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.

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

This spatial data identifies breeding zones used by forest managers and forest genetic associations to manage provincial forest genetic assets. The data: * shows the boundaries of breeding zones * identifies the primary or target species within each zone Species are associated with certain breeding programs, seed orchards and progeny (descendant) testing installations.

PURPOSE:This Archer fiord data is associated with a larger program ArcticCORE, which was created to fulfill knowledge gaps and develop long term protection in the extremely remote Tuvaijuittuq region. The main objectives of this expedition were to improve our comprehension of the key drivers for productive capacity, diversity and ecosystem structure in areas connected to Baffin Bay and Tuvaijuittuq, including Archer fiord.DESCRIPTION:ArcticCORE is a 5-year broader program aiming to characterize Tuvaijuittuq’s unique ecosystem and its influence and connectivity with the adjacent ecosystems to inform sustainable management and conservation initiatives in Tuvaijuittuq and the eastern Arctic. In an Arctic Ocean with rapidly declining sea ice, Tuvaijuittuq area retains the oldest and thickest sea ice, and can act as a refuge for ice-dependent species. This program aims to characterize the Arctic marine ecosystem and establish baseline measurements for future comparisons in the region. From 2023, water collection was carried out at four stations throughout Archer Fiord and analyzed for primary productivity, chlorophyll a, phytoplankton flow cytometry and phytoplankton taxonomy down to the lowest identifiable level. These data will contribute to a better understanding of the key drivers for productive capacity, diversity and ecosystem structure in Archer fiord. Characterization of these upstream areas are relevant for an ecosystem-based approach to fisheries management in Baffin Bay, a priority for DFO and an intrinsic part of mandated activities, as they influence the ecosystem and fisheries resources downstream.

Description:In the coming decades, warming and deoxygenation of marine waters are anticipated to result in shifts in the distribution and abundance of fishes, with consequences for the diversity and composition of fish communities. Here, we combine fisheries-independent trawl survey data spanning the west coast of the USA and Canada with high-resolution regional ocean models to make projections of how 34 groundfish species will be impacted by changes in temperature and oxygen in British Columbia (BC) and Washington. In this region, species that are projected to decrease in occurrence are roughly balanced by those that are projected to increase, resulting in considerable compositional turnover. Many, but not all, species are projected to shift to deeper depths as conditions warm, but low oxygen will limit how deep they can go. Thus, biodiversity will likely decrease in the shallowest waters (less than 100 m), where warming will be greatest, increase at mid-depths (100–600 m) as shallow species shift deeper, and decrease at depths where oxygen is limited (greater than 600 m). These results highlight the critical importance of accounting for the joint role of temperature, oxygen and depth when projecting the impacts of climate change on marine biodiversity.The rasters available in this dataset project the occurrence of each of the 34 groundfish species in a 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models (BCCM and NEP36). Each projection layer is provided as the mean projected occurrence as well as the lower and upper 95% confidence interval of projected occurrence.Methods:Estimated species response curves:We estimated how the observed distribution of groundfish species is determined by temperature, dissolved oxygen and seafloor depth using data from fisheries-independent scientific research trawls spanning the entire American and Canadian west coast. We included data from 4 surveys (NOAA West Coast, NOAA Alaska, NOAA Bering or DFO Pacific) from 2000 to 2019. For each species, we modelled occurrences in the coastwide trawl dataset using a generalized linear model (GLM) using the sdmTMB package in R v. 4.0.2. The predictors were temperature, log dissolved oxygen, log depth and survey. We included quadratic terms for temperature and log depth to allow species occurrences to peak at intermediate values. We fitted a breakpoint function for log dissolved oxygen to reflect the fact oxygen is a limiting factor. We assessed the forecasting accuracy of the SDM by comparing how well a model fitted to only data from 2000 to 2010 could forecast species’ occurrences in trawls within our focal region for the period of 2011–2019. We assessed all 77 groundfish species that were present in the overall trawl dataset, however the final analysis included only the 34 species for which the models had adequate forecasting ability.Projecting groundfish biodiversity changes:We based our groundfish biodiversity change projections on two regional models that downscale climate projections: the British Columbia Continental Margin model (BCCM) and the North-Eastern Pacific Canadian Ocean Ecosystem model (NEP36-CanOE). We used a historical baseline of 1986–2005 and future projected values for 2046–2065 based on RCP 4.5 and 8.5 emissions scenarios. Using the models that we validated in our forecasting accuracy assessment, we projected the occurrence of each species in each 3 km^2 grid cell for the historical baseline, as well as for two emissions scenarios, from each of the two regional ocean models.Uncertainties:Source survey data was collected by consistent methods with survey-grade GPS for all years included. Data quality is expected to be high. Modeled data are at 3 km resolution. Outputs are as accurate as source input models and are deemed to be of high quality and accurate based upon the precision of model inputs.Projecting biodiversity responses to climate change involves considerable uncertainty and our approach allows us to quantify some aspects of this. Of the uncertainty that we could quantify, roughly half was due to uncertainty in our SDMs and the remainder was due to regional ocean model uncertainty or scenario uncertainty. This amount of uncertainty in the SDMs is typical, stemming from the fact that contemporary species distributions are also influenced by other factors that we have not included in our model. In addition, although oxygen demand is understood to vary with temperature, limitations in the implementation of breakpoint models prevented us from estimating a temperature-dependent oxygen breakpoint. However, although somewhat unrealistic, this limitation is unlikely to have greatly increased the uncertainty in our SDMs because low oxygen concentrations occurred almost exclusively at depths where temperature variation and projected change was small.To reduce uncertainty due to year-to-year variation in climate, our model projections are based on 20-year climatologies with a future period that is far enough ahead to ensure that changes are unambiguously due to greenhouse gases. We have made projections based on two different emissions scenarios, and two different regional ocean models that are both downscaled from the same global model, the second generation Canadian Earth System Model (CanESM2), using different downscaling techniques. While the BCCM model was run inter-annually and then averaged to produce the climatologies, the NEP36 model used atmospheric climatologies with augmented winds to force the ocean model and produce representative climatologies. Comparing these regional projections provides an estimate of the uncertainty across different regional downscaling models and methods. We find that the projected impacts of climate change on the groundfish community are more sensitive to the differences in the regional ocean models than they are to the emissions scenarios used. However, these differences are in magnitude (changes tend to be larger based on NEP36 compared with the BCCM) rather than in direction, with both models resulting in similar overall patterns of biodiversity change and turnover for the groundfish community. Over the 60-year time period (1986–2005 versus 2046–2065) used in our study, our projections suggest that groundfish community changes are similar regardless of the scenario used.