Age-dependent leaf physiology and consequences for crown-scale carbon uptake during the dry season in an Amazon evergreen forest

Seasonal variations of leaf ecophysiological traits and strategies of co-occurring evergreen and deciduous trees in white oak forest in the central Himalaya

The present study investigates the seasonal variations in leaf ecophysiological traits and strategies employed by co-occurring evergreen and deciduous tree species within a white oak forest (Quercus leucotrichophora A. Camus) ecosystem in the central Himalaya. Seasonal variations in physiological, morphological, and chemical traits were observed from leaf initiation until senescence in co-occurring deciduous and evergreen tree species. We compared various parameters, including net photosynthetic capacity (Aarea and Amass), leaf stomatal conductance (gswarea and gswmass), transpiration rate (Earea and Emass), specific leaf area (SLA), mid-day water potential (Ψmd), leaf nitrogen (N) and phosphorus (P) concentration, leaf total chlorophyll concentration, photosynthetic nitrogen- and phosphorus-use efficiency (PNUE and PPUE), and water use efficiency (WUE) across four evergreen and four deciduous tree species. Our findings reveal that evergreen and deciduous trees exhibit divergent strategies in coping with seasonal changes, which are crucial for their survival and growth. Deciduous trees consistently exhibited significantly higher photosynthetic rates, transpiration rates, mass-based N and P concentrations (Nmass and Pmass), mass-based chlorophyll concentration (Chlmass), SLA, and leaf Ψmd, while maintaining lower leaf structural investments throughout the year compared to evergreen trees. These findings indicate that deciduous trees achieve greater assimilation rates per unit mass and higher nutrient-use efficiency. Physiological, morphological, and leaf N and P concentrations were higher in the summer (fully expanded leaf) than in the fall (senesced leaf). These insights provide valuable contributions to our understanding of tree species coexistence and their ecological roles in temperate forest ecosystems, with implications for forest management and conservation in the Himalayan region.

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.

Functional traits and trait coordination change over the life of a leaf in a tropical fern species

PREMISE

Plant ecological strategies are often defined by the integration of underlying traits related to resource acquisition, allocation, and growth. Correlations between key traits across diverse plants suggest that variation in plant ecological strategies is largely driven by a fast-slow continuum of plant economics. However, trait correlations may not be constant through the life of a leaf, and it is still poorly understood how trait function varies over time in long-lived leaves.

METHODS

Here, we compared trait correlations related to resource acquisition and allocation across three different mature frond age cohorts in a tropical fern species, Saccoloma inaequale.

RESULTS

Fronds exhibited high initial investments of nitrogen and carbon, but with declining return in photosynthetic capacity after the first year. In the youngest fronds, we found water-use efficiency to be significantly lower than in the oldest mature fronds due to increased transpiration rates. Our data suggest that middle-aged fronds are more efficient relative to younger, less water-use efficient fronds and that older fronds exhibit greater nitrogen investments without higher photosynthetic return. In addition, several trait correlations expected under the leaf economics spectrum (LES) do not hold within this species, and some trait correlations only appear in fronds of a specific developmental age.

CONCLUSIONS

These findings contextualize the relationship between traits and leaf developmental age with those predicted to underlie plant ecological strategy and the LES and are among the first pieces of evidence for when relative physiological trait efficiency is maximized in a tropical fern species.

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.

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.

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.

Transcription Profiles Reveal Age-Dependent Variations of Photosynthetic Properties and Sugar Metabolism in Grape Leaves (Vitis vinifera L.)

Leaves, considered as the ‘source’ organs, depend on the development stages because of the age-dependent photosynthesis and assimilation of leaves. However, the molecular mechanisms of age-dependent limitations on the function of leaves are seldom reported. In the present study, the photosynthesis-related characteristics and photoassimilates were investigated in grape leaves at six different age groups (Ll to L6) at micro-morphological, biochemical, and molecular levels. These results showed lower expression levels of genes associated with stomatal development, and chl biosynthesis resulted in fewer stomata and lowered chlorophyll a/b contents in L1 when compared to L3 and L5. The DEGs between L5 and L3/L1 were largely distributed at stomatal movement, carbon fixation, and sucrose and starch metabolism pathways, such as STOMATAL ANION CHANNEL PROTEIN 1 (SLAC1), FRUCTOSE-1,6-BISPHOSPHATE ALDOLASE (FBA1), SUCROSE-PHOSPHATE SYNTHASE (SPP1), and SUCROSE-PHOSPHATE PHOSPHATASE (SPS2, 4). These genes could be major candidate genes leading to increased photosynthesis capacity and sugar content in L5. The accumulation of starch grains in the chloroplast and palisade tissue of L5 and higher transcription levels of genes related to starch biosynthesis in L5 further supported the high ability of L5 to produce photoassimilates. Hence, our results provide insights for understanding different photosynthetic functions in age-dependent leaves in grape plants at the molecular level.

Local hydrological gradients structure high intraspecific variability in plant hydraulic traits in two dominant central Amazonian tree species

Addressing the intraspecific variability of functional traits helps understand how climate change might influence the distribution of organismal traits across environments, but this is notably understudied in the Amazon, especially for plant hydraulic traits commonly used to project drought responses. We quantified the intraspecific trait variability of leaf mass per area, wood density, and xylem embolism resistance for two dominant central Amazonian tree species, along gradients of water and light availability, while accounting for tree age and height. Intraspecific variability in hydraulic traits was high, with within-species variability comparable to the whole-community variation. Hydraulic trait variation was modulated mostly by the hydrological environment, with higher embolism resistance of trees growing on deep-water-table plateaus compared with shallow-water-table valleys. Intraspecific variability of leaf mass per area and wood density was mostly modulated by intrinsic factors and light. The different environmental and intrinsic drivers of variation among and within individuals lead to an uncoupled coordination among carbon acquisition/conservation and water-use traits. Our findings suggest multivariate ecological strategies driving tropical tree distributions even within species, and reflect differential within-population sensitivities along environmental gradients. Therefore, intraspecific trait variability must be considered for accurate predictions of the responses of tropical forests to climate change.

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.

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.

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.

Including leaf trait information helps empirical estimation of jmax from vcmax in cool-temperate deciduous forests

Understanding the uncertainty in the parameterization of the two photosynthetic capacity parameters, leaf maximum carboxylation rate (Vcmax), and maximum electron transport rate (Jmax), is crucial for modeling and predicting carbon fluxes in terrestrial ecosystems. In gas exchange models, to date, Jmax is typically estimated from Vcmax based on a linear regression. However, recent studies have revealed that this relationship varies, dependent upon species, leaf groups, and time, so it is doubtful that the regression applies universally. Furthermore, far less is known regarding how other leaf traits affect the regression. In this study we analyzed the two key photosynthetic parameters and popularly measurable leaf traits, leaf chlorophyll concentration and leaf mass per area (LMA), of cool-temperate forest stands in Japan, aiming to construct a simple regression applicable to temperate deciduous forests, at least. The analysis was based on a long-term field dataset covering years of data for both sunlit and shaded leaves at different altitudes. Results showed that the best-fitted slope of the regression differed markedly from those previously reported, which were typically acquired from sunlit leaves. LMA had a significant effect on the regression, producing the lowest root mean square errors and the highest ratio of performance to deviation values (RPD = 2.017). Although more data are needed to validate in other ecosystems, our approach at least provides a promising way to substantially improve photosynthesis model predictions, by introducing leaf traits into the popular empirical regression of Jmax against Vcmax, and ultimately to better understand the functioning of the photosynthetic machinery.

Accurate Simulation of Both Sensitivity and Variability for Amazonian Photosynthesis: Is It Too Much to Ask?

Estimates of Amazon rainforest gross primary productivity (GPP) differ by a factor of 2 across a suite of three statistical and 18 process models. This wide spread contributes uncertainty to predictions of future climate. We compare the mean and variance of GPP from these models to that of GPP at six eddy covariance (EC) towers. Only one model's mean GPP across all sites falls within a 99% confidence interval for EC GPP, and only one model matches EC variance. The strength of model response to climate drivers is related to model ability to match the seasonal pattern of the EC GPP. Models with stronger seasonal swings in GPP have stronger responses to rain, light, and temperature than does EC GPP. The model to data comparison illustrates a trade-off inherent to deterministic models between accurate simulation of a mean (average) and accurate responsiveness to drivers. The trade-off exists because all deterministic models simplify processes and lack at least some consequential driver or interaction. If a model's sensitivities to included drivers and their interactions are accurate, then deterministically predicted outcomes have less variability than is realistic. If a GPP model has stronger responses to climate drivers than found in data, model predictions may match the observed variance and seasonal pattern but are likely to overpredict GPP response to climate change. High or realistic variability of model estimates relative to reference data indicate that the model is hypersensitive to one or more drivers.

The transcriptome landscapes of citrus leaf in different developmental stages

The temporal expression profiles of citrus leaves explain the sink-source transition of immature leaves to mature leaves and provide knowledge regarding the differential responses of mature and immature leaves to biotic stress such as citrus canker and Asian citrus psyllid (Diaphorina citri). Citrus is an important fruit crop worldwide. Different developmental stages of citrus leaves are associated with distinct features, such as differences in susceptibilities to pathogens and insects, as well as photosynthetic capacity. Here, we investigated the mechanisms underlying these distinctions by comparing the gene expression profiles of mature and immature citrus leaves. Immature (stages V3 and V4), transition (stage V5), and mature (stage V6) Citrus sinensis leaves were chosen for RNA-seq analyses. Carbohydrate biosynthesis, photosynthesis, starch biosynthesis, and disaccharide metabolic processes were enriched among the upregulated differentially expressed genes (DEGs) in the V5 and V6 stages compared with that in the V3 and V4 stages. Glucose level was found to be higher in V5 and V6 than in V3 and V4. Among the four stages, the largest number of DEGs between contiguous stages were identified between V5 and V4, consistent with a change from sink to source, as well as with the sucrose and starch quantification data. The differential expression profiles related to cell wall synthesis, secondary metabolites such as flavonoids and terpenoids, amino acid biosynthesis, and immunity between immature and mature leaves may contribute to their different responses to Asian citrus psyllid infestation. The expression data suggested that both the constitutive and induced gene expression of immunity-related genes plays important roles in the greater resistance of mature leaves against Xanthomonas citri compared with immature leaves. The gene expression profiles in the different stages can help identify stage-specific promoters for the manipulation of the expression of citrus traits according to the stage. The temporal expression profiles explain the sink-source transition of immature leaves to mature leaves and provide knowledge regarding the differential responses to biotic stress.

Understanding water and energy fluxes in the Amazonia: Lessons from an observation-model intercomparison

Tropical forests are an important part of global water and energy cycles, but the mechanisms that drive seasonality of their land-atmosphere exchanges have proven challenging to capture in models. Here, we (1) report the seasonality of fluxes of latent heat (LE), sensible heat (H), and outgoing short and longwave radiation at four diverse tropical forest sites across Amazonia-along the equator from the Caxiuanã and Tapajós National Forests in the eastern Amazon to a forest near Manaus, and from the equatorial zone to the southern forest in Reserva Jaru; (2) investigate how vegetation and climate influence these fluxes; and (3) evaluate land surface model performance by comparing simulations to observations. We found that previously identified failure of models to capture observed dry-season increases in evapotranspiration (ET) was associated with model overestimations of (1) magnitude and seasonality of Bowen ratios (relative to aseasonal observations in which sensible was only 20%-30% of the latent heat flux) indicating model exaggerated water limitation, (2) canopy emissivity and reflectance (albedo was only 10%-15% of incoming solar radiation, compared to 0.15%-0.22% simulated), and (3) vegetation temperatures (due to underestimation of dry-season ET and associated cooling). These partially compensating model-observation discrepancies (e.g., higher temperatures expected from excess Bowen ratios were partially ameliorated by brighter leaves and more interception/evaporation) significantly biased seasonal model estimates of net radiation (R_n ), the key driver of water and energy fluxes (LE ~ 0.6 R_n and H ~ 0.15 R_n ), though these biases varied among sites and models. A better representation of energy-related parameters associated with dynamic phenology (e.g., leaf optical properties, canopy interception, and skin temperature) could improve simulations and benchmarking of current vegetation-atmosphere exchange and reduce uncertainty of regional and global biogeochemical models.

Unique contributions of chlorophyll and nitrogen to predict crop photosynthetic capacity from leaf spectroscopy

The photosynthetic capacity or the CO2-saturated photosynthetic rate (Vmax), chlorophyll, and nitrogen are closely linked leaf traits that determine C4 crop photosynthesis and yield. Accurate, timely, rapid, and non-destructive approaches to predict leaf photosynthetic traits from hyperspectral reflectance are urgently needed for high-throughput crop monitoring to ensure food and bioenergy security. Therefore, this study thoroughly evaluated the state-of-the-art physically based radiative transfer models (RTMs), data-driven partial least squares regression (PLSR), and generalized PLSR (gPLSR) models to estimate leaf traits from leaf-clip hyperspectral reflectance, which was collected from maize (Zea mays L.) bioenergy plots with diverse genotypes, growth stages, treatments with nitrogen fertilizers, and ozone stresses in three growing seasons. The results show that leaf RTMs considering bidirectional effects can give accurate estimates of chlorophyll content (Pearson correlation r=0.95), while gPLSR enabled retrieval of leaf nitrogen concentration (r=0.85). Using PLSR with field measurements for training, the cross-validation indicates that Vmax can be well predicted from spectra (r=0.81). The integration of chlorophyll content (strongly related to visible spectra) and nitrogen concentration (linked to shortwave infrared signals) can provide better predictions of Vmax (r=0.71) than only using either chlorophyll or nitrogen individually. This study highlights that leaf chlorophyll content and nitrogen concentration have key and unique contributions to Vmax prediction.

An optimality-based model explains seasonal variation in C3 plant photosynthetic capacity

The maximum rate of carboxylation (V_cmax ) is an essential leaf trait determining the photosynthetic capacity of plants. Existing approaches for estimating V_cmax at large scale mainly rely on empirical relationships with proxies such as leaf nitrogen/chlorophyll content or hyperspectral reflectance, or on complicated inverse models from gross primary production or solar-induced fluorescence. A novel mechanistic approach based on the assumption that plants optimize resource investment coordinating with environment and growth has been shown to accurately predict C3 plant V_cmax based on mean growing season environmental conditions. However, the ability of optimality theory to explain seasonal variation in V_cmax has not been fully investigated. Here, we adapt an optimality-based model to simulate daily V_cmax,25C (V_cmax at a standardized temperature of 25°C) by incorporating the effects of antecedent environment, which affects current plant functioning, and dynamic light absorption, which coordinates with plant functioning. We then use seasonal V_cmax,25C field measurements from 10 sites across diverse ecosystems to evaluate model performance. Overall, the model explains about 83% of the seasonal variation in C3 plant V_cmax,25C across the 10 sites, with a medium root mean square error of 12.3 μmol m^-2  s^-1 , which suggests that seasonal changes in V_cmax,25C are consistent with optimal plant function. We show that failing to account for acclimation to antecedent environment or coordination with dynamic light absorption dramatically decreases estimation accuracy. Our results show that optimality-based approach can accurately reproduce seasonal variation in canopy photosynthetic potential, and suggest that incorporating such theory into next-generation trait-based terrestrial biosphere models would improve predictions of global photosynthesis.

Amazon rainforest photosynthesis increases in response to atmospheric dryness

Earth system models predict that increases in atmospheric and soil dryness will reduce photosynthesis in the Amazon rainforest, with large implications for the global carbon cycle. Using in situ observations, solar-induced fluorescence, and nonlinear machine learning techniques, we show that, in reality, this is not necessarily the case: In many of the wettest parts of this region, photosynthesis and biomass tend to increase with increased atmospheric dryness, despite the associated reductions in canopy conductance to CO₂ These results can be largely explained by changes in canopy properties, specifically, new leaves flushed during the dry season have higher photosynthetic capacity than the leaves they replace, compensating for the negative stomatal response to increased dryness. As atmospheric dryness will increase with climate change, our study highlights the importance of reframing how we represent the response of ecosystem photosynthesis to atmospheric dryness in very wet regions, to accurately quantify the land carbon sink.

Optimal leaf life strategies determine Vc,max dynamic during ontogeny

Leaf photosynthetic properties, for example the maximum carboxylation velocity or V_c,max , change with leaf age due to ontogenetic processes. This study introduces an optimal dynamic allocation scheme to model changes in leaf-level photosynthetic capacity as a function of leaf biochemical constraints (costs of synthesis and defence), nitrogen availability and other environmental factors (e.g. light). The model consists of a system of equations describing RuBisCO synthesis and degradation within chloroplasts, defence and ageing at leaf levels, nitrogen transfer and carbon budget at plant levels. Model results show that optimal allocation principles explained RuBisCO dynamics with leaf age. An approximated analytical solution can reproduce the basic pattern of RuBisCO and V_c,max in rice and in two tropical tree species. The model also reveals leaf life complementarities that remained unexplained in previous approaches, as the interplay between V_c,max at maturation, life span and the decline in photosynthetic capacity with age. Furthermore, it explores the role of defence, which is not implemented in current models. This framework covers some of the existing gaps in integrating multiple processes across plant organs (chloroplast, leaf and whole plant) and is a first-step towards representing mechanistically leaf ontogenetic processes into physiological and ecosystem models.

Acclimation of leaf respiration consistent with optimal photosynthetic capacity

Plant respiration is an important contributor to the proposed positive global carbon-cycle feedback to climate change. However, as a major component, leaf mitochondrial ('dark') respiration (Rd ) differs among species adapted to contrasting environments and is known to acclimate to sustained changes in temperature. No accepted theory explains these phenomena or predicts its magnitude. Here we propose that the acclimation of Rd follows an optimal behaviour related to the need to maintain long-term average photosynthetic capacity (Vcmax ) so that available environmental resources can be most efficiently used for photosynthesis. To test this hypothesis, we extend photosynthetic co-ordination theory to predict the acclimation of Rd to growth temperature via a link to Vcmax , and compare predictions to a global set of measurements from 112 sites spanning all terrestrial biomes. This extended co-ordination theory predicts that field-measured Rd and Vcmax accessed at growth temperature (Rd,tg and Vcmax,tg ) should increase by 3.7% and 5.5% per degree increase in growth temperature. These acclimated responses to growth temperature are less steep than the corresponding instantaneous responses, which increase 8.1% and 9.9% per degree of measurement temperature for Rd and Vcmax respectively. Data-fitted responses proof indistinguishable from the values predicted by our theory, and smaller than the instantaneous responses. Theory and data are also shown to agree that the basal rates of both Rd and Vcmax assessed at 25°C (Rd,25 and Vcmax,25 ) decline by ~4.4% per degree increase in growth temperature. These results provide a parsimonious general theory for Rd acclimation to temperature that is simpler-and potentially more reliable-than the plant functional type-based leaf respiration schemes currently employed in most ecosystem and land-surface models.

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

A clear understanding of the dynamics of photosynthetic capacity is crucial for accurate modeling of ecosystem carbon uptake. However, such dynamical information is hardly available and has dramatically impeded our understanding of carbon cycles. Although tremendous efforts have been made in coupling the dynamic information of photosynthetic capacity into models, using “proxies” rooted from the close relationships between photosynthetic capacity and other available leaf parameters remains the popular selection. Unfortunately, no consensus has yet been reached on such “proxies”, leading them only applicable to limited cases. In this study, we aim to identify if there are close relationships between the photosynthetic capacity (represented by the maximum carboxylation rate, Vcmax) and leaf traits for mature broadleaves within a cold temperature deciduous forest. This is based on a long-term in situ dataset including leaf chlorophyll content (Chl), leaf nitrogen concentration (Narea, Nmass), leaf carbon concentration (Carea, Cmass), equivalent water thickness (EWT), leaf mass per area (LMA), and leaf gas exchange measurements from which Vcmax was derived, for both sunlit and shaded leaves during leaf mature periods from 2014 to 2019. The results show that the Vcmax values of sunlit and shaded leaves were relatively stable during these periods, and no statistically significant interannual variations occurred (p > 0.05). However, this is not applicable to specific species. Path analysis revealed that Narea was the major contributor to Vcmax for sunlit leaves (0.502), while LMA had the greatest direct relationship with Vcmax for shaded leaves (0.625). The LMA has further been confirmed as a primary proxy if no leaf type information is available. These findings provide a promising way to better understand photosynthesis and to predict carbon and water cycles in temperate deciduous forests.

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO₂ uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (g_s ) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and leaf water potential (Ψ_leaf ), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (V_c,max ), leaf water content, the degree of isohydry, and predawn Ψ_leaf . We first investigated how the choice of four commonly used leaf-level g_s models with and without the inclusion of Ψ_leaf as an additional predictor variable influence the ability to predict g_s , and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of Ψ_leaf did not improve model performance and that the models that represent the response of g_s to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each g_s model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R²  = 0.66, n = 15), highlighting a potential path towards improved model parameterization. This study advances understanding of g_s dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

Cryptic phenology in plants: Case studies, implications, and recommendations

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Leaf reflectance spectroscopy captures variation in carboxylation capacity across species, canopy environment and leaf age in lowland moist tropical forests

Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (V_c,max ) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting V_c,max is still lacking. Here the ability of leaf spectroscopy for rapid estimation of V_c,max was tested. V_c,max was estimated using traditional gas exchange methods, and measured reflectance spectra and leaf age in leaves sampled from tropical forests in Panama and Brazil. These data were used to build a model to predict V_c,max from leaf spectra. The results demonstrated that leaf spectroscopy accurately predicts V_c,max of mature leaves in Panamanian tropical forests (R²  = 0.90). However, this single-age model required recalibration when applied to broader leaf demographic classes (i.e. immature leaves). Combined use of spectroscopy models for V_c,max and leaf age enabled construction of the V_c,max -age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in V_c,max , assuming sufficient sampling across diverse species, leaf ages and canopy environments. This finding will aid development of remote sensing approaches that can be used to characterize V_c,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models.

Seasonal and drought-related changes in leaf area profiles depend on height and light environment in an Amazon forest

Seasonal dynamics in the vertical distribution of leaf area index (LAI) may impact the seasonality of forest productivity in Amazonian forests. However, until recently, fine-scale observations critical to revealing ecological mechanisms underlying these changes have been lacking. To investigate fine-scale variation in leaf area with seasonality and drought we conducted monthly ground-based LiDAR surveys over 4 yr at an Amazon forest site. We analysed temporal changes in vertically structured LAI along axes of both canopy height and light environments. Upper canopy LAI increased during the dry season, whereas lower canopy LAI decreased. The low canopy decrease was driven by highly illuminated leaves of smaller trees in gaps. By contrast, understory LAI increased concurrently with the upper canopy. Hence, tree phenological strategies were stratified by height and light environments. Trends were amplified during a 2015-2016 severe El Niño drought. Leaf area low in the canopy exhibited behaviour consistent with water limitation. Leaf loss from short trees in high light during drought may be associated with strategies to tolerate limited access to deep soil water and stressful leaf environments. Vertically and environmentally structured phenological processes suggest a critical role of canopy structural heterogeneity in seasonal changes in Amazon ecosystem function.