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.

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.

Regional Deterministic Air Quality Analysis (RDAQA) is an objective analysis of surface pollutants that combines numerical forecasts from the Regional Air Quality Deterministic Prediction System (RAQDPS) with hourly observations from various monitoring networks in North America, including the Canadian measurement networks operated by the provinces, territories and certain cities, as well as the various American networks in the context of the AIRNow program administered by US/EPA (US Environmental Protection Agency). RDAQA analysis provides the best description of current air quality conditions, and is used to inform the public, meteorologists in the various Environment and Climate Change Canada forecasting offices, Health Canada and other users about the distribution of air pollutants near the ground, and the performance of forecasting models. Each hour, a preliminary product is available approximately one hour after the observation measurement time, while final and Firework products are available approximately two hours after the measurement time. The preliminary and final products contain analysis of the chemical constituents O3, SO2, NO, NO2, PM2.5 (fine particles with diameters of 2.5 micrometers or less) and PM10 (coarse particles with diameters of 10 micrometers or less), while the Firework product contains analysis of PM2.5 and PM10.

The Okanagan Lake kokanee shore spawner data set is comprised of multiple combined data sets. The historical data sets for the years 1974, 77, 78, 79 and 80 and more recent data sets collected from 2001 to 2016, and 2018. The historical data was derived from information collected in the field and hand drawn onto air photographs. Ministry staff circled Okanagan Lake in a boat one time each year and recorded fish numbers and spawner locations onto air photographs that were digitized in 2006 to make up the historical data set. This data set may not capture the peak reach count for these years. The data collected from 2001 to 2018 was derived from boat counts undertaken along the shoreline of Okanagan, Wood and Kalamalka Lakes. A GPS was used to record shore spawner locations and numbers. Multiple counts were undertaken over the entire spawning cycle and covered the peak spawning period for each year of data provided. The data collected for Christina Lake began in 2003 and ended in 2006. Christina Lake kokanee spawn at night in late December and early January. Kokanee spawning redd locations are available for the 2003/2004 count. Kokanee enumerations were undertaken at night for the 2004/2005 and 2005/2006 seasons and spawning redds were counted at the end of spawning cycle. For these two years there is both spawning and redd count data available.

These datasets show commercial fisheries catch weight landings of directed fisheries and bycatch from the Scotian Shelf, the Bay of Fundy, and Georges Bank from NAFO Divisions 4VWX and the Canadian portions of 5Y and 5Z. Five-year composite maps (2019–2023) that aggregate catches for each map series are publicly available. The maps aggregate catch weight (kg) per 10 km2 hexagon grid cell for selected species, species groupings and gear types to identify important fishing areas. These maps can support decision making in coastal and oceans management, including marine spatial planning, environmental emergency response operations and protocols, Marine Stewardship Council certification processes, marine protected area networks, and ecological risk assessment.Reported catch locations may have rounded coordinates or contain errors. Although some errors have been corrected, it is assumed that additional errors remain in the data. These datasets have been filtered to comply with the Government of Canada's privacy policy. Privacy assessments were conducted to identify DFO unit areas containing data with less than five vessel IDs, licence IDs or fisher IDs. If this threshold was not met, catch weight locations were withheld from these unit areas to protect the identity or activity of individual vessels or companies.Maps were created for the following species, species groupings and gear types:1. Groundfish (all species)2. Groundfish Bottom Trawl3. Groundfish Bottom Longline4. Groundfish Gillnet5. Groundfish (seasonal composites)6. Atlantic Cod7. Atlantic Cod, Haddock and Pollock8. Flatfish9. Atlantic Halibut10. Greenland Halibut (Turbot)11. Hagfish12. Cusk13. Dogfish14. Redfish15. Red Hake16. Silver Hake17. White Hake18. Monkfish19. Sculpin20. Skate21. Wolffish22. Squid23. Herring24. Mackerel25. Herring and Mackerel Gillnet26. Purse Seine27. Large Pelagics28. Bluefin Tuna29. Other Tuna30. Swordfish31. Snow Crab32. Other Crab33. Scallop34. Scallop (seasonal composites)35. Offshore Clam36. Shrimp Trawl37. Shrimp Trap38. Offshore Lobster39. Disputed Zone Area 38B Lobster40. Whelk

The Ontario Hydro Network (OHN) is a provincial medium scale originating from data with regional scales of 1: 10,000 in Southern Ontario, 1: 20,000 in Northern Ontario and 1: 50,000 in the Far North. The shoreline is taken from the OHN - Waterbody data class. This data is used for cartographic purposes and web mapping services. This product requires the use of geographic information system (GIS) software. [Ontario Hydro Network (OHN) User Guide (Word)](https://www.sdc.gov.on.ca/sites/MNRF-PublicDocs/EN/CMID/OHN%20-%20UserGuide.docx)

This dataset contains two geotiff layers. The first layer (1) represents the coastwide distribution of Dungeness crab as predicted from a geostatistical model. The model predicts the mean coastwide probability of Dungeness crab detection using trap sampling gear. The second layer (2) represent the uncertainty in those predictions. Detailed descriptions of these data products can be found in Nephin et al. (2023) and the code used to produce them can be found at https://gitlab.com/dfo-msea/dungeness-sdm/.The objectives of this work was to model the habitat of Dungeness crab (_Metacarcinus magister_), a data-limited coastal marine species, to evaluate the efficacy of data integration when making predictions to geographic areas larger than the area covered by any one data source. In British Columbia, Dungeness crab are sampled regionally and sporadically with a variety of sampling gears and survey protocols, making them an ideal case study to investigate whether the integration of disparate surveys can improve habitat predictions. To that aim, we assemble data from dive, trawl, and baited-trap surveys to generate six candidate generalized linear mixed-effect models with spatial random fields. This dataset contains the mean (1) and difference (2) between the Survey-effect and Gear-effect model predictions.

The lobster recruitment project is run by the Fishermen and Scientist Research Society (FSRS) through DFO funding. Fishermen participating in the lobster recruitment project collect information about lobster in their fishing area by fishing 2-5 scientific project traps (SPTs) (fished in fixed locations) within the regular commercial season. The SPTs used in all fishing areas are smaller than commercial traps and designed to primarily catch juvenile lobsters below the legal-size limit.These traps are additional to the vessel's legal number of commercial traps. The lobster recruitment project has more than 120 fishers participating from all LFAs along the Atlantic coast of Nova Scotia from LFA 27 in Cape Breton to LFA 35 in the Bay of Fundy (excluding LFA 28, who have not participated to-date).The number of fishermen per LFA and number of SPTs per fisherman are decided on by the LFA Advisory Committees. This decision considers how much additional effort they were comfortable having in the LFA (i.e. number of SPTs) and from how many traps each fisher could be reasonably expected to collect data. It is also important to have fishers dispersed enough to maximize study footprint. DFO Science consults on project design. SAMPLING METHODS: The fishers record the number, sex and length of lobsters captured in each SPT, as well as presence of eggs, tags or v-notch. Bottom temperatures are monitored by placing a temperature recorder in one of their SPTs for the entire lobster season.Cite this data as: Tibbets-Scott, S., Zisserson, B. Data of: Fishermen and Scientist Research Society (FSRS) Lobster Recruitment Trap Project. Published: November 2020. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/a88f9b4d-b59a-44f6-ae7e-d36550266940

This table contains data on whole and partial condemnation and slaughtering from 2015 to 2022.This table contains data on whole and partial condemnation and slaughtering from 2015 to 2022. The data is also classified by its Slaughter Class: Cattle, Swine, Chiken,  Spent layer hens, Ducks, Geese, Rabbits, Spent Breeder hens, 5 kg and under, Bison, Elk, Goats, Horses, Lambs, Llama/Alpaca, Mature turkey, over 11 kg, over 5 but no more than 7 kg, over 7 but no more than 9 kg, over 9 but no more than 11 kg, Sheep, and Wild boars.Field Names (Field Alias): Field description.SlaughterFigureID (SlaughterFigureID): unique indexed number assigned to each record in the database. BodyPart (BodyPart): code for the different body parts affected in partial condemnations. CondemnationReasonCode (CondemnationReasonCode): code for all the different reasons for condemnation. CondemnationType (CondemnationType): This identifies whether the condemnations are either Whole or Partial. SlaughterYear (SlaughterYear): Year when the slaughter occurred. NumberSlaughtered (NumberSlaughtered): Total number of animals slaughtered during the indicated period of time. NumberCondemned (NumberCondemned): Total number of animals condemned (whole) or total  number of parts of animals condemned (partial) during the indicated period of time. SlaughterClass (SlaughterClass): Species or class of the animal or part of the animal condemned. Quarter (Quarter): Number of the quarter.                   - January to March – 1                     - April to June – 2                     - July to September – 3                     - October to December - 4 QuarterYear (Quarter/Year): Corresponding quarter and year.

A magnitude 5 earthquake scenario along an unnamed fault located about 15 km north-northeast of Burnaby City Hall and directly south of Mt Elsay. This fault is not known to be active, but this scenario represents a small but damaging event in the North Shore Mountains.