Monitoring for adaptive management of burned sagebrush-steppe rangelands: addressing variability and uncertainty on the 2015 Soda Megafire

Plant community trajectories following livestock exclusion for conservation vary and hinge on initial invasion and soil‐biocrust conditions in shrub steppe

“…Communities with an initial mean cheatgrass cover of >20% in 2007 (i.e., “becoming invaded” state) had cheatgrass cover increase to 40% by 2021. The 20% cheatgrass threshold identified here as portending conversion of mixed perennial communities to annual grasslands is notable, because (1) the threshold has been presumed by land managers to identify where or when cheatgrass is likely to transform plant‐community and ecosystem integrity, and (2) the threshold has thus guided significant land‐management decisions and action—and both of these points are despite few datasets and analyses available to corroborate the threshold (Creutzburg et al, 2022; Germino et al, 2021). The increased cheatgrass abundance is a serious threat because of the accompanying greater likelihood of wildfire, and cheatgrass would be expected to increase further in the post‐fire environment, imposing a competitive disadvantage to perennials and thereby engaging the cheatgrass‐fire cycle (Brooks et al, 2004).…”

“…While some studies have found neutral or sometimes desirable effects of grazing on cheatgrass depending on site context (Davies et al, 2009, 2021; Davies, Bates, & Boyd, 2016; Davies, Bates, Boyd, & Svejcar, 2016), inappropriate livestock grazing is considered to have promoted cheatgrass expansion in sagebrush steppe by causing the selective loss of perennial herbs that are most suited to competing with cheatgrass (Condon & Pyke, 2018a, 2018b; Pyke et al, 2016; Reisner et al, 2013; Williamson et al, 2020). Thus, temporary or permanent cessation of grazing has become by far the most common type of restoration intervention in sagebrush steppe, albeit a passive one (e.g., post‐fire rest; Germino et al, 2021). Vegetation changes after livestock exclusion are thus of high interest.…”

Modeling of fire spread in sagebrush steppe using FARSITE: an approach to improving input data and simulation accuracy

“…Grazing or browsing wildlife included primarily mule deer (Odocoileus hemionus) and pronghorn antelope (Antilocapra americana), Greater sage-grouse (Centrocercus urophasianus), and a range of small mammals (e.g., lagomorphs, ground squirrels). Considerable postfire restoration/rehabilitation treatments were conducted on the Soda burned area (Germino et al 2022) and were followed by the installation of a network of linear fuel breaks to help protect the landscape and investments from reburning (Soda Fire Fuel Breaks Environmental Impact Statement, 2017). Thus, there is keen interest in understanding how simulated fire risks relate to vegetation recovery and vegetation management options for this particular landscape, which first requires assessing how well fire can be modeled across the landscape.…”

“…As an alternative way to establish fuels across the burned area, we created a coarse land-cover type map by starting with readily available data from the USDA Rangeland Analysis Platform (RAP), which combines field monitoring plots from the US Bureau of Land Management (BLM) Assessment, Inventory and Monitoring program and the NRCS National Resources Inventory, as well as historic Landsat satellite records to generate yearly predictions of continuous cover (from 0 to 100%) for trees, shrubs, perennial grasses, annual herbaceous cover, and bare soil (Jones et al 2018;Allred et al 2021). Though extensive post-fire monitoring data were available for the Soda Fire (e.g., over 2000 plots/year from 2016 to 2020; Germino et al 2018Germino et al , 2022Davidson et al 2019;Applestein and Germino 2021), pre-fire vegetation and fuel data were scarce, and thus, modeled data (i.e., RAP) was relied upon for retrospective fire simulation modeling. This extensive post-fire monitoring data, however, allowed us to assess the accuracy of RAP data across our subject landscape.…”

“…This extensive post-fire monitoring data, however, allowed us to assess the accuracy of RAP data across our subject landscape. For example, mean absolute error in agreement between RAP remotely sensed vegetation cover and USGS field monitoring data were ±7% for annual herbaceous cover measured in 2020 (Allred et al 2021;Applestein and Germino 2022). Unsupervised maximum likelihood classification of RAP 2014 vegetation cover maps was used to classify the pre-Soda landscape into land-cover types.…”