Laura G. Perry Catherine S. Jarnevich Patrick B. Shafroth 20220926 tabular data https://doi.org/10.5066/P9LIB2TF Laura G. Perry Catherine S. Jarnevich Patrick B. Shafroth 2022 publication Ecosphere, in review We developed habitat suitability models for occurrence of three invasive riparian woody plant taxa of concern to Department of Interior land management agencies, as well as for three dominant native riparian woody taxa. Study taxa were non-native tamarisk (saltcedar; Tamarix ramosissima, Tamarix chinensis), Russian olive (Elaeagnus angustifolia) and Siberian elm (Ulmus pumila) and native plains/Fremont cottonwood (Populus deltoides ssp. monilifera and ssp. wislizenii, Populus fremontii), narrowleaf cottonwood (Populus angustifolia), and black cottonwood (Populus balsamifera ssp. trichocarpa and ssp. balsamifera). We generally followed the modeling workflow developed in Young et al. 2020. We developed models using five algorithms with VisTrails: Software for Assisted Habitat Modeling [SAHM 2.1.2]. We accounted for uncertainty related to sampling bias by using two alternative sources of background samples: random (10,000 spatially-filtered (50-kilometer [km]) random background samples) and Salix (10,000 randomly-selected occurrence records of Salix spp.). We constructed model ensembles with the 5 models for each taxon (five algorithms) with each background method, as well as with all 10 models for each taxon (five algorithms by two background methods), for three different occurrence likelihood thresholds (1st percentile, 10th percentile, and MSS (maximum sensitivity and specificity)). We also used the model ensembles to identify major watersheds where each taxon was under-represented in occurrence records relative to predicted habitat suitability, to evaluate risk of undetected or future invasion. For each 6-digit hydrological unit (HUC6, USGS Watershed Boundary Dataset) within the study area, we calculated the difference between actual occurrence record density and the density of occurrence records that would be expected if occurrence records were distributed among watersheds in proportion to habitat suitability in MaxSS 10-model ensembles. This data bundle contains the merged data sets used to create the models, occurrence locations that were used for independent assessments of model accuracy (not used in model training), the raster files associated with each taxon, and tabular summaries of actual and expected occurrence record densities by HUC6. The spatial data are organized in a separate folder for each taxon, each containing 9 rasters. Each of the rasters represent the following: 1) X1st_random - ensemble of 5 models with random background data and 1st percentile threshold 2) X10th_random - ensemble of 5 models with random background data and 10th percentile threshold 3) MaxSS_random - ensemble of 5 models with random background data and MaxSS threshold 4) X1st_Salix_1st - ensemble of 5 models with random background data and 1st percentile threshold 5) X10th_Salix - ensemble of 5 models with random background data and 10th percentile threshold 6) MaxSS_Salix - ensemble of 5 models with random background data and MaxSS threshold 7) X1st_combined - ensemble of 10 models with random and Salix background data and 1st percentile threshold 8) X10th_combined - ensemble of 10 models with random and Salix background data and 10th percentile threshold 9) MaxSS_combined - ensemble of 10 models with random and Salix background data and MaxSS threshold The bundle documentation files are: 1) 'RiparianSDMs_main.xml' (this file), which contains the project-level metadata 2) 'ModelTrainingData.csv' contains the merged data set used to create the models, including location and environmental data. 3) 'IndependentAssessmentData.csv' contains the data set used to assess accuracy of model predictions (occurrence locations not used for model training) 4) XX.tif where XX is the raster type explained above in taxa subfolders. 5) 'HUC6Summaries.csv' contains tabular summaries of actual and expected occurrence record densities by HUC6. 6) 'bison_citations.txt' contains the different data sources with occurrences from the BISON database. This file specifically describes the ModelTrainingData.csv that includes the location data and associated predictor variable values used to train the habitat suitability models. (1) to provide information and documentation on the potential distribution of tamarisk, Russian olive, and Siberian elm in the conterminous western USA, (2) to compare the potential distributions of these invasive riparian trees to potential distributions of dominant native riparian cottonwoods, and (3) to evaluate risks of future or undetected invasion by tamarisk, Russian olive, and Siberian elm in major watersheds of the western USA. 1980 2020 date of source data Complete None planned western United States -124.9400 -100.0000 49.0000 31.1700 ISO 19115 Topic Category biota None Species Distribution modeling VisTrails SAHM habitat suitability Tamarix spp. Elaeagnus angustifolia Ulmus pumila Populus spp. Software for Assisted Habitat Modeling riparian invasive plants USGS Metadata Identifier USGS:62b21808d34e74f0d80f17a4 None United States None Tamarix ramosissima Tamarix chinensis Elaeagnus angustifolia Ulmus pumila Populus deltoides ssp. monilifera Populus deltoides var. wislizenii Populus angustifolia Populus balsamifera ssp. balsamifera Populus balsamifera ssp. trichocarpa Populus fremontii Salix Kingdom Plantae plants Subkingdom Viridiplantae green plants Infrakingdom Streptophyta land plants Superdivision Embryophyta Division Tracheophyta vascular plants tracheophytes Subdivision Spermatophytina spermatophytes seed plants Class Magnoliopsida Superorder Caryophyllanae Order Caryophyllales Family Tamaricaceae tamarisk Genus Tamarix tamarisk Species Tamarix ramosissima tamarix salt cedar tamarisk saltcedar TSN: 22310 Species Tamarix chinensis fivestamen tamarisk tamarisk China tamarisk saltcedar Chinese tamarisk tamarix Chinese saltcedar five-stamen tamarisk TSN: 22308 Superorder Rosanae Order Rosales Family Elaeagnaceae oleasters Genus Elaeagnus silverberry oleaster Species Elaeagnus angustifolia Russian olive Russian-olive Trebizond-date oleaster TSN: 27770 Family Ulmaceae elms Genus Ulmus elm Species Ulmus pumila Siberian elm Chinese elm TSN: 19057 Order Malpighiales Family Salicaceae willows Genus Populus cottonwood Species Populus deltoides plains cottonwood common cottonwood eastern cottonwood Subspecies Populus deltoides ssp. monilifera plains cottonwood TSN: 22447 Subspecies Populus deltoides ssp. wislizeni Rio Grande cottonwood Species Populus angustifolia narrowleaf cottonwood TSN: 22452 Species Populus balsamifera balsam poplar Species Populus trichocarpa black cottonwood Species Populus fremontii cottonwood Fremont's cottonwood TSN: 22459 Genus Salix willow TSN: 22476 None. Please see 'Distribution Info' for details. None. Users are advised to read the dataset's metadata thoroughly to understand appropriate use and data limitations. Catherine S Jarnevich U.S. Geological Survey, ROCKY MOUNTAIN REGION Research Ecologist mailing address

2150 Centre Avenue Bldg C

Fort Collins CO 80526 US 970-226-9439 970-226-9230 jarnevichc@usgs.gov The U.S. Geological Survey Invasive Species Program provided funding, and the U.S. Geological Survey Fort Collins Science Center participated in the project. Created using version 2.2 of the VisTrails/SAHM package Nicholas E. Young Catherine S. Jarnevich Helen R. Sofaer Ian Pearse Julia Sullivan Peder Engelstad Thomas J. Stohlgren 20200309 publication PLOS ONE vol. 15, issue 3 n/a Public Library of Science (PLoS) ppg. e0229253 https://doi.org/10.1371/journal.pone.0229253 Before ensembling layers, we ensured that the training and average 10-fold cross validation AUC were greater than 0.7 and had a difference less than 0.05. After layers were ensembled, maps were visualized for consistency and ecological plausibility. We evaluated location data for accuracy including taxonomic and spatial coordinates. All known synonyms and U.S. Department of Agriculture (USDA) Plants Database acronyms were collected (excluding subspecies, variants, and hybrids) using the Integrated Taxonomic Information System (ITIS; www.itis.gov) as an authoritative taxonomy in the R library ‘taxize’. We filtered observations by observation type (observation or specimen only), observation date (1980 to 2020), and coordinate uncertainty (≤ 30 meters [m]). We removed any records with coordinates corresponding to state or country centroids or other identifiable geographic and taxonomic errors. We also checked the entire dataset for duplicate records. Locations compared with reported distributions. Data set is considered complete for the information presented, as described in the abstract. Users are advised to read the rest of the metadata record carefully for additional details. A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Stephen E. Fick Robert J. Hijmans 201710 raster digital data International Journal of Climatology vol. 37, issue 12 n/a Wiley ppg. 4302-4315 https://doi.org/10.1002/joc.5086 https://www.worldclim.org/data/worldclim21.html Digital and/or Hardcopy 1981 2010 date of source data WorldClim: BioClim Potential predictors included: Anneal Mean Temperature (BIO1), Mean Diurnal Range(BIO2), Isothermality (BIO3), Temperature Seasonality (Standard Deviation, BIO4A), Max Temperature Of Warmest Month (BIO5), Min Temperature Of Coolest Month (BIO6), Temperature Annual Range (BIO7), Mean Temperature Of Wettest Quarter (BIO8), Mean Temperature Of Driest Quarter (BIO9), Mean Temperature Of Warmest Quarter (BIO10), Mean Temperature Of Coolest Quarter (BIO11), Precipitation Of Wettest Month (BIO13), Precipitation Of Driest Month (BIO14), Precipitation Seasonality (Cv) (BIO15), Precipitation Of Wettest Quarter (BIO16), Precipitation Of Driest Quarter (BIO17), Precipitation Of Warmest Quarter (BIO18), and Precipitation Of Coldest Quarter (BIO19) Christina M. Kennedy James Oakleaf David M. Theobald Sharon Baruch-Mordo Joseph Kiesecker 2018 raster digital data n/a figshare https://doi.org/10.6084/m9.figshare.7283087.v1 https://figshare.com/articles/Global_Human_Modification/7283087 Digital and/or Hardcopy 2016 median date of source data Global Human Modification Potential predictors included: Global Human Modification Index Glynn Hulley Zhengmin Wan 2013 raster digital data https://modis-land.gsfc.nasa.gov/temp.html Digital and/or Hardcopy 2001 2013 date of source data Land Surface Temperature (MODIS) Potential predictors included: Land surface temperature (MODIS) US Geological Survey National Geospatial Program 20090201 raster digital data https://www.usgs.gov/core-science-systems/national-geospatial-program/national-map Digital and/or Hardcopy 2009 date of source data Topography Potential predictors included: Elevation, Slope, and Topographic Position Index (TPI). We derived slope as the rate of maximum change in elevation from each raster cell. TPI is the mean difference in elevation between a center point and all surrounding points within a square moving window, divided by the standard deviation of elevation within the window; we derived TPI using moving window sizes=0.4, 4.0, 16.0 km2. U.S. Environmental Protection Agency (USEPA) U.S. Geological Survey 2020 vector digital data n/a US Geological Survey https://www.sciencebase.gov/catalog/item/56c38ad8e4b0946c6520aa52 https://www.epa.gov/waterdata/get-nhdplus-national-hydrography-dataset-plus-data 24000 Digital and/or Hardcopy 2012 date of source data NHDPlus Potential predictors included: Distance to Water, Estimated Mean Annual Flow, Stream Order, Cumulative Watershed Drainage Area, Upstream Ditch Abundance, and HUC12 Ditch Abundance. We calculated minimum distance to surface water (flowlines and water bodies) using Euclidean distance in ArcMap 10.6.1. Mean annual flows were gage-adjusted estimates calculated using the Enhanced Unit Runoff Method (EROM, Q0001E). Drainage area was total cumulative drainage area. We calculated ditch abundance as the proportion of total flowline length composed of ditches, canals, and pipelines within each HUC12 and in the upstream watershed of each HUC12. We aggregated total ditch and flowline lengths upstream of each HUC12 using the HUCAgg package in R (Blodgett, 2017, https://github.com/USGS-R/HUCAgg, accessed 1 November 2018). John T. Abatzoglou 20111221 raster digital data International Journal of Climatology vol. 33, issue 1 n/a Wiley ppg. 121-131 https://doi.org/10.1002/joc.3413 https://app.climateengine.org/climateEngine Digital and/or Hardcopy 1981 2010 date of source data Climate engine Potential predictors downloaded from site included gridMET based on PRISM and NLDAS-2: Mean Annual (Oct-Sep) Precipitation, Mean Spring (Mar-May) Precipitation, Mean Spring (Mar-May) PET, Mean Summer (Jun-Aug) PET, Minimum Winter (Dec-Feb) Temperature, Mean Spring (Mar-May) Temperature, Minimum Spring (Mar-May) Temperature, and Minimum Autumn (Sep-Nov) Temperature Gabriel B. Senay Stefanie Bohms Ramesh K. Singh Prasanna H. Gowda Naga M. Velpuri Henok Alemu James P. Verdin 20130513 raster digital data JAWRA Journal of the American Water Resources Association vol. 49, issue 3 n/a Wiley ppg. 577-591 https://doi.org/10.1111/jawr.12057 https://doi.org/10.1016/j.agwat.2010.10.014 https://doi.org/10.3390/s7060979 https://doi.org/10.1016/j.rse.2013.07.013 https://app.climateengine.org/climateEngine Digital and/or Hardcopy 2003 2017 date of source data Evapotranspiration Potential predictors included: Mean Spring (Mar-May) Evapotranspiration and Mean Summer (Jun-Aug) Evapotranspiration (note: all values are mean monthly) The University of Georgia - Center for Invasive Species and Ecosystem Health 20201201 tabular digital data https://www.eddmaps.org/ Digital and/or Hardcopy 1980 2020 observed EDDMAPS Species location data Global Biodiversity Information Facility 20210930 tabular digital data https://doi.org/10.15468/dl.2xcus9 Digital and/or Hardcopy 1980 2020 observed GBIF sources Species location data Biodiversity Information Serving Our Nation 20201201 tabular digital data Individual data sets provided by BISON can be found in the attached file, bison_citations.txt. https://bison.usgs.gov Digital and/or Hardcopy 1980 2020 observed BISON data Species location data Gregor T. Auble Jonathan M. Friedman Patrick B. Shafroth Michael F. Merigliano Michael L. Scott 20121011 tabular digital data https://pubs.usgs.gov/ds/708/ Digital and/or Hardcopy 1996 2002 observed Auble ecological data Species location data Eduardo Gonzalez Anna A. Sher Robert M. Anderson Robin F. Bay Daniel W. Bean Gabriel J. Bissonnete Berenger B. David J. Cooper Kara Dohrenwend Kim D. Eichhorst Deborah Kennard Rebecca Harms-Weissinger Annie L. Henry Lori J. Makarick Steven M. Ostoja Lindsay V. Reynolds W. Wright Robinson Patrick B. Shafroth 20170417 tabular digital data https://datadryad.org/stash/dataset/doi:10.5061/dryad.70mt1 Digital and/or Hardcopy 2000 2014 observed Gonzalez ecological data Species location data Laura G. Perry Lindsay V. Reynolds Patrick B. Shafroth 20180619 tabular digital data https://www.sciencebase.gov/catalog/item/5aba7932e4b081f61abb4c20 Digital and/or Hardcopy 2012 observed Perry ecological dataset Species location data Southwestern Environmental Information Network (SEINET) 20201201 tabular digital data https://swbiodiversity.org/seinet/ Digital and/or Hardcopy 1980 2020 observed SEINet data Species location data US Army Corps of Engineers 2018 tabular digital data https://nid.sec.usace.army.mil/ Digital and/or Hardcopy 2018 observed National Inventory of Dams (NID) Potential predictors included: Total Upstream Reservoir Storage Volume and HUC12 Reservoir Storage Volume. Corrected dam locations in the state of Wyoming were obtained from the Wyoming State Engineer’s Office (N. Graves, 2019, pers. comm.). Prior to reservoir storage calculations, we removed duplicate storage records for large reservoirs with multiple dams, removed chemical storage reservoirs (e.g., tailings, ash, and evaporation ponds), and corrected locations of dams mislocated within the wrong USA county. We aggregated total reservoir storage upstream of each HUC12 using the HUCAgg package in R (Blodgett, 2017, https://github.com/USGS-R/HUCAgg). Suming Jin Collin Homer Limin Yang Patrick Danielson Jon Dewitz Congcong Li Zhe Zhu George Xian Danny Howard 2018 raster digital data Jin, S. M., Homer, C., Yang, L. M., Danielson, P., Dewitz, J., Li, C. C., . . . Howard, D. (2019). Overall Methodology Design for the United States National Land Cover Database 2016 Products. Remote Sensing, 11(24). doi:10.3390/rs11242971 https://www.mrlc.gov/data/nlcd-2016-land-cover-conus https://doi.org/10.3390/rs11242971 Digital and/or Hardcopy 2001 2004 observed NLCD Potential predictors included: 2001 Impervious Cover, 2004 Local Developed Cover, 2004 Local Agricultural Cover, 2004 HIC12 Developed Cover, and 2004 HUC12 Agricultural Cover. We derived percent cover by developed (classes 21-24) and agricultural (classes 81-82) land-uses within each 90-m pixel and within each HUC12. US Geological Survey National Hydrography 20170411 vector digital data https://www.usgs.gov/national-hydrography/watershed-boundary-dataset Digital and/or Hardcopy 2016 observed Watershed Boundary Dataset 12-digit watershed boundaries (HUC12) for calculations of watershed-scale ditch abundance, reservoir storage, and land cover. 6-digit watershed boundaries (HUC6) for calculations of actual and expected occurrence record densities by watershed. USDI-BLM 20210101 tabular digital data Denver Bureau of Land Management USDI-BLM – Assessment, Inventory, and Monitoring (AIM) Terrestrial Indicators Raw Dataset. Version 2021. Denver: Bureau of Land Management. January 2021–January 2022. https://www.blm.gov/AIM/AIMDataPortal. https://www.blm.gov/AIM/AIMDataPortal Digital and/or Hardcopy 20110101 20210101 observed Terrestrial AIM Species location data Occurrence data was acquired using the Species Occurrence Data Script (https://doi.org/10.5281/zenodo.5745220) from online occurrence data repositories including Global Biodiversity Information Facility (GBIF), Biodiversity Information Serving Our Nation (BISON), and the Early Detection and Distribution Mapping System (EDDMapS), and other repositories including the Bureau of Land Management’s (BLM) Assessment, Inventory and Management database and the BLM and National Park Service’s (NPS) National Invasive Species Information Management System (NISIMS). AIM and NISIMS were obtained directly from the BLM and NPS by request. In addition, occurrence data were acquired from published datasets from large-scale ecological studies, including Auble et al. (2012), Gonzalez et al. (2017) and Perry et al. (2018) (see "Source Inputs" for more details). Occurrence data were used as inputs in VisTrails Software for Assisted Habitat Modeling (SAHM) workflows to create each distribution layer. 20210501 Albers Conical Equal Area 20.0 60.0 -96.0 40.0 0.0 0.0 row and column 98.4693338923368 98.46933389233682 meters North_American_Datum_1983 GRS 1980 6378137.0 298.2572221010042 ModelTrainingData.csv Comma Separated Value (CSV) file containing data. Producer Defined Taxon Scientific name of species Producer Defined Scientific name Background Background method flag Producer Defined random Random background locations Producer defined Salix Target background locations Producer defined X X coordinate in Alber's Equal Area Producer Defined -2235446.054 -279155.7975 meters Y Y coordinate in Alber's Equal Area Producer Defined -1362299.592 1323648.428 meters responseBinary Flag indicating if presence or background location Producer Defined 1 Presence location Producer defined -9998 Background location Producer defined bio_01_1981_2010_800m Annual mean temperature Producer Defined NA No data Producer defined -5.76432 23.8117 degree Celsius bio_02_1981_2010_800m between the maximum and minimum temperature for each month) Producer Defined NA No data Producer defined 2.61753 21.1818 degree Celsius bio_03_1981_2010_800m Isothermality (how much the day-to-night temperatures oscillate relative to the summer-to-winter oscillations) Producer Defined NA No Data Producer defined 24.9729 69.9806 Percent bio_04_1981_2010_800m Temperature seasonality (standard deviation ×100) Producer Defined NA No data Producer defined 1.06962 12.3004 index bio_04a_1981_2010_800m Temperature Seasonality (standard deviation ×100) Producer Defined NA No Data Producer defined 4.52844 53.4343 index bio_05_1981_2010_800m Maximum temperature of the warmest month Producer Defined NA No data Producer defined 12.1559 46.0 degree Celsius bio_06_1981_2010_800m Minimum temperature of the coldest month Producer Defined NA No Data Producer defined -21.194 12 degree Celsius bio_07_1981_2010_800m Temperature annual range (difference between minimum temperature of the coldest month and maximum temperature of the warmest month) Producer Defined NA No data Producer defined 6.0 50.9979 degrees Celsius bio_08_1981_2010_800m Mean temperature of the wettest quarter Producer Defined -12.5112 32.5 degree Celsius bio_09_1981_2010_800m Mean temperature of the driest quarter Producer Defined NA No Data Producer defined -13.6667 32.5 degree Celsius bio_10_1981_2010_800m Mean temperature of the warmest quarter Producer Defined NA No data Producer defined 3.49056 35.5 degree Celsius bio_11_1981_2010_800m Mean temperature of the coldest quarter Producer Defined NA No Data Producer defined -13.7934 15.3333 degree Celsius bio_12_1981_2010_800m Annual precipitation Producer Defined 40.111 4979.15 mm bio_13_1981_2010_800m Precipitation of the wettest month Producer Defined 7.08927 883.83 mm bio_14_1981_2010_800m Precipitation of the driest month Producer Defined 0.0 105.666 mm bio_15_1981_2010_800m Precipitation seasonality (ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation (i.e. coefficient of variation)) Producer Defined NA No Data Producer defined 6.78502 109.219 mm bio_16_1981_2010_800m Precipitation of the wettest quarter Producer Defined 17 2331.0 mm bio_17_1981_2010_800m Precipitation of the driest quarter Producer Defined 0.0 444.164 mm bio_18_1981_2010_800m Precipitation of the warmest quarter Producer Defined NA No Data Producer defined 0.0 464.809 mm Dist_to_NHDwater_mosaic Distance to water derived from NHDPlusV2 Producer Defined NA No Data Producer defined 0.0 22212.4 ETa_Jun_Aug_2003_2018 Mean monthly evapotranspiration of the summer (June through August) Producer Defined NA No Data Producer defined 0.0 803.7134 mm ETa_Mar_May_2003_2018 Mean monthly evapotranspiration of the early spring (March through May) Producer Defined 0 688.490457 mm gHM The global Human Modification map (HM) provides a cumulative measure of human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global datasets with a median year of 2016. Producer Defined NA No Data Producer defined 0 0.981385 index HUC12PercentAg Percent of the HUC12 (hydrologic unit code) the location falls in defined as agriculture by the 2016 National Land Cover Dataset (NLCD) Producer Defined NA No Data Producer defined 0 99 Percent HUC12PercentDev Percent of the HUC12 (hydrologic unit code) the location falls in defined as developed by the 2016 National Land Cover Dataset (NLCD) Producer Defined NA No Data Producer defined 0 98 Percent HUC_DAMHA.MperSQKM HUC 12 reservoir storage Producer Defined 0 37787.8 ha-m km-2 HUC_percent_ditch HUC12 ditch abundance Producer Defined 0 1 Percent/100 MAF_catchment_mosaic mean annual flow by NHDPlus catchment Producer Defined NA No Data Producer defined 0 254297 cfs MODLSTDCLIMM20011201rgb36001800_na2 Land surface temperature/emissivity Producer Defined NA No Data Producer defined 36 172 index NED_30m_ContigUSA_integer_90mwest Elevation Producer Defined NA No Data Producer defined -68051 3895050 m * 1000 NLCD2004simpleAg_90m_percent local agricultural cover Producer Defined NA No Data Producer defined 0.0 100.0 Percent NLCD2004simpleDev_90m_percent Local developed cover Producer Defined NA No Data Producer defined 0.0 100.0 Percent NLCDImp2001_albers Impervious surface cover from NLCD Producer Defined NA No Data Producer defined 0 127 PPT_ETo_Jun_Aug_1981_2018 Potential water deficit of summer months calculated by averaging potential evapotranspiration subtracted from precipitation over a given time period. Producer Defined NA No Data Producer defined -1036.54969 27.72298615 mm PPT_ETo_Mar_May_1981_2018 Potential water deficit of early spring months calculated by averaging potential evapotranspiration subtracted from precipitation over a given time period. Producer Defined NA No Data Producer defined 0 1324.616 mm PPT_Mar_May_1981_2018 Mean precipitation of early spring (March-May) Producer Defined NA No Data Producer defined 0.0 1324.616239 mm PPT_Oct_Sept_1981_2017 Mean annual precipitation Producer Defined NA No Data Producer defined 0.0 5741.6352560000005 mm slope_degrees_NED_90m_west_int Slope Producer Defined NA No Data Producer defined 0 113 degree SO_catchment_mosaic Stream order Producer Defined NA No Data Producer defined 0 9 index TMEAN_March_May_1981_2017.tmean Mean temperature of spring months (March through June) Producer Defined NA No Data Producer defined -4.365239431 24.52220948 degree Celcius TMIN_Dec_Feb_1981_2010_mean Minimum temperature of winter months (December through February) is the monthly mean minimum temperature over the given time period. Producer Defined NA No Data Producer defined -20.0678 10.75336 degree Celcius TMIN_March_May_1981_2017.tmmn Minimum temperature of spring months (March through May) is the monthly mean minimum temperature over the given time period. Producer Defined << empty cell >> No Data Producer defined -10.57835482 16.65699014 degree Celcius TMIN_Sept_Nov_1981_2017.tmmn Minimum temperature of fall months (September through November) is the monthly mean minimum temperature over the given time period. Producer Defined NA No Data Producer defined -8.434240342999999 19.33737627 degree Celcius tpi133_cleaned_int90m topographic position index 16-km2 Producer Defined NA No Data Producer defined -359.328 608.386 index tpi21_cleaned90m topographic position index 0.4-km2 Producer Defined NA No Data Producer defined -222.957 284.81 index tpi67_cleaned90m topographic position index 4-km2 Producer Defined NA No Data Producer defined -8.43424 19.33738 index UP_DAMHA_MperSQKM upstream reservoir storage Producer Defined NA No Data Producer defined 0 1203.91 ha-m km-2 upditch upstream ditch abundance Producer Defined NA No Data Producer defined 0 1 %/100 WDA_catchment_mosaic drainage area by NHDPlus catchment Producer Defined NA No Data Producer defined 0 611546 km2 U.S. Geological Survey GS ScienceBase mailing address

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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. Digital Data https://doi.org/10.5066/P9LIB2TF None 20220926 Fort Metadata Specialist U.S. Geological Survey, Fort Collins Science Center Fort Metadata Specialist mailing

2150 Centre Avenue Bldg C

Fort Collins CO 80526 United States 970-226-9100 970-226-9230 fortdatamanagement@usgs.gov FGDC Biological Data Profile of the Content Standard for Digital Geospatial Metadata FGDC-STD-001.1-1999