An interactive web application illustrating the locations of commercial fisheries, commercial fish species production (kg) for the 2016 calendar year.This interactive web application shows the locations of commercial fisheries and commercial fish species production (kg) in Manitoba, by community. It names the communities involved in the industry, shows the number of fishers by community and also shows the location of packing sheds across Manitoba. For each location, pop ups provides additional information, including the round weight (kg) by species for the 2016 calendar year. This application is populated by the web map: Manitoba Commercial Fishing Industry Map.

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.

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.

This dataset displays the Canadian geographic ranges of the priority species identified under the Pan-Canadian Approach for Transforming Species at Risk Conservation in Canada (“Pan-Canadian Approach”). These species include Barren-ground Caribou (including the Dolphin and Union population); Greater Sage-Grouse; Peary Caribou; Wood Bison; Caribou, Boreal population (“Boreal Caribou”); and Woodland Caribou, Southern Mountain population (“Southern Mountain Caribou”). The priority species were chosen following a number of criteria and considerations in collaboration with federal, provincial, and territorial partners. These include, but were not limited to, the species' ecological role on a regional or national scale, their conservation status and achievability of conservation outcomes, their social and cultural value (particularly to Indigenous peoples), and the leadership/partnership opportunities that they present. Delivering conservation outcomes for targeted priority species can have significant co-benefits for other species at risk, and wildlife in general. For more information on the Pan-Canadian Approach and the priority species, see https://www.canada.ca/en/services/environment/wildlife-plants-species/species-risk/pan-canadian-approach.html.This dataset includes: 1) the range for the Boreal Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/2253); 2) the local populations for the Southern Mountain Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/1309); 3) the range for the Greater Sage-Grouse (see https://species-registry.canada.ca/index-en.html#/consultations/1458); 4) local populations for the Peary Caribou (see https://species-registry.canada.ca/index-en.html#/consultations/3657); 5) range for the Barren-ground Caribou (see https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 6) range for the Barren-ground Caribou, Dolphin and Union population (https://www.maps.geomatics.gov.nt.ca/Html5Viewer/index.html?viewer=NWT_SHV English only); 7) range for the Wood Bison (see https://species-registry.canada.ca/index-en.html#/consultations/2914).

This theme presents observations of invasive exotic species (IAS)transmitted and validated using the Sentinelle tool, an EEE detection system.An invasive exotic species is a plant, animal or microorganism (virus,bacteria or fungi) that are introduced outside of their natural range. Sonestablishment or its spread may pose a threat to the environment,the economy or society. The species listed are species of fauna and floraconcerning (or potentially worrying) for Quebec's biodiversity. Ellesinclude EEE present in Quebec and EEE not listed in Quebec atmonitor.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Science Branch of Fisheries and Oceans Canada (DFO) in the Newfoundland and Labrador (NL) region has been conducting multispecies research vessel (RV) surveys using a stratified random survey design since the early 1970s. The DFO RV survey dataset represents the longest time series of species data in the NL region, making it ideal for mapping the average relative densities of species over time. Average relative density maps depict the interpolated densities (calculated from kg/tow) of fish species or functional groups. These densities are averaged over each time series (Engel and Campelen) and include data from all available seasons, so they represent persistent areas of relatively high and low densities for that species or functional group for the duration of the time series, independent of season. These maps are well suited as decision support tools related to conservation areas and marine spatial planning. These maps can also inform other processes that require information on areas important to marine fish, such as environmental assessments. Spring, fall, and winter data from the DFO RV survey between 1981 and 2017, inclusive, were used for the analysis. Due to a gear change from an Engel 145 Hi-Lift Otter Trawl to a Campelen 1800 Shrimp Trawl in 1995, the time series is treated as two separate datasets. NAFO Divisions 2J3KLNOP were sampled during the Engel time series and Division 2H was added for the Campelen time series. The data were filtered prior to use so that only core strata (areas consistently sampled across years) were included, resulting in most deep water and inshore sets being excluded in this analysis. Weight per tow (kg/tow; standardized for tow length for each gear type) data for fish, shrimp, and crab species were extracted from the database, and all successful sets from regular multispecies surveys were used for analyses. Eight fish functional groups (groups of species of similar size and diet) were identified based on the RV survey dataset: small benthivores, medium benthivores, large benthivores, piscivores, plank-piscivores, planktivores, shrimp, and forage fish. Data for each functional group were mapped three ways: all species, dominant species (i.e. top 90% biomass), and non-dominant species. In total, 40 dominant species and/or at-risk species (i.e. COSEWIC endangered, threatened, special concern; SARA; DFO/NAFO depleted) were mapped individually. To identify the average relative density, independent of seasonality, the spring, fall, and winter survey sets were compiled into a composite dataset using a log transformation on the biomass (kg/tow). For functional groups, these values were then standardized across each group. Absences (0 kg/tow catch values) were included. A continuous raster with a 4x4km resolution was generated through ordinary kriging. The raster was clipped to an 8-km buffer of the RV survey extent and the zero values were then removed. The results of this process are maps depicting the average relative density of fish functional groups and selected individual species during both the Engel (1981-1995) and Campelen (1995-2017) time series. Note that the original units (e.g. kg/tow) are no longer relevant due to data processing. Cell values are not comparable between groups or species; when mapping, all numeric values should be removed from the labels and legend, with relative qualifiers (“high” and “low”) used instead. More detailed information can be found in Wells et al. (2021). References: Wells, N.J., Pretty, C., Warren, M., Novaczek, E. and Koen-Alonso, M. 2021. Average Relative Density of Fish Species and Functional Groups in the Newfoundland and Labrador Shelves Bioregion from 1981-2017. Can. Tech. Rep. Fish. Aquat. Sci. 3427: viii + 76 p.

Marine protected areas (MPAs) are one among a number of spatial management tools, and are defined as areas that are established for the long-term, and managed through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values.Currently, Fisheries and Oceans Canada has a number of MPAs designated under the Oceans Act and Areas of Interest for new MPAs at various stages of progress towards designation. These areas are ecologically significant, with species and/or features that require special management consideration.  An Oceans Act MPA can be established for any of the six conservation purposes outlined in the Act:• The conservation and protection of commercial and non-commercial fishery resources, including marine mammals, and their habitats;  • The conservation and protection of endangered or threatened marine species, and their habitats;  • The conservation and protection of unique habitats;  • The conservation and protection of marine areas of high biodiversity or biological productivity; • The conservation and protection of any other marine resource or habitat as is necessary to fulfill the mandate of the Minister; and• The conservation and protection of marine areas for the purposes of maintaining ecological integrity

Dataset of species/gear type commercial fisheries from 2012 to 2021 in the Eastern Canada Regions. Only fish harvested from the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions are included (Species Sought).The data was obtained from Statistical Services, Fisheries and Oceans Canada (DFO) and consists of commercial species/gear type landings data from 2012 to 2021 taken from Northwest Atlantic Fisheries Organization (NAFO) Subareas 0, 2, 3, 4 and 5 and fished in the NL, Maritimes, Gulf, Quebec and Eastern Arctic regions. The layer was created by overlaying a 2 minute hexagonal grid (approx. 10km2 cell) on species/gear type commercial fisheries point data and summing the total landings by weight reported for each cell over the ten year period. Therefore, the value of each grid cell is equal to the total species/gear type landings in kg from 2012 to 2021 for the area, and may represent many fishing events from several vessels over the ten year period. All landings are from Canadian vessels greater than 35-ft, and does not include information pertaining to international fishing vessels (i.e., St. Pierre). Individuals should exercise caution when interpreting this data. Data has not been altered and is mapped from the original logbook entry for each record prior to amalgamation. Data may contain errors such as inaccurate or nonviable coordinates, landed weights and/or species identification. For example, cases of fishing events reported in a NAFO Division with corresponding coordinates falling outside that particular NAFO Division or fishing events which appear to be located on a land mass due to rounding errors in the original entries. Such cases were excluded from the dataset. Only one location is given for each fishing event; therefore, a fishing activity that would normally cover a large area (i.e., trawling) is only shown in a single location. Some species may not include all records or locations where activity is taking place due to regional differences in permissions for mapping, or because the fishery is only partially georeferenced (e.g. Lobster). The locations/areas shown should only be used as an estimation of fishing intensity and a general guide of where particular species/gear type fishing occurs. This dataset has been privacy screened to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify NAFO unit areas containing data with less than five vessel IDs, license IDs and fisher IDs. If this threshold was not met, catch weight locations have been withheld from these statistical areas to protect the identity or activity of individual vessels or companies. In some instances, permissions were obtained to map species or gears with a limited number of vessels, licenses, or fisher ids. The withheld areas are indicated by the unit area that has been removed and given a weight of -9999.

This inventory, conducted from September 26th to October 3th, 2019, aimed to describe the community structure of macroalgae and benthic macroinvertebrates of five small estuaries of the Upper North Shore of Quebec, namely Barthélemy Bay and the Colombier, Mistassini, Franquelin and Saint-Nicolas rivers. This inventory is part of a doctoral study of Valentine Loiseau on the global changes in the St. Lawrence system, mainly the study of marine benthic communities in response to changes of salinity, to ensure proper management of the environment in the face of future changes. The main objective is to describe the structure and the levels of specific diversities of mediolittoral communities of benthic macroinvertebrates and macroalgae along a salinity gradient. These five small estuaries were selected because of their similar size, hard substrates and easy access. Three levels of hypoosmotic stress (low, medium, high) and one control level (seawater) were used for each of the selected estuaries, with eight quadrats per stress level. Quadrat positions were randomly selected but had to meet two criteria: (1) regular height in the foreshore to control the influence of other stresses (temperature, exposure); and (2) presence of at least one macroalga to maintain homogeneity. A percentage cover by macroalgal and macroinvertebrate species was estimated, and then all organisms were weighed by species and size group. The salinity of the nearest water point was measured at mid-tide with a portable refractometer and a Castaway-type CTD (Conductivity-Temperature-Density) probe. The inventory was done using a stratified random sampling design and the sampling unit was a quadrat measuring 25 x 25 cm. The three files provided (DarwinCore format) are complementary and are linked by the "eventID" key. The "event_information" file includes the generic information of the quadrat, including date and location. The "additional_information_event_and_occurrence" file includes salinity and substrate type of the quadrat, as well as the total weight of all individuals of the same species caught in the quadrat extrapolated to one square metre of surface. For nudibranchs and barnacles, weight was estimated from the size of the individuals so that they were not removed from the environment. The "taxon_occurrence" file includes the taxonomic inventory of macroalgal and benthic macroinvertebrate species observed in the quadrat, identified to the lowest possible species or taxonomic level and biomass by identified species.For quality control, organisms were identified on the field using the following guide: Chabot, Robert et Anne Rossignol. 2003. Algues et faune du littoral du Saint-Laurent maritime : Guide d'identification. Institut des Sciences de la mer de Rimouski, Rimouski ; Pêches et Océans Canada (Institut Maurice-Lamontagne), Mont-Joli. 113 pages. The taxonomy was checked against the World Register of Marine Species (WoRMS) to match recognized standards and using the R obistools and worrms libraries. The WoRMS match was placed in the "scientificNameID" field of the occurrence file. All sample locations were spatially validated. This project was funded by DFO Coastal Environmental Baseline Program under Canada’s Oceans Protection Plan. This initiative aims to acquire environmental baseline data contributing to the characterization of important coastal areas and to support evidence-based assessments and management decisions for preserving marine ecosystems.

Emerald Basin on the Scotia Shelf off Nova Scotia, Canada, is home to a globally unique population of the glass sponge Vazella pourtalesi. Through the analysis of both in situ photographs and trawl catch data from annual multispecies bottom-trawl surveys, we examined community composition, species density, and abundance of epibenthos and fish associated with V. pourtalesi compared to locations without this sponge. Using generalized linear models and analysis of similarities, the importance of V. pourtalesi in enhancing species density and abundance of the associated epibenthic community was assessed against that of the hard substrate on which it settles. Our results indicated that the megafaunal assemblage associated with V. pourtalesi was significantly different in composition and higher in species density and abundance compared to locations without V. pourtalesi. Analysis of similarity of trawl catch data indicated that fish communities associated with the sponge grounds are significantly different from those without V. pourtalesi, although no species were found exclusively on the sponge grounds. Our study provides further evidence of the role played by sponge grounds in shaping community structure and biodiversity of associated deep-sea epibenthic and fish communities. The mechanism for biodiversity enhancement within the sponge grounds formed by V. pourtalesi is likely the combined effect of both the sponge itself and its attachment substrate, which together comprise the habitat of the sponge grounds. We also discuss the role of habitat provision between the mixed-species tetractinellid sponges of the Flemish Cap and the monospecific glass sponge grounds of Emerald Basin. Please refer to the following citation for additional details on the data:Hawkes N, Korabik M, Beazley L, Rapp HT, Xavier JR, Kenchington E (2019) Glass sponge grounds on the Scotian Shelf and their associated biodiversity. Mar Ecol Prog Ser 614:91-109. https://doi.org/10.3354/meps12903Cite this data as: Hawkes, Nickolas; Korabik, Michelle; Beazley, Lindsay; Rapp, Hans Tore; Xavier, Joana; Kenchington, Ellen (2019) Glass sponge grounds on the Scotian Shelf and their associated biodiversity. Published September 2023.Ocean Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/83c8e9af-ad3a-40bc-b1b7-d1ed4a069330