Background

The Soda Megafire and management treatments following it

The first large wildfire to occur after release of the Secretarial Order¹⁰ was the 2015 Soda Megafire (>∼20k ha; 100,000 acres), which burned ∼113,000 ha (∼279,000 acres) of sagebrush steppe and a substantial amount of greater sage-grouse habitat in the Owhyee Mountains of the Northern Great Basin, along the Idaho/Oregon border. The fire moved rapidly from August 10-15, partially in view of the National Interagency Fire Center, the Cities of Boise, Idaho and Vale, Oregon, and the greater Treasure Valley metropolis of Idaho (Fig. 1). Approximately 72% of the burned area is managed by BLM which included 105 grazing units (allotments or pastures in Idaho and Oregon), 14% is managed by the State of Idaho, and 14% is owned by private landowners. The burn area was predominately Wyoming big sagebrush (Artemisia tridentata ssp. wyomingensis) and had highly variable terrain soils, spanning from 700 m (2297 feet) elevation at the low-elevation ecotonal border with salt desert to the upper-elevation limit of Wyoming big sagebrush, near ∼1800 m (5905 feet). The burn area thus encompassed a wide range of biophysical conditions thought to confer either low resistance to exotic annual grass invasion and low resilience to wildfire, to greater resistance and resilience at higher elevations.¹⁵ Resistance and resilience in this context are enhanced by relatively cooler and wetter conditions, and by the abundance of resprouting and deep-rooted perennial grasses that can endure disturbances and water deficits and are better able to competitively preempt annual grasses than other plant types.¹⁵ Above-ground biomass was nearly completely burned across the fire area, leaving a black/charred landscape with few patches of un- or partially burned vegetation. There was considerable concern about annual grass invasion into these burned sagebrush steppe habitats.

Figure 1.

Field-data point (plot) locations where plots were measured on the Soda Megafire area. Variation in elevation is also shown in the fire area (lower map), and the location of the fire area in the United States is shown (upper map).

The federal, state, and local response to the Soda Megafire in Idaho and Oregon was more intensive than most previous post-fire management responses, and all elements of the treat-observe-learn-repeat cycle of adaptive management were used. The thoroughness of combustion over a large area combined with proximity or relation to Boise, NIFC, and the Secretarial Order all appeared help motivate the intensive adaptive management response. The project has accordingly become a national focus for understanding management of burned sagebrush steppe, in terms of national and regional level investment and attention. Examples of attention include multiple visits from the Secretary of the Department of Interior and directors of the BLM along with over 600 visitors from many federal and state offices observing the outcomes in dozens of field trips from 2016-2021, and presentations to many other people in the USA and abroad.

Figure 2.

Spatial and temporal patterns of post-fire treatments applied to the Soda Megafire area, burned in summer 2015. “First year” refers to fall/winter of 2015/2016, “Second year” is fall/winter of 2016/2017, and “Third year” is fall/winter of 2017/2018.

Management responses after the Soda Megafire were relatively more intensive and complex compared to post-fire interventions made after previous wildfires, due to size of the Soda Megafire and number of resources effected. The treatments included 1) temporary removal of wild horses, 2) deferment of livestock grazing, initially planned for either two growing seasons or until management objectives for recovery were met, and 3) herbicide and/or seeding treatments applied to many large polygons (Fig. 2). Other treatments not targeted by the monitoring described here included 4) hazardous material site assessment and clean-up, 5) road improvements, 6) fence repair, and 7) repairing damage to recreational trails.

The vegetation treatments included 1) aerial-broadcast seeding of sagebrush combined with three common forb species (small burnet, yarrow, and alfalfa; Sanguisoriba minor Scop., Achillea millefolium L., Medicado sativa L.) from helicopters in late fall and winter 2015/2016, with repeat seedings applied in subsequent years on small areas, 2) drill or aerial-broadcast seeding of grasses, 3) spraying with the pre-emergent herbicide imazapic (Fig. 2), 4) smaller scale and localized seedings of other shrubs and high-diversity mixes of forbs, and 5) ∼1 M plantings of sagebrush (mean density of 44,733±12,916 seedlings per 5 ha, and 5 ha [12.3 acres] was the mean planting unit area).¹⁶ Seeded perennial grass species included cultivars of bluebunch, Siberian, crested, Snake River, and thickspike wheatgrasses, squirreltail, Great-Basin Wildrye, Sherman big bluegrass, and Idaho fescue [Pseudoregneria spicata (Pursh) A. Love, Agropyron fragile (Roth) Candargy, A. cristatum L. Gaertn., Elymus wawaiensis J. Carlson & Barkworth, E. lanceolatum (Scribn. & J.G. Sm.) Gould, and E. elymoides (Raf.) Swezey; Leymus cinereus (Scribn. & Merr.) A. Love, Poa secunda Presl = P. ampla, and Festuca idahoensis Elmer, respectively]. Smaller areas of bitterbrush (Purshia tridentata (Pursh)DC) or low sagebrush (A. arbuscula Nutt.) or a diverse seed mix of native forbs preferred by greater sage-grouse were also applied.

The overlay of treatment types, years of treatment application, and grazing units with unique grazing plans created a complex mosaic of many areas requiring separate evaluation and decision making. This management complexity was compounded by the additional complexity created by variation in elevation, topography, soils, pre-fire vegetation condition, and other factors. Thus, a robust monitoring effort was needed to support adaptive management and evaluate treatment success.

The structure of sampling and setting objectives

The complexity of treatments required about 10 different objectives, each tailored to a specific vegetation or treatment type, which required a correspondingly sizable list of measurement variables and techniques. The science available to guide development of the objectives was and still is only partially complete due to gaps in published research, making some objectives hard to determine. A working group, referred to as the Soda Partners group (hereafter, “Partners”), consisting of many federal, state, and non-governmental agencies and their staff met frequently during the project and provided key input, discussion, and consensus on objectives and interpretation of vegetation responses. The Partners' vision for the burned area was to sustain or increase resistance and resilience, such that the landscape would not require intensive treatment intervention after subsequent fires. USGS and BLM led the details of the sampling plan, sampling procedures, and general logistics for the sampling. BLM delegated the actual sampling, data processing, statistics, and their development to the USGS. Objectives for the various treatments were based on quantitative thresholds, and were specific to different response variables, plant species or functional groups, and treatment types. The BLM and Partners needed the objectives and monitoring for them to be applied equivalently across the many pastures and other management boundaries including Oregon.

The objectives for perennial grasses were intended to ensure they had adequate abundance resulting from multiple individuals of substantial size, and the stands had sufficient reproduction to ensure self-replacement. For drill or aerial broadcast seedings of perennial grasses, treatment objectives were that perennial grasses had average foliar cover ≥ 20%, average density of ≥ 3 plants/m², and median basal diameter ≥ 7.62 cm (3 inches). The 20% cover threshold was obtained from studies done at local scales,¹⁷ across the Great Basin,¹⁸ or management guidance documents.¹⁹ The density thresholds were from Evans and Young's²⁰ local-scale study, and basal diameter thresholds were derived from expert opinion. The sampling omitted the native bunchgrass Sandberg bluegrass, because this species was perceived by the land managers as less likely to outcompete EAGs compared to deep-rooted perennial grasses. As a sidenote, Sandberg bluegrass may have a soil-water use and phenology that more closely matches EAGs, especially cheatgrass.²¹ These traits cause its growth and survival to be more sensitive to weather compared to deeper-rooted perennials that are more likely to endure and resume growth when drought ends. The role of Sandberg's bluegrass in resistance and resilience clearly requires more evaluation.

Additional variables for objectives included seed production, abundance of roots in terms of root depth and lateral spread, and seasonal precipitation each year. Objectives did not originally articulate quantitative thresholds for seed production and seasonal precipitation effects, owing to a lack of literature support for them. Objectives for perennial grass recovery were similar for non-seeded areas (referred to as a “natural recovery”), except that the threshold basal diameter was increased to 10.16 cm (4 inches) for bunchgrasses (which presumably were mainly from resprouting plants). In application, the number of plots that had perennial grasses with seed heads and roots deeper than EAGs were compared to the total number of plots in the area of concern. These objectives for grass seeding were based on the limited amount of published science available, and this dearth of information presented another source of uncertainty to the monitoring and assessment. Uncertainty resulted from either limited reproduction of evidence either within or among studies, in measurement technique (e.g., whether cover measurements included previous years' biomass), and from how well the thresholds would apply to different species and environmental conditions (e.g., site climate or weather).

For aerial-broadcast seeding of shrubs and forbs, the objective result was an average of 1 plant per 10 m² (108 ft²) in suitable areas. The “suitable area” qualification was important because it formally acknowledged that emergence of these seeded species would be variable and patchy, and that the assessment should emphasize success as establishment primarily where sagebrush could reasonably be expected to establish. Supporting literature to quantitatively define “suitable areas” was not available at the time, however the resulting monitoring data provided considerable insight on factors that promote or inhibit sagebrush recovery. A specific density objective was not set for the relatively small areas that received sagebrush plantings and thus was not part of the broader monitoring implemented by the USGS, but land managers in the study area generally anticipate approximately half of outplants survive. Objectives for enhanced forb seed mixes were 0.5 plant per 10 m² (108 ft²) in suitable areas, and >40% survival of planted sagebrush after two years. For herbicide applications, the objective was average foliar cover of EAGs ≤ 20%.

Sampling and data techniques used

The sampling methods were field-based and quantitative measurements of plant cover, density, size, and root extension or recording general level of seed production that were specific to treatment objectives. Additionally, nominal ratings or coarse measurements of soil-surface conditions, indicators of livestock or wildlife [greater sage grouse, rabbits (Brachylagus idahoensis; Sylvilagus sp.), hares (Lepus sp.), deer (Odocoileus hemionus)], and pathogens were recorded because of their ease of incorporation and value in explaining plant and ecosystem responses. Most techniques for monitoring non-forested upland plant communities are time-intensive and are suited primarily for detecting temporal trends over longer periods of time for a low spatial density of plots, such as the field methods used BLM Assessment Inventory and Monitoring (AIM) strategy. Methods for the AIM strategy involve measuring vegetation cover using line-point intercept on three relatively long transects, belt transects for density, measurements of bare soil gaps, and many other optional features that require several hours for a team to sample a plot. A quantitative sampling-effort analysis available from a nearby landscape indicated that 2000 plots across the burned area were required to determine what cover values represent 80% confidence intervals around the mean cover of dominant plant types, specifically bunchgrasses and EAGs (CI; equivalent to 1 plot/54 ha, [1 plot/133 acres], see  Appendix (mmc1.docx)). Methods were needed that would enable sampling of many plots over a large landscape in a short amount of time. Sampling locations were determined using a stratified-random approach, using the 1 plot per 54 ha plot density along with exclusion zones along roads and other site factors that could cause plots to not be representative of the area around them. Specifically, locations were rejected if they were in areas with slopes steeper than 40 degrees, occurred within 18 m (59 feet) of roads or 400 m (1312 feet) of a water source (thereby avoiding intense livestock impacts); or if cobbles, rocks, or trails made up >20% of the area.

Up to 20 concurrent field technicians collected data during the monitoring campaigns, with sampling staggered across the elevation gradients in effort to collect data at the time of peak biomass, during the few months each year when deciduous or EAG vegetation is green. Data were collected into Survey123 in ArcCollector (ESRI, Redlands CA) and compiled into a geo-database, from which the field data were downloaded and entered into a Microsoft Access database and subject to processing and quality control screening.

Considerable advance thought and planning was invested into a system of quality assurance for data collection/inputs and quality control of data outputs²² to ensure that data would be transparent, easily retrieved, reliable, and ultimately able to serve as the basis for pivotal decisions regarding re-treatments or grazing resumption. For possible contested decision making, the data needed to be able to withstand scrutiny, and moreover the data were a feature that could build trust and appreciation for objectivity among the stakeholders, managers, and scientists. A “10% rule” was applied for all data for quality control: after the final stages of data rollup, 10% of data were randomly selected and recomputed from raw data. If any errors were detected, they were fixed, the source of the error was determined with attention to whether the error source was systemic. Additionally, BLM personnel independently reviewed all final data for inconsistencies. Quality assurance measures prior to data collections included 1) establishment of both monitoring plans and detailed protocols that were updated with iterative reviews and revisions by multiple BLM and USGS participants, 2) production of “A Field Guide to the Soda Wildfire Flora”²³ along with plant taxonomy training sessions provided by local botanical authorities, 3) uniform training sessions required of all technicians each year, 4) carefully designing the digital menus for which data were recorded to provide uniformity in responses and, for many variables, providing redundancy that could be used to check for errors, and 5) periodically having technicians independently measure a small number (3-5) of the same test plots to allow comparison of their assessments and to estimate a standard error attributed to measurement teams. As an example of redundancy, two different methods of estimating plant cover were used (visual estimation on a large plot and grid-point intercept on a photo of a small plot) to compare the results. Another critical assurance feature was the extraction of cover data from photographs that were archived and easily retrieved for data review.

At the coordinates for each plot, perspective photographs were first taken in four cardinal directions, to aid interpretation by data end users and at two central plots, for the purpose of quantifying community cover, two aerial photos were captured from 2 m height of 2×3 m ground areas (6.6×9.8 feet; Figs 3, 4, see Germino et al.^24,25 and Applestein et al.²⁶ for more overall monitoring method detail; 100 grid points total per plot were digitally analyzed using grid-point intercept to species).²⁶ Species lists were recorded for each 2×3 m (6.6×9.8 feet) phtoto area and the surrounding 13-m (42.7 foot) radius area (the latter was referred to as a “rapid assessment”), as well as unguided visual estimation of cover by function group in the 13-m (42.7 feet) radius plot. A frequency density method was used to quantify density (plants/area) of shrubs (by height class), seeded forbs, and select plants of interest (e.g., exotic forbs, in one sampling year), in which the number of target plants were detected in the first area in which there were at least 3 individuals, starting with the inner 1 m² (10.8 ft²), then to a circular area with radius of 5.5 m (18 feet), and so forth to a radius of 18 m (59 feet; ∼1000 m², [0.25 acres]). Density of grasses in drill-seeded areas was measured in three 1-m² locations (Figs. 3 and 4).

Next, grass traits were measured on the first 5 perennial bunchgrasses encountered (excluding the small-statured Sandberg bluegrass) using the frequency-density approach, including height (to tallest leaf), basal diameter, percent of basal area killed by fire, and percent of grass plants producing seed by species (binned, <50% of plants, 50-75%, >75%). The depth and lateral extension of perennial grass roots were measured in drill seeded areas and for a limited number of other contexts in the first three years of monitoring. Depth of EAG roots were also measured for a limited number of plots and years, and presence of pathogens on cheatgrass were also measured.

Figure 3.

Spatial layout of the primary variables sampled for the Soda Monitoring. Top: Two 2×3 m rectangle areas captured in aerial photographs (see Fig. 4), three 1×1 m areas in which grasses from drill seedings were measured, and concentric circular areas that density of shrubs and forbs were measured in. Right: the method for finding the first five bunchgrasses to measure basal diameter and height (upper right drawing).

Additional measurements that varied by year included recording of scat presence from cows, deer, greater sage-grouse, and rabbits; percent of grasses that appeared to have been grazed within a 12-m (42.6 feet) radius plot area. Soil traits recorded included the “pedoderm” classification, which is the characteristics of the soil surface (e.g, biocrust, erosion pavement, base soil) that very strongly affect germination and vegetation recovery.^24,27 Another soil trait was soil-patch connectivity known as “resource retention” classification and is an indicator of ecosystem water and nutrient functioning.²⁷

All combined, these methods provided the measurements needed to address the objectives and supplemental information to explain the vegetation responses and their ecosystem impacts, with a focus on recovery of perennial plants or invasion by EAGs.

Examples of insights from the sampling, for sagebrush and exotic annual grasses

The higher density of sampling in the Soda Megafire monitoring has improved the spatial “grain” with which we understand the relationships of key vegetation types among each other and to either environmental variation or treatment effects. The resulting dataset is among the only available that can quantify what “suitable microsites” are for recovery of sagebrush or forbs. Additionally, the fine grain of these time-series allows for concise tests of the assumptions made for setting thresholds. The relatively rapid availability of fine-grained data also allowed identification of re-treatment opportunities within the critical first post-fire year, such as reapplication of herbicides, and more importantly provided the information about variability among and within pastures to guide grazing resumption decisions. Some of the more trans-formative knowledge advancements required time to develop and be peer-reviewed and accepted, and thus are intended to contribute to more future ESR efforts on other wildfire areas.

The greater detection efficiency of the sampling revealed that the abundance of small, newly established sagebrush was greatly enhanced by seeding, or by fertile-island microsites where sagebrush existed before fire, or by features of the soil-surface “skin” (e.g., whether covered by bare grain soil, crusts, etc), and, as was previously known, elevation.²⁴ Additionally, the data reveal several relationships among recovery of dominant species to be more complex than initially assumed, such as a hump-shaped relationship of sagebrush to perennial-bunchgrass abundances²⁴ rather than the traditionally perceived negative relationship.²⁰ The hump-shaped relationship would be expected if both sagebrush and perennial grasses benefitted from incrementally better growing conditions until an optimal perennial grass cover is evident (which was 40% cover, in this case). Above this optima, additional perennial grass abundances appear to inhibit sagebrush recovery.

Figure 4.

Examples of photos used in monitoring. Top: perspective landscape photograph. Bottom left: capturing aerial photograph from 2 m height. Bottom right: example of the resulting aerial photograph to be digitally analyzed using grid-point intercept.

The sampling effort also detected initial sagebrush establishment where it was seeded into areas with warm and dry soils (mesic and aridic soil temperature and moisture classes, respectively), which are generally deemed to be unresponsive to sagebrush seeding,⁷ but no other published reports used the intensity and timing of sampling used on the Soda Megafire monitoring, and thus these other reports could not have detected the important patterns revealed. A classification and regression tree (CART) was established from these data that provided mappable, quantitative criteria for identifying what the “suitable microsites” are for sagebrush recovery. While these findings were not (and could not have been) available in time to guide most of the sagebrush seeding efforts, the information had application to determination of whether sagebrush seeding was successful or not. For example, where sagebrush was seeded into areas lacking suitable microsites, a lack of subsequent sagebrush recovery would not necessarily imply a failure to meet objectives. The information could be used to create maps in other, newer burned areas that identify areas where sagebrush seeding is more likely to be successful.

In addition to these monitoring-enabled advances in understanding sagebrush recovery, advances were made in understanding natural and management-treatment controls of EAG invasion, and which variables could be measured to best predict vulnerability of sites to EAG invasion after fire.^25,29,30 Because land treatments are not applied randomly to burned areas, correctly selecting untreated areas as controls is both important but often challenging because of the myriad site factors affecting EAG and other vegetation that must be similar among the treated and control areas in order to generate a fair and unconfounded comparison. The high density of plots across the landscape provided a rich set of candidate control plots to use for comparison, leading to more resolute comparisons of EAG response to herbicide applications.²⁹ The resulting data revealed that imazapic spraying could be deferred until after the initial greenup (instead of immediately applying to freshly burned soils), allowing managers to observe the initial vegetation recovery and reaping a benefit of greater soil stability and less risk of herbicide drift off-site on eroding soils.²⁹ Moreover, there was little evidence of negative effects of the delayed spraying on perennials that had already resprouted or germinated, thus indicating a clear prospect for combining a phased seeding or planting with herbicide application provided that initial recovery of EAGs was less than a threshold 40% cover²⁹. Having many plots also provided the statistical power to elucidate key causal relationships between EAGs and factors such as natural pathogens, specifically head smut (Ustillago bullata) that can obliterate cheatgrass seed crops.³¹ Restorationists have considered these pathogen outbreaks as natural bio-herbicide opportunities. The annual resampling in the Soda Megafire monitoring revealed that head smut abundances could be predicted by previous-year abundances of cheatgrass, distance to unburned refugia, and warm-wet weather, but the abundance of the pathogen did not diminish subsequent abundances of cheatgrass.³¹

Neither the novel monitoring of perennial grass roots nor the traditional “tug test” (resistance of grass root to upward pulling of shoot) were as strong a predictor of EAG invasion as were basal diameter (diameter of root-foliage interface crown) and the spacing of bunchgrasses as quantified by size of basal gaps (size of bare soil patches between perennial grasses).²⁵ Furthermore, the detail in the relationships of EAGs to interspace gaps helped refine the apparent threshold diameter of gaps for EAG dominance (∼60 cm).²⁵ These insights were useful in their preliminary stage for helping managers interpret how well the vegetation data satisfied the treatment objectives and are in consideration for application to monitoring in other DOI programs.

“Heat maps”or gridded maps of the presence or abundance of key dominant species are one of the most useful ways to represent vegetation recovery on large, burned areas to inform management, and modeling is required for this (e.g., Fig. 5). The robust Soda Megafire monitoring data provided an opportunity to compare and contrast the different approaches that could be used for making the predictive models.²⁸ The analysis was intended to help researchers and managers identify how to create the most informative vegetation recovery maps based off of whatever field sampling they have available (either intensive like for the Soda Megafire or relatively sparser sampling in other ESR efforts), along with digital geographic data that is available for all areas (typically gridded, e.g., elevation, soils, climate). Specifically, Barnard et al.²⁸ evaluated 11 modeling frameworks for creating gridded maps of sagebrush recovering on the Soda Megafire area, comparing readily available machine learning, inferential (hypothesis testing, e.g., with general linear models), and ensemble (i.e., multiple) approaches. Model inputs in Barnard et al.²⁸ included various combinations of field monitoring and/or digital geographic data. The exercise revealed that the best modeling approach was indeed based on the field monitoring data. More importantly, Barnard et al.²⁸ revealed that agreement between the different modeling approaches will vary across the burn area (for predicting sagebrush) as a function of precipitation, dominance of EAG grasses, and microsite patterning. Thus, uncertainty in how post-fire vegetation varies between field-sampling locations can be addressed with predictive modeling, though the reliability of modeling outputs will likely vary among areas within a megafire scar.

Figure 5.

Annual grass abundances in the initial greenup following the 2015 Soda Megafire. The size and blue-color saturation of round symbols shows the percent cover of annual grasses as measured by the “rapid assessment” of ∼1000 plots (pAg). Background colors shows the estimates of the “Real-Time Cheatgrass Cover” from the months just prior to the fire (Boyte and Wylie³²; “boyt” in the legend).

Real-time use of the monitoring data to inform post-fire decisions, including on grazing resumption

The hallmark of adaptive management is using observations (data) collected on treatments or other actions to modify or improve follow-on actions. An example of this occurred during the first post-fire growing season (2016), where a relatively rapid assessment of EAG emergence patterns was made across the burn area in May and June and the resulting information was used to inform additional treatment needs in that same year. A top priority of the managers was to detect and act on areas of increased EAGs, and so the rapid assessment protocol was used to estimate EAG cover on 1,000 plots, in a short period of two months from the start of sampling (Fig. 5). The resulting information was used to identify and justify additional herbicide applications, which were shown to have reasonable short-term effectiveness if EAGs had not already exceeded a foliar cover of about 40%.²⁹

Monitoring data were used to inform grazing resumption decisions for each pasture, with data collection, processing, and summarization made to match the time at which grazing resumption decisions needed to occur. BLM managers prepared documents for each pasture that summarized the monitoring findings relative to treatment objectives and included representative site photographs. This information was shared openly through field trips and other communications with permittees, stakeholders, the Partners group, and BLM's “Interdisciplinary Teams”assigned to make recommendations on the decisions. Managers or their agents requested to verify the data by reviewing the methods, data, or aerial photos from which cover was determined, which was easily facilitated. The data were used to keep pastures closed or alternatively permit livestock grazing resumption prior to treatment objectives being met from the monitoring data. Other factors were considered in determining grazing resumption, in addition to the monitoring data and objectives. The objectives were developed based on best available data but assumptions regarding them were nonetheless required. The monitoring data are currently being used to formally evaluate how the recovery patterns by 2020 compare with the quantitative thresholds measured in 2016-2018, before or as livestock were returned to pastures (e.g., thresholds for perennial grasses: mean cover >20%, mean basal diameter > 7.6 cm (3 inches), density > 3 plants/m² (> 3 plants/10.8 ft²).). The resulting information can be used to refine the objectives for future ESR efforts.

Broader considerations

The measurements of vegetation recovery after the Soda Megafire are an example of adaptive monitoring in support of the adaptive management loop because 1) the monitoring was structured around clear and quantitative objectives set by a broad interagency partnership, customized to the different restoration treatments that began implementation prior to monitoring, 2) the data were processed, summarized, and interpreted in a timely fashion, 3) the findings were used to guide follow-on management decisions such as with additional herbicide treatments and grazing resumption, and 4) the cycle was repeated iteratively, including new treatments, and monitoring methods were adjusted as the project developed. The workflow for monitoring produced for the Soda Megafire strengthened management decisions by enabling the decisions to be more 1) objective, based on how the measured vegetation recovery (including variance) related to quantitative thresholds for desired plant recovery, 2) transparent and tractable, with strict adherence to monitoring protocols made available to all interested stakeholders and the ability to readily retrieve plot photos for verification, 3) broadly accepted by the diverse participants, as a result of open participation in devising objectives, protocols, and data interpretations, and 4) available in time for the key decision making events, which were often within a week or so of data collection. As described above, the workflow was critical for guiding the decisions on grazing resumption for the 105 pastures and was key for decision making on rapid response to emerging EAG outbreaks, for example. While it is unlikely that other ESR projects would use the exact monitoring plan applied to the Soda Megafire, by adopting the basic elements of the workflow, other ESR projects can similarly strengthen the basis for their management decisions.

Declaration of Competing Interest

M.J.G. writes the Browsing the Literature column for Rangelands, but he was not involved in the review and decision process for this manuscript. The content of sponsored issues of Rangelands is handled with the same editorial independence and single-blind peer review as that of regular issues.