separated in three files:The "Activité_décapodes_DDAO_decapods_event_en" file contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The "Occurrence_décapodes_DDAO_decapods_en" file contains the taxonomic occurrences.The "ADN_décapodes_DDAO_decapods_DNA_en" file contains the DNA derived data.For further details, please refer to the technical report available in the supporting document named "citations_references.csv". USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

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.

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.

This geodatabase includes hotspot maps of 1) nearshore habitat richness, 2) diversity (fish and invertebrates), and 3) biomass (using catch per unit effort of fish and invertebrates), as well as two layers showing the spatial extent of the diversity and biomass hotspot analyses. Full details and methods can be found in the Rubidge et al. 2018 CSAS Research Document 2018/053 available here or at https://waves-vagues.dfo-mpo.gc.ca/Library/40759842.pdf. These data were reviewed as part of a Canadian Science Advisory Secretariat (CSAS) regional peer review process on Nov 1-2, 2017.Habitat Richness Hotspots: Because there are no systematic surveys of nearshore species that span the entire coastline of Northern Shelf Bioregion, the nearshore habitat richness hotspots were developed as a proxy for species diversity in nearshore areas. Habitat richness was calculated from eight habitat features: eelgrass, surfgrass, canopy-forming kelp, estuaries, areas of high rugosity, and hard, mixed, and soft substrate. The number of features within 1 km x 1 km planning units was counted, and hotspots were identified using the Getis-Ord G* tool in ArcGIS. Planning units with Gi_Bin values of 3 (99% confidence) were classified as habitat richness hotspots.Diversity and Biomass Hotspots: Hotspots of fish and invertebrate diversity and biomass were developed as proxies for spatial patterns of productivity in the Northern Shelf Bioregion. Diversity (Shannon diversity) and biomass (kg/hour or count/hook/hour) were calculated from DFO synoptic trawl and outside hard-bottom longline (HBLL) survey catch records. The outside HBLL survey was previously referred to as Pacific Halibut Management Area (PHMA) survey. The synoptic trawl and HBLL surveys have complementary spatial coverage, with the HBLL surveys occurring in more coastal areas (20–260 m) and the synoptic trawl surveys occurring on deeper shelf areas (50–1300 m). Hotspots were identified using the Getis-Ord G* tool in ArcGIS for five separate analyses: fish biomass (trawl), fish diversity (trawl), fish diversity (longline), invertebrate biomass (trawl), and invertebrate diversity (trawl). Using the Minimum Bounding Geometry Tool, convex hull polygons were drawn around groups of hotspot points (Gi_Bin values of 1, 2, or 3; confidence ≥90%) containing 10 or more points. The resulting polygons were then buffered by 1 km and manually edited where needed to exclude any large areas of the polygons that did not include hotspot points.

This dataset provides two landscape-level indices derived for two hierarchical ecological frameworks in Canada—Soil Landscapes of Canada (SLC) and EcoDistrict polygons—across the country’s agricultural regions. The first index, the Shannon Diversity Index, quantifies spatial variability and crop diversity present in the 2021 agricultural landscape. The second, the Rotation Turbulence Index, measures temporal variability by capturing the intensity of crop rotation changes within fields from 2011 to 2021. Both indices are subsequently aggregated to their respective reporting-boundary polygons.

In celebration of the tremendous diversity of tree species that tell the story of our culture and history, the NCC released in September 2020 a compilation of close to 170 remarkable trees across Canada’s Capital region entitled A Living Legacy: Remarkable Trees of Canada’s Capital. An interactive map and downloadable book are available for free on the NCC’s website and will allow the public to discover distinctive features of these trees, revealing a story of the beauty of our natural heritage through the rich diversity of species thriving within Canada’s Capital. This compilation features trees according to their commonalities, which can include their physical relationship with the land, the fact that they were a source of food for Indigenous peoples, or for their contribution to the forest industry.https://ncc-ccn.gc.ca/remarkable-treeshttps://ncc-ccn.maps.arcgis.com/apps/MapJournal/index.html?appid=a9ba98fb7e8b4c2ba9be337235b95291

Ecological Reserves are part of a network of Designated Areas. The goal of the network is to create and maintain a comprehensive, dynamic and accessible data set (digital map) that accurately defines land areas in Saskatchewan that have various levelUnique ecological reserves that are designed to protect representative areas of natural landscapes and to conserve biological diversity.

The data represents an assessment of biodiversity risk for the agricultural area of Alberta in 2002. Biodiversity risk refers to the loss of biological diversity, or the variety of plant and animal life in agricultural landscapes. This map, created in ArcGIS, tries to show where biodiversity could be threatened, such as in areas with significant habitat that coincide with areas of greater agricultural economic activity. Biodiversity is believed to affect the overall health of the environment.

Monitoring programs are an important component of Marine Protected Area (MPA) management, providing requisite information on the state of, and changes in, protected ecosystems. Monitoring is required to gauge the efficacy of MPAs towards their conservation objectives and provides information needed to evaluate the benefits provided to biodiversity from restricted access. However, in Nova Scotia’s coastal zone, there is a lack of baseline data, including fish diversity and community structure in macrophyte beds, which makes monitoring intractable. In 2017, the Eastern Shore Islands was identified as a coastal Area of Interest (AOI) for the potential establishment of an MPA. In 2018 an overview was conducted, detailing the spatial and temporal ecological attributes of the AOI. This information revealed a unique coastal ecosystem associated with a dense archipelago and relatively natural seascape. The abundance of plant and algal biogenic habitats within the area was assumed to host a diversity of juvenile fish species. The primary objective of this project is to begin development of a long-term biodiversity monitoring program in the Eastern Shore Islands and other coastal Areas of Interest for conservation planning. We propose implementing this program with the use of direct (beach seines, scuba diving, and stable isotope sampling) and indirect (environmental DNA - eDNA) sampling. Environmental DNA (eDNA) is a useful tool to examine marine biodiversity in a non-invasive way, on a small spatial scale. eDNA can be easily collected and filtered and is becoming increasingly cost efficient to sequence and may be a useful marine protected area monitoring tool. While eDNA generally yields comparable results to traditional sampling techniques in terms of biodiversity captured, little is known on how eDNA signals fluctuate across years (or even days to weeks). We will compare species detections using eDNA metabarcoding to visual surveys (scuba and seine nets) to census eelgrass beds across the coastal zone, providing a baseline and time series of species diversity on which to base long-term monitoring. This project will generate inventories of eelgrass bed locations, and fish and invertebrate diversity within eelgrass beds. We additionally collect fish length distribution data to examine seasonal and inter-annual trends in size structure over time. The data generated from direct and indirect sampling will provide a comprehensive and ongoing catalog of species diversity and community structure in coastal eelgrass beds, as well as best-practices for sampling eDNA in the coastal environment.Cite this data as: Jeffery, N.W., Pettitt-Wade, H., Van Wyngaarden, M., and Stanley, R.R.E. Maritimes Coastal Biodiversity Monitoring Program – Beach Seining.Published: December 2023. Coastal Ecosystems Science Division, Maritimes region, Fisheries and Oceans Canada, Dartmouth NS. https://open.canada.ca/data/en/dataset/dbbcb23a-d018-4b70-b8ec-89997aded770

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.