Distribution of White Spruce in Alaska. An Open Access prediction surface from climatic and bioclimatic parameters using ESRI GRID formats.

Metadata also available as

Metadata:


Identification_Information:
Citation:
Citation_Information:
Originator: Bettina Ohse
Originator: Falk Huettmann
Publication_Date: summer 2008
Title:
Distribution of White Spruce in Alaska. An Open Access prediction surface from climatic and bioclimatic parameters using ESRI GRID formats.
Edition: no 1
Series_Information:
Series_Name: Distribtion of Tree Species in Alaska
Publication_Information:
Publisher: Bettina Ohse, Falk Huettmann, Steffi Ickert-Bond
Online_Linkage: <http://science.nature.nps.gov/nrdata>
Description:
Abstract:
This open access data set contains a spatially gridded distribution of White Spruce in Alaska (ESRI GRID format), predicted from climatic and bioclimatic parameters (temperature, precipitation, elevation, and aspect). A species distribution model, developed by Bettina Ohse, was used to determine the ecological niche of the species based on the environmental variables. The model was developed within TreeNet, a classification and regression tree software. The ecological niche was then projected into geographical space, resulting in a predictive map of the species distribution in Alaska (4km resolution, tested accuracy of c. 95 %). We used ArcGIS 9.2. Data sources were freely available for the global public, and so were all tools used (prediction algorithms and specific GIS tools). We promote these data and this concept as a role model how to model plant distributions in wilderness areas and for overcoming data gaps in species distributions world-wide. We encourage the use and update of these data for further updating of this concept and its findings.
Purpose:
Display and/or analyses requiring spatially distributed occurrence of WHite Spruce in alaska. This dataset provides high resolution data that should serve researchers concerned with species biogeography, wildlife habitat, nature conservation planning, or natural resource management as a source of information and basis of further investigation. This model is also meant as a baseline for further exploration of opportunities of plant species distribution modeling for the last remaining wilderness areas and can be used as a baseline for assessing and predicting effects of climate change or land use change on tree species ranges.
Supplemental_Information:
Most wilderness areas still lack accurate distribution information on tree species. Mapping of species is often done with roughly interpolated polygons and area restricted expert maps for such regions (Hulten 1968; Viereck and Little 2007), as data sampling in remote areas is difficult to conduct and often not affordable. This situation definitely applies to Alaska. However, wildlife research, vegetation type modeling and adaptive resource management call for better data on specific tree species distribution (Calef et al. 2005). Thus, there was a need for other approaches to efficiently obtain high-quality maps on the distribution of tree species, such as a species distribution model (SDM). SDMs are widely applied for the study of plant and animal species distribution (e.g. Engler et al. 2004; Franklin 1998; Guisan et al. 1998; Wisz et al. 2008), and have been reviewed extensively (e.g. Guisan and Zimmerman 2000). Here we applied a predictive GIS modeling approach, using freely available digital data on species occurrences and (bio-)climatic parameters (Ohse et al. 2009).The shown map is a result of a model approach, that derives spatially explicit distribution maps with a resolution of 4km. Here we try to show how data gaps can be overcome world-wide and help improve spatial decision-making for global sustainability.
Time_Period_of_Content:
Time_Period_Information:
Single_Date/Time:
Calendar_Date: 1950-2000
Currentness_Reference:
period from which most of the point observations were taken (1950-2000)
Status:
Progress: Complete
Maintenance_and_Update_Frequency: As needed
Spatial_Domain:
Bounding_Coordinates:
West_Bounding_Coordinate: -2301787.77313
East_Bounding_Coordinate: 1730212.22687
North_Bounding_Coordinate: 2748069.78588
South_Bounding_Coordinate: 108069.78588
Altitude_Distance_Units: meters
Keywords:
Theme:
Theme_Keyword_Thesaurus: none
Theme_Keyword: SDM
Theme_Keyword: White spruce
Theme_Keyword: map
Theme_Keyword: tree species
Theme:
Theme_Keyword_Thesaurus: National Park Service Theme Category Thesaurus
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Place:
Place_Keyword: Alaska
Access_Constraints: none
Use_Constraints: high statewide accuracy, accuracy on local scale not guaranteed
Point_of_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Bettina Ohse
Contact_Organization: EWHALE Lab University of Alaska Fairbanks
Contact_Position: graduate student
Contact_Electronic_Mail_Address: ftbo1@uaf.edu
Contact_Electronic_Mail_Address: bettina.ohse@web.de
Browse_Graphic:
Browse_Graphic_File_Name: SDM_PicGla_best_model.jpg
Browse_Graphic_File_Description:
Map showing predicted distribution of White Spruce in Alaska as derived by the best-performing model
Browse_Graphic_File_Type: JPEG
Browse_Graphic:
Browse_Graphic_File_Name: SDM_PicGla_four_models.jpg
Browse_Graphic_File_Description:
Map comparing predicted distribution of White Spruce in Alaska as derived by the four best-performing models
Browse_Graphic_File_Type: JPEG
Data_Set_Credit: citation
Security_Information:
Security_Classification_System: none
Security_Classification: Unclassified
Security_Handling_Description: none
Native_Data_Set_Environment:
Microsoft Windows XP Version 5.1 (Build 2600) Service Pack 2; ESRI ArcCatalog 9.2.0.1324
Taxonomy:
Taxonomic_Classification:
Taxon_Rank_Name: Kingdom
Taxon_Rank_Value: Plantae
Taxonomic_Classification:
Taxon_Rank_Name: Subkingdom
Taxon_Rank_Value: Tracheobionta
Taxonomic_Classification:
Taxon_Rank_Name: Division
Taxon_Rank_Value: Coniferophyta
Taxonomic_Classification:
Taxon_Rank_Name: Class
Taxon_Rank_Value: Pinopsida
Taxonomic_Classification:
Taxon_Rank_Name: Order
Taxon_Rank_Value: Pinales
Taxonomic_Classification:
Taxon_Rank_Name: Family
Taxon_Rank_Value: Pinaceae
Taxonomic_Classification:
Taxon_Rank_Name: Genus
Taxon_Rank_Value: Picea
Taxonomic_Classification:
Taxon_Rank_Name: Species
Taxon_Rank_Value: Picea glauca
Applicable_Common_Name: white spruce
Applicable_Common_Name: black hills spruce
Applicable_Common_Name: canadian spruce
Applicable_Common_Name: cat spruce
Applicable_Common_Name: porsild spruce
Applicable_Common_Name: skunk spruce
Applicable_Common_Name: western white spruce

Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
Index of relative occurrence was predicted by the model with values ranging from 0 to 1 (floating point values). This map classifies the values into 5 equal classes for easier application.
Quantitative_Attribute_Accuracy_Assessment:
Attribute_Accuracy_Value: 0 to 1
Attribute_Accuracy_Explanation: Index of relative occurrence, rounded off to 2 decimals
Logical_Consistency_Report:
All data were based on a longterm time period (species data 1928-2005, climatic variables 1961-1990). Similar quality assurance procedures were used with all input data sets.
Positional_Accuracy:
Horizontal_Positional_Accuracy:
Horizontal_Positional_Accuracy_Report:
Accuracy of this data set is based on the specification of the species dataset that was used to train the model. The stated accuracy of the species dataset ranges from 0 to 3615m, depending on the specific sample point. Accordingly we chose 4km as an appropriate resolution.
Quantitative_Horizontal_Positional_Accuracy_Assessment:
Horizontal_Positional_Accuracy_Value: 4km
Horizontal_Positional_Accuracy_Explanation: based on training dataset used to build the model
Vertical_Positional_Accuracy:
Vertical_Positional_Accuracy_Report:
Accuracy of this data set is based on the specification of the species dataset that was used to train the model. The stated accuracy of the species dataset ranges from 0 to 3615m, depending on the specific sample point. Accordingly we chose 4km as an appropriate resolution.
Quantitative_Vertical_Positional_Accuracy_Assessment:
Vertical_Positional_Accuracy_Value: 4km
Vertical_Positional_Accuracy_Explanation: based on training dataset used to build the model
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator:
Dr. Christopher Daly of the Spatial Climate Analysis Service at Oregon State University.
Publication_Date: 20020408
Title: Alaska Average Monthly or Annual Mean Temperature, 1961-90
Geospatial_Data_Presentation_Form: raster digital data
Online_Linkage: <http://www.climatesource.com/ak/fact_sheets/meta_tmean_ak.html>
Source_Scale_Denominator: 2km grid
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: tmean
Source_Contribution:
mean temperature of april, may, june, september, and difference in mean temperature july-january used as climatic variable for building the model
Source_Information:
Source_Citation:
Citation_Information:
Originator:
Dr. Christopher Daly of the Spatial Climate Analysis Service at Oregon State University.
Publication_Date: 20020408
Title: Alaska Average Monthly or Annual Precipitation, 1961-90
Geospatial_Data_Presentation_Form: raster digital data
Online_Linkage: <http://www.climatesource.com/ak/fact_sheets/meta_precip_ak.html>
Source_Scale_Denominator: 2km grid
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: ppt
Source_Contribution:
precipitation of august, april, may, and growing season (may-sept) grow used as climatic variables for building the model
Source_Information:
Source_Citation:
Citation_Information:
Originator: U.S. Geological Survey EROS Alaska Field Office
Publication_Date: 19961210
Title: Alaska 1km Digital Elevation Model
Geospatial_Data_Presentation_Form: raster digital data
Online_Linkage: <http://agdcftp1.wr.usgs.gov/pub/projects/fhm/elevmeta.html>
Source_Scale_Denominator: 1km grid
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Source_Currentness_Reference: publication date
Source_Citation_Abbreviation: elevation
Source_Contribution:
used as bioclimatic variable for building the model, used for calculating aspect for use as bioclimatic variable for building the model
Source_Information:
Source_Citation:
Citation_Information:
Title:
Herbarium (ALA), University of Alaska Museum of the North, Fairbanks, AK
Geospatial_Data_Presentation_Form: tabular digital data
Online_Linkage: <http://arctos.database.museum/SpecimenSearch.cfm>
Source_Scale_Denominator: point location accuracy: 3615m
Type_of_Source_Media: online
Source_Time_Period_of_Content:
Source_Currentness_Reference: date of download (05-22-2008)
Source_Citation_Abbreviation: Arctos Database
Source_Contribution:
species locations used as training dataset for building the model
Process_Step:
Process_Description:
Free online data on confirmed species locations and on climatic and bioclimatic variables were overlayed in GIS and a regular grid over the entire state of Alaska was created. Also, preinformed pseudo-absence points were created.
Process_Date: 2008
Process_Step:
Process_Description:
Variable values were extracted to the confirmed presence points, to pseudo-absence points, and to all points of the regular grid.
Process_Date: 2008
Process_Step:
Process_Description:
Model development, i.e. derivation of index of relative occurrence according to the environmental parameters
Process_Date: 2008
Process_Step:
Process_Description:
Application of model to all gridpoints, i.e. extrapolation to the entire state
Process_Date: 2008
Process_Step:
Process_Description:
Mapping the values of predicted index of relative occurrence for white spruce in Alaska
Process_Date: 2008

Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: Grid Cell
Row_Count: 660
Column_Count: 1008

Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Geodetic_Model:
Horizontal_Datum_Name: North American Datum of 1983
Ellipsoid_Name: GRS_1980
Semi-major_Axis: 6378137.000000000000000000
Denominator_of_Flattening_Ratio: 298.257222101000020000

Entity_and_Attribute_Information:
Detailed_Description:
Entity_Type:
Entity_Type_Label: predicted index of relative occurrence
Attribute:
Attribute_Label: Pixel Value
Attribute_Label: Rel Index of Occurrence
Attribute_Definition: Value of Pixel
Attribute_Definition: Rel. Index of Occurrence 0 to 1
Attribute_Definition_Source: UAF
Attribute_Definition_Source: UAF

Distribution_Information:
Distribution_Liability:
The authors, or the National Park Service shall not be held liable for improper or incorrect use of the data described and/or contained herein. These data and related graphics (i.e. GIF or JPG format files) are not legal documents and are not intended to be used as such. The information contained in these data is dynamic and may change over time. The data are not better than the original sources from which they were derived. It is the responsibility of the data user to use the data appropriately and consistent within the limitations of geospatial data in general and these data in particular. The related graphics are intended to aid the data user in acquiring relevant data; it is not appropriate to use the related graphics as data. The authors or the National Park Service gives no warranty, expressed or implied, as to the accuracy, reliability, or completeness of these data. It is strongly recommended that these data are directly acquired from an NPS server and not indirectly through other sources which may have changed the data in some way. Although these data have been processed successfully on computer systems at the National Park Service, no warranty expressed or implied is made regarding the utility of the data on other systems for general or scientific purposes, nor shall the act of distribution constitute any such warranty. This disclaimer applies both to individual use of the data and aggregate use with other data.
Standard_Order_Process:
Fees: None
Resource_Description: Map Files in ESRI and jpg format.
Technical_Prerequisites: none (Computer and ideally a GIS)

Metadata_Reference_Information:
Metadata_Date: 20080826
Metadata_Contact:
Contact_Information:
Contact_Person_Primary:
Contact_Person: Bettina Ohse
Contact_Organization: EWHALE Lab University of Alaska Fairbanks
Contact_Position: PhD graduate student
Contact_Address:
Address_Type: 419 Irving I
City: Fairbanks
State_or_Province: Alaska
Postal_Code: 99775
Contact_TDD/TTY_Telephone: +1 907 474 7882
Contact_Electronic_Mail_Address: bettina.ohse@uni-greifswald.de
Metadata_Standard_Name: FGDC Content Standards for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001-1998
Metadata_Extensions:
Online_Linkage: <http://www.esri.com/metadata/esriprof80.html>
Profile_Name: ESRI Metadata Profile
Metadata_Time_Convention: local time
Metadata_Access_Constraints: none
Metadata_Use_Constraints: none
Metadata_Security_Information:
Metadata_Security_Classification_System: none
Metadata_Security_Classification: Unclassified
Metadata_Security_Handling_Description: none

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