Aeolian mass flux data for the Colorado Plateau (other, 2023)
These data were compiled to measure airborne horizontal mass flux of sediments moved by wind across soils, climates, vegetation types, and land uses on the Colorado Plateau. Objectives of our study were to quantify spatial and temporal patterns in wind erosion and further our understanding of how soil and site setting, climate, and land uses are controlling wind erosion and horizontal mass flux. These data represent seasonal cumulative horizontal mass flux as measured using passive aspirated sediment traps, Big Spring Number Eight (BSNE) samplers. These data were collected in Grand and San Juan counties, Utah, and Mesa County, Colorado, USA between August 2017 and November 2020. These data were collected by the U.S. Geological Survey, [...]

Snow-free Growing Degree-days 0-60 Days Post Snow Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated in the 60 days after the disappearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute SFGDD, daily estimates of temperatures above a base of 5 degrees Celsius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates from individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D049')  metadata <- sdp_get_metadata('R4D049')    For more information about rSDP, visit the package homepage.

Dryad dataset awaiting publication (2023)
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Snowpack Persistence Day of Year Mean (1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the persistence of the seasonal snowpack from 1993 - 2022.  Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the mean of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D061')  metadata <- sdp_get_metadata('R4D061')    For more information about rSDP, visit the package homepage.

Snowpack Proportional Reduction in Late Season Freezing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of fall snow on late season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free freezing degree-days were accumulated from day-of-year 195 to day-of-year 305 (July 15th to November 1st  in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates than freezing degree days (FDD). This map represents the proportional reduction in late season freezing potential from 2002 to 2021 using the formula (FDD - SFFDD) / FDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D053')  metadata <- sdp_get_metadata('R4D053')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Late Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D040')  metadata <- sdp_get_metadata('R4D040')    For more information about rSDP, visit the package homepage.

Air Temperature Freezing Degree-days Early Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values. 20-year means computed from 2002 to 2021 are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D010')  metadata <- sdp_get_metadata('R4D010')    For more information about rSDP, visit the package homepage.

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

Dryad dataset awaiting publication (2023)
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Video In Situ Snowfall Sensor (VISSS) data for Ny-Ålesund (2021-2022) (2023)
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Video In Situ Snowfall Sensor (VISSS) data for Hyytiälä (2021-2022) (2023)
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Air Temperature Freezing Degree-days Annual Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. These maps represent annual values computed from 2002 to 2021. 20-year averages (2002 - 2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D009')  metadata <- sdp_get_metadata('R4D009')    For more information about rSDP, visit the package homepage.

2017 Meander C and O floodplain groundwater chemistry from the East River, Colorado. - Christian Dewey; Marco Keiluweit; P. S. Nico; Craig R. Anderson; Patricia Fox (other, 2023)
This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Floodplain piezometers in the Pumphouse vicinity (Meander C: MCP, MCB; Meander O: MOP) were sampled on daily to weekly time scales during summer-fall 2017. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], dissolved oxygen [DO], Fe(II), and groundwater level) along with laboratory measurements (dissolved inorganic carbon [DIC], dissolved organic carbon [DOC], metals and major cations, anions [chloride, sulfate, nitrate], and dissolved ammonium). The chemistry data is contained in a csv file, sampling protocol in a pdf document, and sample locations in both a kmz file which can be opened in Google Earth and a csv file.

Supplementary material from "Emergent social structure is typically not associated with survival in a facultatively social mammal" (2023)
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Air Temperature Freezing Degree-days Late Season Timeseries - Ian K Breckheimer (s3, 2023)
These are map of annual fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. These maps represent annual values. 20-year means computed from 2002 to 2021 are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D011')  metadata <- sdp_get_metadata('R4D011')    For more information about rSDP, visit the package homepage.

Snow-free Growing Degree-days Late Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D048')  metadata <- sdp_get_metadata('R4D048')    For more information about rSDP, visit the package homepage.

Snowpack Onset Day of Year Standard Deviation (1993-2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of interannual variability in the day of year (i.e., "Julian Day") of the onset of the seasonal snowpack.  Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  To facilitate computation of snow cover duration, day of year is scaled from January 1st of the following year.  This means that negative values represent days before January 1st.  For example, a snow onset on November 11th, 2021 (Day of Year 315) would be represented in the 2022 map with a value of -50 (315 - 365). These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ), with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the 30-year standard deviation from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D060')  metadata <- sdp_get_metadata('R4D060')    For more information about rSDP, visit the package homepage.

Atmospheric Surface Flux Station #50 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023 (2023)
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Atmospheric Surface Flux Station #30 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023 (2023)
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Snow-free Growing Degree-days Early Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days that were estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. These are annual estimates computed from 2002 to 2022. 20-year means (2002-2022) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D020')  metadata <- sdp_get_metadata('R4D020')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Early Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days that were estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D013')  metadata <- sdp_get_metadata('R4D013')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Late Season Mean (2002-2022) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 197 to day-of-year 305  (July 16th to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D039')  metadata <- sdp_get_metadata('R4D039')    For more information about rSDP, visit the package homepage.

Snowpack Duration Standard Deviation (Water Year 1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of interannual variability in the number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the mean of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D058')  metadata <- sdp_get_metadata('R4D058')    For more information about rSDP, visit the package homepage.

Supplementary material from "The social microbiome: gut microbiome diversity and abundance are negatively associated with sociality in a wild mammal" (2023)
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Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), November 2022-June 2023 (2023)
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Snow-free Growing Degree-days Late Season Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D047')  metadata <- sdp_get_metadata('R4D047')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Early Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days that were estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D038')  metadata <- sdp_get_metadata('R4D038')    For more information about rSDP, visit the package homepage.

Potentilla demographic and environmental data for Rainbow Meadows, Elk Meadows, and Cabin Clearing, 2018 - 2020. - Kelly A Carscadden (other, 2023)
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and (not included here) the Rocky Mountain Biological Laboratory (RMBL). This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.

Snow-free Growing Degree-days Early Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days that were estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D046')  metadata <- sdp_get_metadata('R4D046')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Early Season Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celsius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D037')  metadata <- sdp_get_metadata('R4D037')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Annual Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also availabile.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D012')  metadata <- sdp_get_metadata('R4D012')    For more information about rSDP, visit the package homepage.

SnowEx21 Senator Beck Basin and Grand Mesa, CO AVIRIS-NG Surface Spectral Reflectance, Version 1 (other, 2023)

The role of environmental variation in mediating fitness tradeoffs for an amphibian polyphenism (2023)
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Snow-free Growing Degree-day 0-60 Days Post Snow Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated in the 60 days after the disappearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute SFGDD, daily estimates of temperatures above a base of 5 degrees Celcius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values. 20-year means computed from 2002 to 2021 are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D022')  metadata <- sdp_get_metadata('R4D022')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Annual Standard Deivation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D030')  metadata <- sdp_get_metadata('R4D030')    For more information about rSDP, visit the package homepage.

SnowEx21 Senator Beck Basin and Grand Mesa, CO AVIRIS-NG Surface Spectral Reflectance, Version 1 (other, 2023)

Snow-free Freezing Degree-days Annual Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps of air temperature and annual maps of snow dynamics are also available. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celsius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D036')  metadata <- sdp_get_metadata('R4D036')    For more information about rSDP, visit the package homepage.

SAIL-Net Raw and Post Corrected POPS Data Fall 2021 - Summer 2023 - E. J. T. Levin; Lay James Gibson (other, 2023)
SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleation particles (INP). This network enables the investigation of small-scale variations in complex terrain.Two datasets are provided - one containing raw data and the other containing post-corrected data.The raw dataset provides the raw data recorded from the POPS which were deployed at each of the six sites. These data are organized by site and broken down into daily data files. The six site names used here are: “gothic”, “irwin”, “cbtop”, “cbmid”, “pumphouse”, and “snodgrass”. These data are not cleaned or post-corrected, but some flags have been added. The data are reported at 1 second time resolution. The post-corrected dataset provides the post-corrected and cleaned data recorded from the POPS which were deployed at each of the six sites. This data are also organized by site (same as those found in the raw data) and broken down into daily data files. Unlike the raw POPS data, these data have already been cleaned to remove what we believe are bad values. These data should be ready to use with no cleaning. For a full description of the cleaning and post-correction process, see the readme.

Air Temperature Growing Degree-days Annual Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D029')  metadata <- sdp_get_metadata('R4D029')    For more information about rSDP, visit the package homepage.

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH - Darren Jackson; Eryan Dai; Maciej Stachura; Jack Elston; Maciej Stachura; Gijs de Boer; Darren Jackson; Eryan Dai; Jack Elston; Janet Intrieri; Gijs de Boer; Janet Intrieri; et al. (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows. uas_L2_yyyymmdd_hhmmss_vX.X.zip where L2 = Level 2 data yyyymmdd = year,month,day hhmmss = hour,minute,second vX.X = version number Time is the flight start time in UTC. The NetCDF file format contained in the zip files has a similar format to the zip files with convention uas_ _L2_yyyymmdd_hhmmss.nc where = vsm, dem, ndvi, or stmp vsm = volumetric soil moisture dem = digital elevation ndvi = normalized difference vegetation index stmp = surface temperature Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products. November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. December 2023 update: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.

Snowpack Onset Day of Year Mean (1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the mean day of year (i.e., "Julian Day") of the onset of the seasonal snowpack.  Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.  To facilitate computation of snow cover duration, day of year is scaled from January 1st of the following year.  This means that negative values represent days before January 1st. For example, a snow onset on November 11th, 2021 (Day of Year 315) would be represented in the 2022 map with a value of -50 (315 - 365). These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ), with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the 30-year mean from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D059')  metadata <- sdp_get_metadata('R4D059')    For more information about rSDP, visit the package homepage.

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado - Richard Niswonger; E. R. Woodburn; E. R. Woodburn; Richard Niswonger; Charuleka Varadharajan; Craig Ulrich; Charuleka Varadharajan; Craig Ulrich; Kenneth Hurst Williams; Kenneth Hurst Williams (ess_dive, 2023)
The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution to represent topographic complexity and a daily timestep accounts for energy and water partitioning between the snowpack, vegetation, soil zone and bedrock. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model accounts for nine stratigraphic units and extends 400 m below land surface. Using this modeling framework we explore historical water budgets (water year 1987-2022) and the influence of seasonal warming and associated mechanisms driving groundwater declines and streamflow loss. A Readme_062424.txt file provides instructions on how to download all files. Input and output files are provided for the historical simulation representative of water years 1987 to 2022 (i.e. baseline) and the all-year +4C warming scenario. Instructions are provided to run the seasonal warming scenarios with warming applied only to the autumn, winter, spring or summer months. Modeled output used in figures for Carroll et al., 2024 are also provided with metadata describing where these data were obtained. This dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Updated on 06-24-2024: The dataset was updated to include model outputs (updated base.zip and 4C.zip files), figure source files (Figure_Source_Files.zip), an updated Readme file (Readme_062424.txt), climate input files (climate_input_files.zip), and the adoption of ESS-DIVE File Level Metadata and CSV reporting formats (inclusion of flmd.csv and dd.csv files).

Table 1 in Description of a new species of Hobbsinella (Crustacea, Bathynellacea, Bathynellidae) from Colorado (USA) based on morphological and molecular characters - Carly B. Bonwell; Steven J. Taylor; Ana Isabel Camacho; Beatriz A. Dorda; Paloma Mas-Peinado; Jenna N. McDONALD; Isabel Rey (other, 2023)
Table 1 (continued on next page). Specimens used in the molecular analyses (*type locality). Abbreviations: asl = above sea level; E = East; m = meters; MNCN = Museo Nacional de Ciencias Naturales de Madrid (Spain); MNCN/ADN = Tissues and DNA collection of the MNCN; N = North; WAMC = Western Australian Museum Collection (in bold); Z = altitude. Species Locality Coordinates Voucher GenBank accession number N E Z MNCN/ADN WAMC 18S COI Iberobathynella imuniensis Camacho, 1987 OUTGROUP *Torca Morteros Cave, Burgos, Spain 43.14786 -3.59539 1285 29166 KC469528 HQ659850 1. Altainella calcarata Camacho, 2020 Aktru River, Altai Mountain, Russia 50.06944 87.75028 2408 54879 MN262081 MN258523 2. Anguillanella callawaensis Perina Camacho, 2019 CA0006, bore hole, Western Australia -20.64445 120.30626 57258 MF042209 MF074337 3. Antrobathynella stammeri (Jakobi, 1954) Ogof Cave, UK 51.831159 -3.686986 54711 MF094714 MF114307 4. Bathynella cf ruffoi Dobbiaco Lake, Italy 46.70682 12.21926 1260 54719 MF436212 MF443327 5. Bathynella cf ruffoi Aurino River, Gais, Italy 46.84611 11.95019 850 54727 MF436213 MF443328 6. Bathynella ruffoi Serban, 1973 *Rienza River, San Sigismondo, Italy 46.81054 11.80044 770 54729 MF436214 MF443329 7. Bathynella sp. Kamni ka Cave, Slovenia 46.32747 14.59031 592 54668 MF094716 MF114309 8. Bathynella sp. Trubarjeva Cave, Slovenia 46.36142 13.77056 1854 54718 MF094715 MF114308 9. Fortescuenella serenitatis Perina Camacho, 2019 * SOM049, bore hole, Western Australia -22.14167 117.5294 57315 MK134939 MK134992 10. Gallobathynella boui Serban, Coineau Delamare Deboutteville, 1971 *La Dev ze Cave, Courniou, France 43.47455 2.71417 365 54600 KP999757 KP974146 11. Hobbsinella edwardensis Camacho Hutchins 2018 Edwards Aquifer, Hays County, Texas, USA 29.88958 -97.93645 177 29943 KP999685 12. Hobbsinella edwardensis Edwards Aquifer, Hays County, Texas, USA 29.88958 -97.93645 177 54640 MF443323 13. Hobbsinella gunnisonensis sp. nov. *Spring Creek, Gunnison County, Colorado, USA 38.75418 -106.76984 2622 54883 MN262078 MN258520 14. Hobbsinella gunnisonensis sp. nov. *Lottis Creek, Gunnison County, Colorado, USA 38.77145 -106.62195 2797 54886 MN262077 MN258519 15. Hobbsinella gunnisonensis sp. nov. *Lottis Creek, Gunnison County, Colorado, USA 38.77145 -106.62195 2797 54887 OQ812159 OQ940715 16. Hobbsinella gunnisonensis sp. nov. *Lottis Creek, Gunnison County, Colorado, USA 38.77145 -106.62195 2797 54888 OQ812160 OQ940716 17. Hobbsinella gunnisonensis sp. nov. *Lottis Creek, Gunnison County, Colorado, USA 38.77145 -106.62195 2797 54889 OQ812161 OQ940717 18. Muccanella cundalinensis Perina Camacho, 2019 CU0046, bore hole, Western Australia -20.54346 120.15983 57340 MN149125 MN136082 N E Z MNCN/ADN WAMC 18S COI 19. Paradoxiclamousella fideli Camacho, Dorda Rey, 2013 CO220, Carnero Spring Cave, Cantabria, Spain 43.28680 -4.47784 156 29735 KC469523 JX121252 20. Paradoxiclamousella fideli *CO069, Pozo Agua Cave, Asturias, Spain 43.28383 -4.52318 374 29746 KC469524 JX121253 21. Paradoxiclamousella fideli CO209, Treslajor Cave, Cantabria, Spain 43.26725 -4.59085 1132 29594 JX121235 JX121249 22. Pilbaranella ethelensis Perina Camacho, 2018 * W088 Pilbara, bore hole, Western Australia -23.39647 119.82126 57292 MF042239 MF074338 23. Vejdovskybathynella caroloi Camacho, 2007 G ndara Cave, Cantabria, Spain 43.19096 -3.58635 760 29877 KC469525 KC469538 24. Vejdovskybathynella edelweiss Camacho, 2007 *Ojo Guare a Cave (OG01), Burgos, Spain 43.03188 -3.65821 724 29478 KP999677 HQ596565 25. Vejdovskybathynella edelweiss Ojo Guare a Cave (OG16), Burgos, Spain 43.03188 -3.65821 724 29414 KP999676 HQ596570 26. Vejdovskybathynella edelweiss Huesos Cave, Cornejo, Burgos, Spain 43.03231 -3.63881 705 29440 KC469513 HQ596571 27. Vejdovskybathynella vasconica Camacho, Dorda Rey, 2013 *Goikoetxe Cave, Vizcaya, Spain 43.36026 -2.70195 149 29646 KC469521 KC469535 28. Vejdovskybathynella sp. 1 Erizos River, Ojo Guare a Cave, Burgos, Spain 43.03188 -3.65821 724 29487 KC469514 HQ596572 29. Vejdovskybathynella sp. 1 R o Chico Cave, Cantabria, Spain 43.19088 -3.58071 573 29728 KC469522 JX121244 30. Vejdovskybathynella sp. 1 Sogalamuela Cave, Burgos, Spain 43.03236 -3.62914 668 103487 OQ812158 OQ940714 31. Vejdovskybathynella sp. 2 Redonda Cave, Burgos, Spain 43.03236 -3.62914 668 29523 KC469515 HQ596573

Water Level Data from Wells PLM1 and PLM6 for the East River Watershed, Colorado (ess_dive, 2023)
This dataset (Williams et al., 2020) contains the original un-QA/QC-ed water level data for PLM1 and PLM6 and has been obsoleted. The data contained within this dataset is not to be used. Refer to Faybishenko et al., 2022 (DOI: 10.15485/1866836) for the latest QA/QC-ed data available via ESS-DIVE.This data set contains water level data for the PLM1 and PLM6 wells. PLM1 and PLM6 are location identifiers used by the Watershed Function SFA project for two groundwater monitoring wells along an elevation gradient located along the lower montane life zone of a hillslope near the Pumphouse location. These wells used to monitor subsurface water and carbon inventories and fluxes at the East River Watershed, Colorado, USA. Complete metadata information on the PLM1 and PLM6 wells are available in the related data package reference Varadharajan C, et al (2020). https://doi.org/10.15485/1660962.Data are reported in .csv files per well. The latitude and longitude of each location are given in a file called locations.csv. These data are used for determining the seasonally dependent flow of groundwater under the PLM hillslope. The downslope flow of groundwater in combination with data on groundwater chemistry can be used to estimate rates of solute export from the hillslope to the floodplain and river.These data products are part of the Watershed Function Scientific Focus Area collection effort to further scientific understanding of biogeochemical dynamics from genome to watershed scales.

Community-level flowering & fitness data across an elevational gradient, Rocky Mountain Biological Lab, 2021-2022 - Leah N. Veldhuisen (other, 2023)
We collected data at three sites in Washington Gulch near the Rocky Mountain Biological Laboratory (RMBL, Gothic, Colorado, USA) from June to August 2021 and 2022. RMBL is located in the East River valley of the West Elk mountains, approximately 10 kilometers from Crested Butte, Colorado. Study sites were located at 2815 m (38°53'50"N, 106°58'43"W), 3165 m (38°57'38"N, 107°01'53"W) and 3380 m (38°58'10"N, 107°01'53"W) in elevation. All sites are approximately 50 m 2 and have similar slope and aspect (Sloat et al. 2015). We randomly defined five 1.2 m x 1.2 m plots within each site. Within the plots, we marked all newly open flowering units once per week from early June to mid-August. We counted flowering units as either a single flower (most species), a flowering head (Asteraceae), or an umbel (Apiaceae). We only counted flowering units once as they opened, so counts were not cumulative. We quantified fitness as successful fruit and seed development at the end of the season. When flowers began to produce fruits, we counted the number of units with fruit, the number of developed fruits produced by each flowering unit, and the number of seeds per fruit. We also recorded the week of flowering for each fruit. We could not count fruits and seeds for all species because they disappeared, were consumed too quickly, or did not produce seeds during our field season.

SnowEx20 Grand Mesa IOP Computed Tomography Snow Microstructure, Version 1 (other, 2023)

Air Temperature Growing Degree-days Late Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. These data represent annual estimates. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D014')  metadata <- sdp_get_metadata('R4D014')    For more information about rSDP, visit the package homepage.

High Resolution Current and Future Climate SnowModel Simulations in the Upper Colorado River Basin - Graham A Sexstone; Theodore B Barnhart; John C Hammond (other, 2023)
This data release contains SnowModel snow evolution simulation output on a 100-meter (m) geospatial grid for a 311 kilometer (km) 300 km model domain in Colorado, United States, encompassing the Colorado and Gunnison River Basin headwaters in the Upper Colorado River Basin. Weather Research and Forecasting (WRF) Model convection-permitting and orography-resolving (4-km grid spacing) regional climate simulations provided the atmospheric forcing conditions to drive SnowModel in both a current and future climate scenario. A pair of continuous 13-water-year (2001-13) WRF model simulations was utilized: (1) a current climate control (CTL) simulation forced using ERA-Interim reanalysis, and (2) a future climate simulation using the pseudo-global-warming (PGW) method that uses the ERA-Interim reanalysis for the same period as (1) and adds an ensemble mean climate delta from 100 years in the future for the most extreme Representative Concentration Pathway (RCP) 8.5 scenario. The six SnowModel simulated outputs provided separately as child items in this data release include (1) air temperature (tair), (2) precipitation (prec), (3) precipitation amount falling as snow (spre), (4) snow water equivalent (swed), (5) liquid water supplied to the soil-snow interface from snowmelt (smlt), and (6) liquid water supplied to the soil-snow or soil-air interface either from snowmelt or rainfall (roff). The simulations used to produce these outputs were conducted on a 100-m geospatial grid. Land cover information (file vege.asc) for the simulation was provided by the 2010 North American Land Change Monitoring System and elevation information (file topo.asc) was provided by the U.S. Geological Survey National Elevation Dataset.

shorvath/MammalianMethylationConsortium: Mammalian methylation + universal pan mammalian clocks - Soo Bin Kwon; Jason Ernst; Amin Haghani; Akelu0918; Adriana Arneson; Shorvath (other, 2023)
This version includes the software packages from the Mammalian Methylation Consortium: 1- Genome coordinates for different species 2- R code for alignment and annotation 3- The array manifest 4- Details on probe design 5- R code for Sesame normalization 6- R code for universal pan-mammalian epigenetic clocks, universal blood epigenetic clocks, universal skin epigenetic clocks, adaptions to Illumina 450k/EPIC array, and an example dataset

Groundwater and Surface Water Flow (GSFLOW) model files for the East River, Colorado (ess_dive, 2023)
The data package contains model input files and executables for the East River, Colorado (750 km2) located in the headwaters of the Upper Colorado River Basin. The code applied is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. A Readme.txt file provides instructions on how to download all files and and execute mode runs. The model contains a 100-m grid resolution to represent topographic complexity and a daily timestep accounts for energy and water partitioning between the snowpack, vegetation, soil zone and bedrock. The land surface model is dynamically linked to a three-dimensional groundwater flow model that allows for streamflow gaining and losing conditions. The groundwater model accounts for nine stratigraphic units and extends 400 m below land surface. Using this modeling framework we explore historical water budgets (water year 1987-2022) and the influence of seasonal warming and associated mechanisms driving groundwater declines and streamflow loss.

Atmospheric Surface Flux Station #30 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023 - Jackson Osborn; Gijs de Boer; Samuel B. Morris; Jonathan Hamilton; Brian Butterworth; Erik Hulm; Michael Gallagher; Matthew Shupe; Janet Intrieri; Benjamin Schmatz; Christopher Cox (other, 2023)
Raw (Level 1) measurements from the Atmospheric Surface Flux Station #30 (ASFS-30) deployed at the Kettle Ponds Annex site (38°56.3686' N, 106°58.1781' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from September 2021 through July 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties. These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.

Per-Sample TPM with Annotations - Jacob West-Roberts; Jacob West-Roberts (other, 2023)
Metatranscriptomic data generated from soil collected at the East River watershed, Crested Butte, CO. Normalized by TPM. Annotations included, and labeled with genome ID for all ORFs appearing on binned scaffolds. (Unbinned sequences labeled with UNK)

Table S1- Soil Chemistry Table - Jacob West-Roberts; Jacob West-Roberts (other, 2023)
Chemical concentration and soil property measurements taken from soil collected at the East River, Crested Butte, CO.

River and hyporheic zone water geochemical data from OsmoSamplers, East River Pumphouse, Colorado, Nov 2017-Sept 2018 - Fredrick Colwell; Andrew Thurber; L. L. Lapham; Jessica Buser-Young (other, 2023)
This data package contains geochemical and river water temperature data from OsmoSamplers deployed at East River, Colorado pump house. There are three data files. The first one contains sulfate (SO4) and chloride (Cl) concentrations from riverwater and hyporheic zone (sediment water interface (SWI), 4 cm below seafloor (cmbsf), and 12 (cmbsf)). The second file contains methane concentrations, methane stable carbon isotope ratios and water isotopes from hyporheic zone porewaters. And the third is from a temperature logger during the deployment. Files are provided as .xls or .csv. This research was performed as part of the SBR funded project “Quantifying Subsurface Biogeochemical Variability in a High Altitude Watershed During Winter Isolation” DE-SC0018328. The novel part of this dataset is that we quantify these geochemical parameters throughout the winter isolation period, which is difficult to measure, as well as within the hyporheic zone. The data files were updated 3/1/2021 because the column label for the water isotopes was incorrect. Also, the csv files were also updated since they were not formatted correctly.

Radon Isotopes and Stable Water Isotopes from Coal Creek Watershed, Colorado (2021) - William Payton Gardner; Wesley M. Brown; Nicholas Thiros; J. N. Christensen; Curtis A. Beutler; Kenneth Hurst Williams (ess_dive, 2023)
The radon isotope and stable water isotope data for Coal Creek Watershed, Colorado, consists of d2H, d18O, and 222Rn values from samples collected at 8 stream location along Coal Creek, samples from 7 groundwater springs within the watershed, and precipitation isotope samples collected by Next Generation Water Observing System (NGWOS) from a collector within the watershed. All stream and spring samples were collected between June and October, 2021, and precipitation isotope samples were collected between November 2020 and September 2021. These data were collected to evaluate how groundwater contributions to Coal Creek originating from a fractured hillslope and alluvial fan respond to summer monsoon rains and seasonal drying. Understanding of groundwater-surface water interactions in montane systems in critical for the future of water availability in the Western US as groundwater contributions are expected to become more important for sustaining summer stream flows. This data package contains: (1) a csv of all radon samples; (2) a csv of all stream and spring isotope samples; (3) a csv of precipitation isotope samples; and (4) a csv of locations for each sampling site. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), November 2022-June 2023 - Michael Gallagher; Christopher Cox; Janet Intrieri; Benjamin Schmatz; Matthew Shupe (other, 2023)
Raw (Level 1) measurements from the Snow Ice Mass Balance Apparatus (SIMBA) deployed at the Avery Picnic site (~ 38°58.345' N, 106°59.811' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from November 2021 through June 2023. The SIMBA, originally designed for observing the mass balance of sea ice, is comprised of a thermistor chain with 2 cm spacing (Jackson et al., 2013). This system was configured for terrestrial snowpack by the manufacturer, SAMS Enterprise, to the specifications for SPLASH. The chain was installed suspended from a tripod and fixed to a rigid plastic bar near in time to the onset of snowpack in November 2022. The lowest 10 cm of the chain were buried within the soil. The top of the chain reached approximately 180 cm above the soil surface and snow was permitted to accumulate around the chain throughout the winter of 2022-2023. In the files, negative values of the "height" vector are below the soil surface and positive levels are above, which may be either snow or air depending on the snow depth. The system also uses a low-power heating cycle to measure thermistor's temperature response time for aiding in determining material interfaces: see Jackson et al. (2013) for details. There are several cautions to be aware of when using these data. The data has been ingested into daily netCDF and metadata (in attributes) have been provided but no quality control has been carried out on this raw version of the data set. From 1 November through 22 December 2022, the sensor obtained profiles every 10 min after which corruption of the configuration file reverted the profiles to every 6 hours (0, 6, 12, and 18 UTC). After 1 January a problem in the firmware caused the system to lose connection to the time-synching GPS network and therefore the clock drifted from January through June 2023 (the maximum potential time stamping error is likely 81 sec). Finally, from 23 March through 4 April 2023, the depth of the snow at the location of the sensor was deeper than 180 cm and thus measurements in the upper part of the snowpack were not observed then. Jackson, K., J. Wilkinson, T. Maksym, D. Meldrum, J. Beckers, C. Haas, and D. Mackenzie (2013) A novel and low-cost sea ice mass balance buoy. Journal of Atmosphere and Oceanic Technology, 30(11), 2676-2688, https://doi.org/10.1175/JTECH-D-13-00058.1.

Data from: Historical fire regimes and contemporary fire effects within sagebrush habitats of Gunnison Sage-grouse - Jessica R. Young; Petar Z. Simic; Manuel K. Lopez; Ellis Q. Margolis (other, 2023)
The historical role of fire in sagebrush ( Artemisia tridentata ) landscapes remains poorly understood yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus ). We reconstructed fire histories from tree-ring fire-scars at sagebrush-forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following European-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fire. Tree-ring fire-scars revealed a history of repeated low-severity fire at sagebrush-forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 41.3 years (ranging from 18.2 to 79.7 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, consistent with spread between sites. Most (70%) of the historical fires burned in the early growing season when strong winds can spread fire through sagebrush. Recent burns, relative to unburned sites exhibited greater reductions in sagebrush ( Artemisia tridentata ; 27% vs. 6%) and concomitant increases in herbaceous (40% vs. 55%) cover. These differences declined with time since fire but persisted for at least two decades. Burns were dominated by a suite of native perennial grasses, forbs, and a re-sprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Burns exhibited slightly increased cover (4% vs. 1%) of a widely-planted non-native perennial grass, crested wheatgrass ( Agropyron cristatum ). Our results suggest that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings are consistent with the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed, and now includes substantially reduced (Endangered Species Act) ESA-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management-research partnerships.

In-situ electrochemical and water quality data; Slate River and East River floodplains, Crested Butte, CO; May 2022-September 2022. - Kristin Boye; Samuel Pierce (other, 2023)
This data package includes a time-series of field measurements from May to September 2022 in groundwater and surface water from the Slate River and East River floodplains in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used. All files are in csv format.

Table 2 in Description of a new species of Hobbsinella (Crustacea, Bathynellacea, Bathynellidae) from Colorado (USA) based on morphological and molecular characters - Isabel Rey; Paloma Mas-Peinado; Steven J. Taylor; Beatriz A. Dorda; Jenna N. McDONALD; Ana Isabel Camacho; Carly B. Bonwell (other, 2023)
Table 2. Specimens of Hobbsinella gunnisonensis Camacho Taylor sp. nov. studied from Gunnison County (Colorado). Collectors: C.N. Bonwell J.J. McDonald. Voucher number of MNCN Artropods (MNCN/ARTP) and Tissues and DNA (MNCN/ADN) Collections. Abbreviation: No = sequencing failed. Locality and sampling site Sample and lot (sex specimens) Voucher MNCN/ARTP 20.04 Size mm Voucher MNCN/ADN 18S COI Holotype Lottis Creek; CO-137 CO-137-4; CO-137-4-5 ( ) 20172 1.12 Paratype Lottis Creek; CO-137 CO-137-4; CO-137-4-5 ( ) 20173 1.03 Paratype Lottis Creek; CO-137 CO-137-4; CO-137-4-5 ( ) 20174 1.21 Paratype Lottis Creek; CO-137 CO-137-2; CO-137-2-4 ( ) 20178 1.00 54885 (abdomen) No No Paratype Lottis Creek; CO-137 CO-137-3; CO-137-3-6 ( ) 20179 1.00 54886 (abdomen) Yes Yes Paratype Lottis Creek; CO-137 CO-137-4; CO-137-4-5 ( ) 20180 1.00 54887 (abdomen) Yes Yes Paratype Lottis Creek; CO-137 CO-137-4; CO-137-4-5 ( ) 20181 1.00 54888 (abdomen) Yes Yes Paratype Lottis Creek; CO-137 CO-137-4; CO-137-4-5 ( ) 54889 (whole) Yes Yes Type series Spring Creek; CO-136 CO-136-3; CO-136-3-10 ( ) 20175 1.00 54883 (abdomen) Yes Yes Type series Spring Creek; CO-136 CO-136-3; CO-136-3-10 ( ) 20176 1.00 54884 (abdomen) No No Type series Spring Creek; CO-136 CO-136-3; CO-136-3-10 ( ) 20177 1.00

Dissolved Oxygen and Temperature Data from the Hyporheic Zone of the East River Watershed July 2017 to October 2018 - Ruby N. Ghosh; Charles McIntire; Michael Freeman; Terry Ball (ess_dive, 2023)
Dissolved oxygen (DO) is critical for aquatic ecosystems. Our focus is on the long-term DO dynamics in hyporheic zone of rivers, which are a function of both transport (hydrologic exchange between river and hyporheic zone) and uptake by biogeochemical reactions or respiration. The study site is the alpine East River watershed in Colorado, USA, meander A downstream from the pump house. Opti O2 probes were deployed in the water column and directly within the river-bed at 10, 20, and 35 cm depth (38°55'23.38"N, 106°57'4.21"W, 9046.88m) to monitor DO and temperature. A continuous data stream from July 24, 2017 to Oct 24, 2018 was autonomously telemetered to the cloud. This 14-month DO and temperature time series were obtained without any servicing for maintenance or data downloads; additionally the ability to remotely verify probe performance during field deployment was essential to confirm data validity during winter freeze-in and hydrological/weather events, such as spring melt and summer monsoons. We investigate the variations in dissolved oxygen dynamics of this snow-pack dominated watershed during a comparatively low flow water year (2018) and a relatively normal water year (2017), enabled by distinctive, in-situ, high frequency (∆t = 5min) sensors that provided a continuous time-series from the undisturbed study site over multiple seasons.

Snow-free Freezing Degree-days Early Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated spring snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D016')  metadata <- sdp_get_metadata('R4D016')    For more information about rSDP, visit the package homepage.

Snowpack_Snodgrass Elevation transect_Winters (2021-22, 2022-23)_AS.csv (other, 2023)
The data set “Snowpack_Snodgrass_AS.csv” consists of the snowpack data measured by the PhenoCams at each site up Snodgrass Mtn, Crested Butte, Co. To calculate snow depth each fall, we placed a 3-meter PVC pipe in the ground in the field of view of the PhenoCam. We spray painted 25 cm alternating increments of red and white, with each meter labeled with a black stripe. Using the downloaded PhenoCam images, a calibration curve of pole height to pixels was calculated. The amount of non-snow-covered pole pixels for each image was quantified, and the snow depth was calculated using the calibration curve. Sites are abbreviated versions of site names and phenocam numbers, i.e., hc7= high_conifer_Snodgrass_7.

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows. uas_L2_yyyymmdd_hhmmss_vX.X.zip where L2 = Level 2 data yyyymmdd = year,month,day hhmmss = hour,minute,second vX.X = version number Time is the flight start time in UTC. The NetCDF file format contained in the zip files has a similar format to the zip files with convention uas_ _L2_yyyymmdd_hhmmss.nc where = vsm, dem, ndvi, or stmp vsm = volumetric soil moisture dem = digital elevation ndvi = normalized difference vegetation index stmp = surface temperature Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products. November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. December 2023 update: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.

NESDIS Global 100 km sea surface temperature (SST) product, 1993 - 2016 - NCEI (other, 2023)
Product shows global sea surface temperatures in degrees C. It is a composite gridded-image derived from 8-km resolution Global SST Observations and is generated daily on a global scale. SST is defined as the skin temperature of the ocean surface water.

Air Temperature Freezing Degree-days Annual Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of variability in accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D024')  metadata <- sdp_get_metadata('R4D024')    For more information about rSDP, visit the package homepage.

Gunnison sage-grouse predicted gene flow (conductance) surfaces, Colorado, United States (other, 2023)
Habitat fragmentation and degradation impacts an organism's ability to navigate the landscape, ultimately resulting in decreased gene flow and increased extinction risk. Understanding how landscape composition impacts gene flow (i.e., connectivity) and interacts with scale is essential to conservation decision-making. We used a landscape genetics approach implementing a recently developed statistical model based on the generalized Wishart probability distribution to identify the primary landscape features affecting gene flow and estimate the degree to which each component influences connectivity for Gunnison sage-grouse (Centrocercus minimus). We were interested in two spatial scales: among distinct populations rangewide and among leks (i.e., breeding grounds) within the largest population, Gunnison Basin. Populations and leks are nested within a landscape fragmented by rough terrain and anthropogenic features, although requisite sagebrush habitat is more contiguous within populations. Our best fit models for each scale confirm the importance of sagebrush habitat in connectivity, although the important sagebrush characteristics differ. For Gunnison Basin, taller shrubs and higher quality nesting habitat were the primary drivers of connectivity, while more sagebrush cover and less conifer cover facilitated connectivity rangewide. Our findings support previous assumptions that Gunnison sage-grouse range contraction is largely the result of habitat loss and degradation. Importantly, we report direct estimates of resistance for landscape components that can be used to create resistance surfaces for prioritization of specific locations for conservation or management (i.e., habitat preservation, restoration, or development) or as we demonstrated, can be combined with simulation techniques to predict impacts to connectivity from potential management actions.

bblonder/aspen_phenology_remote_sensing: v1.0.0 - Benjamin Blonder (other, 2023)
Initial release.

Malaise_trap_ReadME.txt (other, 2023)
Data set to accompany the paper " Extensive regional variation in the phenology of insects and their response to temperature across North America" published in Ecology (2023) See below for a full list of authors and addresses. Authors are also listed in the main data file, "Malaise_trap_Data_2019-2021.txt", which is a tab-delimited file. See the ReadMe file for a description of the variables in the main data file. There are also separate tab-delimited files for the "repeatability" analyses, which contain a subset of data from the main data file, and an excel file with the tree swallow laying date data used to estimate the start of the three main phenological periods. Here, we describe the phenology and abundance of insects collected in Malaise traps using a standardized protocol at 96 sites in open, grassy habitats across North America (n = 1041 samples). Over three years (2019-2021), we found strong regional differences in the phenology of insects and their response to temperature, even within the same taxonomic group, habitat type and time of season. At a smaller scale, insect biomass at different traps operating on the same days was correlated up to about 75 km apart. This geographic and phenological variation has not been studied well, and it is a major source of controversy in previous analyses of insect declines that have aggregated studies from different locations and time periods. Using our standardized protocol, we estimate that 5 or 1% declines in insect abundance across North America could be detected with just 1 or 5 more years of sampling, respectively, of a study such as ours (131 samples/year). Thus, standardized monitoring networks such as this have the potential to answer important questions about the distribution and abundance of insects at a continental scale in a relatively short time frame. Authors and addresses Peter O. Dunn Dept. of Biol. Sci., Univ. of Wisconsin-Milwaukee Insiyaa Ahmed U.S. Army Corps of Engineers, Rivers Project Office Elise Armstrong Dept. of Biology, Siena Heights Univ. Natasha Barlow Birds Canada Malcolm A. Barnard Inst. of Marine Sci., Univ. of N.C. at Chapel Hill Marc Bélisle Dépt. de Biologie, Univ. de Sherbrooke T.J. Benson Ill. Nat. Hist. Survey, Prairie Res. Inst., Univ. of Illinois at Urbana-Champaign Lisha L. Berzins Dept. of Biology, Univ. of Saskatchewan Chloe Boynton Canadian Wildl. Service, Environ. and Climate Change Canada T. Anders Brown Environ. Sci. Program and Biol. Dept., Trinity College Melissa Cady Alaska Peninsula and Becharof Nat. Wildl. Refuges, U.S. Fish and Wildl. Service Kyle Cameron Birds Canada Xuan Chen Dept. of Biology, Salisbury Univ. Robert G. Clark Wildl. Res. Div., Environ. and Climate Change Canada, and Dept. of Biol., Univ. of Saskatchewan Ethan D. Clotfelter Dept. of Biol. and Environ. Studies, Amherst College Kara Cromwell Div. Biol. Sciences, University of Montana Russell D. Dawson Dept. of Ecosys. Sci. and Manag., Univ.of North. British Columbia Elsie Denton US Dept. of Agri., Agri. Res. Service, Rangeland and Meadow Forage Unit Andrew Forbes Dept. of Biology, Univ. of Iowa Kendrick Fowler Hawthorne Valley Farmscape Ecol. Program Kevin C. Fraser Dept. of Biol. Sci., Univ. of Manitoba Dany Garant Dépt. de Biologie, Univ. de Sherbrooke Megan Hiebert Birds Canada Claire Houchen Dept. of Biol. Sci., George Washington Univ. Jennifer Houtz Dept. of Ecol. and Evol. Biol., Cornell Univ. Tara L. Imlay Dept. of Biology, Dalhousie Univ., and Pacific Wildl. Res. Centre, Canadian Wildl. Serv. Brian D. Inouye Dept. of Biol. Sci., Florida State Univ. David W. Inouye Dept. of Biology, Univ. of Maryland, and Rocky Mountain Biol. Lab. Michelle Jackson A Rocha Canada Andrew Jacobson Dept. of Environ. and Sustain., Catawba College Kristin Jayd Dept. of Entomol., Univ. of Maryland Christy Juteau A Rocha Canada Andrea Kautz Carnegie Mus. of Nat. Hist., Powdermill Nature Res. Caroline Killian Environ. Sci. Program and Biol. Dept., Trinity College Elliot Kinnear Dept. of Biol. Sci., Univ. of Manitoba Kimberly J Komatsu Smithsonian Environ. Res. Center Kirk Larsen Dept. of Biology, Luther College Andrew Laughlin Dept. of Environ. Studies, UNC Asheville Marty Leonard Dept. of Biol., Dalhousie Univ. Valerie Levesque-Beaudin Centre for Biodiversity Genomics, Univ. of Guelph Ryan Leys Birds Canada and Biol. Dept., Univ. of Waterloo Elizabeth Long Mohonk Preserve Stephen C. Lougheed Dept. of Biology, Queen's Univ. Stuart Mackenzie Birds Canada Jen Marangelo Missoula Butterfly House & Insectarium Colleen Miller Dept. of Ecol. and Evol. Biol., Cornell Univ. Brenda Molano-Flores Ill. Nat. Hist. Survey, Prairie Res. Inst., Univ. of Illinois at Urbana-Champaign Christy Morrissey Dept. of Biol. and School of Environ. and Sustain., Univ. of Saskatchewan Emony Nicholls Ministry of Forests, Lands and Nat. Res. Operations Jessica M. Orlofske Biol. Sci. Dept., University of Wisconsin - Parkside Ian S. Pearse U.S. Geol. Survey, Fort Collins Sci. Center Fanie Pelletier Dépt. de Biologie, Univ. de Sherbrooke Amber L. Pitt Environ. Sci. Program and Biol. Dept., Trinity College Joseph Poston Dept. of Biology, Catawba College Danielle Racke Claytor Nature Center, Univ. of Lynchburg Jeannine A. Randall Dept. of Ecosys. Sci. and Manag., Univ.of North. British Columbia Matthew L. Richardson Center for Urban Research, Engagement and Scholarship, Univ. of the District of Columbia Olivia Rooney Dept. of Ecol. and Evol. Biol., Cornell Univ. A. Rose Ruegg Dept. of Environ. Studies, UNC Asheville Scott Rush Dept of Wildl., Fisheries and Aquaculture, Mississippi State Univ. Sadie Ryan Dept. of Geog. and Emerging Pathogens Inst., Univ. of Florida Mitchell Sadowski Biol. Dept., Westfield State Univ. Ivana Schoepf Dept. of Science, Univ. of Alberta - Augustana Lindsay Schulz Cultural and Environ. Resource Manag., Central Washington Univ. Brenna Shea Missoula Butterfly House & Insectarium Thomas N. Sheehan The Jones Center at Ichauway Lynn Siefferman Dept. of Biology, Appalachian State Univ. Derek Sikes Univ. of Alaska Museum, Univ. of Alaska Fairbanks Mark Stanback Dept. of Biology, Davidson College John D. Styrsky Dept. of Biology, Univ. of Lynchburg Conor C. Taff Dept. of Ecol. and Evol. Biol., Cornell Univ. Jennifer J. Uehling Dept. of Ecol. and Evol. Biol., Cornell Univ. Kathleen Uvino Environ. Science, Univ.of Jamestown Thomas Wassmer Dept. of Biology, Siena Heights Univ. Kathryn Weglarz Biol. Dept., Westfield State Univ. Megan Weinberger Dept. of Biology, Univ. of Iowa John Wenzel Carnegie Mus. of Nat. Hist., Powdermill Nature Res. Linda A. Whittingham Dept. of Biol. Sci., Univ. of Wisconsin-Milwaukee

Raw soil carbon dioxide, moisture, and temperature data from a mixed conifer and aspen forest stands in the East River Watershed of Colorado June 2011-December 2021. - Mariah Carbone (ess_dive, 2023)
These datasets contain raw data from adjacent mixed-conifer (Abies lasiocarpa and Picea engelmannii) and deciduous (Populus tremuloides) forest stands in the Elk Mountains at the Rocky Mountain Biological Laboratory in Gothic, Colorado (38.9592◦ N, longitude: 106.9898◦ W and elevation of 2880 m). Each site contains five soil carbon dioxide (CO2) gas probes and three soil temperature/moisture sensors connected to a data logger. Soil temperature, moisture, and CO2 (ppm) concentrations were recorded each hour. There are three attached CSV files; two CSV files are of the raw data collected in the respective forest types, conifer: "Carbone_conifer.csv" and deciduous: "Carbone_aspen.csv". The third file "Carbone_Headers.csv" is a shared headers file for both datasets. Basic data processing and QA/QC was done on the raw data sets. This research was performed to investigate the response of the soil CO2 to inter-annual and seasonal variability of moisture inputs in mountainous ecosystems.

NESDIS 14 km sea surface temperature for North America, 1993 to 2016 (other, 2023)
This product presents local sea surface temperatures (degrees C). It is a composite gridded-image derived from 8-km resolution SST observations collected by Advanced High Resolution Radiometer (AVHRR) and HIRS (High Resolution Infrared Radiation Sounder) instruments onboard NOAA and Metop-A satellites. It is generated every 48 hours for North America. SST is defined as the skin temperature of the ocean surface water. Data may be output as netCDF and other formats (GIF, ASCII, spreadsheet). The pixel resolution is .125 x .125 degrees.

Aerobic respiration controls on shale weathering, Geochimica et Cosmochimica Acta, 2023: Dataset (ess_dive, 2023)
This data package was generated in order to support the development of a deep-time weathering model and to assess the coupling between shale weathering and aerobic respiration in the paper “Aerobic respiration controls on shale weathering” by Stolze et al., Geochimica et Cosmochimica Acta (2023). The package contains two csv files providing the average CO2(g) concentration profiles [ppm] and mineral concentration profiles [wt%], respectively. The CO2(g) concentration profiles were measured in the vicinity of the monitoring well PLM2 between January 2018 and April 2019. The gas samples were collected in the unsaturated zone to a depth of 1.52 m. The mineral concentration profiles were determined by X-Ray diffraction (XRD). The XRD measurements were performed on sub-core samples collected in the monitoring well PLM3 down to a depth of 7.01 m. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type.

Aerobic respiration controls on shale weathering, Geochimica et Cosmochimica Acta, 2023: Dataset - Zhi Li; Zhi Li; Lucien Stolze; Zhi Li; Lucien Stolze; Sergio Carrero; Lucien Stolze; Benjamin Gilbert; Sergio Carrero; Bhavna Arora; Benjamin Gilbert; Bhavna Arora; et al. (ess_dive, 2023)
This data package was generated in order to support the development of a deep-time weathering model and to assess the coupling between shale weathering and aerobic respiration in the paper “Aerobic respiration controls on shale weathering” by Stolze et al., Geochimica et Cosmochimica Acta (2023). The package contains two csv files providing the average CO2(g) concentration profiles [ppm] and mineral concentration profiles [wt%], respectively. The CO2(g) concentration profiles were measured in the vicinity of the monitoring well PLM2 between January 2018 and April 2019. The gas samples were collected in the unsaturated zone to a depth of 1.52 m. The mineral concentration profiles were determined by X-Ray diffraction (XRD). The XRD measurements were performed on sub-core samples collected in the monitoring well PLM3 down to a depth of 7.01 m. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Update on 2024-05-28: Revised versions of the CSV data files (CO2_data_GCA_Stolze_et_al_2023.csv and XRD_data_GCA_Stolze_et_al_2023.csv) were made to apply ESS-DIVE's CSV reporting format guidelines. Updated versions of the File Level Metadata (v2_20240528_flmd.csv) and Data Dictionary (v2_20240528_dd.csv) files were updated to reflect the changes made to the CSV files.

Air Temperature Freezing Degree-days Late Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 196 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D028')  metadata <- sdp_get_metadata('R4D028')    For more information about rSDP, visit the package homepage.

Maps of multiple future threats and stable areas for Gunnison sage-grouse habitats across three scenarios (2016-2070) - Nathan D Van Van Schmidt (other, 2023)
This dataset contains a series of maps of projected threats and current state of habitats for the threatened Gunnison sage-grouse (Centrocercus minimus). The maps are 30-m spatially explicit projections of current habitats (2016) and future landscape change (by 2070) for the full range of the species’ seven extant populations, which cover diverse ecoregions of the imperiled sagebrush biome of the western United States. Risk maps assessed projected changes caused by climate change-driven shifts in sagebrush habitats, mesic habitats, pinyon-juniper encroachment, and invasive annual grasses, as well as potentially synergistic risk of development and wildfire. We used a scenario-based risk assessment framework based on the U.S. Fish & Wildlife Service’s Species Status Assessment for Gunnison Sage-grouse. This aligned individual projections for each threat into three combinatory scenarios of change: Pessimistic (P; hot and dry future climate, high development), Continuation (C; moderately hot, moderate development), and Optimistic (O; warm and wet, low development). An additional set of habitat Vulnerability (V) maps integrates current and future datasets across scenarios to map areas that are currently partially degraded, fully degraded, sometimes at-risk across future scenarios, always at-risk, or stable both currently and in the future. Lastly, Overall species vulnerability maps describe cumulative vulnerability to all six threats as a mean weighted by relevance to Gunnison sage-grouse habitat selection and the sum number of threats that were at-risk in future projections.

Meteorological and hydrological parameters for 17 locations of meteorological stations of the East River Watershed - Bhavna Arora; Dipankar Dwivedi; Boris Faybishenko; Eoin L. Brodie (ess_dive, 2023)
The Data Package includes a set of csv files of input meteorological parameters for locations of 17 meteorological stations within the East River watershed. These meteorological datasets were downloaded from the (1) PRISM database--monthly precipitation, air temperature (minimum, mean, and maximum), vapor pressure deficit (minimum and maximum), and dewpoint temperature, and (2) NCEP/NCAR Reanalysis database – wind database. The datasets were used for calculations of the Potential Evapotranspiration (ETo), Actual Evapotranspiration (ET), Standard Precipitation Index (SPI) , Standard Evapotranspiration-Precipitation Index (SPEI) for the period from 1966 to 2021. The main research questions addressed are: the evaluation of the long-term temporal trends of climatic parameters, hierarchical clustering, and areal mapping/zonation of the East River watershed. Calculations were conducted in the Rstudio environment. The dataset additionally includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. The input datasets were downloaded from (a) PRISM database (the Northwest Alliance for Computational Science and Engineering at the Oregon State University), and (b) NCEP/NCAR Reanalysis database.

NESDIS Global 50-km twice weekly sea surface temperature (SST) product, 2001 to 2016 - NCEI (other, 2023)
Product shows global sea surface temperatures in degrees C. It is a composite gridded-image derived from 8-km resolution Global SST Observations and is generated twice weekly (Tuesday and Saturday) on a global scale. SST is defined as the skin temperature of the ocean surface water.

A field-validated ensemble species distribution model of Eriogonum pelinophilum, an endangered subshrub in Colorado, USA (other, 2023)
Understanding the suitable habitat of endangered species is crucial for agencies such as the Bureau of Land Management to plan management and conservation. However, few species distribution models are directly validated, potentially limiting their application. In preparation for a Species Status Assessment of clay‐loving wild buckwheat (Eriogonum pelinophilum), an endangered subshrub found in southwest Colorado, we ran a series of species distribution models to estimate the species' potential occupied habitat and validated these models in the field. A 1‐meter resolution digital elevation model derived from LiDAR and a high‐resolution geology mapping helped identify biologically relevant characteristics of the species' habitat. We employed a weighted ensemble model based on two Random Forest and one Boosted Regression Tree model, and the discrimination performance of the ensemble model was high (AUC-PR = 0.793). We then conducted a systematic field survey of model habitat suitability predictions, during which we discovered 55 new subpopulations of the species and demonstrated that new species observations were strongly associated with model predictions (p < .0001, Cliff's delta = 0.575). We then further refined our original models by incorporating the additional species occurrences collected in the field survey, a new explanatory variable, and a more diverse set of models. These iterative changes to the model marginally improved performance (AUC‐PR = 0.825). Direct validation of species distribution models is extremely rare, and our field survey provides strong validation of our model results. This helps increase confidence in utilizing predictions in planning. The final model predictions greatly improve the Bureau of Land Management's understanding of the species' habitat and increase our ability to consider potential habitat in planning land use activities such as road development and travel management.

NESDIS 14 km sea surface temperature for North America, 1993 to 2016 - NCEI (other, 2023)
This product presents local sea surface temperatures (degrees C). It is a composite gridded-image derived from 8-km resolution SST observations collected by Advanced High Resolution Radiometer (AVHRR) and HIRS (High Resolution Infrared Radiation Sounder) instruments onboard NOAA and Metop-A satellites. It is generated every 48 hours for North America. SST is defined as the skin temperature of the ocean surface water. Data may be output as netCDF and other formats (GIF, ASCII, spreadsheet). The pixel resolution is .125 x .125 degrees.

Snow-free Freezing Degree-days 0-60 days Post-snow Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated in the 60 days after the dissapearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D018')  metadata <- sdp_get_metadata('R4D018')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Annual Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D015')  metadata <- sdp_get_metadata('R4D015')    For more information about rSDP, visit the package homepage.

kcarrowsmith/communityBEF: Ecosphere2023 - Kaysee Arrowsmith (other, 2023)
Code and data associated with Ecosphere manuscript Arrowsmith et al. 2023.

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 - Fadji Maina; Fadji Maina; P. James Dennedy-Frank; Fadji Maina; E. R. Woodburn; P. James Dennedy-Frank; E. R. Woodburn; P. James Dennedy-Frank; Fadji Maina; E. R. Woodburn; Fadji Maina; P. James Dennedy-Frank; et al. (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

Leaf gas exchange in Ipomopsis aggregata under manipulated snowmelt timing and summer precipitation - J. M. Powers; Ayaka Paul; Jocelyn Navarro; Diane R. Campbell (other, 2022)
Vegetative traits of plants can respond directly to changes in the environment, such as those occurring under climate change. That phenotypic plasticity could be adaptive, maladaptive, or neutral. We manipulated the timing of spring snowmelt and amount of summer precipitation in factorial combination and examined the responses of photosynthetic rate, stomatal conductance, and intrinsic water-use efficiency (iWUE) in the subalpine herb Ipomopsis aggregata. The experiment was repeated in three years differing in natural timing of snowmelt. A 50% reduction in summer precipitation reduced stomatal conductance and iWUE. Combining natural and experimental variation, earlier snowmelt reduced soil moisture, photosynthetic rate and stomatal conductance, and increased iWUE. Earlier snowmelt is a strong signal of climate change and can change expression of leaf morphology and gas exchange traits, just as reduced precipitation can. Stomatal conductance showed adaptive plasticity under some conditions.

LAS Digital logs for the Mancos Shale, Candy Lane 1 (CL-1) Core in Montrose County, Colorado - Kira K Timm (other, 2022)
The Candy Lane 1 (CL-1) core was acquired in November 2005 as part of a multidisciplinary effort by the U.S. Geological Survey, the Bureau of Land Management, and the Bureau of Reclamation to investigate the Mancos Shale in the Gunnison Gorge National Conservation Area, Montrose County, Colorado. The Upper Cretaceous Mancos core was taken from the NE 1/4 sec. 8, T. 50 N., R. 9 W. corresponding to approximately lat 38.61717° N., long 107.90174° W. The CL-1 core contains part of the lower portion of the Mancos Shale Formation; Mancos Formation members in ascending order include the Bridge Creek Limestone (partial), Fairport, Blue Hill, Juana Lopez, Montezuma Valley, and Niobrara (partial). After coring was completed, the USGS collected geophysical data between the depths of 13 to 552 feet. Logs include gamma ray, caliper, conductivity, resistivity, porosity, density, and temperature. The digital logs for gamma ray (GR), resistivity (RES), and density (DEN) corresponding to those published in Open File Report 2009-1294 are identified in the name of the three Log ASCII Standard (LAS) files.

Occurrence Download (other, 2022)
A dataset containing 10742 species occurrences available in GBIF matching the query: { "DatasetKey" : [ "is Rocky Mountain Biological Laboratory" ] } The dataset includes 10742 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0101451-220831081235567/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

NOAA/WDS Paleoclimatology - Simic fire data from Timber Sale, Western Slope - IMPD USTRS002 - Petar Z. Simic; Manuel K. Lopez; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

Demographic consequences of changes in environmental periodicity - Daniel T. Blumstein; Maria Paniw; Eva Conquet; Kenneth B. Armitage; Julien G. A. Martin (other, 2022)
The fate of natural populations is mediated by complex interactions among vital rates, which can vary within and among years. While the effects of random, among-year variation in vital rates have been studied extensively, relatively little is known about how periodic, non-random variation in vital rates affects populations. This knowledge gap is potentially alarming as global environmental change is projected to alter common periodic variations, such as seasonality. We investigated the effects of changes in vital-rate periodicity on populations of three species representing different forms of adaptation to periodic environments: the yellow-bellied marmot (Marmota flaviventer), adapted to strong seasonality in snowfall; the meerkat (Suricata suricatta), adapted to inter-annual stochasticity as well as seasonal patterns in rainfall; and the dewy pine (Drosophyllum lusitanicum), adapted to fire regimes and periodic post-fire habitat succession. To assess how changes in periodicity affect population growth, we parameterized periodic matrix population models and projected population dynamics under different scenarios of perturbations in the strength of vital-rate periodicity. We assessed the effects of such perturbations on various metrics describing population dynamics, including the stochastic growth rate, log λS. Overall, perturbing the strength of periodicity had strong effects on population dynamics in all three study species. For the marmots, log λS decreased with increased seasonal differences in adult survival. For the meerkats, density dependence buffered the effects of perturbations of periodicity on log λS. Finally, dewy pines were negatively affected by changes in natural post-fire succession under stochastic or periodic fire regimes with fires occurring every 30 years, but were buffered by density dependence from such changes under presumed more frequent fires or large-scale disturbances. We show that changes in the strength of vital-rate periodicity can have diverse but strong effects on population dynamics across different life histories. Populations buffered from inter-annual vital-rate variation can be affected substantially by changes in environmentally-driven vital-rate periodic patterns; however, the effects of such changes can be masked in analyses focusing on inter-annual variation. As most ecosystems are affected by periodic variations in the environment such as seasonality, assessing their contributions to population viability for future global-change research is crucial.

Data from: Comparative impacts of long-term trends in snowmelt and species interactions on plant population dynamics - Mary V. Price; Alison K. Brody; Nickolas M. Waser; Rebecca E. Irwin; Diane R. Campbell (other, 2022)
Climate change can impact plant fitness and population persistence directly through changing abiotic conditions and indirectly through its effects on species interactions. Pollination and seed predation are important biotic interactions that can impact plant fitness, but their impact on population growth rates relative to the role of direct climatic effects is unknown. We combined 13 years of experiments on pollen limitation of seed set and pre-dispersal seed predation in Ipomopsis aggregata , a subalpine wildflower, with a long-term demographic study that has documented declining population growth with earlier spring snowmelt date. We determined how pollen limitation and seed predation changed with snowmelt date over 21 years and incorporated those effects into an integral projection model to assess relative impacts of biotic factors on population growth. Both pollen limitation and the difference in stigma pollen load between pollen-supplemented and control plants declined over years. Neither pollen limitation nor seed predation changed detectably with snowmelt date, suggesting an absence of indirect effects of that specific abiotic factor on these indices of biotic interactions. The projected biotic impacts of pollen limitation and seed predation on population growth rate were small compared to factors associated with snowmelt date. Providing full pollination would delay the projected date when earlier snowmelt will cause populations to fall below replacement by only 14 years. Synthesis. Full pollination and elimination of seed predation would not compensate for the strong detrimental effects of early snowmelt on population growth rate, which in I. aggregata appears driven largely by abiotic environmental factors. The reduction over two decades in pollen limitation also suggests that natural selection on floral traits may weaken with continued climate change. These results highlight the value of studying both abiotic factors and biotic interactions to understand how climate change will influence plant populations.

Carry-over effects of larval food stress on adult energetics and life history in a nectar-feeding butterfly - Carol L. Boggs (other, 2022)
Stressful juvenile developmental conditions can affect performance and fitness later in life. In holometabolous insects such as butterflies, development under stressful conditions may lead to smaller adult size, lower reproductive output and shorter lifespan. However, how larval developmental stress affects energy intake and expenditure in adult individuals is poorly understood. We subjected last-instar larvae of Speyeria mormonia Edwards (Lepidoptera: Nymphalidae) to periodic dietary restriction (DR) to examine the allocation of energy and nutrients among different life-history processes. We measured adult food intake, resting metabolic rate (RMR), metabolic flight capacity, lifespan, and reproductive output. Consistent with pressure to disperse from a poor environment while maintaining offspring number, we predicted that stressed individuals would have increased adult food intake and higher flight capacity. Adult body size was strongly reduced. Contrary to predictions, we found no compensatory adult feeding. Mass-adjusted flight metabolic rate was reduced, suggesting poor dispersal capacity. Larval DR did not affect adult lifespan, nor did the rate of metabolic senescence change. Larval DR did affect RMR, as stressed females had a steeper slope between RMR and body mass, which may reflect differences in physiological activity due to condition. Fecundity decreased less than predicted based on body mass. Instead of investing in flight capacity, females increased relative allocation to reproduction, which may partly buffer against poor environmental conditions. Understanding the interplay of energy acquisition and allocation to life history traits across the life cycle is vital for predicting responses to environmental change.

Integrated hydrological model results for Lower Triangle Region in East River Watershed, Colorado, WYs 2016 and 2017 - Ilhan Özgen-Xian; Utkarsh Mital; Craig Ulrich; Qina Yan; Zexuan Xu; Ralf Loritz; Sergi Molins; Ilhan Özgen-Xian; Carl I. Steefel; Sergi Molins; Zexuan Xu; Utkarsh Mital; et al. (other, 2022)
Summary This data package contains numerical simulation results of integrated hydrology in Lower Triangle Region in East River Watershed, Colorado. The system is forced with DAYMET precipitation and climate data of the region for the water years 2016 and 2017. The results are computed on triangular multi-resolution meshes with resolutions ranging from 10 meter to 80 meter. The purpose of the data is to assess the influence of surface-subsurface exchange on distributed and aggregated hydrological response. Material and Methods Data has been generated by the Advanced Terrestrial Simulator (ATS) v1.0. The output format of ATS for spatially distributed data is HDF5 and can be viewed, for example, using VisIt or ParaView. The format of point data is plain text. README content The uploaded files have been created using the unix split(1) command to limit the size of each individual package. Files can be merged under a unix system through: $ cat OZGEN_ETAL_2022.zip.partaa OZGEN_ETAL_2022.zip.partab OZGEN_ETAL_2022.zip.partac > output.zip Unzip via $ unzip output.zip or using a graphical environment.

NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH - Sergio Pezoa; Laura Bianco; Bianca Adler; James Wilczak; Ludovic Bariteau; Timothy Djalalova; James Wilczak; Sergio Pezoa; Laura Bianco; Timothy Djalalova; Bianca Adler; Ludovic Bariteau (other, 2022)
This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. The file naming conventions for the .DAT files are as follows: NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT with yyyy: Year mm: Month dd: Day HH: Hour when the first sample was written to the file The time stamp of all data is in UTC.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH (other, 2022)
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009). The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm -1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior. The file format is netcdf and the file naming conventions are NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf with yyyy: Year mm: Month dd: Day The time stamp of all data is in UTC. Selected basic variables are (many more provided): Name Dimension Unit base_time Single value Seconds (since 00 UTC 1 Jan 1970) time_offset Time Second (since base_time) hour Time Hours since 00UTC this day height Height km AGL temperature Time, Height C, temperature waterVapor Time, Height g/kg, water vapor mixing ratio theta Time, Height K, potential temperature pressure Time, Height hPa, pressure rh Time, Height %, relative humidity dewpt Time, Height C, dew point temperature thetae Time, Height K, equivalent potential temperature sigma_temperature Time, Height C, 1-sigma uncertainty temperature sigma_waterVapor Time, Height g/kg, 1-sigma uncertainty water vapor cdfs_temperature Time, Height cumulative degrees of freedom for temperature cdfs_waterVapor Time, Height cumulative degrees of freedom for water vapor Bold variables are the main retrieved profiles, from which the other variables are derived. Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model. Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. References: Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17. Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968. Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.

Gothic Colorado WInter 2022 Snow Depth Lidar - Ian K Breckheimer; Ian K Breckheimer (other, 2022)
Gothic Colorado Winter 2022 Snow Depth Lidar from Jan7, Jan 26, and Feb 14 2022. Additionally, includes snow off.

Occurrence Download (other, 2022)
A dataset containing 905116 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Specimen", { "or" : [ "DatasetKey is The New York Botanical Garden Herbarium (NY)", "DatasetKey is University of New Mexico Herbarium", "DatasetKey is University of Colorado Museum of Natural History Herbarium Vascular Plant Collection", "DatasetKey is Rocky Mountain Biological Laboratory", "DatasetKey is Harvard University Herbaria: All Records", "DatasetKey is Colorado State University, Charles Maurer Herbarium" ] }, "OccurrenceStatus is Present", { "or" : [ "StateProvince is Colorado", "StateProvince is ArIzona", "StateProvince is Utah", "StateProvince is New Mexico", "StateProvince is Wyoming", "StateProvince is South Dakota", "StateProvince is Nebraska", "StateProvince is Kansas", "StateProvince is Oklahoma", "StateProvince is Texas" ] } ] } The dataset includes 905116 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0347300-210914110416597/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

Gothic Colorado WInter 2022 Snow Depth Lidar (other, 2022)
Gothic Colorado Winter 2022 Snow Depth Lidar from Jan7, Jan 26, and Feb 14 2022. Additionally, includes snow off.

Data from: Genetic and spatial variation in vegetative and floral traits across a hybrid zone - Maya Midzik; Geoffrey Broadhead; Robert A. Raguso; Mascha Bischoff; Diane R. Campbell (other, 2022)
Premise: Genetic variation influences potential for evolution to rescue populations from impacts of environmental change. Most studies of genetic variation in fitness-related traits focus on either vegetative or floral traits, with few on floral scent. How vegetative and floral traits compare in potential for adaptive evolution is poorly understood. Results: Vegetative traits SLA and WUE varied greatly among planting sites, while showing weak or no genetic variation among source populations. SLA and trichomes responded plastically to snowmelt date, and SLA exhibited within-population genetic variation. All aspects of floral morphology varied genetically among source populations, and corolla length, corolla width, and sepal width varied genetically within populations. Heritability was not detected for volatiles, due to high environmental variation, although one terpene had high evolvability and two terpenes correlated genetically with sepal width, associated with high emission from that tissue. Environmental variation across sites was weak for floral morphology and stronger for volatiles and vegetative traits. Three of 4 volatiles showed inheritance departing from additive. Conclusions: Results indicate stronger genetic potential for evolutionary responses to selection in floral morphology compared with scent and vegetative traits, while finding potentially adaptive plasticity in some vegetative traits.

Life history consequences of climate change in hibernating mammals: A review (2022)
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HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

HEC-HMS models of major rainfall induced floods in ungauged mountain basins of Colorado (other, 2022)
This dataset includes the models used in the journal article entitled, "Parameter Estimation for Models of Major Rainfall Induced Floods in Ungauged Mountain Basins of Colorado," published in the Journal of Hydrology Regional Studies. Hydrologic Engineering Center – Hydrologic Modeling System (HEC-HMS) models are included for the following river basins and major storms in the Front Range and San Juan Range of Colorado: North Fork of the Big Thompson river (1976 storm), Big Thompson River (2013 storm), South Boulder Creek (2013 storm), Bear Creek (2013 storm), Cheyenne Creek (1997 and 2013 storms), Saguache Creek (1999 storm), Vallecito Creek (1970 and 2006 storms), and Mineral Creek (2004 and 2006 storms). In each case, the model produces streamflow hydrographs based on the provided precipitation hyetographs. For each case, calibrated and uncalibrated versions of the models are included. The models also include models that estimate the streamflow for design storms that were generated from the Regional Extreme Precipitation Study.

SnowEx Meteorological Station Measurements from Grand Mesa, CO, Version 1 (other, 2022)

Integrated hydrological model results for Lower Triangle Region in East River Watershed, Colorado, WYs 2016 and 2017 (other, 2022)
Summary This data package contains numerical simulation results of integrated hydrology in Lower Triangle Region in East River Watershed, Colorado. The system is forced with DAYMET precipitation and climate data of the region for the water years 2016 and 2017. The results are computed on triangular multi-resolution meshes with resolutions ranging from 10 meter to 80 meter. The purpose of the data is to assess the influence of surface-subsurface exchange on distributed and aggregated hydrological response. Material and Methods Data has been generated by the Advanced Terrestrial Simulator (ATS) v1.0. The output format of ATS for spatially distributed data is HDF5 and can be viewed, for example, using VisIt or ParaView. The format of point data is plain text. README content The uploaded files have been created using the unix split(1) command to limit the size of each individual package. Files can be merged under a unix system through: $ cat OZGEN_ETAL_2022.zip.partaa OZGEN_ETAL_2022.zip.partab OZGEN_ETAL_2022.zip.partac > output.zip Unzip via $ unzip output.zip or using a graphical environment.

SnowEx20 Grand Mesa Snow Depth from Snow Pole Time-Lapse Imagery, Version 1 (other, 2022)

SnowEx Meteorological Station Measurements from Grand Mesa, CO Raw, Version 1 (other, 2022)

Aspen ploidy level and cover maps from NEON Airborne Observation Platform, 2018, Crested Butte, Colorado - Philip G. Brodrick; K. Dana Chadwick; Benjamin Blonder (ess_dive, 2022)
Maps of aspen ploidy (diploid or triploid) developed using National Ecological Observatory Network (NEON) Airborne Observation Platform visible to shortwave infrared imaging spectroscopy data that were acquired over the Upper East River, Colorado in 2018. Maps of aspen distribution, as well as other land cover types that were used to screen for aspen dominated pixels are also provided. Each image file is provided as a GeoTiff with internal tiling and LZW compression. For use of ploidy maps, it is necessary to mask out non-aspen and shaded pixels. Shaded pixels are provided in an associated data package. For full details, please see associated publication and methods description. This dataset is associated with the publication in Ecological Applications.

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

Machine Learning Assisted Gap-Filled Discharge Data for the East River Community Watershed, Colorado, for Water Years 2014-2021 - Carroll Rosemary; Michelle Newcomer; Kenneth Hurst Williams (ess_dive, 2022)
This dataset contains a collection of machine learning assisted gap-filled discharge data created for all discharge stations across the East River Watershed, Colorado. This data was generated by using raw discharge data collected by Rosemary Carroll, and conducting a random forest machine learning analysis to gap-fill discharge data across all years at the hourly time level. Discharge data with gaps creates problems for analysis of measured and modeled fluxes of carbon and nitrogen exported out of each sub-watershed. Gap-filled data is also required as an input to surface water models, which helps to address our main research question related to how snowmelt timing impacts the timing and magnitude of nitrogen exports. Data is provided in one csv file.

NOAA/WDS Paleoclimatology - Woodhouse and Lukas 2004 TreeFlow Colorado Streamflow Reconstructions (other, 2022)
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 Climate Reconstruction. The data include parameters of climate reconstructions|tree ring with a geographic location of Colorado, United States Of America. The time period coverage is from 567 to -52 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

SnowEx Meteorological Station Measurements from Grand Mesa, CO Raw, Version 1 (other, 2022)

Field data for the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park - Open Format Data Package (other, 2022)
These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

Data used in Wainwright, H.M. et al. 2021, “Watershed zonation through hillslope clustering for tractably quantifying above- and belowground watershed heterogeneity and functions” (2022)
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Data for “Herbivory damage but not plant disease under experimental warming is dependent on weather for three subalpine grass species”, Rocky Mountain Biological Laboratory, Gothic, Colorado, 2015-2017. (2022)
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Supplementary material from "Current and lagged climate affects phenology across diverse taxonomic groups" (2022)
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An Equation of State Unifies Diversity, Productivity, Abundance and Biomass (data sets) (2022)
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Supplementary material from "Life-history traits predict responses of wild bees to climate variation" (2022)
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SnowEx20 Grand Mesa Snow Depth from Snow Pole Time-Lapse Imagery, Version 1 (other, 2022)

Field data for the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park - Open Format Data Package (other, 2022)
These data were converted from the originally delivered Microsoft Access PLOTs database from the Vegetation Mapping Inventory Project of Black Canyon of the Gunnison National Park. These comma-delimited data tables contain(s) vegetation mapping plot classification and accuracy assessment data, as well as summary information about the data itself. If a table is empty, then it was empty in the original database.

Temperature and floral density data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2021 - Jessica R. K. Forrest (other, 2022)
Data were collected as part of a study of population dynamics of solitary, cavity-nesting Hymenoptera. Nesting structures (“trap-nests”) were established at five study sites along an elevational gradient around the Rocky Mountain Biological Laboratory in 2013. Two additional study sites were added in 2014, and one of the original study sites was dropped at the end of 2015. At each site, a HOBO data-logger placed under a centrally located trap-nest records air temperatures hourly. Floral densities are recorded at each site, typically 1-2 times per week, throughout the growing season, for specific plant taxa known to be used as pollen sources by cavity-nesting bees.

Gage Height data from February through September for years 2016-19 at Gunnison River near Grand Junction, Colorado - Mark F Henneberg; Laura A Hempel (other, 2022)
Gage height values from U.S. Geological Survey (USGS) streamflow-gaging station 09152500 for the specified period are presented in comma separated value (CSV) format. Values encompass the monthly date range from February 1st to September 30th for each year, 2016 through 2019.

Occurrence Download (other, 2022)
A dataset containing 648 species occurrences available in GBIF matching the query: { "DatasetKey" : [ "is Rocky Mountain Biological Laboratory Mammal Collection" ] } The dataset includes 648 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0164883-220831081235567/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH (other, 2022)
This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. The file naming conventions for the .DAT files are as follows: NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT with yyyy: Year mm: Month dd: Day HH: Hour when the first sample was written to the file The time stamp of all data is in UTC.

stemkov/pheno_skew: Phenological skews (2022)
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SnowEx Meteorological Station Measurements from Grand Mesa, CO, Version 1 (other, 2022)

Stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases in a mountainous watershed (Crested Butte, Colorado). - Markus Bill (other, 2022)
Temperature and precipitation variability in response to climate change affects water cycling of watersheds and can potentially impact water quality, water availability, elemental and molecular fluxes, and biogeochemical processes. Here we report the measurements of light stable isotopes and greenhouse gases analysis to a large multidisciplinary project addressing watershed function. Water hydrogen (H) and oxygen (O) stable isotopes of snow and water saturated soil were measured to constrain source contributions to groundwater and streamflow. In an effort to better constrain existing nitrogen and carbon cycling models in high-altitude ecosystems, carbon dioxide (CO2), nitrous oxide (N2O), methane (CH4) greenhouse gases concentrations and stable isotope signatures of CO2, dissolved inorganic carbon (DIC), and water were measured. This dataset includes one CSV file and one Excel file containing data including stable isotopes of carbon dioxide (CO2), dissolved inorganic carbon (DIC), water and concentrations of greenhouse gases. The CSV and Excel files contain the same information/data but have included both versions for accessibility.

Surface electrical resistivity tomography, magnetic, and gravity surveys in Redwell Basin and the greater East River watershed near Crested Butte, Colorado, 2017 - Bethany L Burton (other, 2022)
Surface electrical resistivity tomography (ERT), time-domain electromagnetics (TEM), nuclear magnetic resonance (NMR), magnetics, and gravity data were acquired in 2016, 2017 and 2018 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin. Five ERT profiles were acquired within Redwell Basin and Brush Creek to map geologic structure at depths up to 40 meters, depending on the subsurface resistivity, using the Advanced Geosciences, Inc. SuperSting R8 resistivity meter. Approximately ten kilometers of total field magnetics data were acquired with a Geometrics G-858 cesium vapor magnetometer that detects changes in deep (tens of meters to kilometers) geologic structure based on variations [...]

Magnetics Data - Bethany L Burton (other, 2022)
Surface electrical resistivity tomography, magnetic, and gravity surveys were conducted in July 2017 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin as part of a broader study of the role of bedrock groundwater in the hydrogeology of mineralized mountain watersheds. Approximately ten kilometers of total field magnetics data were acquired on July 29, 2017 with a Geometrics G-858 cesium vapor magnetometer that detects changes in deep (tens of meters to kilometers) geologic structure based on variations in the magnetic properties of different formations. This data release includes the raw and processed magnetics data. They are provided as digital data, and data fields are defined [...]

Floodplain hydrostratigraphy from sedimentology, geophysics, and remote sensing - H. F. Malenda; J. Randall; K. Singha (other, 2022)
This file includes the data published in: Malenda, H.F., Sutfin, N.A., Stauffer, S., Guryan. G., Rowland, J.C., Williams, K.H., and Singha, K. (2019). From Grain to Floodplain: Evaluating heterogeneity of floodplain hydrostatigraphy using sedimentology, geophysics, and remote sensing. Earth Surface and Planetary Landforms, doi:10.1002/esp.4613. Floodplain stratigraphy, a major structural element of alluvial aquifers, is a fundamental component of floodplain heterogeneity, hydraulic conductivity, and connectivity. Watershed-scale hydrological models often simplify floodplains by modeling them as largely homogeneous, which inherently overlooks natural floodplain heterogeneity and anisotropy and their effects on hydrologic processes such as groundwater flow and transport and hyporheic exchange. This study, conducted in the East River Basin, Colorado, USA, combines point-, meander-, and floodplain-scale data to explore the importance of detailed field studies and physical representation of alluvial aquifers. We combine sediment core descriptions, hydraulic conductivity estimates from slug tests, ground-penetrating radar (GPR), historical maps of former channels, LiDAR-based elevation and Normalized Difference Vegetation Index data to infer 3-D fluvial stratigraphy. We compare and contrast stratigraphy of two meanders with disparate geometries to explore floodplain heterogeneity and connectivity controls on flow and transport. We identify buried point bars, former channels, and overbank deposits using GPR, corroborated by point sediment descriptions collected during piezometer installment and remotely sensed products. We map heterogeneous structural features that should control resultant flow and transport; orientation and connectivity of these features would control residence times important in hydrologic models.

Strontium isotopic data from the Mount Emmons-Redwell area, Crested Butte, Colorado - Philip L Verplanck (other, 2022)
This U.S. Geological Survey (USGS) data release contains strontium isotopic data from water and rock samples collected between 2000 and 2019 from the Mount Emmons area, central Colorado. The data include strontium isotopic compositions, 87Sr/86Sr, for surface- and groundwater samples collected from streams, springs, draining mines, piezometers, and drill holes and for leachates of rock samples collected from surface outcrops and drill core. Rock sample isotopic data are results from two-step leaching of samples from various lithologies within the study area. Drill core rock and water samples were collected from holes drilled in 2017 and 2018 as part of a U.S. Geological Survey and Department of Energy investigation of groundwater flowpaths [...]

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

NOAA/WDS Paleoclimatology - Woodhouse and Lukas 2004 TreeFlow Colorado Streamflow Reconstructions (other, 2022)
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 Climate Reconstruction. The data include parameters of climate reconstructions|tree ring with a geographic location of Colorado, United States Of America. The time period coverage is from 567 to -52 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

NOAA/WDS Paleoclimatology - Woodhouse et al. 2006 Updated Streamflow Reconstructions for the Upper Colorado River Basin - NCEI (other, 2022)
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 Climate Reconstruction. The data include parameters of climate reconstructions|tree ring with a geographic location of Colorado, United States Of America. The time period coverage is from 425 to -49 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA - Mark S. Raleigh; Hannah M. Bonner; Eric Smyth; Eric E. Small (other, 2022)
This dataset contains meteorology and snow observation data collected at sites in the southwestern Colorado Rocky Mountains during water years 2019-2021. Data collection had an emphasis on paired open-forest sites and included three forested elevations. In total, we present 270 snow pit observations, 4,019 snow depth measurements, and three years of meteorological forcing from two weather stations (one in a meadow, the other in an adjacent forest). The dataset is described in a forthcoming publication of the same name: A meteorology and snow dataset from adjacent forested and meadow sites at Crested Butte, CO, USA (Bonner et al., 2022). All snow observation and meteorological forcing data are available as both .nc and .mat files. Additionally, original digitized copies of snow pit observations are provided as .gsheet/.xlxs files. This dataset will continue to be updated, via this repository, as additional years of data are collected.

NOAA/WDS Paleoclimatology - Woodhouse 2003 Gunnison River Basin Snow Water Equivalent Reconstruction - NCEI (other, 2022)
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 Climate Reconstruction. The data include parameters of instrumental with a geographic location of Colorado, United States Of America. The time period coverage is from 381 to -49 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

Gravity Data - Bethany L Burton (other, 2022)
Surface electrical resistivity tomography, magnetic, and gravity surveys were conducted in July 2017 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin as part of a broader study of the role of bedrock groundwater in the hydrogeology of mineralized mountain watersheds. Ten gravity stations were acquired with a LaCoste and Romberg G-model relative gravimeter to map density variations. This data release includes principal facts for the gravity stations provided as digital data. Data fields are defined in the data dictionary.

Electrical Resistivity Tomography (ERT) Data - Bethany L Burton (other, 2022)
Surface geophysical surveys were conducted from 2016 to 2018 in the greater East River Watershed near Crested Butte Colorado with a focused effort in Redwell Basin as part of a broader study of the role of bedrock groundwater in the hydrogeology of mineralized mountain watersheds. Five electrical resistivity tomography (ERT) profiles were acquired within Redwell Basin and Brush Creek to map geologic structure at depths up to 40 meters, depending on the subsurface resistivity, using the Advanced Geosciences, Inc. SuperSting R8 resistivity meter. This data release includes the raw and processed resistivity data as well as inverted resistivity models. All are provided as digital data, and data fields for each file type are defined in [...]

Whole rock major, minor, and trace element geochemistry of the upper part of the Mount Emmons-Redwell porphyry molybdenum (Climax-type) deposit, Redwell Basin, Crested Butte, Colorado - Jeffrey L Mauk (other, 2022)
This U.S. Geological Survey (USGS) data release provides whole rock major, minor, and trace element geochemical data from the fluorine-rich Mount Emmons-Redwell porphyry molybdenum (Climax-type) deposit (Mt. Emmons-Redwell deposit), located approximately 5.6 km (3.5 mi) northwest of Crested Butte, Colorado. The Mt. Emmons-Redwell deposit partly underlies Redwell Basin on the northwest flank of Mount Emmons in the west-central region of the Colorado Mineral Belt. Mineralization of the Mt. Emmons-Redwell deposit occurs at depth as vein, veinlet, stockwork, and fracture-hosted Climax-type mineralization [308 Mt at 0.32% Mo (Galey, 1978; Guarnera and Anderson, 2007)], and is associated with the Mt. Emmons igneous complex. Samples were [...]

NOAA/WDS Paleoclimatology - Simic fire data from Old Monarch Pass High, Western Slope - IMPD USOMH001 - Manuel K. Lopez; Petar Z. Simic; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Antelope Hill, Western Slope - IMPD USAEH001 - Manuel K. Lopez; Petar Z. Simic; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

R code for Demographic consequences of changing environmental periodicity - Maria Paniw; Daniel T. Blumstein; Julien G. A. Martin; Eva Conquet; Kenneth B. Armitage (other, 2022)
These R scripts contain the code to replicate the analyses performed in Demographic consequences of changing environmental periodicity , Ecology. Vital-rate estimation We used the demographic data of each species to model periodic differences in vital rates for each life-cycle stage using generalized linear models and mixed models (GLMs for the deterministic vital rates in dewy pines and GLMMs for all other vital rates). More specifically, we tested for the effect of season on marmot and meerkat vital rates and of post-fire habitat states (time since fire, TSF) on dewy pine vital rates. For all three species, we estimated stage-specific survival and probability of transition to another stage using a binomial distribution, and reproductive output using a Poisson distribution. In addition, we estimated meerkat helper emigration probability and dewy-pine flowering probability using a binomial distribution. In addition to fixed effects, we incorporated year random effects in all appropriate models to model year-specific differences among season-specific average vital-rates (for marmots and meerkats) or among vital-rate averages (for dewy pines). For the meerkats and dewy pines, we also incorporated the fixed effect of density on vital rates. For each modeled vital rate, we first selected the best random-effect structure where appropriate (i.e., testing whether a random effect on the average vital rate and the slope between seasons outperformed a random effect on the average vital rate only). We then selected the best fixed-effect variables using the Akaike Information Criterion corrected for small sample size (AICc). When the difference in AICc values was not significant (dAICc<2), we picked the model with fewer parameters or the most biologically relevant. For models using a Poisson distribution, we tested for over- and under-dispersion in the best model and fitted over- and under-dispersed models with a quasi-Poisson distribution when necessary. Population projections We used the predictions of the vital-rate models described above to parameterize periodic matrix population models (MPMs) and project the population dynamics for 100 years according to two scenarios—control and perturbed—to assess the effect of changes in vital-rate periodicity on the stochastic growth rate (log λ S ), the variance of 100 annual growth rates (var(log λ )), and the probability of quasi-extinction ( p qext , i.e., the ratio of simulations leading to quasi-extinction out of 500 simulations, with a threshold set at 15% of the minimum observed abundance or number of reproductive individuals for the marmot and meerkat populations, and at 50% of the minimum observed aboveground or seed bank abundance for the dewy pines). Yellow-bellied marmot and meerkat (seasonal dynamics) For the meerkats and marmots, we projected population dynamics under changing patterns of vital rates for which the year random effect in the GLMM was applied both on the mean vital-rate estimate (i.e., the model intercept) and the difference between seasons. That is, for the marmots, we assessed the population responses to changes in the seasonal patterns of yearling and non-reproductive and reproductive adult survival. For the meerkats, we did so for subadult, helper, and dominant survival, helper emigration, transition from helper to dominant, and helper and dominant recruitment. We used the above models to predict season- and year-specific vital rates and parameterize periodic matrix population models (MPMs) for each season. We then simulated population dynamics by projecting MPMs representing low- (LS) and high-seasonality (HS) years for each of the vital rates. Half of all years in which a vital rate was estimated were considered as LS and the other half as HS. That is, for a given vital rate, we defined the threshold between LS and HS years as the 50th percentile of all year-specific absolute differences between seasons. In addition, we used a control scenario, where we projected the population dynamics using MPMs representing both high- and low-seasonality years indistinctly. We performed 500 simulations, each starting with the same population vector, and projected the population dynamics for 100 years. For each simulation, we randomly selected 100 years corresponding to each scenario (LS, HS, or control). In each step of the simulation, all vital rates were predicted based on the same randomly selected year. This allowed us to maintain within-year vital-rate correlation. The predicted vital rates were then used to build the corresponding period-specific MPM. Dewy pine (Multi-year habitat succession and periodicity in fire regimes) For dewy pines, we simulated two distinct types of fire regimes, each with two frequencies: (1) periodic burning occurring systematically every 15 or 30 years, and (2) stochastic fires occurring on average every 15 or 30 years. In all simulations, dewy pines transition deterministically through the first four post-fire states, TSF 0 to TSF 3 . Under periodic fires, once in the fifth state, TSF >3 , the population remains in that state until the next fire (15 or 30 years after the previous fire) and then transitions to TSF 0 . Under stochastic fires, the population can transition from TSF >3 to TSF 0 conditional on fire frequency ( p ). This is done using a Markov-chain approach. In addition to the different periodic patterns in fire regimes, we perturbed periodic patterns in habitat succession in natural heathlands by introducing an additional human disturbance (i.e., using vital rates from populations under a browsing-induced disturbance) first only in the years of the last post-fire state (TSF >3 ), and then increasingly in the previous states (i.e., in TSF 3 and >3 , in TSF 2, 3, and >3 , etc.) until all post-fire states of a natural population were perturbed. We used TSF-specific vital-rate predictions from our models and rates on seed-bank dynamics to build MPMs for each TSF. We used these MPMs to project population dynamics for 100 years using 500 simulations, in each iteration randomly sampling among MPMs describing one of the two natural populations. In addition, for each iteration in the stochastic post-fire state TSF >3 , we randomly sampled a year-specific MPM. We increased browsing pressure by replacing MPMs associated with natural habitat conditions by MPMs parameterized with vital rates estimated from human-disturbed populations, for any given TSF. We then compared scenarios of increasing anthropogenic pressures to the control scenario (i.e., natural populations with no browsing perturbation) in each fire regime. Population responses to vital-rate periodic patterns under density dependence For the meerkats and dewy pines, projections of population dynamics incorporated density dependence. That is, at each iteration of the simulations, population density was estimated and used to predict vital rates and parameterize an MPM from these predictions at the next iteration. We then compared density-dependent projections to ones where the density input during parameter estimation was fixed at constant average values obtained from the observed data. Analysis of the simulations results For all three systems and for each simulation, we recorded the stochastic growth rate log λ S . We also investigated the effect of changes in vital-rate patterns on the variance in 100 annual log λ , var(log λ ), and the quasi-extinction probability p qext . We checked the overlap of the distributions of each metric (i.e., the mean and the 2.5th and 97.5th percentiles) across the 500 simulations between two scenarios. We considered a metric to differ between scenarios when 95% of the distributions (i.e., between the 2.5th and 97.5th percentiles) did not overlap. Comparing population sensitivity to changes in periodic environmental patterns vs. stochastic environmental variation We compared our results from periodic population models to “classic” assessments of population fitness sensitivity to stochastic environmental variation. To do so, for marmots and meerkats, we computed the stochastic elasticities of the population growth rate to changes in the mean and standard deviation of vital rates. We then calculated the relative importance of the stochastic elasticity of the growth rate due to changes in the variability of vital rates compared to changes in their mean. For dewy pines, as the effects of periodic patterns consisted of changing the sequences of post-fire habitat states, we explored the link between the effects of human-induced disturbances in various post-fire habitat states and the role of these states in shaping population dynamics. We thus used the megamatrix approach to calculate the elasticity of the population growth rate to each post-fire habitat state.

NOAA/WDS Paleoclimatology - Simic fire data from Needle Creek, Western Slope - IMPD USNEC001 - Petar Z. Simic; Ellis Q. Margolis; Manuel K. Lopez; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH - Timothy Myers; Timothy Myers; James Wilczak; Timothy Djalalova; Bianca Adler; Laura Bianco; James Wilczak; Bianca Adler; Timothy Djalalova; Laura Bianco (other, 2022)
This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009). The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm -1 and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances. In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior. The file format is netcdf and the file naming conventions are NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf with yyyy: Year mm: Month dd: Day The time stamp of all data is in UTC. Selected basic variables are (many more provided): Name Dimension Unit base_time Single value Seconds (since 00 UTC 1 Jan 1970) time_offset Time Second (since base_time) hour Time Hours since 00UTC this day height Height km AGL temperature Time, Height C, temperature waterVapor Time, Height g/kg, water vapor mixing ratio theta Time, Height K, potential temperature pressure Time, Height hPa, pressure rh Time, Height %, relative humidity dewpt Time, Height C, dew point temperature thetae Time, Height K, equivalent potential temperature sigma_temperature Time, Height C, 1-sigma uncertainty temperature sigma_waterVapor Time, Height g/kg, 1-sigma uncertainty water vapor cdfs_temperature Time, Height cumulative degrees of freedom for temperature cdfs_waterVapor Time, Height cumulative degrees of freedom for water vapor Bold variables are the main retrieved profiles, from which the other variables are derived. Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model. Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. References: Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17. Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1. Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968. Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.

Valve Ultrastructure and Description of a new Luticola D. G. Mann (Bacillariophyceae) species from Colorado, USA - Jeremy Greifenstein; J. P. Kociolek; Rachel Shea (other, 2022)
This dataset contains the digitized treatments in Plazi based on the original journal article Shea, Rachel, Greifenstein, Jeremy, Kociolek, J. P. (2022): Valve Ultrastructure and Description of a new Luticola D. G. Mann (Bacillariophyceae) species from Colorado, USA. Phytotaxa 554 (3): 269-277, DOI: 10.11646/phytotaxa.554.3.5AbstractA new species of the diatom genus Luticola D.G.Mann is described from a wet meadow environment from Gunnison County, Colorado, based on light and scanning electron microscopy. The new species has rugose valves, with longitudinal interruptions in the striae. Unlike other species of the genus, L. coloradiana sp. nov. has a simple structure of the internal stigma, lacking the complex opening seen in all other species in the genus. The new species resembles L. plausibilis (Hustedt) D.G.Mann, described from Franz Josef Land in the Arctic, but differs in valve shape and density of the striae. Luticola coloradiana sp. nov. also resembles L. plausibiloides Metzeltin et al., a species described from subtropical Uruguay, but differs in striae density and the rugose nature of the valves. Further research is needed to assess if the similar species from the Arctic and subtropical South America also possess the unique stigma structure seen in L. coloradiana sp. nov.

Global Daily Surface Blue-sky Albedo Climatology and Land Cover Climatology Dataset from 20-year MODIS Products (500m) - Aolin Jia; Dongdong Wang; Yunyue Yu; Jingjing Peng; Shunlin Liang (other, 2022)
Only the first days of each month were uploaded to Zenodo due to the data storage limitation, and the full dataset is available at http://glass.umd.edu/albedo_clim/. Surface albedo plays a critical role in climate, hydrological, and biogeochemical modeling and weather forecasting. Therefore, precisely mapping surface albedo climatology globally is necessary to better parameterize environmental systems. We generated a new global surface blue-sky actual and snow-free albedo climatology dataset from 20-year MODIS products from the Google Earth Engine (GEE). The 500m global surface blue-sky daily albedo climatology dataset follows the basic MODIS product format and employed the sinusoidal projection. It includes historical and snow-free blue-sky albedo climatology data. For application convenience, the land cover climatology of MODIS product (MCD12Q1) is also generated and attached. The International Geosphere-Biosphere Programme (IGBP) and PFT classification climatology of MCD12Q1 since 2001 were also generated.

oysteiop/EvolDiv: Publication release (other, 2022)
Analyses of evolvability and population divergence

Western United States UCLA Daily Snow Reanalysis, Version 1 - Steve Margulis; Yufei Liu; Yilin Fang (other, 2022)

Sulfur x-ray absorption spectroscopy data from bedrock shale, soil, and floodplain sediment from the East River, Colorado watershed. - Sergio Carrero; Christian Dewey; Marco Keiluweit; P. S. Nico; Craig R. Anderson; H. R. Naughton; Patricia Fox (other, 2022)
This dataset includes sulfur x-ray absorption near edge spectroscopy (XANES) data collected on solid samples as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the speciation of sulfur and degree of shale weathering in solid samples across the watershed and their impact on riverine export of sulfate. Solid samples, including shale, hillslope soil, and floodplain sediment samples collected in 2016-2018, were analyzed by bulk XANES. Density fractions of hillslope soil, including a light fraction representing particulate organic matter and a heavy fraction representing minerals are also analyzed by bulk XANES. Micro-focused XANES on a weathered shale sample (PLM6, 2.7 m) was also performed. Sample locations are included in the 'sample meta data.csv' file, and XANES data for each sample type are included in csv files.

Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 R1 - S-C. Kao; P.E. Thornton; M.M. Thornton; Y. Wei; R. Shrestha (other, 2022)
This dataset provides Daymet Version 4 R1 data as gridded estimates of daily weather parameters for North America, Hawaii, and Puerto Rico. Daymet variables include the following parameters: minimum temperature, maximum temperature, precipitation, shortwave radiation, vapor pressure, snow water equivalent, and day length. The dataset covers the period from January 1, 1980, to December 31 (or December 30 in leap years) of the most recent full calendar year for the Continental North America and Hawaii spatial regions. Data for Puerto Rico is available starting in 1950. Each subsequent year is processed individually at the close of a calendar year. Daymet variables are provided as individual files, by variable and year, at a 1 km x 1 km spatial resolution and a daily temporal resolution. Areas of Hawaii and Puerto Rico are available as files separate from the continental North America. Data are in a North America Lambert Conformal Conic projection and are distributed in a standardized Climate and Forecast (CF)-compliant netCDF file format. In Version 4 R1, all 2020 and 2021 files were updated to improve predictions especially in high-latitude areas. It was found that input files used for deriving 2020 and 2021 data had, for a significant portion of Canadian weather stations, missing daily variable readings for the month of January. NCEI has corrected issues with the Environment Canada ingest feed which led to the missing readings. The revised 2020 and 2021 Daymet V4 R1 files were derived with new GHCNd inputs. Files outside of 2020 and 2021 have not changed from the previous V4 release.

climlab/climlab: Version 0.8.1 - Andrew Williams; Sebastian Meßlinger; Paul Gierz; Brian Rose; Ryan Abernathey; Xoviat; Chris Cardinale; Moritz Kreuzer (other, 2022)
A major refactor of the internals: all the Fortran code has been moved into external companion packages climlab-rrtmg, climlab-cam3-radiation, and climlab-emanuel-convection. Climlab is now (once again!) a pure Python package. Builds of these helper packages are available through conda-forge and will be automatically installed as dependencies by conda / mamba. The climlab source repo also moved to https://github.com/climlab/climlab There should be no breaking changes to the user-facing API. The major motivation for this change was to (vastly) simplify the development and testing of new-and-improved climlab internals (coming soon).

Data from the assessment of sediment-retention ponds near Delta, Colorado, 2019 - Rodney J Richards (other, 2022)
In 2019, soil samples were collected at selected sediment-retention ponds in the Gunnison Gorge National Conservation Area, near Delta, Colorado. This dataset includes pond location, pond characteristics, soil-moisture data, soil-chemistry data, and geophysical data. Pond locations were surveyed using real-time kinematic Global Navigation Satellite Systems (RTK-GNSS) methods and incorporated as a single shapefile in Ponds.zip. Pond characteristics were assessed, and observations were made for assigned surficial-geologic unit, vegetation, check-dam condition, check-dam height, water holding capacity, and stream condition entering and exiting the pond. Pond-characteristic data are incorporated as a single comma-separated values (.csv) file (PondInventory.csv). Soil moisture and chemistry data characterize soil moisture, specific conductance, pH, total dissolved solids, and chloride at selected depths in the soil profile. Soil moisture and chemistry data are presented in a .csv file (SoilData.csv). Geophysical surveys were conducted using a Geophex GEM-2, a hand-held multi-frequency electromagnetic induction sensor. The geophysical surveys were conducted to evaluate soil conductivity differences in and around pond locations and are presented as the unprocessed data from the instrument as a .csv file in GEM-2 data.zip.

NOAA/WDS Paleoclimatology - Simic fire data from North Powderhorn 2, Western Slope - IMPD USNPH001 - Ellis Q. Margolis; Petar Z. Simic; Manuel K. Lopez; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Iola Valley, Western Slope - IMPD USIAV001 - Manuel K. Lopez; Petar Z. Simic; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

Data-model files associated with the manuscript "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" - X. Chen; Pin Shuai; Dipankar Dwivedi; E. T. Coon; Utkarsh Mital (other, 2022)
This data package contains the model inputs and outputs used in "The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses" (Shuai et al., 2022 HESS). The data.zip file contains the data used to drive the model simulations. The model.zip file contains the XML input file for ATS. The notebook.zip file contains the Jupyter notebooks for pre- and post- processing model results. The figures.zip file contains the raw figures associated with the manuscript. Meteorological forcing plays a critical role in accurately simulating the watershed hydrological cycle. With the advancement of high-performance computing and the development of integrated watershed models, simulating the watershed hydrological cycle at high temporal (hourly to daily) and spatial resolution (10s of meters) has become efficient and computationally affordable. These hyperresolution watershed models require high resolution of meteorological forcing as model input to ensure the fidelity and accuracy of simulated responses. In this study, we utilized the Advanced Terrestrial Simulator (ATS), an integrated watershed model, to simulate surface and subsurface flow and land surface processes using unstructured meshes at the Coal Creek Watershed near Crested Butte (Colorado). We compared simulated watershed hydrologic responses including streamflow, and distributed variables such as evapotranspiration, snow water equivalent (SWE), and groundwater table driven by three publicly available, gridded meteorological forcings (GMFs) -- Daily Surface Weather and Climatological Summaries (Daymet), Parameter-elevation Regressions on Independent Slopes Model (PRISM), and North American Land Data Assimilation System (NLDAS). By comparing various spatial resolutions (ranging from 400 m to 4 km) of PRISM, the simulated streamflow only becomes marginally worse when spatial resolution of meteorological forcing is coarsened to 4 km (or 30% of the watershed area). However, the 4 km resolution has much worse performance than finer resolution in spatially distributed variables such as SWE. Using temporally disaggregated PRISM, we compared models forced by different temporal resolutions (hourly to daily), sub-daily resolution preserves the dynamic watershed responses (e.g., diurnal fluctuation of streamflow) that are absent in results forced by daily resolution. Conversely, the simulated streamflow shows better performance using daily resolution compared to that using sub-daily resolution. Our findings suggest that the choice of GMF and its spatiotemporal resolution depends on the quantity of interest and its spatial and temporal scale, which may have important implications on model calibration and watershed management decisions.

NOAA/WDS Paleoclimatology - Simic fire data from Yellow Pine Ridge South, Western Slope - IMPD USYPS001 - Ellis Q. Margolis; Petar Z. Simic; Manuel K. Lopez; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Old Monarch Pass Low, Western Slope - IMPD USOML001 - Ellis Q. Margolis; Manuel K. Lopez; Petar Z. Simic; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Sapinero Mesa, Western Slope - IMPD USSOM001 - Ellis Q. Margolis; Manuel K. Lopez; Petar Z. Simic; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

NOAA/WDS Paleoclimatology - Simic fire data from Meyer West, Western Slope - IMPD USMYW001 - Petar Z. Simic; Manuel K. Lopez; Ellis Q. Margolis; Jessica R. Young (other, 2022)
The historical role of fire in sagebrush (Artemisia tridentata) landscapes remains poorly understood, yet is important to inform management and conservation of obligate species such as the threatened Gunnison Sage-grouse (GUSG; Centrocercus minimus). We reconstructed fire histories from tree-ring fire scars at sagebrush–forest ecotones (10 sites, 111 trees) to better understand the role of fire in sagebrush landscapes of the Upper Gunnison Basin (UGB), Colorado, and how fire may have changed following Euro-American settlement. We assessed likely influences of historical fire by surveying plant composition and structure at 100 sagebrush sites with and without recent (2001–2020) fires. Tree-ring fire scars revealed a history of repeated low-severity fire at sagebrush–forest ecotones until 1892, followed by over a century without fire. Between 1684 and 1892, the mean fire interval (MFI) among sites averaged 49.6 years (ranging from 18.2 to 119 years). Fire over this period occurred synchronously at two or more sites on average every 23.6 years, potentially indicative of spread between sites. Most (70%) of the historical fires burned in the early growing season, consistent with times of strong wind. Recent burns exhibited reductions in sagebrush cover (5% vs. 25% in unburned sites) and concomitant increases in herbaceous cover (55% vs. 40%). These differences declined over time but persisted for at least two decades. Burned sites were dominated by native perennial grasses, forbs, and resprouting shrub species. Historically, such openings may have served as seasonal GUSG habitat. Our results indicate that parts of the UGB sagebrush landscapes were characterized historically by frequent fire and dynamic vegetation mosaics that included open, grassy patches. These findings support the use of prescribed fire to restore and maintain this ecological process and vegetation heterogeneity. However, the contemporary context for fire has changed and now includes substantially reduced, Endangered Species Act (ESA)-listed GUSG populations, increased risk of non-native plant invasion, and climate warming. These circumstances highlight new risks, information needs, and opportunities for key knowledge co-production via management–research partnerships.

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

QA/QC-ed Groundwater Level Time Series in PLM-1 and PLM-6 Monitoring Wells, East River, Colorado - Boris Faybishenko; Roelof Versteeg (ess_dive, 2022)
Performed Quality Assurance and Quality Control (QA/QC) analysis of measured groundwater levels in monitoring wells PLM-1 and PLM-6, including identification and flagging of duplicated values of timestamps, gap filling of missing timestamps and water levels, removal of abnormal/bad and outliers of measured water levels. The purpose of the QA/QC analysis was to test the application of different QA/QC methods and the development of regular (1-hour and 1-day) time series datasets, which can be served as benchmark for testing other QA/QC techniques, and will be applicable for ecohydrological modeling. The package includes a Readme file, two R Markdown html files, a series of 8 csv files: two input files of irregular measured groundwater levels, two output files with QA/QC flagging of original data, and four QA/QC-ed regular time series datasets.

ParFlow-CLM model simulation files for Maina et al., Journal of Hydrology, 2022 (ess_dive, 2022)
This dataset contains the files to run a ParFlow-CLM integrated hydrologic model simulation for Maina et al., HESS, 2022. It also contains the associated daily pressure output of those simulations. Simulations are for a hillslope-similarity approach based on seasonal groundwater changes in the East River, Colorado. Comparisons are made to different clustering, or functional zonation approaches. We assess the ability of these clustering approaches to identify and categorize hillslopes with similar static characteristics, hydroclimate, land surface processes, and subsurface dynamics in a mountainous watershed. ParFlow binary files (.pfb) are model specific, and can be modified/viewed with conversion to .sa, .silo, or .vtk file formats. ParFlow input scripts are in the .tcl file format. Associated post-processing scripts are included as .f90 files.

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO (other, 2022)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2020. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

Occurrence of plants in plots along the Gunnison River, Colorado, 1990-2017 - Abigail M Eurich; Gregor T Auble; Michael L Scott; Polly P Gibson; Jonathan M Friedman (other, 2022)
In 1990 the US Geological Survey Fort Collins Science Center and National Park Service Water Resources Division established 133 permanent 1meter (m) X 2 m plots along the Gunnison River near Warner Point in Black Canyon of the Gunnison National Park. A one-dimensional hydraulic model was calibrated to determine the inundation necessary to inundate each plot, and this can be combined with daily flow data to determine the inundation duration or percentage of time a plot is under water. Occurrence of all plant species in these plots was determined in late July of 1990, 1994, 2001, 2006 and 2013. This data set includes 3 csv files of plot data. One file gives characteristics of plots, including plot name, inundating discharge, mean number of species occurring in the plot, mean vegetative cover, Mean Sorensen Index and mean inundation duration with flows exponentially weighted for recency using a half-life of 1.5 years. A second file gives characteristics of all the plant species that have occurred in the plots including scientific and common names, plant family, life span, occurrence of rhizomes, height, total occurrences across years, median inundating discharge of plots containing the plant, half-life for exponential weighting of flows selected by regressions relating species occurrence to flow in each sample year, slope, probability and variance of half-life regressions, and the optimum inundation duration, probability and variance of logistic regressions relating occurrence of species in all sample years to flow. A third file documents occurrence of species in plots in all five sample years as well as the inundation duration, number of species and total vegetative cover in each plot in each sample year. The 1990 data were interpreted by Auble et al. (1991 and 1994), and all five years of data were interpreted by Friedman et al. (2022). This data set also includes a library of repeat views of the river bars containing the plots photographed from the same set of 21 camera locations in 1990, 1994, 2001, 2006, 2013 and 2017, a total of 117 images. Finally, this data set also includes a csv file listing the years each view was photographed and the approximate latitude and longitude of each camera location.

Bumble bee occurrences of North America from 1805–2020 - Leithen M'Gonigle; Laura Melissa Guzman; Hanna Jackson; Lora Morandin; Sarah Johnson; L. L. Richardson (other, 2022)
Bumble bee occurrence data used for "Climate change winners and losers among North American bumble bees": These data comprise 649 407 specimen records from 48 species and spans 1805–2020. These records have been compiled from a variety of collections and sources with reputable origin. Data contributors are listed in: https://www.leifrichardson.org/bbna.html

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO - James H Den Uyl; M. L. Mullins; M. L. Mullins; Emily H Mooney; James H Den Uyl; M. L. Mullins; Emily H Mooney; James H Den Uyl; M. L. Mullins; James H Den Uyl; Emily H Mooney; Emily H Mooney (other, 2022)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2022. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO (other, 2022)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2020. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

Long-term changes in flowering synchrony reflect climatic changes across an elevational gradient - de Manincor Natasha; Fisogni Alessandro; Rafferty Nicole E.; Bertelsen C. David (other, 2021)
These are the data with the accompanying R code used in the article Long-term changes in flowering synchrony reflect climatic changes across an elevational gradient , by Fisogni A, de Manincor N, Bertelsen CD, and Rafferty NE. We provide the raw data on flowering phenology, temperature and precipitation data in the study area, and overlap estimates used to evaluate temporal changes within and between elevations and their relationship with changing climatic variables. Data are .txt files with tab separated values. The raw dataset was created by C. David Bertelsen from field observations performed from 1984 to 2019 along a fixed transect in the Santa Catalina Mountains, Arizona, USA.

1 m Resolution Canopy Height Estimates for the Upper Gunnison Basin derived from 2015 and 2019 LiDAR Data - Ian K Breckheimer (s3, 2021)
This is a map of vegetation canopy height above the ground for the Upper Gunnison River Basin based on 2015 and 2019 LiDAR data. Height is measured in meters. This dataset was generated from the original point clouds using a pit-free algorithm implemented in the R package lidR. The ground-classified points were first used to generate a bare-earth surface, which was subtracted from the point cloud elevation. The canopy surface was then modeled with a pitfree algorithm using the ground-normalized point cloud. Topographic artifacts and structures were removed from the dataset by clipping building footprints present in the Bing buildings dataset.

East River Surface and Pore Water FTICR-MS Data Associated with “Implications of sample treatment on characterization of the riverine environmental metabolome” (ess_dive, 2021)
Surface and pore water samples were collected from distributed locations around Meander A in East River (Crested Butte, CO, USA) during the summer of 2018. This dataset consists of the characterization of dissolved organic matter using 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS; in .xml format) analyzed through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory) using two different sample pre-treatment methods (solid phase extraction, no solid phase extraction) and two electrospray ionization methods (ESI-positive, ESI-negative) to identify the impacts of these choices on the final dataset. This study aimed to understand how the different sample processing methods influence the resulting FTICR-MS dataset. Any published work that utilizes data presented in this dataset should cite the dataset using the DOI number. If using the dataset, please acknowledge the U.S. Department of Energy (DOE) Office of Science (BER) Subsurface Biogeochemical Research (SBR) program, the Environmental Molecular Sciences Laboratory (EMSL), and Worldwide Hydrobiogeochemical Observation Network for Dynamic River Systems (WHONDRS). The publication DOI will be added to this abstract when available.

Dataset for temporal influences on selenium partitioning, trophic transfer, and exposure in a major U.S. river - Haldre S. Rogers; James J Roberts; Jessica (Contractor) E Brandt; Patricia A Nease; Craig A Stricker; Travis S Schmidt (other, 2021)
The trace element selenium is an essential element with a narrow window between concentrations needed to support life and those that cause toxicity to egg laying organisms. Selenium bioaccumulation in aquatic organisms is primarily the result of trophic transfer through food webs and is poorly predicted by dissolved concentrations in freshwater bodies. To better understand the hydrologic and biological dynamics that control selenium accumulation into fishes of the Lower Gunnison River Basin (Colorado), ecosystem scale selenium accumulation models were developed from data collected between June 2015 and October 2016.

Hyporheic, Floodplain, and Surface Water (on Floodplain and River) Geochemical Datasets, and Shapefiles on Meander C at the East River, Colorado. (ess_dive, 2021)
The purpose of this dataset was to sample surface water and groundwater geochemistry at random locations in the East River, Colorado to provide a broad spatial dataset of geochemistry. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. We conducted this random sampling at multiple time points including during the snowmelt flooding stage, during peak flood, during the falling limb of flood, and during baseflow. This datasets contain many datafiles. We include flat text files of surface water geochemistry including surface water on the floodplain and surface water in the river, geochemistry of floodplain pore-water samples, and geochemistry of hyporheic pore-water samples. All datasets are also included as shapefiles so location geochemistry is co-located with sampling locations and sampling depths. This datasets also has the locations of small channels, and hollows that are on top of floodplain Meander C. These small channels funnel surface water from the hillslope through the floodplain and out to the river. All text datasets can be analyzed with software such as Excel or R. All shapefiles can be viewed with freely available R packages such as Leaflet.

Frost sensitivity of leaves and flowers of subalpine plants is related to tissue type and phenology - Paul J. CaraDonna; Justin A. Bain (other, 2021)
Harsh abiotic conditions–such as low temperatures that lead to spring and summer frost events in high-elevation and high-latitude ecosystems–can have strong negative consequences for plant growth, survival, and reproduction. Despite the predicted increase in episodic frost events under continued climate change in some ecosystems, our general understanding of the factors associated with frost sensitivity of reproductive and vegetative plant structures in natural plant communities is limited. The timing of growth and reproduction may be an important strategy by which plants can avoid frost. In this study, we experimentally investigated the frost sensitivity of eight long-lived perennial herbaceous plant species from a subalpine ecosystem in the Colorado Rocky Mountains, USA. The study taxa represent four congeneric pairs from four flowering plants families; within each pair is a species with early and late growth and reproductive phenology. Thus, we control for evolutionary history–and therefore additional traits shared through common ancestry– to some degree, while examining the influence of phenology on frost sensitivity. Specifically, we compared frost sensitivity of vegetative and reproductive structures for each species, and asked whether frost sensitivity was similar between species within congeneric pairs, or instead was related to phenology (i.e. differences in the timing of growth and reproduction). For most species (6 of 8), flowers were more sensitive to frost than leaves. Within most congeneric pairs (3 of 4), the leaves of species with later phenology were significantly more sensitive to frost than the leaves of species with earlier phenology. For flowers, the later-flowering species were more sensitive in two of the four congeneric pairs. Synthesis: This study contributes to our general understanding of factors related to interspecific differences in plant sensitivity to episodic frost events of naturally occurring species. The increased frost sensitivity of reproductive structures compared to vegetative structures may be a widespread pattern for long-lived perennial plants. Furthermore, we find evidence for a trade-off between phenology and frost sensitivity, whereby species with later phenology exhibit higher frost sensitivity compared to species with earlier phenology. These results have implications for plant populations, species interactions, and ecological communities.

Multi-year census of arthropod abundance on the plant Ligusticum porteri near Gothic, CO (other, 2021)
The purpose of this study was to track year-to-year variation in aphid abundance on the host plant Ligusticum porteri (Apiaceae). We censused arthropod abundance on the flowering stalks of L. porteri weekly in June-August from 2012 to 2020. The censuses took place in ten L. porteri populations near the Rocky Mountain Biological Laboratory in Gothic, CO. While the same populations were used across years, we randomly selected ten flowering plants in each population in each year (N = 100 plants per year). Observations focused on colonization by the aphid Aphis asclepiadis, its mutualist ants, and natural enemies. When found, we counted other arthropods as well, identifying them to Family or Order in the field. We also counted the number of host plant flowering stalks and inflorescences (umbel). In 2016, we began tracking flowering phenology using a numerical score (0-8). To track senescence, we used a qualitative score (TB=turning brown and AB=all brown) for terminal and primary umbels.

3 m Resolution Digital Elevation Model for the Upper Gunnison Domain derived from 2015 and 2019 LiDAR Data - Ian K Breckheimer (s3, 2021)
This is a 3 m resolution Digital Elevation Model (DEM) for the Upper Gunnison River domain derived from public LiDAR datasets. The primary data source was a 2019 LiDAR collection for Gunnison County. A small portion of the upper basin was not covered by this dataset, and for those areas, data from a 2015 acquisition was substituted. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. This dataset is distinct from, but covers overlapping geographic areas as, LiDAR datasets collected as part of the 2018 NEON Aerial Observation Platform campaign in the Upper East River (Goulden T ; Hass B ; Brodie E ; Chadwick K D ; Falco N ; Maher K ; Wainwright H ; Williams K (2020): NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters. Watershed Function SFA. doi:10.15485/1617203), and the 2015 LiDAR scan contracted by the Department of Energy for the East River and Washington Gulch (Wainwright H ; Williams K (2017): LiDAR collection in August 2015 over the East River Watershed, Colorado, USA. Watershed Function SFA. doi:10.21952/WTR/1412542). The current dataset has a higher point density (9.4 points / square meter) than the 2018 NEON AOP data, and covers a larger geographic area than the 2015 DOE data.

ammilten/ERTFaultMonteCarlo v1.0-zenodo - Alex Miltenberger (other, 2021)
No description provided.

1 m Resolution Digital Elevation Model for the Upper Gunnison Domain derived from 2015 and 2019 LiDAR Data - Ian K Breckheimer (s3, 2021)
This is a 1 m resolution Digital Elevation Model (DEM) for the Upper Gunnison River domain derived from public LiDAR datasets. The primary data source was a 2019 LiDAR collection for Gunnison County. A small portion of the upper basin was not covered by this dataset, and for those areas, data from a 2015 acquisition was substituted. Source datasets include August 2015 and August 2019 discrete-return LiDAR point clouds collected by Quantum Geospatial for terrain mapping purposes on behalf of the Colorado Hazard Mapping Program and the Colorado Water Conservation Board. Both datasets adhere to the USGS QL2 quality standard. This dataset is distinct from, but covers overlapping geographic areas as, LiDAR datasets collected as part of the 2018 NEON Aerial Observation Platform campaign in the Upper East River (Goulden T ; Hass B ; Brodie E ; Chadwick K D ; Falco N ; Maher K ; Wainwright H ; Williams K (2020): NEON AOP Survey of Upper East River CO Watersheds: LAZ Files, LiDAR Surface Elevation, Terrain Elevation, and Canopy Height Rasters. Watershed Function SFA. doi:10.15485/1617203), and the 2015 LiDAR scan contracted by the Department of Energy for the East River and Washington Gulch (Wainwright H ; Williams K (2017): LiDAR collection in August 2015 over the East River Watershed, Colorado, USA. Watershed Function SFA. doi:10.21952/WTR/1412542). The current dataset has a higher point density (9.4 points / square meter) than the 2018 NEON AOP data, and covers a larger geographic area than the 2015 DOE data.

MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 61 - George A. Riggs; Dorothy K. Hall (other, 2021)

Does environmental heterogeneity drive functional trait variation? A test in montane and alpine meadows - Stark; Jordan (other, 2021)
While community-weighted means of plant traits have been linked to mean environmental conditions at large scales, the drivers of trait variation within communities are not well understood. Local environmental heterogeneity (such as microclimate variability), in addition to mean environmental conditions, may decrease the strength of environmental filtering and explain why communities support different amounts of trait variation. Here, we assess two hypotheses: first, that more heterogeneous local environments and second, that less extreme environments, should support a broader range of plant strategies and thus higher trait variation. We quantified drivers of trait variation across a range of environmental conditions and spatial scales ranging from sub-meter to tens of kilometers in montane and alpine plant communities. We found that, within communities, both environmental heterogeneity and environmental means are drivers of trait variation. However, the importance of each environmental factor varied depending on the trait. Our results indicate that larger-scale trait-climate linkages that hold across communities also apply at small spatial scales, suggesting that microclimate variation within communities is a key driver of community functional diversity. Microclimatic variation provides a potential mechanism for helping to maintaining diversity in local communities and also suggests that small-scale environmental heterogeneity should be measured as a better predictor of functional diversity.

Environmental tracer data from surface water and groundwater samples collected in Redwell Basin near Crested Butte, Colorado, 2017-2019 - Andrew H Manning (other, 2021)
This dataset contains environmental tracer data from surface water and groundwater samples collected by the U.S. Geological Survey in Redwell Basin, an alpine watershed in the Elk Mountains near the town of Crested Butte, Colorado. The basin is underlain by interbedded shale and sandstone that have been variably hydrothermally altered and silicified by local magmatic intrusions. Samples were collected from 2017 to 2019 from bedrock monitoring wells completed at multiple depths, shallow hand-installed piezometers and mini boreholes, springs, mine adits, and streams. The tracer data include stable isotopes of water (oxygen-18 and deuterium), tritium, dissolved noble gases (He, Ne, Ar, Kr, Xe, and helium-3/helium-4 ratio), sulfur hexafluoride, and other dissolved atmospheric gases.

Data for Context-dependent biotic interactions control plant abundance across altitudinal environmental gradients, 2014, 2016, Colorado, USA. - Stephanie N. Kivlin; Melanie R Kazenel; Joshua S. Lynn; Jennifer A. Rudgers (other, 2021)
Many biotic interactions influence community structure, yet most distribution models for plants have focused on plant competition or used only abiotic variables to predict plant abundance. Furthermore, biotic interactions are commonly context-dependent across abiotic gradients. For example, plant-plant interactions can grade from competition to facilitation over temperature gradients. We used a hierarchical Bayesian framework to predict the abundances of 12 plant species across a mountain landscape and test hypotheses on the context-dependency of biotic interactions over abiotic gradients. We combined field-based estimates of six biotic interactions (foliar herbivory and pathogen damage, fungal root colonization, fossorial mammal disturbance, plant cover, and plant diversity) with abiotic data on climate and soil depth, nutrients, and moisture. All biotic interactions were significantly context-dependent along temperature gradients. Results supported the stress gradient hypothesis: As abiotic stress increased, the strength or direction of the relationship between biotic variables and plant abundance generally switched from negative (suggesting suppressed plant abundance) to positive (suggesting facilitation/mutualism). For half of the species, plant cover was the best predictor of abundance, suggesting that the prior focus on plant-plant interactions is well-justified. Explicitly incorporating the context-dependency of biotic interactions generated novel hypotheses about drivers of plant abundance across abiotic gradients and may improve the accuracy of niche models.

Mammalian herbivores restrict the altitudinal range limits of three alpine grass species (transplant and herbivore exclusion experiment and demographic data from natural populations), West Elk Mountains, Colorado, USA 2015-2018 (other, 2021)
Though rarely experimentally tested, biotic interactions have long been hypothesized to limit low-elevation range boundaries of species. We tested the effects of herbivory on three alpine-restricted plant species by transplanting plants below (novel), at the edge (limit), or in the center (core) of their current elevational range and factorially fencing-out above- and belowground mammals in the West Elk Mountains, Colorado, USA from 2015-2018. Herbivore damage was greater in range limit and novel habitats than in range cores. Exclosures increased plant biomass and reproduction more in novel habitats than in range cores, suggesting demographic costs of novel interactions with herbivores. We then used demographic models to project population growth rates, which increased 5-20% more under herbivore exclosure at range limit and novel sites than in core habitats. Our results identify mammalian herbivores as key drivers of the low-elevation range limits of alpine plants and indicate that upward encroachment of herbivores could trigger local extinctions by depressing plant population growth.

Microclimate observations associated with snowmelt experiment gradient sites, East River, Colorado, 2017 to 2020 (ess_dive, 2021)
The timing of snowmelt in mountain systems is a main driver of vegetation phenology and production, as well as recharge of soil moisture and ground water. Decreases in maximum snowpack and warmer spring temperatures have led to a higher frequency of early snowmelt. This study combines a natural elevation gradient with an experimental manipulation of snowmelt timing to assess the effect of earlier melt on hillslope and watershed ecohydrological processes. This data package includes csv files with plot and sub-plot level observations of soil temperature (SM_ST.csv), soil moisture (SM_SWC.csv), air temperature (SM_AirT.csv), and relative humidity (SM_rH.csv). The date range of this dataset includes a pre-treatment year (2017), two treatment years (2018 and 2019), and a post-treatment year (2020). Location IDs associated with this data package are: ER-LM, WG-UM, WG-LS, ER-US, and XX-AL. Related data packages include: “Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018” and “Sensor-based phenology from snowmelt experiment gradient, East River, Colorado, 2017 to 2020”

Dissolved-Selenium Concentrations and Loads in the Lower Gunnison River Basin, Colorado, as Part of the Selenium Management Program (ver. 5.0, September 2025) - Patrick C Longley; Joel W Homan; Mark F Henneberg (other, 2021)
The U.S. Geological Survey (USGS), in cooperation with the Bureau of Reclamation, completed a review of dissolved selenium data collected from the Selenium Management Program network during each water year (WY) in the lower Gunnison River Basin, in western Colorado. The data tables include dissolved-selenium concentrations, selected streamflow data, and computed loads as well as dissolved-selenium regression model calibration and output tables. Concentration and streamflow data are compiled from the USGS National Water Information System database. Tables include monitoring location identification information, data collection time frames, concentration data, as well as calculations of mean, median, select percentiles, and percentage reduction in loads. Tables are organized by each year, with a zipped folder for WY 2018 (October 1, 2017 through September 30, 2018), WY 2019 (October 1, 2018 through September 30, 2019), WY 2020 (October 1, 2019 through September 30, 2020), WY 2021 (October 1, 2020 through September 30, 2021), WY 2022 (October 1, 2021 through September 30, 2022), WY 2023 (October 1, 2022 through September 30, 2023), and WY 2024 (October 1, 2023 through September 30, 2024). Within each zipped folder is a metadata .xml file describing each comma-separated value (.csv) data file contained therein. The methods used to complete this review are available in Henneberg (2018) and Mayo and Leib (2012). Version 5.0 contains additional zipped folder for WY2024, which was not included in the original data release. Although previous versions are available from the author, all records in previous versions can be found in version 5.0. First posted - April 23, 2021 (available from author) Revised - September 2, 2022 (version 2.0) Revised - August 8, 2023 (version 3.0) Revised - March 4, 2025 (version 4.0) Revised - September 24, 2025 (version 5.0)

The effect of demographic correlations on the stochastic population dynamics of perennial plants - Aldo; Compagnoni (other, 2021)
Understanding the influence of environmental variability on population dynamics is a fundamental goal of ecology. Theory suggests that, for populations in variable environments, temporal correlations between demographic vital rates (e.g., growth, survival, reproduction) can increase (if positive) or decrease (if negative) the variability of year-to-year population growth. Because this variability generally decreases long-term population viability, vital rate correlations may importantly affect population dynamics in stochastic environments. Despite long-standing theoretical interest, it is unclear whether vital rate correlations are common in nature, whether their directions are predominantly negative or positive, and whether they are of sufficient magnitude to warrant broad consideration in studies of stochastic population dynamics. We used long-term demographic data for three perennial plant species, hierarchical Bayesian parameterization of population projection models, and stochastic simulations to address the following questions: (1) What are the sign, magnitude, and uncertainty of temporal correlations between vital rates? (2) How do specific pairwise correlations affect the year-to-year variability of population growth? (3) Does the net effect of all vital rate correlations increase or decrease year-to-year variability? (4) What is the net effect of vital rate correlations on the long-term stochastic population growth rate (λS)? We found only four moderate to strong correlations, both positive and negative in sign, across all species and vital rate pairs; otherwise, correlations were generally weak in magnitude and variable in sign. The net effect of vital rate correlations ranged from a slight decrease to an increase in the year-to-year variability of population growth, with average changes in variance ranging from -1% to +22%. However, vital rate correlations caused virtually no change in the estimates of λS (mean effects ranging from -0.01% to +0.17%). Therefore, the proportional changes in the variance of population growth caused by demographic correlations were too small on an absolute scale to importantly affect population growth and viability. We conclude that in our three focal populations and perhaps more generally, vital rate correlations have little effect on stochastic population dynamics. This may be good news for population ecologists, because estimating vital rate correlations and incorporating them into population models can be data-intensive and technically challenging.

East River Watershed Stable Water Isotope Data in Precipitation, Snowpack and Snowmelt 2016-2020 (ess_dive, 2021)
Stable water isotopes (d18O, d2H and d-excess) are important tracers in hydrologic research to understand water partitioning between vegetation, groundwater, and runoff but are rarely applied to large watersheds with persistent snowpack and complex topopgraphy. Data were collected for the Lawrence Berkeley National Laboratory Watershed Function Science Focus Area supported by the U.S Department of Energy in the East River, CO Hydrologic Unit Code (140200010) with limited data also collected in adjacent watersheds Ohio Creek and Taylor River. Data are provided in csv and includes isotopic information for precipitation (years 2014-2016), snowmelt (years 2016-2017) and snowpits (years 2016-2020). Snowpit data contain depth resolved information at 10 cm intervals for density, snow water equivalent (SWE) and stable water isotopes. Bulk isotopic data for 86 snowpits contain depth, SWE, density and SWE-weighted isotope values. Sampling locations and elevations are provided within the data files, while kmz files are provided to view sampling locations using Google Earth software.

Habitat preference of an herbivore shapes the habitat distribution of its host plant - Gloss Parris T; Noah K Whiteman; Humphrey Nicolas M; Frazier Jimmy; Joseph; Affeldt III; Lee Andrew D; Alexandre (other, 2021)
Plant distributions can be limited by habitat-biased herbivory, but the proximate causes of such biases are rarely known. Distinguishing plant-centric from herbivore-centric mechanisms driving differential herbivory between habitats is difficult without experimental manipulation of both plants and herbivores. Here, we tested alternative hypotheses driving habitat-biased herbivory in bittercress (Cardamine cordifolia), which is more abundant under the shade of shrubs and trees (shade) than in nearby meadows (sun) where herbivory is intense from the specialist fly Scaptomyza nigrita. This system has served as a textbook example of habitat-biased herbivory driving a plant's distribution across an ecotone, but the proximate mechanisms underlying differential herbivory are still unclear. First, we found that higher S. nigrita herbivory in sun habitats contrasts sharply with their preference to attack plants from shade habitats in laboratory-choice experiments. Second, S. nigrita strongly preferred leaves in simulated sun over simulated shade habitats, regardless of plant source habitat. Thus, herbivore preference for brighter, warmer habitats overrides their preference for more palatable shade plants. This promotes the sun-biased herbivore pressure that drives the distribution of bittercress into shade habitats.

Snow Depth on April 7th 2019 for Upper East River Derived from Airborne Snow Observatory Data - Thomas Painter (s3, 2021)
This is a 3m map of snow depth derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset has been clipped and resampled to the standard 3m SDP grid, and is derived directly from: Painter, T. 2018. ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/KIE9QNVG7HP0. Small areas outside of the UER region, but within the extent of this map had missing data. These gaps were filled with linear regression relating 2018 and 2019 ASO snow depths (where one map was available), or regressions related snow depth to elevation (where neither 2018 nor 2019 data was available). Peak snow depths in 2018 were near the 80th percentile of the long-term average estimated from the Butte SNOTEL station.

Rainbow trout diet and invertebrate drift data from 2012-2015 for the Colorado River, Grand Canyon, Arizona (other, 2021)
These data were compiled to explore the foraging ecology of Rainbow Trout (Oncorhynchus mykiss) in the Colorado River, Grand Canyon, Arizona. These data represent samples characterizing the availability of drifting invertebrate prey (hereafter, drift) and use of these invertebrate prey by rainbow trout determined by gut samples (hereafter, diets). Drift and diet sampling occurred in five distinct reaches downstream of Glen Canyon Dam (river kilometer (rkm) from the dam): (I) rkm 16.3-21.7; (II) rkm 52.8-58.3; (III) rkm 86.6-91.9; (IV) rkm 122.0-123.6; and (V) rkm 127.1-129.6. Samples were taken on 12 trips in April, July, September and January from April 2012 through January 2015. The aquatic prey base consists of 5 primary taxa; two [...]

Digital Data from Mineral Investigation of Sangre de Cristo Wilderness Study Area, Alamosa, Custer, Fremont, Huerfano, and Saguache Counties, Colorado, USA (other, 2021)
This Data Release provides tabular and geospatial data digitized by the U.S. Geological Survey (USGS) from a U.S. Bureau of Mines (USBoM) report titled Mineral Investigation of Sangre de Cristo Wilderness Study Area, Alamosa, Custer, Fremont, Huerfano, and Saguache Counties, Colorado. The original preliminary paper report, numbered Mineral Land Assessment 65-83 (MLA 65-83; Ellis and others, 1983), presented the results of a mineral resource potential survey of areas in and around the Sangre de Cristo Range flanking the northeastern extent of San Luis Valley in south-central Colorado. Included in this Data Release are georeferenced scans of the regional plate maps, digitized sample site locations, analytical chemistry, and structural [...]

Early snowmelt and warming experiments to study plant phenology - Diana K Jerome; William K. Petry; Amy M. Iler; Kailen A. Mooney (other, 2021)
Phenology - the timing of life events - determines how a species’ life cycle aligns with the abiotic and biotic environment, however, climate change has altered the environmental cues organisms use to track climate leading to shifts in phenology. In high latitude environments, phenological shifts in plants are associated with both temperature and the timing of snowmelt, but the mechanism underlying the effect of snowmelt on phenology remains unclear. Here we aim to disentangle the effects of experimental warming and earlier snowmelt on the phenology of three long lived perennial wildflowers. In the summer of 2019, we factorially crossed passive warming with early snowmelt timing within a subalpine plant community in the Colorado Rocky Mountains at the Rocky Mountain Biological Laboratory to understand the individual effects of these aspects of climate change.

Winter Travel Time from Crested Butte for the Upper East River Domain - Ian K Breckheimer (s3, 2021)
This map represents the estimated on-road and off-road travel time in minutes from Crested Butte via the fastest travel means available (snowmobiles excluded). Estimates apply for winter (snow-on) conditions. This map was generated with the cost distance GRASS GIS module (r.cost) using estimated travel speeds (in minutes / pixel) as the cost function. Corresponding speeds in km / hr are below: Highway: 80 kph Paved road: 55 km kph Dirt / 4wd road: 5 kph Trail: 5 kph Off-trail in meadow/open : flat 3 kph > 45% slope 0.01 kph Off-trail in forest: flat 2 kph > 45% slope 0.01 kpg Surface water: 0.01 km/hr

Summer Travel Time from Gothic for the Upper East River Domain - Ian K Breckheimer (s3, 2021)
This map represents the estimated on-road and off-road travel time in minutes from Crested Butte via the fastest travel means available (snowmobiles excluded). Estimates apply for summer (snow-off) conditions after all roads have been opened to vehicle traffic. This map was generated with the cost distance GRASS GIS module (r.cost) using estimated travel speeds (in minutes / pixel) as the cost function. Corresponding speeds in km / hr are below: Highway: 80 kph, Paved road: 55 km kph Dirt / 4wd road: 15 kph Trail: 5 kph Off-trail in meadow/open : flat 3 kph > 45% slope 0.01 kph Off-trail in forest: flat 2 kph > 45% slope 0.01 kpg Surface water: 0.01 km/hr

Genome-resolved metagenomics and metatranscriptomics of microbial communities in three meander-bound floodplain soils along the East River, Colorado. (ess_dive, 2021)
This dataset comprises paired environmental and genomic data for soil samples collected across meander-bound floodplains G (ERMG), L (ERML) and Z (ERMZ). A total of 96 soil samples (32 per floodplain) from the ~ 10-25 cm layer were collected in September 2015, during base flow conditions. An additional 32 soil samples were collected from ERML in September 2016, including three samples from a depth just above the water table. A subset of samples (14) collected in 2016 were used for metatranscriptomics as well as metagenomics. The data package includes: 1) A spread sheet with sample names, location and depth by meander-bound floodplain. 2) Total organic carbon (TOC), total inorganic carbon (TIC), total carbon (TC = TOC + TIC), total nitrogen (TN), and the TOC to TN ratio (TOC:N) from 2015 subsamples. 3) Soil chemistry for 2016 subsamples. 4) A spread sheet with elevation, Easting, Northing, and topographic position index (TPI) for each site sampled in 2015. 5) A kml file, which can be viewed in Google Earth, with the locations of the 96 soil sampling sites along the East River Watershed Floodplain in Colorado. Metagenomics and metatranscriptomics data, as well as reconstructed genomes, are available elsewhere (see data package methods and bioRxiv preprint for links to the data). 6) Two sets of reconstructed genomes with overlapping members (see bioRxiv preprint for description and associated data). Metagenomes and metatranscriptomes are available elsewhere (see data package methods and bioRxiv preprint). To view tar.gz files, the following software can be used: https://www.gnu.org/software/tar/, https://www.7-zip.org/.

Drone ortho basemap of the Gothic Townsite, May 25th 2019 - Ian K Breckheimer (s3, 2021)
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in sunny conditions on May 25th 2019, collecting RGB images using the built-in camera on a DJI Mavic 2 Pro. The raw imagery had an approximately 3cm ground sample distance (GSD). Images were processed in Agisoft Metashape Software (version 1.6) to derive a georeferenced dense point cloud and 3D model. Images were orthorectified and combined into a mosaic using the 3D model. This dataset has been georeferenced using 14 ground-control points, but has not been radiometrically calibrated.

Drone ortho basemap of the Gothic Townsite, September 25th, 2019 - Ian K Breckheimer (s3, 2021)
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in Sunny conditions on September 25th, 2019, collecting RGB images using the built-in camera on a DJI Mavic 2 Pro. The raw imagery had an approximately 3cm ground sample distance (GSD). Images were processed in Agisoft Metashape Software (version 1.6) to derive a georeferenced dense point cloud and 3D model. Images were orthorectified and combined into a mosaic using the 3D model. This dataset has been georeferenced using 14 ground-control points, but has not been radiometrically calibrated.

Gap-filled meteorological data (2011-2020) and modeled potential evapotranspiration data from the KCOMTCRE2 WeatherUnderground weather station, from the East River Watershed, Colorado. - David Brian Rogers; Michelle Newcomer (other, 2021)
This dataset is a gap-filled meteorological dataset (years 2011-2020) that includes modeled potential evapotranspiration from a station near the pumphouse on the East River (KCOMTCRE2 WeatherUnderground ) near the top of the ridge at the Pumphouse PLM wells. These datasets are useful as model inputs. The KOMTCRE2 station is located on Mt. Crested Butte in Colorado. More information can be found at: https://www.wunderground.com/dashboard/pws/KCOMTCRE2/table/2020-12-7/2020-12-7/daily. The purpose of this dataset is to use as input to hydrological and flow-reactive transport models as meteorological forcing. One attached file includes metadata files with definitions for all variables and units. Subsequent files include the same time series dataset, but aggregated and gap-filled at different time-stamp intervals. These include 5 minute time-series, 15 minute time-series, 30 minute time-series, 1 hour time-series, daily time-series, and hourly and daily modeled evapotranspiration.

Total metals & anion concentration data; Slate River floodplain, Crested Butte, CO; May 2020-September 2020 - Samuel Pierce; John R. Bargar (other, 2021)
This data package includes processed and undiluted measurements for metal and anion concentrations from pore water (groundwater) samples from the Slate River floodplain of Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? Samples were collected between May and September of 2020. These measurements were all recorded at the Arizona Laboratory for Emerging Contaminants (ALEC) at the University of Arizona located in Tucson, AZ. Groundwater samples were extracted from a network of installed rhizon (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the river floodplain. All water samples were shaded from sun exposure during extraction from the subsurface and preserved at 4C until measured at ALEC.Analysis by ICP-MS:Measurements for total metals were made on the Agilent 7700x ICP-MS (for total metals) – Agilent Technologies, Santa Clara, CA.The analytical QA/QC protocol was adapted from US EPA Method 200.8 for analysis by ICP-MS. Calibration standards were prepared from multi-element stock solution (Sigma-Aldrich Multielement standard solution for ICP, St. Louis, MO) using matrix matched to sample solutions (either 2% HCl or HNO3 from AriStar Plus,grade acids from VWR Scientific). Calibration curves include at least 7 points with correlation coefficients > 0.995. The QC protocol includes a continuing calibration blank (CCB), a continuing calibration verification (CCV) solution and at least one quality control sample (QCS) to be analyzed just after calibration and again after every 12 samples and at the completion of the run. The QCS solutions are from an independent source, such as NIST SRM 1643e - Trace Elements in Water, or QCS solutions from High Purity Standards (Charleston, SC). Acceptable QC responses must be between 90 and 110% of the certified value. An internal standard (Rh) is added via on-line addition into the sample line using a mixing tee.Analysis by Ion Chromatography (Anions):The protocol follows Method 4110 in Standard Methods for Examination of Water and Wastewater.The instrument used is the Thermo Scientific Dionex ICS-6000 using AS+AG22 column set for anion analysis with isocratic method using sodium carbonate eluent. Detection is by chemical suppression of eluent conductivity. Quality control solutions and mixed analyte standards purchased from Inorganic Ventures, Christiansburg, VA.All files are in csv format.

MODIS/Terra+Aqua BRDF/Albedo Daily L3 Global - 500m V061 - Crystal Schaaf; Zhuosen Wang (other, 2021)

Drone ortho basemap of the Gothic Townsite, July 22nd 2019 - Ian K Breckheimer (s3, 2021)
This is a visible (RGB) orthomosaic derived from UAV imagery via Structure from Motion processing. UAV flights were performed in cloudy conditions on July 22nd, 2019, collecting RGB images using the built-in camera on a DJI Mavic 2 Pro. The raw imagery had an approximately 3cm ground sample distance (GSD). Images were processed in Agisoft Metashape Software (version 1.6) to derive a georeferenced dense point cloud and 3D model. Images were orthorectified and combined into a mosaic using the 3D model. This dataset has been georeferenced using 14 ground-control points, but has not been radiometrically calibrated.

Styled 2019 snow depth basemap of the Upper East River domain - Ian K Breckheimer (s3, 2021)
This is a styled basemap showing snow depth on April 7th 2019 derived from repeat LiDAR data collection by the Airborne Snow Observatory. This dataset is derived directly from: Painter, T. 2019. ASO L4 Lidar Snow Depth 3m UTM Grid, Version 1. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. doi: https://doi.org/10.5067/KIE9QNVG7HP0. Small areas outside of the UER region, but within the extent of this map had missing data. These gaps were filled with linear regression relating 2018 and 2019 ASO snow depths (where one map was available), or regressions related snow depth to elevation (where neither 2018 nor 2019 data was available). Peak snow depths in 2019 were near the 80th percentile of the long-term average estimated from the Butte SNOTEL station. The color scale ranges from <20 cm (gray), through red (20 - 100 cm), yellow (100 - 200 cm), green (200 - 300 cm), and blue (300 - 400+ cm).

Styled slope and aspect basemap of the Upper Gunnison domain - Ian K Breckheimer (s3, 2021)
This dataset is a styled basemap depicting topographic slope and aspect of Upper Gunnison domain using a rainbow color scale with contour lines, optimized for fast display on web maps and mobile devices. The underlying datasets are available as part of Release 3 of the RMBL Spatial Data Platform. In this map style, flat areas are depicted as white, and steeper slopes are depicted as darker colors. The color of the slope depends on the direction of the steepest slope (topographic aspect), with blue representing north, green representing east, yellow representing south, and red representing west-facing aspects. Slopes approaching 90 degrees (vertical cliffs) are depicted as black. Light contour lines have 20 m elevation spacing, and dark lines have 100 m spacing.