Research and Publications
View the infographic.
View the Forest Service Bulletin summary.
View a list of information and tools for applying these concepts.
View the executive summary.
The Science Framework for Conservation and Restoration of the Sagebrush Biome is a two-part guide to managing sagebrush ecosystems in the West and was developed by an extensive interagency team of scientists and managers. It uses the concepts of resilience to disturbance (ability to recover) and resistance to invasive annual grasses across three geographic scales (sagebrush biome, ecoregions, and local sites) to prioritize conservation and restoration actions in areas where they are likely to have the greatest benefits.
Part 1 provides the science basis and decision-support tools for prioritizing areas and strategies for management.
Part 2 focuses on management considerations and tradeoffs for applying the information in Part 1, including monitoring and adaptive management, climate adaptation, wildfire and vegetation management, nonnative invasive plant management, application of National Seed Strategy concepts, livestock grazing management, and wild horse and burro considerations.
Linear fuel breaks may help reduce wildfire intensity and spread, and at the same time improve firefighting effectiveness, but their ecological impacts may include habitat loss and fragmentation, as well as facilitation of species movement. There is very little peer‐reviewed science available to inform land managers about the ecological effects of fuel breaks. As such, land managers may face trade‐offs with uncertain outcomes: either substantially alter habitats with fuel breaks to potentially minimize wildfire impacts or risk increased habitat loss and degradation from wildfire. The Great Basin region of the western US offers an opportunity to better understand the relative costs and benefits of fuel breaks, and to identify key knowledge gaps
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In this study, we aim to advance the optimization of daily large fire containment strategies for ground-based suppression resources by leveraging fire risk assessment results commonly used by fire managers in the western USA. We begin from an existing decision framework that spatially overlays fire risk assessment results with pre-identified potential wildland fire operational delineations (PODs), and then clusters PODs into a response POD (rPOD) using a mixed integer program (MIP) model to minimize expected loss. We improve and expand upon this decision framework through enhanced fire modeling integration and refined analysis of probabilistic and time-sensitive information. Specifically, we expand the set of data inputs to include raster layers of simulated burn probability, flame length probability, fire arrival time, and expected net value change, all calculated using a common set of stochastic weather forecasts and landscape data.
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Although we detected shifts in the relative abundance of several bee and plant genera along the fire severity gradient, the two most abundant bee genera (Bombus and Halictus) responded positively to high fire severity despite differences in their typical foraging ranges. Our study demonstrates that within a large wildfire mosaic, severely burned forest contained the most diverse wild bee communities. This finding has particularly important implications for biodiversity in fire-prone areas given the expected expansion of wildfires in the coming decades.
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Fire refugia and the seed sources they contain fostered tree regeneration in severely burned patches. Management practices that reduce refugia within post-fire landscapes may negatively influence essential forest recovery processes.
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On average, one third of the area burned by predicted wildfires was non-local, meaning that the source ignition was on a different land tenure. Land tenures with smaller parcels tended to receive more incoming fire on a proportional basis, while the largest fires were generated from ignitions in national parks, national forests, public and tribal lands. Among the 11 western States, the amount and pattern of cross-boundary fire varied substantially in terms of which land tenures were mostly exposed, by whom and to what fire sizes. We also found spatial variability in terms of community exposure among States, and more than half of the predicted structure exposure was caused by ignitions on private lands or within the wildland-urban interface areas. This study addressed gaps in existing wildfire risk assessments, that do not explicitly consider cross-boundary fire transmission and do not identify the sources of fire. The results can be used by State, Federal, and local fire planning organizations to help improve risk mitigation programs.
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Comparisons of observed and simulated historical area burned indicate simulated future fire vulnerability could be underestimated by 3% in the Sierra Nevada and overestimated by 3% in the Rocky Mountains. Projections show that water‐limited forests in the Rocky Mountains, Southwest, and Great Basin regions will be the most vulnerable to future drought‐related mortality, and vulnerability to future fire will be highest in the Sierra Nevada and portions of the Rocky Mountains. High carbon‐density forests in the Pacific coast and western Cascades regions are projected to be the least vulnerable to either drought or fire. Importantly, differences in climate projections lead to only 1% of the domain with conflicting low and high vulnerability to fire and no area with conflicting drought vulnerability. Our drought vulnerability metrics could be incorporated as probabilistic mortality rates in earth system models, enabling more robust estimates of the feedbacks between the land and atmosphere over the 21st century.
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Here, we show >20 yr were required for adaptive differences to emerge among 13 populations of a widespread shrub (sagebrush, Artemisia tridentata ssp wyomingensis) collected from around the western United States and planted into common gardens. Additionally, >10 yr were required for greater survival of local populations, that is, local adaptation, to become evident. Variation in survival was best explained by the combination of populations’ home ecoregion combined with grouping of minimum temperature and aridity.
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The variation in predictive power among a suite of modeling frameworks underscores the importance of a model comparison and refinement approach that evaluates multiple models and data groups, and selects variables based on their contribution to predictive power. The enhanced understanding of factors influencing restoration outcomes accomplished by this framework has the potential to aid the adaptive management process for improving future restoration outcomes.
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The agreement between Landsat and phenocam Normalized Difference Vegetation Index for short-statured plant communities of the Great Basin, shows promise for monitoring landscape and regional-level plant phenology across large areas and time periods, with phenocams providing a more comprehensive understanding of plant phenology at finer spatial scales, and Landsat extending the historical record of observations.