Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision‐making at multiple scales. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on‐the‐ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatiotemporal‐explicit, pixel‐level estimates of uncertainty. We evaluated the model with 5,780 on‐the‐ground vegetation plots removed from the training data. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Evaluation with additional datasets that were not part of the training dataset, and that varied in geographic range, method of collection, scope and size, revealed similar metrics. Model performance increased across all functional groups compared to the previously produced fractional product. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel‐level uncertainty. The new product is available on the Rangeland Analysis Platform ( https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on‐the‐ground data, expert knowledge, land use history, scientific literature and other sources of information when making interpretations. When being used to inform decision‐making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

1. Operational satellite remote sensing products are transforming rangeland management and science. Advancements in computation, data storage, and processing have removed barriers that previously blocked or hindered the development and use of remote sensing products. When combined with local data and knowledge, remote sensing products can inform decision making at multiple scales.2. We used temporal convolutional networks to produce a fractional cover product that spans western United States rangelands. We trained the model with 52,012 on-the-ground vegetation plots to simultaneously predict fractional cover for annual forbs and grasses, perennial forbs and grasses, shrubs, trees, litter, and bare ground. To assist interpretation and to provide a measure of prediction confidence, we also produced spatially-explicit, pixel-level estimates of uncertainty. We evaluated the model with 5,780 on-the-ground vegetation plots removed from the training data.3. Model evaluation averaged 6.3% mean absolute error and 9.6% root mean squared error. Model performance increased across all functional groups compared to the previously produced fractional product. 4. The advancements achieved with the new rangeland fractional cover product expand the management toolbox with improved predictions of fractional cover and pixel-level uncertainty. The new product is available on the Rangeland Analysis Platform (https://rangelands.app/), an interactive web application that tracks rangeland vegetation through time. This product is intended to be used alongside local on-the-ground data, expert knowledge, land use history, scientific literature, and other sources of information when making interpretations. When being used to inform decision-making, remotely sensed products should be evaluated and utilized according to the context of the decision and not be used in isolation.

Fire suppression and exclusion, the historically dominant paradigm of fire management, has resulted in major modifications of fire-dependent ecosystems worldwide. These changes are partially credited with a recent increase in wildfire number and extent, as well as more extreme fire behavior. Fire and herbivory historically interacted, and research has shown that the interaction creates a unique mosaic of vegetation heterogeneity that each disturbance alone does not create. Because fire and grazing have largely been decoupled in modern times, the degree to which the interaction affects fuels and fire regimes has not yet been quantified. We evaluated effects of fire-only and pyric herbivory on rangeland fuels and fire behavior simulated using BehavePlus at four sites across the southern Great Plains. We predicted patches managed via pyric herbivory would maintain lower fuel loads, and less intense simulated fire behavior than fire alone. We found that time since fire was a significant predictor of fuel loads and simulated fire behavior characteristics at all sites. Fuel loads and simulated fire behavior characteristics (flame length and rate of spread) increased with increasing time since fire in all simulated weather scenarios. Pyric herbivory mediated fuel accumulation at all sites. Mean fuel loads in fire-only treatments exceeded 5000 kg/ha within 24 months, but pyric herbivory treatments remained below 5000 kg/ha for approximately 36 months. Simulated flame lengths in fire-only treatments were consistently higher (up to 3 9 ) than in pyric herbivory treatments. Similarly, fire spread rates were higher in fire-only than in pyric herbivory treatments in all simulated weather conditions. Although all sites had potential to burn in the most extreme weather conditions, pyric herbivory reduced fuel accumulations, flame lengths, and rates of spread across all weather patterns simulated. These reductions extended the amount of time standard wildland firefighting techniques remain effective. Therefore, incorporating pyric herbivory into fuel management practices, in areas of high herbaceous productivity, increases the effectiveness of fuel treatments.

Interactions between large herbivores and their food supply are central to the study of population dynamics. We assessed temporal and spatial patterns in meadow plant biomass over a 23-year period for meadow complexes that were spatially linked to three distinct populations of Roosevelt elk (Cervus elaphus roosevelti) in northwestern California. Our objectives were to determine whether the plant community exhibited a tolerant or resistant response when elk population growth became irruptive. Plant biomass for the three meadow complexes inhabited by the elk populations was measured using Normalized Difference Vegetation Index (NDVI), which was derived from Landsat 5 Thematic Mapper imagery. Elk populations exhibited different patterns of growth through the time series, whereby one population underwent a complete four-stage irruptive growth pattern while the other two did not. Temporal changes in NDVI for the meadow complex used by the irruptive population suggested a decline in forage biomass during the end of the dry season and a temporal decline in spatial variation of NDVI at the peak of plant biomass in May. Conversely, no such patterns were detected in the meadow complexes inhabited by the nonirruptive populations. Our findings suggest that the meadow complex used by the irruptive elk population may have undergone changes in plant community composition favoring plants that were resistant to elk grazing.

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