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Summary: 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 (gs) 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 gs 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. [ABSTRACT FROM AUTHOR]

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Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs) 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 (Ψleaf), 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 (Vc,max), leaf water content, the degree of isohydry, and predawn Ψleaf. We first investigated how the choice of four commonly used leaf‐level gs models with and without the inclusion of Ψleaf as an additional predictor variable influence the ability to predict gs, 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 Ψleaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs 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 gs dynamics over seasonal drought, and identifies a practical, trait‐based approach to improve modeling of carbon and water exchange in tropical forests. [ABSTRACT FROM AUTHOR]

Summary: Understanding the pronounced seasonal and spatial variation in leaf carboxylation capacity (Vc,max) is critical for determining terrestrial carbon cycling in tropical forests. However, an efficient and scalable approach for predicting Vc,max is still lacking.Here the ability of leaf spectroscopy for rapid estimation of Vc,max was tested. Vc,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 Vc,max from leaf spectra.The results demonstrated that leaf spectroscopy accurately predicts Vc,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 Vc,max and leaf age enabled construction of the Vc,max–age relationship solely from leaf spectra, which agreed with field observations. This suggests that the spectroscopy technique can capture the seasonal variability in Vc,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 Vc,max in moist tropical forests and enable an efficient means to parameterize and evaluate terrestrial biosphere models. [ABSTRACT FROM AUTHOR]

Nonstructural carbohydrates (NSCs) are essential for maintenance of plant metabolism and may be sensitive to short‐ and long‐term climatic variation. NSC variation in moist tropical forests has rarely been studied, so regulation of NSCs in these systems is poorly understood. We measured foliar and branch NSC content in 23 tree species at three sites located across a large precipitation gradient in Panama during the 2015–2016 El Niño to examine how short‐ and long‐term climatic variation impact carbohydrate dynamics. There was no significant difference in total NSCs as the drought progressed (leaf P = 0.32, branch P = 0.30) nor across the rainfall gradient (leaf P = 0.91, branch P = 0.96). Foliar soluble sugars decreased while starch increased over the duration of the dry period, suggesting greater partitioning of NSCs to storage than metabolism or transport as drought progressed. There was a large variation across species at all sites, but total foliar NSCs were positively correlated with leaf mass per area, whereas branch sugars were positively related to leaf temperature and negatively correlated with daily photosynthesis and wood density. The NSC homoeostasis across a wide range of conditions suggests that NSCs are an allocation priority in moist tropical forests. Nonstructural carbohydrates (NSCs), the organic compounds that drive plant metabolism, have rarely been studied in moist tropical forests, so their regulation in these systems is poorly understood. We measured foliar and branch NSCs in canopy trees across a large precipitation gradient in Panama during the 2015–2016 El Niño to examine how short‐ and long‐term climatic variation impact carbohydrate dynamics. There was a large variation in NSCs across species; however, there was no change in total NSCs as the drought progressed nor across the rainfall gradient, suggesting that NSCs are an allocation priority in moist tropical forests. [ABSTRACT FROM AUTHOR]

Trees generally maintain a small safety margin between the stem water potential (Ψstem) reached during seasonal droughts and the Ψstem associated with their mortality. This pattern may indicate that species face similar mortality risk during extreme droughts. However, if tree species vary in their ability to regulate Ψstem, then safety margins would poorly predict drought mortality. To explore variation among species in Ψstem regulation, I subjected potted saplings of six tropical tree species to extreme drought and compared their responses with well-watered plants and pretreatment reference plants. In the drought treatment, soil water potential reached <-10 MPa, yet three species, Bursera simaruba (L.) Sarg., Cavanillesia platanifolia (Bonpl.) Kunth and Cedrela odorata L. had 100% survival and maintained Ψstem near -1 MPa (i.e., desiccation-avoiding species). Three other species, Cojoba rufescens (Benth.) Britton and Rose, Genipa americana L. and Hymenaea courbaril L. had 50%, 0% and 25% survival, respectively, and survivors had Ψstem <-6 MPa (i.e., desiccation-susceptible species). The desiccation-avoiding species had lower relative water content (RWC) in all organs and tissues (root, stem, bark and xylem) in the drought treatment than in the reference plants (means 72.0-90.4% vs 86.9-97.9%), but the survivors of the desiccation-susceptible C. rufescens had much lower RWC in the drought treatment (44.5-72.1%). Among the reference plants, the desiccation-avoiding species had lower tissue density, leaf-mass fraction and lateral-root surface area (LRA) than the desiccation-susceptible species. Additionally, C. platanifolia and C. odorata had reduced LRA in the drought treatment, which may slow water loss into dry soil. Together, these results suggest that the ability to regulate Ψstem during extreme drought is associated with functional traits that favor retention of stored water and that safety margins during seasonal drought poorly predict survival during extreme drought. [ABSTRACT FROM AUTHOR]

Tropical leaf phenology—particularly its variability at the tree-crown scale—dominates the seasonality of carbon and water fluxes. However, given enormous species diversity, accurate means of monitoring leaf phenology in tropical forests is still lacking. Time series of the Green Chromatic Coordinate (GCC) metric derived from tower-based red–greenblue (RGB) phenocams have been widely used to monitor leaf phenology in temperate forests, but its application in the tropics remains problematic. To improve monitoring of tropical phenology, we explored the use of a deep learning model (i.e. superpixel-based Residual Networks 50, SP-ResNet50) to automatically differentiate leaves from non-leaves in phenocam images and to derive leaf fraction at the tree-crown scale. To evaluate our model, we used a year of data from six phenocams in two contrasting forests in Panama. We first built a comprehensive library of leaf and non-leaf pixels across various acquisition times, exposure conditions and specific phenocams. We then divided this library into training and testing components. We evaluated the model at three levels: 1) superpixel level with a testing set, 2) crown level by comparing the model-derived leaf fractions with those derived using image-specific supervised classification, and 3) temporally using all daily images to assess the diurnal stability of the model-derived leaf fraction. Finally, we compared the model-derived leaf fraction phenology with leaf phenology derived from GCC. Our results show that: 1) the SP-ResNet50 model accurately differentiates leaves from non-leaves (overall accuracy of 93%) and is robust across all three levels of evaluations; 2) the model accurately quantifies leaf fraction phenology across tree-crowns and forest ecosystems; and 3) the combined use of leaf fraction and GCC helps infer the timing of leaf emergence, maturation and senescence, critical information for modeling photosynthetic seasonality of tropical forests. Collectively, this study offers an improved means for automated tropical phenology monitoring using phenocams. [ABSTRACT FROM AUTHOR]