A national map of Canadian Fire Behaviour Prediction (FBP) Fuel Types (FT) developed from public data sources. The resolution of the raster grid is 30m, classified from the Spatialized Canadian National Forest Inventory (SCANFI) dataset, ecozones of Canada, and the National Burned Area Composite (NBAC). The purpose of the dataset is to characterize Canadian forests into fuel types for use in Fire Behaviour Prediction calculations as well as for situational awareness of national fire potential.

11 tagged Blue whales (Balaenoptera musculus) were tracked during the daytime movements as well as the feeding behaviour in the St. Lawrence River estuary. Kernel density was applied to derminate the high density feeding areas of all individuals combined (30, 40, 50, 60, 75, 95 %).Doniol-Valcroze T, Lesage V, Giard J, Michaud R, 2012. Challenges in marine mammal habitat modelling: evidence of multiple foraging habitats from the identification of feeding events in blue whales. Endang Species Res, Vol. 17 : 255–268, doi : 10.3354/esr00427(English version only)

Acoustic telemetry allows detailed observations of the movement behaviour of many species and as tags get smaller, smaller organisms may be tagged. The number of studies using acoustic telemetry to evaluate marine invertebrate movement is growing, but novel attachment methods include unknowns about the effects of tagging procedures on individual survival and behaviour. This study compared methods of tag attachment on green sea urchins (Strongylocentrotus droebachiensis) to determine the feasibility of using acoustic transmitters to track echinoid movement. Four tagging methods were compared in the lab and tag retention, urchin condition, and survival analysed. Two tagging methods (Dyneema® fishing line and T-bar tags) were evaluated in the field using an existing acoustic telemetry array. Urchins were tagged and the study area revisited one week and 2 months post-release by scuba divers to estimate movement and tag retention. The best methods in the lab, with high tag retention, survival, and minimal effects on urchin condition, were fishing line methods. T-bar tags, although showing high tag retention, caused significant mortality and had deleterious long-term effects on urchin condition and behaviour. After 2 months in the field, as in the lab, fishing line was a more effective tagging method. Urchins tagged with fishing line showed increased estimates of space occupancy compared to T-bar-tagged urchins and a single fishing-line tagged individual was found by divers in good health after 80 days. Combined, these laboratory and field results demonstrate the feasibility of using acoustic telemetry to observe urchin movement. Results strongly suggest that surgical attachment methods that minimize injuries at the attachment site should be prioritized for echinoid tagging studies. Together, lab and field tests indicate that acoustic telemetry is a promising method to examine marine echinoid movement over ecologically relevant spatial and temporal scales.The data available includes the laboratory data (tag retention, survival, diameter, wet weight, gonad weight and condition/righting time) and the field data (metadata and acoustic telemetry detections for tagged individuals, results of diver searches and 2-day estimates of movement measured in the field). Data from the laboratory experiment and diver observations in the field have been verified and undergone a control for quality. Acoustic telemetry detections are raw detection files (unfiltered); see the published article for a description of how the data were treated for analyses (https://doi.org/10.1186/s40317-022-00309-8).

Most of these events involved community evacuations, significant structural loss and/or involvement of a Ministry of Natural Resources (MNR) Emergency Response Officer. Events include those assigned to MNR by an Order-In-Council under the Emergency Management and Civil Protection Act as well as events where MNR provided requested emergency response assistance. These events fall into one of ten type categories: * dam failure * drought /low water * erosion * flood * forest fire * soil and bedrock instability * Petroleum Resource Center event * EMO requested assistance * continuity of operations event * other requested assistance This product requires the use of geographic information system (GIS) software.

Displays the levels of escalating enforcement applied by health officers to enforce the Manitoba Food and Food Handling Establishment Regulations when food safety violations are not corrected in the prescribed period of timeThis table displays the frequency in which health officers applied escalating levels of enforcement to food processors who did not correct food safety violations in the prescribed amount of time indicated in the inspection. Food processors are given a period of time to correct food safety violations based on the associated risk to food safety. Escalating enforcement is only applied if the violation is not corrected in the prescribed period of time. This table was created by the Food Safety and Inspection Branch - Agriculture and Resource Development department.Field Names (Field Alias): Field description. Period (Period):The period for which the data was collected. Either quarterly or yearly. Year (Year): The specific year of the previously mentioned data collection period. Date (Date): The dates of the calendar year in which the data was collected during a period. Enforcement_Type (Enforcement Type): The individual levels of escalating enforcement applied by health officers. The values for this field are "Warning letter", "Verbal warning", "Offence notice issued", "Closure orders issued", "Suspensions", "Products seized and destroyed", "Products seized and held". Total (Total): the number of occurrences a level of escalating enforcement was applied in a period.

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.

An opening's disturbance and silviculture activities reported into RESULTS. Most activities are within opening boundaries with the exception of broadcast treatments. An opening may have more than one activities associated with it. Activities may also overlap each other. Reporting of disturbance and silviculture attribute information is a mandatory requirement while the map is optional. This is part of the Silviculture and Land status Tracking dataset, which includes tracking harvesting and silviculture obligations on Crown Land

These are the specific silviculture activities that took place within an opening in order to meet silviculture obligations after a disturbance. These activities may have been conducted over the entire opening or just a portion of the opening, and multiple activities may overlap. This database is the most current information available, but will be updated as new information is added.ACTIVITY_CATEGORY - This is the broad category of activity, such as disturbance, site preparation, planting, or survey.ACTIVITY_NAME - This is the specific activity that occurred in the opening.ACTIVITY_AREA - This is the measured within the opening that the activity occurred on.ACTIVITY_STATUS - This is the status of the activity which is either planned, started, or completed.ACTIVITY_COMPLETED_DATE - This is the date that the activity was finished.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)

DEPRECATED: During a declared emergency event, this dataset will include the communities affected by Evacuation Order Orders and Alerts, which represents the populated communities and First Nations in the province, and their current evacuation status.

The blue whale (Balaenopterus musculus) is a wide-ranging cetacean that can be found in all oceans, inhabiting coastal and oceanic habitats. In the North Atlantic, little is known about blue whale distribution and genetic structure, and if whether animals found in Icelandic waters, the Azores, or Northwest Africa are part of the same population as those from the Northwest Atlantic. In the Northwest Atlantic, seasonal movements of blue whales and habitat use, including the location of breeding and wintering areas, are poorly understood.The behaviour of remotely-monitored animals can be inferred from a time series of location data. This is because animals tend to demonstrate stochasticity in their movement paths as a result of spatial variation in environmental characteristics, such as topography or prey density (Curio 1976; Gardner et al. 1989; Turchin 1991; Wiens et al. 1993). Predators are expected to decrease travel speed and/or increase turning frequency and turning angle when a suitable resource, e.g., food patch, is encountered (Turchin 1991), otherwise known as area-restricted search (ARS). In contrast, animals in transit or travelling tend to move at faster and more regular speeds, with infrequent and smaller turning angles (Kareiva and Odell 1987; Turchin 1998).Based on satellite telemetry to track the seasonal movements of 24 blue whales from eastern Canada in 2002 and from 2010 to 2015, it was possible to estimate trajectories and locations where ARS behaviour of blue whales was inferred at a 4h time interval.To assess blue whale movements and behavior, a Bayesian switching statespace model (SSSM) was applied to Argos-derived telemetry data (Jonsen et al. 2005; Jonsen et al. 2013). An SSSM essentially estimates animal location at fixed time intervals, movement parameters and behavioral patterns.Two important sources of uncertainty can be measured separately: estimation error resulting from inaccurate observations (Argos location error) and process variability linked to the stochasticity of the movement process (behavior mode estimation) (Jonsen et al. 2003; Patterson et al. 2008).The points visible on land are the result of errors in the Argos geographic position calculation. They have been deliberately left unchanged to assess the performance of the model, which was able to clean up some positions, but not all.Lesage, V., Gavrilchuk, K., Andrews, R.D., and Sears, R. 2016. Wintering areas, fall movements and foraging sites of blue whales satellite-tracked in the Western North Atlantic. DFO Can. Sci. Advis. Sec. Res. Doc. 2016/078. v + 38 p.