As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping, across known caribou ranges, as of 2010. The attached dataset comprises the second 5-year update (first one in 2015) bringing the data up to 2020.The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008-2010. Since then, anthropogenic disturbances within 51 caribou ranges across Canada were remapped every five years to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The ranges were defined by individual provinces and territories across Canada. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for a caribou resource selection function (habitat modeling) and to assess overall disturbance levels on each caribou ranges. As with the 2010 mapping project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat 30-metre multi-band imagery at a viewing scale of 1:50,000. The same concept was followed for the 2015 and 2020 disturbance mapping and any additional disturbance features that were observed since the original mapping date, were added. The 2015 database was used as a starting point for the 2020 database. Unlike the previous iteration, features were not removed in the mapping process which was a decision made in the name of time. Interpretation was carried out based on the most recent cloud free imagery available up to mid fall for a given year. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. Polygonal disturbances included: cutblocks, harvest (added in 2020), mines, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. For each type of anthropogenic disturbance, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Features were only digitized if they were clearly visible in the Landsat imagery at the prescribed viewing scale. In comparison to the previous mapping protocol, one enhancement to the mapping process in 2020 was the addition of CFS harvest polygons (Ref: NRCan-CFS NTEMS; Wulder 2020) into the database prior to interpretation. This considerably reduced the digitizing time for polygons and accelerated the data collection process. The CFS harvest polygons were checked before inclusion, removing some which had been generated erroneously in their process.A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. Subsequently, the vector data of individual linear and polygonal disturbances were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons for the past forty years (CNFDB 1981-2020) were merged into the buffered anthropogenic footprint in order to create an overall disturbance footprint. These buffered datasets were used in the calculation of range disturbance levels and for integrated risk assessment analysis.

Anthropogenic and natural constraints of the revised land use and development plan of the City of Laval.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

This data layer presents the results of a modeling of net anthropogenic inputs of nitrogen and phosphorus (NANI/NAPI) carried out by Professor Roxane Maranger and Stéphanie Shousha from the University of Montreal as part of a partnership with the Ministry of the Environment, the Fight against Climate Change, Wildlife and Parks (MELCCFP). The modeling was carried out using the Net Anthropogenic Nitrogen/Phosphorus Input method applied for the first time in Quebec by ([Goyette et al., 2016]) (https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fagupubs.onlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2016GB005384&data=05%7C02%7CAntoine.Prince%40environnement.gouv.qc.ca%7Ce9a3e849691c4a3f9bc008de4e23f624%7C4262d4ec5a674957abb6bf78aca6a6f5%7C0%7C0%7C639034113587157844%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C 0% 7C%7C%7C&sdata=a7ktxc6ea9u4thdsbor9ojrkspdjgwno%2Fbaizkkkuaq%3D&reserved=0)) then refined in ([Shousha & Maranger, 2024] (https://can01.safelinks.protection.outlook.com/?url=https%3A%2F%2Fbesjournals.onlinelibrary.wiley.com%2Fdoi%2Ffull%2F10.1111%2F1365-2664.14733&data=05%7C02%7CAntoine.Prince%40environnement.gouv.qc.ca%7Ce9a3e849691c4a3f9bc008de4e23f624%7C4262d4ec5a674957abb6bf78aca6a6f5%7C0%7C0%7C639034113587193473%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=AdT31R8 IHMEEYRZZ1HGMTRTUAEE8XMRMAQBRNAUJDN 4% 3D&reserved=0)). The model is based on the premise that a region imports nitrogen and phosphorus to support its population and agricultural activities.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Description:This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021.The data available here are the outputs of NEP36-CanOE_RCP 8.5; a projection of the 2046-2065 climate for the no mitigation scenario RCP 8.5.Methods:This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans.The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone.Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes.While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016).Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditionsData Sources:Model outputUncertainties:These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability.

Description:This dataset consists of three simulations from the Northeastern Pacific Canadian Ocean Ecosystem Model (NEP36-CanOE) which is a configuration of the Nucleus for European Modelling of the Ocean (NEMO) V3.6. The historical simulation is an estimate of the 1986-2005 mean climate. The future simulations project the 2046-2065 mean climate for representative concentration pathways (RCP) 4.5 (moderate mitigation scenario) and 8.5 (no mitigation scenario). Each simulation is forced by a climatology of atmospheric forcing fields calculated over these 20 year periods and the winds are augmented with high frequency variability, which introduces a small amount of interannual variability. Model outputs are averaged over 3 successive years of simulation (the last 3, following an equilibration period); standard deviation among the 3 years is available upon request. For each simulation, the dataset includes the air-sea carbon dioxide flux, monthly 3D fields for potential temperature, salinity, potential density, total alkalinity, dissolved inorganic carbon, nitrate, oxygen, pH, total chlorophyll, aragonite saturation state, total primary production, and monthly maximum and minimum values for oxygen, pH, and potential temperature. The data includes 50 vertical levels at a 1/36 degree spatial resolution and a mask is provided that indicates regions where these data should be used cautiously or not at all. For a more detailed description please refer to Holdsworth et al. 2021.The data available here are the outputs of NEP36-CanOE_RCP 4.5; a projection of the 2046-2065 climate for the moderate mitigation scenario RCP 4.5.Methods:This study uses a multi-stage downscaling approach to dynamically downscale global climate projections at a 1/36° (1.5 − 2.25 km) resolution. We chose to use the second-generation Canadian Earth System model (CanESM2) because high-resolution downscaled projections of the atmosphere over the region of interest are available from the Canadian Regional Climate Model version 4 (CanRCM4). We used anomalies from CanESM2 with a resolution of about 1° at the open boundaries, and the regional atmospheric model, CanRCM4 (Scinocca et al., 2016) for the surface boundary conditions. CanRCM4 is an atmosphere only model with a 0.22° resolution and was used to downscale climate projections from CanESM2 over North America and its adjacent oceans.The model used is computationally expensive. This is due to the relatively high number of points in the domain (715 × 1,021 × 50) and the relatively complex biogeochemical model (19 tracers). Therefore, rather than carrying out interannual simulations for the historical and future periods, we implemented a new method that uses atmospheric climatologies with augmented winds to force the ocean. We show that augmenting the winds with hourly anomalies allows for a more realistic representation of the surface freshwater distribution than using the climatologies alone.Section 2.1 describes the ocean model that is used to estimate the historical climate and project the ocean state under future climate scenarios. The time periods are somewhat arbitrary; 1986–2005 was chosen because the Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations end in 2005 as no community-accepted estimates of emissions were available beyond that date (Taylor et al., 2009); 2046–2065 was chosen to be far enough in the future that changes in 20 year mean fields are unambiguously due to changing GHG forcing (as opposed to model internal variability) (e.g., Christian, 2014), but near enough to be considered relevant for management purposes.While it is true that 30 years rather than 20 is the canonical value for averaging over natural variability, in practice the difference between a 20 and a 30 year mean is small (e.g., if we average successive periods of an unforced control run, the variance among 20 year means will be only slightly larger than for 30 year means). Also, there is concern that longer averaging periods are inappropriate in a non-stationary climate (Livezey et al., 2007; Arguez and Vose, 2011). We chose 20 year periods because they are adequate to give a mean annual cycle with little influence from natural variability, while minimizing aliasing of the secular trend into the means. As the midpoints of the two time periods are separated by 60 years, the contribution of natural variability to the differences between the historical and future simulations is negligible e.g., (Hawkins and Sutton, 2009; Frölicher et al., 2016).Section 2.2 describes how climatologies derived from observations were used for the initialization and open boundary conditions for the historical simulations and pseudo-climatologies were used for the future scenarios. The limited availability of observations means that the years used for these climatologies differs somewhat from the historical and future periods. Section 2.3 details the atmospheric forcing fields and the method that we developed to generate winds with realistic high-frequency variability while preserving the daily climatological means from the CanRCM4 data. Section 2.4 shows the equilibration of key modeled variables to the forcing conditionsData Sources:Model outputUncertainties:These climate projections are downscaled from a single global climate model (CanESM2/CanRCM4) because the cost of ensembles is presently prohibitive. Our experimental design uses climatological forcing for each time period so the differences between them are almost entirely due to anthropogenic forcing with little effect of natural variability.

As part of a scientific assessment of critical habitat for boreal woodland caribou (Environment Canada 2011, see full reference in accompanying documentation), Environment Canada's Landscape Science and Technology Division was tasked with providing detailed anthropogenic disturbance mapping, across known caribou ranges, as of 2015. This data comprises a 5-year update to the mapping of 2008-2010 disturbances, and allows researchers to better understand the attributes that have a known effect on caribou population persistence. The original disturbance mapping was based on 30-metre resolution Landsat-5 imagery from 2008 -2010. The mapping process used in 2010 was repeated using 2015 Landsat imagery to create a nationally consistent, reliable and repeatable geospatial dataset that followed a common methodology. The methods developed were focused on mapping disturbances at a specific point of time, and were not designed to identify the age of disturbances, which can be of particular interest for disturbances that can be considered non-permanent, for example cutblocks. The resultant datasets were used for a caribou resource selection function (habitat modeling) and to assess overall disturbance levels on each caribou ranges. Anthropogenic disturbances within 51 caribou ranges across Canada were mapped. The ranges were defined by individual provinces and territories across Canada. Disturbances were remapped across these ranges using 2015 Landsat-8 satellite imagery to provide the most up-to-date data possible. As with the 2010 mapping project, anthropogenic disturbance was defined as any human-caused disturbance to the natural landscape that could be visually identified from Landsat imagery with 30-metre multi-band imagery at a viewing scale of 1:50,000. A minimum mapping unit MMU of 2 ha (approximately 22 contiguous 30-metre pixels) was selected. Each disturbance feature type was represented in the database by a line or polygon depending on their geometric description. Polygonal disturbances included: cutblocks, mines, reservoirs, built-up areas, well sites, agriculture, oil and gas facilities, as well as unknown features. Linear disturbances included: roads, railways, powerlines, seismic exploration lines, pipelines, dams, air strips, as well as unknown features. For each type of anthropogenic disturbance, a clear description was established (see Appendix 7.2 of the science assessment) to maintain consistency in identifying the various disturbances in the imagery by the different interpreters. Features were only digitized if they were visible in the Landsat imagery at the prescribed viewing scale. A 2nd interpreter quality-control phase was carried out to ensure high quality, complete and consistent data collection. For this 2015 update an additional, separate higher-resolution database was created by repeating the process using 15-metre panchromatic imagery. For the 30-metre database only, the line and poly data were buffered by a 500-metre radius, representing their extended zone of impact upon boreal caribou herds. Additionally, forest fire polygons were merged into the anthropogenic footprint in order to create an overall disturbance footprint. These buffered datasets were used in the calculation of range disturbance levels and for integrated risk assessment analysis.

The excessive input of nitrogen derived from human land-use activities remains a major cause of the eutrophication of coastal ecosystems around the world. However, little data exist on rates of nutrient pollution or its potential impacts to coastal ecosystems in Atlantic Canada. To fill this knowledge gap, a Nitrogen Loading Model (NLM) framework was applied to determine the Total Nitrogen Load (kg TN / yr) from point and non-point source inputs (wastewater, atmospheric deposition, land use, fertilizer applications, and regional industries) in 109 coastal watersheds bordering the Bay of Fundy and Scotian Shelf. To evaluate the potential impact of nitrogen loading, two indicators were calculated for 40 coastal embayments: (1) ∆N, a measure of nitrogen residency that predicts dissolved oxygen problems; and (2) the estuary loading rate, a predictor of the potential for loss of submerged aquatic vegetation. This project was funded by Fisheries and Oceans Canada through a Strategic Program for Ecosystem-based Research and Advice (SPERA) grant. This research has been published in the scientific literature (Kelly et al. 2021). Kelly, N.E., Guijarro-Sabaniel, J. and Zimmerman, R., 2021. Anthropogenic nitrogen loading and risk of eutrophication in the coastal zone of Atlantic Canada. Estuarine, Coastal and Shelf Science, 263, p.107630. doi: https://doi.org/10.1016/j.ecss.2021.107630Cite this data as: Kelly, N.E., Guijarro-Sabaniel, J. and Zimmerman, R. Data of: Estimates of anthropogenic nitrogen loading and eutrophication indicators for the Bay of Fundy and Scotian Shelf. Published: February 2022. Coastal Ecosystems Science Division, Fisheries and Oceans Canada, Dartmouth, N.S. https://open.canada.ca/data/en/dataset/08746031-1970-4bf6-b6d4-3de2715c8634

Fisheries and Oceans Canada has conducted a cumulative human impact mapping analysis for Pacific Canada to support ongoing Marine Spatial Planning. Cumulative impact mapping (CIM) combines spatial information on human activities, habitats, and a matrix of vulnerability weights into an intuitive relative ‘cumulative impact score’ that shows where cumulative human impacts are greatest and least. To map cumulative impacts, a recently developed ecosystem vulnerability assessment for Pacific Canadian waters (Murray et al. 2022) was combined with spatial information on thirty-eight (38) different habitat types and forty-five (45) human activities following the methodology from Halpern et al.(2008) and Murray et al. (2015). The cumulative impact map is provided in a 1x1 km grid used for oceans management by Fisheries and Oceans Canada. For further information, please contact the data provider.

BiologicEcologic ISO Feature Dataset symbolization and publication. September 5, 2017.

This dataset documents the infauna occurrences collected from 2021 to 2023 during the Canadian Beaufort Sea Marine Ecosystem Assessment (CBS-MEA) conducted by the Department of Fisheries and Oceans (DFO). This scientific program focuses on the integration of oceanography, food web linkages, physical-biological couplings, and spatial and interannual variabilities.The program also aims to expand the baseline coverage of species diversity, abundances, and habitat associations in previously unstudied areas of the Beaufort Sea and Western Canadian Archipelago. The study took place mainly in the Canadian Beaufort Sea and the Amundsen Gulf. Sampling is done along transects at fixed stations in the study area. Catches are collected using a 50 x 50 cm box-corer. 2 or 3 box core is collected per station to obtain replicates. A total of 29 stations were sampled for infauna in 2021, 15 in 2022 and 25 in 2023 between 10-653 m depth. Half of the box corer (0.125 m2) is sampled for infauna taxonomy. The first 20 cm of sediment are collected and sieved through a 0.5 mm mesh sieve. The samples are preserved in seawater-formaldehyde solution (10 % v/v). In the lab, infauna is identified to the lowest taxon level possible.The data are presented in two files:The "Activité_endofaune_CBSMEA_infauna_event_en" file which contains information about missions, stations and deployments, which are presented under a hierarchical activity structure.The "Occurrence_endofaune_CBSMEA_infauna_en" file that contains the taxonomic occurrences.