Trails designed for hiking.attribut:ID - Unique identifier **This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Species Distribution Modeling (SDM) and Wildlife Habitat Ratings (WHR) project boundaries contains (study areas) and attributes describing each project (project level metadata), plus links to the locations of other data associated with the project (e.g. reports, WHR polygon datasets, plotfiles). SDM predicts the suitability of different environments for occupation by particular species, and the likelihood that those suitable habitats are occupied. WHR are also known as wildlife habitat interpretations and most commonly use TEM data as a means to identify specific habitats. This layer is derived from the STE_TEI_PROJECT_BOUNDARIES_SP layer by filtering on the PROJECT_TYPE attribute. Project types include: WHR, SDM, PEMWHR, PEMSDM, TEMWHR, TEMSDM, TEMPRW, NEMPRW, TEMSEW, BEIWHR, BEISDM, SEIWHR, SDM, and SOILSW. Current version: v11 (published on 2024-10-03) Previous versions: v10 (published on 2023-11-14), v9 (published on 2023-03-01), v8 (published on 2016-09-01)

Contains the boundaries of current British Columbia Strategic Land and Resource Plans (SLRPs). The plans can be accessed [here](https://www2.gov.bc.ca/gov/content/industry/crown-land-water/land-use-planning/regions). SLRPs provide direction for Crown land use through the establishment of broad land use goals, planning zone designations, objectives and strategies. This layer represents an integrated regional consensus-based process, which requires public and First Nations participation to produce a SLRP for review and approval by government. SLRPs establish direction on land and resource use and specify broad resource management objectives and strategies. Historical plan types include SRMPs, LRMPs, RLUPs and coastal plans. Current, non-retired SLRP boundaries are included in this layer, where RETIREMENT_DATE is blank. RETIREMENT_DATE is the field that stores the retirement date. If RETIREMENT_DATE is empty, the feature is the current shape. All SLRP shapes (past/retired and present/current) are in the layer [Strategic Land and Resource Plans - All](https://catalogue.data.gov.bc.ca/dataset/298d1034-c1be-4fd1-ad4b-d00ad5ab4b88). ** Please review the Data Quality section below.**

**Attention: there is a new version of this product (Pre-CanLaD v2)** Pre-CanLaD v2 can be found here: https://doi.org/10.23687/8d49698f-40f9-40da-b097-a3f4c90adf5a This data product aimed to extend the existing pre-1985 disturbance history record by mapping wildfire, harvest, and insect outbreaks in Canadian forests between 1965 and 1984. Our geospatial data processing methodology relied on multi-layer perceptrons (MLP) trained on spectral recovery signatures to map and age these disturbances. Specific years were not assigned to insect outbreaks due to the lack of dependable training and validation data. In order to provide a more accurate data product that is compatible with existing datasets (e.g. provincial forest inventories), we used these reliable, but incomplete datasets to correct our predictions of disturbance type and year whenever they were available. Coupled with the updated Canada Landsat Disturbance (CanLaD) data product (Guindon et al. 2017), we are thus able to obtain a pan-Canadian 30m resolution disturbance history record from 1965 until 2023. The full description of the methodology and the exhaustive validation analyses are described in detail in Correia et al. (2024). The following limitations should be taken into account when using this dataset: • It is recommended to group disturbance age predictions into age classes, as this should reduce the noise present in the disturbance age estimation models. • Fire-harvest misclassification seems to be particularly common in transition zones like the southern edge of the Boreal Shield, where fires and harvest are both relatively common. • There seems to be an overestimation of 1965 fires due to a misclassification of burnt areas older than 1965 in northern, less productive areas as belonging to the beginning of our time series. • We likely detected mostly high-severity burnt areas that depict complete mortality, since the faster recovery of low-severity burns makes them more challenging to detect. • Insect outbreak detections were mostly associated with the historic eastern spruce budworm outbreak of the 1970s. Even though pixel-level insect disturbance year was not predicted, realistic estimates can be obtained by cross-checking our data product with historic reports. The following raster layers are available: • canlad_1965_1984_disturbanceType: Estimated disturbance type o 2 = Fire o 3 = Harvest o 4 = Insect • canlad_1965_1984_disturbanceYear: Estimated disturbance year o Numeric value from 1965 to 1984 • canlad_1965_1984_correctionMask: Raster indicating which predictions have been corrected with external datasets o 0 = Unconfirmed disturbance o 1 = Confirmed fire o 2 = Confirmed harvest Please cite this data product as: Correia, D. L. P., L. Guindon, and M. A. Parisien. 2024. Canada-wide Landsat-based 30-m resolution product of disturbance detection prior to 1984. https://doi.org/10.23687/660b7c6a-cdec-4c02-90c7-d63e91825c42 References: Correia, D. L. P., L. Guindon, and M. A. Parisien. 2024. Extending Canadian forest disturbance history maps prior to 1985. Ecosphere [in press]. Guindon, L., P. Villemaire, R. St-Amant, P.Y. Bernier, A. Beaudoin, F. Caron, M. Bonucelli and H. Dorion. 2017. Canada Landsat Disturbance (CanLaD): a Canada-wide Landsat-based 30-m resolution product of fire and harvest detection and attribution since 1984. https://doi.org/10.23687/add1346b-f632-4eb9-a83d-a662b38655ad

“Pacific Rim National Park Reserve - Total Ecosystem Forest Carbon Density” is the annual carbon density (tonnes carbon per hectare) within Pacific Rim’s forested ecosystems over a 31-year period from 1990 to 2020. Total Ecosystem Forest Carbon Density includes aboveground and belowground biomass, soil carbon, and dead organic matter. Total Ecosystem Forest Carbon Density was estimated for 31 national parks using the Generic Carbon Budget Model (GCBM), a spatially explicit carbon budget model developed by Canadian Forest Service which uses forest inventory, disturbance, and mean annual temperature data along with yield data to estimate growth and merchantable volume for dominant tree species. Species- and Ecozone-specific equations are then used to convert merchantable volume to aboveground and belowground biomass carbon. Ecozones were classified according to Canada Ecological Land Classification Level 1. The GCBM simulates carbon dynamics to produce spatially explicit estimations of carbon stocks and fluxes. The model simulates and tracks carbon stocks, transfers between Intergovernmental Panel on Climate Change-defined pools, and other metrics including net ecosystem production, net biome production, and emissions of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O) in annual time steps. The stocks and fluxes are also tracked by disturbance event (e.g., forest fires, insect outbreaks). Total Ecosystem Forest Carbon Density accounts for the effects of natural and anthropogenic disturbances, including wildfires, prescribed burns, and insect outbreaks. These products have a spatial resolution of 30m. This information is part of the Parks Canada Carbon Atlas Series. To obtain a copy of this report, please contact changementclimatique-climatechange@pc.gc.ca. When using this data, please cite as follows: Sharma, T., Kurz, W.A., Fellows, M., MacDonald, A.L., Richards, J., Chisholm, C., Seutin, G., Richardson, K., Keenleyside, K. (2023). Parks Canada Carbon Atlas Series: Carbon Dynamics in the Forests of Canada’s National Parks. Scientific Report. Parks Canada Agency, Gatineau, QC, Canada, 104 p.

During the period of 1987 to 1994, Robin J. LeBrasseur and N. Brent Hargreaves lead a juvenile salmon predation research project in Alberni Inlet and Barkley Sound BC. This dataset contains the research survey catch data and individual fish examinations data.

Mapping of agricultural production zoning in the urban planning code (CDU) on the territory of Laval.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

These data sets provide information pertaining to shrimp and bycatch estimates associated with otter-trawling and trapping (November, 2000) and beam-trawling (February, 2001) in Simoom Sound. Data sets were compiled and formatted by Meagan Mak. Abstract from report: As part of a project investigating possible modification of marine ecosystems by shrimp trawling and trapping, we obtained information on catches offish, shrimp, prawns , and bycatch organisms as well as weight, sex ratios , egg location and colIateral damage to several species of pandalids and eualids. Focusing on the humpback shrimp (Pandalus hypsinotus), we assessed damage to the rostrum, carapace, abdomen, and tail fan on specimens caught by beam trawling, otter trawling, and trapping. Data are given from a preliminary study conducted in Northumberland Channel in June 2000 and more comprehensive sampling from Simoom Sound in November 2000 and February 2001.

First Responders identifies the location of ambulance, fire, police and coast guard facilities in British Columbia.

The Steller sea lion (Eumetopias jubatus) is the largest species of sea lion with males weighing as much as 1,100 kg and females as much as 350 kg. In Canada, the Steller can be spotted along the rocky coast of British Columbia (BC). These highly mobile marine mammals typically travel alone or in small groups, but they congregate in large numbers at traditional rookeries and haul-outs during the mating and pupping season. Females generally live longer (up to 30 years) than males (usually up to 20 years). The population was severely depleted in Canada but following its protection in 1970, the size of the adult population has more than doubled. Trends in the abundance of Steller sea lions in BC have been assessed based on a series of standardized, province-wide aerial surveys conducted during the breeding season (27-June to 06-July) between 1971 and 2021. Additional ad-hoc surveys during the fall, winter, and spring periods were completed to assess sea lion distribution outside of the breeding season. Surveys targeted historically occupied rookeries and haul-out sites with nearby areas also monitored for potential shifts in distribution. Both datasets contain counts that have been collected from sightings of individuals from 1971 through 2021. The updated standard breeding season survey counts data file consolidates and extends two previous datasets – one covering 1971 through 2013 and the other covering the summer portion of the 2016/2017 surveys. The non-breeding season count data was previously limited to the fall-winter portion of the 2016/2017 surveys and the updated data covers the entire study period to date.