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Abstract Coastal ecosystems such as mangroves, salt marshes, and seagrasses sequester large amounts of carbon per unit area due to their high productivity and sediment accumulation rates. However, only a handful of studies have examined carbon sequestration in coastal dunes, which are shaped by biophysical feedback between aeolian sediment transport and burial-tolerant vegetation. The goal of this study was to measure carbon storage and identify the factors that influence its variability along the foredunes of the US Outer Banks barrier islands of North Carolina. Specifically, differences in carbon stocks (above- and belowground biomass and sand), dune grass abundance, and sand supply were measured among islands, cross-shore dune profile locations, and dune grass species. Carbon varied among aboveground grass biomass (0.1 ± 0.1 kg C m⁻²), belowground grass biomass (1.1 ± 1.6 kg C m⁻³), and sand (0.9 ± 0.6 kg C m⁻³), with the largest amount in belowground grass stocks. Aboveground grass carbon stocks were comparable to those in eelgrass beds and salt marshes on a per-area basis, while sediment carbon values in our study system were lower than those in other coastal systems, including other dune locations. Additionally, sand carbon density was positively related to patterns in dune sand supply and grass abundance, reflecting a self-reinforcing vegetation-sediment feedback at both high and low sand accumulation rates. 

Abstract Sea surface height observations provided by satellite altimetry since 1993 show a rising rate (3.4 mm yr⁻¹) for global mean sea level. While on average, sea level has risen 10 cm over the last 30 years, there is considerable regional variation in the sea level change. Through this work, we predict sea level trends 30 years into the future at a 2° spatial resolution and investigate the future patterns of the sea level change. We show the potential of machine learning (ML) in this challenging application of long-term sea level forecasting over the global ocean. Our approach incorporates sea level data from both altimeter observations and climate model simulations. We develop a supervised learning framework using fully connected neural networks (FCNNs) that can predict the sea level trend based on climate model projections. Alongside this, our method provides uncertainty estimates associated with the ML prediction. We also show the effectiveness of partitioning our spatial dataset and learning a dedicated ML model for each segmented region. We compare two partitioning strategies: one achieved using domain knowledge and the other employing spectral clustering. Our results demonstrate that segmenting the spatial dataset with spectral clustering improves the ML predictions. Significance StatementLong-term projections are needed to help coastal communities adapt to sea level rise. Forecasting multidecadal sea level change is a complex problem. In this paper, we show the promise of machine learning in producing such forecasts 30 years in advance and over the global ocean. Continued improvements in prediction skills that build on this work will be vital in sea level rise adaptation efforts. 

Abstract Tropical forests are increasingly threatened by deforestation and degradation, impacting carbon storage, climate regulations and biodiversity. Restoring these ecosystems is crucial for environmental sustainability, yet monitoring these efforts poses significant challenges. Secondary forests are in a constant state of flux, with growth depending on multiple factors.Remote sensing technologies offer cost‐effective, scalable and transferable solutions, advancing forest restoration monitoring towards more accurate, efficient and real‐time data analysis and interpretation. This review provides a comprehensive evaluation of the current state and advancements in remote sensing technologies applied to monitoring tropical forest restoration.Synthesis and applications: This review brings together the state of the art of remote sensing technologies, such as very‐high‐resolution RGB imagery, multi‐ and hyperspectral imaging, lidar, radar and thermal‐infrared technologies and their applicability in monitoring forest restoration. In conclusion, this review emphasizes the potential of remote sensing technologies, coupled with advanced computational techniques, to enhance global efforts towards effective and sustainable forest restoration monitoring. 

Abstract Recent work established a backbone reference tree and phylogenetic placement pipeline for identification of arbuscular mycorrhizal fungal (AMF) large subunit (LSU) rDNA environmental sequences. Our previously published pipeline allowed any environmental sequence to be identified as putative AMF or within one of the major families. Despite this contribution, difficulties in implementation of the pipeline remain. Here, we present an updated database and pipeline with (1) an expanded backbone tree to include four newly described genera and (2) several changes to improve ease and consistency of implementation. In particular, packages required for the pipeline are now installed as a single folder (conda environment) and the pipeline has been tested across three university computing clusters. This updated backbone tree and pipeline will enable broadened adoption by the community, advancing our understanding of these ubiquitous and ecologically important fungi. 

Abstract Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km²) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world. 

ABSTRACT Arbuscular mycorrhizal fungi (AMF, phylum Glomeromycota) are essential to plant community diversity and ecosystem functioning. However, increasing human land use represents a major threat to native AMF globally. Characterizing the loss of AMF diversity remains challenging because many taxa are undescribed, resulting in poor documentation of their biogeography and family‐level disturbance sensitivity. We survey sites representing native and human‐altered ecosystems across the American continents—in Alaska, Kansas, and Brazil—to shed light on these gaps. Using a recently developed pipeline for phylogenetic placement of eDNA, we find evidence for three putative novel clades within the Glomeromycota, sister toEntrophosporaceae,Glomeraceae, andArchaeosporaceae, with evidence for geographic structuring. We further find that taxa in theDiversisporaceae,Glomeraceae, andEntrophosporaceaerelatively high families are overrepresented and more diverse in temperate samples. By contrast, the diversity of taxa that cannot be placed into a family is higher in tropical samples, suggesting that tropical sites harbor relatively high undescribed AMF diversity. Moreover, we find evidence thatEntrophosporaceaeis more tolerant, whileGlomeraceaeis more sensitive to disturbance. These results underscore the vast undescribed diversity of AMF while highlighting a way forward to systematically improve our understanding of AMF biogeography and response to human disturbance. 

Abstract Dryland ecosystems cover 40% of our planet's land surface, support billions of people, and are responding rapidly to climate and land use change. These expansive systems also dominate core aspects of Earth's climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite their indispensable ecosystem services and high vulnerability to change, drylands are one of the least understood ecosystem types, partly due to challenges studying their heterogeneous landscapes and misconceptions that drylands are unproductive “wastelands.” Consequently, inadequate understanding of dryland processes has resulted in poor model representation and forecasting capacity, hindering decision making for these at‐risk ecosystems. NASA satellite resources are increasingly available at the higher resolutions needed to enhance understanding of drylands' heterogeneous spatiotemporal dynamics. NASA's Terrestrial Ecology Program solicited proposals for scoping a multi‐year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. A primary goal of the scoping study is to gather input from the scientific and data end‐user communities on dryland research gaps and data user needs. Here, we provide an overview of the ARID team's community engagement and how it has guided development of our framework. This includes an ARID kickoff meeting with over 300 participants held in October 2023 at the University of Arizona to gather input from data end‐users and scientists. We also summarize insights gained from hundreds of follow‐up activities, including from a tribal‐engagement focused workshop in New Mexico, conference town halls, intensive roundtables, and international engagements. 

Abstract Ecosystem responses to disturbance depend on the nature of the perturbation and the ecological legacies left behind, making it critical to understand how climate‐driven changes in disturbance regimes modify resilience properties of ecosystems. For coral reefs, recent increases in severe marine heat waves now co‐occur with powerful storms, the historic agent of disturbance. While storms kill coral and remove their skeletons, heat waves bleach and kill corals but leave their skeletons intact. Here, we explored how the material legacy of dead coral skeletons modifies two key ecological processes that underpin coral reef resilience: the ability of herbivores to control macroalgae (spatial competitors of corals), and the replenishment of new coral colonies. Our findings, grounded by a major bleaching event at our long‐term study locale, revealed that the presence of structurally complex dead skeletons reduced grazing on turf algae by ~80%. For macroalgae, browsing was reduced by >40% on less preferred (unpalatable) taxa, but only by ~10% on more preferred taxa. This enabled unpalatable macroalgae to reach ~45% cover in 2 years. By contrast, herbivores prevented macroalgae from becoming established on adjacent reefs that lacked skeletons. Manipulation of unpalatable macroalgae revealed that the cover reached after 1 year (~20%) reduced recruitment of corals by 50%. The effect of skeletons on juvenile coral growth was contingent on the timing of settlement relative to the disturbance. If corals settled directly after bleaching (before macroalgae colonized), dead skeletons enhanced colony growth by 34%, but this benefit was lost if corals colonized dead skeletons a year after the disturbance once macroalgae had proliferated. These findings underscore how a material legacy from a changing disturbance regime can alter ecosystem resilience properties by disrupting key trophic and competitive interactions that shape post‐disturbance community dynamics. 

Abstract A key challenge in ecology is understanding how multiple drivers interact to precipitate persistent vegetation state changes. These state changes may be both precipitated and maintained by disturbances, but predicting whether the state change will be fleeting or persistent requires an understanding of the mechanisms by which disturbance affects the alternative communities. In the sagebrush shrublands of the western United States, widespread annual grass invasion has increased fuel connectivity, which increases the size and spatial contiguity of fires, leading to postfire monocultures of introduced annual grasses (IAG). The novel grassland state can be persistent and is more likely to promote large fires than the shrubland it replaced. But the mechanisms by which prefire invasion and fire occurrence are linked to higher postfire flammability are not fully understood. A natural experiment to explore these interactions presented itself when we arrived in northern Nevada immediately after a 50,000 ha wildfire was extinguished. We hypothesized that the novel grassland state is maintained via a reinforcing feedback where higher fuel connectivity increases burn severity, which subsequently increases postfire IAG dispersal, seed survivorship, and fuel connectivity. We used a Bayesian joint species distribution model and structural equation model framework to assess the strength of the support for each element in this feedback pathway. We found that prefire fuel connectivity increased burn severity and that higher burn severity had mostly positive effects on the occurrence of IAG and another nonnative species and mostly negative or neutral relationships with all other species. Finally, we found that the abundance of IAG seeds in the seed bank immediately after a fire had a positive effect on the fuel connectivity 3 years after the fire, completing a positive feedback promoting IAG. These results demonstrate that the strength of the positive feedback is controlled by measurable characteristics of ecosystem structure, composition, and disturbance. Further, each node in the loop is affected independently by multiple global change drivers. It is possible that these characteristics can be modeled to predict threshold behavior and inform management actions to mitigate or slow the establishment of the grass–fire cycle, perhaps via targeted restoration applications or prefire fuel treatments.