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    This publication contains thirteen (13) maps of different biogeochemical and soil properties of forest ecosystems of Canada’s managed forest. A scientific article gives additional details on the methodology: Paré, D., Manka, F., Barrette, J., Augustin, F., Beguin, J. 2021. Indicators of site sensitivity to the removal of forest harvest residues at the sub-continental scale: mapping, comparisons, and challenges. Ecol. Indicators. https://dx.doi.org/10.1016/j.ecolind.2021.107516

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    The AAFC Land Use Time Series is a culmination and curated meta-analysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

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    This deep water substrate bottom type model was created to aid in habitat modeling, and to complement the nearshore bottom patches. It was created from a combination of bathymetrically-derived layers in addition to bottom type observations. Using random forest classification, the relationship between observed substrates and bathymetric derivatives was estimated across the entire area of interest. The raster is categorized into: 1) Rock, 2) Mixed, 3) Sand, 4) Mud

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    The 2005 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

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    Greenland Sharks (Somniosus microcephalus) are estimated to have the highest longevity of any invertebrate (392 ± 120 years), making bycatch a significant concern (Nielsen et al. 2016). However, in the Newfoundland and Labrador (NL) region, accurate estimates of bycatch are not often available for the species (Simpson et al. 2021). To address this, species distribution models (SDMs) were generated to delineate habitat suitability for the species throughout the NL region in order to identify areas where a higher rate of bycatch is expected to occur. Observations of Greenland shark bycatch recorded by At-Sea Observers (ASOs) in NL (1983 – 2019), Spain (1999 – 2017), and by the Northwest Atlantic Fisheries Organization (NAFO) Secretariat (2014 – 2019) were compiled to generate a presence-only dataset. Multiple environmental variables were assessed for collinearity, and non-collinear variables (Bathymetry and mean monthly bottom temperature for March and November (1990 – 2015)) were retained for use in the SDM. MaxEnt (maximum entropy) software was used to model habitat suitability because it is a presence-only modelling program that is able to account for a lack of absence data by comparing the environmental conditions at occurrence locations to those at randomly selected background points. Overall, the results indicated that habitat suitability for Greenland Shark was highest in deeper waters along the shelf edge in NAFO Divisions 3OP, and the Laurentian Channel, but also extended along the edge of the Labrador shelf, the Grand Banks, and deeper areas along the continental shelf such as the Hawke Channel, Funk Island Deep, and the slopes of Saglek, Nain, and Hamilton Banks. Beyond the Economic Exclusive Zone (EEZ) and within the NAFO regulatory area (NRA), suitable habitat was also present within the Flemish Pass, and along the slope of the Flemish Cap and shelf edge in NAFO Divisions 3NO (Simpson et al. 2021). More detailed information can be found in Simpson et al. 2021. References: Nielsen, J., R. B. Hedeholm, J. Heinemeier, P. G. Bushnell, J. S. Christiansen, J. Olsen, C. B. Ramsey, R. W. Brill, M. Simon, K. F. Steffensen, J. F. Steffensen. 2016. Eye lens radiocarbon reveals centuries of longevity in the Greenland shark (Somniosus microcephalus). Science 353 (6300):702-704 Simpson, M. R., Gullage, L., Konecny, C., Ollerhead, N., Treble, M.A., Nogueira, A., González-Costas, F. 2021. Spatial-temporal variation in Greenland shark (Somniosus microcephalus) bycatch in the NAFO Regulatory Area. NAFO SCR Doc. 21/028

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    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.

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    The 2015 AAFC Land Use is a culmination and curated metaanalysis of several high-quality spatial datasets produced between 1990 and 2021 using a variety of methods by teams of researchers as techniques and capabilities have evolved. The information from the input datasets was consolidated and embedded within each 30m x 30m pixel to create consolidated pixel histories, resulting in thousands of unique combinations of evidence ready for careful consideration. Informed by many sources of high-quality evidence and visual observation of imagery in Google Earth, we apply an incremental strategy to develop a coherent best current understanding of what has happened in each pixel through the time series.

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    The mapping depicts the relative abundance of wedge ice in upper permafrost at a national scale. The mapping is based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019). The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

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    The mapping depicts a first-order estimate of the combined volumetric percentage of excess ice in the top 5 m of permafrost from segregated, wedge, and relict ice. The estimates for the three ice types are based on modelling by O'Neill et al. (2019) (https://doi.org/10.5194/tc-13-753-2019), and informed by available published values of ground ice content and expert knowledge. The mapping offers an improved depiction of ground ice in Canada at a broad scale, incorporating current knowledge on the associations between geological and environmental conditions and ground ice type and abundance. It provides a foundation for hypothesis testing related to broad-scale controls on ground ice formation, preservation, and melt.

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    A relative exposure index (REI), unweighted fetch, effective fetch, and other fetch-based indices (i.e., sum, minimum) were calculated for the Newfoundland and Labrador (NL) Shelves bioregion. Due to the extensive coastline of the study region, this analysis was conducted for a 5km buffered region along the coast at a spatial resolution of 250m. Detailed methods on the selection of input points for the NL bioregion are included below. Methods Preprocessing and input point selection: Land boundary files were obtained for Eastern Canada and the Canadian Arctic (NrCan 2017) at a scale of 1:50,000 as well as for Saint Pierre and Miquelon (Hijmans 2015), and the New England states (GADM 2012) however the scale at which these layers were produced is unknown. Land boundary files were merged into a single land polygon layer and watercourses reaching for in-land and/or above sea level were clipped from this polygon layer (Greyson 2021). A 5km buffer was generated around the NL provincial boundary. This buffer was then clipped by all land polygons to remove areas overlapping land polygons within the study area. All buffer segments intersecting the NAFO divisions within the NL bioregion were selected and the Union tool in ArcGIS Pro (v. 2.7.2) was used to fill-in gaps within the buffered area, creating a more continuous polygon. The buffered layer was then dissolved, and the NL provincial boundary polygon was erased from the buffered layer to create the study area polygon. A 250m fishnet was created and clipped to the study area (5km buffer layer) and the feature to point tool was used (with the “inside parameter checked”) to convert this grid into a point layer (approx. 1,000,000 points). The spatial resolution for all subsequent analyses was matched to the fishnet grid at 250m. References GADM database of Global Administrative Areas (2012). Global Administrative Areas, version 2.0. (accessed 2 December 2020). www.gadm.org Greyson, P (2021) Land boundary file for Eastern Canada, the Canadian Arctic, the New England States and Saint Pierre and Miquelon. [shapefile]. Unpublished data. Hijmans, R. and University of California, Berkeley, Museum of Vertebrate Zoology. (2015). First-level Administrative Divisions, Saint Pierre and Miquelon, 2015. UC Berkeley, Museum of Vertebrate Zoology. Available at: http://purl.stanford.edu/bz573nv9230 Natural Resources Canada (2017) Administrative Boundaries in Canada - CanVec Series - Administrative Features - Open Government Portal. (accessed 2 December 2020). https://open.canada.ca/data/en/dataset/306e5004-534b-4110-9feb-58e3a5c3fd97.