North American models of habitat quality and migration potential under climate change

Metadata:
Identification_Information:
Citation:
Citation_Information:
Originator: Prasad, Anantha M.
Originator: Peters, Matthew P.
Originator: Pedlar, John H.
Originator: McKenney, Daniel W.
Originator: Mora, Franz
Publication_Date: 2024
Title:
North American models of habitat quality and migration potential under climate change
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Fort Collins, CO
Publisher: Forest Service Research Data Archive
Other_Citation_Details:
Updated 30 August 2024
Online_Linkage: https://doi.org/10.2737/RDS-2024-0020
Description:
Abstract:
Modeled habitat suitability for 326 trees species across North America under 1991-2020 climate conditions and projected future conditions (2070-2100) were created using a multi-model ensemble (MME) approach that correlates individual tree species relative abundance to climate and topographic data. The associated files include relative abundance (i.e., habitat suitability), derived from tree basal area and number of stems, for the species according to USDA Forest Service Forest Inventory and Analysis data, modeled under current conditions, and two future scenarios. Species percent composition from Canada’s National Forest Inventory, as well as relative abundance from Mexican inventory data for a few species, were combined with data from the United States. Additionally, the colonization likelihoods (computed via a migration model that simulates historical migration using current abundance) of potential newly suitable habitats are provided to assess natural migration of species under the future scenarios.

This data publication includes the following raster files for each species: 1) actual relative abundance derived from national forest inventory data from Canada, Mexico, and the United States; 2) modeled relative abundance indicating suitability under climate conditions from 1991-2020; 3-4) modeled relative abundance indicating habitat suitability under projected climate conditions for two future (2070-2100) scenarios (SSP2-4.5 and SSP5-8.5); and 5-6) fifteen-class combination of potential habitat quality (HQ) and colonization likelihoods (CL) for two future (2070-2100) scenarios (SSP2-4.5 and SSP5-8.5). Maps associated with each of these raster files are also provided. Additionally, image files containing statistical boxplots of 1) elevation, 2) mean annual precipitation, and 3) mean annual temperature for each species for actual inventory data, and habitat suitability modeled under current (1991-2020) and two future (2070-2100) scenarios (SSP2-4.5 and SSP5-8.5). The tabular summary data associated with these boxplots are also included.
Purpose:
Knowing where suitable habitat for a species could exists, how changes may unfold as a result of changing climatic conditions, and how likely tree species are to naturally migrate into potentially new habitats allows resource managers to plan for future conditions.
Supplemental_Information:
These data were originally published on 06/17/2024. It came to our attention that the raster files had a data mask inconsistently applied to the model inputs, resulting in coastal pixels having values that should not be considered. We have corrected the models and associated output files. For more details on this change, see the Process Steps section. On 08/30/2024 this data publication was updated to include the corrected files, including minor metadata updates.

See Prasad et al. (2020; https://doi.org/10.1111/ddi.13078) for details about migration simulations and Prasad et al. (in review) for details about model parameterization.
Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 199101
Ending_Date: 209912
Currentness_Reference:
Publication date
Status:
Progress: Complete
Maintenance_and_Update_Frequency: None planned
Spatial_Domain:
Description_of_Geographic_Extent:
Data for North America are represented as 20 kilometer (km) grids, within the provided bounding coordinates. The latitude and longitude values associated with each data layer represent the lower left (southwest) corner of the grid.

While the grids have equal dimensions in terms of degrees in a geographic coordinate system and are often referred to as 20 km grids, the grid areas are not equal in terms of square km, varying in area with change in latitude.
Bounding_Coordinates:
West_Bounding_Coordinate: -180.00000
East_Bounding_Coordinate: 180.00000
North_Bounding_Coordinate: 90.00000
South_Bounding_Coordinate: 2.72297
Keywords:
Theme:
Theme_Keyword_Thesaurus: ISO 19115 Topic Category
Theme_Keyword: climatologyMeteorologyAtmosphere
Theme:
Theme_Keyword_Thesaurus: National Research & Development Taxonomy
Theme_Keyword: Climate change
Theme_Keyword: Climatology
Theme:
Theme_Keyword_Thesaurus: None
Theme_Keyword: modeled niche
Theme_Keyword: potential suitable habitat
Theme_Keyword: tree species
Theme_Keyword: relative abundance
Theme_Keyword: importance value
Place:
Place_Keyword_Thesaurus: None
Place_Keyword: United States
Place_Keyword: Canada
Place_Keyword: Mexico
Taxonomy:
Keywords/Taxon:
Taxonomic_Keyword_Thesaurus:
None
Taxonomic_Keywords: multiple species
Taxonomic_Keywords: plants
Taxonomic_Keywords: vegetation
Taxonomic_System:
Classification_System/Authority:
Classification_System_Citation:
Citation_Information:
Originator: USDA Forest Service, Forest Inventory and Analysis
Publication_Date: 2017
Title:
Forest Inventory and Analysis Database
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: St. Paul, MN
Publisher: USDA Forest Service, Northern Research Station
Other_Citation_Details:
FIADB_1.6.1.00 Last updated Sat Apr 15 17:00:16 CDT 2017
Online_Linkage: https://apps.fs.usda.gov/fia/datamart/datamart.html
Online_Linkage: https://doi.org/10.2737/RDS-2001-FIADB
Taxonomic_Procedures:
General_Taxonomic_Coverage:
See \Data\Species_List.csv for detailed information about the species included in this publication.
Access_Constraints: None
Use_Constraints:
These data were collected using funding from the U.S. Government and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Prasad, Anantha M.; Peters, Matthew P.; Pedlar, John H.; McKenney, Daniel W.; Mora, Franz. 2024. North American models of habitat quality and migration potential under climate change. Updated 30 August 2024. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2024-0020
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Person: Matthew Peters
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 359 Main Road
City: Delaware
State_or_Province: OH
Postal_Code: 43015
Country: USA
Contact_Voice_Telephone: 740-368-0063
Contact_Electronic_Mail_Address: matthew.p.peters@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Point_of_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Person: Anantha Prasad
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 359 Main Road
City: Delaware
State_or_Province: OH
Postal_Code: 43015
Country: USA
Contact_Voice_Telephone: 740-368-0103
Contact_Electronic_Mail_Address: anantha.prasad@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Data_Set_Credit:
This project was funded by the USDA Forest Service, Northern Research Station and the Canadian Forest Service, Natural Resources Canada. Support was also provided by the Comisión Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO), México.


Author Information:

Anantha M. Prasad
USDA Forest Service, Northern Research Station
https://orcid.org/0000-0002-4645-6260

Matthew P. Peters
USDA Forest Service, Northern Research Station
https://orcid.org/0000-0002-4793-0075

John H. Pedlar
Great Lakes Forestry Centre, Natural Resources Canada
https://orcid.org/0000-0001-5831-1731

Daniel W. McKenney
Great Lakes Forestry Centre, Natural Resources Canada
https://orcid.org/0000-0002-8538-2145

Franz Mora
Comisión Nacional para el Conocimiento y Uso de la Biodiversidad
Native_Data_Set_Environment:
Version 10.0 (Build 19045) ; Esri ArcGIS 13.1.2.41833
Cross_Reference:
Citation_Information:
Originator: Prasad, Anantha M.
Originator: Pedlar, John H.
Originator: Peters, Matt P.
Originator: McKenney, Daniel W.
Originator: Iverson, Louis R.
Originator: Matthews, Steve N.
Originator: Adams, Bryce T.
Publication_Date: 2020
Title:
Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Diversity and Distributions
Issue_Identification: 26(9): 1142-1159
Online_Linkage: https://doi.org/10.1111/ddi.13078
Online_Linkage: https://research.fs.usda.gov/treesearch/60748
Cross_Reference:
Citation_Information:
Originator: Prasad, Anantha M.
Originator: Peters, Matthew P.
Originator: Pedlar, John H.
Originator: Gougherty, Andy V.
Originator: McKenney, Daniel W.
Originator: Mora, Francis
Originator: Matthews, Steve N.
Originator: McNulty, Steve G.
Originator: Mata, Lauro Lopez
Publication_Date: Unknown
Title:
North American tree species habitat and migration dynamics under current and future climates – a macroscale synthesis
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Journal of Biogeography
Other_Citation_Details:
[In review]
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Xgboost: A scalable tree boosting system
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Publisher: Association for Computing Machinery
Other_Citation_Details:
In: KDD '16: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016 August; San Francisco, CA. pp. 785–794
Online_Linkage: https://doi.org/10.1145/2939672.2939785
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Originator: Becker, Marc
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R package version 1.3.0.
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Originator: Wehberg, J.
Originator: Wichmann, V.
Originator: Böhner, J.
Publication_Date: 2015
Title:
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Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: Geoscientific Model Development
Issue_Identification: 8: 1991-2007
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Analytical_Tool:
Analytical_Tool_Description:
ranger: A Fast Implementation of Random Forests
Tool_Access_Information:
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Tool_Access_Instructions:
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Tool_Citation:
Citation_Information:
Originator: Wright, Marvin N.
Publication_Date: 2023
Title:
ranger: A Fast Implementation of Random Forests
Geospatial_Data_Presentation_Form: software
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Originator: Ziegler, Andreas
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ranger: a fast implementation of random forests for high dimensional data in C++ and R
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Series_Information:
Series_Name: Journal of Statistical Software
Issue_Identification: 77(1): 1-17
Online_Linkage: https://doi.org/10.18637/jss.v077.i01
Back to Top
Data_Quality_Information:
Attribute_Accuracy:
Attribute_Accuracy_Report:
The methods of statistically downscaling climate values from General Circulation Models (GCMs) from their native coarse resolution to finer spatial resolutions that are more relevant to managers and evaluating broad regional trends are well documented. The downscaled data used to calculate climatic indices used methods widely accepted by the scientific community. Since climate projections carry an inherent degree of error and uncertainty, 30-year averages of monthly values were used to reduce the uncertainty of projections. Therefore, these data represent a potential range of change that might be expected under the scenarios of shared socioeconomic pathways during this century.

Please note: These data are a product of modeling, and as such carries an inherent degree of error and uncertainty. Users should read and fully comprehend the metadata and other available documentation prior to data use. These data represent potential habitat suitability and colonization likelihoods, not specifically where a species might exist. Analyses and interpretations should be conducted at regions greater than or equal to 16,000 km (6,177.6 square miles [mi²]).
Logical_Consistency_Report:
The data are logically consistent. The consistency was verified as part of the quality assurance that occurred during data analysis.
Completeness_Report:
There are no missing data as far as we know. However, data are not provided for species if model performance was considered too low (R² below 10 percent).
Lineage:
Source_Information:
Source_Citation:
Citation_Information:
Originator: AdaptWest Project
Publication_Date: 2021
Title:
Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.01
Geospatial_Data_Presentation_Form: raster digital data
Publication_Information:
Publication_Place: Corvallis, OR
Publisher: Conservation Biology Institute
Online_Linkage: https://adaptwest.databasin.org/pages/adaptwest-climatenav71/
Larger_Work_Citation:
Citation_Information:
Originator: Wang, Tongli
Originator: Hamann, Andreas
Originator: Spittlehouse, Dave
Originator: Carroll, Carlos
Publication_Date: 2016
Title:
Locally downscaled and spatially customizable climate data for historical and future periods for North America
Geospatial_Data_Presentation_Form: journal article
Series_Information:
Series_Name: PLoS ONE
Issue_Identification: 11(6): e0156720
Online_Linkage: https://doi.org/10.1371/journal.pone.0156720
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1991
Ending_Date: 2100
Source_Currentness_Reference:
Publication Date
Source_Citation_Abbreviation:
AdaptWest Project (2021)
Source_Contribution:
Gridded current and projected future climate data for North America at a spatial resolution of 1 km², were obtained for the 30-year period 1991–2020 to represent the current baseline and align with the forest inventory records. Climate projections for the 2070–2099 period were based on CMIP6 outputs from eight general circulation models (GCM) and two emissions pathways (SSP2-4.5 and SSP5-8.5). The GCMs included ACCESS-ESM1.5, CNRM-ESM2-1, EC-Earth3, GFDL-ESM4, GISS-E2-1-G, MIROC6, MPI-ESM1.2-HR, and MRI-ESM2.0 for which individual projections were averaged to reduce noise and uncertainties among the models. The Shared Socioeconomic Pathways (SSP) are the latest greenhouse gas emission scenarios used by the Intergovernmental Panel on Climate Change. The SSP2-4.5 and SSP5-8.5 scenarios were used to bracket the plausible conditions that may occur during this century. See Mahony et al. (2022) for details on GCMs.


Mahony, Colin R.; Wang, Tongli; Hamann, Andreas; Cannon, Alex J. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 42(11): 5871–5891. https://doi.org/10.1002/joc.7566

O’Neill, B.C.; Tebaldi, C.; Van Vuuren, D.P.; Eyring, V.; Friedlingstein, P.; Hurtt, G.; Knutti, R.; Kriegler, E.; Lamarque, J.-F.; Lowe, J.; Meehl, G.A.; Moss, R.; Riahi, K.; Sanderson, B.M. 2016. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geoscientific Model Development. 9(9): 3461–3482. https://doi.org/10.5194/gmd-9-3461-2016
Source_Information:
Source_Citation:
Citation_Information:
Originator: CONAFOR
Publication_Date: 2009
Title:
Inventario Nacional Forestal y de Suelos, Informe de Resultados 2004–2009
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publisher: National Forestry Commission of Mexico
Online_Linkage: https://snmf.cnf.gob.mx/datos-del-inventario/
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2004
Ending_Date: 2009
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
Mexican NFI
Source_Contribution:
For each tree species, individuals having a diameter at breast height greater than roughly 3 inches (7.3 centimeters) were used to calculate an importance value from the number of stems (e.g., relative density) and basal area (e.g., relative dominance). The relative density and dominance of a species were divided by the total density or dominance of all species recorded at the inventory plot, multiplied by 50, and added together to calculate an importance value, i.e., relative abundance, for a species that ranged from 0–100.
Source_Information:
Source_Citation:
Citation_Information:
Originator: Danielson, Jeffrey J.
Originator: Gesch, Dean B.
Publication_Date: 2011
Title:
Global multi-resolution terrain elevation data 2010 (GMTED2010)
Geospatial_Data_Presentation_Form: document
Series_Information:
Series_Name: Open-File Report
Issue_Identification: 2011-1073
Publication_Information:
Publisher: U.S. Geological Survey
Other_Citation_Details:
23 p.
Online_Linkage: https://doi.org/10.3133/ofr20111073
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 19700101
Ending_Date: 20220117
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
Danielson and Gesch (2011)
Source_Contribution:
A digital elevation model of North America having a spatial resolution of 500 meters was obtained from the U.S. Geological Survey. The elevation values were aggregated to a 20 × 20 km (20 km²) grid by calculating the mean value and other topographic metrics were derived.
Source_Information:
Source_Citation:
Citation_Information:
Originator: National Forest Inventory
Publication_Date: 2021
Title:
First Remeasurement Standard Reports
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: Canada
Publisher: National Forest Inventory
Online_Linkage: https://nfi.nfis.org/en/statisticalreports
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 2007
Ending_Date: 2017
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
Canadian NFI
Source_Contribution:
Species information is obtained from a network of 2 × 2 km photo plots across Canada in a regular grid. For each tree identified to the species, the percent composition among the forested area of the photo plot was calculated to represent individual species importance or relative dominance. This data reported 80 tree species and an additional 20 species identified to the genus.
Source_Information:
Source_Citation:
Citation_Information:
Originator: USDA Forest Service, Forest Inventory and Analysis
Publication_Date: 2017
Title:
Forest Inventory and Analysis Database
Geospatial_Data_Presentation_Form: tabular digital data
Publication_Information:
Publication_Place: St. Paul, MN
Publisher: USDA Forest Service, Northern Research Station
Other_Citation_Details:
FIADB_1.6.1.00 Last updated Sat Apr 15 17:00:16 CDT 2017
Online_Linkage: https://apps.fs.usda.gov/fia/datamart/datamart.html
Online_Linkage: https://doi.org/10.2737/RDS-2001-FIADB
Type_of_Source_Media: Online
Source_Time_Period_of_Content:
Time_Period_Information:
Range_of_Dates/Times:
Beginning_Date: 1998
Ending_Date: 2018
Source_Currentness_Reference:
Ground Condition
Source_Citation_Abbreviation:
USDA FIA
Source_Contribution:
For each tree species, individuals having a diameter at breast height greater than or equal to 5 inches (12.7 centimeters) were used to calculate an importance value from the number of stems (e.g., relative density) and basal area (e.g., relative dominance). The relative density and dominance of a species were divided by the total density or dominance of all species recorded at the inventory plot, multiplied by 50, and added together to calculate an importance value, i.e., relative abundance, for a species that ranged from 0–100.

O’Connell, B.M.; Conkling, B.L.; Wilson, A.M.; Burrill, E.A.; Turner, J.A.; Pugh, S.A.; Christiansen, G.; Ridley, T.; Menlove, J. 2017. The Forest Inventory and Analysis Database: Database description and user guide version 7.0 for Phase 2. U.S. Department of Agriculture, Forest Service. 830 p. https://apps.fs.usda.gov/fia/datamart/datamart.html
Process_Step:
Process_Description:
Summary description:

The MME consisted of five machine learning algorithms: 1) bagging, 2) randomForest, 3) extremeForests, 4) Stochastic gradient boosting, and 5) Xgboost.

Relative abundance of individual tree species, 32 climate indices, and seven elevation derived indices were aggregated to 20 km² grids. Climate data for the 30-year periods 1991-2020 and 2070-2100 were obtained from the AdaptWest Project with future projections based on CMIP6 outputs from eight general circulation models (GCM) and two emissions pathways (SSP2-4.5 and SSP5-8.5). The GCMs included ACCESS-ESM1.5, CNRM-ESM2-1, EC-Earth3, GFDL-ESM4, GISS-E2-1-G, MIROC6, MPI-ESM1.2-HR, and MRI-ESM2.0 for which individual projections were averaged to reduce noise and uncertainties among the models. The Shared Socioeconomic Pathways (SSP) provide the latest greenhouse gas emission scenarios used by the Intergovernmental Panel on Climate Change. The SSP2-4.5 and SSP5-8.5 scenarios were used to bracket the plausible conditions that may occur during this century.

The consensus average of the MME is used to predict habitat suitability and ensure the main trends from all models are incorporated reducing unique predictions from some algorithms. Where one or more of the five models predict zero habitat suitability, the MME consensus average is set to zero as a consensus did not result among the models. Colonization likelihoods are derived from the consensus model by simulating long distance dispersals as a fat-tailed inverse power function applied to the fragmented forested landscape. Migration simulations are calculated until the end of the century based on a historical migration rate of 50 km/century and an inverse power function with a search window of 500 km (Prasad et al. 2020).


Prasad, Anantha; Pedlar, John; Peters, Matt; McKenney, Daniel W.; Iverson, Louis; Matthews, Steve; Adams, Bryce. 2020. Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change. Diversity and Distributions. 26(9): 1142-1159. https://doi.org/10.1111/ddi.13078 and https://research.fs.usda.gov/treesearch/60748
Source_Used_Citation_Abbreviation:
AdaptWest Project (2021)
Process_Date: Unknown
Process_Step:
Process_Description:
Data aggregation:

The response variable, relative abundance (i.e., species importance value) and thirty-nine environmental predictor variables, derived from climate and elevation datasets, were aggregated from native resolutions to 20 km² grids to model individual species importance values or habitat suitability. The mean values of continuous data were used to represent conditions within each grid cell.

See Prasad et al. (in review) for more details.


Prasad, Anantha M.; Peters, Matthew P.; Pedlar, John H.; Gougherty, Andy V.; McKenney, Daniel W.; Mora, Francis; Matthews, Steve N.; McNulty, Steve G.; Mata, Lauro Lopez. [In review]. North American tree species habitat and migration dynamics under current and future climates – a macroscale synthesis. Journal of Biogeography.
Source_Used_Citation_Abbreviation:
AdaptWest Project (2021), Canadian NFI, Danielson and Gesch (2011), Mexican NFI, USDA FIA
Process_Date: 2022
Process_Step:
Process_Description:
Modifying the response surface:

The model response surface (i.e., dependent variable) of relative abundance is based on surveys from national forest inventories from Canada, Mexico, and the United States. Each country’s inventory has specific methods for sampling the forested land, density of sampling plots, and protocols for reporting trees. Canadian surveys consisted of photo interpretation plots which estimate percent composition of species identified within the image. Both Mexican and United States surveys report physical characteristics of individual trees and environmental conditions of the sample sites. When relative abundance was aggregated to 20 km² grids, many adjacent grids contained few or no inventory data. These gaps and low samples were modified using focal 3 × 3 moving window to average among neighboring grids with inventory data. Where focal means resulted in zero, the original aggregated relative abundance was retained. For grids within the United States, a 5 × 5 focal window was used for grids containing no inventory data and a 3 × 3 window when fewer than four inventory plots where within a 20 km² grid.

The modified response surface attempts to reduce artificial data peaks resulting from fewer inventory plots being aggregated and fill gaps across the landscape.
Source_Used_Citation_Abbreviation:
Canadian NFI, Mexican NFI, USDA FIA
Process_Date: 2022
Process_Step:
Process_Description:
Modeling habitat suitability:

A MME was used to predict habitat suitability of individual tree species from relative abundance and 39 environmental conditions consisting of climate and topographic information. The MME consisted of five machine learning algorithms: 1) bagging (Breiman 1996), 2) randomForest (Breiman 2001), 3) extremeForests (Geurts et al. 2006), 4) Stochastic gradient boosting (Friedman 2002), and 5) Xgboost (Chen et al. 2016). All algorithms were implemented in R with bagging, randomForest, and extremeForests implemented from the ranger package (Wright et al. 2017), while Stochastic gradient boosting and Xgboost were implemented from the gbm (Greg et al. 2024) and xgboost (Chen et al. 2023) packages respectively.

The consensus average of the MME is used to predict habitat suitability and ensure the main trends from all models are incorporated reducing unique predictions from some algorithms. Where one or more of the five models predict zero habitat suitability, the MME consensus average is set to zero as a consensus did not result among the models.

See Prasad et al. (in review) for details about model parameterization.


Breiman, Leo. 1996. Bagging predictors. Machine Learning. 24(2): 123–140. https://doi.org/10.1007/BF00058655

Breiman, Leo. 2001. Random forest. Machine Learning. 45(1): 5-32. https://doi.org/10.1023/A:1010933404324

Chen, Tianqi; Guestrin, Carlos. 2016. Xgboost: A scalable tree boosting system. In: KDD '16: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016 August; San Francisco, CA. pp. 785–794. https://doi.org/10.1145/2939672.2939785

Chen, Tianqi; He, Tong; Benesty, Michael; Khotilovich, Vadim; Tang; Yuan; Cho, Hyunsu; Chen, Kailong; Mitchell, Rory; Cano, Ignacio; Zhou, Tianyi; Li, Mu; Xie, Junyuan; Lin, Min; Geng, Yifeng; Li, Yutian; Yuan, Jiaming. 2023. xgboost: Extreme Gradient Boosting. R package version 1.7.5.1, https://CRAN.R-project.org/package=xgboost.

Friedman, Jerome H. 2002. Stochastic gradient boosting. Computational Statistics & Data Analysis. 38(4): 367–378. https://doi.org/10.1016/S0167-9473(01)00065-2

Geurts, Pierre; Ernst, Damien; Wehenkel, Louis. 2006. Extremely randomized trees. Mach Learning. 63: 3–42. https://doi.org/10.1007/s10994-006-6226-1

Greg, Ridgeway; Edwards, Daniel; Kriegler, Brian; Schroedl, Stefan; Southworth, Harry; Greenwell, Brandon; Boehmke, Bradley; Cunningham, Jay; GBM Developers. 2024. gbm: Generalized Boosted Regression Models. R package version 2.1.9, https://CRAN.R-project.org/package=gbm.

Prasad, Anantha M.; Peters, Matthew P.; Pedlar, John H.; Gougherty, Andy V.; McKenney, Daniel W.; Mora, Francis; Matthews, Steve N.; McNulty, Steve G.; Mata, Lauro Lopez. [In review]. North American tree species habitat and migration dynamics under current and future climates – a macroscale synthesis. Journal of Biogeography.

Wright, Marvin N.; Ziegler, Andreas. 2017. ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software. 77(1): 1-17. https://doi.org/10.18637/jss.v077.i01
Source_Used_Citation_Abbreviation:
AdaptWest Project (2021), Canadian NFI, Danielson and Gesch (2011), Mexican NFI, USDA FIA
Process_Date: 20230817
Process_Step:
Process_Description:
Defining habitat quality:

For each tree species, the multi-model ensemble predicted habitat suitability was classified into habitat quality as low (1–6), medium (7–15), and high (16–100). The habitat quality is used to estimate colonization likelihoods by providing a baseline of general abundance across the landscape from which seed dispersals can be simulated into new habitats.
Source_Used_Citation_Abbreviation:
habitat suitability from previous step
Process_Date: 20230913
Process_Step:
Process_Description:
Estimating colonization likelihoods:

Simulation of natural migration was performed to estimate future colonization likelihoods (CL) from within and beyond the current range extent of suitable habitat based on historical migration rates of trees. Long distance dispersals were simulated from a fat-tailed inverse power function applied to the fragmented forested landscape. The migration simulation assumes no climatic constraints as each tree species migrates based on current habitat quality and time to reproductive maturity. Migration simulations are calculated until the end of the century based on a historical migration rate of 50 km/century and an inverse power function with a search window of 500 km. The resulting migration model represents a CL that ranges from 0–100, where 0 indicates no colonization and 100 the maximum colonization likelihood within 80 years.

See Prasad et al. (2020) and Prasad et al. (in review) for more details.


Prasad, Anantha; Pedlar, John; Peters, Matt; McKenney, Daniel W.; Iverson, Louis; Matthews, Steve; Adams, Bryce. 2020. Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change. Diversity and Distributions. 26(9): 1142-1159. https://doi.org/10.1111/ddi.13078 and https://research.fs.usda.gov/treesearch/60748

Prasad, Anantha M.; Peters, Matthew P.; Pedlar, John H.; Gougherty, Andy V.; McKenney, Daniel W.; Mora, Francis; Matthews, Steve N.; McNulty, Steve G.; Mata, Lauro Lopez. [In review]. North American tree species habitat and migration dynamics under current and future climates – a macroscale synthesis. Journal of Biogeography.
Source_Used_Citation_Abbreviation:
habitat quality from previous step
Process_Date: 20230915
Process_Step:
Process_Description:
Combing habitat quality and colonization likelihoods:

The combination of habitat quality (HQ) and colonization likelihoods (CL) represents a tree species potential current and future habitat suitability and the potential migration within this century. The HQCL data indicates where habitat is suitable, how likely a species could migrate to newly suitable habitat, and where no migration is likely to occur. The estimated CL was classified into uncolonized (0), low (1–10), medium (11–30), and high (31–100) and combined with HQ to form a 15-class dataset. The classes include low, medium, and high for HQ, and occupied, null, low, medium, and high for CL.

See Prasad et al. (2020) and Prasad et al. (in review) for more details.


Prasad, Anantha; Pedlar, John; Peters, Matt; McKenney, Daniel W.; Iverson, Louis; Matthews, Steve; Adams, Bryce. 2020. Combining US and Canadian forest inventories to assess habitat suitability and migration potential of 25 tree species under climate change. Diversity and Distributions. 26(9): 1142-1159. https://doi.org/10.1111/ddi.13078 and https://research.fs.usda.gov/treesearch/60748

Prasad, Anantha M.; Peters, Matthew P.; Pedlar, John H.; Gougherty, Andy V.; McKenney, Daniel W.; Mora, Francis; Matthews, Steve N.; McNulty, Steve G.; Mata, Lauro Lopez. [In review]. North American tree species habitat and migration dynamics under current and future climates – a macroscale synthesis. Journal of Biogeography.
Source_Used_Citation_Abbreviation:
habitat quality and colonization likelihood from previous steps
Process_Date: 20230915
Process_Step:
Process_Description:
DATA CORRECTIONS

The previous version of the data (published on 06/17/2024) contained raster tiff files (CurrentPredicted_Consensus_sp[XXX].tif, SSP2-45_HQCL_sp[XXX].tif, SSP2-45_Predicted_Consensus_sp[XXX].tif, SSP5-85_HQCL_sp[XXX].tif, and SSP5-85_Predicted_Consensus_sp[XXX].tif), where XXX is the tree species code used by the U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) program that had a non-model mask inconsistently applied to the input layers. This resulted in pixels along the coast to reflect values of habitat suitability that should not be considered. This minor error, which has been corrected, also impacts the summary files (elv_[REGION]_boxplot_sp[XXX]_v2.png, elv_[REGION]_stats_sp[XXX]_v2.csv, map_[REGION]_boxplot_sp[XXX]_v2.png, map_[REGION]_stats_sp[XXX]_v2.csv, mat_[REGION]_boxplot_sp[XXX]_v2.png, and mat_[REGION]_stats_sp[XXX]_v2.csv), where REGION is either Canada-US, Mexico-US, or US-Only and the map files ([REGION]_CurrentPredicted_Consensus_sp[XXX]_v2.png, [REGION]_FuturePredicted_Consensus_sp[XXX]_v2.png, and [REGION]_HQCL_Consensus_sp[XXX]_v2.png). Corrected files were made available on 08/30/2024.
Process_Date: 202408
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Spatial_Data_Organization_Information:
Direct_Spatial_Reference_Method: Raster
Raster_Object_Information:
Raster_Object_Type: GridCell
Row_Count: 409
Column_Count: 378
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Spatial_Reference_Information:
Horizontal_Coordinate_System_Definition:
Planar:
Map_Projection:
Map_Projection_Name: Albers Conical Equal Area
Albers_Conical_Equal_Area:
Standard_Parallel: 20
Standard_Parallel: 60.00000
Longitude_of_Central_Meridian: -96.00000
Latitude_of_Projection_Origin: 40.00000
False_Easting: 0
False_Northing: 0
Planar_Coordinate_Information:
Planar_Coordinate_Encoding_Method: Coordinate Pair
Coordinate_Representation:
Abscissa_Resolution: 20000
Ordinate_Resolution: 20000
Planar_Distance_Units: Meters
Geodetic_Model:
Horizontal_Datum_Name: World Geodetic System of 1984
Ellipsoid_Name: World Geodetic System of 1984
Semi-major_Axis: 6378137.0000
Denominator_of_Flattening_Ratio: 298.257224
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Entity_and_Attribute_Information:
Overview_Description:
Entity_and_Attribute_Overview:
Below you will find a list and description of the files included in this data publication.


VARIABLE DESCRIPTION FILE

\Data\_variable_descriptions.csv: Comma-separated values (CSV) file containing a list and description of variables found in all data files. (A description of these variables is also provided in the metadata below.)

Attributes include:

Filename = Name of data file

Variable = Name of variable

Units = Units (if applicable)

Precision = Precision (if applicable)

Description = Description of variable



DATA FILES - Summary Species Information

\Data\Species_List.csv: CSV file containing information about each species in this data publication, such as the scientific name, common name, USDA Forest Service Forest Inventory and Analysis (FIA) species code [XXX], status indicating if the inventory distribution is 'mapped' or 'mapped and modeled', source of the inventory data, and R² indicating model performance.

Attributes include:

Index = Numeric sequence to order list

Scientific Name = Scientific name for species

Common Name = Common name for species

Species Code = Unique code assigned to FIA species, based on FIADB_1.6.1.00 (FIADB 2017)

STATUS = Indication of whether the species was 'mapped' using actual inventory records aggregated to 20-km grids or 'mapped and modeled' under current and projected climate change scenarios

Inventory = Source of forest inventory data used to map and model the species ('Canada-US', 'Mexico-US', 'US-only')

Percent Rsquare = Percent R² of the multi-model ensemble indicating model performance [precision = 0.1]



DATA FILES - Species-level Data

There are 326 species included in this data publication. Each species has its own folder designated by the species' scientific name. The same set of files provided for each species and described using the following:

[SCIENTIFIC_NAME]= scientific name for species
[XXX] = FIA species code (which are defined in \Data\Species_List.csv)
[REGION] = region of national forest inventory data


1. \Data\[SCIENTIFIC_NAME]\Actual_sp[XXX].tif: Georeferenced (GeoTIFF) raster file (and associated files) containing actual relative abundance derived from national forest inventory data from Canada, Mexico, and the United States. Data are provided with North America as a 20 × 20 kilometer (km) grid for each species [XXX].

Attributes include:

Value = Integer values representing the relative abundance of the species


2. \Data\[SCIENTIFIC_NAME]\CurrentPredicted_Consensus_sp[XXX].tif: GeoTIFF raster file (and associated files) containing modeled relative abundance indicating habitat suitability under climate conditions for the period 1991-2020 and topographic information across North America. Data are provided with North America as a 20 × 20 km grid for each species [XXX]. This file is not included for species with R² below 10 percent.

Attributes include:

Value = Integer values representing the relative abundance of the species modeled under climate condition for the period 1991-2020


3. \Data\[SCIENTIFIC_NAME]\elv_[REGION]_boxplot_sp[XXX]_v2.png: Portable Network Graphic format (PNG) image file containing statistical boxplots of elevation (meters [m]) for each species [XXX] and the specified region ([REGION] = canUS, MexUS, or USonly) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models. Potentially four boxplots representing elevation from 20-km grid containing actual inventory data, and habitat suitability modeled under current (1991-2020), SSP2-4.5, and SSP5-8.5 in the future (2070-2100). For species with R² below 10 percent, current and future boxplots were omitted.


4. \Data\[SCIENTIFIC_NAME]\elv_[REGION]_stats_sp[XXX]_v2.csv: CSV file containing the values used to plot the statistical boxplots of elevation the associated file elv_[REGION]_boxplot_sp[XXX]_v2.png. Four columns contain elevation (m) values from 20-km grid containing actual inventory data, and habitat suitability modeled under current (1991-2020), SSP2-4.5, and SSP5-8.5 in the future (2070-2100). For species with R² below 10 percent, current and future boxplots were omitted.

Attributes include:

Stats = Type of statistics represented (Min = Minimum, Mean = Mean, Max = Maximum, and multiple quarteriles Q##)

Actual = Actual elevation (m) from inventory data

CurMod = Elevation (m) from habitat suitability modeled under current (1991-2020) climate conditions

SSP2-45 = Elevation (m) from habitat suitability modeled under SSP2-4.5, future (2070-2100) climate conditions

SSP5-85 = Elevation (m) from habitat suitability modeled under SSP5-8.5, future (2070-2100) climate conditions


5. \Data\[SCIENTIFIC_NAME]\map_[REGION]_boxplot_sp[XXX]_v2.png: PNG image file containing statistical boxplots of mean annual precipitation (millimeters [mm]) for each species [XXX] and the specified region ([REGION] = canUS, MexUS, or USonly) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models. Climate data for the periods 1991-2020 and 2071-2100 were used. Potentially four boxplots representing precipitation from 20-km grid containing actual inventory data, and habitat suitability modeled under current (1991-2020), SSP2-4.5, and SSP5-8.5 in the future (2070-2100). For species with R² below 10 percent, current and future boxplots were omitted.


6. \Data\[SCIENTIFIC_NAME]\map_[REGION]_stats_sp[XXX]_v2.csv: CSV file containing the values used to plot the statistical boxplots of mean annual precipitation (mm) for each species [XXX] and the specified region ([REGION] = canUS, MexUS, or USonly) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models. Climate data for the periods 1991-2020 and 2071-2100 were used. Four columns contain precipitation from 20-km grid containing actual inventory data, and habitat suitability modeled under current (1991-2020), SSP2-4.5, and SSP5-8.5 in the future (2070-2100). For species with R² below 10 percent, current and future boxplots were omitted.

Attributes include:

Stats = Type of statistics represented (Min = Minimum, Mean = Mean, Max = Maximum, and multiple quarteriles Q##)

Actual = Actual mean annual precipitation (mm) from inventory data

CurMod = Mean annual precipitation (mm) from habitat suitability modeled under current (1991-2020) climate conditions

SSP2-45 = Mean annual precipitation (mm) from habitat suitability modeled under SSP2-4.5, future (2070-2100) climate conditions

SSP5-85 = Mean annual precipitation (mm) from habitat suitability modeled under SSP5-8.5, future (2070-2100) climate conditions


7. \Data\[SCIENTIFIC_NAME]\mat_[REGION]_boxplot_sp[XXX]_v2.png: PNG image file containing statistical boxplots of mean annual temperature (degrees Celsius [°C]) for each species [XXX] and the specified region ([REGION] = canUS, MexUS, or USonly) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models. Climate data for the periods 1991-2020 and 2071-2100 were used. Potentially four boxplots representing precipitation from 20-km grid containing actual inventory data, and habitat suitability modeled under current (1991-2020), SSP2-4.5, and SSP5-8.5 in the future (2070-2100). For species with R² below 10 percent, current and future boxplots were omitted.


8. \Data\[SCIENTIFIC_NAME]\mat_[REGION]_stats_sp[XXX]_v2.csv: CSV file containing the values used to plot the statistical boxplots of mean annual temperature (°C) for each species [XXX] and the specified region ([REGION] = canUS, MexUS, or USonly) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models. Climate data for the periods 1991-2020 and 2071-2100 were used. Four columns contain temperature from 20-km grid containing actual inventory data, and habitat suitability modeled under current (1991-2020), SSP2-4.5, and SSP5-8.5 in the future (2070-2100). For species with R² below 10 percent, current and future boxplots were omitted.

Attributes include:

Stats = Type of statistics represented (Min = Minimum, Mean = Mean, Max = Maximum, and multiple quarteriles Q##)

Actual = Actual mean annual temperature (°C) from inventory data

CurMod = Mean annual temperature (°C) from habitat suitability modeled under current (1991-2020) climate conditions

SSP2-45 = Mean annual temperature (°C) from habitat suitability modeled under SSP2-4.5, future (2070-2100) climate conditions

SSP5-85 = Mean annual temperature (°C) from habitat suitability modeled under SSP5-8.5, future (2070-2100) climate conditions


9. \Data\[SCIENTIFIC_NAME]\[REGION]_Actual_sp[XXX]_v2.png: PNG image file containing mapped relative abundance for each species [XXX] and the specified region ([REGION] = Canada-US, Mexico-US, or US-Only) indicating the source of inventory data, reported by national forest inventories from Canada, Mexico, and United States across North America.


10. \Data\[SCIENTIFIC_NAME]\[REGION]_CurrentPredicted_Consensus_sp[XXX]_v2.png: PNG image file containing mapped relative abundance for each species [XXX] and the specified region ([REGION] = Canada-US, Mexico-US, or US-Only) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models under climate conditions for the period 1991-2020 and topographic information across North America. This file is not included for species with R² below 10 percent.


11. \Data\[SCIENTIFIC_NAME]\[REGION]_FuturePredicted_Consensus_sp[XXX]_v2.png: PNG image file containing mapped relative abundance for each species [XXX] and the specified region ([REGION] = Canada-US, Mexico-US, or US-Only) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models under climate conditions for the period 2071-2100 and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) and topographic information across North America. This file is not included for species with R² below 10 percent.


12. \Data\[SCIENTIFIC_NAME]\[REGION]_HQCL_Consensus_sp[XXX]_v2.png: PNG image file containing mapped habitat quality (HQ) and colonization likelihoods (CL) for each species [XXX] and the specified region ([REGION] = Canada-US, Mexico-US, or US-Only) indicating the source of inventory data, corresponding to habitat suitability from version 2 (v2) models under climate conditions for the period 2071-2100 and shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5) and topographic information across North America. This file is not included for species with R² below 10 percent.


13. \Data\[SCIENTIFIC_NAME]\SSP2-45_HQCL_sp[XXX].tif: GeoTIFF raster file (and associated files) containing the fifteen-class combination of potential habitat quality (HQ) and colonization likelihoods (CL) for the future (2070-2100) SSP2-4.5 scenario. Data are provided with North America as a 20 × 20 km grid for each species [XXX]. This file is not included for species with R² below 10 percent.

Attributes include:

Value = A fifteen-class value representing the combination of potential habitat quality (HQ) and colonization likelihoods (CL) for projected climate conditions for the period 2070-2100 and SSP2-4.5 scenario. HQ was reclassified into 3 classes (HQlow, HQmed, HQhigh) and CL was reclassified into five classes (Occupied, CLnull, CLlow, CLmed and CLhigh).
1 = No data
5 = Low habitat quality (HQlow) and occupied
6 = Low habitat quality (HQlow) and no colonization likelihood (CLnull)
7 = Low habitat quality (HQlow) and low colonization likelihood (CLlow)
8 = Low habitat quality (HQlow) and medium colonization likelihood (CLmed)
9 = Low habitat quality (HQlow) and high colonization likelihood (CLhigh)
10 = Medium habitat quality (HQmed) and occupied
11 = Medium habitat quality (HQmed) and no colonization likelihood (CLnull)
12 = Medium habitat quality (HQmed) and low colonization likelihood (CLlow)
13 = Medium habitat quality (HQmed) and medium colonization likelihood (CLmed)
14 = Medium habitat quality (HQmed) and high colonization likelihood (CLhigh)
15 = High habitat quality (HQhigh) and occupied
16 = High habitat quality (HQhigh) and no colonization likelihood (CLnull)
17 = High habitat quality (HQhigh) and low colonization likelihood (CLlow)
18 = High habitat quality (HQhigh) and medium colonization likelihood (CLmed)
19 = High habitat quality (HQhigh) and high colonization likelihood (CLhigh)


14. \Data\[SCIENTIFIC_NAME]\SSP2-45_Predicted_Consensus_sp[XXX].tif: GeoTIFF raster file (and associated files) containing modeled relative abundance indicating habitat suitability under projected climate conditions for the period 2070-2100 and SSP2-4.5 scenario. Data are provided with North America as a 20 × 20 km grid for each species [XXX]. This file is not included for species with R² below 10 percent.

Attributes include:

Value = Integer values representing the relative abundance or habitat suitability of the species modeled under projected climate conditions for the period 2070-2100 and SSP2-4.5 scenario


15. \Data\[SCIENTIFIC_NAME]\SSP5-85_HQCL_sp[XXX].tif: GeoTIFF raster file (and associated files) containing the fifteen-class combination of potential habitat quality (HQ) and colonization likelihoods (CL) for the future (2070-2100) SSP5-5.5 scenario. Data are provided with North America as a 20 × 20 km grid for each species [XXX]. This file is not included for species with R² below 10 percent.

Attributes include:

Value = A fifteen-class value representing the combination of potential habitat quality (HQ) and colonization likelihoods (CL) for projected climate conditions for the period 2070-2100 and SSP5-8.5 scenario. HQ was reclassified into 3 classes (HQlow, HQmed, HQhigh) and CL was reclassified into five classes (Occupied, CLnull, CLlow, CLmed and CLhigh).
1 = No data
5 = Low habitat quality (HQlow) and occupied
6 = Low habitat quality (HQlow) and no colonization likelihood (CLnull)
7 = Low habitat quality (HQlow) and low colonization likelihood (CLlow)
8 = Low habitat quality (HQlow) and medium colonization likelihood (CLmed)
9 = Low habitat quality (HQlow) and high colonization likelihood (CLhigh)
10 = Medium habitat quality (HQmed) and occupied
11 = Medium habitat quality (HQmed) and no colonization likelihood (CLnull)
12 = Medium habitat quality (HQmed) and low colonization likelihood (CLlow)
13 = Medium habitat quality (HQmed) and medium colonization likelihood (CLmed)
14 = Medium habitat quality (HQmed) and high colonization likelihood (CLhigh)
15 = High habitat quality (HQhigh) and occupied
16 = High habitat quality (HQhigh) and no colonization likelihood (CLnull)
17 = High habitat quality (HQhigh) and low colonization likelihood (CLlow)
18 = High habitat quality (HQhigh) and medium colonization likelihood (CLmed)
19 = High habitat quality (HQhigh) and high colonization likelihood (CLhigh)


16. \Data\[SCIENTIFIC_NAME]\SSP5-85_Predicted_Consensus_sp[XXX].tif: GeoTIFF raster file (and associated files) containing modeled relative abundance indicating habitat suitability under projected climate conditions for the period 2070-2100 and SSP5-8.5 scenario. Data are provided with North America as a 20 × 20 km grid for each species [XXX]. This file is not included for species with R² below 10 percent.

Attributes include:

Value = Integer values representing the relative abundance or habitat suitability of the species modeled under projected climate conditions for the period 2070-2100 and SSP5-8.5 scenario
Entity_and_Attribute_Detail_Citation:
Prasad, Anantha M.; Peters, Matthew P.; Pedlar, John H.; Gougherty, Andy V.; McKenney, Daniel W.; Mora, Francis; Matthews, Steve N.; McNulty, Steve G.; Mata, Lauro Lopez. [In review]. North American tree species habitat and migration dynamics under current and future climates – a macroscale synthesis. Journal of Biogeography.

USDA Forest Service, Forest Inventory and Analysis. 2017. Forest Inventory and Analysis Database. St. Paul, MN: USDA Forest Service, Northern Research Station. FIADB_1.6.1.00 Last updated Sat Apr 15 17:00:16 CDT 2017. https://apps.fs.usda.gov/fia/datamart/datamart.html and https://doi.org/10.2737/RDS-2001-FIADB
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Distribution_Information:
Distributor:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Research and Development
Contact_Position: Research Data Archivist
Contact_Address:
Address_Type: mailing and physical
Address: 240 West Prospect Road
City: Fort Collins
State_or_Province: CO
Postal_Code: 80526
Country: USA
Contact_Voice_Telephone: see Contact Instructions
Contact Instructions: This contact information was current as of August 2024. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Resource_Description: RDS-2024-0020
Distribution_Liability:
Metadata documents have been reviewed for accuracy and completeness. Unless otherwise stated, all data and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. However, neither the author, the Archive, nor any part of the federal government can assure the reliability or suitability of these data for a particular purpose. The act of distribution shall not constitute any such warranty, and no responsibility is assumed for a user's application of these data or related materials.

The metadata, data, or related materials may be updated without notification. If a user believes errors are present in the metadata, data or related materials, please use the information in (1) Identification Information: Point of Contact, (2) Metadata Reference: Metadata Contact, or (3) Distribution Information: Distributor to notify the author or the Archive of the issues.
Standard_Order_Process:
Digital_Form:
Digital_Transfer_Information:
Format_Name: CSV
Format_Version_Number: see Format Specification
Format_Specification:
Comma-separated values file
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2024-0020
Digital_Form:
Digital_Transfer_Information:
Format_Name: TIFF
Format_Version_Number: see Format Specification
Format_Specification:
Georeferenced (GeoTIFF) raster file (and associated files)
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2024-0020
Digital_Form:
Digital_Transfer_Information:
Format_Name: PNG
Format_Version_Number: see Format Specification
Format_Specification:
Portable Network Graphic format
Digital_Transfer_Option:
Online_Option:
Computer_Contact_Information:
Network_Address:
Network_Resource_Name: https://doi.org/10.2737/RDS-2024-0020
Fees: None
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Metadata_Reference_Information:
Metadata_Date: 20240830
Metadata_Contact:
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Person: Matthew Peters
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 359 Main Road
City: Delaware
State_or_Province: OH
Postal_Code: 43015
Country: USA
Contact_Voice_Telephone: 740-368-0063
Contact_Electronic_Mail_Address: matthew.p.peters@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Contact_Information:
Contact_Organization_Primary:
Contact_Organization: USDA Forest Service, Northern Research Station
Contact_Person: Anantha Prasad
Contact_Position: Ecologist
Contact_Address:
Address_Type: mailing and physical
Address: 359 Main Road
City: Delaware
State_or_Province: OH
Postal_Code: 43015
Country: USA
Contact_Voice_Telephone: 740-368-0103
Contact_Electronic_Mail_Address: anantha.prasad@usda.gov
Contact Instructions: This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS.
Metadata_Standard_Name: FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata
Metadata_Standard_Version: FGDC-STD-001.1-1999
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https://www.fs.usda.gov/rds/archive/products/RDS-2024-0020/_metadata_RDS-2024-0020.html