Predicting Recovery of Sagebrush Ecosystems Across the Sage-grouse Range from Remotely Sensed Vegetation Data
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, projections of future conditions, 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. Current extensions include developing spatial optimization tools based on vegetation trend estimates and modeling and predicting trends in other land health components including bare ground, perennial herbaceous, and annual herbaceous cover.
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
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, projections of future conditions, 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. Current extensions include developing spatial optimization tools based on vegetation trend estimates and modeling and predicting trends in other land health components including bare ground, perennial herbaceous, and annual herbaceous cover.
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.