USGS researchers are using remote-sensing and other broadscale datasets to study and predict recovery of sagebrush across the sage-grouse range, assessing influence of disturbance, restoration treatments, soil moisture, and other ecological conditions on trends in sagebrush cover. The results will be used to inform conservation prioritization models, economic analyses, climate change projections, and more.
Project Need
Arid shrublands face growing threats from disturbances such as wildfire, drought, and invasive species. In western North America, these threats are increasingly altering the sagebrush (Artemisia species) biome, degrading habitat for species of conservation concern such as greater sage-grouse (Centrocercus urophasianus). Effective restoration is needed to combat these processes, but understanding the conditions determining when, where, and at what rate sagebrush recovery will occur is a pressing research need for prioritizing and implementing restoration actions across the vast and heterogeneous sagebrush landscape.
Approach
We are developing a framework for modeling and predicting sagebrush recovery across the biome by leveraging a suite of datasets that span broad spatiotemporal extents. We are using these data to evaluate the influence of restoration treatments (for example, aerial seeding, herbicide application) and environmental conditions (for example, soil moisture, perennial herbaceous cover, wildfire) on trends in post-disturbance cover of sagebrush, with an emphasis on understanding differences between sites that are recovering naturally and those that received restoration treatments. We are also using the results of these models to develop spatially explicit projections for sagebrush recovery (that is, return to pre-disturbance sagebrush cover), conditional on disturbance, restoration practice, and environmental conditions.


Anticipated Benefits
Our results will provide context-dependent estimates of treatment efficacy and sagebrush recovery rates, as well as spatially explicit predictions of sagebrush cover 30 years after disturbance and probability of recovery to pre-disturbance sagebrush cover. These results will serve as inputs for economic cost-effectiveness analyses, restoration prioritization tools, and other scientific endeavors to ensure managers have the tools and information they need to effectively steward the sagebrush biome in a rapidly changing world.
Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET): A USGS-facilitated Decision-support Tool for Sagebrush Ecosystem Conservation and Restoration Actions
Optimization of Management Actions for Restoration Success and Wildlife Populations
Economic Implications of Sagebrush Treatment and Restoration Practices Across the Great Basin and Wyoming
Using Long-Term Remote Sensing and an Automated Reference Toolset To Estimate and Predict Post-Development Recovery Potential
Assessing vegetation recovery from energy development using a dynamic reference approach
Using remote sensing products to predict recovery of vegetation across space and time following energy development
- Overview
USGS researchers are using remote-sensing and other broadscale datasets to study and predict recovery of sagebrush across the sage-grouse range, assessing influence of disturbance, restoration treatments, soil moisture, and other ecological conditions on trends in sagebrush cover. The results will be used to inform conservation prioritization models, economic analyses, climate change projections, and more.
Project Need
Arid shrublands face growing threats from disturbances such as wildfire, drought, and invasive species. In western North America, these threats are increasingly altering the sagebrush (Artemisia species) biome, degrading habitat for species of conservation concern such as greater sage-grouse (Centrocercus urophasianus). Effective restoration is needed to combat these processes, but understanding the conditions determining when, where, and at what rate sagebrush recovery will occur is a pressing research need for prioritizing and implementing restoration actions across the vast and heterogeneous sagebrush landscape.
Approach
We are developing a framework for modeling and predicting sagebrush recovery across the biome by leveraging a suite of datasets that span broad spatiotemporal extents. We are using these data to evaluate the influence of restoration treatments (for example, aerial seeding, herbicide application) and environmental conditions (for example, soil moisture, perennial herbaceous cover, wildfire) on trends in post-disturbance cover of sagebrush, with an emphasis on understanding differences between sites that are recovering naturally and those that received restoration treatments. We are also using the results of these models to develop spatially explicit projections for sagebrush recovery (that is, return to pre-disturbance sagebrush cover), conditional on disturbance, restoration practice, and environmental conditions.
Aerial seeding on a sagebrush steppe restoration project in Utah (USFWS). Sources/Usage: Public Domain. Visit Media to see details.Sagebrush seedlings growing in a greenhouse (BLM). Sources/Usage: Public Domain. Visit Media to see details.Planting sagebrush (BLM). Anticipated Benefits
Our results will provide context-dependent estimates of treatment efficacy and sagebrush recovery rates, as well as spatially explicit predictions of sagebrush cover 30 years after disturbance and probability of recovery to pre-disturbance sagebrush cover. These results will serve as inputs for economic cost-effectiveness analyses, restoration prioritization tools, and other scientific endeavors to ensure managers have the tools and information they need to effectively steward the sagebrush biome in a rapidly changing world.
- Science
Prioritizing Restoration of Sagebrush Ecosystems Tool (PReSET): A USGS-facilitated Decision-support Tool for Sagebrush Ecosystem Conservation and Restoration Actions
Sagebrush ecosystems represent one of the most imperiled systems in North America and face continued and widespread degradation due to multiple factors including climate change, invasive species, and increased human development. Effective sagebrush management must consider how to best conserve and restore habitats to stem the decline of species that rely on them, especially given limited...Optimization of Management Actions for Restoration Success and Wildlife Populations
USGS researchers, in collaboration with the Wyoming Landscape Conservation Initiative and other partners, are developing a statistically based prioritization tool that will aid agencies in their management decisions.Economic Implications of Sagebrush Treatment and Restoration Practices Across the Great Basin and Wyoming
USGS and Colorado State University researchers are conducting analyses and predictions of sagebrush recovery in the Great Basin and Wyoming and assess the role of weather, soils, and reseeding treatments.Using Long-Term Remote Sensing and an Automated Reference Toolset To Estimate and Predict Post-Development Recovery Potential
USGS scientists are using a time-varying approach to monitor and predict recovery of sagebrush ecosystems following disturbance. - Publications
Assessing vegetation recovery from energy development using a dynamic reference approach
Ecologically relevant references are useful for evaluating ecosystem recovery, but references that are temporally static may be less useful when environmental conditions and disturbances are spatially and temporally heterogeneous. This challenge is particularly acute for ecosystems dominated by sagebrush (Artemisia spp.), where communities may require decades to recover from disturbance. We demonsAuthorsAdrian P. Monroe, Travis W. Nauman, Cameron L. Aldridge, Michael O'Donnell, Michael C. Duniway, Brian S. Cade, Daniel Manier, Patrick J. AndersonUsing remote sensing products to predict recovery of vegetation across space and time following energy development
Using localized studies to understand how ecosystems recover can create uncertainty in recovery predictions across landscapes. Large archives of remote sensing data offer opportunities for quantifying the spatial and temporal factors influencing recovery at broad scales and predicting recovery. For example, energy production is a widespread and expanding land use among many semi-arid ecosystems ofAuthorsAdrian P. Monroe, Cameron L. Aldridge, Michael O'Donnell, Daniel Manier, Collin Homer, Patrick J. Anderson - Partners