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773 results for “Colorado”
Long-term record of lake and stream biogeochemistry from the Loch Vale Watershed, Rocky Mountain National Park, Colorado, USA: 1981-2024
The Loch Vale Watershed (LVWS) Project is a long-term research and monitoring program that addresses watershed-scale ecosystem processes, particularly as they respond to atmospheric deposition and climate variability. The LVWS is a 7-km2 high-altitude basin located within Rocky Mountain National Park in the Colorado Front Range (Colorado, United States of America). This dataset includes year-round measurements of physical water parameters, nutrients, major ions, trace metals, silica, and chlorophyll collected from lakes and streams within the LVWS basin. Related data entities: Scanned field notebooks from the Loch Vale Watershed Project from 1981-2023 are available via this published data release: https://www.sciencebase.gov/catalog/item/6723cba2d34e4f57573e8e45. Quality assurance reports from the Loch Vale Watershed Project are available for specific time periods and can be found at the following locations: 1983-1987: included in this data release under "Other Entities", file name LWVS_QAreport_1983to1987_Denning 1988: included in this data release under "Other Entities", file name LWVS_QAreport_1988_Denning 1989-1990: included in this data release under "Other Entities", file name LWVS_QAreport_1989to1990_Edwards 1995-1998: https://doi.org/10.3133/ofr99111 1999-2002: https://doi.org/10.3133/ofr20041306 2003-2009: https://doi.org/10.3133/ofr20111137 2010-2019: https://doi.org/10.3133/tm1D9 The most recent methods manual is included in full in this data release under "Other Entities", file name "LVWS Methods Manual". Please refer to this manual for the detailed methods.
Langenheim Plant Species Data (1953) and Associated Resurvey Datasets (2014), Gunnison Basin, Colorado, USA
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 seve
Life histories of the perennial geophyte Erythronium grandiflorum (Liliaceae) in Colorado subalpine transplant garden from annual measurements, 1991 onward
In an outdoor garden at Irwin, Colorado, we established glacier lily plants in open-bottomed PVC pots that protected them from gopher attack. The initial cohorts were excavated from field sites as mature corms of unknown age. Later cohorts were grown from seed, so their ages are known. Each spring since 1991, we have noted fruit and flower production. In August, after the aboveground parts have died back, we exhume the plants, wash off the soil, weigh the corms, characterize their morphology, photograph them, and replant them. If a corm splits, we replant the pieces in separate pots. The study is ongoing, with 264 plants in 2019. Main findings through 2020: plants produce 0-4 flowers per year, depending on size; most plants flower each year; death is rare, with many plants having survived the entire study; setting a fruit reduces corm substantially (cost of reproduction); plants appear to regulate weight by adjusting flower production, and by splitting; genotypes vary in splitting propensity. Oddly, mortality is higher in very large corms than in mid-sized ones. Evidence for senescence is scant.
Temperature, floral density, and Osmia pollen usage data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2023
Data were collected as part of a study of population dynamics of solitary, cavity-nesting Hymenoptera. Nesting structures ("trap-nests") were established at five study sites along an elevational gradient around the Rocky Mountain Biological Laboratory in 2013. Two additional study sites were added in 2014, and one of the original study sites was dropped at the end of 2015. At each site, a HOBO data-logger placed under a centrally located trap-nest records air temperatures hourly. Floral densities are recorded at each site, typically 1-2 times per week, throughout the growing season, for specific plant taxa known to be used as pollen sources by cavity-nesting bees. In addition, pollen samples are taken from the nests of cavity-nesting bees and the constituent plant taxa identified by microscopic comparison with a reference pollen collection from the study area.
25-meter elevation lattice grid, Niwot Ridge LTER Project Area, Colorado
25-meter lattice made from the Niwot Ridge LTER TIN model (ltertin). This dataset was made to support hierarchical GIS databases at the Niwot Ridge LTER. Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
3.23-meter elevation lattice grid, Martinelli Snowfield, Niwot Ridge LTER, Colorado
Martinelli snow field lattice. This dataset is part of the Martinelli grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
1-meter elevation lattice grid, Martinelli Snowfield, Niwot Ridge LTER, Colorado
Resampled version of Martinelli snow field lattice grid (martlat) with finer resolution. This dataset is part of the Martinelli grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
5-meter elevation contours, Martinelli Snowfield, Niwot Ridge LTER, Colorado
Martinelli snow field contour lines. This dataset is part of the Martinelli grid geographic information system (GIS). Additional information concerning the Niwot Ridge LTER hierarchical GIS can be found in Walker et al. (1993).
Annual snow survey, Green Lakes Valley, Niwot Ridge, Colorado, 2013 - ongoing.
Yearly snow surveys were conducted in the Green Lakes Valley in the City of Boulder Watershed at the estimated peak of snowpack in late spring. Over a period of several days, surveying teams (1 to several people) traversed valley slopes measuring snow depth with avalanche probes. Locations of each depth measurement were recorded as waypoints in Garmin hand-held GPS units. Snow depths were recorded on standardized field sheets along with dates, recorder names, waypoint numbers, and comments.
Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation
<div> <div> <p>The dataset accompanying the manuscript titled "Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation" provides comprehensive information on streamflow patterns in the Colorado River since 2000. The dataset is needed to run the analysis available on GitHub available <a href="https://github.com/dlhogan97/Spring-Precipitation-Effect-CO-River.git">here</a>. This is version 2, please use this version for the most up-to-date results.</p> <p><strong>Please read the accompanying README (available in the README.md file) for individual file descriptions and file nesting strategy that should be employed to easily reproduce this analysis.</strong></p> <p>The dataset covers a range of variables related to streamflow and precipitation, including but not limited to discharge measurements, seasonal variations, and relevant meteorological data. The primary focus of the dataset is to elucidate the observed streamflow deficits in the Colorado River, attributing these changes to decreased spring precipitation.</p> <p>Key features of the dataset include:</p> <ul> <li> <p>Time Coverage: The dataset spans a specified time range that aligns with the investigation into recent streamflow deficits in the Colorado River between 1964 and 2022.</p> </li> <li> <p>Spatial Scope: It includes data from relevant monitoring stations along within the Upper Colorado River, but focusing in the hydrologically vital headwater regions, providing a spatially distributed perspective.</p> </li> <li> <p>Variables: The dataset encompasses a variety of variables essential for understanding streamflow dynamics, with a particular emphasis on the impact of reduced spring precipitation.</p> </li> </ul> <p>Researchers and stakeholders interested in hydrological patterns, climate-driven changes, and water resource management in the Colorado River Basin will find this dataset valuable. It serves as a foundational resource for reproducibility, further analysis, and collaboration within the scientific community. The dataset is deposited on Zenodo to facilitate open access, sharing, and citation for broader research endeavors.</p> </div> </div>
Measurements and model simulations of iodine monoxide (IO) radical, water vapor (H2O), nitrogen dioxide (NO2) radical, formaldehyde (HCHO), gaseous elemental mercury (Hg0), and oxidized mercury (HgII) at Storm Peak Laboratory, Colorado, during April 2022
<p>This dataset was compiled to accompany the manuscript Lee et al., titled "Elevated Tropospheric Iodine over the Central Continental United States: Is Iodine a Major Oxidant of Atmospheric Mercury?", submitted to <em>AGU Geophysical Research Letters</em>.</p> <p> </p> <p><strong>file01</strong> contains two example spectral proofs for iodine monoxide (IO) radical measured by the University of Colorado Multi-AXis Differential Optical Absorption Spectroscopy (CU MAX-DOAS) instrument at Storm Peak Laboratory, CO (SPL; 3220 meters above sea level; 40.455 degrees North; 106.745 degrees West) during April 2022.</p> <p><strong>file02</strong> contains oxygen collision-induced absorption (O2-O2) slant column densities (SCDs) measured in a spectral fit window from 350 to 388 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file03</strong> contains O2-O2 SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file04</strong> contains IO SCDs measured in a spectral fit window from 417.5 to 438 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file05</strong> contains water vapor (H2O) SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file06</strong> contains nitrogen dioxide (NO2) radical SCDs measured in a spectral fit window from 425 to 490 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file07</strong> contains formaldehyde (HCHO) SCDs measured in a spectral fit window from 328,5 to 359 nm by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file08</strong> contains the profiles of pressure, temperature, O2-O2, ozone (O3), NO2, and H2O derived from ECMWF CAMS reanalysis (April 2022 at SPL) and used in the radiative transfer model McArtim3 to calculate weighting functions for the trace gas profile inversions of IO, H2O, NO2, and HCHO.</p> <p><strong>file09</strong> contains the a priori profiles used for the IO profile inversions during April 2022 at SPL. One profile assumes a "flat" profile shape with a constant volume mixing ratio of 0.10 pptv throughout the atmosphere. The other profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file10</strong> contains the a priori profile used for the H2O profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file11</strong> contains the a priori profile used for the NO2 profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file12</strong> contains the a priori profile used for the HCHO profile inversions during April 2022 at SPL. The profile is adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average.</p> <p><strong>file13</strong> contains the IO tropospheric vertical column densities (VCDtrop; surface to 12 km), volume mixing ratios near instrument altitude (VMRinstr), and degrees of freedom (DoF) measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file14</strong> contains the H2O VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file15</strong> contains the NO2 VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file16</strong> contains the HCHO VCDtrop, VMRinstr, and DoF measured by the CU MAX-DOAS instrument at SPL from April 1 to April 30, 2022.</p> <p><strong>file17</strong> contains GEOS-Chem simulated temperature, relative humidity, IO VCDtrop & VMRinstr, H2O VCDtrop & VMRinstr, NO2 VCDtrop & VMRinstr, HCHO VCDtrop & VMRinstr, and bromine monoxide (BrO) radical VCDtrop & VMRinstr at SPL from April 1 to April 30, 2022.</p> <p><strong>file18</strong> contains the gaseous elemental mercury (Hg0) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file19</strong> contains the oxidized mercury (HgII) measured by the Utah State University dual-channel mercury system at SPL from April 1 to April 30, 2022.</p> <p><strong>file20</strong> contains the GEOS-Chem simulated Hg0 and HgII at SPL from April 1 to April 30, 2022.</p> <p><strong>file21</strong> contains the profiles of pressure, temperature, relative humidity, BrO, bromine atom (Br), methane (CH4), chlorine monoxide (ClO) radical, chlorine atom (Cl), carbon monoxide (CO), Hg0, peroxy radical (HO2), IO, iodine atom (I), NO2, hydroxyl radical (OH), and O3 used as constraints for the gas-phase mercury box model. All profiles except IO and I are adapted from the GEOS-Chem April 2022 daytime (SZA < 85) average. The IO profile was calculated by scaling the GEOS-Chem April 2022 daytime (SZA < 85) average below 12 km by the average observed IO VCDtrop during April 2022. The I atom profile was calculated by multiplying the scaled IO profile by the ratio of unscaled I / unscaled IO profiles from GEOS-Chem.</p> <p> </p> <p><strong>file22</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file23</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file24</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file25</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file26</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file27</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file28</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file29</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file30</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file31</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file32</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file33</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file34</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file35</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file36</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file37</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file38</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file39</strong> contains the time-resolved gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p> </p> <p><strong>file40</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file41</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file42</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file43</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file44</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file45</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file46</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file47</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file48</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgOH</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file49</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file50</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file51</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at half the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file52</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file53</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file54</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at the same rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file55</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>8 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file56</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>9.5 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p> <p><strong>file57</strong> contains a profile of the gas-phase mercury box model output assuming that <strong>HgI forms at twice the rate as HgBr</strong> and that the Hg-I bond strength is <strong>11 kcal / mol</strong>, using <strong>HgBr</strong> as reference for the B-value in the HgI equilibrium coefficient.</p>
Multi-year measurements of tree motion from an accelerometer on a spruce tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of a <em>Picea engelmannii</em> (engelmann spruce) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p> <p> </p>
Multi-year measurements of tree motion from an accelerometer on a fir tree near Niwot Ridge, Colorado
<p>This repository includes 12 Hz three-axis acceleration data from an accelerometer mounted to the bole of an <em>Abies lasiocarpa</em> (subalpine fir) next to the C-1 Ameriflux tower at Niwot Ridge LTER, Colorado, USA. The data were recorded from November 2014 through August 2020. More information on the installation can be found in Raleigh et al. (in review, Water Resources Research).</p> <p>The data are stored in netCDF files, chunked based on the collection date when the data were downloaded from the accelerometer.</p> <p><strong>File metadata:</strong></p> <p>Filename</p> <p>GCDC_L01_Raw_Data_Niwot_TreeXX_collection_YYYYMMDD.nc</p> <p>where</p> <p>XX = tree number (01 = spruce, 02 = fir)</p> <p>YYYYMMDD = year (YYYY), month (MM), and day (DD) of data collection</p> <p> </p> <p>Each netCDF includes four variables:</p> <p>1. serial_date = time increment (fractional days), as defined by Matlab: "A serial date number represents the whole and fractional number of days from a fixed, preset date (January 0, 0000) in the proleptic ISO calendar." The serial dates are in mountain standard time (MST) with no adjustments for daylight savings.</p> <p>2. Ax = acceleration in the vertical direction (counts)</p> <p>3. Ay = acceleration in the east-west direction (counts)</p> <p>4. Az = acceleration in the north-south direction (counts)</p> <p>To convert the "counts" unit to gravitational units (g), divide Ax, Ay, and Az each by 2048, as explained in the manufacturer's user manual.</p> <p> </p>
WARM experiment Soil Microbial Function, RMBL Colorado, 2021
We examined how abiotic (warming), and biotic (presence of dominant plant species) factors interact to affect soil microbial processes in montane meadow ecosystems at high and low elevations at the WaRM experimental sites near the Rocky Mountain Biological Laboratory in Colorado in the West Elk range of the southern Rocky Mountains in Colorado, USA, during the summer 2021 growing season. The low elevation site (low site) is at 2740 m elevation (38.715, -106.823) in an open meadow without tree cover, and the dominant plant species is a flowering forb, Wyethia amplexicaulis. The high elevation site (high site) (3460 m, 38.992, -107.067) is also described as open meadow with no tree cover and is dominated by Juncus drummondii, a monocot, grass-like herb. The low and high elevation sites have a mean summertime temperature of 14.9 and 10.9°C respectively, and a mean summertime precipitation of 143 and 151 mm The WaRM experimental design is a 2 × 2 factorial warming × dominant plant species removal experiment deployed at the high elevation site and the low elevation site. Each of the four treatments are replicated 8 times, for total of 32 plots (each of which is 2 × 2 m) at each elevation with warming imposed via transparent hexagonal open-top chambers (OTCs), 1.5 m in diameter, in the center of each warming plot and the dominant plant species (listed above) removed via clipping at soil level within removal plots. Treatments at this site have been deployed each summer (June-August) since 2013. We analyzed multiple soil microbial responses at three times throughout the growing season: pre-growing season [low site; approx. May 26, high site; approx. July 6], peak-growing season [low site; approx. June 23, high site; approx. July 21], and post-growing season [low site; approx. August 18, high site; approx. Sept 14]. We measured edaphic characteristics including volumetric soil water content. We measured soil microbial functions including soil respiration, microbial metabolic
Colorado Motus Prioritization Tool Data
Addressing survival and movement of priority migratory avian species of concern along the Pacific Flyway is paramount for their conservation. Yet, the migratory life stage is understudied in many avian species. The Motus radiotelemetry receiver network is an established system for tracking survival and movement of avian species. This network is an international collaborative that successfully identifies stopover site duration, connected migratory routes, post-fledging dispersal and survival, and adult survival and fidelity on a landscape-scale; parameters that cannot be easily estimated using non-tagged birds. While the Motus network is highly connected in eastern North America, the western part of the continent is lagging in coverage and connectivity, limiting the ability to obtain sample sizes large enough to robustly model demographic parameters from tagged birds. Thus, the expansion of the Motus network is a high priority for Pacific Flyway State Agencies. To date, no method exists for determining priority locations for new Motus receiving stations. With collaborations from States and the Canadian Province of British Columbia, we used eBird citizen scientist data to prioritize strategic locations for new Motus receiving stations throughout the Pacific Flyway. We model priority species’ co-occupancy of varying abundance states (i.e., absent, present, abundant, abundant in multiple weeks) with spatially varying Landsat (red and near infrared), water, land cover types, and weather covariates while accounting for variable detection with temporally varying survey effort covariates. Using occupancy model predictions, we identify high-use areas of the Pacific Flyway for establishing new Motus receiving towers that have high probabilities of intercepting high presence and /or abundance of multiple species of interest in a series of predictive occupancy maps. This package contains all the necessary files to recreate the data analysis, print out maps based on predictions
SGS-LTER Standard Production Data: 1983-2008 Annual Aboveground Net Primary Production on the Central Plains Experimental Range, Nunn, Colorado, USA 1983-2008, ARS Study Number 6 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/700/1. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. The objective of the long-term ANPP study is to monitor long-term net above ground primary production of the shortgrass steppe community by species. There are 6 sites: ridgetop (ridge), midslope (mid), swale, ESA (replicate 1 not 2), Section 25 (SEC 25), and owl-creek (OC). Each site is located in a different landscape position or soil type on the shortgrass steppe and may be grazed or not. Ridgetop, midslope and swale are grazed and are sampled along a catena. Section 25 is grazed and is located in an upload grassland. ESA is an ungrazed upland grassland an is the control from the Ecosystem Stress Area experiment. Owl Creek is ungrazed and is located in the lowland along the owl creek drainage. There are 3 transects with 5 plots in each transect. Plots in the grazed locations are protected by cages. Because this is a monitoring effort, true replicates across the landscape are not
SGS-LTER Long-Term Monitoring Project: Vegetation Cover on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1999 -2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/140/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83458. The abundance and diversity of small mammals in shortgrass steppe is strongly influenced by the structure and composition of vegetation. Vegetation structure provides cover from predators and harsh abiotic conditions. Plant species composition affects the types of seeds and herbaceous material available to granivores and herbivores, and influences arthropod populations, which are important prey for the omnivorous species that dominate in shortgrass steppe. Both vegetation structure and plant community composition are sensitive to the availability of precipitation as well as the activity of large mammalian herbivores. In 1999, we began measuring vegetation structure and plant community composition on the three grassland and three shrubland trapping webs where we live-trap small mammals
SGS-LTER Long-term Monitoring Project: Spotlight Rabbit Count on the Central Plains Experimental Range, Nunn, Colorado, USA 1994-2006, ARS Study Number 98 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/136/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83448. Rabbits are the most important small-mammal herbivores in shortgrass steppe, and may significant influence the physiognomy and population dynamics of herbaceous plants and woody shrubs. Rabbits also are the most important prey of mammalian carnivores such as coyotes and large raptors such as golden eagles and great horned owls. Two hares (Lepus californicus, L. townsendii) and one cottontail rabbit (Sylvilagus audubonii) occur in shortgrass steppe. In 1994, we initiated long-term studies to track changes in relative abundance of rabbits on the Central Plains Experimental Range (CPER). On four nights each year (one night each season, usually on new moon nights in January, April, July, October), we drove a 32-km route consisting of pasture two-track and gravel roads on the CPER. This wa
SGS-LTER Long-Term Montioring Project: Arthropod Pitfall Trapping on Small Mammal Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1998-2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/134/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83450. With the exception of heteromyids, eg kangaroo rats and pocket mice, most small rodents in shortgrass steppe are omnivorous. Depending on season, arthropods (insects and arachnids) make up 40-85% of the diet of grasshopper mice and thirteen-lined ground squirrels, the most widespread rodents in northern shortgrass steppe. Small mammals are among the most important predators of ground-dwelling macroarthropods and herbivorous insects provide a direct resource link between weather and plant production. Understanding temporal variability in the abundance of arthropods is central to determining the mechanisms that drive small rodent populations. At present, there are no long-term studies of arthropods in shortgrass steppe, despite the important role that these taxa play in grassland food w
SGS-LTER Long-Term Monitoring Project: Small Mammals on Trapping Webs on the Central Plains Experimental Range, Nunn, Colorado, USA 1994 -2006, ARS Study Number 118 (Reformatted to the ecocomDP Design Pattern)
This data package is formatted as an ecocomDP (Ecological Community Data Pattern). For more information on ecocomDP see https://github.com/EDIorg/ecocomDP. This Level 1 data package was derived from the Level 0 data package found here: https://pasta.lternet.edu/package/metadata/eml/knb-lter-sgs/137/17. The abstract below was extracted from the Level 0 data package and is included for context: This data package was produced by researchers working on the Shortgrass Steppe Long Term Ecological Research (SGS-LTER) Project, administered at Colorado State University. Long-term datasets and background information (proposals, reports, photographs, etc.) on the SGS-LTER project are contained in a comprehensive project collection within the Digital Collections of Colorado (http://digitool.library.colostate.edu/R/?func=collections&collection_id=3429). The data table and associated metadata document, which is generated in Ecological Metadata Language, may be available through other repositories serving the ecological research community and represent components of the larger SGS-LTER project collection. Additional information and referenced materials can be found: http://hdl.handle.net/10217/83452. Small mammals (rabbits, rodents) are integral components of semiarid ecosystems because of their roles as consumers of plants, seeds and arthropods, as soil disturbance agents, and as food for raptors, snakes and mammalian carnivores. Because of their vagility and intermediate trophic position, populations of small mammals may track changes in vegetation and the abiotic environment that may result from shifts in land-use and other anthropogenic disturbances. However, these populations are variable over space and time, and their response to environmental changes may not be immediately apparent given their behavioral flexibility and relatively long life-spans and generation times. Patterns in the distribution and abundance of small mammals thus may simultaneously reflect and affect the
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.