Improving light use efficiency models via the introduction of both the diffuse fraction and radiation scalar

Surface solar radiation, both its components and intensity, is pivotal in determining vegetation light use efficiency (LUE) and is essential for accurately estimating gross primary production (GPP) in ecosystems. This study introduces two key parameters: the diffuse photosynthetic photon flux density fraction (fdPPFD) to account for the diffuse fertilization effect (DFE) and the radiation scalar to reflect the impact of radiation intensity on leaf-level LUE. Leveraging these parameters, we developed two novel LUE models: the Big-leaf Diffuse-fraction Radiation-scalar LUE (BDR-LUE) model, adapted from traditional big-leaf LUE models, and the Two-leaf Diffuse-fraction Radiation-scalar LUE (TDR-LUE) model, based on conventional two-leaf LUE frameworks. These models were calibrated and validated using data from 32 FLUXNET sites representing six vegetation types with available diffuse PPFD measurements. The results reveal the following key findings: (1) Both new models, particularly the TDR-LUE model, deliver superior performance compared to conventional big-leaf and two-leaf LUE models; (2) The BDR-LUE model reduces the root mean square error (RMSE) by at least 12.75 % compared to the conventional Eddy Covariance-LUE (EC-LUE) model; (3) The TDR-LUE model achieves an RMSE reduction of at least 13.54 % compared to the established Two-Leaf LUE (TL-LUE) model; (4) Both models effectively capture annual and monthly variations in vegetation GPP; (5) While the BDR-LUE model outperforms the TDR-LUE model for croplands, it shows lower accuracy for other vegetation types. These findings underscore the importance of integrating fdPPFD and the radiation scalar into LUE models. The proposed models demonstrate substantial potential for enhancing GPP estimates in terrestrial ecosystems.

A General Model for the Seasonal to Decadal Dynamics of Leaf Area

Leaf phenology, represented at the ecosystem scale by the seasonal dynamics of leaf area index (LAI), is a key control on the exchanges of CO2, energy, and water between the land and atmosphere. Robust simulation of leaf phenology is thus important for both dynamic global vegetation models (DGVMs) and land-surface representations in climate and Earth System models. There is no general agreement on how leaf phenology should be modeled. However, a recent theoretical advance posits a universal relationship between the time course of "steady-state" gross primary production (GPP) and LAI-that is, the mutually consistent LAI and GPP that would pertain if weather conditions were held constant. This theory embodies the concept that leaves should be displayed when their presence is most beneficial to plants, combined with the reciprocal relationship of LAI and GPP via (a) the Beer's law dependence of GPP on LAI, and (b) the requirement for GPP to support the allocation of carbon to leaves. Here we develop a global prognostic LAI model, combining this theoretical approach with a parameter-sparse terrestrial GPP model (the P model) that achieves a good fit to GPP derived from flux towers in all biomes and a scheme based on the P model that predicts seasonal maximum LAI as the lesser of an energy-limited rate (maximizing GPP) and a water-limited rate (maximizing the use of available precipitation). The exponential moving average method is used to represent the time lag between leaf allocation and modeled steady-state LAI. The model captures satellite-derived LAI dynamics across biomes at both site and global levels. Since this model outperforms the 15 DGVMs used in the TRENDY project, it could provide a basis for improved representation of leaf-area dynamics in vegetation and climate models.

Assessing and improving the high uncertainty of global gross primary productivity products based on deep learning under extreme climatic conditions

Gross Primary Productivity (GPP) is a crucial indicator of the carbon fixed by plants through photosynthesis, playing a vital role in understanding and managing ecological and environmental processes. However, global warming, characterized by elevated temperatures, water shortage, and increased drought stress, has significantly impacted GPP. Various GPP products based on different algorithms and input data have been developed, but their performance under extreme climatic conditions remains unverified. This study evaluated the consistency and accuracy of eight global GPP products from 2003 to 2014 using flux towers data. The results show that GPP products performed well under overall conditions, with an average correlation coefficient (R2) of 0.604, and Penman-Monteith-Leuning-version-2 (PMLv2) showed the best performance (R2 = 0.664). However, under extreme climatic conditions like high temperature, high vapor pressure deficit (VPD), and drought, the accuracy significantly dropped (R2 = 0.3), with Global-dataset-of-solar-induced-chlorophyll-fluorescence (GOSIF) being the most affected. Accuracy was lower in croplands (CRO) and grasslands (GRA). To enhance accuracy under extreme climatic conditions, GPP products were used as inputs to a Convolutional Neural Network (CNN) based on ECMWF-Reanalysis-5th-Generation (ERA5) meteorological data and compared with random forests (RF). Four GPP products significantly contributed to the model, with a cumulative contribution of 80.3 %. Under extreme climatic conditions, CNN significantly improved the estimation accuracy of GPP and outperformed RF. The optimal values for R2 and the root mean square error (RMSE) were 0.905 (increase by at least 201.7 %) and 7.708 gC m-2 8d-1 (decrease by at least 50.7 %). The model also performed well at 20 independent validation sites (R2 = 0.783). This study offers a method to improve GPP estimation under extreme climatic conditions, unrestricted by time and space.

Interannual variations in grassland carbon fluxes and attribution of influencing factors in Qilian Mountains, China

Clarifying the driving factors of grassland carbon sequestration is essential for understanding its role in the regional carbon balance. However, there is a lack of studies on the upscaling of carbon flux in the Qilian Mountains (QLMs) and the driving factors of its interannual variation (IAV). Based on long-term eddy covariance observations in the QLMs, this study estimated the net ecosystem CO2 exchange (NEE), gross primary productivity (GPP), and ecosystem respiration (ER) of the QLMs grassland using four machine learning methods (random forest regression (RF), extremely randomized tree regression (ETR), support vector regression (SVR), and extreme gradient boosting (XGBoost)) to obtain the optimal estimation model. Subsequently, the spatiotemporal variations of GPP, ER, and NEE in the QLMs grasslands were conducted in a comprehensive analysis. The factors influencing the IAV of carbon flux, the contribution of monthly NEE to NEE IAV, and the contribution of different grassland types of NEE to NEE IAV were explored. Our findings revealed that the accuracy and resolution of the grassland carbon flux estimated by the RF method in this study are higher than those of global products. The grassland exhibited a weak carbon sink from 2000 to 2022, with an average NEE of -26.46 ± 6.80 g Cm-2 yr-1, and it acted as a carbon sink from May to September. The spatial distribution pattern of carbon sequestration was "low in the northwest and high in the southeast". LAI was the key driving factors of IAV for GPP and ER, while NEE IAV was primarily influenced by precipitation and temperature. Climate and vegetation factors primarily regulated NEE IAV by affecting the GPP and ER of plants, and NEE IAV was primarily driven by GPP. Furthermore, NEE in alpine meadows and alpine steppes dominated the NEE IAV of the entire grassland, and summer NEE contributed the most to the NEE IAV. The results will help us to better understand the carbon cycling mechanism in grassland ecosystems and provide new data support and a theoretical foundation for regional carbon cycling research.

Comprehensive physiological, transcriptomic, and metabolomic analyses revealed the regulation mechanism of evergreen and cold resistance of Pinus koraiensis needles

As a significant fruit and timber tree species among conifers, Pinus koraiensis remains it evergreen status throughout the harsh winters of the north, a testament to its intricate and prolonged evolutionary adaptation. This study delves into the annual trends of physiological indicators, gene expression levels, and metabolite accumulation to dissect the seasonal adaptability of P. koraiensis needles. Chlorophyll content reaches its zenith primarily between July and September, whereas carotenoids persist until spring. Additionally, notable seasonal variations are observed in the levels of soluble sugar and protein. Transcriptome data is categorized into four distinct stages: spring (S2), summer (S3-S4), autumn (S5), and winter (S6-S1). The differential expression of transcription factor genes, including bHLH, MYB-related, AP2/ERF, C3H, and NAC, provides insights into the needles' seasonal adaptations. Analysis of chlorophyll and carotenoid metabolism, sugar metabolism, and the MAPK signaling pathway identifies PSY5 (Cluster-50735.3), AMY13 (Cluster-37114.0), pgm1 (Cluster-46022.0), and MEKK1-1 (Cluster-33069.0) may as potential key genes involved in sustaining the needle's evergreen nature and cold resistance. Ultimately, a comprehensive annual adaptability map for P. koraiensis is proposed, enhancing understanding of its responses to seasonal variations.

Leaf stomatal configuration and photosynthetic traits jointly affect leaf water use efficiency in forests along climate gradients

Water use efficiency (WUE) represents the trade-off between carbon assimilation and water loss in plants. It remains unclear how leaf stomatal and photosynthetic traits regulate the spatial variation of leaf WUE in different natural forest ecosystems. We investigated 43 broad-leaf tree species spanning from cold-temperate to tropical forests in China. We quantified leaf WUE using leaf δ13C and measured stomatal traits, photosynthetic traits as well as maximum stomatal conductance (G w max ) and maximum carboxylation capacity (V c max ). We found that leaves in cold-temperate forests displayed 'fast' carbon economics, characterized by higher leaf nitrogen, Chl, specific leaf area, andV c max , as an adaptation to the shorter growing season. However, these leaves exhibited 'slow' hydraulic traits, with larger but fewer stomata and similarG w max , resulting in higher leaf WUE. By contrast, leaves in tropical forests had smaller and denser stomata, enabling swift response to heterogeneous light conditions. However, this stomatal configuration increased potential water loss, and coupled with their low photosynthetic capacity, led to lower WUE. Our findings contribute to understanding how plant photosynthetic and stomatal traits regulate carbon-water trade-offs across climatic gradients, advancing our ability to predict the impacts of climate changes on forest carbon and water cycles.

Water deficit and storm disturbances co-regulate Amazon rainforest seasonality

Canopy leaf abundance of Amazon rainforests increases in the dry season but decreases in the wet season, contrary to earlier expectations of water stress adversely affecting plant functions. Drivers of this seasonality, particularly the role of water availability, remain debated. We introduce satellite-based ecophysiological indicators to demonstrate that Amazon rainforests are constrained by water during dry seasons despite light-driven canopy greening. Evidence includes a shifted partitioning of photosynthetically active radiation toward more isoprene emissions and synchronized declines in leaf and xylem water potentials. In addition, we find that convective storms attenuate light-driven ecosystem greening in the late dry season and then reverse to net leaf loss in the wet season, improving rainforest leaf area predictability by 24 to 31%. These findings highlight the susceptibility of Amazon rainforests to increasing risks of drought and windthrow disturbances under warming.

A leaf age-dependent light use efficiency model for remote sensing the gross primary productivity seasonality over pantropical evergreen broadleaved forests

Tropical and subtropical evergreen broadleaved forests (TEFs) contribute more than one-third of terrestrial gross primary productivity (GPP). However, the continental-scale leaf phenology-photosynthesis nexus over TEFs is still poorly understood to date. This knowledge gap hinders most light use efficiency (LUE) models from accurately simulating the GPP seasonality in TEFs. Leaf age is the crucial plant trait to link the dynamics of leaf phenology with GPP seasonality. Thus, here we incorporated the seasonal leaf area index of different leaf age cohorts into a widely used LUE model (i.e., EC-LUE) and proposed a novel leaf age-dependent LUE model (denoted as LA-LUE model). At the site level, the LA-LUE model (average R2 = .59, average root-mean-square error [RMSE] = 1.23 gC m-2 day-1) performs better than the EC-LUE model in simulating the GPP seasonality across the nine TEFs sites (average R2 = .18; average RMSE = 1.87 gC m-2 day-1). At the continental scale, the monthly GPP estimates from the LA-LUE model are consistent with FLUXCOM GPP data (R2 = .80; average RMSE = 1.74 gC m-2 day-1), and satellite-based GPP data retrieved from the global Orbiting Carbon Observatory-2 (OCO-2) based solar-induced chlorophyll fluorescence (SIF) product (GOSIF) (R2 = .64; average RMSE = 1.90 gC m-2 day-1) and the reconstructed TROPOspheric Monitoring Instrument SIF dataset using machine learning algorithms (RTSIF) (R2 = .78; average RMSE = 1.88 gC m-2 day-1). Typically, the estimated monthly GPP not only successfully represents the unimodal GPP seasonality near the Tropics of Cancer and Capricorn, but also captures well the bimodal GPP seasonality near the Equator. Overall, this study for the first time integrates the leaf age information into the satellite-based LUE model and provides a feasible implementation for mapping the continental-scale GPP seasonality over the entire TEFs.

Characterization and Identification of NPK Stress in Rice Using Terrestrial Hyperspectral Images

Due to nutrient stress, which is an important constraint to the development of the global agricultural sector, it is now vital to timely evaluate plant health. Remote sensing technology, especially hyperspectral imaging technology, has evolved from spectral response modes to pattern recognition and vegetation monitoring. This study established a hyperspectral library of 14 NPK (nitrogen, phosphorus, potassium) nutrient stress conditions in rice. The terrestrial hyperspectral camera (SPECIM-IQ) collected 420 rice stress images and extracted as well as analyzed representative spectral reflectance curves under 14 stress modes. The canopy spectral profile characteristics, vegetation index, and principal component analysis demonstrated the differences in rice under different nutrient stresses. A transformer-based deep learning network SHCFTT (SuperPCA-HybridSN-CBAM-Feature tokenization transformer) was established for identifying nutrient stress patterns from hyperspectral images while being compared with classic support vector machines, 1D-CNN (1D-Convolutional Neural Network), and 3D-CNN. The total accuracy of the SHCFTT model under different modeling strategies and different years ranged from 93.92% to 100%, indicating the positive effect of the proposed method on improving the accuracy of identifying nutrient stress in rice.

Amazon forest biogeography predicts resilience and vulnerability to drought

Amazonia contains the most extensive tropical forests on Earth, but Amazon carbon sinks of atmospheric CO2 are declining, as deforestation and climate-change-associated droughts1-4 threaten to push these forests past a tipping point towards collapse5-8. Forests exhibit complex drought responses, indicating both resilience (photosynthetic greening) and vulnerability (browning and tree mortality), that are difficult to explain by climate variation alone9-17. Here we combine remotely sensed photosynthetic indices with ground-measured tree demography to identify mechanisms underlying drought resilience/vulnerability in different intact forest ecotopes18,19 (defined by water-table depth, soil fertility and texture, and vegetation characteristics). In higher-fertility southern Amazonia, drought response was structured by water-table depth, with resilient greening in shallow-water-table forests (where greater water availability heightened response to excess sunlight), contrasting with vulnerability (browning and excess tree mortality) over deeper water tables. Notably, the resilience of shallow-water-table forest weakened as drought lengthened. By contrast, lower-fertility northern Amazonia, with slower-growing but hardier trees (or, alternatively, tall forests, with deep-rooted water access), supported more-drought-resilient forests independent of water-table depth. This functional biogeography of drought response provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia's most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience.

Unraveling the drivers and impacts of leaf phenological diversity in a subtropical forest: A fine-scale analysis using PlanetScope CubeSats

Leaf phenology variations within plant communities shape community assemblages and influence ecosystem properties and services. However, questions remain regarding quantification, drivers, and productivity impacts of intra-site leaf phenological diversity. With a 50-ha subtropical forest plot in China's Heishiding Provincial Nature Reserve (part of the global ForestGEO network) as a testbed, we gathered a unique dataset combining ground-derived abiotic (topography, soil) and biotic (taxonomic diversity, functional diversity, functional traits) factors. We investigated drivers underlying leaf phenological diversity extracted from high-resolution PlanetScope data, and its influence on aboveground biomass (AGB) using structural equation modeling (SEM). Our results reveal considerable fine-scale leaf phenological diversity across the subtropical forest landscape. This diversity is directly and indirectly influenced by abiotic and biotic factors (e.g. slope, soil, traits, taxonomic diversity; r2 = 0.43). While a notable bivariate relationship between AGB and leaf phenological diversity was identified (r = -0.24, P < 0.05), this relationship did not hold in SEM analysis after considering interactions with other biotic and abiotic factors (P > 0.05). These findings unveil the underlying mechanism regulating intra-site leaf phenological diversity. While leaf phenology is known to be associated with ecosystem properties, our findings confirm that AGB is primarily influenced by functional trait composition and taxonomic diversity rather than leaf phenological diversity.

Contrasting carbon cycle along tropical forest aridity gradients in West Africa and Amazonia

Tropical forests cover large areas of equatorial Africa and play a substantial role in the global carbon cycle. However, there has been a lack of biometric measurements to understand the forests' gross and net primary productivity (GPP, NPP) and their allocation. Here we present a detailed field assessment of the carbon budget of multiple forest sites in Africa, by monitoring 14 one-hectare plots along an aridity gradient in Ghana, West Africa. When compared with an equivalent aridity gradient in Amazonia, the studied West African forests generally had higher productivity and lower carbon use efficiency (CUE). The West African aridity gradient consistently shows the highest NPP, CUE, GPP, and autotrophic respiration at a medium-aridity site, Bobiri. Notably, NPP and GPP of the site are the highest yet reported anywhere for intact forests. Widely used data products substantially underestimate productivity when compared to biometric measurements in Amazonia and Africa. Our analysis suggests that the high productivity of the African forests is linked to their large GPP allocation to canopy and semi-deciduous characteristics.

Anisotropic Rotunda-Shaped Carboxymethylcellulose/Carbon Nanotube Aerogels Supported Phase Change Materials for Efficient Solar-Thermal Energy Conversion

For the drawbacks of phase change materials such as poor shape stability and weak solar-thermal conversion ability, a rotunda-shaped carboxymethylcellulose/carbon nanotube aerogel (CA) with three-dimensional network was constructed by freeze casting with a special mold, and then impregnated with polyethylene glycol (PEG) in this work. The PEG/CA had an enthalpy of 183.21 J/g, and a thermal conductivity of 0.324 W m-1 K-1, which was 57 % higher than the pure PEG. The ability of PEG/CA to convert solar energy to thermal energy was a positive correlation between the inclusion of CNTs and the composite material's thermal conductivity. Under simulated sunlight, its solar-thermal conversion efficiency reaches 94.41 %, and after 10 min of irradiation, the surface temperature can reach 65 °C and the internal temperature can reach 44.67 °C. This rotunda-shaped PEG/CA is promising for the efficient use of renewable solar energy due to its strong solar-thermal conversion and thermal storage capabilities.

Historical tree phenology data reveal the seasonal rhythms of the Congo Basin rainforest

Tropical forest phenology directly affects regional carbon cycles, but the relation between species-specific and whole-canopy phenology remains largely uncharacterized. We present a unique analysis of historical tropical tree phenology collected in the central Congo Basin, before large-scale impacts of human-induced climate change. Ground-based long-term (1937-1956) phenological observations of 140 tropical tree species are recovered, species-specific phenological patterns analyzed and related to historical meteorological records, and scaled to characterize stand-level canopy dynamics. High phenological variability within and across species and in climate-phenology relationships is observed. The onset of leaf phenophases in deciduous species was triggered by drought and light availability for a subset of species and showed a species-specific decoupling in time along a bi-modal seasonality. The majority of the species remain evergreen, although central African forests experience relatively low rainfall. Annually a maximum of 1.5% of the canopy is in leaf senescence or leaf turnover, with overall phenological variability dominated by a few deciduous species, while substantial variability is attributed to asynchronous events of large and/or abundant trees. Our results underscore the importance of accounting for constituent signals in canopy-wide scaling and the interpretation of remotely sensed phenology signals.

Data science competition for cross-site individual tree species identification from airborne remote sensing data

Data on individual tree crowns from remote sensing have the potential to advance forest ecology by providing information about forest composition and structure with a continuous spatial coverage over large spatial extents. Classifying individual trees to their taxonomic species over large regions from remote sensing data is challenging. Methods to classify individual species are often accurate for common species, but perform poorly for less common species and when applied to new sites. We ran a data science competition to help identify effective methods for the task of classification of individual crowns to species identity. The competition included data from three sites to assess each methods' ability to generalize patterns across two sites simultaneously and apply methods to an untrained site. Three different metrics were used to assess and compare model performance. Six teams participated, representing four countries and nine individuals. The highest performing method from a previous competition in 2017 was applied and used as a baseline to understand advancements and changes in successful methods. The best species classification method was based on a two-stage fully connected neural network that significantly outperformed the baseline random forest and gradient boosting ensemble methods. All methods generalized well by showing relatively strong performance on the trained sites (accuracy = 0.46-0.55, macro F1 = 0.09-0.32, cross entropy loss = 2.4-9.2), but generally failed to transfer effectively to the untrained site (accuracy = 0.07-0.32, macro F1 = 0.02-0.18, cross entropy loss = 2.8-16.3). Classification performance was influenced by the number of samples with species labels available for training, with most methods predicting common species at the training sites well (maximum F1 score of 0.86) relative to the uncommon species where none were predicted. Classification errors were most common between species in the same genus and different species that occur in the same habitat. Most methods performed better than the baseline in detecting if a species was not in the training data by predicting an untrained mixed-species class, especially in the untrained site. This work has highlighted that data science competitions can encourage advancement of methods, particularly by bringing in new people from outside the focal discipline, and by providing an open dataset and evaluation criteria from which participants can learn.

Asymmetric response of Amazon forest water and energy fluxes to wet and dry hydrological extremes reveals onset of a local drought-induced tipping point

Understanding the effects of intensification of Amazon basin hydrological cycling-manifest as increasingly frequent floods and droughts-on water and energy cycles of tropical forests is essential to meeting the challenge of predicting ecosystem responses to climate change, including forest "tipping points". Here, we investigated the impacts of hydrological extremes on forest function using 12+ years of observations (between 2001-2020) of water and energy fluxes from eddy covariance, along with associated ecological dynamics from biometry, at the Tapajós National Forest. Measurements encompass the strong 2015-2016 El Niño drought and La Niña 2008-2009 wet events. We found that the forest responded strongly to El Niño-Southern Oscillation (ENSO): Drought reduced water availability for evapotranspiration (ET) leading to large increases in sensible heat fluxes (H). Partitioning ET by an approach that assumes transpiration (T) is proportional to photosynthesis, we found that water stress-induced reductions in canopy conductance (Gs ) drove T declines partly compensated by higher evaporation (E). By contrast, the abnormally wet La Niña period gave higher T and lower E, with little change in seasonal ET. Both El Niño-Southern Oscillation (ENSO) events resulted in changes in forest structure, manifested as lower wet-season leaf area index. However, only during El Niño 2015-2016, we observed a breakdown in the strong meteorological control of transpiration fluxes (via energy availability and atmospheric demand) because of slowing vegetation functions (via shutdown of Gs and significant leaf shedding). Drought-reduced T and Gs , higher H and E, amplified by feedbacks with higher temperatures and vapor pressure deficits, signaled that forest function had crossed a threshold, from which it recovered slowly, with delay, post-drought. Identifying such tipping point onsets (beyond which future irreversible processes may occur) at local scale is crucial for predicting basin-scale threshold-crossing changes in forest energy and water cycling, leading to slow-down in forest function, potentially resulting in Amazon forests shifting into alternate degraded states.

Proof of evidence of changes in global terrestrial biomes using historic and recent NDVI time series

Climate change affects plant dynamics and functioning of terrestrial ecosystems. This study aims to investigate temporal changes in global vegetation coverage and biomes during the past three decades. We compared historic annual NDVI time series (1982, 1983, 1984 and 1985) with recent ones (2015, 2016, 2017 and 2018), captured from NOAA-AVHRR satellite observations. To correct the NDVI time series for missing data and outliers, we applied the Harmonic Analysis of Time Series (HANTS) algorithm. The NDVI time series were decomposed in their significant amplitude and phase given their periodic fluctuation, except for ever green vegetation. Our findings show that the average NDVI values in most biomes have increased significantly (F-value<0.01) by 0.05 ndvi units over during the past three decades, except in tundra, and deserts and xeric shrublands. The highest rates of change in the harmonic components were observed in the northern hemisphere, mainly above 30° latitude. Worldwide, the mean annual phase reduced by 9° corresponding to a 9 days shift in the beginning of the growing season. Annual phases in the recent time series reduced significantly as compared to the historic time series in the five major global biomes: by 14.1, 14.8, 10.6, 9.5, and 22.8 days in boreal forests/taiga; Mediterranean forests, woodlands, and scrubs; temperate conifer forests; temperate grasslands, savannas, and shrublands; and deserts, and xeric shrublands, respectively. In tropical and subtropical biomes, however, changes in the annual phase of vegetation coverage were not statistically significant. The decrease in the level of phases and acceleration of growth and changes in plant phenology indicate the increase in temperature and climate changes of the planet.

Large diurnal compensatory effects mitigate the response of Amazonian forests to atmospheric warming and drying

Photosynthesis and evapotranspiration in Amazonian forests are major contributors to the global carbon and water cycles. However, their diurnal patterns and responses to atmospheric warming and drying at regional scale remain unclear, hindering the understanding of global carbon and water cycles. Here, we used proxies of photosynthesis and evapotranspiration from the International Space Station to reveal a strong depression of dry season afternoon photosynthesis (by 6.7 ± 2.4%) and evapotranspiration (by 6.1 ± 3.1%). Photosynthesis positively responds to vapor pressure deficit (VPD) in the morning, but negatively in the afternoon. Furthermore, we projected that the regionally depressed afternoon photosynthesis will be compensated by their increases in the morning in future dry seasons. These results shed new light on the complex interplay of climate with carbon and water fluxes in Amazonian forests and provide evidence on the emerging environmental constraints of primary productivity that may improve the robustness of future projections.

The role of ecosystem transpiration in creating alternate moisture regimes by influencing atmospheric moisture convergence

The terrestrial water cycle links the soil and atmosphere moisture reservoirs through four fluxes: precipitation, evaporation, runoff, and atmospheric moisture convergence (net import of water vapor to balance runoff). Each of these processes is essential for sustaining human and ecosystem well-being. Predicting how the water cycle responds to changes in vegetation cover remains a challenge. Recently, changes in plant transpiration across the Amazon basin were shown to be associated disproportionately with changes in rainfall, suggesting that even small declines in transpiration (e.g., from deforestation) would lead to much larger declines in rainfall. Here, constraining these findings by the law of mass conservation, we show that in a sufficiently wet atmosphere, forest transpiration can control atmospheric moisture convergence such that increased transpiration enhances atmospheric moisture import and results in water yield. Conversely, in a sufficiently dry atmosphere increased transpiration reduces atmospheric moisture convergence and water yield. This previously unrecognized dichotomy can explain the otherwise mixed observations of how water yield responds to re-greening, as we illustrate with examples from China's Loess Plateau. Our analysis indicates that any additional precipitation recycling due to additional vegetation increases precipitation but decreases local water yield and steady-state runoff. Therefore, in the drier regions/periods and early stages of ecological restoration, the role of vegetation can be confined to precipitation recycling, while once a wetter stage is achieved, additional vegetation enhances atmospheric moisture convergence and water yield. Recent analyses indicate that the latter regime dominates the global response of the terrestrial water cycle to re-greening. Evaluating the transition between regimes, and recognizing the potential of vegetation for enhancing moisture convergence, are crucial for characterizing the consequences of deforestation as well as for motivating and guiding ecological restoration.

Short-term variation in leaf-level water use efficiency in a tropical forest

The representation of stomatal regulation of transpiration and CO₂ assimilation is key to forecasting terrestrial ecosystem responses to global change. Given its importance in determining the relationship between forest productivity and climate, accurate and mechanistic model representation of the relationship between stomatal conductance (g_s ) and assimilation is crucial. We assess possible physiological and mechanistic controls on the estimation of the g₁ (stomatal slope, inversely proportional to water use efficiency) and g₀ (stomatal intercept) parameters, using diurnal gas exchange surveys and leaf-level response curves of six tropical broadleaf evergreen tree species. g₁ estimated from ex situ response curves averaged 50% less than g₁ estimated from survey data. While g₀ and g₁ varied between leaves of different phenological stages, the trend was not consistent among species. We identified a diurnal trend associated with g₁ and g₀ that significantly improved model projections of diurnal trends in transpiration. The accuracy of modeled g_s can be improved by accounting for variation in stomatal behavior across diurnal periods, and between measurement approaches, rather than focusing on phenological variation in stomatal behavior. Additional investigation into the primary mechanisms responsible for diurnal variation in g₁ will be required to account for this phenomenon in land-surface models.

Declining Amazon biomass due to deforestation and subsequent degradation losses exceeding gains

In the Amazon, deforestation and climate change lead to increased vulnerability to forest degradation, threatening its existing carbon stocks and its capacity as a carbon sink. We use satellite L-Band Vegetation Optical Depth (L-VOD) data that provide an integrated (top-down) estimate of biomass carbon to track changes over 2011-2019. Because the spatial resolution of L-VOD is coarse (0.25°), it allows limited attribution of the observed changes. We therefore combined high-resolution annual maps of forest cover and disturbances with biomass maps to model carbon losses (bottom-up) from deforestation and degradation, and gains from regrowing secondary forests. We show an increase of deforestation and associated degradation losses since 2012 which greatly outweigh secondary forest gains. Degradation accounted for 40% of gross losses. After an increase in 2011, old-growth forests show a net loss of above-ground carbon between 2012 and 2019. The sum of component carbon fluxes in our model is consistent with the total biomass change from L-VOD of 1.3 Pg C over 2012-2019. Across nine Amazon countries, we found that while Brazil contains the majority of biomass stocks (64%), its losses from disturbances were disproportionately high (79% of gross losses). Our multi-source analysis provides a pessimistic assessment of the Amazon carbon balance and highlights the urgent need to stop the recent rise of deforestation and degradation, particularly in the Brazilian Amazon.

The other side of tropical forest drought: do shallow water table regions of Amazonia act as large-scale hydrological refugia from drought?

Tropical forest function is of global significance to climate change responses, and critically determined by water availability patterns. Groundwater is tightly related to soil water through the water table depth (WT), but historically neglected in ecological studies. Shallow WT forests (WT < 5 m) are underrepresented in forest research networks and absent in eddy flux measurements, although they represent c. 50% of the Amazon and are expected to respond differently to global-change-related droughts. We review WT patterns and consequences for plants, emerging results, and advance a conceptual model integrating environment and trait distributions to predict climate change effects. Shallow WT forests have a distinct species composition, with more resource-acquisitive and hydrologically vulnerable trees, shorter canopies and lower biomass than deep WT forests. During 'normal' climatic years, shallow WT forests have higher mortality and lower productivity than deep WT forests, but during moderate droughts mortality is buffered and productivity increases. However, during severe drought, shallow WT forests may be more sensitive due to shallow roots and drought-intolerant traits. Our evidence supports the hypothesis of neglected shallow WT forests being resilient to moderate drought, challenging the prevailing view of widespread negative effects of climate change on Amazonian forests that ignores WT gradients, but predicts they could collapse under very strong droughts.

Spatiotemporal dynamic of subtropical forest carbon storage and its resistance and resilience to drought in China

Subtropical forests are rich in vegetation and have high photosynthetic capacity. China is an important area for the distribution of subtropical forests, evergreen broadleaf forests (EBFs) and evergreen needleleaf forests (ENFs) are two typical vegetation types in subtropical China. Forest carbon storage is an important indicator for measuring the basic characteristics of forest ecosystems and is of great significance for maintaining the global carbon balance. Drought can affect forest activity and may even lead to forest death and the stability characteristics of different forest ecosystems varied after drought events. Therefore, this study used meteorological data to simulate the standardized precipitation evapotranspiration index (SPEI) and the Biome-BGC model to simulate two types of forest carbon storage to quantify the resistance and resilience of EBF and ENF to drought in the subtropical region of China. The results show that: 1) from 1952 to 2019, the interannual drought in subtropical China showed an increasing trend, with five extreme droughts recorded, of which 2011 was the most severe one; 2) the simulated average carbon storage of the EBF and ENF during 1985-2019 were 130.58 t·hm^-2 and 78.49 t·hm^-2, respectively. The regions with higher carbon storage of EBF were mainly concentrated in central and southeastern subtropics, where those of ENF mainly distributed in the western subtropic; 3) The median of resistance of EBF was three times higher than that of ENF, indicating the EBF have stronger resistance to extreme drought than ENF. Moreover, the resilience of two typical forest to 2011 extreme drought and the continuous drought events during 2009 - 2011 were similar. The results provided a scientific basis for the response of subtropical forests to drought, and indicating that improve stand quality or expand the plantation of EBF may enhance the resistance to drought in subtropical China, which provided certain reference for forest protection and management under the increasing frequency of drought events in the future.

Thermal sensitivity across forest vertical profiles: patterns, mechanisms, and ecological implications

Rising temperatures are influencing forests on many scales, with potentially strong variation vertically across forest strata. Using published research and new analyses, we evaluate how microclimate and leaf temperatures, traits, and gas exchange vary vertically in forests, shaping tree, and ecosystem ecology. In closed-canopy forests, upper canopy leaves are exposed to the highest solar radiation and evaporative demand, which can elevate leaf temperature (T_leaf ), particularly when transpirational cooling is curtailed by limited stomatal conductance. However, foliar traits also vary across height or light gradients, partially mitigating and protecting against the elevation of upper canopy T_leaf . Leaf metabolism generally increases with height across the vertical gradient, yet differences in thermal sensitivity across the gradient appear modest. Scaling from leaves to trees, canopy trees have higher absolute metabolic capacity and growth, yet are more vulnerable to drought and damaging T_leaf than their smaller counterparts, particularly under climate change. By contrast, understory trees experience fewer extreme high T_leaf 's but have fewer cooling mechanisms and thus may be strongly impacted by warming under some conditions, particularly when exposed to a harsher microenvironment through canopy disturbance. As the climate changes, integrating the patterns and mechanisms reviewed here into models will be critical to forecasting forest-climate feedback.

The legacy effects of rubber defoliation period on the refoliation phenology, leaf disease, and latex yield

The leaf phenology of trees has received particular attention for its crucial role in the global water and carbon balances, ecosystem, and species distribution. However, current studies on leaf phenology have mainly focused on temperate trees, while few studies including tropical trees. Little attention has been paid to globally extensive industrial plantations. Rubber plantations are important to both the local and global economies. In this study, we investigated the legacy effects of defoliation phenology on the following year's leaf flushing, leaf disease, and also latex yield of rubber trees, an economically important tree to local people and the world. Results show that extended duration of defoliation increased the subsequent duration of refoliation and rates of infection by powdery mildew disease, but led to reduced latex yield in March. This legacy effect of rubber defoliation may relate to the carbohydrate reserved in the trees. A longer duration of defoliation would consume more reserved carbohydrates, reducing available reserves for disease defense and latex production. Extended duration of defoliation period was associated with either a lower temperature before the cessation of latex tapping in October-November and/or a higher temperature after the cessation of latex tapping in December-January. Leaf falling signals the end of photosynthetic activities in deciduous trees. Thus, the leaf falling phenology will impact ecological processes involving rubber trees. Our findings indicated that the inclusion of defoliation periods in future rubber trees' research, will be crucial to furthering our understanding of leaf flushing, powdery mildew disease, and latex yield.

Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Feedback in tropical forests of the Anthropocene

Tropical forests are complex systems containing myriad interactions and feedbacks with their biotic and abiotic environments, but as the world changes fast, the future of these ecosystems becomes increasingly uncertain. In particular, global stressors may unbalance the feedbacks that stabilize tropical forests, allowing other feedbacks to propel undesired changes in the whole ecosystem. Here, we review the scientific literature across various fields, compiling known interactions of tropical forests with their environment, including the global climate, rainfall, aerosols, fire, soils, fauna, and human activities. We identify 170 individual interactions among 32 elements that we present as a global tropical forest network, including countless feedback loops that may emerge from different combinations of interactions. We illustrate our findings with three cases involving urgent sustainability issues: (1) wildfires in wetlands of South America; (2) forest encroachment in African savanna landscapes; and (3) synergistic threats to the peatland forests of Borneo. Our findings reveal an unexplored world of feedbacks that shape the dynamics of tropical forests. The interactions and feedbacks identified here can guide future qualitative and quantitative research on the complexities of tropical forests, allowing societies to manage the nonlinear responses of these ecosystems in the Anthropocene.

Seasonal shifts in isoprenoid emission composition from three hyperdominant tree species in central Amazonia

Volatile isoprenoids regulate plant performance and atmospheric processes, and Amazon forests comprise the dominant source to the global atmosphere. Still, there is a poor understanding of how isoprenoid emission capacities vary in response to ecophysiological and environmental controls in Amazonian ecosystems. We measured isoprenoid emission capacities of three Amazonian hyperdominant tree species - Protium hebetatum, Eschweilera grandiflora, Eschweilera coriacea - across seasons and along a topographic and edaphic environmental gradient in the central Amazon. From wet to dry season, both photosynthesis and isoprene emission capacities strongly declined, while emissions increased among the heavier isoprenoids: monoterpenes and sesquiterpenes. Plasticity across habitats was most evident in P. hebetatum, which emitted sesquiterpenes only in the dry season, at rates that significantly increased along the hydro-topographic gradient from white sands (shallow root water access) to uplands (deep water table). We suggest that emission composition shifts are part of a plastic response to increasing abiotic stress (e.g. heat and drought) and reduced photosynthetic supply of substrates for isoprenoid synthesis. Our comprehensive measurements suggest that more emphasis should be placed on other isoprenoids, besides isoprene, in the context of abiotic stress responses. Shifting emission compositions have implications for atmospheric responses because of the strong variation in reactivity among isoprenoid compounds.

Drought resilience of conifer species is driven by leaf lifespan but not by hydraulic traits

Increased droughts impair tree growth worldwide. This study analyzes hydraulic and carbon traits of conifer species, and how they shape species strategies in terms of their growth rate and drought resilience. We measured 43 functional stem and leaf traits for 28 conifer species growing in a 50-yr-old common garden experiment in the Netherlands. We assessed: how drought- and carbon-related traits are associated across species, how these traits affect stem growth and drought resilience, and how traits and drought resilience are related to species' climatic origin. We found two trait spectra: a hydraulics spectrum reflecting a trade-off between hydraulic and biomechanical safety vs hydraulic efficiency, and a leaf economics spectrum reflecting a trade-off between tough, long-lived tissues vs high carbon assimilation rate. Pit aperture size occupied a central position in the trait-based network analysis and also increased stem growth. Drought recovery decreased with leaf lifespan. Conifer species with long-lived leaves suffer from drought legacy effects, as drought-damaged leaves cannot easily be replaced, limiting growth recovery after drought. Leaf lifespan, rather than hydraulic traits, can explain growth responses to a drier future.

Plant hydraulics, stomatal control, and the response of a tropical forest to water stress over multiple temporal scales

Many tropical regions are experiencing an intensification of drought, with increasing severity and frequency. The ecosystem response to these changes is still highly uncertain. On short time scales (from diurnal to seasonal), tropical forests respond to water stress by physiological controls, such as stomatal regulation and phenological adjustment, to cope with increasing atmospheric water demand and reduced water supply. However, the interactions among biological processes and co-varying environmental factors that determine the ecosystem-level fluxes are still unclear. Furthermore, climate variability at longer time scales, such as that generated by ENSO, produces less predictable effects because it depends on a highly stochastic combination of factors that might vary among forests and even between events in the same forest. This study will present some emerging patterns of response to water stress from 5 years of water, carbon, and energy fluxes observed on a seasonal tropical forest in central Panama, including an increase in productivity during the 2015 El Niño. These responses depend on the combination of environmental factors experienced by the forest throughout the seasonal cycle, in particular, increase in solar radiation, stimulating productivity, and increasing vapor pressure deficit (VPD) and decreasing soil moisture, limiting stomata opening. These results suggest a critical role of plant hydraulics in mediating the response to water stress over a broad range of temporal scales (diurnal, intraseasonal, seasonal, and interannual), by acclimating canopy conductance to light and VPD during different soil moisture regimes. A multilayer photosynthesis model coupled with a plant hydraulics scheme can reproduce these complex responses. However, results depend critically on parameters regulating water transport efficiency and the cost of water stress. As these costs have not been properly identified and quantified yet, more empirical research is needed to elucidate physiological mechanisms of hydraulic failure and recover, for example embolism repair and xylem regrowth.

Vegetative phenologies of lianas and trees in two Neotropical forests with contrasting rainfall regimes

Among tropical forests, lianas are predicted to have a growth advantage over trees during seasonal drought, with substantial implications for tree and forest dynamics. We tested the hypotheses that lianas maintain higher water status than trees during seasonal drought and that lianas maximize leaf cover to match high, dry-season light conditions, while trees are more limited by moisture availability during the dry season. We monitored the seasonal dynamics of predawn and midday leaf water potentials and leaf phenology for branches of 16 liana and 16 tree species in the canopies of two lowland tropical forests with contrasting rainfall regimes in Panama. In a wet, weakly seasonal forest, lianas maintained higher water balance than trees and maximized their leaf cover during dry-season conditions, when light availability was high, while trees experienced drought stress. In a drier, strongly seasonal forest, lianas and trees displayed similar dry season reductions in leaf cover following strong decreases in soil water availability. Greater soil moisture availability and a higher capacity to maintain water status allow lianas to maintain the turgor potentials that are critical for plant growth in a wet and weakly seasonal forest but not in a dry and strongly seasonal forest.

Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño

The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of [Formula: see text]. [Formula: see text] is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data-RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high [Formula: see text] may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high [Formula: see text] ([Formula: see text] ≥ 0.3) appear to have a stronger capacity to recover than low-[Formula: see text] ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics.

Bridge to the future: Important lessons from 20 years of ecosystem observations made by the OzFlux network

In 2020, the Australian and New Zealand flux research and monitoring network, OzFlux, celebrated its 20th anniversary by reflecting on the lessons learned through two decades of ecosystem studies on global change biology. OzFlux is a network not only for ecosystem researchers, but also for those 'next users' of the knowledge, information and data that such networks provide. Here, we focus on eight lessons across topics of climate change and variability, disturbance and resilience, drought and heat stress and synergies with remote sensing and modelling. In distilling the key lessons learned, we also identify where further research is needed to fill knowledge gaps and improve the utility and relevance of the outputs from OzFlux. Extreme climate variability across Australia and New Zealand (droughts and flooding rains) provides a natural laboratory for a global understanding of ecosystems in this time of accelerating climate change. As evidence of worsening global fire risk emerges, the natural ability of these ecosystems to recover from disturbances, such as fire and cyclones, provides lessons on adaptation and resilience to disturbance. Drought and heatwaves are common occurrences across large parts of the region and can tip an ecosystem's carbon budget from a net CO2 sink to a net CO2 source. Despite such responses to stress, ecosystems at OzFlux sites show their resilience to climate variability by rapidly pivoting back to a strong carbon sink upon the return of favourable conditions. Located in under-represented areas, OzFlux data have the potential for reducing uncertainties in global remote sensing products, and these data provide several opportunities to develop new theories and improve our ecosystem models. The accumulated impacts of these lessons over the last 20 years highlights the value of long-term flux observations for natural and managed systems. A future vision for OzFlux includes ongoing and newly developed synergies with ecophysiologists, ecologists, geologists, remote sensors and modellers.

The Resilience of Vegetation to the 2009/2010 Extreme Drought in Southwest China

The 2009/2010 extreme drought in southwest China (SWC) was a “once-in-a-century” drought event, which caused unprecedented damage to the regional ecology and socioeconomic development. The event provided a chance to explore the resilience of vegetation growth and productivity to the extreme drought. Here, we used the self-calibrating Palmer drought severity index (scPDSI) to describe the characteristics of the extreme drought. Vegetation growth and productivity indices, including the normalized difference vegetation index (NDVI), leaf area index (LAI), and gross primary productivity (GPP), were applied to analyze the resilience of different vegetation types to the extreme drought. Our results showed that the extreme drought event occurred mainly in Yunnan Province, Guizhou Province, central and northern Guangxi Zhuang Autonomous Region, and northwestern Sichuan Province. The spatial heterogeneity of the extreme drought was related to the temperature increase and water deficit. During the extreme drought, the vegetation growth and productivity of evergreen broadleaf forest were the least suppressed, whereas cropland was greatly suppressed. The recovery of cropland was higher than that of evergreen broadleaf forest. NDVI and LAI were recovered in more than 80% of the drought-affected area within 5 months, whereas GPP required a longer time to recover. Moreover, the results of multiple linear regression showed that an increase in surface soil moisture was able to significantly improve the resistance of vegetation NDVI and LAI in evergreen broadleaf forest, evergreen needleleaf forest, evergreen broadleaf shrubland, deciduous broadleaf shrubland, and grassland. Our study highlights the differences in the resilience of different vegetation types to extreme drought and indicates that surface soil moisture is an important factor affecting vegetation resistance in SWC.

An Overview of the Isoprenoid Emissions From Tropical Plant Species

Terrestrial vegetation is the largest contributor of isoprenoids (a group of biogenic volatile organic compounds (BVOCs)) to the atmosphere. BVOC emission data comes mostly from temperate regions, and less is known about BVOC emissions from tropical vegetation, even though it is estimated to be responsible for >70% of BVOC emissions. This review summarizes the available data and our current understanding of isoprenoid emissions from tropical plant species and the spatial and temporal variation in emissions, which are strongly species-specific and regionally variable. Emission models lacking foliar level data for tropical species need to revise their parameters to account for seasonal and diurnal variation due to differences in dependencies on temperature and light of emissions from plants in other ecosystems. More experimental information and determining how emission capacity varies during foliar development are warranted to account for seasonal variations more explicitly.

Changes in leaf functional traits with leaf age: when do leaves decrease their photosynthetic capacity in Amazonian trees?

Most leaf functional trait studies in the Amazon basin do not consider ontogenetic variations (leaf age), which may influence ecosystem productivity throughout the year. When leaf age is taken into account, it is generally considered discontinuous, and leaves are classified into age categories based on qualitative observations. Here, we quantified age-dependent changes in leaf functional traits such as the maximum carboxylation rate of ribulose-1,5-biphosphate carboxylase/oxygenase (Rubisco) (Vcmax), stomatal control (Cgs%), leaf dry mass per area and leaf macronutrient concentrations for nine naturally growing Amazon tropical trees with variable phenological strategies. Leaf ages were assessed by monthly censuses of branch-level leaf demography; we also performed leaf trait measurements accounting for leaf chronological age based on days elapsed since the first inclusion in the leaf demography, not predetermined age classes. At the tree community scale, a nonlinear relationship between Vcmax and leaf age existed: young, developing leaves showed the lowest mean photosynthetic capacity, increasing to a maximum at 45 days and then decreasing gradually with age in both continuous and categorical age group analyses. Maturation times among species and phenological habits differed substantially, from 8 ± 30 to 238 ± 30 days, and the rate of decline of Vcmax varied from -0.003 to -0.065 μmol CO2 m-2 s-1 day-1. Stomatal control increased significantly in young leaves but remained constant after peaking. Mass-based phosphorus and potassium concentrations displayed negative relationships with leaf age, whereas nitrogen did not vary temporally. Differences in life strategies, leaf nutrient concentrations and phenological types, not the leaf age effect alone, may thus be important factors for understanding observed photosynthesis seasonality in Amazonian forests. Furthermore, assigning leaf age categories in diverse tree communities may not be the recommended method for studying carbon uptake seasonality in the Amazon, since the relationship between Vcmax and leaf age could not be confirmed for all trees.

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO₂ emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/T_air ) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/T_air and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. We found that the top drivers of LST/T_air , explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/T_air variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/T_air is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

Assessing Drought Response in the Southwestern Amazon Forest by Remote Sensing and In Situ Measurements

Long-term meteorological analyzes suggest an increase in air temperature and a decrease in rainfall over the Amazon biome. The effect of these climate changes on the forest remains unresolved, because field observations on functional traits are sparse in time and space, and the results from remote sensing analyses are divergent. Then, we analyzed the drought response in a ‘terra firme’ forest fragment in the southwestern Amazonia, during an extreme drought event influenced by ENSO episode (2015/2017), focusing on stem growth, litter production, functional traits and forest canopy dynamics. We use the Moderate Resolution Imaging Spectroradiometer (MODIS), corrected by Multi-Angle Implementation of Atmospheric Correction (MAIAC) to generate the enhanced vegetation index (EVI) and green chromatic coordinate (Gcc) vegetation indices. We monitor stem growth and measure the functional traits of trees in situ, such as the potential at which the plant loses 50% of hydraulic conductivity (P50), turgor loss point (πTLP), hydraulic safety margin (HSM) and isohydricity. Our results suggest that: (a) during the dry season, there is a smooth reduction in EVI values (browning) and an increase in the wet season (greening); (b) in the dry season, leaf flush occurs, when the water table still has a quota at the limit of the root zone; (c) the forest showed moderate resistance to drought, with water as the primary limiting factor, and the thickest trees were the most resistant; and (d) a decline in stem growth post-El-Niño 2015/2016 was observed, suggesting that the persistence of negative rainfall anomalies may be as critical to the forest as the drought episode itself.

Dry Season Transpiration and Soil Water Dynamics in the Central Amazon

With current observations and future projections of more intense and frequent droughts in the tropics, understanding the impact that extensive dry periods may have on tree and ecosystem-level transpiration and concurrent carbon uptake has become increasingly important. Here, we investigate paired soil and tree water extraction dynamics in an old-growth upland forest in central Amazonia during the 2018 dry season. Tree water use was assessed via radial patterns of sap flow in eight dominant canopy trees, each a different species with a range in diameter, height, and wood density. Paired multi-sensor soil moisture probes used to quantify volumetric water content dynamics and soil water extraction within the upper 100 cm were installed adjacent to six of those trees. To link depth-specific water extraction patterns to root distribution, fine root biomass was assessed through the soil profile to 235 cm. To scale tree water use to the plot level (stand transpiration), basal area was measured for all trees within a 5 m radius around each soil moisture probe. The sensitivity of tree transpiration to reduced precipitation varied by tree, with some increasing and some decreasing in water use during the dry period. Tree-level water use scaled with sapwood area, from 11 to 190 L per day. Stand level water use, based on multiple plots encompassing sap flow and adjacent trees, varied from ∼1.7 to 3.3 mm per day, increasing linearly with plot basal area. Soil water extraction was dependent on root biomass, which was dense at the surface (i.e., 45% in the upper 5 cm) and declined dramatically with depth. As the dry season progressed and the upper soil dried, soil water extraction shifted to deeper levels and model projections suggest that much of the water used during the month-long dry-down could be extracted from the upper 2-3 m. Results indicate variation in rates of soil water extraction across the research area and, temporally, through the soil profile. These results provide key information on whole-tree contributions to transpiration by canopy trees as water availability changes. In addition, information on simultaneous stand level dynamics of soil water extraction that can inform mechanistic models that project tropical forest response to drought.

Representation of Leaf-to-Canopy Radiative Transfer Processes Improves Simulation of Far-Red Solar-Induced Chlorophyll Fluorescence in the Community Land Model Version 5

Recent advances in satellite observations of solar-induced chlorophyll fluorescence (SIF) provide a new opportunity to constrain the simulation of terrestrial gross primary productivity (GPP). Accurate representation of the processes driving SIF emission and its radiative transfer to remote sensing sensors is an essential prerequisite for data assimilation. Recently, SIF simulations have been incorporated into several land surface models, but the scaling of SIF from leaf-level to canopy-level is usually not well-represented. Here, we incorporate the simulation of far-red SIF observed at nadir into the Community Land Model version 5 (CLM5). Leaf-level fluorescence yield was simulated by a parametric simplification of the Soil Canopy-Observation of Photosynthesis and Energy fluxes model (SCOPE). And an efficient and accurate method based on escape probability is developed to scale SIF from leaf-level to top-of-canopy while taking clumping and the radiative transfer processes into account. SIF simulated by CLM5 and SCOPE agreed well at sites except one in needleleaf forest (R ² > 0.91, root-mean-square error <0.19 W⋅m^-2⋅sr^-1⋅μm^-1), and captured the day-to-day variation of tower-measured SIF at temperate forest sites (R ² > 0.68). At the global scale, simulated SIF generally captured the spatial and seasonal patterns of satellite-observed SIF. Factors including the fluorescence emission model, clumping, bidirectional effect, and leaf optical properties had considerable impacts on SIF simulation, and the discrepancies between simulate d and observed SIF varied with plant functional type. By improving the representation of radiative transfer for SIF simulation, our model allows better comparisons between simulated and observed SIF toward constraining GPP simulations.

Forest fragmentation impacts the seasonality of Amazonian evergreen canopies

Predictions of the magnitude and timing of leaf phenology in Amazonian forests remain highly controversial. Here, we use terrestrial LiDAR surveys every two weeks spanning wet and dry seasons in Central Amazonia to show that plant phenology varies strongly across vertical strata in old-growth forests, but is sensitive to disturbances arising from forest fragmentation. In combination with continuous microclimate measurements, we find that when maximum daily temperatures reached 35 °C in the latter part of the dry season, the upper canopy of large trees in undisturbed forests lost plant material. In contrast, the understory greened up with increased light availability driven by the upper canopy loss, alongside increases in solar radiation, even during periods of drier soil and atmospheric conditions. However, persistently high temperatures in forest edges exacerbated the upper canopy losses of large trees throughout the dry season, whereas the understory in these light-rich environments was less dependent on the altered upper canopy structure. Our findings reveal a strong influence of edge effects on phenological controls in wet forests of Central Amazonia.

Phenology is the dominant control of methane emissions in a tropical non-forested wetland

Tropical wetlands are a significant source of atmospheric methane (CH₄), but their importance to the global CH₄ budget is uncertain due to a paucity of direct observations. Net wetland emissions result from complex interactions and co-variation between microbial production and oxidation in the soil, and transport to the atmosphere. Here we show that phenology is the overarching control of net CH₄ emissions to the atmosphere from a permanent, vegetated tropical swamp in the Okavango Delta, Botswana, and we find that vegetative processes modulate net CH₄ emissions at sub-daily to inter-annual timescales. Without considering the role played by papyrus on regulating the efflux of CH₄ to the atmosphere, the annual budget for the entire Okavango Delta, would be under- or over-estimated by a factor of two. Our measurements demonstrate the importance of including vegetative processes such as phenological cycles into wetlands emission budgets of CH₄.

Tropical wetlands are a significant but understudied source of methane. Here, methane emissions were measured over three years in a perennial tropical swamp in the Okavango Delta, Botswana, finding phenology was the overarching control of emissions.

The CO2 record at the Amazon Tall Tower Observatory: A new opportunity to study processes on seasonal and inter-annual scales

High-quality atmospheric CO₂  measurements are sparse in Amazonia, but can provide critical insights into the spatial and temporal variability of sources and sinks of CO₂ . In this study, we present the first 6 years (2014-2019) of continuous, high-precision measurements of atmospheric CO₂ at the Amazon Tall Tower Observatory (ATTO, 2.1°S, 58.9°W). After subtracting the simulated background concentrations from our observational record, we define a CO₂ regional signal ( ΔCO2obs ) that has a marked seasonal cycle with an amplitude of about 4 ppm. At both seasonal and inter-annual scales, we find differences in phase between ΔCO2obs and the local eddy covariance net ecosystem exchange (EC-NEE), which is interpreted as an indicator of a decoupling between local and non-local drivers of ΔCO2obs . In addition, we present how the 2015-2016 El Niño-induced drought was captured by our atmospheric record as a positive 2σ anomaly in both the wet and dry season of 2016. Furthermore, we analyzed the observed seasonal cycle and inter-annual variability of ΔCO2obs together with net ecosystem exchange (NEE) using a suite of modeled flux products representing biospheric and aquatic CO₂ exchange. We use both non-optimized and optimized (i.e., resulting from atmospheric inverse modeling) NEE fluxes as input in an atmospheric transport model (STILT). The observed shape and amplitude of the seasonal cycle was captured neither by the simulations using the optimized fluxes nor by those using the diagnostic Vegetation and Photosynthesis Respiration Model (VPRM). We show that including the contribution of CO₂ from river evasion improves the simulated shape (not the magnitude) of the seasonal cycle when using a data-driven non-optimized NEE product (FLUXCOM). The simulated contribution from river evasion was found to be 25% of the seasonal cycle amplitude. Our study demonstrates the importance of the ATTO record to better understand the Amazon carbon cycle at various spatial and temporal scales.

A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

Relationships among productivity, leaf phenology, and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests, which contribute 25% of terrestrial productivity. On the one hand, as moisture availability declines, trees shed leaves to reduce transpiration and the risk of hydraulic failure. On the other hand, increases in light availability promote the replacement of senescent leaves to increase productivity. Here, we provide a comprehensive framework that relates the seasonality of climate, leaf abscission, and leaf productivity across the evergreen broadleaved tropical/subtropical forest biome. The seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 forests. Where rainfall and light covary positively, litterfall and productivity also covary positively and are always greater in the wetter sunnier season. Where rainfall and light covary negatively, litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate; otherwise productivity is smaller in the drier sunnier season. This framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

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Three climate-phenology regimes are identified across tropical and subtropical forest biomes

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Where light and water limit plant in dry season, litterfall and productivity peak in sunny wet season

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Where light or water alternately limits plant, productivity peaks in wet season with low litterfall

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Where water does not limit plant, litterfall and productivity peak in sunny dry season

Rapid estimation of photosynthetic leaf traits of tropical plants in diverse environmental conditions using reflectance spectroscopy

Tropical forests are one of the main carbon sinks on Earth, but the magnitude of CO₂ absorbed by tropical vegetation remains uncertain. Terrestrial biosphere models (TBMs) are commonly used to estimate the CO₂ absorbed by forests, but their performance is highly sensitive to the parameterization of processes that control leaf-level CO₂ exchange. Direct measurements of leaf respiratory and photosynthetic traits that determine vegetation CO₂ fluxes are critical, but traditional approaches are time-consuming. Reflectance spectroscopy can be a viable alternative for the estimation of these traits and, because data collection is markedly quicker than traditional gas exchange, the approach can enable the rapid assembly of large datasets. However, the application of spectroscopy to estimate photosynthetic traits across a wide range of tropical species, leaf ages and light environments has not been extensively studied. Here, we used leaf reflectance spectroscopy together with partial least-squares regression (PLSR) modeling to estimate leaf respiration (R_dark25), the maximum rate of carboxylation by the enzyme Rubisco (V_cmax25), the maximum rate of electron transport (J_max25), and the triose phosphate utilization rate (T_p25), all normalized to 25°C. We collected data from three tropical forest sites and included leaves from fifty-three species sampled at different leaf phenological stages and different leaf light environments. Our resulting spectra-trait models validated on randomly sampled data showed good predictive performance for V_cmax25, J_max25, T_p25 and R_dark25 (RMSE of 13, 20, 1.5 and 0.3 μmol m^-2 s^-1, and R² of 0.74, 0.73, 0.64 and 0.58, respectively). The models showed similar performance when applied to leaves of species not included in the training dataset, illustrating that the approach is robust for capturing the main axes of trait variation in tropical species. We discuss the utility of the spectra-trait and traditional gas exchange approaches for enhancing tropical plant trait studies and improving the parameterization of TBMs.

Thermal optima of gross primary productivity are closely aligned with mean air temperatures across Australian wooded ecosystems

Gross primary productivity (GPP) of wooded ecosystems (forests and savannas) is central to the global carbon cycle, comprising 67%-75% of total global terrestrial GPP. Climate change may alter this flux by increasing the frequency of temperatures beyond the thermal optimum of GPP (T_opt ). We examined the relationship between GPP and air temperature (Ta) in 17 wooded ecosystems dominated by a single plant functional type (broadleaf evergreen trees) occurring over a broad climatic gradient encompassing five ecoregions across Australia ranging from tropical in the north to Mediterranean and temperate in the south. We applied a novel boundary-line analysis to eddy covariance flux observations to (a) derive ecosystem GPP-Ta relationships and T_opt (including seasonal analyses for five tropical savannas); (b) quantitatively and qualitatively assess GPP-Ta relationships within and among ecoregions; (c) examine the relationship between T_opt and mean daytime air temperature (MDTa) across all ecosystems; and (d) examine how down-welling short-wave radiation (Fsd) and vapour pressure deficit (VPD) influence the GPP-Ta relationship. GPP-Ta relationships were convex parabolas with narrow curves in tropical forests, tropical savannas (wet season), and temperate forests, and wider curves in temperate woodlands, Mediterranean woodlands, and tropical savannas (dry season). Ecosystem T_opt ranged from 15℃ (temperate forest) to 32℃ (tropical savanna-wet and dry seasons). The shape of GPP-Ta curves was largely determined by daytime Ta range, MDTa, and maximum GPP with the upslope influenced by Fsd and the downslope influenced by VPD. Across all ecosystems, there was a strong positive linear relationship between T_opt and MDTa (Adjusted R² : 0.81; Slope: 1.08) with T_opt exceeding MDTa by >1℃ at all but two sites. We conclude that ecosystem GPP has adjusted to local MDTa within Australian broadleaf evergreen forests and that GPP is buffered against small Ta increases in the majority of these ecosystems.

Seasonal changes in GPP/SIF ratios and their climatic determinants across the Northern Hemisphere

Satellite-derived sun-induced chlorophyll fluorescence (SIF) has been increasingly used for estimating gross primary production (GPP). However, the relationship between SIF and GPP has not been well defined, impeding the translation of satellite observed SIF to GPP. Previous studies have generally assumed a linear relationship between SIF and GPP at daily and longer time scales, but support for this assumption is lacking. Here, we used the GPP/SIF ratio to investigate seasonal variations in the relationship between SIF and GPP over the Northern Hemisphere (NH). Based on multiple SIF products and MODIS and FLUXCOM GPP data, we found strong seasonal hump-shaped patterns for the GPP/SIF ratio over northern latitudes, with higher values in the summer than in the spring or autumn. This hump-shaped GPP/SIF seasonal variation was confirmed by examining different SIF products and was evident for most vegetation types except evergreen broadleaf forests. The seasonal amplitude of the GPP/SIF ratio decreased from the boreal/arctic region to drylands and the tropics. For most of the NH, the lowest GPP/SIF values occurred in October or September, while the maximum GPP/SIF values were evident in June and July. The most pronounced seasonal amplitude of GPP/SIF occurred in intermediate temperature and precipitation ranges. GPP/SIF was positively related to temperature in the early and late parts of the growing season, but not during the peak growing months. These shifting relationships between temperature and GPP/SIF across different months appeared to play a key role in the seasonal dynamics of GPP/SIF. Several mechanisms may explain the patterns we observed, and future research encompassing a broad range of climate and vegetation settings is needed to improve our understanding of the spatial and temporal relationships between SIF and GPP. Nonetheless, the strong seasonal variation in GPP/SIF we identified highlights the importance of incorporating this behavior into SIF-based GPP estimations.

Spectroscopy outperforms leaf trait relationships for predicting photosynthetic capacity across different forest types

Leaf trait relationships are widely used to predict ecosystem function in terrestrial biosphere models (TBMs), in which leaf maximum carboxylation capacity (V_c,max ), an important trait for modelling photosynthesis, can be inferred from other easier-to-measure traits. However, whether trait-V_c,max relationships are robust across different forest types remains unclear. Here we used measurements of leaf traits, including one morphological trait (leaf mass per area), three biochemical traits (leaf water content, area-based leaf nitrogen content, and leaf chlorophyll content), one physiological trait (V_c,max ), as well as leaf reflectance spectra, and explored their relationships within and across three contrasting forest types in China. We found weak and forest type-specific relationships between V_c,max and the four morphological and biochemical traits (R²  ≤ 0.15), indicated by significantly changing slopes and intercepts across forest types. By contrast, reflectance spectroscopy effectively collapsed the differences in the trait-V_c,max relationships across three forest biomes into a single robust model for V_c,max (R²  = 0.77), and also accurately estimated the four traits (R²  = 0.75-0.94). These findings challenge the traditional use of the empirical trait-V_c,max relationships in TBMs for estimating terrestrial plant photosynthesis, but also highlight spectroscopy as an efficient alternative for characterising V_c,max and multitrait variability, with critical insights into ecosystem modelling and functional trait ecology.

Improving modeling of ecosystem gross primary productivity through re-optimizing temperature restrictions on photosynthesis

The terrestrial ecosystem gross primary productivity (GPP) plays an important role in the global carbon cycle and ecosystem functions. However, the estimates of GPP still have large uncertainties due to insufficient understanding of the photosynthesis-temperature relationship and maximum light use efficiency (LUE_max). We used satellite-derived proxies of GPP to derive optimum, minimum, and maximum temperature for photosynthesis at the ecosystem scale, which was then used to construct a new temperature stress expression. This study improves the MODIS-based light use efficiency model through coupling the optimized LUE_max with the new proposed temperature stress expression. The new model (R² = 0.81, RMSE = 17.8 gC m^-2 (16 d)^-1) performed better than the MODIS GPP products (R² = 0.67, RMSE = 30.4 gC m^-2 (16 d)^-1), especially for evergreen broadleaf forests and croplands. The mean annual GPP over China is 5.7 ± 0.27 PgC, and the GPP significantly increased by 0.046 ± 0.006 PgC year^-1 during 2001-2018. This study provides a potential method for future projections of terrestrial ecosystem functioning.