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In the version of this Article originally published, the first sentence of the Acknowledgements “We would like to thank D. Papale, M. Zapparoli, L. Portoghesi, M. di Porcia e Brugnera, and H. Verbeeck for feedback that helped improve the manuscript.” should have been “We would like to thank: F. Berzaghi's PhD thesis committee: D. Papale, M. Zapparoli and L. Portoghesi; A. Swann and S. Wasser for their conception of this idea and their mentorship during the initial work on the project; and M. di Porcia e Brugnera and H. Verbeeck for feedback that helped improve the manuscript.” This has now been amended.

Meteorological extreme events such as El Niño events are expected to affect tropical forest net primary production (NPP) and woody growth, but there has been no large-scale empirical validation of this expectation. We collected a large high-temporal resolution dataset (for 1-13 years depending upon location) of more than 172 000 stem growth measurements using dendrometer bands from across 14 regions spanning Amazonia, Africa and Borneo in order to test how much month-to-month variation in stand-level woody growth of adult tree stems (NPPstem) can be explained by seasonal variation and interannual meteorological anomalies. A key finding is that woody growth responds differently to meteorological variation between tropical forests with a dry season (where monthly rainfall is less than 100 mm), and aseasonal wet forests lacking a consistent dry season. In seasonal tropical forests, a high degree of variation in woody growth can be predicted from seasonal variation in temperature, vapour pressure deficit, in addition to anomalies of soil water deficit and shortwave radiation. The variation of aseasonal wet forest woody growth is best predicted by the anomalies of vapour pressure deficit, water deficit and shortwave radiation. In total, we predict the total live woody production of the global tropical forest biome to be 2.16 Pg C yr-1, with an interannual range 1.96-2.26 Pg C yr-1 between 1996-2016, and with the sharpest declines during the strong El Niño events of 1997/8 and 2015/6. There is high geographical variation in hotspots of El Niño-associated impacts, with weak impacts in Africa, and strongly negative impacts in parts of Southeast Asia and extensive regions across central and eastern Amazonia. Overall, there is high correlation (r = -0.75) between the annual anomaly of tropical forest woody growth and the annual mean of the El Niño 3.4 index, driven mainly by strong correlations with anomalies of soil water deficit, vapour pressure deficit and shortwave radiation.This article is part of the discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

Improving tropical forest current biomass estimates can help more accurately evaluate ecosystem services in tropical forests. The Global Ecosystem Dynamics Investigation (GEDI) lidar provides detailed 3D forest structure and height data, which can be used to improve above‐ground biomass estimates. However, there is still debate on how best to predict tropical forest biomass using GEDI data. Here we compare stand biomass predicted by GEDI data with the observed data of 2,102 inventory plots in tropical forests and find that adding a remotely sensed (RS) trait map of leaf mass area (LMA) significantly (P < 0.001) improves field biomass predictions, but by only a small amount (r2 = 0.01). However, it may also help reduce the bias of the residuals because there was a negative relationship between both LMA (r2 of 0.34) and percentage of phosphorus (%P, r2 = 0.31) and residuals. Leaf spectral data (400–1,075 nm) from 523 individual trees along a Peruvian tropical forest elevation gradient predicted Diameter at Breast height (DBH) (the critical measurement underlying plot biomass) with an r2 = 0.01 and LMA predicts DBH with an r2 = 0.04. Other data sets may offer further improvements and max temperature (Tmax) predicts Amazonian biomass residuals with an r2 of 0.76 (N = 66). Finally, for a network of net primary production (NPP) and gross primary production (GPP) plots (N = 21), leaf traits predicted with remote sensing are better at predicting fluxes than structure variables. Overall, trait maps, especially future improved ones produced by Surface Biology Geology, may improve biomass and carbon flux predictions by a small but significant amount. Plain Language Summary: Improving predictions of tropical forest biomass can help us to fight climate change. In this paper, we tried to improve tropical forest biomass predictions of satellite lidar (Global Ecosystem Dynamics Investigation) by adding remote sensed estimates of leaf traits. Leaf traits like leaf mass area or phosphorus slightly improved predictions of forest biomass with both a ground data set and a remotely sensed data set. Further, remotely sensed trait data could help explain the differences in the prediction of remotely sensed biomass compared with field derived biomass (residuals). Maximum temperature, but not soil fertility, also improved biomass predictions. Remotely sensed leaf traits were better than structure, like tree height, for predicting net primary production (NPP) and gross primary production (GPP). Future improved hyperspectral satellite data may be used to further improve predictions of biomass, NPP and GPP. Key Points: Both field measured and remotely sensed trait data improved biomass predictions, but only by a small amountIn the Amazon region, maximum temperature, but not soil fertility, improved biomass predictionsTrait data was better than structure data for predicting tropical forest net primary production and gross primary production [ABSTRACT FROM AUTHOR]

Aim: Tropical elevation gradients are natural laboratories to assess how changing climate can influence tropical forests. However, there is a need for theory and integrated data collection to scale from traits to ecosystems. We assess predictions of a novel trait-based scaling theory, including whether observed shifts in forest traits across a broad tropical temperature gradient are consistent with local phenotypic optima and adaptive compensation for temperature. Location: An elevation gradient spanning 3,300 m and consisting of thousands of tropical tree trait measures taken from 16 1-ha tropical forest plots in southern Perú, where gross and net primary productivity (GPP and NPP) were measured. Time period: April to November 2013. Major taxa studied: Plants; tropical trees. Methods: We developed theory to scale from traits to communities and ecosystems and tested several predictions. We assessed the covariation between climate, traits, biomass and GPP and NPP. We measured multiple traits linked to variation in tree growth and assessed their frequency distributions within and across the elevation gradient. We paired these trait measures across individuals within 16 forests with simultaneous measures of ecosystem net and gross primary productivity. Results: Consistent with theory, variation in forest NPP and GPP primarily scaled with forest biomass, but the secondary effect of temperature on productivity was much less than expected. This weak temperature dependence appears to reflect directional shifts in several mean community traits that underlie tree growth with decreases in site temperature. Main conclusions: The observed shift in traits of trees that dominate in more cold environments is consistent with an ‘adaptive/acclimatory’ compensation for the kinetic effects of temperature on leaf photosynthesis and tree growth. Forest trait distributions across the gradient showed overly peaked and skewed distributions, consistent with the importance of local filtering of optimal growth traits and recent shifts in species composition and dominance attributable to warming from climate change. Trait-based scaling theory provides a basis to predict how shifts in climate have and will influence the trait composition and ecosystem functioning of tropical forests.

High-resolution spectroscopy can be used to measure leaf chemical and structural traits. Such leaf traits are often highly correlated to other traits, such as photosynthesis, through the leaf economics spectrum. We measured VNIR (visible-near infrared) leaf reflectance (400–1,075 nm) of sunlit and shaded leaves in ~150 dominant species across ten, 1 ha plots along a 3,300 m elevation gradient in Peru (on 4,284 individual leaves). We used partial least squares (PLS) regression to compare leaf reflectance to chemical traits, such as nitrogen and phosphorus, structural traits, including leaf mass per area (LMA), branch wood density and leaf venation, and “higher-level” traits such as leaf photosynthetic capacity, leaf water repellency, and woody growth rates. Empirical models using leaf reflectance predicted leaf N and LMA (r2 > 30% and %RMSE

Leaf aging is a fundamental driver of changes in leaf traits, thereby regulating ecosystem processes and remotely sensed canopy dynamics. We explore leaf reflectance as a tool to monitor leaf age and develop a spectra-based partial least squares regression (PLSR) model to predict age using data from a phenological study of 1099 leaves from 12 lowland Amazonian canopy trees in southern Peru. Results demonstrated monotonic decreases in leaf water (LWC) and phosphorus (Pmass ) contents and an increase in leaf mass per unit area (LMA) with age across trees; leaf nitrogen (Nmass ) and carbon (Cmass ) contents showed monotonic but tree-specific age responses. We observed large age-related variation in leaf spectra across trees. A spectra-based model was more accurate in predicting leaf age (R2  = 0.86; percent root mean square error (%RMSE) = 33) compared with trait-based models using single (R2  = 0.07-0.73; %RMSE = 7-38) and multiple (R2  = 0.76; %RMSE = 28) predictors. Spectra- and trait-based models established a physiochemical basis for the spectral age model. Vegetation indices (VIs) including the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), normalized difference water index (NDWI) and photosynthetic reflectance index (PRI) were all age-dependent. This study highlights the importance of leaf age as a mediator of leaf traits, provides evidence of age-related leaf reflectance changes that have important impacts on VIs used to monitor canopy dynamics and productivity and proposes a new approach to predicting and monitoring leaf age with important implications for remote sensing.

We used leaf gas exchange, sap flow, and eddy covariance measurements to investigate whether high temperature substantially limits CO2 uptake at the LBA-ECO (Large-scale Biosphere-Atmosphere) km-83 tropical forest site in Brazil. Leaf-level temperature-photosynthesis curves, and comparisons of whole-canopy net ecosystem CO2 exchange (NEE) with air temperature, showed that CO2 uptake declined sharply during warm periods. Observations of ambient leaf microclimate showed that leaves oscillate between two states: a cool, dimly lit stage and a hot, brightly illuminated stage where leaf temperatures are often greater than 35°C. The leaf-level rates of photosynthesis decreased when shaded leaves (∼ambient air temperature and < 500 μmol m−2 s−1) were transferred into a prewarmed, brightly illuminated chamber (35° to 38°C and 1000 μmol m−2 s−1), coincident with increased leaf temperature, increased evaporative demand, and stomatal closure. The rates of whole-canopy CO2 uptake calculated at 5-min intervals increased initially at the onset of sunny periods that followed extended cloudy periods, but then decreased as the sunlight continued, leaf temperature and evaporative demand increased, and canopy conductance decreased. The forest at km-83 appears to be close to a high temperature threshold, above which CO2 uptake drops sharply. This sensitivity results in part from the covariance between leaf temperature and leaf illumination; the brightly illuminated leaves that contribute disproportionately to canopy photosynthesis are warmed to the point that leaf gas exchange is curtailed.

We used in situ and satellite measurements to investigate the seasonal patterns of leaf area index (LAI) and gross ecosystem CO2 exchange (GEE) by an evergreen tropical forest. The forest experienced a dry season from June through November. The rates of light-saturated CO2 uptake (GEE) were comparatively high from December through March and low from May through July. In situ measurements showed that LAI varied seasonally, with a minimum from May through September. Leaf production and leaf abscission were reduced from December through April. Leaf abscission increased in May, which reduced LAI. High rates of leaf abscission and production occurred from July through September associated with leaf turnover. Leaf abscission decreased abruptly in October, while production continued, which rapidly increased LAI. Leaf phenology was not directly correlated with changes in soil water. The seasonal cycle of in situ LAI differed markedly from the seasonal cycles of in situ normalized difference vegetation index (NDVI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15 LAI product. We hypothesize that the NDVI and MOD15 seasonality at the site is driven partly by seasonal changes in leaf age and leaf reflectance. We developed three simple models to investigate the causes of GEE seasonality. The first two models showed that the seasonal changes in LAI alone, and the effects of leaf age on leaf-level photosynthesis alone, could not account for the observed GEE seasonality. The third model showed that the combined effect of seasonal changes in LAI and seasonal changes in leaf age and leaf photosynthesis was sufficient to account for the observed GEE seasonality.

We used a controlled-environment leaf gas-exchange system and the micrometeorological technique eddy covariance to determine whether circadian rhythms constrain the rates of leaf and canopy gas exchange in an Amazonian forest over a day. When exposed to continuous and constant light for 20 to 48 hours leaves of eleven of seventeen species reduced their photosynthetic rates and closed their stomata during the normally dark period and resumed active gas exchange during the normally light period. Similarly, the rate of whole-forest CO2 uptake at a predetermined irradiance declined during the late afternoon and early morning and increased during the middle of the day. We attribute these cycles to circadian rhythms that are analogous to ones that have been reported for herbaceous plants in the laboratory. The importance of endogenous gas exchange rhythms presents a previously unrecognized challenge for efforts to both interpret and model land-atmosphere energy and mass exchange.