Surface underway measurements of partial pressure (or fugacity) of carbon dioxide, salinity and water temperature collected from the sailing vessel HOLCIM during the Vendée Globe 2024 race in the Atlantic Ocean, Pacific Ocean, Indian Ocean from 2024-11-10 to 2025-01-25 (NCEI Accession 0310919) - NCEI (other, 2026)
This dataset includes data collected from the race sailing vessel HOLCIM during the Vendée Globe 2024 race. A flow-through membrane pCO2 sensor is integrated with the SubCTech Ocean pack. The vessel sailed around the world, from and back to Les Sables D'Olonnes (France), sampling in the Atlantic Ocean, Indian Ocean and Pacific Ocean. The most recent calibration of this sensor occurred before the race, in fall 2024. The system is composed of only one temperature sensor, and SST and membrane temperature are assumed to be the same (even if this hypothesis could be flawed, and should be improved in the future). This system is equipped with three pressure sensor, one measuring atmospheric pressure, one measuring the differential pressure between the membrane and the LICOR, and the LICOR pressure. To estimate the pressure of the measurement we added the differential pressure and the LICOR pressure. Since the system is not vented, it is not at atmospheric pressure, and the readings of the pressure are therefore not following the atmospheric pressure variations but more the cycles of the system. We acknowledge that the salinity is noisy, we did not do any processing or filtering to remove the noise.

Linked collectors and determiners for: Rocky Mountain Biological Laboratory. - Bionomia (other, 2026)
Natural history specimen data linked to collectors and determiners held within, "Rocky Mountain Biological Laboratory". Claims or attributions were made on Bionomia by volunteer Scribes, https://bionomia.net/dataset/e82092b5-e2b0-4db8-8cca-22e11272af6b using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, https://gbif.org/dataset/e82092b5-e2b0-4db8-8cca-22e11272af6b . Formatted as a Frictionless Data package.

Rocky Mountain Biological Laboratory - Southern Rocky Mountain Herbaria (other, 2026)
Vascular Plants curated within the Rocky Mountain Biological Laboratory Herbarium. Specimens date to 1929 and are primarily from the East River and upper Gunnison basin.

Reduced Surface Hoar in a Warming World— SUMMA Model Output and Codes - William Rudisill; William Rudisill (other, 2026)
SUMMA model output for a single site in the East River Watershed, Colorado using downscaled forcings from the WUS-D3 dataset of downscaled CMIP6 GCMS. For a full description, see <>.

SUMMA model output for surface hoar study - William Rudisill (other, 2026)
SUMMA model output for a single site in the East River Watershed, Colorado using downscaled forcings from the WUS-D3 dataset of downscaled CMIP6 GCMS. For a full description, see <>.

Passive and Active Seismic Data Collected Near Beaver Pond Locations in Storrs, CT; Willington, CT; and Gunnison County, CO in 2025 - Lijing Wang; David M Rey; Eric A White; Martin Briggs; Fritz J Jean; Yeonju Kim; Annie E Tucker; Samuel Pierce (other, 2026)
This data release documents seismic data collected immediately adjacent to beaver ponds and in their surrounding floodplain environments using active and passive seismic measurements at three sites. These sites are located in the University of Connecticut (UConn) Forest in Storrs, Connecticut; Fenton-Ruby Park in Willington, Connecticut; and in Gunnison County, Colorado. Horizontal-to-vertical spectral ratio (HVSR) measurements were acquired using three-component Tromino seismometers (MoHo, S.R.L.) at non-inundated locations during late summer 2025 (51 total HVSR points). Resonance frequencies were identified from Fourier spectra using GRILLA software (MoHo, S.R.L.). Active 1D Multichannel Analysis of Surface Waves (MASW) test data were collected to estimate 1D seismic shear-wave velocity (Vs) structure at the UConn Forest and Fenton-Ruby Park sites using GRILLA (2 total MASW points). We used literature estimates of Vs for the Gunnison County site. We then converted the resonance frequencies to sediment thickness (depth to bedrock) using a range of plausible sediment shear-wave velocities to demonstrate potential uses of the geophysical data. The average inferred bedrock depth for the UConn Forest was 10.6 +/- 0.8 m; Fenton-Ruby Park was 5.3 +/- 0.9 m; and Gunnison County was 18 +/- 1.9 m. The uncertainty of these example calculations is based on how distinct the HVSR resonance frequency response was, as evaluated automatically with GRILLA software.

Total dissolved-solid loads in the Upper Colorado River Basin during high-flow hydrologic events from 2021 to 2023 (other, 2026)
This U.S. Geological Survey (USGS) data release contains data that allows for calculation of total dissolved-solid loads for high-flow and baseflow conditions in the Upper Colorado River Basin (UCRB) collected between 2021 and 2023. High-flow hydrologic events are critical periods for salinity transport but are often underrepresented in existing datasets due to limited sampling and modeling constraints. To address this gap, samples were collected at five USGS streamgage sites: San Rafael River near Green River, Utah (09328500); Uncompahgre River at Delta, Utah (09149500); Dirty Devil River above Poison Springs Wash near Hanksville, Utah (09333500); Loutsenhizer Arroyo below North River Road near Delta, Colorado (383946107595301); and Virgin River below confluence of Beaver Dam Wash, Nevada (365342113551701). The dataset includes measurements of total dissolved solids (TDS), specific conductance, and stream discharge, which were used to refine estimation techniques for peak-flow conditions.

UAV Imagery of Marmot Burrows in Colorado - Isla Duporge; Isla Duporge (other, 2026)
UAV flights were conducted between August 26, 2024, and September 1, 2024, at and around the Rocky Mountain Biological Laboratory in the East River Valley, Gothic, Colorado, USA (38°57′ N, 106°59′ W; approximately 2900 m elevation). The study area included marmot colonies located in two core zones, categorized as “higher elevation” and “lower elevation” sites. Marmots were actively foraging on natural vegetation, a behavior that continues until mid-September/early-October when they retreat to their hibernacula until the following mid-April or early May.Imagery was collected using a DJI Air 2S equipped with a 1-inch CMOS image sensor, capable of capturing high-resolution images with a 20 MP resolution. The camera has a pixel size of 2.4 µm and a focal length of 8.38 mm, ensuring detailed spatial resolution suitable for ecological applications. Images were captured at a native resolution of 5472 × 3648 pixels.UAV flights were conducted at altitudes ranging from 10 m to 120 m above ground level, with images systematically captured at 10 m intervals.

Graphical presentation of the daily mean discharge in cubic feet per second of The Uncompahgre River (other, 2026)

Geophysical survey associated with NEON AOP survey, East River, CO 2018 - Abdoulaye Balde; Jack Lamb; Orgil Tuvshintugs; Jiancong Chen; Nicola Falco; Haruko M. Wainwright; Baptiste Dafflon (ess_dive, 2026)
The package contains data layers developed and used in Falco et al. 2024: “EcoImaging: Advanced Sensing to Investigate Plant and Abiotic Hierarchical Spatial Patterns in Mountainous Watersheds". The package is part of the DOE Watershed Function Science Focus Area (SFA) project and includes geophysical measurements collected at the East River, Colorado, in conjunction with the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) survey conducted in June 2018. This dataset provide soil geophysical information and were used to investigate soil-plant relationships. The dataset consists of: - NEON_2018_EMI_survey.zip: the electromagnetic induction (EMI) survey as shape-file; - NEON_plot_TDR.csv: plot‑level data from Time‑Domain Reflectometry (TDR) measurements, providing: * volumetric water content (VWC) in percent (%); * soil temperature in degrees Celsius (°C); - file level metadata (flmd.csv) - data dictionary (dd.csv) file This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

SOS: Terrestrial Scanning Lidar L5 Downwind Tower Raw Point Clouds. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Raw point clouds from the terrestrial scanning lidar #5 (L5) that was deployed on the downwind flux tower near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The raw point clouds provide information about the distribution of blowing snow particles in the air in addition to valuable information related to vegetation structure and spring vegetation growth.

Surface Water Quality Data from Beaver-Impacted Streams; Trail Creek and East River, Colorado 2025 - Jessica Pall; Samuel Pierce (ess_dive, 2026)
This data package contains surface water chemistry measurements collected in 2025 to evaluate how beaver damming and low-tech process-based stream restoration influence water quality and metal mobility in mountainous headwater systems of the Upper Colorado River Basin. Sampling was conducted at Trail Creek (Taylor Park watershed, Colorado), a tributary undergoing restoration through installation of low-tech process-based structures (i.e., beaver dam analogs), and at off-channel beaver ponds within the East River floodplain (East River watershed, Colorado). Samples were collected along longitudinal transects spanning upstream control reaches, beaver-influenced ponded reaches, and downstream segments. Additional samples were collected from near-surface pore waters within a beaver dam seepage face. The dataset includes concentrations of major and trace elements measured by inductively coupled plasma–mass spectrometry (ICP-MS) and inductively coupled plasma–optical emission spectrometry (ICP-OES), major anions measured by ion chromatography (IC), and dissolved organic carbon (DOC; reported as non-purgeable organic carbon, NPOC). Samples were size-fractionated at 0.45 micrometers (µm), 0.22 µm, and 0.02 µm to distinguish particulate (>0.45 µm), colloidal (0.22–0.02 µm), and dissolved (<0.02 µm) fractions. The data package consists of comma-separated value (.csv) files containing tabulated chemical concentration data, sample metadata (site identifiers, geographic coordinates, sampling dates, fraction type), and quality control flags. All files are provided in open, non-proprietary formats that can be accessed using standard data analysis software such as Microsoft Excel, R, Python, MATLAB, or other programs capable of reading .csv files. Units, detection limits, and analytical methods are documented in accompanying metadata files. The dataset is designed to support analyses of (1) how beaver impoundment and restoration structures alter elemental partitioning and transport, (2) the role of iron and organic carbon in mediating trace metal mobility, and (3) reach-scale changes in water quality across restoration gradients. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

SOS: Terrestrial Scanning Lidar L3 Upwind West Tower Raw Point Clouds. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Raw point clouds from the terrestrial scanning lidar #3 (L3) that was deployed on the upwind west flux tower near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The raw point clouds provide information about the distribution of blowing snow particles in the air in addition to valuable information related to vegetation structure and spring vegetation growth.

CHESS 2025: Leaf Area Index (LAI) for meadow, shrub, tree, and understory vegetation - Piper Lovegreen; Markus Bill; Cassidy Bromka; Anna Herzog; Andres Villa; Travis Guy; Ian K Breckheimer; Amanda Henderson; Sophia Todorov; Nicola Falco; H. Marshall Worsham (other, 2026)
This dataset contains Leaf Area Index (LAI) measurements made as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Data were collected in the Upper Gunnison Basin, Colorado, across three study domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). Field observations of LAI were collected within 72 hours of airborne data collection by the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. LAI measurements were collected using the LICOR LAI-2200C Plant Canopy Analyzer following protocols outlined in the instrument manual (LI-COR 2019). Sampling targeted four distinct vegetation types: meadows, shrubs, trees, and aspen forest understory. We have archived data separately by site type because different field methods were used for each. At meadow sites, measurements were made at the four corners of 1m x 1m plots, with the instrument moving inward toward the center of the plot. At shrub sites, we measured the canopies of individual shrubs. At tree sites, we made measurements within a 10m x 10m subplot centered around a focal tree, with 30 observations taken on a regular grid. At aspen understory sites, we measured overstory trees following the tree protocol and understory herbaceous vegetation following the meadow protocol. All measurements included above-canopy (A) and below-canopy (B) readings, with specific protocols for scattering correction measurements in direct-sun conditions. Data were processed using the R package `rlai` (Worsham 2025). This package includes functions to calculate LAI, gap fraction, apparent clumping factor (Ω), scattering correction, and other canopy metrics. Package contents: Full file descriptions appear in ‘flmd.csv’. Files named according to the convention ‘lai_*_summary_data_cleaned.csv’ contain summary values of LAI, apparent clumping factor (Ωapp), and scattering correction factors for each site. These are the analysis-ready products that most data users will work with. Files named ‘lai_*_metadata_cleaned.csv’ contain additional site-level observations made during field collection. We have also archived intermediate and supplementary data for users who wish to check our processing approach or apply alternative methods. ‘raw_lai_2200C.zip’ contains the raw files as read from the LI-COR instrument, with no processing applied, in TXT format. The zip archive contains subdirectories by site type, which are further subdivided by sampling area. Filenames correspond to the sampling site number. ‘intermediate_results.zip’ contains detailed output from the processing routines, in JSON format. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘scattering_correction_logs.zip’ contains logfiles from the implementation of Kobayashi et al.'s (2013) scattering correction algorithm. The logfiles report values of several parameters at each iteration of the algorithm, as the model converges toward a stable solution. They are intended for users who want to verify scattering correction performance. The zip archive contains subdirectories by site type; filenames correspond to the sampling site number. ‘spot_checks.csv’ reports LAI and other values for a small number of files processed with LI-COR FV2200 software (LI-COR 2013) using the same control parameters as in our R-based approach. Additional metadata are provided in a data dictionary describing column names and definitions (dd.csv), and in a file-level metadata file (flmd.csv). All zip files can be expanded with common archive utilities. TXT, CSV, and JSON files can be ingested into R or Python computing environments or read in common text editor utilities. Geospatial information: Geospatial data for mapping measurement site locations are in the files CHESS_polygons_lai_UTM.geojson, CHESS_polygons_shrub_UTM.geojson, and CHESS_polygons_meadow_UTM.geojson in the companion geospatial package for the 2025 CHESS campaign, ‘CHESS 2025: Location data for field observations and sampling’ (Henderson et al., 2026). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. * Todorov and Worsham are co–first authors.

SOS: Terrestrial Scanning Lidar L2 Upwind East Tower Raw Point Clouds. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Raw point clouds from the terrestrial scanning lidar #2 (L2) that was deployed on the upwind east flux tower near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The raw point clouds provide information about the distribution of blowing snow particles in the air in addition to valuable information related to vegetation structure and spring vegetation growth.

Graphical presentation of the daily mean discharge in cubic feet per second of The Uncompahgre River (other, 2026)

Reduced Surface Hoar in a Warming World— SUMMA Model Output and Codes (other, 2026)
SUMMA model output for a single site in the East River Watershed, Colorado using downscaled forcings from the WUS-D3 dataset of downscaled CMIP6 GCMS. For a full description, see <>.

Meteorological and Soil Data from Ecohydrology Sensor Towers at Pump House and Snodgrass Mountain in East River Watershed, Colorado, 2019-2025 - Stijn Wielandt; Alexander W. Newman; Chunwei Chou; Yuxin Wu; Luca Peruzzo; Benjamin Gilbert; Eoin L. Brodie; Baptiste Dafflon; Curtis A. Beutler; Amanda Henderson; Kenneth Hurst Williams (other, 2026)
This data package includes hourly meteorological and soil sensor data at eight ecohydrology monitoring sites in East River Watershed, Colorado as part of the Watershed Function Scientific Focus Area (WFSFA) research led by Lawrence Berkeley National Lab (LBNL). Four field sites were located on the hillslope of East River (ER) near Pump House (PH) at Mount Crested Butte (ER-PHS1 to 4), and the other four are in the Snodgrass Mountain (SG) area (SG-EHS5 to 8). In terms of vegetation cover, three sites are in montane grasslands (ER-PHS1, ER-PHS2, and SG-EHS5), three are below evergreen conifer canopy (ER-PHS3, SG-EHS6, and SG-EHS7), and two are below deciduous aspen canopy (ER-PHS4 and SG-EHS8). The monitoring period began in October 2019 at the East River sites, in October 2020 at SG-EHS5 and SG-EHS6, and in October 2021 at SG-EHS7 and SG-EHS8. In September 2024, all four East River sites were fully retired. The four Snodgrass Mountain sites remain active. Each site is equipped with a comprehensive suite of meteorological sensors on a tripod and soil sensors that measure weather, energy fluxes, and soil variables. This data package includes measurements from ten different types of sensors and up to thirteen individual sensors per site, including (1) a weather station (measurement height ranges from 2.8~3.8 meters (m) above ground), (2) a quantum sensor for photosynthetic active radiation (PAR) (2.4~3.3m), (3) a net radiometer (1.7~2.1m), (4) an infrared radiometer (1.6~2.2m), (5) a sonic distance sensor (1.5~1.9m), (6) a soil carbon dioxide (CO2) flux chamber (0m), (7) a soil heat flux plate (-0.05m below ground), (8) a soil oxygen sensor (-0.3m), (9) a soil water potential sensor (-0.3m), and (10) soil water content sensors at 3~4 depths (-1.15 ~ -0.1m). A total of twenty-three variables is reported in this data package, including (1) atmospheric variables: air temperature (TA), atmospheric pressure (PA), vapor pressure (VP), and vapor pressure deficit (VPD), (2) precipitation variables: rain precipitation (P) and snow depth (D_SNOW), (3) energy fluxes variables: four-component net radiation (NETRAD) (shortwave/longwave incoming/outgoing radiation, SW_IN, SW_OUT, LW_IN, LW_OUT), photosynthetic photon flux density (PPFD), and soil heat flux (G), (4) soil variables: soil water content (SWC), soil water potential (SWP), soil temperature (TS), soil bulk electrical conductivity (COND_SOIL), and soil gaseous oxygen concentration (O2_SOIL), (5) wind variables: two-dimensional wind speed (WS), gust speed (WS_MAX), and wind direction (WD), and (6) surface variables: surface infrared temperature (T_CANOPY) and soil CO2 flux (CO2_SOIL). Please see the Methods section for data processing and QA/QC steps taken to generate the hourly datasets. The following files are included in this data package (notes on version: vx-{y}, where x is the metadata version, and y is the data version, when applicable): (1) “metadata_site_vx-{y}.csv” - a site metadata file that summarizes location information of all sites, including site ID, description, coordinates, timeframe, elevation, and vegetation cover, (2) “metadata_instrument_vx-{y}.csv” - an instrument metadata file that summarizes sensor information of all sites, including sensor manufacturer and model, measurement height, and sampling and averaging interval of all variables, (3) "data_{SITE_ID}_vx-{y}.csv" - eight data files that contain hourly data of each site indicated by {SITE_ID} in the filename, (4) “/figure/data_{SITE_ID}_vx-{y}.png" - eight figures that help visualize data of each site indicated by {SITE_ID} in the filename, (5) “/photo/*” - photos of each site indicated by {SITE_ID} in the filename, and (6) four file level metadata (flmd.csv) and data dictionary (*_dd.csv) files that summarize file, header, column, and variable information of all files. Notes: (1) Measurement height: Each variable name is followed by conventional positional qualifiers “H_V_R”, where H indicates the relative horizontal positions of that specific variable, V the vertical positions, and R the replicates. In this data package, only the vertical qualifier V varies, and V increases from the highest vertical position (V=1) to the lowest. Variables with the same qualifier are not necessarily measured by the same sensor, and the same variable with the same qualifier across different sites are not necessarily measured at the same height. Please refer to “metadata_instrument.csv” for the sensor information and measurement heights, and whether a variable is measured below the canopy. (2) Variable availability: Snow depth is not available at ER-PHS3 and SG-EHS7. SWC, soil temperature, and soil bulk EC at the deepest depth (<-1m) are not available at SG-EHS6 and SG-EHS7. The missing value code for numeric variables is -9999, except for SWP. For SWP, the missing value code is +9999, because SWP values are negative. (3) Sampling frequency: Please refer to “metadata_instrument.csv” for the increase of sampling frequency of some variables from 30-min to 1-min at ER-PHS1 to 4 in July 2020. (4) Sensors: While the methods of each sensor are not detailed, all sensors are commercially available, and their methods can be found in their manuals. Please refer to “metadata_instrument.csv” for the sensor manufacturer and model information. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

SOS: Terrestrial Scanning Lidar Derived Land/Snow Surfaces. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Surface grids of the snow, ground, and vegetation surfaces derived from the six terrestrial scanning lidars that were deployed on three flux towers near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The data were collected to support the analysis of snow depth, snow surface variability, snow redistributions, and sublimation particularly from blowing snow.

Forage Resources in Warming and Removal Plots, Almont, CO, 2019 (other, 2026)
This is data collected to explore the impacts of warming and dominant species removal on the quantity and quality of plants for cattle foraging. The data were collected from the Colorado low elevation site (Almont) of the Warming and Removal in Mountains experiment which examines the direct and indirect impacts of climate change on plant and soil communities. Treatments include a control, warming (+1.5C), removal of the dominant species (Wyethia Amplexicalus), and both warming and dominant species removal. The dataset includes data that were collected in 2019 as well as historical data from the site. From 2019 we have in situ air temperature contained in and soil temperature data and an assessment of plant cover from every plot. We then have a compiled set of plant traits for each of the nine most common species including the leaf nitrogen, crude protein content, and forage quality class which are used for analysis on forage quality. The dataset also includes the annual plant cover data collected at peak season from 2013 to 2021 which was compared to daily temperature and precipitation data from the same date range collected by the National Oceanic and Atmospheric Administration. All reported figures and statistics published can be created from this data package.

CHESS 2025: Field-collected vegetation attributes and site photos - Hillary Xu; Erin Carroll; Piper Lovegreen; Sophia Todorov; K. Dana Chadwick; Amanda Henderson; Ian K Breckheimer; C.F. (Rick) Williams; Nicola Falco; H. Marshall Worsham (other, 2026)
This dataset represents field observations of vegetation samples collected as part of the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Samples were collected in the field using tablet computers and digital forms, with target data differing by sample type (individual trees, individual shrubs, or 1-meter square plots of meadow and subshrub vegetation). Field samples were collected within 72 hours of airborne data collection using the National Ecological Observatory Network’s Aerial Observation Platform (NEON AOP). The NEON AOP collected waveform LiDAR (Light Detection and Ranging) and imaging spectrometer data in 426 spectral bands from the visible to shortwave infrared. Remote sensing data for the project is available on ESS-DIVE (DOI and citation to be added upon publication). Field data collected included canopy height and per-species horizontal proportional cover for meadow plots, species identity and height information for shrubs, as well as species identity, height, diameter at breast height, and health assessment information for trees. Photos of the focal site and surrounding landscape were taken for all sampling sites and are included in this archive. Green leaves or needles were collected for plant trait and foliar chemistry analysis. This data is archived separately (DOI and citation to be added upon publication). High-precision geospatial data for each sample (crown perimeter polygons for trees and shrubs, plot boundaries for meadow plots) is available here (Henderson et al., 2026). Field and remote sensing protocols largely followed those of a previous field and airborne imaging campaign performed in 2018 (described in Chadwick et al. 2020). Field data from the 2018 campaign can be found here (Chadwick et al., 2020 doi:10.15485/1618130). Because different field measurements were taken for meadow, shrub, and tree sites, data from these three sample types are archived as separate tables (chess_meadow_site_cleaned.csv, chess_shrub_site_cleaned.csv, chess_tree_site_cleaned.csv). Meadow proportional cover data is stored in a separate table (chess_meadow_cover_cleaned.csv). Taxonomy was treated identically between sample types, and the dataset shares a common set of voucher specimens (chess_voucher_IDs_cleaned.csv), as well as a single species list (chess_species_list_cleaned.csv). All taxonomic determinations were performed to the species level, and adhere to the Global Biodiversity Information Facility (GBIF) backbone taxonomy as of January 10th, 2026 (GBIF Secretariat 2023). CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

UAV Imagery of Marmot Burrows in Colorado (other, 2026)
UAV flights were conducted between August 26, 2024, and September 1, 2024, at and around the Rocky Mountain Biological Laboratory in the East River Valley, Gothic, Colorado, USA (38°57′ N, 106°59′ W; approximately 2900 m elevation). The study area included marmot colonies located in two core zones, categorized as “higher elevation” and “lower elevation” sites. Marmots were actively foraging on natural vegetation, a behavior that continues until mid-September/early-October when they retreat to their hibernacula until the following mid-April or early May.Imagery was collected using a DJI Air 2S equipped with a 1-inch CMOS image sensor, capable of capturing high-resolution images with a 20 MP resolution. The camera has a pixel size of 2.4 µm and a focal length of 8.38 mm, ensuring detailed spatial resolution suitable for ecological applications. Images were captured at a native resolution of 5472 × 3648 pixels.UAV flights were conducted at altitudes ranging from 10 m to 120 m above ground level, with images systematically captured at 10 m intervals.

Data From: "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater" - Nicholas Thiros; Erica R. Siirila-Woodburn; Michelle Newcomer; William Rudisill (ess_dive, 2026)
This repository contains the data and code associated with the paper titled "Warming and snow loss increase reliance on old groundwater in a Colorado River headwater," published in Nature Geoscience, 2026. This study seeks to answer how various ages of groundwater interact with mountainous streamflow in mountainous headwaters such as the East River. It includes various model-data processing scripts, primarily for ParFlow-CLM analysis of simulated water years 2015-2021, and two numerical warming experiments (+2.5 and +4.0 degrees C), including run scripts, forcing scripts, and post-processing, as well as comparison to observation datasets, detailed below. This data requires the use of R (.r, .rmd), Python (.py), Jupyter Notebook or Jupyter Lab (.ipynb), ParFLOW-CLM, EcoSLIM. Further information on the use of all file formats mentioned below (e.g. .tff. .nc) are provided within the associated scripts and directory where the files are located. Contents & Usage ASO/: ​​Contains the bash and python scripts used to convert airborne snow observatory (ASO) data (ASO, 2023) in various data formats (georeferenced tiff file, NetCDF, UTM, and to latitude/longitude) then regrided to the ParFlow equivalent grid. Output data are in regrid_regll_data.zip and subsequently visualized and analyzed in plot_and_compare.py for Supplementary Figures A14 and A15. The wksht_ASO_comparison.xlsx spreadsheet is used to calculate the data for Supplementary Figure A16. EcoSLIM/: Contains the scripts and input files to run the EcoSLIM particle tracking simulations (/run_scripts) and the post-processing python script (/plot_scripts/eco_agedist_plots.ipynb). Jasechko et al./: Contains the jupyter notebook (Extract_Elevation.ipynb) to determine the outlet elevations of the 260 watersheds used in Jasechko et al. (2016), and the corresponding table, Table_S1_Watersheds_alt.csv. Used to create Supplementary Information Figure A2. PLM_Wells/: Contains the QA/QC-ed groundwater level time series of the PLM-1 and PLM-6 Monitoring Wells from Faybishenko et al. (2023), reformatted to water years used for Supplementary Figures A19 and and A20. ParFlow/: Contains the input files and run scripts to run ParFlow-CLM (/run_scripts), the python and tool command language (Tcl) scripts to create and distribute the ParFlow forcing simulation files (/forcing), and various scripts and intermediary files to analyze the model outputs (/post_process). SQUIRE/: Contains the processing scripts and intermediary files for the Surface QUantitatIve pRecipitation Estimation (SQUIRE) data (Grover, 2023) used to generate Supplementary Figure A18. USGS_Streamflow/: Contains the raw and gap-filled United States Geological Survey streamflow data (U.S. Geological Survey, 2026) used at the Almont station (site number 09112500). Gap-filling is performed in the R script with data from the Taylor station (site number 09110000). (/USGS_09112500_EAST_RIVER_AT_ALMONT_GAP_FILLED/code_almont_streamflow_gap_fill.Rmd). discharge/: Contains the gap-filled discharge data at the Watershed Function SFA East River pumphouse site (Newcomer et al., 2022) used to generate Supplementary Figure A13 and to compute hourly Nash-Sutcliffe model efficiency coefficients (NSE) in Table A4. snotel_and_flux_tower/: Contains the snow telemetry data (U.S. Department of Agriculture, 2024) from the Butte (site ID 380) and Schofield (site ID 737) stations, reformatted by water year, accessed with the snotelr R package. Used to create Supplementary Figure A17. Also contains the flux tower observational data (FluxTower_Pumphouse_ESS-DIVE.ET_only.h.txt) from Ryken et al. (2022) and sap flux transpiration data (MaxB_Transpiration_5Sites.daily_sums.h.txt) from Ryken (2021), used to create Supplementary Figures A22 and A23, respectively. Raw EcoSLIM model outputs are in excess of 24TB, and are stored on National Energy Research Scientific Computing Center (NERSC) and publicly available via the external link provided in the paper.

SOS: Terrestrial Scanning Lidar L1 Upwind East Tower Raw Point Clouds. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Raw point clouds from the terrestrial scanning lidar #1 (L1) that was deployed on the upwind east flux tower near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The raw point clouds provide information about the distribution of blowing snow particles in the air in addition to valuable information related to vegetation structure and spring vegetation growth.

Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability" - Brian Gulick; Curtis Beulter; John Sanders; Sharon Bone; Brian Gulick; Benjamin Gilbert; Nicholas J. Bouskill; Langlang Li; Curtis Beulter; John Sanders; Matthias Sprenger; Sharon Bone; et al. (ess_dive, 2026)
The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Mountain Basin Controls on the Snow-to-Streamflow Signal: An AIC-Weighted Multiple Linear Regression Framework - Erica R. Siirila-Woodburn; Beatrice Gordon; Charuleka Varadharajan; Christine Albano; Jessica D. Lundquist; Kenneth Hurst Williams (other, 2026)
A regression-based analysis quantifies how basin characteristics modulate the snow-to-streamflow signal. First, we use the ERA5-Land reanalysis gridded product (European Centre for Medium Range Weather Forecasts reanalysis 5 -Land component) for 4,655 hydrologic unit code - 10 (HUC10) mountain basins across the western United States (US) for water years 1987–2024. Linear regressions are performed for peak snow water equivalent (SWE) and annual streamflow for each mountain basin. Models use ordinary least squares in Python’s statsmodels package. After which, an Akaike Information Criterion (AIC)–weighted ensemble multiple linear regression (MLR) framework with 47 watershed traits is used to predict the linear regression coefficient of determination (r-squared) defining the ability of peak SWE to predict annual streamflow across all mountain basin. Predictor sets are constrained to avoid multicollinearity by excluding models with variance inflation factors (VIF) greater than 5. Mountain basin traits included in the MLR include seasonal climate, topography, vegetation type and structure, and bedrock geology. Accepted models are considered if their AIC is within 2.0 of the model with the minimum AIC, or best model. To compare predictor influence across acceptable models, we computed standardized regression coefficients. To evaluate structural redundancy among models, we constructed binary inclusion vectors for each acceptable model, denoting whether a predictor was present (1) or absent (0). Core predictor variables are defined as occurring in at least 67% of the acceptable models. For this regional analysis, only one model was found acceptable, with higher snow-to-streamflow translation (higher r-squared) occurring in colder mountain basins with higher relative winter precipitation, more snow accumulation and a lower fraction of annual precipitation that falls in the spring and summer. The second component of the data package uses previously published, high-resolution output from an integrated hydrological model of the East River watershed using the U.S. Geological Survey Groundwater and Surface water Flow model (GSFLOW, doi:10.15485/1998576). East River MLR expands upon the approach described above to explore the response of five streamflow metrics—annual streamflow, runoff efficiency, 7-day minimum flow, low-flow duration, and non-perennial stream fraction to snow system indicators including peak SWE, snow-covered area, snow disappearance date, and the fraction of basin area characterized by low-to-no snow, as well as seasonal precipitation and temperature, and annual hydrologic variables representing soil moisture, evapotranspiration (ET), the partitioning of incoming precipitation to evapotranspiration (ET/P), groundwater storage, and groundwater inflow to streams. MLR was done on all water years (P0: 1987-2024) and for each period as determined in the split analysis using pooled regression techniques (P1: 1987-2011 and P2: 2012-2024) to evaluate shifting predictor variable emphasis on streamflow generation. Results indicate that since 2012, peak SWE has lost statistical strength in its prediction of annual streamflow and runoff efficiency, and the indirect influence of spring temperature has emerged as critically important. Low-flow metrics remain largely influenced by soil moisture, vegetation water use and groundwater inflows with summer precipitation becoming a direct influence on minimum summer flow. Together, these data and Python-based analysis tools provide a framework for identifying the key watershed characteristics that control how streamflow responds to snow from year to year. The package also helps quantify uncertainty in statistical models and assess how snow–streamflow relationships vary across regions and over time. This dataset contains comma-separated values files (.csv), text files (.txt), python code files (.py), figure files (.png), and shapefiles (.cpg, .dbf, .prj, .sbn, .sbx, .shp, .xml). Further details on file contents and MLR execution can be found in the readme file and the FLMD files. Work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

SOS: Terrestrial Scanning Lidar L6 Downwind Tower Raw Point Clouds. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Raw point clouds from the terrestrial scanning lidar #6 (L6) that was deployed on the downwind flux tower near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The raw point clouds provide information about the distribution of blowing snow particles in the air in addition to valuable information related to vegetation structure and spring vegetation growth.

Data from: Wild foundress queen bumble bees make numerous, short foraging trips and exhibit frequent nest failure: Insights from trap-nesting and RFID tracking - William Grover; S. Hollis Woodard (other, 2026)
The overwhelming majority of research on wild bumble bees has focused on the social colony stage. Nest-founding queens in the early season are difficult to study because incipient nests are challenging to find in the wild, and the foundress queen's flight period is very short relative to the entire nesting period. As a result, natural history information on foundress queens is exceedingly rare. New methodological approaches are needed to adequately study this elusive life stage. We trap-nested wild queen bumble bees in artificial nest boxes in Gothic, Colorado, and used a custom-built radio frequency identification (RFID) system to continuously record queen foraging activity (inferred from entering and exiting the nest) for the majority of their spring flight periods. Foundress queens made frequent, short foraging trips, which tended to increase in duration over the course of the flight period. All queens who produced adult workers ceased foraging within approximately one week after workers emerged in the nest. We observed frequent nest failure among foundress queens: fewer than one quarter of queens who laid eggs in nest boxes went on to produce reproductive gynes at the end of the season. We also report nest characteristics and curious phenomena we observed, including conspecific nest invasion and queens remaining outside the nest overnight. We present this trap-nesting and subsequent RFID tracking method as a valuable, albeit resource-intensive, path forward for uncovering new information about the elusive, incipient life stage of wild bumble bees.

Herbarium specimens reliably track plant phenological responses to climate change in understudied montane biomes - shijia peng; Charles Davis; S. J. Mazer; Brian D. Inouye; Tadeo Hernan Ramirez-Parada; Sydne Record; Sydne Record; S. J. Mazer; Charles Davis; Brian D. Inouye; shijia peng; Tadeo Hernan Ramirez-Parada (other, 2026)
File: Peng_et_al._20206.zip Description: There are three folders here. The Data folder contains the raw specimen phenology data and the RMBL phenology data. The Analyses folder contains the data frames used to build the linear mixed-effects models. The Codes folder contains R codes

CHESS 2025: Location data for field observations and sampling - Sophia Todorov; Nicola Falco; Ian K Breckheimer; Amanda Henderson; H. Marshall Worsham (other, 2026)
This dataset represents geolocation data associated with field observations and sampling from the Colorado Headwaters Ecological Spectroscopy Study (CHESS) during June and July of 2025. Location data were collected using Trimble DA2 Global Navigation Satellite System (GNSS) receivers with Trimble Catalyst 2 centimeter (cm) positioning service and the Environmental Systems Research Institute (Esri) Field Maps mobile app. Files in this data package include meadow site polygons, shrub site polygons, tree site polygons and stem point locations, and Leaf Area Index (LAI) plot polygons (.geojson). The geojson files can be opened with open-source GIS software (e.g, QGIS). A csv file is also provided with point coordinates for all locations. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Herbarium specimens reliably track plant phenological responses to climate change in understudied montane biomes (other, 2026)
File: Peng_et_al._20206.zip Description: There are three folders here. The Data folder contains the raw specimen phenology data and the RMBL phenology data. The Analyses folder contains the data frames used to build the linear mixed-effects models. The Codes folder contains R codes

Total dissolved-solid loads in the Upper Colorado River Basin during high-flow hydrologic events from 2021 to 2023 (other, 2026)
This U.S. Geological Survey (USGS) data release contains data that allows for calculation of total dissolved-solid loads for high-flow and baseflow conditions in the Upper Colorado River Basin (UCRB) collected between 2021 and 2023. High-flow hydrologic events are critical periods for salinity transport but are often underrepresented in existing datasets due to limited sampling and modeling constraints. To address this gap, samples were collected at five USGS streamgage sites: San Rafael River near Green River, Utah (09328500); Uncompahgre River at Delta, Utah (09149500); Dirty Devil River above Poison Springs Wash near Hanksville, Utah (09333500); Loutsenhizer Arroyo below North River Road near Delta, Colorado (383946107595301); and Virgin River below confluence of Beaver Dam Wash, Nevada (365342113551701). The dataset includes measurements of total dissolved solids (TDS), specific conductance, and stream discharge, which were used to refine estimation techniques for peak-flow conditions.

SOS: Terrestrial Scanning Lidar L4 Upwind West Tower Raw Point Clouds. Version 1.0 - Ethan Gutmann; Jessica D. Lundquist (other, 2026)
Raw point clouds from the terrestrial scanning lidar #4 (L4) that was deployed on the upwind west flux tower near Crested Butte, Colorado for the SOS (Sublimation of Snow) campaign. The raw point clouds provide information about the distribution of blowing snow particles in the air in addition to valuable information related to vegetation structure and spring vegetation growth.

Forage Resources in Warming and Removal Plots, Almont, CO, 2019 (other, 2026)
This is data collected to explore the impacts of warming and dominant species removal on the quantity and quality of plants for cattle foraging. The data were collected from the Colorado low elevation site (Almont) of the Warming and Removal in Mountains experiment which examines the direct and indirect impacts of climate change on plant and soil communities. Treatments include a control, warming (+1.5C), removal of the dominant species (Wyethia Amplexicalus), and both warming and dominant species removal. The dataset includes data that were collected in 2019 as well as historical data from the site. From 2019 we have in situ air temperature contained in and soil temperature data and an assessment of plant cover from every plot. We then have a compiled set of plant traits for each of the nine most common species including the leaf nitrogen, crude protein content, and forage quality class which are used for analysis on forage quality. The dataset also includes the annual plant cover data collected at peak season from 2013 to 2021 which was compared to daily temperature and precipitation data from the same date range collected by the National Oceanic and Atmospheric Administration. All reported figures and statistics published can be created from this data package.

Rocky Mountain Biological Laboratory Mammal Collection - CVColl Portal (other, 2026)
Mammal collection housed at The Rocky Mountain Bioloagical Laboratory. Most specimens are student preparations from Mammalogy course taught at RMBL since ca. 1949.

Depth-resolved sagebrush root metabolomics, rhizosphere microbial communities, and geochemistry at the East River Watershed - Langlang Li; Dusan Velickovic; Nathalie Munoz; J. N. Christensen; Benjamin Gilbert; Nicholas J. Bouskill; Markus Bill; Wenming Dong; Kenneth Hurst Williams (other, 2026)
This data set consists of results from soil nutrient profile, untargeted metabolomics, mass spec imaging, and amplicon sequencing. Data for soil nutrient profile includes common cations (Ca, Mg, Na, and K etc.) extracted from 3 digesting steps – ammonia acetate (for exchangeable cations), nitric acid (for acid dissolved fraction), and hydrofluoric acid/perchloric acid (HF/HClO4) for whole soil digestion. It also includes concentration of organic carbon, inorganic nitrogen (ammonia and nitrate) and phosphorus (Bray-1 P and nitric acid extract), and total nitrogen and phosphorus. Data for untargeted metabolomics includes metabolomic profile for root exudate/tissues and soil extracts from depths at surface soil to saprolite, that were measured using gas chromatography – mass spectrometry (GC-MS), and liquid chromatography – tandem mass spectrometry (LC-MS/MS). Data for mass spec imaging includes spatial distribution of metabolites that were detected and annotated with Fourier transformation ion cyclotron resonance mass spectrometer (FTICR-MS). Data for amplicon sequencing includes the base paired 16S and ITS ribosomal RNA sequences from Miseq Illumina sequencing. All samples were collected from 2 sampling campaign October 2022 and June 2023. Collectively, these datasets enable a mechanistic evaluation of how nutrient acquisition, especially nitrogen and phosphorus, differs between shallow roots operating in soil and deep roots functioning within the fractured bedrock zone. All files are provided as comma-separated values (CSV) fies (.csv) and (GZIP) file (.gz). The compressed .gz FASTQ files can be read directly in R using the dada2 package as part of the amplicon sequence analysis workflow. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research was performed on a project award 60563 (https://dx.doi.org/10.46936/expl.proj.2022.60563/60008727) from the Environmental Molecular Sciences Laboratory, a DOE Office of Science User Facility sponsored by the Biological and Environmental Research program under Contract No. DE-AC05-76RL01830.

Seasonal Precipitation Classification during Surface Atmosphere Integrated Field Laboratory Campaign - Joseph O'Brien; Robert Jackson; Scott Collis; Adam Theisen; Zachary Sherman; Maxwell Grover; Bhupendra Raut; Daniel Feldman; Matthew Tuftedal (other, 2026)
The Surface Atmosphere Integrated Field Laboratory (SAIL) campaign, conducted from September 2021 to June 2023 in Crested Butte, Colorado, aimed to characterize precipitation processes in the Upper Colorado River Basin (UCRB). This increased observations of snowfall accumulation in this hydrologically significant watershed would be useful for quantitative precipitation estimates (QPE). Therefore, the Surface Quantitative Precipitation Estimate (SQUIRE) product was developed using the ARM-supported Colorado State University (CSU) X-band Precipitation Radar. Although SQUIRE will be only released for snowfall, by categorizing precipitation types, users can effectively utilize relevant datasets under diverse meteorological conditions. Moreover, the dataset facilitates validation of the QPE product and the analysis of seasonal variations in precipitation types at the surface. Hydrometeors classes are organized based on their phase and physical characteristics mapping the CSU (both winter Summer) and Py-ART classifications into four groups. 1. Liquid Precipitation: includs drizzle, rain, and large raindrops. 2. Frozen Snow and Ice : Pure Snow, combining ice crystals, aggregates, and vertically oriented ice structures. 3. Dense and Large frozen hydrometeors: including low- and high-density graupel and dry hail. 4.Melting: Wet Snow and Melting Hail, hydrometeors exhibiting both liquid and frozen characteristics.

Data from Stewart et al. 2026 "Organic Colloid Composition in Variable-Redox Porewaters within a Mountainous Floodplain" - Vincent Noel; Brandy Stewart; Kristin Boye (ess_dive, 2026)
Redox gradients, often driven by changes in sediment moisture levels in porous, heterogeneous groundwater systems, create dynamic conditions that may promote the production and transport of colloids within natural waters. While much research has focused on the inorganic composition of colloids, the organic composition remains less well understood. Organic matter (OM) in colloids may associate with minerals, complex metal ions, and serve as an electron donor for microbial respiration; therefore, its composition is of high interest. We examined the composition of porewater OM along a redox gradient in a riparian soil located along the Slate River in Crested Butte, Colorado, USA as a function of depth (90, 130, 200, and 350 cm below ground surface). All depths were oxic to suboxic, except 200 cm, where the products of iron and sulfate reduction were observed concomitant with an increase in dissolved and/or colloidal OM, pH, alkalinity, and conductivity. We investigated the composition of porewater using correlated scanning transmission X-ray microscopy and transmission electron microscopy. We observed a change in the OM chemistry from carboxylate-rich at the 200 cm depth (where it was also enmeshed with non-crystalline iron) to phenol- and substituted-aromatic-rich at other depths. Radiocarbon dating revealed carbon in porewater at 200 cm was younger than depths above and below. Soil porewater can flow down into the underlaying gravel bed during baseflow conditions, thus we speculate whether riparian porewater could transport iron- and carboxylate-rich organic colloids into surrounding surface waters through the gravel bed conduit. This data package includes geochemical data (.csv) as a function of depth from porewaters in OBJ1. Measurements included are pH, conductivity, TOC, Fe concentration, sulfate concentration, dissolved oxygen. Additionally radiocarbon dating age of carbon in samples is provided. Additionally, this dataset contains metadata files (.csv) describing the methods, file contents, and column headers.

Surface Ocean CO2 Atlas Database Version 2025 (SOCATv2025) (NCEI Accession 0304549) - NCEI (other, 2025)
The ocean absorbs one quarter of the global CO2 emissions from human activity. The community-led Surface Ocean CO2 Atlas (www.socat.info) is key for the quantification of ocean CO2 uptake and its variation, now and in the future. SOCAT version 2025 has quality-controlled in situ surface ocean fCO2 (fugacity of CO2) measurements on ships, moorings, sailing yachts, autonomous and drifting surface platforms for the global ocean and coastal seas from 1957 to 2025. The main SOCAT synthesis and gridded products contain fCO2 values with an estimated accuracy of better than 5 μatm. Sensor fCO2 data with an estimated accuracy of better than 10 μatm are separately available. During secondary quality control, marine scientists assign a flag to each data set, as well as WOCE flags of 2 (good), 3 (questionable) or 4 (bad) to individual fCO2 values. Data sets are assigned flags of A and B for an estimated accuracy of better than 2 μatm, flag of C (and D) for an accuracy of better than 5 μatm and a flag of E for an accuracy of better than 10 μatm. Bakker et al. (2016) describe the quality control criteria used from SOCAT version 3 onward. SOCAT quality control cookbooks provide quality control updates (www.socat.info), with (Gkritzalis et al., 2024) used for version 2025. Quality control comments for individual data sets can be accessed via the SOCAT Data Set Viewer (www.socat.info). All data sets, where data quality has been deemed acceptable, have been made public. The main SOCAT synthesis files and the gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Access to data sets with an estimated accuracy of better than 10 µatm (flag of E) and fCO2 values with flags of 3 and 4 is via additional data products and the Data Set Viewer (Table 8 in Bakker et al., 2016). SOCAT publishes a global gridded product with a 1° longitude by 1° latitude resolution without gap filling. A second product with a higher resolution of 0.25° longitude by 0.25° latitude is available for the coastal seas. The gridded products contain all data sets with an estimated accuracy of better than 5 µatm (data set flags of A to D) and fCO2 values with a WOCE flag of 2. Gridded products are available monthly, per year and per decade. Two powerful, interactive, online viewers, the Data Set Viewer and the Gridded Data Viewer (www.socat.info), enable investigation of the SOCAT synthesis and gridded data products. SOCAT data products can be downloaded. Matlab code is available for reading these files. Ocean Data View also provides access to the SOCAT data products (www.socat.info). SOCAT data products are discoverable, accessible and citable. The SOCAT Data Use Statement (www.socat.info) asks users to generously acknowledge the contribution of SOCAT scientists by invitation to co-authorship, especially for data providers in regional studies, and/or reference to relevant scientific articles. It also asks users to cite the relevant SOCAT data set, the relevant methods paper(s), and to use acknowledgement text (https://socat.info/index.php/citing-socat/). The SOCAT website (www.socat.info) provides a single access point for online viewers, downloadable data sets, the Data Use Statement, a list of contributors and an overview of scientific publications on SOCAT and using SOCAT. Automation of data upload and initial data checks have allowed annual releases of SOCAT from version 4 onwards. Automation of metadata upload is ongoing. SOCAT is used for quantification of ocean CO2 uptake and ocean acidification and for evaluation of earth system models and sensor data. SOCAT products inform on ocean CO2 uptake in the annual Global Carbon Budget since 2013. SOCAT is a key element of the World Meteorological Organization’s (WMO) Global Greenhouse Gas Watch (G3W) program and is a key resource for Copernicus’ evaluations. The annual SOCAT releases by the SOCAT scientific community contribute to United Nations (UN) Sustainable Development Goal (SDG) 13 and SDG 14 (Life Below Water), and to the UN Decade of Ocean Science for Sustainable Development. However, since 2022 SOCAT critically relies on support provided by the Pacific Marine Environmental Laboratory of the National Oceanic and Atmospheric Administration in the US. This has been sufficient to keep the basic operation running, however this limited support has resulted in SOCAT data architecture not being updated, leading to a system with limited resilience that is highly vulnerable to external factors. Hundreds of peer-reviewed scientific publications and high-impact reports cite SOCAT. The SOCAT community-led synthesis product is a key step in the value chain based on in situ surface ocean carbon measurements, which provides policy makers with critical information on ocean CO2 uptake for policy makers. The need for accurate knowledge of global ocean CO2 uptake and its (future) variation makes sustained funding of in situ surface ocean CO2 observations and their synthesis imperative.

Analytical Spectral Device Fieldspec3 foliar and soil reflectance survey, East River, CO 2023 - A. Joshua West; Angie Vasquez; Kathleen Grant; Lauren Hechinger; K. Dana Chadwick (other, 2025)
Foliar and soil spectra colleted during a 2023 metal hotspot survey in Gunnison County, CO.

SPLASH Field Study; CLAMPS2 Doppler Lidar Vertical Stare Data (NCEI Accession 0304847) - NCEI (other, 2025)
These files contain 24-hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. While not conducting other scans, the lidar directs the beam to zenith, allowing for the measurement of vertical velocity. These data were collected during the SPLASH project. Data collection and processing: For the SPLASH campaign, the CLAMPS2 Doppler lidar collected PPI scans at 60 deg elevation every 5 minutes. The Doppler lidar provides range-resolved, line-of-sight measurements of radial velocity, intensity (signal-to-noise ratio [SNR]+1), and attenuated backscatter. This measurement of vertical velocity is much more direct and high quality than that provided with the VAD scan files. The provided files provide the intensity field (SNR+1), which can be used as a ‘filter’ for noise. A good rule of thumb cutoff is 1.01. Data format: Data are provided in netcdf format. The typical naming convention is clampsdlfpC2.c1.YYYYMMDD.HHmmss.cdf, following closely to ARM file naming convention. The files have time and height dimensions.

SunJammer Technology Demonstration - NCEI (other, 2025)
The Sunjammer Project is a NASA funded contract to L’Garde Inc. to fly a solar sail demonstration for a period of approximately one year. L’Garde is also partnered in this mission with Space Services Holdings Inc. (SSHI) who will conduct all commercial activities associated with the mission such as memorial services, advertising, sponsorship (SSHI fill in) NASA will provide a government frequency allocation for the conduct of this mission. NASA, as the government user of the government acquired frequency will maintain authority to terminate the mission transmissions throughout the operating life of the Sunjammer mission.

SPLASH Field Study; CLAMPS2 Doppler Lidar VAD Data (NCEI Accession 0304717) - NCEI (other, 2025)
These files contain 24-hour periods of data collected from the CLAMPS2 Halo Streamline XR+ Doppler lidar. The Doppler lidar conducts regular conical scans at a set elevation angle. These data are then passed through a typical VAD algorithm to retrieve horizontal wind speed and direction profiles. These data were collected during the SPLASH project. Data collection and processing: For the SPLASH campaign, the CLAMPS2 Doppler lidar collected PPI scans at 60 deg elevation every 5 minutes. The Doppler lidar provides range-resolved, line-of-sight measurements of radial velocity, intensity (signal-to-noise ratio [SNR]+1), and attenuated backscatter. In the case of PPI scans meant for VAD analysis, these data are passed through a VAD code to produce profiles of horizontal wind speed and direction. Vertical velocity is also provided, but it is not as high quality as vertical velocity more directly measured by vertical stares. The provided files provide the intensity field (SNR+1), which can be used as a ‘filter’ for noise. A good rule of thumb cutoff is 1.01. Data format: Data are provided in netcdf format. The typical naming convention is clampsdlvad1turnC2.c1.YYYYMMDD.HHmmss.cdf, following closely to ARM file naming convention. The files have time and height dimensions.

SPLASH Field Study; Spectral Surface Albedo Observations from NOAA GML/GRAD and CIRES at Kettle Ponds (CKP) (NCEI Accession 0305473) - NCEI (other, 2025)
Overview The NOAA Global Monitoring Laboratory (GML), Radiation, Aerosols, and Cloud Division (G-RAD), in collaboration with the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder, conducted spectral surface albedo measurements at — Kettle Ponds (CKP) — as part of the NOAA Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. Spectral albedo values were derived from combined upwelling and downwelling hemispheric irradiance measurements in seven narrow spectral bands centered at nominal wavelengths of 415, 500, 670, 870, 940, and 1625 nm. Downwelling irradiance was measured using an upward-facing Multifilter Rotating Shadowband Radiometer (MFRSR). Upwelling irradiance was measured using a downward-facing Multifilter Radiometer (MFR) mounted approximately 2 meters above ground level. Measurements were recorded at a 20-second interval. Due to the sequential nature of MFRSR observations — which reconstruct downwelling irradiance from irradiances during various shadowband positions — each timestamp represents neither a purely instantaneous nor a time-averaged measurement. The full measurement cycle is completed within eight seconds prior to each reported timestamp. Calibration Approach Instrument responsivity calibrations were performed using: Laboratory lamp calibrations for the MFRSR (downwelling) In-field side-by-side calibration for the MFR (upwelling) Relative calibration between the MFR and MFRSR reduced propagated uncertainty in the albedo product. Surface albedo was computed as the simple ratio of the calibrated upwelling to downwelling irradiance at each wavelength. Note: The NOAA/GML/GRAD calibration facility does not currently support absolute responsivity calibration at 1625 nm. Consequently, irradiance values at this wavelength carry an unknown scaling factor. However, a side-by-side calibration was performed, enabling valid and consistent albedo retrievals at 1625 nm. Data Quality Considerations (notes on both locations) During winter, both instruments were occasionally buried in snow when snow depths exceeded their 2-meter installation height. These periods — more frequent at CKP than CBC (Brush Creek) — were not removed from the dataset and may be associated with degraded data quality. Users should exercise caution when analyzing data from winter periods, particularly at CKP, due to potential signal degradation caused by snow coverage over the instruments. Data from the CKP site are affected by several quality issues, particularly prior to 2022-11-03. These include intermittent data loss, occasional jumps in the MFRSR signal at 670 nm, and oscillations in the MFR signal, most notably at 1625 nm. While much of the data remains usable, users are advised to apply increased scrutiny when using CKP observations before 2022-11-03. Data quality improved significantly after 2022-11-03. Data Availability Spectral surface albedo data are available for the following date ranges: CKP: 2021-12-20 through 2023-08-15 File Naming Convention Files are named as follows: HTML Markup Was Removed.HTML Markup Was Removed.HTML Markup Was Removed.nc Example: ckp.spectral_surface_albedo.20230623.nc ckp – Site identifier spectral_surface_albedo – Data product descriptor 20230623 – Data collection date (YYYYMMDD) File Format All files are in NetCDF format and conform to the CF-1.8 standard. Each file includes the following key variables: surface_albedo — Spectral surface albedo derived from the ratio of upwelling and downwelling irradiance. Unit: 1. downwelling_hemispheric_irradiance — Global downwelling irradiance from MFRSR. Unit: W m-2 nm-1. upwelling_hemispheric_irradiance — Upwelling irradiance from MFR. Unit: W m-2 nm-1. downwelling_hemispheric_irradiance_1625nm — Relative downwelling irradiance at 1625 nm. Unit: 1. upwelling_hemispheric_irradiance_1625nm — Relative upwelling irradiance at 1625 nm. Unit: 1. solar_zenith_angle — Solar zenith angle in degrees. solar_azimuth_angle — Solar azimuth angle in degrees. channel_wavelength_mfr — MFR channel central wavelengths in nanometers. channel_wavelength_mfrsr — MFRSR channel central wavelengths in nanometers.HTML Markup Was RemovedHTML Markup Was RemovedHTML Markup Was Removed

SPLASH Field Study; NOAA and CIRES Ceilometer Cloud Data Products at Brush Creek, Colorado, 2021-10-19 to 2023-08-14 (NCEI Accession 0301003) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners conducted a field study (SPLASH - the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology). This field study established a state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. The ultimate goal of this project is improved prediction of weather and water in the Colorado mountains and beyond to inform societal preparedness and response. The NOAA Global Monitoring Laboratory (GML) Radiation, Aerosols, and Cloud Division (G-RAD) and the Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, operated Vaisala CL51 ceilometers at the Kettle Ponds (CKP) and Brush Creek (CBC) observatory field sites during the SPLASH experiment. The cloud product files were produced in order to condense the wealth of level 0 data streams and instrument health checks in order to focus on the primary data observations/products these ceilometers produce; namely attenuated backscatter coefficient profiles (aerosol and cloud hydrometeor backscatter) cloud detection status, and cloud base height above ground level. Note, Vaisala’s BL View retrieved boundary layer height estimates are not included in these files but will be archived as separate data product/data files. The files were produced daily, from 00:00:00 UTC to 23:59:59 UTC at each location. The CL51 ceilometers were deployed in early- to mid-October 2021, with data collection occurring from 2021-10-19 through 2023-08-14 at the Brush Creek site. A separate archive is available for the data collected at the Kettle Ponds location.

AmeriFlux US-UR4 Gunnison - UCRB (other, 2025)
This is the AmeriFlux version of the carbon flux data for the site US-UR4 Gunnison - UCRB. Site Description - This site is located in Gunnison, Colorado, within a floodplain at a notable elevation. The region experiences cold winters with significant snow cover and cool summers. The ecosystem is homogeneous over an extended distance, providing a stable environment for consistent and reliable environmental studies.

SPLASH Field Study; NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe (NCEI Accession 0303808) - NCEI (other, 2025)
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. 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.

Metagenome-assembled genomes measured at 3 depths during snowmelt period in East River, CO (March, May, and June, September 2017) - Harry Beller; Elizabeth Eder; Nikola Tolic; Emiley A. Eloe-Fadrosh; Yangquanwei Zhong; Allison Wong; Malak M. Tfaily; David W. Hoyt; Jillian F. Banfield; Patrick O. Sorensen; Jason G. Toyoda; Shi Wang; et al. (ess_dive, 2025)
Snowmelt is a critical biogeochemical period that accounts for large nitrogen (N) export events from high-elevation watersheds. Soil microbial populations bloom and immobilize N during snowmelt, yet the population size crashes in spring, which releases a pulse of soil N. We sought to discover the N sources fueling this microbial bloom and determine the fate of N following microbial die-off. Here, focusing on the snowmelt period within a headwater catchment of the Upper Colorado River Basin (East River, CO), we deployed strain-resolved metagenomics to identify the metabolic pathways and processes that mobilize soil N during and after snowmelt. Soil metagenome samples were taken from 6 snowpits from 3 depths (0-5cm, 5-15cm, >15cm) at 4 time points during snowmelt period (March 2017, May 2017, and June 2017, September 2017) generating 48 metagenomes. We reconstructed 474 metagenome-assembled genomes (MAGs) across all metagenomes. All 48 metagenomes were sequenced at JGI and raw data can be found under JGI (Joint Genome Institute) GOLD Study Gs0135149. Metagenome assemblies from IMG under the same study were used for genome binning. This dataset (1) a zip file of 474 MAGs (as fasta files, Gs0135149_bins_tar.gz), (2) sample metadata file with sample IGSNs (International Generic Sample Numbers) (samples.csv), (3) bounding box coordinates for the sampled locations (Gs0135149.kml), (4) metagenome metadata file listing IMG/M (Integrated Microbial Genomes/Metagenomes) metagenome accessions linking samples to metagenomes (metagenomes.csv), (5) location metadata file (locations.csv), (6) file-level metadata file (flmd.csv) and (7) data dictionary (dd.csv) file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

SPLASH Field Study; NOAA GML Brush Creek & Kettle Ponds Snow Depth Estimates (NCEI Accession 0302850) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners conducted a field study (SPLASH - the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology). This field study established a state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. The ultimate goal of this project is improved prediction of weather and water in the Colorado mountains and beyond to inform societal preparedness and response. This dataset contains three-times daily estimates of snow depth at the Brush Creek and Kettle Ponds sites. Estimates are made via camera and manual identification of marks on a seven foot snow stake. Because the estimates are subjective, they should only be used to supplement analysis.

Sindewald et al - Identifying alpine treeline species using high-resolution WorldView-3 multispectral imagery and convolutional neural networks dataset (other, 2025)
The dataset contains region of interest (ROI) polygons for six treeline species found in Rocky Mountain National Park, CO. The tree and shrub species include limber pine (Pinus flexilis), willow (Salix glauca, Salix brachycarpa, and hybrids), Engelmann spruce (Picea engelmannii), subalpine fir (Abies lasiocarpa), glandular birch (Betula glandulosa), and quaking aspen (Populus tremuloides). The ROIs correspond to WorldView-3 image data obtained from Maxar, collected on July 21, 2020, with 0% cloud cover and an off-nadir angle of 16.8 degrees. WV-3 data include a panchromatic (black-and-white) band with 31 cm spatial resolution and eight multispectral bands with 1.24 m resolution: coastal blue (400-450 nm), blue (450-510nm), green (510-580 nm), yellow (585-625 nm), red (630-690 nm), red edge (705-745 nm), near-infrared 1 (N-IR1, 770-895 nm), and near-infrared 2 (N-IR2, 860-1040 nm). Details on orthorectification and atmospheric correction can be seen in the attendant paper. The dataset also includes a 10 m digital elevation model (DEM) from the U.S. Geological Survey EROS Data Center, interpolated to 1.24 m resolution using a cubic spline resampling method. The image data were used to train convolutional neural networks (CNNs) to do pixel-based species classification. The CNN inputs were image chips (patches), each centered on an ROI pixel. The data are therefore provided in the form of these image patches. Additionally, a csv file is provided with mean radiance values for each ROI polygon for each multispectral WV-3 band with species labels. These data accompany a paper titled "Identifying alpine treeline species using high-resolution WorldView-3 multispectral imagery and convolutional neural networks dataset", under review in the Biogeosciences special issue, "Treeline ecotones under global change: linking spatial patterns to ecological processes". Authors include Laurel A. Sindewald 1 , Ryan Lagerquist 2 , Matthew D. Cross 3 , Theodore A. Scambos 4 , Peter J. Anthamatten 5 , Diana F. Tomback 1 1 Department of Integrative Biology, University of Colorado Denver, Denver, 80204, USA 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, 80521, USA 3 Department of Geography and the Environment, University of Denver, Denver, 80208, USA 4 Earth Science and Observation Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, 80309, USA 5 Department of Geography and Environmental Sciences, University of Colorado Denver, Denver, 80204, USA

Data for: Predicting the contribution of single trait evolution to rescuing a plant population from demographic impacts of climate change - Justin Kipness; J. M. Powers; Diane R. Campbell (other, 2025)
Evolutionary adaptation can allow a population to persist in the face of a new environmental challenge. With many populations now threatened by environmental change, it is important to understand whether this process of evolutionary rescue is feasible under natural conditions, yet work on this topic has been largely theoretical. We used unique long-term data to parameterize deterministic and stochastic models of the contribution of one trait to evolutionary rescue using field estimates for the subalpine plant Ipomopsis aggregata and hybrids with its close relative I. tenuituba. In the absence of evolution or plasticity, the two studied populations are projected to go locally extinct due to earlier snowmelt under climate change, which imposes drought conditions. Phenotypic selection on specific leaf area (SLA) was estimated in 12 years and multiple populations. Those data on selection and its environmental sensitivity to annual snowmelt timing in the spring were combined with previous data on heritability of the trait, phenotypic plasticity of the trait, and the impact of snowmelt timing on mean absolute fitness. Selection favored low values of SLA (thicker leaves). The evolutionary response to selection on that single trait was insufficient to allow evolutionary rescue by itself, but in combination with phenotypic plasticity it promoted evolutionary rescue in one of the two populations. The number of years until population size would stop declining and begin to rise again was heavily dependent upon stochastic environmental changes in snowmelt timing around the trend line. Our study illustrates how field estimates of quantitative genetic parameters can be used to predict the likelihood of evolutionary rescue. Although a complete set of parameter estimates are generally unavailable, it may also be possible to predict the general likelihood of evolutionary rescue based on published ranges for phenotypic selection and heritability and the extent to which early snowmelt impacts fitness.

Compilation of actual evapotranspiration and vegetation indices along critical riparian zones on the Navajo Nation from 2013-2023 (other, 2025)
These data were compiled for monitoring riparian zone trends and changes in the Navajo Nation as part of a study to document riparian ecosystem health and its water use in support of potential restoration efforts. The objective of our study was to monitor the short and medium-term effects on the riparian vegetation in relation to evapotranspiration changes, drought, and other hydrological processes, along some critical riparian zones in the Navajo Nation. These data represent time series of vegetation greenness and water use for the years 2013 to 2023. These data were collected from the spaceborne mission Landsat 8 which carries the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors for an area within the Navajo [...]

Rhizosphere Soil Biogeochemical Data and Photosynthetic Data of Vicia Faba in a Rhizobox - Yilin Fang; Zoe Cardon; Steve B. Yabusaki; M. Tildon Jones; Mariela Garcia Arredondo; Marco Keiluweit (other, 2025)
Here we share the data in column format via csv files for pH, redox, and dissolved oxygen collected at hourly resolution from microelectrodes. Dissolved organic carbon concentrations collected from TOC are also provided in a similar format but are composited samples from hourly microdialysis collection. This provided resolution of diel rhizosphere dynamics belowground. Plant physiological data was also collected at every 5 min for 24 hr cycles in order to capture diel dynamics aboveground. This data was used to parameterize the reaction transport model eSTOMP-ROOTS, which examines the rhizosphere biogeochemistry of a growing Vicia faba plant. The aim was to investigate plant activity and belowground biogeochemical processes, particularly their impact on mineral-organic associations in the rhizosphere. We combined in-situ rhizosphere microsensor and plant physiological measurements with a 3-D plant-soil reactive transport model to explore the behavior of dissolved organic carbon (DOC) in the rhizosphere. Over several days, microdialysis probes placed at the root-soil interface in live soil showed distinct daily patterns of DOC concentration in the pore water. Spikes in DOC concentrations during the day aligned with peaks in leaf-level photosynthesis, accompanied by decreasing redox potential and dissolved oxygen levels, and increasing pH in the rhizosphere. This new mechanistic modeling framework, which integrates aboveground plant physiological data with non-destructive, high-resolution monitoring of rhizosphere processes, offers significant potential for studying the factors that control carbon storage in soils.

Data and scripts associated with “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” (v2) - Peter Raymond; Byron Crump; Ted Bambakidis; Kevin Ryan; Beck Powers-McCormack; Vanessa A. Garayburu-Caruso; James C. Stegen; Brieanne Forbes; Shaoda Liu (other, 2025)
This data package is associated with the publication “Riverine dissolved organic matter transformations increase with watershed area, water residence time, and Damköhler numbers in nested watersheds” submitted to Biogeochemistry by Ryan et al., 2024 (DOI: https://doi.org/10.1007/s10533-024-01169-5). This study aims to investigate fundamental and transferable drivers of dissolved organic matter (DOM) diversity across five nested watersheds within the contiguous United States. DOM diversity was explored using ultrahigh-resolution Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS). The samples and the unprocessed FTICR-MS data used in this study are publicly available on the Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) data repository (see DOIs below). The data for the Willamette, Gunnison, Connecticut, and Deschutes basins were collected as part of a collaboration between the Watershed Rules of Life (WROL) project and Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS). The data for the Yakima River basin (YRB) was collected by the PNNL River Corridor SFA. The raw, unprocessed FTICR-MS data with additional (meta)data can be found at doi:10.15485/1895159 for WROL samples and doi:10.15485/1898912 for YRB samples. This data package contains the processed data used in the associated manuscript. This package also contains ancillary geospatial, hydrological, and geochemical information that supports the interpretation of the FTICR-MS data within Ryan et al., 2024. This data package is associated with the GitHub repository found at https://github.com/WHONDRS-Hub/rcsfa-RC4-WROL-YRB_DOM_Diversity. This data package was originally published August 2024. It was updated January 2025 (modified files). See the change history in the readme more details. At the directory level, the data package is comprised of three folders: (1) data, (2) output, and (3) src; and five additional files including the data dictionary (file ending in "_dd.csv”) and file-level metadata (file ending in “_flmd.csv”). The “src” folder contains the scripts used to process the FTICR data, conduct the analyses, and produce the manuscript figures. The inputs for these scripts are in the “data” folder and the returned outputs in the “output” folder. Inputs include temporal and spatial metadata associated with the sampling efforts, processed FTICR data, and total and normalized putative biochemical transformations per sample. Outputs include cleaned and combined data presented as tables, descriptive statistics, and plots. The file-level metadata file lists all files contained in this data package and descriptions for each. The data dictionary describes the units and definitions for each tabular data column or row header.

Data for Unraveling the Drivers of Water Shortage across Spatial Scales and Sectors in Colorado's West Slope River Basins - Sai Veena Sunkara; Patrick Reed (other, 2025)
Results from Sunkara et al. submitted to Earth's Future. All code to reproduce the experiment and make the figures can be found here: https://github.com/ssaiveena/Sunkara-etal_2025_WestSlope For a detailed guide to data in this repository, see the README.txt file. Shortage output from .xdd files generated by StateMod was compressed into .parquet files in the Uncertain_scenarios folder. Reservoir output from .xre files can be found in the "Reservoir" directory within the Uncertain_scenarios folder. We adopted the CDSS naming convention: cm = Upper Colorado River Basin gm = Gunnison River Basin ym = Yampa River Basin wm = White River Basin sj = Southwest Basin

Trembling aspen (Populus tremuloides) water potentials from clones with varying aspect and understory composition (other, 2025)
Predawn and midday plant water potential measurements, collected on terminal twigs using a scholander-type pressure chamber (PMS Instruments, Corvallis, OR, USA). Measurements were collected from 1-3 canopy dominant or co-dominant raments in 12 mature trembling aspen ( Populus tremuloides ) near aspen's low elevation range edge in the San Juan National Forest in southwest Colorado, USA. Clones were either growing on north-facing (n=4) or south-facing slopes (n=8) and had either grassy understories (n=6) or shrubby understories dominated by Symphiocarpus albus (n=6). Measurements were collected in the early growing season (2010-06-23), in the mid growing season but prior to the onset of the summer monsoon (2010-07-16) or in the late growing season after the onset of the monsoon (2010-08-17). Data are published using the PSINet data submission template (https://psinetrcn.github.io/). This data is a portion of the data analyzed in: Anderegg LDL, Anderegg WRL, Abatzoglou J, Hausladen AM, Berry JA . 2013 . Drought characteristics role in widespread aspen forest mortality across Colorado, USA. Global Change Biology 19 : 1526 1537.

Data from "A Bayesian Record Linkage Approach to Applications in Tree Demography Using Overlapping LiDAR Scans" - Lane Drew; Andee Kaplan; Ian K Breckheimer (other, 2025)
Processed LiDAR data and environmental covariates from 2015 and 2019 LiDAR scans in the Vicinity of Snodgrass Mountain (Western Colorado, USA), in a geographic subset used in primary analysis for the research paper. This package contains LiDAR-derived canopy height maps for 2015 and 2019, crown polygons derived from the height maps using a segmentation algorithm, and environmental covariates supporting the model of forest growth. 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. The point cloud data were processed using the R package lidR to generate a canopy height model representing maximum vegetation height above the ground surface, using a pit-free algorithm. This dataset was compiled to assess how spatial patterns of tree growth in montane and subalpine forests are influenced by water and energy availability. Understanding these growth patterns can provide insight into forest dynamics in the Southern Rocky Mountains under changing climatic conditions. This dataset contains .tif, .csv, and .txt files. 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.

Plant community data from Rocky Mountain Biological Laboratory (CO, USA) transplant experiment 2017-2023 - Anonymous (other, 2025)
These two datasets include plant community data from the transplant experiment at the Rocky Mountain Biological Laboratory. We conducted this experiment at three sites near the Rocky Mountain Biological Laboratory (RMBL, Gothic, Colorado, USA) from June to September 2017-2023. The dataset "Abundance_2017_COTransplant has raw abundance data from before transplantation. The dataset "Cover_2018-2023_COTransplant" has percent cover data for all plots after transplantation. See the README for a full description of each file and Methods below for details on the field protocol used to collect both datasets.

Site and endmember spectra of terrestrial vegetation and soils for the Colorado Headwaters Ecological Spectroscopy Study, June-July 2025 - Regina Eckert; Kathleen Kanaley; Erin Carroll; Regina Eckert; Kathleen Kanaley; Regina Eckert; Erin Carroll; Kathleen Kanaley; Erin Carroll; K. Dana Chadwick; K. Dana Chadwick; K. Dana Chadwick; et al. (ess_dive, 2025)
This dataset provides site and endmember spectra collected during the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign. The site spectra were collected to help validate airborne hyperspectral data acquired by the National Ecological Observatory Network's aerial observation platform (NEON AOP). Endmember spectra were collected to augment existing spectral libraries with additional samples of bare surfaces and non-photosynthetic vegetation. All measurements were acquired with an Analytical Spectral Devices (ASD) FieldSpec4 Hi-Res NG (Next Generation) spectroradiometer, which records radiance at 1nm (nanometer) intervals from the ultraviolet to the short-wave infrared (350-2500 nm). The dataset includes spectra measured at meadow sites where the CHESS team also collected vegetation samples for trait analyses. The site spectra were collected with the ASD FieldSpec4 palm grip attachment using an 8° field-of-view foreoptic. Site spectra are integrated measurements of the entire surface within the foreoptic’s field of view. For site-level spectra, the sun is the illumination source. A Spectralon panel mounted on a tripod was used for instrument optimization and white reference measurements for all site spectra. Site spectra were acquired within two hours of solar noon and within 48 hours of a NEON AOP overflight. Site spectra are labeled by date, sampling area, and site number according to the naming conventions of the CHESS campaign’s data management plan. The dataset also contains endmember spectra in the following categories: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare (soil/rock), and flowers. Endmember measurements were acquired using either the contact probe or the leaf clip attachments of the ASD FieldSpec4. In these configurations, the bulb inside the spectrometer provides the light source for the measurements. The spectrometer was optimized and white reference measurements were recorded using the circular white pucks attached to the contact probe and leaf clip. Because they do not rely on solar illumination, contact probe and leaf clip measurements were collected during a broader time frame than the palm grip site spectra. Some endmembers were measured at CHESS meadow sites, while others were collected within the larger sampling area or in nearby locations (e.g. Gothic Townsite) with similar characteristics. Radiance, reflectance, and metadata files are split into three subfolders according to measurement type: proximal/palm grip (prx), contact probe (cp), and leaf clip (lc). Radiance spectra are provided in ASD file format (.asd file extension). All ASD files can be opened using the provided scripts. Metadata is provided in two formats: CSV file format (no geolocation) and GEOJSON file format (includes geolocation for each spectra). The dataset includes a set of pre-processed reflectance spectra as CSV files (yyyymmdd_rfl.csv). The python scripts and jupyter notebook used to calculate reflectance spectra from the ASD radiance data is included here and was previously published at: https://doi.org/10.3334/ORNLDAAC/2446. There is also a folder of JPEG photographs corresponding to selected spectra. We include a protocol document with detailed steps for ASD FieldSpec4 assembly and operations. This data additionally contains a file level metadata (flmd.csv) and data dictionary (dd.csv) file. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June to October 2020) - Anna N. Rasmussen (ess_dive, 2025)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June to October 2020 at two locations (OBJ1 and OBJ2) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the Stanford Linear Accelerator Center (SLAC) National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 30 cm depth below surface to just above the cobble layer (~190-250 cm depth) at discrete depths every 40 cm for microbial analyses. A total of 35 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2848 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also 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. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Replication data for: Fig 11.1, Top right, GPDs, Colony sizes and compositions by year, 18 Dec 2025 - John Hoogland (other, 2025)
FILE DESCRIPTION Keywords: Gunnison's prairie dog, Colony sizes and colony compositions by year File name = Fig 11.1, Top right, GPDs, Colony sizes and compositions by year, 18 Dec 2025 SM males = sexually mature GPD males, some of them yearlings SM females = yearling and adult fems, all of which copulated, with 1-2 possible exceptions SIM yrl males = sexually immature yearling males, for which we found no evidence for copulation from either behavior or reproductive condition SOME gunnison males DID copulate as yearlings. If so, then I show these sexually active yearlings males as SM males These data have been checked TWO TIMES on 13 April 2020 SM = Sexually Mature = Sexually Active SI = Sexually Immature = Sexually Inactive YRL = Yearling Colony sizes are and other counts are for May of each year. If a GPD was alive for any part of May, then I used him or her in my calculation of Colony size--unless the UPD disappeared in the first day or two of May OLDFile name = GPDs, Colsize, colcomps, babies, 13 April 2020 This file checked TWO TIMES by JLH on 31 March 2020. Note: These three GPD data sets are from THREE DIFFERENT STUDY SITES. PEFO = Petrified Forest National Park RMC = Redondo Meadow Colony of Valles Caldera National Preserve VCC = Visitor Colony of Valles Caldera National Preserve Remember: For all three GPD colonies, I studied PART OF ONE LARGE COLONY I call that part the "Study site" or "Study area. "For more details on this point, see Chapters 4, 9, and 11. On 24 June 2020, I deleted 91 and BB6 from 31-R19A in 1994 This change affected colony size and colony density and also clan size and clan density and also number of SM females OLD File name = GPDs, Colsize, colcomps, babies, 25 June 2020 These data used for Fig 11.1 in Hoogland 2026. This file ready for longterm storage on 15 Feb 2025. Note: Counts of emergent juveniles in this EXCEL file INCLUDE babies never captured Weanling = juvenile at first emergence from the natal burrow entrance = baby Colony size = number of adults and yearlings in May For this EXCEL file, adult = prairie dog that was >=2 years old For this EXCEL file, yearlings were young adults that emerged from the natal nursery-burrow >9 months ago, but <20 months ago. File name = Fig 11.1, Top right, GPDs, Colony sizes and compositions by year, 18 Dec 2025

Scripts and data to produce figures for the SVS2 paper submitted to GMD (other, 2025)
The Soil, Vegetation, and snow model version 2.0 (SVS2) is a land surface model developed by Environment and Climate Change Canada (ECCC) to be used in the context of land surface prediction. SVS2 is described in a manuscript submitted to GMD (https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3396/). This repository contains the scripts and data used to generate the figures presented in the manuscript. It is divided into two subfolders: point_scale_simulations_SCB/ contains the scripts and data to generate Figures 3, 4 and 5 for the simulations at Snow Crested Butte. distributed_simulations/ contains the scripts and data to generate Figures 2 and 6 to 11 for the distributed simulations along a transect in Eastern Canada. Observations used in this evaluation are shared in this repository: Snow pit data are distributed in the folder distributed_simulations/data/obs/snow_pits/ Snow cover data from GMASI are distributed in the folder distributed_simulations/data/obs/gmasi Information about the ERA5 atmospheric forcing are provided in the folder distributed_simulations/forcing. The configuration files used for the 1D and 2D simulations presented in the manuscript are also available in their respective subfolders. Readme files are present in the different subfolders to guide the user.

Groundwater and river water elevations and temperature from 2017 to 2022 across Meander Z in the East River Watershed, Colorado - Christian Dewey; Patricia Fox (ess_dive, 2025)
This dataset includes groundwater and river water elevations and temperature data collected in the East River watershed located in the Upper Colorado River Basin. The data were collected in order to investigate the coupling between hydrology and biogeochemical processes in the floodplain. Data was collected at ten groundwater locations in Meander Z (MZ), located just upstream of the confluence with Brush Creek and two river locations directly adjacent to Meander Z from 2017-2019. From 2019-2022, data was collected at five groundwater locations in Meander Z. Note that location names, not location identifiers (IDs), are used in the related publication Dewey et al. (2022). Both location IDs and names are included in data files. Files in this dataset include the main data files for each location zipped into a single folder (waterlevel_data.zip), an installation methods file describing sensor installation (InstallationMethods.csv), a file containing field metadata including GPS (Global Positioning System) coordinates and ground surface elevations (transducers_locations.csv). This dataset also 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. This dataset conforms to the ESS-DIVE hydrological reporting format. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Data and scripts from: “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification” - Timothy Johnsen; Lavanya Ramakrishnan; Jonathan Skone; Xiangyu Bi; Charuleka Varadharajan; Chunwei Chou; Yuxin Wu (ess_dive, 2025)
This data package includes data and scripts from the manuscript “Denoising autoencoder for reconstructing sensor observation data and predicting evapotranspiration: noisy and missing values repair and uncertainty quantification”. The study addressed common challenges faced in environmental sensing and modeling, including uncertain input data, missing sensor observations, and high-dimensional datasets with interrelated but redundant variables. Point-scaled meteorological and soil sensor observations were perturbed with noises and missing values, and denoising autoencoder (DAE) neural networks were developed to reconstruct the perturbed data and further predict evapotranspiration. This study concluded that (1) the reconstruction quality of each variable depends on its cross-correlation and alignment to the underlying data structure, (2) uncertainties from the models were overall stronger than those from the data corruption, and (3) there was a tradeoff between reducing bias and reducing variance when evaluating the uncertainty of the machine learning models. This package includes: (1) Four ipython scripts (.ipynb): “DAE_train.ipynb” trains and evaluates DAE neural networks, “DAE_predict.ipynb” makes predictions from the trained DAE models, “ET_train.ipynb” trains and evaluates ET prediction neural networks, and “ET_predict.ipynb” makes predictions from trained ET models. (2) One python file (.py): “methods.py” includes all user-defined functions and python codes used in the ipython scripts. (3) A “sub_models” folder that includes five trained DAE neural networks (in pytorch format, .pt), which could be used to ingest input data before being fed to the downstream ET models in ‘ET_train.ipynb” or ‘ET_predict.ipynb’. (4) Two data files (.csv). Daily meteorological, vegetation, and soil data is in “df_data.csv”, where “df_meta.csv” contains the location and time information of “df_data.csv”. Each row (index) in “df_meta.csv” corresponds to each row in “df_data.csv”. These data files are formatted to follow the data structure requirements and be directly used in the ipython scripts, and they have been shuffled chronologically to train machine learning models. The meteorological and soil data was collected using point sensors between 2019-2023 at (4.a) Three shrub-dominated field sites in East River, Colorado (named “ph1”, “ph2” and “sg5” in “df_meta.csv”, where “ph1” and “ph2” were located at PumpHouse Hillslopes, and “sg5” was at Snodgrass Mountain meadow) and (4.b) One outdoor, mesoscale, and herbaceous-dominated experiment in Berkeley, California (named “tb” in “df_meta.csv”, short for Smartsoils Testbed at Lawrence Berkeley National Lab). - See "df_data_dd.csv" and "df_meta_dd.csv" for variable descriptions and the Methods section for additional data processing steps. See "flmd.csv" and "README.txt" for brief file descriptions. - All ipython scripts and python files are written in and require PYTHON language software.

Range size and local abundance data for angiosperm communities across an elevation gradient, Rocky Mountain Biological Lab, 2021-2022 - Brian J. Enquist; Katrina M Dlugosch; Verónica Zepeda; Leah N. Veldhuisen (other, 2025)
This dataset contains abundance and range size data for angiosperm communities at three sites in Washington Gulch near the Rocky Mountain Biological Laboratory (RMBL, Gothic, Colorado, USA) for 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, and contained five 1.2 m x 1.2 m plots each. We identified all vascular plants to species level. Each plot was sampled once per year near the peak of the growing season (approximately mid-July, depending on the year and elevation). In each plot, we counted all individuals of every species. To quantify abundance, we averaged local abundance across all five plots at each site and the two data collection years, and then ranked species by averaged abundance within each site (highest to lowest). We calculated range size as Extent of Occurrence (EOO) and Area of Occupancy (AOO). We calculated AOO and EOO with GBIF data using the ‘red’ package. We then ranked species by AOO within each site (largest to smallest).

Foliar element determination from field survey in association with the National Ecological Observatory Network Airborne Observation Platform survey, East River, Colorado 2018 - Maceo Hastings Porro; Maeve McCormick; Andea Scott; Kathleen Grant; Katharine Maher; Samuel Pierce; Amanda Henderson; K. Dana Chadwick (ess_dive, 2025)
The purpose of this dataset is to support research aimed at understanding the coupling between hydrologic and biogeochemical processes at watershed scale, particularly the relationship between aboveground vegetation characteristics and subsurface soil properties. These data are intended to inform and calibrate models of catchment-scale biogeochemical fluxes, including rock-derived nutrient cycling, and they were procured to address the following questions: (1) What is the distribution of vegetation characteristics across the study catchments? (2) Are foliar concentrations of rock-derived nutrients related to underlying lithology and soil availability, or are these signals masked by biotic nutrient cycling and retention processes? This data package contains foliar elemental data collected during the 2018 National Ecological Observatory Networks (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, Colorado. Folair samples were collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds and contain a mixture of vegetation including meadow, shrub, and tree foliar samples. The samples were processed using aqua regia digestion and analyzed for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES). The data package includes: (1) raw foliar elemental data files in CSV and PDF formats, (2) quality control certificates in PDF format, and (3) an aggregated CSV file containing all elemental measurements compiled across samples. No specialized software is required to access or use these files.

Langenheim Plant Species Data (1953) and Associated Resurvey Datasets (2014), Gunnison Basin, Colorado, USA - Stephanie Zorio; Stephanie Zorio; C.F. (Rick) Williams; Leah N. Veldhuisen (other, 2025)
Quantitative plant abundance data were collected from the same 121 sites at two time periods separated by 65 years (1948-1952 and 2012-2014) in the Colorado Rocky Mountains to examine changes in plant community composition. The sites range in elevation from 2600m to 4100m. Approximately 30 sites were sampled from each of four habitat types: sagebrush (2528-3119m, n=27 sites), spruce-fir forest understory (3001-3520m, n=31 sites) , upland herb = montane meadow (3124-3850m, n=30 sites), and alpine (3549-4013m, n=33 sites). The earlier data set was collected by Jean H. Langenheim (1953, 1962) and consisted of counts of species occurrences along approximately 100m paced transects, noting species touching her boot tip every pace (n=100 sampled points per site). The later data set was collected by Stephanie D. Zorio (2015, 2016) consisting of counts of species occurrences every 1m along 300m transects (n=300 sampled points per site). The sites resurveyed by Zorio (2015, Zorio et al. 2016) were placed as close as possible to the original sites based on the written descriptions of Langenheim, but are only approximate. The GPS coordinates given for the resurveyed sites are the centerpoint of 2 perpendicular 150m transects, one across the slope and the other perpendicular to the slope. GPS coordinates for alpine sites along narrow ridges are the start and end points of three 100m transects along the ridge. Georeferenced localities and environmental site data are presented in Table 2: Lang Zorio Env Site Data. Langenheim’s original data were extracted from tables in her thesis (Langenheim 1953). These data omitted species that occurred in fewer than 14% of sites of a given habitat type (constancy). Species that occurred at very low frequencies (<1% per site) were only denoted as an x in the tables. Zorio converted these to frequencies of 0.5 for quantitative comparative purposes. This data set contains 157 species from 27 families across all sites and habitats. Species in several difficult to identify genera were lumped at the genus level. The original taxonomic nomenclature is retained. This data set is presented in Table 1: Lang Data 1953 Thesis. Data are given with species in rows and sites in columns for each habitat separately. Data are raw counts of species/site. For comparison with the more recent survey results, taxonomomy was updated to that of Weber and Wittmann (2012). All taxa (n=199) are combined into one data matrix with sites in rows and species in columns. These data are presented in Table 3: Langenheim data for use with Zorio Data. Complete species counts from the 2012-2014 resurveys (n=300 samples per site) are presented in Table 4: Zorio Veg Matrix All Species. As in Table 3, sites are in rows and species (n=311) are in columns. These data will be the most appropriate for comparison with future resurveys. The reduced data set of 2012-2014 surveys used to compare with Langenheims original data is presented in Table 5: Zorio_AAAR_Div by 3 & 14% Constancy. As noted above, all species with less than 14% constancy were dropped from the dataset and counts were divided by 3 to standardize the sampling effort between surveys. The abundance and constancy change dataset, Table 6, shows species' rank abundance, relative abundance and constancy for both 1953 and 2014, grouped by community type. There are then two columns for the change from 1953 to 2014: one for change in abundance and one for change in constancy. Each species has a unique value for each community type where it occurs. The elevation change dataset, Table 7, shows the change in species' mean elevation from 1953 to 2014. These data are not grouped by community type, as species' mean elevations can change across community types. Each species has only one elevation change value. Literature Cited: Langenheim, J.H. 1953. Plant-Ecological Reconnaissance of the Crested Butte Area, Gunnison County Colorado. Draft of published doctoral dissertation, Dept. of Biological Sciences, University of Minnesota, Minneapolis, 235 pp. Langenheim, J.H. 1962. Vegetation and environmental patterns in the Crested Butte area, Gunnison County, Colorado. Ecological Monographs, 32:249-285. Weber, W.A., & Wittmann, R.C. 2012. Colorado Flora: Western Slope. Fourth Ed. Boulder: University Press of Colorado, 608pp. Zorio, S.D. 2015. 70 Years of Vegetation Change in the East River Basin, CO, USA. Master’s thesis, Department of Biological Sciences, Idaho State University, Pocatello, 127 pp. Zorio, S.D, Williams, C.F., & Aho, K.A. 2016. Sixty-five years of change in montane plant communities in western Colorado, USA. Arctic, Antarctic and Alpine Research, 48: 703-722.

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (September 2019) - Bradley Tolar; John R. Bargar; Christopher Francis; Anna N. Rasmussen; Kristin Boye (ess_dive, 2025)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken September 2019 at one locations (OBJ1) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the Stanford Linear Accelerator Center (SLAC) National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 6 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 2562 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also 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. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Electrical Resistivity Tomography data from 2016 to 2018 at the Lower Montane site in the East River Watershed, Colorado - John Peterson; Emmanuel Leger; Baptiste Dafflon (ess_dive, 2025)
This dataset contains time-lapse Electrical Resistivity Tomography (ERT) data along a transect located on the northeast-facing hillslope at the lower montane site (Pumphouse site) in the upper East River Watershed. The monitoring dataset covers the period from November 2, 2016, to August 6, 2018. In addition, the archive also contains a baseline dataset from October 9, 2016. The ERT transect consisted of 128 electrodes with an electrode spacing of 1.25 m. The acquisition system was located in the middle of the transect, about 50 m on one side, and included an MPT (Multi-Phase Technologies) ERT system, a mini computer, and batteries with solar panels. Acquisition occurred daily under normal circumstances. The first 16 electrodes (from the upper end of the transect) could not be used after the cable was damaged during the 2017–2018 winter. Also, due to multiple failures in the power system, the temporal resolution of the data is much lower in 2018 compared to 2016 and 2017. The data have been processed and used in Dafflon et al., 2023, and the baseline dataset was used in Falco et al., 2019 (see reference list). This archive contains the measurements (ER.zip containing csv files) for each of the 326 acquisition times and a filtered version where only electrodes 17 to 128 are included (ERT_sm.zip containing csv files). The archive also contains the baseline dataset and two acquisitions with full reciprocals (ERT_RB.zip containing csv files), as well as all the raw MPT files (ERT_raw_MTP.zip). The geometry (electrode position and elevation) is provided in Universal Transverse Mercator (UTM) 13N Geoid2012AB in the file named ERT_Location.csv. The archive contains 1 *.csv data files, four *.zip files, and three metadata *.csv files. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Priority Faults for Improving Seismic Hazard Models in the Intermountain West Region (other, 2025)
Abstract This data release includes a list of high-priority hazardous faults and associated spatial data (regional polygons and buffered fault traces) for the Intermountain West (IMW) region of the United States. These are the top five faults or regions of concern per IMW state, based on the 2025 (version 1.0) meeting of the Basin and Range Province Earthquake Working Group. In general, the list includes important, understudied faults where additional research would help to improve regional earthquake rupture forecasts and hazard modeling, reduce earthquake risk, and expand our knowledge of earthquake processes in the IMW region. This information will be used to guide IMW-specific priorities for research included in the USGS Earthquake Hazards Program external research priorities. Disclaimer: This database has been approved for release by the U.S. Geological Survey (USGS). Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, the database is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Introduction In February 2025, the Basin and Range Province Earthquake Working Group (BRPEWG; Utah Geological Survey, 2025a) met in Salt Lake City, Utah to discuss priority faults and topics in the Intermountain West (IMW) region. BRPEWG members and state representatives joined the meeting from Arizona, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, and Wyoming. Discussions followed previous meetings on fault and topical priorities convened by the U.S. Geological Survey (USGS) (Crone and others, 2009; Briggs and Hammond, 2011; Koehler and Anderson, 2019; Briggs and Gold, 2015). The overall goal was to present and rank the top five faults or regions of concern in each IMW state and update the priority fault list included in Briggs and Gold (2015). This information will be used to guide IMW-specific priorities for research included in the U.S. Geological Survey (USGS) Earthquake Hazards Program external research priorities. Criteria Used to Select Faults of Concern State representatives each presented a list of the top five faults or regions of concern in their states. Criteria for inclusion varied by state but mostly followed three themes: (1) High-risk faults, such as those crossing or proximal to urbanized regions or critical facilities. (2) Faults or regions lacking adequate characterization for seismic or displacement hazard analyses (for example, 3D fault geometry, paleoearthquake history, or slip rate). (3) Faults that can facilitate regional or topical earthquake research, such as characterizing faults in an unstudied region or advancing our understanding of earthquake rupture behavior. Faults of Concern in the IMW Region The following list of high-priority hazardous faults and regions was presented at the 2025 BRPEWG meeting. These are the top five faults or regions of concern considered most important to understanding earthquake hazards and reducing earthquake risk in each IMW state. Notably, this is a subjective and incomplete list; additional faults in the IMW may meet the criteria included above but not be listed below. Spatial data associated with this list include buffered fault traces and regional polygons; for individual fault traces, refer to USGS (2025). Arizona: Big Chino–Little Chino faults, Needles Graben faults, Toroweap fault, Hurricane fault, and Bellemont fault. Colorado: Gore Range frontal fault, Williams Fork Mountains fault, Sawatch fault (southern section), Poncha fault, and Uncompahgre Plateau region faults. Idaho: Lost River–Lemhi–Beaverhead faults, Sawtooth–Boulder Front faults, West Ola Valley–Jakes Creek–Big Flat faults, Long Valley faults, and Grand Valley–Rexburg–Heise fault. Montana: Bitterroot Valley and Missoula area faults, Butte area and Deer Lodge Valley faults, Helena Valley and Canyon Ferry Lake faults, Mission–Swan–South Fork Flathead faults, and Northwest Greater Yellowstone–Centennial Tectonic Belt faults. For more information, refer to Montana Bureau of Mines and Geology (2025). New Mexico: Alamogordo fault, Albuquerque basin region, border faults on the west side of Tularosa basin, Southern Sangre de Cristo fault, and Pajarito fault system. Nevada: Carson Range front faults, distributed faults in the North Valleys area of Reno, Frenchman Mountain fault, intra-basin faults in Las Vegas Valley, and northern Walker Lane faults. For additional information, refer to Nevada Bureau of Mines and Geology (2025). Oregon (IMW portion): Klamath–Sky Lakes fault system, Chemult–LaPine Graben fault system, Bend Area fault system, Grande Ronde Valley fault system, and Goose Lake Graben fault system. Wyoming: Rock Creek fault, Grand Valley fault, Southeast Yellowstone region faults, Jackson Hole region faults, and Stagner Creek fault. Utah: Wasatch fault zone, Cache Valley regional faults, Southern Utah regional faults, Tooele and Rush Valley regional faults, and Great Salt Lake and Utah Lake faults. For additional information, refer to Utah Geological Survey (2025b). Priority faults and regions were provided by BRPEWG State Representatives, including Jeri Ben Horin, Arizona Geological Survey; James McCalpin and Enrique Chon, Colorado Geological Survey; Zach Lifton, Idaho Geological Survey; Yann Gavillot and Mike Stickney, Montana Bureau of Mines and Geology; Dan Koning, New Mexico Bureau of Geology & Mineral Resources, New Mexico Institute of Mining & Technology; Richard Koehler, Nevada Bureau of Mines and Geology, University of Nevada, Reno; Lalo Guerrero, Oregon Department of Geology and Mineral Industries; James Mauch, Wyoming State Geological Survey; and Adam Hiscock and Rachel Adam, Utah Geological Survey. Explanation of Data Spatial data for IMW faults and regions of concern are included with this data release. Faults (for example, the Wasatch fault zone), grouped faults (for example, the Sawtooth–Boulder Front faults), and regions (for example, faults within the Uncompahgre Plateau region) are represented as polygons and organized into a single geographic information systems (GIS) file in both shapefile and Google Earth keyhole markup language (kml) formats. For individual or grouped faults, a polygon forming a 2.5-km-wide buffer (5 km total width) around the Qfaults trace is provided. Polygons for regional areas are also included and intended to represent areas of interest and/or distributed faulting. Justification for priority faults and regions is included in a plain text, comma separated value (csv) file. Fault and region polygons have the following fields: Name – Name of the IMW fault, grouped faults, or region. For example, Sawatch fault (individual fault), San Andres fault system (grouped faults), and Uncompahgre Plateau region faults (region). Fault naming convention follows the USGS Quaternary fault and fold database of the United States (Qfaults; USGS, 2025); regional names provided by BRPEWG state representatives. Type – Type of polygon. Fault – individual fault; grouped fault – one or more faults grouped; region – area of distributed faulting or general area of concern. Faults – Faults included in a regional polygon or set of grouped faults. For example, the Cache Valley region faults include the East Cache and West Cache faults; the San Andres fault system includes the San Andres Mountains and Organ Mountain faults. For individual faults, this field is NULL. State – State within which the majority of the fault, grouped faults, or region is located. Source – Source of spatial data. “Buffered USGS Qfaults traces” indicates USGS (2025) fault traces with a 2.5-km-wide buffer added. Other sources of data, including regional polygons, are provided. NSHM – Whether the fault or faults are included as earthquake sources in the 2023 update of the USGS National Seismic Hazard Model (Hatem and others, 2021). Partial – some but not all faults within a region or fault grouping are included in the model. Notes – Short statement provided by BRPEWG state representatives and only included in the csv file. This field includes justification for the priority fault designation, information on data gaps or remaining questions, and/or how additional research would help characterize hazard and reduce risk in the IMW region. References Crone, A.J., Haller, K.M., and Maharrey, J.Z., 2009, Evaluation of hazardous faults in the Intermountain West region—Summary and recommendations of a workshop: U.S. Geological Survey Open-File Report 2009-1140, 72 p. Briggs, R.W., and Hammond, W.C., 2011, Evaluation of geodetic and geologic datasets in the northern Walker Lane—summary and recommendations of the workshop, U.S. Department of the Interior: U.S. Geological Survey Open-File Report 2011-1282. Briggs, R., and Gold, R., 2015, U.S. Geological Survey workshop summary: Evaluation of hazardous faults in the Intermountain West region—2015 update, in Lund, W.R., editor, Proceedings volume, Basin and Range Province Seismic Hazards Summit III: Utah Geological Survey Miscellaneous Publication 15-5, variously paginated. Hatem, A.E., Collett, C.M., Briggs, R.W., Gold, R.D., Angster, S.J., Powers, P.M., Field, E.H., Anderson, M., Ben-Horin, J.Y., Dawson, T., and others, 2021, Earthquake geology inputs for the U.S. National Seismic Hazard Model (NSHM) 2023 (western U.S.) (ver. 3.0, December 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9AWINWZ. Koehler, R.D., and Anderson, J.G., 2019, 2018 Working Group on Nevada Seismic Hazards – Summary and recommendations of the workshop, Nevada Bureau of Mines and Geology Open-File Report 19-2, 44 p. Nevada Bureau of Mines and Geology, 2025, Earthquake research priorities for Nevada, EHP FY22, available at http://www.nbmg.unr.edu/_docs/Earthquakes/NBMG_priorities_NEHRP.pdf, accessed February 2025. Montana Bureau of Mines and Geology, 2025, GIS Data Hub: Geohazards, available at https://gis-data-hub-mbmg.hub.arcgis.com/pages/geohazards, accessed February 2025. Utah Geological Survey, 2025a, Basin and Range Province Earthquake Working Group, available at: https://geology.utah.gov/hazards/info/workshops/working-groups/basin-and-range-earthquakes/, accessed February 2025. Utah Geological Survey, 2025b, Utah Quaternary fault parameters, available at: https://geology.utah.gov/hazards/info/workshops/working-groups/q-faults/, accessed February 2025. U.S. Geological Survey, 2025, Quaternary fault and fold database of the United States, available at https://www.usgs.gov/programs/earthquake-hazards/faults, accessed February 2025.

East River Pore Water FTICR-MS Data associated with "Geomorphic and Hydrogeochemical Controls on Fine-Scale Hyporheic Microbiome Assembly and Function" - Audrey Sawyer; Mikayla Borton; K. D. Harris; Robert E. Danczak; Michael J. Wilkins; C. M. Saup; Annika S. Nelson; Rosalie K. Chu; James C. Stegen; Kenneth Hurst Williams (other, 2025)
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). This study aimed to understand organic matter assemblages differ within a streambed with differing geomorphology and the interplay between streambed organic matter and microbiome function. 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.

Temperature influences pollinator' choice of floral partners independently of community composition (2025)
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Replication data for: Fig 6.1, Dates of capture and ages of victims for single American Badger, 03 Nov 2025 - John Hoogland (other, 2025)
FILE DESCRIPTION Keywords: American badger, dates of capture, age of victims, Gunnison's prairie dogs Filename = Fig 6.1, Dates of capture and ages of victims for single American badger, 03 Nov 2025. Number of Gunnison’s prairie dogs (and their approximate ages) captured and carried to home-den per day by a single female American Badger (Becky) from 05 June 2018 through 07 July 2018. A gunnison adult had first emerged from its natal nursery-burrow >11 months ago, and a juvenile had first emerged <2 months ago. “Age unknown” was either a large juvenile or a small adult. All data shown here are from a single study-area of Gunnison's prairie dogs at Valles Caldera National Preserve in 2018. Data in this file were checked one time by Samantha Kagel, and one time by John Hoogland shortly after creation of file in 2019. Filename = Fig 6.1, Dates of capture and ages of victims for single American badger, 03 Nov 2025. These data used for Fig 6.1 in Hoogland 2026. This file ready for longterm storage on 27 May 2025.

Atmospheric Surface Flux Station #50 measurements (level 2 Processed), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023 (2025)
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Cold-Air Pooling Maps for Colorado (100 m) and the East River Watershed Region (30 m) - John Cramblitt; Jessica D. Lundquist (other, 2025)
Cold-air pooling (CAP) mapped across Colorado at 100m resolution and the region surrounding the East River watershed, CO at 30m resolution. Classifications were determined using a Python implementation of the algorithm originally developed in Lundquist et al., 2008, applied to digital elevation models (DEMs) from the USGS (2024). This dataset is released in support of an upcoming manuscript studying CAP distributions across these two regions (currently in preparation). It contains two NetCDF (.nc) files structured as follows: Variables- elevation: elevation at each pixel (meters) - slope: slope at each pixel (degrees)- rank: proportion of surrounding pixels (within a square of radius r) which are lower in elevation than the given pixel. - curvature: terrain curvature at each pixel (1/m)- CAP: cold-air pooling signal at each pixel. Values: 1 = CAP, 0 no signal, -1 = no CAP. Coordinates- lon: longitude (degrees, WGS84)- lat: latitude (degrees, WGS84)- xv_dist: cross-valley distance (meters). A critical input defining the critical radii r in rank and curvature calculations. Files- Colorado_100m.nc: CAP classifications at 100m-resolution across the Colorado mountains for cross-valley distances from 10,000m to 1,000m (every 500m). Users may select the value which best describes their region of interest. - EastRiver_30m.nc: CAP classifications at 30m-resolution across terrain surrounding the East River watershed for a single cross-valley distance of 3,000m. For further detail on algorithm design and function, including the calculation of rank and curvature, CAP classification criteria, and choice of cross-valley distance, please refer to Lundquist et al., 2008 or the linked GitHub repository, which will be publicly released upon manuscript publication.For questions, please contact jcramblitt@berkeley.edu.

Data from: Betting on rains that do not come: Monsoon failure and leaf area overshoot relate to increased tree mortality from drought (other, 2025)
Structural overshoots, where biomass is overallocated to tree leaf area compared to sapwood area, could result in lethal stress during droughts. Climate change may alter climatic cues that drive leaf area production, such as temperature and precipitation, as well as seasonal dynamics that underlie summer rainfall due to the North American Monsoon (NAM). Combined, this could lead to temporal mismatches between leaf area-driven water demand and availability, and increased drought-induced mortality events. We used leaf area to sapwood area ratios to investigate the prevalence of overshoots and whether overshoots increase drought-induced mortality. We measured populations of aspen spanning the northern transition zone of the NAM during and following severe droughts. We observed increased overshoots and drought-induced mortality in southern latitude populations that rely more on summer monsoon rainfall. Changes in convective activity from low snowpack the preceding winter may be a climatic driver of heightened summer monsoon rainfall in the region and therefore may also trigger increased production of leaf area during wetter summers. Our results suggest that an overshoot of leaf area to sapwood area (AL:AS) ratios is associated with drought-induced tree mortality and highlight that climate-change driven alterations to the NAM could have major consequences for tree species’ acclimation to environment change. Supplemental data files provide for: Kerr KL, Anderegg LDL, Trugman AT, Anderegg WRL (2025) Betting on rains that do not come: Monsoon failure and leaf area overshoot relate to increased tree mortality from drought. Functional Ecology. Data were gathered from national forests (NF) throughout Utah and Colorado, USA in the summers of 2014, 2018, 2019, 2020, and 2021. See Methods for details.

Rapid Geomorphic Assessment Data for the Upper East River Watershed, Brown and Calumet Counties, Wisconsin - Heidi M Broerman; James D Blount; Isaac J Mevis; Faith A Fitzpatrick (other, 2025)
The East River is an agricultural tributary to the Lower Fox River and the Environmental Protection Agency (EPA) Green Bay Area of Concern (AOC) in northeast Wisconsin. This tributary has known excessive phosphorus (P) and suspended sediment loads that contribute to habitat related impairments and downstream algal blooms. As part of the Great Lakes Restoration Initiative (GLRI), the U.S. Geological Survey conducted a stream corridor sediment budget study to estimate the spatial distribution of near-channel erosion and in-channel deposition of sediment and sediment-bound P. The Upper East River watershed is largely located in southern Brown County, with some headwater streams in northern Calumet and Manitowoc counties. In August 2022, rapid geomorphic assessments (RGAs) were completed in 15 stream reaches throughout the watershed to assess streambank erosion and streambed deposition. Additionally, samples of streambank and soft, bed sediment were collected in the reaches and analyzed for total phosphorus concentrations and particle size. The results of these field-based assessment and sample analyses were used to estimate annual streambank erosion and volume of soft sediment streambed deposition within each reach.

Priority Faults for Improving Seismic Hazard Models in the Intermountain West Region (other, 2025)
Abstract This data release includes a list of high-priority hazardous faults and associated spatial data (regional polygons and buffered fault traces) for the Intermountain West (IMW) region of the United States. These are the top five faults or regions of concern per IMW state, based on the 2025 (version 1.0) meeting of the Basin and Range Province Earthquake Working Group. In general, the list includes important, understudied faults where additional research would help to improve regional earthquake rupture forecasts and hazard modeling, reduce earthquake risk, and expand our knowledge of earthquake processes in the IMW region. This information will be used to guide IMW-specific priorities for research included in the USGS Earthquake Hazards Program external research priorities. Disclaimer: This database has been approved for release by the U.S. Geological Survey (USGS). Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, the database is released on condition that neither the USGS nor the U.S. Government shall be held liable for any damages resulting from its authorized or unauthorized use. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Introduction In February 2025, the Basin and Range Province Earthquake Working Group (BRPEWG; Utah Geological Survey, 2025a) met in Salt Lake City, Utah to discuss priority faults and topics in the Intermountain West (IMW) region. BRPEWG members and state representatives joined the meeting from Arizona, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, and Wyoming. Discussions followed previous meetings on fault and topical priorities convened by the U.S. Geological Survey (USGS) (Crone and others, 2009; Briggs and Hammond, 2011; Koehler and Anderson, 2019; Briggs and Gold, 2015). The overall goal was to present and rank the top five faults or regions of concern in each IMW state and update the priority fault list included in Briggs and Gold (2015). This information will be used to guide IMW-specific priorities for research included in the U.S. Geological Survey (USGS) Earthquake Hazards Program external research priorities. Criteria Used to Select Faults of Concern State representatives each presented a list of the top five faults or regions of concern in their states. Criteria for inclusion varied by state but mostly followed three themes: (1) High-risk faults, such as those crossing or proximal to urbanized regions or critical facilities. (2) Faults or regions lacking adequate characterization for seismic or displacement hazard analyses (for example, 3D fault geometry, paleoearthquake history, or slip rate). (3) Faults that can facilitate regional or topical earthquake research, such as characterizing faults in an unstudied region or advancing our understanding of earthquake rupture behavior. Faults of Concern in the IMW Region The following list of high-priority hazardous faults and regions was presented at the 2025 BRPEWG meeting. These are the top five faults or regions of concern considered most important to understanding earthquake hazards and reducing earthquake risk in each IMW state. Notably, this is a subjective and incomplete list; additional faults in the IMW may meet the criteria included above but not be listed below. Spatial data associated with this list include buffered fault traces and regional polygons; for individual fault traces, refer to USGS (2025). Arizona: Big Chino–Little Chino faults, Needles Graben faults, Toroweap fault, Hurricane fault, and Bellemont fault. Colorado: Gore Range frontal fault, Williams Fork Mountains fault, Sawatch fault (southern section), Poncha fault, and Uncompahgre Plateau region faults. Idaho: Lost River–Lemhi–Beaverhead faults, Sawtooth–Boulder Front faults, West Ola Valley–Jakes Creek–Big Flat faults, Long Valley faults, and Grand Valley–Rexburg–Heise fault. Montana: Bitterroot Valley and Missoula area faults, Butte area and Deer Lodge Valley faults, Helena Valley and Canyon Ferry Lake faults, Mission–Swan–South Fork Flathead faults, and Northwest Greater Yellowstone–Centennial Tectonic Belt faults. For more information, refer to Montana Bureau of Mines and Geology (2025). New Mexico: Alamogordo fault, Albuquerque basin region, border faults on the west side of Tularosa basin, Southern Sangre de Cristo fault, and Pajarito fault system. Nevada: Carson Range front faults, distributed faults in the North Valleys area of Reno, Frenchman Mountain fault, intra-basin faults in Las Vegas Valley, and northern Walker Lane faults. For additional information, refer to Nevada Bureau of Mines and Geology (2025). Oregon (IMW portion): Klamath–Sky Lakes fault system, Chemult–LaPine Graben fault system, Bend Area fault system, Grande Ronde Valley fault system, and Goose Lake Graben fault system. Wyoming: Rock Creek fault, Grand Valley fault, Southeast Yellowstone region faults, Jackson Hole region faults, and Stagner Creek fault. Utah: Wasatch fault zone, Cache Valley regional faults, Southern Utah regional faults, Tooele and Rush Valley regional faults, and Great Salt Lake and Utah Lake faults. For additional information, refer to Utah Geological Survey (2025b). Priority faults and regions were provided by BRPEWG State Representatives, including Jeri Ben Horin, Arizona Geological Survey; James McCalpin and Enrique Chon, Colorado Geological Survey; Zach Lifton, Idaho Geological Survey; Yann Gavillot and Mike Stickney, Montana Bureau of Mines and Geology; Dan Koning, New Mexico Bureau of Geology & Mineral Resources, New Mexico Institute of Mining & Technology; Richard Koehler, Nevada Bureau of Mines and Geology, University of Nevada, Reno; Lalo Guerrero, Oregon Department of Geology and Mineral Industries; James Mauch, Wyoming State Geological Survey; and Adam Hiscock and Rachel Adam, Utah Geological Survey. Explanation of Data Spatial data for IMW faults and regions of concern are included with this data release. Faults (for example, the Wasatch fault zone), grouped faults (for example, the Sawtooth–Boulder Front faults), and regions (for example, faults within the Uncompahgre Plateau region) are represented as polygons and organized into a single geographic information systems (GIS) file in both shapefile and Google Earth keyhole markup language (kml) formats. For individual or grouped faults, a polygon forming a 2.5-km-wide buffer (5 km total width) around the Qfaults trace is provided. Polygons for regional areas are also included and intended to represent areas of interest and/or distributed faulting. Justification for priority faults and regions is included in a plain text, comma separated value (csv) file. Fault and region polygons have the following fields: Name – Name of the IMW fault, grouped faults, or region. For example, Sawatch fault (individual fault), San Andres fault system (grouped faults), and Uncompahgre Plateau region faults (region). Fault naming convention follows the USGS Quaternary fault and fold database of the United States (Qfaults; USGS, 2025); regional names provided by BRPEWG state representatives. Type – Type of polygon. Fault – individual fault; grouped fault – one or more faults grouped; region – area of distributed faulting or general area of concern. Faults – Faults included in a regional polygon or set of grouped faults. For example, the Cache Valley region faults include the East Cache and West Cache faults; the San Andres fault system includes the San Andres Mountains and Organ Mountain faults. For individual faults, this field is NULL. State – State within which the majority of the fault, grouped faults, or region is located. Source – Source of spatial data. “Buffered USGS Qfaults traces” indicates USGS (2025) fault traces with a 2.5-km-wide buffer added. Other sources of data, including regional polygons, are provided. NSHM – Whether the fault or faults are included as earthquake sources in the 2023 update of the USGS National Seismic Hazard Model (Hatem and others, 2021). Partial – some but not all faults within a region or fault grouping are included in the model. Notes – Short statement provided by BRPEWG state representatives and only included in the csv file. This field includes justification for the priority fault designation, information on data gaps or remaining questions, and/or how additional research would help characterize hazard and reduce risk in the IMW region. References Crone, A.J., Haller, K.M., and Maharrey, J.Z., 2009, Evaluation of hazardous faults in the Intermountain West region—Summary and recommendations of a workshop: U.S. Geological Survey Open-File Report 2009-1140, 72 p. Briggs, R.W., and Hammond, W.C., 2011, Evaluation of geodetic and geologic datasets in the northern Walker Lane—summary and recommendations of the workshop, U.S. Department of the Interior: U.S. Geological Survey Open-File Report 2011-1282. Briggs, R., and Gold, R., 2015, U.S. Geological Survey workshop summary: Evaluation of hazardous faults in the Intermountain West region—2015 update, in Lund, W.R., editor, Proceedings volume, Basin and Range Province Seismic Hazards Summit III: Utah Geological Survey Miscellaneous Publication 15-5, variously paginated. Hatem, A.E., Collett, C.M., Briggs, R.W., Gold, R.D., Angster, S.J., Powers, P.M., Field, E.H., Anderson, M., Ben-Horin, J.Y., Dawson, T., and others, 2021, Earthquake geology inputs for the U.S. National Seismic Hazard Model (NSHM) 2023 (western U.S.) (ver. 3.0, December 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9AWINWZ. Koehler, R.D., and Anderson, J.G., 2019, 2018 Working Group on Nevada Seismic Hazards – Summary and recommendations of the workshop, Nevada Bureau of Mines and Geology Open-File Report 19-2, 44 p. Nevada Bureau of Mines and Geology, 2025, Earthquake research priorities for Nevada, EHP FY22, available at http://www.nbmg.unr.edu/_docs/Earthquakes/NBMG_priorities_NEHRP.pdf, accessed February 2025. Montana Bureau of Mines and Geology, 2025, GIS Data Hub: Geohazards, available at https://gis-data-hub-mbmg.hub.arcgis.com/pages/geohazards, accessed February 2025. Utah Geological Survey, 2025a, Basin and Range Province Earthquake Working Group, available at: https://geology.utah.gov/hazards/info/workshops/working-groups/basin-and-range-earthquakes/, accessed February 2025. Utah Geological Survey, 2025b, Utah Quaternary fault parameters, available at: https://geology.utah.gov/hazards/info/workshops/working-groups/q-faults/, accessed February 2025. U.S. Geological Survey, 2025, Quaternary fault and fold database of the United States, available at https://www.usgs.gov/programs/earthquake-hazards/faults, accessed February 2025.

The impact of warming on peak-season ecosystem carbon uptake is influenced by dominant species in warmer sites - Rose Brinkhoff; Aimee T Classen; Nathan J. Sanders (other, 2025)
Climatic warming affects ecosystem-scale carbon fluxes directly through its impact on photosynthesis and respiration, and indirectly by altering the plant community. This study reports on a 10-year factorial warming and dominant plant species removal experiment, conducted in a high- and a low-elevation montane meadow, to explore how dominant plants modify the effect of warming on the carbon cycle over time and in different locations. At the low-elevation site, warming increased peak-season net carbon uptake in most years, primarily due to higher primary productivity. This effect was observed only in plots where the dominant species was present. Net ecosystem carbon uptake was generally positive, but it often shifted from net carbon uptake to net carbon release when the dominant species was removed, particularly in dry years. Surprisingly, the high-elevation site showed no response to the warming or plant removal treatments. Synthesis: These findings highlight that dominant plant species can modify the impacts of warming on carbon fluxes. However, the effects of warming and plant species removal on the carbon cycle vary spatially and temporally. This study provides valuable insights into how both abiotic and biotic factors influence ecosystem carbon cycling and source-sink dynamics.

Scripts and data to produce figures for the SVS2 paper submitted to GMD (other, 2025)
The Soil, Vegetation, and snow model version 2.0 (SVS2) is a land surface model developed by Environment and Climate Change Canada (ECCC) to be used in the context of land surface prediction. SVS2 is described in a manuscript submitted to GMD (https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3396/). This repository contains the scripts and data used to generate the figures presented in the manuscript. It is divided into two subfolders: point_scale_simulations_SCB/ contains the scripts and data to generate Figures 3, 4 and 5 for the simulations at Snow Crested Butte. distributed_simulations/ contains the scripts and data to generate Figures 2 and 6 to 11 for the distributed simulations along a transect in Eastern Canada. Observations used in this evaluation are shared in this repository: Snow pit data are distributed in the folder distributed_simulations/data/obs/snow_pits/ Snow cover data from GMASI are distributed in the folder distributed_simulations/data/obs/gmasi Information about the ERA5 atmospheric forcing are provided in the folder distributed_simulations/forcing. The configuration files used for the 1D and 2D simulations with SVS2 presented in the manuscript are also available in their respective subfolders. The configuration files for the 1D simulations with SVS1 at Snow Crested Butte are also available. Readme files are present in the different subfolders to guide the user.

Data for: Increasing aridity may threaten the maintenance of a plant defense polymorphism - William Morris (other, 2025)
It is unclear how environmental change influences standing genetic variation in wild populations. Here, we characterized environmental conditions that protect vs. erode polymorphic chemical defenses in Boechera stricta (Brassicaceae), a short-lived perennial wildflower. By manipulating drought and herbivory in a four-year field experiment, we measured the effects of driver variation on vital rates of genotypes varying in defense chemistry and then assessed interacting driver effects on total fitness (estimated as each genotype’s lineage growth rate, λ) using demographic models. Drought and herbivory interacted to shape vital rates, but contrasting defense genotypes had equivalent total fitness in many environments. Defense polymorphism thus may persist under a range of conditions; however, ambient field conditions fall close to the boundary of putatively polymorphic environment space, and increasing aridity may drive populations to monomorphism. Consequently, elevated intensity and/or frequency of drought under climate change may erode genetic variation for defense chemistry in B. stricta. NOTE: A previously published version of this dataset was removed due to licensing issues. Please use the updated version now available.

Langenheim Plant Species Data (1953) and Associated Resurvey Datasets (2014), Gunnison Basin, Colorado, USA (other, 2025)
Quantitative plant abundance data were collected from the same 121 sites at two time periods separated by 65 years (1948-1952 and 2012-2014) in the Colorado Rocky Mountains to examine changes in plant community composition. The sites range in elevation from 2600m to 4100m. Approximately 30 sites were sampled from each of four habitat types: sagebrush (2528-3119m, n=27 sites), spruce-fir forest understory (3001-3520m, n=31 sites) , upland herb = montane meadow (3124-3850m, n=30 sites), and alpine (3549-4013m, n=33 sites). The earlier data set was collected by Jean H. Langenheim (1953, 1962) and consisted of counts of species occurrences along approximately 100m paced transects, noting species touching her boot tip every pace (n=100 sampled points per site). The later data set was collected by Stephanie D. Zorio (2015, 2016) consisting of counts of species occurrences every 1m along 300m transects (n=300 sampled points per site). The sites resurveyed by Zorio (2015, Zorio et al. 2016) were placed as close as possible to the original sites based on the written descriptions of Langenheim, but are only approximate. The GPS coordinates given for the resurveyed sites are the centerpoint of 2 perpendicular 150m transects, one across the slope and the other perpendicular to the slope. GPS coordinates for alpine sites along narrow ridges are the start and end points of three 100m transects along the ridge. Georeferenced localities and environmental site data are presented in Table 2: Lang Zorio Env Site Data. Langenheim’s original data were extracted from tables in her thesis (Langenheim 1953). These data omitted species that occurred in fewer than 14% of sites of a given habitat type (constancy). Species that occurred at very low frequencies (<1% per site) were only denoted as an x in the tables. Zorio converted these to frequencies of 0.5 for quantitative comparative purposes. This data set contains 157 species from 27 families across all sites and habitats. Species in several difficult to identify genera were lumped at the genus level. The original taxonomic nomenclature is retained. This data set is presented in Table 1: Lang Data 1953 Thesis. Data are given with species in rows and sites in columns for each habitat separately. Data are raw counts of species/site. For comparison with the more recent survey results, taxonomomy was updated to that of Weber and Wittmann (2012). All taxa (n=199) are combined into one data matrix with sites in rows and species in columns. These data are presented in Table 3: Langenheim data for use with Zorio Data. Complete species counts from the 2012-2014 resurveys (n=300 samples per site) are presented in Table 4: Zorio Veg Matrix All Species. As in Table 3, sites are in rows and species (n=311) are in columns. These data will be the most appropriate for comparison with future resurveys. The reduced data set of 2012-2014 surveys used to compare with Langenheims original data is presented in Table 5: Zorio_AAAR_Div by 3 & 14% Constancy. As noted above, all species with less than 14% constancy were dropped from the dataset and counts were divided by 3 to standardize the sampling effort between surveys. Literature Cited: Langenheim, J.H. 1953. Plant-Ecological Reconnaissance of the Crested Butte Area, Gunnison County Colorado. Draft of published doctoral dissertation, Dept. of Biological Sciences, University of Minnesota, Minneapolis, 235 pp. Langenheim, J.H. 1962. Vegetation and environmental patterns in the Crested Butte area, Gunnison County, Colorado. Ecological Monographs, 32:249-285. Weber, W.A., & Wittmann, R.C. 2012. Colorado Flora: Western Slope. Fourth Ed. Boulder: University Press of Colorado, 608pp. Zorio, S.D. 2015. 70 Years of Vegetation Change in the East River Basin, CO, USA. Master’s thesis, Department of Biological Sciences, Idaho State University, Pocatello, 127 pp. Zorio, S.D, Williams, C.F., & Aho, K.A. 2016. Sixty-five years of change in montane plant communities in western Colorado, USA. Arctic, Antarctic and Alpine Research, 48: 703-722.

Constraining Bedrock Groundwater Residence Times in a Mountain System with Environmental Tracer Observations and Bayesian Uncertainty Quantification: Modeling and Data Package - P. James Dennedy-Frank; William Payton Gardner; Nicholas Thiros; Erica R. Siirila-Woodburn; Kenneth Hurst Williams (other, 2025)
Groundwater residence times provide fundamental descriptions of hydrologic dynamics and mixing processes in mountainous watersheds. Yet, few observational datasets that can constrain groundwater residence times over broad timescales are available in high elevation mountain systems. Here we present field observations from May 2021 of dissolved noble gases (He, Ne, Ar, Kr, and Xe), Chloroflourcarbons (CFCs), Sulfurhexaflouride (SF6), and tritium (3H) sampled from the Pumphouse Lower Montane study site (wells PLM1, PLM6, and PLM7) within the East River Watershed, Colorado. The presented noble gas (PLM_noblegas_2021.csv) and environmental tracer (PLM_tracers_2021.csv) observation datasets, along with the associated modeling scripts, aide in quantifying groundwater residence times and recharge conditions in a high elevation mountain system. Furthermore, the modeling scripts quantify groundwater residence time and noble gas recharge condition uncertainties using a novel Markov-chain Monte Carlo approach. All data modeling scripts are written in the Python code.

Scripts and data to produce figures for the SVS2 paper submitted to GMD (other, 2025)
The Soil, Vegetation, and snow model version 2.0 (SVS2) is a land surface model developed by Environment and Climate Change Canada (ECCC) to be used in the context of land surface prediction. SVS2 is described in a manuscript submitted to GMD (https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3396/). This repository contains the scripts and data used to generate the figures presented in the manuscript. It is divided into two subfolders: · point_scale_simulations_SCB/ contains the scripts and data to generate Figures 3, 4 and 5 for the simulations at Snow Crested Butte. · distributed_simulations/ contains the scripts and data to generate Figures 2 and 6 to 11 for the distributed simulations along a transect in Eastern Canada. Observations used in this evaluation are shared in this repository: o Snow pit data are distributed in the folder distributed_simulations/data/obs/snow_pits/ o Snow cover data from GMASI are distributed in the folder distributed_simulations/data/obs/gmasi The configuration files used for the 1D and 2D simulations presented in the manuscript are also available in their respective subfolders. Readme files are present in the different subfolders to guide the user.

Plant and carbon data, snowmelt manipulation experiment, Rocky Mountain Biological Laboratory (RMBL), 2023 - Olivia K Vought; Peter Falb; Hannah B Shulman; Aimee T Classen; Stephanie N. Kivlin; Ian K Breckheimer; Amanda Henderson (other, 2025)
These data are from a 2023 snowmelt manipulation experiment in Vera Meadow at the Rocky Mountain Biological Laboratory. We experimentally advanced the snowmelt date in a montane meadow by approximately 12 days using black shade cloths and assessed the effect on plant and carbon dynamics. We measured net ecosystem exchange, gross primary productivity, and soil respiration using a Li-COR 7500 five times biweekly from June to August, plant community composition using the pin-drop method five times biweekly from June to August, and root biomass nine times using bulk soil cores. Using drone imagery, we measured the Normalized Difference Vegetation Index (NDVI). This data package is completed.

Atmospheric Surface Flux Station #30 measurements (level 2 Processed), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), September 2021-July 2023 (2025)
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Recreational trail traffic counts and trail proximity as a driver of ungulate landscape utilization - Chloe Beaupre; Alissa Bevan; Kevin A. Blecha; Jessica R. Young (other, 2025)
With continual growth in recreational trail use, it is becoming increasingly complicated to balance demands for outdoor recreation opportunities with wildlife conservation. To better understand how mule deer (Odocoileus hemionus) and Rocky Mountain elk (Cervus canadensis) respond to trail-based recreation, we deployed remote cameras in a paired study design to assess ungulate encounter rates relative to recreation traffic counts and distance from trails. Our methodology allowed us to estimate the magnitude of human activity on public land trails which can be challenging, but is a key step in understanding recreational effects on animal utilization, especially if any interaction in trail proximity and human traffic rates exist. Paired cameras provided a comparison of animal encounter rates on and off-trail at varying trail proximities, and the on-trail camera also yielded daily recreation traffic counts to assess how animals respond to varying traffic for each camera pair. Elk avoided busier trails and showed a strong positive response to distance from trails, while mule deer exhibited a positive response to higher traffic counts and no measurable effect of trail proximity. We identified a distance to trail threshold of 600-655 m, where elk utilization shifted from lower-than-expected utilization to higher-than-expected, indicating this elk population was redistributed to some degree within 57.5% of the study area’s extent. Quantifying recreation traffic counts was key to understanding species’ responses to recreation, highlighting the need for managers to consider both trail placement and human traffic regulation to mitigate recreation impacts.

bsu-wrudisill/CRF_UCRB: release 2 - William Rudisill (other, 2025)
release 2

AmeriFlux US-UR4 Gunnison - UCRB (other, 2025)
This is the AmeriFlux version of the carbon flux data for the site US-UR4 Gunnison - UCRB. Site Description - This site is located in Gunnison, Colorado, within a floodplain at a notable elevation. The region experiences cold winters with significant snow cover and cool summers. The ecosystem is homogeneous over an extended distance, providing a stable environment for consistent and reliable environmental studies.

Geochemistry and Strontium Isotopes for Coal Creek Watershed, Colorado, 2021-2022 - Wenming Dong; Curtis A. Beutler; J. N. Christensen; Kenneth Hurst Williams (other, 2025)
The geochemistry and strontium isotope data for Coal Creek Watershed, Colorado, consists of cation, anion, and 87Sr/87Sr isotope values from samples collected at 8 stream location along Coal Creek, samples from two groundwater springs within the watershed, and a shallow subsurface piezometer. All stream and spring samples were collected between June and October, 2021, and the shallow, near stream piezometer sample was collected in July of 2022. These data were collected to evaluate how groundwater contributions to Coal Creek originating from shallow vs deep flow paths respond 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 cation samples; (2) a csv of all anion samples; (3) a csv of all 87Sr/87Sr 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.

SnowEx20 Grand Mesa IOP Ground-Based Passive Microwave Radiometer Measurements, Version 1 (other, 2025)

leahgibson/sailnet_paper_analysis_and_figures: SAIL-Net Analysis and Figures (paper accepted) - Lay James Gibson (other, 2025)
Contains the code for the figure generation and analysis of the manuscript "Measurement Report: An investigation of the spatiotemporal variability of aerosol in the mountainous terrain of the Upper Colorado River Basin from SAIL-Net" (Gibson et al., 2025) accepted to EGU's Journal of Atmospheric Chemistry and Physics.

Surface Water Disinfection Byproducts and Organic Matter Characterization Data Associated with: “Disinfection byproducts formed during drinking water treatment reveal an export control point for dissolved organic matter in a subalpine headwater stream” - Stephanie S. Lau; William A. Mitch; Jonathon O. Sharp; Amy E. Goldman; Laura T. Leonard; Jason G. Toyoda; Gary F. Vanzin; Lupita Renteria; Sophia A. McKever; Robert E. Danczak; Vanessa A. Garayburu-Caruso; Brieanne Forbes; et al. (other, 2025)
This dataset is associated with the publication “Disinfection byproducts formed during drinking water treatment reveal an export control point for dissolved organic matter in a subalpine headwater stream” published in Water Research X (Leonard et al. 2022; https://doi.org/10.1016/j.wroa.2022.100144). The associated study analyzed temporal trends from the Town of Crested Butte water treatment facility and synoptic sampling at Coal Creek in Crested Butte, Colorado, US. This work demonstrates how drinking water quality archives combined with synoptic sampling and targeted analyses can be used to identify and understand export control points for dissolved organic matter. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) metadata and international geo-sample number (IGSN) mapping file; (4) dissolved organic carbon (DOC), ultraviolet absorbance at 254 nanometers (UV254), total nitrogen (TN), and specific ultraviolet absorbance (SUVA) data; (5) disinfection bioproduct formation potential (DBP-FP) data; (6) readme; (7) methods codes; (8) water collection protocol; (9) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory); and (10) folder of excitation emissions matrix (EEM) spectra. The FTICR folder contains a file of DOC (measured as non-purgeable organic carbon; NPOC) used for FTICR sample preparation. The FTICR folder also contains three subfolders: one subfolder containing the raw .xml data files, one containing the processed data, and the other containing instructions for using Formularity (https://omics.pnl.gov/software/formularity) and an R script to process the data based on the user's specific needs. All files are .csv, .pdf, .dat, .R, .ref, or .xml

Colorado Cold-Air Pooling Maps and SNOTEL Station Classifications (other, 2025)
This dataset contains maps of cold-air pooling (CAP) across Colorado at 100m resolution (Colorado_100m.nc) and the region surrounding the East River watershed, CO at 30m resolution (EastRiver_30m.nc). Classifications were determined using a Python implementation of the algorithm originally developed in Lundquist et al. (2008), applied to digital elevation models (DEMs) accessed and resampled from the U.S. Geologic Survey 3D Elevation Program (USGS, 2024) using the py3dep Python module (Chegini, 2021). Additionally, the SNOTEL_CAP_classifications.csv file contains CAP signals for 110 of the 117 Colorado SNOw TELemetry (SNOTEL) weather stations, derived from statistical analysis on observations and the automated algorithm classifications in Colorado_100m.nc. This dataset is released in support of an upcoming manuscript studying CAP distributions and mountain weather station representativeness across these two regions (currently in preparation). The two NetCDF (.nc) files are structured as follows: Variables elevation: elevation at each pixel (meters). CAP: cold-air pooling signal at each pixel. Values: 1 = CAP, 0 = no-signal, -1 = no-CAP. spatial_ref: contains attributes with coordinate system metadata The EastRiver_30m.nc file additionally contains the three variables used to determine CAP signal: slope (degrees) rank: the fractional height of a pixel relative to its neighbors curvature: see the formula in Lundquist et al. (2008) or Cramblitt (2025) Coordinates x: easting (NAD83 / Conus Albers) y: northing (NAD83 / Conus Albers) xv_dist: cross-valley distance (meters). A critical input defining the critical radii r in rank and curvature calculations. The SNOTEL_CAP_classifications.csv file is modified from site metadata obtained with the USDA NRCS National Water and Climate Center Air and Water Database Report Generator (https://wcc.sc.egov.usda.gov/reportGenerator). The appended columns include: EOF CAP Signal: CAP signal derived from empirical orthogonal function (EOF) analysis on recorded daily-minimum temperatures at 110 of Colorado's 117 SNOTEL stations. Negative values indicate CAP; positive values indicate no-CAP. Algorithm CAP Signal: CAP classifications for 110 of the 117 Colorado SNOTEL stations derived from the values in Colorado_100m.nc (3000 m cross-valley distance). Values: 1 = CAP, 0 = no-signal, -1 = no-CAP. Files Colorado_100m.nc: CAP classifications at 100m-resolution across the Colorado mountains for cross-valley distances from 10,000m to 250m (every 500m, with the addition of 250m). Users may select the value which best describes their region of interest. EastRiver_30m.nc: CAP classifications at 30m-resolution across terrain surrounding the East River watershed for a single cross-valley distance of 3,000m. SNOTEL_CAP_classifications.csv: CAP classifications for 110 of the 117 Colorado SNOTEL Stations determined by EOF analysis (column titled EOF CAP Signal) and an automated algorithm (column titled Algorithm CAP Signal). For further detail on algorithm design and function, including the calculation of rank and curvature, CAP classification criteria, and choice of cross-valley distance, please refer to Lundquist et al. (2008) or the linked GitHub repository. In-depth discussion on algorithm implementation and constraints, EOF classifications, and algorithm-EOF disagreement will be released in an upcoming manuscript. Version History Version 2.0.0 updated the contents of all three files and renamed SNOTEL_CAP_classifications.csv to Colorado_SNOTEL_CAP.csv. The previous version was missing information near the state borders, which was corrected here by expanding the spatial domain in Colorado_100m.nc and adjusting the CAP classifications in Colorado_SNOTEL_CAP.csv accordingly. The updated files also reflect the recent corrections to the calculation of curvature and handling of coordinate systems in v2.0.0 of the CAP algorithm (Cramblitt, 2025). The effect of these corrections is relatively minor, but do affect the CAP signal at a few SNOTEL stations and notably the coordinate reference system has been changed from WGS84 (EPSG:4326) to NAD83/Conus Albers (EPSG:5070). The variables 'slope', 'curvature', and 'rank' were removed from the Colorado_100m.nc file to decrease file size and allow for more manageable downloads. These variables are available upon request. Version 1.1.0 added the SNOTEL_CAP_classifications.csv file to the dataset. For questions, please contact jcramblitt@berkeley.edu.

Cosmogenic Nuclide Burial Isochron Data for the Central City Gravel, Malta Gravel, and Lava Creek B Ash Locations: Classic Locations of Early to Middle Pleistocene Deposits, South Platte and Arkansas Rivers, Central Colorado, USA (other, 2025)
This data release includes Al-26/Be-10 cosmogenic nuclide concentrations and burial isochron ages for three locations in central Colorado, USA, with preferred burial ages ranging from ~2 Ma to 600 ka, that provide constraints on geologic mapping and chronostratigraphic correlations of Pleistocene Colorado Rocky Mountain (CRM)-Great Plains deposits. This new dataset helps to validate original identification and characterization of the deposits by renowned work of early to late 20th century geologists. In addition, this dataset is critical for any models of geomorphic development of modern Colorado Rocky Mountain (CRM)-Great Plains depositional systems, including analyses of regional relationships involving the Malta Gravel, Nussbaum Alluvium, Rocky Flats Alluvium, Verdos Alluvium, and Slocum Alluvium (Scott, 1960; Tweto, 1961; Epis and others, 1976, 1980). Paired with cosmogenic analyses for the Nussbaum Alluvium at Baculite Mesa (1.30 ± 0.08 Ma) (Odom and others, 2024) and a glacial advance of the Laurentide icesheet of Balco and Rovey (2010) at 1.31 ± 0.13 Ma, this dataset provides the chronologic constraints for onset of Pleistocene Rocky Mountain river incision and Great Plains river entrenchment for the South Platte and Arkansas Rivers, and the establishment of the current river positions. Three classic localities of previously mapped and described deposits were sampled in the field for cosmogenic nuclide analysis. A summary of the characteristics of these sites is as follows: (1) The Central City gravel is a 160 meter-thick (525 feet), matrix-supported, pebble to boulder gravel with a silty sand matrix, nonstratified to crude stratification, and no distinct clast imbrication (Kellogg and others, 2008) lying within the CRM Front Range foothills along Clear Creek at an elevation of approximately 2,430 m/7,960 ft asl. It was originally identified as the first Pleistocene glacial deposit emanating from ice caps developed on the Continental Divide across the CRM Front Range (Spurr and others, 1908; Atwood and Atwood, 1938). More recent work has placed this deposit as a Neogene/Miocene gravel associated with the Ogalalla Formation on the Great Plains (Kellogg and others, 2008), and disregarded its glacial origin and designation as till by the initial investigators. Seven samples of cobbles and clayey, silty sand matrix were measured for an Al-26/Be-10 burial isochron. Two samples, 17-CO-047-CEN and 18-CO-002-CEN, featured depressed Al-26/Be-10 ratios indicative of prior burial and were excluded from further calculations. The final burial isochron for this site yielded a burial age of 1.44 ± 0.26 Ma (1σ analytical uncertainty). (2) The Pliocene-early Pleistocene, Malta Gravel (Tweto, 1961; McCalpin and others, 2012; Ruleman and others, 2018) is a reddish-brown to yellow-red, angular to subangular, pebble to cobble gravel with a silty sand matrix exposed in a railroad cut ~5 km/3.1 mi west of Leadville, Colorado at the headwaters of the Arkansas River (McCalpin and others, 2012). It was originally defined as an early-middle Pleistocene glaciofluvial deposit (Tweto, 1961), but was more recently identified as non-glacial in origin and possibly within the uppermost section of the Neogene Dry Union Formation (McCalpin and others, 2012; Kellogg and others, 2017). Twelve samples of sand and cobbles were collected for analyses approximately 5 meters (16.4 feet) below the deposit surface and below a heavily oxidized pedogenic horizon overlain by pre-Bull Lake glaciofluvial deposits of early or middle Pleistocene age. One sample, 17-CO-039-LV-Ac, featured a depressed Al-26/Be-10 ratio and was excluded from further calculations. The final burial isochron for this site yielded a burial age of 1.97 ± 0.04 Ma (1σ analytical uncertainty). (3) Lava Creek B ash location in the central upper Arkansas River valley, north of Buena Vista, CO is a heavily oxidized, fluvially reworked tephra within a faulted and fractured outcrop of clayey, silty sand deposits, overlain by pre-Bull Lake glacial deposits of middle Pleistocene age (Scott, 1975; Kellogg and others, 2017). The ash lies within the uppermost Dry Union Formation (Kellogg and others, 2013). Probable seismogenic sand dikes are pervasive throughout the exposure. Twelve samples for analyses were collected approximately 9 meters (29.5 feet) below the surface and 40 centimeters (15.8 inches below the basal Lava Creek B ash layer within a silty sand and pebble gravel. One sample, 17-CO-032-BV-A, featured a depressed Al-26/Be-10 ratio indicative of prior burial and was excluded from further calculations. All other samples had nearly identical mean Be-10 concentrations clustering between 601,000-651,000 atoms/gram (at/g) and Al-26 concentrations clustering between 2,913,000-3,429,000 at/g. These overlapping concentrations precluded a burial isochron calculation and necessitated minimum/maximum burial dating. The mean minimum burial age was ~570 ka and mean maximum burial age was ~690 ka, overlapping with the preferred sanidine 40Ar/39Ar isochron date of 631.3 ± 4.3 ka for the Lava Creek B ash (Matthews and others, 2015). These new ages provide a middle Pleistocene age for the minimum age of the Dry Union Formation, and chronostratigraphic correlation to the uppermost Dry Union Formation with the uppermost Santa Fe Group within the San Luis Valley (Ruleman and others, 2021). References: Atwood, W.W., and Atwood, W.W., 1938, Opening of the Pleistocene in the Rocky Mountains of the United States, The Journal of Geology, v. 46, no. 3, p. 239–247, https://www.jstor.org/stable/30081297 Balco, Greg, and Rovey, C.W., 2010, Absolute chronology for major Pleistocene advances of the Laurentide Ice Sheet, Geology, v. 38, no. 9, p. 795–798, doi: https://doi.org/10.1130/G30946.1 Epis, R.C., Scott, G.R., Taylor, R.B., and Chapin, C.E. 1976, Cenozoic volcanic, tectonic, and geomorphic features of central Colorado, Professional Contributions of the Colorado School of Mines No. 8, p. 323-338. Epis, R.C., Scott, G.R., Taylor, R.B., and Chapin, C.E., 1980, Summary of Cenozoic geomorphic, volcanic, and tectonic features of central Colorado and adjoining areas, in Kent, H.C., Porter, K.W., eds., Colorado Geology: Denver Colo., Rocky Mountain Association of Geologists, p. 135-156. Kellogg, K.S., Shroba, R.R., Bryant, Bruce, and Premo, W.R., 2008, Geologic map of the Denver West 30’ x 60’ quadrangle, north-central Colorado: U.S. Geological Survey Scientific Investigations Map 3000, scale 1:100,000, 48-p. pamphlet. Kellogg, K.S., Lee, Keenan, Premo, W.R., and Cosca, M.A., 2013, Geologic map of the Harvard Lakes 7.5' quadrangle, Park and Chaffee Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3267, 22 p., scale 1;24,000, https://dx.doi.org/10.3133/sim3267. Kellogg, K.S., Shroba, R.R., Ruleman, C.A., Bohannon, R.G., McIntosh, W. C., Premo, W.R., Cosca, M.A., Moscati, R.J., and Brandt, T.R., 2017, Geologic map of the upper Arkansas River valley region, north-central Colorado: U.S. Geological Survey Scientific Investigations Map 3382, pamphlet 70 p., 2 sheets, scale 1:50,000, https://doi.org/10.3133/sim3382. Matthews, N.E., Vazquez, J.A., and Calvert, A.T., 2015, Age of the Lava Creek supereruption and magma chamber assembly at Yellowstone based on 40Ar/39Ar and U-Pb dating of sanidine and zircon crystals, Geochemistry, Geophysics, and Geosystems, v. 16, no. 8, p. 2508-2528, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GC005881. McCalpin, J.P., Funk, Jonathan, and Mendel, David, 2012, Leadville South Quadrangle Geologic Map, Lake County, Colorado, Colorado Geological Survey Open-File Report 12-06, 54 p., 2 plates, 1:24,000 scale. Odom, W.E., Ruleman, C.A., Sterne, E.J., O'Keeffe, M.K., and Morgan, M.L., 2024, Cosmogenic Al-26/Be-10 Isochron Burial Data for the Nussbaum Gravel at Baculite Mesa, CO: U.S. Geological Survey data release, https://doi.org/10.5066/P1B2MCHG. Ruleman, C.A., Brandt, T.R., Caffee, M.W., and Goehring, B.M., 2018, Geologic map of the Leadville North 7.5’ quadrangle, Eagle and Lake Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3400, 1:24,000, https://doi.org/10.3133/sim3400. Ruleman, C.A., and Brandt, T.R., 2021, Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3475, 2 sheets, scale 1:75,000, https://doi.org/10.3133/sim3475. Scott, G.R., 1960, Subdivision of the Quaternary Alluvium East of the Front Range Near Denver, Colorado, Bulletin of the Geological Society of America, v. 71, p. 1541-1544. Scott, G.R., 1975, Reconnaissance geologic map of the Buena Vista quadrangle, Chaffee and Park Counties, Colorado, U.S. Geological Survey Miscellaneous Field Studies Map MF-657, 1:62,500 scale. Spurr, J.E., Garrey, G.H., and Ball, S.H., 1908, Economic Geology of the Georgetown Quadrangle, Together with the Empire District, U.S. Geological Survey Professional Paper 63, 507 p., 4 plates. Tweto, Ogden, 1961, Late Cenozoic events of the Leadville district and upper Arkansas Valley, Colorado, Geological Survey Research 1961, short papers in the geologic and hydrologic sciences Articles 1-146, U.S. Geological Survey Professional Paper, 424-B, p. B133-B135.

Life History and Demography of Astragalus microcymbus - Michelle DePrenger-Levin; Richard Levy (other, 2025)
Astragalus microcymbus, the skiff milkvetch, is a perennial forb endemic to Gunnison and Saguache counties in Colorado, United States. In 1995 Denver Botanic Gardens scientists began monitoring individual A. microcymbus plants to track longevity and reproductive rates, ultimately recording plant length, reproductive condition, life stage, and herbivory damage on an annual basis. This resource provides occurrence and measurement data in the form of a sampling event dataset.

SPLASH Field Study; Atmospheric Surface Flux Station #30 measurements (level 2 Processed), Sep2021-Jul2023 (NCEI Accession 0303959) - NCEI (other, 2025)
Processed (Level 2) measurements and derived variables 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 of the surface energy budget, momentum flux, near-surface meteorology, and soil properties. Measurements of high-resolution 3-dimensional winds were observed at a nominal height of 4.6 m (depending on snow depth). Measurements of upwelling broadband radiation and meteorology observed from a nominal height of 2.9 m. The measurements are included in three netCDF files per day. The "sledmet" files are comprised of 1-min averages of measured and derived variables, including near-surface meteorology, surface skin temperature, snow depth, radiative fluxes, and conductive fluxes. The "sledseb" files are 10-min averages of the same variables as in the 1-min files and also include calculations of turbulent sensible and latent heat fluxes, momentum flux, and associated diagnostics, surface stress, and Monin-Obukhov parameters using both eddy covariance and bulk methodologies, all valid for the 10-min intervals. Both of these file types also contain a "_qc" variable paired with each measurement variable, or family of variables, that is a temporally-matched quality control code: 0 = good data, 1 = caution (data may be suspect), 2 = bad data, and -1 = missing (no data was collected). The "10hz" files include 3-dimensional winds and gas densities of water vapor and carbon dioxide that are quality-controlled, aggregated to a regular 10-Hz temporal grid, and (for winds) rotated into the earth coordinate frame. A detailed documentation of the measurement conditions, the processing steps taken to construct this data set, and other caveats and uncertainties will be provided in an accompanying published data manuscript.

Size-Resolved Chemical Composition of Particles Collected Using STAC at the Ground Site During the SAIL Campaign in Gunnison, Colorado - Nurun Nahar Lata; Zezhen Cheng; Ashfiqur Rahman (other, 2025)
Aerosol particles were collected using a four-stage Size and Time-resolved Aerosol Collector (STAC) during the SAIL field campaign. Each stage of STAC separates particles into distinct aerodynamic size fractions with 50% cut-off diameters: Stage A: 2.27 µm Stage B: 0.615 µm Stage C: 0.421 µm Stage D: 0.119 µm Each stage provides both size- and time-resolved sampling, enabling investigation of particle composition across different atmospheric regimes. Only a subset of samples was selected for analysis based on prevailing meteorological conditions (e.g., temperature, humidity, and air-mass influence) to capture representative aerosol types under distinct weather patterns. Collected substrates were first examined under Scanning Electron Microscopy (SEM) to evaluate particle loading, morphology, and spatial distribution. Subsequently, Computer-Controlled Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (CCSEM/EDX) was performed to obtain size-resolved elemental composition of individual particles. A rule-based classification scheme was applied to categorize particles into major compositional groups (e.g., biological, carbonaceous, dust, sulfate, Na-rich, and mixed types). This dataset provides high-resolution morphological and chemical information on atmospheric particles collected during the SAIL campaign, offering insights into the influence of meteorology on aerosol composition and mixing state.

Data from: Influence of plant reproductive systems on the evolution of hummingbird pollination - Judith Eberz; Lea Sophie Dannenberg; Katrin Becker; Stefan Abrahamczyk; Nayara Atella-Hödtke; Bastian Steudel; Maximilian Weigend (other, 2025)
Many hummingbird-pollinated plant species evolved from bee-pollinated ancestors independently in many different habitats in North and South America. The mechanisms leading to these transitions are not completely understood. We conducted pollination and germination experiments and analysed additional reproductive traits in three sister species pairs of which one species is bee- and the other hummingbird-pollinated. All hummingbird-pollinated species showed higher seed set and germination rates in cross-pollinated than in self-pollinated flowers. In the self-compatible, bee-pollinated sister species this difference did not exist. As expected, seed set and germination rate were higher after cross-pollination in the largely self-incompatible genus Penstemon independently of the pollination syndrome. However, the bird-pollinated species produce only half of the amount of ovules and pollen grains per flower compared to the bee-pollinated sister species. This indicates that hummingbird pollination is much more efficient in self-incompatible populations because hummingbirds waste less pollen and provide higher outcrossing rates. Therefore, hummingbird pollination is less resource costly. Overall, we suggest that hummingbirds may increase the reproductive success compared to bees, influencing the evolution of hummingbird pollination in ecosystems with diverse bee assemblages.

MariaPaniw/Comparative-demography-project: comp_demo_scale_up (2025)
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DevonKerins/HydrologicalDynamicsinHeadwaterCatchments: Input and data analysis files for "Controls From Above and Below: snow, soil, and steepness drive diverging trends of subsurface water and streamflow dynamics" (2025)
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Supplementary material from "Nutrient niche dynamics among wild pollinators" - Justin A. Bain; Jane E. Ogilvie; Paul J. CaraDonna; William K. Petry (other, 2025)
Food underpins fitness and ecological interactions, yet how nutrient availability shapes species interactions in natural communities remains poorly understood. Most nutritional ecology research focuses on laboratory or single-species systems, limiting insight into how nutrient use and nutrient niche dynamics occur in complex, multi-species assemblages in the wild. We combined long-term plant–pollinator interaction data with pollen macronutrient analyses to examine how wild bumble bees exploit macronutrients and whether they occupy distinct nutrient niches. Pollen macronutrient composition varied across plant species and over the season, with protein-rich pollen peaking in spring and lipid- and carbohydrate-rich pollen increasing by late summer. Across this nutrient landscape, bumble bee species occupied two distinct macronutrient niches: one high in protein and low in lipid and carbohydrate, and the other lower in protein but moderate in lipid and carbohydrate. Nutrient niche partitioning was associated with differences in feeding morphology and colony life stage (but not phenology). We found little evidence that nutrient niche breadth differed among species or was explained by feeding morphology or colony life stage. Our results extend nutritional ecology to a multi-species context, provide evidence for nutrient niche partitioning among wild pollinators, and highlight the need to consider species-specific nutritional requirements in pollinator conservation.

River bank erosion and accretion rates, and planform metrics of the East River, downstream of Gothic Colorado over intervals between 1955 and 2015 - J. C. Rowland; Sophie Stauffer (other, 2025)
This dataset provides the tabular summary of analysis of an alluvial floodplain reach of the East River, downstream of Gothic, CO near Crested Butte. The measurements include bank erosion and accretion rates, channel width, bank curvature, and the aspect/orientation of the river banks. The measurements were derived from binary masks of the location of the river channel from aerial and satellite imagery collected on seven dates over a sixty-year timespan: 1955, 1973, 1983, 1990, 2001, 2011, and 2015. The masks were analyzed using the Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM) software detailed in Rowland et al. 2016. The masks used in this analysis can be found an accompanying dataset (DOI: ). Rates of change along the East River were measured over a total of 8-time intervals: 1955-1973; 1955-2015; 1973-1983; 1983-1990; 1990-2001; 2001-2011; 2001-2015; and 2011-2015. In files with “summary” in the name, the data is provided at a pixel level, where each mapped bank pixel has an associated erosion or accretion value, a channel width, a curvature value, and an aspect each river and time period will have an individual file. Files with “Segments” in name provide data that is averaged along segments of the rivers. These segments are approximately 10 channel widths in length. In addition to erosion and accretion rates, the segment-based results include area measurements of erosion and accretion, islands, and channels. The number of islands is also included.

Data from: Drivers of spring migration phenology in Rocky Mountain elk (other, 2025)
By migrating, ungulates take advantage of cyclical fluctuations in resources, which allows them to persist at greater population numbers than they would in the absence of these seasonal movements. We sought to identify the drivers of spring elk (Cervus canadensis) migration and evaluate how well individuals were able to optimize access to forage prior to departure, while migrating, and upon arrival on summer range. Specifically, we investigated the timing and duration of spring migration in four Colorado elk herds to test how forage quality and snow dynamics pre-, during, and post-migration influenced elk departure from winter range and the length of time spent migrating. Our analyses revealed significant heterogeneity among and within herds. Overall, spatiotemporal dynamics in forage and snow emerged as critical drivers influencing migratory phenology, but the discrete covariates associated with forage and snow were herd-specific. We did not find marked sub-strategies wherein some herds or individuals optimized a specific component of migration, but rather found that elk varied in their ability to optimize access to forage at all times analyzed. Our findings suggest that elk exhibit a flexible response to environmental cues, adjusting migration timing and duration in accordance with local conditions. These results contribute to a deeper understanding of the mechanisms driving ungulate migration and emphasize the importance of considering dynamic environmental factors in studies of migration phenology. Further, they emphasize the importance of forage conditions not just on the migratory route, but also on winter and summer range. Additionally, they provide baseline knowledge of elk migrations in Colorado, useful information for ensuring the conservation and persistence of these migratory routes and the productivity they facilitate.

SPLASH Field Study; Atmospheric Surface Flux Station #50 measurements (level 2 Processed), Oct2021-Jun2023 (NCEI Accession 0303960) - NCEI (other, 2025)
Processed (Level 2) measurements and derived variables from the Atmospheric Surface Flux Station #50 (ASFS-50) deployed at the Avery Picnic site (38°58.3455' N, 106°59.8113' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from October 2021 through June 2023. The ASFS measured variables of the surface energy budget, momentum flux, near-surface meteorology, and soil properties. Measurements of high-resolution 3-dimensional winds were observed at a nominal height of 4.6 m (depending on snow depth). Measurements of upwelling broadband radiation and meteorology observed from a nominal height of 2.9 m. The measurements are included in three netCDF files per day. The "sledmet" files are comprised of 1-min averages of measured and derived variables, including near-surface meteorology, surface skin temperature, snow depth, radiative fluxes, and conductive fluxes. The "sledseb" files are 10-min averages of the same variables as in the 1-min files and also include calculations of turbulent sensible and latent heat fluxes, momentum flux, and associated diagnostics, surface stress, and Monin-Obukhov parameters using both eddy covariance and bulk methodologies, all valid for the 10-min intervals. Both of these file types also contain a "_qc" variable paired with each measurement variable, or family of variables, that is a temporally-matched quality control code: 0 = good data, 1 = caution (data may be suspect), 2 = bad data, and -1 = missing (no data was collected). The "10hz" files include 3-dimensional winds and gas densities of water vapor and carbon dioxide that are quality-controlled, aggregated to a regular 10-Hz temporal grid, and (for winds) rotated into the earth coordinate frame. A detailed documentation of the measurement conditions, the processing steps taken to construct this data set, and other caveats and uncertainties will be provided in an accompanying published data manuscript.

Genomic and Transcriptomic Analysis of the Whirling Disease-Resistant Gunnison River Rainbow Trout - Supplementary Data - Abdullah Gharamah; Abdullah Gharamah (other, 2025)
Supplementary tables and figures

Data from: Different aspects of dominance are not equivalent when testing for trade-offs in ant communities (2025)
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Bstricta climate change dataset (other, 2025)

Genomic and Transcriptomic Analysis of the Whirling Disease-Resistant Gunnison River Rainbow Trout - Supplementary Data (other, 2025)
Supplementary tables and figures

igraph for R: R interface of the igraph library for graph theory and network analysis - Szabolcs Horvát; Tamás Nepusz; Vincent Traag; Kirill Müller; Fabio Zanini; Gábor Csárdi; Daniel Noom (other, 2025)
Testing Tweak tests that use the graph package.

Cosmogenic Nuclide Burial Isochron Data for the Central City Gravel, Malta Gravel, and Lava Creek B Ash Locations: Classic Locations of Early to Middle Pleistocene Deposits, South Platte and Arkansas Rivers, Central Colorado, USA (other, 2025)
This data release includes Al-26/Be-10 cosmogenic nuclide concentrations and burial isochron ages for three locations in central Colorado, USA, with preferred burial ages ranging from ~2 Ma to 600 ka, that provide constraints on geologic mapping and chronostratigraphic correlations of Pleistocene Colorado Rocky Mountain (CRM)-Great Plains deposits. This new dataset helps to validate original identification and characterization of the deposits by renowned work of early to late 20th century geologists. In addition, this dataset is critical for any models of geomorphic development of modern Colorado Rocky Mountain (CRM)-Great Plains depositional systems, including analyses of regional relationships involving the Malta Gravel, Nussbaum [...]

Model Scripts for "Old-Aged Groundwater Contributes to Mountain Hillslope Hydrologic Dynamics" - Matthias Sprenger; James Dennedy-Frank; Payton Gardner; Nicholas Thiros; Erica R. Siirila-Woodburn; Kenneth Hurst Williams (ess_dive, 2025)
The partitioning of water inputs between deep and shallow groundwater flow paths is a fundamental processes, yet is challenging to observe. Numerical models provide a valuable tool to further develop insights on these groundwater mixing processes. This repository contains scripts to run the ParFlow-CLM and EcoSLIM integrated hydrologic models along the Pumphouse Hillslope in the East River Watershed and associated python scripts to process model outputs. Files includes input decks for the associated models, python scripts, and text and css files to support the model runs and processing. The 2-D hillslope model simulates from 2000 to 2021 using transient forcing conditions from the publicly available NLDAS-2 dataset. We develop an ensemble of models with variable hydrogeologic and soil parameters. Model outputs from the ensemble of runs are compared to to water level observations at the PLM1 and PLM6 wells published at (https://data.ess-dive.lbl.gov/view/doi:10.15485/1866836) and groundwater mean ages published at (https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1960042. The model results are used to evaluate hillslope water mass-balance transience and mixing dynamics between groundwater flow paths with young (<10 year) and old (>10 year) ages.

United States Coast Pilot (volume 1 through 9) - NCEI (other, 2025)
The United States Coast Pilot is a series of 9 nautical books that cover a wide variety of information important to navigators of U.S. coastal and intercoastal waters, and the waters of the Great Lakes. Most of this information cannot be shown graphically on the standard nautical charts and is not readily available elsewhere. The subjects of the Coast Pilot include, but are not limited to, channel descriptions, anchorages, bridge and cable clearances, currents, tide and water levels, prominent features, pilotage, towage, weather, ice conditions, wharf descriptions, dangers, routes, traffic separation schemes, small-craft facilities and federal regulations applicable to navigations.

Supplementary material from "Shorter seasonal snow cover poses a risk to solitary bee populations in a mountainous ecosystem" (2025)
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Petrogenic organic carbon estimates for two Rocky Mountain soils underlain by Cretaceous Mancos Shale (2025)
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Bulk density, grain size, carbon, and nitrogen content of floodplain fine sediment along the East River, Colorado collected from 2014 to 2017 - Nicholas Sutfin; M. Ryan King; J. C. Rowland; Wenming Dong (other, 2025)
The dataset is comprised of grain size, bulk density, carbon and nitrogen content of soils sampled on the floodplain of the East River 4 to 11 km downstream of Gothic, CO near Crested Butte. This dataset was collected to characterize the sedimentological properties and carbon inventory of the East River floodplain. The data help quantify the flux and storage of sediment and carbon between the East River and the alluvial floodplain bounding the river. The samples were collected between 2014 and 2017. The soil samples come from a total of 156 locations and were sampled in the upper 40 to 60 cm of the floodplain and in abandoned channel segments or oxbows. Not all samples were analyzed for all properties, but 26 samples were analyzed for bulk density, full particle size distribution, carbon, and nitrogen content. An additional 48 samples were analyzed for particle size fraction greater than and less than 2 mm and total organic content determined by loss on ignition. The dataset is comprised of a total of six separate data files based on the sample type and analysis and another file lists the sample identifications and locations of samples with a complete suite of analyses.

Wet and dry plastic deposition data for western US National Atmospheric Deposition Program sites (2017-2019) - Janice Brahney (other, 2025)
Raw plastic counts and calculated plastic deposition rates for 11 National Atmospheric Deposition Program (NADP) sites in the western USA recorded from 2017-2019. Samples were collected to determine plastic deposition rates in remote locations and identify the potential sources of plastic to the atmosphere. Samples were collected using an Aerochem Metrics Wet and Dry sampler. Wet deposition refers to the material retained on a 0.45-micron filter. Dry deposition refers to the dry only deposition collected using a Dry Side Insert (DSI) (Brahney et al. 2020). Plastics are identified as either fibers or particles (includes small colored films). Wet deposition rates are recorded weekly and dry plastic deposition rates are based on monthly sampling intervals. Sites included are Grand Canyon National Park (AZ03), Joshua Tree National Park (CA67), Rocky Mountain National Park (CO98), Niwot Ridge (CO02), Ashley National Forest (UT95), East River SFA (CO10)), Craters of the Moon National Park (ID03), Great Basin National Park (NV05), Canyonlands National Park (UT09), Bryce Canyon Nationa Park (UT99), and Wind River Range (WY06). Data collection is being published in collaboration with the Department of Energy's research project, "Constraining physical understanding of aerosol loading, biogeochemistry, and snowmelt hydrology from hillslope to watershed scale in the East River Scientific Focus Areas.

Raw Data published in PeerJ, 2021 for Crested Butte decomposition field study 2017-2019 - Laura T. Leonard; Jonathon O. Sharp; Eoin L. Brodie; Kenneth Hurst Williams (other, 2025)
This data package contains text files that describe geochemical measurements collected from 2017-2019 during isolated conifer needle decomposition field studies in Crested Butte, Colorado. The geochemical measurements were collected across three elevations (2,800–3,500 m) ranging from montane to subalpine ecoregions. The data sets within include total carbon and nitrogen content and fourier-transform infrared spectroscopy (FTIR) results for the initial needles collected in 2016 and after decomposition in 2019. Data collection results from August 2020 are also included from when the experimental plots were removed to understand final concentrations of soil extractable carbon and nitrogen content as well as mass balances from litter bag deployments. Soil porewater results are also provided from 2017-2019 for DOC, TN, UV254, and specific UV absorbance (SUVA) analyses at 15 cm soil depth. Gas flux raw data provides CO2, CH4, N2O, and NH3 measurements above needle decomposition over the three study years. Finally, soil samples for microbial DNA extractions were collected from the upper soil depth. This raw data is available in the NCBI SRA database under SRA accession numbers PRJNA605259 and PRJNA715914. These data sets were generated to investigate the isolated decomposition of spruce and lodgepole conifer needles. The goal of this work was to determine the roles of elevation, soil type, seasonal changes in soil moisture, and snowmelt timing on litter decomposition processes. Results from this work are detailed in the reference paper "Effect of elevation, season and accelerated snowmelt on biogeochemical processes during isolated conifer needle litter decomposition. DOI: 10.7717/peerj.11926."

Occurrence Download - GBIF.org User; GBIF.org User; GBIF.org User; GBIF.org User; GBIF.org User; GBIF.org User (other, 2025)
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/0015415-251025141854904/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

SPLASH Field Study; BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature (NCEI Accession 0301536) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners participated in the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH). This field study installed a comprehensive, state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. 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 collected during the SPLASH campaign. These data were collected near Avery Picnic and Kettle Ponds in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023.

2D reactive transport model of shale chemical weathering and biogeochemical fluxes along a mountainous hillslope, East River Watershed, Colorado: Input files and simulation results - Dwivedi Dipankar; Lucien Stolze; Sergi Molins; Wenming Dong; Alexander W. Newman; Curtis A. Beutler; Carl I. Steefel; Kenneth Hurst Williams (ess_dive, 2025)
This data package contains input files and simulation results for a two-dimensional (2D) reactive transport model used to quantitatively analyze the coupled hydrological and biogeochemical processes governing shale weathering and associated biogeochemical fluxes under realistic environmental conditions in the high-elevation East River Watershed. These data support the conclusions presented in Stolze et al. (Water Resources Research, under review), "Model-based interpretation of solute exports and carbon partitioning during shale weathering in a mountainous hillslope". The model simulates atmospheric-subsurface gas exchange, subsurface water flow, and shale weathering processes under dynamic, year-scale conditions along a shale-underlain hillslope located in the East River watershed. The simulations were performed using the PFLOTRAN flow and reactive transport code and executed on the Perlmutter supercomputer to leverage its large-scale parallel computing capabilities. The data package contains two zipped folders, "model_input_files" and "simulation_results", and one readme.txt file. "model_input_files" contains the necessary input files to run the calibrated base-base model presented in Stolze et al. (Water Resources Research, under review). "simulation_results" contains a single hdf5 file ("Output_2D_hillslope_model.h5") which includes the results of simulation performed using the base-case model. This file can be opened with HDFView 3.1.4, Python, or MATLAB. "readme.txt" contains relevant information about the base-case model and provides guidelines on how to run the associated input files provided in the folder "model_input_files". Furthermore, readme.txt provides information regarding the model results provided in "Output_2D_hillslope_model.h5" such as matrix dimensionality and output units. Field datasets used to evaluate model performance were collected at three monitoring wells located along a hillslope transect (PLM1, PLM2, and PLM3). Dissolved ion concentration data were collected from November 2016 to October 2021 for Ca, Mg, DIC, Na, K, SO4 (Dong et al., 2025 - dic_npoc_data_2014_2024.zip - DOI:10.15485/1660459; Williams et al., 2025 - anion_data_2014_2024.zip - DOI:10.15485/1668054; Dong et al., 2025 - cation_data_2014_2024.zip - DOI:10.15485/1668055). Note that we used the files named er_PLM1_xx_yy, er_PLM2_xx_yy, and er_PLM3_xx_yy where xx stands for the name of the aqueous species and yy stands for the depth where the measurements were performed. Soil water content ([0 - 1] m) and water table depth were collected from November 2016 to October 2021 (Wan et al., 2024 - Dynamic_water_table__depthsFig2b.csv and Soil_water_content_Fig4e.csv - DOI:10.15485/2322567). Gaseous CO2 concentration were collected from October 2020 to December 2021(Wan et al., 2024 - Soil_CO2_concentrations_Fig4h.csv - DOI:10.15485/2322567) Gaseous CO2 flux from the subsurface to the atmosphere were collected in the vicinity of PLM2 from October 2019 to May 2022 (Wu et al., 2025). Soil microbial biomass concentration was measured from August 2016 to June 2017 (Sorensen et al., 2019 - 2017_East_River_Pumphouse_Microbial_Biomass__1_.csv - DOI:10.15485/1577267) All field data are published as CSV files compatible with Microsoft Excel, MATLAB, and Python, or as text files. The coordinates of the monitoring wells and the CO2(g) flux sensor in the coordinate system WGS84 are: -PLM1: [38.9197710 ; -106.9492750] -PLM2: [38.9201580 ; -106.9487170] -PLM3: [38.9207843 ; -106.9483668] -PLM4: 38.9210060 ; -106.9479528] -CO2(g) flux sensor: [38.9199180 ; -106.9489906] ------------------------------------------------------------------------------------------- This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy User Facility using NERSC award BER-ERCAP 23980, BER-ERCAP 28550, and BER-ERCAP 33789.

Meteorological Variables and Energy Fluxes at the Pumphouse Site, Crested Butte, CO 2017-2019 - Reed M. Maxwell; Anna Ryken; David Gochis (ess_dive, 2025)
This data contains output from the pumphouse eddy covariance tower that includes shortwave radiation, longwave radiation, net radiation, air temperature, relative humidity, as well as sensible, latent, and ground heat fluxes. Also included is calculated evapotranspiration from the latent heat flux and the latent heat of vaporization. All data are on a daily timestep and displayed in Mountain Time. The data has been processed, and Quality Assurance / Quality Control (QA/QC) was done, but any daily gaps in the data have not been filled in. This research was funded by the Department of Energy and performed as part of the Watershed Function Scientific Focus Area. This research aimed to constrain evapotranspiration in a high-elevation catchment. The dataset includes one comma-separated values (CSV) data file (EddyCovariance_MeteorlogicalVariables_CrestedButtePumphouse.csv). Additionally, three metadata CSV files are included: (1) location metadata file (locations.csv), which contains location metadata and coordinates; (2) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; and (3) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Global Bee Interaction Data - Katja C Seltmann; Global Biotic Interaction Community; Jorrit H. Poelen; Global Biotic Interaction Community; Katja C Seltmann; Jorrit H. Poelen; Jorrit H. Poelen; Katja C Seltmann; Global Biotic Interaction Community; Jorrit H. Poelen; Global Biotic Interaction Community; Katja C Seltmann; et al. (other, 2025)
Last modified: January 09, 2025 IntroductionThis dataset comprises all bee interactions indexed by Global Biotic Interactions (GloBI; Poelen et al. 2014). It is published quarterly by the Big Bee Project (Seltmann et al. 2021) to summarize all available knowledge about bee interactions from natural history collections, community science observations (i.e., iNaturalist), and the literature. Interactions include flower visitation, parasitic interactions (mite, viral), lecty, and many others. Data DescriptionPlease see the integration process page to better understand how Global Biotic Interactions combines datasets from various sources. The complete interaction dataset for all species can be accessed via https://www.globalbioticinteractions.org/data. Data is filtered for unique records based on the interaction description and source citation. Archives contain full data records and unique filtered records in tab-delimited format. Dataset column name definitions https://api.globalbioticinteractions.org/interactionFields or https://api.globalbioticinteractions.org/interactionFields Duplicate records occur in the database because more than one provider shares information. This is most frequently occuring in museum specimen data and duplicates can be identified evaluating the institutionCode, collectionCode and catalogNumber fields. The file catalogNumber_counts.tsv groups records by these three fields for this dataset, but does not filter out duplicate records. Additionally, this dataset includes the citation information provided by the data publisher. The provided sourceCitation may not include information about the primary provider (often the natural history collection) the specimen data originates and the catalogNumber should be referenced to understand the original source of the data. If you know of a missing dataset, or wish to share your dataset, please contact us! Metrics Date Total bee records 07-17-2020 232,906 01-24-2021 257,738 11-17-2021 226,160 06-01-2022 286,818 11-07-2022 429,308 01-18-2024 842,819 07-03-2024 1,109,057 01-09-2025 1,223,768 Date Andrenidae Apidae Colletidae Halictidae 07-17-2020 73,463 106,222 20,821 58,880 01-24-2021 77,824 120,919 21,376 63,945 11-17-2021 25,535 134,517 10,568 43,070 06-01-2022 78,016 144,827 20,409 64,054 11-07-2022 84,172 171,378 30,792 79,155 01-18-2024 166,473 334,224 63,847 171,931 07-03-2024 289,400 371,953 83,337 190,562 01-09-2025 204,565 686,195 70,724 241,856 Date Megachilidae Melittidae Stenotritidae 07-17-2020 44,449 2,511 23 01-24-2021 48,856 2,624 18 11-17-2021 37,001 995 9 06-01-2022 54,516 2,994 18 11-07-2022 61,391 2,396 24 01-18-2024 100,814 5,088 442 07-03-2024 162,587 4,964 438 01-09-2025 126,113 5,928 441 Included files filter_and_count_bee_families.sh - script for separating bee records into family and counting number of records for each family citation-count.sh - script for counting citations counts.sh - script for counting number of records per family family_counts.tsv - counts by family unique_citations.tsv - list of references/sources providing bee interaction data and the number of interactions provided by each reference globi-bees-filtered_file.tsv.gz - list of all bee interaction data indexed on Global Biotic Interactions from GloBI version 2025-Jan-09 produced by [filter_and_count_bee_families.sh]. interactions.tsv.gz - archive of the full Global Biotic Interaction dataset on January 9, 2025. Downloaded from https://www.globalbioticinteractions.org Interaction Sources count source 255946 USGS Biodiversity Information Serving Our Nation (BISON) IPT 203993 http://iNaturalist.org is a place where you can record what you see in nature, meet other nature lovers, and learn about the natural world. 113413 Digital Bee Collections Network, 2014 (and updates). Version: 2015-03-18. National Science Foundation grant DBI 0956388; PBI: Phytophagous Insects as a Model Group for Documenting Planetary Biodiversity (Insecta: Heteroptera: Miridae: Orthotylinae, Phylinae). Version: 08 Mar 2016. National Science Foundation grant DBI#0316495; Tri-Trophic Thematic Collection Network, 2014 (and updates). Version: 08 Mar 2016. http://tcn.amnh.org/. National Science Foundation grant(s) EF#1115081, EF#1115103, EF#1115080, EF#1115144, EF#1115191, EF#1115104, EF#1115115 111712 University of Kansas Natural History Museum 95131 ecdysis - a portal for live-managing arthropod occurrence data 74835 Symbiota Collections of Arthropods Network (SCAN) 62736 Frost Entomological Museum, Pennsylvania State University 28517 PaDIL Bee records from the Pests and Diseases Image Library, http://www.padil.gov.au. 27114 Guzman, Laura Melissa; Kelly, Tyler; Elle, Elizabeth, 2022, "A dataset for pollinator diversity and their interactions with plants in the Pacific NorthWest", https://doi.org/10.5683/SP3/WTEZNH, Borealis, V1 24564 University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. 23731 Carril OM, Griswold T, Haefner J, Wilson JS. (2018) Wild bees of Grand Staircase-Escalante National Monument: richness, abundance, and spatio-temporal beta-diversity. PeerJ 6:e5867 https://doi.org/10.7717/peerj.5867 18739 A. Thessen. 2014. Species associations extracted from EOL text data objects via text mining. 18003 Pensoft Darwin Core Archives available via Integrated Publication Toolkit 15754 Schwarz, Benjamin et al. (2021). Data from: Temporal scale-dependence of plant-pollinator networks [Dataset]. Dryad. https://doi.org/10.5061/dryad.qz612jmbp 10211 Pensoft Darwin Core Archives with associateTaxa columns 8752 Web of Life. http://www.web-of-life.es . 7590 Nick Balfour, Maria Clara Castellanos, Chris Johnson, Dave Goulson, Andrew Philippides. 2023. The Database of Pollinator Interactions (DoPI). Accessed at https://www.sussex.ac.uk/lifesci/ebe/dopi/ on 2023-12-01. 6367 Purdue Entomological Research Collection 6190 Redhead, J.W.; Coombes, C.F.; Dean, H.J.; Dyer, R.; Oliver, T.H.; Pocock, M.J.O.; Rorke, S.L.; Vanbergen, A.J.; Woodcock, B.A.; Pywell, R.F. (2018). Plant-pollinator interactions database for construction of potential networks. NERC Environmental Information Data Centre. https://doi.org/10.5285/6d8d5cb5-bd54-4da7-903a-15bd4bbd531b 5629 https://mangal.io - the ecological interaction database. 5451 University of Michigan Museum of Zoology, Division of Insects 5316 Clint Otto, Russ Bryant, and Ned H. Euliss Jr., 2020, The U.S. Geological Survey Pollinator Library Dataset: U.S. Geological Survey. https://doi.org/10.5066/P9DSS3VL 5297 Eardley C, Coetzer W. 2016. Catalogue of Afrotropical Bees. 4700 University of Colorado Museum of Natural History Entomology Collection 4684 National Database Plant Pollinators. Center for Plant Conservation at San Diego Zoo Global. Accessed via https://saveplants.org/national-collection/pollinator-search/ on 2020-06-05. 4557 Allen-Perkins, Alfonso, Magrach, Ainhoa, Dainese, Matteo, Garibaldi, Lucas A., Kleijn, David, Rader, Romina, Reilly, James R., et al. 2022. "CropPol: A Dynamic, Open and Global Database on Crop Pollination." Ecology 103(3): e3614. https://doi.org/10.1002/ecy.3614 4502 Natural History Collections managed by Arctos (https://arctosdb.org) accessed via https://vertnet.org . 4243 Seltmann, K., Van Wagner, J., Behm, R., Brown, Z., Tan, E., & Liu, K. (2020). BID: A project to share biotic interaction and ecological trait data about bees (Hymenoptera: Anthophila). UC Santa Barbara: Cheadle Center for Biodiversity and Ecological Restoration. Retrieved from https://escholarship.org/uc/item/1g21k7bf 3709 Arizona State University Hasbrouck Insect Collection 3630 Mycology Collections Data Portal (MyCoPortal). https://mycoportal.org 3590 Maiorano, L., Montemaggiori, A., Ficetola, G.F., O’Connor, L. & Thuiller, W. (2020), Data from: Tetra-EU 1.0: a species-level trophic meta-web of European tetrapods, Dryad, Dataset, https://doi.org/10.5061/dryad.jm63xsj7b 3552 Fricke, E.C., Svenning, J. Accelerating homogenization of the global plant–frugivore meta-network. Nature 585, 74–78 (2020). https://doi.org/10.1038/s41586-020-2640-y 3140 University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC 3124 CaraDonna, P.J. 2020. Temporal variation in plant-pollinator interactions, Rocky Mountain Biological Laboratory, CO, USA, 2013 - 2015 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/27dc02fe1655e3896f20326fed5cb95f (Accessed 2021-04-16). 3121 LaManna, JA, Burkle, LA, Belote, RT, Myers, JA. Biotic and abiotic drivers of plant–pollinator community assembly across wildfire gradients. J Ecol. 2020; 00: 1– 14. https://doi.org/10.1111/1365-2745.13530 . 3041 Ollerton, J., Trunschke, J. ., Havens, K. ., Landaverde-González, P. ., Keller, A. ., Gilpin, A.-M. ., Rodrigo Rech, A. ., Baronio, G. J. ., Phillips, B. J., Mackin, C. ., Stanley, D. A., Treanore, E. ., Baker, E. ., Rotheray, E. L., Erickson, E. ., Fornoff, F. ., Brearley, F. Q. ., Ballantyne, G. ., Iossa, G. ., Stone, G. N., Bartomeus, I. ., Stockan, J. A., Leguizamón, J., Prendergast, K. ., Rowley, L., Giovanetti, M., de Oliveira Bueno, R., Wesselingh, R. A., Mallinger, R., Edmondson, S., Howard, S. R., Leonhardt, S. D., Rojas-Nossa, S. V., Brett, M., Joaqui, T., Antoniazzi, R., Burton, V. J., Feng, H.-H., Tian, Z.-X., Xu, Q., Zhang, C., Shi, C.-L., Huang, S.-Q., Cole, L. J., Bendifallah, L., Ellis, E. E., Hegland, S. J., Straffon Díaz, S., Lander, T. A. ., Mayr, A. V., Dawson, R. ., Eeraerts, M. ., Armbruster, W. S. ., Walton, B. ., Adjlane, N. ., Falk, S. ., Mata, L. ., Goncalves Geiger, A. ., Carvell, C. ., Wallace, C. ., Ratto, F. ., Barberis, M. ., Kahane, F. ., Connop, S. ., Stip, A. ., Sigrist, M. R. ., Vereecken, N. J. ., Klein, A.-M., Baldock, K. ., & Arnold, S. E. J. . (2022). Pollinator-flower interactions in gardens during the COVID-19 pandemic lockdown of 2020. Journal of Pollination Ecology, 31, 87–96. https://doi.org/10.26786/1920-7603(2022)695 2823 Robert L. Minckley San Bernardino Valley from the year 2000 to 2011. 2757 Harvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University. 2038 Sarah E. Miller. 07/06/2017. Information extracted from dataset https://www.idigbio.org/portal/recordsets/db4bb0df-8539-4617-ab5f-eb118aa3126b. 1681 Klompen H, Johnson N (2018). Ohio State Acarology Laboratory (OSAL), Ohio State University. Museum of Biological Diversity, The Ohio State University. Accessed via https://mbd-db.osu.edu/hol/taxon_name/05fbf4bb-f8e1-404e-a27c-759d345aa4d0 on 2023-11-06 hash://sha256/fb23140e60f4889de35ae174b2570cf294012bff4f2c8c419c292af51c98c25f 1619 Grundler MC (2020) SquamataBase: a natural history database and R package for comparative biology of snake feeding habits. Biodiversity Data Journal 8: e49943. https://doi.org/10.3897/BDJ.8.e49943 1522 Brose, U. (2018). GlobAL daTabasE of traits and food Web Architecture (GATEWAy) version 1.0 [Data set]. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. https://doi.org/10.25829/IDIV.283-3-756 1430 Sarah E Miller. 6/19/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html. 1126 Giselle Muschett & Francisco E. Fontúrbel. 2021. A comprehensive catalogue of plant – pollinator interactions for Chile 1091 Magrach, Ainhoa et al. (2017), Data from: Plant-pollinator networks in semi-natural grasslands are resistant to the loss of pollinators during blooming of mass-flowering crops, Dryad, Dataset, https://doi.org/10.5061/dryad.k0q1n 1057 Sarah E Miller. 4/18/2016. Species associations from Wardeh, M. et al. Database of host-pathogen and related species interactions, and their global distribution. Sci. Data 2:150049 doi: 10.1038/sdata.2015.49 (2015) 1057 Wardeh, M., Risley, C., McIntyre, M. et al. 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The diversity and evolution of pollination systems in large plant clades: Apocynaceae as a case study. Ann Bot. 2019 Jan 23;123(2):311-325. doi: 10.1093/aob/mcy127. PMID: 30099492; PMCID: PMC6344220. 590 National Museum of Natural History, Smithsonian Institution IPT RSS Feed 558 Sarah E Miller. 6/22/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html. 544 Pardee, G.L., Ballare, K.M., Neff, J.L., Do, L.Q., Ojeda, D., Bienenstock, E.J., Brosi, B.J., Grubesic, T.H., Miller, J.A., Tong, D. and Jha, S., 2023. Local and Landscape Factors Influence Plant-Pollinator Networks and Bee Foraging Behavior across an Urban Corridor. Land, 12(2), p.362. https://www.mdpi.com/2073-445X/12/2/362 493 RCPol: Online Pollen Catalogs Network. 2016. https://rcpol.org.br/ 484 Hurlbert, A. H., Olsen, A. M., Sawyer, M. M., and Winner, P. M. 2021. Avian Diet Database. https://doi.org/10.5281/zenodo.5151056 450 Sarah E Miller. 6/25/2015. Species associations manually extracted from Robertson, C. 1929. Flowers and insects: lists of visitors to four hundred and fifty-three flowers. Carlinville, IL, USA, C. Robertson. 437 The Albert J. Cook Arthropod Research Collection 293 Global Web Database (http://globalwebdb.com): an online collection of food webs. Accessed via https://www.globalwebdb.com/Service/DownloadArchive on 2017-10-12. 290 Sarah E Miller. 06/10/2015. Species associations manually extracted from Chamberlin, W. J. The Buprestidae of North America, Exclusive of Mexico, a Catalogue including Synonomy, Bibliography, Distribution, Type Locality and Hosts of Each Species,. 1926. 282 BENSCH, S., HELLGREN, O. and PÉREZ‐TRIS, J. (2009), MalAvi: a public database of malaria parasites and related haemosporidians in avian hosts based on mitochondrial cytochrome b lineages. Molecular Ecology Resources, 9: 1353-1358. https://doi.org/10.1111/j.1755-0998.2009.02692.x 271 California Academy of Sciences Entomology and Entomology Type Collection 263 Food Webs and Species Interactions in the Biodiversity of UK and Ireland (Online). 2017. Data provided by Malcolm Storey. Also available from http://bioinfo.org.uk. 262 Canterbury Museum. (2024). Canterbury Museum (CMNZ) collection insect specimen-plant flower interactions (0.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11694302 248 WorldFAIR pilot data from: VisitationData_Luisa_Carvalheiro. 248 http://invertebrates.si.edu/parasites.htm 245 Eneida L. Hatcher, Sergey A. Zhdanov, Yiming Bao, Olga Blinkova, Eric P. Nawrocki, Yuri Ostapchuck, Alejandro A. Schäffer, J. Rodney Brister, Virus Variation Resource – improved response to emergent viral outbreaks, Nucleic Acids Research, Volume 45, Issue D1, January 2017, Pages D482–D490, https://doi.org/10.1093/nar/gkw1065 . 213 Groom, Q.J., Maarten De Groot, M. & Marčiulynienė, D. (2020) Species interation data manually extracted from literature for species . 205 @article{Sabino_2022, doi = {10.1016/j.agee.2022.108012}, url = {https://doi.org/10.1016%2Fj.agee.2022.108012}, year = 2022, month = {sep}, publisher = Elsevier BV, volume = {335}, pages = {108012}, author = {William Sabino and Luciano Costa and Tamires Andrade and Juliana Teixeira and Gustavo Araújo and André Luís Acosta and Luísa Carvalheiro and Tereza Cristina Giannini}, title = {Status and trends of pollination services in Amazon agroforestry systems}, journal = {Agriculture, Ecosystems & Environment}} 201 University of Hawaii Insect Museum 182 Palomares, M.L.D. and D. Pauly. Editors. 2018. SeaLifeBase. World Wide Web electronic publication. www.sealifebase.org, version (10/2018). 181 Brigham Young University Arthropod Museum 181 Stokland, J.; Dahlberg, A.; Meyke, E.; Schigel, D.; Siitonen, J. (2006) The Nordic saproxylic database - a comprehensive overview of the biological diversity in dead wood. 1st European Congress of Conservation Biology - "Diversity for Europe". August 2006, Hungary. Book of Abstracts. Society of Conservation Biology (USA) & Blackwell Publishing (UK) p. 159 . 164 Stephens, P. R., Pappalardo, P. , Huang, S. , Byers, J. E., Farrell, M. J., Gehman, A. , Ghai, R. R., Haas, S. E., Han, B. , Park, A. W., Schmidt, J. P., Altizer, S. , Ezenwa, V. O. and Nunn, C. L. (2017), Global Mammal Parasite Database version 2.0. Ecology, 98: 1476-1476. doi:10.1002/ecy.1799 143 Allen Hurlbert. 2017. Avian Diet Database. 134 Bartomeus, Ignasi (2013): Plant-Pollinator Network Data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.154863.v1 133 Sarah E Miller. 12/13/2016. Species associations manually extracted from Onstad, D.W. EDWIP: Ecological Database of the World's Insect Pathogens. Champaign, Illinois: Illinois Natural History Survey, [23/11/2016]. http://insectweb.inhs.uiuc.edu/Pathogens/EDWIP. 129 Yale University Peabody Museum Collections Data Portal 128 Llewelyn, J., Strona, G., Dickman, C.R., Greenville, A.C., Wardle, G.M., Lee, M.S.Y., Doherty, S., Shabani, F., Saltré, F. and Bradshaw, C.J.A. (2023), Predicting predator–prey interactions in terrestrial endotherms using random forest. Ecography e06619. https://doi.org/10.1111/ecog.06619 124 Field Museum of Natural History IPT 120 Shaw, LP, Wang, AD, Dylus, D, et al. The phylogenetic range of bacterial and viral pathogens of vertebrates. Mol Ecol. 2020; 29: 3361– 3379. https://doi.org/10.1111/mec.15463 119 Species Interactions of Australia Database (SIAD): Helping us to understand species interactions in Australia and beyond. http://www.discoverlife.org/siad/ . 118 WIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection 100 Carlson, C.J. et al., 2021. The Global Virome in One Network (VIRION): an atlas of vertebrate-virus associations. Available at: http://dx.doi.org/10.1101/2021.08.06.455442 99 Mihara, T., Nishimura, Y., Shimizu, Y., Nishiyama, H., Yoshikawa, G., Uehara, H., Hingamp, P., Goto, S., and Ogata, H.; Linking virus genomes with host taxonomy. Viruses 8, 66 doi:10.3390/v8030066 (2016). 92 Brose, U. et al., 2005. Body sizes of consumers and their resources. Ecology, 86(9), pp.2545–2545. Available at: https://doi.org/10.1890/05-0379. 92 San Diego Natural History Museum 79 Cristina Preda and Quentin Groom. 2014. Species associations manually extracted from literature. 66 US National Museum of Natural History Ixodes Records 57 Farr, David F.; Rossman, Amy Y.; Castlebury, Lisa A. (2021). United States National Fungus Collections Fungus-Host Dataset. Ag Data Commons. https://doi.org/10.15482/USDA.ADC/1524414. 53 Lintulaakso, K., Tatti, N. and Žliobaitė, I., 2023. Quantifying mammalian diets. Mammalian Biology, 103(1), pp.53-67. https://doi.org/10.1007/s42991-022-00323-6 53 Soleto-Casas RC and Simões N (2020). Parasitic and commensal invertebrates of echinoderms from American Tropical And Subtropical Atlantic manually extracted from literature. 51 Froese, R. and D. Pauly. Editors. 2018. FishBase. World Wide Web electronic publication. www.fishbase.org, version (10/2018). 45 Olito, Colin; Fox, Jeremy W. (2015), Data from: Species traits and abundances predict metrics of plant–pollinator network structure, but not pairwise interactions, Dryad, Dataset, https://doi.org/10.5061/dryad.7st32 43 Price Institute of Parasite Research, School of Biological Sciences, University of Utah 40 Sarah E Miller. 9/19/2017. Species associations manually extracted from Benesh, D. P., Lafferty, K. D. and Kuris, A. (2017), A life cycle database for parasitic acanthocephalans, cestodes, and nematodes. Ecology, 98: 882. doi:10.1002/ecy.1680 38 http://gomexsi.tamucc.edu 37 Pocock, Michael J. O.; Evans, Darren M.; Memmott, Jane (2012), Data from: The robustness and restoration of a network of ecological networks, Dryad, Dataset, https://doi.org/10.5061/dryad.3s36r118 36 Quentin J. Groom. 2020. Species interactions of species on the List of invasive alien species of Union concern 36 University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection 34 Museum for Southwestern Biology (MSB) Parasite Collection 34 Sarah E Miller. 6/20/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html. 32 Ballantyne, Gavin; Baldock, Katherine C. R.; Willmer, Pat G. (2015), Data from: Constructing more informative plant-pollinator networks: visitation and pollen deposition networks in a heathland plant community, Dryad, Dataset, https://doi.org/10.5061/dryad.17pp3 29 Sarah E Miller. 5/17/2016. Wenzel, Rupert L., and Vernon J. Tipton. Appendix: Classified List of Hosts and Parasites. Chicago, Ill.: Field Museum of Natural History, 1966. 26 Sarah E Miller. 9/19/2016. Species associations extracted from Graystock, P., Blane, E.J., McFrederick, Q.S., Goulson, D. and Hughes, W.O., 2016. Do managed bees drive parasite spread and emergence in wild bees?. International Journal for Parasitology: Parasites and Wildlife, 5(1), pp.64-75. 25 Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology 23 Agosti, Donat. 2020. Transcription of Linné, C. von, 1758. Systema naturae per regna tria naturae secundum classes, ordines, genera, species, cum characteribus, differentiis, synonymis, locis. Available at: http://dx.doi.org/10.5962/bhl.title.542 . 23 Sherman, Aja C.; Geiselman, Cullen; Simons, Nancy B.; Upham, Nathan S.; Poelen, Jorrit H.; Reeder, DeeAnn M.; Bertolino, Sandro; Groom, Quentin; Phelps, Kendra; Agosti, Donat; Willoughby, Anna R. In Preparation. Bat-Co-Roosting Database develop by the Biodiversity-related knowledge hub on COVID-19. 23 Udy, Kristy; Reininghaus, Hannah; Scherber, Christoph; Tscharntke, Teja (2020), Data from: Plant-pollinator interactions along an urbanization gradient from cities and villages to farmland landscapes, Dryad, Dataset, https://doi.org/10.5061/dryad.4mw6m906s 20 IPBES. (2016). The assessment report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on pollinators, pollination and food production. Table 2.4.3 p88 Zenodo. https://doi.org/10.5281/zenodo.3402857 19 Seltzer, Carrie; Wysocki, William; Palacios, Melissa; Eickhoff, Anna; Pilla, Hannah; Aungst, Jordan; Mercer, Aaron; Quicho, Jamie; Voss, Neil; Xu, Man; J. Ndangalasi, Henry; C. Lovett, Jon; J. Cordeiro, Norbert (2015): Plant-animal interactions from Africa. figshare. https://dx.doi.org/10.6084/m9.figshare.1526128 18 Jakovos Demetriou and Quentin Groom 2014. Species associations of Sceliphron manually extracted from literature. 17 Species Connect. https://speciesconnect.com 14 Fabricia Sousa Paz, Carlos Eduardo Pinto, Rafael Melo de Brito, Vera Lucia Imperatriz-Fonseca, Tereza Cristina Giannini, Edible Fruit Plant Species in the Amazon Forest Rely Mostly on Bees and Beetles as Pollinators, Journal of Economic Entomology, Volume 114, Issue 2, April 2021, Pages 710–722, https://doi.org/10.1093/jee/toaa284 14 Raymond, B., Marshall, M., Nevitt, G., Gillies, C., van den Hoff, J., Stark, J.S., Losekoot, M., Woehler, E.J., and Constable, A.J. (2011) A Southern Ocean dietary database. Ecology 92(5):1188. Available from http://dx.doi.org/10.1890/10-1907.1 . Data set supplied by Ben Raymond. 14 Scientific Committee on Antarctic Research. (2023). SCAR Southern Ocean Diet and Energetics Database (2023-04-04) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7796465 hash://md5/e41e29d8fb3c2d731f292ec08798cf6b hash://md5/05abf23c0b9e5f4bc721ff407455af0a hash://sha256/7a344b858ab8d1daeca1da49843e8bf957f1116ff9e10a29176ab5c02cb49bef 13 Geiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org 12 Gaden S. Robinson; Phillip R. Ackery; Ian Kitching; George W Beccaloni; Luis M. Hernández (2023). HOSTS (from HOSTS - a Database of the World's Lepidopteran Hostplants) [Data set resource]. Natural History Museum. https://data.nhm.ac.uk/dataset/hosts/resource/877f387a-36a3-486c-a0c1-b8d5fb69f85a via Natural History Museum (2023). Data Portal query on 1 resources created at 2023-05-24 11:19:42.032183 PID https://doi.org/10.5519/qd.bsucrxdz 12 Gandhi, K. J. K., & Herms, D. A. (2009). North American arthropods at risk due to widespread Fraxinus mortality caused by the Alien Emerald ash borer. Biological Invasions, 12(6), 1839–1846. doi:10.1007/s10530-009-9594-1. 12 Meyer R.S., et al., Beach environmental DNA fills gaps in photographic biomonitoring to track spatiotemporal community turnover across 82 phyla. Environmental DNA, submitted June 3, 2019. 9 Consortium of Small Vertebrate Collections 9 Urban M, Cuzick A, Rutherford K, Irvine A, Pedro H, Pant R, Sadanadan V, Khamari L, Billal S, Mohanty S, Hammond-Kosack KE. PHI-base: a new interface and further additions for the multi-species pathogen-host interactions database. Nucleic Acids Res. 2017 Jan 4;45(D1):D604-D610. doi: 10.1093/nar/gkw1089. Epub 2016 Dec 3. PMID:27915230 8 Sarah E Miller. 7/6/2016. Arctos collection. 5 International Council for the Exploration of the Sea (ICES). Year of The Stomach Datasets. 4 Sarah E Miller. 4/20/2015. Species associations manually extracted from various papers and articles from site https://repository.si.edu 4 Sarah E Miller. 5/28/2015. Arnaud, Paul Henri. A Host-parasite Catalog of North American Tachinidae (Diptera). Washington, D.C.: U.S. Dept. of Agriculture, Science and Education Administration, 1978. 4 Sarah E Miller. 7/7/2016. Text gathered from Wirta, H.K., Vesterinen, E.J., Hambäck, P.A., Weingartner, E., Rasmussen, C., Reneerkens, J., Schmidt, N.M., Gilg, O. and Roslin, T., 2015. Exposing the structure of an Arctic food web. Ecology and evolution, 5(17), pp.3842-3856. 4 Semantic Prototypes in Research Ecoinformatics (SPIRE). Data provided by Joel Sachs. See also http://ebiquity.umbc.edu/get/a/publication/297.pdf . 4 University of California Santa Barbara Herbarium 3 Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org 3 Gippet, J.M.W., Bates, O.K., Moulin, J. et al. The global risk of infectious disease emergence from giant land snail invasion and pet trade. Parasites Vectors 16, 363 (2023). https://doi.org/10.1186/s13071-023-06000-y 3 Jorrit H. Poelen. 2017. Species interactions associated with known species interaction datasets. 3 Ministerio del Ambiente, Agua y Transición Ecológica de Ecuador - MAATE. 3 Sarah E Miller. 9/15/2016. Species associations extracted from http://parasiticplants.siu.edu/index.html. 3 Sarah E Miller. 9/3/2015. Species associations manually extracted from JSTOR. 3 Schriml, L. M., Arze, C., Nadendla, S., Ganapathy, A., Felix, V., Mahurkar, A., … Hall, N. (2009). GeMInA, Genomic Metadata for Infectious Agents, a geospatial surveillance pathogen database. Nucleic Acids Research, 38(Database), D754–D764. doi:10.1093/nar/gkp832 2 Carnegie Invertebrate Zoology Collection 2 F. Gabriel. Muñoz. 2017. Palm-Animal frugivore associations extracted from literature with Biodiversity Observations Miner for SouthEast Asia. 2 Ferrer-Paris, José R.; Sánchez-Mercado, Ada Y.; Lozano, Cecilia; Zambrano, Liset; Soto, José; Baettig, Jessica; Leal, María (2014): A compilation of larval host-plant records for six families of butterflies (Lepidoptera: Papilionoidea) from available electronic resources. figshare. http://dx.doi.org/10.6084/m9.figshare.1168861 2 Paleo Digitization Working Group. Biological associations extracted from fossil specimens. 2 Quentin J. Groom. 2020. Bat interation data manually extracted from literature. 2 Sarah E. Miller. 04/14/2015. Extracted from literature Scott, J.A. 1986. The Butterflies of North America. Stanford University Press, Stanford, CA 2 Strona, G., Palomares, M. L. D., Bailly, N., Galli, P., & Lafferty, K. D. (2013). Host range, host ecology, and distribution of more than 11 800 fish parasite species. Ecology, 94(2), 544–544. doi:10.1890/12-1419.1 2 Thessen AE. 2017. Biotic Interactions in Greenland. GloBI. 10.5281/zenodo.266824 1 Bourlat SJ, Koch M, Kirse A, Langen K, Espeland M, Giebner H, Decher J, Ssymank A, Fonseca VG (2023) Metabarcoding dietary analysis in the insectivorous bat Nyctalus leisleri and implications for conservation. Biodiversity Data Journal 11: e111146. https://doi.org/10.3897/BDJ.11.e111146 1 De Rojas M, Doña J, Dimov I (2020) A comprehensive survey of Rhinonyssid mites (Mesostigmata: Rhinonyssidae) in Northwest Russia: New mite-host associations and prevalence data. Biodiversity Data Journal 8: e49535. https://doi.org/10.3897/BDJ.8.e49535 1 Deans, Andrew (2021). Catalog of Rose Gall, Herb Gall, and Inquiline Gall Wasps (Hymenoptera: Cynipidae) of the United States, Canada, and Mexico 1 Gunther KA et al. 2014 Dietary breadth of grizzly bears in the Greater Yellowstone Ecosystem. Ursus 25(1):60-72 1 HiveTracks WorldFAIR Test Data. 1 Pinnegar, J.K. (2014). DAPSTOM - An Integrated Database & Portal for Fish Stomach Records. Version 4.7. Centre for Environment, Fisheries & Aquaculture Science, Lowestoft, UK. February 2014, 39pp. 1 Sarah E Miller. 3/4/2015. Species associations manually extracted from http://onlinelibrary.wiley.com/doi/10.1111/j.1474-919X.2009.00907.x/suppinfo. 1 Sarah E Miller. 5/21/2015. Text gathered from http://www.biodiversitylibrary.org/ 1 Vanderweyen A, Fraiture A, Groom Q, Desmet P, Reyserhove L (2019). Catalogue of the Rust Fungi of Belgium. Botanic Garden Meise. 1 Vanderweyen, A., & Fraiture, A. (2009). Catalogue des Uredinales de Belgique, 1re partie, Chaconiaceae, Coleosporiaceae, Cronartiaceae, Melampsoraceae, Phragmidiaceae, Pucciniastraceae, Raveneliaceae et Uropyxidaceae. Lejeunia, Revue de Botanique|Vanderweyen, A., & Fraiture, A. (2009). Catalogue des Uredinales de Belgique, 2ème partie, Pucciniaceae (sauf Puccinia)(suite 2). Lejeunia, Revue de Botanique.|Vanderweyen, A., & Fraiture, A. (2012). CATALOGUE DES UREDINALES DE Belgique 3ème partie Pucciniaceae (genre Puccinia). Lejeunia, Revue de Botanique. 1 Zeke Marshall. 2021. Species interactions manually extracted from literature. References Poelen JH, Simons JD, Mungall CJ (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2014.08.005 Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. https://doi.org/10.3897/biss.5.74037 Poelen, JS & Seltmann, KS (2024) Bees Only Please: Bees Only Please: Selecting Hundreds of Thousands of Possible Bee Interactions Using a Laptop, Open Datasets, and Small (but Mighty) Commandline Tools. https://www.globalbioticinteractions.org/2024/06/07/bees-only-please Ascher, J. S. and J. Pickering (2020) Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila). http://www.discoverlife.org/mp/20q?guide=Apoidea_species. Acknowledgements This project is supported by the National Science Foundation. Award numbers: DBI:2102006, DBI:2101929, DBI:2101908, DBI:2101876, DBI:2101875, DBI:2101851, DBI:2101345, DBI:2101913, DBI:2101891 and DBI:2101850

Foliar element determination from field survey in association with an Analytical Spectral Device Fieldspec3 survey, East River, CO 2023 - K. Dana Chadwick; A. Joshua West; Lauren Hechinger; Kathleen Grant; Angie Vasquez (other, 2025)
Foliar elements were determined for samples collected in the field during a 2023 survey in Gunnison County, CO that also included paired data collection using an ASD FieldSpec3 spectrometer. Sampling locations were selected based on metal hotspots identified using remote sensing data and foliar elemental data from the 2018 National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) imaging spectroscopy and lidar surveys in Gunnison County, CO. The samples collected across the East River, Washington Gulch, Slate River, and Coal Creek watersheds include a mixture of vegetation including meadow, shrub, and tree foliar samples. Foliar samples underwent aqua regia digestion for elemental determination on inductively coupled plasma optical emission spectrometry (ICP-OES). This data package contains the raw foliar elemental data for all foliar samples, metadata associated with the foliar samples collected, and a certificate of analysis for this project.

SPLASH Field Study; NOAA PSL Soil Moisture and Surface Temperature Probe Data at Avery Picnic and Kettle Ponds, Colorado (NCEI Accession 0302136) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners conducted a field study (SPLASH - the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology). This field study established a state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. The ultimate goal of this project is improved prediction of weather and water in the Colorado mountains and beyond to inform societal preparedness and response. This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth. The data were collected around the SPLASH campaign areas 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. The elements provided in this dataset are Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches). The data were collected between June 1st, 2022 and September 18th, 2023 under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office.

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (ess_dive, 2025)
This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

SPLASH Field Study; NOAA GML Surface Radiation Budget and Near-Surface Meteorology Data at Kettle Ponds and Brush Creek, Colorado (NCEI Accession 0302673) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners conducted a field study (SPLASH - the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology). This field study established a state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. The ultimate goal of this project is improved prediction of weather and water in the Colorado mountains and beyond to inform societal preparedness and response. SPLASH was complemented by a concurrent Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign led by the Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility. This dataset contains Surface Energy Balance data collected at the Kettle Ponds (CKP) and Brush Creek (CBC), Colorado sites from October 2021 through August 2023. The data are provided in daily files of one minute data in ASCII text format.

2013 Pleasure Craft and Sailing Vessel Density - NCEI (other, 2025)
Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. This dataset represents the density of pleasure craft and sailing vessel traffic in 2013 for the contiguous United States offshore waters from pleasure craft and sailing vessels with AIS transponders in 100 meter grid cells. The dataset is best interpreted using a high to low density scale and does not represent actual vessel counts.

SPLASH Field Study; Continuous snow temperature profiles from the Snow Ice Mass Balance Apparatus (SIMBA) level 1 Raw, Nov2022-Jun2023 (NCEI Accession 0304095) - NCEI (other, 2025)
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 HTML Markup Was Removed

SPLASH Field Study; NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data from 2021-10-21 to 2022-01-28 (NCEI Accession 0303807) - NCEI (other, 2025)
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).

2016 Vessel Transit Counts by Type - NCEI (other, 2025)
Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell. This record represents the metadata for each of these vessel types: Cargo, Fishing, Passenger, Pleasure Craft and Sailing, Tanker, and Tug and Towing.

Trembling aspen (Populus tremuloides) water potentials from clones with varying aspect and understory composition (other, 2025)
Predawn and midday plant water potential measurements, collected on terminal twigs using a scholander-type pressure chamber (PMS Instruments, Corvallis, OR, USA). Measurements were collected from 1-3 canopy dominant or co-dominant raments in 12 mature trembling aspen ( Populus tremuloides ) near aspen's low elevation range edge in the San Juan National Forest in southwest Colorado, USA. Clones were either growing on north-facing (n=4) or south-facing slopes (n=8) and had either grassy understories (n=6) or shrubby understories dominated by Symphiocarpus albus (n=6). Measurements were collected in the early growing season (2010-06-23), in the mid growing season but prior to the onset of the summer monsoon (2010-07-16) or in the late growing season after the onset of the monsoon (2010-08-17). Data are published using the PSINet data submission template (https://psinetrcn.github.io/). This data is a portion of the data analyzed in: Anderegg LDL, Anderegg WRL, Abatzoglou J, Hausladen AM, Berry JA . 2013 . Drought characteristics role in widespread aspen forest mortality across Colorado, USA. Global Change Biology 19 : 1526 1537.

Data from: Fitness costs and benefits of a non-native floral resource for subalpine solitary bees - Jessica R.K. Forrest; Charlotte M. Cahill; Paul J. CaraDonna (other, 2025)
Organisms inhabiting seasonal environments must fit their life cycle into a limited time window while also synchronizing periods of resource consumption with timing of resource availability. Introduced non-native species, which often differ in phenology from natives, can alter and expand the seasonal window of resource availability for native consumers, providing potential fitness benefits. However, if these non-native resources are nutritionally unsuitable for native consumers, their presence could elicit foraging behaviour that proves maladaptive—i.e., they could act as an ecological trap. Here, we used multi-year field observations and a laboratory experiment to investigate the impacts of a common non-native plant species on two components of fitness in three solitary bee species (all specialist consumers of pollen from the plant family Asteraceae) native to the Colorado Rocky Mountains. First, we tested whether individual bees that collect pollen from the non-native common dandelion (Taraxacum officinale) produce more offspring than those that do not, thanks to the unusually early flowering phenology of the non-native. Second, we compared survival of bee larvae experimentally reared on Taraxacum pollen to that of larvae reared on native Asteraceae pollen. Bees that used at least some non-native Taraxacum pollen produced more potentially viable offspring, but larval survival was significantly reduced for bee larvae experimentally fed provisions dominated by Taraxacum pollen. Therefore, survival costs may negate the potential fitness benefits of early nesting, indicating that non-native floral resource use may act as an ecological trap for native bees. Using a series of simple simulations informed by our results, we explore the fitness effects of non-native floral resource use, demonstrating that the net cost or benefit depends on how bees respond to resource shortages. Our results highlight the importance of considering organisms’ full life cycles when evaluating the fitness consequences of resource availability and species introductions.

Solute chemistry for streams draining geomorphic features and varying land cover gradients in the East River watershed, Colorado - Matthew Winnick; J. R. Slosson; I. J. Larsen; José Marmolejo-Cossío (other, 2025)
This dataset contains solute chemistry data and GPS coordinates for surface water samples collected in the East River watershed in the Elk and West Elk Ranges of Colorado from August 3rd through August 11th, 2022. Samples were collected from streams and groundwater seeps as part of a comprehensive analysis within the watershed of how different geomorphic features (such as landslides and rock glaciers) along with variations in land cover (such as the presence of vegetation) affect stream water quality in this Rocky Mountain headwater environment. This dataset includes concentrations of the following in a single csv file: fluoride, chloride, sulfate, nitrate, calcium, potassium, magnesium, sodium, silicon, and total organic carbon (TOC). All concentrations are reported in mg/L. The sulfate data in this version have been updated to account for an earlier ion chromatograph instrument method error whereby sulfate and bromide peaks overlapped in calibration solution samples, biasing calculated sulfate concentrations low by 30-50%. Sulfate data reported herein are from a re-run of the samples performed in December 2024 using a corrected instrument method. All samples were stored under refrigerated conditions and filtered on initial collection.

SPLASH Field Study; Brush Creek Flux Data for Publication (NCEI Accession 0305465) - NCEI (other, 2025)
These files contain the Surface Flux and turbulence data at the Brush Creek site during the deployment of NOAA's Physical Science Laboratory deployment of the atmospheric profile systems in late 2021 and early 2022.

Mineralogy of floodplain sediments from Meanders C, O, and Z in the East River Watershed, CO, USA - Sergio Carrero; P. S. Nico; Patricia Fox (ess_dive, 2025)
This dataset includes bulk X-ray diffraction data from floodplain sediments 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 biogeochemical cycling and other river corridor processes on riverine export of solutes. Sediment cores were collected from Meander C, Meander O, and Meander Z in July 2016 to September 2017 to depths of approximately 40-95 cm. Sample metadata including locations, depths, and sample dates are included in a csv file ("sample_list_and_locations.csv"). The file "diffraction_data.csv" contains raw diffraction data, and mineral quantification is in the file "mineral_abundance.csv". This dataset also 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.

Integrated Hourly Meteorological Database of 20 Meteorological Stations (1981-2022) for Watershed Function SFA Hydrological Modeling - Boris Faybishenko (ess_dive, 2025)
This dataset contains (a) a script “R_met_integrated_for_modeling.R”, and (b) associated input CSV files: 3 CSV files per location to create a 5-variable integrated meteorological dataset file (air temperature, precipitation, wind speed, relative humidity, and solar radiation) for 19 meteorological stations and 1 location within Trail Creek from the modeling team within the East River Community Observatory as part of the Watershed Function Scientific Focus Area (SFA). As meteorological forcings varied across the watershed, a high-frequency database is needed to ensure consistency in the data analysis and modeling. We evaluated several data sources, including gridded meteorological products and field data from meteorological stations. We determined that our modeling efforts required multiple data sources to meet all their needs. As output, this dataset contains (c) a single CSV data file (*_1981-2022.csv) for each location (20 CSV output files total) containing hourly time series data for 1981 to 2022 and (d) five PNG files of time series and density plots for each variable per location (100 PNG files). Detailed location metadata is contained within the Integrated_Met_Database_Locations.csv file for each point location included within this dataset, obtained from Varadharajan et al., 2023 doi:10.15485/1660962. This dataset also includes (e) a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata and (f) a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type. Review the (g) ReadMe_Integrated_Met_Database.pdf file for additional details on the script, methods, and structure of the dataset. The script integrates Northwest Alliance for Computational Science and Engineering’s PRISM gridded data product, National Oceanic and Atmospheric Administration’s NCEP-NCAR Reanalysis 1 gridded data product (through the `RCNEP` R package, Kemp et al., doi:10.32614/CRAN.package.RNCEP), and analytical-based calculations. Further, this script downscales the input data into hourly frequency, which is necessary for the modeling efforts.

Multiple RGB ortho-mosaics and digital surface models in 2017 and 2018 across the Lower Montane site in the East River Watershed, Colorado - John Peterson; Emmanuel Leger; Baptiste Dafflon (ess_dive, 2025)
Aerial imagery was collected at the Lower Montane site (Pumphouse) in the East River Watershed, Colorado during the spring, summer, and fall seasons of 2017 and 2018 to improve the understanding of seasonal vegetation dynamics and their drivers. The datasets include Red-Green-Blue (RGB) ortho-mosaics and digital surface models (DSMs) inferred from the Unoccupied Aerial System (UAS) acquired aerial RGB imagery for June 3, June 19, July 7, and August 14, 2017, and for March 14, April 26, June 1, June 18, July 6, and August 7, 2018. Real-Time Kinematic Global Positioning System (RTK-GPS) surveyed Ground control points (GCPs) were used to increase the reconstruction accuracy. The reconstructed RGB mosaics and DSMs have been trimmed to cover a similar spatial domain. The accuracy of the RGB mosaics is considered high (~10 cm). DSM accuracy is highest (~10 cm) where sufficient GCPS are available, and more difficult to assess elsewhere (see reconstruction reports for uncertainty estimates). The dataset includes a total of 20 GeoTIFF (.tif) files, 10 PDF (.pdf) files, 3 data CSV (.csv) files, and 2 metadata CSV (.csv) files. Feel free to contact the authors with any questions or collaboration interests. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2015 Vessel Transit Counts by Type - NCEI (other, 2025)
Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell. This record represents the metadata for each of these vessel types: Cargo, Fishing, Passenger, Pleasure Craft and Sailing, Tanker, and Tug and Towing.

Scripts and data to produce figures for the SVS2 paper submitted to GMD - Manon Gaillard; Florent Domine; Chris Derksen; Vincent Fortin; Nathalie Gauthier; Alain Royer; Stephane Belair; Giulia Mazzotti; Nicolas Leroux; Georgina Woolley; Maria Abramowicz; Matthieu Lafaysse; et al. (other, 2025)
The Soil, Vegetation, and snow model version 2.0 (SVS2) is a land surface model developed by Environment and Climate Change Canada (ECCC) to be used in the context of land surface prediction. SVS2 is described in a manuscript submitted to GMD (https://egusphere.copernicus.org/preprints/2025/egusphere-2025-3396/). This repository contains the scripts and data used to generate the figures presented in the manuscript. It is divided into two subfolders: point_scale_simulations_SCB/ contains the scripts and data to generate Figures 3, 4 and 5 for the simulations at Snow Crested Butte. distributed_simulations/ contains the scripts and data to generate Figures 2 and 6 to 11 for the distributed simulations along a transect in Eastern Canada. Observations used in this evaluation are shared in this repository: Snow pit data are distributed in the folder distributed_simulations/data/obs/snow_pits/ Snow cover data from GMASI are distributed in the folder distributed_simulations/data/obs/gmasi Information about the ERA5 atmospheric forcing are provided in the folder distributed_simulations/forcing. The configuration files used for the 1D and 2D simulations with SVS2 presented in the manuscript are also available in their respective subfolders. The configuration files for the 1D simulations with SVS1 at Snow Crested Butte are also available. Readme files are present in the different subfolders to guide the user.

Time-lapse imagery in 2017 and 2018 at the Lower Montane site in the East River Watershed, Colorado - John Peterson; Baptiste Dafflon (ess_dive, 2025)
Time-lapse imagery was collected using an automated RGB camera mounted on a pole at the base of the northeast-facing hillslope at the Lower Montane site in the East River Watershed, Colorado. The imagery was intended to support a better understanding of plant dynamics and their controls during the growing season. The dataset includes RGB images archived in four zip files (containing imagery in JPEG format), corresponding to photos taken from the hillslope and the adjacent floodplain during 2017 and 2018. A fifth zip file contains a few AVI movies that compare imagery between the two years. The AVI files can be read with most media players applications. The archive contains a total of five *.zip files and three csv metadata files (flmd.csv, dd.csv, and locations.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (ess_dive, 2025)
This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (May to September 2018) - Scott Fendorf; Christopher Francis; Kristin Boye; Anna N. Rasmussen; Craig R. Anderson; Marco Keiluweit (ess_dive, 2025)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2018 in May (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) near the Meander C/Pumphouse floodplain sites of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (30-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (70-80 cm depth below surface). Sediments were homogenized from the 10 cm core for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0141020. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 478 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also 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. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2019) - Scott Fendorf; Christopher Francis; Anna N. Rasmussen; Kristin Boye; Craig R. Anderson; Marco Keiluweit (ess_dive, 2025)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2019 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) near the Meander C/Pumphouse floodplain sites of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (30-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (70-80 cm depth below surface). Sediments were homogenized from the 10 cm core for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0141020. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 436 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also 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. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Cosmogenic Nuclide Burial Isochron Data for the Central City Gravel, Malta Gravel, and Lava Creek B Ash Locations: Classic Locations of Early to Middle Pleistocene Deposits, South Platte and Arkansas Rivers, Central Colorado, USA (other, 2025)
This data release includes Al-26/Be-10 cosmogenic nuclide concentrations and burial isochron ages for three locations in central Colorado, USA, with preferred burial ages ranging from ~2 Ma to 600 ka, that provide constraints on geologic mapping and chronostratigraphic correlations of Pleistocene Colorado Rocky Mountain (CRM)-Great Plains deposits. This new dataset helps to validate original identification and characterization of the deposits by renowned work of early to late 20th century geologists. In addition, this dataset is critical for any models of geomorphic development of modern Colorado Rocky Mountain (CRM)-Great Plains depositional systems, including analyses of regional relationships involving the Malta Gravel, Nussbaum [...]

Data from: Betting on rains that do not come: Monsoon failure and leaf area overshoot relate to increased tree mortality from drought (other, 2025)
Structural overshoots, where biomass is overallocated to tree leaf area compared to sapwood area, could result in lethal stress during droughts. Climate change may alter climatic cues that drive leaf area production, such as temperature and precipitation, as well as seasonal dynamics that underlie summer rainfall due to the North American Monsoon (NAM). Combined, this could lead to temporal mismatches between leaf area-driven water demand and availability, and increased drought-induced mortality events. We used leaf area to sapwood area ratios to investigate the prevalence of overshoots and whether overshoots increase drought-induced mortality. We measured populations of aspen spanning the northern transition zone of the NAM during and following severe droughts. We observed increased overshoots and drought-induced mortality in southern latitude populations that rely more on summer monsoon rainfall. Changes in convective activity from low snowpack the preceding winter may be a climatic driver of heightened summer monsoon rainfall in the region and therefore may also trigger increased production of leaf area during wetter summers. Our results suggest that an overshoot of leaf area to sapwood area (AL:AS) ratios is associated with drought-induced tree mortality and highlight that climate-change driven alterations to the NAM could have major consequences for tree species’ acclimation to environment change. Supplemental data files provide for: Kerr KL, Anderegg LDL, Trugman AT, Anderegg WRL (2025) Betting on rains that do not come: Monsoon failure and leaf area overshoot relate to increased tree mortality from drought. Functional Ecology. Data were gathered from national forests (NF) throughout Utah and Colorado, USA in the summers of 2014, 2018, 2019, 2020, and 2021. See Methods for details.

2017 Vessel Transit Counts by Type - NCEI (other, 2025)
Automatic Identification Systems (AIS) are a navigation safety device that transmits and monitors the location and characteristics of many vessels in U.S. and international waters in real-time. In the U.S. the Coast Guard and industry collect AIS data, which can also be used for a variety of coastal planning purposes. NOAA and BOEM have worked jointly to re-task and make available some of the most important records from the U.S. Coast Guard's national network of AIS receivers. This dataset represents annual vessel transit counts summarized at a 100 m by 100 m geographic area. A single transit is counted each time a vessel track passes through, starts, or stops within a 100 m grid cell. This record represents the metadata for each of these vessel types: Cargo, Fishing, Passenger, Pleasure Craft and Sailing, Tanker, and Tug and Towing.

Supplementary material from "Multilevel selection on individual and group social behaviour in the wild" (2025)
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Sea Surface Temperature (14 KM North America) - NCEI (other, 2025)
Product shows local sea surface temperatures (degrees C). It is a composite gridded-image derived from 8-km resolution SST Observations. It is generated every 48 hours for North America. SST is defined as the skin temperature of the ocean surface water.

SPLASH Field Study; Spectral Surface Albedo Observations from NOAA GML/GRAD and CIRES at Brush Creek (CBC) (NCEI Accession 0305474) - NCEI (other, 2025)
Overview The NOAA Global Monitoring Laboratory (GML), Radiation, Aerosols, and Cloud Division (G-RAD), in collaboration with the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder, conducted spectral surface albedo measurements at — Brush Creek (CBC) — as part of the NOAA Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. Spectral albedo values were derived from combined upwelling and downwelling hemispheric irradiance measurements in seven narrow spectral bands centered at nominal wavelengths of 415, 500, 670, 870, 940, and 1625 nm. Downwelling irradiance was measured using an upward-facing Multifilter Rotating Shadowband Radiometer (MFRSR). Upwelling irradiance was measured using a downward-facing Multifilter Radiometer (MFR) mounted approximately 2 meters above ground level. Measurements were recorded at a 20-second interval. Due to the sequential nature of MFRSR observations — which reconstruct downwelling irradiance from irradiances during various shadowband positions — each timestamp represents neither a purely instantaneous nor a time-averaged measurement. The full measurement cycle is completed within eight seconds prior to each reported timestamp. Calibration Approach Instrument responsivity calibrations were performed using: Laboratory lamp calibrations for the MFRSR (downwelling) In-field side-by-side calibration for the MFR (upwelling) Relative calibration between the MFR and MFRSR reduced propagated uncertainty in the albedo product. Surface albedo was computed as the simple ratio of the calibrated upwelling to downwelling irradiance at each wavelength. Note: The NOAA/GML/GRAD calibration facility does not currently support absolute responsivity calibration at 1625 nm. Consequently, irradiance values at this wavelength carry an unknown scaling factor. However, a side-by-side calibration was performed, enabling valid and consistent albedo retrievals at 1625 nm. Data Quality Considerations (notes on both locations) During winter, both instruments were occasionally buried in snow when snow depths exceeded their 2-meter installation height. These periods — more frequent at CKP (Kettle Ponds) than CBC — were not removed from the dataset and may be associated with degraded data quality. Users should exercise caution when analyzing data from winter periods, particularly at CKP, due to potential signal degradation caused by snow coverage over the instruments. Data Availability Spectral surface albedo data are available for the following date ranges: CBC: 2021-10-17 through 2023-06-23 File Naming Convention Files are named as follows: HTML Markup Was Removed.HTML Markup Was Removed.HTML Markup Was Removed.nc Example: cbc.spectral_surface_albedo.20230623.nc cbc – Site identifier spectral_surface_albedo – Data product descriptor 20230623 – Data collection date (YYYYMMDD) File Format All files are in NetCDF format and conform to the CF-1.8 standard. Each file includes the following key variables: surface_albedo — Spectral surface albedo derived from the ratio of upwelling and downwelling irradiance. Unit: 1. downwelling_hemispheric_irradiance — Global downwelling irradiance from MFRSR. Unit: W m-2 nm-1. upwelling_hemispheric_irradiance — Upwelling irradiance from MFR. Unit: W m-2 nm-1. downwelling_hemispheric_irradiance_1625nm — Relative downwelling irradiance at 1625 nm. Unit: 1. upwelling_hemispheric_irradiance_1625nm — Relative upwelling irradiance at 1625 nm. Unit: 1. solar_zenith_angle — Solar zenith angle in degrees. solar_azimuth_angle — Solar azimuth angle in degrees. channel_wavelength_mfr — MFR channel central wavelengths in nanometers. channel_wavelength_mfrsr — MFRSR channel central wavelengths in nanometers.HTML Markup Was RemovedHTML Markup Was RemovedHTML Markup Was Removed

Sindewald et al - Identifying alpine treeline species using high-resolution WorldView-3 multispectral imagery and convolutional neural networks dataset (other, 2025)
The dataset contains region of interest (ROI) polygons for six treeline species found in Rocky Mountain National Park, CO. The tree and shrub species include limber pine (Pinus flexilis), willow (Salix glauca, Salix brachycarpa, and hybrids), Engelmann spruce (Picea engelmannii), subalpine fir (Abies lasiocarpa), glandular birch (Betula glandulosa), and quaking aspen (Populus tremuloides). The ROIs correspond to WorldView-3 image data obtained from Maxar, collected on July 21, 2020, with 0% cloud cover and an off-nadir angle of 16.8 degrees. WV-3 data include a panchromatic (black-and-white) band with 31 cm spatial resolution and eight multispectral bands with 1.24 m resolution: coastal blue (400-450 nm), blue (450-510nm), green (510-580 nm), yellow (585-625 nm), red (630-690 nm), red edge (705-745 nm), near-infrared 1 (N-IR1, 770-895 nm), and near-infrared 2 (N-IR2, 860-1040 nm). Details on orthorectification and atmospheric correction can be seen in the attendant paper. The dataset also includes a 10 m digital elevation model (DEM) from the U.S. Geological Survey EROS Data Center, interpolated to 1.24 m resolution using a cubic spline resampling method. The image data were used to train convolutional neural networks (CNNs) to do pixel-based species classification. The CNN inputs were image chips (patches), each centered on an ROI pixel. The data are therefore provided in the form of these image patches. Additionally, a csv file is provided with mean radiance values for each ROI polygon for each multispectral WV-3 band with species labels. These data accompany a paper titled "Identifying alpine treeline species using high-resolution WorldView-3 multispectral imagery and convolutional neural networks dataset", under review in the Biogeosciences special issue, "Treeline ecotones under global change: linking spatial patterns to ecological processes". Authors include Laurel A. Sindewald 1 , Ryan Lagerquist 2 , Matthew D. Cross 3 , Theodore A. Scambos 4 , Peter J. Anthamatten 5 , Diana F. Tomback 1 1 Department of Integrative Biology, University of Colorado Denver, Denver, 80204, USA 2 Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, 80521, USA 3 Department of Geography and the Environment, University of Denver, Denver, 80208, USA 4 Earth Science and Observation Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, 80309, USA 5 Department of Geography and Environmental Sciences, University of Colorado Denver, Denver, 80204, USA

Colorado Cold-Air Pooling Maps and SNOTEL Station Classifications - John Cramblitt; John Cramblitt; John Cramblitt; Jessica D. Lundquist; Jessica D. Lundquist; Jessica D. Lundquist (other, 2025)
This dataset contains maps of cold-air pooling (CAP) across Colorado at 100m resolution (Colorado_100m.nc) and the region surrounding the East River watershed, CO at 30m resolution (EastRiver_30m.nc). Classifications were determined using a Python implementation of the algorithm originally developed in Lundquist et al. (2008), applied to digital elevation models (DEMs) accessed and resampled from the U.S. Geologic Survey 3D Elevation Program (USGS, 2024) using the py3dep Python module (Chegini, 2021). Additionally, the SNOTEL_CAP_classifications.csv file contains CAP signals for 110 of the 117 Colorado SNOw TELemetry (SNOTEL) weather stations, derived from statistical analysis on observations and the automated algorithm classifications in Colorado_100m.nc. This dataset is released in support of an upcoming manuscript studying CAP distributions and mountain weather station representativeness across these two regions (currently in preparation). The two NetCDF (.nc) files are structured as follows: Variables elevation: elevation at each pixel (meters). CAP: cold-air pooling signal at each pixel. Values: 1 = CAP, 0 = no-signal, -1 = no-CAP. spatial_ref: contains attributes with coordinate system metadata The EastRiver_30m.nc file additionally contains the three variables used to determine CAP signal: slope (degrees) rank: the fractional height of a pixel relative to its neighbors curvature: see the formula in Lundquist et al. (2008) or Cramblitt (2025) Coordinates x: easting (NAD83 / Conus Albers) y: northing (NAD83 / Conus Albers) xv_dist: cross-valley distance (meters). A critical input defining the critical radii r in rank and curvature calculations. The SNOTEL_CAP_classifications.csv file is modified from site metadata obtained with the USDA NRCS National Water and Climate Center Air and Water Database Report Generator (https://wcc.sc.egov.usda.gov/reportGenerator). The appended columns include: EOF CAP Signal: CAP signal derived from empirical orthogonal function (EOF) analysis on recorded daily-minimum temperatures at 110 of Colorado's 117 SNOTEL stations. Negative values indicate CAP; positive values indicate no-CAP. Algorithm CAP Signal: CAP classifications for 110 of the 117 Colorado SNOTEL stations derived from the values in Colorado_100m.nc (3000 m cross-valley distance). Values: 1 = CAP, 0 = no-signal, -1 = no-CAP. Files Colorado_100m.nc: CAP classifications at 100m-resolution across the Colorado mountains for cross-valley distances from 10,000m to 250m (every 500m, with the addition of 250m). Users may select the value which best describes their region of interest. EastRiver_30m.nc: CAP classifications at 30m-resolution across terrain surrounding the East River watershed for a single cross-valley distance of 3,000m. SNOTEL_CAP_classifications.csv: CAP classifications for 110 of the 117 Colorado SNOTEL Stations determined by EOF analysis (column titled EOF CAP Signal) and an automated algorithm (column titled Algorithm CAP Signal). For further detail on algorithm design and function, including the calculation of rank and curvature, CAP classification criteria, and choice of cross-valley distance, please refer to Lundquist et al. (2008) or the linked GitHub repository. In-depth discussion on algorithm implementation and constraints, EOF classifications, and algorithm-EOF disagreement will be released in an upcoming manuscript. Version History Version 2.0.0 updated the contents of all three files and renamed SNOTEL_CAP_classifications.csv to Colorado_SNOTEL_CAP.csv. The previous version was missing information near the state borders, which was corrected here by expanding the spatial domain in Colorado_100m.nc and adjusting the CAP classifications in Colorado_SNOTEL_CAP.csv accordingly. The updated files also reflect the recent corrections to the calculation of curvature and handling of coordinate systems in v2.0.0 of the CAP algorithm (Cramblitt, 2025). The effect of these corrections is relatively minor, but do affect the CAP signal at a few SNOTEL stations and notably the coordinate reference system has been changed from WGS84 (EPSG:4326) to NAD83/Conus Albers (EPSG:5070). The variables 'slope', 'curvature', and 'rank' were removed from the Colorado_100m.nc file to decrease file size and allow for more manageable downloads. These variables are available upon request. Version 1.1.0 added the SNOTEL_CAP_classifications.csv file to the dataset. For questions, please contact jcramblitt@berkeley.edu.

SPLASH Field Study; HELiX UAS Solar Irradiance data from 2022-03-12 to 2022-06-04 (NCEI Accession 0303584) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners conducted a field study (SPLASH - the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology). This field study established a state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. The ultimate goal of this project is improved prediction of weather and water in the Colorado mountains and beyond to inform societal preparedness and response. This dataset includes data files collected during flights of the HELiX uncrewed aircraft system (UAS). The system was equipped with stabilized up and downward looking pyranometers which measure the incident solar energy, and when used together they can provide information on the reflectivity of the surface. Flights were conducted at the Avery Picnic and Kettle Ponds SPLASH sites during spring 2022 and capture the evolution of the snow surface during spring snow melt.

Data from: 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' - L. M. Kueppers; Thomas Powell; L. M. Kueppers; Thomas Powell; Thomas Powell; L. M. Kueppers; Haruko M. Wainwright; H. Marshall Worsham; Nicola Falco; Haruko M. Wainwright; Nicola Falco; H. Marshall Worsham; et al. (ess_dive, 2025)
This package archives the core data used for analysis and inference in 'Abiotic influences on continuous conifer forest structure across a subalpine watershed' (Worsham et al., 2025). All data were collected in the East River, Washington Gulch, Slate River, and Coal Creek watersheds of Colorado. In the paper, we quantified the relative influence of climate, topographic, edaphic, and geologic factors on conifer stand structure and composition, and their functional relationships, at the watershed scale. We used waveform LiDAR data to derive spatially continuous stand structure metrics. We fused these with a species-level classification map to estimate tree species abundance. We applied generalized additive and generalized boosted models to evaluate the covariability of structural and compositional metrics with abiotic variables. The package contains the essential products required for reproducing our analysis and the tables and figures reported in the publication. The products comprise four classes: (1) geospatial data, (2) tabular data used for inferential analysis, (3) tabular data describing analytical results and performance statistics, and (4) a data user guide. (1) includes discretized waveform LiDAR data, locations and attributes of individual tree crowns, sampling locations and domain boundaries, a canopy height model, and raster files of estimated forest structural and compositional metrics at 100 m grid scale. (2) includes all response and explanatory variable values applied in inferential models. Response variables include conifer forest stand density, basal area, 95th percentile height, quadratic mean diameter, and others. Explanatory variables include climatic water deficit, actual evapotranspiration, elevation, heat load, soil available water content, and others. (3) includes results of training and testing several individual tree detection (ITD) algorithms, as well as inferential modeling results. (4) is a PDF user guide for this data package, including detailed descriptions and data dictionaries for all files. The data package root contains 17 assets: 8 compressed tape archive (.tar.gz) files, 5 comma-separated values (.csv) files, 3 Geographic Tagged Image File Format (GeoTIFF) (.tif) files, and 1 Portable Document Format (.pdf) file. The compressed .tar.gz archives contain ESRI shapefiles (.shp) .tif, compressed LASer (.laz), and .csv files. The archives must first be decompressed using the widely distributed command-line software utility TAR. All other files, including constituent files within the .tar.gz archives, can be opened in the open-source R statistical computing environment. Alternatively, .csv files may also be read in any simple text editor software or Microsoft Excel. Geospatial files including .shp and .tif files can also be opened in GIS software, such as QGIS (open-source) or ESRI ArcGIS (proprietary). The .pdf Data User Guide can be read with Adobe Acrobat Reader or other compatible readers.

Metagenome-assembled genomes from topsoils collected during NEON campaign in East River, CO (06/14/2018-06/28/2018) - Shi Wang; Patrick O. Sorensen; Ulas Karaoz; Dana Chadwick; Alexander Polussa; Katharine Maher; Eoin L. Brodie; Nicholas J. Bouskill; Markus Bill; Haruko M. Wainwright; Nicola Falco; Kenneth Hurst Williams (ess_dive, 2025)
The Watershed Function Science Focus Area (WF SFA) at Lawrence Berkeley National Lab is working to build a mechanistic understanding of the distribution and dynamics of biogeochemical processes in mountainous watersheds and their response to perturbation. In June 2018, the NEON (National Ecological Observatory Network) Airborne Observatory Platform (AOP) performed a taskable airborne imaging campaign to collect visible to shortwave infrared (VSWIR) imaging spectroscopy and LiDAR data across 330 km2 in the Upper East River at Crested Butte, CO. We conducted a parallel ground sampling campaign to sample vegetation traits, as well as soil physical, chemical, and microbiological characteristics. We collected these samples from 438 sites across 12 locations spanning much of the elevation, topographic, and geologic variability across the study area. A subset of 250 samples were used for soil metagenomics which is presented here. In addition, at each site, vegetation samples were collected to measure species-specific leaf water content and leaf mass area, foliar elemental composition and foliar CN stable isotope ratios. Soil samples were collected to measure soil physical properties which include bulk density and soil texture analysis. A suite of soil chemical properties was measured from the samples collected at each site, including pH, organic matter, concentrations exchangeable cations, total elemental composition, and the concentrations of extractable N pools (e.g. total free amino acids, ammonium, nitrate, dissolved organic N, and total dissolved N). Additionally, we have measured soil microbial biomass CN stoichiometry. Here, we present 1982 metagenome-assembled genomes (MAGs) for the bacterial and archaeal community from topsoil collected from during NEON 2018 campaign. All metagenomes were sequenced at JGI (Joint Genome Institute) (GOLD Study ID: Gs0149986). Metagenomes were assembled using JGI Metagenome Workflow (10.1128/mSystems.00804-20). The dataset includes (1) zip files for 1982 MAG fasta files (neon_genomes1-5.tar.gz, split into 5 tarballs to keep tarballs under 0.5 GB), (2) neon_Gs0149986_samples_soilproperties_metagenomes.csv: the sample information together with the accession numbers for the underlying metagenomes and the associated soil physical and chemical measurements in NMDC (National Microbiome Data Collaborative) compliant format, (3) neon_Gs0149986.kml: location bounding box file for the sampled locations, (4) samples.csv: sample metadata file used to register Internationall Generic Sample Numbers (IGSNs), (5) flmd.csv: file level metadata file, and (6) dd.csv: data dictionary file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

SnowEx20 Grand Mesa IOP Ground-Based Passive Microwave Radiometer Measurements, Version 1 (other, 2025)

Site and endmember spectra of terrestrial vegetation and soils for the Colorado Headwaters Ecological Spectroscopy Study, June-July 2025 (ess_dive, 2025)
This dataset provides site and endmember spectra collected during the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign. The site spectra were collected to help validate airborne hyperspectral data acquired by the National Ecological Observatory Network's aerial observation platform (NEON AOP). Endmember spectra were collected to augment existing spectral libraries with additional samples of bare surfaces and non-photosynthetic vegetation. All measurements were acquired with an Analytical Spectral Devices (ASD) FieldSpec4 Hi-Res NG (Next Generation) spectroradiometer, which records radiance at 1nm (nanometer) intervals from the ultraviolet to the short-wave infrared (350-2500 nm). The dataset includes spectra measured at meadow sites where the CHESS team also collected vegetation samples for trait analyses. The site spectra were collected with the ASD FieldSpec4 palm grip attachment using an 8° field-of-view foreoptic. Site spectra are integrated measurements of the entire surface within the foreoptic’s field of view. For site-level spectra, the sun is the illumination source. A Spectralon panel mounted on a tripod was used for instrument optimization and white reference measurements for all site spectra. Site spectra were acquired within two hours of solar noon and within 48 hours of a NEON AOP overflight. Site spectra are labeled by date, sampling area, and site number according to the naming conventions of the CHESS campaign’s data management plan. The dataset also contains endmember spectra in the following categories: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare (soil/rock), and flowers. Endmember measurements were acquired using either the contact probe or the leaf clip attachments of the ASD FieldSpec4. In these configurations, the bulb inside the spectrometer provides the light source for the measurements. The spectrometer was optimized and white reference measurements were recorded using the circular white pucks attached to the contact probe and leaf clip. Because they do not rely on solar illumination, contact probe and leaf clip measurements were collected during a broader time frame than the palm grip site spectra. Some endmembers were measured at CHESS meadow sites, while others were collected within the larger sampling area or in nearby locations (e.g. Gothic Townsite) with similar characteristics. Radiance, reflectance, and metadata files are split into three subfolders according to measurement type: proximal/palm grip (prx), contact probe (cp), and leaf clip (lc). Radiance spectra are provided in ASD file format (.asd file extension). All ASD files can be opened using the provided scripts. Metadata is provided in two formats: CSV file format (no geolocation) and GEOJSON file format (includes geolocation for each spectra). The dataset includes a set of pre-processed reflectance spectra as CSV files (yyyymmdd_rfl.csv). The python scripts and jupyter notebook used to calculate reflectance spectra from the ASD radiance data is included here and was previously published at: https://doi.org/10.3334/ORNLDAAC/2446. There is also a folder of JPEG photographs corresponding to selected spectra. We include a protocol document with detailed steps for ASD FieldSpec4 assembly and operations. This data additionally contains a file level metadata (flmd.csv) and data dictionary (dd.csv) file. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability" (ess_dive, 2025)
The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Replication data for Fig 4.1, Gunnison uterine litter size vs litter size at first juvenile emergence, 19 Sept 2025. - John Hoogland (other, 2025)
FILE DESCRIPTION File name = Fig 4.1, Gunnison uterine litter size vs litter size at first juvenile emergence, 19 Sept 2025. These data used for Fig 4.1 (top and bottom) in Hoogland 2026. Top graph: Number of different uterine litter sizes for GPDs in late pregnancy, all determined by using a Sonosite portable ultrasound machine. I had one data point for each GPD female per year, but considered data from the same female in different years to be independent. Bottom graph: Differences between uterine GPD litter size vs GPD litter size for the same females at first juvenile emergence. These differences were not statistically significant (P = 0.267, Wilcoxon matched-pairs signed-ranks test). For each gunnison female shown here, I used one data point, which was the difference between uterine litter size in late pregnancy vs litter size at first juvenile emergence from the natal nursery-burrow. For this figure, 0 = no difference; 1 = difference of 1 juvenile; and so forth. Numbers for each uterine litter size can be calculated directly from the top histogram of this Fig 4.1 of Hoogland 2026. Sample sizes for GPD uterine litter sizes in top graph differ from (smaller) GPD litter sizes in bottom graph because many females in late pregnancy did not eventually wean a litter. This file ready for longterm storage on 24 January 2025.

Cosmogenic Nuclide Burial Isochron Data for the Central City Gravel, Malta Gravel, and Lava Creek B Ash Locations: Classic Locations of Early to Middle Pleistocene Deposits, South Platte and Arkansas Rivers, Central Colorado, USA (other, 2025)
This data release includes Al-26/Be-10 cosmogenic nuclide concentrations and burial isochron ages for three locations in central Colorado, USA, with preferred burial ages ranging from ~2 Ma to 600 ka, that provide constraints on geologic mapping and chronostratigraphic correlations of Pleistocene Colorado Rocky Mountain (CRM)-Great Plains deposits. This new dataset helps to validate original identification and characterization of the deposits by renowned work of early to late 20th century geologists. In addition, this dataset is critical for any models of geomorphic development of modern Colorado Rocky Mountain (CRM)-Great Plains depositional systems, including analyses of regional relationships involving the Malta Gravel, Nussbaum Alluvium, Rocky Flats Alluvium, Verdos Alluvium, and Slocum Alluvium (Scott, 1960; Tweto, 1961; Epis and others, 1976, 1980). Paired with cosmogenic analyses for the Nussbaum Alluvium at Baculite Mesa (1.30 ± 0.08 Ma) (Odom and others, 2024) and a glacial advance of the Laurentide icesheet of Balco and Rovey (2010) at 1.31 ± 0.13 Ma, this dataset provides the chronologic constraints for onset of Pleistocene Rocky Mountain river incision and Great Plains river entrenchment for the South Platte and Arkansas Rivers, and the establishment of the current river positions. Three classic localities of previously mapped and described deposits were sampled in the field for cosmogenic nuclide analysis. A summary of the characteristics of these sites is as follows: (1) The Central City gravel is a 160 meter-thick (525 feet), matrix-supported, pebble to boulder gravel with a silty sand matrix, nonstratified to crude stratification, and no distinct clast imbrication (Kellogg and others, 2008) lying within the CRM Front Range foothills along Clear Creek at an elevation of approximately 2,430 m/7,960 ft asl. It was originally identified as the first Pleistocene glacial deposit emanating from ice caps developed on the Continental Divide across the CRM Front Range (Spurr and others, 1908; Atwood and Atwood, 1938). More recent work has placed this deposit as a Neogene/Miocene gravel associated with the Ogalalla Formation on the Great Plains (Kellogg and others, 2008), and disregarded its glacial origin and designation as till by the initial investigators. Seven samples of cobbles and clayey, silty sand matrix were measured for an Al-26/Be-10 burial isochron. Two samples, 17-CO-047-CEN and 18-CO-002-CEN, featured depressed Al-26/Be-10 ratios indicative of prior burial and were excluded from further calculations. The final burial isochron for this site yielded a burial age of 1.44 ± 0.26 Ma (1σ analytical uncertainty). (2) The Pliocene-early Pleistocene, Malta Gravel (Tweto, 1961; McCalpin and others, 2012; Ruleman and others, 2018) is a reddish-brown to yellow-red, angular to subangular, pebble to cobble gravel with a silty sand matrix exposed in a railroad cut ~5 km/3.1 mi west of Leadville, Colorado at the headwaters of the Arkansas River (McCalpin and others, 2012). It was originally defined as an early-middle Pleistocene glaciofluvial deposit (Tweto, 1961), but was more recently identified as non-glacial in origin and possibly within the uppermost section of the Neogene Dry Union Formation (McCalpin and others, 2012; Kellogg and others, 2017). Twelve samples of sand and cobbles were collected for analyses approximately 5 meters (16.4 feet) below the deposit surface and below a heavily oxidized pedogenic horizon overlain by pre-Bull Lake glaciofluvial deposits of early or middle Pleistocene age. One sample, 17-CO-039-LV-Ac, featured a depressed Al-26/Be-10 ratio and was excluded from further calculations. The final burial isochron for this site yielded a burial age of 1.97 ± 0.04 Ma (1σ analytical uncertainty). (3) Lava Creek B ash location in the central upper Arkansas River valley, north of Buena Vista, CO is a heavily oxidized, fluvially reworked tephra within a faulted and fractured outcrop of clayey, silty sand deposits, overlain by pre-Bull Lake glacial deposits of middle Pleistocene age (Scott, 1975; Kellogg and others, 2017). The ash lies within the uppermost Dry Union Formation (Kellogg and others, 2013). Probable seismogenic sand dikes are pervasive throughout the exposure. Twelve samples for analyses were collected approximately 9 meters (29.5 feet) below the surface and 40 centimeters (15.8 inches below the basal Lava Creek B ash layer within a silty sand and pebble gravel. One sample, 17-CO-032-BV-A, featured a depressed Al-26/Be-10 ratio indicative of prior burial and was excluded from further calculations. All other samples had nearly identical mean Be-10 concentrations clustering between 601,000-651,000 atoms/gram (at/g) and Al-26 concentrations clustering between 2,913,000-3,429,000 at/g. These overlapping concentrations precluded a burial isochron calculation and necessitated minimum/maximum burial dating. The mean minimum burial age was ~570 ka and mean maximum burial age was ~690 ka, overlapping with the preferred sanidine 40Ar/39Ar isochron date of 631.3 ± 4.3 ka for the Lava Creek B ash (Matthews and others, 2015). These new ages provide a middle Pleistocene age for the minimum age of the Dry Union Formation, and chronostratigraphic correlation to the uppermost Dry Union Formation with the uppermost Santa Fe Group within the San Luis Valley (Ruleman and others, 2021). References: Atwood, W.W., and Atwood, W.W., 1938, Opening of the Pleistocene in the Rocky Mountains of the United States, The Journal of Geology, v. 46, no. 3, p. 239–247, https://www.jstor.org/stable/30081297 Balco, Greg, and Rovey, C.W., 2010, Absolute chronology for major Pleistocene advances of the Laurentide Ice Sheet, Geology, v. 38, no. 9, p. 795–798, doi: https://doi.org/10.1130/G30946.1 Epis, R.C., Scott, G.R., Taylor, R.B., and Chapin, C.E. 1976, Cenozoic volcanic, tectonic, and geomorphic features of central Colorado, Professional Contributions of the Colorado School of Mines No. 8, p. 323-338. Epis, R.C., Scott, G.R., Taylor, R.B., and Chapin, C.E., 1980, Summary of Cenozoic geomorphic, volcanic, and tectonic features of central Colorado and adjoining areas, in Kent, H.C., Porter, K.W., eds., Colorado Geology: Denver Colo., Rocky Mountain Association of Geologists, p. 135-156. Kellogg, K.S., Shroba, R.R., Bryant, Bruce, and Premo, W.R., 2008, Geologic map of the Denver West 30’ x 60’ quadrangle, north-central Colorado: U.S. Geological Survey Scientific Investigations Map 3000, scale 1:100,000, 48-p. pamphlet. Kellogg, K.S., Lee, Keenan, Premo, W.R., and Cosca, M.A., 2013, Geologic map of the Harvard Lakes 7.5' quadrangle, Park and Chaffee Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3267, 22 p., scale 1;24,000, https://dx.doi.org/10.3133/sim3267. Kellogg, K.S., Shroba, R.R., Ruleman, C.A., Bohannon, R.G., McIntosh, W. C., Premo, W.R., Cosca, M.A., Moscati, R.J., and Brandt, T.R., 2017, Geologic map of the upper Arkansas River valley region, north-central Colorado: U.S. Geological Survey Scientific Investigations Map 3382, pamphlet 70 p., 2 sheets, scale 1:50,000, https://doi.org/10.3133/sim3382. Matthews, N.E., Vazquez, J.A., and Calvert, A.T., 2015, Age of the Lava Creek supereruption and magma chamber assembly at Yellowstone based on 40Ar/39Ar and U-Pb dating of sanidine and zircon crystals, Geochemistry, Geophysics, and Geosystems, v. 16, no. 8, p. 2508-2528, https://agupubs.onlinelibrary.wiley.com/doi/full/10.1002/2015GC005881. McCalpin, J.P., Funk, Jonathan, and Mendel, David, 2012, Leadville South Quadrangle Geologic Map, Lake County, Colorado, Colorado Geological Survey Open-File Report 12-06, 54 p., 2 plates, 1:24,000 scale. Odom, W.E., Ruleman, C.A., Sterne, E.J., O'Keeffe, M.K., and Morgan, M.L., 2024, Cosmogenic Al-26/Be-10 Isochron Burial Data for the Nussbaum Gravel at Baculite Mesa, CO: U.S. Geological Survey data release, https://doi.org/10.5066/P1B2MCHG. Ruleman, C.A., Brandt, T.R., Caffee, M.W., and Goehring, B.M., 2018, Geologic map of the Leadville North 7.5’ quadrangle, Eagle and Lake Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3400, 1:24,000, https://doi.org/10.3133/sim3400. Ruleman, C.A., and Brandt, T.R., 2021, Surficial geology of the northern San Luis Valley, Saguache, Fremont, Custer, Alamosa, Rio Grande, Conejos, and Costilla Counties, Colorado: U.S. Geological Survey Scientific Investigations Map 3475, 2 sheets, scale 1:75,000, https://doi.org/10.3133/sim3475. Scott, G.R., 1960, Subdivision of the Quaternary Alluvium East of the Front Range Near Denver, Colorado, Bulletin of the Geological Society of America, v. 71, p. 1541-1544. Scott, G.R., 1975, Reconnaissance geologic map of the Buena Vista quadrangle, Chaffee and Park Counties, Colorado, U.S. Geological Survey Miscellaneous Field Studies Map MF-657, 1:62,500 scale. Spurr, J.E., Garrey, G.H., and Ball, S.H., 1908, Economic Geology of the Georgetown Quadrangle, Together with the Empire District, U.S. Geological Survey Professional Paper 63, 507 p., 4 plates. Tweto, Ogden, 1961, Late Cenozoic events of the Leadville district and upper Arkansas Valley, Colorado, Geological Survey Research 1961, short papers in the geologic and hydrologic sciences Articles 1-146, U.S. Geological Survey Professional Paper, 424-B, p. B133-B135.

Metagenome-assembled genomes from East River floodplain sediments near Crested Butte, CO, USA (June to September 2017) - Scott Fendorf; Christopher Francis; Kristin Boye; Anna N. Rasmussen; Craig R. Anderson; Marco Keiluweit (ess_dive, 2025)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken in 2017 in June (flooded conditions) and September (drained conditions) at two locations (MCB1 and MCB3) in an active meander (Meander C) of the East River. Sediment cores were collected from 2 depths, a near-surface, generally unsaturated depth (15-40 centimeter (cm) depth below surface) and a deeper depth influenced by flooding with redoximorphic features (50-88 cm depth below surface). Sediments were homogenized from the ~10 cm cores for microbial analyses. A total of 24 metagenomes were sequenced through the Joint genome institute (JGI) corresponding to 8 samples sequenced in triplicate. These metagenomes can be found under Genomes Online Database (GOLD) sequencing project: Gs0151851. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 405 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also 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. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

2019 Meander C and Meander Z floodplain groundwater chemistry from the East River Watershed, CO, USA - Christian Dewey; P. S. Nico; Craig R. Anderson; Patricia Fox; Marco Keiluweit (ess_dive, 2025)
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. Data includes samples from two intra-meander zones: Meander C, in the Pumphouse vicinity, and Meander Z, just upstream of the confluence with Brush Creek. Floodplain piezometers installed along two transects across Meander C (MCP and MCB wells) and Meander Z (MZA and MZB wells) were sampled on daily to weekly time scales during summer-fall 2019. Some river water grab samples are also included. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], dissolved oxygen [DO], 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). Files are included in this dataset include: sample locations and depths in both a kmz file which can be opened in Google Earth and a csv file, aqueous geochemistry data in a csv files for Meander C and Meander Z, and analytical detection limits in a csv file. This dataset also 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.

Airborne Magnetic and Radiometric Survey, Colorado Mineral Belt, Mid Block, 2023 - United States Geological Survey (other, 2025)
This data release provides digital flight-line and gridded data for a high-resolution airborne magnetic and radiometric survey over the central portion of the Colorado Mineral Belt, central Colorado. The Colorado Mineral Belt is a broad, northeast-southwest trending alignment of historic mining districts that have produced multiple types of commodities, including critical minerals that are vital to the Nation's security and economy. The survey was acquired as part of the USGS Earth Mapping Resource Initiative (Earth MRI) to improve our understanding of the fundamental geologic framework underpinning the Colorado Mineral Belt. This release covers the central portion of the belt (“mid” block), which includes a wide swath from the Powderhorn mining district to the southwest, the Brown’s Canyon mining district to the east, and the Spring Creek mining district in the north, including parts of Gunnison, Chaffee, and Saguache Counties. Previous reports have identified several critical minerals in this region, including beryllium, fluorspar, graphite, lithium, manganese, niobium, REEs, tantalum, titanium, uranium, vanadium, and zinc. Data for this survey were acquired by NV5 Geospatial, Inc. and is sub-contractors Precision GeoSurveys, Inc. and EDCON-PRJ, Inc under contract with the USGS. The survey was flown in two segments, with the first in October 2023 and the second July through August of 2024, following a break in operations for winter weather. The survey used a helicopter equipped with a magnetometer mounted in a stinger extending from the nose of the aircraft and a gamma-ray spectrometer stowed onboard. The helicopter pilots followed pre-planned flight paths in a grid-like pattern, with north-south lines spaced 200 meters apart and east-west lines spaced 1,000 meters apart. Lines were flown 100 meters above ground as much as possible to maximize detection of gamma-rays and resolve details of the magnetic field. This clearance could be realized in areas of low relief but higher clearances, as much as 200-500 meters, were required over rugged terrain and populated areas for safety reasons. Areas with restricted airspace, such as Wilderness Areas, were avoided. A total of 40,111 linear kilometers of data were collected along the lines, covering a 6,863 square-kilometer irregular area. EDCON-PRJ performed extensive data processing after completion of flying and delivered the final data and report in November 2024.

Snow Depth Datasets for Snodgrass Catchment, Colorado, Water Year 2022-2023 - Chen Wang; Lijing Wang; Baptiste Dafflon (ess_dive, 2025)
This data package presents snow depths data from distributed temperature probes at 18 locations near Snodgrass catchment, Colorado. These data show that snow melt-out dates are approximately one or two weeks later under evergreen forests compared to other vegetation types even at the same elevation. These data were collected to understand how snowmelt heterogeneity impacts headwater hydrology, including streamflow and groundwater levels. They were also used to compare with process-based model simulations of snow depth to evaluate whether the model accurately represents snowmelt dynamics and their effects on headwater hydrology. Snow_DTPs_locations.csv includes all probes locations and their associated elevation and vegetation types. Snow_Depth_Snodgrass_WY2022_2023.csv includes processed snow depths datasets for Water Year (WY) 2022 and 2023. This dataset also 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. Several probes have recordings for WY 2021.

Stream bed topography in 2016 at the Lower Montane site in the East River Watershed, Colorado - John Peterson; Baptiste Dafflon (ess_dive, 2025)
This dataset contains stream bed topography data from the Lower Montane site in the East River Watershed, Colorado. It is intended to support hydro-biogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). The stream bed was surveyed using a RTK-GPS (Real-Time Kinematic Global Positioning System) and a total station at hundreds of locations during two field campaigns, conducted in October 2015 and October 2016. The two surveys were merged into a single product under the assumption of minimal changes in stream bed topography between the years. A LiDAR (Light Detection and Ranging) derived ground surface elevation product from 2015 (see reference below) was used to interpolate the stream bed topography and estimate water depth at the time of the LiDAR survey. The dataset includes three GeoTIFF products, two *.csv data files, and three *.csv metadata files. Feel free to contact the author with any questions.

Geochemistry and Environmental Tracer Data for Groundwater, Stream Water, and Sediment from the East Mancos River, Colorado, 2023 - Andrew H Manning (other, 2025)
This U.S. Geological Survey (USGS) data release contains data from stream water, groundwater, and stream sediment samples collected in August 2023 in the East Mancos River watershed in southwestern Colorado. Data were collected as part of a study aimed at identifying sources of elevated metal concentrations in the stream. Eighteen streambank wells were installed in pairs at nine locations to capture the emerging groundwater from the left and right banks (relative to downstream-facing direction) and a synoptic sampling campaign was conducted to quantify stream metal loads. A continuous, instream injection of sodium bromide (NaBr) was initiated at the head of the ~17 km study reach prior to the synoptic sampling campaign and maintained [...]

Temperature, Humidity, and Time-Lapse Video Data from the East River Watershed, Water Year 2024 - John Cramblitt; Ethan Gutmann; Jessica D. Lundquist (ess_dive, 2025)
This dataset contains timelapse videos and distributed measurements of air temperature, relative humidity, dewpoint, and soil temperature across the East River Basin from October 3rd, 2023 to August 12th, 2024. Instruments were deployed in 14 sites as part of the DOE Grant: Seasonal Cycles Unravel Mysteries of Missing Mountain Water organized by lead-PI Dr. Jessica Lundquist (University of Washington), and co-PIs Dr. Rosemary Carroll (Desert Research Institute) and Dr. Ethan Gutmann (National Center for Atmospheric Research). Data are made available for other projects interested in surface climate or hydrologic processes in complex terrain. Measurements are taken with low cost data loggers strapped to trees or buried just below the soil surface. Timelapse videos are availabe from vantage points at three sites. Images from sites AP BONUS and AP5 provide insight into large-scale seasonal snow cover variability across nearby mountains. Images from site EL2 show smaller scale snow patterns across a nearby meadow. Files contained in this dataset are named and organized by site and variable type (air measurements, ground measurements, or timelapse video). Air and ground measurements are packaged in LoggerData.zip, and timelapse videos are stored in TimelapseVideos.zip. File-level metadata contains details for each file included in the dataset. A data dictionary provides units and descriptions for column or row names in all files. Locations metadata describes site characteristics, locations, and associated GPS methods. Dataset update 2025-03-03: Resolved header and datetime formatting inconsistencies within LoggerData.zip files KP1_Air, KP3_Air, KP3_Ground, KP6_Ground, AP3_Ground, AP4_Air, and AP6_Air.

Groundwater elevation data for monitoring wells within the East and Taylor River basins, Colorado (USA) - Austin Shirley; Lijing Wang; Matthias Sprenger; Alexander W. Newman; Curtis A. Beutler; Kenneth Hurst Williams (ess_dive, 2025)
This dataset is comprised of temporal variations in groundwater elevation data for the 24 monitoring wells located throughout the East River watershed. Seasonal to annual variations in groundwater elevations are a critical property of mountainous watersheds needed to understand both hydrological and below ground biogeochemical processes. Such data serve as a critical constraint for numerical models describing coupled groundwater-surface water behavior within the watershed. Additionally, the offset between the maximum and minimum groundwater elevations defines the extent of the bedrock weathering zone, with annual excursions in the groundwater hydrographic (i.e., the rising and falling hydrographic limbs) imposing primary controls on bedrock saturation state and redox conditions that govern biogeochemical reactions impacting nitrogen, carbon, and metals cycling. Manufacturer-specific software is used to download pressure data from each transducer, with broadly available spreadsheet software (e.g. Microsoft Excel) used to convert temporal variations in water pressure to elevations in units of meters above mean sea level. As additional monitoring wells are installed within the East River watershed and new groundwater monitoring wells are installed in the Taylor River watershed, temporal groundwater elevation data will be included as a part of this master dataset. Details regarding the metadata associated with each monitoring well location, including well depths, screened intervals, well location coordinates, and bedrock type, are included, as is a standard operating procedure for generating groundwater elevation data from water pressure values recorded by the pressure transducers. This dataset includes: (1) a zip file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.zip), containing (a) PNG of groundwater hydrographs, (b) a CSV file with groundwater elevation data, and (c) CSV file containing metadata organized by location; (2) an Excel file (East_River_Watershed_Compiled_Groundwater_Elevation_Data_Plots.xlsx) with the groundwater elevation data, groundwater hydrographs, and metadata organized by location; (3) a Word file (Groundwater_elevation_data_protocols.docx) and a PDF file version (Groundwater_elevation_data_protocols.pdf) containing field protocols and methods; (4) a location metadata (locations.csv) file; (5) a file level metadata (flmd.csv); and (6) data dictionary (dd.csv) file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Soil moisture and temperature from 2019 to 2024 along northeast- and southwest-facing hillslopes at the Lower Montane site in the East River Watershed, Colorado - Baptiste Dafflon (ess_dive, 2025)
Soil moisture, temperature, and electrical conductivity have been monitored at multiple depths (between 10 and 50 cm) at 4 locations along a northeast-facing slope and 3 locations on the opposite southwest-facing slope at the Lower Montane site in the East River Watershed, Colorado, from Oct 2019 to Oct 2024. The purpose of this data is to inform hydro-biogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). Two locations on the northeast-facing slope were reinstalled in 2020 due to damage from wildlife, and thus data for these sites are provided in two distinct files. Overall, the data are reported in 9 CSV files containing the measurements, and the locations are provided in the Sensor_Location.csv file. There is a total of 10 *.csv data files and 3 *.csv metadata files. Older datasets associated with the northeast-facing slope are provided in another archive (see reference). 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. Feel free to contact the author with any questions or collaboration interests.

60-meter Segment Stream Network of the Upper East River Watershed, Brown County, Wisconsin - Faith A Fitzpatrick; Heidi M Broerman; James D Blount (other, 2025)
The East River is an agricultural tributary to the Lower Fox River and the Environmental Protection Agency (EPA) Green Bay Area of Concern (AOC) in northeast Wisconsin. This tributary has known excessive phosphorus (P) and suspended sediment loads that contribute to habitat related impairments and downstream algal blooms. As part of the Great Lakes Restoration Initiative (GLRI), the U.S. Geological Survey conducted a stream corridor sediment budget study to estimate the spatial distribution of near-channel erosion and in-channel deposition of sediment and sediment-bound phosphorus (P). The Upper East River watershed is largely located in southern Brown County, with some headwater streams in northern Calumet and Manitowoc counties. In August 2022, erosion and deposition were assessed along 15 stream reaches using rapid geomorphic assessments (RGAs) and sediment samples were collected and analyzed for total phosphorus concentrations. The results of these field-based estimates were later applied to an expanded 60-meter stream network constructed from a hydro-enforced 10-meter digital elevation model in a Geographic Information System (GIS) in order to spatially map areas of streambank erosion and streambed deposition.

Montane Conifer, Aspen, Meadow, and Sagebrush Metagenome Resolved Genomes and Traits in East River Watershed, Colorado, USA - Preston Tasoff; Jillian F. Banfield; Ulas Karaoz; Eoin L. Brodie; Haruko M. Wainwright; Kenneth Hurst Williams (ess_dive, 2025)
Climate change is driving vegetation shifts in mountain watersheds, with unknown impacts on biogeochemical cycles. We hypothesize that these shifts will reshape soil microbiomes and associated biogeochemical processes. As a part of Lawrence Berkeley National Laboratory (LBNL) Watershed Science Focus Area (SFA), we assessed microbiome and microbial functional trait differences between soils under conifer, aspen, forby meadows, and sagebrush across the East River Watershed, CO, controlling for elevation and aspect. Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal communities from soils 0-20cm in depth across three locations in the watershed—Headwaters, Upper Reaches, and Lower Reaches from August 3-11th 2016. Each location was further subdivided into two blocks, with one block on a west facing aspect, and two on the east aspect of the valley. Within blocks, two samples per vegetation type were taken (one at each depth). This resulted in 66 samples, which were sequenced at JGI and can be found under the Joint Genome Institute (JGI) Genomes Online Database (GOLD) sequencing project Gs0118068. Metagenomes were assembled through an inhouse pipeline (see methods), binned using four autobinners (concoct, maxbin2, metabat2, and vamb) and consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>75%) and contamination (<25%), and dereplicated at 95% ANI using drep. The dataset includes a zip file of 687 genomes (Vegtype_MAGS.zip), the accession numbers for the underlying metagenomes, a csv file with MAG quality metrics and taxonomy from Genome Taxonomy Database (GTDB) and National Center for Biotechnology Information (NCBI) taxonomic representative genome proteins (EastRiver_Vegtype_drep_genome_info.csv), and a file containing MAG quality metrics and taxonomy (gtdb_drep_bin_taxonomy.csv). The dataset additionally includes a sample metadata file (EastRiver_Vegtype_sample_metadata.csv), a metadata file used to register associated samples with IGSNs (International Generic Sample Numbers) (samples.csv), a Google KML file for the sampled locations (sample_collection_sites.kml), a location metadata file (locations.csv), a file-level metadata file (flmd.csv), and a data dictionary (dd.csv) file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

Data from: Drivers of spring migration phenology in Rocky Mountain elk (other, 2025)
By migrating, ungulates take advantage of cyclical fluctuations in resources, which allows them to persist at greater population numbers than they would in the absence of these seasonal movements. We sought to identify the drivers of spring elk (Cervus canadensis) migration and evaluate how well individuals were able to optimize access to forage prior to departure, while migrating, and upon arrival on summer range. Specifically, we investigated the timing and duration of spring migration in four Colorado elk herds to test how forage quality and snow dynamics pre-, during, and post-migration influenced elk departure from winter range and the length of time spent migrating. Our analyses revealed significant heterogeneity among and within herds. Overall, spatiotemporal dynamics in forage and snow emerged as critical drivers influencing migratory phenology, but the discrete covariates associated with forage and snow were herd-specific. We did not find marked sub-strategies wherein some herds or individuals optimized a specific component of migration, but rather found that elk varied in their ability to optimize access to forage at all times analyzed. Our findings suggest that elk exhibit a flexible response to environmental cues, adjusting migration timing and duration in accordance with local conditions. These results contribute to a deeper understanding of the mechanisms driving ungulate migration and emphasize the importance of considering dynamic environmental factors in studies of migration phenology. Further, they emphasize the importance of forage conditions not just on the migratory route, but also on winter and summer range. Additionally, they provide baseline knowledge of elk migrations in Colorado, useful information for ensuring the conservation and persistence of these migratory routes and the productivity they facilitate.

Supplemental Tables for "Phylogenetic patterns over sixty-five years of vegetation change across a montane elevation gradient" by Veldhuisen et al. - Leah N. Veldhuisen (other, 2025)
This repository contains three supplemental tables for Veldhuisen et al.'s paper titled "Phylogenetic patterns over sixty-five years of vegetation change across a montane elevation gradient." Tables 1 2 contain species replacements in the Smith Brown (2018) phylogeny for the short and full dataset used in the paper. Table 3 contains raw phyogenetic signal results for all community types, with and without lumped data.

Metagenome-assembled genomes from Slate River floodplain sediments near Crested Butte, CO, USA (June 2018) - John R. Bargar; Bradley Tolar; Christopher Francis; Kristin Boye; Anna N. Rasmussen (ess_dive, 2025)
Microorganisms play a key role in cycling nutrients and contaminants in the terrestrial environment depending on their genetic potential. Here, we present metagenome-assembled genomes (MAGs) for the bacterial and archaeal community in floodplain sediment samples taken June 2018 at two locations (OBJ1 and OBJ2) near the confluence of the Oh-Be-Joyful Creek and Slate River. The site is one of the field sites in focus for the Stanford Linear Accelerator Center (SLAC) National Accelerator Laboratory Groundwater Quality Science Focus Area (SFA) program. Sediment samples from a deep soil pit were collected from 50 to 150 cm depth below surface at discrete depths every 20 cm for microbial analyses. A total of 12 metagenomes were sequenced through the Joint Genome Institute (JGI) and can be found under Genomes Online Database (GOLD) sequencing project: Gs0142591. Metagenomes were assembled, binned, and refined using metawrap to generate MAGs (>50% complete and < 10% contamination based on checkM scores). This dataset includes a zip file of 1233 MAG fasta files and a csv file with quality, taxonomic classification (Genome Taxonomy Database Release RS220), and metagenome accessions for MAGs. This dataset also 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. Part of this work was performed at SLAC Accelerator Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-76SF00515.

Compilation of actual evapotranspiration and vegetation indices along critical riparian zones on the Navajo Nation from 2013-2023 (other, 2025)
These data were compiled for monitoring riparian zone trends and changes in the Navajo Nation as part of a study to document riparian ecosystem health and its water use in support of potential restoration efforts. The objective of our study was to monitor the short and medium-term effects on the riparian vegetation in relation to evapotranspiration changes, drought, and other hydrological processes, along some critical riparian zones in the Navajo Nation. These data represent time series of vegetation greenness and water use for the years 2013 to 2023. These data were collected from the spaceborne mission Landsat 8 which carries the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors for an area within the Navajo [...]

Colorado Cold-Air Pooling Maps and SNOTEL Station Classifications (other, 2025)
This dataset contains maps of cold-air pooling (CAP) across Colorado at 100m resolution (Colorado_100m.nc) and the region surrounding the East River watershed, CO at 30m resolution (EastRiver_30m.nc). Classifications were determined using a Python implementation of the algorithm originally developed in Lundquist et al., 2008, applied to digital elevation models (DEMs) from the USGS (2024). Additionally, the SNOTEL_CAP_classifications.csv file contains CAP signals for 110 of the 117 Colorado SNOw TELemetry (SNOTEL) weather stations, derived from statistical analysis on observations and the automated algorithm classifications in Colorado_100m.nc. This dataset is released in support of an upcoming manuscript studying CAP distributions across these two regions (currently in preparation). The two NetCDF (.nc) files are structured as follows: Variables- elevation: elevation at each pixel (meters) - slope: slope at each pixel (degrees)- rank: proportion of surrounding pixels (within a square of radius r) which are lower in elevation than the given pixel. - curvature: terrain curvature at each pixel (1/m)- CAP: cold-air pooling signal at each pixel. Values: 1 = CAP, 0 = no-signal, -1 = no-CAP. Coordinates- lon: longitude (degrees, WGS84)- lat: latitude (degrees, WGS84)- xv_dist: cross-valley distance (meters). A critical input defining the critical radii r in rank and curvature calculations. The SNOTEL_CAP_classifications.csv file is modified from site metadata obtained with the USDA NRCS National Water and Climate Center Air and Water Database Report Generator (https://wcc.sc.egov.usda.gov/reportGenerator). The appended columns include:- EOF CAP Signal: CAP signal derived from empirical orthogonal function (EOF) analysis on recorded daily-minimum temperatures at 110 of Colorado's 117 SNOTEL stations. Negative values indicate CAP; positive values indicate no-CAP.- Algorithm CAP Signal: CAP classifications for 110 of the 117 Colorado SNOTEL stations derived from the values in Colorado_100m.nc (3000 m cross-valley distance). Values: 1 = CAP, 0 = no-signal, -1 = no-CAP. Files- Colorado_100m.nc: CAP classifications at 100m-resolution across the Colorado mountains for cross-valley distances from 10,000m to 1,000m (every 500m). Users may select the value which best describes their region of interest. - EastRiver_30m.nc: CAP classifications at 30m-resolution across terrain surrounding the East River watershed for a single cross-valley distance of 3,000m. - SNOTEL_CAP_classifications.csv: CAP classifications for 110 of the 117 Colorado SNOTEL Stations determined by EOF analysis (column titled EOF CAP Signal) and an automated algorithm (column titled Algorithm CAP Signal). For further detail on algorithm design and function, including the calculation of rank and curvature, CAP classification criteria, and choice of cross-valley distance, please refer to Lundquist et al., 2008 or the linked GitHub repository. In-depth discusson on algorithm implementation and constraints, EOF classifications, and algorithm-EOF disagreement will be released in an upcoming manuscript.Version 1.1.0 added the SNOTEL_CAP_classifications.csv file to the dataset.For questions, please contact jcramblitt@berkeley.edu.

Metagenome-assembled genomes from topsoils along a hillslope water gradient across early snowmelt to late summer in East River, CO - Jillian F. Banfield; Preston Tasoff; Ulas Karaoz; Eoin L. Brodie (ess_dive, 2025)
Drought is changing the American Mountain West at unprecedented rates with unknown consequences to soil microbiome composition and function. As a part of LBNL Watershed Science Focus Area (SFA), we investigated shifts in microbial community and transcriptional activity on a subalpine conifer-meadow transition zone throughout the summer of 2023 as soil dried down. This work took place in Crested Butte, CO on Snodgrass mountain, using a proxy for drought conditions. Here we present metagenome assembled genomes (MAGs) for the bacterial and archaeal community at 0-10cm from three sites along a hillslope water gradient across five timepoints from early snowmelt to late summer. 42 metagenomes were sequenced at Joint Genome Institute (JGI) and can be found under the JGI GOLD (Genomes Online Database) sequencing project Gs0166660. Metagenomes were assembled through an inhouse pipeline (see methods), binned using four autobinners (concoct, maxbin2, metabat2, and vamb) and consolidated using dastool. The consolidated bins from all metagenomes were pooled, filtered by completeness (>70%) and contamination (<10%), and dereplicated at 95% ANI using drep. This dataset (1) a zip file of 157 MAGs (as fasta files, Gs0166660_bins_tar.gz), (2) sample metadata file with sample IGSNs (International Generic Sample Numbers) (samples.csv), (3) bounding box coordinates for the sampled locations (Gs0166660.kml), (4) metagenome assembly and coassembly metadata file listing IMG/M (Integrated Microbial Genomes/Metagenomes) metagenome accessions linking samples to metagenomes (EastRiver_Drought_ESSDive_Metadata.csv), (5) location metadata file (locations.csv), (6) file-level metadata file (flmd.csv) and (7) data dictionary (dd.csv) file. This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

SPLASH Field Study; NOAA NSSL CLAMPS2 TROPoe Retrievals from 2021-10-21 to 2022-01-18 (NCEI Accession 0303080) - NCEI (other, 2025)
From fall 2021 through summer 2023, NOAA and research partners conducted a field study (SPLASH - the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology). This field study established a state-of-the-art observing network in the East River watershed of the Colorado mountains with a goal of advancing weather and water prediction capabilities in areas with complex terrain. The ultimate goal of this project is improved prediction of weather and water in the Colorado mountains and beyond to inform societal preparedness and response. SPLASH was complemented by a concurrent Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign led by the Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility. These files contain 24-hour periods of retrieved thermodynamic profiles derived from observations collected by the CLAMPS2 Atmospheric Emitted Radiance Interferometer (AERI). These retrievals were processed in the IDL (e.g., the AERIoe version) of the TROPoe algorithm. Quicklook imagery is included. The AERI is capable of measuring IR radiances throughout the depth of the atmosphere with a wavenumber resolution 1 cm-1 and temporal resolution of ~20 seconds on a clear sky day. Complete details and citations are included in the provided documentation.

Site and endmember spectra of terrestrial vegetation and soils for the Colorado Headwaters Ecological Spectroscopy Study, June-July 2025 (ess_dive, 2025)
This dataset provides site and endmember spectra collected during the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS) campaign. The site spectra were collected to help validate airborne hyperspectral data acquired by the National Ecological Observatory Network's aerial observation platform (NEON AOP). Endmember spectra were collected to augment existing spectral libraries with additional samples of bare surfaces and non-photosynthetic vegetation. All measurements were acquired with an Analytical Spectral Devices (ASD) FieldSpec4 Hi-Res NG (Next Generation) spectroradiometer, which records radiance at 1nm (nanometer) intervals from the ultraviolet to the short-wave infrared (350-2500 nm). The dataset includes spectra measured at meadow sites where the CHESS team also collected vegetation samples for trait analyses. The site spectra were collected with the ASD FieldSpec4 palm grip attachment using an 8° field-of-view foreoptic. Site spectra are integrated measurements of the entire surface within the foreoptic’s field of view. For site-level spectra, the sun is the illumination source. A Spectralon panel mounted on a tripod was used for instrument optimization and white reference measurements for all site spectra. Site spectra were acquired within two hours of solar noon and within 48 hours of a NEON AOP overflight. Site spectra are labeled by date, sampling area, and site number according to the naming conventions of the CHESS campaign’s data management plan. The dataset also contains endmember spectra in the following categories: photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare (soil/rock), and flowers. Endmember measurements were acquired using either the contact probe or the leaf clip attachments of the ASD FieldSpec4. In these configurations, the bulb inside the spectrometer provides the light source for the measurements. The spectrometer was optimized and white reference measurements were recorded using the circular white pucks attached to the contact probe and leaf clip. Because they do not rely on solar illumination, contact probe and leaf clip measurements were collected during a broader time frame than the palm grip site spectra. Some endmembers were measured at CHESS meadow sites, while others were collected within the larger sampling area or in nearby locations (e.g. Gothic Townsite) with similar characteristics. Radiance, reflectance, and metadata files are split into three subfolders according to measurement type: proximal/palm grip (prx), contact probe (cp), and leaf clip (lc). Radiance spectra are provided in ASD file format (.asd file extension). All ASD files can be opened using the provided scripts. Metadata is provided in two formats: CSV file format (no geolocation) and GEOJSON file format (includes geolocation for each spectra). The dataset includes a set of pre-processed reflectance spectra as CSV files (yyyymmdd_rfl.csv). The python scripts and jupyter notebook used to calculate reflectance spectra from the ASD radiance data is included here and was previously published as: { cite Regina ORNL DAAC }. There is also a folder of JPEG photographs corresponding to selected spectra. We include a protocol document with detailed steps for ASD FieldSpec4 assembly and operations. This data additionally contains a file level metadata (flmd.csv) and data dictionary (dd.csv) file. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgment: This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004) and was funded by EMIT Extended Mission Phase E Science.

Data from: Variation in season length and development time is sufficient to drive the emergence and coexistence of social and solitary behavioral strategies - Ned Wingreen; Simon Levin; Dee Ruttenberg; Sarah Kocher (other, 2024)
Season length and its associated variables can influence the expression of social behaviors, including the occurrence of eusociality in insects. Among bees, ants, and wasps, social behaviors can vary widely across environmental gradients, both within and between different species. While numerous theoretical models have been developed to examine the traits in a species’ life history that underlie the emergence and maintenance of eusociality, the impact of seasonality on this process is largely uncharacterized. Here, we present a theoretical model that builds on these foundational models by incorporating season length and offspring development time. The integration of these environmental and life history traits into a single, individual-focused model enables detailed examination of how these factors can shape the costs and benefits of social living. We find that longer season lengths and faster brood development times are sufficient to favor the emergence and maintenance of a social strategy, while shorter seasons favor a solitary one. We also identify a range of season lengths where social and solitary strategies can coexist. Moreover, our theoretical predictions are well-matched to the natural history and behavior of two, flexibly-eusocial bee species, suggesting our model can make realistic predictions about the evolution of different social strategies. Broadly, this work reveals the crucial role that environmental conditions can have in shaping social behavior and its evolution and underscores the need for further models that explicitly incorporate such variation to study evolutionary trajectories of eusociality. The above data is needed to reproduce Figure 4 in this manuscript, showing the matching between our theoretical predictions and the weather data.

Data and Code for "Detecting context-dependence in the expression of life history tradeoffs" (2024)
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Decomposing an elevational gradient in predation by insectivorous birds - Kailen A. Mooney; Luis Abdala-Roberts; Carla Vázquez-González (other, 2024)
Insectivorous birds have ecologically important effects on prey abundance, behavior, and evolution, and through top-down control birds indirectly reduce herbivory and promote plant growth. While several studies sought to characterize biogeographic patterns in top-down control by birds, variation in bird predation along elevational gradients is not well characterized both in terms of its commonness and the mechanisms underlying such variation. Here we characterized variation in bird predation along a 700m montane elevation gradient using artificial clay caterpillars, assessing the roles of variation in aridity, other elevational effects not associated with aridity (e.g., most notably growing season length) and bird abundance and diversity. Multivariate models revealed attack rates increasing with aridity (when controlling for the effects of elevation) and elevation (when controlling for aridity). Because aridity declines with elevation, elevational patterns were not detectable in a univariate analysis. Bird abundance (but not diversity) decreased with elevation (but not aridity) and did not provide an explanation for our results, suggesting the underlying mechanisms were behaviorally based. We speculate that the declining abundance of insect prey with elevation and aridity leads to increased bird foraging efforts and thus the likelihood of attacking clay caterpillars. If widespread, these dynamics have important consequences for both the interpretation of predation bioassays generally and for our understanding of the multivariate drivers of variation top-down control by predators and predation risks experienced by prey.

Data for "Diurnal trends and meteorological factors influencing the variability of fluorescent bioaerosol in Mt. Crested Butte, Colorado during SAIL" with higher fluorescence threshold. - Allison Aiken; Abu Sayeed Md Shawon; Katherine Benedict (other, 2024)
We deployed a Wideband Integrated Bioaerosol Sensor (WIBS‐Neo, DMT) at Crested Butte Mountain to the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. During 2022, we measured and calculated the size distribution of fluorescence bioaerosol particles (FBAPs), and non-biogenic aerosol particles (NBAPs). We used higher Time (MDT): Time Diameter Bin Edges: Diameter in microns. FBAP: Different types of fluorescence bioaerosol particles NFBAP: Number concentration of non-fluorescence particles in #/cubic centimeters A_size: Number concentration of A type particles in #/cubic centimeters at each size bin B_size: Number concentration of B type particles in #/cubic centimeters each size bin C_size: Number concentration of C type particles in #/cubic centimeters each size bin AB _size : Number concentration of AB type particles in #/cubic centimeters each size bin BC _size : Number concentration of BC type particles in #/cubic centimeters each size bin AC _size : Number concentration of AC type particles in #/cubic centimeters each size bin ABC _size : Number concentration of ABC type particles in #/cubic centimeters each size bin

VISSS Raw data from SAIL at Gothic from November 2022 to June 2023 - Maximilian Maahn; Veronika Ettrichraetz; Isabelle Steinke (other, 2024)
A Video in situ Snowfall Sensor (VISSS) records shadow images of precipitating hydrometeors from two perspective at a 90 degree angle with a frequency of 140 Hz. From this, hydrometeor properties such as particle size, shape, complexity, degree of riming, and fall velocity can be derived. The VISSS was set up next to the ARM mobile facility during the SAIL project located in the East River Watershed near Crested Butte, Colorado from November 2022 to June 2023. The deployment was funded by the CORSIPP project (Characterization of orography-influenced riming and secondary ice production and their effects on precipitation rates using radar polarimetry and Doppler spectra) funded by the German Research Foundation (DFG). More information about the VISSS can be found in Maahn et al. 2023 (https://dx.doi.org/10.5194/egusphere-2023-655 )

Airborne Magnetic and Radiometric Survey, Colorado Mineral Belt, Southwest Block, 2023 - Patricia G Macqueen; V. J. S Grauch; April D Allen Langhans (other, 2024)
This data release provides digital flight-line and gridded data for a high-resolution airborne magnetic and radiometric survey over the southwestern portion of the Colorado Mineral Belt, southwestern Colorado. The Colorado Mineral Belt is a broad, northeast-southwest trending alignment of historic mining districts that have produced multiple types of commodities, including critical minerals that are vital to the Nation's security and economy. The survey was acquired as part of the UGSS Earth Mapping Resource Initiative (Earth MRI) to improve our understanding of the fundamental geologic framework underpinning the Colorado Mineral Belt. This release covers the southwestern portion of the belt (southwest block), which includes a wide swath from the Gunnison and San Juan mining districts on the northeast to the La Plata mining district on the southwest, including parts of Chaffee, Gunnison, Saguache, Hinsdale, Mineral, Ouray, San Miguel, San Juan, Dolores, Montezuma, and La Plata Counties. Previous reports have identified several critical minerals in this region, including aluminum, antimony, fluorspar, manganese, tungsten, vanadium, and zinc. Data for this survey were acquired by NV5 Geospatial, Inc. and is sub-contractors Precision GeoSurveys, Inc. and EDCON-PRJ, Inc under contract with the USGS. The survey was flown in September and October of 2023 using a helicopter equipped with a magnetometer mounted in a stinger extending from the nose of the aircraft and a gamma-ray spectrometer stowed onboard. The helicopter pilots followed pre-planned flight paths in a grid-like pattern, with north-south lines spaced 200 meters apart and east-west lines spaced 1,000 meters apart. Lines were flown 100 meters above ground as much as possible to maximize detection of gamma-rays and resolve details of the magnetic field. This clearance could be realized in areas of low relief but higher clearances, as much as 200-500 meters, were required over rugged terrain and populated areas for safety reasons. Areas with restricted airspace, such as Wilderness Areas, were avoided. A total of 28,472 linear kilometers of data were collected along the lines, covering a 4,719 square-kilometer irregular area. EDCON-PRJ performed extensive data processing after completion of flying and delivered the final data and report in April 2024.

C1-cycling MAGs from Slate River, CO (other, 2024)
Metagenome-assembled genomes (MAGs) assembled from riparian floodplain sediment (30 to 150 cm below surface height) for C1-cycling microorganisms discussed in the paper Diverse and unconventional methanogens, methanotrophs, and methylotrophs in metagenome-assembled genomes from subsurface sediments of the Slate River floodplain, Crested Butte, CO USA

iRON_Soil Moisture_Calibrated (other, 2024)
**Error has been found in Glenwood Springs station data. The 8in depth soil moisture sensor was mislabeled as the 40in depth sensor and vice versa for data sets uploaded prior to February 2024. Datasets uploaded after Feb 22, 2024 have the sensor labels corrected for all dates going back to installation.*****An error has been caught in time codes. The time codes currently read GMT-5:00. The CORRECT time code is GMT-7:00 for all datasets. Files with FullRecord in the name are in correct, UTC time codes*** These data are the complete data set from installation through March 15, 2018 for AGCI's interactive Roaring Fork Observation Network (iRON). The network consists of 9 stations in the Southern Rockies (Colorado, USA). Descriptions of soil type and site locations can be found at: www.agci.org/iron/stations Please note: Measurements taken for rain are recorded via tipping bucket rain gauge and may not be accurate for winter precipitation events (e.g. snow). Error readings are represented by -888.88, -138.25, or -162.469 represents a sensor error. Absent data is represented by N/A. Sensors used for soil moisture readings are Decagon EC-5 Dielectric probe (5cm/2in depth); Decagon 10-HS (20cm/8in; 50cm/20in depths). All other equipment is Onset, and the logger device is the RX3000 model. Data collection is generally set to every 20 minutes (most stations) or every hour (Independence Pass station), but maintenance resets, etc. may occasionally have contributed to other collection intervals, e.g. every 5 minutes. Conversely, on occasions of logger failure, there may be full days of data absent. Snow data are available for Northstar Transition Zone and Independence Pass by request. Updated calibrations (V2) are anticipated in the summer of 2018 for the following stations: Glassier Ranch (3), Glenwood Springs (5), Northstar Transition Zone (7), Spring Valley (8) Some instruments have been replaced over the life of a station. In 2015, all original U30 logger boxes were replaced with the RX3000 model. For complete metadata or records of instrumentation, please contact project advisor E. Osenga at eliseo at agci.org. **Glassier Ranch site is a wetland and the soils are often completely saturated (leading to sensor difficulties in taking accurate readings) much of the year.

Effects of herbivory by a translocated butterfly on plant size and seed set of Lonicera involucrata - Danielle Scott; R. H. Rosenberg; Nitin Ravikanthachari; Charlotte Wayne; L. L. Burch; Rachel Powell; Carol L. Boggs (other, 2024)
Assisted migration consists of the introduction of a species to previously inhabited areas or to new suitable regions. Such introductions have been touted as a viable tool for conserving earth's biodiversity. However, both the likely success of assisted migrations and the impacts on local communities are hotly debated. Empirical data on the local impacts of assisted migration are particularly lacking. We examined the short and long time-scale effects of herbivory on Lonicera involucrata (Richards) Banks ex. Spreng (Caprifoliaceae) after an introduction of Euphydryas gillettii Barnes (Lepidoptera: Nymphalidae: Melitaeini) to Gunnison County, Colorado via an assisted migration in 1977. The plant is the primary larval hostplant for the butterfly. We quantified plant seed production, plant survival, and population stage structure in two sets of observational experiments. We found that herbivory by E. gillettii increased L. involucrata reproduction on an annual time scale, independent of plant size and local microhabitat characteristics. Over the time since the butterfly’s introduction, herbivory by E. gillettii resulted in a plant population structure biased towards smaller plants in the butterfly introduction and satellite sites compared to sites without the butterfly. Our results highlight the importance of studying effects of assisted migrations on native populations at different temporal scales. As assisted migration becomes an indispensable tool for species conservation, our work adds to the understanding of the multi-trophic impacts of assisted introductions on local populations and communities.

Gold-et-al_2024_EarthsFuture - Rohini Gupta; Patrick Reed (other, 2024)
Results from Gold et al. submitted to Earth's Future. All code to reproduce the experiment and make the figures can be found here: https://github.com/davidfgold/Gold-etal_2024_EarthsFuture For a detailed guide to data in this repository, see the README.txt file. Shortage output from .xdd files generated by StateMod was compressed into .parquet files. Reservoir output from .xre files can be found in the "Reservoir" directory. We adopted the CDSS naming convention: cm = Upper Colorado River Basin gm = Gunnison River Basin ym = Yampa River Basin wm = White River Basin sj = Southwest Basin

Upland Vegetation and Soils Monitoring for the Northern Colorado Plateau Network: 2009-2023 - Data Package - Sarah Karinen; Aneth Wight; Rebecca Weissinger; Sarah Topp; Carolyn Livensperger; H. D. Thomas; Abigail Hobbs; Steven Garman; Joseph Brehm; Dana L. Witwicki (other, 2024)
Uplands are land areas lying above the elevation where flooding generally occurs—areas found beyond riparian zones. Uplands represent the vast majority of land area in the Northern Colorado Plateau Network (NCPN), and include rock outcrops, badlands, shrublands, grasslands, woodlands, and forests. Upland vegetation provides energy to other trophic levels, habitat structure for various organisms, and is a significant component of species diversity. The ability of uplands to retain soil and nutrients, absorb and release water, and buffer high-runoff precipitation events is a major influence on riparian condition. Upland ecosystems are easily disturbed and slow to recover, yet several NCPN units contain relatively undisturbed examples of grasslands and shrublands. Historic land uses include livestock and timber production. More recently, recreational use has increased. To effectively manage uplands, the National Park Service needs to know the impacts of these uses. Uplands monitoring includes measuring soil stability, hydrologic function, biological soil crusts, and plant community characteristics. The NCPN uses a rotating panel survey design to select randomized sampling plots that are visited every 1–2 years, followed by a relatively long interval between revisits (3–6 years). This design minimizes the chances that sensitive, arid-ecosystem plots will be damaged by repeated visits in successive years and is a cost-effective way to estimate the health of upland ecosystems across a large area. While the sampling has largely followed the survey design, there have been minor deviations due to budget fluctuations, weather, and Covid, resulting in fewer plots being sampled than intended in some years. NCPN uplands monitoring is intended to strike a balance between increasing fundamental understanding of these systems and providing managers with early warning of undesirable change. The NCPN monitors upland vegetation and soils at 10 units: Arches National Park, Black Canyon of the Gunnison National Park, Bryce Canyon National Park, Canyonlands National Park, Capitol Reef National Park, Cedar Breaks National Monument, Curecanti National Recreation Area, Dinosaur National Monument, Timpanogos Cave National Monument, and Zion National Park.

Groundwater and Surface Water Flow (GSFLOW) model files to explore bedrock circulation depth and porosity in Copper Creek, Colorado - Kenneth Hurst Williams (ess_dive, 2024)
This data package contains integrated hydrological model input and output files for Copper Creek, Colorado (24 km2), a tributary of the East River located in the headwaters of the Upper Colorado River Basin. The model code is the U.S. Geological Survey (USGS) Groundwater and Surface Water Flow (GSFLOW) model. The model contains a 100-m grid resolution and a daily timestep. 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 contains 12 model layers and extends 400 m below land surface. The original Copper Creek model was modified to contain geologic layers representing saprolite, shallow bedrock, and deep bedrock. Endmember depth versus hydraulic conductivity relationships and porosity values for fractured crystalline rock are simulated. For the shallow case, median flow depths occur in the shallow saprolite at depths <8 m, while the deep case promotes a median groundwater flow depth of 100 m. With this modeling framework we compare streamflow response to a plausible worst-case drought lasting up to five years. Streamflow metrics of analysis include average streamflow, fraction of stream network that is dry, no-flow duration, average groundwater flow to streams and time to recovery following the drought. Results and implications are presented in a paper submitted to Geophysical Research Letters titled, "The role of bedrock circulation depth and porosity in mountain streamflow response to prolonged drought" by Rosemary WH. Carroll, Andrew H. Manning and Kenneth H Williams. A Readme.txt file provides instructions on how to download all model files and execute each model scenario. In addition to the GSFLOW output/prms/copper_drought.csv file containing daily basin water stores and fluxes (refer to GSFLOW manual) and the output/prms/copper_drought_statvar.dat file with output defined in the gsflow3.control file (refer to GSFLOW Manual), output files also include spatially distributed daily values of total evapotranspiration, canopy evaporation, precipitation, snowfall, infiltration, snow water equivalent, potential evapotranspiration, recharge, sublimation, soil moisture, contributing interflow, water table elevations, changes in groundwater storage, groundwater evapotranspiration, interbasin groundwater flow (limited to the alluvium below the stream outlet), and surface-groundwater exchanges within the river system.

Unoccupied Aerial System-mounted image velocimetry and Doppler velocity radar data for computation of river velocity and discharge collected at seven locations in Colorado in 2023: Videos (other, 2024)
This child item contains the raw videos captured by small Unoccupied Aircraft Systems (sUAS) at each field site. It also includes a summary file with metadata for each video. Each field site is abbreviated in various files in this data release. File and folder names quickly identify which site a particular file or dataset represents. The following abbreviations are used: ACS: Anthracite Creek at Somerset, Colorado, USA BRA: Blue River below Dillon, Colorado, USA (collected in August 2023) BRJ: Blue River below Dillon, Colorado, USA (collected in June 2023) CRG: Colorado River below Glenwood Springs, Colorado, USA CRR: Colorado River above Roaring Fork River at Glenwood Springs, Colorado, USA ERW: Eagle River below Milk Creek near [...]

SnowEx20 Grand Mesa IOP Lidar Data, Version 1 (other, 2024)

Dataset: RumbleOn, Inc. (RMBL) Stock Performance - Nitiraj Kulkarni; Jagadish Tawade; Jagadish Tawade; Nitiraj Kulkarni (other, 2024)
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Data from: Phylogeny does not predict the outcome of heterospecific pollen-pistil interactions in a species-rich alpine plant community - James V. Jones; Asstan Cisse; Jennifer L. Williams (other, 2024)
Premise: Co-occurring plant species that share generalist pollinators often exchange pollen. This heterospecific pollen transfer (HPT) impacts male and female reproductive success through pollen loss and reductions in seed set, respectively. The resulting fitness cost of HPT imposes selection on reproductive traits (e.g. floral color and shape), yet we currently lack strong predictors for the post-pollination fate of heterospecific pollen, especially within community and phylogenetic contexts. Methods: We investigated the fate of heterospecific pollen at three distinct stages of plant reproduction: 1) pollen germination on the stigma, 2) pollen tube growth in the style, and 3) fertilization of ovules. We experimentally crossed eleven naturally co-flowering species in the sub-alpine meadows of the Colorado Rocky Mountains, across a spectrum of phylogenetic relatedness. Using generalized linear mixed models and generalized linear models, we evaluated the effect of parental species identity and phylogenetic relatedness on pollen tube growth at each reproductive stage. Key Results: We found that heterospecific pollen tubes can germinate and grow within pistils at each reproductive stage, even when parental species are >100My divergent. There was no significant effect of phylogenetic distance on heterospecific pollen success and no evidence for a mechanism that suspends heterospecific pollen germination or pollen tube growth within heterospecific stigmas or styles. Conclusions: Our results show that even in communities where HPT is common, pre-zygotic post-pollination mechanisms do not provide strong barriers to interspecific fertilization. HPT can result in the loss of ovules even between highly diverged plant species.

Data from: Flying by night: Comparing nocturnal pollinator networks over time in the Colorado Rocky Mountains - Daria Syskine (other, 2024)
Because pollen-transport networks tend to vary widely over short periods of time but remain consistent over longer periods of time, it is important to account for study length when characterizing pollen-transport network structure. The study of nocturnal pollen-transport networks independently from diurnal pollinator networks has also been emphasized, as nocturnal pollen-transport networks are an often overlooked ecosystem component. Here, we systematically compare the properties of moth pollen-transport pollinator networks in Colorado, USA, over the course of a season, and test for a link between specialization and pollinator morphology. We sampled moths for pollen over nine weeks in June through August 2021. We then constructed pollen-transport networks, and used mixed models to test for differences in network indices between different parts of the 2021 season; we also tested for a relationship between moth proboscis length, moth presence across seasons, and the number of flower species pollinated. As expected, we found high rates of species turnover and plant-pollinator interactions over the summer, and we found that longer proboscis lengths – as well as length of presence over the summer – were predictive of higher generalization within individual species. However, variation in the overall structure of pollen-transport networks was relatively low over the course of the season, with linkage density and robustness being the only metrics that changed. We suggest that even with high rates of species turnover, nocturnal moth pollen-transport networks may maintain their overall structure over a season, with variations in some metrics possibly resulting from variations in the availability of floral resources. Our study highlights the necessity of continuing to research moth pollen-transport networks over different temporal scales, as aggregating pollination data over time can obfuscate patterns in temporal variation in network structure.

Data from: A multi-year case study highlighting the influence of hydrological conditions on epidemic dynamics in a natural plant pathosystem - Madison Machado; Peyton Smith; Keenan Duggal; Juliana Jiranek; Jessica L. Metcalf (other, 2024)
The scale of influence of hydrological and thermal conditions on disease remains uncertain for most wild plant pathosystems, thus restricting our ability to predict the impacts of climate change. Analysis of the spatiotemporal spread of a fungal rust pathogen throughout four naturally occurring flax populations over the course of five growing seasons reveals relationships between epidemic magnitude and snow cover, relative humidity and temperature, as well as an unexpectedly significant effect of severe drought on disease progression. These results indicate that climate change will likely disrupt wild plant epidemics, and points to a need for further epidemiological studies characterizing the effects of environmental conditions on population-level disease dynamics in natural pathosystems.

AE33 Aethalometer Records at RMBL [AMF2] from April 2022 to October 2023 - Ryan Moffet; Temitope Olayemi; Alexander Laskin; Steven Sharpe (other, 2024)
The dataset presents atmospheric mass loadings of black carbon (BC) and brown carbon (BrC), during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign that took place from 2021 – 2023 in Gothic, CO. A Magee Scientific aethalometer (model AE33) was deployed at the Rocky Mountain Biological Laboratory, 200 m north of AMF2 to monitor mass loadings of optically absorbing aerosols. The instrument inlet was equipped with a BGI SCC 1.829 ambient cyclone with a particle cutoff of 2.5 μm and placed 5 m above ground level. The dataset reports hour-resolved BC mass loadings at 370, 470, 520, 590, 660, 880, and 950 (BC370, BC470, BC 520, BC590, BC660, BC880, BC950 in ng/m3) reported by the instrument built-in algorithm, which calculates mass loadings from the rate of change of the attenuation of light transmitted through the aerosol-laden filter. BC880 is the operationally defined reference of the BC concentration. Hour-resolved absorbance at each wavelength were calculated from their respective mass absorption cross-section in inverse megameters (Mm-1). The absorption Angstrom exponent (AAE) for BrC and BC were calculated for each hour. BrC AAE (AAE_BrC) was calculated from the slope of Log(wavelength) versus Log(absorbance) based on the measurements at 370, 470, and 520 nm. BC AAE (AAE_BrC) was calculated from the slope of Log(wavelength) versus Log(absorbance) based on the measurements at 590, 660, 880, and 950 nm. The aethalometer record spans time periods of 4/5/2022 – 10/3/2022 and 12/8/2022 – 10/9/2023. The ambient CO2 concentration recorded by a Vaisala CARBOCAP carbon dioxide probe GMP343 is also reported in ppm.

Black Canyon of the Gunnison National Park and Curecanti National Recreation Area 1959-2024 Water Quality Data from the BLCA_CURE_WQ Project as of 2024-01-19 - Nicki Gibney; Dean Tucker; Dean Tucker (other, 2024)
This data package was created 2024-10-17 14:21:45 by NPSTORET and includes selected project, location, and result data. The data derive from three sources: (1) samples collected by the park and analyzed by the USGS National Water Quality Lab that aren’t in the USGS National Water Information System (NWIS); (2) field measurements and observations or lab analyses conducted by park staff that aren’t in NWIS; and (3) NWIS data collected by the USGS that aren’t part of the monitoring program at Black Canyon of the Gunnison National Park and Curecanti National Recreation Area. Data contained in the Black Canyon of the Gunnison National Park and Curecanti National Recreation Area NPSTORET back-end file (BLCACURE_NPSTORET_BE_20240403_0716.accdb) were filtered to include: Organization: - BLCACURE: Black Canyon of the Gunnison NP and Curecanti NRA Station: - Include Trip QC And All Station Visit Results Value Status: - Accepted or Certified (exported as Final) or Final Excluded 9670 results because the parameter is not in WQX/EQuIS. The data package is organized into six data tables: - Projects.csv - describes the purpose and background of the monitoring efforts - Locations.csv - documents the attributes of the monitoring locations/stations - Results.csv - contains the field measurements, observations, and/or lab analyses for each sample/event/data grouping - HUC.csv - enumerates the domain of allowed values for 8-digit and 12-digit hydrologic unit codes utilized by the Locations datatable - Characteristics.csv - enumerates the domain of characteristics available in NPSTORET to identify what was sampled, measured or observed in Results - Taxon.csv - enumerates the domain of taxa available in NPSTORET to identify the organism sampled for Results Period of record for filtered data is 1959-06-16 to 2024-01-19. This data package is a snapshot in time of one National Park Service project. The most current data for this project, which may be more or less extensive than that in this data package, can be found on the Water Quality Portal at: https://www.waterqualitydata.us/data/Result/search?project=BLCA_CURE_WQ

Data associated with Quaternary Research manuscript Latest Pleistocene glacial chronology and paleoclimate reconstruction for the East River watershed, Colorado, USA - Brendon Quirk; A. J. Hidy; I. J. Larsen (ess_dive, 2024)
The data here are associated with Quaternary Research manuscript Latest Pleistocene glacial chronology and paleoclimate reconstruction for the East River watershed, Colorado, USA and include data associated with cosmogenic exposure and depth profile dating as well as glacier-climate numerical modeling. Reconstructing Pleistocene glaciation timing and extent is vital for understanding paleoclimate. While late Pleistocene glaciation has been studied extensively in western North American mountain ranges, the glacial history of the western Elk Range in Colorado remains understudied, particularly in the East River watershed, a site of intense scientific focus. Here we use cosmogenic nuclide exposure and depth–profile dating methods to determine the timing of glaciation in the East River watershed. We use glacier modeling to reconstruct paleoglacier extents and quantify past climate conditions. Our findings indicate that the East River glacier retreated from its maximum position approximately 17–18 ka, moving to recessional positions between 13–15 ka, before experiencing more substantial retreat to high-elevation cirques around 12.9 ka. Glacier modeling suggests that the maximum ice extents at 17–18 ka could have been sustained by temperature depressions of approximately −6.5°C compared to modern conditions, assuming consistent precipitation. Additionally, the ice position at 13–15 ka could have been supported by temperature depressions of around −4.0°C. These results offer insights into the deglaciation timeline in the East River watershed and broader western Elk Range as well as paleoclimate conditions during the late Pleistocene, which may aid future research on critical zone evolution in the East River watershed. The data files include: 1. Table 1 In situ-produced 10Be sample data (.pdf, .csv, .xlsx) 2. Table 2 In situ-produced 10Be exposure age results (.pdf, .csv, .xlsx) 3. S1_A CRONUS calculator input assuming no erosion (.csv) 4. S1_B CRONUS calculator input assuming erosion (.csv) 5. S1_C CRONUS calculator results and comparison (.csv) 6. S2_A In situ-produced 10Be sample data for depth profile (.csv) 7. S2_B In situ-produced 10Be depth profile model input (.csv) 8. S2_C In situ-produced 10Be depth profile model results (.csv) 9. S3 Monthly cloudiness, rH, and windspeed data used in glacier climate model (.csv) 10. S4 Glacier climate model results (.csv)