Biology subjects

Allred, B. W.

Publications and source records attributed to Allred, B. W..

11 recordsLinked to original sources

bioRxiv2026

Exclusion of fire and grazing does not reduce annual grass invasion in a sagebrush steppe ecosystem

Disturbance is widely recognized as a catalyst of invasion, but growing evidence suggests even protected communities are susceptible to severe infestation. We used k[i]pukas--naturally isolated patches of minimally-disturbed vegetation surrounded by lava flows--as a large-scale natural experiment to test the long-term biotic resistance of protected sagebrush ecosystems threatened by Bromus tectorum and other invasive annual grasses. Employing a robust causal inference approach combining matching with regression adjustment, we compared protected communities within k[i]pukas to otherwise similar communities exposed to contemporary disturbance regimes. Despite their near-total protection from fire and livestock grazing, k[i]pukas were extensively invaded by annual grasses (18.9 {+/-} 0.28% cover), with abundance comparable to or slightly exceeding disturbed sites (16.7 {+/-} 0.29% cover). These findings challenge the notion that protection from disturbance confers effective long-term resistance to invasion, instead demonstrating that invaders can establish, proliferate, and drive ecosystem transformation where favorable abiotic conditions prevail. Our findings reveal the limits of passive protection as a conservation strategy, suggesting active management may be necessary to prevent ecosystem degradation by aggressive invaders.

ecology↗

bioRxiv2025

A 0.6-meter resolution canopy height and structure model for the contiguous United States

Above-ground vertical structure is a critical variable for ecosystem monitoring, carbon accounting, and land management. However, the high cost and limited coverage of airborne lidar hinder its widespread application. To address this, we developed NAIP-CHM, a 0.6-meter resolution canopy height and structure model (CHM) covering the contiguous United States, derived from National Agriculture Imagery Program (NAIP) aerial imagery. Unlike forestry-specific models that exclude human-made features, NAIP-CHM characterizes the full vertical structure of the landscape including vegetation, buildings, and infrastructure. We utilized a U-Net convolutional neural network with attention mechanisms and environmental conditioning, training and validating the model with a peer-reviewed, publicly available dataset of 22.8 million co-registered NAIP imagery and lidar-derived CHM pairs, with stratified sampling to ensure robustness in open-canopy ecosystems. The model achieved a pixel-wise root mean square error (RMSE) of 2.28 meters and an r2 of 0.87. Forested sites alone produced an r2 of 0.82 and RMSE of 3.82 meters. We provide the dataset, source code, and cloud-based tools to enable broad application without requiring specialized computational resources.

ecology↗

bioRxiv2025

Estimating rangeland fractional cover and canopy gap size class with Sentinel-2 imagery

Rangelands are extensive ecosystems, providing important ecosystem services while undergoing continuous change. As a result, improved monitoring technologies can help better characterize vegetation change. Satellite remote sensing has proven effective in this regard, tracking vegetation dynamics at broad and fine scales. We leveraged the spatial, spectral, and temporal resolution of Sentinel-2 satellites to estimate fractional cover and canopy gap across rangelands of the western United States. We produced annual, 10 m spatial resolution estimates of fractional cover and canopy gap size class for years 2018 to 2024. Fractional cover estimates include that of common plant functional types (annual forb and grass, bareground, littler, perennial forb and grass, shrub, tree) and select genera (including invasive annual grass species, pinyon-juniper species, and sagebrush species); canopy gap size classes include gap sizes 25 to 50, 51 to 100, 101 to 200, and greater than 200 cm. We make these data available as Cloud Optimized GeoTIFFs, organized as 75x75 km tiles covering the 17 western states of the United States.

ecology↗

bioRxiv2024

Canopy height model and NAIP imagery pairs across CONUS

Canopy height models (CHM) provide detailed environmental vertical structure information and are an important indicator and input for ecological and geospatial applications. These models are often spatiotemporally inconsistent, necessitating additional modeling to scale them in space and time. Yet, such scaling is hindered by a lack of spatially diverse data. To address this, we use United States Geological Survey 3D Elevation Program lidar data to produce 22,796,764 one meter resolution CHM chips, stratified across the dominant land covers of the conterminous United States. For each CHM, we pair a matching time-aligned aerial image from the United States Department of Agriculture National Agriculture Imagery Program. This dataset can be used to train models for large scale CHM production.

ecology↗

bioRxiv2022

Mapping Tree Cover Expansion in Montana, U.S.A. Rangelands using High-Resolution Historical Aerial Imagery

Worldwide, trees are colonizing rangelands with high conservation value. The introduction of trees into grasslands and shrublands causes large-scale changes in ecosystem structure and function, which have cascading impacts to ecosystem services, biodiversity, and agricultural economies. Satellites are increasingly being used to track tree cover at continental to global scales, but these methods can only provide reliable estimates of change over recent decades. Given the slow pace of tree cover expansion, remote sensing techniques that can extend this historical record provide critical insights into the magnitude of environmental change. Here, we estimate conifer expansion in rangelands of the northern Great Plains, United States, North America, using historical aerial imagery from the mid-20th century and modern aerial imagery. We analyzed 19.3 million hectares of rangelands in Montana, USA, using a convolutional neural network (U-Net architecture) and cloud computing to detect tree features and tree cover change. Our bias-corrected results estimate 3.0 {+/-} 0.2 million hectares of conifer tree cover expansion in Montana rangelands, which accounts for 15.4% of the total study area. Overall accuracy was greater than 91%, but producer accuracy was lower than user accuracy (0.60 vs. 0.88). Nonetheless, the omission errors were not spatially correlated, indicating that the method is reliable for estimating the spatial extent of tree cover expansion. Using the model results in conjunction with historical and modern imagery allows for effective communication of the scale of tree expansion while overcoming the recency effect caused by shifting environmental baselines.

ecology↗

bioRxiv2022

Regional context for balancing sagebrush- and woodland-dependent songbird needs with targeted pinyon-juniper management in the sagebrush biome

Tree expansion among historic grassland and shrubland systems is a global phenomenon, which results in dramatic influences on ecosystem processes and wildlife populations. In the western US, pinyon-juniper woodlands have expanded by as much as six-fold among sagebrush steppe landscapes since the late nineteenth century, with demonstrated negative impacts to the behavior, demography, and population dynamics of species that rely on intact sagebrush rangelands. Notably, greater sage-grouse (Centrocercus urophasianus) are unable to tolerate even low conifer cover, which can result in population declines and local extirpation. Removing expanding conifer cover has been demonstrated to increase sage grouse population growth rates and sagebrush-obligate songbird abundance. However, advances in restoring sagebrush habitats have been met with concern about unintended impacts to species that rely on conifer woodlands, notably the pinyon jay (Gymnorhinus cyanocephalus) whose population declines are distinctive among birds breeding in pinyon-juniper woodlands. We modeled indices to abundance in relation to multi-scale habitat features for nine songbirds reliant on both sagebrush and pinyon-juniper woodlands for breeding. Findings demonstrate that targeted sage grouse habitat restoration under the Sage Grouse Initiative is not at odds with protection of pinyon jay populations. Rather, conifer management has largely occurred in the northern sagebrush ecosystem where models suggest that past cuts likely benefit Brewers sparrow and sage thrasher while avoiding pinyon jay habitat. Extending our spatial modeling further south beyond the sagebrush biome could better equip conservationists with more comprehensive decision-support, particularly where pinyon jays face additional pressures of drought-induced tree mortality.

ecology↗

bioRxiv2021

Where there's smoke, there's fuel: predicting Great Basin rangeland wildfire

Wildfires are a growing management concern in western US rangelands, where invasive annual grasses have altered fire regimes and contributed to an increased incidence of catastrophic large wildfires. Fire activity in arid, non-forested ecosystems is thought to be largely controlled by interannual variation in fuel amount, which in turn is controlled by antecedent weather. Thus, long-range forecasting of fire activity in rangelands should be feasible given annual estimates of fuel quantity. Using a 32 yr time series of spatial data, we employed machine learning algorithms to predict the relative probability of large (>405 ha) wildfire in the Great Basin based on fine-scale annual and 16-day estimates of cover and production of vegetation functional groups, weather, and multitemporal scale drought indices. We evaluated the predictive utility of these models with a leave-one-year-out cross-validation, building spatial hindcasts of fire probability for each year that we compared against actual footprints of large wildfires. Herbaceous aboveground biomass production, bare ground cover, and long-term drought indices were the most important predictors of burning. Across 32 fire seasons, 88% of the area burned in large wildfires coincided with the upper 3 deciles of predicted fire probabilities. At the scale of the Great Basin, several metrics of fire activity were moderately to strongly correlated with average fire probability, including total area burned in large wildfires, number of large wildfires, and maximum fire size. Our findings show that recent years of exceptional fire activity in the Great Basin were predictable based on antecedent weather-driven growth of fine fuels and reveal a significant increasing trend in fire probability over the last three decades driven by widespread changes in fine fuel characteristics.

ecology↗

bioRxiv2021

Biome-scale woody encroachment threatens conservation potential and sustainability of U.S. rangelands

O_LIRangelands of the United States provide ecosystem services that benefit society and rural economies. Native tree encroachment is often overlooked as a primary threat to rangelands due to the slow pace of tree cover expansion and the positive public perception of trees. Still, tree encroachment fragments these landscapes and reduces herbaceous production, thereby threatening habitat quality for grassland wildlife and the economic sustainability of animal agriculture. C_LIO_LIRecent innovations in satellite remote sensing permit the tracking of tree encroachment and the corresponding impact on herbaceous production. We analyzed tree cover change and herbaceous production across the western United States from 1990 to 2019. C_LIO_LIWe show that tree encroachment is widespread in U.S. rangelands; absolute tree cover has increased by 50% (77,323 km2) over 30 years, with more than 25% (684,852 km2) of U.S. rangeland area experiencing tree cover expansion. Since 1990, 302 {+/-} 30 Tg of herbaceous biomass have been lost. Accounting for variability in livestock biomass utilization and forage value reveals that this lost production is valued at between $4.1 - $5.6 billion U.S. dollars. C_LIO_LISynthesis and applications: The magnitude of impact of tree encroachment on rangeland loss is similar to conversion to cropland, another well-known and primary mechanism of rangeland loss in the U.S. Prioritizing conservation efforts to prevent tree encroachment can bolster ecosystem and economic sustainability, particularly among privately-owned lands threatened by land-use conversion. C_LI

ecology↗

bioRxiv2021

The elevational ascent and spread of exotic annual grasslands in the Great Basin, USA

AimIn the western US, sagebrush (Artemisia spp.) and salt desert shrublands are rapidly transitioning to communities dominated by exotic annual grasses, a novel and often self-reinforcing state that threatens the economic sustainability and conservation value of rangelands. Climate change is predicted to directly and indirectly favor annual grasses, potentially pushing transitions to annual grass dominance into higher elevations and north-facing aspects. We sought to quantify the expansion of annual grass-dominated vegetation communities along topographic gradients over the past several decades. LocationOur analysis focused on rangelands among three ecoregions in the Great Basin of the western US, where several species of exotic annual grasses are widespread among shrub and perennial grass-dominated vegetation communities. MethodsWe used recently developed remote sensing-based rangeland vegetation data to produce yearly maps of annual grass-dominated vegetation communities spanning the period 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. ResultsWe documented more than an eight-fold increase in annual grass-dominated area (to >77,000 km2) occurring at an average rate of >2,300 km2 yr-1. In 2020, annual grasses dominated one fifth (19.8%) of Great Basin rangelands. This rapid expansion is associated with a broadening of the topographic niche, with widespread movement into higher elevations and north-facing aspects. Main conclusionsAccelerated, strategic intervention is critically needed to conserve the fragile band of rangelands being compressed between annual grassland transitions at lower elevations and woodland expansion at higher elevations.

ecology↗

bioRxiv2020

Annual and 16-day rangeland production estimates for the western United States

Rangeland production is a foundational ecosystem service and resource upon which livestock, wildlife, and people depend. Capitalizing on recent advancements in the use of remote sensing data across rangelands we provide estimates of herbaceous rangeland production from 1986-2019 at 16-day and annual time steps and 30m resolution across the western United States. A factorial comparison of this dataset and three national scale datasets is presented, and we highlight a multiple lines of evidence approach when using production estimates in decision-making. Herbaceous aboveground biomass at this scale and resolution provides critical information applicable for management and decision-making, particularly in the face of annual grass invasion and woody encroachment of rangeland systems. These readily available data remove analytical and technological barriers allowing immediate utilization for monitoring and management.

ecology↗

bioRxiv2020

Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty

O_LIOperational 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. C_LIO_LIWe 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. C_LIO_LIModel 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. C_LIO_LIThe 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. C_LI

ecology↗