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In a new Northwest CASC supported study, USGS researchers use historic data sets to predict future vegetation cover in sagebrush ecosystems under different climate change scenarios.

Many sagebrush-dominated ecosystems in the United States are managed by federal agencies who are tasked with balancing human use with conservation. With their cattle-filled grasslands, herds of wild horses, and charismatic endemic species, like sage grouse, these shrublands have high economic, cultural, and environmental value to western communities. Yet they are highly vulnerable to fires, species invasions, and droughts associated with climate change, making it increasingly important for land stewards to look to the future when making management decisions.

In a study recently published in Remote Sensing, USGS researchers combine historic data sets and state-of-the-art mapping technology to predict how sagebrush ecosystems will respond to future climate shifts. The team used data from the National Land Cover Dataset “Back-in-Time” project, a USGS-funded resource that tracks historic vegetation patterns across the United States. They quantified three categories of land coverage (bare ground, herbaceous, and shrub) across three DOI managed sites in Oregon and Nevada from 1985-2018: Beaty’s Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. The authors then applied knowledge from these historic patterns to predictive models, generating estimates of potential land cover changes over the next thirty years. They examined sagebrush ecosystems under two future climate change scenarios: a business as usual model, which assumes future values will be comparable to those from the previous thirty years, and an Intergovernmental Panel on Climate Change (IPCC) representative concentration pathway (RCP 8.5) model, which computes future temperature and precipitation values using the highest levels of forecasted greenhouse gas emissions.

They found that vegetation cover has been historically connected to precipitation and temperature levels, indicating that climate does contribute to slow land cover changes that may not be obvious in short time frames. Using this insight, their models predicted that climate change will alter land cover over the next thirty years, with shrub areas increasing and bare ground and herbaceous areas decreasing under the most extreme climate predictions. This will make the studied landscapes more homogeneous over time, with the largest effects predicted to occur in the Hart Mountain site. These predicted vegetation shifts are not as dramatic as expected, indicating that sagebrush ecosystems may be somewhat robust to some effects of climate change. The results support prior findings that climate contributes to slow composition changes in sagebrush ecosystems that are often undetectable in traditional remote sensing land cover changes studies. It’s important to note, however, that the model did not consider the effects of fire, grazing, or land management, which will likely play important roles in shaping future sagebrush ecosystems.

The authors hope these predictive models will help federal land managers develop targeted management plans for identifying vegetation cover changes that may affect habitat condition and availability for species of interest on DOI sagebrush lands in the face of climate change.

This work is part of the Identifying Historical Drivers of Vegetation Change to Inform Future Management of Federal Lands in the Northern Great Basin project funded by the Northwest CASC.

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