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Originator: Prasad, Anantha M. Originator: Peters, Matthew P. Originator: Matthews, Stephen N. Originator: Gougherty, Andrew V. Publication_Date: 2026 Title: Tree pests rarely fill the geographical ranges of their host species, but host range filling may increase with future climate change Geospatial_Data_Presentation_Form: journal article Series_Information: Series_Name: Global Ecology and Biogeography Other_Citation_Details: [In press] 1 -165.6 -69.2 54.4 9.9 Phytophagous insects, pathogens, and parasitic plants (collectively referred to as tree pests) are important components of forest ecosystems. Understanding their current distributions, as well as where they could potentially occur in the future, is vital for assessing tree species vulnerability to pest damage. Our data will aid landowners and natural resource managers in planning for the future. <div style='text-align:Left;'><div><div><p><span>Habitat suitability models were created for 85 pest species that negatively impact tree species across the USA and Canada. Each model was produced as an ensemble of species distribution models that associated the occurrence of pest species with climate factors, topographical characteristics, host tree abundance, and, for non-native pest species, a dispersal metric capturing their spatiotemporal spread from their origin site in North America. Pest species occurrences from 1991-2025 were gathered from multiple online sources, including the USDA Forest Service, Forest Inventory and Analysis program; USDA Forest Service Forest, Insect and Disease Survey database; Global Biodiversity Information Facility; Canadian Forest Service Forest Invasive Alien Species; and numerous regional surveys. This data publication includes raster and image files of current projected distributions (1991-2020) of 85 pest species, as well as projected distributions that incorporate future climate scenarios (2070-2100) corresponding to high and low greenhouse gas emissions and future tree distribution scenarios in which trees do or do not migrate in response to future climate change. Each map is presented as both a probability surface (ranging from 0 to 1000) and a binary classification (0 and 1).</span></p></div></div></div> National Research & Development Taxonomy; climate change; climate change effects; ecology; ecosystems; environment; forest & plant health; insects; pest management; plant diseases; natural resource management & use; forest management; North America; Canada; Mexico; United States; <div style='text-align:Left;'><div><div><p><span>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:</span></p><p><span>Clipp, Hannah L.; Prasad, Anantha M.; Peters, Matthew P.; Matthews, Stephen N.; Gougherty, Andrew V. 2026. Current and potential future distributions of 85 pest species affecting trees in North America. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2026-0035</span></p><p><span>USDA Use Limitations:</span></p><p><span>The USDA Forest Service makes no warranty, expressed or implied, including the warranties of merchantability and fitness for a particular purpose, nor assumes any legal liability or responsibility for the accuracy, reliability, completeness or utility of these geospatial data, or for the improper or incorrect use of these geospatial data. These geospatial data and related maps or graphics are not legal documents and are not intended to be used as such. The data and maps may not be used to determine title, ownership, legal descriptions or boundaries, legal jurisdiction, or restrictions that may be in place on either public or private land. Natural hazards may or may not be depicted on the data and maps, and users should exercise due caution. The data are dynamic and may change over time. The user is responsible to verify the limitations of the geospatial data and to use the data accordingly.</span></p><p><span>Additionally, The U.S. Forest Service waives copyright and related rights in the work worldwide through the CC0 (which can be found at https://creativecommons.org/public-domain/cc0/).</span></p><p><span>Non-Discrimination Statement:</span></p><p><span>In accordance with Federal civil rights law and U.S. Department of Agriculture (USDA) civil rights regulations and policies, the USDA, its Agencies, offices, and employees, and institutions participating in or administering USDA programs are prohibited from discriminating based on race, color, national origin, religion, sex, disability, age, marital status, family/parental status, income derived from a public assistance program, political beliefs, or reprisal or retaliation for prior civil rights activity, in any program or activity conducted or funded by USDA (not all bases apply to all programs). Remedies and complaint filing deadlines vary by program or incident.</span></p><p><span>Persons with disabilities who require alternative means of communication for program information (e.g., Braille, large print, audiotape, American Sign Language, etc.) should contact the State or local Agency that administers the program or contact USDA through the Telecommunications Relay Service at 711 (voice and TTY). Additionally, program information may be made available in languages other than English.</span></p><p><span>To file a program discrimination complaint, complete the USDA Program Discrimination Complaint Form, AD-3027, found online at How to File a Program Discrimination Complaint and at any USDA office or write a letter addressed to USDA and provide in the letter all of the information requested in the form. To request a copy of the complaint form, call (866) 632-9992. Submit your completed form or letter to USDA by: (1) mail: U.S. Department of Agriculture, Office of the Assistant Secretary for Civil Rights, 1400 Independence Avenue, SW, Mail Stop 9410, Washington, D.C. 20250-9410; (2) fax: (202) 690-7442; or (3) email: program.intake@usda.gov.</span></p><p><span>USDA is an equal opportunity provider, employer, and lender.</span></p></div></div></div> This project was funded by the USDA Forest Service, Northern Research Station. Author Information: Hannah L. Clipp USDA Forest Service, Northern Research Station (former ORISE postdoctoral researcher) https://orcid.org/0000-0002-8555-7398 Anantha M. Prasad USDA Forest Service, Northern Research Station (former employee) https://orcid.org/0000-0002-4645-6260 Matthew P. Peters USDA Forest Service, Northern Research Station https://orcid.org/0000-0002-4793-0075 Stephen N. Matthews USDA Forest Service, Northern Research Station https://orcid.org/0000-0001-9175-7778 Andrew V. Gougherty USDA Forest Service, Northern Research Station https://orcid.org/0000-0002-3905-8539 National Research & Development Taxonomy climate change climate change effects ecology ecosystems environment forest & plant health insects pest management plant diseases natural resource management & use forest management North America Canada Mexico United States EPSG 6.18.3(9.3.1.2) 2 1 0 -20037507.067200 300971.458400 -20037507.067200 30240971.458386 20037507.842788 30240971.458386 20037507.842788 300971.458400 0.387794 15270971.458393 2004 19997.512430 1497 20000.000000 Band_1 84.000000 1.000000 32 False False False False False 20260903 Attribute_Accuracy_Report: The methods of statistically downscaling climate values obtained from general circulation models (GCMs), converting 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 were generated following 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, they carry an inherent degree of error and uncertainty. Users should read and fully comprehend the metadata and other available publications prior to using the data. These data represent potential habitat suitability but not necessarily pest damage, outbreaks, occur might exist. Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos. 2016. Locally downscaled and spatially customizable climate data for historical and future periods for North America. PLoS One. 11(6): e0156720. https://doi.org/10.1371/journal.pone.0156720 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. 1-21. https://doi.org/10.1002/joc.7566 Logical_Consistency_Report: These data are logically consistent. The consistency was verified as part of the quality assurance that occurred during data analysis. Completeness_Report: To our knowledge, there are no missing data. However, data were not available for tree species if their model performance was considered too low (R² below 10 percent) and thus, for some pest species, the included set of host tree species may not have been fully complete but still deemed sufficient for model training purposes. Methodology_Description: OVERVIEW To map the distributions of 85 pests of tree species in North America, we first modeled pest species occurrences as a function of (up to) ten predictors, including climate, topography, host tree abundance, and spatiotemporal dispersal from introduction site(s) using an ensemble modeling approach that combined outputs from nine different model algorithms. We then used the ensemble model to forecast pest species distributions to future (end-of-century) climate conditions and explored multiple future scenarios, including high and low greenhouse gas emissions scenarios and whether or not tree species migrate in response to climate change. FOCAL PEST SPECIES SELECTION For this dataset, we sought to include a diversity of pests in our analyses, encompassing phytophagous insects, pathogens, and parasitic plants, as well as a combination of native and non-native species. We initially considered 462 pest species for which we gathered occurrence data from across North America and then applied various selection criteria to yield a subset of focal species. We prioritized pests that were well-documented as causing severe harm to one or more host trees native to North America (e.g., listed in Liebhold et al. 2013 or Potter et al. 2019) and for which the occurrence data were representative of the entire known geographical range in North America. Ultimately, we selected a total of 85 pest species, encompassing 63 insects, 13 pathogens, and 9 plant parasites. The insects comprised 6 feeding guilds: bark beetles (N = 10), defoliators (N = 35), gallmakers (N = 1), sap feeders (N = 9), weevils (N = 3), and wood borers (N = 5); the pathogens included cankers (N = 3), foliage and shoot diseases (N = 2), root diseases / decay (N = 6), and rusts (N = 2); and the parasitic plants consisted of both dwarf mistletoes (N = 5) and true mistletoes (N = 4). Of the 85 total species, 60 are considered native to North America, and 25 were introduced from another continent between 1835 and 2014. Most of the non-native species (N = 17) were first detected in North America during the 20th century, while 3 focal pest species were first detected after 2000. Distributions of the pest species within North America ranged from local endemic populations that were restricted to a single state or province to widespread, cosmopolitan populations that occurred in nearly every state and province. PEST OCCURRENCE DATA We obtained historical and contemporary occurrence data for the 85 pest species from multiple sources, including the USDA Forest Service, Forest, Insect and Disease Survey database (https://www.fs.usda.gov/science-technology/data-tools-products/fhp-mapping-reporting/detection-surveys); USDA Forest Service Forest, Inventory and Analysis program (Woudenberg et al. 2010), Canadian Forest Service Forest Invasive Alien Species database (Nealis et al. 2016); British Columbia Aerial Overview Surveys (Ministry of Forests 2023), Ontario Forest Disease and Insect Damage Event database (Government of Ontario 2023), Quebec Natural Disturbance Data (Ministry of Forests, Wildlife and Parks 2023), Global Biodiversity Information Facility (2025), and MycoPortal (Miller and Bates 2017). Because the data were stored in multiple formats and used different geographical projections, we synonymized occurrence data by first transforming all projections to WGS 84. Then, for any data that were stored as polygons, we overlaid the polygons on a 10 arc-minute grid, and any grid cell that intersected a polygon was kept as a point occurrence. To avoid pseudo-replication, occurrence data were further filtered by raster cell, such that each individual cell was associated with a single occurrence point rather than multiple occurrences. Finally, because pest damage can be misattributed to particular pests and small pests can be misidentified based on visual characteristics, we removed outlier points that fell outside of the known contemporary distribution of each pest species. To do so, for each pest, we identified any points that had a nearest neighbor distance beyond 3 standard deviations from the mean (calculated from all nearest neighbor distances). Next, we manually checked whether these points represented true occurrences or may have been erroneous by comparing the location of outlier points to published maps of pest distributions or written descriptions of distributions. From this process, we removed an average of 2.6 outlier points (0.5% of the total data) across all focal pest species and a range of 0–15 outlier points (0.0–7.2% of the total data) for individual pest species. Ultimately, total sample sizes of retained occurrence points for individual pest species ranged from 27–5,029 (mean = 1,216). PREDICTOR VARIABLES To make projections of contemporary and future pest distributions, we used a combination of predictor variables that included climate factors, topographical characteristics, host tree abundance, and, for non-native pests, a dispersal metric to capture their spatiotemporal spread. We obtained current and future projected climate and topography data from AdaptWest (2022). These data were represented as a raster with a spatial resolution of 20 km. All predictor variables were selected for their low collinearity (r < 0.7) and ecological relevance to our focal pest species. The six climate metrics included annual heat moisture index, average winter temperature, mean annual relative humidity, average winter precipitation, average summer precipitation, and Hogg’s climate moisture index (i.e., a drought metric that reflects the difference between precipitation and potential evapotranspiration; Hogg et al. 2013) , while the two topographical metrics included elevation and ruggedness (i.e., a measure of elevational variability within a pixel). Future climate projections consisted of a low-emissions scenario (SSP2-4.5) and a high-emissions scenario (SSP5-8.5) for the end of the century (2070–2100) and were derived from an eight-model ensemble (AdaptWest Project 2022; Mahony et al. 2022). For the host tree abundance variable, we first compiled a comprehensive list of host tree species information from a variety of sources, including the Centre for Agriculture and Bioscience International Digital Library (CABI 2024) and European Plant Protection Organization global database (EPPO 2024), as well as primary literature. We then obtained contemporary and projected future relative abundance data corresponding to as many host tree species as possible from Prasad et al. (2024, 2025). These data were represented as individual rasters with spatial resolutions of 20 km across the North American continent. Contemporary relative abundance datasets were compiled from national forest inventories for the periods 2007 and 2017 for Canada, 2004–2009 for Mexico, and 1998–2018 for the United States and modeled for the period 1991–2020. The two future relative abundance datasets corresponded to the low-emissions and a high-emissions climate scenarios for the end of the century. The future projections were additionally constrained by colonization likelihood, such that relative abundance for individual host tree species was set to 0 in areas that were unlikely to be colonized by natural migration by the end of the century, based on historic migration rates (Prasad et al. 2025). Finally, we calculated the sum of the individual relative abundances for all host tree species corresponding to each focal pest species to create a single predictor variable representing aggregate host tree relative abundance. Host tree species for which we lacked relative abundance data were not included in the aggregate metric, but these tree species tended to be either non-native in North America, or have very restricted ranges such that they were not well captured by forest inventories. For non-native focal pest species, we generated a contemporary and projected future dispersal metric, which was calculated as the quotient of the geographical distance from the primary site(s) of introduction in North America divided by the elapsed number of years since initial discovery. This metric is similar to our previous approach to accounting for spread of non-native species, but also included information on the time since discovery (Gougherty et al. 2024). For future projections, the elapsed number of years was adjusted to the end of the century. In general, we expected this metric to represent the pace at which introduced pests have been able to spread and, for future projections, indicate the potential rate of expansion, assuming that pests maintain the same rate of spread. However, we note that this metric should be considered conservative, as some pests are prone to rare long-distance dispersal events that cannot be easily accounted for in statistical models. Introduction sites and dates were compiled from the CABI Digital Library and primary literature sources (Ward et al. 2019). For non-native pest species that were introduced independently into eastern and western North America (e.g., balsam woolly adelgid [Adelges piceae], white pine blister rust, satin moth [Leucoma salicis]), distances and years since introduction were calculated from the nearest introduction site. SPECIES DISTRIBUTION MODELING AND ANALYSIS For native pest species, we modeled species occurrence as a function of the six climate metrics, two topographical metrics, and aggregate host tree metric, and for non-native species we included the spatiotemporal dispersal metric. Using the “biomod2” package (Thuiller et al. 2009, 2025) in R (R Core Team 2023), we used an ensemble modeling approach that combined results from nine different model algorithms. First, we formatted our input data with the “BIOMOD_FormatingData” function, using the random selection strategy to generate ten pseudo-absence datasets, with the number of pseudo-absence points equal to three times the number of pest occurrence points. Next, we ran a series of single species distribution models with the “BIOMOD_Modeling” function. We selected a total of nine model algorithms: Artificial Neural Network, Classification Tree Analysis, Flexible Discriminant Analysis, Generalized Additive Model, Generalized Boosting Model (i.e., Boosted Regression Tree), Maximum Entropy (i.e., MAXENT), Maximum Entropy 2 (i.e., MAXNET), Random Forest, and eXtreme Gradient Boosting Training. For model algorithms that implemented a model formula, we followed a standardized formula, based on the set of predictor variables: (a) all climate and topographical metrics incorporated as purely additive, for a total of eight parameters; (b) all climate, topographical, and aggregate host tree metrics incorporated as purely additive, for a total of nine parameters; (c) all climate and topographical metrics interacting individually with the dispersal metric, for a total of 17 parameters; and (d) all climate, topographical, and aggregate host tree metrics interacting individually with the dispersal metric, for a total of 19 parameters. Thus, while we used the recommended “bigboss” option (provided by the biomod2 team to optimize for species distribution modeling) as our base model optimization strategy, we also specified the modeling options as user-defined. To calibrate and validate the models, we used a random cross-validation procedure to split the data a total of five times, with 80% of the data used for calibration and 20% of the data used for validation. We further evaluated the models with the following metrics calculated within the “BIOMOD_Modeling” function: true skill statistic (TSS), relative operating characteristic (ROC), Cohen’s Kappa, and the Boyce index. Finally, we ran three permutations to estimate variable importance, which characterizes the impact that each variable has on model predictions and is calculated by randomizing the variable of interest and computing the correlation between original and shuffled variables. After the single species distribution models were run for each focal pest species, we created and evaluated an ensemble set of models and predictions with the “BIOMOD_EnsembleModeling” function. We considered all single species distribution models and combined them all to build the ensemble models, using the TSS evaluation metric and a threshold of 0.7 to exclude single models from ensemble model building based on their validation scores. The first ensemble algorithm that we computed was the weighted mean, in which probabilities from the single species distribution models were weighted according to their evaluation scores and summed. Weights were assigned to each model proportionally to their TSS evaluation scores. The second ensemble algorithm that we computed was the coefficient of variation (CV; i.e., standard deviation divided by mean) of probabilities over the single species distribution models. The CV is a measure of uncertainty (rather than a measure of probability of occurrence), with higher values indicating higher uncertainty. Both types of ensemble models were evaluated using TSS, ROC, and Cohen’s Kappa, and variable importance was estimated over three permutations. Ultimately, model fit of the single species models and weighted mean ensemble models was assessed using sensitivity (i.e., the percentage of true positives that are correctly predicted), specificity (i.e., the percentage of true negatives that are correctly predicted), and TSS (which combines sensitivity and specificity) for calibration and validation (if applicable). Finally, we used the “BIOMOD_EnsembleForecasting” function to project the ensemble species distribution models with the contemporary and future metrics. In addition to projecting the contemporary distributions of the focal pest species, we considered four future projection scenarios for each set of predictor variables: (i) low-emissions future climate scenario with no change in host tree relative abundance; (ii) low-emissions future climate scenario with corresponding change in aggregate host tree relative abundance; (iii) high-emissions future climate scenario with no change in host tree relative abundance; and (iv) high-emissions future climate scenario with corresponding change in aggregate host tree relative abundance. To produce binary projection maps, we used the “get_predictions” function and selected the TSS evaluation metric to transform predictions into binary values. Source_Information: Source_Citation: Citation_Information: Originator: AdaptWest Project Publication_Date: 2022 Title: Gridded current and projected climate data for North America at 1km resolution, generated using the ClimateNA v7.3 software Geospatial_Data_Presentation_Form: database Online_Linkage: https://adaptwest.databasin.org Online_Linkage: https://adaptwest.databasin.org/pages/adaptwest-climatena/ Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2022 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: AdaptWest Source_Contribution: We collected contemporary and future spatial climate data for North America from AdaptWest. For further information and citation refer to: Wang, T.; Hamann, A.; Spittlehouse, D.; Carroll, C. 2016. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS One. 11(6): e0156720. https://doi.org/10.1371/journal.pone.0156720 Mahony, C.R.; Wang, T.; Hamann, A.; Cannon, A. J. 2022. A global climate model ensemble for downscaled monthly climate normals over North America. International Journal of Climatology. 1-21. https://doi.org/10.1002/joc.7566 Source_Information: Source_Citation: Citation_Information: Originator: CABI Publication_Date: 2024 Title: CABI Digital Compendium Geospatial_Data_Presentation_Form: document Publication_Information: Publication_Place: Wallingford, UK Publisher: CAB International Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2024 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: CABI (2024) Source_Contribution: We collected host information for 85 forest tree pests in North America from CABI. Source_Information: Source_Citation: Citation_Information: Originator: EPPO Publication_Date: 2024 Title: EPPO global database Geospatial_Data_Presentation_Form: database Publication_Information: Publisher: European and Mediterranean Plant Protection Organization Online_Linkage: https://gd.eppo.int/ Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2024 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: EPPO (2024) Source_Contribution: We collected host information for 85 forest tree pests in North America from EPPO. Source_Information: Source_Citation: Citation_Information: Originator: The Global Biodiversity Information Facility Publication_Date: 2025 Title: Tree pests Geospatial_Data_Presentation_Form: database Publication_Information: Publisher: GBIF.org Other_Citation_Details: Filtered export of GBIF occurrence data Online_Linkage: https://doi.org/10.15468/DD.XRKJQB Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2025 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: GBIF Source_Contribution: We collected geographic coordinates for 85 forest tree pests in North America from GBIF. Source_Information: Source_Citation: Citation_Information: Originator: Government of Ontario Publication_Date: 2023 Title: Forest disease damage event Geospatial_Data_Presentation_Form: database Publication_Information: Publisher: Ontario GeoHub Online_Linkage: https://geohub.lio.gov.on.ca/datasets/lio::forest-disease-damage-event/about Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2023 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Goverment of Ontario (2023) Source_Contribution: We collected geographic coordinates for forest pest damage events in Ontario from this source. Source_Information: Source_Citation: Citation_Information: Originator: Miller, Andrew N. Originator: Bates, Scott T. Publication_Date: 2017 Title: The Mycology collections portal (MyCoPortal) Geospatial_Data_Presentation_Form: document Series_Information: Series_Name: IMA Fungus Issue_Identification: 8(2): A65-A66 Online_Linkage: https://doi.org/10.1007/BF03449464 Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2017 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Miller and Bates (2017) Source_Contribution: We collected geographic coordinates for fungal forest pathogens in North America from MyCoPortal. Source_Information: Source_Citation: Citation_Information: Originator: Ministry of Forests Publication_Date: 2023 Title: Aerial overview survey summary reports—Province of British Columbia Geospatial_Data_Presentation_Form: document Publication_Information: Publication_Place: Province of British Columbia Online_Linkage: https://www2.gov.bc.ca/gov/content/industry/forestry/managing-our-forest-resources/forest-health/aerial-overview-surveys/summary-reports Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2023 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Ministry of Forests (2023) Source_Contribution: We collected geographic coordinates for forest pest damage events in British Columbia from this source. Source_Information: Source_Citation: Citation_Information: Originator: Ministry of Forests, Wildlife and Parks Publication_Date: 2023 Title: Natural disturbance data - insect: hemlock looper - open government portal Geospatial_Data_Presentation_Form: database Online_Linkage: https://open.canada.ca/data/en/dataset/9a77a0c0-d50e-47ff-a832-4ac93e7d2db7 Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2023 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Ministry of Forests, Wildlife and Parks (2023) Source_Contribution: We collected geographic coordinates for forest pest damage events in Quebec from this source. Source_Information: Source_Citation: Citation_Information: Originator: Nealis, V. G. Originator: DeMerchant, I. Originator: Langor, D. Originator: Noseworthy, M. K. Originator: Pohl, G. Originator: Porter, K. Originator: Shanks, E. Originator: Turnquist, R. Originator: Waring, V. Publication_Date: 2016 Title: Historical occurrence of alien arthropods and pathogens on trees in Canada Geospatial_Data_Presentation_Form: journal article Series_Information: Series_Name: Canadian Journal of Forest Research Issue_Identification: 46(2): 172-180 Online_Linkage: https://doi.org/10.1139/cjfr-2015-0273 Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2016 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Nealis et al. (2016) Source_Contribution: We collected geographic coordinates for invasive forest tree pests in Canada from Nealis et al. (2016). Source_Information: Source_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 Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2099 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Prasad et al. (2024) Source_Contribution: We collected contemporary and future host tree abundances for 85 forest tree pests in North America from Prasad et al. (2024). Source_Information: Source_Citation: Citation_Information: Originator: Woudenberg, Sharon W. Originator: Conkling, Barbara L. Originator: O'Connell, Barbara M. Originator: LaPoint, Elizabeth B. Originator: Turner, Jeffrey A. Originator: Waddell, Karen L. Publication_Date: 2010 Title: The Forest Inventory and Analysis database: Database description and users manual version 4.0 for Phase 2 Geospatial_Data_Presentation_Form: document Series_Information: Series_Name: General Technical Report Issue_Identification: RMRS-GTR-245 Publication_Information: Publication_Place: Fort Collins, CO Publisher: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station Other_Citation_Details: 336 p. Online_Linkage: https://doi.org/10.2737/RMRS-GTR-245 Type_of_Source_Media: Online Source_Time_Period_of_Content: Time_Period_Information: Range_of_Dates/Times: Beginning_Date: 1991 Ending_Date: 2010 Source_Currentness_Reference: Publication Date Source_Citation_Abbreviation: Woudenberg et al. (2010) Source_Contribution: We collected geographic coordinates for forest tree pests in the United States from Woudenberg et al. (2010). Process_Step: Process_Description: Because we assumed that pest species are restricted in forests to host tree species distributions, we additionally masked all projections by the current or future aggregate host tree occurrence, depending on the future projection scenario. Thus, the projected pest species distributions were all constrained by their respective aggregate host tree ranges. Process_Date: Unknown 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. INFORMATIONAL FILES (2) 1. \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.) Columns include: Filename = name of data file Variable = name of variable Units = units (if applicable) Description = description of variable 2. \Data\species_list.csv: CSV file containing a list of the 85 pest species along with the pest type, nativity status, and validation statistics. (The scientific name of these pest species is referred to as [SCI_NAME] in the file descriptions below.) Columns include: Scientifc Name = Scientific name of pest Native = Whether pest in native (1) or non-native (0) in North America Type = Functional group of pest (may include bark beetles, cankers, defoliators, foliage and shoot diseases, gallmakers, parasitic plants, root diseases or decay, rusts, sap feeders, weevils, wood borers) SSM Mean Sensitivity (%) = Mean sensitivity of individual models used to predict pest species distributions (percentage) SSM Mean Specificity (%) = Mean specificity of individual models used to predict pest species distributions (percentage) SSM Mean TSS Calibration = Mean true skill statistic of individual models used to predict pest species distributions for occurrences used in calibration SSM Mean TSS Validation = Mean true skill statistic of individual models used to predict pest species distributions for occurrences used in validation EM Sensitivity (%) = Sensitivity of ensemble model used to predict pest species distributions (percentage) EM Specificity (%) = Specificity of ensemble model used to predict pest species distributions (percentage) EM TSS Calibration = True skill statistic of ensemble model used to predict pest species distributions DATA FILES (85 species x 5 projections x 2 visualizations x 3 files per model = 2550 files) There is a folder for each of the 85 pest species (\Data\[SCI_NAME]) and for each of the 10 projection visualizations there will be a i) Portable Network Graphic image file (*.png), ii) a georeferenced (GeoTIFF) raster file (*.tif), and iii) the associated *.tif.xml. The 10 projections (which used the following variables in the model: climate, host abundance, and a metric of spread for non-native species) include: Probability Models - 1. [SCI_NAME]_CURRENT_PROJECTION_MODEL_climate-and-trees-and-dispersal_PROBABILITY.* The CURRENT distribution inferred from a species distribution model. Values range from 0-1000 representing habitat suitability for the pest. 2. [SCI_NAME]_FUTURE_PROJECTION_high-emissions_dynamic-trees_MODEL_climate-and-trees-and-dispersal_PROBABILITY.* The projected future distribution inferred from a species distribution model. FUTURE climate conditions assume a HIGH emission scenario where host trees MIGRATE in response to climate change. Values range from 0-1000 representing habitat suitability for the pest. 3. [SCI_NAME]_FUTURE_PROJECTION_high-emissions_static-trees_MODEL_climate-and-trees-and-dispersal_PROBABILITY.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a HIGH emission scenario where host trees DO NOT MIGRATE in response to climate change. Values range from 0-1000 representing habitat suitability for the pest. 4. [SCI_NAME]_FUTURE_PROJECTION_low-emissions_dynamic-trees_MODEL_climate-and-trees-and-dispersal_PROBABILITY.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a LOW emission scenario where host trees MIGRATE in response to climate change. Values range from 0-1000 representing habitat suitability for the pest. 5. [SCI_NAME]_FUTURE_PROJECTION_low-emissions_static-trees_MODEL_climate-and-trees-and-dispersal_PROBABILITY.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a LOW emission scenario where host trees DO NOT MIGRATE in response to climate change. Values range from 0-1000 representing habitat suitability for the pest. Threshold Models - 6. [SCI_NAME]_CURRENT_PROJECTION_MODEL_climate-and-trees-and-dispersal_THRESHOLD.* The CURRENT distribution inferred from a species distribution model. Values are thresholded to zero or one (absence or presence) based on the value that maximizes the true still statistic. 7. [SCI_NAME]_FUTURE_PROJECTION_high-emissions_dynamic-trees_MODEL_climate-and-trees-and-dispersal_THRESHOLD.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a HIGH emission scenario where host trees MIGRATE in response to climate change. Values are thresholded to zero or one (absence or presence) based on the value that maximizes the true still statistic. 8. [SCI_NAME]_FUTURE_PROJECTION_high-emissions_static-trees_MODEL_climate-and-trees-and-dispersal_THRESHOLD.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a HIGH emission scenario where host trees DO NOT MIGRATE in response to climate change. Values are thresholded to zero or one (absence or presence) based on the value that maximizes the true still statistic. 9. [SCI_NAME]_FUTURE_PROJECTION_low-emissions_dynamic-trees_MODEL_climate-and-trees-and-dispersal_THRESHOLD.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a LOW emission scenario where host trees MIGRATE in response to climate change. Values are thresholded to zero or one (absence or presence) based on the value that maximizes the true still statistic. 10. [SCI_NAME]_FUTURE_PROJECTION_low-emissions_static-trees_MODEL_climate-and-trees-and-dispersal_THRESHOLD.* The projected FUTURE distribution inferred from a species distribution model. FUTURE climate conditions assume a LOW emission scenario where host trees DO NOT MIGRATE in response to climate change. Values are thresholded to zero or one (absence or presence) based on the value that maximizes the true still statistic. Entity_and_Attribute_Detail_Citation: Clipp, Hannah L.; Prasad, Anantha M.; Peters, Matthew P.; Matthews, Stephen N.; Gougherty, Andrew V. [2026, In press]. Tree pests rarely fill the geographical ranges of their host species, but host range filling may increase with future climate change. Global Ecology and Biogeography. USDA Forest Service, Research and Development Research Data Archivist 240 West Prospect Road Fort Collins CO 80526 US This contact information was current as of July 2026. For current information see Contact Us page on: https://doi.org/10.2737/RDS. CSV, Tiff, PNG https://doi.org/10.2737/RDS-2026-0035 Andrew V. Gougherty USDA Forest Service, Northern Research Station Research Ecologist 359 Main Road Delware Ohio 43015 andrew.gougherty@usda.gov 740-368-0097 This contact information was current as of original publication date. For current information see Contact Us page on: https://doi.org/10.2737/RDS. 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