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).

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) 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 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.

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).

This dataset contains rasters of vegetation refugia and habitat exposure variables for the state of California. Two potential future climate scenarios were used: warmer and wetter (CNRM-CM5), and hotter and drier (MIROC-ESM) & 2 emission scenarios: a higher level one that represents our current trajectory (RCP 8.5) and a lower level one that represents a more optimistic scenario (RCP 4.5). The vegetation exposure models used aims to help in assessing potential climatic stress to vegetation communities and this dataset contains the statewide data for use in assessing the potential risk to each of the California Allotments. Current and future vegetation stress was determined by integrating the hydroclimate data with a detailed 2015 map of the spatial patterns of California’s vegetation community types, and examining how climate conditions will change at those locations using 9 hydroclimatic variables (30-year averages) from the Basin Characterization Model. The main habitat exposure outputs contain rasters all of the climate exposure results: 1 historic run: 1981-2010 and 12 future runs: 3 time periods (2010-2039, 2040-2069, 2070-2099) under 2 emission scenarios and 2 climate scenarios as well as reclassified rasters where the outputs were binned into 5 groups. To distinguish refugia areas from high-stress areas in the climate exposure results above, the team classified the climate frequency distribution for each vegetation type, which are labeled as CA refugia combined 45 and 85 for the respective RCP. Finally, the team looked at the spatial patterns of just refugia for the 2 climate models to identify areas where they align, defined as CA refugia concensus.

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).