Moose distribution, season of habitat use, and habitat values are determined by local wildlife biologist relying on observations, surveys, and radio/satellite data. For use in large-scale planning and reporting.Habitat definitions:Crucial value - habitat on which the local population of a wildlife species depends for survival because there are no alternative ranges or habitats available. Crucial value habitat is essential to the life history requirements of a wildlife species. Degradation or unavailability of crucial habitat will lead to significant declines in carrying capacity and/or numbers of wildlife species in question.Substantial value - habitat used by a wildlife species but is not crucial for population survival. Degradation or unavailability of substantial value habitat will not lead to significant declines in carrying capacity and/or numbers of the wildlife species in question.

Moose habitat and movement throughout Colorado.

Connectivity describes how well a landscape facilitates or impedes the movement of animals. Maximizing connectivity is a common management goal, especially for large mammals like moose (Alces americanus americanus) that occupy large home ranges and have the capacity to move long distances. Moose in the northeastern US (encompassing the states of Vermont, New Hampshire, Massachusetts, Maine, Connecticut, and Rhode Island) represent a management priority and are expected to decline due to the near-term impacts of climate change and landscape development that will alter the distribution of habitats across the region. Large-scale maps of moose connectivity are unavailable but would provide an important resource for management planning to improve moose persistence in the landscape. We used an omnidirectional circuit-theory approach to model and map moose connectivity across the six states in the northeastern US. The approach involved integrating a distribution map developed from an occurrence model and a resistance map developed from expert opinion data, along with home range information and current landcover maps to depict expected movement flow. The data release includes 1 CSV file that contains expert-elicited responses regarding moose occurrence and resistence to movement. The release also includes 6 rasters (1 and 2) the Omniscape inputs files named "source.tif" and "resistance.tif"; (3) the connectivity raster using a 0-threshold "source" input named "cumulative_current_map_raw0.tif"; (4) the Omniscape connectivity raster using a 0.2-threshold "source" input named "cumulative_current_map_raw02.tif"; (5) and (6) the respective normalized connectivity rasters, named "normalized_map_crop0.tif" and "normalized_map_crop02.tif". The latter two rasters can be categorized into flow categories if desired: impeded (areas with less current than in a resistance-free landscape), diffuse (areas with as much current as a resistance-free landscape), intensified (areas with more current than a resistance-free landscape), and channelized (areas with much more current than a resistance-free landscape).

Yukon Flats National Wildlife Refuge (YKF NWR) and Koyukuk NWR (KUK NWR), U.S. Fish and Wildlife Service (USFWS), initiated a project with the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center to acquire map products needed for moose habitat assessment. The objective of this work was to create a suite of products which included: Estimated Vegetation Heights, probability of Willow Estimates, and Vegetation Type Maps. These products are based on spectral characteristics found in bands 2 through 7 of Landsat 8 OLI scenes processed to surface reflectance, acquired in summer of 2013, and late winter of 2014. Training data was collected by fixed wing aircraft and helicopter by USFWS refuge staff, and extrapolated by the methods described. This project, “ Yukon Flats NWR willow mapping” (PI: Delia Vargas Kretsinger) was funded through the USFWS Inventory and Monitoring program via an Interagency Agreement between the USGS EROS and the USFWS – Alaska Regional Office. The data products are provisional in nature and are intended to support USFWS land management decisions. These data have not been validated with independent test data but received favorable qualitative assessment by local field experts.

The objectives of this work were to map the distribution of the 2 most important habitat associations to moose in the Southern Rockies in order to develop predictive seasonal habitat models. Two separate datasets were the result of this work, including Deciduous forest and riparian shrublands (Riparian Areas for Moose Winter Habitat Selection, Medicine Bow National Forest, Wyoming). Purpose: The objectives of this work were to map the distribution of the 2 most important habitat associations to moose in the Southern Rockies in order to develop predictive seasonal habitat models.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This is a raster map of moose (Alces alces) distribution in the New England region of the northeastern United States. The value of each pixel in the map is an estimate of the species probability of occurrence. Occurrence estimates were calculated from species-specific distribution models fit using expert-opinion data and generalized linear mixed modeling. For details on the collection of expert opinion data, the modeling process, and the development of the distribution maps, please see: Pearman-Gillman, S, J. E. Katz, R. Mickey, J. Murdoch, and T. Donovan. 2020. Predicting wildlife distribution patterns in New England USA with expert elicitation techniques. Global Ecology and Conservation 21:e00853.

Animal movements, needed to acquire food resources, avoid predation risk, and find breeding partners, are influenced by annual and circadian cycles. Decisions related to movement reflect a quest to maximize benefits while limiting costs, especially in heterogeneous landscapes. Predation by wolves (Canis lupus) has been identified as the major driver of moose (Alces alces) habitat selection patterns, and linear features have been shown to increase wolf efficiency to travel, hunt and kill prey. However, few studies have described moose behavioral response to roads and logging in Canada in the absence of wolves. We thus characterized temporal changes (i.e., day phases and biological periods) in eastern moose (Alces alces americana) habitat selection and space use patterns near a road network in a wolf-free area located south of the St. Lawrence River (eastern Canada). We used telemetry data collected on 18 females between 2017 and 2019 to build resource selection functions and mixed linear regressions to explain variations in habitat selection patterns, home-range size and movement rates. Female moose selected forest stands providing forage when movement was not impeded by snow cover (i.e., spring/green-up, summer/rearing, fall/rut) and stands offering protection against incidental predation during calving. In winter, home-range size decreased with an increasing proportion of stands providing food and shelter against harsh weather, limiting the energetic costs associated with movement. Our results reaffirmed the year-round aversive effect of roads, even in the absence of wolves, but the magnitude of this avoidance differed between day phases, being lower during the “dusk-night-dawn” phase, perhaps due to a lower level of human activity on and near roads. Female moose behavior in our study area was similar to what was observed in landscapes where moose and wolves cohabit, suggesting that the risk associated with humans, perceived as another type of predator, and with incidental predators (coyote Canis latrans, black bear Ursus americanus), equates that of wolf predation in heavily managed landscapes. Methods Telemetry data were collected on 20 moose (2 males, 18 females; 151,029 GPS locations) between 2017 and 2019. Capture and handling took place in February and March 2017, and our protocols were approved by the ministère de l’Environnement, de la Lutte contre les Changements Climatiques, de la Faune et des Parcs (hereafter MELCCFP; wildlife management permit SEG # 2017-02-10-010-01-S-F) and by the Animal Welfare Committee of the Université du Québec à Rimouski (hereafter UQAR; certificate CPA #68-17-183). We delineated 5 biological periods (winter, spring/green-up, calving, summer/rearing, fall/rut) and 2 day phases (day, dusk-night-dawn) in order to consider the temporal variations in moose behavior associated with these factors. We determined the cut-off dates of the biological periods by identifying breaks in the distribution of mean movement rates in function of Julian days and using the available knowledge on moose ecology (Hundertmark, 2007; Leblond et al., 2010); we did so for each individual-year combination. We defined day phases using the official sunrise and sunset times (National Research Council Canada, 2021): the day was bounded by the 60-minute period following sunrise to the 60-minute period preceding sunset, and dusk-night-dawn was bounded by the 60-minute period preceding sunset to the 60-minute period following sunrise. We defined landcover types using 1: 20,000 ecoforestry maps published by the ministère des Ressources naturelles et des Forêts (hereafter MRNF) and combined information from two mapping exercises (4th and 5th decennial inventories) to create updated annual maps to account for anthropogenic disturbance, and fit the GPS data collected from our collared moose (from 2017 to 2019). We regrouped the map polygons into a total of 8 landcover types relevant to moose ecology based on stand cover, composition, height, age, disturbance, land types, and representativity. We estimated movement rates (in m/h) for each individual and for each step (i.e., the trajectory linking two successive locations spaced by a 2 h interval) using Euclidean distances. We delineated seasonal home ranges using the kernel method based on Brownian bridges (Horne et al., 2007). We characterized moose habitat selection patterns using resource selection functions (hereafter RSF; Manly et al., 2002) with the different landcover types and other covariates (elevation, slope, day phase, presence of forest and paved roads in buffer zone) for each biological period. For each biological period, we retained only combinations of individual ID – year for which we had data for the entire or nearly the entire duration of the biological period (i.e., ~4% of the ID – year were removed from the dataset for the statistical analyses). We compared moose space use patterns (movement rates and home-range sizes) between biological periods using an analysis of variance (ANOVA) with repeated measures followed by a multiple comparison test (Tukey). The RSF we used to describe the habitat selection patterns was a mixed logistic regression contrasting GPS locations (coded 1) with random points (coded 0) with different combinations of the following independent variables: landcover types, topography variables, day phases, and the presence of forest and paved roads in the buffer zone around each location.

Maps of Nova Scotia showing distribution of (Fig. A1) moose populations and protected areas; (Fig. A2) moose habitat suitability values; (Fig. A3) road density and moose pellet presence/absence; (Fig. A4) contiguous areas of natural cover 10,000 ha; (Fig. A5) roadless areas; (Fig. A6) uneven-aged forest stands; (Fig. A7) combined cover for contiguous natural cover 10,000 ha, roadless areas, and uneven-aged forest stands; (Fig. A8) areas of primary priority combining natural areas, 10,000 ha, uneven-aged forest stands, and roadless areas; (Fig. A9) species at risk globally or provincially; (Fig. A10) highest rarity-weighted richness values; (Fig. A11) significant ecosites; (Fig. A12) signigficant old and unique forest stands; (Fig. A13) areas of primary priority for special elements; highest habitat suitability and population densities for (Fig. A14) American moose, (Fig. A15) American marten, and (Fig. A16) Northern Goshawk; (Fig. A17) 47 core areas selected by priority sites for representation, special elements, and focal species; (Fig. A18) cost-surface for American marten; and (Fig. A19) least-cost paths for American marten.

Moose (Alces americanus) is a large herbivore that inhabits mainly forests, wetlands and riparian areas. It is an important subsistence resource in the YKL study area. This dataset provides the most up-to-date spatial distribution of Moose (Alces americanus) rutting concentrations within the YKL study area for the analysis of the Terrestrial Fine-Filter Conservation Element and Management Question #6. We heads-up digitized seasonal Moose concentrations; including winter ranges, from scanned distribution maps from the Alaska habitat management guide. During calving, rutting, and winter, moose are generally found concentrated around riparian areas. According to ADFG management reports, the majority of radio-collared animals within Game Management Units 17, 19, 21, and 24 are generally non-migratory, which is supported by the substantial overlap in seasonal range maps.

Moose habitat and movement throughout Colorado.

Yukon Flats National Wildlife Refuge (YKF NWR) and Koyukuk NWR (KUK NWR), U.S. Fish and Wildlife Service (USFWS), initiated a project with the U.S. Geological Survey Earth Resources Observation and Science (EROS) Center to acquire map products needed for moose habitat assessment. The objective of this work was to create a suite of products which included: Estimated Vegetation Heights, probability of Willow Estimates, and Vegetation Type Maps. These products are based on spectral characteristics found in bands 2 through 7 of Landsat 8 OLI scenes processed to surface reflectance, acquired in summer of 2013, and late winter of 2014. Training data was collected by fixed wing aircraft and helicopter by USFWS refuge staff, and extrapolated by the methods described. This project, “ Yukon Flats NWR willow mapping” (PI: Delia Vargas Kretsinger) was funded through the USFWS Inventory and Monitoring program via an Interagency Agreement between the USGS EROS and the USFWS – Alaska Regional Office. The data products are provisional in nature and are intended to support USFWS land management decisions. These data have not been validated with independent test data but received favorable qualitative assessment by local field experts.

Capability mapping showing provincially significant winter ranges from CORE for moose, bighorn sheep, mule deer, goat, black bear, grizzly bear and caribou. Disclaimer: This is older strategic scale mapping information that may be superseded in some areas with more detailed TEM mapping information

Wildlife Key Areas (WKA) are locations used by wildlife for critical, seasonal life functions. WKAs are identified by interpreting observed locations of wildlife at key times of year, not through intensive habitat assessment. Polygons derived from interviews with locals and from GIS interpretation of wildlife/habitat surveys. GIS interpretation follows criteria specific for taxon and/or populations of taxon. Key Areas are based on observed locations of wildlife at key times of year, not on habitat assessment. With new information, boundaries and designations of Key Areas can change and additional Key Areas can be identified. Furthermore, Key Areas are not the only sites important for wildlife. Other information sources can identify other sites important for wildlife for reasons outside the scope of the WKA Inventory Program. Updates to Key Areas occur only periodically. For the most current information, please consult with the Regional Biologist for your area of interest. If you have questions or would like to contribute to the WKA database, please contact the WKA Inventory Program (wka@yukon.ca).Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon’s digital map data collection.For more information: geomatics.help@yukon.ca

Wildlife Key Areas (WKA) are locations used by wildlife for critical, seasonal life functions. WKAs are identified by interpreting observed locations of wildlife at key times of year, not through intensive habitat assessment. Polygons derived from interviews with locals and from GIS interpretation of wildlife/habitat surveys. GIS interpretation follows criteria specific for taxon and/or populations of taxon. Key Areas are based on observed locations of wildlife at key times of year, not on habitat assessment. With new information, boundaries and designations of Key Areas can change and additional Key Areas can be identified. Furthermore, Key Areas are not the only sites important for wildlife. Other information sources can identify other sites important for wildlife for reasons outside the scope of the WKA Inventory Program. Updates to Key Areas occur only periodically. For the most current information, please consult with the Regional Biologist for your area of interest. If you have questions or would like to contribute to the WKA database, please contact the WKA Inventory Program ( wka@yukon.ca ). Distributed from GeoYukon by the Government of Yukon . Discover more digital map data and interactive maps from Yukon's digital map data collection. For more information: geomatics.help@yukon.ca

description: This is a series of letters and maps that were exchanged between Dr. Ueckermann in Germany and David Spencer at Kenai National Moose Range. The letters focus on moose hunting.; abstract: This is a series of letters and maps that were exchanged between Dr. Ueckermann in Germany and David Spencer at Kenai National Moose Range. The letters focus on moose hunting.

The wildlife values area and site datasets represent the consolidation of 13 wildlife data classes collected by the Ministry of Natural Resources and Forestry. The data estimates locations used by wildlife for various reasons, including:

breeding calving and fawning denning feeding staging nesting wintering general habitat areas nurseries travel corridors

Locations are represented as points (site) or polygons (area) and may be related to a specific species or described more generally.

Wildlife values data is most often used to support policy and legislation associated with the Crown Forest Sustainability Act. The data may also be used to inform a wide range of resource management activities and decisions.

There are additional sensitive features related to provincially tracked species and species at risk that are not available as part of the open data package. Sensitive features are subject to licensing and approvals and may be requested by contacting lio@ontario.ca.

This class has related tables. Wildlife Values Area related tables

Additional Documentation

Wildlife Values Area - Data Description (PDF)

Wildlife Values Area - Documentation (Word)

Wildlife Values User Guide (PDF)

Status On going: data is being continually updated Maintenance and Update Frequency

As needed: data is updated as deemed necessary

Contact

Christine Phair, Integration Analyst, Integration Branch, christine.phair@ontario.ca

The Hudson Bay Lowlands (HBL) is the wettest ecozone in Canada with 80% of its area covered by wetlands. It forms the third largest wetland in the world and is composed almost entirely of permafrost and non-frozen subarctic peatlands that store more carbon in the first 2 m of soil than the total carbon stored in any other ecozone in Canada. The HBL is also home to large mammals including polar bears, caribou, moose, and provides important breeding habitat for migratory birds. Surface water dynamics in the HBL are both a consequence and a driver of climate change, impacting evapotranspiration, permafrost thaw and carbon budgets under wetting / drying conditions as well as wildlife habitat. Previously, dynamic surface water products were generated from historical Landsat data to inform surface water trends in the HBL based on binary classifications of land versus water at 30 m spatial resolution (Olthof and Rainville, 2022). However, the HBL contains many water features smaller than 30 m, including streams and patterned fens that require a sub-pixel mapping approach to depict these small features as the percent water fraction within each pixel footprint. The annual surface water products in this dataset were created by leveraging an existing binary dynamic surface water product (Olthof and Rainville, 2022) to implement adaptive physical linear spectral unmixing models. The result is a spatially and temporally comprehensive Landsat sub-30m surface water time-series over the HBL from 1985 to 2021 that can be used to help researchers and policymakers address issues around climate change and wildlife. Following the Government of Canada Open Data initiative, these original dynamic surface water maps are available to the public. More information on the creation of this dataset can be found in the associated research paper at https://www.sciencedirect.com/science/article/pii/S0034425723004467?ref=pdf_download&fr=RR-2&rr=84402791cff87154.