Abstract
Sagebrush-dominant ecosystems in the western United States are highly vulnerable to climatic variability. To understand how these ecosystems will respond under potential future conditions, we correlated changes in National Land Cover Dataset “Back-in-Time” fractional cover maps from 1985-2018 with Daymet climate data in three federally managed preserves in the sagebrush steppe ecosystem: Beaty Butte Herd Management Area, Hart Mountain National Antelope Refuge, and Sheldon National Wildlife Refuge. Future (2018 to 2050) abundance and distribution of vegetation cover were modeled at a 300-m resolution under a business-as-usual climate (BAU) scenario and a Representative Concentration Pathway (RCP) 8.5 climate change scenario. Spatially explicit map projections suggest that climate influences may make the landscape more homogeneous in the near future. Specifically, projections indicate that pixels with high bare ground cover become less bare ground dominant, pixels with moderate herbaceous cover contain less herbaceous cover, and pixels with low shrub cover contain more shrub cover. General vegetation patterns and composition do not differ dramatically between scenarios despite RCP 8.5 projections of +1.2 °C mean annual minimum temperatures and +7.6 mm total annual precipitation. Hart Mountain National Antelope Refuge is forecast to undergo the most change, with both models projecting larger declines in bare ground and larger increases in average herbaceous and shrub cover compared to Beaty Butte Herd Management Area and Sheldon National Wildlife Refuge. These scenarios present plausible future outcomes intended to guide federal land managers to identify vegetation cover changes that may affect habitat condition and availability for species of interest.
Highlights
A common approach for constructing a record of contemporary landscape change over large geographic areas is land-cover change analysis using multi-decadal observations made with Earth Observation Systems
Pixels characterized by higher bare ground cover exhibit less climate sensitivity than those with higher vegetation cover
Historical changes served as the empirical foundation of a business as usual scenario intended to model the effect of historical trends into the future, and these rates were modified based on Representative Concentration Pathway (RCP) 8.5 climate projections to create a climate-change scenario intended to show how climate change may lead to different land cover distributions under the direst scenario projected by IPCC
Summary
A common approach for constructing a record of contemporary landscape change over large geographic areas is land-cover change analysis using multi-decadal observations made with Earth Observation Systems. A new generation of mapping efforts capitalizes on these advances, producing fractional rangeland vegetation cover at a 30-m scale, with annual time-steps, across vast landscapes [3,4]. The NLCD Back in Time (BIT) project uses 30m Landsat data to generate annual fractional cover maps for different land cover types from 1985-2018 [4]. And spatially rich outputs like BIT provide a long list of benefits, such as the identification of abrupt change events across a map time series, the tracking of gradual changes over time (i.e., succession, recovery, etc.), the ability to link short-term change with drivers where comparable data exist (i.e., climate), and the opportunity to create empirical land-cover projections based on more precise estimates of change [5,6]. Chronicling historical changes and deconstructing causes of land change are important steps towards identifying current impacts and mitigating future impacts [7,8]
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