Search

Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic influences are unknown. While many studies have projected the effects of climate change using several modeling approaches, none has evaluated the impacts to fractional component cover at a 30‐m resolution across the full sagebrush (Artemisia spp.) biome. We used fractional component cover data for rangeland functional groups and weather data from the 1985 to 2018 reference period in conjunction with soils and topography data to develop empirical models describing the spatiotemporal variation in component cover. To investigate the ramifications of future change across the western United States, we extended models based on historical relationships over the reference period to model landscape effects based on future weather conditions from two emission scenarios and three time periods (2020s, 2050s, and 2080s). We tested both generalized additive models (GAMs) and regression tree models, finding that the former led to superior spatial and statistical results. Our results indicate more xeric vegetation across most of the study area, with an increasing dominance of non‐sagebrush shrubs, annual herbaceous cover, and bare ground over herbaceous and sagebrush cover in both the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. In general, both scenarios yielded similar results, but RCP 8.5 tended to be more extreme, with greater change relative to the reference period. Results demonstrate that in cool sites some degree of warming to growing season maximum temperature or nongrowing season minimum temperature could be beneficial to sagebrush and shrub growth. However, warming nongrowing season maximum temperature was beneficial to shrub, but not to sagebrush growth. Our results inform rangeland managers of potential future vegetation composition, cover, and species distributions, which could improve prioritization of conservation and restoration efforts.

Understanding the factors that influence vegetation responses to disturbance is important because vegetation is the foundation of food resources, wildlife habitat, and ecosystem properties and processes. We integrated vegetation cover data derived from field plots and remotely sensed Landsat images in two focal areas over a 37‐yr period (1979–2016) to investigate how historical changes to community composition influence contemporary responses of vegetation to fire in sagebrush ecosystems in the Great Basin. Our objectives were (1) to quantify the magnitude and direction of change in the cover of native and exotic plant functional groups in relation to their exposure to fire; (2) to relate plant community changes to their historical composition, exposure to fire, and environmental conditions; and (3) to test for consistency of trends revealed by vegetation cover data derived from field plots and Landsat images. Historical (1979–1981) field data originated from 298 locations, Landsat‐derived data and contemporary (2011–2016) field data originated from 448 locations, and an expanded set of locations were included in some analyses of Landsat‐derived data. We found that areas burned by fire since the 1980s had higher annual herbaceous cover than unburned areas both historically and contemporarily. Models revealed a significant interaction between historical community composition and exposure to fire, which suggests that plots with historically high herbaceous cover were more susceptible to burning. Trends revealed by field and Landsat‐derived cover data were only partially consistent, potentially due in part to methods used to predict cover values from Landsat images, and the time period over which each data set was collected. Our results suggest that burned areas historically occupied by sagebrush‐dominated plant communities may have been invaded by exotic annuals prior to burning, possibly because of prior land uses, and after burning, have now transitioned to a persistent herbaceous‐dominated state. This type of state transition has important consequences for forage quality, wildlife habitat, soil nutrients, and future disturbances, such as drought and wildfire.

The need to monitor change in sagebrush steppe is urgent due to the increasing impacts of climate change, shifting fire regimes, and management practices on ecosystem health. Remote sensing provides a cost‐effective and reliable method for monitoring change through time and attributing changes to drivers. We report an automated method of mapping rangeland fractional component cover over a large portion of the Northern Great Basin, USA, from 1986 to 2016 using a dense Landsat imagery time series. Our method improved upon the traditional change vector method by considering the legacy of change at each pixel. We evaluate cover trends stratified by climate bin and assess spatial and temporal relationships with climate variables. Finally, we statistically evaluate the minimum time density needed to accurately characterize temporal patterns and relationships with climate drivers. Over the 30‐yr period, shrub cover declined and bare ground increased. While few pixels had >10% cover change, a large majority had at least some change. All fractional components had significant spatial relationships with water year precipitation (WYPRCP), maximum temperature (WYTMAX), and minimum temperature (WYTMIN) in all years. Shrub and sagebrush cover in particular respond positively to warming WYTMIN, resulting from the largest increases in WYTMIN being in the coolest and wettest areas, and respond negatively to warming WYTMAX because the largest increases in WYTMAX are in the warmest and driest areas. The trade‐off of lowering temporal density against removing cloud‐contaminated years is justified as temporal density appears to have only a modest impact on trends and climate relationships until n ≤ 6, but multi‐year gaps are proportionally more influential. Gradual change analysis is likely to be less sensitive to n than abrupt change. These data can be used to answer critical questions regarding the influence of climate change and the suitability of management practices.