Zircon U-Pb data for Paleoproterozoic metasedimentary rocks of the Gunnison block, central Colorado, USA - Ian W. Hillenbrand (other, 2024)
This data release includes raw detrital zircon U-Pb data for Paleoproterozoic metasedimentary rocks of the Gunnison block, central Colorado, USA. Detrital zircon U-Pb data for five samples of metasedimentary rock (totaling 1459 zircon U-Pb dates) were acquired via laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) at the University of Arizona LaserChron Center. The data provide constraints on the sediment provenance, maximum depositional age, and timing of metamorphism of these metasedimentary rocks, providing insights on the crustal and tectonic evolution of the Gunnison block and Yavapai Province.

Additional file 2 of Virus diversity and activity is driven by snowmelt and host dynamics in a high-altitude watershed soil ecosystem - Clement Coclet; Simon Roux; Simon Roux; Emiley A. Eloe-Fadrosh; Clement Coclet; Shi Wang; Emiley A. Eloe-Fadrosh; Shi Wang; Ulas Karaoz; Ulas Karaoz; Patrick O. Sorensen; Patrick O. Sorensen; et al. (other, 2024)
Additional file 2: Supplementary Table 1. Description of the 46 IMG metagenomes and 43 metatranscriptomes (related to Fig. 1A). Supplementary Table 2. Metadata for all the East River Watershed soil samples (related to Fig. 1A). Supplementary Table 3. DNA vOTU table with RPKM for each vOTU by sample, contig information, taxonomy and host prediction (related to Fig. 1C). Supplementary Table 4. RNA vOTU table with RPKM for each vOTU by sample, contig information, taxonomy and host prediction (related to Fig. 1D). Supplementary Table 5. Detailed information of RNA vOTUs linking RdRP Set 1 (related to Fig. 2). Viral taxonomy is based on Coat protein (CP) from Callanan et al., (2020) [80] and RdRP marker genes. "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 6. Detailed information of RNA vOTUs linking RdRP Set 2 (related to Suplementary Fig. 1). "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 7. Detailed information of RNA vOTUs linking RdRP Set 3 (related to Suplementary Fig. 1). "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 8. Detailed information of RNA vOTUs linking RdRP Set 4 (related to Suplementary Fig. 1)."Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 9. Detailed information of RNA vOTUs linking RdRP Set 5 (related to Suplementary Fig. 1). "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 10. Output table generated by vConTACT2 analysis with all the taxonomic information to reference genomes, as well as all the clustering information (related to Fig. 1C). Supplementary Table 11. UNIFRAC distances between this study and environmental studies (related to Fig. 2C). “Hillary” dataset was described in Hillary et al., (2022) [14], “Starr” was described in Starr et al., (2019) [29], "Callanan" was described in Callanan et al., (2020) [80], and "Wolf" was described in Wolf et al., (2020) [79]. Supplementary Table S12. PERMANOVA testing of contribution of season, depth and location on total DNA and RNA viral community and active DNA and RNA viral community (related to Fig. 3BCD) Supplementary Table 13. Significance of changes in total and active DNA and RNA vOTU abundance between months by ANOVA and post-hoc tests (related to Fig. 3EFG). Supplementary Table 14. Functional annotation of protein families against PHROGS database (related to Fig. 4C). Supplementary Table 15. Information (RPKM for each vOTU by sample, contig information, taxonomy and host prediction) of active DNA vOTUs exhibiting significant changes in abundance (n = 144) determined by STAMP analyses presented in Table S13 (related to Fig. 5A). Supplementary Table 16. Ecological strategies assigned to archaeal and bacterial OTUs from Sorensen et al., (2020) [6].

WARM experiment Soil Microbial Function, RMBL Colorado, 2021 - Sydne Spinella; Jennie R McLaren (other, 2024)
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 functional diversity, microbial biomass carbon and nitrogen, and extracellular enzyme potential activity.

Occurrence Download (other, 2024)
A dataset listing the 1870 species recorded in GBIF matching the query: { "DatasetKey" : [ "is Rocky Mountain Biological Laboratory" ] } The dataset's 1870 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0084094-240626123714530/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

Late Paleozoic and Early Mesozoic Structural History of the Uncompahgre Front- 19600101 (other, 2024)
This is an examination the late Paleozoic and Early Mesozoic structural history of the Uncompahgre front and the Paradox Basin.

Percent plant cover, Warming and Removal in Mountains (WaRM) experiment, Rocky Mountain Biological Laboratory, 2013-2022 - Kenna E Rewcastle; C. M. Prager; Rose Brinkoff; Jeremiah A Henning; Nathan J. Sanders; Olivia K Vought; Aimee T Classen (other, 2024)
These data were collected from 2013 to 2022 near the Rocky Mountain Biological Laboratory in Colorado. They are from a climate change experiment that manipulated temperature using open-top chambers to passively warm the air and plant community composition by removing the dominant species in a factorial design at two elevations. We measured the total percent cover of all the plots during the peak season each year, and in 2022, we also measured the total percent cover and species diversity every week. From 2022, air temperature, soil temperature, and soil moisture are also included.

Additional file 2 of Virus diversity and activity is driven by snowmelt and host dynamics in a high-altitude watershed soil ecosystem (other, 2024)
Additional file 2: Supplementary Table 1. Description of the 46 IMG metagenomes and 43 metatranscriptomes (related to Fig. 1A). Supplementary Table 2. Metadata for all the East River Watershed soil samples (related to Fig. 1A). Supplementary Table 3. DNA vOTU table with RPKM for each vOTU by sample, contig information, taxonomy and host prediction (related to Fig. 1C). Supplementary Table 4. RNA vOTU table with RPKM for each vOTU by sample, contig information, taxonomy and host prediction (related to Fig. 1D). Supplementary Table 5. Detailed information of RNA vOTUs linking RdRP Set 1 (related to Fig. 2). Viral taxonomy is based on Coat protein (CP) from Callanan et al., (2020) [80] and RdRP marker genes. "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 6. Detailed information of RNA vOTUs linking RdRP Set 2 (related to Suplementary Fig. 1). "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 7. Detailed information of RNA vOTUs linking RdRP Set 3 (related to Suplementary Fig. 1). "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 8. Detailed information of RNA vOTUs linking RdRP Set 4 (related to Suplementary Fig. 1)."Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 9. Detailed information of RNA vOTUs linking RdRP Set 5 (related to Suplementary Fig. 1). "Putative_Viral_order", "Putative_Viral_family", "Putative_host" columns are based on predictions made with a custom script (See Materials). Supplementary Table 10. Output table generated by vConTACT2 analysis with all the taxonomic information to reference genomes, as well as all the clustering information (related to Fig. 1C). Supplementary Table 11. UNIFRAC distances between this study and environmental studies (related to Fig. 2C). “Hillary” dataset was described in Hillary et al., (2022) [14], “Starr” was described in Starr et al., (2019) [29], "Callanan" was described in Callanan et al., (2020) [80], and "Wolf" was described in Wolf et al., (2020) [79]. Supplementary Table S12. PERMANOVA testing of contribution of season, depth and location on total DNA and RNA viral community and active DNA and RNA viral community (related to Fig. 3BCD) Supplementary Table 13. Significance of changes in total and active DNA and RNA vOTU abundance between months by ANOVA and post-hoc tests (related to Fig. 3EFG). Supplementary Table 14. Functional annotation of protein families against PHROGS database (related to Fig. 4C). Supplementary Table 15. Information (RPKM for each vOTU by sample, contig information, taxonomy and host prediction) of active DNA vOTUs exhibiting significant changes in abundance (n = 144) determined by STAMP analyses presented in Table S13 (related to Fig. 5A). Supplementary Table 16. Ecological strategies assigned to archaeal and bacterial OTUs from Sorensen et al., (2020) [6].

hydrokeira/Si_RandomForest: Si Random Forest Analysis Code (other, 2024)
This release contains code to replicate random forest analyses associated with the JGR-Biogeoscience paper "Climate, hydrology, and nutrients control the seasonality of Si concentrations in rivers".

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 (other, 2024)
This data package was created 2024-04-03 16:31:54 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.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 five 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 - HUC12.csv - enumerates the domain of allowed values for 12-digit hydrologic unit codes utilized by the Locations datatable - 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

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.

Colony counts and environmental covariates of the American white pelican colony on Gunnison Island, UT, USA - Aimee Van Tatenhove; Russell Norvell; Clark Rushing; Erica F Stuber; John Neill (other, 2024)
Annual colony counts of American white pelican (Pelecanus erythrorhynchos) at Gunnison Island, Utah, USA, and selected environmental covariates hypothesized to influence colony population dynamics at this location. Environmental covariates include mean springtime Great Salt Lake water levels, minimum springtime air temperature, mean annual Pacific Decadal Oscillation index values, and mean annual Southern Oscillation Index values across the study period.

Data from: Climate mediates the tradeoffs associated with phenotypic plasticity in an amphibian polyphenism (2024)
[object Object]

Dataset: RumbleOn, Inc. (RMBL) Stock Performance (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.

Global Bee Interaction Data (other, 2024)
Last modified: July 3, 2024 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. Currently, version 3.0 of this dataset is recommended. Katja C. Seltmann, & Global Biotic Interaction Community. (2024). Global Bee Interaction Data (v3.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10552937 Data DescriptionPlease see the [integration process page](https://www.globalbioticinteractions.org/process) 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. Summary statistics about the dataset can be found in the bees-only-review.pdf file. This review of all bee data indexed by Global Biotic Interactions was created using GloBI’s Interaction Data Review Report Framework via repository https://github.com/Big-Bee-Network/select-bee-interactions.sh. 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 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 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 Included Resources count sourceCitation 219440 Symbiota Collections of Arthropods Network (SCAN) 156437 University of Kansas Natural History Museum 150780 Digital Bee Collections Network, 2014 (and updates). Version: 2015-03-18. National Science Foundation grant DBI#0956388 134657 USGS Biodiversity Information Serving Our Nation (BISON) IPT 126820 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. 44522 PaDIL Bee records from the Pests and Diseases Image Library, http://www.padil.gov.au. 38658 University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. 27711 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 15506 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 14666 Web of Life. http://www.web-of-life.es . 14577 Pensoft Darwin Core Archives available via Integrated Publication Toolkit 13447 University of Colorado Museum of Natural History Entomology Collection 13296 https://mangal.io - the ecological interaction database. 10705 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. 8529 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 8014 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 7630 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). 6921 Purdue Entomological Research Collection 6911 Arizona State University Hasbrouck Insect Collection 6430 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 . 6288 Pensoft Darwin Core Archives with associateTaxa columns 6269 Eardley C, Coetzer W. 2016. Catalogue of Afrotropical Bees. 6114 University of Michigan Museum of Zoology, Division of Insects 5089 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 3860 Giselle Muschett & Francisco E. Fontúrbel. 2021. A comprehensive catalogue of plant – pollinator interactions for Chile 3720 Frost Entomological Museum, Pennsylvania State University 3670 Natural History Collections managed by Arctos (https://arctosdb.org) accessed via https://vertnet.org . 3620 Sarah E Miller. 6/19/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html. 3581 Robert L. Minckley San Bernardino Valley from the year 2000 to 2011. 3581 University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC 3581 University of New Hampshire Donald S. Chandler Entomological Collection 2242 Sarah E. Miller. 07/06/2017. Information extracted from dataset https://www.idigbio.org/portal/recordsets/db4bb0df-8539-4617-ab5f-eb118aa3126b. 2223 Bartomeus, Ignasi (2013): Plant-Pollinator Network Data. figshare. Dataset. https://doi.org/10.6084/m9.figshare.154863.v1 2110 Illinois Natural History Survey Insect Collection 2074 Florida State Collection of Arthropods 2035 Ed Baker; Ian J. Kitching; George W. Beccaloni; Amoret Whitaker et al. (2016). Dataset: NHM Interactions Bank. Natural History Museum Data Portal (data.nhm.ac.uk). https://doi.org/10.5519/0060767 1762 Poelen, Jorrit H. (2023). A biodiversity dataset graph: Biological Associations in TaxonWorks hash://sha256/a4d651aac5220487835e6178511886e98b845b2d98cb7c5447fb2b042e0654d2 hash://md5/849edbe55e31e54ea5cdaba0188c5655 (0.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.8253729 1681 Harvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University. 1563 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 1365 Sarah E Miller. 5/30/2016. Interations from various papers. 1281 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) 1102 University of California Santa Barbara Invertebrate Zoology Collection 1086 Cohen JM, Sauer EL, Santiago O, Spencer S, Rohr JR. 2020. Divergent impacts of warming weather on wildlife disease risk across climates. Science. doi:10.1126/science.abb1702 939 Allen Hurlbert. 2017. Avian Diet Database. 918 Texas A&M University Insect Collection 906 Del Risco, A.A., Montoya, Á.M., García, V. et al. Data synthesis and dynamic visualization converge into a comprehensive biotic interaction network: a case study of the urban and rural areas of Bogotá D.C.. Urban Ecosyst (2021). https://doi.org/10.1007/s11252-021-01133-3 872 Cristina Preda and Quentin Groom. 2014. Species associations manually extracted from literature. 754 United States Geological Survey (USGS) Pollinator Library. https://www.npwrc.usgs.gov/pollinator. 752 Sarah E Miller. 6/22/2015. Species associations manually extracted from datasets https://www.nceas.ucsb.edu/interactionweb/resources.html. 750 RCPol: Online Pollen Catalogs Network. 2016. https://rcpol.org.br/ 744 Classen, Alice; Steffan-Dewenter, Ingolf (2020): Plant-pollinator interactions along an elevational gradient on Mt. Kilimanjaro. PANGAEA, https://doi.org/10.1594/PANGAEA.911390 704 Yale University Peabody Museum Collections Data Portal 677 The Albert J. Cook Arthropod Research Collection 541 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 524 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/12/2/362 511 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. 511 The International Barcode of Life Consortium (2016). International Barcode of Life project (iBOL). Occurrence dataset https://doi.org/10.15468/inygc6 454 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 342 Mycology Collections Data Portal (MyCoPortal). 2020. https://mycoportal.org 292 Global Web Database (http://globalwebdb.com): an online collection of food webs. Accessed via https://www.globalwebdb.com/Service/DownloadArchive on 2017-10-12. 268 University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection 241 University of Hawaii Insect Museum 168 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. 153 California Academy of Sciences Entomology and Entomology Type Collection 127 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 114 Kari Lintulaakso. 2023. MammalBase Diet Database. 106 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.283-3-756 104 Groom, Q.J., Maarten De Groot, M. & Marčiulynienė, D. (2020) Species interation data manually extracted from literature for species . 96 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 . 93 Jakovos Demetriou and Quentin Groom 2014. Species associations of Sceliphron manually extracted from literature. 92 San Diego Natural History Museum 80 Price Institute of Parasite Research, School of Biological Sciences, University of Utah 59 National Museum of Natural History, Smithsonian Institution IPT RSS Feed 56 Poelen, JH (2016). Plant pathogen-host interactions scraped from Common Names of Plant Diseases published by the American Phytopathological Society at http://www.apsnet.org/publications/commonnames/Pages/default.aspx using Samara, a Planteome (http://planteome.org) plant-trait scraper. 50 Florez-Montero, G.L., Muylaert, R.L., Nogueira, M.R., Geiselman, C., Santana, S.E., Stevens, R.D., Tschapka, M., Rodrigues, F.A. and Mello, M.A.R. (2022), NeoBat Interactions: A data set of bat–plant interactions in the Neotropics. Ecology. Accepted Author Manuscript e3640. https://doi.org/10.1002/ecy.3640 50 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 39 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 37 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. 36 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). 36 Quentin J. Groom. 2020. Species interactions of species on the List of invasive alien species of Union concern 33 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 30 Brigham Young University Arthropod Museum 24 Geiselman, Cullen K. & Sarah Younger. 2020. Bat Eco-Interactions Database. www.batbase.org 24 Geiselman, Cullen K. and Tuli I. Defex. 2015. Bat Eco-Interactions Database. www.batplant.org 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 . 21 Species Connect. https://speciesconnect.com 17 http://invertebrates.si.edu/parasites.htm 14 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 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. 12 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. 10 University of California Santa Barbara Herbarium 9 Field Museum of Natural History IPT 8 Brose, U. et al., 2005. Body sizes of consumers and their resources. Ecology, 86(9), pp.2545–2545. Available at: http://dx.doi.org/10.1890/05-0379. 8 Strong, Justin S., and Shawn J. Leroux. 2014. "Impact of Non-Native Terrestrial Mammals on the Structure of the Terrestrial Mammal Food Web of Newfoundland, Canada." PLOS ONE 9 (8): e106264. https://doi.org/10.1371/journal.pone.0106264 7 Chen L, Liu B, Wu Z, Jin Q, Yang J, 2017. DRodVir: A resource for exploring the virome diversity in rodents. J Genet Genomics. 44(5):259-264. 5 Froese, R. and D. Pauly. Editors. 2018. FishBase. World Wide Web electronic publication. www.fishbase.org, version (10/2018). 5 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. 4 Aja Sherman, Cullen Geiselman. 2021. Bat Co-Roosting Database 4 Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology 4 Mollentze, Nardus, & Streicker, Daniel G. (2019). Viral zoonotic risk is homogenous among taxonomic orders of mammalian and avian reservoir hosts (Version 1.0.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3516613 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 Sarah E Miller. 9/15/2016. Species associations extracted from http://parasiticplants.siu.edu/index.html. 4 Sarah E. Miller. 04/14/2015. Extracted from literature Scott, J.A. 1986. The Butterflies of North America. Stanford University Press, Stanford, CA 4 Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. 2 Deans, Andrew (2021). Catalog of Rose Gall, Herb Gall, and Inquiline Gall Wasps (Hymenoptera: Cynipidae) of the United States, Canada, and Mexico 2 Jorrit H. Poelen. 2017. Species interactions associated with known species interaction datasets. 2 Museum for Southwestern Biology (MSB) Parasite Collection 2 Sarah E Miller. 4/20/2015. Species associations manually extracted from various papers and articles from site https://repository.si.edu 2 Seltmann, Katja C. 2020. Biotic species interactions about ticks manually extracted from literature. 2 Species Interactions of Australia Database (SIAD): Helping us to understand species interactions in Australia and beyond. http://www.discoverlife.org/siad/ . 1 Chen L, Liu B, Yang J, Jin Q, 2014. DBatVir: the database of bat-associated viruses. Database (Oxford). 2014:bau021. doi:10.1093/database/bau021 1 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 1 Gunther KA et al. 2014 Dietary breadth of grizzly bears in the Greater Yellowstone Ecosystem. Ursus 25(1):60-72 1 Sarah E Miller. 7/6/2016. Arctos collection. Included files bee_data_BID.sh - script for separating bee records into family uniq_citations.tsv - list of unique citations indicating bee interactions Andrenidae_data_unique.tsv - Andrenidae records Apidae_data_unique.tsv - Apidae records Colletidae_data_unique.tsv - Colletidae records Halictidae_data_unique.tsv - Halictidae records Megachilidae_data_unique.tsv - Megachilidae records Melittidae_data_unique.tsv - Melittidae records Stenotritidae_data_unique.tsv - Stenotritidae records bees-only-interactions.tsv.zip - list of all bee interaction data indexed on Global Biotic Interactions from GloBI version 2024-06-07 produced by https://github.com/Big-Bee-Network/select-bee-interactions.sh bees-only-review.pdf - Review of all bee data indexed by Global Biotic Interactions using GloBI’s Interaction Data Review Report Framework via repository https://github.com/Big-Bee-Network/select-bee-interactions.sh catalogNumber_counts.tsv - counts by catalogNumber in dataset. Duplicate catalog numbers indicate duplicated data shared by multiple data providers. ReferencesGloBI Community. (2024). Global Biotic Interactions: Interpreted Data Products hash://md5/946f7666667d60657dc89d9af8ffb909 hash://sha256/4e83d2daee05a4fa91819d58259ee58ffc5a29ec37aa7e84fd5ffbb2f92aa5b8 (0.7) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.11552565. 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

C1-cycling MAGs from Slate River, CO - Anna N. Rasmussen; Anna N. Rasmussen (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

Water Vapor Isotopic Composition During SAIL - Joseph Galewsky (other, 2024)
Measurements of the stable isotopic composition of water vapor were collected between 21 November 2022 and 1 June 2023 at the SAIL campaign main site in Gothic Colorado. The data were obtained using a Los Gatos Research Triple Water Vapor Isotopic Analyzer from a heated and insulated inlet mounted on the roof of the guest instrument van. Standards were run periodically during the campaign and the data were corrected for humidity-dependent biases and were normalized to the VSMOW/SLAP scale. Uncertainties in dD and d18O are estimated to be 2.28 permil and 0.38 permil, respectively.

Dust mass and horizontal aeolian sediment flux data from a sampler network on the Colorado Plateau, USA (other, 2024)
These data were compiled to assess time series data of aeolian sediment collections across varying climates, vegetation cover, and land uses on the Colorado Plateau. The objectives of our study were to interpret aeolian erosion and deposition processes and measure horizontal sediment flux over the span of two decades amid rapidly changing climate conditions and multiple land use changes within the study area. These data represent the seasonally accumulated horizontal sediment flux from different landscapes within the Colorado Plateau measured using Big Springs Number Eight (BSNE) dust samplers. These data were collected in Grand, San Juan, and Wayne County, Utah and Mesa Country, Colorado with sample collections ranging from 1998 to [...]

Fractional Live Cell Count for the paper "Diurnal trends and meteorological factors influencing the variability of fluorescent bioaerosol in Mt. Crested Butte, Colorado during SAIL" - Alejandro Gutierrez; Abu Sayeed Md Shawon; Katherine Benedict; Allison Aiken (other, 2024)
We deployed an Andersen’s Impactor for 24 hrs. to collect filter sample of bioaerosol particles at Mt. Crested Butte During Surface Atmosphere Integrated Field Laboratory (SAIL) campaign. It has six stages which bioaerosol particles were separated and collected in those stages based on their aerodynamic size. A flow cytometer was used for measuring the live cell counts for the bioaerosol particles during that 24 hour period. Here we present Relative abundance of that live cell counts at each stage. Stage: Stages of the Impactor Andersen’s used. Diameter: Range of aerodynamic diameter of the live cells collected at each stage (in micron). Fraction: Relative abundance of the live cells at each stage.

Data for "Diurnal trends and meteorological factors influencing the variability of fluorescent bioaerosol in Mt. Crested Butte, Colorado during SAIL" - Katherine Benedict; Abu Sayeed Md Shawon; Allison Aiken (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). 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 N_A: Number concentration of A type particles in #/cubic centimeters N_B: Number concentration of B type particles in #/cubic centimeters N_C: Number concentration of C type particles in #/cubic centimeters N_AB: Number concentration of AB type particles in #/cubic centimeters N_BC: Number concentration of BC type particles in #/cubic centimeters N_AC: Number concentration of AC type particles in #/cubic centimeters N_ABC: Number concentration of ABC type particles in #/cubic centimeters

NOAA GML and CIRES/Uni. of Colorado-Boulder Ceilometer Cloud Data Products at Kettle Ponds (CKP) for SPLASH - Christian Herrera; Joseph Sedlar; S. C. O. T. T. Stierle; Hagen Telg (other, 2024)
Cloud base height / vertical visibility and attenuated backscatter coefficient profiles from NOAA GML's Vaisala CL51 ceilometer deployed at Kettle Ponds (CKP). Data have been sampled to nearest 1-min. See Readme document for details.

hmworsham/er-forest-structure: Abiotic influences on continuous conifer forest structure across a subalpine watershed: RSE review archive - H. Marshall Worsham (other, 2024)
This release archives the code base for the manuscript Worsham et al., "Abiotic influences on continuous conifer forest structure across a subalpine watershed," submitted to Remote Sensing of Environment on July 18, 2024. This archive enables reviewers to view and access analytical code "as-is", i.e., as was implemented for the reported analysis, without subsequent modification. Updates: Links in several files in the directory 'inst/notebooks/tables_figures' were refreshed to ingest source data from the public ESS-DIVE repository at doi:10.15485/2404585 after that dataset was published. A later release containing any requested revisions is expected after the review process.

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 (ver. 2.0, February 2026) (other, 2024)
A series of field measurements of surface water velocity derived from video and Doppler velocity radar collected by small unoccupied aircraft systems (sUAS) and portable sensors were collected at seven locations in Colorado, USA, during the summer of 2023. The measurements were utilized to compute surface velocity and discharge using the Probability Concept, Large-Scale Particle Image Velocimetry (LSPIV), and Space-Time Image Velocimetry (STIV) methods. This data release includes the original videos, radar spectra, and ancillary data necessary to produce the surface water velocity and streamflow results. Data are grouped into sections (child items) based on the data type and purpose: Ancillary Scripts: this child item contains Microsoft Windows batch and Python script files that reproduce the processing steps used for specific files associated with image calibration, collating video metadata, and extraction of velocity data from associated hydroacousticdischarge measurements. Calibration Images: this child item contains images with labeled ground control points and associated pixel coordinate locations for each dataset. The images are used to calibrate image velocimetry results into real-world units (meters, for example). The images also indicate the pixel positions corresponding to the location of the streamflow measurement cross-section used for each dataset. Cross Section Geometry: this child item contains the bathymetry cross-sectional survey information for each dataset. Discharge Measurements: this child item contains the raw acoustic Doppler velocity profiler (ADCP) and acoustic Doppler velocimeter (ADV) measurements collected by U.S. Geological Survey hydrographers at each site to validate image and Doppler radar velocimetry results. Ground Control: this child item contains point correspondence information for each field site. The point real-world coordinates and their respective pixel locations are included. Radar Spectra: this child item contains Doppler velocity radar spectra values for selected field sites. Velocity and Discharge: this child item contains discharge measurements, channel area, surface velocities, and derived discharges using the entropy-based probability concept and four image velocimetry algorithms for each field site. Videos: this child item contains the raw videos captured by sUAS for selected field sites. 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 Wolcott, Colorado, USA MCA: Maroon Creek near Aspen, Colorado, USA RFG: Roaring Fork at Glenwood Springs, Colorado, USA The ERW and RFG sites contain radar spectra but not associated image velocimetry data, and the CRG site contains image velocimetry data but no associated radar spectra. Version History Summary: First Published: September 2024 Version 2.0: February 2026

Late Paleozoic and Early Mesozoic Structural History of the Uncompahgre Front- 19600101 (other, 2024)
This is an examination the late Paleozoic and Early Mesozoic structural history of the Uncompahgre front and the Paradox Basin.

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: Ground Control (other, 2024)
This child item contains information about associating the calibration images and video to real-world distances using ground control points for each field site. The data included here can be used to provide a pixel ground scale distance 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 [...]

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: Cross-Section Geometry (other, 2024)
This child item contains bathymetric measurements of the channel geometry suitable for use in computing cross-sectional area for each field site. 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 Wolcott, Colorado, USA MCA: [...]

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023 (ess_dive, 2024)
This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method. The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground.

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023 - Lizzy Paulus; Lizzy Paulus; Brandy Stewart; Brandy Stewart; Lizzy Paulus; Lizzy Paulus; Brandy Stewart; Brandy Stewart; Samuel Pierce; Eoin L. Brodie; Samuel Pierce; Eoin L. Brodie; et al. (ess_dive, 2024)
This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method. The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground.

Hydrological control of chemical weathering and rock-carbon fluxes: East River, Colorado - Wesley M. Brown; Jiamin Wan; Tetsu Tokunaga; Anh Phuong Tran; Alexander W. Newman; Wenming Dong; Curtis A. Beutler; Markus Bill; Amanda Henderson; Kenneth Hurst Williams (ess_dive, 2024)
This data package is used in the manuscript entitled “Hydrological control of chemical weathering and rock-carbon fluxes”. The field study was conducted in a lower montane hillslope of the East River watershed, underlain by Mancos Shale, within the lower 140 m section of a transect that extends nearly 1 km to its local peak. The data in this package, in CSV file format, were collected from Fall 2016 to fall 2021, including depth-resolved dynamic water table depths, subsurface water fluxes, solid phase (soil to bedrock) chemical compositions, geochemical properties of porewater and pore-gas, including radio carbon concentrations. The detailed methods of field studies, laboratory chemical analyses, and calculations are described in the Methods section and in the dataset file: Wan_et_al_Methods.pdf. 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.

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data - Bridget Hass; Tristan Goulden; Bridget Hass; Bridget Hass; Tristan Goulden; L. M. Kueppers; Tristan Goulden; Katharine Maher; L. M. Kueppers; L. M. Kueppers; Katharine Maher; Katharine Maher; et al. (ess_dive, 2024)
The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf).

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

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.

Recent Upper Colorado River Streamflow Declines Driven by Loss of Spring Precipitation - Jessica D. Lundquist (other, 2024)
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 here. This is version 2, please use this version for the most up-to-date results. 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. 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. Key features of the dataset include: 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. 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. Variables: The dataset encompasses a variety of variables essential for understanding streamflow dynamics, with a particular emphasis on the impact of reduced spring precipitation. 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.

Laboratory Optical Measurements Collected for Surface Water Samples Collected within Fraser River in the Upper Colorado River Basin from September 2021 through August 2023 - Jacob A. Fleck (other, 2024)
Optical spectra reported here are collected from 17 surface water sampling sites within the Fraser River, a headwaters drainage of the Upper Colorado River Basin in the central western United States. The sample collection was conducted as part of the partnership between the United States Geological Survey’s (USGS) Next Generation Water Observation System (NGWOS) and Proxies Project, in coordination with the USGS Colorado Water Science Center and California Water Science Center, and the East Grand Water Quality Board.

Geophysical and Environmental Monitoring Data, and Subsurface Flow Modelling Results for Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO - Sebastian Uhlemann; Stijn Wielandt; Chunwei Chou; Yuxin Wu; Baptiste Dafflon; Kenneth Hurst Williams (ess_dive, 2024)
This dataset includes geoelectrical monitoring data acquired between October 2021 and November 2022, soil moisture and temperature data, groundwater data obtained from borehole SNIB covering the period from June 2021 to September 2022, and hydrological modelling results. The data were acquired to investigate how variations in bedrock type and topography, and vegetation cover control subsurface flow dynamics. To provide insights into the subsurface flow dynamics and their controls, a monitoring transect was installed at the Chicken Bone Meadow, Mt. Snodgrass, Crested Butte, CO, measuring the spatio-temporal variations of soil moisture, soil and snow temperature, subsurface electrical resistivity variations, and groundwater dynamics. Field data are organized in a folder structure, with Electrical Resistivity Tomography (ERT) data being provided as one file per measurement, and data of the soil moisture and temperature sensors being provided as text files covering the entire monitoring period. The ‘Locations.csv’ file contains the location of all sensors, given in NAD83 – UTM Zone 13N. ERT monitoring data has been processed to filter data based on reciprocal errors (data with errors > 30% were removed), a linear error model was fitted to each survey, and to ensure a constant set of measurements for time-lapse inversion, filtered data were interpolated and assigned a 100% measurement error. Soil moisture and temperature data were acquired at 15 min intervals, and averaged to provide 1h data. Weather data and borehole data (groundwater depth, conductivity and temperature) were acquired at 30 min intervals, and are provided as daily measurements; all measurements are averaged, except of precipitation values, which are given as daily accumulation. The hydrological model was set up along the ERT monitoring transect, and net infiltration was used as surface boundary condition and derived from the weather data. Four different results are provided, (1) results for a parameterization using hydraulic permeability and porosity as derived from the ERT data through petrophysical relationships, and (2) three simplified model results, using 1 to 3 geological layers above the bedrock. Modelling was performed using PFLOTRAN, and for each model the PFLOTRAN input files are provided. The result files include weekly hydrological modelling results (e.g., saturation, velocities, pressures), as well as the model parameterization. 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.

Argon ages near Parker, AZ, and Needles, CA - Morgan W Nasholds (other, 2024)
Reported here are argon age data for samples collected to better understand lower Colorado River corridor landscape evolution, the history of the Colorado River, and define the age of geologic units being mapped. Samples of basalts, ashes, and sand were collected in the Cactus Plain southeast of Parker, AZ, the Whipple Mountain piedmont north-northwest of Parker, AZ and washes east-southeast of Needles, CA. Ar/Ar measurements were collected using a Thermo-Fisher Scientific ARGUS VI multi-collector mass spectrometer at the New Mexico Bureau of Geology and Mineral Resources in Socorro, NM. These data were collected as part of efforts to study and map the Lower Colorado River corridor conducted by the U.S. Geological Survey.

Partial pressure of carbon dioxide, sea surface salinity, temperature and barometric pressure collected from surface underway observations during the racing sailing vessel Malizia Ocean Challenge cruises in the Atlantic Ocean, South Pacific Ocean, Indian Ocean from 2023-02-26 to 2023-05-29 (NCEI Accession 0292120) - NCEI (other, 2024)
This dataset includes surface underway data collected during the racing sailing vessel Malizia Ocean Challenge cruises in the Atlantic Ocean, South Pacific Ocean, Indian Ocean from 2023-02-26 to 2023-05-29. These data include partial pressure of carbon dioxide in the water, sea surface salinity, sea surface temperature and barometric pressure. The instruments used to collect these data include carbon dioxide (CO2) gas analyzer, shower head chamber equilibrator for autonomous carbon dioxide (CO2) measurement.

Daily water stable isotopes, transpiration, and matrix potential data for an aspen and engelmann stand in the East River Watershed - Stefan Seeger; Max Berkelhammer; Matthias Sprenger (ess_dive, 2024)
We provide daily stable isotope (2H & 18O) ratios in soil water and xylem (plant stem) water, as well as the sap flow (transpiration) and the soil's matric potential at a forested site near Gothic, Colorado, in the East River catchment. We measured the stable isotopic composition of the transpiration and the daily transpiration flux sum of three aspen and three engelmann spruce. In both forest stands, we installed a soil profile and measured the soil matric potential at 15, 30, and 60 cm depth as well as the stable isotopes of soil pore water at 5, 10, 30, 60, and 90 cm depths. All isotope measurements were done in situ via vapor probes connected to a cavity ring down spectrometer (Picarro L1240i). We further report the daily meteorological data observed at billy barr near our study site. We also provide for each tree the relative share of root water uptake derived from the isotope measurements via a Bayesian mixing model (MixSIAR). The daily data is provided as a time series in "Iso_MP_Sap_DataDaily_ESSDiveUpload.csv" and the units are provided in "dd.csv"; the location of the instrumented trees and soil profiles are given as latitude and longitude coordinates saved as CSV and KMZ files; and a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata. The data was gathered to investigate the short-term changes of the water sources (i.e., variation of root water uptake from different soil depths) of the studied subalpine trees.

SPLASH Field Study; Snow-level Radar FMCW Moments at Kettle Ponds, Colorado, 2021-09-29 to 2023-09-06 (NCEI Accession 0289123) - NCEI (other, 2024)
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. Among the measurements taken in this study were snow-level measurements, using a snow-level radar that utilizes modernized frequency-modulated, continuous wave (FM-CW) technology. Snow level, the altitude in the atmosphere where snow changes into rain, is a critical parameter influencing runoff in mountainous watersheds because it determines the surface area of the watershed that will be exposed to rain versus snow. When the snow level is above most or all of the terrain in a watershed, a storm is more likely to produce enough rapid runoff to cause flooding. If the snow level is low in a watershed, then a storm increases the snowpack, providing valuable storage of water for potential later use. This archive contains the raw snow-level radar moments (reflectivity, radial velocity, spectral width, and related variables) at the Kettle Ponds, Colorado site.

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 (ver. 2.0, February 2026) (other, 2024)
A series of field measurements of surface water velocity derived from video and Doppler velocity radar collected by small unoccupied aircraft systems (sUAS) and portable sensors were collected at seven locations in Colorado, USA, during the summer of 2023. The measurements were utilized to compute surface velocity and discharge using the Probability Concept, Large-Scale Particle Image Velocimetry (LSPIV), and Space-Time Image Velocimetry (STIV) methods. This data release includes the original videos, radar spectra, and ancillary data necessary to produce the surface water velocity and streamflow results. Data are grouped into sections (child items) based on the data type and purpose: Ancillary Scripts: this child item contains Microsoft Windows batch and Python script files that reproduce the processing steps used for specific files associated with image calibration, collating video metadata, and extraction of velocity data from associated hydroacousticdischarge measurements. Calibration Images: this child item contains images with labeled ground control points and associated pixel coordinate locations for each dataset. The images are used to calibrate image velocimetry results into real-world units (meters, for example). The images also indicate the pixel positions corresponding to the location of the streamflow measurement cross-section used for each dataset. Cross Section Geometry: this child item contains the bathymetry cross-sectional survey information for each dataset. Discharge Measurements: this child item contains the raw acoustic Doppler velocity profiler (ADCP) and acoustic Doppler velocimeter (ADV) measurements collected by U.S. Geological Survey hydrographers at each site to validate image and Doppler radar velocimetry results. Ground Control: this child item contains point correspondence information for each field site. The point real-world coordinates and their respective pixel locations are included. Radar Spectra: this child item contains Doppler velocity radar spectra values for selected field sites. Velocity and Discharge: this child item contains discharge measurements, channel area, surface velocities, and derived discharges using the entropy-based probability concept and four image velocimetry algorithms for each field site. Videos: this child item contains the raw videos captured by sUAS for selected field sites. 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 Wolcott, Colorado, USA MCA: Maroon Creek near Aspen, Colorado, USA RFG: Roaring Fork at Glenwood Springs, Colorado, USA The ERW and RFG sites contain radar spectra but not associated image velocimetry data, and the CRG site contains image velocimetry data but no associated radar spectra. Version History Summary: First Published: September 2024 Version 2.0: February 2026

Surface Ocean CO2 Atlas Database Version 2024 (SOCATv2024) (NCEI Accession 0293257) - NCEI (other, 2024)
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 2024 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 oceans and coastal seas from 1957 to 2023. The main 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 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, flags 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 in SOCAT versions 3 to 2024. 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. 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 and using SOCAT. Automation of data upload and initial data checks allows annual releases of SOCAT from version 4 onward. SOCAT is used for quantification of ocean CO2 uptake and ocean acidification and for evaluation of climate models and sensor data. SOCAT products inform the annual Global Carbon Budget since 2013. The annual SOCAT releases by the SOCAT scientific community are a Voluntary Commitment for United Nations Sustainable Development Goal 14.3 (Reduce Ocean Acidification) (#OceanAction20464). More broadly the SOCAT releases contribute to UN SDG 13 (Climate Action) and SDG 14 (Life Below Water), and to the UN Decade of Ocean Science for Sustainable Development. 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 inorganic carbon measurements of the oceans, which provides policy makers with critical information on ocean CO2 uptake in climate negotiations. 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.

SnowEx20 Grand Mesa IOP Snow Water Equivalent and Snow Density, Version 1 (other, 2024)

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023 (ess_dive, 2024)
This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method. The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground. Update on 2024-10-18: Updates were made to the 2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv and dd.csv files to correct units (ppb instead of ppm).

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data (ess_dive, 2024)
The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf).

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.

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: Radar Spectra (other, 2024)
This child item contains Doppler radar velocimetry spectra measurements for each field site where the radars were deployed. 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 Wolcott, Colorado, USA MCA: Maroon Creek near Aspen, [...]

SPLASH Field Study; Ott Disdrometer Raw & Stats Observations at Brush Creek, Colorado, 2021-10-19 to 2023-09-03 (NCEI Accession 0287380) - NCEI (other, 2024)
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. Among the instruments used in this study were disdrometers, which are optical instruments mounted on a tripod that use a laser to measure the size and fall speed of precipitation particles – such as raindrops, snowflakes, or hail – that fall through the laser beam. This archive consists of Disdrometer observations taken at the site at Brush Creek, Colorado. The raw observations and processed (stats) observations are included.

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: Cross-Section Geometry (other, 2024)
This child item contains bathymetric measurements of the channel geometry suitable for use in computing cross-sectional area for each field site. 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 Wolcott, Colorado, USA MCA: [...]

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: Ground Control (other, 2024)
This child item contains information about associating the calibration images and video to real-world distances using ground control points for each field site. The data included here can be used to provide a pixel ground scale distance 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 [...]

SPLASH Field Study; Surface Meteorological Observations at Roaring Judy, Colorado, 2021-09-28 to 2023-09-05 (NCEI Accession 0287379) - NCEI (other, 2024)
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. Objectives included research to apply SPLASH observations and the associated processes to evaluate and improve NOAA's latest suite of modeling tools, including the Unified Forecast System, Rapid Refresh Forecast System, and National Water Model. 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 archive consists of Surface Meteorological observations taken at the Roaring Judy, Colorado site.

Occurrence Download (other, 2024)
A dataset containing 215207 species occurrences available in GBIF matching the query: { "and" : [ "BasisOfRecord is Specimen", "Country is United States of America", "DatasetKey is one of (SJSU - Carl W. Sharsmith Herbarium, San Jose State University, Natural History Museum (London) Collection Specimens, University of Alaska, Anchorage Herbarium Vascular Plants, Whitman College Herbarium Vascular Plants, Rocky Mountain Research Station (Boise, Idaho, USA), USDA Forest Service Herbarium Vascular Plants, University of Alberta Vascular Plant Herbarium (ALTA-VP), Herbarium SUNY Oswego, Botanical Database of Southern Africa (BODATSA): Botanical Collections, National Herbarium of Victoria (MEL) AVH data, University of Puget Sound, Slater Museum Herbarium Vascular Plants, Rocky Mountain Biological Laboratory, Indiana University of Pennsylvania, A.G. Shields Herbarium, University of Wisconsin-LaCrosse, Montana State University Billings, CHAS Botany Collection (Arctos), Eastern Washington University Herbarium Vascular Plants., University of Cincinnati, Margaret H. Fulford Herbarium - Vascular Plants, UCSC Herbarium - Kenneth S. Norris Center for Natural History, University of California Santa Cruz)", "TaxonKey is Tracheophyta" ] } The dataset includes 215207 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0203400-240321170329656/datasets/export for details. Data from some individual datasets included in this download may be licensed under less restrictive terms.

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 [...]

TRAC Time Tags of individual particle samples collected for microscopy analysis at RMBL [AMF2] from June 2023 to August 2023 - Ryan Moffet; Temitope Olayemi; Alexander Laskin; Steven Sharpe (other, 2024)
The dataset presents the time tags of individual particle samples collected on microscopy substrates during the Surface Atmosphere Integrated Field Laboratory (SAIL) campaign in Gothic, CO. A Time Resolved Aerosol Collector was deployed at the Rocky Mountain Biological Laboratory, 200 m north of AMF2 to collect ambient particles. The instrument inlet was placed 5 m above ground level. Particles are deposited on 400 mesh copper carbon-B film TEM grids (Ted Pella 01814-F). Samples are sealed and currently stored at Purdue University. A subset of the samples can be shared. Please contact dataset authors for more information. The dataset reports the sample start times in UTC and MST along with the sample ID, used to guide its location and retrieval from the storage plate. TRAC sampling started on June 17, 2023, and ended on July 31, 2023.

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: Radar Spectra (other, 2024)
This child item contains Doppler radar velocimetry spectra measurements for each field site where the radars were deployed. 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 Wolcott, Colorado, USA MCA: Maroon Creek near Aspen, [...]

Dust mass and horizontal aeolian sediment flux data from a sampler network on the Colorado Plateau, USA (other, 2024)
These data were compiled to assess time series data of aeolian sediment collections across varying climates, vegetation cover, and land uses on the Colorado Plateau. The objectives of our study were to interpret aeolian erosion and deposition processes and measure horizontal sediment flux over the span of two decades amid rapidly changing climate conditions and multiple land use changes within the study area. These data represent the seasonally accumulated horizontal sediment flux from different landscapes within the Colorado Plateau measured using Big Springs Number Eight (BSNE) dust samplers. These data were collected in Grand, San Juan, and Wayne County, Utah and Mesa Country, Colorado with sample collections ranging from 1998 to [...]

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: Discharge Measurements (other, 2024)
This child item contains the raw acoustic Doppler velocity profiler (ADCP) measurements collected by U.S. Geological Survey (USGS) hydrographers at each site to validate Image and Radar velocimetry results. The discharge measurements were collected as part of routine and standard streamflow gaging direct measurement workflows at established USGS gaging locations. The zip file included in this release contains folders for each site. Within each folder are the associated raw ADCP files. For all field sites except BRJ, official discharge records are approved and available in the National Water Information System (NWIS) database. For those measurements with NWIS official measurements, the Site Visit Notes report is included (portable document [...]

SnowEx20 Grand Mesa IOP Snow Water Equivalent and Snow Density, Version 1 (other, 2024)

Upper Colorado River Basin: Monitoring sites and water chemistry data used to develop a specific conductance - salinity proxy model - R Blaine McCleskey (other, 2024)
Salinity levels in streams and tributaries of the Colorado River Basin have been a major concern for years. Recently, the United States Geological Survey’s (USGS) Next Generation Water Observing System (NGWOS) program expanded stream monitoring networks including the number of sites where continuous (15-minute) specific conductance is measured in the Colorado River Headwaters and Gunnison River subbasins located east of the Colorado-Utah state line (hereafter, UCOL). Salinity and total dissolved solids (TDS) can be estimated using specific conductance and water type as a proxy (McCleskey et al., 2023); thus, the UCOL is an ideal basin to apply the proxy. The data presented in this data release, including monitoring site information [...]

The NOAA NCEI Global Marine Microplastics Data Collection (1972-present) - NCEI (other, 2024)
Microplastics (i.e., plastics measuring less than 5mm) pollution is a growing problem affecting coastal communities, marine ecosystems, aquatic life, and human health. The widespread occurrence of marine microplastics, and the need to curb its threats, require expansive, and continuous monitoring. While microplastic research has increased in recent years and generated significant volumes of data, there is a lack of a robust, open access, and long-term aggregation of this data. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) marine microplastic data collection provides a global open access to marine microplastic data on an easily discoverable and accessible GIS web map and data portal (https://www.ncei.noaa.gov/products/microplastics). The objective of this data portal is to develop a repository where microplastics data are aggregated, archived, and served in a user friendly, consistent, and reliable manner. This work contributes to NCEI’s efforts towards data standardization, integration, harmonization, and interoperability among national and international collaborators for monitoring global marine microplastics.

SAIL-Net POPS Data Fall 2021 - Summer 2023 - L. D. Gibson (other, 2024)
SAIL-Net was a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleation particles (INP). This network enables the investigation of small-scale variations in complex terrain. This dataset provides particles size distribution concentrations from the POPS. These data are also available on the ARM Data Discovery: https://doi.org/10.5439/2203692.

Maps of habitat suitability improvement potential for the Gunnison Sage-grouse (Centrocercus minimus) satellite populations in Southwestern Colorado - Michael S. O'Donnell; Nathan D Van Van Schmidt (other, 2024)
Habitat restoration efforts to conserve wildlife species are often conducted along a range of local site conditions, with limited information available to gauge relative outcomes for habitat suitability among sites and identify those that may lead to the greatest returns on restoration investment. We leveraged existing resource selection function models to generate heatmaps of spatially varying habitat suitability improvement potential for the Gunnison Sage-grouse (Centrocercus minimus) based on a suite of habitat restoration actions deployed across crucial habitats within six remaining satellite populations. We first simulated expected change in model covariates (habitat features) from a suite of restoration actions (increasing sagebrush, herbaceous, or litter cover, non-sagebrush shrub management, installation of mesic improvement structures, and removal of invasive plants) to generate modified input layers for each. We then reran the original models using these modified layers and calculated the predicted change in habitat suitability across space. The resulting heatmaps identify areas with the greatest improvement potential for each restoration action to help guide strategic restoration planning for the species. This data release includes a set of 76 total raster files. This includes: 38 heatmaps illustrating predicted change in Gunnison Sage-grouse habitat suitability across space following habitat restoration actions (either single or combined), 32 categorized heatmaps showing areas where 1) new habitat was created, 2) non-habitat remained non-habitat despite management interventions, or 3) negative changes in suitability were observed, and 6 heatmaps illustrating predicted changes in suitability following new or worsening plant invasions (for example, cheatgrass, represented by annual herbaceous, or pinyon-juniper). Raster file names are coded by the name of the satellite population, the season the model is representing, the restoration action applied, and whether the maps were categorized. The codes are listed below: CSb = Cerro Summit-Cimarron-Sims Mesa Breeding CSs = Cerro Summit-Cimarron-Sims Mesa Summer CRb = Crawford Breeding CRs = Crawford Summer DCb = Dove Creek Breeding PMb = Pinon Mesa Breeding PMs = Pinon Mesa Summer PPb = Poncha Pass Breeding PPs = Poncha Pass Summer SMb = San Miguel Breeding SMs = San Miguel Summer ahrb_inv = annual herbaceous invasion (increase in cover) ahrb_rm = annual herbaceous removal (decrease in cover) combo = combined actions hrb_incr = increase herbaceous cover lit_incr = increase litter cover mes_impr = mesic improvements (increase in area) pj_inv = pinyon-juniper invasion (increase in area) pj_rm = pinyon-juniper removal (decrease in area) pns_decr = decrease non-sagebrush shrub cover pns_incr = increase non-sagebrush shrub cover pos_incr = increase percent other sagebrush cover (non-big sagebrush cover) sgc_incr = increase sagebrush cover sgh_incr = increase sagebrush height shh_decr = decrease shrub height C = CATEGORIZED Maps V = UNCATEGORIZED Maps X = INVASION (categorized) Maps

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: Discharge Measurements (other, 2024)
This child item contains the raw acoustic Doppler velocity profiler (ADCP) measurements collected by U.S. Geological Survey (USGS) hydrographers at each site to validate Image and Radar velocimetry results. The discharge measurements were collected as part of routine and standard streamflow gaging direct measurement workflows at established USGS gaging locations. The zip file included in this release contains folders for each site. Within each folder are the associated raw ADCP files. For all field sites except BRJ, official discharge records are approved and available in the National Water Information System (NWIS) database. For those measurements with NWIS official measurements, the Site Visit Notes report is included (portable document [...]

Data from: Pre- and post- treatment fold change plasma proteomics in metastatic NSCLC patients (2024)
[object Object]

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023 (ess_dive, 2024)
This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method. The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground.

Video In Situ Snowfall Sensor (VISSS) data for Ny-Ålesund (July 2022 - December 2023) (2024)
[object Object]

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.

Data and scripts for the manuscript "Herbivory and water availability interact to shape the adaptive landscape in the perennial forb, Boechera stricta". (2024)
[object Object]

Dataset for Quantifying Dissolved Organic Matter Scaling Relationships and Trends in Watersheds (2024)
[object Object]

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.

NEON AOP Survey of Upper East River CO Watersheds: Waveform LiDAR Binary Data (ess_dive, 2024)
The waveform Light Detection and Ranging (LiDAR) data in this package were generated through a National Ecological Observatory Network Airborne Observation Platform (NEON AOP) acquisition over watersheds of interest surrounding Crested Butte, Colorado. The remote sensing imagery acquired by the NEON AOP supports an interdisciplinary project on hydrology, biogeochemistry, and ecosystem functioning in a snow-dominated headwater environment. These waveform LiDAR data enable spatially continuous estimation of vegetation structure parameters to facilitate analyses of the major environmental drivers of structural and compositional variability. The package contains 97 compressed file archives in 7-zip (.7z) format, each corresponding to one acquisition flightpath. Within each .7z archive is a set of constituent files describing properties of the LiDAR waveforms, such as return intensity, geolocation, outgoing pulse and other behavior of the sensor and signals. Once downloaded, the files must first be unzipped using the widely distributed command-line software utility 7z, using the command '7z x \[filename\].7z \[target_directory\]'. All files within the .7z archives can be opened in IDL, MatLab, or the open-source R statistical computing environment. Further details about the data package are in the attached user guide (neon_aop_crbu_waveformlidar_userguide.pdf).

Total metal, carbon, anion, iron speciation, and sulfide concentrations; Slate River, East River, and Trail Creek surface water and floodplains, Crested Butte, CO; May 2023–August 2023 (ess_dive, 2024)
This data package comprises analytical results and metadata from stream and groundwater samples collected from the Slate River, East River, Trail Creek, and their respective floodplains. This dataset contains five files: (1) a samples file (2023_SFA_Field_samples.csv) that contains site information; (2) a chemical analysis data file (2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv) that contains sample analysis values; (3) a file-level metadata file (flmd.csv) that lists each file contained in the dataset with associated metadata; (4) a data dictionary file (dd.csv) that contains column/row headers used throughout the files along with definitions, units, and data types; and (5) a methods file (methods.csv) that contains ID, type, description, instrument, and lab information for each method. The samples’ anion concentrations were measured using ion chromatography (IC), total metal concentrations using inductively coupled plasma emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS), non-purgeable organic carbon using total organic carbon (TOC) analysis, dissolved sulfide concentration using methylene blue spectrophotometry, and iron speciation using the ferrozine assay. To support bulk chemical analyses and colloid characterization, samples were collected from multiple depths ranging from the surface to 3.5 meters below ground. Update on 2024-10-18: Updates were made to the 2023_FieldWaterSampleData_IC__ICPOES__ICPMS__TOC__Fe__S_chem_data.csv and dd.csv files to correct units (ppb instead of ppm).

SPLASH Field Study; Snow-level Radar FMCW Moments at Brush Creek, Colorado, 2021-12-20 to 2023-09-05 (NCEI Accession 0289129) - NCEI (other, 2024)
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. Among the measurements taken in this study were snow-level measurements, using a snow-level radar that utilizes modernized frequency-modulated, continuous wave (FM-CW) technology. Snow level, the altitude in the atmosphere where snow changes into rain, is a critical parameter influencing runoff in mountainous watersheds because it determines the surface area of the watershed that will be exposed to rain versus snow. When the snow level is above most or all of the terrain in a watershed, a storm is more likely to produce enough rapid runoff to cause flooding. If the snow level is low in a watershed, then a storm increases the snowpack, providing valuable storage of water for potential later use. This archive contains the raw snow-level radar moments (reflectivity, radial velocity, spectral width, and related variables) at the Brush Creek, Colorado site.

SPLASH Field Study; Ott Disdrometer Raw & Stats Observations at Kettle Ponds, Colorado, 2021-09-30 to 2023-09-05 (NCEI Accession 0287378) - NCEI (other, 2024)
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. Among the instruments used in this study were disdrometers, which are optical instruments mounted on a tripod that use a laser to measure the size and fall speed of precipitation particles – such as raindrops, snowflakes, or hail – that fall through the laser beam. This archive consists of Disdrometer observations taken at the site at Kettle Ponds, Colorado. The raw observations and processed (stats) observations are included.

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.

Scaling landscape fire history in sagebrush: Wildfires not historically frequent in the main population of threatened Gunnison Sage-grouse (other, 2024)
The main population of 5,000 Threatened Gunnison sage-grouse (GUSG; Centrocercus minimus) in Colorado depends on sagebrush that are killed by wildfires, with recovery taking decades, so frequent fire is a threat, but did it occur historically? Early land surveys showed that the historical (preindustrial) fire rotation (FR), the expected period to burn area equal to a focal land area, was 90-143 years in GUSG ranges, which is not frequent fire ( 25 years). However, recent research, based on fire scars on trees at ten sites near sagebrush, suggested some frequent fire historically in the main population. That study was not spatial, essential to estimate FR, so spatial data were created in GIS with land-survey reconstructions, survey dates, fire-scar sites, Thiessen polygons around sites, and sagebrush. The previous study assumed fires that burned 2+ sites likely burned across sagebrush. Historical FRs were calculated several ways over a common period. A recovery estimate of FR was 90-135 years, a land-survey estimate 82-131 years, and three spatial scar-based estimates 93-107 years, showing agreement. However, comparing land-survey and fire-scar results showed that using fire scars spatially only 43% matched land surveys. Detailed analysis showed that 10 fire-scar sites were insufficient to detect historical fire sizes and distributions across the large 168,753 ha sagebrush area. An adequate historical fire reconstruction could require 45-60 fire-scar sites, making only 30,000 ha of sagebrush feasible. Using the two remaining methods, which cross-validate, showed frequent fire did not occur historically in the study area, as historical FRs were 82-135 years.

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.

tatemeehan/SnowEx2020_BSU_pE_GPR: Multipolarization Radargram Processing SnowEx 2020 Grand Mesa IOP (2024)
[object Object]

2016 Floodplain Geochemistry from the East River Watershed, Colorado - Bhavna Arora; Dipankar Dwivedi; P. S. Nico; Patricia Fox (ess_dive, 2023)
This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Floodplain piezometers installed in several meanders (Meander C, Meander D, and Meander O) in the vicinity of the Pumphouse were sampled at several dates in 2016. Data includes in-field measurements (pH, electrical conductivity, oxidation reduction potential, dissolved oxygen, dissolved Fe(II), dissolved sulfide, and groundwater level) along with laboratory measurements (dissolved inorganic carbon, dissolved organic carbon, metals and major cations). Five files are included in this dataset, including: sample locations and depths in a csv file (metadata.csv), aqueous geochemistry data in a csv file, analytical detection limits in a csv file, a file-level metadata (flmd.csv) file, and a data dictionary (dd.csv) file. Data that was below the detection limit is reported as one half of the detection limit and missing data is listed as "-9999".

Potentilla demographic and environmental data for Rocky Mountains of Colorado (Niwot LTER & RMBL), 2018 - 2020. - Kelly A Carscadden (other, 2023)
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and the Rocky Mountain Biological Laboratory (RMBL). This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.

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

ATS (Advanced Terrestrial Simulator) integrated hydrology and reactive transport model output in Copper Creek, Colorado. - Ilhan Özgen-Xian; Dipankar Dwivedi; Zexuan Xu; Sergi Molins; Carl I. Steefel (other, 2023)
This dataset is generated using the ATS (Advanced Terrestrial Simulator) model at Copper Creek, Colorado, the largest catchment in the East River watershed. ATS is an integrated hydrology and reactive transport model to simulate the Concentration-Discharge (C-Q) relationship, and is used to quantify the geochemical export and understand how watersheds respond to climate disturbances. Key hydrogeochemical variables, including discharge, precipitation, evapotranspiration, and geochemical concentrations, were simulated and output at a daily frequency at the Copper Creek outlet. A group of hydrology and geochemical model variables over the spatial domain are output at a bi-monthly frequency. The model used 1-km resolution daily frequency Daymet meteorological forcing, and mineral composition from previous studies. The simulations are performed on NERSC. The datasets include the input files for running the ATS (Advanced Terrestrial Simulator) simulation, including the mesh file, input script and geochemical model files. The output files from the ATS (Advanced Terrestrial Simulator) are the daily time-series discharge and geochemical concentration simulated at the outlet of Copper Creek, and the bi-monthly spatial output over the simulation domain.

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

Meteorological data from the Billy Bar site at the East River, Colorado. - David Gochis; Roelof Versteeg; Boris Faybishenko; Kenneth Hurst Williams (other, 2023)
This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.

Air Temperature Growing Degree-days Early Season Mean (2002-2022) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the mean of annual values computed from 2002 to 2021. Annual estimates are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D031')  metadata <- sdp_get_metadata('R4D031')    For more information about rSDP, visit the package homepage.

2017 Meander C sediment characterization from the East River, Colorado - Frances Griswold; Genevieve Goebel; Sam Ying; Malak M. Tfaily; Scott Fendorf; Craig R. Anderson; Marco Keiluweit; Patricia Fox; P. S. Nico (ess_dive, 2023)
This dataset includes characterization data collected on sediment samples from Meander C as part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected to investigate the nature of mineral-organic associations across the meander transect and with depth. Samples were collected in 2017, and analyzed using sequential extractions (water, pyrophosphate, HCl, and dithionite) and density fractionation. Data collected include: texture, pH, root biomass, and metals (Fe, Al, Ca, Si) of bulk sediment samples; C and N of heavy fraction; and extractable OC, Fe, and Al, and organic matter characterization using high-resolution mass spectrometry (FT-ICR-MS) of sequential extractions. Sample locations are included in the ‘sample_metadata.xlsx” file. Sediment characterization data is included in the accompanying csv files: bulk and density fractions (MCB2017_density-and-bulk-fractions.csv), sequential extractions (MCB2017_sequential-extractions.csv), and FT-ICR-MS data from sequential extractions in three files to be read by the R package ftmsRanalysis (MCB2017_FTICRMS-sequential-edata.csv, MCB2017_FTICRMS-sequential-emeta.csv, and MCB2017_FTICRMS-sequential-fdata.csv). Sediment was collected in triplicate; ID’s refer to individual samples.

Data from: Linking microenvironment modification to species interactions and demography in an alpine plant community - Rozália E Kapás; Benjamin Blonder (other, 2023)
Individual plants can modify the microenvironment within their spatial neighborhood. However, the consequences of microenvironment modification for demography and species interactions remain unclear at the community scale. In a study of co-occurring alpine plants, we (1) determined the extent of species-specific microclimate modification by comparing temperature and soil moisture between vegetated and non-vegetated microsites for several focal species. We (2) determined how vital rates (survival, growth, fecundity) of all species varied in response to aboveground and belowground vegetative overlap with inter- and intraspecific neighbors as proxies for microenvironment modification. For (1), surface temperatures were buffered (lower maximums and higher minimums) and soil moisture was higher below the canopies of most species compared to non-vegetated areas. For (2), vegetative overlap predicted the most vital rates, although the effect varied depending on whether aboveground or belowground overlap was considered. Vital rate response to microenvironment-modification proxies (vegetative overlap) was also frequently context-dependent with respect to plant size and macroclimate. Microenvironment modification and spatial overlapping of individuals are key drivers of demography and species interactions in this alpine community.

Model files for estimating snow dynamics and stable water isotopes across the East River, CO. (ess_dive, 2023)
A coupled hydrologic and snowpack stable water isotope model is used to assesses controls on isotopic inputs across the East River, Colorado, a large, mountainous basin. The hydrologic model uses the semi-empirical, spatially distributed and publicly available U.S. Geological Survey numerical code Precipitation-Modelling Runoff System (PRMS). Water and energy are tracked daily through the atmosphere, canopy and subsurface at a 100-m grid resolution. The isotope mass balance model follows previous work by Ala-aho et al. (2017) to track stable isotopes entering the soil system as snowmelt or rain. Water stores and fluxes needed for the isotope model use hydrologic model output for each timestep and model modeled grid location. This data package provides all files related to the hydrologic model (executable, input and output files), the isotopic model calibration and the historic isotopic model (water years 2015-2020). The isotope model source code and executable are provided. The file "readme.txt" includes information on the file structure, steps to run the model, and description of included folders.

Manual soil CO2 flux, temperature and water content measurements from the Snodgrass and Copper Creek elevation transect (other, 2023)
The manual_soil_measurements_2022_2023.csv data set contains all of the manually measured soil CO 2 efflux, volumetric water content and soil temperature during field visits to the Rocky Mountain Biological Laboratory. In 2022, 16 PVC collars were installed at each site, there are two sets of collars, (n=4) PVC collars that are ~20” in diameter and placed in a 1x1 m square around the soil pit (Collar ID’s 1,2,3,4). The remaining collars (n=12, diameter= 4”) are placed within the forest type at each site, with four transects of three collars spaced at 3, 6 and 10m from the soil pit. For both types of collars, the soil CO 2 flux is measured using Flux Puppy, a field-portable system built around the Li-Cor 800 series IRGA (Carbone et al. 2019) Lids are placed on the collars and CO 2 is allowed to accumulate for 4 minutes (Large collars) and 3 minutes in (small collars). The flux is calculated by extracting the slope from the CO 2 accumulation curve using methods developed by (Trumbore lab).The large collars measurements were taken first, and lids were left on to accumulate CO 2 for radiocarbon samples. The small chambers were measured up to three times between 10:00-16:00 hr. Manual measurements were collected during four site visits in 2022, and one in 2023. Each soil CO 2 flux is paired with manual vwc (Campell scientific Hydrosense) and soil temperature (Thermofisher) measurements at the collars. Vwc measurements were take 3 per collar to account for spatial heterogeneity around each collar. Soil temperature when taken, was taken during the flux measurements. Soil temp1 was taken during the first flux measurement at that collar, soil temp 2 and the second etc..Carbone, M. S., B. Seyednasrollah, T. T. Rademacher, D. Basler, J. M. Le Moine, S. Beals, J. Beasley, A. Greene, J. Kelroy, and A. D. Richardson. 2019. Flux Puppy – An open-source software application and portable system design for low-cost manual measurements of CO2 and H2O fluxes. Agricultural and Forest Meteorology 274:1–6.

Air Temperature Freezing Degree-days Annual Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D023')  metadata <- sdp_get_metadata('R4D023')    For more information about rSDP, visit the package homepage.

SAIL-Net CloudPuck CCN Data - E. J. T. Levin; Lay James Gibson (other, 2023)
SAIL-Net is a DOE funded project in the East River Watershed near Crested Butte, Colorado with the goal of advancing our understanding of aerosol-cloud interactions in complex, mountainous regions. Through the deployment of a network of six low cost microphysics nodes in Fall 2021 in the same domain at the SAIL campaign, SAIL-Net provides data on aerosol size distributions, cloud condensation nuclei (CCN), and ice nucleations particles (INP). This network enables the investigation of small-scale variations in complex terrain.This specific dataset provides the cleaned data recorded from the CloudPuck, an in house instrument made by Handix Scientific that counts CCN concentrations. The CloudPuck was deployed at the sites for the summer/fall of 2022 before winter conditions were too harsh to maintain the instrument. For more information on the the CloudPuck or how the raw data are processed, see the read me.

Manual soil CO2 flux, temperature and water content measurements from the Snodgrass and Copper Creek elevation transect - Austin Simonpietri; Mariah Carbone; Andrew D. Richardson; Andrew D. Richardson; Austin Simonpietri; Mariah Carbone; Andrew D. Richardson; Austin Simonpietri; Mariah Carbone (other, 2023)
The manual_soil_measurements_2022_2023.csv data set contains all of the manually measured soil CO 2 efflux, volumetric water content and soil temperature during field visits to the Rocky Mountain Biological Laboratory. In 2022, 16 PVC collars were installed at each site, there are two sets of collars, (n=4) PVC collars that are ~20” in diameter and placed in a 1x1 m square around the soil pit (Collar ID’s 1,2,3,4). The remaining collars (n=12, diameter= 4”) are placed within the forest type at each site, with four transects of three collars spaced at 3, 6 and 10m from the soil pit. For both types of collars, the soil CO 2 flux is measured using Flux Puppy, a field-portable system built around the Li-Cor 800 series IRGA (Carbone et al. 2019) Lids are placed on the collars and CO 2 is allowed to accumulate for 4 minutes (Large collars) and 3 minutes in (small collars). The flux is calculated by extracting the slope from the CO 2 accumulation curve using methods developed by (Trumbore lab).The large collars measurements were taken first, and lids were left on to accumulate CO 2 for radiocarbon samples. The small chambers were measured up to three times between 10:00-16:00 hr. Manual measurements were collected during four site visits in 2022, and one in 2023. Each soil CO 2 flux is paired with manual vwc (Campell scientific Hydrosense) and soil temperature (Thermofisher) measurements at the collars. Vwc measurements were take 3 per collar to account for spatial heterogeneity around each collar. Soil temperature when taken, was taken during the flux measurements. Soil temp1 was taken during the first flux measurement at that collar, soil temp 2 and the second etc..Carbone, M. S., B. Seyednasrollah, T. T. Rademacher, D. Basler, J. M. Le Moine, S. Beals, J. Beasley, A. Greene, J. Kelroy, and A. D. Richardson. 2019. Flux Puppy – An open-source software application and portable system design for low-cost manual measurements of CO2 and H2O fluxes. Agricultural and Forest Meteorology 274:1–6.

Classified channel masks of the East River, Colorado, U.S.A and areas of floodplain erosion and accretion ranging from 1955 to 2015. - oel Rowland; Sophie Stauffer (other, 2023)
This dataset provides four sets of geotiffs used for the mapping and analysis an alluvial floodplain reach of the East River, downstream of Gothic, CO, U.S.A near Crested Butte. The files include binary masks of the river channel at five in intervals from 1955 to 2015. Another set of rasters provide a map of the channel centerline pixels of the river for each date. Also included in the dataset are rasters of the areas of channel change due to migration over 8 time intervals. The masks were generated 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 to create the channel centerlines and the change area rasters. The change masks were generated for the following time periods: 1955-1973; 1955-2015; 1973-1983; 1983-1990; 1990-2001; 2001-2011; 2001-2015; and 2011-2015.

Geochemistry of East River, Colorado floodplain sediments from Meander Z and Meander Y collected in 2017. - P. S. Nico; Patricia Fox (other, 2023)
This dataset includes sediment geochemical characterization 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 Z (MZA, MZB) and Meander Y (MYP) just upstream of the confluence with Brush Creek in September 2017 to depths of approximately 40-95 cm in 15-20 cm intervals. Analyses include moisture content, particle size analysis, total inorganic and total organic carbon, and extractions by 1 M KCl (for adsorbed NH4), anoxic HCl (for Fe(II) content), ammonium oxalate (for amorphous Fe-oxide content), and dithionite (for crystalline Fe-oxide content). Sample locations are included in both a kmz file, which can be opened in Google Earth, and a csv file, and methods are included as a text document. All characterization data is summarized in a Microsoft Excel file, and is also included as individual .csv files for each extraction (ammonium oxalate, anoxic HCl, dithionite, and KCl) and for bulk sediment data (moisture content, particle size analysis, TIC, and TOC).

Landscape Flowering Phenology Field Data for Sites in the Vicinity of Crested Butte, CO. - Ian K Breckheimer (other, 2023)
This dataset represents field observations of reproductive development (flowering phenology) in 135 species of flowering plants collected at 12 field sites in the vicinity of Crested Butte, Colorado starting in 2019. Sites were visited approximately weekly from early May until early August, and all species in flower were recorded in 25 segments along a 50m transect at each site, and species were recorded if they were within 1m of either side of the transect. Datasets included in this package are 1) cleaned field observations of flowering phenology, 2) taxonomic identity of all recorded species, 3) spatial data representing the location of the center of each transect segment, and 4) spatial data representing the segment polygons.

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows. uas_L2_yyyymmdd_hhmmss.zip where L2 = Level 2 data yyyymmdd = year,month,day hhmmss = hour,minute,second Time is the flight start time in UTC. The NetCDF file format contained in the zip files has a similar format to the zip files with convention uas_ _L2_yyyymmdd_hhmmss.nc where = vsm, dem, ndvi, or stmp vsm = volumetric soil moisture dem = digital elevation ndvi = normalized difference vegetation index stmp = surface temperature Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products. November 2023 update: Version 2.0 added which has flight data from 2022 and 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data and a mask was applied to the soil moisture retrieval that masks data over water surfaces.

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH (other, 2023)
This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected 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 from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363. Two file formats are provided: one version is text csv format and the second version is in NetCDF. Volumetric water content calculations: Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC = 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: Mwet, Mdry = mass (g) of wet and dry soil respectively Vtot = total soil volume (ml) w = density of water (1g/ml) A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset. vwc_calculated = vwc_probe * slope + intercept slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3 Definitions: TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%). EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. The VWC measured by TDR is an average over the length of the waveguide. Soil Characteristics: Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows: Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture ------------------------------------------------------------------------------------------------------ KEP1 2 43 35 22 Loam AYP 2 40 35 25 Loam KPA 2 35 42 22 Loam ------------------------------------------------------------------------------------------------------

Measurement of particulated matter (PM1, PM2.5, PM10) using a PM sensor during the SAIL campaign in Gothic, CO and Mt. Crested Butte, CO - Katherine Benedict; Allison Aiken; Abu Sayeed Md Shawon (other, 2023)
We deployed another PM sensor (Modulair-PM, QuantAQ) to measure the mass concentration of particulate matter (PM) for three size cuts at both the main site (M1) and supplementary site (S2) of the SAIL from 14 June 2022 to 14 June 2023. The instruments provide the mass concentration of PM1, PM2.5 and PM10. Mass concentrations were calculated based on measurements made with a nephelometer and an OPC. We used Quant-AQ's algorithm [please see their documentation here and the references within] to determine the mass concentrations reported. Here, we present the time series of sample relative humidity, temperature, pressure, and the mass concentration of PM1, PM2.5 and PM10. We also present the size distribution of the aerosol particles within a diameter range of 0.35 - 40 micron, measured by the OPC of the pm-modulair. However, we encourage caution while using the size distribution data, since it is operated only with the factory calibration. Abstract and description of the campaign can be found here : https://www.arm.gov/research/campaigns/amf2022ssb.

Leipzig University W-Band Cloud Radar, Gothic (Colorado), SAIL Campaign Second Winter (15.11.2022 - 05.06.2023) - Heike Kalesse-Los; Anton Kötsche; Veronika Ettrichraetz; Isabelle Steinke; Maximilian Maahn (other, 2023)
The instrument is a polarimetric W-Band simultaneous transmission simultaneous reception (STSR) Doppler Cloud Radar manufactured by Radiometer Physics GmbH (RPG, instrument type RPG-FMCW-94-DP). It was deployed about 500m away from the ARM M1 facility in Gothic (CO) near the Ore House between Nov 15, 2022 and Jun 5, 2023 at an altitude of 2913 m. The Doppler cloud radar was mounted on a prototype of a cold temperature scanner manufactured by RPG. The dataset contains common radar variables like equivalent radar reflectivity factor, mean Doppler velocity, Doppler spectrum width as well as common polarimetric radar variables like differential phase shift (PhiDP), specific differential phase shift (KDP), correlation coefficient (RHV) and differential reflectivity (ZDR). Between Nov 15, 2022, and Feb 7, 2023 the radar was operated at 40° constant elevation and 151° azimuth (towards the KAZR at the AMF1). Between Feb 7 and Mar 15, 2023 the following scan cycle was repeated in 15min intervals: zenith pointing measurements, RHI scans from 0° to 90° and 90° to 0° elevation at 151° azimuth, constant elevation measurements at 40° elevation and 151° azimuth and PPI scans at 85° elevation for wind retrievals (one PPI per hour). Between Mar 15 18:18 UTC and Apr 8, 2023 11:04 UTC, the scanner was stuck at 72° elevation, which resulted in corrupted files for this period. For that period, the files containing data are uploaded but should be treated with care. Between Apr 8 and Apr 14, 2023 the scanning pattern described above was performed again. Afterwards, except for May 17 and May 18, 2023 where scanning patterns as described above were performed, measurements continued at 40° elevation and 151° azimuth until May 22, 2023 09:00 UTC. The scanning pattern was then started again, but without hourly PPI measurements until May 31, 2023. Between May 31 and Jun 5, 2023 solely zenith-pointing observations were performed. The data provided here are the RPG Level 1 (L1) files, Doppler spectra Level 0 (L0) files are available upon request.

Snow-free Growing Degree-days Late Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of accumulated fall snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. These maps represent annual values computed from 2002 to 2022.20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D021')  metadata <- sdp_get_metadata('R4D021')    For more information about rSDP, visit the package homepage.

Bioaerosol Properties using WIBS from SAIL on Mt. Crested Butte, CO - Allison Aiken; Abu Sayeed Md Shawon; Katherine Benedict (other, 2023)
We deployed a Wideband Integrated Bioaerosol Sensor (WIBS-Neo, DMT) to measure different properties of fluorescent bioaerosol particles (FBAP) during SAIL. Our measurements were collected at the supplementary site on Crested Butte Mountain from inside the ARM Aerosol Observing System (AOS) sampling from the AOS inlet. We had two deployment periods at SAIL during biologically active seasons: summer 2022 (15 June 2022 - 13 September 2022) and spring 2023 (21 March 2023 - 19 June 2023). The WIBS is an online single-particle measurement that detects FBAPs (within a size range of 0.5 - 30 microns in diameter) based on the excitation and emission wavelengths of the individual particles. Using two xenon lamps, the WIBS excites FBAPs at 280 nm and 370 nm. Their emission is detected across two wavebands of 310-400 nm and 420-650 nm. We classified the FBAPs into seven different categories (A, B, C, AB, BC, AC, and ABC) using the classification scheme in Perring et. al. (2015) [1]. Here, we report one-minute averaged number concentration of FBAPs (total and by category) and NFBAPs. We also present one-minute-averaged size distributions and the asymmetry factor (AF, a surrogate for shape) of all FBAPs and NFBAPs. The logarithmic bin width of the size bins is the same as the average bin width of the AOS's optical particle counter (OPC, Grimm) for the range of sizes in which they overlap (26 bins from 0.5 - 30 microns). AF of the particles ranges from 0-100 and is divided into five bins with a linear spacing at increments of 20. The smallest AF bin represents more spherical particles while the largest bin represents more rod-shaped particles. We recommend referring to the readme file for more details of the variables present in the files. [1] Perring, A. E., et al. (2015), Airborne observations of regional variation in fluorescent aerosol across the United States, J. Geophys. Res. Atmos., 120, 1153-1170, doi:10.1002/2014JD022495. Abstract and description of the campaign can be found here: https://www.arm.gov/research/cam

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added which has flight data from 2022 and 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data and a mask was applied to the soil moisture retrieval that masks data over water surfaces. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0.

Sapflow and xylem water isotopes from Snodgrass Mountain, East River Watershed, Colorado USA. Space and time dynamics of transpiration in the East River watershed: biotic and abiotic controls (ess_dive, 2023)
This dataset includes sapflux and stable water isotopes of soil water and xylem water for aspen, fir and spruce trees along the Snodgrass Mountain transect in the East River Watershed, Colorado USA. The purpose of generating this dataset was to understand: (1) the total flux of water being used by trees in the watershed, (2) separate the component of transpiration that was derived from recent precipitation vs. older water such as winter precipitation of groundwater and (3) understand how total water use and water sources for the trees varies between species and position on a hillslope. The data were collected from May 2019 until October 2022. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm hr-1 and as mm hr -1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. The xylem water isotope data were collected approximately weekly from each of the trees instrumented with sap flux. The stems were collected and the water extracted using classic cryogenic methods. Isotope measurements were done using a Picarro 2140i analyzer. We provide an estimate of the Seasonal Origin Index for each measurements following Allen et al., 2019 (10.5194/hess-23-1199-2019) where values of -1 equate to trees relying on winter precipitation and +1 tree relying on summer precipitation. We also provide the simultaneous sap flux for each isotope measurement when this data was available. Please note this is an update to an earlier data repository with the same name that only included 2019 data. The new dataset was posted in January 2023 and is inclusive of the original 2019 data but now includes 2021 and 2022 data.

Data for: Pollinator and habitat-mediated selection as potential contributors to ecological speciation in two closely related species - Madison Crowell; Diane R. Campbell; J. M. Powers (other, 2023)
In ecological speciation, incipient species diverge due to natural selection that is ecologically based. In flowering plants, different pollinators could mediate that selection (pollinator-mediated divergent selection) or other features of the environment that differ between habitats of two species could do so (environment-mediated divergent selection). Although these mechanisms are well understood, they have received little rigorous testing, as few studies of divergent selection across sites of closely related species include both floral traits that influence pollination and vegetative traits that influence survival. This study employed common gardens in sites of the two parental species and a hybrid site, each containing advanced generation hybrids along with the parental species, to test these forms of ecological speciation in plants of the genus Ipomopsis . Three vegetative traits (specific leaf area, leaf trichomes, and photosynthetic water-use efficiency) and five floral traits (corolla length and width, anther insertion, petal color, nectar production) were analyzed for impacts on fitness components (survival to flowering and seeds per flower, respectively). These traits exhibited strong clines across the elevational gradient in the hybrid zone, with narrower clines in theory reflecting stronger selection or higher genetic variance. Plants with long corollas and inserted anthers had higher seeds per flower at the I. tenuituba site, whereas selection favored the reverse condition at the I. aggregata site, a signature of divergent selection. In contrast, no divergent selection due to variation in survival was detected on any vegetative trait. Selection within the hybrid zone most closely resembled selection within the I. aggregata site. Across traits, the strength of divergent selection was not significantly correlated with width of the cline, which was better predicted by evolvability (standardized genetic variance). These results support the role of pollinator-mediated divergent selection in ecological speciation and illustrate the importance of genetic variance in determining divergence across hybrid zones.

Air Temperature Growing Degree-days Early Season Standard Deviation (2002-2022) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the standard deviation of annual values computed from 2002 to 2021. Annual estimates are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D032')  metadata <- sdp_get_metadata('R4D032')    For more information about rSDP, visit the package homepage.

A pathogen's spatial range is not constrained by geographical features in the flax rust pathosystem - Juliana Jiranek; Keenan Duggal; Jessica L. Metcalf (other, 2023)
In this study, we performed several transect surveys over the course of the 2021 summer field season to assess potential ecogeographical range determinants for Lewis flax ( Linum lewisii ) and its pathogen, flax rust ( Melamspora lini ), in the area surrounding the Rocky Mountain Biological Laboratory in Gothic, Colorado. Additionally, we used generalized additive models to examine the effects of host population density and metapopulation structure on disease presence and prevalence.

Snowpack Proportional Reduction in Freezing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of snow on growing season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celsius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates than freezing degree days (FDD). This map represents the proportional reduction in freezing potential from 2002 to 2021 using the formula (FDD - SFFDD) / FDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D051')  metadata <- sdp_get_metadata('R4D051')    For more information about rSDP, visit the package homepage.

Data from: Individual life histories: Neither slow nor fast, just diverse - Sandra Hamel; F. Guillaume Blanchet; Marcel E. Visser; Céline Teplitsky; Fanie Pelletier; James A. Mills; Bernt-Erik Saether; Martijn van de Pol; Jean-Michel Gaillard; Christophe Barbraud; John Yarrall; Karine Delord; et al. (other, 2023)
The slow-fast continuum is known to structure variation in life-history strategies across species. Within populations, it is also assumed to structure individual life histories, yet evidence of its existence remains unclear. We formally assessed the presence of a slow-fast continuum of life histories both within populations and across species using detailed individual-based data for 17 bird and mammal species with contrasting life histories. We estimated adult lifespan, age at first reproduction, breeding frequency and fecundity, and identified the main axes of variation using Principal Component Analyses. The slow-fast continuum was the main axis of life-history variation across species, but within populations individual variation did not follow the slow-fast continuum in any species. This suggests that individual life histories are neither slow nor fast, but rather follow an idiosyncratic pattern across species because of relative differences in the importance of processes such as stochasticity, density dependence, and variation in individual performance.

Pre-dispersal seed predation obscures the detrimental effect of dust on wildflower reproduction - fruit data - Price; Mary V. (other, 2023)
Premise of the Research. Seed production by flowering plants depends on abiotic and biotic factors whose interacting effects may be hidden. We previously reported that exposure to dust from unpaved roads reduced the average amount of pollen on flowers of Ipomopsis aggregata, but did not consistently reduce mean seed set per fruit. Here we explore one possible explanation—that the expected detrimental effect of lower pollen loads on seed production was obscured because dust reduces not only pollination success but also attack by a pre-dispersal seed predator. Methodology. Over three consecutive summers we scored the fates of over 4,000 flowers, comparing those on hand-dusted I. aggregata plants to those on clean control plants in the same natural populations. We censused plants throughout each flowering season, marking phenological cohorts of flowers and examining them for eggs of the Anthomyiid fly Hylemya sp. We subsequently scored expanded fruits for seed loss to Hylemya larvae. Pivotal Results. Control plants consistently had higher egg loads and rates of seed loss to larvae than dusty plants. Furthermore, most eggs appeared early in the season when flowers are most likely to set fruit and ovule numbers are highest. In agreement with earlier studies, unparasitized fruits from dusty plants did not contain fewer seeds than fruits from control plants. But when we simulated a system without Hylemya by assigning each parasitized fruit the average number of seeds for unparasitized flowers in the same phenological cohort, control plants out-produced dusty plants. Conclusions. Pre-dispersal seed predation likely contributed to the previous lack of a negative effect of dust on seed set in I. aggregata. This study illustrates how exploring unexpected experimental outcomes can reveal important ecological interactions, and how the mechanistic understanding so gained can improve the ability to generalize results to other systems.

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

Snowpack Proportional Reduction in Early Season Freezing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of spring snow on early season freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celsius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates than freezing degree days (FDD). This map represents the proportional reduction in early season freezing potential from 2002 to 2021 using the formula (FDD - SFFDD) / FDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D052')  metadata <- sdp_get_metadata('R4D052')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days Annual Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D035')  metadata <- sdp_get_metadata('R4D035')    For more information about rSDP, visit the package homepage.

Snowpack Proportional Reduction in Early Season Growing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) in spring and early summer for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free growing degree-days (SFGDD) were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celsius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of growing potential for snow-dominated climates than growing degree days (GDD). This map represents the proportional reduction in growing potential from 2002 to 2021 using the formula (GDD - SFGDD) / GDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D055')  metadata <- sdp_get_metadata('R4D055')    For more information about rSDP, visit the package homepage.

Air Temperature Freezing Degree-days Late Season Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of fall accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D027')  metadata <- sdp_get_metadata('R4D027')    For more information about rSDP, visit the package homepage.

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

bsu-wrudisill/NRCSSnotel-Downloader: 1st Release - Wrudisill; William Rudisill (other, 2023)
1st release

bsu-wrudisill/precipitation_inference_publish: pre-release - Wrudisill; William Rudisill (other, 2023)
pre-release

bsu-wrudisill/WRF-Run: first release pt 2 - William Rudisill (other, 2023)
"Experimental" scripts to run WRF in a variety of configurations. Designed to facilitate experimenting with run settings, boundary/initial conditions, etc.

Snow-free Growing Degree-days Annual Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D043')  metadata <- sdp_get_metadata('R4D043')    For more information about rSDP, visit the package homepage.

Continuous sensor suite measurements of weather, energy, soil and flux at four PumpHouse sites at the East River, Colorado. - Yuxin Wu; Chunwei Chou; Luca Peruzzo; Baptiste Dafflon (ess_dive, 2023)
The purpose of this package is to monitor energy and water fluxes using a comprehensive set of sensors integrated on a tripod set up at 4 locations at the East River watershed in Colorado in the Upper Colorado River Basin as part of the Scientific Focus Area (SFA) research led by the Lawrence Berkeley National Lab (LBNL). This is a continuous sensor suite measurements of weather, energy, soil and flux at four East River pumphouse sites (ER-PHS1, ER-PHS2, ER-PHS3 and ER-PHS4) include: (1) soil CO2 concentration, CO2 flux and heat flux from soil surface; (2) photosynthetic active radiation flux at 1 m; (3) incoming/outcoming short and long wave radiation at 1 m, and user calculated net short and long wave radiation; (4) oxygen concentration and surface infrared temperature; (5) soil volumetric water content, temperature, and electrical conductivity at 10, 30, 60, 115 cm depth 115 cm depth; (6) soil matric potential and temperature at 30 cm depth; (7) incoming short wave radiation at 3 m height; (8) rainfall precipitation; (9) lightning strikes and strike distances; (10) wind direction and speed, maximum wind gust at 3 m; (11) air temperature, vapor pressure and barometric pressure at 3 m; (12) X _axis_level and Y_axis_level_(degree), tilt of the ATMOS 41 sensor; (13) maximum rainfall rate; (14) relative humidity sensor temperature at 3 m; (15) internally calculated vapor pressure deficit at 3 m with vapor pressure, relative humidity and sensor temperature measurements; (16) user calculated vapor pressure.These datasets contain data from Oct 11 2019 to March 20, 2020, and new datasets will be updated on annual basis. Files uploaded in Excel and Word, with copies in csv and pdf format.

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

Air Temperature Freezing Degree-days Early Season Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of spring accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the mean of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D025')  metadata <- sdp_get_metadata('R4D025')    For more information about rSDP, visit the package homepage.

Data from: Two-year bee, or not two-year bee? How voltinism is affected by temperature and season length in a high-elevation solitary bee - Regan Cross; Paul J. CaraDonna; Jessica R. K. Forrest (other, 2023)
Organisms must often make developmental decisions without complete information about future conditions. This uncertainty—for example, about the duration of conditions favorable for growth—can favor bet-hedging strategies. Here, we investigated the causes of life-cycle variation in Osmia iridis, a bee exhibiting a possible bet-hedging strategy with co-occurring one- and two-year life cycles. One-year bees reach adulthood quickly but die if they fail to complete pupation before winter; two-year bees adopt a low-risk, low-reward strategy of postponing pupation until the second summer. We reared larval bees in incubators in various experimental conditions and found that warmer—but not longer—summers, and early birth dates, increased the frequency of one-year life cycles. Using in situ temperature measurements and developmental trajectories of laboratory- and field-reared bees, we estimated degree-days required to reach adulthood in a single year. Local long-term (1950–2015) climate records reveal that this heat requirement is met in only ~7% of summers, suggesting that the observed distribution of life cycles is adaptive. Warming summers will likely decrease average generation times in these populations. Nevertheless, survival of bees attempting one-year life cycles—particularly those developing from late-laid eggs—will be <100%; consequently, we expect the life-cycle polymorphism to persist.

Minimum 2m Air Temperature Monthly Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of monthly averages of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These data were derived from daily maps, which are also available. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D008')  metadata <- sdp_get_metadata('R4D008')    For more information about rSDP, visit the package homepage.

Colorado Elevation Gradient Snowmelt Manipulation Plant Phenology 2017-2018. - Chelsea Wilmer; Heidi Steltzer (other, 2023)
This dataset includes observations of plant phenology at five locations along an elevation gradient in Crested Butte, Colorado. Observations occurred over the years 2017 and 2018. Observations taken in 2018 are associated with an experimental early snowmelt manipulation. The file PhenologyData_complete.txt contains raw data of phenology observations for each year, at each site, for each species within 1mX1m subplots. The file Phenology Data Package.docx contains information about sites, plots, and plant phenophases. These observations were taken to assess the temporal impact of early snowmelt perturbations on plant growth in the East River Valley. These data can be used to inform when and where plants may be using water resources throughout the season and how the phenology of plant communities in the watershed may respond to early snowmelt.

Mammalian herbivores restrict the altitudinal range limits of three alpine grass species - Tom E. X. Miller; Jennifer A. Rudgers; Joshua S. Lynn (other, 2023)
Though rarely experimentally tested, biotic interactions have long been hypothesized to limit low-elevation range boundaries of species. We tested the effects of herbivory on three alpine-restricted plant species by transplanting plants below (novel), at the edge (limit), or in the center (core) of their current elevational range and factorially fencing-out above- and belowground mammals in the West Elk Mountains, Colorado, USA from 2015-2018. Herbivore damage was greater in range limit and novel habitats than in range cores. Exclosures increased plant biomass and reproduction more in novel habitats than in range cores, suggesting demographic costs of novel interactions with herbivores. We then used demographic models to project population growth rates, which increased 5-20% more under herbivore exclosure at range limit and novel sites than in core habitats. Our results identify mammalian herbivores as key drivers of the low-elevation range limits of alpine plants and indicate that upward encroachment of herbivores could trigger local extinctions by depressing plant population growth.

Snow-free Growing Degree-days Annual Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D044')  metadata <- sdp_get_metadata('R4D044')    For more information about rSDP, visit the package homepage.

Snow-free Growing Degree-days 0-60 Days Post Snow Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated in the 60 days after the dissapearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute SFGDD, daily estimates of temperatures above a base of 5 degrees Celcius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the standard deviation of annual values computed from 2002 to 2021. Estimates from individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D050')  metadata <- sdp_get_metadata('R4D050')    For more information about rSDP, visit the package homepage.

Dynamically Downscaled (WRF) 1km, Hourly Meteorological Conditions 1987-2020. East/Taylor Watersheds. - Caroline Nash; Alejandro Flores; Allison N. Vincent; William Rudisill (other, 2023)
This dataset contains meteorological output from the Weather Research and Forecasting (WRF) version 3.8.1. This dataset has been created to 1) investigate hydrometeorological processes impacting the East River and water-delivery to the Critical Zone, and 2) provide meteorological forcing data for distributed Earth-science modeling applications in the East River watershed. Variables have a 1 kilometer spatial resolution and hourly temporal resolution and encompass a rectangular region encompassing the East and Taylor River watersheds, Colorado, near the town of Crested Butte. WRF was forced using Climate Forecast System Reanalysis (CFSR) lateral boundary conditions. Each .zip file contains one "water year" of data (October 1 -- September 30; i.e. water year 2017 starts October 1, 2016 and ends September 30, 2017). Each zip folder contains 12 netcdf (.nc) files containing one month of hourly data each and are approximately 250mb. Model timestamps are in UTC time. The files contain the following data variables: EAST_MASK: binary mask of the watershed region TAYLOR_MASK: binary mask of the watershed region GLW downwelling longwave radiation (w/m2) HR_PRCP: Hourly Precipitation Rate (mm/hr). Includes all hydrometeors (solid+liquid). HFX NoahMP LSM total grid-cell modelled sensible heat flux (w/m2) [positive towards atmosphere] LH NoahMP LSM total grid-cell modelled latent heat flux (w/m2) [positive towards atmosphere; can be converted to ET] PSFC Surface Barometric Pressure (hPa) Q2 Two-meter specific humidity (kg/kg) SWDOWN Downwelling shortwave solar radiation (w/m2) SWNORM Terrain-normal downwelling shortwave radiation (w/m2) T2 Two-meter air temperature (deg K) U10 10-m U-component of wind velocity (m/s) V10 10m V-component of wind velocity (m/s) XLAT Latitude of grid-center point XLONG Longitude of grid-center point XTIME Model timestamp, **in UTC**

BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 o N,106.996855 o W) and Kettle Ponds (38.942005 o N,106.973006 o W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 19th, 2022. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows. uas_L2_yyyymmdd_hhmmss.zip where L2 = Level 2 data yyyymmdd = year,month,day hhmmss = hour,minute,second Time is the flight start time in UTC. The NetCDF file format contained in the zip files has a similar format to the zip files with convention uas_<var>_L2_yyyymmdd_hhmmss.nc where <var> = vsm, dem, ndvi, or stmp vsm = volumetric soil moisture dem = digital elevation ndvi = normalized difference vegetation index stmp = surface temperature Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.

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

Snowpack Proportional Reduction in Late Season Growing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) in late summer and fall for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free growing degree-days (SFGDD) were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute SFGDD, daily estimates of temperatures above a base of 5 degrees Celsius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of growing potential for snow-dominated climates than growing degree days (GDD). This map represents the proportional reduction in growing potential from 2002 to 2021 using the formula (GDD - SFGDD) / GDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D056')  metadata <- sdp_get_metadata('R4D056')    For more information about rSDP, visit the package homepage.

Snowpack Persistence Day of Year Standard Deviation (1993-2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of interannual variability in the day of year (i.e., "Julian Day") of the persistence of the seasonal snowpack.  Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the standard deviation of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D062')  metadata <- sdp_get_metadata('R4D062')    For more information about rSDP, visit the package homepage.

Snow-free Freezing Degree-days 0-60 Days Post Snow Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated in the 60 days after the dissapearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates from individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D042')  metadata <- sdp_get_metadata('R4D042')    For more information about rSDP, visit the package homepage.

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

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0. December 2023 update: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.

Maximum 2m Air Temperature Daily Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D004')  metadata <- sdp_get_metadata('R4D004')    For more information about rSDP, visit the package homepage.

Manual soil CO2 flux, temperature and water content measurements from the Snodgrass and Copper Creek elevation transect (other, 2023)
The manual_soil_measurements_2022_2023.csv data set contains all of the manually measured soil CO 2 efflux, volumetric water content and soil temperature during field visits to the Rocky Mountain Biological Laboratory. In 2022, 16 PVC collars were installed at each site, there are two sets of collars, (n=4) PVC collars that are ~20” in diameter and placed in a 1x1 m square around the soil pit (Collar ID’s 1,2,3,4). The remaining collars (n=12, diameter= 4”) are placed within the forest type at each site, with four transects of three collars spaced at 3, 6 and 10m from the soil pit. For both types of collars, the soil CO 2 flux is measured using Flux Puppy, a field-portable system built around the Li-Cor 800 series IRGA (Carbone et al. 2019) Lids are placed on the collars and CO 2 is allowed to accumulate for 4 minutes (Large collars) and 3 minutes in (small collars). The flux is calculated by extracting the slope from the CO 2 accumulation curve using methods developed by (Trumbore lab).The large collars measurements were taken first, and lids were left on to accumulate CO 2 for radiocarbon samples. The small chambers were measured up to three times between 10:00-16:00 hr. Manual measurements were collected during four site visits in 2022, and one in 2023. Each soil CO 2 flux is paired with manual vwc (Campell scientific Hydrosense) and soil temperature (Thermofisher) measurements at the collars. Vwc measurements were take 3 per collar to account for spatial heterogeneity around each collar. Soil temperature when taken, was taken during the flux measurements. Soil temp1 was taken during the first flux measurement at that collar, soil temp 2 and the second etc..Carbone, M. S., B. Seyednasrollah, T. T. Rademacher, D. Basler, J. M. Le Moine, S. Beals, J. Beasley, A. Greene, J. Kelroy, and A. D. Richardson. 2019. Flux Puppy – An open-source software application and portable system design for low-cost manual measurements of CO2 and H2O fluxes. Agricultural and Forest Meteorology 274:1–6.

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

2018 Meander C floodplain groundwater chemistry from the East River Watershed, Colorado. - Marco Keiluweit; P. S. Nico; Craig R. Anderson; Patricia Fox; Christian Dewey (other, 2023)
This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Floodplain piezometers installed along two transects across Meander C in the Pumphouse vicinity (MCP and MCB wells) were sampled on daily to weekly time scales during summer-fall 2018. Some river water grab samples are also included. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], dissolved oxygen [DO], Fe(II), and groundwater level) along with laboratory measurements (dissolved inorganic carbon [DIC], dissolved organic carbon [DOC], metals and major cations, anions [chloride, sulfate, nitrate], and dissolved ammonium). Five files are included in this dataset, including: sample locations and depths in both a kmz file which can be opened in Google Earth and a csv file, sampling protocol as a pdf document, aqueous geochemistry data in a csv file, and analytical detection limits in a csv file. Keywords CATEGORICAL:NONE

QA/QC of the East River, Colorado, discharge and geochemical time series datasets (Almont, BCC, and Pump House) to be used for modeling of hydrogeochemical balance. - Boris Faybishenko; Patricia Fox (other, 2023)
The following datasets were QA/QC-ed (Quality Assurance/Quality Control): 1. Brush Creek Confluence (BCC) discharge data (from Helen Malenda, USGS, Colorado School of Mines), which were calculated using the pressure transducer data and rating curves. The original 15 min time series data were presented as mean daily discharge. 2. Almont discharge data from United States Geological Survey (USGS). The original data were in 15 min time intervals, and were averaged to mean daily discharge time series. 3. Pump House discharge data as mean daily discharge (downloaded from the SFA portal). 4. BCC and Pump House chemistry data from SFA data portal and/or original spreadsheets provided by Roelof Versteeg. The following challenging QA/QC problems of the datasets were resolved: Missing data with the duration of gaps up to >1 month; Duplicated dates; Anomalies and outliers of discharge and concentrations; Time stamps of measurements of the discharge and concentrations are not aligned (hydrogeochemical balance calculations require the timestamps to be aligned). All QA/QC-ed datasets are given as csv files. The csv files were prepared using the xts files with multiple worksheets, which are also included in the data packages. Figures of the QA/QC-ed datasets are given in the jpeg and pdf formats. The QA/QC-ed datasets have been used to quantify discharge and chemical concentrations in river water in order to understand riverine exports of water and dissolved constituents in the East River watershed. These datasets served as a basis in the presentation given by P. Fox et al. at the 2021 Goldschmidt Conference.

Total metals, carbon, nitrogen & anion concentration data; Slate River & East River floodplains, Crested Butte, CO; May 2022-October 2022 - Samuel Pierce (ess_dive, 2023)
This data package includes processed and undiluted measurements for metal, total carbon, total nitrogen, and anion concentrations from pore water (groundwater) and surface water samples from the Slate River and East River floodplains of Crested Butte, CO, focus field sites for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? Samples were collected between May and October of 2022. These measurements were all recorded at the Arizona Laboratory for Emerging Contaminants (ALEC) at the University of Arizona located in Tucson, AZ. Groundwater samples were extracted from a network of installed rhizon (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the river floodplain. All water samples were shaded from sun exposure during extraction from the subsurface and preserved at 4C until measured at ALEC. Analysis by ICP-MS (metals): Measurements for total metals were made on the Agilent 7700x ICP-MS (for total metals) – Agilent Technologies, Santa Clara, CA. The analytical QA/QC protocol was adapted from US EPA Method 200.8 for analysis by ICP-MS. Calibration standards were prepared from multi-element stock solutions (SPEX Certiprep, Metuchen, NJ). Calibration curves include at least 7 points with correlation coefficients > 0.995. The QC protocol includes a continuing calibration blank (CCB), a continuing calibration verification (CCV) solution and at least one quality control sample (QCS) to be analyzed just after calibration and again after every 12 samples and at the completion of the run. The QCS solutions are from an independent source, such as NIST SRM 1643e - Trace elements in water, or QCS solutions from High Purity Standards (Charleston, SC).  Acceptable QC responses must be between 90 and 110% of the certified value. Lastly, a suitable internal standard (usually Rh, In, Ga or Ge) is added using on-line addition into the sample line and mixing tee. Analysis by Shimadzu TOC-L (TOC/TN): The TOC-L system is a combustion technique where liquid samples are injected and combusted into CO2 for carbon detection by non-dispersive infrared (NDIR) and NO for detection by chemiluminescence. A calibration curve using five standard solutions between 0.1 and 7 ppm for carbon and 0.05 and 3.5 ppm for nitrogen is made for each type of measurement with a linearity >0.99. All samples, standards, and QC’s are prepared in 24mL scintillation vials that have been baked for 4hrs at 475 Cº and made using RO water (18.2mΩ). QC’s include a calibration blank check (CCB), continuing calibration check (CCC), and a certified reference material check (CRM). All QC’s are within ±10% error and are run before and after each batch of samples. Samples are diluted and rerun if any measurement concentrations are above the highest standard. Analysis by Ion Chromatography (Anions): The instrument used is the Thermo Scientific Dionex ICS-6000 using AS+AG22 column set for anion analysis with sodium carbonate eluent. A calibration curve using five standard solutions between 5 and 250 umol/L is made with a linearity >0.99. Standards and QC’s are prepared in 15mL polypropylene conical tubes, pipetted along with the samples into 1.5mL polypropylene vials. Dilutions are made using RO water (18.2mΩ). QC’s include a calibration blank check (CCB), continuing calibration check (CCC), and a certified reference material check (CRM). All QC’s are within ±10% error and are run before and after each batch of samples. Samples are diluted and rerun if any measurement concentrations are above the highest standard. All files are in csv format.

Soil water content, matric potential, carbon dioxide and oxygen concentrations, Oct 2018-Dec 2021, Slate River Floodplain, Crested Butte, Colorado - Katharine Maher; Zach Perzan; John R. Bargar; Kristin Boye (other, 2023)
This data package includes a time series of soil sensor data (temperature, water content, bulk electrical conductivity, porewater dissolved oxygen and porewater dissolved carbon dioxide) in a vertical profile from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package includes: (1) soil temperature, volumetric water content and electrical conductivity at 40, 60 and 82.5 cm depth; (2) soil matric potential at 40, 60, 79 and 100 cm depth; (3) soil CO2 concentrations at 40, 60 and 82.5 cm depth; and (4) soil oxygen concentrations at 60, 82.5, 100, 135, 170 and 182 cm depth. Both the carbon dioxide and oxygen sensors are optical sensors that can measure the partial pressure of oxygen in both saturated and unsaturated conditions. Unfortunately, soil CO2 in the profile is unexpectedly high and above the sensor calibration range (0-25,000 ppm). In addition, soil CO2 sensors failed within a year of deployment, so we only report CO2 data from 2019-2020.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 4 "*data.csv" files, one for each calendar year in the dataset. Each "*data.csv" file has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding for each measured parameter. Because sensors have been added over time, not every sensor has data dating back to Oct 2018. Note that there is a data gap over winter 2019-2020 due to a power outage. While this repository currently only contains data through December 2021, the dataset will be updated as additional years are collected and processed.

Maximum 2m Air Temperature Monthly Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of monthly averages of daily maximum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These data were derived from daily maps, which are also available. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D007')  metadata <- sdp_get_metadata('R4D007')    For more information about rSDP, visit the package homepage.

Atmospheric Surface Flux Station #50 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023 - Erik Hulm; Samuel B. Morris; Gijs de Boer; Brian Butterworth; Jackson Osborn; Jonathan Hamilton; Michael Gallagher; Benjamin Schmatz; Matthew Shupe; Janet Intrieri; Christopher Cox (other, 2023)
Raw (Level 1) measurements 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 comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties. These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.

Snow-free Freezing Degree-days Late Season Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated fall snow-free freezing potential (snow-free freezing degree days, SFFDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated from day-of-year 197 to day-of-year 305  (July 16th to November 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. These maps represent annual values computed from 2002 to 2022. 20-year means (2002-2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D017')  metadata <- sdp_get_metadata('R4D017')    For more information about rSDP, visit the package homepage.

Humpback chub (Gila cypha) capture histories and growth data for two areas in the Colorado River network from 2009-2022 and 2017-2022 - Charles B. Yackulic (other, 2023)
These data were compiled for a manuscript entitled 'Vital rates of a burgeoning population of Humpback Chub in western Grand Canyon. Objective(s) of our study were to compare survival and growth of humpback chub in western Grand Canyon to that from the more established metapopulation in eastern Grand Canyon that spawns in the Little Colorado River (LCR). These data represent capture histories of fish based on mark-recapture data. These data were collected in eastern Grand Canyon (CR-east study site) and in the western Grand Canyon (CR-west study site). The CR-east reach sites are located at the lower 13.56 rkm of the LCR and in the mainstem of the Colorado River at river kilometers (rkm) 126.2-128.7 (2009-2016) and was expanded to [...]

Meteorological, reference evapotranspiration and estimated transpiration data, July 2020-Dec 2021, Slate River Floodplain, Crested Butte, Colorado - Katharine Maher; Zach Perzan; John R. Bargar; Kristin Boye (other, 2023)
This data package includes a time series of meteorological data (air temperature, relative humidity, barometric pressure, precipitation, wind speed, soil heat flux, incoming shortwave radiation, incoming longwave radiation, outgoing shortwave radiation and outgoing longwave radiation) from the Slate River floodplain outside Crested Butte, Colorado, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The package also includes reference evapotranspiration calculated via the Penman-Monteith equation and an estimate of willow (Salix spp.) evapotranspiration using the method of Irmak et al., J. Hydrology, 2013. Due to two malfunctioning sensors (incoming shortwave radiation from Oct 2020-May 2021 and incoming longwave radiation from May 2021-March 2022), observed net radiation and calculated evapotranspiration are only included from July-October 2020. However, users may be able to extend calculated evapotranspiration using estimates of these two parameters from other sources. Both sensors have been replaced and data beyond 2021 be uploaded to this repository when it is available.Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 3 "*data.csv" files, two for meteorological data for 2020 and 2021 and one for reference evapotranspiration and transpiration data from 2020 ("SLT_OBJ2_PenmanMonteith_2020_data.csv"). Each "*data.csv" file of meteorological data has a corresponding "*_InstallationMethods.csv" file that describes the location, sensor model, sensor serial number and other metadata corresponding to each measured parameter. We also include a Jupyter notebook ("PenmanMonteithET_Calculations.ipynb") that details the assumptions and equations used to calculate daily and hourly reference evapotranspiration and daily willow transpiration from raw meteorological data.

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

Specific conductivity, pH, dissolved oxygen, water temperature and alkalinity in-situ data; Slate River floodplain, Crested Butte, CO; March 2021-October 2021. (ess_dive, 2023)
This data package includes a time-series of field measurements from March to October 2021 in groundwater and surface water from the Slate River floodplain in Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data package includes 5 data files, one for each measured variable: specific conductivity, pH, dissolved oxygen, water temperature and alkalinity. All measurements were recorded in the field immediately after water sampling. Groundwater samples were extracted from a network of installed rhizons (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the floodplain. In addition to the data files, there is a terminology file explaining the terms used, a file level metadata file, and a sensor file with metadata about the sensors used. All files are in csv format.

Groundwater level elevation and temperature data, Oct 2018-Dec 2021, Slate River Floodplain, Crested Butte, CO. (ess_dive, 2023)
This data package includes a time series of water level and temperature measurements from October 2018 to December 2021 in groundwater and surface water from the Slate River floodplain outside Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? The data were recorded by pressure transducers were installed in four types of piezometers: 1) a network of gravel bed ("GB") drive-point piezometers with a 6-inch screen interval installed all at ~330 cm below ground surface. 2) a network of piezometers screened across the water table, used to measure evapotranspiration ("ET") using the White method. Each of these piezometers is screened along almost its entire length. 3) a suite nested piezometers used the measure the vertical hydraulic gradient ("VHG") across the fines-cobble interface. Each of these piezometers uses a 6-inch screen length. 4) a group of piezometers scattered across the boundaries of the floodplain, used to monitor boundary conditions ("BC") flowing into and out of the floodplain. With the exception of "SR-BD-WT", each of these piezometers is screened along its entire length. Within the data package, "FLMD.csv" describes file-level metadata and "dd.csv" defines column headers and universal terms across the dataset. The data package includes 11 "*data.csv" files, one for each piezometer type for each calendar year. Because piezometers have been added over time, not every sensor has data dating back to Oct 2018. Each "*data.csv" file has a corresponding "*_InstallationMethods.csv" file that describes the location, elevation, screen depth, sediment type and sensor metadata for each piezometer and pressure transducer.

Snow-free Growing Degree-days Early Season Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated spring snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days that were estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates for individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D045')  metadata <- sdp_get_metadata('R4D045')    For more information about rSDP, visit the package homepage.

Air Temperature Growing Degree-days Late Season Mean (2002-2022) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the mean of annual values computed from 2002 to 2021. Annual estimates are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D033')  metadata <- sdp_get_metadata('R4D033')    For more information about rSDP, visit the package homepage.

Cation Data for the East River Watershed, Colorado (2014-2021) (ess_dive, 2023)
This data package contains mean values for cation concentration for water samples taken from the East River Watershed in Colorado. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of 37 elements of interest simultaneously for the East River Watershed, Colorado groundwater and surface water samples to inform insights on the biogeochemistry processes within the watershed. The East River is part of the Watershed Function Scientific Focus Area (WFSFA) located in the Upper Colorado River Basin, United States. Inductively coupled plasma mass spectrometry (ICP-MS) has been used to measure the concentrations of 37 elements of interest simultaneously for Rifle and East River Watershed, Colorado groundwater and surface water samples. The instrumentation, Elan DRC II, PerkinElmer SCIEX, automatically switches among the three models necessary to analyze all 37 elements. These 37 elements include: (1) Lithium (Li), Beryllium (Be), Boron (B), Soldium (Na), Magnesium (Mg), Aluminium (Al), Silicon (Si), Phosphorus (P), Titanium (Ti), Cobalt (Co), Nickel (Ni), Copper (Cu), Zinc (Zn), Germanium (Ge), Arsenic (As), Rubidium (Rb), Strontium (Sr), Zirconium (Zr), Molybdenum (Mo), Silver (Ag), Cadmium (Cd), Tin (Sn), Antimony (Sb), Caesium (Cs), Barium (Ba), Europium (Eu), Lead (Pb), Thorium (Th), Uranium (U) using santadard model, argon Ar as reaction gas, (2) Potassium (K), Calcium (Ca), Vanadium (V), Chromium (Cr), Manganese (Mn), Iron (Fe) using dynamic reaction cell (DRC) model, ammonia NH3 as reaction gas, and (3) Phosphorus (P) and Selenium (Se) using DRC model, oxygen O2 as reaction gas. Note for the samples with higher concentrations of chloride (Cl-), asenic (As) concentrations were analysed with DRC model (oxygen O2 as reaction gas) to avoid the interference of chloride. All samples were prepared/diluted with 2% (v/v) ultrapure nitric acid in Milli-Q water (18.2 mega ohm-cm), and analyzed under a rigorous quality assurance and quality control (QA/QC) process. This data package contains (1) a zip file (cation_data_2014-2021.zip) containing a total of 4,517 files: 4,516 data files of cation data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (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 terms/column_headers used throughout the files along with a definition, units, and data type. There are a total of 99 locations containing isotope data. Update on 4/11/2021, Added Detemination of Method Detection Limits (MDLs) for ICP-MS document, which can be accessed as a PDF or with Microsoft Word. Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated cation data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) removed suffix and prefix on two variables (“aqberylliumion_asberyllium” and “aqlithiumion_aslithium”), (5) added -9999 for empty numerical cells, and (6) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv).

Air Temperature Growing Degree-days Late Season Standard Deviation (2002-2022) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in accumulated fall growing potential (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Temperature estimates represent conditions approximately 2m above the ground surface. Growing degree-days were accumulated from day-of-year 197 to day-of-year 305 (July 16th to November 1st in a non-leap year). To compute GDD, daily maximum temperatures above 5 degrees Celcius (minimal growth conditions for most plants) were summed across all focal days. This map represents the standard deviation of annual values computed from 2002 to 2021. Annual estimates are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D034')  metadata <- sdp_get_metadata('R4D034')    For more information about rSDP, visit the package homepage.

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

Stable Water Isotope Data for the East River Watershed, Colorado (2014-2021) (ess_dive, 2023)
The stable water isotope data for the East River Watershed, Colorado, consists of delta2H (hydrogen) and delta18O (oxygen) values from samples collected at multiple, long-term monitoring sites including streams, groundwater wells, springs, and a precipitation collector used to establish a local meteoric water line (LMWL) for the watershed. These locations represent important and/or unique end-member locations for which stable isotope values can be diagnostic of the connection between precipitation inputs as snow and rain and riverine export. Such locations include drainages underline entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of water isotope values from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on water export. Such data may be combined with stream gaging stations co-located at each surface water monitoring site to relate seasonal variations in water export to their stable isotopic signature. Data for liquid water delta2H and delta18O values are reported in units of parts per thousand (per-mil; ‰). This data package contains (1) a zip file (isotope_data_2014-2021.zip) containing a total of 69 files: 68 data files of isotope data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location and a locations.csv (1 file) with latitude and longitude for each location; (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 terms/column_headers used throughout the files along with a definition, units, and data type. There are a total of 34 locations containing isotope data. Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated isotope data for all locations up to 2021-12-31 and (2) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv).

Pre-dispersal seed predation obscures the detrimental effect of dust on wildflower reproduction - flowering and egg phenology - Mary V.; Price (other, 2023)
Premise of the Research.  Seed production by flowering plants depends on abiotic and biotic factors whose interacting effects may be hidden. We previously reported that exposure to dust from unpaved roads reduced the average amount of pollen on flowers of Ipomopsis aggregata, but did not consistently reduce mean seed set per fruit.  Here we explore one possible explanation—that the expected detrimental effect of lower pollen loads on seed production was obscured because dust reduces not only pollination success but also attack by a pre-dispersal seed predator.              Methodology.  Over three consecutive summers we scored the fates of over 4,000 flowers, comparing those on hand-dusted I. aggregata plants to those on clean control plants in the same natural populations.  We censused plants throughout each flowering season, marking phenological cohorts of flowers and examining them for eggs of the Anthomyiid fly Hylemya sp.  We subsequently scored expanded fruits for seed loss to Hylemya larvae.             Pivotal Results.  Control plants consistently had higher egg loads and rates of seed loss to larvae than dusty plants.  Furthermore, most eggs appeared early in the season when flowers are most likely to set fruit and ovule numbers are highest.  In agreement with earlier studies, unparasitized fruits from dusty plants did not contain fewer seeds than fruits from control plants.  But when we simulated a system without Hylemya by assigning each parasitized fruit the average number of seeds for unparasitized flowers in the same phenological cohort, control plants out-produced dusty plants.             Conclusions.  Pre-dispersal seed predation likely contributed to the previous lack of a negative effect of dust on seed set in I. aggregata.  This study illustrates how exploring unexpected experimental outcomes can reveal important ecological interactions, and how the mechanistic understanding so gained can improve the ability to generalize results to other systems.

Air Temperature Freezing Degree-days Early Season Standard Deviation (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of temporal variability in spring accumulated freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data. The original daily maps are also available. Freezing degree-days were accumulated from day-of-year 60 to day-of-year 196 (March 1st to July 15th in a non-leap year). To compute freezing degree days, temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days and multiplied by negative 1. This map represents the standard deviation of annual values computed from 2002 to 2021.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D026')  metadata <- sdp_get_metadata('R4D026')    For more information about rSDP, visit the package homepage.

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

Snow-free Freezing Degree-days 0-60 Days Post Snow Mean (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of accumulated snow-free freezing potential (freezing degree days, FDD) for the Upper Gunnison domain, derived from daily minimum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free freezing degree-days were accumulated in the 60 days after the dissapearance of seasonal snowpack.  In the absence of snow, the accumulation period started on day-of-year 60 (March 1st in a non-leap year). To compute freezing degree days, daily estimates of temperatures below -5 degrees Celcius (considered harmful to most plants) were summed across all days estimated to be free of snow cover and multiplied by negative 1. Because snowpack insulates the ground surface and soil from low temperatures, this estimate can be a more appropriate measure of freezing potential for snow-dominated climates. This map represents the mean of annual values computed from 2002 to 2021. Estimates from individual years are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D041')  metadata <- sdp_get_metadata('R4D041')    For more information about rSDP, visit the package homepage.

Snowpack Persistence Day of Year Yearly Timeseries - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the day of year (i.e. ,  "Julian Day") of the persistence of the seasonal snowpack.  Specifically these are estimates of the first day of bare ground derived from long-term time-series of Landsat TM,  ETM,  and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a heirarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022),  and greater than 90% of temporal variation across the full 1993 - 2022 time-series. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D001')  metadata <- sdp_get_metadata('R4D001')    For more information about rSDP, visit the package homepage.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 19th, 2022. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added which has flight data from 2022 and 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data and a mask was applied to the soil moisture retrieval that masks data over water surfaces.

Experimental test of the combined effects of water availability and flowering time on pollinator visitation and seed set - M. Kate Gallagher; Diane R. Campbell (other, 2023)
Climate change is likely to alter both flowering phenology and water availability for plants. Either of these changes alone can affect pollinator visitation and plant reproductive success. The relative impacts of phenology and water, and whether they interact in their impacts on plant reproductive success remain, however, largely unexplored. We manipulated flowering phenology and soil moisture in a factorial experiment with the subalpine perennial Mertensia ciliata (Boraginaceae). We examined responses of floral traits, floral abundance, pollinator visitation, and composition of visits by bumblebees vs. other pollinators. To determine the net effects on plant reproductive success, we also measured seed production and seed mass. Reduced water led to shorter, narrower flowers that produced less nectar. Late flowering plants produced fewer and shorter flowers. Both flowering phenology and water availability influenced pollination and reproductive success. Differences in flowering phenology had greater effects on pollinator visitation than did changes in water availability, but the reverse was true for seed production and mass, which were enhanced by greater water availability. The probability of receiving a flower visit declined over the season, coinciding with a decline in floral abundance in the arrays. Among plants receiving visits, both the visitation rate and percent of non-bumblebee visitors declined after the first week and remained low until the final week. We detected interactions of phenology and water on pollinator visitor composition, in which plants subject to drought were the only group to experience a late-season resurgence in visits by solitary bees and flies. Despite that interaction, net reproductive success measured as seed production responded additively to the two manipulations of water and phenology. Commonly observed declines in flower size and reward due to drought or shifts in phenology may not necessarily result in reduced plant reproductive success, which in M. ciliata responded more directly to water availability. The results highlight the need to go beyond studying single responses to climate changes, such as either phenology of a single species or how it experiences an abiotic factor, in order to understand how climate change may affect plant reproductive success.

Potentilla flowering phenology for Cabin Clearing, Elk Meadows and Rainbow Meadows, 2019. - Kelly A Carscadden (other, 2023)
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and (not included here) the Rocky Mountain Biological Laboratory (RMBL). Since 2018, I have been monitoring populations at Rainbow Meadows Lower, Rainbow Meadows Upper, Elk Meadows South, and Cabin Clearing - sites that vary in cinquefoil composition. In 2019, my team and I recorded reproductive phenophases (e.g., vegetative, buds, flowering, setting seed) on tagged plants in June, July, and August. When plants began flowering, we estimated floral counts. Characterizing flowering phenology of parent and hybrid species in each site sheds light on the potential for ongoing interbreeding in different environments. NWT phenological data is shared here, and soil moisture and trait data from demographic monitoring are provided separately. This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.

Snow-free Growing Degree-days Annual Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of annual accumulated snow-free growing potential (snow-free growing degree days, SFGDD) for the Upper Gunnison domain, derived from daily maximum air temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. The original daily maps are also available. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (the minimum growth threshold for many plants) were summed across all days estimated to be free of snow. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of the energy available for biological processes in snow-dominated climates. These maps represent annual values computed from 2002 to 2022. 20-year averages (2002 - 2021) are also available.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D019')  metadata <- sdp_get_metadata('R4D019')    For more information about rSDP, visit the package homepage.

Total metals, sulfur and organic carbon data; Slate River floodplain, Crested Butte, CO; March 2021-October 2021 (ess_dive, 2023)
This data package includes processed and undiluted measurements for metal, sulfur and organic carbon concentrations from pore water (groundwater) samples from the Slate River floodplain of Crested Butte, CO, a focus field site for the SLAC Floodplain Hydro-Biogeochemistry SFA. The data was generated as part of the work targeting the overarching research question for the SLAC SFA: How do ubiquitous subsurface interfaces mediate molecular-scale biogeochemical processes and groundwater quality in floodplains and watersheds? Samples were collected between March and October of 2021. These measurements were all recorded at the Environmental Measurements Facility (EM-1) at Stanford University in Stanford, CA. Groundwater samples were extracted from a network of installed rhizon (Rhizosphere Research Products, part no. 19.60.21F, 0.6 micrometer mesh size) and piezometer wells within the river floodplain. All water samples were shaded from sun exposure during extraction from the subsurface and preserved at 2C until measured at EM-1. Analysis by ICP-MS: Measurements for total metals were performed on an inductively coupled plasma optical emission spectrometer (ICP-OES; iCAP 6300, Thermo Scientific, Cambridge, U.K.). Calibration standards were prepared from the mulit element stock solution (Sigma-Aldrich Multielement standard for ICP, St. Louis, MO) using matrix matched to sample solutions (2% HNO3). Calibration curves included 5 points with correlation coefficients of >0.99. The QC protocol includes a continuing calibration blank and quality control samples that are analyzed just after calibration and again every 20 samples and at the completion of the run. Acceptable QC responses must be between 90-110% of the certified value. Analysis by Total Organic Carbon: Dissolved organic carbon concentrations were quantified on a total organic carbon (TOC) analyzer (TOC-L, Shimadzu, Kyoto, Japan) running the NPOC method. Standard curves were developed using an Organic Carbon Standard from RICCA Chemical Company (Arlington,TX). A series of 4 to 6 standards were automatically diluted by the instrument in a concentration range that spans that of the samples. A blank sample was run just after the calibration curve and at the end of the run. A QC sample was run every 25 samples. Acceptable QC responses must be between 90-110% of the certified value. All files are in csv format.

Plant phenology, aphid colony growth, and honeydew deposition data - Mooney; Emily (other, 2023)
Changing phenological cues can lead to trophic mismatch for plants and herbivores, and this often shifts herbivore feeding to plant stages of lower quality. Temperature can also mediate how herbivores respond to plant quality, leading to temperature-by-phenology interactions. We examined how both temperature and host plant phenology impact aphid abundance and their mutualism with ants. Our study system was composed of aphids (Aphis asclepiadis) that colonize flowering stalks of the host plant, Ligusticum porteri. Abundance of this aphid species is dependent on mutualism with several ant species. To understand how host plant phenology and temperature affect aphid abundance, we experimentally accelerated snow melt date by two weeks, which correspondingly advanced flowering phenology. Then, we factorially combined this phenology treatment with open top warming chambers surrounding aphid colonies. We tracked aphid colony growth and interactions with ants, and results showed the greatest colony growth at cooler, ambient temperatures on host plants without accelerated phenology. These colonies also showed the highest levels of honeydew deposition relative to their overall size. Our findings show that trophic mismatch decreases aphid abundance, and changes to the ant-aphid mutualism exacerbate this effect.

Facilitation strength across environmental and beneficiary trait gradients in stream communities - Benjamin Tumolo (other, 2023)
Ecosystem engineers modify habitats in ways that facilitate other community members by ameliorating harsh conditions. The strength of such facilitation is predicted to be influenced by both beneficiary traits and abiotic context. One key trait of animals that could control the strength of facilitation is beneficiary body size because it should determine how beneficiaries fit within and exploit stress ameliorating habitat modifications. However, few studies have measured how beneficiary body size relates to facilitation strength along environmental gradients. We examined how the strength of facilitation by net-spinning caddisflies on invertebrate communities in streams varied along an elevation gradient and based on traits of the invertebrate beneficiaries. We measured whether use of silk retreats as habitat concentrated invertebrate density and biomass compared to surrounding rock-surface habitat and whether the use of retreat habitat varied across body sizes of community members along the gradient. We found that retreats substantially concentrated the densities of a diversity of taxa including eight different Orders, and this effect was greatest at high elevations. Caddisfly retreats also concentrated invertebrate biomass more as elevation increased. Body size of invertebrates inhabiting retreats was lower than that of surrounding rock habitats at low elevation sites, however body size between retreats and rocks converged at higher elevation sites. Additionally, the body size of invertebrates found in retreats varied within and across taxa. Specifically, caddisfly retreats functioned as a potential nursery for taxa with large maximal body sizes. However, the patterns of this taxa-specific nursery effect were not influenced by elevation unlike the patterns observed based on community level body size.Collectively, our results indicate that invertebrates use retreats in earlier life stages or when they are smaller in body size independent of life stage. Furthermore, our analysis suggests that facilitation strength intensifies as elevation increases within stream invertebrate communities. Further consideration of how trait variation and environmental gradients interact to determine the strength and direction of biotic interactions will be important as species ranges and environmental conditions continue to shift.

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

Data from: Unraveling the ecological and evolutionary impacts of a plant invader on the pollination of a native plant - Wilnelia Recart; Diane R. Campbell (other, 2023)
Interactions between a native plant species and its pollinators, herbivores, or microbiome can be affected by the presence of non-native plant species. Non-native plant species are altering plant-pollinator interactions, yet we know little about how these non-native species influence natural selection. In addition, year-to-year variation in flowering could influence the impacts of non-native species on reproductive success in native plants and the strength and direction of pollinator-mediated selection. We examined whether the presence of the highly invasive plant Linaria vulgaris influenced average pollinator visitation, species composition of floral visitors, or pollinator-mediated selection in the native Penstemon strictus. In the field, we conducted small scale L. vulgaris inflorescence removals, that were repeated through three years. Pollinator-mediated selection on the floral trait of platform length was examined by determining the relationships between platform length and visitation, between visitation and seed production, and by calculating net selection based on seed production. We found that the presence of L. vulgaris on a small spatial scale facilitated pollinator visitation rates to P. strictus but did not influence pollinator-mediated selection on platform length. Pollinator visitation varied across years, as did the relationship between seed production and pollinator visitation, and the relationship between pollinator visitation and platform length. Although components of selection varied across years, no net selection on platform length was detected in any of the three years. Our results show how the presence of an invasive plant and year-to-year variation in plant-pollinator interactions affect the pollination and components of pollinator-mediated selection in native plants.

Anion Data for the East River Watershed, Colorado (2014-2022) (ess_dive, 2023)
The anion data for the East River Watershed, Colorado, consists of fluoride, chloride, sulfate, nitrate, and phosphate concentrations collected at multiple, long-term monitoring sites that include stream, groundwater, and spring sampling locations. These locations represent important and/or unique end-member locations for which solute concentrations can be diagnostic of the connection between terrestrial and aquatic systems. Such locations include drainages underlined entirely or largely by shale bedrock, land covered dominated by conifers, aspens, or meadows, and drainages impacted by historic mining activity and the presence of naturally mineralized rock. Developing a long-term record of solute concentrations from a diversity of environments is a critical component of quantifying the impacts of both climate change and discrete climate perturbations, such as drought, forest mortality, and wildfire, on the riverine export of multiple anionic species. Such data may be combined with stream gauging stations co-located at each monitoring site to directly quantify the seasonal and annual mass flux of these anionic species out of the watershed. This data package contains (1) a zip file (anion_data_2014-2022.zip) containing a total of 345 data files of anion data from across the Lawrence Berkeley National Laboratory (LBNL) Watershed Function Scientific Focus Area (SFA) which is reported in .csv files per location; (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 terms/column_headers used throughout the files along with a definition, units, and data type. Update on 6/10/2022: versioned updates to this dataset was made along with these changes: (1) updated anion data for all locations up to 2021-12-31, (2) removal of units from column headers in datafiles, (3) added row underneath headers to contain units of variables, (4) restructure of units to comply with CSV reporting format requirements, and (5) the addition of the file-level metadata (flmd.csv) and data dictionary (dd.csv) were added to comply with the File-Level Metadata Reporting Format. Update on 2022-09-09: Updates were made to reporting format specific files (file-level metadata and data dictionary) to correct swapped file names, add additional details on metadata descriptions on both files, add a header_row column to enable parsing, and add version number and date to file names (v2_20220909_flmd.csv and v2_20220909_dd.csv). Update on 2022-12-20: Updates were made to both the data files and reporting format specific files. Conversion issues affecting ER-PLM locations for anion data was resolved for the data files. Additionally, the flmd and dd files were updated to reflect the updated versions of these files. Available data was added up until 2022-03-14.

Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008-2019; daily temporal resolution; 400 m spatial resolution. - J. B. Brown; Dipankar Dwivedi; Carl I. Steefel; Utkarsh Mital (other, 2023)
This dataset provides gridded meteorological forcing data (specifically, daily precipitation and daily mean air temperature). The dataset has been generated by downscaling the Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset from a spatial resolution of 800 m to 400 m. The time period is 2008-2019, and the mapped area is East Taylor subbasin in Upper Colorado. The data are in the form of NetCDF files, arranged by year. Compared to the PRISM dataset, the dates of the downscaled dataset are shifted backwards by one (e.g., downscaled data for May 25 corresponds to PRISM data for May 26). This temporal shifting makes the meteorological forcing correspond more closely to the prescribed date. The NetCDF format is a standard raster format that can be read using any Geographic Information System (GIS) software; plenty of modules exist in popular scripting languages (such as Python, R and Matlab) that can also be used to read NetCDF files. East_Taylor_Tavg_PRISM400 corresponds to mean air temperature. East_Taylor_Precip_PRISM400.zip corresponds to precipitation. Dataset update on March 9, 2022: Uploaded new data files that are identical to the earlier files but are now in the NetCDF format, arranged by year. Earlier, the files were in the GeoTiff format, arranged by date.

Snowpack Duration Yearly Timeseries - Ian K Breckheimer (s3, 2023)
These maps represent annual estimates of the number of days of continuous seasonal snowpack from 1993 - 2022. The maps are derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat, and OLI imagery starting in 1993.  Annual estimates represent the number of days of continuous snow cover from the fall of the previous year to the spring of the current year. For example, the 1993 estimate is the number of days of snowpack cover from fall 1992 to spring 1993. This is often called a "water year" estimate. These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents annual estimates from 1993 - 2022. The 30-year mean is also available. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D003')  metadata <- sdp_get_metadata('R4D003')    For more information about rSDP, visit the package homepage.

Novel host plant unmasks heritable variation in plant preference within an insect population - Rachel A Steward; Rebecca S Epanchin-Niell; Carol L. Boggs (other, 2023)
Introductions of novel plant species can disturb the historical resource environment of herbivorous insects, resulting in strong selection to either adopt or exclude the novel host. However, an adaptive response depends on heritable genetic variation for preference or performance within the targeted herbivore population, and it is unclear how heritability of host-use preference may differ between novel and historical hosts. Pieris macdunnoughii butterflies in the Rocky Mountains lay eggs on the nonnative mustard Thlaspi arvense, which is lethal to their offspring. Heritability analyses revealed considerable sex-linked additive genetic variation in host preference within a population of this butterfly. This was contrary to general predictions about the genetic basis of preference variation, which are hypothesized to be sex-linked between populations but autosomal within populations. Evidence of sex-linkage disappeared when butterflies were tested on methanol-based chemical extracts, suggesting these chemicals in isolation may not be the primary driver of female choice among available host plants. Although unexpected, evidence for within-population sex-linked genetic variation in preference for T. arvense over native hosts indicates that persistent maladaptive oviposition on this lethal plant must be maintained by alternative evolutionary dynamics such as migration- or drift-selection balance or pleiotropic constraints.

Lead isotopic compositions of airborne particulates collected from snow and from dry deposition in the East River Watershed, Colorado and at the Southern Sierra Critical Zone Observatory, California. - Ugwumsinachi Nwosu; P. A. O'Day; J. N. Christensen (other, 2023)
The data set consists of lead (Pb) isotopic ratios (206Pb/207Pb and 208Pb/207Pb, as well as 206Pb/204Pb, 207Pb/204Pb, 208Pb/204Pb) for particulate samples collected from snow and dry deposition. This was part of a study (O’Day et al. 2020) that was looking at the speciation of phosphorous in splits of these samples to evaluate the flux and bio-availability of phosphorous to the mountain sites in the study. The Pb isotopic data allow sourcing of long-range transported Pb, specifically the fraction of Pb sourced in Asia, and so a comparison of the relative distances of PM sources between the high and low elevation sites. Samples were collected at the East River Watershed, CO (dry deposition collected over Sept.-Oct. 2016, show sampling winter 2017) and at the Southern Sierra Critical Zone Observatory (dry deposition collected over Sept-Nov 2016, July-Sept 2017, June-July 2017). The data includes Pb concentration data, as well as calculated fraction of the Pb sourced in Asia. This data set was published in part in O’Day et al. (2020) Phosphorus Speciation in Atmospherically Deposited Particulate Matter and Implications for Terrestrial Ecosystem Productivity. ES&T, https://dx.doi.org/10.1021/acs.est.9b06150

Herbarium-Derived Phenological Data in North America - Isaac Park; Charles Davis; Tadeo Hernan Ramirez-Parada; S. J. Mazer; Sydne Record (other, 2023)
We present infrastructure for developing large-scale and long-term phenological datasets across multiple herbaria, as well as a sample dataset that has been acquired from the digital archives of 440 distinct herbaria across North America and further processed to evaluate phenological status. This dataset contains 2,319,672 specimen records of plants collected while reproductively active. These data have been modified to explicitly codify the observed phenological status of each specimen at the time of collection, and to remove specimens for which information essential to assessing their phenology or the corresponding climate conditions in the year and location of collection were missing. As different collectors have used distinct taxonomic schema over space and time in documenting the specimens being collected, these data were also rectified into a single unified taxonomic schema to ensure that consistent taxon names were used throughout the dataset. Further, this data has been united with long-term and annual climate conditions in the year and location of collection, as derived from PRISM climate data (https://www.prism.oregonstate.edu/). To date, this data includes 2,319,672 specimens across 25,429 plant taxa. However, this represents a living dataset that will continue to be updated as digitization efforts proceed and additional digital specimen records become available.

Pollinator visitation on Na-enriched plants in a subalpine meadow - Thiago Gonçalves Souza; Ethan VanValkenburg; Nathan J. Sanders; Paul J. CaraDonna (other, 2023)
Many plants have evolved nutrient rewards to attract pollinators to flowers, but most research has focused on the sugar content of floral nectar resources. Concentrations of sodium in floral nectar (a micronutrient in low concentrations in nectar) can vary substantially both among and within co-occurring species. Sodium concentrations in floral nectar might play an important and underappreciated role in plant-pollinator interactions, especially because many animals, including pollinators, are sodium-limited in nature. Yet, the consequences of variation in sodium concentrations in floral nectar have gone largely unexplored. Here, we investigate whether enriching floral nectar with sodium influences the composition, diversity, and abundance of pollinator interactions. We experimentally enriched sodium concentrations in four plant species in a subalpine meadow in Colorado, USA. We found that flowers with sodium-enriched nectar received more visits from a greater diversity of pollinator visitors throughout the season. Each pollinator species foraged more frequently on flowers enriched with sodium and showed evidence of other changes to pollinator foraging behavior, including greater dietary evenness. These findings are consistent with the 'salty nectar hypothesis,' providing evidence for the importance of sodium limitation in pollinators and suggesting that even small nectar constituents can shape plant-pollinator interactions.

Maternal survival costs in an asocial mammal: Data and analysis - L. M. Aubry (other, 2023)
Maternal characteristics, social dynamics, and environmental factors can all influence reproduction and survival as well as shape trade-offs that might arise between these components of fitness. Short-lived mammals like the golden-mantled ground squirrel (GMGS; Callospermophilus lateralis) tend to maximize effort towards current reproduction at the expense of survival but may be complicated by other aspects of the species’ life history and environment.. Here, we use 25 years of data (1995-2020) collected from a population of GMGS at the Rocky Mountain Biological Research Laboratory in Gothic, Colorado, to test the effect of several maternal characteristics (e.g., age, experience, timing of litter emergence), social context (e.g., litter sex ratio, kin density), and environmental context (e.g., date of bare ground, length of vegetative growing season) on survival of reproductive female GMGS using Cox proportional hazard models. Our results indicated that social dynamics (i.e., density) and environmental conditions (i.e., standardized first day of permanent snow cover and length of growing season) explained significant variation in annual maternal survival, while maternal characteristics did not. A higher density of related breeding females and the total number of females (both related and unrelated to the focal mother) were associated with an increase in the mortality hazard. A later standardized date of the first day of permanent snow cover and a shorter growing season both reduced the maternal mortality hazard. Together, our results suggest that factors extrinsic to the squirrels affect maternal survival, and thus may also influence local population growth and dynamics in GMGS and other short-lived, territorial mammal species.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH - Maciej Stachura; Eryan Dai; Darren Jackson; Jack Elston; Jack Elston; Maciej Stachura; Darren Jackson; Gijs de Boer; Eryan Dai; Janet Intrieri; Gijs de Boer; Eryan Dai; et al. (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour. November 2023 update: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data. Version 2.1 adds data file uas_L3_20221018_171650_v2.1.nc that was missing in Version 2.0. December 2023 update: Version 2.2 updated soil moisture data with a wet bias in v2.1 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.

Marmot capture history data and growing season length data - B. D. Gerber; L. S. Cordes; Kenneth B. Armitage; D. Z. Childs; Julien G. A. Martin; Paul J. CaraDonna; Daniel T. Blumstein (other, 2023)
Seasonal environmental conditions shape the behavior and life history of virtually all organisms. Climate change is modifying these seasonal environmental conditions, which threatens to disrupt population dynamics. It is conceivable that climatic changes may be beneficial in one season but result in detrimental conditions in another because life-history strategies vary between these time periods. We analyzed the temporal trends in seasonal survival of yellow-bellied marmots ( Marmota flaviventer ) and explored the environmental drivers using a 40-y dataset from the Colorado Rocky Mountains (USA). Trends in survival revealed divergent seasonal patterns, which were similar across age-classes. Marmot survival declined during winter but generally increased during summer. Interestingly, different environmental factors appeared to drive survival trends across age-classes. Winter survival was largely driven by conditions during the preceding summer and the effect of continued climate change was likely to be mainly negative, whereas the likely outcome of continued climate change on summer survival was generally positive. This study illustrates that seasonal demographic responses need disentangling to accurately forecast the impacts of climate change on animal population dynamics. We were able to impute body mass for each individual twice during each year following their first capture using a similar approach to Ozgul et al. (2010) (for more details on the modeling procedure see SI Appendix within the main paper). Body mass measurements were log-transformed.

Temperature, floral density, and Osmia pollen usage data from seven study sites around the Rocky Mountain Biological Laboratory, Colorado: 2013-2022. - Lydia H. Wong; Jessica R. K. Forrest (other, 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.

Raw soil carbon dioxide, moisture, temperature and micrometeorological data in the East River Watershed, Colorado June 2021-October 2022. (DE-SC0021139). Ecohydrological controls on root and microbial respiration in the East River watershed of Colorado (ess_dive, 2023)
This dataset contains raw data from four tripod stations along an elevation gradient on Snodgrass Mountain in the East River Watershed, CO, USA. Each station contains a datalogger connected to 3 soil Carbon Dioxide CO2 gas probes, 3 soil temperature/moisture sensors and a micrometeorological station. Sensors are scanned every minute, and the 30 minute average is reported. The file snodgrass_soil_ESS.csv contains raw data and a row of column descriptors and units of measurements. No data processing or QA/QC was done on the raw data sets. This research was performed to investigate the ecohydrological linkages of belowground carbon processes in the East River watershed forested communities to better understand how these ecosystems will respond to a changing cold-season moisture input.

WHONDRS Surface Water Dissolved Organic Carbon and FTICR-MS across Stream Orders in Four United States Watersheds in 2019 and 2020. (ess_dive, 2023)
This dataset supports the broader Watershed Rules of Life (WROL) study examining spatial and temporal biogeochemical and microbial relationships across the Connecticut River, Deschutes River, Gunnison River, and Willamette River watersheds. The dataset provides geochemistry and organic matter characterization data generated from surface water collected from August 2019 to September 2020 in Colorado, Connecticut, Oregon, and Vermont. Related data were collected and will be published separately in collaboration with Raymond and Crump as part of WROL. This dataset is comprised of one main data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) field metadata; (5) dissolved organic carbon (DOC, measured as non-purgeable organic carbon, NPOC) data and averages; (6) surface water sampling protocol; (7) methods codes; (8) international geo-sample number (IGSN) mapping file; and (9) folder of high resolution characterization of organic matter via 12 Tesla Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS) generated through the Environmental Molecular Sciences Laboratory (EMSL; https://www.pnnl.gov/environmental-molecular-sciences-laboratory). The FTICR folder contains two subfolders, one containing the .xml data files 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, .R, .ref, or .xml.

Data from: “Significant stream chemistry response to temperature variations in a high-elevation mountain watershed” (ess_dive, 2023)
High-elevation mountain regions, central to global freshwater supply, are experiencing more rapid warming than low-elevation locations. High-elevation streams are therefore potentially critical indicators for earth system and water chemistry response to warming. Here we present concerted hydroclimatic and biogeochemical data from Coal Creek, Colorado in the central Rocky Mountains at elevations of 2700 to 3700 m, where air temperatures have increased by about 2 °C since 1980. We analyzed water chemistry every other day from 2016 to 2019. Water chemistry data indicate distinct responses of different solutes to inter-annual hydroclimatic variations. Specifically, the concentrations of solutes from rock weathering are stable inter-annually. Solutes that are active in soils, including dissolved organic carbon, vary dramatically, with double to triple peak concentrations occurring during snowmelt and in warm years. We advocate for consistent and persistent monitoring of high elevation streams to record early glimpse of earth surface response to warming.1) Coal Creek stream were sampled and measured to answer the question if there is a significant stream chemistry response to climate change in high-elevation watershed2) The file contains data regarding discharge, water temperature and stream chemistry data for 2016-2019 year. Stream chemistry includes dissolved organic and inorganic carbon, chloride, nutrients, cations. The data collection efforts were carried out by the DOE-funded "Advancing a Watershed Hydro-Biogeochemical Theory: Linking Water Travel Time and Reaction Rates Under Changing Climate" project and Watershed Function SFA.

Stream discharge data collected within the East River, Colorado for the Lawrence Berkeley National Laboratory Watershed Function Science Focus Area (water years 2019 to 2020). - Alexander W. Newman; Curtis A. Beutler; Kenneth Hurst Williams (other, 2023)
This data package contains East River, Colorado stream discharge and temperature data for water years 2019 to 2020. This data were collected to understand hydrological processes occurring in the East River Watershed, Colorado, which is part of the Lawrence Berkeley National Laboratory Watershed Function Scientific Focus Area. Data includes instantenous observed discharge using salt dillution and accoustic doppler velocimeter techniques, raw pressure transducer downloaded data, sub-hourly temperature as well as corrected water level and associated stream discharge and mean daily values. Notes on water level corrections, rating curve development and metadata provided. Note that 2019 provisional discharge data finalized with added information from 2020, and a new site SR-NL1 added in 2020. Data are uploaded in Excel files labeled with location, variable, and year, and can be accessed using Microsoft Excel. Files include the location metadata, as well as notes for understanding the data. Please contact Rosemary Carroll for any support in accessing the files.

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

2018 Meander Y and Meander Z floodplain groundwater chemistry from the East River watershed, Colorado - Patricia Fox; Craig R. Anderson; Christian Dewey; Marco Keiluweit; P. S. Nico (other, 2023)
This dataset includes groundwater geochemistry data from floodplain piezometers collected as a part of the Watershed Function Scientific Focus Area (SFA) located in the Upper Colorado River Basin. The data were collected in order to investigate the role of hyporheic exchange and other river corridor processes on riverine export of solutes. Floodplain piezometers installed in Meander Z (MZA, MZB wells) and Meander Y (MYP wells) just upstream of the confluence with Brush Creek were sampled on daily to weekly time scales during summer-fall 2018. Some river water grab samples are also included. Data includes in-field measurements (pH, electrical conductivity [EC], oxidation reduction potential [ORP], 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). Five files are included in this dataset, including: sample locations and depths in both a kmz file which can be opened in Google Earth and a csv file, sampling protocol as a pdf document, aqueous geochemistry data in a csv file, and analytical detection limits in a csv file.

CLM simulated data at three FLUXNET sites and three SNOTEL sites in the Colorado River Basin. - Jiancong Chen; Anh Phuong Tran; S. S. Hubbard; Haruko M. Wainwright; Baptiste Dafflon (other, 2023)
This data package includes 6 csv files that include Community Land Model (CLM) simulations for three Fluxnet sites (i.e., US-NR1, US-GLE and US-VCM) and three SNOTEL sites (i.e., ER-BT, ER-SP and ER-PK) in the Upper Colorado River Basin area. Data inputs include meteorological forcing data (obtained from Fluxnet and SNOTEL databases) and CLM-simulated evapotranspiration (ET) data from 2005 to 2016. These datasets support the development of subseasonal regime approach based on statistics driven Hidden Markov Model to assess intra- and inter-annual variability of ET, which improves our understanding of climate change's impacts on water resources and ecosystem dynamics in the Colorado River Basin.

Soil moisture and temperature data along the northeast facing hillslope at the Lower Montane site in the East River Watershed, Colorado. - Emmanuel Leger; Baptiste Dafflon (other, 2023)
Soil moisture, temperature and electrical conductivity have been monitored with 5TE sensors (Decagon Inc.) located at 10 and 50 cm depth at 4 different locations along the northeast-facing slope at the Lower Montane site in the East River Watershed, Colorado from Oct 2016 to Oct 2018. The purpose of this data is to inform hydrobiogeochemical analyses for the Watershed Function Scientific Focus Area (SFA). The data is reported in 4 csv files labeled their by location identifiers (ER-SMN1-5) and location metadata is provided in SM_loc.csv. 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.

Bedrock weathering rates, reactive nitrogen influxes and effluxes, and nitrous oxide emissions rates from the Pumphouse Hillslope, East River Watershed, Colorado. - Tetsu Tokunaga; Jiamin Wan (other, 2023)
Atmospheric nitrous oxide contributes directly to global warming, yet models of the nitrogen cycle do not account for bedrock, the largest pool of terrestrial nitrogen, as a source of nitrous oxide. Although it is known that release rates of nitrogen from bedrock are large, there is an incomplete understanding of the connection between bedrock-hosted nitrogen and atmospheric nitrous oxide. Here, we quantify nitrogen fluxes and mass balances at a hillslope underlain by marine shale. We found that at this site bedrock weathering contributes 78% of the subsurface reactive nitrogen, while atmospheric sources (commonly regarded as the sole sources of reactive nitrogen in pristine environments) account for only the remaining 22%. About 56% of the total subsurface reactive nitrogen denitrifies, including 14% emitted as nitrous oxide. The remaining reactive nitrogen discharges in porewaters to a floodplain where additional denitrification likely occurs. We also found that the release of bedrock nitrogen occurs primarily within the zone of the seasonally fluctuating water table and suggest that the accumulation of nitrate in the vadoes zone, often attributed to fertilization and soil leaching, may also include contributions from weathered nitrogen-rich bedrock. Our hillslope study suggests that under oxygenated and moisture-rich conditions, weathering of deep, nitrogen-rich bedrock makes an important contribution to the nitrogen cycle. The data files are in Excel, which can be accessed using Microsoft Office, and consist of many data sets from the Pumphouse Hillslope PLM (Pumphouse Lower Montane) 1, 2, 3, and 4. They include soil to rock (0-10 meters) solid phase minerals and elements compositions; time- and depth-resolved pore-water chemistry and pore-gas compositions; time- and depth-resolved water table depths and water fluxes; subsurface weathering rates; nitrogen influxes and effluxes and mass balance. The attached paper is in a Word document, which can be accessed using Microsoft Office, and in pdf format.

Basin Characteristics and Salinity and Selenium Loads and Yields for Selected Subbasins in the Lower Gunnison River Basin, Western Colorado, 1992?2013 - Cory A. Williams; Michael R Stevens; Rachel G Gidley (other, 2023)
Mitigating the effects of salinity (total dissolved solids) and selenium on water quality in the lower Gunnison River Basin (LGRB) of western Colorado is a major concern for land managers. In cooperation with the Bureau of Reclamation, the U.S. Geological Survey completed a study to develop multiple linear regression models to estimate salinity and selenium loads. The objective of the study was to refine existing models by improving the regressions and using additional geospatial and water-quality data. This data release presents the basin characteristics and loads used to calibrate the models. Also provided are salinity and selenium yield rasters determined by empirical Bayesian kriging of loads estimated by the models. The yield rasters define the study area boundary. The "Lower_Gunnison_River Basin_Data.7z" file contains subfolders for the salinity (TDS) and selenium (Se) data. Both subfolders contain raster datasets used to compute basin characteristics (variables), a shapefile of basin characteristics and loads used as input to the models, a raster dataset of predicted yields, and individual metadata files for the shapefile and raster data. The raster datasets also represent the boundary of the study area.

Minimum 2m Air Temperature Daily Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of daily minimum air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D005')  metadata <- sdp_get_metadata('R4D005')    For more information about rSDP, visit the package homepage.

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

Snowpack Proportional Reduction in Growing Degree Days (2002-2021) - Ian K Breckheimer (s3, 2023)
This is a map of the influence of snow on the energy available for plant growth (growing degree days, GDD) for the Upper Gunnison domain, derived from daily maximum temperature maps interpolated from weather station and microclimate sensor data combined with Landsat-derived estimates of the timing of seasonal snowpack departure and onset. Snow-free growing degree-days were accumulated from day-of-year 60 to day-of-year 305 (March 1st to November 1st in a non-leap year). To compute growing degree days, daily estimates of temperatures above a base of 5 degrees Celcius (a minimum growth threshold for many plants) were summed across all days estimated to be free of snow cover. Because snowpack insulates the ground surface and soil from the atmosphere, this estimate can be a more appropriate measure of growing potential for snow-dominated climates than growing degree days (FDD). This map represents the proportional reduction in growing potential from 2002 to 2021 using the formula (GDD - SFGDD) / GDD.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D054')  metadata <- sdp_get_metadata('R4D054')    For more information about rSDP, visit the package homepage.

Snowpack Duration Mean (Water Year 1993 - 2022) - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the mean number of days of continuous seasonal snowpack from 1993 - 2022. This map is derived from estimates of the first and last day of bare ground derived from long-term time-series of Landsat TM, ETM, and OLI imagery starting in 1993.  These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ) with a time-series analysis of a spectral snow index (NDSI) using a hierarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022), and greater than 90% of temporal variation across the full 1993 - 2022 time-series. This data represents the mean of annual estimates from 1993 - 2022. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D057')  metadata <- sdp_get_metadata('R4D057')    For more information about rSDP, visit the package homepage.

Potentilla plot soil moisture for Cabin Clearing, Elk Meadows and Rainbow Meadows, 2019. - Kelly A Carscadden (other, 2023)
To understand parent-hybrid dynamics in cinquefoil (Potentilla) species in the Colorado Rocky Mountains, I am estimating environmental overlap among parents and hybrids, interbreeding among parents and hybrids, and hybrid population growth in multiple natural populations at NWT and (not included here) the Rocky Mountain Biological Laboratory (RMBL). Since 2018, I have been monitoring populations at Rainbow Meadows Lower, Rainbow Meadows Upper, Elk Meadows South, and Cabin Clearing - sites that vary in cinquefoil composition. In 2019, my team and I recorded reproductive phenophases (e.g., vegetative, buds, flowering, setting seed) on tagged plants in June, July, and August. When plants began flowering, we estimated floral counts. Characterizing flowering phenology of parent and hybrid species in each site sheds light on the potential for ongoing interbreeding in different environments. NWT soil moisture are provided here, demographic and phenological data are shared separately. This data was collected to test broad hypotheses about hybrid-parent dynamics in changing montane environments.

BST/NOAA PSL Level 3 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH (other, 2023)
This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). While Level 2 provides each product at their highest retrieved spatial resolution, Level 3 provides all four products on a common grid at each flight location. These data were collected near Avery Picnic (38.972425 o N,106.996855 o W) and Kettle Ponds (38.942005 o N,106.973006 o W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 19th, 2022. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies, Inc. Each Level 3 NetCDF file contains all four UAS parameters at a flight location interpolated to a common rectilinear grid at ~50 cm resolution. Soil moisture retrievals were downscaled to a higher resolution grid using bilinear interpolation while surface temperature, NDVI, and digital elevation were upscaled to a lower resolution grid using conservative interpolation. The data was regridded using the Python package xESMF which is based on code developed for the Earth System Modeling Framework (ESMF) project. The file name convention for the Level 3 NetCDF files is as follows. uas_L3_yyyymmdd_hhmmss_vx.x.nc where L3 = Level 3 data yyyymmdd = year,month,day hhmmss = hour,minute,second x.x = version number Time is the flight start time in UTC. Version number description is provided in the NetCDF global attributes. Note that each flight location using the E2 aerial platform required two flights with different starting flight times for the soil moisture and the other three products. The flight start time is the time of the first flight. The total time for the two flights at each location was ~1 hour.

NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH - William Currier; Jake Longenecker; Jake Longenecker; William Currier; Darren Jackson; Maciej Stachura; Maciej Stachura; Darren Jackson; Gijs de Boer; Janet Intrieri; Gijs de Boer; Janet Intrieri (other, 2023)
This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected 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 from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363. Two file formats are provided: one version is text csv format and the second version is in NetCDF. Volumetric water content calculations: Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC = 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: Mwet, Mdry = mass (g) of wet and dry soil respectively Vtot = total soil volume (ml) w = density of water (1g/ml) A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset. vwc_calculated = vwc_probe * slope + intercept slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3 Definitions: TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%). EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. The VWC measured by TDR is an average over the length of the waveguide. Soil Characteristics: Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows: Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture ------------------------------------------------------------------------------------------------------ KEP1 2 43 35 22 Loam AYP 2 40 35 25 Loam KPA 2 35 42 22 Loam ------------------------------------------------------------------------------------------------------

Snowpack Onset Day of Year Yearly Timeseries - Ian K Breckheimer (s3, 2023)
This dataset represents an estimate of the day of year (i.e. ,  "Julian Day") of the onset of the seasonal snowpack.  Specifically these are estimates of the last day of bare ground derived from long-term time-series of Landsat TM,  ETM,  and OLI imagery starting in 1993.  To facilitate computation of snow cover duration,  day of year is scaled from January 1st of the following year.  This means that negative values represent days before January 1st.  For example,  a snow onset on November 11th,  2021 (Day of Year 315) would be represented in the 2022 map with a value of -50 (315 - 365). These maps combine monthly ground snow cover fraction maps from the USGS Landsat Collection 2 Level 3 fSCA Statistics (https://doi.org/10.5066/F7VQ31ZQ),  with a time-series analysis of a spectral snow index (NDSI) using a heirarchical Bayesian model (Gao et al.  2021).  The combination of these two approaches allows reconstruction of detailed annual snow persistence maps from sparse imagery time-series (Landsat data have an 8 to 16-day return interval in the absence of clouds). A comparison of these data to independent in-situ observations from SNOTEL and microclimate sensors show that these products capture about 85% of spatial variation in snow persistence for recent years (2021-2022),  and greater than 90% of temporal variation across the full 1993 - 2022 time-series. References: Gao, X., Gray, J. M., & Reich, B. J. (2021). Long-term, medium spatial resolution annual land surface phenology with a Bayesian hierarchical model. Remote Sensing of Environment, 261, 112484. https://doi.org/10.1016/j.rse.2021.112484    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D002')  metadata <- sdp_get_metadata('R4D002')    For more information about rSDP, visit the package homepage.

Dryad dataset awaiting publication (2023)
[object Object]

Average 2m Air Temperature Monthly Timeseries - Ian K Breckheimer (s3, 2023)
These are maps of monthly averages of daily average air temperature for the Upper Gunnison domain measured in degrees C. Estimates were derived from weather station and microclimate sensor data using Bayesian regression-kriging, incorporating topographic and vegetation structure covariates. These data were derived from daily maps, which are also available. Daily averages were estimated from daily minimums and maximums using the equation (Tmax + Tmin) / 2. To reduce storage space, original estimates were multiplied by 10000 and stored as integer data. Divide the map values by 10000 to retrieve the original estimates.    In addition to the download links on this page, you can access this dataset and metadata using the rSDP R Package:    #devtools::install_github('rmbl-sdp/rSDP')  library(rSDP)  dataset <- sdp_get_raster('R4D006')  metadata <- sdp_get_metadata('R4D006')    For more information about rSDP, visit the package homepage.