Larger, more frequent wildfires in arid and semi‐arid ecosystems have been associated with invasion by non‐native annual grasses, yet a complete understanding of fine fuel development and subsequent wildfire trends is lacking. We investigated the complex relationships among weather, fine fuels, and fire in the Great Basin, USA. We first modeled the annual and time‐lagged effects of precipitation and temperature on herbaceous vegetation cover and litter accumulation over a 26‐year period in the northern Great Basin. We then modeled how these fine fuels and weather patterns influence subsequent wildfires. We found that cheatgrass cover increased in years with higher precipitation and especially when one of the previous 3 years also was particularly wet. Cover of non‐native forbs and native herbs also increased in wet years, but only after several dry years. The area burned by wildfire in a given year was mostly associated with native herb and non‐native forb cover, whereas cheatgrass mainly influenced area burned in the form of litter derived from previous years’ growth. Consequently, multiyear weather patterns, including precipitation in the previous 1–3 years, was a strong predictor of wildfire in a given year because of the time needed to develop these fine fuel loads. The strong relationship between precipitation and wildfire allowed us to expand our inference to 10,162 wildfires across the entire Great Basin over a 35‐year period from 1980 to 2014. Our results suggest that the region's precipitation pattern of consecutive wet years followed by consecutive dry years results in a cycle of fuel accumulation followed by weather conditions that increase the probability of wildfire events in the year when the cycle transitions from wet to dry. These patterns varied regionally but were strong enough to allow us to model annual wildfire risk across the Great Basin based on precipitation alone.

Our study objectives were to model the aboveground biomass in a xeric shrub-steppe landscape with airborne light detection and ranging (Lidar) and explore the uncertainty associated with the models we created. We incorporated vegetation vertical structure information obtained from Lidar with ground-measured biomass data, allowing us to scale shrub biomass from small field sites (1 m subplots and 1 ha plots) to a larger landscape. A series of airborne Lidar-derived vegetation metrics were trained and linked with the field-measured biomass in Random Forests (RF) regression models. A Stepwise Multiple Regression (SMR) model was also explored as a comparison. Our results demonstrated that the important predictors from Lidar-derived metrics had a strong correlation with field-measured biomass in the RF regression models with a pseudo R² of 0.76 and RMSE of 125 g/m² for shrub biomass and a pseudo R² of 0.74 and RMSE of 141 g/m² for total biomass, and a weak correlation with field-measured herbaceous biomass. The SMR results were similar but slightly better than RF, explaining 77–79% of the variance, with RMSE ranging from 120 to 129 g/m² for shrub and total biomass, respectively. We further explored the computational efficiency and relative accuracies of using point cloud and raster Lidar metrics at different resolutions (1 m to 1 ha). Metrics derived from the Lidar point cloud processing led to improved biomass estimates at nearly all resolutions in comparison to raster-derived Lidar metrics. Only at 1 m were the results from the point cloud and raster products nearly equivalent. The best Lidar prediction models of biomass at the plot-level (1 ha) were achieved when Lidar metrics were derived from an average of fine resolution (1 m) metrics to minimize boundary effects and to smooth variability. Overall, both RF and SMR methods explained more than 74% of the variance in biomass, with the most important Lidar variables being associated with vegetation structure and statistical measures of this structure (e.g., standard deviation of height was a strong predictor of biomass). Using our model results, we developed spatially-explicit Lidar estimates of total and shrub biomass across our study site in the Great Basin, U.S.A., for monitoring and planning in this imperiled ecosystem.

The U.S. federal government has recently committed to the difficult task of slowing and managing the invasive grass‐fire cycle in sagebrush steppe, where property, livelihoods, and entire ecosystems are at risk. To safely manage this crisis, the government recently proposed to construct about 17,700 km of fuel breaks and millions of hectares of fuel reduction treatments in six western states. A challenge for resource managers will be the strategic placement of these land treatments. We investigated the need for this massive effort from the perspective of protecting previous rehabilitation and restoration seeding investments, including over 3,400 seedings implemented from 1990 to 2019 covering about 24,540 km ² . We found that portions of over 26% of these seedings have since burned representing nearly 17% of this seeded area. Locations that had recurrent wildfire had repeat treatments and thus multiple investments in the same location. Our analysis supports the management actions aimed at protecting remaining sagebrush and restoration investments, especially in areas where the invasive grass‐fire cycle is most pervasive. Given the decades required for most sagebrush to recover after wildfire, the urgency of this management intervention is evident. The specific details, placement, and effectiveness of these interventions could influence outcomes and potential unintended consequences.

Ecological droughts are deficits in soil–water availability that induce threshold-like ecosystem responses, such as causing altered or degraded plant-community conditions, which can be exceedingly difficult to reverse. However, ‘ecological drought’ can be difficult to define, let alone to quantify, especially at spatial and temporal scales relevant to land managers. This is despite a growing need to integrate drought-related factors into management decisions as climate changes result in precipitation instability in many semi-arid ecosystems. We asked whether success in restoration seedings of the foundational species big sagebrush (Artemisia tridentata) was related to estimated water deficit, using the SoilWat2 model and data from >600 plots located in previously burned areas in the western United States. Water deficit was characterized by: (1) the standardized precipitation-evapotranspiration index (SPEI), a coarse-scale drought index, and (2) the number of days with wet and warm conditions in the near-surface soil, where seeds and seedlings germinate and emerge (i.e. days with 0–5 cm deep soil water potential >−2.5 MPa and temperature above 0 °C). SPEI, a widely used drought index, was not predictive of whether sagebrush had reestablished. In contrast, wet-warm days elicited a critical drought threshold response, with successfully reestablished sites having experienced seven more wet-warm days than unsuccessful sites during the first March following summer wildfire and restoration. Thus, seemingly small-scale and short-term changes in water availability and temperature can contribute to major ecosystem shifts, as many of these sites remained shrubless two decades later. These findings help clarify the definition of ecological drought for a foundational species and its imperiled semi-arid ecosystem. Drought is well known to affect the occurrence of wildfires, but drought in the year(s) after fire can determine whether fire causes long-lasting, negative impacts on ecosystems.