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.

PURPOSE:Provide early detection of newly arrived Aquatic Invasive Species (AIS) and determine the spread, establishment and spatial distribution of existing AIS within marine waters of the southern Gulf of St. Lawrence (sGSL), DFO Gulf Region boundaries (northern and eastern coastal shores of NB, Gulf shore of NS, and PEI shoreline).DESCRIPTION:DFO Science monitors for AIS in the Gulf Region. The data collected from DFO's biofouling monitoring program provides an overview of the distribution and abundance of Aquatic Invasive Species (AIS) in the Gulf Region. This information can be used by the general public, scientists and DFO managers.Monitoring program targeting aquatic invasive species (AIS). Native biofouling species are not included in this dataset. Botrylloides violaceus: Violet tunicateBotryllus schlosseri: Golden star tunicateCiona intestinalis: Vase tunicateStyela clava: Clubbed tunicateCaprella mutica*: Japanese skeleton shrimpMembranipora membranacea: Coffin box bryozoanCarcinus maenas*: European green crabCodium fragile*: Oyster thief algaJuxtacribrilina mutabilis**: Ribbed bryozoan (JCM)*indicates species that are not included as percent cover as they are not accurately captured by the sampling method, but are included as detections.**indicates species was only integrated fully into the monitoring protocol starting in 2025.Included here is a dataset of detection and percent cover data of AIS, as well as a monitoring station dataset. Environmental data collected, including from temperature loggers, are stored but not included here. PARAMETERS COLLECTED:Air and water temperature, salinity, depth, dissolved oxygen, weather conditions, list of biofouling AIS, percent cover of AIS on PVC plates, and incidental detection of other species and AIS in surrounding area. Only species data is pushed to the Open Data platform.NOTES ON QUALITY CONTROL:Each sample and species is processed and identified in a standardized fashion using standardized DFO Science AIS protocols and taxonomic references. Data is manually entered into DFO Gulf Region's AIS Science biofouling database and randomly verified for accuracy.SAMPLING METHODS:Biofouling monitoring is conducted using PVC collector plates that are deployed in the water column approximately 1 meter below the sea surface in the spring of each year. Biofouling organisms settle on these plates which are collected in the fall of the same year. Abundances of AIS are given as percent plate cover. Physico-chemical data including temperature, conductivity, and depth as well as weather conditions are noted at each geo-referenced biofouling monitoring site during initial deployment and at time of retrieval. All biofouling organisms settled on the underside of the PVC plates are noted and percent cover of each AIS is estimated. Any AIS present at the location is also noted. A HOBO temperature and light logger was deployed.USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.

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

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.

Greenland halibut (Reinhardtius hippoglossoides) occupy deep waters off Newfoundland and Labrador and are a commercially important species. The Groundfish Section, Fisheries and Oceans Canada – Newfoundland and Labrador Region implemented an acoustic tagging program to better understand the movement and habitat use for this species. This program began in 2021, and was further developed as a results of two DFO Competitive Science Research Fund project between 2022-2026.Fish were caught via trawl or longline, VEMCO acoustic transmitters surgically implanted, and released at site of capture. Detections occurred from acoustic receivers deployed by the Groundfish section and/or from receiver arrays associated with the Oceans Tracking Network (e.g. Northern Cod Acoustic Telemetry Array [NCAT]). Raw detection data are downloaded from receivers and sent to the Ocean Tracking Network for formatting and archiving. Data provided here summarize reported detections from 2021-2024 as part of a master’s project at Memorial University of Newfoundland. Release locations, number and location of detections, defined movement class, and detection duration are summarized by individual. Location information includes the position of the receiver the fish was detected at, and the North Atlantic Fisheries Organization (NAFO) Divisions where fish were detected in.

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).

This data publication contains a set of files in which areas affected by fire or by harvest from 1984 to 2015 are identified at the level of individual 30m pixels on the Landsat grid. Details of the product development can be found in Guindon et al (2018). The change detection is based on reflectance-corrected yearly summer (July and August) Landsat mosaics from 1984 to 2015 created from individual scenes developed from USGS reflectance products (Masek et al, 2006; Vermote et al, 2006). Briefly, the change detection method uses a six-year temporal signature centered on the disturbance year to identify fire, harvest and no change. The signatures were derived from visually-interpreted disturbance or no-change polygons that were used to fit a decision tree model. The method detects about 91% of the areas harvested and 85% of the areas burned across Canada’s forests over the study period, but overestimates areas disturbed in the two initial and mostly in the two final years of the 1985 to 2015 time series. This is caused by the absence of appropriate pre-disturbance and post-disturbance data for the model-based detection and attribution. Disturbance coverage in those four years should therefore be used with caution. As in Guindon et al (2014), the method was designed to minimize commission errors and has a disturbance class attribution success rate of about 98%. The attribution success rate of disturbance year for fire is of about 69% for the exact year and of about 99% when attribution to the following year is also considered as a success. This common one-year lag is mostly due to the use of mid-summer Landsat mosaics for the analysis that will cause spring and fall events of the same year to be attributed to successive years. For example, a fire that occurred in the fall of 2004 (after July and August), will be detected and attributed to 2005, while for a fire that occurred in the spring of 2004 will be detected and attributed to 2004. The presence of clouds and shadows or image availability causes 10% of missing data annually and therefore can too delay the capture of events. The data provides uniform spatial and temporal information on fire and harvest across all provinces and territories of Canada and is intended for strategic-level analysis. Since no attention was given to other minor disturbances such as mining, road or flooding, the product should not be used for their identification. Finally, calibration datasets were developed for only three major forest pests (mountain pine beetle, eastern spruce budworm and forest tent caterpillar), but were folded within the “no-change” class in order to minimize commission errors for fire and harvest . Less common pests for which validation datasets are hard to develop were not considered and therefore could in rare circumstances generate false fire events. Considering that area having two (3.3%) to three disturbances (less than 1%) events are not common, only the most recent disturbance is provided, overlapping older disturbances in these rare case. ## Please cite this dataset as: 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 ## Scientific article citation: The creation, validation and limitations of the CanLaD product are described in the Supplementary Material file associated with the following article: Guindon, L.; Bernier, P.Y.; Gauthier, S.; Stinson, G.; Villemaire, P.; Beaudoin, A. 2018. Missing forest cover gains in boreal forests explained. Ecosphere, 9 (1) Article e02094. doi:10.1002/ecs2.2094. ## Cited references: Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008.

This theme presents observations of invasive exotic species (IAS)transmitted and validated using the Sentinelle tool, an EEE detection system.An invasive exotic species is a plant, animal or microorganism (virus,bacteria or fungi) that are introduced outside of their natural range. Sonestablishment or its spread may pose a threat to the environment,the economy or society. The species listed are species of fauna and floraconcerning (or potentially worrying) for Quebec's biodiversity. Ellesinclude EEE present in Quebec and EEE not listed in Quebec atmonitor.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

PURPOSE:Assess the distribution of freshwater mussels, including the Brook Floater (Alasmidonta varicosa), the Eastern Pearlshell (Margaritifera margaritifera), and the Yellow Lampmussel (Lampsilis cariosa) in New Brunswick.DESCRIPTION:This dataset contains the results of work undertaken from 2017 to 2024 to assess the distribution of freshwater mussels using environmental DNA (eDNA) surveys and species-specific qPCR assays. USE LIMITATION:To ensure scientific integrity and appropriate use of the data, we would encourage you to contact the data custodian.