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.

To ensure informed decision-making, management, and conservation of marine ecosystems along the British Columbia (BC) coastline, a comprehensive understanding of marine communities and habitats is necessary. Marine planning initiatives, including environmental incident response planning and the development of a marine protected area network, have long been challenged by significant gaps in our knowledge of the distribution of many nearshore marine species. This lack of critical information can impede our ability to effectively manage and protect vulnerable species and ecosystems.   We developed a set of species distribution models (single-SDMs) using data collected from the benthic habitat mapping survey. These models correlate species occurrence data with environmental variables. We used these models to make predictions of the probability of occurrence of each species across the entire BC coast. A cluster analysis of the single-SDM predictions was then used to determine biotopes, which are distinct combinations of physical and chemical water properties and associated biological communities.   We defined and mapped the spatial distribution and probability of nine nearshore macrofauna epibenthic biotopes along with three physiotopes based on environmental conditions. The definition of the biotopes includes correlated species, the type of substrate, and range of environmental conditions (slope, depth, current, temperature, salinity, and tidal current) occurring within each biotope. The biotopes defined here are a first, but important, step in quantitative bioregionalization for the Pacific region as the resolution of nearshore marine community information has been lacking in BC and has hindered marine spatial initiatives.  This dataset contains 71 of the 72 single-SDMs (which excludes Northern abalone, an endangered species), the spatial distribution of 9 biotopes and 3 physiotopes, and the species richness calculated as the sum of occurrence probabilities across all 72 species.

The data layer (.shp) presented is the result of an unsupervised classification method for classifying seafloor habitat on German Bank (off South West Nova Scotia, Canada). This method involves separating environmental variables derived from multibeam bathymetry (Slope, Curvature) and backscatter (principal components: Q1, Q2, and Q3) into spatial units (i.e. pixels) and classifying the acoustically separated units into 5 habitat classes (Reef, Glacial Till, Silt, Silt with Bedforms, and Sand with Bedforms) using in situ data (imagery). Benthoscape classes (synonymous to landscape classifications in terrestrial ecology) describe the geomorphology and biology of the seafloor and are derived from elements of the seafloor that were acoustically distinguishable. Unsupervised classifications (acoustic classifications) optimized at 15 classes using Idrisi CLUSTER method (pixel based) Number representing the benthoscape classes (CLASS) derived from in situ imagery and video (See Brown et al., 2012, Figure 3, Table 1). Benthoscape classes (See Brown et al., 2012, Figure 3). Reference: Brown, C. J., Sameoto, J. A., & Smith, S. J. (2012). Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research, 72, 1–13. https://doi.org/10.1016/j.seares.2012.04.009 Cite this data as: Brown, C. J., Sameoto, J. A., & Smith, S. J. Data of: Benthoscape Map of German Bank. Published: February 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/b7f81d4a-2cb6-4393-b35b-e536ec63e834

Fish holding areas (i.e. areas of concentration of salmon and herring, both adult and juvenile) in coastal British Columbia. Attribute information includes overall relative importance, species of salmon and other fish observed at various times throughout the year as well as information on spawning activity and larvae. 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.

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

The data layer (.tif) presented are the results of using MaxEnt to produce a single species habitat map for Sea Scallop (Placopecten magellanicus) on German Bank (off South West Nova Scotia, Canada). Presence data derived from videos and still images were compared against environmental variables derived from multibeam bathymetry (Slope, Curvature, Aspect and Bathymetric Position Index (BPI)), and backscatter data (principal components: Q1, Q2, and Q3). Results represent a probability of habitat suitability for Sea Scallop on German Bank. Probability of suitability: The probability that a given habitat is suitable for a species based on presence data and underlying environmental variables (i.e. probability of species occurrence). Reference: Brown, C. J., Sameoto, J. A., & Smith, S. J. (2012). Multiple methods, maps, and management applications: Purpose made seafloor maps in support of ocean management. Journal of Sea Research, 72, 1–13. https://doi.org/10.1016/j.seares.2012.04.009 Cite this data as: Brown, C. J., Sameoto, J. A., & Smith, S. J. Data of: Distribution of Sea Scallop on German Bank. Published: February 2021. Population Ecology Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/2bb98a09-5daf-42c4-94e8-e5de718b821d

Description Conservation 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.

Biological samples of Geoduck Clams have been collected during surveys in British Columbia as part of the broader survey objectives of determining Geoduck density, distribution and population structure. Samples of Geoducks were collected from 41 locations throughout British Columbia between 1993 and 2002. Clams were measured for total weight, shell length, shell weight and were aged. Biological parameters are presented here for individual clams sampled. See Bureau D., W. Hajas, N.W. Surry, C.M. Hand, G. Dovey and A. Campbell. 2002. Age, size structure and growth parameters of Geoducks (Panopea abrupta, Conrad 1849) from 34 locations in British Columbia sampled between 1993 and 2000. Can Tech. Rep. Fish. Aquat. Sci. 2413: 84 p. and Bureau D., W. Hajas, C.M. Hand and G. Dovey. 2003. Age, size structure and growth parameters of Geoducks (Panopea abrupta, Conrad 1849) from seven locations in British Columbia sampled in 2001 and 2002. Can. Tech. Rep. Fish. Aquat. Sci. 2494: 29 p.

Saskatchewan's woodland caribou range is divided into two conservation units, based on the ecozone boundaries of the boreal shield (SK1) and the boreal plain (SK2). The SK2 Caribou Conservation Unit is further divided into three administrative units: SK2 East, SK2 Central and SK2 West. The SK1 (Boreal Shield) Caribou Conservation Unit encompasses the rocky shield, sandy plains and many lakes of northern Saskatchewan. The SK2 (Boreal Plain) Caribou Conservation Unit encompasses the more productive mixed-wood forests and lakes of central Saskatchewan, including large areas of low-lying peatlands. While these two units represent important differences in ecological conditions (e.g., habitat types, fire regimes, landforms, etc.) and human land use and management (e.g., overall levels and types of land use, fire management, etc.), the boundary between SK1 and SK2 does not represent a population boundary, as caribou move freely between the two areas. The large size of the SK2 Caribou Conservation Unit (i.e., 109,717 km2) is not well suited for range assessment and range planning activities, given the large variation in ecological conditions, habitat types, land use, and natural disturbance regimes across the Boreal Plain of Saskatchewan. As a result, three smaller caribou administrative units within SK2 were developed: SK2 East, SK2 Central and SK2 West. SK2 West is further subdivided into two smaller management subunits. At present, the SK1 area has not been sub-divided into administrative units. Find out more about woodland caribou and what the province is doing to manage their habitat and protect their populations: https://www.saskatchewan.ca/business/environmental-protection-and-sustainability/wildlife-and-conservation/wildlife-species-at-risk/woodland-caribou-program

Distribution of dabbling duck species habitat in coastal British Columbia showing relative abundance (RA) by season 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.