On the continental scale, climate is an important determinant of the distributions of plant taxa and ecoregions. To quantify and depict the relations between specific climate variables and these distributions, we placed modern climate and plant taxa distribution data on an approximately 25-kilometer (km) equal-area grid with 27,984 points that cover Canada and the continental United States (Thompson and others, 2015). The gridded climatic data include annual and monthly temperature and precipitation, as well as bioclimatic variables (growing degree days, mean temperatures of the coldest and warmest months, and a moisture index) based on 1961-1990 30-year mean values from the University of East Anglia (UK) Climatic Research Unit (CRU) CL 2.0 dataset (New and others, 2002), and absolute minimum and maximum temperatures for 1951-1980 interpolated from climate-station data (WeatherDisc Associates, 1989). As described below, these data were used to produce portions of the "Atlas of relations between climatic parameters and distributions of important trees and shrubs in North America" (hereafter referred to as "the Atlas"; Thompson and others, 1999a, 1999b, 2000, 2006, 2007, 2012a, 2015). Evolution of the Atlas Over the 16 Years Between Volumes A & B and G: The Atlas evolved through time as technology improved and our knowledge expanded. The climate data employed in the first five Atlas volumes were replaced by more standard and better documented data in the last two volumes (Volumes F and G; Thompson and others, 2012a, 2015). Similarly, the plant distribution data used in Volumes A through D (Thompson and others, 1999a, 1999b, 2000, 2006) were improved for the latter volumes. However, the digitized ecoregion boundaries used in Volume E (Thompson and others, 2007) remain unchanged. Also, as we and others used the data in Atlas Volumes A through E, we came to realize that the plant distribution and climate data for areas south of the US-Mexico border were not of sufficient quality or resolution for our needs and these data are not included in this data release. The data in this data release are provided in comma-separated values (.csv) files. We also provide netCDF (.nc) files containing the climate and bioclimatic data, grouped taxa and species presence-absence data, and ecoregion assignment data for each grid point (but not the country, state, province, and county assignment data for each grid point, which are available in the .csv files). The netCDF files contain updated Albers conical equal-area projection details and more precise grid-point locations. When the original approximately 25-km equal-area grid was created (ca. 1990), it was designed to be registered with existing data sets, and only 3 decimal places were recorded for the grid-point latitude and longitude values (these original 3-decimal place latitude and longitude values are in the .csv files). In addition, the Albers conical equal-area projection used for the grid was modified to match projection irregularities of the U.S. Forest Service atlases (e.g., Little, 1971, 1976, 1977) from which plant taxa distribution data were digitized. For the netCDF files, we have updated the Albers conical equal-area projection parameters and recalculated the grid-point latitudes and longitudes to 6 decimal places. The additional precision in the location data produces maximum differences between the 6-decimal place and the original 3-decimal place values of up to 0.00266 degrees longitude (approximately 143.8 m along the projection x-axis of the grid) and up to 0.00123 degrees latitude (approximately 84.2 m along the projection y-axis of the grid). The maximum straight-line distance between a three-decimal-point and six-decimal-point grid-point location is 144.2 m. Note that we have not regridded the elevation, climate, grouped taxa and species presence-absence data, or ecoregion data to the locations defined by the new 6-decimal place latitude and longitude data. For example, the

These data tables describe biogeoclimatic units for Western North America. These data were assembled as inputs to the Climate Change Informed Species Selection (CCISS) framework. The CCISS framework is built on Biogeoclimatic Ecosystem Classification (BEC). CCISS uses spatial climatic analogs (BEC subzone/variants) to make inferences about future tree species suitability, known as biogeoclimatic projections. Creating species suitability projections for the future climates of British Columbia requires finding climate analogs in Alberta and the Western US. For Alberta, we adapted the Ecological Classification of Alberta (e.g., Archibald et al. 1996), with 21 natural subregions (Natural Regions Committee 2006) as the biogeoclimatic map units and 167 ecological sites as the site series units. For Washington, Idaho, Montana, Oregon, northern California, and northwestern Wyoming, we use a draft biogeoclimatic ecosystem classification for the Western US developed by Del Meidinger and Will MacKenzie. Biogeoclimatic units are detailed in the: Western North America Biogeoclimatic Units Attribute Table. The CCISS tool predicts climate change implications to tree species environmental suitability at a site series level. We have compiled sites series information for Western North America biogeoclimatic units, detailed in; Site Series Information Table and Edatopic Space Table.

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Plant Macrofossil. The data include parameters of plant macrofossil (population abundance) with a geographic location of Ontario, Canada. The time period coverage is from 13904 to 13880 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st percentile, 10th percentile, and maximum of sensitivity-specificity ). This data bundle contains a single file of occurrence data with abundance information (Nonnative_plants_US.csv) and a subfolder for each species that contains the two raster files associated with the species. Each of the two rasters represent the following: species_code for current climate and species_code.2c for predictions under a +2C climate change scenario. The bundle documentation files are: 1) 'project_metdata.xml' (this file) which contains the project-level metadata 2) Nonnative_plants_US.csv is the occurrence and abundance data. 3) XX.tif where XX is the species code with current climatic conditions and species code with '2c' appended for habitat suitability predictions with +2C of climate change.

This is a dataset containing aggregated non-native plant occurrence and abundance data for the contiguous United States. We used these data to develop habitat suitability models for species found in the Eastern United States using locations with 5% cover or greater. We adapted the INHABIT modeling workflow (Young et al. 2020), using a consistent set of climatic predictors that were important in the INHABIT models. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.2.2]. We accounted for sampling bias by using the target background approach, and constructed model ensembles using the five models for each species for three different thresholds (conservative to targeted;1st percentile, 10th percentile, and maximum of sensitivity-specificity ). This data bundle contains a single file of occurrence data with abundance information (Nonnative_plants_US.csv) and a subfolder for each species that contains the two raster files associated with the species. Each of the two rasters represent the following: species_code for current climate and species_code.2c for predictions under a +2C climate change scenario. The bundle documentation files are: 1) 'project_metdata.xml' (this file) which contains the project-level metadata 2) Nonnative_plants_US.csv is the occurrence and abundance data. 3) XX.tif where XX is the species code with current climatic conditions and species code with '2c' appended for habitat suitability predictions with +2C of climate change.

This spatial data product consists of projected climate conditions classified by biogeoclimatic (BGC) unit across Western North America. Projections can be accessed interactively through the spatial module of the CCISS tool, where users can view projections at both local and provincial scales. Users can download raster data (TIF files) for the entire province or for predefined subregions, for five 20-year periods of the 21st century: 2001-2020, 2021-2040, 2041-2060, 2061-2080, and 2081-2100. The data available are as follows: • Ensemble vote winner BGC subzone/variant from an ensemble of 60 global climate model projections (5 rasters – 1 per time period) • Ensemble vote winner BGC zone (5 rasters – 1 per time period) • BGC projections for 5 global climate model simulations that represent the variation in the 60-member ensemble (25 rasters – 5 simulations x 5 time periods) • BGC projections for observed climates of the 1961-1990 and 2001-2020 periods (2 rasters) • Estimated climatic novelty for all BGC projections (available for all 37 rasters) • Tree Species Environmental Suitability projections (225 rasters - 5 time periods x 3 edatopes x 15 species) • Tree Species Environmental Suitability change (225 rasters - 5 time periods x 3 edatopes x 15 species) ***For data access and downloads, see the CCISS Spatial tab of the CCISS tool and Documentation > Instructions > CCISS Spatial.***

We mapped potential climate change refugia for riparian areas in the central and western USA for 2040-2069 and 2070-2099. Riparian refugia are existing riparian areas that are projected to maintain riparian vegetation and associated ecological function under plausible future climates. Four input variables were included in the riparian refugia index: two landscape variables that represent where existing riparian areas may be more resilient to climatic changes (riparian connectedness and landscape diversity) and two climate variables that reflect projected exposure to climate change (runoff and warm days). For the climate variables, we considered two global circulation models: moderately hot and wet (CNRM-CM5) and hot and dry (IPSL-CM5A-MR) under RCP 8.5. The climate variables represented the projected change from a historical baseline (1971-2000) for two future 30-year time periods, mid-century (2040-2069) and late century (2070-2099). The four input variables of uniform pixel size were assigned equal weights and layered together using ArcGIS Pro's Suitability Modeler to create an index for riparian refugial quality. Here we provide raster layers for the riparian refugia index and three of the four input variables including riparian connectedness, runoff, and warm days. The fourth input variable, landscape diversity, was produced by The Nature Conservancy and is available online at The Nature Conservancy's Resilient and Connected Network. The four climate scenarios (CNRM-CM5 2040-2069, CNRM-CM5 2070-2099, IPSL-CM5A-MR 2040-2069, and IPSL-CM5A-MR 2070-209) are included as individual rasters for the riparian refugia index, runoff, and warm days, and are zipped into each base folder. We also provide a geodatabase that contains all the data (riparian refugia index, riparian connectedness, runoff, and warm days).

We mapped potential climate change refugia for riparian areas in the central and western USA for 2040-2069 and 2070-2099. Riparian refugia are existing riparian areas that are projected to maintain riparian vegetation and associated ecological function under plausible future climates. Four input variables were included in the riparian refugia index: two landscape variables that represent where existing riparian areas may be more resilient to climatic changes (riparian connectedness and landscape diversity) and two climate variables that reflect projected exposure to climate change (runoff and warm days). For the climate variables, we considered two global circulation models: moderately hot and wet (CNRM-CM5) and hot and dry (IPSL-CM5A-MR) under RCP 8.5. The climate variables represented the projected change from a historical baseline (1971-2000) for two future 30-year time periods, mid-century (2040-2069) and late century (2070-2099). The four input variables of uniform pixel size were assigned equal weights and layered together using ArcGIS Pro’s Suitability Modeler to create an index for riparian refugial quality. Here we provide raster layers for the riparian refugia index and three of the four input variables including riparian connectedness, runoff, and warm days. The fourth input variable, landscape diversity, was produced by The Nature Conservancy and is available online at The Nature Conservancy’s Resilient and Connected Network. The four climate scenarios (CNRM-CM5 2040-2069, CNRM-CM5 2070-2099, IPSL-CM5A-MR 2040-2069, and IPSL-CM5A-MR 2070-209) are included as individual rasters for the riparian refugia index, runoff, and warm days, and are zipped into each base folder. We also provide a geodatabase that contains all the data (riparian refugia index, riparian connectedness, runoff, and warm days).

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Plant Macrofossil. The data include parameters of plant macrofossil (population abundance) with a geographic location of Ontario, Canada. The time period coverage is from 14341 to 14315 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Data points intensively sampling 46 North American biomes were used to predict the geographic distribution of biomes from climate variables using the Random Forests classification tree. Techniques were incorporated to accommodate a large number of classes and to predict the future occurrence of climates beyond the contemporary climatic range of the biomes. Errors of prediction from the statistical model averaged 3.7%, but for individual biomes, ranged from 0% to 21.5%. In validating the ability of the model to identify climates without analogs, 78% of 1528 locations outside North America and 81% of land area of the Caribbean Islands were predicted to have no analogs among the 46 biomes. Biome climates were projected into the future according to low and high greenhouse gas emission scenarios of three General Circulation Models for three periods, the decades surrounding 2030, 2060, and 2090. Prominent in the projections were (1) expansion of climates suitable for the tropical dry deciduous forests of Mexico, (2) expansion of climates typifying desertscrub biomes of western USA and northern Mexico, (3) stability of climates typifying the evergreen–deciduous forests of eastern USA, and (4) northward expansion of climates suited to temperate forests, Great Plains grasslands, and montane forests to the detriment of taiga and tundra climates. Maps indicating either poor agreement among projections or climates without contemporary analogs identify geographic areas where land management programs would be most equivocal. Concentrating efforts and resources where projections are more certain can assure land managers a greater likelihood of success.