Elizabeth K. Orning Bryan C. Tarbox Catherine S. Jarnevich Lindy Garner James R. Meldrum Cameron L. Aldridge 20260218 model (ST-Sim model libraries) Orning, E.K., B.C. Tarbox, C.S. Jarnevich, L. Garner, J.R. Meldrum, and C.L. Aldridge. 2025. Regional state-and-transition simulation models of invasive annual grass across the sagebrush biome (2022-2042): U.S. Geological Survey data release, https://doi.org/10.5066/P1YKOV8D. https://doi.org/10.5066/P1YKOV8D Elizabeth K. Orning Bryan C. Tarbox Catherine S. Jarnevich Lindy Garner James R. Meldrum Cameron L. Aldridge 20260301 publication Biological Conservation vol. 315 n/a Elsevier BV ppg. 111720 https://doi.org/10.1016/j.biocon.2026.111720 These data are spatially explicit state-and-transition simulation models (STSM) of invasive annual grass (IAG) vegetation dynamics for Region 3, containing datasets for model extents 3A (northcentral Wyoming) and 3B (southwest Wyoming/northeast Utah). The STSM was built using to simulate and track IAG growth and spread as well as response to management actions. Spatially explicit models were built as paired model extents (A/B) for five regions that differed in underlying site conditions, susceptibility to invasion, and the amounts of IAG cover present (seedbank, <8% cover, 8-15% cover, 15-50% cover, >50% cover, uninvaded), representing a range of simulated IAG outcomes across the sagebrush biome. Two scenarios representing alternative IAG management strategies were simulated for each model landscape. Management actions and treatment effort levels were based on historic treatment area polygon data for IAG management, represented treating approximately 1% of IAG infested areas of a landscape, and emphasized allocating treatment actions at a higher rate to areas with more IAG cover. Each STSM model library database contains multiple model scenarios and their associated outputs, and each database includes scenarios for the paired extents modeled for Region 3. The selected extents represented a range of underlying site conditions, IAG cover, and invasion dynamics present in Wyoming sagebrush landscapes and varied in size (3A=811,446 ha, 3B=480,856 ha). These models were developed and designed to help managers build informed conservation plans in sagebrush landscapes. The model outputs are intended for use in combination with local knowledge and expertise to inform strategic decision-making but do not represent predictions of expected future invasive annual grass cover. The ST-Sim file structure includes three components: 1) IAG.ssim.input folder that houses the input files used by ST-Sim, 2) the IAG.ssim.output folder which houses the scenario outputs used by ST-Sim for visualization and export of data, and 3) IAG.ssim file which is opened by ST-Sim to provide an interface to the model and outputs (i.e., ST-Sim model library stored as a SQLite database). This data release also includes end-of-simulation 5-year average invaded cover and extrapolated invaded cover datasets created from this model's simulated spatial outputs. Additional details on the ST-Sim model parameterization and invaded cover raster creation can be found in the appendices (Appendix B and C) for the larger work citation. Users are asked to cite the larger work and this data release if the database and STSM framework are used to model new study areas. 2022 2042 simulated starting with 2022 ground conditions Complete None planned Region 3A (Wyoming) and 3B (Wyoming/Utah) in Wyoming, USA -112.1923 -107.5207 44.1946 40.9694 ISO 19115 Topic Category biota environment None state-and-transition simulation model vegetation change invasive annual grass annual grass cover Sagebrush biome USGS Thesaurus invasive species vegetation state and transition modeling decision support methods USGS Metadata Identifier USGS:68a3b29fd4be0258122e0d73 None Intermountain West Ecoregion Wyoming Utah United States None Bromus tectorum Kingdom Plantae Subkingdom Viridiplantae Infrakingdom Streptophyta Superdivision Embryophyta Division Tracheophyta Subdivision Spermatophytina Class Magnoliopsida Superorder Lilianae Order Poales Family Poaceae Genus Bromus Species Bromus tectorum TSN: 40524 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. Elizabeth K. Orning USGS - ROCKY MOUNTAIN REGION Biologist mailing and physical

Fort Collins Science Center, NRRC Bldg C

Fort Collins CO 80526 US 970-226-9309 eorning@usgs.gov Environment as of Metadata Creation: Microsoft Windows 11 (version 23H2 (Build 22631.4890) Experience Pack 1000.22700.1067.0); Virtual machine (16 CPU, 64GB RAM); Esri ArcGISPro© (version 3.4.0, copyright 2024); ApexRMS SyncroSim© software (version 2.5.9 with minimum version 3.4.5 stsim (ST-Sim) package, copyright 2024) No formal attribute accuracy tests were conducted. See larger work citation for a detailed description of STSM model development and evaluation. No formal logical consistency tests were conducted. A literature review of field studies across the area and consultation with experts provided input for the STSM model components and their relationships. See larger work citation for a detailed description of model development and evaluation. Data set is considered complete for the information presented, as described in the abstract. Users are advised to refer to the larger work citation for a detailed description of model development, including definitions, omissions, assumptions, and rationale for these choices. A formal accuracy assessment of the horizontal positional information in the data set has not been conducted. All data sources (STSM model input and output, and invaded cover layers) reflect 30-meter (m) by 30-m spatial resolution. A formal accuracy assessment of the vertical positional information in the data set has either not been conducted, or is not applicable. Kevin Doherty David M Theobald Martin C Holdrege Lief A Wiechman John B Bradford 20220826 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p94y5cdv Digital and/or Hardcopy 2017 2020 ground condition SCD management zone classes Data were used as strata to track management outcomes in ST-Sim simulations. Michael S O'Donnell Daniel J Manier 20221024 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9ulgc03 Digital and/or Hardcopy 1981 2010 ground condition Soil climate Data were used to define initial ecological site conditions used to vary vegetation growth and transitions in ST-Sim simulations. Devendra Dahal Stephen Boyte Neal J Pastick Logan J Megard Kory Postma 20211220 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9gc5jvg Digital and/or Hardcopy 2021 publication date REPMs cheatgrass cover Data were used to develop invasive annual grass state classes to initialize model. Catherine S Jarnevich Jillian M Laroe Peder Engelstad Brandon R Hays Grace C Henderson Demetra A Williams Keana S Shadwell Ian S Pearse Janet S Prevey Helen R Sofaer 20221102 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9v54h5k Digital and/or Hardcopy 2022 publication date INHABIT cheatgrass suitability Data were used to develop invasive annual grass state classes to initialize model and to establish where management transitions could occur in ST-Sim simulations. 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 http://rangeland.ntsg.umt.edu/data/rap/rap-vegetation-cover/ Digital and/or Hardcopy 2017 2021 ground condition Rangeland analysis program (RAP) perennial grass (2017-2021) Data were used to define initial ecological site conditions used to vary vegetation growth and transitions in ST-Sim simulations. Michael O'Donnell David R Edmunds Cameron Aldridge Julie A Heinrichs Adrian P Monroe Peter S Coates Brian G Prochazka Steve Hanser Lief A Wiechman 20221201 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p9d1k0lx Digital and/or Hardcopy 2022 publication date Hierarchically nested spatial scales Data were used to define model extents for ST-Sim simulations. David Pilliod Justin Welty Michelle Jeffries 20190417 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p98obols Digital and/or Hardcopy 1917 2022 ground condition Land treatment digital library Data were used to develop management scenario for ST-Sim simulations. Michelle I. Jeffries Sean P. Finn 20190513 Dataset https://www.sciencebase.gov U.S. Geological Survey https://doi.org/10.5066/p950h8hs Digital and/or Hardcopy 2019 publication date Sagebrush biome boundary Data were used to define model extents for ST-Sim simulations. James M. Omernik Glenn E. Griffith 20140916 publication Environmental Management vol. 54, issue 6 n/a Springer Science and Business Media LLC Omernik, J.M., and G.E. Griffith. 2014. Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management 54(6):1249–1266. https://doi.org/10.1007/s00267-014-0364-1. https://doi.org/10.1007/s00267-014-0364-1 Digital and/or Hardcopy 2014 publication date Ecoregions Data were used to define model extents for ST-Sim simulations. Creation of Region 02 STSMs: All of the simulations described here and in the larger citation were created and run using the Syncrosim© software platform (version 2.5.9 with minimum version 3.4.5 stsim (ST-Sim) package, copyright 2024 ApexRMS); versions of the software exist for Windows (64-bit Windows 7 or higher) and Linux (running Mono). The following steps are required to run regional models in SyncroSim using the ST-Sim package: 1. Download and install the latest Windows version of the free SyncroSim (with ST-Sim) software, available at: https://syncrosim.com/download/ 2. Download and unzip the case study SyncroSim model files from the Science Base Repository provided as data for this project. 3. Use the SyncroSim software to open the library file called IAG_Region03_tabular.ssim (for tabular only dataset) or IAG_Region03_spatial.ssim (for single iteration spatial and tabular dataset). Users are encouraged to make use of the documentation and reference materials ApexRMS has available for SyncroSim (https://docs.syncrosim.com/) and ST-Sim (https://docs.stsim.net/), including tutorials (https://docs.stsim.net/getting_started/tutorials.html) and publications that detail the STSM framework (Daniel et al., 2016), as well as examples of application (Daniel et al., 2017). For each invasive annual grass (IAG) regional STSM, landscapes were classified into discrete IAG cover state classes and stratified by resistance and resilience (R&R) soil moisture and temperature regime classifications. R&R provided a base for incorporating variation in the model, affecting transition probabilities as well as vegetation growth and treatment effects. Sagebrush Conservation Design classifications of the landscape were included in models as a secondary management zone strata to track simulated outcomes relative to core sagebrush areas of interest. State classes were defined based on IAG percent cover class bins and spatially initialized. STSMs included transitions among state classes from IAG growth and spread and management actions. Invaded cover was tracked in models as a state class attribute for each timestep, calculated as the predicted hectares of all IAG cover classes. IAG cover state classes, invaded cover state class attributes, and transitions associated with different management scenarios were run for a 20-year duration using an annual timestep. Initial model conditions corresponded to the year 2022; simulations were repeated for 10 Monte Carlo realizations (tabular libraries only). Several steps were used to prepare spatial data inputs for use in SyncroSim and to create regional STSMs of IAG vegetation for sagebrush landscapes. Users are encouraged to review the larger work citation and Appendix B which hold further details on model parameterization. The following processing steps were used to prepare and create data inputs for SyncroSim: Step 2: Creation of regional model extents Step 3: Creation of resistance and resilience (R&R) strata Step 4: Creation of SCD management zone strata Step 5: Creation of IAG vegetation state class spatial initialization inputs Step 6: Vegetation growth and management transitions Step 7: IAG management scenarios Step 8: IAG SyncroSim library & scenario set up Step 9: Additional SyncroSim file organization & naming convention details for spatial data NOTE: All spatial condition rasters (strata, vegetation state) were projected into NAD83 (Datum) and the Contiguous Albers Equal Area Conical USGS coordinate system and formatted for input to SyncroSim as single-band, 30x30 (double precision, 64-bit depth) TIFF files with ‘No Data’ values = -9999. References: (1) ApexRMS., 2016. State-and-transition simulation models. https://apexrms.com/ (accessed 15 February 2021). (2) Daniel, C.J., Frid, L., Sleeter, B.J., Fortin, M.J., 2016. State-and-transition simulation models: a framework for forecasting landscape change. Methods in Ecology & Evolution 7(11), 1413-1423. https://doi.org/10.1111/2041-210X.12597 (3) Daniel, C.J., Ter-Mikaelian, M.T., Wotton, B.M., Rayfield, B., Fortin, M.J., 2017. Incorporating uncertainty into forest management planning: timber harvest, wildfire, and climate change in the boreal forest. Forest Ecology & Management 400, 542-554. https://doi.org/10.1016/j.foreco.2017.06.039 2024 Creation of regional STSM extents: A regional sampling approach of hierarchically nested model extents was used to implement 10 regional simulation models. An existing framework of nested spatial scales (O'Donnell et al. 2019, 2022a,b) was used to select extents based on IAG complexity (see main text 'Methods' for full description). Level 13 climate clusters were used to define five sample regions across the sagebrush biome (Jeffries and Finn 2019). Two level 3 neighborhood clusters (NC) were selected within each region (see Appendix B, Table S4 and S5). Eastern Range and Great Basin climate clusters overlapped more than one ecoregion (Omernik and Griffith, 2014), and three climate clusters were defined as distinct clusters (Bi-State, Washington, Jackson Hole Wyoming; O’Donnell et al., 2022a,b). The Great Basin climate cluster overlapped the Intermountain West and Southern Great Basin ecoregions, and the Eastern Range climate cluster overlapped all three ecoregions. Two neighborhood clusters (one from each overlap zone) were selected to serve as model extents in our sample design and were considered climate cluster-ecoregion outliers (Region 4). Two neighborhood cluster model extents were maintained per ecoregion. Given their smaller size, one neighborhood cluster was selected from the Bi-State and Washington climate clusters (Region 5). The Jackson Hole climate cluster was excluded from our sample design as the climatic effects and regional conditions were like those of the Wyoming climate cluster. Selected extents were: NC-ID-206 (Idaho), NC-ID-201 (Oregon), NC-ID-100 (Montana), NC-ID-131 (Wyoming), NC-ID-273 (Wyoming), NC-ID-256 (Wyoming/Utah), NC-ID-145 (Nevada), NC-ID-77 (Utah), NC-ID-6 (California/Nevada), NC-ID-14 (Washington). The following steps were used in ArcGISPro to create model extraction extents: Step 2.1: Selected NC polygons were exported from attribute tables as single polygon .shp files and converted to raster using the 'Polygon to raster' tool. Step 2.2: The 'Reclassify' tool was used to create binary rasters for NC (value=1) and 'NoData' as (value=0). Regional extents (column x rows): 1A = 2216 x 2535; 2386171 (top), 2310121 (bottom), -1661239 (left), -1594759 (right) 1B = 2174 x 2305; 2439661 (top), 2370511 (bottom), -1934869 (left), -1869649 (right) 2A = 3872 x 2135; 2821621 (top), 2757571 (bottom), -1034779 (left), -918619 (right) 2B = 2181 x 3046; 2391361 (top), 2299981 (bottom), -810199 (left), -744769 (right) 3A = 3436 x 2624; 2416291 (top), 2337571 (bottom), -1056649 (left), -953569 (right) 3B = 2262 x 2362; 2172421 (top), 2101561 (bottom), -1292599 (left), -1224739 (right) 4A = 3318 x 2742; 2176171 (top), 2093911 (bottom), -1649269 (left), -1549729 (right) 4B = 2321 x 3903; 2071471 (top), 1954381 (bottom), -1416799 (left), -1347169 (right) 5A = 2062 x 2350; 1975231 (top), 1904731 (bottom), -2017789 (left), -1955929 (right) 5B = 2211 x 2466; 2902291 (top), 2828311 (bottom), -1863529 (left), -1797199 (right) References: (1) Jeffries, M.I., and Finn, S.P., 2019, The Sagebrush Biome Range Extent, as Derived from Classified Landsat Imagery: U.S. Geological Survey data release, https://doi.org/10.5066/P950H8HS. (2) O'Donnell, M.S., D.R. Edmunds, C.L. Aldridge, J.A. Heinrichs, P.S. Coates, B.G. Prochazka, and S.E. Hanser. 2019. Designing multi-scale hierarchical monitoring networks for wildlife to support management: a sage-grouse case study. Ecosphere 10(9):e02872. https://doi.org/10.1002/ecs2.2872. (3) O'Donnell, M.S., D.R. Edmunds, C.L. Aldridge, J.A. Heinrichs, A.P. Monroe, P.S. Coates, B.G. Prochazka, S.E. Hanser, and L.A. Wiechman. 2022a. Defining biologically relevant and hierarchically nested population units to inform wildlife management. Ecology and Evolution 12:e9565. https://doi.org/10.1002/ece3.9565. (4) O'Donnell, M. S., D.R. Edmunds, C.L. Aldridge, J.A. Heinrichs, A.P. Monroe, P.S. Coates, B.G. Prochazka, S.E. Hanser, and L.A. Wiechman. 2022b. Hierarchically nested and biologically relevant range-wide monitoring frameworks for greater sage-grouse, western U.S. U.S. Geological Survey Data Release. https://doi.org/10.5066/P9D1K0LX. (5) Omernik, J.M., and G.E. Griffith. 2014. Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management 54(6):1249–1266. https://doi.org/10.1007/s00267-014-0364-1. 2023 Creation of resistance and resilience (R&R) strata: In the model, the landscape was divided into different resistance and resilience (R&R) strata representing underlying ecological site condition and potential risk of invasion. R&R classes were updated from Chambers et al. 2014 concepts and derived from continuous estimates of soil temperature and moisture regime (STMR) data (O’Donnell & Manier 2022a,b) and Rangeland Analysis Program (RAP) 5-year average (2017-2021) perennial forb and grass (PFG) coverage data (v3.0; Allred et al. 2021). To generate R&R strata, STMR classes were 1) converted to four classes (low, low-medium, high-medium, high) following Maestas et al. (2016) using the estimated relationships with annual herbaceous cover (O’Donnell and Manier 2022a,b), 2) modified based on perennial herbaceous cover (using 5-year mean cover), whereby low perennial cover (≤7%) downgraded the R&R class and high perennial cover (>=20%) upgraded the R&R class (Chambers et al. 2014, Germino et al. 2022), and 3) distilled into three R&R classes based on equal intervals. The following steps were used in ArcGISPro to create R&R strata inputs for SyncroSim: Step 3.1: Used 'Cell Statistics' tool to calculate a 5-year average (MEAN) value for 2017-2021 PFG cover; interim float point raster Step 3.2: Converted step 3.1 raster to an integer raster using the 'Raster Calculator' tool; OutRas = Int(InRas + 0.5) Step 3.3: Used ‘Reclassify’ tool to convert step 3.2 raster to three-class raster where PFG cover 0–7% = –1, 8–19% = 0, and 20% or greater = 1. Step 3.4 Used ‘Reclassify’ tool to create four-class raster based on STMR classes where STMR 11xx–2500 = 1, 25xx = 2, 3000–54xx = 3, and 5500 or greater = 4. Step 3.5: Used ‘Raster Calculator’ tool to add step 3.3 raster to step 3.4 raster, creating a raster with values ranging from 0 to 5. Step 3.6: Used ‘Reclassify’ tool to convert step 3.5 raster to three-class raster where 0–1 = 1, 2–3 = 2, and 4–5 = 3 (i.e., low, medium and high, respectively). Step 3.7: The 'Extract by Mask' tool was used to clip step 3.6 raster to the sagebrush biome and regional model extents. Raster classification codes: 1 = High 2 = Moderate 3 = Low References: (1) Allred, B.W., B.T. Bestelmeyer, C.S. Boyd, C. Brown, K.W. Davies, M.C. Duniway, L.M. Ellsworth, T.A. Erickson, S.D. Fuhlendorf, T.V. Griffiths, V. Jansen, M.O. Jones, J. Karl, A. Knight, J.D. Maestas, J.J. Maynard, S.E. McCord, D.E. Naugle, H.D. Starns, D. Twidwell, and D.R. Uden. 2021. Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution 12(5):841-849. http://dx.doi.org/10.1111/2041-210x.13564. (2) Chambers, J.C., R.F. Miller, D.I. Board, D.A. Pyke, B.A. Roundy, J.B. Grace, E.W. Schupp, and R.J. Tausch. 2014. Resilience and resistance of sagebrush ecosystems: implications for state and transition models and management treatments. Rangeland Ecology & Management 67(5), 440-454. https://doi.org/10.2111/REM-D-13-00074.1. (3) Germino, M.J., C.R. Kluender, and C.R. Anthony. 2022. Plant community trajectories following livestock exclusion for conservation vary and hinge on initial invasion and soil-biocrust conditions in shrub steppe. Conservation Science and Practice 4(12):e12838. https://doi.org/10.1111/csp2.12838. (4) Maestas, J.D., S.B. Campbell, J.C. Chambers, M. Pellant, and R.F. Miller. 2016. Tapping soil survey information for rapid assessment of sagebrush ecosystem resilience and resistance. Rangelands 38(3):120-128. https://doi.org/10.1016/j.rala.2016.02.002. (5) O'Donnell, M.S. and D.J. Manier. 2022a. Soil-climate estimates in the western United States: climate averages (1981-2010): U.S. Geological Survey data release, https://doi.org/10.5066/P9ULGC03. (6) O'Donnell, M.S. and D.J. Manier. 2022b. Spatial estimates of soil moisture for understanding ecological potential and risk: a case study for arid and semi-arid ecosystems. Land 11(10): 1856. https://doi.org/10.3390/land11101856. 2023 Creation of SCD management zone strata: The SCD separates landscapes into core sagebrush areas (CSA), growth opportunity areas (GOA), other rangeland areas (ORA), and non-sagebrush areas (NSA) based on the results of an evaluation of sagebrush ecological integrity (SEI) under multiple threats (Doherty et al. 2022a). We used the 2022 categorical 4-class SCD classification of SEI values (Doherty et al. 2022b) as SCD management zone strata in the model. InRas called: SEI_2017_2020_30m_Current.tif = SCD classification layer for biome The following steps were used in ArcGISPro to create SCD Zone spatial strata inputs for SyncroSim: Step 4.1: The 'Extract by mask' tool was used to clip the "SEI_2017_2020_30_Current.tif" raster to each regional model extent. Step 4.2: Each regional extent clip (step 4.1) was exported as a single-band, 30x30 (double precision, 64-bit depth) TIFF file with ‘No Data’ values set to -9999. Raster classification codes: 0 = Non-sagebrush areas 1 = Core sagebrush areas 2 = Growth opportunity areas 3 = Other rangeland areas References: (1) Doherty, K., D.M. Theobald, J.B. Bradford, L.A. Wiechman, G. Bedrosian, C.S. Boyd, M. Cahill, P.S. Coates, M.K. Creutzburg, M.R. Crist, S.P. Finn, A.V. Kumar, C.E. Littlefield, J.D. Maestas, K.L. Prentice, B.G. Prochazka, T.E. Remington, W.D. Sparklin, J.C. Tull, Z. Wurtzebach, and K.A. Zeller. 2022a. A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081. (2) Doherty, K., Theobald, D.M., Holdrege, M.C., Wiechman, L.A., and Bradford, J.B., 2022b, Biome-wide sagebrush core habitat and growth areas estimated from a threat-based conservation design: U.S. Geological Survey data release, https://doi.org/10.5066/P94Y5CDV. 2022 Creation of IAG vegetation state class spatial initialization inputs: Cover class bins used as IAG state classes in the model reflect broad treatment thresholds and IAG threat level classifications used to define sagebrush ecological integrity in the SCD (Doherty et al. 2022). For model initialization, landscapes were classified into seven discrete IAG cover state classes (undetected seedbank, detected seedbank, <8% cover, 8-15% cover, 15-50% cover, >50% cover, uninvaded:susceptible). State classes were derived from cheatgrass fractional cover (Dahal et al. 2022) and cheatgrass habitat suitability (Jarnevich et al. 2023). To initialize detected and undetected seedbank states, we combined a measure of proximity to existing cover with habitat suitability and 0% cover and ‘no data’ values. Cells that were ≤ 30m from any cell with cover (i.e., >1% IAG cover) and mapped to have 0% cover were defined as detected seedbank; cells that were ≤ 30m from any existing cover, susceptible to IAG invasion, and mapped to no data, were defined as undetected seedbank. Cells mapped to have 0% cover or ‘no data’ that were > 30m from any cell with mapped cover and susceptible to invasion were defined as an ‘uninvaded: susceptible’ state while cells unsusceptible to invasion were masked and not included in simulation models. After states are initialized in the model, additional undetected cover class states (<8%, 8-15%, 15-50% cover) are generated within the model as the undetected seedbank state grows and transitions (see Fig. 1 of main text). In addition, an uninvaded: previous cover state class exists to track cells on the landscape that previously held IAG cover, were treated and transitioned to uninvaded but remain susceptible to reinvasion processes. InRas called: ensemble_10th_masked = INHABIT 2022 cheatgrass habitat suitability Cheatgrass_2022_PercentCover = 2022 July01 cheatgrass fractional cover The following steps were used in ArcGISPro to create IAG state class inputs for SyncroSim: Step 5.1: The10th percentile masked 2022 INHABIT cheatgrass habitat suitability raster was resampled from 98m to 30m using the 'Resample' tool and the 'Nearest' neighbor center point cell sampling technique. Step 5.2: The 'Reclassify' tool was used to reclassify step 5.1 output raster into a binary layer for > 3 model predicted cheatgrass occurrence; the -32767 (No Data) and 100 (masked) values were reclassed to 0. Step 5.3: The 'Extract by Mask' tool was used to clip step 5.2 raster to the sagebrush biome and regional model extents. Step 5.4: The 'Reclassify' tool was used to reclassify the 2022 Cheat grass fractional cover raster to an interim 5-class raster based continuous cover value grouping (1 = 1-8% IAG, 2 = 8-15% IAG, 3 = 15-50% IAG, 4 = >50% IAG, 0 = potential seedbank). Step 5.5: 0 and 255 value cells were reclassified based on proximity to detected IAG classes (values 1-4, step 5.4) to build detected (values=0) and undetected (values=255) 'seedbank' state classes. Step 5.6: The 'Reclassify' tool was used to create a binary proximity to IAG layer by reclassifying step 5.4 raster to single '1' class for all detected IAG classes (1-4); '0' reclassed to 'NoData'. Step 5.7: The 'Euclidean Distance' tool was used on step 5.6 output to create a continuous distance-to-detected-IAG invaded cell raster. Step 5.8: A conditional statement was used in the 'Raster Calculator' tool to reclass step 5.7 output to a binary layer with 1 = values <30m, 0 = values >30m. Con statement: Con("step5.7OutRas"<=30, 1, 0). Step 5.9: A conditional statement was used in the 'Raster Calculator' tool to classify IAG state class bins by Cheatgrass cover (step 5.4) and undetected seedbank class by susceptibility (step 5.3) and proximity to detected infestations (step 5.8 output). Con statement: Con("Cheatgrass_2022_PercentCover">=1,Con(("Cheatgrass_2022_PercentCover">=1) & ("Cheatgrass_2022_PercentCover"<=8),1,Con(("Cheatgrass_2022_PercentCover">8) & ("Cheatgrass_2022_PercentCover"<=15),2,Con(("Cheatgrass_2022_PercentCover">15) & ("Cheatgrass_2022_PercentCover"<=50),3,Con(("Cheatgrass_2022_PercentCover">50) & ("Cheatgrass_2022_PercentCover"<=100),4,Con("Cheatgrass_2022_PercentCover">100,Con("IAG_SCDclass_30mED_SBB"==1,7,Con("Cheatgrass_susceptibility_SBB"==1,11,-9999)),-9999))))),Con("Cheatgrass_2022_PercentCover"==0,Con("IAG_SCDclass_30mED_SBB"==1,0,Con("Cheatgrass_susceptibility_SBB"==1,11,-9999)),-9999)) Step 5.10: Used the 'Reclassify' tool to reclass step 5.9 output into classes in above Table (i.e., -9999 set to NoData) Step 5.11: Used 'Extract by Mask' tool to clip step 5.10 output to the sagebrush biome and regional model extents. Raster classification codes: 0 = Detected seedbank 1 = <8% IAG cover 2 = 8-15% IAG cover 3 = 15-50% IAG cover 4 = Converted (>50% IAG cover) 7 = Undetected seedbank 11 = Uninvaded: susceptible References: (1) Dahal, D., Boyte, S.P., Parajuli, S., Pastick, N.J., Oimoen, M.J., Megard, L.J., and Shrestha, D., 2021, Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrass in the Sagebrush Biome, USA, 2016 - 2021 (ver. 2.0, December 2022): U.S. Geological Survey data release, https://doi.org/10.5066/P9GC5JVG. (2) Doherty, K., D.M. Theobald, J.B. Bradford, L.A. Wiechman, G. Bedrosian, C.S. Boyd, M. Cahill, P.S. Coates, M.K. Creutzburg, M.R. Crist, S.P. Finn, A.V. Kumar, C.E. Littlefield, J.D. Maestas, K.L. Prentice, B.G. Prochazka, T.E. Remington, W.D. Sparklin, J.C. Tull, Z. Wurtzebach, and K.A. Zeller. 2022b. A sagebrush conservation design to proactively restore America’s sagebrush biome: U.S. Geological Survey Open-File Report 2022–1081, 38 p., https://doi.org/10.3133/ofr20221081. (3) Jarnevich, C.S., LaRoe, J., Engelstad, P., Hays, B., Henderson, G., Williams, D., Shadwell, K., Pearse, I.S., Prevey, J.S., Sofaer, H.R., 2023, INHABIT species potential distribution across the contiguous United States (ver. 3.0, February 2023): U.S. Geological Survey data release, https://doi.org/10.5066/P9V54H5K. 2023 Vegetation growth and management transitions: IAG dispersal and spread were simulated using a Weibull distribution of annual spread distances applied spatially based on IAG cover class state (dispersal distance kernel). Dispersal introduces seeds to uninvaded locations susceptible to IAG, after which a seedbank state is established through a probabilistic transition. From the seedbank state, cells advance to the <8% cover class state through a probabilistic cover establishment transition. After cover establishment, IAG growth (i.e., patch infill) follows a deterministic pattern in the model, with transitions to the next higher cover class at a rate of increase equivalent to 0.5–3% cover per year, depending on ecological site conditions with the fastest rates applied to Low R&R classified cells (see Appendix B, Table S1 of main text). Without management, IAG cover continues to infill up to 50% cover, with transition to a converted state (>50% cover) simulated using conservative probabilities representing aspatial fire effects (see main text for full conceptual STSM description). Management transitions included one inventory action (IAG occurrence detection) and three treatment actions (early detection, rapid response (EDRR), aerial herbicide application, and native grass seeding). In the model, the probability of detection through inventory actions increases with increasing IAG cover (see Table 2, main text) and treatment actions in the model could only occur at sites where IAG was detected. The effectiveness of actions (25-100%) varied by R&R strata and determined the amount of IAG cover reduction. In the model, EDRR represents a collection of actions including field survey, herbicide application, and post-treatment monitoring. EDRR treatment targeted low abundance infestations (i.e., <8% states) while aerial herbicide application (AHA) treatments targeted converted and moderate to high cover infestations (i.e., state classes >8%). Native grass seeding (NGS) was considered a secondary treatment action targeted toward moderate to high cover infestations (i.e., state classes 8-50%), excluding IAG converted states, and was applied less frequently to high infestations (15-50% class). NGS treatment was broken down into two pieces: the impact of native grass restoration on the growth of IAGs (represented in the model through a patch infill deceleration factor), and the probability of a cell receiving grass seeding in addition to AHA. For infestations that received NGS, the patch infill deceleration factor increases the time to deterministic transitions between cover classes (see Appendix B, Table S1 and Fig. S4 of main text). 2023 IAG management scenarios: Two scenarios were simulated for each regional landscape to test the regional sampling framework and establish baseline IAG outcomes. A 'No Management' scenario representing growth and transition of IAG cover classes with no inventory or management and a 'Reactive Management' scenario representing management based on allocating treatment actions at a higher rate to areas with more IAG cover versus areas with less IAG cover (ReActMgmt). Effort and allocation of actions for the Reactive Management scenario were derived from empirical analysis of past treatment records (Land Treatment Digital Library; Pilliod et al. 2019) with the intention of representing a hypothetical management approach counterfactual to a spatially prioritized strategy like that of the SCD (e.g., one focused on addressing lower cover infestations earlier in the invasion process in key areas of sagebrush landscapes). Management actions were constrained to ensure the area treated per yearly timestep was consistent with treating approximately 1% of IAG infested areas of a landscape and historic treatment effort (see Table 1 and 3 of main text). In the management scenario, 34% of target amounts (hectares) were allocated to treating of <8% IAG cover, 21% was allocated to treating 8-15% IAG cover, 40% was allocated to treating 15-50% IAG cover, and 5% was allocated to treating >50% cover. References: (1) Pilliod, D.S., Welty, J.L., and Jeffries, M.I., 2019, USGS Land Treatment Digital Library Data Release: A centralized archive for land treatment tabular and spatial data (ver. 5.0, July 2022): U.S. Geological Survey data release, https://doi.org/10.5066/P98OBOLS (accessed 1 December 2022). 2024 IAG SyncroSim library & scenario set up: Users are encouraged to read the ST-Sim documentation to understand the organization and properties outlined below. The models, parameters, and results are organized in multiple ST-Sim library databases (stored as an SQLite databases), which can be viewed through the SyncroSim interface, using R programming (rsyncrosim package), or Python programming (pysyncrosim package) languages. Each regional folder contains a _tabular.ssim and _spatial.ssim library. Tabular libraries contain 10 iteration, tabular only output data. Spatial libraries contain a single iteration with both tabular and spatial output data. There are two scenarios for each regional IAG STSM, and only spatial input conditions (vegetation, site condition, SCD zones) vary between scenarios modeled for regions. Each model scenario is composed of 'Sub Scenarios' (Dependencies), which hold the parameter values that make up a given scenario's properties. Sub Scenarios are organized by the major scenario properties (e.g., Run Control, Pathways, Initial Conditions). Simulation results are located in a 'Results' file within each simulated regional scenario. References: (1) ST-Sim documentation, https://docs.stsim.net/reference/properties.html 2024 Additional SyncroSim file organization & naming convention details for spatial/tabular data: Interested and new STSM users are encouraged to use SyncroSim software to explore and visualize these data. Within the SyncroSim file structure, these data are organized by scenario (i.e., .ssim.input and .ssim.output folders), and STSM model scenario output includes “Spatial State”, “Spatial State Attribute”, "Spatial Transition", and "Spatial Transition Event". When viewed within SyncroSim, scenario names accompany each scenario to identify regional model extents and management action details. A Scenario Lookup .csv file is included with these data (described in 'Entity and Attribute Information') for reference and use by those viewing data outside the SyncroSim platform. Tabular result scenario IDs: Scenario-227 = No management for 3A Scenario-228 = Reactive management for 3A Scenario-235 = No management for 3B Scenario-236 = Reactive management for 3B Spatial result scenario IDs: Scenario-227 = No management for 3A Scenario-228 = Reactive management spatial results for 3A Scenario-235 = No management for 3B Scenario-236 = Reactive management for 3B Scenario spatial output folders: stsim_OutputSpatialState = spatial output rasters for State Class stsim_OutputSpatialStateAttribute = spatial output rasters for invaded cover State Attribute stsim_OutputSpatialTransition = spatial output rasters for Transitions (management actions only) stsim_OutputSpatialTransitionEvent = spatial output rasters for Transition Events (management actions only) General TIFF file terms: it = iteration (0-10) sc = state class ts = timestep (2022-2042) tg = transition group tge = transition group event sa = state attribute ST-Sim State class spatial output raster cell value codes: 0 = Detected: Seedbank 1 = Detected: Invaded - Cover <8% 2 = Detected: Invaded - Cover 8-15% 3 = Detected: Invaded - Cover 15-50% 4 = Detected: Converted (>50% cover) 7 = Undetected: Seedbank 8 = Undetected: Invaded - Cover <8% 9 = Undetected: Invaded - Cover 8-15% 10 = Undetected: Invaded - Cover 15-50% 11 = Uninvaded: susceptible 12 = Uninvaded: previous cover ST-Sim State Attribute spatial output raster (sa_183.it1.tsXXXX) cell value codes: 0.025 = previous cover 0.05 = IAG seedbank 0.15 = IAG cover <8% 0.30 = IAG cover 8-15% 0.75 = IAG cover 15-50% 1.0 = IAG cover >50% ST-Sim Transition spatial output raster cell value codes: 0 – No transition occurred 1 = Defend Treatment - EDRR 1 2 = Defend Treatment - EDRR 2 3 = Grow Treatment - AHA&NGS 1 4 = Grow Treatment - AHA&NGS 2 5 = Grow Treatment - AHA 1 6 = Grow Treatment - AHA 2 7 = Mitigate Treatment - AHA 1 8 = Mitigate Treatment - AHA 2 9 = Mitigate Treatment - AHA 3 10 = Invasion: Cover Establishment 11 = Growth 12 = Invasion: Seedbank Establishment 13 = Invasion: Converted 14 = Inventory - Success 15 = Inventory - Failure 16 = EDRR1 - Failure ST-Sim Transition Event spatial output raster cell value codes: 0 – No transition occurred 1 = Defend Treatment - EDRR 1 2 = Defend Treatment - EDRR 2 3 = Grow Treatment - AHA&NGS 1 4 = Grow Treatment - AHA&NGS 2 5 = Grow Treatment - AHA 1 6 = Grow Treatment - AHA 2 7 = Mitigate Treatment - AHA 1 8 = Mitigate Treatment - AHA 2 9 = Mitigate Treatment - AHA 3 14 = Inventory - Success 15 = Inventory - Failure 16 = EDRR1 - Failure ST-Sim transition groups: 171 - Inventory: Inventory - Success, Inventory - Failure 158 - EDRR1: Defend Treatment - EDRR1, EDRR1 - Failure 136 - EDRR2: Defend Treatment - EDRR2 161 - Grow 8-15%: Grow Treatment - AHA&NGS1, Grow Treatment - AHA1 160 - Grow 15-50%: Grow Treatment - AHA&NGS2, Grow Treatment - AHA2 148 - Mitigate Treatment - AHA1 149 - Mitigate Treatment - AHA2 150 - Mitigate Treatment - AHA3 2024 Albers Conical Equal Area 29.5 45.5 -96.0 23.0 0.0 0.0 coordinate pair 0.6096 0.6096 meters North_American_Datum_1983 GRS 1980 6378137.0 298.257222101 Scenario_Lookup Comma Separated Value (CSV) file containing details on file organization of model outputs (for user reference outside SyncroSim). Producer Defined CC_short Acronym identifying the regional climate cluster model study sites were nested under. Producer Defined GB Great Basin climate cluster Producer defined ER Eastern Range climate cluster Producer defined Wyo Wyoming climate cluster Producer defined BiS Bi-State climate cluster Producer defined Wash Washington climate cluster Producer defined NCSR_short Short name identifying regionally paired individual neighborhood cluster model study sites. Producer Defined 1A Level 3 neighborhood climate cluster 206 model extent in Idaho, hierarchically nested in the Great Basin (GB) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined 1B Level 3 neighborhood climate cluster 201 model extent in Oregon, hierarchically nested in the Great Basin (GB) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined 2A Level 3 neighborhood climate cluster 100 model extent in Montana, hierarchically nested in the Eastern Range (ER) climate cluster, within the Great Plains (GP) ecoregion. Producer defined 2B Level 3 neighborhood climate cluster 131 model extent in Wyoming, hierarchically nested in the Eastern Range (ER) climate cluster, within the Great Plains (GP) ecoregion. Producer defined 3A Level 3 neighborhood climate cluster 273 model extent in Wyoming, hierarchically nested in the Wyoming (Wyo) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined 3B Level 3 neighborhood climate cluster 256 model extent along the Wyoming-Utah border, hierarchically nested in the Wyoming (Wyo) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined 4A Level 3 neighborhood climate cluster 145 model extent in Nevada, hierarchically nested in the Great Basin (GB) climate cluster, within the Southern Great Basin (SGB) ecoregion. Producer defined 4B Level 3 neighborhood climate cluster 77 model extent in Utah, hierarchically nested in the Eastern Rang (ER) climate cluster, within the Southern Great Basin (SGB) ecoregion. Producer defined 5A Level 3 neighborhood climate cluster 6 model extent in Nevada, hierarchically nested in the Bi-State (BiS) climate cluster, within the Southern Great Basin (SGB) ecoregion. Producer defined 5B Level 3 neighborhood climate cluster 14 model extent along the Wyoming-Utah border, hierarchically nested in the Washington (Wash) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined Neighborhood_cluster_sample_region Individual neighborhood cluster regional model study site names. Producer Defined IMW-GB-lv3-206 Level 3 neighborhood climate cluster 206 model extent in Idaho, hierarchically nested in the Great Basin (GB) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined IMW-GB-lv3-201 Level 3 neighborhood climate cluster 201 model extent in Oregon, hierarchically nested in the Great Basin (GB) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined GP-ER-lv3-100 Level 3 neighborhood climate cluster 100 model extent in Montana, hierarchically nested in the Eastern Range (ER) climate cluster, within the Great Plains (GP) ecoregion. Producer defined GP-ER-lv3-131 Level 3 neighborhood climate cluster 131 model extent in Wyoming, hierarchically nested in the Eastern Range (ER) climate cluster, within the Great Plains (GP) ecoregion. Producer defined IMW-Wyo-lv3-273 Level 3 neighborhood climate cluster 273 model extent in Wyoming, hierarchically nested in the Wyoming (Wyo) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined IMW-Wyo-lv3-256 Level 3 neighborhood climate cluster 256 model extent along the Wyoming-Utah border, hierarchically nested in the Wyoming (Wyo) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined SGB-GB-lv3-145 Level 3 neighborhood climate cluster 145 model extent in Nevada, hierarchically nested in the Great Basin (GB) climate cluster, within the Southern Great Basin (SGB) ecoregion. Producer defined SGB-ER-lv3-77 Level 3 neighborhood climate cluster 77 model extent in Utah, hierarchically nested in the Eastern Rang (ER) climate cluster, within the Southern Great Basin (SGB) ecoregion. Producer defined SGB-BiS-lv3-6 Level 3 neighborhood climate cluster 6 model extent in Nevada, hierarchically nested in the Bi-State (BiS) climate cluster, within the Southern Great Basin (SGB) ecoregion. Producer defined IMW-Wash-lv3-14 Level 3 neighborhood climate cluster 14 model extent along the Wyoming-Utah border, hierarchically nested in the Washington (Wash) climate cluster, within the Intermountain West (IMW) ecoregion. Producer defined SyncroSim_library SyncroSim library containing either 'spatial' or 'tabular' results. Spatial.ssim libraries contain a single iteration of spatial output results. Tabular.ssim libraries contain 10 iteration tabular only results. Producer Defined IAG_Region01_spatial.ssim Spatial results for region 1A and 1B. Producer defined IAG_Region02_spatial.ssim Spatial results for region 2A and 2B. Producer defined IAG_Region03_spatial.ssim Spatial results for region 3A and 3B. Producer defined IAG_Region04_spatial.ssim Spatial results for region 4A and 4B. Producer defined IAG_Region05_spatial.ssim Spatial results for region 5A and 5B. Producer defined IAG_Region01_tabular.ssim Tabular results for region 1A and 1B. Producer defined IAG_Region02_tabular.ssim Tabular results for region 2A and 2B. Producer defined IAG_Region03_tabular.ssim Tabular results for region 3A and 3B. Producer defined IAG_Region04_tabular.ssim Tabular results for region 4A and 4B. Producer defined IAG_Region05_tabular.ssim Tabular results for region 5A and 5B. Producer defined ScenarioID Unique scenario ID generated by ST-Sim. All IDs start with “Scenario-“ followed by a numeric value ranging from 1 to 3 digits (e.g., “Scenario-6” or “Scenario-108”. Producer Defined Unique scenario ID generated by ST-Sim. All IDs start with “Scenario-“ followed by a numeric value ranging from 1 to 3 digits (e.g., “Scenario-6” or “Scenario-108”. ScenarioName Descriptive name provided for IAG management scenarios simulated for each site. Names describe the site (region, sample area), management action, and spatial prioritization. Producer Defined Site: 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B No management: NoMgmt Reactive management: ReAct Mgmt Spatial priority: NoPriority Other: Initial Conditions, Transition Spatial Multipliers ScenarioResultsID Unique scenario ID generated by ST-Sim. All IDs start with “Scenario-“ followed by a numeric value ranging from 1 to 3 digits (e.g., “Scenario-6” or “Scenario-108”. Producer Defined Unique scenario ID generated by ST-Sim. All IDs start with “Scenario-“ followed by a numeric value ranging from 1 to 3 digits (e.g., “Scenario-6” or “Scenario-108”. InvadedCover5yrAvgRasters Identifies if end-of-simulation 5-year average invaded cover layers were generated for given scenario (Y/N). Producer Defined N Average invaded cover layers were not produced for this scenario. Producer defined Y Average invaded cover layers were produced for this scenario. Producer defined OutRas_file_name Provides the output raster name (.tif) for any average invaded cover files generated. Producer Defined NA Average invaded cover layer not produced, so output file name is not applicable. Producer defined GB: Great Basin climate cluster ER: Eastern Range climate cluster Wyo: Wyoming climate cluster BiS: Bi-State climate cluster Wash: Washington climate cluster Region: 1A, 1B, 2A, 2B, 3A, 3B, 4A, 4B, 5A, 5B Management: NoMgmt, ReActMgmt Model Output: State Classes Invasive annual grass state classes (tabular & geospatial raster) Producer Defined State Classes Tabular and spatial (raster) data of the area and proportion of landscape, stratum (resistance and resilience; RR), and secondary stratum (SCD management zones) in each of 11 IAG cover state classes for each scenario (n=2), timestep, and model iteration (tabular data n=10, spatial data n = 1). Plots of these data can be viewed within the ST-Sim software or data can be exported. All spatial data share the same spatial reference information (see Spatial Reference Information section). Producer Defined -9999 No Data Producer defined 0 = Detected: Seedbank 1 = Detected: Invaded - Cover <8% 2 = Detected: Invaded - Cover 8-15% 3 = Detected: Invaded - Cover 15-50% 4 = Detected: Converted (>50% cover) 7 = Undetected: Seedbank 8 = Undetected: Invaded - Cover <8% 9 = Undetected: Invaded - Cover 8-15% 10 = Undetected: Invaded - Cover 15-50% 11 = Uninvaded: susceptible 12 = Uninvaded: previous cover Model Output: Transitions Invasive annual grass transitions (tabular & geospatial raster) Producer Defined Transitions Tabular and spatial data of the amount of vegetation and management transitions for every scenario, timestep, and model iteration. Plots of these data can be viewed within ST-Sim or data exported from the ST-Sim software package. Producer Defined -9999 No Data Producer defined 0 – No transition occurred 1 - Defend Treatment - EDRR 1 2 - Defend Treatment - EDRR 2 3 - Grow Treatment - AHA&NGS 1 4 - Grow Treatment - AHA&NGS 2 5 - Grow Treatment - AHA 1 6 - Grow Treatment - AHA 2 7 - Mitigate Treatment - AHA 1 8 - Mitigate Treatment - AHA 2 9 - Mitigate Treatment - AHA 3 10 - Invasion: Cover Establishment 11 - Growth 12 - Invasion: Seedbank Establishment 13 - Invasion: Converted 14 - Inventory - Success 15 - Inventory - Failure 16 - EDRR1 - Failure Model Output: Transition Events Invasive annual grass transition events (tabular & geospatial raster) Producer Defined Transition Events Tabular and spatial data of management transition events for every scenario, timestep, and model iteration. Plots of these data can be viewed within ST-Sim or data exported from the ST-Sim software package. Producer Defined -9999 No Data Producer defined 0 – No transition event occurred 1 - Defend Treatment - EDRR 1 2 - Defend Treatment - EDRR 2 3 - Grow Treatment - AHA&NGS 1 4 - Grow Treatment - AHA&NGS 2 5 - Grow Treatment - AHA 1 6 - Grow Treatment - AHA 2 7 - Mitigate Treatment - AHA 1 8 - Mitigate Treatment - AHA 2 9 - Mitigate Treatment - AHA 3 14 - Inventory - Success 15 - Inventory - Failure 16 - EDRR1 - Failure Model Output: State Attributes Invasive annual grass state attributes (tabular & geospatial raster) Producer Defined State Attributes Tabular and spatial data of predicted hectares of invaded cover (combined detected/undetected IAG cover state classes) for every scenario, timestep, and model iteration. Plots of these data can be viewed within ST-Sim or data exported from the ST-Sim software package. Producer Defined -9999 No Data Producer defined 0.025 = previous cover 0.05 = IAG seedbank 0.15 = IAG cover <8% 0.30 = IAG cover 8-15% 0.75 = IAG cover 15-50% 1.0 = IAG cover >50% U.S. Geological Survey - ScienceBase mailing address

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