Search

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 (R2 = 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. [ABSTRACT FROM AUTHOR]

Context: Plant 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. Objectives: Annual 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. Methods: We 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. Results: We 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. Conclusions: We 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. [ABSTRACT FROM AUTHOR]

Aims: Invasion by annual grasses (IAGs) and concomitant increases in wildfire are impacting many drylands globally, and an understanding of factors that contribute to or detract from community resistance to IAGs is needed to inform postfire restoration interventions. Prefire vegetation condition is often unknown in rangelands but it likely affects variation in postfire invasion resistance across large burned scars. Whether satellite‐derived products like the Rangeland Analysis Platform (RAP) can fulfill prefire information needs and be used to parametrize models of fire recovery to inform postfire management of IAGs is a key question. Methods: We used random forests to ask how IAG abundances in 669 field plots measured in the 2‐3 years following megafires in sagebrush steppe rangelands of western USA responded to RAP estimates of annual:perennial prefire vegetation cover, the effects of elevation, heat load, postfire treatments, soil moisture–temperature regimes, and land‐agency ratings of ecosystem resistance to invasion and resilience to disturbance. Results: Postfire IAG cover measured in the field was 22¯$$ \overline{22} $$% and RAP‐estimated prefire annual herbaceous cover was 15.7¯$$ \overline{15.7} $$%. The random forest model had an R2 of 0.36 and a root‐mean‐squared error (RMSE) of 4.41. Elevation, postfire herbicide treatment, and prefire estimates from RAP for the ratio of annual:perennial and shrub cover were the most important predictors of postfire IAG cover. Threshold‐like relationships between postfire IAG cover and the predictors indicate that maintaining annual:perennial cover below 0.4 and shrub cover below <10% prior to wildfire would decrease invasion, at low elevations below 1400 m above sea level. Conclusion: Despite known differences between RAP and field‐based estimates of vegetation cover, RAP was still a useful predictor of variation in IAG abundances after fire. IAG management is oftentimes reactive, but our findings indicate impactful roles for more inclusively addressing the exotic annual community, and focusing on prefire maintenance of annual:perennial herbaceous and shrub cover at low elevations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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 levelAt the ecoregion level, vegetation recovered to prewildfire levels with the exception of one ecoregion for a single functional groupWildfire‐driven vegetation degradation appears localized and represents extreme cases within larger wildfires [ABSTRACT FROM AUTHOR]

Screening is a strategy for detecting undesirable change prior to manifestation of symptoms or adverse effects. Although the well-recognized utility of screening makes it commonplace in medicine, it has yet to be implemented in ecosystem management. Ecosystem management is in an era of diagnosis and treatment of undesirable change, and as a result, remains more reactive than proactive and unable to effectively deal with today's plethora of non-stationary conditions. In this paper, we introduce spatial imaging-based screening to ecology. We link advancements in spatial resilience theory, data, and technological and computational capabilities and power to detect regime shifts (i.e., vegetation state transitions) that are known to be detrimental to human well-being and ecosystem service delivery. With a state-of-the-art landcover dataset and freely available, cloud-based, geospatial computing platform, we screen for spatial signals of the three most iconic vegetation transitions studied in western USA rangelands: (1) erosion and desertification; (2) woody encroachment; and (3) annual exotic grass invasion. For a series of locations that differ in ecological complexity and geographic extent, we answer the following questions: (1) Which regime shift is expected or of greatest concern? (2) Can we detect a signal associated with the expected regime shift? (3) If detected, is the signal transient or persistent over time? (4) If detected and persistent, is the transition signal stationary or non-stationary over time? (5) What other signals do we detect? Our approach reveals a powerful and flexible methodology, whereby professionals can use spatial imaging to verify the occurrence of alternative vegetation regimes, image the spatial boundaries separating regimes, track the magnitude and direction of regime shift signals, differentiate persistent and stationary transition signals that warrant continued screening from more concerning persistent and non-stationary transition signals, and leverage disciplinary strength and resources for more targeted diagnostic testing (e.g., inventory and monitoring) and treatment (e.g., management) of regime shifts. While the rapid screening approach used here can continue to be implemented and refined for rangelands, it has broader implications and can be adapted to other ecological systems to revolutionize the information space needed to better manage critical transitions in nature.