FluWatchers is an online health surveillance system. It helps monitor the spread of flu-like illness across Canada. FluWatchers relies on Canadians to volunteer 15 seconds of their time each week to answer 2 questions about their health. To be a Fluwatcher, sign up at https://cnphi.canada.ca/fluWatcher/registerNote: Only areas where there are five or more weekly reporters are included in the map. The reported rates of cough and fever are a reflection of the surveillance data available to FluWatch at the time of production. Delays in reporting of data may cause data to change retrospectively.

This series of datasets has been created by AAFC’s National Agroclimate Information Service (NAIS) of the Agro-Climate, Geomatics and Earth Observations (ACGEO) Division of the Science and Technology Branch. The Canadian Drought Monitor (CDM) is a composite product developed from a wide assortment of information such as the Normalized Difference Vegetation Index (NDVI), streamflow values, Palmer Drought Index, and drought indicators used by the agriculture, forest and water management sectors. Drought prone regions are analyzed based on precipitation, temperature, drought model index maps, and climate data and are interpreted by federal, provincial and academic scientists. Once a consensus is reached, a monthly map showing drought designations for Canada is digitized. AAFC’s National Agroclimate Information Service (NAIS) updates this dataset on a monthly basis, usually by the 10th of every month to correspond to the end of the previous month, and subsequent Canadian input into the larger North American Drought Monitor (NA-DM).The drought areas are classified as follows: D0 (Abnormally Dry) – represents an event that occurs once every 3-5 years;D1 (Moderate Drought) – represents an event that occurs every 5-10 years;D2 (Severe Drought) – represents an event that occurs every 10-20 years;D3 (Extreme Drought) – represents an event that occurs every 20-25 years; andD4 (Exceptional Drought) – represents an event that occurs every 50 years. Impact lines highlight areas that have been physically impacted by drought. Impact labels specify the longitude and magnitude of impacts.The impact labels are classified as follows:S – Short-Term, typically less than 6 months (e.g. agriculture, grasslands).L – Long-Term, typically more than 6 months (e.g. hydrology, ecology).

Prior to January 1, 2008, fishing divisions were administrative units to manage, monitor, assess and regulate recreational fisheries. Each zone was based on angler usage and ecological/geographic patterns. Refer to [Fisheries Management Zone for boundaries after 2008](/dataset/fisheries-management-zone).

An abiotic damage event is a non-biological event -- such as wind or an ice storm -- that has damaged areas of forested land. Abiotic damage event information is mainly used to: * generate summary maps for these events at a general or provincial scale * monitor the extent of damage for forest fire prevention purposes * calculate gross timber volume loss estimates caused by these events This product requires the use of geographic information system (GIS) software.

The Coastal Biodiversity Trawl Survey for the Passamaquoddy Bay was conducted annually between July to October from 2009 to 2019. This survey was intended to monitor long-term change in local species presence, habitat utilization, and health. The sampling activities support coastal research in fisheries, aquaculture, marine protected areas, and ecosystem change. Data collected prior to 2013 are generally not recommended for comparative analysis due to changes in vessel, sampling effort, and protocols.

As part of the development of a nationally-consistent sampling design within the Aquaculture Monitoring Program (AMP), this data reports mesozooplankton assemblages observed at nine coastal shellfish aquaculture sites, located across four DFO regions, with sampling across months, tide phases, and sampling locations. In most sites, strong spatial effects were observed, while tide effects were generally less important for structuring the mesozooplankton communities. Seasonality emerged as an essential factor to design an efficient monitoring program. This dataset represents the first large-scale Canadian coastal study using imaging technology for plankton taxonomic Identification.Cite this data as: Finnis, S., Guyondet, T., McKindsey, C.W., Arseneau, J., Barrell, J., Duhaime, J., Filgueira, R., Gallardi, D., Gaspard, D., Gibb, O., Goodwin, C., Hua, K., Macdonald, T., Milne, R., Lacoursière-Roussel, A. 2023. Guidance on sampling effort to monitor mesozooplankton communities at Canadian bivalve aquaculture sites using an optical imaging system. Can. Tech. Rep. Fish. Aquat. Sci. 3581: vii + 101 p

Bivalve aquaculture has direct and indirect effects on plankton communities, which are highly sensitive to short-term (seasonal, interannual) and long-term climate changes, although how these dynamics alter aquaculture ecosystem interactions is poorly understood. Here, we investigate seasonal patterns in plankton abundance and community structure spanning several size fractions from 0.2 µm up to 5 mm, in a deep aquaculture embayment in northeast Newfoundland, Canada. Using flow cytometry and FlowCam imaging, we observed a clear seasonal relationship between fraction sizes driven by water column stratification (freshwater input, nutrient availability, light availability, water temperature). Plankton abundance decreased proportionally with increasing size fraction, aligning with size spectra theory. Within the bay, greater mesozooplankton abundance, and a greater relative abundance of copepods, was observed closest to the aquaculture lease. No significant spatial effect was observed for phytoplankton composition. While the months of August to October showed statistically similar plankton composition and size spectra slopes (i.e., food chain efficiency) and could be used for interannual variability comparisons of plankton composition, sampling for longer periods could capture long-term phenological shifts in plankton abundance and composition related to various processes, including climate change. Conclusions provide guidance on optimal sampling to monitor and assess aquaculture pathways of effects.Cite this data as: Sharpe H, Lacoursière-Roussel A, Gallardi D (2024). Ecological insight of seasonal plankton succession to monitor shellfish aquaculture ecosystem interactions. Version 3.2. Fisheries and Oceans Canada. Sampling event dataset. https://doi.org/10.25607/2ujdvh

This dataset provides the background radiation dose results from Health Canada’s Canadian Radiological Monitoring Network (CRMN) monitoring sites. More information about the CRMN network can be found on the Health Canada website (see link below).This background radiation data contains both “monitoring” and “transit” dosimeters starting in 2016. The historical background radiation dose data can be found on the Open Data portal. A transit dosimeter is sent along with the monitoring dosimeter to determine if there is a significant dose recorded by the dosimeter while it is in transit to the sampling station. The transit dosimeter is shipped out with a station monitor, and shipped back with the station monitor from the previous quarter. The monitoring dosimeters are deployed over a longer time (around three months) than the transit dosimeters (around 3 weeks). This difference largely explains the lower recorded dose values for the transit dosimeter. The results provided for the monitoring and transit dosimeters are expressed as ambient dose equivalent to a cesium source, in units of millisieverts (mSv). The measured dose rate is reported in mSv/day. The external dose can be attributed almost exclusively to natural radiation (of terrestrial and cosmic origin) with fluctuations based on several factors including location, soil characteristics, and seasonal changes. The map shows the approximate sampling location for each monitoring station. Stations are found within the associated location range.

Data show where pathogens - fungal, bacillial or viral - have caused damage by reducing growth rates, tree vigor or have killed trees. Examples of forest diseases include White Pine Blister Rust, Armillaria Root Rot etc. The Government of Ontario tracks forest damage events to help proactively manage the detrimental effects to our forests. We monitor the threat and spread of invasive forest pest insect species in Ontario. The data is also important to the Forest Management Planning process in calculating timber volume loss within affected areas. This product requires the use of geographic information system (GIS) software.

Miscellaneous events are often the result of the cumulative impact of a combination of abiotic, insect and disease agents or events. For example, Aspen decline where repeated infestations of Forest Tent Caterpillar, are combined with several seasons of prolonged drought. The Government of Ontario tracks forest damage events to help proactively manage the detrimental effects to our forests.   We monitor the threat and spread of invasive forest pest insect species in Ontario. The data is also important to the Forest Management Planning process in calculating timber volume loss within affected areas. This product requires the use of geographic information system (GIS) software.