Fuel Treatment Response Groups for Fire Prone Sagebrush Landscapes

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Chambers, Jessi L. Brown, Matthew C. Reeves, Eva K. Strand, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3167529/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Nov, 2023 Read the published version in Fire Ecology → Version 1 posted 4 You are reading this latest preprint version Abstract Background Sagebrush shrublands in the Great Basin, US, are experiencing widespread increases in wildfire size and area burned resulting in new policies and funding to implement fuel treatments. However, we lack the spatial data needed to optimize the types and locations of fuel treatments across large landscapes and mitigate fire risk. To address this, we developed Treatment Response Groups (TRGs) – sagebrush and pinyon-juniper vegetation associations that differ in resilience to fire and resistance to annual grass invasion (R&R) and thus responses to fuel treatments. Results We developed spatial layers of the dominant sagebrush associations by overlaying LANDFIRE Existing Vegetation Type, Biophysical Setting, and Mapping Zone, extracting vegetation plot data from the LANDFIRE 2016 LF Reference Database for each combination, and identifying associated sagebrush, grass, shrub, and tree species. We derived spatial layers of pinyon-juniper (PJ) cover and expansion phase within the sagebrush associations from the Rangeland Analysis Platform and identified persistent PJ woodlands from the LANDFIRE Biophysical Setting. TRGs were created by overlaying dominant sagebrush associations, with and without PJ expansion, and new indicators of resilience and resistance. We assigned appropriate fuel treatments to the TRGs based on prior research on treatment responses. The extent of potential area to receive fuel treatments was constrained to 52,940 km2 (18.4%) of the dominant sagebrush associations (272,501 km 2 ) largely because of extensive areas of low R&R (68.9%), which is expected to respond poorly to treatment. Prescribed fire was assigned to big sagebrush associations with moderate or higher resilience and moderately low or higher resistance (14.2%) due to higher productivity, fuels, and recovery potential. Mechanical treatments were assigned to big sagebrush associations with moderately low resilience and to low, black, and mixed low sagebrush associations with moderately low or higher R&R (4.2%) due to lower productivity, fuels, and recovery potential. Persistent PJ woodlands represent high value resources and were not assigned treatments (9%). Conclusions Mapped TRGs can help identify the dominant sagebrush associations and determine appropriate fuel treatments at project area scales and provide the basis for quantitative wildfire risk assessments and outcome-based scenario planning to prioritize fuel treatment investments at landscape scales. Great Basin sagebrush fuel treatments ecological resilience resistance to invasion treatment durability fire behavior pinyon-juniper expansion persistent woodlands Fuel Treatment Response Groups Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION There is a critical need to understand the effects of fuel treatments across large sagebrush landscapes. More area burned in shrubland and herbaceous ecosystems (56%) than in forested, tree-dominated ecosystems (44%) from 2000 to 2020 across the western U.S. (Crist 2023 ). During this time period shrubland and herbaceous ecosystems experienced increasing trends in area burned, number of burned patches, and fire sizes (Crist 2023 ). Major anthropogenic drivers include fire suppression policies (Bates and Davies 2020), an increase in the human footprint (Leu et al. 2008 ), and greater numbers of human fire starts (Fusco et al. 2016 ). The primary ecosystem drivers include (1) the invasion of exotic annual grasses, which increase continuous fine fuels that cure earlier in the growing season and result in large increases in fire frequency and extent (Bradley et al. 2018 , Chambers et al. 2019 ), and (2) the expansion of pinyon and juniper tree species into the shrublands, where stand infilling results in a new strata of crown fuel and increased risk of high severity fire (Strand et al. 2013 , Miller et al. 2019 ). In addition, climate warming is exacerbating invasion by the exotic annual grasses (Bradley et al. 2016 ) and resulting in longer fire seasons and more severe fire weather (Abatzoglou and Kolden 2013 , Abatzoglou et al. 2018 ). The consequences of these altered fire regimes are increased risks to human life and property, high fire management costs, loss of cultural and economic resources, risk of type-conversions to annual grasses, and diminished habitat for a wide variety of sagebrush dependent species (Coates et al. 2016 ). The increase in wildfire extent and severity across the western US has resulted in agency strategies, such as US Department of the Interior’s Integrated Rangeland Fire Management Strategy (USDOI 2015) and the US Forest Service’s Wildfire Crisis Strategy (USDA Forest Service 2022 ), to increase capacity to prevent and suppress wildfires or otherwise reduce wildfire risk. The increase in wildfire activity also has resulted in significant new funding for the management agencies and their collaborators to implement fuel treatments through the Infrastructure Investment and Jobs Act (IIJA 2021) and Inflation Reduction Act (IRA 2022). The purpose of these fuel treatments is to reduce or redistribute burnable material with the goal of decreasing fire intensity or burn severity (Reinhardt et al. 2008 ). Ideally, fuel treatments are implemented in a manner that has reliable and durable effects on fire behavior (Ellsworth et al. 2022 , Williams et al in press) and that improves ecological resilience to future wildfires and resistance to invasive annual plants (Chambers et al. 2014a , b ; 2019 ). Although the knowledge base needed to effectively implement the number of fuel treatments at the scales proposed is growing, important information gaps remain. Recent reviews of fuel treatment effectiveness indicate that the adverse effects of wildfires can be mitigated within fuel treatments to varying degrees (e.g., Kalies et al. 2016 , Urza et al. 2023 , Chambers et al. in progress), but that we lack the information needed to optimize the types and locations of fuel treatments and mitigate fire risk across large landscapes (McKinney et al. 2022 ). Outcome-based scenario planning in conjunction with quantitative wildfire risk assessment is an effective tool for prioritizing fuel treatment investments at landscape scales (e.g., Ager et al. 2017 ). In sagebrush landscapes, generalized spatial data for assessing fuels and fire risk is available across the western US at 30 m spatial resolution through platforms such as LANDFIRE (LANDFIRE 2023 ) and the Rangeland Analysis Platform (RAP) (Allred et al. 2021 , USDA ARS 2023). However, we lack the necessary spatial data to determine fuel treatment outcomes at these scales. In sagebrush ecosystems the responses to fuel treatments are highly dependent on the predominant vegetation types as well as their resilience to wildfire and fuel treatments (resilience) and resistance to invasive annual grasses (resistance) (Miller et al. 2013 , 2019 ; Chambers et al. 2014a , b ; 2019 ). New ecologically relevant and climate sensitive indicators of resilience and resistance based on climate and water availability have been developed recently (Chambers et al. 2023 ). However, information is also needed on the locations and extents of the dominant sagebrush associations, persistent woodlands (PJ; pinyon and/or juniper), and the different phases (successional stages) of PJ expansion into the shrublands (Miller et al. 2019 ). For example, prescribed fire may be effective for reducing woody fuels and fire risk while maintaining ecological function in mountain big sagebrush ( Artemisia tridentata ssp. vaseyana ) associations with high productivity, historically frequent fire return intervals, and relatively high resilience and resistance (Chambers et al. 2014b ; 2019 ). However, use of prescribed fire could lead to conversion to invasive annual grasses and development of annual grass – fire cycles in Wyoming big sagebrush ( A. tridentata ssp. wyomingensis ) associations with low productivity, historically long fire return intervals, and relatively low resilience and resistance (Chambers et al. 2014b , Ellsworth et al. 2022 , Pyke et al. 2022 ). In sagebrush associations experiencing PJ expansion, information is needed on the phase of expansion as resilience and resistance often decrease as the phase of expansion and thus tree cover increases and it becomes more difficult to implement treatments successfully (Miller et al. 2019 , Freund et al. 2021 ). In addition, the various sagebrush associations are characterized by different levels of site productivity and support different amounts of tree cover, both of which influence the types of fuel treatments that are feasible (Miller et al. 2019 ). Finally, it is important to recognize that persistent woodlands, which are historical, tree-dominated communities where site conditions (soils and climate) and disturbance regimes are inherently favorable for pinyon and/or juniper and the trees were an important landscape component prior to 20th century expansion (Miller et al. 2019 ). Here, we developed fuel treatment response groups to describe the potential for fuel treatments to reduce fire risk and prioritize fuel treatment investments across large sagebrush landscapes. We focused on the Great Basin because of the magnitude of annual grass invasion (Bradley et al. 2018 , Chambers et al. 2019 ) and the large area of high fire risk (Short et al. 2023 ). We defined treatment response groups (TRGs) as sagebrush and PJ vegetation associations that differ in their relative resilience and resistance and have specific responses to woody fuel treatments. The TRGs were based on five spatial data layers: (1) the dominant sagebrush associations; (2) persistent PJ woodlands (pinyon and/or juniper); (3) the different phases (successional stages) of PJ expansion; and (4 and 5) new, climate-sensitive indicators of resilience and resistance for sagebrush ecosystems based on climate and water availability (Chambers et al. 2023 ). We assigned woody fuel treatments (prescribed fire and mechanical thinning) to each of the TRGs based on the dominant sagebrush association, the presence and phases of PJ expansion, environmental characteristics, and relative resilience and resistance. We asked: (1) What is the extent of the dominant sagebrush associations with and without PJ expansion and how are they distributed within the ecoregions in the Great Basin? (2) What are the resilience and resistance categories of the dominant sagebrush associations with and without PJ expansion? (3) What are the extents of the different TRGs and how do they differ among sagebrush associations and ecoregions? We discuss the implications of the results for locating and implementing fuel treatments across Great Basin landscapes. METHODS Study area We focused on the three ecoregions that comprise the Great Basin of the western United States: Central Basin and Range, Northern Basin and Range, and Snake River Plain (US EPA, 2022). These ecoregions are part of the western Cold Deserts and have mid-latitude climates with warm to hot summers and cold winters (Wiken 2011). Most precipitation arrives during the winter months and the ecosystems are characterized largely by shrubland and woodland vegetation. Fire risk varies across the study area but is generally high in much of the Northern Basin and Range and Snake River Plain (Short et al. 2023 ). Spatial data layers To identify the dominant sagebrush associations for the three focal ecoregions, we first overlaid the Existing Vegetation Type (EVT), Biophysical Setting (BPS), and LANDFIRE map zone (Rollins 2009 ; LANDFIRE 2020a , b ). We extracted and compiled the values of the EVT, BPS, and map zone found at each vegetation plot within the LANDFIRE 2016 Reference Database (LFRDB; LANDFIRE 2016 ) to account for all unique combinations of these three layers. We selected all LFRDB vegetation plot records from the resulting database with sagebrush species, extracted the species information for each plot, and summed and ranked the number of instances of the individual sagebrush species for each unique combination of EVT, BPS, and map zone. We also summed and ranked the number of instances of the top five shrub and top three tree and grass species present within the LFRDB vegetation plots. We assigned the dominant sagebrush associations based on the dominant sagebrush species present as well as the associated grasses, shrubs, and trees. Using this process, we derived seven sagebrush associations: basin big sagebrush ( Artemisia tridentata ssp. tridentata ), Wyoming big sagebrush ( A. tridentata ssp. wyomingensis ), Wyoming/Basin big sagebrush ( A. tridentata ssp. wyomingensis and A. tridentata ssp. tridentata combined), mountain big sagebrush ( A. tridentata ssp. vaseyana ), black sagebrush ( A. nova ), low sage ( A. arbuscula and A. rigida ), and mixed low sagebrush ( A. arbuscula and A. nova with minor amounts of A. tridentata ssp. vaseyana and A. tridentata ssp. wyomingensis ). For the BPSs characterized by sagebrush species that were not included in the original analyses, we determined the dominant sagebrush association based on the BPS descriptions of the dominant species and included them in our data layer. The tabular dominant sagebrush associations were joined to the spatial data layer describing EVT, BPS and map zone. This enabled the sagebrush associations to be shown in a spatially explicit manner. We assigned “NA” to the agriculture, developed, barren, open water, snow and ice categories in the EVT data layer. Those areas that were not associated with a BPS assigned to a dominant sagebrush association were designated the category “Not Sage.” To evaluate PJ ( Pinus monophylla , Juniperus occidentalis and/or J. osteosperma ) cover within the dominant sagebrush associations, we used Rangeland Analysis Platform (RAP) (USDA ARS 2023) data from 2020. Fuel treatments are conducted primarily in the early phases (Phase I and II successional stages) of tree expansion (Miller et al. 2015 ; 2019 ), and it is important to designate these phases on the landscape. In addition, the various sagebrush associations are characterized by different levels of site productivity and thus support different amounts of tree cover (Miller et al. 2019 ). Therefore, we mapped sagebrush associations with relatively low site productivity, including low, black or mixed low sagebrush, as PJ Phase I where tree cover was 1–10% tree cover, Phase II where tree cover was 10–20%, or Phase III where tree cover was > 20%. We mapped sagebrush associations with higher site productivity, including basin big, basin/Wyoming big, mountain big, or Wyoming big sagebrush as PJ Phase I where tree cover was 1–10% tree cover, Phase II where tree cover was 10–30%, or Phase III where tree cover was > 30%. The RAP does not identify persistent woodlands so to characterize and map them, we identified the LANDFIRE BPS categories that represented persistent pinyon and juniper woodlands within the study area ( Table S1) . The BPS classifications are based on the current biophysical environment and estimated historical disturbance regimes and are intended to not include areas experiencing recent expansion of PJ. To develop the final map layer, we overlaid the dominant sagebrush associations with the three phases of PJ expansion and the persistent woodlands. We refined the map layer by removing those areas in LANDFIRE EVT dominated by tree species other than pinyon or juniper, including quaking aspen ( Populus tremuloides ) and curl-leaf mountain mahogany ( Cercocarpus ledifolius ) ( Table S2 ). To characterize the relative resilience and resistance of the dominant sagebrush associations and PJ expansion areas, we used ecologically relevant and climate-sensitive indicators based on climate and soil water availability variables derived from ecohydrological simulations (see Chambers et al. 2023 for details) ( Figs. S1 and S2 ). Data compilation and mapping All five spatial data layers (dominant sagebrush associations, persistent PJ woodlands, expansion PJ, resilience, and resistance) were output as 30-m rasters in the Albers CONUS projection. They were then overlaid to create a single layer representing the different treatment response groups. The initial base TRG layer contained many complex patch shapes and single 30x30-m TRG patches, so we refined the layer by smoothing with two sequential 9-cell moving window majority filters. The resilience and resistance data layers contained some spatial gaps because of missing data within some of the soil polygons that were used to map the resilience and resistance layers (Chambers et al. 2023 ), but visual inspection of satellite imagery showed that some of the gaps appeared to contain contiguous sagebrush stands. We therefore filled the resulting TRG spatial gaps by expanding (nibbling) neighboring valid TRG values outward into the regions lacking TRG data. Because some of the resilience and resistance data gaps included types of land cover other than sagebrush, we removed any nibbled data that covered areas not expected to contain sagebrush according to its BPS. We masked out areas with land covers designated as barren, agriculture, developed, or forest being sure not to exclude PJ areas (Reeves and Mitchell 2011). All analyses were performed in R and ArcGIS (Higmans 2023, R Core Development Team, 2023). Assignment of fuel treatments to treatment response groups The information used to develop the resilience and resistance categories (Chambers et al. 2023 ) was also used to assign treatments to the TRGs. We first developed generalized ecological types for each of the focal ecoregions based on USDA Natural Resources Conservation Service Soil Survey Information (USDA NRCS, 2020) and Ecological Site Descriptions (USDA NRCS, 2022) (see Chambers et al. 2023 for details). Ecological types are a category of land with a distinctive (i.e., mappable) combination of environmental components, specifically, climate, geology, geomorphology, soils, and potential natural vegetation (Winthers et al. 2005 ). The ecological sites within each ecoregion were grouped into ecological types based on similarities in climate, soils, and vegetation as well as the concept for the site as indicated by the ecological site description. Natural resource experts then categorized the relative resilience and resistance of the ecological types in each ecoregion. To facilitate comparison across ecoregions, the resilience categorization was based largely on the abiotic characteristics (i.e., climate and soils) that determined the potential response of the ecological types to disturbance (Chambers et al. 2014a ). To obtain consistent ratings among ecological types for resistance to annual grass invasion, categorization focused on Bromus tectorum . Climate suitability and soils were considered the primary determinants of resistance, but resource availability and competition from perennial herbaceous species were also considered (Chambers et al. 2014a ). To assign treatments to the TRGs, we compiled the ecological types into groups that reflected the dominant sagebrush associations and PJ expansion areas. The groups had similar vegetation associations, environmental conditions, and resilience and resistance categories ( Table S3 ). We used information on the long-term effects of fuel treatments from recent literature reviews (Chambers et al., in process; Miller et al. 2013 , 2019 ) and analyses of the 10-yr regional Sagebrush Steppe Treatment Evaluation Project data (SageSTEP.org; McIver et al. 2011, McIver and Brunson 2014) to assign fuel treatments to each group. Fuel treatments were assigned to the TRGs based on three criteria: (1) treatments were durable in that they resulted in a longer-term (10 + years post-treatment) decrease in fire risk, (2) treatments resulted in maintaining or increasing resilience as measured by an increase in perennial herbs and sagebrush establishment, and (3) treatments did not decrease resistance to invasion and resulted in little to no increase in invasive annual grasses and forbs. Thus, prescribed fire treatments were assigned to TRGs characterized by big sagebrush associations with and without PJ expansion that had moderate or higher resilience and moderately low or higher resistance (e.g., Chambers et al. 2014b , Bates et al. 2019 , Freund et al. 2021 , Ellsworth et al. 2022 , Pyke et al. 2022 , Williams et al. in press). Mechanical tree removal treatments that leave understory shrubs in place, such as cut and remove and mastication, were assigned to big sagebrush associations experiencing PJ expansion that had moderately low resilience or resistance, and to low sagebrush, black sagebrush, and mixed low sagebrush associations with PJ expansion that had moderately low or higher resilience or resistance (e.g., Chambers et al. 2014b , Freund et al. 2021 , Roundy et al. 2020 , Williams et al. in press). Treatments were not assigned to TRGs with low resilience or resistance, regardless of sagebrush association, due to generally low recovery potential; nor to low, black, and mixed low sagebrush associations without tree expansion due to generally low productivity and fuels as well as lower recovery potential (Miller et al. 2019 ). In addition, treatments were not assigned to Phase III woodlands due to reduced recovery potential and difficulty in implementing treatments (Bates et al. 2013 , Miller et al. 2019 ). Persistent woodlands were considered high value resources and were not assigned treatments. We aggregated the base TRG layer to represent various combinations of the assigned fuel treatments. For example, we combined the dominant sagebrush associations with moderate or higher resilience and moderately low or higher resistance that were assigned the prescribed fire treatment into a common TRG. And we pooled low sagebrush with moderately low or higher resilience and resistance assigned a potential mechanical treatment. We mapped and evaluated the relative extents of these different combinations. RESULTS Dominant sagebrush associations The dominant sagebrush associations within the study area covered 272,502 km 2 (Fig. 1 , Table S4 ). These associations were characterized largely by Wyoming sagebrush (86,379 km 2 or 31.7%), followed closely by Wyoming/basin big sagebrush (79,259 km 2 or 29.1%), then mountain big sagebrush (39,988 km 2 or 14.7%) and black sagebrush (31,807 km 2 or 11.7%). Lesser amounts of low sagebrush (14,862 km 2 or 5.5%), mixed low sagebrush (12,449 km 2 or 4.6%), and basin big sagebrush (7,757 km 2 or 2.8%) occurred within the study area. The distribution of dominant sagebrush associations differed across ecoregions, with relatively more black sagebrush and Wyoming/basin big sagebrush in the Central Basin and Range, more low sagebrush and basin big sagebrush in the Northern Basin and Range, and more Wyoming sagebrush in the Snake River Plain (Fig. 1 , Table S5 ). Pinyon and juniper expansion areas and persistent woodlands The area of the dominant sagebrush associations without PJ expansion covered 145,731 km 2 or 53.5%, while the area with PJ expansion encompassed 126,771 km 2 or 46.5% (Fig. 2 , Table S4) . Over a third of the area characterized by sagebrush associations had Phase I expansion (106,241 km 2 or 36.9%). Phase II and III expansion areas were much less extensive (17,296 km 2 or 6.0% and 14,386 km 2 or .05%, respectively). Persistent PJ woodlands, which were their own category, covered 22,636 km 2 and were much smaller in area than the expansion woodlands. As with the dominant sagebrush associations, PJ woodlands and expansion areas were not equally distributed across the ecoregions (Fig. 2 , Table S4 ). The Central Basin and Range contained a higher overall amount of expansion woodland (82,592 km 2 ) than the Northern Basin and Range (42,702 km 2 ) and Snake River Plain (12,629 km 2 ) and had more PJ expansion in Phases II and III than the other ecoregions. In addition, almost 90% of the persistent woodlands were in the Central Basin and Range. The Northern Basin and Range contained the greatest proportion of sagebrush dominated associations without PJ expansion (69,787 km 2 ). The least amount of sagebrush with or without PJ was found in the Snake River Plain (27,174 km 2 ). Resilience and resistance categories The dominant sagebrush associations were characterized by higher amounts of low and moderately low resilience (RSL-L 189,037 km 2 or 69.3%; RSL-ML 29,697 km 2 or 10.9%) and resistance (RST-L 126,830 km 2 or 46.5%; RST-ML 99,109 km 2 or 36.4%) than moderate to high resilience (RSL-M + MH + H 53,766 km 2 or 19.7%) and resistance (RSL-M + MH + H 46,564 km 2 or 17.1%) when evaluated as a whole (Fig. 3 , Table S5, S6 ). However, large differences in resilience and resistance existed both among the dominant sagebrush associations and ecoregions (Fig. 3 , Tables S5, S6 ). The highest levels of resilience and resistance generally occurred within sagebrush associations characterized by cooler and/or wetter environment conditions: mountain big sagebrush (RSL M + MH + H 25,960 km 2 or 64.9%; RST M + MH + H 23,071 km 2 or 57.7%), low sagebrush (RSL M + MH + H 6,408 km 2 or 45.3%; RST M + MH + H 6,738 km 2 or 45%) and then basin big sagebrush (RSL M + MH + H 2,796 km 2 or 36.0%;, RST M + MH + H 2,297 km 2 or 29.6%). In contrast, those associations with generally warmer and drier conditions had the lowest resilience and resistance: Wyoming big sagebrush and the combination of Wyoming and basin big sagebrush (RSL-L 137,084 km 2 or 83%, RST-L 99,724 km 2 or 60%); black sagebrush (RSL-L 23,663 km 2 or 74.4%, RST-L 8,242 km 2 or 25.9%); and mixed low sagebrush (RSL-L 8,953 km 2 or 71.9%; RST-L 6,015 km 2 or 48.3%). The resilience and resistance of the different sagebrush associations were generally higher in the Northern Basin and Range or Snake River Plain than the Central Basin and Range, except for Wyoming big sagebrush and the combination of Wyoming and basin big sagebrush which were low consistently. Resilience and resistance within the dominant sagebrush associations experiencing pinyon and juniper expansion was generally lowest for Phase I (RSL-L 66,577 km 2 or 67.5%, RST-L 42,686 km 2 or 43.3%) ( Fig. 4 , Tables S7, S8 ). Resilience also was relatively low within Phase II (RSL-L 8,817 km 2 or 58.2%) and Phase III (RSL-L 5,903 km 2 or 45.3%). However, resistance tended to be moderately low within Phase II (RST-ML 9,013 km 2 or 59.5%) and Phase III (RST-ML 9,689 km 2 or 74.4%). Values for persistent woodlands were generally like those for Phase II and Phase III. As for the sagebrush associations, resilience and resistance were generally higher in the Northern Basin and Range or Snake River Plain. The persistent woodlands also had higher resilience and resistance in these northern ecoregions, but the persistent woodlands comprised much smaller percentages of the landscape in these ecoregions. Treatment response groups The TRGs assigned the prescribed fire treatment comprised 40,905 km 2 or 15.1% of the landscape dominated by big sagebrush associations (Tables 1 , S9; Figs. 5 , S3). More of the prescribed fire area was characterized by sagebrush associations with PJ expansion (23,962 km 2 or 8.8%) than sagebrush associations with no expansion (16,979 km 2 or 6.3%) and by mountain big sagebrush (27,078 km 2 or 66.2%) than Wyoming big sagebrush and the combination of Wyoming big sagebrush and basin big sagebrush (13,827 km 2 or 33.8%). The TRGs assigned the mechanical tree removal treatment in PJ expansion areas comprised a much smaller amount of the landscape dominated by big sagebrush associations – 12,035 km 2 or 4.5% (Table 1 , Fig. 5 ). Mechanical treatment areas assigned to big sagebrush associations with ML resilience or resistance were larger (6,917 km 2 or 2.6%) than those assigned to low, black and mixed low sagebrush associations with ML resilience and resistance or higher (5,118 km 2 or 1.9%). Table 1 Areal extents of the condensed list of Treatment Response Groups (TRGs) within the Northern Basin and Range (NBR), Snake River Plain (SRP), and Central Basin and Range (CBR). The area of each TRG is reported in km 2 and percentage (%) of the individual ecoregions and of all ecoregions combined. Figure 5 is the map of these TRGs. Central Basin and Range Northern Basin and Range Snake River Plain All Ecoregions Treatment Response Group (TRG) Treatment Area Area Area Area km 2 % km 2 % km 2 % km 2 % Mtn big sage, no PJ, H-M&H-ML R&R Prescribed fire 2,743 1.8% 4,584 4.1% 1,826 6.3% 9,153 3.1% Mtn big sage, PJ Phase I&II, H-M&H-ML R&R Prescribed fire 4,890 3.1% 11,555 10.3% 1,480 5.1% 17,925 6.0% Mtn big sage, PJ Phase I&II, ML&H-ML R&R Mechanical 1,350 0.9% 896 0.8% 13 0.0% 2,259 0.8% WY or basin big sage, no PJ, H-M&H-ML R&R Prescribed fire 2,036 1.3% 4,476 4.0% 1,314 4.6% 7,826 2.6% WY or basin big sage, PJ Phase I&II, H-M&H-ML R&R Mechanical 1,774 1.1% 3,285 2.9% 941 3.3% 6,001 2.0% WY or basin big sage, PJ Phase I&II, ML&H-ML R&R Mechanical 2,941 1.9% 1,413 1.3% 304 1.1% 4,658 1.6% All big sage, low R&R No treatment 76,555 49.1% 64,742 57.8% 22,364 77.6% 163,662 55.2% Mixed low, low, & black sage, no PJ, low fuels No treatment 25,202 16.2% 15,000 13.4% 504 1.8% 40,706 13.7% Mixed low sage, PJ Phase I&II, ML&H-ML R&R Mechanical 370 0.2% 384 0.3% 19 0.1% 773 0.3% Low sage, PJ Phase I&II, ML&H-ML R&R, Mechanical 66 0.0% 2,046 1.8% 5 0.0% 2,116 0.7% Black sage, PJ Phase I&II, ML&H-ML R&R Mechanical 1,868 1.2% 361 0.3% 1 0.0% 2,229 0.8% All sage, PJ Phase III No treatment 12,069 7.7% 770 0.7% 8 0.0% 12,848 4.3% PJ persistent woodland No treatment 23,979 15.4% 2,431 2.2% 34 0.1% 26,444 8.9% Large areas of the big sagebrush associations were not assigned treatments (163,662 km 2 or 60.6%) because they had either low resilience or low resistance (Table 1 , Fig. 5 ). In addition, large areas of the low, black and mixed low sagebrush associations that lacked PJ expansion (40,706 km 2 or 13.7%) were not assigned fuel treatments due to generally low fuels. Areas designated as Phase III were not assigned treatments (12,848 km 2 or 4.3%) due to low durability and recovery potential. And because of the ecological importance of persistent woodlands, they were not assigned treatments (26,444 km 2 ). DISCUSSION The treatment response groups (TRGs) provide the information needed to optimize the types and locations of fuel treatments to mitigate fire risk across Great Basin landscapes. In this region, the response to fuel treatments depends on the vegetation association, its response to disturbance, and its susceptibility to invasion by invasive annual grasses (Chambers et al. 2014a , b , 2019 ; Roundy et al. 2018 ; Freund et al. 2021 ). We identified and mapped TRGs based on the dominant sagebrush associations, the expansion woodlands and phase of expansion, the persistent woodlands, and the relative resilience and resistance of each of these vegetation associations. Potential fuel treatments were assigned to the TRGs based on their durability, or likelihood of decreasing longer-term fire risk, and their likelihood of maintaining or increasing ecological resilience and resistance to invaders. The extent of potential fuel treatments was constrained by the extensive area of low resilience and resistance with low recovery potential and high susceptibility to invasive annual grasses. Restricting treatments to higher resilience and resistance areas helped ensure that treatments would increase durability and help maintain or increase resilience and resistance over time. At local scales, the spatial layers that comprise the TRGs can be used to help identify project areas and determine appropriate treatments. At landscape scales, the TRGs can be used in outcome-based scenario planning in conjunction with quantitative wildfire risk assessment to prioritize fuel treatment investments (e.g., Ager et al. 2017 ). Relationship of TRGs to treatment areas and types The area with potential for fuel treatments was relatively small (52,940 km 2 or 19.3%) in comparison to the overall extent of the dominant sagebrush associations in the Great Basin (272,502 km 2 ). Suitable treatment areas were constrained primarily by the large extents of the sagebrush associations characterized by low resilience and/or resistance (68.9%). Fuel treatments in low resilience and resistance areas typically show little to no increase in perennial grasses and forbs, low recruitment of sagebrush, and increases in invasive annual grasses in big sagebrush associations without PJ expansion (Davies et al. 2012 , Swanson et al. 2016 , Chambers et al. 2021 , Pyke et al. 2022 ) and with PJ expansion (Roundy et al. 2018 , Chambers et al. 2021 , Freund et al. 2021 ). Increases in invasive annual grasses following fuel treatments in big sagebrush associations with relatively low resilience and resistance can result in longer-term (10-yr) increases in herbaceous fuel and changes in fire behavior. In sagebrush associations without PJ expansion, both prescribed fire and mechanical shrub removal (mowing) resulted in reduced modeled flame lengths and rates of spread, but in no change in reaction intensity (Ellsworth et al. 2022 ). Except in those rare cases where the understory is characterized by ~ 20% cover of perennial native grasses and forbs and minimal invasive annual grasses (Davies 2008 , Chambers et al. 2014b ), the potential decrease in fire behavior is not worth the risk of conducting fuel treatments that may result in conversion to annual grass dominance and the development of invasive grass – fire cycles (Miller et al. 2013 , 2019 ). The area assigned to prescribed fire was larger than that assigned to mechanical treatments. Prescribed fire was assigned to big sagebrush associations with moderate or higher resilience and was found primarily at higher elevations with the largest area in the Northern Basin and Range (Fig. 5 ). The prescribed fire area assigned to big sagebrush associations with PJ expansion was greater than that without PJ. Most of the big sagebrush areas with higher resilience and resistance that were assigned prescribed fire have higher productivity, fuel, and burn probabilities (Short et al. 2023 ) and burned more frequently historically (Miller and Rose 1999 , Miller and Heyerdahl 2008 ). Prescribed fire treatments in moderate or higher resilience areas usually result in increases in perennial grasses and forbs, moderately high recruitment of sagebrush, and little to no increase in invasive annual grasses in big sagebrush associations without PJ expansion (Ellsworth and Kaufman 2017, Chambers et al. 2017 ) and with PJ expansion (Roundy et al 2018 , Freund et al. 2021 ). Cooler climatic conditions coupled with competition from perennial herbs typically limits establishment and growth of invasive annual grasses (Chambers et al. 2007 , Roundy and Chambers 2021) and recruitment of PJ is restricted until reestablishment of sagebrush nurse plants (Chambers et al. 1999 , 2001; Urza et al. 2019 ). Longer-term (10-yr) increases in herbaceous fuel and decreases in shrub and tree fuel following prescribed fire are common (Williams et al. in press). Modeled fire behavior of Phase I and II PJ expansion areas after prescribed fire resulted in increased rate of spread and higher flame lengths, but reductions in reaction intensity in Phase I and in the initial years after treatment in Phase II (Williams et al. in press). These changes in plant community composition and fire behavior likely reflect those observed historically when fires burned more frequently. Prescribed fire treatments that mimic the historic, heterogeneous burns that occurred in these sagebrush associations (e.g., Miller and Heyerdahl 2008 ) will likely be most successful in maintaining or increasing their resilience and resistance. Mechanical tree removal treatments covered a smaller extent than prescribed fire because they were assigned only to big sage associations with moderately low resilience and moderately low or higher resistance (2.4%), and to low, black and mixed low sagebrush (1.8%) with moderately low or higher resilience and resistance. These sagebrush associations with these resilience and resistance categories typically have relatively low productivity, fuel, and burn probabilities and burned infrequently in the past (Miller et al. 2013 , 2019 ). Under these conditions prescribed fire can be difficult to implement and can result in significant increases in invasive annual grasses over time (Freund et al. 2021 ). However, mechanical treatments that remove the trees but leave the shrubs in place can result in increases in cover of perennial grasses and forbs and shrubs with smaller increases in invasive annual grass cover, particularly on cooler and wetter sites (Freund et al. 2021 ). The type of mechanical treatment influences both ecological outcomes and longer-term fire behavior. Cut and leave treatments increase woody surface fuels and to a lesser degree herbaceous fuels, which elevate modeled surface fire intensity, flame length, and especially rate of spread but remove the risk of canopy fire (Williams et al. in press). Cut and remove treatments may be a better option than cut and leave due to less remaining woody surface fuels, except in Phase I expansion where cut and leave treatments have less effect on fuels and fire behavior (Williams et al. in press). However, cut and remove treatments increase both shrub and herbaceous fuels over time and are associated with potential increases in surface fire intensity, flame length, and rate of spread. In addition, these treatments may promote localized increases in invasive annuals as a result of broadcast burning of slash (O’Connor et al 2013 ) or pile burning (Redmond et al. 2014 ). Mastication treatments result in a high abundance of compacted 1-hr and 10-hr woody surface fuels that likely burn at lower intensity and at a slower rate (Kreye et al. 2014 ). However, prolonged smoldering may result in increased duff consumption, soil heating, and root injury (Busse et al. 2005 ). Furthermore, mastication may result in smothering residual plants and reducing seedling establishment in shredded piles, and like the other treatments, increase the potential for invasive plants due to competitive release (Young et al. 2015 ). Low resilience and resistance big sagebrush associations with high ecological integrity represent high value resource areas that merit protective management to prevent the development of uncharacteristic fire regimes (Chambers et al. in progress). Although fuel treatments are not appropriate in these areas, fuel breaks in areas of high fire risk may help facilitate wildfire suppression by reducing fire behavior through fuels modification and allowing safe access points for containment by firefighters (Shinneman et al. 2018 , 2019 ). A recent retrospective analysis indicated that fuel breaks were least effective in low resilience and resistance sagebrush associations composed primarily of woody fuels, particularly under high temperature and low precipitation conditions (Weise et al. 2023 ). However, fuel breaks were more effective in areas that were readily accessible and dominated by fine fuels (Weise et al. 2023 ). This indicates that routinely maintained fuel breaks in areas of invasive annual grass and forb dominance adjacent to high value sagebrush associations may provide necessary access points for firefighters and help prevent transmission of wildfires into these high value resource areas. TRG map layers and spatial data needs The spatial data layers currently available through the LANDFIRE and RAP platforms allowed us to develop the dominant sagebrush associations, areas of PJ expansion delineated by woodland development phase, and areas of persistent PJ woodlands. Careful examination of aerial photos and cross-checking of aerial extents of the different data layers indicated acceptable accuracy. Our assessment of the dominant sagebrush associations confirmed the general patterns discussed by other authors (West 1983 ) and quantified for the first time the areas of the different associations. The dominant sagebrush associations in the Great Basin were characterized largely by Wyoming big sagebrush and Wyoming/basin big sagebrush (61%) followed by mountain big sagebrush (15%), black sagebrush (12%) and then low sagebrush, mixed low sagebrush, and basin big sagebrush (3–5% each). Developing the spatial data of the dominant sagebrush associations from Landsat imagery at 30 m spatial scales and monitoring their changes over time would facilitate larger scale analyses not only of fire risk, but also sagebrush habitat. The RAP layer provided continuous cover of sagebrush associations with PJ cover and this allowed designation of the three woodland development phases. Similar to other research we found that PJ expansion was widespread in the Great Basin (Miller et al. 2008 , 2019 ; Morford et al. 2022 ) with almost half of the area covered by the dominant sagebrush associations experiencing expansion. Most of the area of expansion was in Phase I (77%) reflecting recent increases in tree cover over the past 30 yrs due to factors such as fire suppression, livestock overgrazing, and increasing atmospheric CO 2 (Miller et al. 2008 , Morford et al. 2022 ). A high percentage of the expansion area was characterized by warmer and drier conditions with low resilience and resistance (59%), which may result in lower productivity, decreased rates of infilling, and lower cover values typical of Phase I (Johnson and Miller 2006 , Miller et al. 2019 ). A limitation of the RAP layer is that it did not identify the areas of persistent PJ woodland. Although the LANDFIRE BPS identified persistent woodlands, most of the area was in the Central Basin and Range and undoubtedly underestimated those in the remainder of the Great Basin. Given the ecological importance of the persistent woodlands (Miller et al. 2019 ), a spatial data layer is needed that more accurately identifies these woodlands across the region. In addition, more research is needed to understand the potential for fuel treatments to reduce fire risk and increase resilience while maintaining woodland ecological values in persistent and high-cover woodland areas (Redmond et al. 2023 ). The new resilience and resistance layers that we used are based on climate and soil water availability metrics (Chambers et al. 2023 ) and represent a refinement over the original combined resilience and resistance layer based on soil temperature and moisture regimes (Maestas et al. 2016 ). Recent applications of these new layers are validating their utility for landscape scale spatial analyses and treatment prioritization (Chambers et al. in press). Conclusions We developed spatial layers of the dominant sagebrush associations, the area of these associations experiencing PJ expansion, and the persistent PJ woodlands that can be used to aid assessments requiring information on the locations and extents of the dominant vegetation types in the Great Basin. We also developed treatment response groups (TRGs) that combined these spatial layers with new indicators of ecological resilience and resistance to the invasive annual grass, cheatgrass, and that indicate the likely responses of the different sagebrush associations to fuel treatments. The TRGs can be used to identify the dominant sagebrush associations and determine the types of fuel treatments that will meet management objectives within project areas. The TRGs can also provide the basis for quantitative wildfire risk assessments and outcome-based scenario planning designed to prioritize fuel treatment investments at landscape scales. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and material The metadata and maps have been submitted for publication in the USDA Research Data Archive. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Joint Fire Sciences Program, Project 19-2-02-11, and USDA Forest Service, Rocky Mountain Research Station, Joint Venture Agreements 20-JV-11221632-038 to L. M. Ellsworth and 21-JV-11221632-033 to J. C. Chambers. Authors' contributions JCC led the writing of the manuscript and JLB, MCR and EKS created the maps. CMT, AKU, and KCS contributed to writing the manuscript. All authors participated in the working group that developed the approach and read and approved the final manuscript. Acknowledgements The manuscript was improved by review comments from Chelcy Miniat. The findings and conclusions in this publication are those of the authors and should not be considered to represent any official USDA or US Government determination or policy. References Abatzoglou, John T., and Crystal A. Kolden. 2013. "Relationships between climate and macroscale area burned in the western United States." 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(FY2020 July official release). USDA Forest Service. 2022. “Confronting the Wildfire Crisis: A Strategy for Protecting Communities and Improving Resilience in America’s Forests.” https://www.fs.usda.gov/managing-land/wildfire-crisis US Department of the Interior [USDOI]. 2015. “An Integrated Rangeland Fire Management Strategy: Final Report to the Secretary of the Interior.” https://www.forestsandrangelands.gov/documents/rangeland/IntegratedRangelandFireManagementStrategy_FinalReportMay2015.pdf US Environmental Protection Agency [US EPA]. 2022. Level III and IV Ecoregions of the Continental United States. https://www.epa.gov/eco-research/level-iii-and-iv-ecoregions-continental-united-states Weise, Cali L., Brianne E. Brussee, Peter S. Coates, Douglas J. Shinneman, Michele R. Crist, Cameron L. Aldridge, Julie A. Heinrichs, and Mark A. Ricca. 2023. "A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome." Journal of Environmental Management 341: 117903. https://doi.org/10.1016/j.jenvman.2023.117903 West, N.E. 1983. Intermountain salt-desert shrubland. In: West, N.E., ed. Temperate deserts and semi-deserts. Amsterdam, The Netherlands: Elsevier Publishing Company: 375–378. Wiken, E. D., F. Jiménez Nava, and Glenn Griffith. 2011. "North American terrestrial ecoregions—level III." Commission for Environmental Cooperation, Montreal, Canada 149. http://ftp//ftp.epa.gov/wed/ecoregions/pubs/NA_TerrestrialEcoregionsLevel3_Final-2june11_CEC.pdf Winthers, E., D. Fallon, J. Haglund, T. DeMeo, G. Nowacki, D. Tart, M. Ferwerda, G. Robertson, A. Gallegos, A Rorick, D. Cleland, and W. Robbie. 2005. Terrestrial Ecological Unit Inventory Technical Guide . Gen. Tech Rep. WO-68. Washington, D.C.: US Forest Service, Ecosystem Management Coordination Staff, Washington Office. http://dx.doi.org/10.13140/RG.2.1.3500.2007 Young, Kert R., Bruce A. Roundy, Stephen C. Bunting, and Dennis L. Eggett. 2015. “Utah juniper and two-needle piñon reduction alters fuel loads.” International Journal of Wildland Fire 24(2): 236–248. https://doi.org/10.1071/WF13163 Cite Share Download PDF Status: Published Journal Publication published 21 Nov, 2023 Read the published version in Fire Ecology → Version 1 posted Reviewers agreed at journal 31 Jul, 2023 Reviewers invited by journal 31 Jul, 2023 Editor assigned by journal 18 Jul, 2023 First submitted to journal 14 Jul, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3167529","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":222964045,"identity":"76a4125c-a792-4860-acfd-1fd9d1933cf1","order_by":0,"name":"Jeanne C. Chambers","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBACxgYow4CBgQ1Ey0HYpGgxJqgFDmBaEhsIaWFub372uKCCQd6c/XTag4976tI3nD+88QPDL5vEBhxaGHuOmRvPOMNguLMnd7vhjGeHczfcSCuWYOxLw61lRoKZNG8bQ4LBgdxt0jwHDgC18BhIMPYcNsblMMb5z79J8/4Dajn/dpv0nwN16Qbnzxj/wKtlBg/QlgaglhtAWxgOMAOtyzGTYPhxWA6nlp6cMmmeYxKGG2683W7Yc+Cw4cwbaWUWiQ1pOLUYth8HeqHGRt7gfO62Bz8O1MnznT+8+caHPzY8OLU0gCkJNOHENlwaGBjkcYj/wa1lFIyCUTAKRhwAAHpxXFSXPYasAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-3111-269X","institution":"USDA Forest Service Rocky Mountain Research Station","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jeanne","middleName":"C.","lastName":"Chambers","suffix":""},{"id":222964046,"identity":"f01a81d4-128d-4e8a-ba00-84f8372b2650","order_by":1,"name":"Jessi L. Brown","email":"","orcid":"","institution":"RMRS: USDA Forest Service Rocky Mountain Research Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jessi","middleName":"L.","lastName":"Brown","suffix":""},{"id":222964047,"identity":"54a32961-789a-44d5-8c4e-49f7e446b9c6","order_by":2,"name":"Matthew C. Reeves","email":"","orcid":"","institution":"RMRS: USDA Forest Service Rocky Mountain Research Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"C.","lastName":"Reeves","suffix":""},{"id":222964048,"identity":"76cf86d8-7e0e-4885-882f-f389a099cbea","order_by":3,"name":"Eva K. Strand","email":"","orcid":"","institution":"University of Idaho","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eva","middleName":"K.","lastName":"Strand","suffix":""},{"id":222964049,"identity":"c86b8a4a-f939-4d3f-af10-9a93615e1c0f","order_by":4,"name":"Lisa M. Ellsworth","email":"","orcid":"","institution":"Oregon State University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"M.","lastName":"Ellsworth","suffix":""},{"id":222964050,"identity":"0e403625-1526-4e01-8879-de7ef96e7d85","order_by":5,"name":"Claire M. Tortorelli","email":"","orcid":"","institution":"UC Davis: University of California Davis","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Claire","middleName":"M.","lastName":"Tortorelli","suffix":""},{"id":222964051,"identity":"948c5d62-6a34-48fe-90d9-68d15c6d5eda","order_by":6,"name":"Alexandra K. Urza","email":"","orcid":"","institution":"RMRS: USDA Forest Service Rocky Mountain Research Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Alexandra","middleName":"K.","lastName":"Urza","suffix":""},{"id":222964052,"identity":"30eaadb4-de7f-4674-8851-bdf65a58b230","order_by":7,"name":"Karen C. Short","email":"","orcid":"","institution":"RMRS: USDA Forest Service Rocky Mountain Research Station","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Karen","middleName":"C.","lastName":"Short","suffix":""}],"badges":[],"createdAt":"2023-07-13 14:04:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3167529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3167529/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s42408-023-00230-2","type":"published","date":"2023-11-21T15:01:45+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41086542,"identity":"82d48a10-ed36-4eda-8d92-36d4f2adafed","added_by":"auto","created_at":"2023-08-04 17:06:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2978318,"visible":true,"origin":"","legend":"\u003cp\u003eCover of dominant sagebrush associations in the Northern Basin and Range (NBR), Snake River Plain (SRP), and Central Basin and Range (CBR). Bar graph at upper right shows the proportion of the area of each dominant sagebrush associations within each ecoregion. Dominant sagebrush associations include low sagebrush (sage) (\u003cem\u003eArtemisia arbuscula\u003c/em\u003e and \u003cem\u003eA. rigida\u003c/em\u003e); black sagebrush (\u003cem\u003eA. nova\u003c/em\u003e); mixed low sagebrush (low sagebrush with minor amounts of mountain big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003evaseyana\u003c/em\u003e) and Wyoming big sagebrush\u003cem\u003e \u003c/em\u003e(\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003ewyomingensis\u003c/em\u003e); basin big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003essp. \u003cem\u003etridentata\u003c/em\u003e), mountain big sagebrush, Wyoming big sagebrush, and mixed Wyoming and basin big sagebrush.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-3167529/v1/ca50e61fbf056686b0868bb2.png"},{"id":41086540,"identity":"f48f107d-4e1f-4ec0-b5db-6dd170cb0396","added_by":"auto","created_at":"2023-08-04 17:06:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2627379,"visible":true,"origin":"","legend":"\u003cp\u003eAreal extent of pinyon-juniper (PJ) expansion areas by phase of expansion, PJ persistent woodlands, and sagebrush associations with no PJ cover in the Northern Basin and Range (NBR), Snake River Plain (SRP), and Central Basin and Range (CBR). Bar graph at the upper right shows the proportion of the area of each category within each ecoregion. PJ areas are comprised of singleleaf pinyon (\u003cem\u003ePinus monophyla\u003c/em\u003e), western juniper (\u003cem\u003eJuniperus occidentalis\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eand/or Utah juniper (\u003cem\u003eJ. osteosperma\u003c/em\u003e).\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-3167529/v1/3e6ef5cd75da6c9b46a9f75c.png"},{"id":41087981,"identity":"de4275ef-43dc-45e9-8663-90a4e4b92143","added_by":"auto","created_at":"2023-08-04 17:14:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100829,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative proportion of the area of the dominant sagebrush (sage) associations within the four resilience (top) and resistance (bottom) categories for the Northern Basin and Range (NBR), Snake River Plain (SRP), Central Basin and Range (CBR). The resilience and resistance categories for the three ecoregions combined are indicated by “All.”\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-3167529/v1/4e4a2358137e73231e8db47e.png"},{"id":41086538,"identity":"a5efbb5c-c892-4d2f-8c5c-28dfa719d5bc","added_by":"auto","created_at":"2023-08-04 17:06:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":94051,"visible":true,"origin":"","legend":"\u003cp\u003eThe relative proportion of the pinyon-juniper (PJ) expansion areas by phase of expansion, PJ persistent woodlands, and dominant sagebrush associations within the four resilience (top) and resistance (bottom) categories for the Northern Basin and Range (NBR), Snake River Plain (SRP), Central Basin and Range (CBR). The resilience and resistance categories for the three ecoregions combined are indicated by “All.”\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-3167529/v1/c562606905ab09e18eb00ed3.png"},{"id":41086539,"identity":"f297d91c-1f2a-43f4-851d-ae78f1e4c055","added_by":"auto","created_at":"2023-08-04 17:06:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":708720,"visible":true,"origin":"","legend":"\u003cp\u003eThe primary treatment response groups (TRGs) with assigned fuel treatments, including prescribed fire, mechanical tree removal, and no treatment, for the Northern Basin and Range (NBR), Snake River Plain (SRP), and Central Basin and Range (CBR). Treatment response groups were based on the dominant sagebrush (sage) association, the presence of pinyon-juniper (PJ) expansion and the phase of expansion, and the resilience and resistance category. The map also includes areas of persistent woodland and areas that are not sagebrush associations. The bar graph at upper right shows the proportional area of the TRGs within each ecoregion. The “Big sage, low R\u0026amp;R, no treatment” combines all mountain big sagebrush (\u003cem\u003eArtemisia tridentata\u003c/em\u003essp. \u003cem\u003evaseyana\u003c/em\u003e), basin big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003etridentata\u003c/em\u003e), Wyoming big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003ewyomingensis\u003c/em\u003e), and Wyoming/basin big sagebrush associations with low resilience and resistance categories.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-3167529/v1/fd539398ec689c69be2c9099.png"},{"id":47146641,"identity":"e65f1289-d868-4a3c-8baa-1ae0d3862d2d","added_by":"auto","created_at":"2023-11-27 15:08:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4021686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3167529/v1/c16b6a05-3ed1-4d9b-9eee-ceb9242afc75.pdf"}],"financialInterests":"","formattedTitle":"Fuel Treatment Response Groups for Fire Prone Sagebrush Landscapes","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThere is a critical need to understand the effects of fuel treatments across large sagebrush landscapes. More area burned in shrubland and herbaceous ecosystems (56%) than in forested, tree-dominated ecosystems (44%) from 2000 to 2020 across the western U.S. (Crist \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). During this time period shrubland and herbaceous ecosystems experienced increasing trends in area burned, number of burned patches, and fire sizes (Crist \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Major anthropogenic drivers include fire suppression policies (Bates and Davies 2020), an increase in the human footprint (Leu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and greater numbers of human fire starts (Fusco et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The primary ecosystem drivers include (1) the invasion of exotic annual grasses, which increase continuous fine fuels that cure earlier in the growing season and result in large increases in fire frequency and extent (Bradley et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Chambers et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and (2) the expansion of pinyon and juniper tree species into the shrublands, where stand infilling results in a new strata of crown fuel and increased risk of high severity fire (Strand et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, climate warming is exacerbating invasion by the exotic annual grasses (Bradley et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and resulting in longer fire seasons and more severe fire weather (Abatzoglou and Kolden \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Abatzoglou et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The consequences of these altered fire regimes are increased risks to human life and property, high fire management costs, loss of cultural and economic resources, risk of type-conversions to annual grasses, and diminished habitat for a wide variety of sagebrush dependent species (Coates et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increase in wildfire extent and severity across the western US has resulted in agency strategies, such as US Department of the Interior\u0026rsquo;s Integrated Rangeland Fire Management Strategy (USDOI 2015) and the US Forest Service\u0026rsquo;s Wildfire Crisis Strategy (USDA Forest Service \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), to increase capacity to prevent and suppress wildfires or otherwise reduce wildfire risk. The increase in wildfire activity also has resulted in significant new funding for the management agencies and their collaborators to implement fuel treatments through the Infrastructure Investment and Jobs Act (IIJA 2021) and Inflation Reduction Act (IRA 2022). The purpose of these fuel treatments is to reduce or redistribute burnable material with the goal of decreasing fire intensity or burn severity (Reinhardt et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Ideally, fuel treatments are implemented in a manner that has reliable and durable effects on fire behavior (Ellsworth et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Williams et al in press) and that improves ecological resilience to future wildfires and resistance to invasive annual plants (Chambers et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003eb\u003c/span\u003e; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Although the knowledge base needed to effectively implement the number of fuel treatments at the scales proposed is growing, important information gaps remain.\u003c/p\u003e \u003cp\u003eRecent reviews of fuel treatment effectiveness indicate that the adverse effects of wildfires can be mitigated within fuel treatments to varying degrees (e.g., Kalies et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Urza et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Chambers et al. in progress), but that we lack the information needed to optimize the types and locations of fuel treatments and mitigate fire risk across large landscapes (McKinney et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Outcome-based scenario planning in conjunction with quantitative wildfire risk assessment is an effective tool for prioritizing fuel treatment investments at landscape scales (e.g., Ager et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In sagebrush landscapes, generalized spatial data for assessing fuels and fire risk is available across the western US at 30 m spatial resolution through platforms such as LANDFIRE (LANDFIRE \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the Rangeland Analysis Platform (RAP) (Allred et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, USDA ARS 2023). However, we lack the necessary spatial data to determine fuel treatment outcomes at these scales.\u003c/p\u003e \u003cp\u003eIn sagebrush ecosystems the responses to fuel treatments are highly dependent on the predominant vegetation types as well as their resilience to wildfire and fuel treatments (resilience) and resistance to invasive annual grasses (resistance) (Miller et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chambers et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003eb\u003c/span\u003e; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). New ecologically relevant and climate sensitive indicators of resilience and resistance based on climate and water availability have been developed recently (Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, information is also needed on the locations and extents of the dominant sagebrush associations, persistent woodlands (PJ; pinyon and/or juniper), and the different phases (successional stages) of PJ expansion into the shrublands (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, prescribed fire may be effective for reducing woody fuels and fire risk while maintaining ecological function in mountain big sagebrush (\u003cem\u003eArtemisia tridentata\u003c/em\u003e ssp. \u003cem\u003evaseyana\u003c/em\u003e) associations with high productivity, historically frequent fire return intervals, and relatively high resilience and resistance (Chambers et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e; \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, use of prescribed fire could lead to conversion to invasive annual grasses and development of annual grass \u0026ndash; fire cycles in Wyoming big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003ewyomingensis\u003c/em\u003e) associations with low productivity, historically long fire return intervals, and relatively low resilience and resistance (Chambers et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e, Ellsworth et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Pyke et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn sagebrush associations experiencing PJ expansion, information is needed on the phase of expansion as resilience and resistance often decrease as the phase of expansion and thus tree cover increases and it becomes more difficult to implement treatments successfully (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, the various sagebrush associations are characterized by different levels of site productivity and support different amounts of tree cover, both of which influence the types of fuel treatments that are feasible (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Finally, it is important to recognize that persistent woodlands, which are historical, tree-dominated communities where site conditions (soils and climate) and disturbance regimes are inherently favorable for pinyon and/or juniper and the trees were an important landscape component prior to 20th century expansion (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, we developed fuel treatment response groups to describe the potential for fuel treatments to reduce fire risk and prioritize fuel treatment investments across large sagebrush landscapes. We focused on the Great Basin because of the magnitude of annual grass invasion (Bradley et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Chambers et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the large area of high fire risk (Short et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We defined treatment response groups (TRGs) as sagebrush and PJ vegetation associations that differ in their relative resilience and resistance and have specific responses to woody fuel treatments. The TRGs were based on five spatial data layers: (1) the dominant sagebrush associations; (2) persistent PJ woodlands (pinyon and/or juniper); (3) the different phases (successional stages) of PJ expansion; and (4 and 5) new, climate-sensitive indicators of resilience and resistance for sagebrush ecosystems based on climate and water availability (Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We assigned woody fuel treatments (prescribed fire and mechanical thinning) to each of the TRGs based on the dominant sagebrush association, the presence and phases of PJ expansion, environmental characteristics, and relative resilience and resistance. We asked: (1) What is the extent of the dominant sagebrush associations with and without PJ expansion and how are they distributed within the ecoregions in the Great Basin? (2) What are the resilience and resistance categories of the dominant sagebrush associations with and without PJ expansion? (3) What are the extents of the different TRGs and how do they differ among sagebrush associations and ecoregions? We discuss the implications of the results for locating and implementing fuel treatments across Great Basin landscapes.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eStudy area\u003c/p\u003e \u003cp\u003eWe focused on the three ecoregions that comprise the Great Basin of the western United States: Central Basin and Range, Northern Basin and Range, and Snake River Plain (US EPA, 2022). These ecoregions are part of the western Cold Deserts and have mid-latitude climates with warm to hot summers and cold winters (Wiken 2011). Most precipitation arrives during the winter months and the ecosystems are characterized largely by shrubland and woodland vegetation. Fire risk varies across the study area but is generally high in much of the Northern Basin and Range and Snake River Plain (Short et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSpatial data layers\u003c/p\u003e \u003cp\u003eTo identify the dominant sagebrush associations for the three focal ecoregions, we first overlaid the Existing Vegetation Type (EVT), Biophysical Setting (BPS), and LANDFIRE map zone (Rollins \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; LANDFIRE \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003eb\u003c/span\u003e). We extracted and compiled the values of the EVT, BPS, and map zone found at each vegetation plot within the LANDFIRE \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e Reference Database (LFRDB; LANDFIRE \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) to account for all unique combinations of these three layers. We selected all LFRDB vegetation plot records from the resulting database with sagebrush species, extracted the species information for each plot, and summed and ranked the number of instances of the individual sagebrush species for each unique combination of EVT, BPS, and map zone. We also summed and ranked the number of instances of the top five shrub and top three tree and grass species present within the LFRDB vegetation plots. We assigned the dominant sagebrush associations based on the dominant sagebrush species present as well as the associated grasses, shrubs, and trees. Using this process, we derived seven sagebrush associations: basin big sagebrush (\u003cem\u003eArtemisia tridentata\u003c/em\u003e ssp. \u003cem\u003etridentata\u003c/em\u003e), Wyoming big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003ewyomingensis\u003c/em\u003e), Wyoming/Basin big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003ewyomingensis\u003c/em\u003e and \u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003etridentata\u003c/em\u003e combined), mountain big sagebrush (\u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003evaseyana\u003c/em\u003e), black sagebrush (\u003cem\u003eA. nova\u003c/em\u003e), low sage (\u003cem\u003eA. arbuscula\u003c/em\u003e and \u003cem\u003eA. rigida\u003c/em\u003e), and mixed low sagebrush (\u003cem\u003eA. arbuscula\u003c/em\u003e and \u003cem\u003eA. nova\u003c/em\u003e with minor amounts of \u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003evaseyana\u003c/em\u003e and \u003cem\u003eA. tridentata\u003c/em\u003e ssp. \u003cem\u003ewyomingensis\u003c/em\u003e). For the BPSs characterized by sagebrush species that were not included in the original analyses, we determined the dominant sagebrush association based on the BPS descriptions of the dominant species and included them in our data layer.\u003c/p\u003e \u003cp\u003eThe tabular dominant sagebrush associations were joined to the spatial data layer describing EVT, BPS and map zone. This enabled the sagebrush associations to be shown in a spatially explicit manner. We assigned \u0026ldquo;NA\u0026rdquo; to the agriculture, developed, barren, open water, snow and ice categories in the EVT data layer. Those areas that were not associated with a BPS assigned to a dominant sagebrush association were designated the category \u0026ldquo;Not Sage.\u0026rdquo;\u003c/p\u003e \u003cp\u003eTo evaluate PJ (\u003cem\u003ePinus monophylla\u003c/em\u003e, \u003cem\u003eJuniperus occidentalis and/or J. osteosperma\u003c/em\u003e) cover within the dominant sagebrush associations, we used Rangeland Analysis Platform (RAP) (USDA ARS 2023) data from 2020. Fuel treatments are conducted primarily in the early phases (Phase I and II successional stages) of tree expansion (Miller et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and it is important to designate these phases on the landscape. In addition, the various sagebrush associations are characterized by different levels of site productivity and thus support different amounts of tree cover (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, we mapped sagebrush associations with relatively low site productivity, including low, black or mixed low sagebrush, as PJ Phase I where tree cover was 1\u0026ndash;10% tree cover, Phase II where tree cover was 10\u0026ndash;20%, or Phase III where tree cover was \u0026gt;\u0026thinsp;20%. We mapped sagebrush associations with higher site productivity, including basin big, basin/Wyoming big, mountain big, or Wyoming big sagebrush as PJ Phase I where tree cover was 1\u0026ndash;10% tree cover, Phase II where tree cover was 10\u0026ndash;30%, or Phase III where tree cover was \u0026gt;\u0026thinsp;30%. The RAP does not identify persistent woodlands so to characterize and map them, we identified the LANDFIRE BPS categories that represented persistent pinyon and juniper woodlands within the study area (\u003cb\u003eTable S1)\u003c/b\u003e. The BPS classifications are based on the current biophysical environment and estimated historical disturbance regimes and are intended to not include areas experiencing recent expansion of PJ. To develop the final map layer, we overlaid the dominant sagebrush associations with the three phases of PJ expansion and the persistent woodlands. We refined the map layer by removing those areas in LANDFIRE EVT dominated by tree species other than pinyon or juniper, including quaking aspen (\u003cem\u003ePopulus tremuloides\u003c/em\u003e) and curl-leaf mountain mahogany (\u003cem\u003eCercocarpus ledifolius\u003c/em\u003e) (\u003cb\u003eTable S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo characterize the relative resilience and resistance of the dominant sagebrush associations and PJ expansion areas, we used ecologically relevant and climate-sensitive indicators based on climate and soil water availability variables derived from ecohydrological simulations (see Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e for details) (\u003cb\u003eFigs. S1 and S2\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eData compilation and mapping\u003c/p\u003e \u003cp\u003eAll five spatial data layers (dominant sagebrush associations, persistent PJ woodlands, expansion PJ, resilience, and resistance) were output as 30-m rasters in the Albers CONUS projection. They were then overlaid to create a single layer representing the different treatment response groups.\u003c/p\u003e \u003cp\u003eThe initial base TRG layer contained many complex patch shapes and single 30x30-m TRG patches, so we refined the layer by smoothing with two sequential 9-cell moving window majority filters. The resilience and resistance data layers contained some spatial gaps because of missing data within some of the soil polygons that were used to map the resilience and resistance layers (Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but visual inspection of satellite imagery showed that some of the gaps appeared to contain contiguous sagebrush stands. We therefore filled the resulting TRG spatial gaps by expanding (nibbling) neighboring valid TRG values outward into the regions lacking TRG data. Because some of the resilience and resistance data gaps included types of land cover other than sagebrush, we removed any nibbled data that covered areas not expected to contain sagebrush according to its BPS. We masked out areas with land covers designated as barren, agriculture, developed, or forest being sure not to exclude PJ areas (Reeves and Mitchell 2011). All analyses were performed in R and ArcGIS (Higmans 2023, R Core Development Team, 2023).\u003c/p\u003e \u003cp\u003eAssignment of fuel treatments to treatment response groups\u003c/p\u003e \u003cp\u003eThe information used to develop the resilience and resistance categories (Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) was also used to assign treatments to the TRGs. We first developed generalized ecological types for each of the focal ecoregions based on USDA Natural Resources Conservation Service Soil Survey Information (USDA NRCS, 2020) and Ecological Site Descriptions (USDA NRCS, 2022) (see Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e for details). Ecological types are a category of land with a distinctive (i.e., mappable) combination of environmental components, specifically, climate, geology, geomorphology, soils, and potential natural vegetation (Winthers et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The ecological sites within each ecoregion were grouped into ecological types based on similarities in climate, soils, and vegetation as well as the concept for the site as indicated by the ecological site description. Natural resource experts then categorized the relative resilience and resistance of the ecological types in each ecoregion. To facilitate comparison across ecoregions, the resilience categorization was based largely on the abiotic characteristics (i.e., climate and soils) that determined the potential response of the ecological types to disturbance (Chambers et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e). To obtain consistent ratings among ecological types for resistance to annual grass invasion, categorization focused on \u003cem\u003eBromus tectorum\u003c/em\u003e. Climate suitability and soils were considered the primary determinants of resistance, but resource availability and competition from perennial herbaceous species were also considered (Chambers et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo assign treatments to the TRGs, we compiled the ecological types into groups that reflected the dominant sagebrush associations and PJ expansion areas. The groups had similar vegetation associations, environmental conditions, and resilience and resistance categories (\u003cb\u003eTable S3\u003c/b\u003e). We used information on the long-term effects of fuel treatments from recent literature reviews (Chambers et al., in process; Miller et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and analyses of the 10-yr regional Sagebrush Steppe Treatment Evaluation Project data (SageSTEP.org; McIver et al. 2011, McIver and Brunson 2014) to assign fuel treatments to each group. Fuel treatments were assigned to the TRGs based on three criteria: (1) treatments were durable in that they resulted in a longer-term (10\u0026thinsp;+\u0026thinsp;years post-treatment) decrease in fire risk, (2) treatments resulted in maintaining or increasing resilience as measured by an increase in perennial herbs and sagebrush establishment, and (3) treatments did not decrease resistance to invasion and resulted in little to no increase in invasive annual grasses and forbs. Thus, prescribed fire treatments were assigned to TRGs characterized by big sagebrush associations with and without PJ expansion that had moderate or higher resilience and moderately low or higher resistance (e.g., Chambers et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e, Bates et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Ellsworth et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Pyke et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Williams et al. in press). Mechanical tree removal treatments that leave understory shrubs in place, such as cut and remove and mastication, were assigned to big sagebrush associations experiencing PJ expansion that had moderately low resilience or resistance, and to low sagebrush, black sagebrush, and mixed low sagebrush associations with PJ expansion that had moderately low or higher resilience or resistance (e.g., Chambers et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e, Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Roundy et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Williams et al. in press). Treatments were not assigned to TRGs with low resilience or resistance, regardless of sagebrush association, due to generally low recovery potential; nor to low, black, and mixed low sagebrush associations without tree expansion due to generally low productivity and fuels as well as lower recovery potential (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, treatments were not assigned to Phase III woodlands due to reduced recovery potential and difficulty in implementing treatments (Bates et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Persistent woodlands were considered high value resources and were not assigned treatments.\u003c/p\u003e \u003cp\u003eWe aggregated the base TRG layer to represent various combinations of the assigned fuel treatments. For example, we combined the dominant sagebrush associations with moderate or higher resilience and moderately low or higher resistance that were assigned the prescribed fire treatment into a common TRG. And we pooled low sagebrush with moderately low or higher resilience and resistance assigned a potential mechanical treatment. We mapped and evaluated the relative extents of these different combinations.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003eDominant sagebrush associations\u003c/p\u003e \u003cp\u003eThe dominant sagebrush associations within the study area covered 272,502 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eTable S4\u003c/b\u003e). These associations were characterized largely by Wyoming sagebrush (86,379 km\u003csup\u003e2\u003c/sup\u003e or 31.7%), followed closely by Wyoming/basin big sagebrush (79,259 km\u003csup\u003e2\u003c/sup\u003e or 29.1%), then mountain big sagebrush (39,988 km\u003csup\u003e2\u003c/sup\u003e or 14.7%) and black sagebrush (31,807 km\u003csup\u003e2\u003c/sup\u003e or 11.7%). Lesser amounts of low sagebrush (14,862 km\u003csup\u003e2\u003c/sup\u003e or 5.5%), mixed low sagebrush (12,449 km\u003csup\u003e2\u003c/sup\u003e or 4.6%), and basin big sagebrush (7,757 km\u003csup\u003e2\u003c/sup\u003e or 2.8%) occurred within the study area. The distribution of dominant sagebrush associations differed across ecoregions, with relatively more black sagebrush and Wyoming/basin big sagebrush in the Central Basin and Range, more low sagebrush and basin big sagebrush in the Northern Basin and Range, and more Wyoming sagebrush in the Snake River Plain (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eTable S5\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePinyon and juniper expansion areas and persistent woodlands\u003c/p\u003e \u003cp\u003eThe area of the dominant sagebrush associations without PJ expansion covered 145,731 km\u003csup\u003e2\u003c/sup\u003e or 53.5%, while the area with PJ expansion encompassed 126,771 km\u003csup\u003e2\u003c/sup\u003e or 46.5% (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable S4)\u003c/b\u003e. Over a third of the area characterized by sagebrush associations had Phase I expansion (106,241 km\u003csup\u003e2\u003c/sup\u003e or 36.9%). Phase II and III expansion areas were much less extensive (17,296 km\u003csup\u003e2\u003c/sup\u003e or 6.0% and 14,386 km\u003csup\u003e2\u003c/sup\u003e or .05%, respectively). Persistent PJ woodlands, which were their own category, covered 22,636 km\u003csup\u003e2\u003c/sup\u003e and were much smaller in area than the expansion woodlands. As with the dominant sagebrush associations, PJ woodlands and expansion areas were not equally distributed across the ecoregions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable S4\u003c/b\u003e). The Central Basin and Range contained a higher overall amount of expansion woodland (82,592 km\u003csup\u003e2\u003c/sup\u003e) than the Northern Basin and Range (42,702 km\u003csup\u003e2\u003c/sup\u003e) and Snake River Plain (12,629 km\u003csup\u003e2\u003c/sup\u003e) and had more PJ expansion in Phases II and III than the other ecoregions. In addition, almost 90% of the persistent woodlands were in the Central Basin and Range. The Northern Basin and Range contained the greatest proportion of sagebrush dominated associations without PJ expansion (69,787 km\u003csup\u003e2\u003c/sup\u003e). The least amount of sagebrush with or without PJ was found in the Snake River Plain (27,174 km\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResilience and resistance categories\u003c/p\u003e \u003cp\u003eThe dominant sagebrush associations were characterized by higher amounts of low and moderately low resilience (RSL-L 189,037 km\u003csup\u003e2\u003c/sup\u003e or 69.3%; RSL-ML 29,697 km\u003csup\u003e2\u003c/sup\u003e or 10.9%) and resistance (RST-L 126,830 km\u003csup\u003e2\u003c/sup\u003e or 46.5%; RST-ML 99,109 km\u003csup\u003e2\u003c/sup\u003e or 36.4%) than moderate to high resilience (RSL-M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 53,766 km\u003csup\u003e2\u003c/sup\u003e or 19.7%) and resistance (RSL-M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 46,564 km\u003csup\u003e2\u003c/sup\u003e or 17.1%) when evaluated as a whole (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTable S5, S6\u003c/b\u003e). However, large differences in resilience and resistance existed both among the dominant sagebrush associations and ecoregions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTables S5, S6\u003c/b\u003e). The highest levels of resilience and resistance generally occurred within sagebrush associations characterized by cooler and/or wetter environment conditions: mountain big sagebrush (RSL M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 25,960 km\u003csup\u003e2\u003c/sup\u003e or 64.9%; RST M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 23,071 km\u003csup\u003e2\u003c/sup\u003e or 57.7%), low sagebrush (RSL M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 6,408 km\u003csup\u003e2\u003c/sup\u003e or 45.3%; RST M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 6,738 km\u003csup\u003e2\u003c/sup\u003e or 45%) and then basin big sagebrush (RSL M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 2,796 km\u003csup\u003e2\u003c/sup\u003e or 36.0%;, RST M\u0026thinsp;+\u0026thinsp;MH\u0026thinsp;+\u0026thinsp;H 2,297 km\u003csup\u003e2\u003c/sup\u003e or 29.6%). In contrast, those associations with generally warmer and drier conditions had the lowest resilience and resistance: Wyoming big sagebrush and the combination of Wyoming and basin big sagebrush (RSL-L 137,084 km\u003csup\u003e2\u003c/sup\u003e or 83%, RST-L 99,724 km\u003csup\u003e2\u003c/sup\u003e or 60%); black sagebrush (RSL-L 23,663 km\u003csup\u003e2\u003c/sup\u003e or 74.4%, RST-L 8,242 km\u003csup\u003e2\u003c/sup\u003e or 25.9%); and mixed low sagebrush (RSL-L 8,953 km\u003csup\u003e2\u003c/sup\u003e or 71.9%; RST-L 6,015 km\u003csup\u003e2\u003c/sup\u003e or 48.3%). The resilience and resistance of the different sagebrush associations were generally higher in the Northern Basin and Range or Snake River Plain than the Central Basin and Range, except for Wyoming big sagebrush and the combination of Wyoming and basin big sagebrush which were low consistently.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eResilience and resistance within the dominant sagebrush associations experiencing pinyon and juniper expansion was generally lowest for Phase I (RSL-L 66,577 km\u003csup\u003e2\u003c/sup\u003e or 67.5%, RST-L 42,686 km\u003csup\u003e2\u003c/sup\u003e or 43.3%) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eTables S7, S8\u003c/b\u003e). Resilience also was relatively low within Phase II (RSL-L 8,817 km\u003csup\u003e2\u003c/sup\u003e or 58.2%) and Phase III (RSL-L 5,903 km\u003csup\u003e2\u003c/sup\u003e or 45.3%). However, resistance tended to be moderately low within Phase II (RST-ML 9,013 km\u003csup\u003e2\u003c/sup\u003e or 59.5%) and Phase III (RST-ML 9,689 km\u003csup\u003e2\u003c/sup\u003e or 74.4%). Values for persistent woodlands were generally like those for Phase II and Phase III. As for the sagebrush associations, resilience and resistance were generally higher in the Northern Basin and Range or Snake River Plain. The persistent woodlands also had higher resilience and resistance in these northern ecoregions, but the persistent woodlands comprised much smaller percentages of the landscape in these ecoregions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTreatment response groups\u003c/p\u003e \u003cp\u003eThe TRGs assigned the prescribed fire treatment comprised 40,905 km\u003csup\u003e2\u003c/sup\u003e or 15.1% of the landscape dominated by big sagebrush associations (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cb\u003eS9;\u003c/b\u003e Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S3). More of the prescribed fire area was characterized by sagebrush associations with PJ expansion (23,962 km\u003csup\u003e2\u003c/sup\u003e or 8.8%) than sagebrush associations with no expansion (16,979 km\u003csup\u003e2\u003c/sup\u003e or 6.3%) and by mountain big sagebrush (27,078 km\u003csup\u003e2\u003c/sup\u003e or 66.2%) than Wyoming big sagebrush and the combination of Wyoming big sagebrush and basin big sagebrush (13,827 km\u003csup\u003e2\u003c/sup\u003e or 33.8%). The TRGs assigned the mechanical tree removal treatment in PJ expansion areas comprised a much smaller amount of the landscape dominated by big sagebrush associations \u0026ndash; 12,035 km\u003csup\u003e2\u003c/sup\u003e or 4.5% (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Mechanical treatment areas assigned to big sagebrush associations with ML resilience or resistance were larger (6,917 km\u003csup\u003e2\u003c/sup\u003e or 2.6%) than those assigned to low, black and mixed low sagebrush associations with ML resilience and resistance or higher (5,118 km\u003csup\u003e2\u003c/sup\u003e or 1.9%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAreal extents of the condensed list of Treatment Response Groups (TRGs) within the Northern Basin and Range (NBR), Snake River Plain (SRP), and Central Basin and Range (CBR). The area of each TRG is reported in km\u003csup\u003e2\u003c/sup\u003e and percentage (%) of the individual ecoregions and of all ecoregions combined. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e is the map of these TRGs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eCentral Basin and Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNorthern Basin and Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSnake River Plain\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eAll Ecoregions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTreatment Response Group (TRG)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ekm\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMtn big sage, no PJ, H-M\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrescribed fire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9,153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMtn big sage, PJ Phase I\u0026amp;II, H-M\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrescribed fire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4,890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17,925\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMtn big sage, PJ Phase I\u0026amp;II, ML\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY or basin big sage, no PJ, H-M\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrescribed fire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4,476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7,826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY or basin big sage, PJ Phase I\u0026amp;II, H-M\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWY or basin big sage, PJ Phase I\u0026amp;II, ML\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4,658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll big sage, low R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76,555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e64,742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.6%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e163,662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed low, low, \u0026amp; black sage, no PJ, low fuels\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e40,706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed low sage, PJ Phase I\u0026amp;II, ML\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow sage, PJ Phase I\u0026amp;II, ML\u0026amp;H-ML R\u0026amp;R,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack sage, PJ Phase I\u0026amp;II, ML\u0026amp;H-ML R\u0026amp;R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMechanical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2,229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll sage, PJ Phase III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12,069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12,848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePJ persistent woodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23,979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26,444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e8.9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLarge areas of the big sagebrush associations were not assigned treatments (163,662 km\u003csup\u003e2\u003c/sup\u003e or 60.6%) because they had either low resilience or low resistance (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In addition, large areas of the low, black and mixed low sagebrush associations that lacked PJ expansion (40,706 km\u003csup\u003e2\u003c/sup\u003e or 13.7%) were not assigned fuel treatments due to generally low fuels. Areas designated as Phase III were not assigned treatments (12,848 km\u003csup\u003e2\u003c/sup\u003e or 4.3%) due to low durability and recovery potential. And because of the ecological importance of persistent woodlands, they were not assigned treatments (26,444 km\u003csup\u003e2\u003c/sup\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe treatment response groups (TRGs) provide the information needed to optimize the types and locations of fuel treatments to mitigate fire risk across Great Basin landscapes. In this region, the response to fuel treatments depends on the vegetation association, its response to disturbance, and its susceptibility to invasion by invasive annual grasses (Chambers et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003eb\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Roundy et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We identified and mapped TRGs based on the dominant sagebrush associations, the expansion woodlands and phase of expansion, the persistent woodlands, and the relative resilience and resistance of each of these vegetation associations. Potential fuel treatments were assigned to the TRGs based on their durability, or likelihood of decreasing longer-term fire risk, and their likelihood of maintaining or increasing ecological resilience and resistance to invaders. The extent of potential fuel treatments was constrained by the extensive area of low resilience and resistance with low recovery potential and high susceptibility to invasive annual grasses. Restricting treatments to higher resilience and resistance areas helped ensure that treatments would increase durability and help maintain or increase resilience and resistance over time. At local scales, the spatial layers that comprise the TRGs can be used to help identify project areas and determine appropriate treatments. At landscape scales, the TRGs can be used in outcome-based scenario planning in conjunction with quantitative wildfire risk assessment to prioritize fuel treatment investments (e.g., Ager et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRelationship of TRGs to treatment areas and types\u003c/p\u003e \u003cp\u003eThe area with potential for fuel treatments was relatively small (52,940 km\u003csup\u003e2\u003c/sup\u003e or 19.3%) in comparison to the overall extent of the dominant sagebrush associations in the Great Basin (272,502 km\u003csup\u003e2\u003c/sup\u003e). Suitable treatment areas were constrained primarily by the large extents of the sagebrush associations characterized by low resilience and/or resistance (68.9%). Fuel treatments in low resilience and resistance areas typically show little to no increase in perennial grasses and forbs, low recruitment of sagebrush, and increases in invasive annual grasses in big sagebrush associations without PJ expansion (Davies et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Swanson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Chambers et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Pyke et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and with PJ expansion (Roundy et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Chambers et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Increases in invasive annual grasses following fuel treatments in big sagebrush associations with relatively low resilience and resistance can result in longer-term (10-yr) increases in herbaceous fuel and changes in fire behavior. In sagebrush associations without PJ expansion, both prescribed fire and mechanical shrub removal (mowing) resulted in reduced modeled flame lengths and rates of spread, but in no change in reaction intensity (Ellsworth et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Except in those rare cases where the understory is characterized by ~\u0026thinsp;20% cover of perennial native grasses and forbs and minimal invasive annual grasses (Davies \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Chambers et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e), the potential decrease in fire behavior is not worth the risk of conducting fuel treatments that may result in conversion to annual grass dominance and the development of invasive grass \u0026ndash; fire cycles (Miller et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe area assigned to prescribed fire was larger than that assigned to mechanical treatments. Prescribed fire was assigned to big sagebrush associations with moderate or higher resilience and was found primarily at higher elevations with the largest area in the Northern Basin and Range (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The prescribed fire area assigned to big sagebrush associations with PJ expansion was greater than that without PJ. Most of the big sagebrush areas with higher resilience and resistance that were assigned prescribed fire have higher productivity, fuel, and burn probabilities (Short et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and burned more frequently historically (Miller and Rose \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, Miller and Heyerdahl \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Prescribed fire treatments in moderate or higher resilience areas usually result in increases in perennial grasses and forbs, moderately high recruitment of sagebrush, and little to no increase in invasive annual grasses in big sagebrush associations without PJ expansion (Ellsworth and Kaufman 2017, Chambers et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and with PJ expansion (Roundy et al \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Cooler climatic conditions coupled with competition from perennial herbs typically limits establishment and growth of invasive annual grasses (Chambers et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Roundy and Chambers 2021) and recruitment of PJ is restricted until reestablishment of sagebrush nurse plants (Chambers et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, 2001; Urza et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Longer-term (10-yr) increases in herbaceous fuel and decreases in shrub and tree fuel following prescribed fire are common (Williams et al. in press). Modeled fire behavior of Phase I and II PJ expansion areas after prescribed fire resulted in increased rate of spread and higher flame lengths, but reductions in reaction intensity in Phase I and in the initial years after treatment in Phase II (Williams et al. in press). These changes in plant community composition and fire behavior likely reflect those observed historically when fires burned more frequently. Prescribed fire treatments that mimic the historic, heterogeneous burns that occurred in these sagebrush associations (e.g., Miller and Heyerdahl \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) will likely be most successful in maintaining or increasing their resilience and resistance.\u003c/p\u003e \u003cp\u003eMechanical tree removal treatments covered a smaller extent than prescribed fire because they were assigned only to big sage associations with moderately low resilience and moderately low or higher resistance (2.4%), and to low, black and mixed low sagebrush (1.8%) with moderately low or higher resilience and resistance. These sagebrush associations with these resilience and resistance categories typically have relatively low productivity, fuel, and burn probabilities and burned infrequently in the past (Miller et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Under these conditions prescribed fire can be difficult to implement and can result in significant increases in invasive annual grasses over time (Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, mechanical treatments that remove the trees but leave the shrubs in place can result in increases in cover of perennial grasses and forbs and shrubs with smaller increases in invasive annual grass cover, particularly on cooler and wetter sites (Freund et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe type of mechanical treatment influences both ecological outcomes and longer-term fire behavior. Cut and leave treatments increase woody surface fuels and to a lesser degree herbaceous fuels, which elevate modeled surface fire intensity, flame length, and especially rate of spread but remove the risk of canopy fire (Williams et al. in press). Cut and remove treatments may be a better option than cut and leave due to less remaining woody surface fuels, except in Phase I expansion where cut and leave treatments have less effect on fuels and fire behavior (Williams et al. in press). However, cut and remove treatments increase both shrub and herbaceous fuels over time and are associated with potential increases in surface fire intensity, flame length, and rate of spread. In addition, these treatments may promote localized increases in invasive annuals as a result of broadcast burning of slash (O\u0026rsquo;Connor et al \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) or pile burning (Redmond et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Mastication treatments result in a high abundance of compacted 1-hr and 10-hr woody surface fuels that likely burn at lower intensity and at a slower rate (Kreye et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). However, prolonged smoldering may result in increased duff consumption, soil heating, and root injury (Busse et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Furthermore, mastication may result in smothering residual plants and reducing seedling establishment in shredded piles, and like the other treatments, increase the potential for invasive plants due to competitive release (Young et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLow resilience and resistance big sagebrush associations with high ecological integrity represent high value resource areas that merit protective management to prevent the development of uncharacteristic fire regimes (Chambers et al. in progress). Although fuel treatments are not appropriate in these areas, fuel breaks in areas of high fire risk may help facilitate wildfire suppression by reducing fire behavior through fuels modification and allowing safe access points for containment by firefighters (Shinneman et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A recent retrospective analysis indicated that fuel breaks were least effective in low resilience and resistance sagebrush associations composed primarily of woody fuels, particularly under high temperature and low precipitation conditions (Weise et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, fuel breaks were more effective in areas that were readily accessible and dominated by fine fuels (Weise et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This indicates that routinely maintained fuel breaks in areas of invasive annual grass and forb dominance adjacent to high value sagebrush associations may provide necessary access points for firefighters and help prevent transmission of wildfires into these high value resource areas.\u003c/p\u003e \u003cp\u003eTRG map layers and spatial data needs\u003c/p\u003e \u003cp\u003eThe spatial data layers currently available through the LANDFIRE and RAP platforms allowed us to develop the dominant sagebrush associations, areas of PJ expansion delineated by woodland development phase, and areas of persistent PJ woodlands. Careful examination of aerial photos and cross-checking of aerial extents of the different data layers indicated acceptable accuracy. Our assessment of the dominant sagebrush associations confirmed the general patterns discussed by other authors (West \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1983\u003c/span\u003e) and quantified for the first time the areas of the different associations. The dominant sagebrush associations in the Great Basin were characterized largely by Wyoming big sagebrush and Wyoming/basin big sagebrush (61%) followed by mountain big sagebrush (15%), black sagebrush (12%) and then low sagebrush, mixed low sagebrush, and basin big sagebrush (3\u0026ndash;5% each). Developing the spatial data of the dominant sagebrush associations from Landsat imagery at 30 m spatial scales and monitoring their changes over time would facilitate larger scale analyses not only of fire risk, but also sagebrush habitat.\u003c/p\u003e \u003cp\u003eThe RAP layer provided continuous cover of sagebrush associations with PJ cover and this allowed designation of the three woodland development phases. Similar to other research we found that PJ expansion was widespread in the Great Basin (Miller et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Morford et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) with almost half of the area covered by the dominant sagebrush associations experiencing expansion. Most of the area of expansion was in Phase I (77%) reflecting recent increases in tree cover over the past 30 yrs due to factors such as fire suppression, livestock overgrazing, and increasing atmospheric CO\u003csub\u003e2\u003c/sub\u003e (Miller et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Morford et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). A high percentage of the expansion area was characterized by warmer and drier conditions with low resilience and resistance (59%), which may result in lower productivity, decreased rates of infilling, and lower cover values typical of Phase I (Johnson and Miller \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA limitation of the RAP layer is that it did not identify the areas of persistent PJ woodland. Although the LANDFIRE BPS identified persistent woodlands, most of the area was in the Central Basin and Range and undoubtedly underestimated those in the remainder of the Great Basin. Given the ecological importance of the persistent woodlands (Miller et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), a spatial data layer is needed that more accurately identifies these woodlands across the region. In addition, more research is needed to understand the potential for fuel treatments to reduce fire risk and increase resilience while maintaining woodland ecological values in persistent and high-cover woodland areas (Redmond et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe new resilience and resistance layers that we used are based on climate and soil water availability metrics (Chambers et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and represent a refinement over the original combined resilience and resistance layer based on soil temperature and moisture regimes (Maestas et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Recent applications of these new layers are validating their utility for landscape scale spatial analyses and treatment prioritization (Chambers et al. in press).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe developed spatial layers of the dominant sagebrush associations, the area of these associations experiencing PJ expansion, and the persistent PJ woodlands that can be used to aid assessments requiring information on the locations and extents of the dominant vegetation types in the Great Basin. We also developed treatment response groups (TRGs) that combined these spatial layers with new indicators of ecological resilience and resistance to the invasive annual grass, cheatgrass, and that indicate the likely responses of the different sagebrush associations to fuel treatments. The TRGs can be used to identify the dominant sagebrush associations and determine the types of fuel treatments that will meet management objectives within project areas. The TRGs can also provide the basis for quantitative wildfire risk assessments and outcome-based scenario planning designed to prioritize fuel treatment investments at landscape scales.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe metadata and maps have been submitted for publication in the USDA Research Data Archive.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Joint Fire Sciences Program, Project 19-2-02-11, and USDA Forest Service, Rocky Mountain Research Station, Joint Venture Agreements 20-JV-11221632-038 to L. M. Ellsworth and 21-JV-11221632-033 to J. C. Chambers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eJCC led the writing of the manuscript and JLB, MCR and EKS created the maps. CMT, AKU, and KCS contributed to writing the manuscript. All authors participated in the working group that developed the approach and read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe manuscript was improved by review comments from Chelcy Miniat. The findings and conclusions in this publication are those of the authors and should not be considered to represent any official USDA or US Government determination or policy.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbatzoglou, John T., and Crystal A. 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Robertson, A. Gallegos, A Rorick, D. Cleland, and W. Robbie. 2005. \u003cem\u003eTerrestrial Ecological Unit Inventory Technical Guide\u003c/em\u003e. Gen. Tech Rep. WO-68. Washington, D.C.: US Forest Service, Ecosystem Management Coordination Staff, Washington Office. http://dx.doi.org/10.13140/RG.2.1.3500.2007\u003c/li\u003e\n\u003cli\u003eYoung, Kert R., Bruce A. Roundy, Stephen C. Bunting, and Dennis L. Eggett. 2015. \u0026ldquo;Utah juniper and two-needle pi\u0026ntilde;on reduction alters fuel loads.\u0026rdquo;\u003cem\u003e International Journal of Wildland Fire \u003c/em\u003e24(2): 236\u0026ndash;248. https://doi.org/10.1071/WF13163 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Great Basin, sagebrush, fuel treatments, ecological resilience, resistance to invasion, treatment durability, fire behavior, pinyon-juniper expansion, persistent woodlands, Fuel Treatment Response Groups","lastPublishedDoi":"10.21203/rs.3.rs-3167529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3167529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSagebrush shrublands in the Great Basin, US, are experiencing widespread increases in wildfire size and area burned resulting in new policies and funding to implement fuel treatments. However, we lack the spatial data needed to optimize the types and locations of fuel treatments across large landscapes and mitigate fire risk. To address this, we developed Treatment Response Groups (TRGs) \u0026ndash; sagebrush and pinyon-juniper vegetation associations that differ in resilience to fire and resistance to annual grass invasion (R\u0026amp;R) and thus responses to fuel treatments.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWe developed spatial layers of the dominant sagebrush associations by overlaying LANDFIRE Existing Vegetation Type, Biophysical Setting, and Mapping Zone, extracting vegetation plot data from the LANDFIRE \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e LF Reference Database for each combination, and identifying associated sagebrush, grass, shrub, and tree species. We derived spatial layers of pinyon-juniper (PJ) cover and expansion phase within the sagebrush associations from the Rangeland Analysis Platform and identified persistent PJ woodlands from the LANDFIRE Biophysical Setting. TRGs were created by overlaying dominant sagebrush associations, with and without PJ expansion, and new indicators of resilience and resistance. We assigned appropriate fuel treatments to the TRGs based on prior research on treatment responses. The extent of potential area to receive fuel treatments was constrained to 52,940 km2 (18.4%) of the dominant sagebrush associations (272,501 km\u003csup\u003e2\u003c/sup\u003e) largely because of extensive areas of low R\u0026amp;R (68.9%), which is expected to respond poorly to treatment. Prescribed fire was assigned to big sagebrush associations with moderate or higher resilience and moderately low or higher resistance (14.2%) due to higher productivity, fuels, and recovery potential. Mechanical treatments were assigned to big sagebrush associations with moderately low resilience and to low, black, and mixed low sagebrush associations with moderately low or higher R\u0026amp;R (4.2%) due to lower productivity, fuels, and recovery potential. Persistent PJ woodlands represent high value resources and were not assigned treatments (9%).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eMapped TRGs can help identify the dominant sagebrush associations and determine appropriate fuel treatments at project area scales and provide the basis for quantitative wildfire risk assessments and outcome-based scenario planning to prioritize fuel treatment investments at landscape scales.\u003c/p\u003e","manuscriptTitle":"Fuel Treatment Response Groups for Fire Prone Sagebrush Landscapes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-04 17:06:17","doi":"10.21203/rs.3.rs-3167529/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2023-07-31T23:36:27+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-31T16:38:23+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-18T12:24:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fire Ecology","date":"2023-07-14T09:15:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fire-ecology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"feco","sideBox":"Learn more about [Fire Ecology](https://www.springer.com/journal/42408)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/feco/default.aspx","title":"Fire Ecology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"321bf2db-6856-4bec-ae7d-8fe4e96b216c","owner":[],"postedDate":"August 4th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-11-27T15:06:56+00:00","versionOfRecord":{"articleIdentity":"rs-3167529","link":"https://doi.org/10.1186/s42408-023-00230-2","journal":{"identity":"fire-ecology","isVorOnly":false,"title":"Fire Ecology"},"publishedOn":"2023-11-21 15:01:45","publishedOnDateReadable":"November 21st, 2023"},"versionCreatedAt":"2023-08-04 17:06:17","video":"","vorDoi":"10.1186/s42408-023-00230-2","vorDoiUrl":"https://doi.org/10.1186/s42408-023-00230-2","workflowStages":[]},"version":"v1","identity":"rs-3167529","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3167529","identity":"rs-3167529","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}