The Aquatic Invasive Species Surveillance Database is a compilation of fish community and habitat data from DFO’s Aquatic Invasive Species and Invasive Carp Program early detection surveillance efforts in Canadian waters of the Great Lakes basin. Data includes: sampling site location, date, fish species and counts, and associated habitat information. Annual project-specific details including purpose/objectives and study methodology are often reported in the DFO Canadian manuscript report of fisheries and aquatic sciences series.

The European Green Crab (EGC) is a high-risk global invader that can devastate coastal marine ecosystems by displacing native species, degrading and disturbing native habitats (including eelgrass), and altering food webs. EGC has recently been detected in the Canadian portion of the Salish Sea. As EGC continue to establish in the region, identifying locations on which to focus limited monitoring resources is an ongoing problem given the vast amount of coastal habitat that could be occupied by the species. A variety of methods can be used to identify highly suitable habitats for EGC at a range of spatial scales. However, none have been evaluated in the context of informing EGC management, nor for the Canadian portion of the Salish Sea. Here we evaluate five individual methods developed to assess habitat suitability for EGC (i.e., MaxEnt, stochastic gradient boosted linear and logistic regression models, a rapid site selection tool, and a qualitative site assessment and ranking tool) and five derived models generated by multiplying the outputs of these individual models. Each model relied on slightly different environmental and habitat input variables affecting EGC invasion success. Thus, rather than identifying a single preferred model, we used a multi-model ensemble approach to identify sites that are expected to be most suitable for the species. The ensemble approach likely increases predictive power by including both environmental and habitat characteristics when identifying priority sites for early detection/monitoring for EGC in the Canadian waters of the Salish Sea. Finally, we describe how the models evaluated here, alone or in combination, could be used to identify additional sites either within the Salish Sea or into new areas.This dataset contains predicted habitat suitability from five models for European Green Crab at beaches in the Salish Sea (British Columbia, Pacific Region).

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

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend.This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application.

This is point GIS coverage consisting of either fire ignition locations or centroids of fire polygons where the exact location of fire ignition were unknown for fires within the Yukon, spanning a period from 1946 to present. Although the temporal scale of the coverage goes back to late 1940's, Yukon-wide fire detection capability was not fully developed until the 1960's. In addition to this, access to regular aerial mapping was not readily available until that same time period. As a result many fires in the 40's and 50's were simply not recorded or poorly mapped, particularly in the north. For that reason, care must be taken when drawing conclusions from these data as it relates to the early years. Starting 2020 on the data was automatically updated using iFMS.Distributed from [GeoYukon](https://yukon.ca/geoyukon) by the [Government of Yukon](https://yukon.ca/maps) . Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: [geomatics.help@yukon.ca](mailto:geomatics.help@yukon.ca)

PURPOSE: Adult salmon logbook data are collected annually from volunteer anglers on the Margaree River. These data are used as an input to the current model for predicting abundance of large and small salmon returns each season in the Margaree River (Breau and Chaput 2012). Logbook data used in the model come from in-season fishing. However, the dataset provided also includes early out-of-season fishing that was conducted by volunteer anglers under a scientific license issued by DFO. This early out-of-season early fishing began in 2015 as a pilot project and ran until 2023. DESCRIPTION: Tabularized data from logbooks of anglers in SFA 18A and 18B PHYSICAL SAMPLE DETAILS: LogbooksUSE LIMITATION: To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

The Forest Inventory Zone(s) (FIZ) were developed to provide a broadly based ecological classification of the forest land in British Columbia. FIZ closely follow the early biogeoclimatic zones developed by Dr. Krajina. The province of British Columbia is split into 12 FIZ zones.

Glaciers and ice masses for the province, derived from aerial imagery flown in the late 1980s and early 1990s. Please refer to the [Glaciers](https://catalogue.data.gov.bc.ca/dataset/glaciers) dataset for recent glacier extents in British Columbia, and [Historical Glaciers](https://catalogue.data.gov.bc.ca/dataset/historical-glaciers) for a comparable historic view.

This dataset is comprised of locations and current information for all schools for Kindergarten to Grade 12 in British Columbia. Indicators are included for schools that offer French programs including: Core French, Early French Immersion, Late French Immersion and Francophone Program.

Zebra Mussel (Dreissena polymorpha) and Quagga Mussel (Dreissena rostriformis bugensis) have a long history of invasion in European and North American freshwater ecosystems, with significant ecological and economic impacts. An ecological risk assessment for these two invasive species for freshwater ecosystems in Canada was completed in April 2022 with the aim to provide science-based guidance to inform management decisions and actions. These include early detection, response planning, and/or regulatory and policy measures aimed at mitigating the potential spread and risk posed by Zebra and Quagga Mussels to Canadian freshwater ecosystems (DFO 2023). The Potential for Introduction (propagule pressure and connectivity), the Potential for Establishment (habitat suitability, including a Calcium-based and Maximum Entropy (MaxEnt)-based model), the Potential for Invasion, and the Ecological Impacts were used to derive Ecological Risk for Zebra and Quagga Mussels in Canada. This assessment did not evaluate the risk to individual waterbodies but rather was conducted at a 9,260 m x 9,260 m grid cell resolution. These high resolution maps are provided here. Maps of Ecological Risk at the sub-drainage level are also provided. Fisheries and Oceans Canada is not responsible for any omissions or errors that may be contained in this dataset and shall not be liable for any losses, financial or otherwise, due to the use of these data. Please credit Wilcox et al. 2024 as the source of the data in any maps, reports, or articles that are printed or published on paper or the Internet.