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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 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 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. 

The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across the contiguous US. Nearly half of the ecoregions experienced destructive fast fires that grew more than 1620 hectares in 1 day. These fires accounted for 78% of structures destroyed and 61% of suppression costs ($18.9 billion). From 2001 to 2020, the average peak daily growth rate for these fires more than doubled (+249% relative to 2001) in the Western US. Nearly 3 million structures were within 4 kilometers of a fast fire during this period across the US. Given recent devastating wildfires, understanding fast fires is crucial for improving firefighting strategies and community preparedness. 

There is a resurgent enthusiasm for Indigenous Knowledges (IK) across settler–colonial institutions of research, education, and conservation. But like fitting a square peg in a round hole, IK are being forced into colonial systems, and then only as marginal alternatives. To address this mismatch, the Traditional Ecological Knowledge Section of the Ecological Society of America (ESA) hosted a 2‐day workshop—entitledElevating Indigenous Knowledges in Ecology—at the 2022 ESA Annual Meeting, which was held on Kanien'keháka (Mohawk) and Ho‐de‐no‐sau‐nee‐ga (Haudenosaunee) territories in Montreal, Canada. This gathering of 21 interdisciplinary Indigenous ecologists included scholars from across the career and professional spectrum. By consensus, workshop participants (including the authors of this article) identified four emergent themes and respective guiding questions as a pathway toward the transformation of settler–colonial institutions into IK‐led spaces. We highlight this pathway to support actions toward systemic change, inspire future directions for Indigenous and non‐Indigenous ecologists, and nurture stronger relationships between Indigenous communities and the Western sciences, toward actualized decoloniality.