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Aim In the western United States, sagebrush (Artemisia spp.) and salt desert shrublands are rapidly transitioning to communities dominated by exotic annual grasses, a novel and self‐reinforcing state that threatens the economic sustainability and conservation value of rangelands. Climate change is predicted to favour annual grasses, potentially pushing transitions to annual grass dominance into higher elevations and north‐facing aspects. We sought to quantify expansion of annual grass‐dominated vegetation communities along topographic gradients over the past several decades. Location Our analysis focused on rangelands among three ecoregions in the Great Basin of the western United States, where several species of exotic annual grasses are widespread among shrub and perennial grass‐dominated vegetation communities. Methods We used recently developed remote sensing‐based rangeland vegetation data to produce yearly maps of annual grass‐dominated vegetation communities spanning 1990–2020. With these maps, we quantified the rate of spread and characterized changes in the topographic distribution (i.e. elevation and aspect) of areas transitioning to annual grass dominance. Results We documented more than an eightfold increase in annual grass‐dominated area since 1990, occurring at an average rate of >2,300 km2 year−1 (0.6% of the area of Great Basin rangelands). In 2020, annual grasses dominated approximately one‐fifth (>77,000 km2) of Great Basin rangelands. This rapid expansion was associated with a broadening topographic niche, with widespread movement into higher elevations and north‐facing aspects consistent with predicted effects of a warming climate. Main conclusions More than a century after first appearing in the region, exotic annual grasses continue to proliferate and establish dominance in new environments across the Great Basin. Accelerated, strategic intervention is critically needed to conserve vulnerable sagebrush and salt desert shrub communities not yet heavily invaded. In this era of warming, future climate provides important context for selecting from among alternative management actions and judging long‐term prospects of success.

Context Satellite products of fractional vegetation cover are often used to manage rangelands. However, they frequently miss the details of heterogeneous landscapes. The use of unoccupied aerial systems (UAS) to produce high spatial resolution rangeland fractional cover maps could fill that gap at local scales. Objectives We evaluated the capabilities of UAS imagery for mapping rangeland fractional vegetation cover in sagebrush steppe communities of the Northern Great Basin, USA. Methods We applied segmentation and machine learning models for image classification, and established regression functions with field-measured herbaceous cover and multiple spectral indices to quantify herbaceous fraction in bare/herbaceous mixed polygons. Finally, we conducted a correlation analysis to compare UAS-derived rangeland fractional cover with satellite-derived products. Results Overall classification accuracies for the UAS-derived rangeland fractional cover maps were high (89–98%). Modified Soil Adjusted Vegetation Index was the most important spectral index for predicting photosynthetic classes and including Brightness Index in a multiple index approach improved classification of shadows and bare ground. Regression models effectively estimated herbaceous fractions within bare/herbaceous mixed polygons with high accuracy (R 2  = 0.71–0.88). UAS-derived rangeland fractional cover estimates captured within-site variability, while satellite-derived products did not, specifically for herbaceous and litter. Conclusions This study demonstrated a workflow using UAS and intensive ground sampling for estimating rangeland fractional cover in sagebrush communities. We found a disagreement between UAS-derived and satellite-derived fractional cover products at two sagebrush communities in the Northern Great Basin. We recommend the application of UAS when estimating rangeland fractional cover at local scales.

We simulated historical grassland aboveground plant productivity (ANPP) across the midwestern and western contiguous United States using the DayCent-UV ecosystem model. For this study we developed new methods for informing DayCent-UV of growing season length and validating its plant productivity estimates for grasslands by utilizing a wide range of data sources at multiple scales, from field observations to remotely sensed satellite data. The model’s phenology was informed by the MODIS MCD12Q2 product, which showed good agreement with in-situ observations of growing season commencement and duration across different grassland ecosystems, and with observed historical trends. Model results from each simulated grid cell were compared to a remote-sensing estimate of grassland plant productivity offered by the Rangeland Analysis Platform (RAP). We determined that a modified RAP ANPP calculation that incorporated total annual precipitation instead of mean annual temperature to estimate the fraction of total productivity allocated to roots improved temporal correlations between RAP and field measurements and between RAP and DayCent-UV, We found that RAP provides a valuable data set for evaluating grassland ANPP predictions from ecosystem and other types of models because it provides estimates of grassland plant productivity over large spatial regions and a long historical period and captures temporal variablilty in plant production. This work provides the foundation for using the DayCent-UV model to predict climate change impacts on grassland cecosystem dynamics in the contiguous US.

ContextPlant communities are undergoing compositional changes that affect ecosystem function. These changes are not always uniform across the landscape due to heterogenous topographic and edaphic conditions. To predict areas most at risk of change, it is necessary to identify the landscape drivers affecting plant abundance.ObjectivesAnnual plants are increasing across the western USA, largely driven by non-native annual invasions. Here, we quantified change in annual plant abundance and identified landscape factors contributing to that change over the past 35 years.MethodsWe focused on Willamette Valley (Oregon) grasslands because they represent a new example in this phenomenon. To understand the spatiotemporal patterns of annual plant abundances between 1986 and 2020, we combined a remote-sensing vegetation cover dataset from the rangeland analysis platform with gridded soils data and topographic variables. We determined the rate of change in percent cover for each 30 × 30 m pixel and regressed cover against heat load, soil depth and sand content for > 5975 hectares to determine areas most sensitive to rising annual cover.ResultsWe found a tendency toward increasing annual cover, with a median gain of + 15% cover among significantly increasing pixels. However, change was uneven across the landscape, with annual cover increasing markedly in areas with high heat load and shallower soils.ConclusionsWe identified steep, south-facing slopes as being particularly sensitive to rising annual cover. Annual plant invasions may be lagging in this region compared to elsewhere in the western USA, but trends here suggest it may just be a matter of time.

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long‐term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C‐cycle processes. Bayesian calibration was conducted using quality‐controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass‐shrub mixture, and grass‐tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE <390 g C m−2) relative to net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE <180 g C m−2). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long‐term network‐based monitoring of vegetation biomass, C fluxes, and SOC stocks. Plain Language Summary Rangelands play a crucial role in providing various ecosystem services, including potential climate change mitigation through increased soil organic carbon (SOC) storage. Accurate estimates of changes in carbon (C) storage are challenging due to the heterogeneous nature of rangelands and the limited availability of field observations. In this work, we leveraged remote sensing observations, tower‐based C flux measurements from over 60 rangeland sites in the Western and Midwestern US, and other environmental data sets to build the process‐based Rangeland Carbon Tracking and Management (RCTM) modeling system. The RCTM system is designed to simulate the past 20 years of rangeland C dynamics and is regionally calibrated. The RCTM system performs well in estimating spatial and temporal rangeland C fluxes as well as spatial SOC storage. Model simulation results revealed increased SOC storage and rangeland productivity driven by annual precipitation patterns. The RCTM system developed by this work can be used to generate accurate spatial and temporal estimates of SOC storage and C fluxes at fine spatial (30 m) and temporal (every 5 days) resolutions, and is well‐suited for informing rangeland C management strategies and improving broad‐scale policy making. Key Points The Rangeland Carbon Tracking and Monitoring System was calibrated to simulate vegetation type‐specific rangeland C dynamics Regional variability in carbon fluxes and soil organic carbon is well represented by a remote sensing‐driven process modeling approach Soil organic carbon stocks in Western and Midwestern US rangelands increased over the past 20 years due to increased precipitation

Wildfires are ecosystem‐level drivers of structure and function in many vegetated biomes. While numerous studies have emphasized the benefits of fire to ecosystems, large wildfires have also been associated with the loss of ecosystem services and shifts in vegetation abundance. The size and number of wildfires are increasing across a number of regions, and yet the outcomes of large wildfire on vegetation at large‐scales are still largely unknown. We introduce an exhaustive analysis of wildfire‐scale vegetation response to large wildfires across North America's grassland biome. We use 18 years of a newly released vegetation data set combined with 1,390 geospatial wildfire perimeters and drought data to detect large‐scale vegetation response among multiple vegetation functional groups. We found no evidence of persistent declines in vegetation driven by wildfire at the biome level. All vegetation functional groups exhibited relatively rapid recovery to pre wildfire ranges of variation across the Great Plains ecoregions, with the exception being a persistent decrease in the abundance of trees in the Northwestern Great Plains. Drought intensity magnified immediate vegetation response to wildfire. Persistent declines in vegetation cover were observed at the scale of single pixels (30 m), suggesting that these responses were localized and represent extreme cases within larger wildfires. Our findings echo over a century of research demonstrating a biome resilient to wildfire. Key Points All vegetation functional groups exhibited relatively rapid recovery at the biome level At the ecoregion level, vegetation recovered to prewildfire levels with the exception of one ecoregion for a single functional group Wildfire‐driven vegetation degradation appears localized and represents extreme cases within larger wildfires