These data were compiled to assess potential changes in the climatic suitability for 66 species (dominant and associate plant species) and forecast climate exposure for 29 major plant communities within major plant communities in the southwestern United States. An objective of our study was that species within plant communities have unique climate suitability signatures and forecast changes in climatic suitability will not be uniform within the species respective communities or among species within the community. The climate suitability spatial models were developed under a modern baseline (1960-90) and future climate scenario (2041-2060) using Maxent and WorldClim temperature and precipitation variables. Plant species were chosen that are characteristic species of plant communities in the southwest as mapped by GAP/LANDFIRE National Terrestrial Ecosystems v1 (USGS-Core Science Analytics, Synthesis, and Library – Gap Analysis Project, 2011). Monthly average minimum and maximum temperature and monthly total precipitation values were acquired from WorldClim v1.4 for current climate conditions and Community Climate System Model 4.0 (CCSM4, Gent et al. 2011) representative concentration pathway (rcp) models, 4.5 and 8.5, for the future climate scenario. The climate exposure spatial models are represented as a composite score of the climate exposure of characteristic plants for each community. Baseline climate exposure rasters represent a baseline climate change and were developed for current climate conditions (~1960-1990) from WordlClim v1.4 data. Climate exposure ratings are forecast for the period 2040-60 using the Community Climate System Model v4 (CCSM4) for Representative Concentration Pathways (RCP) 4.5 and 8.5. Climate exposure is indicated as a categorical score (1-5) that is a composite of climate suitability scores for characteristic plant species identified for each plant community and represents a range of climate exposure ratings from unfavorable to best climatic suitability. Plant communities are represented as mapped by the USGS – Core Science Analytics, Synthesis, and Library – Gap Analysis Project, 2011 (Gap Landcover).

These data were compiled to assess potential changes in the climatic suitability for 66 species (dominant and associate plant species) within major plant communities in the southwestern United States. An objective of our study was that species within plant communities have unique climate suitability signatures and forecast changes in climatic suitability will not be uniform within the species respective communities or among species within the community. We developed these spatial models of climate suitability under a modern baseline (1960-90) and future climate scenario (2041-2060) using Maxent and WorldClim temperature and precipitation variables. Plant species were chosen that are characteristic species of plant communities in the southwest as mapped by GAP/LANDFIRE National Terrestrial Ecosystems v1 (USGS-Core Science Analytics, Synthesis, and Library – Gap Analysis Project, 2011). Monthly average minimum and maximum temperature and monthly total precipitation values were acquired from WorldClim v1.4 for current climate conditions and Community Climate System Model 4.0 (CCSM4, Gent et al. 2011) representative concentration pathway (rcp) models, 4.5 and 8.5, for the future climate scenario.

These data were compiled to forecast climate exposure for 29 major plant communities in the southwestern United States to changing climate under two future climate change scenarios. An objective of our study was that species within plant communities have unique climate suitability signatures and forecast changes in climatic suitability will not be uniform within the species respective communities or among species within the community. We developed these spatial models where climate exposure is represented as a composite score of the climate exposure of characteristic plants for each community. Baseline climate exposure rasters represent a baseline climate change and were developed for current climate conditions (~1960-1990) from WordlClim v1.4 data. Climate exposure ratings are forecast for the period 2040-60 using the Community Climate System Model v4 (CCSM4) for Representative Concentration Pathways (RCP) 4.5 and 8.5. Climate exposure is indicated as a categorical score (1-5) that is a composite of climate suitability scores for characteristic plant species identified for each plant community and represents a range of climate exposure ratings from unfavorable to best climatic suitability. Plant communities are represented as mapped by the USGS – Core Science Analytics, Synthesis, and Library – Gap Analysis Project, 2011 (Gap Landcover).

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

This dataset was constructed from readily available open source climate and vegetation data, like Landsat. This dataset represents the vegetation and climate conditions for a large number of points across the major deserts of the SW USA. The dataset was constructed in order to use the climate pivot point approach (Munson et al. 2013) at the landscape level. Originally this dataset was much larger but we were looking to study a pure vegetation signal and therefore developed a detailed masking procedure to remove fire, slope, human, and floodplain effects. The vegetation classification originally came from SW regap, though we have refined / regrouped the data. The vegetation classification for each point is representative of the dominant vegetation in the 30m area, but by no means is it the only vegetation there. In the pivot point methodology we look to understand how the vegetation production in a single year relates to long term mean production, these columns are included in the dataset. Lastly, this time series data was composited to the warm and cold season since the deserts studied had productivity/ climate events at different times of year. The definition of season is; warm season (July – September),and cold season (October – March).

These data were compiled to provide a quantitative, spatially explicit estimate of ecological resilience and resistance (R&R) under ambient and projected future climate conditions. Objective of our study was to understand where and why climate change will alter the distribution of ecological resilience and resistance in the sagebrush biome throughout the 21st century. To accomplish this, we pursued four specific objectives: we estimated the new R&R indicators under future climate conditions and quantified changes from historical conditions; we developed a continuous R&R index that integrates probability information from the underlying predictive R&R models; we assessed the robustness of projected changes in R&R to uncertainty in future climate conditions. These data represent spatially-explicit estimates of ecological resilience and resistance (R&R; categorical indicators, probabilities, continuous indices) under ambient and downscaled projected historical and future climate conditions (historical, RCP 4.5, and RCP 8.5 CMIP5 scenarios). These data were created in rangelands and open woodlands across the sagebrush biome in 2023. These data were created by a collaboration between Northern Arizona University and the U.S. Geological Survey, Southwest Biological Science Center based on modeling which utilized predictive R&R models utilizing ecological and climate metrics which were based on soil properties (NRCS), ambient climate data (gridMET), and downscaled climate projections (MACAv2-METDATA). These data can be used to assess geographic patterns in resilience and resistance under ambient and projected future climate conditions.

These data were compiled to assess potential impacts of altered operations of Glen Canyon Dam on riparian plant resources. Objective(s) of our study were to quantify potential responses of specific vegetation metrics. These data represent predicted changes in vegetation metrics based on the data in the Interim Guidelines and LTEMP_SEIS folders. These data were collected in Grand Canyon and Glen Canyon below the dam from 2014-2019. These data were collected by USGS SBSC scientists through ground-based vegetation surveys. These data can be used to predict responses of vegetation metrics only to the specific alterations to dam operations that were simulated.

These data are species distribution information assembled for assessing the impacts of land-use barriers, facilitative interactions with other species, and loss of long-distance animal dispersal on predicted species range patterns for four common species in pinyon-juniper woodlands in the western United States. The layers in the data release are initial distribution records of two kinds: point occurrence records and a raster layer for the general vegetation types where the species is a co-dominant, compiled from other sources. Both types of data are the baseline information in species distribution models for the associated publication(see Larger Work Citation).

Data was collected to characterize the conditions under which sagebrush occurs after seeding and wildfire in the Great Basin, and used to parameterize models used to explore adaptive seeding approaches. Data includes plot level field data on sagebrush occurrence, density, weather, and soil moisture conditions in the year that seeding after wildfire occurred. Weather data includes both average annual summaries and average weather at 5-day intervals from day 1-250 of the year of seeding. Also included are summaries of annual temperature and soil moisture conditions from 1979 to 2016 and model predictions of the probability of sagebrush establishment in each of these years.