Predicted Calving and Post-calving Season Resource Use of the Porcupine Caribou Herd During 2012–2018 With Future Projections for the 2030s, 2040s, and 2050s

Identification_Information:

Citation:

Citation_Information:

Originator: Severson, J.P. (ORCID: 0000-0002-1754-6689)
Originator: Johnson, H.E. (ORCID: 0000-0001-5392-7676)
Originator: Arthur, S.M.
Originator: Leacock, W.B.
Originator: Suitor, M.J.
Publication_Date: 20210618
Title:

Geospatial_Data_Presentation_Form: raster and vector digital data
Publication_Information:

Publication_Place: Anchorage, Alaska
Publisher: U.S. Geological Survey, Alaska Science Center

Other_Citation_Details:

Suggested Citation: Severson, J.P., Johnson, H.E., Arthur, S.M., Leacock, W.B., Suitor, M.J., 2021. Predicted calving and post-calving season resource use of the Porcupine Caribou Herd during 2012–2018 with future projections for the 2030s, 2040s, and 2050s: U.S. Geological Survey data release, https://doi.org/10.5066/P9TTRPAC

Online_Linkage: https://doi.org/10.5066/P9TTRPAC
Larger_Work_Citation:

Citation_Information:

Originator: U.S. Geological Survey, Alaska Science Center
Publication_Date: 2010
Title:

USGS Changing Arctic Ecosystems: Measuring and forecasting the response of wildlife populations to changes in ecosystem processes on the Arctic Coastal Plain

Geospatial_Data_Presentation_Form: website
Series_Information:

Series_Name: Alaska Science Portal
Issue_Identification: 279

Publication_Information:

Publication_Place: Anchorage, Alaska
Publisher: U.S. Geological Survey, Alaska Science Center

Other_Citation_Details:

This is a link to the broader USGS Alaska Science Center research project supported by these data. Users will find a description of the research project and links to associated reports, publications, and data products.

Online_Linkage: https://alaska.usgs.gov/portal/project.php?project_id=279

Description:

Abstract:

This dataset contains rasters and polygon shapefiles related to predicted resource use of the Porcupine Caribou Herd (PCH) during the calving (26 May–10 June) and post-calving (11–30 June) seasons in Alaska and the Yukon Territory. Resource selection was analyzed for each season using random forest models, which compared female caribou GPS collar locations (2012–2018) to available locations within the study area. The models assessed the influence of annual variation in spring phenology (dates of snowmelt, onset of vegetation greenness, and the 50% maximum NDVI value) on caribou resource use, while also accounting for static landcover and topographic variables. For each year GPS collar data were collected (2012–2018), we provide rasters of predicted relative probabilities of use (ranging from 0 to 1) during the calving and post-calving seasons given year-specific phenological conditions. We also provide annual polygons depicting "suitable" habitat, which were delineated from the rasters based on probability of use thresholds that minimized the mean class error (i.e., the average of the used and available location error rates; calving threshold: 0.534, post-calving threshold: 0.522). In addition to annual rasters and polygons, we provide an average raster and polygon across the study period (2012–2018) for each season.

We then projected future PCH calving and post-calving resource use based on predicted climate-driven shifts in spring phenology. We first used random forest regression to model phenology dates as a function of temperature, precipitation, and topographic covariates. We then obtained decadal averages of climate projections from the Coupled Model Inter-Comparison Project 5 for Representative Concentration Pathway 8.5 for the 2030s, 2040s, and 2050s. We used the climate projections to predict future dates of spring snowmelt and vegetation phenology across the study area, which were then applied to our resource selection models to project future PCH calving and post-calving resource use. For each decade in the 2030s, 2040s and 2050s, we provide a raster of the average predicted probability of use and a polygon depicting average suitable habitat. Decadal averages were used to reduce annual variation in spatial predictions so trends in suitable habitat could be more easily compared across time periods. Probabilities of use could not be estimated for pixels with missing phenology data, and thus, those pixels are not assigned probability values.

We also provide a shapefile of the study area boundary, which defined habitat availability for resource selection models and the extent of our spatial predictions. The study area was delineated from a 95% contour of an annually-weighted kernel density estimate of caribou locations during the combined calving and post-calving seasons and buffered by the mean daily caribou movement rate in the location dataset (13 km).

Purpose:

The U.S. Fish and Wildlife Service requested information on the influence of annual variation in spring phenology on calving and post-calving distributions of the Porcupine Caribou Herd (PCH), and how these distributions were likely to shift in the future under changing climate conditions. The rasters and shapefiles for 2012–2018 illustrate annual shifts in PCH calving and post-calving distributions, particularly during years with early (e.g., 2015) and late (e.g., 2018) spring phenology. The rasters and polygons for future decades depict average expected trends in resource use based on projected dates of spring phenology, and static landcover and topographic variables. It is important to note that future projections do not encompass the annual variation in habitat use that would be expected among years.

Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2021

Currentness_Reference: observed

Status:

Progress: Complete
Maintenance_and_Update_Frequency: None planned

Spatial_Domain:

Description_of_Geographic_Extent: North Slope of Alaska and northwestern Canada
Bounding_Coordinates:

West_Bounding_Coordinate: -147.5113
East_Bounding_Coordinate: -135.7438
North_Bounding_Coordinate: 70.4995
South_Bounding_Coordinate: 68.0161

Keywords:

Theme:

Theme_Keyword_Thesaurus: USGS Metadata Identifier
Theme_Keyword: USGS:ASC389

Theme:

Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: Biota
Theme_Keyword: Environment

Theme:

Theme_Keyword_Thesaurus: NASA GCMD Earth Science Keyword Thesaurus
Theme_Keyword: Animals/Vertebrates
Theme_Keyword: Mammals
Theme_Keyword: Telemetry
Theme_Keyword: Landscape ecology
Theme_Keyword: Range changes
Theme_Keyword: Herbivory
Theme_Keyword: Plant phenology

Theme:

Theme_Keyword_Thesaurus: USGS CSA Biocomplexity Thesaurus
Theme_Keyword: Herbivores
Theme_Keyword: Site Fidelity
Theme_Keyword: Seasonal distribution
Theme_Keyword: Seasonal movement

Theme:

Theme_Keyword_Thesaurus: USGS Thesaurus
Theme_Keyword: Wildlife
Theme_Keyword: Migratory species
Theme_Keyword: Terrestrial ecosystems
Theme_Keyword: Tundra ecosystems
Theme_Keyword: Animal behavior
Theme_Keyword: Phenology
Theme_Keyword: Geospatial datasets
Theme_Keyword: Animal tracking

Theme:

Theme_Keyword_Thesaurus: None
Theme_Keyword: Caribou
Theme_Keyword: Rangifer tarandus granti
Theme_Keyword: Resource selection
Theme_Keyword: Climate change
Theme_Keyword: Machine learning
Theme_Keyword: Random forest
Theme_Keyword: Arctic

Place:

Place_Keyword_Thesaurus: USGS Geographic Names Information System (GNIS)
Place_Keyword: Alaska
Place_Keyword: Arctic Coastal Plain
Place_Keyword: Arctic National Wildlife Refuge

Place:

Place_Keyword_Thesaurus: NGA GEOnet Names Server
Place_Keyword: Canada
Place_Keyword: Yukon
Place_Keyword: Ivvavik National Park

Taxonomy:

Keywords/Taxon:

Taxonomic_Keyword_Thesaurus: None
Taxonomic_Keywords: Animals
Taxonomic_Keywords: Mammals
Taxonomic_Keywords: Ungulates

Taxonomic_System:

Classification_System/Authority:

Classification_System_Citation:

Citation_Information:

Originator: ITIS Integrated Taxonomic Information System
Publication_Date: Unknown
Title: ITIS Integrated Taxonomic Information System
Geospatial_Data_Presentation_Form: online database
Publication_Information:

Publication_Place: online
Publisher: ITIS-North America

Other_Citation_Details:

Taxonomic details retrieved May 25, 2021 from the Integrated Taxonomic Information System online database https://www.itis.gov

Online_Linkage: https://www.itis.gov

Classification_System_Modifications:

ITIS currently considers the subspecies Rangifer tarandus granti invalid. However, here we follow numerous recent scientific publications which consider the Porcupine Caribou Herd to be barren-ground caribou of the subspecies Rangifer tarandus granti.

Taxonomic_Procedures:

Caribou were identified by skilled observers in the field based on general appearance.

Taxonomic_Classification:

Taxon_Rank_Name: Kingdom
Taxon_Rank_Value: Animalia
Taxonomic_Classification:

Taxon_Rank_Name: Subkingdom
Taxon_Rank_Value: Bilateria
Taxonomic_Classification:

Taxon_Rank_Name: Infrakingdom
Taxon_Rank_Value: Deuterostomia
Taxonomic_Classification:

Taxon_Rank_Name: Phylum
Taxon_Rank_Value: Chordata
Taxonomic_Classification:

Taxon_Rank_Name: Subphylum
Taxon_Rank_Value: Vertebrata
Taxonomic_Classification:

Taxon_Rank_Name: Infraphylum
Taxon_Rank_Value: Gnathostomata
Taxonomic_Classification:

Taxon_Rank_Name: Superclass
Taxon_Rank_Value: Tetrapoda
Taxonomic_Classification:

Taxon_Rank_Name: Class
Taxon_Rank_Value: Mammalia
Taxonomic_Classification:

Taxon_Rank_Name: Subclass
Taxon_Rank_Value: Theria
Taxonomic_Classification:

Taxon_Rank_Name: Infraclass
Taxon_Rank_Value: Eutheria
Taxonomic_Classification:

Taxon_Rank_Name: Order
Taxon_Rank_Value: Artiodactyla
Taxonomic_Classification:

Taxon_Rank_Name: Family
Taxon_Rank_Value: Cervidae
Taxonomic_Classification:

Taxon_Rank_Name: Subfamily
Taxon_Rank_Value: Capreolinae
Taxonomic_Classification:

Taxon_Rank_Name: Genus
Taxon_Rank_Value: Rangifer
Taxonomic_Classification:

Taxon_Rank_Name: Species
Taxon_Rank_Value: Rangifer tarandus
Applicable_Common_Name: reindeer
Applicable_Common_Name: caribou
Applicable_Common_Name: TSN: 180701
Taxonomic_Classification:

Taxon_Rank_Name: Subspecies
Taxon_Rank_Value: Rangifer tarandus granti
Applicable_Common_Name: barren-ground caribou

Access_Constraints: None
Use_Constraints:

It is requested that the authors and the USGS Alaska Science Center be cited for any subsequent publications that reference this dataset.

Point_of_Contact:

Contact_Information:

Contact_Organization_Primary:

Contact_Organization: U.S. Geological Survey, Alaska Science Center

Contact_Address:

Address_Type: Mailing and Physical
Address: 4210 University Drive
City: Anchorage
State_or_Province: Alaska
Postal_Code: 99508
Country: USA

Contact_Voice_Telephone: 907-786-7000
Contact_Electronic_Mail_Address: ascweb@usgs.gov

Data_Set_Credit:

U.S. Fish and Wildlife Service, Porcupine Caribou Technical Committee, Yukon Department of Environment, Alaska Department of Fish and Game

Cross_Reference:

Citation_Information:

Originator: Severson, J.P.
Originator: Johnson, H.E.
Originator: Arthur, S.M.
Originator: Leacock, W.B.
Originator: Suitor, M.J.
Publication_Date: 2021
Title:

Spring Phenology Drives Range Shifts in a Migratory Arctic Ungulate with Key Implications for the Future

Geospatial_Data_Presentation_Form: journal article
Series_Information:

Series_Name: Global Change Biology
Issue_Identification: TBD

Publication_Information:

Publication_Place: online
Publisher: Wiley

Other_Citation_Details:

Severson, J.P., Johnson, H.E., Arthur, S.M., Leacock, W.B., Suitor, M.J. 2021. Spring phenology drives range shifts in a migratory Arctic ungulate with key implications for the future. Global Change Biology doi:10.1111/gcb.15682

Online_Linkage: https://doi.org/10.1111/gcb.15682

Data_Quality_Information:

Attribute_Accuracy:

Attribute_Accuracy_Report:

Attribute values of pixels represent predicted relative probability of use. Random forest resource selection models were produced to optimize prediction accuracy. Cross-validation among years was used to optimize tree size. Models were selected using area under the curve (calving: 0.942; post-calving: 0.919). Spatial predictions were visually compared to caribou locations.

Logical_Consistency_Report:

Attribute values fall within the expected range (0-1). "NA" and missing values in the rasters represent missing data in the phenology covariates (snow in pixel never melted or vegetation in pixel never turned green in a given year; e.g., in persistent snow fields), which could not be run through the random forest models. Suitable habitat polygons were visually compared to the caribou locations.

Completeness_Report:

Predictions were only made within the study area boundary (included). Missing values in rasters account for a minimal portion of the study area and represent pixels with missing phenology covariate data (phenology event never occurred; e.g., snow did not melt or vegetation did not turn "green" in a given year; e.g., in persistent snow fields), which could not be run through the random forest models.

Positional_Accuracy:

Horizontal_Positional_Accuracy:

Horizontal_Positional_Accuracy_Report:

Caribou locations were collected via GPS collars with accuracy generally occurring at less than the pixel size of our covariates (minimum: 30-m resolution). Refer to citations of source data for underlying positional accuracy of habitat covariates.

Lineage:

Methodology:

Methodology_Type: Field
Methodology_Description:

Location data for female caribou (>=3 years old) marked with GPS collars in the Porcupine Caribou Herd during 2012-2018 and 26 May to 30 June were acquired from the Porcupine Caribou Technical Committee (archived by Yukon Environment). Caribou were captured by the Alaska Department of Fish and Game and Yukon Environment by net-gunning from a helicopter during February-March. GPS collars recorded locations every 2-5 hours.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Geographic Information Network of Alaska (GINA)
Publication_Date: 2020
Title: Modis-derived snow metrics
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place: online
Publisher: GINA, University of Alaska, Fairbanks

Other_Citation_Details:

Geographic Information Network of Alaska (GINA). 2020. Modis-derived snow metrics. Data accessible at https://gina.alaska.edu/projects/modis-derived-snow-metrics [Subset Query: spatial_extent=study area boundary; temporal_extent=2012-2018 for resource selection models and 2001-2017 for phenology models]

Online_Linkage: https://gina.alaska.edu/projects/modis-derived-snow-metrics

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 202002

Source_Currentness_Reference: Date accessed

Source_Citation_Abbreviation: GINA Snow Metrics
Source_Contribution: Snowmelt data for phenology predictions

Source_Information:

Source_Citation:

Citation_Information:

Originator: Geographic Information Network of Alaska
Publication_Date: 2020
Title: Modis-derived NDVI metrics
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place: online
Publisher: GINA, University of Alaska, Fairbanks

Other_Citation_Details:

Geographic Information Network of Alaska (GINA). 2020. Modis-derived NDVI metrics. Data accessible at https://gina.alaska.edu/projects/modis-derived-ndvi-metrics [Subset Query: spatial_extent=study area boundary; temporal_extent=2012-2018 for resource selection models and 2001-2017 for phenology models]

Online_Linkage: https://gina.alaska.edu/projects/modis-derived-ndvi-metrics

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 202002

Source_Currentness_Reference: Date accessed

Source_Citation_Abbreviation: GINA NDVI Metrics
Source_Contribution: Greenness data for phenology predictions

Source_Information:

Source_Citation:

Citation_Information:

Originator: U.S. Geological Survey
Publication_Date: 2020
Title: USGS Digital Elevation Model (DEM)
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place: online
Publisher: U.S. Geological Survey

Other_Citation_Details:

U.S. Geological Survey. 2020. USGS Digital Elevation Model (DEM). Data accessible at https://www.usgs.gov/core-science-systems/ngp/tnm-delivery [Subset Query: spatial_extent=study area boundary]

Online_Linkage: https://www.usgs.gov/core-science-systems/ngp/tnm-delivery

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 202002

Source_Currentness_Reference: Date accessed

Source_Citation_Abbreviation: USGS DEM
Source_Contribution: Derived terrain metrics from DEM

Source_Information:

Source_Citation:

Citation_Information:

Originator: National Oceanic and Atmospheric Administration
Publication_Date: 2017
Title:

NOAA Global Self-consistent, Hierarchical, High-resolution Geography Database (ver2.3.7, June 2017)

Geospatial_Data_Presentation_Form: vector digital data
Publication_Information:

Publication_Place: online
Publisher: National Oceanic and Atmospheric Administration

Other_Citation_Details:

National Oceanic and Atmospheric Administration. 2017. NOAA Global Self-consistent, Hierarchical, High-resolution Geography Database (ver 2.3.7, June 2017). Data accessible at https://www.ngdc.noaa.gov/mgg/shorelines [Subset Query: spatial_extent=study area boundary]

Online_Linkage: https://www.ngdc.noaa.gov/mgg/shorelines

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2017

Source_Currentness_Reference: publication date

Source_Citation_Abbreviation: NOAA Shorelines
Source_Contribution:

Used to define the northern boundary of the study area and to measure distance to coast for phenology models

Source_Information:

Source_Citation:

Citation_Information:

Originator: Wang, J.A.
Originator: Sulla-Menashe, D.
Originator: Woodcock, C.E.
Originator: Sonnentag, O.
Originator: Keeling, R.F.
Originator: Friedl, M.A.
Publication_Date: 2019
Title:

ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014

Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place: online
Publisher:

Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC), Oak Ridge, Tennessee, USA

Other_Citation_Details:

Wang, J.A., Sulla-Menashe, D., Woodcock, C.E., Sonnentag, O., Keeling, R.F., Friedl, M.A. 2019. ABoVE: Landsat-derived Annual Dominant Land Cover Across ABoVE Core Domain, 1984-2014. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1691 [Subset Query: spatial_extent=study area boundary; temporal_extent=2014 landcover]

Online_Linkage: https://doi.org/10.3334/ORNLDAAC/1691

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 2019

Source_Currentness_Reference: publication date

Source_Citation_Abbreviation: ABoVE Landcover
Source_Contribution: Used 2014 data for landcover during our study period.

Source_Information:

Source_Citation:

Citation_Information:

Originator: Scenarios Network for Alaska and Arctic Planning (SNAP)
Publication_Date: 2020
Title: Dynamically Downscaled ERA-Interim Historical Weather Data
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place: online
Publisher:

International Arctic Research Center, University of Alaska, Fairbanks

Other_Citation_Details:

Scenarios Network for Alaska and Arctic Planning (SNAP). 2020. Dynamically Downscaled ERA-Interim Historical Weather Data. Data accessible at http://ckan.snap.uaf.edu/dataset/historical-and-projected-dynamically-downscaled-climate-data-for-the-state-of-alaska-and-surrou [Subset Query: spatial_extent=study area boundary; temporal_extent=2001-2017 for phenology models]

Online_Linkage:

http://ckan.snap.uaf.edu/dataset/historical-and-projected-dynamically-downscaled-climate-data-for-the-state-of-alaska-and-surrou

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 202005

Source_Currentness_Reference: Date accessed

Source_Citation_Abbreviation: SNAP ERA-Interim Weather
Source_Contribution: Weather data used to produce phenology prediction models

Source_Information:

Source_Citation:

Citation_Information:

Originator: Scenarios Network for Alaska and Arctic Planning (SNAP)
Publication_Date: 2020
Title: Dynamically Downscaled Projected Climate Data
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:

Publication_Place: online
Publisher:

International Arctic Research Center, University of Alaska, Fairbanks

Other_Citation_Details:

Scenarios Network for Alaska and Arctic Planning (SNAP). 2020. Dynamically Downscaled Projected Climate Data. Data accessible at http://ckan.snap.uaf.edu/dataset/historical-and-projected-dynamically-downscaled-climate-data-for-the-state-of-alaska-and-surrou [Subset Query: spatial_extent=study area boundary; temporal_extent=2010-2019 for bias correction and 2030-2059 for phenology projections. Averaged both available climate models (GFDL-CM3 and NCAR-CCSM4)]

Online_Linkage:

http://ckan.snap.uaf.edu/dataset/historical-and-projected-dynamically-downscaled-climate-data-for-the-state-of-alaska-and-surrou

Type_of_Source_Media: online database
Source_Time_Period_of_Content:

Time_Period_Information:

Single_Date/Time:

Calendar_Date: 202005

Source_Currentness_Reference: Date accessed

Source_Citation_Abbreviation: SNAP Climate Data
Source_Contribution: Climate projection data used to predict future phenology

Process_Step:

Process_Description:

We produced an annually-weighted kernel density of the locations, and defined the study area and availability as the 95% contour buffered by the mean daily movement rate of the caribou in the data set (13 km). We removed all locations falling outside the boundary and individuals which joined the Central Arctic Herd. We split the dataset into calving season (26 May to 10 June) and post-calving season (11 June to 30 June) and removed animals with <50 locations/season/year. Available locations were generated throughout the study area at a ratio of 1:5 (used:available) locations.

Process_Date: 2020

Process_Step:

Process_Description:

Habitat data included landcover (ABoVE Landcover), topography metrics (slope, aspect, topographic position, ruggedness derived from USGS DEM), snowmelt date (GINA Snow Metrics), onset of greenness date (GINA NDVI Metrics), and 50% max NDVI date (derived from GINA NDVI Metrics assuming constant rate of change between onset of greenness and maximum NDVI). Barren and sparsely vegetated landcover classes were combined because they were similar and interspersed with each other and were then split into "montane" and "riverine" sparsely vegetated classes based on topographic characteristics. Habitat covariates were used at the pixel scale and were also calculated within buffers including 500-m, 1-km, 2-km, 5-km, and 10-km scales.

Random forest resource selection models were produced for each season to compare used to available locations with all habitat covariates at each scale as explanatory variables. Random forests were limited to a maximum depth of 13 based on cross-validation among years to avoid overfitting and to optimize generalization to new years. Variables were eliminated recursively by dropping the least important variable and rerunning the model until the most simple model with high predictive power was identified based on the "area under the curve." Optimal thresholds were produced by minimizing the mean class error rate between used and available locations. The resource selection models were used to predict to the study area, and pixel values above the threshold was considered "suitable."

Source_Used_Citation_Abbreviation: ABoVE Landcover
Source_Used_Citation_Abbreviation: USGS DEM
Source_Used_Citation_Abbreviation: GINA Snow Metrics
Source_Used_Citation_Abbreviation: GINA NDVI Metrics
Process_Date: 2020

Process_Step:

Process_Description:

Phenology prediction models were produced using random forest regression to predict phenology dates. Phenology data during 2001-2017 comprised the response variable. Explanatory included all topographic variable and scales used in the resource selection models, along with distance to coast (calculated from NOAA Shorelines), elevation (USGS DEM), and spring weather (i.e., temperature, precipitation, and snow-water equivalent from SNAP ERA-Interim Weather 2001-2017). Similar to the resource selection variable elimination, the least important variables were removed until the most simple model with high predictive power could be identified based on the "area under the curve."

Future phenology dates were predicted by running decadal averages of RCP 8.5 climate projections (SNAP Climate Data) through the phenology prediction models for the 2030s, 2040s, and 2050s. Future phenology estimates were run through the resource selection models to predict future probability of use for the future decades.

Source_Used_Citation_Abbreviation: NOAA Shorelines
Source_Used_Citation_Abbreviation: USGS DEM
Source_Used_Citation_Abbreviation: SNAP ERA-Interim Weather
Source_Used_Citation_Abbreviation: SNAP Climate Data
Process_Date: 2020

Spatial_Data_Organization_Information:

Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:

Raster_Object_Type: Grid Cell
Row_Count: 785
Column_Count: 1771
Vertical_Count: 1

Spatial_Reference_Information:

Horizontal_Coordinate_System_Definition:

Planar:

Map_Projection:

Map_Projection_Name: Albers Conical Equal Area
Albers_Conical_Equal_Area:

Standard_Parallel: 55.0
Standard_Parallel: 65.0
Longitude_of_Central_Meridian: -154.0
Latitude_of_Projection_Origin: 50.0
False_Easting: 0.0
False_Northing: 0.0

Planar_Coordinate_Information:

Planar_Coordinate_Encoding_Method: row and column
Coordinate_Representation:

Abscissa_Resolution: 250
Ordinate_Resolution: 250

Planar_Distance_Units: meters

Geodetic_Model:

Horizontal_Datum_Name: North_American_Datum_1983
Ellipsoid_Name: GRS 1980
Semi-major_Axis: 6378137
Denominator_of_Flattening_Ratio: 298.257222101004

Entity_and_Attribute_Information:

Detailed_Description:

Entity_Type:

Entity_Type_Label: caribou_porcupineHerd_studyAreaBoundary
Entity_Type_Definition:

Folder of geospatial files outlining the Study Area Boundary used to define availability for resource selection analysis and to define the spatial prediction extent. The boundary was produced by calculating the 95% contour of the annually-weighted kernel density estimate, which was buffered by 13 km (the average daily movement rate of caribou during our study period). Presented in both ESRI shapefile (SHP) and Keyhole Markup Language (KML) formats.

Entity_Type_Definition_Source: Author defined

Attribute:

Attribute_Label: FID
Attribute_Definition: Internal feature number
Attribute_Definition_Source: ESRI
Attribute_Domain_Values:

Unrepresentable_Domain:

Sequential unique whole numbers that are automatically generated.

Attribute:

Attribute_Label: Shape
Attribute_Definition: Feature geometry
Attribute_Definition_Source: ESRI
Attribute_Domain_Values:

Unrepresentable_Domain: Coordinates defining the features.

Attribute:

Attribute_Label: X1
Attribute_Definition: Polygon ID
Attribute_Definition_Source: Author defined
Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 1.0
Range_Domain_Maximum: 1.0

Attribute:

Attribute_Label: area_km2
Attribute_Definition: Area of polygon
Attribute_Definition_Source: Author defined
Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 33375.923399920895
Range_Domain_Maximum: 33375.923399920895
Attribute_Units_of_Measure: km^2

Detailed_Description:

Entity_Type:

Entity_Type_Label: caribou_porcupineHerd_habitatUse
Entity_Type_Definition:

Folder (with subfolders) of raster files indicating the relative probability of habitat use for the Porcupine Caribou Herd during calving (26 May to June 10) and post-calving (11 June to 30 June) season in 2012-2018, 2012-2018 average, 2030s, 2040s, and 2050s. Presented in GeoTIFF format.

Entity_Type_Definition_Source: Author defined

Attribute:

Attribute_Label: Value
Attribute_Definition: Relative probability of use (0-1)
Attribute_Definition_Source: Author defined
Attribute_Domain_Values:

Range_Domain:

Range_Domain_Minimum: 0
Range_Domain_Maximum: 1
Attribute_Units_of_Measure: probability

Detailed_Description:

Entity_Type:

Entity_Type_Label: caribou_porcupineHerd_suitableHabitat
Entity_Type_Definition:

Folder (with subfolders) of polygons representing the habitat classified as "suitable". Raster values above the threshold (calving: 0.534; post-calving: 0.522) were classified as "suitable." Thresholds were determined by maximizing the mean classification rate of each response class (i.e., used and available locations) in the resource selection models for the Porcupine Caribou Herd during calving and post-calving season in 2012-2018, 2012-2018 average, 2030s, 2040s, and 2050s. Attributes were not used. All polygons in a single shapefile represent the modeled suitable habitat for the specified year and season. Presented in ESRI shapefile (SHP) format) and Keyhole Markup Language (KML) formats.

Entity_Type_Definition_Source: Author defined

Attribute:

Attribute_Label: FID
Attribute_Definition: Internal feature number.
Attribute_Definition_Source: ESRI
Attribute_Domain_Values:

Unrepresentable_Domain:

Sequential unique whole numbers that are automatically generated.

Attribute:

Attribute_Label: Shape
Attribute_Definition: Feature geometry.
Attribute_Definition_Source: ESRI
Attribute_Domain_Values:

Unrepresentable_Domain: Coordinates defining the features.

Attribute:

Attribute_Label: ID
Attribute_Definition:

ID signifying that all polygons are part of the same distribution (i.e., for all polygons in shapefile, ID = 1).

Attribute_Definition_Source: Author defined
Attribute_Domain_Values:

Unrepresentable_Domain:

All ID's are the same (ID = 1) signifying that all polygons in the shapefile came from the same distribution. The multi-part polygon (ID = 1) was split into separate polygons for computational purposes (separate polygons defined by "FID").

Predicted Calving and Post-calving Season Resource Use of the Porcupine Caribou Herd During 2012–2018 With Future Projections for the 2030s, 2040s, and 2050s

Metadata: