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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.

Considering the global intensification of aridity in tropical biomes due to climate change, we need to understand what shapes the distribution of drought sensitivity in tropical plants. We conducted a pantropical data synthesis representing 1117 species to test whether xylem-specific hydraulic conductivity (KS), water potential at leaf turgor loss (ΨTLP) and water potential at 50% loss of KS (ΨP50) varied along climate gradients. The ΨTLP and ΨP50 increased with climatic moisture only for evergreen species, but KS did not. Species with high ΨTLP and ΨP50 values were associated with both dry and wet environments. However, drought-deciduous species showed high ΨTLP and ΨP50 values regardless of water availability, whereas evergreen species only in wet environments. All three traits showed a weak phylogenetic signal and a short half-life. These results suggest strong environmental controls on trait variance, which in turn is modulated by leaf habit along climatic moisture gradients in the tropics., Ecology Letters, 25 (12), ISSN:1461-023X, ISSN:1461-0248

Deep-water access is arguably the most effective, but under-studied, mechanism that plants employ to survive during drought. Vulnerability to embolism and hydraulic safety margins can predict mortality risk at given levels of dehydration, but deep-water access may delay plant dehydration. Here, we tested the role of deep-water access in enabling survival within a diverse tropical forest community in Panama using a novel data-model approach. We inversely estimated the effective rooting depth (ERD, as the average depth of water extraction), for 29 canopy species by linking diameter growth dynamics (1990-2015) to vapor pressure deficit, water potentials in the whole-soil column, and leaf hydraulic vulnerability curves. We validated ERD estimates against existing isotopic data of potential water-access depths. Across species, deeper ERD was associated with higher maximum stem hydraulic conductivity, greater vulnerability to xylem embolism, narrower safety margins, and lower mortality rates during extreme droughts over 35years (1981-2015) among evergreen species. Species exposure to water stress declined with deeper ERD indicating that trees compensate for water stress-related mortality risk through deep-water access. The role of deep-water access in mitigating mortality of hydraulically-vulnerable trees has important implications for our predictive understanding of forest dynamics under current and future climates.

The representation of stomatal regulation of transpiration and CO

Leaf mass per area (LMA) is a key plant trait, reflecting tradeoffs between leaf photosynthetic function, longevity, and structural investment. Capturing spatial and temporal variability in LMA has been a long-standing goal of ecological research and is an essential component for advancing Earth system models. Despite the substantial variation in LMA within and across Earth's biomes, an efficient, globally generalizable approach to predict LMA is still lacking. We explored the capacity to predict LMA from leaf spectra across much of the global LMA trait space, with values ranging from 17 to 393 g m-2 . Our dataset contained leaves from a wide range of biomes from the high Arctic to the tropics, included broad- and needleleaf species, and upper- and lower-canopy (i.e. sun and shade) growth environments. Here we demonstrate the capacity to rapidly estimate LMA using only spectral measurements across a wide range of species, leaf age and canopy position from diverse biomes. Our model captures LMA variability with high accuracy and low error (R2 = 0.89; root mean square error (RMSE) = 15.45 g m-2 ). Our finding highlights the fact that the leaf economics spectrum is mirrored by the leaf optical spectrum, paving the way for this technology to predict the diversity of LMA in ecosystems across global biomes.

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.

Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system. publishedVersion

International audience; Transpiration in humid tropical forests modulates the global water cycle and is a key driver of climate regulation. Yet, our understanding of how tropical trees regulate sap flux in response to climate variability remains elusive. With a progressively warming climate, atmospheric evaporative demand [i.e., vapor pressure deficit (VPD)] will be increasingly important for plant functioning, becoming the major control of plant water use in the twenty-first century. Using measurements in 34 tree species at seven sites across a precipitation gradient in the neotropics, we determined how the maximum sap flux velocity (vmax) and the VPD threshold at which vmax is reached (VPDmax) vary with precipitation regime [mean annual precipitation (MAP); seasonal drought intensity (PDRY)] and two functional traits related to foliar and wood economics spectra [leaf mass per area (LMA); wood specific gravity (WSG)]. We show that, even though vmax is highly variable within sites, it follows a negative trend in response to increasing MAP and PDRY across sites. LMA and WSG exerted little effect on vmax and VPDmax, suggesting that these widely used functional traits provide limited explanatory power of dynamic plant responses to environmental variation within hyper-diverse forests. This study demonstrates that long-term precipitation plays an important role in the sap flux response of humid tropical forests to VPD. Our findings suggest that under higher evaporative demand, trees growing in wetter environments in humid tropical regions may be subjected to reduced water exchange with the atmosphere relative to trees growing in drier climates.

Metadata describe the ancillary information needed for data preservation and independent interpretation, comparison across heterogeneous datasets, and quality assessment and quality control (QA/QC). Environmental observations are vastly diverse in type and structure, can be taken across a wide range of spatiotemporal scales in a variety of measurement settings and approaches, and saved in multiple formats. Thus, well-organized, consistent metadata are required to produce usable data products from diverse environmental observations collected across field sites. However, existing metadata reporting protocols do not support the complex data synthesis and model-data integration needs of interdisciplinary earth system research. We developed a metadata reporting framework (FRAMES) to enable management and synthesis of observational data that are essential in advancing a predictive understanding of earth systems. FRAMES utilizes best practices for data and metadata organization enabling consistent data reporting and compatibility with a variety of standardized data protocols. We used an iterative scientist-centered design process to develop FRAMES, resulting in a data reporting format that incorporates existing field practices to maximize data-entry efficiency. Thus, FRAMES has a modular organization that streamlines metadata reporting and can be expanded to incorporate additional data types. With FRAMES's multi-scale measurement position hierarchy, data can be reported at observed spatial resolutions and then easily aggregated and linked across measurement types to support model-data integration. FRAMES is in early use by both data originators (persons generating data) and consumers (persons using data and metadata). In this paper, we describe FRAMES, identify lessons learned, and discuss areas of future development.

Forest leaf area has enormous leverage on the carbon cycle because it mediates both forest productivity and resilience to climate extremes. Despite widespread evidence that trees are capable of adjusting to changes in environment across both space and time through modifying carbon allocation to leaves, many vegetation models use fixed carbon allocation schemes independent of environment, which introduces large uncertainties into predictions of future forest responses to atmospheric CO2 fertilization and anthropogenic climate change. Here, we develop an optimization-based model, whereby tree carbon allocation to leaves is an emergent property of environment and plant hydraulic traits. Using a combination of meta-analysis, observational datasets, and model predictions, we find strong evidence that optimal hydraulic-carbon coupling explains observed patterns in leaf allocation across large environmental and CO2 concentration gradients. Furthermore, testing the sensitivity of leaf allocation strategy to a diversity in hydraulic and economic spectrum physiological traits, we show that plant hydraulic traits in particular have an enormous impact on the global change response of forest leaf area. Our results provide a rigorous theoretical underpinning for improving carbon cycle predictions through advancing model predictions of leaf area, and underscore that tree-level carbon allocation to leaves should be derived from first principles using mechanistic plant hydraulic processes in the next generation of vegetation models.

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 Nino. 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.

During droughts, leaves are predicted to act as 'hydraulic fuses' by shedding when plants reach critically low water potential (Ψplant ), thereby slowing water loss, stabilizing Ψplant and protecting against cavitation-induced loss of stem hydraulic conductivity (Ks ). We tested these predictions among trees in seasonally dry tropical forests, where leaf shedding is common, yet variable, among species. We tracked leaf phenology, Ψplant and Ks in saplings of six tree species distributed across two forests. Species differed in their timing and extent of leaf shedding, yet converged in shedding leaves as they approached the Ψplant value associated with a 50% loss of Ks and at which their model-estimated maximum sustainable transpiration rate approached zero. However, after shedding all leaves, the Ψplant value of one species, Genipa americana, continued to decline, indicating that water loss continued after leaf shedding. Ks was highly variable among saplings within species and seasons, suggesting a minimal influence of seasonal drought on Ks . Hydraulic limits appear to drive diverse patterns of leaf shedding among tropical trees, supporting the hydraulic fuse hypothesis. However, leaf shedding is not universally effective at stabilizing Ψplant , suggesting that the main function of drought deciduousness may vary among species.

Bark water vapour conductance ( g bark ) is a rarely considered functional trait. However, for the few tree species measured to date, it appears high enough to create stem water deficits associated with mortality during droughts, when access to water is limited. I tested whether g bark correlates with stem water deficit during drought conditions in two datasets of tropical trees: one of saplings in forest understories during an annual dry season and one of potted saplings in a shadehouse during extreme drought conditions. Among all 14 populations of eight species measured, g bark varied more than 10-fold (0.86–12.98 mmol m −2 s −1 ). In the forest understories, g bark was highly correlated with stem water deficit among four deciduous species, but not among evergreen species that likely maintained access to soil water. In the shadehouse, g bark was positively correlated with stem water deficit and mortality among all six species. Overall, tree species with higher g bark suffer higher stem water deficit when soil water is unavailable. Incorporating g bark into soil–plant–atmosphere hydrodynamic models may improve projections of plant mortality under drought conditions.

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.

Climate change is expected to lead to increases in drought frequency and severity, with deleterious effects on many ecosystems. Stomatal responses to changing environmental conditions form the backbone of all ecosystem models, but are based on empirical relationships and are not well-tested during drought conditions. Here, we use a dataset of 34 woody plant species spanning global forest biomes to examine the effect of leaf water potential on stomatal conductance and test the predictive accuracy of three major stomatal models and a recently proposed model. We find that current leaf-level empirical models have consistent biases of over-prediction of stomatal conductance during dry conditions, particularly at low soil water potentials. Furthermore, the recently proposed stomatal conductance model yields increases in predictive capability compared to current models, and with particular improvement during drought conditions. Our results reveal that including stomatal sensitivity to declining water potential and consequent impairment of plant water transport will improve predictions during drought conditions and show that many biomes contain a diversity of plant stomatal strategies that range from risky to conservative stomatal regulation during water stress. Such improvements in stomatal simulation are greatly needed to help unravel and predict the response of ecosystems to future climate extremes. Funding for this research was provided by NSF DEB EF-1340270 and the Climate Mitigation Initiative at the Princeton Environmental Institute, Princeton University. SL acknowledges financial support from the China Scholarship Council (CSC). VRD acknowledges funding from Ramón y Cajal fellowship (RYC-2012-10970). BTW was supported by the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research. DJC acknowledges funding from the National Science Centre, Poland (NN309 713340). WRLA was supported in part by NSF DEB 1714972.

Plantations of Tectona grandis in Central America are widely perceived to suppress forest regeneration in their understories, yet few studies have tested this assumption. We surveyed the understory woody vegetation growing in 7-year-old stands of T. grandis and the native tree species Terminalia amazonia in a plantation in western Panama that was managed with both commercial timber and biodiversity conservation objectives. We predicted that if T. grandis suppressed forest regeneration then the understories of T. grandis stands would have a lower density of woody stems, smaller stems, and fewer species than stands of T. amazonia. None of our predictions were supported. Densities of woody stems were 0.56 ± 0.21 m−2 (mean ± SE) and 0.64 ± 0.10 m−2 in T. grandis and T. amazonia understories, respectively. Stem height structure was similar under both species, where stems

Trees’ resistance to fire-induced mortality increases with bark thickness, which varies widely among species and generally increases with stem diameter. Because dry forests are more fire-prone than wetter forests, bark may be thicker in these forests. However, where disturbances such as hurricanes suppress stem diameter, trees may not obtain fire-resistant bark thickness. In two hurricane-prone Caribbean dry-forest types in Puerto Rico—deciduous forest and scrub forest—we measured bark thickness on 472 stems of 25 species to test whether tree species obtain bark thicknesses that confer fire resistance, whether bark is thicker in the fire-prone scrub forest than in the deciduous forest, and how bark thickness in Caribbean dry forest compares with other tropical ecosystems. Only 5% of stems within a deciduous-forest stand had bark thickness that would provide < 50% probability of top-kill during low-intensity fire. In contrast, thicker-barked trees dominated the scrub forest, suggesting that fires influenced it. Compared with trees of similar diameter in other regions of the tropics, bark in Caribbean dry forest was thinner than in savanna, similar to other seasonally dry forests, and thicker than moist-to-wet forests. Dry-forest species appear to invest more in fire-resistance than species from wetter forests. However, Caribbean dry forests remain highly vulnerable to fire because the trees rarely reach large enough diameters to be fire resistant.

Ecosystem models have difficulty predicting plant drought responses, partially from uncertainty in the stomatal response to water deficits in soil and atmosphere. We evaluate a 'supply-demand' theory for water-limited stomatal behavior that avoids the typical scaffold of empirical response functions. The premise is that canopy water demand is regulated in proportion to threat to supply posed by xylem cavitation and soil drying. The theory was implemented in a trait-based soil-plant-atmosphere model. The model predicted canopy transpiration (E), canopy diffusive conductance (G), and canopy xylem pressure (Pcanopy ) from soil water potential (Psoil ) and vapor pressure deficit (D). Modeled responses to D and Psoil were consistent with empirical response functions, but controlling parameters were hydraulic traits rather than coefficients. Maximum hydraulic and diffusive conductances and vulnerability to loss in hydraulic conductance dictated stomatal sensitivity and hence the iso- to anisohydric spectrum of regulation. The model matched wide fluctuations in G and Pcanopy across nine data sets from seasonally dry tropical forest and pinon-juniper woodland with

We surveyed Lepidoptera found on 11 species of Inga (Fabaceae:Mimosoideae) co-existing on Barro Colorado Island, Panama, to evaluate factors influencing diet choice. Of the 47 species of caterpillars (747 individuals) recorded, each fed on a distinct set of Inga .I n the field, 96% of the individuals were found on young leaves. Growth rates of caterpillars that were fed leaves in the laboratory were 60% higher on young leaves compared to mature leaves. When caterpillars were fed leaves of nonhost Inga, they grew more slowly. These data provide support for a link between preference and performance. However, among hosts on which larvae normally occurred, faster growth rates were not associated with greater host electivity (the proportion of larvae found on each host species in the field, corrected for host abundance). Growth rates on normal hosts were positively correlated with leaf expansion rates of the host, and fast expansion was associated with leaves with higher nutritional content. Detailed studies on a gelechiid leaf roller, the species with the largest diet breadth, allowed us to assess the importance of factors other than growth that could influence diet electivity. This species showed a 1.7-fold difference in growth rate among Inga hosts and faster growth on species with fast-expanding leaves. However, there was no correlation between caterpillar growth rate and abundance on different host species. Instead, abundance of the gelechiid on each Inga species was significantly correlated with the temporal predictability of food (synchrony of leaf flushing) and was negatively correlated with competition (amount of leaf area removed by species other than the gelechiid). Although rates of parasitism were high (23- 43%), there were no differences among hosts. Parasitism was also not related to measures of escape, such as growth rates of caterpillars, leaf expansion rates, and synchrony of leaf production. Together, food availability, parasitism, and competition explained 84% of the variation in host preference by the gelechiid. We suggest that these ecological interactions may be particularly important in determining diet choice initially and that preferences may be reinforced by subsequent divergence in host chemistry and/or the herbivore's ability to tolerate the secondary metabolites.

Tree species in seasonally dry tropical forests likely vary in their drought-survival mechanisms. Drought-deciduousness, which reduces water loss, and low wood density, which may permit dependence on stored water, are considered key traits. For saplings of six species at two distinct sites, we studied these and two associated traits: the seasonal amount of water released per stem volume (“water released”) and the hydraulic capacitance of the stem (C). Two deciduous species with low stem density, Cavanillesia platanifolia and Bursera simaruba, had high C and high dry-season stem water potential (Ψ stem), but differed in dry-season water released. C. platanifolia did not use stored water during the dry season whereas B. simaruba, in a drier forest, released stored water. In both, water released was highest while flushing leaves, suggesting that stored water supports leaf flushing. In contrast, two deciduous species with intermediate stem density, Annona hayesii and Genipa americana, had intermediate C, low dry-season Ψ stem, and high seasonal change in water released. Meanwhile, two evergreen species with intermediate stem density, Cojoba rufescens and Astronium graveolens, had relatively low C, low dry-season Ψ stem, and intermediate seasonal change in water released. Thus, at least three, distinct stored-water-use strategies were observed. Additionally, bark relative water content (RWC) decreased along with Ψ stem during the dry season while xylem RWC did not change, suggesting that bark-stored water buffers Ψ stem seasonally. Together these results suggest that seasonal use of stored water and change in Ψ stem are associated with functional groups that are characterized by combinations of deciduousness and stem density.