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.

These datasets provide information pertaining to sediment grain size, porosity, organic content, total carbon and nitrogen concentrations, trace element concentrations, chlorophyll and phaeopigment concentrations, and meiofauna and macrofaunal abundance in Simoom Sound between November, 2000, and February, 2001. Data formatting of files were performed by Meagan Mak. Sutherland et al (2023) covers the benthic component of a broader project investigating potential modification of marine ecosystems by shrimp trawling and trapping on the central coast of British Columbia. Sediment and infaunal samples were collected before and after fishing with commercial fishing gear consisting of otter-trawl, beam-trawl, and trap-lines. Simoom Sound was sampled in November 2000 and February 2001. Tabulated data of sediment characteristics that include sediment grain size, porosity, carbon and nitrogen content, trace-element, and chlorophyll concentrations are presented in this report. In addition, the infaunal data are comprised of both macrofaunal and meiofaunal communities.

The Broad Ecosystem Inventory (BEI) Classification provides broad regional information about the distribution of ecosystems throughout the province and the value of these ecosystems to wildlife. This is done in order to facilitate the use of wildlife information in broad provincial and regional land and resource planning initiatives. Broad Ecosystem Units are mapped based on imagery of the provincial land base generally captured at a scale of 1:250,000. BEI mapping represents forest conditions from approximately 1995 to 2000. The BEI Classification was used to produce wildlife habitat capability and suitability mapping following Provincial Wildlife Habitat Ratings (WHR) Standards (Resource Inventory Committee 1999). Habitat classifications were based on BEI units. BEI units were evaluated and rated to determine the habitat potential (capability) and current habitat conditions (suitability) for selected dry forests and grassland birds within each seral stage within each Broad Ecosystem Unit (and related site modifier variation) within the framework of Ecosections and Biogeoclimatic Sub-zone/Variants for their ability to supply the species’ necessary life requisites. The data represented by the habitat capability and suitability maps for this project was obtained from a team consisting of a bird species expert, a habitat expert, and a data manager. A regional expert review was conducted in September 2002 involving Species at Risk Biologists, Habitat Biologists, and Wildlife Biologists from the Cariboo, Thompson, Okanagan Regions.

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.

HRL, 6 háupplausnargagnalög: yfirborðsgegndræpi, skógar (trjákrónuþéttleiki), skógar (barrtré/lauftré), graslendi, votlendi, vötn. Rastagögn, 20 m myndpunktsstærð, upprunaleg og endurbætt gagnalög og skýrslur, ISN2004. Hægt er að sækja gögnin á niðurhalssíðu Landmælinga Íslands. Nánari upplýsingar um hvert lag fylgja gögnunum. HRL, 6 High Resolution Layers: imperviousness, tree cover density, forest type, grasslands, wetlands, permanent water bodies. Raster data, 20 m pixel size, intermediate and enhanced results, data and verification/enhancement reports, ISN2004. The datasets can be downloaded from the National Land Survey of Iceland Download Site where more details information about each layer are included.

Seed Planning Unit (SPU) geometry is comprised of Seed Planning Zone geometry delineated by elevation ranges. Each SEED_PLAN_UNIT_ID relates to a SEED_PLAN_ZONE for a specific tree species and minimum/maximum elevation in meters. Seed Planning Units only apply to Genetic Class A (orchard/parent tree). Some coastal species also have a latitude range. The geometry can be joined to the SEED_PLAN_UNIT table using SEEED_PLAN_UNIT_ID to determine the Seed Planning Zone, tree species and elevation range.

Distribution of Bald Eagle habitat in coastal British Columbia showing relative abundance (RA) and overall relative importance (RI). RI is based on project region and not on the province as a whole. CRIMS is a legacy dataset of BC coastal resource data that was acquired in a systematic and synoptic manner from 1979 and was intermittently updated throughout the years. Resource information was collected in nine study areas using a peer-reviewed provincial Resource Information Standards Committee consisting of DFO Fishery Officers, First Nations, and other subject matter experts. There are currently no plans to update this legacy data.

Biodiversity Corridors in the Kamloops Forest District showing dispersal routes of wildlife. This data is a legacy dataset, circa 1998, from the Ministry of Forests Forest Development Plan (FDP) datasets. Additional details are unknown. For historical reference.

The atlas provides printable maps, Web Services and downloadable data files representing seabirds at-sea densities in eastern Canada. The information provided on the open data web site can be used to identify areas where seabirds at sea are found in eastern Canada. However, low survey effort or high variation in some areas introduces uncertainty in the density estimates provided. The data and maps found on the open data web site should therefore be interpreted with an understanding of this uncertainty. Data were collected using ships of opportunity surveys and therefore spatial and seasonal coverage varies considerably. Densities are computed using distance sampling to adjust for variation in detection rates among observers and survey conditions. Depending on conditions, seabirds can be difficult to identify to species level. Therefore, densities at higher taxonomic levels are provided. more details in the document: Atlas_SeabirdsAtSea-OiseauxMarinsEnMer.pdf. By clicking on "View on Map" you will visualize a example of the density measured for all species combined from April to July - 2006-2020. ESRI REST or WMS map services can be added to your web maps or opened directly in your desktop mapping applications. These are alternatives to downloading and provide densities for all taxonomical groups and species as well as survey effort.

A list of the various 'regional' (zone/subzone/variant/phase) ecological units of the current biogeoclimatic ecosystem classification. It is consistent with the biogeoclimatic subzone/variant map (BEC_BIOGEOCLIMATIC_POLY, version 13.1, July 8, 2026). At this 'regional' level, vegetation, soils and topography are used to infer the climate and to identify geographic areas that have relatively uniform climate. These geographic areas are termed biogeoclimatic units. The basic biogeoclimatic unit is the Subzone. These units are grouped into Zones and may be further subdivided into variants based on further refinements of climate (e.g., wetter, drier, snowier). The map units of the Biogeoclimatic map are mapped to the highest possible thematic resolution - subzone or variant. In some cases, where further sampling is required to define the unit climatically, polygons are labelled as an undifferentiated unit (e.g. CWH un)