This report describes an analysis of Maritimes Region inshore lobster logbook data reported at a grid level, including Bay of Fundy Disputed Zone data reported at the coordinate level. Annual and composite (2015–2019) grid maps were produced for landings, number of trap hauls, and the same series standardized by grid area, as well as maps of catch weight per number of trap hauls as an index of catch per unit effort (CPUE). Spatial differences in fishing pressure, landings, and CPUE are indicated, and potential mapping applications are outlined. Mapping the distribution and intensity of inshore lobster fishing activity has management applications for spatial planning and related decision support. The lack of region-wide latitude and longitude coordinates for inshore lobster effort and landings limits the utility of commercial logbook data for marine spatial planning purposes.

Oceanographic data from stationary moorings deployed as part of the Centre for Marine Applied Research's (CMAR) Coastal Monitoring Program.

Significant climate change impacts are highly likely in all Canadian marine and freshwater basins, with effects increasing over time (DFO 2012). Climate models project that ecosystems and fisheries across Canada will be disrupted into the foreseeable future (Lotze et al. 2019; Bryndum-Buchholz et al. 2020; Tittensor et al. 2021; Boyce et al. 2024). Despite its imminence, climate change is infrequently factored into Canada’s primary marine conservation strategies, such as spatial planning (O’Regan et al. 2021) or fisheries management (Boyce et al. 2021; Pepin et al. 2022). The Climate Risk Index for Biodiversity (CRIB) was developed to assess climate risk for marine species in a quantitative, spatially explicit, and scalable manner, supporting climate-informed decision-making. It has been used to evaluate climate risks for marine life globally (Boyce et al. 2022), regionally (Lewis et al. 2023; Boyce et al. 2024; Keen et al. 2023), for fisheries (Boyce et al. 2024), and in support of spatial conservation planning (Keen et al. 2023). This dataset contains climate vulnerability and risk estimates from the CRIB framework adapted to consider warming at both the sea surface and its bottom for 145 marine species of conservation or fisheries interest across Canada’s marine territory. Climate risk is available at a 0.25-degree resolution under two contrasting emission scenarios to 2100. For each species, location, and scenario, 12 climate indexes, three vulnerability dimensions, and an overall vulnerability and risk score are provided. The accompanying report describes the data, methods, and workflow used to calculate risk. This report also guides the interpretation of these data to inform and support climate-informed decision-making in Canada.

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 (2024). MBON POLE TO POLE: SANDY BEACH BIODIVERSITY OF SOUTHWEST NEW BRUNSWICK, CANADA. Version 1.5. Caribbean OBIS Node. Samplingevent dataset. https://ipt.iobis.org/mbon/resource?r=sandybeachesbayoffundynb&v=1.5

Fisheries landings and effort mapping of the inshore lobster fishery on the DFO Maritimes Region statistical grid (2012-2014). This report describes an analysis of Maritimes Region inshore lobster logbook data reported at a grid level, including Bay of Fundy Grey Zone data reported at the coordinate level. Annual and composite (2012–2014) grid maps were produced for landings, number of license-days fished, number of trap hauls, and the same series standardized by grid area, as well as maps of catch weight per number of trap hauls as an index of catch per unit effort (CPUE). Spatial differences in fishing pressure, landings, and CPUE are indicated, and potential mapping applications are outlined. Mapping the distribution and intensity of inshore lobster fishing activity has management applications for spatial planning and related decision support. The lack of region-wide latitude and longitude coordinates for lobster effort and landings limits the utility of commercial logbook data for marine spatial planning purposes.

Bay and Channel (fresh and marine) features and associated names

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 rocky shores of the Musquash Harbour and Mispec Bay, New Brunswick, Canada, using the MBON P2P sampling protocol for rocky shores, with funding from the Government of Canada's Coastal Environmental Baseline Program.Citation: Reinhart B, Cooper A, Nason R (2023). MBON POLE TO POLE: ROCKY SHORE BIODIVERSITY OF MUSQUASH HARBOUR AND MISPEC BAY. Version 1.4. Caribbean OBIS Node. Samplingevent dataset. https://ipt.iobis.org/mbon/resource?r=rockyshoresbayoffundynb&v=1.4

This dataset contains the modelled and observed data used in the publication "Fjord circulation permits persistent subsurface water mass in a long, deep mid-latitude inlet" by Laura Bianucci et al., DFO Ocean Sciences Division, Pacific Region (published in the journal Ocean Science in 2024). An application of the Finite Volume Community Ocean Model (FVCOM v4.1) was run from May 24 to June 27, 2019 in the Discovery Islands region of British Columbia, Canada. Observed temperature and salinity profiles available in this area during this time period are included in the dataset, along with the modelled values at the same times and locations.

Description:Biophysical Units: Under the Pacific Marine Ecological Classification System (PMECS; DFO 2016; Rubidge et al. 2016), biophysical units are areas of distinct physiographic and oceanographic conditions and processes that shape species composition at spatial extents of 1000s of km. Geomorphic units:Geomorphic units or geozones are discrete geomorphological structures at the scale of 100s of km that are assumed to have distinctive biological assemblages (e.g., plateaus, ridges, seamounts, canyons). Although the spatial scale of geomorphic units is nested within biophysical units, a single geomorphic unit such as a trough may span more than one biophysical unit. The following 5 layers are included in this geodatabase:1. Biophysical_Units_L4A - Predicted PMECS Biophysical Units (Level 4A) output from the random forest analysis2. Biophysical_Units_L4B - Predicted PMECS Biophysical Units (Level 4B) output from the random forest analysis3. Biophysical_Units_ProbAssign_L4AB - Layer showing the probability that a grid cell was assigned to a given biophysical unit in the final random forest predictive modelling step4. Cluster_L4AB - Layer showing the output of species assemblage cluster analysis5. Geomorphic_Units - Geomorphic units for the BC coast that combines geomorphic units produced by Rubidge et al. 2016) and Proudfoot and Robb (2022).Methods:Biophysical Units:Rubidge et al. (2016) used a two-step process to identify biophysical units in British Columbia. First, a cluster analysis based on the similarity of species composition was used to group sites with similar species into distinct biological assemblages. Second, a random forest analysis was used to identify environmental correlates of the biological assemblages identified by the cluster analysis and to predict and assign the biological assemblage present in areas with too few biological data. Two different similarity thresholds were used to identify two levels (4A, 4B) of biophysical units; see Rubidge et al. (2016) for details. Indicator species for each assemblage (biophysical unit) were also identified.Geomorphic units:Rubidge et al. (2016) used the benthic terrain modeller (BTM) tool with broad and fine-scale benthic positioning index (BPI) parameters to define geomorphic units on the continental shelf in the Northern Shelf Bioregion and the continental slope in both the Northern Shelf Bioregion and Southern Shelf Bioregion. In 2022, geomorphic units were produced for the Strait of Georgia and Southern Shelf Bioregions following the same methods as Rubidge et al. (2016) (Proudfoot and Robb 2022). The geomorphic units produced as part of the PMECS process were merged with the geomorphic units produced for the Strait of Georgia and Southern Shelf bioregions to produce a continuous spatial data product representing geomorphic units for the Canadian Pacific continental shelf and slope. After merging, the geomorphic units produced in 2016 were unchanged (i.e., they are consistent with the original geomorphic units described in Rubidge et al. 2016).Data Sources:From Rubidge et al. (2016): Species data was taken from Fisheries and Oceans Canada (DFO) standardized fisheries-independent research surveys: groundfish trawl and long-line (2003-2013), Tanner Crab trawl and trap (2000–2006), and Dungeness Crab trap (2000–2014). Environmental data came from NASA, the Canadian Hydrographic Service, Fisheries and Oceans Canada, Bio-ORACLE, and elsewhere (details in Rubidge et al. 2016). From Proudfoot and Robb (2022): bathymetry data came from Natural Resources Canada (details in Proudfoot and Robb 2022).Uncertainties:The data is intended for use at the bioregional scale, and caution should be used for finer-scale analyses.

In Canada, DFO assessments have reported a high probability of significant climate change impacts in all marine and freshwater basins, with effects increasing over time (DFO 2012a, 2012b), while climate projections indicate that ecosystems and fisheries will be disrupted into the foreseeable future (Lotze et al. 2019b; Bryndum-Buchholz et al. 2020; Tittensor et al. 2021; Boyce et al. 2022c). Despite its imminence, climate change is infrequently factored into Canada’s primary marine conservation strategies, such as spatial planning (O’Regan et al. 2021) or fisheries management (Boyce et al. 2021a; Pepin et al. 2022). The Climate Risk Index for Biodiversity was developed to assess climate risk for marine species in a quantitative, spatially explicit, and scalable way to better support climate-informed decision-making. It has been used to evaluate climate risks for marine life globally (Boyce et al. 2022a), regionally (Lewis et al. 2023), and for fisheries (Boyce et al. 2022c). These data present results from application of the CRIB framework to estimate average climate risks associated with sea surface warming across 2,959 species throughout the Canadian marine territory under contrasting future emission scenarios. In the Technical Report accompanying this data publication, we use Atlantic cod (Gadus morhua) as an example to describe the approach’s data, methods, and outputs, and to transparently and tangibly show how it quantifies risk and can inform and support climate-informed decision-making in Canada. Cite this data as: Boyce, D., Greenan, B., Shackell, N. Data of:A climate risk index for marine life across the Canadian exclusive economic zone.Published: January 2024. Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S.https://open.canada.ca/data/en/dataset/2a0b3298-2bcc-49a0-a745-af56ed0462f1