Náttúrulegt birkilendi á Íslandi er kortlagning yfir alla náttúrulega birkiskóga og birkikjarr á Íslandi. Helstu upplýsingar eru hæð, þekja og aldur. Skilið er á milli núverandi hæðar og aldur fullvaxta birkis. Það er gert samkvæmt alþjóðlegum skilgreiningum um hæð trjágróðurs þar sem miðað er við hæð fullvaxta skógar. Birki var fyrst kortlagt á árunum 1972-1975 og var unnin leiðrétting á gögnunum og gerðar frekari greiningar á árunum 1987-1991. Gögnin voru því komin nokkuð til ára sinna þegar ákveðið var að hefja endurkortlagningu á öllu náttúrulegu birki á Íslandi. Fór sú vinna fram á árunum 2010-2014 og er núverandi þekja því afrakstur þeirrar vinnu. Flatarmál náttúrulegs birkis á Íslandi er 150.600 ha. Frá árinu 1987 hefur flatarmál birkis með sjálfsáningu aukist um 9% og nemur 13.000 ha. Gögnin voru upphaflega hugsuð fyrir mælikvarða 1:15.000, hins vegar var talsvert stór hluti landsins kortlagður í mælikvarða 1:5000 – 1:10.000.

Atlantic salmon postsmolts were surveyed via surface trawling during 2001 and 2003. These data were provided to the Coastal Oceanography and Ecosystem Research section of Fisheries and Oceans Canada. These data, and information from subsequent tagging studies were considered to estimate the likelihood of presence of Atlantic salmon within the Area Response Plan regions. Atlantic salmon presence varies seasonally and this spatial information should be used in conjunction with the temporal information in the attribute table. A version of this dataset was created for the National Environmental Emergency Center (NEEC) following their data model and is available for download in the Resources section. Cite this data as: Lazin, G., Hamer, A.,Corrigan, S., Bower, B., and Harvey, C. Data of: Likelihood of presence of Atlantic Salmon in Area Response Planning pilot areas. Published: June 2018. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/436cdf90-9d6b-4784-938b-feec48844a67

Likelihood of Presence of Harbour Porpoise in the Bay of Fundy and Port Hawkesbury Area Response Plan. The Coastal Oceanography and Ecosystem Research section (DFO Science) reviewed reported opportunistic whale sightings and local knowledge sources to estimate areas where Harbour Porpoises are seasonally present and delineate these areas. A version of this dataset was created for the National Environmental Emergency Center (NEEC) following their data model and is available for download in the Resources section. Cite this data as: Lazin, G., Hamer, A.,Corrigan, S., Bower, B., and Harvey, C. Data of: Likelihood of Presence of Harbour Porpoise in Area Response Planning Pilot Areas. Published: June 2018. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/58ea48ab-f052-48ab-9c18-4353e51b8bea

The National Ecological Framework for Canada's "Surficial Geology by Ecodistrict” dataset contains tables that provide surficial geology information with the ecodistrict framework polygons. It provides codes that characterize surficial geology (unconsolidated geologic materials) and their English and French-language descriptions as well as information about the area and percentage of the polygon that the material occupies.

Bluefin tuna landings are reported to the Department of Fisheries and Oceans and stored in the Maritime Fishery Information System Database. This database was queried in January 2016 for all reported landings of Bluefin tuna in coastal Nova Scotia. Longline data was excluded due to location uncertainties associated with the gear. Bluefin tuna sightings are also reported opportunistically to the DFO Whale Sightings Database. The Coastal Oceanography and Ecosystem Research section considered these landings and sightings to estimate the presence of Bluefin tuna within the Area Response Plan areas. Bluefin tuna presence varies seasonally and this spatial information should be used in conjunction with temporal information. A version of this dataset was created for the National Environmental Emergency Center (NEEC) following their data model and is available for download in the Resources section. Cite this data as: Lazin, G., Hamer, A.,Corrigan, S., Bower, B., and Harvey, C. Data of: Likelihood of presence of Bluefin Tuna in Area Response Planning pilot areas. Published: June 2018. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, St. Andrews, N.B. https://open.canada.ca/data/en/dataset/0c3b25df-f831-43e8-a8ac-336e1467c4fe

The National Ecological Framework for Canada's "Surficial Geology by Ecoprovince” dataset contains tables that provide surficial geology information with the ecoprovince framework polygons. It provides codes that characterize surficial geology (unconsolidated geologic materials) and their English and French-language descriptions as well as information about the area and percentage of the polygon that the material occupies.

The B.C. Conservation Data Centre’s spatial view of publicly available, known locations of species and ecological communities at risk. This spatial view is split into the "Publicly Available Occurrences" layer and "(Extirpated and Historical) Publicly Available Occurrences" layer. The Extirpated and Historical layer includes element occurrences that have a last observation date greater than 40 years ago and element occurrences that are extirpated due to general habitat loss or degradation of the environment in the area. Use the field ‘Rank Description’ to differentiate between Historical and Extirpated element occurrences in this layer. All element occurrences are polygons: the size of the polygon usually reflects the locational uncertainty associated with the source data, represented with varying sized circles. Some polygons may be larger to reflect the actual area covered by the element occurrence. The field "Representational Accuracy" is used to communicate how accurately the polygon reflects the actual area covered by the element. If you do not find an element occurrence in your area of interest, this means there are none currently mapped in the CDC database. The best way to verify whether an area contains a species or ecosystem at risk is to have do a detailed assessment of the property during the appropriate season.

Biogeoclimatic Ecosystem Classification (BEC) subzone\variant\phase boundaries with percent protected, number of overlapping protected areas and other attributes added as a result of geoprocessing in the Protected Area System Overview (PASO) application. Protected area and park representation by BEC unit provides a small scale ecosystem classification context for natural resource planning processes such as; management plans, land use zoning, environmental risk assessment, landscape analysis, habitat supply, and management of high priority species. Biogeoclimatic subzones are the basic unit of the BEC system. Subzones are grouped into biogeoclimatic zones to create more generalized units, and subdivided into biogeoclimatic variants and phases to create more specific or climatically homogeneous units. For more information on the BEC system see: http://www.for.gov.bc.ca/hre/becweb/. For important warnings about using this data for spatial analysis see the Data Quality section of the metadata

Description: Spatial information on ecologically important species is needed to support marine spatial planning initiatives in British Columbia’s (BC) marine environment. For data deficient taxa, such as shark species, species distribution models that integrate presence-absence data from different sources can be used to predict their coastwide distributions. Here we provide spatial estimates of the distribution of Blue Shark (Prionace glauca), Salmon Shark (Lamna ditropis), Pacific Sleeper Shark (Somniosus pacificus) and Bluntnose Sixgill Shark (Hexanchus griseus). These estimates were generated using spatial generalized linear mixed effects models and are based on data from two scientific surveys and the commercial hook and line, midwater trawl and bottom trawl fisheries. For each species, we provide predicted probability of occurrence and prediction uncertainty at a 3 km resolution for the British Columbia coast, and parameter estimates for model covariates (depth, slope, year, data source). Results show variable predicted distributions across species, with Blue Shark and Pacific Sleeper Shark showing higher probability of presence along the continental slope, while Salmon Shark show low probability of occurrence coastwide and Bluntnose Sixgill Shark show the highest probability of occurrence in the Strait of Georgia. The results from this study can support ongoing marine spatial planning initiatives in the BC and support the conservation and management of these important species. Methods: Data Sources The species distribution models (SDMs) are based on data from two fishery independent scientific surveys and from the commercial hook and line fishery, which are all conducted within Canadian Pacific waters. The scientific surveys include the Fisheries and Oceans Canada (DFO) hard bottom longline surveys and the International Pacific Halibut Commission (IPHC) fishery-independent setline survey. The study area is bound by the outer convex hull of these three data sources. Other DFO research surveys, such as the groundfish synoptic bottom trawl surveys, midwater trawl surveys and sablefish trap surveys were investigated as potential data sources, but were found to have insufficient presence observations for the species of interest to warrant their inclusion in the analysis. For more information on the details of the source data please refer to Proudfoot et al. 2024. Modelling Approach and Comparison For each species, we fit a suite of generalized linear mixed effects models (GLMMs) using the sdmTMB package (Anderson et al. 2022). For each species, we fit four models, each with a different set of fixed effects/environmental predictors. Additionally, we compared the predictive power of four models for each species, with each model having a different combination of environmental predictors (i.e., slope, depth, slope + depth, none). A summary of the candidate models is provided in Table 2 of Proudfoot et al. 2024. For each species, we selected the model with the highest predictive accuracy (assessed using the predicted log likelihood based on the cross-validation) as the best fit. Spatial Species Distribution Predictions We made predictions of species occurrence using the selected model and a 3 km resolution spatial prediction grid. Our predictions were made for the entire BC coast, and species distribution predictions were made using models fit to the full dataset, as opposed to models fit using cross-validation. We made predictions with year set to 2014 (the approximate midpoint of the dataset) and type set to IPHC (the dataset with the most even spatial distribution of data points). Uncertainties: Because limited survey and commercial catch data exists for deep areas off the continental shelf, predictions in these areas are likely more uncertain than predictions on the shelf. To illustrate this, uncertainty (standard deviation derived from the 500 simulated values from the joint precision matrix of selected models) was mapped across the full study area for each species. Additionally, because these models are based on data that likely do not span the full spatiotemporal extent of the species’ habitat (i.e., mid depths, surface waters, and data across all seasons may not be captured), these results illustrate a snapshot of occurrence but do not account for more complex migration and movement patterns undertaken by these species.

Fisheries and Oceans Canada (DFO) conducts an annual summer multidisciplinary scientific survey with a bottom trawl in the Estuary and the northern Gulf of St. Lawrence since 1984. Over the years this survey has been conducted on four vessels: the MV Lady Hammond (1984-1990), the CCGS Alfred Needler (1990-2005), the CCGS Teleost (2004-2022) and the CCGS Cabot (2022-present). It is important to note that the objectives, the methods used and the identification of the species during these surveys have improved over time in response to DFO requests and mandates. The data are therefore not directly comparable between these surveys. However, comparative analyses have been carried out between vessels, and conversion factors are available on request for a number of species. The specificities of the missions onboard the CCGS Teleost are described below. Objectives: 1. Assess groundfish and northern shrimp population abundance and condition 2. Assess environmental conditions 3. Conduct a biodiversity inventory of benthic and demersal megafauna 4. Assess phytoplankton and mesozooplankton abundance 5. Monitor the pelagic ecosystem 6. Inventory marine mammals populations 7. Inventory seabirds populations 8. Collect samples for various research projects Survey description The survey covers the Estuary and the northern Gulf of St. Lawrence, that is the divisions 4R, 4S and the northern part of division 4T of the Northwest Atlantic Fisheries Organization (NAFO). Since 2008, coverage of division 4T has been increased in the upstream part of the Lower Estuary in order to sample the depths between 37 and 183 m. A stratified random sampling strategy is used for this survey and the area of the study area is 118,587 km². The fishing gear used on the CCGS Teleost is a four-sided Campelen 1800 shrimp trawl equipped with a Rockhopper footgear (“bicycle”). The trawl lengthening and codend are equipped with a 12.7-mm knotless nylon lining. Standard trawling tows last 15 minutes, starting from the time the trawl touches the sea floor. The target towing speed is 3 knots. Data For each fishing tow, the catch is sorted and weighed by taxa; individuals are then counted and biological data are collected on a subsample. For fish, crab and squid, size and weight are measured by individual and, for some species, sex, gonad maturity, and the weight of certain organs (stomach, liver, gonads) are also evaluated. The soft rays of the anal fin are counted for redfish, and the otoliths are sampled for several species such as Atlantic cod, Atlantic halibut, Greenland halibut, and witch flounder. A roughly 2-kg shrimp sample is sorted and weighed by species (and by stage of maturity for northern shrimp). The shrimps are measured individually. The other invertebrates are counted (no individual measurements) and photographed. The biological data are divided into 4 files: a “Metadata” file containing set information, a “Catches” file containing catches per set for fish taxa, a a “Carbio” file containing biological and morphometric measurements per individual and a “Shrimps” file containing information on shrimp catches. It's important to note that this is raw data. Only set considered successful are retained. In each set, all species are kept, with a few exceptions. For more information please contact the data management team (gddaiss-dmsaisb@dfo-mpo.gc.ca).