Forests worldwide are increasingly exposed to soil drought under climate change, with their fates depending on the ability to maintain essential functions like water transport (hydraulics) and photosynthesis. Acclimation is expected to mitigate drought impacts, but the extent to which trees acclimate remains largely untested, which limits our predictive confidence. Here, we examined 24 physiological attributes from 40 globally distributed forest throughfall reduction (TFE) experiments and found no evidence that trees adjusted their hydraulic or photosynthetic systems in response to drought. Key attributes related to embolism resistance, hydraulic efficiency, leaf nutrients, and Rubisco carboxylation capacity remained unchanged, regardless of coniferous or broadleaf trees, local precipitation levels, or the duration and severity of drought treatments. However, drought-induced declines in tissue water potentials, combined with unchanged embolism resistance, led to narrower hydraulic safety margins and thus an increased risk of hydraulic failure. Although net photosynthetic rate declined significantly due to stomatal closure, nonstructural carbohydrates (starch and sugars) remained stable, suggesting a shift in carbohydrate allocation toward storage. These findings indicate that trees maintain their hydraulic and photosynthetic capacities under drier conditions, enabling them to maximize carbon assimilation on favorable periods following rainfall, while facing an increased risk of hydraulic failure during drought. Physiological acclimation is unlikely to mitigate future drought impacts, while tree mortality from hydraulic failure is likely to increase.

Climate change, particularly the associated increase in extreme events and disturbances, threatens the numerous environmental, social, and economic benefits that forests provide, both locally and globally. Heat and drought pose significant risks to forest ecosystems; the anticipated future climate is expected to exacerbate this trend. Management interventions should aim to maximise the provision of ecological functions amid the uncertain conditions ahead. A better understanding of the mechanisms regulating forest responses to drought, heat, pests, and diseases - and how management interventions interact with these - is necessary for evidence-based, climate-smart forest management. We first provide an overview of the ecophysiological mechanisms that drive the loss of ecosystem functioning induced by heat and drought. We then explore how various commonly adopted management interventions at the stand level - such as tree species selection and mixture, stand density regulation, measures to optimise stand structure, tree height, and age distribution, as well as nutrient management - may positively or negatively influence forest ecophysiological responses to heat and drought. In this work, we present a mechanism-based critical assessment of forest management practices to support climate-smart forestry/forest management in response to shifting environmental and climatic conditions.

Coastal upland forests are exposed to intensifying precipitation regimes and sea level rise, increasing tree mortality and transforming these coastal forests into wetland ecosystems. While the ultimate outcome of long-term exposure to these perturbations is known to be an ecosystem state change from upland forest to wetland, the resistance of forests to the first novel exposure to flooding and salinity is relatively unknown. The Terrestrial Ecosystem Manipulation to Probe the Effects of Storm Treatments (TEMPEST) experiment uses ecosystem-scale (2000 m2) experimental flooding plots to decouple two distinct disturbances associated with hydrological extremes: (1) freshwater saturation of soils and flooding (e.g., from heavy precipitation) and (2) salinization from storm surge by saturating and flooding soils with brackish water. Here we describe the immediate effects of the experimental flooding treatments on hydrologic, biogeochemical and vegetation ecosystem components following the first novel experimental ecosystem-scale flooding event in TEMPEST. Following a 9-hour experimental treatment, the system's hydrology was temporarily and significantly impacted, but there were subtle effects on biogeochemical and vegetation components of the ecosystem. This suggests that this temperate deciduous forest was resistant to a single novel flooding event, even if the water is saline. Most biogeochemical parameters monitored in the soil, porewater, and groundwater responded similarly between freshwater and saltwater treatments relative to the control plot. However, we show that even a single episodic event can cause large transient shifts in belowground conditions that drive physiological changes in coastal forest functions, such as soil moisture and oxygen levels. Such responses may impact how the system responds to future perturbations.

Coastal forests are increasingly vulnerable to climate change and sea-level rise, with flooding and salinity driving transitions to marsh-dominated ecosystems. Using the coastal version of FATES-Hydro, we conducted 30-year simulations at two coastal forest sites-a broadleaf swamp white oak stand at Lake Erie and a conifer loblolly pine stand at Chesapeake Bay-under historical climate and elevated CO2 (+100 ppm) and temperature (+1.5 degrees C) scenarios. Elevated CO2 increased net primary productivity at both sites, while warming alone intensified hydraulic stress and accelerated mortality, particularly in the conifer stand. Simulations show that elevated temperatures intensify vapor pressure deficit and hydraulic stress on trees already experiencing salinity- and submersion-driven water stress, increasing tree mortality beyond what would be expected in a non-water-limited environment. Marsh expansion partially compensated for tree loss at the Lake Erie site but reduced ecosystem productivity in the conifer forest at Chesapeake Bay. Our results highlight how differences in stand structure, phenology, and local hydrology modulate ecosystem trajectories under climate change, emphasizing the importance of demographic and community-level processes for predicting the fate of coastal forests.

Ongoing climate warming may profoundly impact terrestrial gross primary productivity (GPP), a key component of the global carbon cycle. However, uncertainty in the relative roles of atmospheric water demand (vapor pressure deficit, VPD) and root-zone soil moisture (SM) limits predictions of drought impacts on GPP. Here, we show that growing-season GPP was more strongly constrained by VPD than SM globally, based on observation-constrained model estimates, satellite retrievals and Dynamic Global Vegetation Model simulations. The importance of VPD increased with higher temperatures and more severe and prolonged droughts. This pattern reflects VPD's critical role in regulating stomatal conductance and plant hydraulic function, both of which are essential for preventing xylem embolism. We further found that P50, the water potential at 50% loss of hydraulic conductivity, was the strongest predictor of their relative influence. Collectively, rising VPD may accelerate declines in terrestrial carbon storage under future warming.

Abstract. Distributed data generation, or data collected from multiple sources and locations using standardized approaches and involving coordination among investigators, has emerged as a powerful approach to meet contemporary demands for scalable environmental knowledge. However, practitioners often lack guidance on best practices for distributed data generation, and a framework classifying its modalities is missing. To address these gaps, we developed a conceptual framework organizing distributed data generation along two axes: participant-based (ranging from highly formalized to highly flexible) and method-based (from experimental to observational). This framework provides common vocabulary across modalities and describes how different approaches affect data generation logistics and outcomes. We propose operational best practices across three critical pillars: outreach, operations, and output (i.e., publications, data), leveraging lessons learned from over 35 existing distributed data projects. Lastly, we explore how emerging artificial intelligence (AI) capabilities may help address longstanding challenges in distributed data generation, including in coordination, adaptive sampling, and cross-project data integration. This perspective provides strategies and identifies opportunities to advance distributed data generation for addressing pressing biogeochemical, environmental, and societal challenges. We underscore the transformative potential of distributed data generation for modern, broad-scale environmental research, and provide guidance on how to realize that potential.

Tropical forest resilience to climate change depends on the rate of vegetation biomass turnover, a key determinant of forest biomass storage potential. However, the large-scale patterns of tropical biomass turnover and their environmental drivers remain poorly understood. Here we estimate kilometre-scale aboveground biomass turnover time (tau(AGB)) across intact Amazonian forests by integrating satellite and field measurements. Our spatial analysis provides continent-scale evidence that tau(AGB) exhibits strong nonlinear responses to climate. Convective storms, a common cause of tropical forest disturbances, are a major climatic driver of tau(AGB) spatial variation, surpassing vapour pressure deficit and precipitation extremes. By the end of this century, projected increases in atmospheric dryness and storm activities are expected to reduce tau(AGB) in the Amazon by-3% (shared socioeconomic pathway SSP 126) to-15% (SSP 585), thereby accelerating biomass turnover. These findings highlight that climatic sensitivities of biomass turnover critically shape tropical forest dynamics and carbon cycling.

Leaf phenology may influence the development of wood structure and hydraulic function across growing seasons, yet the roles of green-up, green-down, and growing season length in regulating xylem anatomy remain unclear. We quantified annual wood anatomy, leaf phenology, and growth in a whole-ecosystem experiment with 5 warming levels (up to +9 °C) and 2 CO2 levels (ambient and +500 ppm) in Picea mariana (conservative spruce) and Larix laricina (acquisitive larch). We identified a phenology-tracheid-growth spectrum, reflecting a trade-off between hydraulic safety (thicker walls, higher tracheid density, and later green-up) and fast growth (wider tracheids, delayed green-down, and longer growing seasons). In spruce, earlier green-up, longer growing seasons, and later green-down increased the latewood hydraulic diameter more than the earlywood. In larch, earlier green-up increased earlywood hydraulic diameter, while later green-down increased latewood mechanical safety via thicker walls. Larch exhibited greater phenological sensitivity to elevated CO2 in regulating wood anatomy than spruce. Warming indirectly increased spruce growth by extending the growing season, which increased the latewood hydraulic diameter and subsequently enhanced overall growth. Warming directly increased larch growth but not through enhanced earlywood hydraulic conductivity. These findings demonstrate the role of divergent phenological adjustments in hydraulic function, with implications for boreal carbon and water fluxes.

Tropical forests play a vital role in the global carbon cycle and land-atmosphere interactions. Estimating tropical forest carbon-water dynamics is challenging due to observational and modeling uncertainties. This study leverages the "Trends and drivers of the regional scale terrestrial sources and sinks of carbon dioxide" (TRENDY) project models and satellite observations to assess changes (2003-2021) in vegetation carbon, gross primary production (GPP), evapotranspiration (ET), and net biosphere production (NBP) in the Amazon and Congo. Atmospheric CO2, climate variability, and land use and land cover changes constrain these variables between 1700 and 2021 with the overall increasing trends of carbon stock and fluxes. The models overestimate vegetation carbon and GPP, while ET and NBP are consistent with observations. Fire-activated models predict lower values for vegetation carbon and GPP, ET, and NBP, aligning more closely with observations. The higher ET from fire-activated models may result from enhanced soil evaporation due to increased canopy openings. Fire-inactivated models could well estimate the magnitudes of NBP. The high vegetation carbon in nitrogen-enabled models points to simulation uncertainties and imbalance in model numbers regarding the nitrogen cycle. Although the nitrogen cycle enhances water use efficiency in both the Amazon and Congo, the models show a higher sensitivity to the nitrogen cycle in the Congo. This study highlights the challenges in accurately representing tropical biogeochemical cycles and the values of satellite products in model evaluations, underscoring the need for standard modeling protocols that address biogeochemical components (e.g., nutrient cycles) to better resolve process-based representations.

Wildfire frequency, intensity, and rate of spread are increasing across the Western U.S, resulting in more severe ecosystem impacts. Significant tree mortality can occur years after fire events, but this has received little attention compared to the immediate tree loss during a fire. We overlapped forest cover loss data with burn severity maps in the U.S. Pacific Northwest and quantified the total and delayed forest canopy loss after fires. We found that wildfires resulted in total canopy loss fraction (CLF) of 84%, 53%, and 22% within 3 years in areas burned at high, moderate, and low severity, respectively. The delayed canopy loss accounted for approximately 1/3, 1/2, and 2/3 of the total canopy loss for high, moderate, and low severity burns. Delayed canopy loss was greater in moist and cool areas than in dry and warm areas, likely because tree species in wetter environments were less adapted to survive when fires did occur. Across all forests, delayed CLF doubled as temperature increased from the climatological mean to a hot anomaly and tripled as vapor pressure deficit increased from a wet anomaly to a dry anomaly. Fire impacts on forest ecosystems are likely to intensify under future climate scenarios as wildfires expand into areas that historically experienced infrequent fires. The impacts can also be exacerbated by more frequent compound extreme events, such as droughts, heatwaves, and fires. These findings highlight the urgent need for targeted forest management strategies, particularly in mesic forests, to mitigate future fire impacts.

IntroductionUnderstanding the mechanisms of tree mortality in tropical ecosystems remains challenging, in part due to the high diversity of tree species and the inherently stochastic nature of mortality. Plant functional traits offer a mechanistic link between plant physiology and performance, yet their ability to predict growth and mortality remains poorly understood. Given recent increases in tree mortality rates in the Amazon forest following extreme drought and wind events, we tested if lower wood density and acquisitive plant functional traits were associated with increased growth and mortality for common co-occurring trees in the Central Amazon.MethodsSeventeen trees of different species with similar sizes but a range in wood density (WD) and wood traits were felled, then assessed for 27 different individual functional parameters, including whole tree architecture, stem xylem anatomical and hydraulic traits and leaf traits. Traits of the individual trees were related to stand-level growth and mortality rates collected periodically over 30 years from nearby permanent inventory plots.ResultsHigher wood density was associated with smaller leaf size, lower foliar base cations, lower stem water content and sapwood fraction, in agreement with the fast-slow plant economics spectrum. Lower wood density was associated with more acquisitive characteristics with greater hydraulic capacity and foliar nutrient concentrations, correlating with greater growth and mortality rates.DiscussionOur results show that lower wood density is part of a coordinated suite of traits linked to high resource acquisition, fast growth, and increased mortality risk, providing a functional framework for predicting species performance and forest vulnerability under future climate stress.

Plant growth and survival are fundamentally constrained by water transport from roots to leaves, impacting carbon assimilation and associated labile carbon pools. However, physiological constraints on growth and survival vary with plant age, due to changes in metabolic sinks and increases in hydraulic path length from rhizosphere to canopy. We investigated crown dieback, growth, hydraulics, carbon assimilation and nonstructural carbohydrate (NSC) storage in relation to increasing basal diameter of two dominant shrub species (Caragana korshinskii and Artemisia ordosica) at the southeastern edge of the Tengger Desert, China. The aim was to identify mechanisms of decreased performance with plant size in dryland shrubs. Clear contrasts in stomatal regulation of leaf water potentials were detected between species. Despite these contrasts, radial growth, hydraulic transport efficiency (Ks), and carbon assimilation similarly declined in both species with increasing plant size, while NSC reserves remained unchanged. Xylem embolism (percentage loss of conductivity) increased with plant size, resulting in significant reductions in carbon assimilation in both species. Results indicate that hydraulic and potentially carbon assimilation constraints, rather than NSC depletion, govern growth-related dryland shrub decline. These findings improve our understanding of how population demography impacts dryland forest response to climate change.

Litterfall is crucial for forest maintenance, serving as a primary mechanism for nutrient return to the nutrientpoor soils of tropical forests. Foliar nutrient resorption likewise represents an important nutrient-conservation mechanism. Yet, little is known about how these processes vary between fast- and slow-growing species in post-logging areas of the Amazon forest. The objective of this study was to quantify litterfall production and the resorption of foliar nutrients in fast-growing and slow-growing tree species of the Central Amazon, in a forest that was experimentally logged in 1987. The study was conducted from May 2022 to April 2023. Litterfall was collected biweekly using four collectors that were systematically distributed beneath the canopy of each monitored tree, totaling 72 collectors. Three fast-growing and three slow-growing species were selected, each with three replicates, totaling 18 monitored individuals. Species-specific samples of fresh (green) and senesced (litter) leaves were collected and analyzed for their nutrient content and resorption efficiency. Fast-growing species had a monthly leaf litter deposition of 13.53 f 1.6 g m- 2 month- 1, compared to 2.59 f 0.4 g m- 2 month- 1 for slow-growing species. The average annual litter production across both functional types was 8.6 f 2.6 Mg ha- 1 year- 1. Nutrient inputs through litterfall were higher in fast-growing species for all elements, particularly nitrogen (N), with 21.92 f 4.9 kg ha- 1 year- 1. Phosphorus (P) and potassium (K) exhibited the highest foliar resorption. P resorption efficiency was 68.3 % in fast-growing species and 57.8 % in slow-growing species. For K, efficiencies were 59.0 % and 41.7 %, respectively. These results highlight the substantial role that fast-growing species play in restoring forest productivity in managed Amazon forests, both through higher litter deposition and nutrient fluxes, and through nutrient conserving-mechanisms such as foliar nutrient resorption.

Tropical forests represent the warmest and wettest of Earth's biomes, but with continued anthropogenic warming, they will be pushed to climate states with no current analogue1,2. Droughts in the tropics are already becoming more intense as they occur at successively higher temperatures3-5. Here we synthesize multiple datasets to assess the effects of hot droughts on a central Amazon forest. First, a more than 30-year record of annually resolved forest demographic data from a selective logging experiment showed higher tree mortality during intense droughts, particularly among fast-growing pioneer species with low wood density. Second, analysis of ecophysiological field measurements from the 2015 and 2023 El Niño droughts identified a soil moisture threshold beyond which transpiration rates rapidly declined. As rainless days beyond this threshold continued, drought conditions intensified, increasing the potential for tree mortality from hydraulic failure and carbon starvation. Third, analyses from the Coupled Model Intercomparison Project Phase 6 demonstrated that under high-emission scenarios, a large area of tropical forest will shift to a hotter 'hypertropical' climate by 2100. Last, under a hypertropical climate, temperature and moisture conditions during typical dry season months will more frequently exceed identified drought mortality thresholds, elevating the risk of forest dieback. Present-day hot droughts are harbingers of this emerging climate, offering a window for studying tropical forests under expected extreme future conditions6-8.

Wildfires impact vegetation mortality and productivity and are increasing in intensity, frequency, and spatial area in the western United States. The rates of vegetation recovery after fires play a major role in the reestablishment of biomass and ecosystem functioning (e.g., structure, resilience, and productivity), but such recovery rates are poorly understood. Here we use remotely sensed data products from the Moderate Resolution Imaging Spectroradiometer (MODIS) to quantify the resistance and resilience of leaf area index (LAI), gross primary production (GPP), and evapotranspiration (ET) to 138 wildfires of various burn severity across the Columbia River basin (CRB) of the Pacific Northwest in 2015. Increasing burn severity caused lower resistance and resilience for all three variables. Resistance and resilience are highest in grasslands, intermediate in savanna, and lowest in needleleaf evergreen forests, consistent with the adaptation of these vegetation types to fire. LAI has consistently lower resistance and resilience than GPP and ET, which is consistent with physical and physiological mechanisms that compensate for reduced LAI. Resilience is influenced by precipitation, vapor pressure deficit (VPD), and burn severity across all three vegetation types; however, burn severity plays a more minor role in grasslands. Increasing wildfire severity will reduce the resistance and resilience and lengthen the recovery time of vegetation structure and fluxes with climate change, with significant consequences for the provision of ecosystem functioning and implications for model predictions.

Interconnected landscape features such as terrestrial-aquatic interfaces play an outsized role in biogeochemical cycles as ecosystem control points, but it is notoriously challenging to characterize these. Here, we document a synoptic sensor network design that is (a) flexible to accommodate diverse ecosystem interfaces and gradients, (b) adaptable to monitoring and modeling needs of small and large projects alike, (c) standardized for intercomparability across sites and field experiments, and (d) adequately replicated to capture heterogeneity of each parameter monitored. This real-time monitoring of surface water, groundwater, soil, and vegetation supports configuration and evaluation of models that span upland, wetland, open water strata, and transitions between them. We established the network at seven sites along the Chesapeake Bay and Lake Erie coastlines, including large-scale flood manipulation experiments in both regions. A central design element is "one data logger program to rule them all"-a collection of sensor-specific modules deployed on 40 loggers controlling similar to 2,000 sensors, with the goal of streamlining maintenance, debugging, and reproducible data processing. The network generates similar to 6 M observations per month, capturing system dynamics at the broad spatial and fine temporal scales needed to initialize and benchmark models; measurement frequency can be modified remotely to capture events. This network design has also revealed behaviors not represented in Earth system models, such as transient groundwater oxygen pulses. Completely documented and open source, this standardized, flexible, and efficient sensor network design can reduce barriers to understanding environmental changes and ecosystem responses across systems and scales.

The radiative effects of wildfires have been traditionally estimated by models using radiative transfer calculations. Assessment of model-predicted radiative effects commonly involves information on observation-based aerosol optical properties. However, lack or incompleteness of this information for dense plumes generated by intense wildfires reduces substantially the applicability of this assessment. Here we introduce a novel method that provides additional observational constraints for such assessments using widely available ground-based measurements of shortwave and spectrally resolved irradiances and aerosol optical depth (AOD) in the visible and near-infrared spectral ranges. We apply our method to quantify the radiative impact of the record-breaking wildfires that occurred in the Western US in September 2020. For our quantification we use integrated ground-based data collected at the Atmospheric Measurements Laboratory in Richland, Washington, USA with a location frequently downwind of wildfires in the Western US. We demonstrate that remarkably dense plumes generated by these wildfires strongly reduced the solar surface irradiance (up to 70% or 450 Wm-2 for total shortwave flux) and almost completely masked the sun from view due to extremely large AOD (above 10 at 500 nm wavelength). We also demonstrate that the plume-induced radiative impact is comparable in magnitude with those produced by a violent volcano eruption occurred in the Western US in 1980 and continental cumuli.

The apparent respiratory quotient (ARQ) of tree stems, defined as the ratio of net stem CO2 efflux (ES_CO2) to net stem O2 influx (ES_O2), offers insights into the balance between local respiratory CO2 production and CO2 transported via the xylem. Traditional static chamber methods for measuring ARQ can introduce artifacts and obscure natural diurnal variations. Here, we employed an open flow-through stem chamber with ambient air coupled with cavity ring-down spectrometry, which uses the molecular properties of CO2 and O2 molecules to continuously measure ES_CO2, ES_O2, and ARQ, at the base of a California cherry tree (Prunus ilicifolia) during the 2024 growing season. Measurements across three stem chambers over 3–11-day periods revealed strong correlations between ES_CO2 and ES_O2 and mean ARQ values ranging from 1.3 to 2.9, far exceeding previous reports. Two distinct diurnal ARQ patterns were observed: daytime suppression with nighttime recovery, and a morning peak followed by gradual decline. Partitioning ES_CO2 into local respiration and xylem-transported CO2 indicated that the latter can dominate when ARQ exceeds 2.0. Furthermore, transported CO2 exhibited a higher temperature sensitivity than local respiration, with both processes showing declining temperature sensitivity above 20 °C. These findings underscore the need to differentiate stem CO2 flux components to improve our understanding of whole-tree carbon cycling.

The structure, function, and dynamics of Earth's terrestrial ecosystems are profoundly influenced by how often (frequency) and how long (duration) they are inundated with water. A diverse array of natural and human-engineered systems experience temporally variable inundation whereby they fluctuate between inundated and non-inundated states. Variable inundation spans extreme events to predictable sub-daily cycles. Variably inundated ecosystems (VIEs) include hillslopes, non-perennial streams, wetlands, floodplains, temporary ponds, tidal systems, storm-impacted coastal zones, and human-engineered systems. VIEs are diverse in terms of inundation regimes, water chemistry and flow velocity, soil and sediment properties, vegetation, and many other properties. The spatial and temporal scales of variable inundation are vast, ranging from sub-meter to whole landscapes and from sub-hourly to multi-decadal. The broad range of system types and scales makes it challenging to predict the hydrology, biogeochemistry, ecology, and physical evolution of VIEs. Despite all experiencing the loss and gain of an overlying water column, VIEs are rarely considered together in conceptual, theoretical, modeling, or measurement frameworks and approaches. Studying VIEs together has the potential to generate mechanistic understanding that is transferable across a much broader range of environmental conditions, relative to knowledge generated by studying any one VIE type. We postulate that enhanced transferability will be important for predicting changes in VIE function in response to global change. Here we aim to catalyze cross-VIE science that studies drivers and impacts of variable inundation across Earth's VIEs. To this end, we complement expert mini-reviews of eight major VIE systems with overviews of VIE-relevant methods and challenges associated with scale. We conclude with perspectives on how cross-VIE science can derive transferable understanding via unifying conceptual models in which the impacts of variable inundation are studied across multi-dimensional environmental space.