Parks Canada’s National Program for Ecological Corridors was initiated to strengthen the network of protected areas across Canada through the creation of ecological corridors. To achieve this goal, Parks Canada sought out to develop tools for a common approach on the scientific and governance aspects of corridor creation and management. The National Priority Areas for Ecological Corridors (NPAECs) were developed using a scientific framework for national-scale prioritization of where ecological corridors are most urgently needed. Improving or maintaining ecological connectivity in these areas will greatly benefit biodiversity conservation and climate change adaptation. The NPAECs were identified based on a methodology that is multivariate, data driven, national in scale, and spatially explicit at a coarse resolution. The Criteria for Ecological Corridors in Canada provide a common approach to ensure ecological corridors are managed and stewarded to maintain or restore effective ecological connectivity, while upholding Indigenous stewardship values. They are derived from the internationally recognized International Union for Conservation of Nature’s Guidelines on Connectivity and adapted to the Canadian context. The NPAECs geographic data layer, the list of datasets used to identify them, the Criteria and their accompanying guidance can be found below. More details and context about both program elements are available on the Program’s webpage (https://parks.canada.ca/nature/science/conservation/corridors-ecologiques-ecological-corridors).

Geometric and conventional representation of the hydrographic network. The 3D hydrographic layer is represented by several natural or physical elements associated with the presence of water. These elements form part of the layers in the digital cartographic compilation.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The Ontario Raw Point Cloud (Imagery-Derived) is elevation point cloud data created from aerial photography from the Geospatial Ontario (GEO) imagery program. It was created using a pixel-autocorrelation process based on aerial photography collected by the imagery contractor for the GEO imagery program. The dataset consists of overlapping tiles in LAZ format and is 6.29 terabytes in size. Tiles are overlapping because the pixel-autocorrelation process extracts elevation values from overlapping stereo photo strips. No classification has been applied to the point cloud, however they are encoded with colour (RGB) values from the source photography. This data is for geospatial tech specialists, and is used by government, municipalities, conservation authorities and the private sector for land use planning and environmental analysis. __Related data__ For a product in non-overlapping tiles with a ground classification applied, see the [Ontario Classified Point Cloud (Imagery-Derived)](https://geohub.lio.gov.on.ca/datasets/febf17330adb4100a22738e1684b5feb). Raster derivatives have been created from the point clouds for some imagery projects. These products may meet your needs and are available for direct download. For a representation of bare earth, see [Ontario Digital Elevation Model (Imagery-Derived)](https://geohub.lio.gov.on.ca/maps/mnrf::ontario-digital-elevation-model-imagery-derived/about). For a model representing all surface features, see the [Ontario Digital Surface Model (Imagery-Derived)](https://geohub.lio.gov.on.ca/maps/mnrf::ontario-digital-surface-model-imagery-derived/about).

Geometric and conventional representation of electrical transmission lines for planning purposes. The geospatial data of electric transmission lines represents only the main infrastructure and is structured according to the following layers of information: - CARTO-SER-ELECTRICITY: A concrete base on which a structure used to support high-voltage electrical cables is based. - Carto-Ser-ele-Tel-Aerien:suspended electrical cable. Symbolic representation, from center pylon to center pylon. These elements form part of the layers in the digital cartographic compilation.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Krill is a generic name for crustaceans of the order Euphausiids, most of which are known to be Thysanoessa raschii and Meganyctiphanes norvegica in eastern Canada. Krill is an important food resource for many marine mammals, in particular the blue whale. The maps show the points of high krill concentration per month from April to November. Each point gives the number of years of high aggregation probability (6 to 10 years). The data were produced from a mathematical model developed in Plourde et al. 2016. The model has allowed to calculate the probability of meeting a strong aggregation of krill over a period of 10 years. High krill aggregations are defined as the 95th percentile of predicted biomass in 10 x 10 km cells covering the Estuary and Gulf of St. Lawrence. Additional Information Plourde, S., Lehoux, C., McQuinn, I.H., and Lesage, V. (2016). Describing krill distribution in the western North Atlantic using statistical habitat models. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/nnn. vi + xx p. Plourde, S., McQuinn, I.H., Lesage, V., Lehoux, C., Joly, P., Bourassa, M-N. in prep. Spatial distribution of krill in eastern Canadian waters: a climatological approach based on historical plankton net and acoustic data. The data are incomplete upstream of Pointe-des-Monts because of the lack of water height anomalies in the area (variable being used to predict aggregations of krill). A less number of years with a high aggregation of krill is thus represented but that should not be interpreted as a less favorable zone compared to areas East of Pointe-des-Monts.

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 conditions Data Sources: Model output Uncertainties: 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.

The Brier Island/Digby Neck area has been identified as an Ecologically and Biologically Significant Area (EBSA) by Fisheries and Oceans Canada and is one of four marine areas within the Bay of Fundy recognised by Parks Canada as of national significance for marine conservation planning. The area is representative of important outer Bay of Fundy features with significant marine mammal, bird, and benthic diversity including potentially important aggregations of sensitive benthic species such as horse mussel and sponge. Much of the information used for this recognition is now over 40 years old and should be re-validated using standardised georeferenced survey methods. As a first phase, a diver-based survey of the sublittoral habitats and associated species was conducted in August and September of 2017 for the Brier Island area. This report summarises the major sublittoral habitat types, species assemblages, and oceanographic conditions observed at 20 locations including Northwest and Southwest Ledges, Gull Rock, Peter’s Island, and Grand Passage. A total of 962 records were made of 178 taxa, consisting of 43 algae and 135 animals. Comparison with historical records largely confirmed the continued presence of unique habitats and species assemblages for which this area was initially recognised as an EBSA. Differences in species richness observed for cryptic and less known taxonomic groups such as sponges and bryozoans were attributable to changes in survey methods and knowledge. Based on these findings, additional surveys of inshore and offshore Brier Island using more quantitative methods developed for other Bay of Fundy EBSAs would further support regional MPA network planning and provide relative scales of species diversity and habitat coverage for this area.

Railway network on the territory of the city. **Collection context** Historical data from the Government of Quebec. Additional data by photointerpretation. **Collection method** Computer-aided mapping. **Attributes** * `ID_VFR` (`integer`): Identifier * `integer`): Identifier * `SOURCE` (`varchar`): Created on * `DATE_MODIFICATION` (`integer`): Identifier * `SOURCE` (`integer`): Modified on * `USER_MODIFICATION` (`varchar`): Modified by For more information, see the metadata on the Isogeo catalog (OpenCatalog link).**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

The National Ecological Framework for Canada's "Land Cover by Ecoprovince” dataset provides land cover information within the ecoprovince framework polygon. It provides landcover codes and their English and French language description as well as information about the percentage of the polygon that the component occupies.

The National Ecological Framework for Canada's "Surficial Geology by Ecodistrict” dataset contains tables that provide surficial geology information with the ecodistrict framework polygons. It provides codes that characterize surficial geology (unconsolidated geologic materials) and their English and French-language descriptions as well as information about the area and percentage of the polygon that the material occupies.