The Marine Biodiversity Observation Network Pole to Pole (MBON P2P) effort seeks to develop a framework for the collection, use and sharing of marine biodiversity data in a coordinated, standardized manner leveraging on existing infrastructure managed by the Global Ocean Observing System (GOOS; IOC-UNESCO), the GEO Biodiversity Observation Network (GEO BON), and the Ocean Biogeographic Information System (OBIS). The MBON Pole to Pole aims to become a key resource for decision-making and management of living resource across countries in the Americas for reporting requirements under the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES), Aichi Targets of the Convention of Biological Diversity (CBD), and the UN 2030 Agenda for Sustainable Development Goals (SDGs).This collection corresponds to the species registered on sandy beaches of the Musquash Harbour, Mispec Bay, and New River Beach, New Brunswick, Canada, using the MBON P2P sampling protocol for sandy beaches, with funding from the Government of Canada's Coastal Environmental Baseline Program.Citation: Reinhart B, Jonah L (2025). MBON POLE TO POLE: SANDY BEACH BIODIVERSITY OF SOUTHWEST NEW BRUNSWICK, CANADA. Version 1.7. Caribbean OBIS Node. Samplingevent dataset. https://ipt.iobis.org/mbon/resource?r=sandybeachesbayoffundynb&v=1.7

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

In 2012 and 2013, Fisheries and Oceans Canada surveyed the benthos in two areas closed to bottom contact fishing, the Narwhal Overwintering and Coldwater Coral Zone (now the Disko Fan Conservation Area, DFCA), and the Hatton Basin Voluntary Coral Protection Zone (now the Hatton Basin Conservation Area, HBCA). Samples were collected following protocols recommended by the Arctic Council’s Circumpolar Biodiversity Monitoring Plan for the purposes of providing baseline data for future monitoring of benthic invertebrates in this sensitive region, and for facilitating pan-Arctic comparisons of benthic communities. Five biodiversity monitoring stations were established, four in the DFCA and one in the HBCA, each of which was fully sampled according to those protocols with Van Veen grabs or box corers, drop cameras and temperature recorders attached to the gear. This report summarises the grab/core-sampled benthic fauna collected during the 2012 survey of the Conservation Areas and complements another report documenting the epibenthos from the camera transects in the DFCA. Here we report on macrofauna in the 1-cm size fraction, and on foraminiferan meiofauna.The data provided is presented in the following report (see related link) :Jacobs, K., Bouchard Marmen, M., Rincón, B., MacDonald, B., Lirette, C., Gibb, O., Treble, M., and Kenchington, E. 2022. Biodiversity Monitoring Stations for Benthic Macrofauna and Meiofauna in the Disko Fan and Hatton Basin Conservation Areas. Can. Tech. Rep. Fish. Aquat. Sci. 3487: vi + 86 p.Cite this data as: Bouchard Marmen, Marieve; Rincon, Beatriz ; MacDonald, Barry; Lirette, Camille; Gibb, Olivia; Treble, Margaret ; Jacobs, Kevin; Kenchington, Ellen (2022). Biodiversity Monitoring Stations for Benthic Macrofauna and Meiofauna in the Disko Fan and Hatton Basin Conservation Areas. Published January 2023. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b7bcff18-698b-4d40-a7bd-13d39925cbeb

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.

This resource documents a dataset of epifauna occurrences collected in 2021 during The Knowledge and Ecosystem-Based Approach in Baffin Bay (KEBABB) program developed by the Department of Fisheries and Oceans Canada (DFO) in collaboration with university partners. The overall objective of KEBABB is to characterize the variability and trends in physical, chemical, and biological oceanographic conditions and food webs supporting fisheries in the connected ecosystems of western Baffin Bay and Lancaster Sound. In 2021, DFO expanded the KEBABB program to Barrow Strait (KEBABS-Knowledge and Ecosystem-Based Approach in Barrow Strait), a key productive area of the Tallurutiup Imanga National Marine Conservation Area. The study took place in the Eastern Canadian Arctic (mainly in Baffin Bay, Davis Strait and Barrow Strait). Sampling is done along transects at fixed stations in the study area. Catches are collected with a 1.5 m Agassiz trawl (5 mm mesh net) for 3 minutes bottom-contact time at a target speed of 1.5 knots and with a 3 m benthic beam trawl (6.4 mm mesh net) for 15 minutes bottom-contact time at a target speed of 3 knots. A total of 16 stations were sampled for epifauna in 2021 between 85-850 m depth. Epibenthic invertebrates are identified to the lowest possible taxonomic level and photographed. All unknown specimens are frozen. In the lab, the identifications are validated or refined with the photos and the frozen specimens.The data are presented in Darwin Core and are separated in two files:The “Activité_épifaune_KEBABB_epifauna_event_en” file which contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The “Occurrence_épifaune_KEBABB_epifauna_en” file that contains the taxonomic occurrences.Further details on sampling can be found in the following report: Pućko, M., Charette, J., Tremblay P., Brulotte S., St-Denis B., Ciastek S., Hedges, K., Kuzyk, Z., Roy V., and Michel, C. 2022. An ecosystem-based approach in the eastern Arctic: KEBABB/S (Knowledge and Ecosystem-Based Approach in Baffin Bay/Barrow Strait) 2021 expedition report. Can. Manuscr. Rep. Fish. Aquat. Sci. 3250: viii + 58 p. https://publications.gc.ca/collections/collection_2022/mpo-dfo/Fs97-4-3250-eng.pdfUSE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

100 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

40 Class - Canadian Ecological Domain Classification from Satellite Data. Satellite derived data including 1) topography, 2) landscape productivity based on photosynthetic activity, and 3) land cover were used as inputs to create an environmental regionalization of the over 10 million km2 of Canada’s terrestrial land base. The outcomes of this clustering consists of three main outputs. An initial clustering of 100 classes was generated using a two-stage multivariate classification process. Next, an agglomerative hierarchy using a log-likelihood distance measure was applied to create a 40 and then a 14 class regionalization, aimed to meaningfully group ecologically similar components of Canada's terrestrial landscape. For more information (including a graphical illustration of the cluster hierarchy) and to cite this data please use: Coops, N.C., Wulder, M.A., Iwanicka, D. 2009. An environmental domain classification of Canada using earth observation data for biodiversity assessment. Ecological Informatics, Vol. 4, No. 1, Pp. 8-22, DOI: https://doi.org/10.1016/j.ecoinf.2008.09.005. ( Coops et al. 2009).

This dataset documents the epifauna occurrences collected from 2021 to 2024 during the Canadian Beaufort Sea Marine Ecosystem Assessment (CBS-MEA) conducted by the Department of Fisheries and Oceans (DFO). This scientific program focuses on the integration of oceanography, food web linkages, physical-biological couplings, and spatial and interannual variabilities. The program also aims to expand the baseline coverage of species diversity, abundances, and habitat associations in previously unstudied areas of the Beaufort Sea and Western Canadian Archipelago. The study took place mainly in the Canadian Beaufort Sea and the Amundsen Gulf. Sampling is done along transects at fixed stations in the study area. Catches are collected with a 3 m benthic beam trawl for 10 minutes bottom-contact time at a target speed of 2 knots and with a modified Atlantic Western IIA otter trawl for 20 minutes bottom-contact time at a target speed of 2.9 knots. A total of 32 stations were sampled for epifauna in 2021, 22 in 2022, 23 in 2023 and 22 in 2024, between 22-655 m depth. Epibenthic invertebrates were identified to the lowest taxonomic level possible and photographed. All unknown specimens are frozen. In the lab, the identifications are validated or refined with the photos and the frozen specimens.The data are presented in Darwin Core and are separated in two files:The "Activité_épifaune_CBSMEA_epifauna_event_en" file which contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The "Occurrence_épifaune_CBSMEA_epifauna_en" file that contains the taxonomic occurrences.

This dataset documents the infauna occurrences collected from 2021 to 2023 during the Canadian Beaufort Sea Marine Ecosystem Assessment (CBS-MEA) conducted by the Department of Fisheries and Oceans (DFO). This scientific program focuses on the integration of oceanography, food web linkages, physical-biological couplings, and spatial and interannual variabilities.The program also aims to expand the baseline coverage of species diversity, abundances, and habitat associations in previously unstudied areas of the Beaufort Sea and Western Canadian Archipelago. The study took place mainly in the Canadian Beaufort Sea and the Amundsen Gulf. Sampling is done along transects at fixed stations in the study area. Catches are collected using a 50 x 50 cm box-corer. 2 or 3 box core is collected per station to obtain replicates. A total of 29 stations were sampled for infauna in 2021, 15 in 2022 and 25 in 2023 between 10-653 m depth. Half of the box corer (0.125 m2) is sampled for infauna taxonomy. The first 20 cm of sediment are collected and sieved through a 0.5 mm mesh sieve. The samples are preserved in seawater-formaldehyde solution (10 % v/v). In the lab, infauna is identified to the lowest taxon level possible.The data are presented in two files:The "Activité_endofaune_CBSMEA_infauna_event_en" file which contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The "Occurrence_endofaune_CBSMEA_infauna_en" file that contains the taxonomic occurrences.

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.