Condon, L.A. Coates, P.S. 2025 tabular digital data ScienceBase U.S. Geological Survey data release https://doi.org/10.5066/P1434E27 Lea A. Condon Brian G. Prochazka Talia J. Gabay Bill E. Davidson Matthew J. Germino Peter S. Coates 202511 publication Science of The Total Environment vol. 1002 n/a Elsevier BV ppg. 178737 https://doi.org/10.1016/j.scitotenv.2025.178737 The frequency and extent of wildfire is increasing globally, necessitating an increased understanding of wildfire effects on ecosystem function. Although soil-stabilizing cyanobacteria can make up a substantial portion of the biotic community in semi-arid and arid rangelands, we currently have a limited understanding of the drivers behind their abundance following wildfire. These organisms contribute to ecosystem functions, including reduced invasion by non-native species and decreased soil erosion, which are common management targets following wildfire. This data was generated to examine the probability of encountering soil-stabilizing cyanobacteria of the order Oscillatoriales following nine recent wildfires in the northern Great Basin of the western U.S. We investigated plots that burned at least once since 2012, with most sites experiencing one or two wildfires, and collected vegetation and soil data. We additionally obtained fire, soil, and ground cover characteristics for each plot. These data were collected for the purpose of understanding the effects of fire on Oscillatoriales abundance. Understanding these relationships could aid land managers in identifying post-fire conditions most favorable to soil-stabilizing cyanobacteria which in turn could help achieve management goals of reducing invasion by fire-promoting non-native annual grasses and post-fire soil erosion. 19840101 20221231 ground condition Complete None planned Northern Great Basin -118.5057 -112.5814 43.9943 40.1504 ISO 19115 Topic Category biota environment None greasewood pinyon-juniper woodlands sagebrush saltbush USGS Thesaurus shrubland ecosystems biological soil crusts soil sciences fires USGS Metadata Identifier USGS:67903cb5d34e28977994d36e Common geographic areas Great Basin Integrated Taxonomic Information System (ITIS) Oscillatoriales U.S. Geological Survey 2013 Online Database https://doi.org/10.5066/F7KH0KBK www.itis.gov expert identifier Kingdom Bacteria bacteria Subkingdom Negibacteria Phylum Cyanobacteriota Class Cyanophyceae blue-green algae Order Oscillatoriales TSN: 180739 No access constraints. Please see 'Distribution Info' for details. No use constraints. These data are marked with a Creative Common CC0 1.0 Universal License. These data are in the public domain and do not have any use constraints. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Questions pertaining to appropriate use or assistance with understanding limitations or interpretation of the data are to be directed to the individuals/organization listed in the Point of Contact section. U.S. Geological Survey Western Ecological Research Center Data Manager mailing and physical

3020 State University Drive, Modoc Hall, Suite 4004

Sacramento California 95819 United States 279-782-0904 gs-b-werc_data_management@usgs.gov U.S. Geological Survey Western Ecological Research Center, U.S. Geological Survey Forest and Rangeland Ecosystem Science Center The ‘solrad’ R package is to be used in surface energy models and estimation of solar positions and components with varying topography, time and locations. The functions calculate solar top-of-atmosphere, open, diffuse and direct components, atmospheric transmittance and diffuse factors, day length, sunrise and sunset, solar azimuth, zenith, altitude, incidence, and hour angles, earth declination angle, equation of time, and solar constant. https://cran.r-project.org/package=solrad https://cran.r-project.org/web/packages/solrad/solrad.pdf Bijan Seyednasrollah 20181105 v.1.0.0 Tools Software n/a The R Foundation https://doi.org/10.32614/CRAN.package.solrad 3 transects were sampled per plot and 3 soil samples were taken per plot in order to comprehensively survey each plot. Additionally, 3 petri dishes were generated per plot for quantifying the abundance of Oscillatoriales and cover values were averaged across the three replicates to get an accurate estimate of cover. Field collected data entries were QA/QC'd. Values obtained from databases were assessed to ensure they fell within expected ranges. Covariate values are standardized to z-score. These data are considered to be complete, and no update is planned. All covariate values are standardized to their z-score. Brady W. Allred Brandon T. Bestelmeyer Chad S. Boyd Christopher Brown Kirk W. Davies Michael C. Duniway Lisa M. Ellsworth Tyler A. Erickson Samuel D. Fuhlendorf Timothy V. Griffiths Vincent Jansen Matthew O. Jones Jason Karl Anna Knight Jeremy D. Maestas Jonathan J. Maynard Sarah E. McCord David E. Naugle Heath D. Starns Dirac Twidwell Daniel R. Uden 20210208 publication Methods in Ecology and Evolution vol. 12, issue 5 n/a Wiley ppg. 841-849 https://doi.org/10.1111/2041-210X.13564 https://rangelands.app/ Digital and/or Hardcopy 20000101 20221231 ground condition Rangeland Analysis Platform Provided percent cover estimates for environmental covariates including annual grass, perennial grass, trees, shrubs, bare ground, and litter. Tomislav Hengl Jorge Mendes de Jesus Gerard B. M. Heuvelink Maria Ruiperez Gonzalez Milan Kilibarda Aleksandar Blagotić Wei Shangguan Marvin N. Wright Xiaoyuan Geng Bernhard Bauer-Marschallinger Mario Antonio Guevara Rodrigo Vargas Robert A. MacMillan Niels H. Batjes Johan G. B. Leenaars Eloi Ribeiro Ichsani Wheeler Stephan Mantel Bas Kempen 20170216 publication PLOS ONE vol. 12, issue 2 n/a Public Library of Science (PLoS) ppg. e0169748 https://doi.org/10.1371/journal.pone.0169748 https://www.soilgrids.org/ Digital and/or Hardcopy 20170216 publication date SoilGrids Provided estimates of soil composition, including clay, silt, and sand. Christopher Daly Michael Halbleib Joseph I. Smith Wayne P. Gibson Matthew K. Doggett George H. Taylor Jan Curtis Phillip P. Pasteris 20080312 publication International Journal of Climatology vol. 28, issue 15 n/a Wiley ppg. 2031-2064 https://doi.org/10.1002/joc.1688 https://www.prism.oregonstate.edu/ Digital and/or Hardcopy 19910101 20201231 ground condition PRISM Provided precipitation and temperature estimates over a 30-year average. Jeff Eidenshink Brian Schwind Ken Brewer Zhi-Liang Zhu Brad Quayle Stephen Howard 20070601 publication Fire Ecology vol. 3, issue 1 n/a Springer Science and Business Media LLC ppg. 3-21 https://doi.org/10.4996/fireecology.0301003 Digital and/or Hardcopy 19840101 20221231 ground condition Monitoring Trends in Burn Severity Provided locations of fires and their estimated intensity. Soil Survey Staff Natural Resources Conservation Service United States Department of Agriculture 20050816 publication Accessed 2/10/2024 https://websoilsurvey.nrcs.usda.gov/app/ Digital and/or Hardcopy 20220101 ground condition Web Soil Survey Provided extra information about the soils such as perecent composition of coarse fragments, cation exchange capacity, pH, nitrogen levels, and more. U.S. Geological Survey 20200606 raster digital data https://www.usgs.gov/the-national-map-data-delivery Digital and/or Hardcopy 20220101 ground condition Digital Elevation Map Provided elevation values, which were also transformed into slope and aspect. Data collection occurred during 2022 across nine recent wildfires in the Great Basin ecoregion (Benwalk [2012, 7 plots], Holloway [2012, 41 plots], Indian Creek [2020, 6 plots], Long Draw [2012, 22 plots], Onaqui [2017, 12 plots], Roosters Comb [2017, 8 plots], Snowstorm [2017, 7 plots], Stout [2012, 2 plots], Wildcat [2017, 7 plots]). A total of 112 plots were included in this data and their distribution by fire varied based on the size of the fire. Although plots were originally placed to account for various restoration treatments, the plots used in this study are those where sites were allowed to naturally recover following fire. Vegetation was surveyed at these sites using line-point intercept over three transects, starting at 5.5 m from plot center and ending at 30 m from plot center. At the 15-m mark on each transect, three soil samples (i.e., “cookies”) were collected down to 4 cm in depth after any litter or duff on the surface had first been scraped away. We took the first 4 cm to focus our sampling on surface soils where we expected the soil-stabilizing cyanobacteria to be present. These organisms live in the soil and not in litter or duff, so removing the organic layers prior to soil sampling, reduced possible dilution of our samples by these materials. This resulted in a total of nine cookies which were pooled to create one composite soil sample for each plot. These soils were stored in dry, ambient conditions for up to six months before being sieved to separate the 2 mm soil fraction from coarser materials. Soils were textured by hand via the ribbon method and the median values of each category were determined from a soil texture triangle. The moistened soil method (MSM) was used to quantify the abundance of soil stabilizing cyanobacteria at each surveyed plot with three replicates of 9 g each in petri dishes that were surveyed at 24, 48, and 168 hours following wetting and illumination. Dishes were surveyed via the point-vertex method at 44 points along a grid to quantify cover. Cover was converted to a proportion (hereafter, probability of presence) by dividing the number of cells containing cyanobacteria by the total number of cells on the plate (i.e., 44). 20240301 We investigated 46 environmental covariates hypothesized to influence Oscillatoriales. Candidate covariates included soil texture derived from the ribbon method as well as vegetation cover products (i.e., tree, shrub, bare ground, perennial herbaceous) for the years 2000 and 2022 available from the Rangeland Analysis Platform (RAP; Allred et al., 2021), summarized at 30-m and 1-km scales. The 2022 RAP products provided insights about vegetation cover conditions following fire events. The difference between the two annual products (2000 and 2022) provided insights about the change in cover conditions stemming from fire presence. Geospatial information on soil properties was investigated using multiple properties mapped by SoilGrids v.2.0 (Hengl et al., 2017), including bulk density of the fine earth fraction, cation exchange capacity, volumetric fraction of coarse fragments, proportion of clay particles, total nitrogen, soil pH, proportion of sand particles, proportion of silt particles, and soil organic carbon content in the fine earth fraction. All SoilGrids soil property data were extracted using depth interval I (0-5 cm). This differs from our field methods where we collected the first 4 cm of soil because SoilGrid is an existing dataset. We maintained the original 250-m resolution for this product. Temperature and precipitation data, in the form of 30-yr Normals, were extracted to each survey plot using the 4-km product available from the PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate group (Daly et al., 2008). Frost-free period, the minimum number of days between last freezing temperature in the spring and first freezing temperature in the fall was determined for each plot and site factors of elevation, slope and aspect were calculated from digital elevation models. Spatially explicit data on fire frequency and burn severity, spanning 2012–2021, were retrieved from the Monitoring Trends in Burn Severity (MTBS) database (Eidenshink et al., 2007). Annual MTBS products were summarized (mean) within a 500-m radius circular window, and the maximum value across the 10-yr period was assigned to each survey plot. Finally, we calculated the amount of direct solar radiation at each survey plot using the previously described topographic variables and the solrad package (v.1.0.0) available in R. Rangeland Analysis Platform SoilGrids PRISM Monitoring Trends in Burn Severity Web Soil Survey Digital Elevation Map 20240301 Cyano_Data_wPlotID.csv Comma Separated Value (CSV) file containing data. Producer Defined PlotID Unique ID assigned. Producer Defined Unique ID assigned to each monitoring plot. No specific schema is applied, it just is required to be unique. Fire Name of wildfire and year the fire occurred. Producer Defined LongDraw_2012 Long Draw fire in 2012. Producer defined IndianCreek_2020 Indian Creek fire in 2020. Producer defined Holloway_2012 Holloway fire in 2012. Producer defined Onaqui_2017 Onaqui fire in 2017. Producer defined RoostersComb_2017 Roosters Comb fire in 2017. Producer defined Wildcat_2017 Wildcat fire in 2017. Producer defined Stout_2012 Stout fire in 2012. Producer defined Benwalk_2012 Benwalk fire in 2012. Producer defined Snowstorm_2017 Snowstorm fire in 2017. Producer defined Oscillatoriales Oscillatoriales absolute cover. These values are not standardized, as this was the response variable in the modeling process. Producer Defined 0.0 1.0 PPT_WSS Expected range of annual precipitation. Values standardized to z-score. Producer Defined -2.184481441 4.450880936 Solar_Radiation The amount of solar energy received in each plot. Values standardized to z-score. Producer Defined -3.379932798 1.880269958 Tree_30m Percent tree cover within 30 meters in 2022. Values standardized to z-score. Producer Defined -0.365460795 7.699299793 Shrub_30m Percent shrub cover within 30 meters in 2022. Values standardized to z-score. Producer Defined -1.581320798 3.080269266 Perennial_Herb_30m Percent cover of perennial grasses within 30 meters in 2022. Values standardized to z-score. Producer Defined -1.966670888 1.929632016 Bare_Ground_30m Percent cover of bare ground within 30 meters in 2022. Values standardized to z-score. Producer Defined -1.434729801 3.932414595 Annual_30m Percent cover of annual grasses within 30 meters in 2022. Values standardized to z-score. Producer Defined -1.696879005 3.070514334 Litter_30m Percent cover of litter in within 30 meters 2022. Values standardized to z-score. Producer Defined -2.32135113 4.20747395 FFD_Minimum Expected minimum number of days between last freezing temperature in spring and the first freezing temperature in fall. 1961 to 1990 were used as the baseline for the "normal" number of frost-free days in a year, and the probability that the baseline will be exceeded in 5 years out of a 10-year window was calculated. Values standardized to z-score. Producer Defined -2.733351173 1.75641197 Aspect Aspect of each plot obtained from a digital elevation map. Values standardized to z-score. Producer Defined -2.031106529 1.84036712 Carbon_WS Global spatial predictions of soil organic carbon content at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -1.297475164 4.088201306 pH_WS Global spatial predictions of soil pH at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -2.617126412 1.657823988 Nitrogen_WS Global spatial predictions of total nitrogen content at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -1.526480427 4.941099722 MTBS_500m_10yr_Maximum The mean of burn severity pixels within a 500 m radius was calculated for each of the most recent 10 years and the maximum value across all years was calculated. Values standardized to z-score. Producer Defined -2.803180999 2.789475803 Bulk_Den_WS Global spatial predictions of bulk density at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -3.328362232 2.731552684 CEC_WS Global spatial predictions of cation exchange capacity at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -2.306952828 2.841576045 Coarse_Frag_WS Global spatial predictions of the volumetric fraction of coarse fragments (>2 mm) at 250-m resolution using depth interval I (0–5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -2.080371621 1.938004512 PPT_30yr_PRISM 30-year average precipitation (rain plus melted snow), obtained from PRISM. Values standardized to z-score. Producer Defined -1.42240776 3.318212092 TMEAN_30yr_PRISM 30-year average temperature, obtained from PRISM. Values standardized to z-score. Producer Defined -3.431093034 1.695484015 Clay_WS Global spatial predictions of the proportion of clay particles (less-than 0.002 mm) at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -1.809209956 2.848790331 Sand_WS Global spatial predictions of the proportion of sand particles (greater-than 0.05/0.063 mm) at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -3.07377703 1.386223746 Silt_WS Global spatial predictions of the proportion of silt particles (greater-than-or-equal-to 0.002 mm and less-than-or-equal-to 0.05/0.063 mm) at 250-m resolution using depth interval I (0-5 cm), obtained from SoilGrids. Values standardized to z-score. Producer Defined -1.68931787 2.891024941 Sand Median percent sand as determined by the ribbon method. Values standardized to z-score. Producer Defined -1.451478501 1.713503123 Silt Median percent silt as determined by the ribbon method. Values standardized to z-score. Producer Defined -1.140673356 2.79042026 Clay Median percent clay as determined by the ribbon method. Values standardized to z-score. Producer Defined -1.884628542 3.159480364 GS ScienceBase U.S. Geological Survey - ScienceBase mailing address

Denver Federal Center, Building 810, Mail Stop 302

Denver CO 80225 United States 1-888-275-8747 sciencebase@usgs.gov Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government. Digital Data (CSV) https://doi.org/10.5066/P1434E27 None 20251117 Lea A. Condon U.S. Geological Survey Western Ecological Research Center mailing and physical

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Reno Nevada 89512 United States 541-750-1030 lcondon@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999