This dataset provides various Ministry of Natural Resources and Forestry business areas with fundamental forest inventory information needed to meet their program mandates.

The porbeagle shark (Lamna nasus), is a species found in Atlantic Canadian waters which is encountered in commercial and recreational fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to porbeagle sharks from 2005 to 2021 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada and the Faroe Islands on commercial, recreational and scientific charters, typically in summer and fall but some over winter when the porbeagle commercial fishery was active in Canada. A variety of tag models were deployed: PAT 4 (n=1), Mk10 (N=41), and MiniPAT (N=15) and 51 of 57 tags reported. One individual shark was recaptured and the physical tag was returned. The porbeagle sharks tagged ranged in size from 76 cm to 249 cm Fork Length (curved); 42 were female, 15 were male. Time at liberty ranged from 4 – 356 days and 14 tags remained on for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

Current communities partnering in a mobile business licence program with neighboring communities. To view the the Mobile Business Licence partnerships in the BC Economic atlas, [click here](https://maps.gov.bc.ca/ess/hm/bcea/?catalogLayers=6081,6120,6082¢er=-13000000,6450000,102100&legendFirst).

The Agri-Environmental Indicator of Risk of Water Contamination by Phosphorus dataset estimates the relative risk of phosphorus loss from Soil Landscapes of Canada agricultural areas to surface water. The data series for this indicator consists of four (4) datasets: Annual P-Balance, Soil-P-Source, Edge of Field and IROWC-P. Products in this data series present results for predefined areas as defined by the Soil Landscapes of Canada (SLC v.3.2) data series, uniquely identified by SOIL_LANDSCAPE_ID values.

**NOTE: Not for operational use. This dataset is the result of an analysis (Aug 2018) of many input layers and as such the accuracy, currency, and completeness of the dataset cannot be relied upon for operational or management decision making.** Land designations that contribute to conservation are spatially-defined areas established through legislation or purchased for the protection of nature and cultural values, the conservation of biological diversity and ecosystem services and the management of natural resources. Over 40 land designations were divided into three broad categories: - **Protected Lands** includes all Parks & Protected Areas and Other Protected Lands with the primary purpose of the long-term conservation of nature and cultural values. - **Resource Exclusion Areas** includes all designations that fully exclude one or two resource activities for the purpose of conservation. - **Spatially Managed Areas** includes all spatial designations managing or limiting development or a resource activity for the purpose of conservation, or a spatial management regime in place to preserve specified elements of biodiversity but where activity is still allowed to occur. Land designations layers were combined into a single layer, with overlaps removed such that areas with overlapping designations were assigned to the highest conservation contribution category. **Data sources:** A list of the source datasets, including links to their sources, more information, and data processing steps is available [**here**](https://raw.githubusercontent.com/bcgov/designatedlands/v0.1.0/sources.csv). Details on methods are available [**here**](https://github.com/bcgov/designatedlands). Previous versions of the data are available [**here**](https://github.com/bcgov/designatedlands/releases).

The white shark (Carcharodon carcharias), is a species found in Atlantic Canadian waters which is encountered in commercial and recreational fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to white sharks from 2016 to the present to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada and the United States (Cape Cod and South Carolina) on scientific charters, typically in summer. Tag models deployed included: Mk10 (N=1), and MiniPAT (N=29) and 22 of 27 tags reported with 3 still at liberty. One individual shark returned to the location of tagging 1 year later and the physical tag was recovered. Another tag was recovered 5 years after deployment. White sharks tagged ranged in size from 259 cm to 459 cm Total Length (curved) estimated; 15 were female, 13 were male, and 2 were of unknown sex. Time at liberty ranged from 48 – 377 days and to date, only 3 tags remained on the shark for the programmed duration. Tagging of white sharks is an ongoing study and data will be updated here when it becomes available. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

All island polygons. Islands may overlap as there are islands within islands (e.g., a lake on an island contains an island). GNIS_NAME_1 contains the most atomic name for the island. For example, there are 3797 "Haida Gwaii" islands. If the island has not been named as part of a more specific group or with an individual name, "Haida Gwaii" is the GNIS_NAME_1 value. GNIS_NAME_2 and GNIS_NAME_3 values are null. If the island has a more specific name, "Haida Gwaii" moves to GNIS_NAME_2, and the more atomic name, such as "Moresby Island" is the GNIS_NAME_1. If the island has an individual name, belongs to a group, and is part of Haida Gwaii, the same logic of naming from most to least specific applies. For example, GNIS_NAME_1 = "George Island", GNIS_NAME_2 = "Copper Islands", GNIS_NAME_3 = "Haida Gwaii".

Likelihood of Presence of Harbour Seal in the Bay of Fundy and Port Hawkesbury Area Response Plan. The Coastal Oceanography and Ecosystem Research section (DFO Science) reviewed science sources and local knowledge sources to estimate where Harbour seals are seasonally present and delineate these areas. As of March 2017, this dataset delineates the presence of Harbour seals in the Bay of Fundy and Port Hawkesbury areas of Nova Scotia designated within the Area Response Planning (ARP), identified under the World Class Tanker Safety System (WCTSS) initiative, based on the Transport Canada Response Organizations Standards. 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 Harbour Seal 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/5bbc1575-4267-44fa-ae35-ee08cc2af8fb

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.2021.107630 Cite 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

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.