US fires became larger, more frequent, and more widespread in the 2000s

Recent fires have fueled concerns that regional and global warming trends are leading to more extreme burning. We found compelling evidence that average fire events in regions of the United States are up to four times the size, triple the frequency, and more widespread in the 2000s than in the previous two decades. Moreover, the most extreme fires are also larger, more common, and more likely to co-occur with other extreme fires. This documented shift in burning patterns across most of the country aligns with the palpable change in fire dynamics noted by the media, public, and fire-fighting officials. &nbsp;</p

Recent droughts in the United States are among the fastest-developing of the last seven decades

Drought has traditionally been characterized as a slow process that requires seasons or even years to fully develop. Recent fast-evolving drying events, however, have challenged our forecasting and response capabilities. A fundamental question emerges: are droughts setting in more quickly? We address this question by evaluating drought intensification rates for the contiguous United States and find that median drought onset rates did not change significantly in 1951&ndash;2021. Conversely, development of fast onset droughts (i.e., the 95th percentile of the drought intensification rates per drought region in a given month of the time series) has been speeding up in recent years across most of the country. As a result, intensification rates of the quickest-onset droughts in 2011&ndash;2021 are among the fastest of the last seven decades. &nbsp;</p

Focus on changing fire regimes: interactions with climate, ecosystems, and society

Fire is a complex Earth system phenomenon that fundamentally affects vegetation distributions, biogeochemical cycling, climate, and human society across most of Earth’s land surface. Fire regimes are currently changing due to multiple interacting global change drivers, most notably climate change, land use, and direct human influences via ignition and suppression. It is therefore critical to better understand the drivers, patterns, and impacts of these changing fire regimes now and continuing into the future. Our review contributes to this focus issue by synthesizing results from 27 studies covering a broad range of topics. Studies are categorized into (i) Understanding contemporary fire patterns, drivers, and effects; (ii) Human influences on fire regimes; (iii) Changes in historical fire regimes; (iv) Future projections; (v) Novel techniques; and (vi) Reviews. We conclude with a discussion on progress made, major remaining research challenges, and recommended directions

Cheatgrass (Bromus tectorum) distribution in the intermountain Western United States and its relationship to fire frequency, seasonality, and ignitions

Cheatgrass (Bromus tectorum) is an invasive grass pervasive across the Intermountain Western US and linked to major increases in fire frequency. Despite widespread ecological impacts associated with cheatgrass, we lack a spatially extensive model of cheatgrass invasion in the Intermountain West. Here, we leverage satellite phenology predictors and thousands of field surveys of cheatgrass abundance to create regional models of cheatgrass distribution and percent cover. We compare cheatgrass presence to fire probability, fire seasonality and ignition source. Regional models of percent cover had low predictive power (34% of variance explained), but distribution models based on a threshold of 15% cover to differentiate high abundance from low abundance had an overall accuracy of 74%. Cheatgrass achieves ≥ 15% cover over 210,000 km2 (31%) of the Intermountain West. These lands were twice as likely to burn as those with low abundance, and four times more likely to burn multiple times between 2000 and 2015. Fire probability increased rapidly at low cheatgrass cover (1–5%) but remained similar at higher cover, suggesting that even small amounts of cheatgrass in an ecosystem can increase fire risk. Abundant cheatgrass was also associated with a 10 days earlier fire seasonality and interacted strongly with anthropogenic ignitions. Fire in cheatgrass was particularly associated with human activity, suggesting that increased awareness of fire danger in invaded areas could reduce risk. This study suggests that cheatgrass is much more spatially extensive and abundant than previously documented and that invasion greatly increases fire frequency, even at low percent cover

A Synthesis of the Effects of Cheatgrass Invasion on US Great Basin Carbon Storage

Non-native, invasive Bromus tectorum (cheatgrass) is pervasive in sagebrush ecosystems in the Great Basin ecoregion of the western United States, competing with native plants and promoting more frequent fires. As a result, cheatgrass invasion likely alters carbon (C) storage in the region. Many studies have measured C pools in one or more common vegetation types: native sagebrush, invaded sagebrush and cheatgrass-dominated (often burned) sites, but these results have yet to be synthesized. We performed a literature review to identify studies assessing the consequences of invasion on C storage in above-ground biomass (AGB), below-ground biomass (BGB), litter, organic soil and total soil. We identified 41 articles containing 386 unique studies and estimated C storage across pools and vegetation types. We used linear mixed models to identify the main predictors of C storage. We found consistent declines in biomass C with invasion: AGB C was 55% lower in cheatgrass (40 ± 4 g C/m2) than native sagebrush (89 ± 27 g C/m2) and BGB C was 62% lower in cheatgrass (90 ± 17 g C/m2) than native sagebrush (238 ± 60 g C/m2). In contrast, litter C was \u3e4× higher in cheatgrass (154 ± 12 g C/m2) than native sagebrush (32 ± 12 g C/m2). Soil organic C (SOC) in the top 10 cm was significantly higher in cheatgrass than in native or invaded sagebrush. SOC below 20 cm was significantly related to the time since most recent fire and losses were observed in deep SOC in cheatgrass \u3e5 years after a fire. There were no significant changes in total soil C across vegetation types. Synthesis and applications. Cheatgrass invasion decreases biodiversity and rangeland productivity and alters fire regimes. Our findings indicate cheatgrass invasion also results in persistent biomass carbon (C) losses that occur with sagebrush replacement. We estimate that conversion from native sagebrush to cheatgrass leads to a net reduction of C storage in biomass and litter of 76 g C/m2, or 16 Tg C across the Great Basin without management practices like native sagebrush restoration or cheatgrass removal

Controls on Interannual Variability in Lightning-Caused Fire Activity in the Western US

Lightning-caused wildfires account for a majority of burned area across the western United States (US), yet lightning remains among the more unpredictable spatiotemporal aspects of the fire environment and a challenge for both modeling and managing fire activity. A data synthesis of cloudto-ground lightning strikes, climate and fire data across the western US from 1992 to 2013 was conducted to better understand geographic variability in lightning-caused wildfire and the factors that influence interannual variability in lightning-caused wildfire at regional scales. Distinct geographic variability occurred in the proportion of fires and area burned attributed to lightning, with a majority of fires in the interior western US attributed to lightning. Lightning ignition efficiency was highest across the western portion of the region due to the concomitance of peak lightning frequency and annual nadir in fuel moisture in mid-to-late summer. For most regions the number of total and dry lightning strikes exhibited strong interannual correlation with the number of lightning-caused fires, yet were a poor predictor of area burned at regional scales. Commonality in climate–fire relationships for regional annual area burned by lightning- versus human-ignited fires suggests climate conditions, rather than lightning activity, are the predominant control of interannual variability in area burned by lightning-caused fire across much of the western US

Fusion neural networks for plant classification: learning to combine RGB, hyperspectral, and lidar data

Airborne remote sensing offers unprecedented opportunities to efficiently monitor vegetation, but methods to delineate and classify individual plant species using the collected data are still actively being developed and improved. The Integrating Data science with Trees and Remote Sensing (IDTReeS) plant identification competition openly invited scientists to create and compare individual tree mapping methods. Participants were tasked with training taxon identification algorithms based on two sites, to then transfer their methods to a third unseen site, using field-based plant observations in combination with airborne remote sensing image data products from the National Ecological Observatory Network (NEON). These data were captured by a high resolution digital camera sensitive to red, green, blue (RGB) light, hyperspectral imaging spectrometer spanning the visible to shortwave infrared wavelengths, and lidar systems to capture the spectral and structural properties of vegetation. As participants in the IDTReeS competition, we developed a two-stage deep learning approach to integrate NEON remote sensing data from all three sensors and classify individual plant species and genera. The first stage was a convolutional neural network that generates taxon probabilities from RGB images, and the second stage was a fusion neural network that &ldquo;learns&rdquo; how to combine these probabilities with hyperspectral and lidar data. Our two-stage approach leverages the ability of neural networks to flexibly and automatically extract descriptive features from complex image data with high dimensionality. Our method achieved an overall classification accuracy of 0.51 based on the training set, and 0.32 based on the test set which contained data from an unseen site with unknown taxa classes. Although transferability of classification algorithms to unseen sites with unknown species and genus classes proved to be a challenging task, developing methods with openly available NEON data that will be collected in a standardized format for 30 years allows for continual improvements and major gains for members of the computational ecology community. We outline promising directions related to data preparation and processing techniques for further investigation, and provide our code to contribute to open reproducible science efforts. &nbsp;</p

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on \u3e100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

Best practices for virtual participation in meetings : experiences from synthesis centers

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