The documented occurrence data package contains 3 datasets that, in combination, help to provide generalized information about woodland caribou locations and survey areas in Saskatchewan. This information may assist users in their efforts to avoid or mitigate impacts to woodland caribou when operating in woodland caribou range. Generalized locations of caribou use have been provided to better reflect their large home ranges. Absence of a hexagon in an area should not be interpreted as absence of woodland caribou.Please read the Data Guide for important information about this product. Download survey boundaries, telemetry occurrence, and sightings/sign. Download the full package, including data guide here. The Woodland Caribou Documented Occurrence public data product is composed of three shapefiles/feature classes: 1. Woodland Caribou Occurrence - Sighting and Sign 2. Woodland Caribou Occurrence - Telemetry 3. Woodland Caribou Survey Boundaries The two occurrence datasets contain a grid of 18 sq km hexagons (tessellation). The inclusion of a hexagon in the dataset indicates that one or more animal sightings or sign, or telemetry points have been documented in that area. Importantly, lack of caribou occurrence (e.g. no hexagon) should not be interpreted as absence of woodland caribou. Rather, data may not have been collected in these areas or incidental or other observations have not been received.   The survey boundaries dataset displays the boundaries of woodland caribou surveys that were completed by or in collaboration with the Ministry of Environment from 2005 to 2024. Boundaries are from multiple sources, and include various types of surveys (fecal pellet collection or telemetry). These boundaries provide context when viewed alongside the woodland caribou occurrence datasets. We expect to see more occurrence locations in areas that have been surveyed. This information may provide context to areas with a seemingly higher number of occurrences.  For a full description of the data, please refer to the Data Guide document available for download on the Saskatchewan GeoHub.

The data in this dataset represent an amalgamation of sea turtle sighting, stranding, and entrapment events, mainly near Newfoundland and Labrador (NL), Canada.This document summarises the detection events data for Leatherback (Dermochelys coriacea), Loggerhead (Caretta caretta), and Green (Chelonia mydas) Turtles that has been collected from opportunistic and systematic survey sources, plus stranding and entrapment records, in the waters of NL from 1946 to 2023. To a much lesser extent there are also detection records for the southern Gulf of St. Lawrence. Scotian Shelf, and northeastern U.S. waters.These detection records are mostly derived from opportunistic reports, so there are rarely data for a report that includes measures of the observer effort expended to make the detection, and rarely associated imagery. During DFO aerial surveys there are measures of effort in most cases, enabling the turtle sightings reports to be used in habitat modelling (e.g., Mosnier et al. 2018).Most of the information variables (such as “Date”, “Latitude”, “Longitude”, “Number of Animals”) have been obtained from the detection report. In some cases data for variables such as “Location Reliability”, “ID Reliability”, “Platform”, and “Strand or Entrapment Outcome” were derived from interpretation of the comments associated with the report, if available. For description of the variables in the dataset see the Data Dictionary.References:Mosnier, A., Gosselin, J.-F., Lawson, J., Plourde, S., and Lesage, V. 2018. Predicting seasonal occurrence of leatherback turtles (Dermochelys coriacea) in eastern Canadian waters from turtle and sunfish (Mola mola) sighting data and habitat characteristics. Can. J. Zool. 97: 464-478. https://doi.org/10.1139/cjz-2018-0167

PURPOSE:The focus of this research is on changes in the distribution of killer whales in the Canadian Arctic, which is within the field of marine biogeography and marine megafauna. Our research details change in killer whale presence and ties it to changes in sea ice coverage. These are novel results, presenting trends in the arrival and departure dates of killer whales into the eastern Canadian Arctic for the first time. We go on to discuss the impacts of these changes on other aspects of Arctic ecosystems and how increasing in killer whale presence might affect other species and the management of those species in Canada. Killer whales are a widespread species of interest, especially in the Canadian Arctic as their presence is tied to multiple aspects of a region rapidly changing from the effects of climate change. DESCRIPTION:This study examines 20 years of killer whale (Orcinus orca) sightings in the eastern Canadian Arctic, drawing from a comprehensive sighting database spanning 1850-2023. Despite inherent biases favoring data collection near communities and coastal areas, spatiotemporal analyses reveal significant shifts in killer whale distribution linked to changing sea ice conditions. We developed a clustering metric representing the mean distance to the five nearest sightings and results show that killer whales are progressively moving away from historically high-use areas and that sighting locations are becoming more dispersed over time. A significant year × sea ice interaction indicates observations occur earlier during their arrival period at lower sea ice concentrations over time, suggesting that declining sea iceconcentration contributes to earlier arrival. Conversely, for departure periods, killer whales are observed farther south later in the year, likely linked to earlier freeze-up at higher latitudes, and are overall observed later into the year over time. This trend has led to a near doubling of their average presence from 26 days in 2002 to 48 days in 2023 (27 July to 13 September) reflecting an extended open-water season. These findings underscore the prolonged seasonal use of Arctic regions by killer whales, driven by diminishing sea ice and expanding openwater habitat. Such shifts highlight potential implications for Arctic marine ecosystems as killer whales increasingly overlap with endemic species.

Locations are indicated based on the information available. If coordinates were not available, the approximate location is indicated using the description associated with the record. Note that effort is not accounted for in this dataset, nor is effort equally distributed throughout the area captured. Data derived from satellite or acoustic tagging are not included in this dataset. Note that not all records are confirmed. DFO Science reviews records and reports and classifies them as either confirmed or unconfirmed, based on the available information (e.g., pictures, videos, descriptions).

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding.Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status.NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest.NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.

Canada's National Forest Inventory (NFI) sampling program is designed to support reporting on forests at the national scale. On the other hand, continuous maps of forest attributes are required to support strategic analyses of regional policy and management issues. We have therefore produced maps covering 4.03 × 106 km2 of inventoried forest area for the 2001 base year using standardised observations from the NFI photo plots (PP) as reference data. We used the k nearest neighbours (kNN) method with 26 geospatial data layers including MODIS spectral data and climatic and topographic variables to produce maps of 127 forest attributes at a 250 × 250 m resolution. The stand-level attributes include land cover, structure, and tree species relative abundance. In this article, we report only on total live aboveground tree biomass, with all other attributes covered in the supplementary data (http://nrcresearchpress.com/doi/suppl/10.1139/cjfr-2013-0401). In general, deviations in predicted pixel-level values from those in a PP validation set are greater in mountainous regions and in areas with either low biomass or sparse PP sampling. Predicted pixel-level values are overestimated at small observed values and underestimated at large ones. Accuracy measures are improved through the spatial aggregation of pixels to 1 km2 and beyond. Overall, these new products provide unique baseline information for strategic-level analyses of forests (https://nfi.nfis.org)Collection:- **[Canada's National Forest Inventory (NFI) 2006](https://open.canada.ca/data/en/dataset/e2fadaeb-3106-4111-9d1c-f9791d83fbf4)**

Polygons containing the date of capture of the Landsat images used to create the first version of the Baseline Thematic Mapping v1 (BTM1). This spatial view is only meaningful in conjunction with the satellite images or the BTM data derived from the satellite images. The images were captured from 1990 to 1997

Canada’s NFI survey was designed to provide an unbiased probability sample of Canada’s forests for long-term strategic monitoring purposes. The target population is Canada’s entire non-Arctic land area. A National Terrestrial Monitoring Framework (NTMF) was created by establishing a systematic 4 km by 4 km sampling grid over all of Canada from a random offshore point. Prior to T0, NFI partners determined that the NFI program would be able to affordably achieve its mission by establishing a 2 km by 2 km (400 ha) “photo plot” at every fifth sampling point on the NTMF (i.e. every 20 km), thereby providing a one percent sample of the target population. This sampling intensity was considered sufficient for national reporting and possible to sustain over the long term with anticipated funding.Photo plots were established across Canada during 2000-2006 (T0). There are 26,139 photo plot survey locations on the 20 km by 20 km grid, of which 18,570 lie inside the target population area. For each photo plot, information is collected on land cover, land use, ownership and protection status.NFI photo plot survey data are stratified by “NFI Unit” for standard estimation and reporting purposes. NFI Units were created by the geographic intersection of Canada’s 10 provinces, 3 territories and 12 non-Arctic terrestrial ecozones. Estimates produced for NFI Units are rolled up to produce standard reports for ecozones, jurisdictions (provinces and territories) and Canada. Some NFI Units are too small to produce robust estimates for with the current sampling intensity, so NFI Unit estimates are not publicly reported. Prince Edward Island (PEI) Atlantic Maritime, for example, is PEI’s only NFI Unit and it is small (1% sampling intensity achieved with only 19 photo plots), so the NFI avoids publishing provincial reports. Information consumers are encouraged to use official statistics produced by provincial and territorial governments for the forests in their jurisdictions. Most provinces are large, however, and the current NFI sampling intensity is sufficient for producing robust NFI reports for those jurisdictions. Special estimation reports can be produced using different ecological or administrative strata, such as the Boreal Zone, or the Managed Forest.NFI photo plots are surveyed on a ten-year cycle. During first re-measurement (T1; 2008-2017), survey intensity was reduced to one photo plot every 40 km across northern Canada (Figure 3) because of budget limitations. The T2 survey (2018-2027) is currently underway.

Species Distribution Models (SDM) were used to predict and identify priority areas for enhanced monitoring of cetaceans in eastern Canadian waters off Nova Scotia, Newfoundland and Labrador. This data set represents information presented in Gomez et al. (2020) and includes sighting records and SDM outputs for ten cetacean species with sufficient records (n > 450) and sightings only for an additional six species. For more information about sighting records including which were included in each SDM, please see Gomez et al. 2020. This study used a compilation of aerial- and vessel-based cetacean sightings data from 1975-2015 assembled in Gomez et al. (2017) from variety of sources. Note that sightings data from many of these sources are not effort-corrected and apparent distribution patterns based on these opportunistic sightings data are biased by when and where survey activities were conducted. Unfavorable weather and reduced visual effort in winter, spring, and autumn likely account for the fewer sighting records in these seasons compared to summer. The dataset does not include dead animal, stranding, entanglement or entrapment data. While some of the databases include records obtained during the whaling period (catches or sightings recorded prior to 1975), for all analyses/modelling conducted in this study, only sightings of free-swimming whales obtained during the post-whaling period (1975-2015) were used. Quality control checks included discarding all records outside of our study area and removing redundant records (identical species, day, month, latitude and longitude).The data used do not reflect any updates or corrections to the databases that have occurred since the data were compiled in 2016. Sightings are not available for download here, please contact the original data sources listed below to obtain raw sightings data. This study represents an important initiative in eastern Canada to highlight key areas for cetacean monitoring in waters off Nova Scotia, Newfoundland and Labrador. Habitats with high suitability are interpreted as areas where cetacean monitoring efforts may be prioritized, and results can help direct future survey efforts. These model outputs used cetacean sightings from several decades and dynamic environmental predictors that used seasonal averages across multiple years. As proxies for prey availability, five predictor environmental variables were selected for the SDM: ocean depth, compound topographic index, sea surface temperature, areas of persistently high chlorophyll-a concentration, and regional chlorophyll-a magnitude. See Gomez et al. (2020) for further details on modelling methods. Persistent patterns over time (between 1975-2015) are the main patterns expected to be captured by these models. Further, SDM results presented here are not the same as species density maps; rather, they portray predicted suitable habitat based on environmental characteristics and sightings data that were not always derived from effort-based surveys. Consequently, the use of these models in marine spatial planning processes should be accompanied by complimentary approaches such as acoustic and visual validation of the SDM results as well as additional monitoring and modeling efforts. Please refer to Gomez et al. (2020) for examples on how to best use these data outputs. Future efforts will focus on using more recent data and improving these models to facilitate the inclusion of cetaceans in marine spatial planning processes that are currently underway. Data sources: Fisheries and Oceans Canada Maritimes region and Newfoundland and Labrador region (Whale Sightings Database, Ocean and Ecosystem Sciences Division, Dartmouth, NS; http://www.inter.dfo-mpo.gc.ca/Maritimes/SABS/popec/sara/Database, MacDonald et. al. 2017) Ocean Biogeographic Information System (OBIS; http://www.iobis.org/), North Atlantic Right Whale Consortium (NARWC; http://www.narwc.org/) Whitehead Lab at Dalhousie University (http://whitelab.biology.dal.ca/) Environment and Climate Change Canada’s (Canadian Wildlife Service) Eastern Canada Seabirds at Sea (ECSAS) program (Gjerdrum et al. 2012). References: Gomez, C., Konrad, C.M., Vanderlaan, A., Moors-Murphy, H.B., Marotte, E., Lawson, J.,Kouwenberg, A-L., Fuentes-Yaco, C., Buren, A. 2020. Identifying priority areas toenhance monitoring of cetaceans in the Northwest Atlantic Ocean. Can. Tech. Rep.Fish. Aquat. Sci. 3370: vi + 103 p. http://waves-vagues.dfo-mpo.gc.ca/Library/40869155.pdfGomez C, Lawson J, Kouwenberg A, Moors-Murphy H, Buren A, Fuentes-Yaco C, Marotte E, Wiersma YF, Wimmer T. 2017. Predicted distribution of whales at risk: identifying priority areas to enhance cetacean monitoring in the Northwest Atlantic Ocean. Endangered Species Research 32:437-458 https://www.int-res.com/abstracts/esr/v32/p437-458/Gjerdrum, C., D.A. Fifield, and S.I. Wilhelm. 2012. Eastern Canada Seabirds at Sea (ECSAS) standardized protocol for pelagic seabird surveys from moving and stationary platforms. 31 Canadian Wildlife Service Technical Report Series No. 515. Atlantic Region. vi + 37 p. MacDonald, D., Emery, P., Themelis, D., Smedbol, R.K., Harris, L.E., and McCurdy, Q. 2017. Marine mammal and pelagic animal sightings (Whalesightings) database: a user’s guide. Can. Tech. Rep. Fish. Aquat. Sci. 3244: v + 44 p.

The documented occurrence data package contains 3 datasets that, in combination, help to provide generalized information about woodland caribou locations and survey areas in Saskatchewan. This information may assist users in their efforts to avoid or mitigate impacts to woodland caribou when operating in woodland caribou range. Generalized locations of caribou use have been provided to better reflect their large home ranges. Absence of a hexagon in an area should not be interpreted as absence of woodland caribou.Please read the Data Guide for important information about this product. Download survey boundaries, telemetry occurrence, and sightings/sign. Download the full package, including data guide here. The Woodland Caribou Documented Occurrence public data product is composed of three shapefiles/feature classes: 1. Woodland Caribou Occurrence - Sighting and Sign 2. Woodland Caribou Occurrence - Telemetry 3. Woodland Caribou Survey Boundaries The two occurrence datasets contain a grid of 18 sq km hexagons (tessellation). The inclusion of a hexagon in the dataset indicates that one or more animal sightings or sign, or telemetry points have been documented in that area. Importantly, lack of caribou occurrence (e.g. no hexagon) should not be interpreted as absence of woodland caribou. Rather, data may not have been collected in these areas or incidental or other observations have not been received.   The survey boundaries dataset displays the boundaries of woodland caribou surveys that were completed by or in collaboration with the Ministry of Environment from 2005 to 2024. Boundaries are from multiple sources, and include various types of surveys (fecal pellet collection or telemetry). These boundaries provide context when viewed alongside the woodland caribou occurrence datasets. We expect to see more occurrence locations in areas that have been surveyed. This information may provide context to areas with a seemingly higher number of occurrences.  For a full description of the data, please refer to the Data Guide document available for download on the Saskatchewan GeoHub.