Coordinated volatile isoprenoid production and leaf turnover protect central Amazon Forest trees against stress

Coordinated volatile isoprenoid production and leaf turnover protect central Amazon Forest trees against stress | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Coordinated volatile isoprenoid production and leaf turnover protect central Amazon Forest trees against stress Michelle Robin, Vinícius de Souza, Joseph Byron, Ülo Niinemets, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7270146/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Climate stress is shifting the Amazon Forest from a carbon sink to a source, highlighting the need to understand tree resilience strategies, including leaf turnover and volatile isoprenoid (VI) production. Dry-season leaf turnover in Amazonian trees is hypothesized as a strategy to avoid drought and herbivory, and VIs protect trees against abiotic and biotic stresses. We measured temperature- and light-driven changes in VI emissions and characteristics of photochemical activity in 12 brevideciduous and evergreen central Amazon woody species. Brevideciduous trees showed greater increases in sesquiterpene emissions with rising temperature. Among these, isoprene emitters showed superior baseline photosynthetic performance, suggesting coordinated VI production and leaf turnover strategies. Moreover, current global VI models consistently overestimated isoprene fluxes by neglecting leaf phenological variability. These findings reveal overlooked phenological controls on Amazonian VI emissions, challenging standard model parametrization and emphasizing leaf-level data to improve predictions of atmospheric chemistry and climate-vegetation feedbacks. Biological sciences/Plant sciences/Plant stress responses/Heat Biological sciences/Ecology/Ecophysiology Biological sciences/Ecology/Climate-change ecology/Phenology isoprene monoterpenes sesquiterpenes BVOCs brevideciduous evergreen leaf-out phenology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction The Amazon Forest covers more than half of the tropical forest area in the world and stores up to 200 Pg of carbon (Malhi & Grace, 2000 ; Nobre et al., 2021 ). Yet, climate stress (e.g., droughts, heatwaves, floods, elevated atmospheric CO 2 and O 3 ) (Calvin et al., 2023 ) is slowly turning it into a net carbon source due to rising tree vulnerability to disturbances like windthrows, fires, insect outbreaks, and pathogens (Gatti et al., 2021 ). Hence, understanding the ecophysiological mechanisms driving stress responses of Amazonian trees is both timely and critical. Such mechanisms include leaf turnover (Aleixo et al., 2019 ) and the production of volatile isoprenoids (VIs) like isoprene (C 5 H 8 ), monoterpenes (C 10 H 16 ) and sesquiterpenes (C 15 H 24 ) (Loreto & Schnitzler, 2010 ). Leaf turnover in the central Amazon Forest is driven by precipitation seasonality, with co-occurring brevideciduous and evergreen trees - though evergreens predominate (Wu et al., 2016 ; Lopes et al., 2016 ; Aleixo et al., 2019 ). During the driest months, brevideciduous trees shed large fractions (or all) of their leaves synchronously, while evergreen trees exhibit fewer, more irregular flushing events (Gonçalves et al., 2020 ; Mesquita Pinho, 2021 ; Botía et al., 2022 ). Drought stress and herbivore avoidance are proposed evolutionary drivers of these patterns (Lopes et al., 2016 ). Since young leaves are softer and more vulnerable to herbivory, studies suggest dry-season flushing evolved to avoid wet-season herbivore pressure (Wright et al. , 1994; Coley & Barone, 1996 ; Lopes et al., 2016 ). Other studies argue it helps mitigate drought stress, as younger leaves may regulate water loss more effectively and brevideciduous trees may be more prone to hydraulic failure (Reich & Borchert, 1988 ; Aleixo et al., 2019 ; Mesquita Pinho, 2021 ). Leaf turnover patterns are also not strictly conserved at the species level and can shift due to stress events and disturbances (Borchert, 1999 ; Cleland et al., 2007 ; Gonçalves et al., 2020 ). Isoprene - and certain light-dependent monoterpenes (Loreto et al., 1996 ; Jardine et al., 2015 ) - are rapidly produced from newly fixed photosynthetic carbon and emitted immediately (Delwiche & Sharkey, 1993 ; Affek & Yakir, 2003 ). They are associated with abiotic stress mitigation and proposed to act by either directly scavenging reactive oxygen species (ROS) (Velikova, 2008 ), acting as sinks for excessive reducing power (Morfopoulos et al., 2013 ), enhancing photochemical efficiency (Pollastri et al., 2014 ; Rodrigues et al., 2020 ), or possibly stabilizing thylakoid membranes (Velikova et al., 2011 ) - though evidence for membrane stabilization is limited (Harvey et al., 2015 ). Recent studies also emphasize that isoprene engages in signaling networks linked to growth and defense responses, regulating carbon allocation under stress (Behnke et al., 2010 ; Lantz et al., 2019 ; Frank et al., 2021 ; Monson et al., 2021 ). In contrast, sesquiterpenes and most monoterpenes accumulate in specialized structures (e.g., resin ducts or glandular trichomes) and are emitted gradually under normal conditions or rapidly upon structural damage (Arneth & Niinemets, 2010 ; Niinemets et al., 2013 ; Rasulov et al., 2019 ; Nagalingam et al., 2023 ). These stored compounds are mostly known as herbivore deterrents and plant signaling molecules (Pichersky & Gershenzon, 2002 ; Fineschi & Loreto, 2012 ). Still, increased monoterpene and sesquiterpene emissions under abiotic stress conditions have also been observed and could reflect an active protective mechanism, or simply derive from increased diffusion due to elevated vapor pressure deficit (VPD) (Jardine et al., 2017 ; Byron et al., 2022 ; Nagalingam et al., 2023 ; Bourtsoukidis et al., 2024 ). In addition to their roles in plant stress responses, VIs also significantly impact atmospheric processes. Upon entering the atmosphere, they rapidly oxidize and decompose in the presence of atmospheric radicals (OH) (Lelieveld et al., 2008 ; Pfannerstill et al., 2018 , 2021 ) and contribute to the formation and growth of secondary organic aerosols and cloud condensation nuclei - influencing light scattering, precipitation, and the radiative balance of the atmosphere (Griffin et al., 1999a ; Pöschl et al., 2010 ; Curtius et al., 2024 ). They also contribute to the formation of tropospheric O 3 in the presence of nitrogen oxides (NO x ) - intensifying the radiative forcing of greenhouse gases (Yáñez-Serrano et al., 2020 ). Moreover, although isoprene dominates VI fluxes (Guenther et al., 2012 ), monoterpenes and sesquiterpenes incur higher carbon losses (Gomes Alves et al., 2022 ), and are considerably more chemically reactive, yielding 2- and 10-times more particle formation than isoprene, respectively (Griffin et al., 1999b ; Kroll et al., 2005 ; Xu et al., 2014 ). With its massive plant biomass and species diversity (Fauset et al., 2015 ; ter Steege et al., 2020 ), the Amazon Forest is estimated as the largest and most chemically diverse source of VIs to the atmosphere (Guenther et al., 2012 ; Yáñez-Serrano et al., 2020 ; Gomes Alves et al., 2023 ). Yet, VI fluxes predicted from current emission models (e.g., Guenther et al., 2012 ) are highly uncertain, as these models rely on coarse emission factors tied to generalized plant functional types (e.g., CLM4 model, Oleson et al., 2010 ). This mechanistic gap is compounded by the use of generalized parameters derived from temperate forest species responses and the limited flux tower data, masking the variability in light and temperature sensitivity across tropical forest species (Mu et al., 2022 ). These simplified assumptions overlook species-specific physiology and leaf phenological types, rarely accounting for the carbon costs of VI emissions under high light and temperature stress. Furthermore, the production of VIs relies on the activity of isoprenoid-specific synthase enzymes and the availability of their common precursor dimethylallyl diphosphate (DMADP), being tightly coupled to photosynthetic activity and varying between species, leaf developmental stages (Schnitzler et al., 1997 ; Niinemets et al., 2004 ; Souza et al., 2025 ) - and possibly leaf phenological types. Warmer and drier climates are expected to favor the selection of brevideciduous trees (Aleixo et al., 2019 ), as well as of isoprene and light-dependent monoterpene emitters - given that emitters often sustain higher photosynthetic rates under heat than non-emitters (Singsaas et al., 1997 ; Pollastri et al., 2019 ; Taylor et al., 2019 ; Byron et al., 2022 ). At the same time, more extreme and frequent stress events will likely promote substantial stress-induced VI emissions, especially from heavier and more reactive stored monoterpenes and sesquiterpenes (Byron et al., 2022 ; Bourtsoukidis et al., 2024 ). Indeed, such shifts have been observed in the Amazon during El Niño years, when reductions in photosynthesis and isoprene emissions coincide with increased release of more reactive and temperature-sensitive monoterpenes and sesquiterpenes (Jardine et al., 2017 ; Pfannerstill et al., 2018 ; Gomes Alves et al., 2022 ). Still, it is not entirely clear how the combined effects of global climate change will affect forest-atmosphere emission feedbacks (Yáñez-Serrano et al., 2020 ; Satake et al., 2024 ), particularly considering the potential for differential emission responses to abiotic stress across leaf phenological types. Hence, in this study, we asked: i) how do VI emissions vary in response to light and temperature changes in different leaf phenological types; ii) how do characteristics of photochemical activity vary between isoprene emitters and non-emitters from different leaf phenological types; and iii) how may the interaction of leaf phenological types and light and temperature changes affect canopy isoprene emission estimates? To answer these questions, we measured the responses of VI emissions, and key leaf gas exchange and chlorophyll fluorescence characteristics - net photosynthesis ( A n ), stomatal conductance ( g s ), photosynthesis temperature optimum ( T opt ), light saturation point ( LSP ), photosynthetic capacity ( A sat ), electron transport rate ( J ), quantum efficiency of photosystem II (ϕ PSII ), and photochemical quenching ( qP ) - to changes in light and temperature conditions in 12 angiosperm species at a central Amazon Forest. Among the species, six were isoprene emitters and six non-emitters, with each group containing three evergreen and three brevideciduous trees. By answering the questions, our study aims to provide deeper ecophysiological knowledge and a better mechanistic understanding of VI emissions across different leaf phenological types. This is fundamental for advancing model parametrization and improving future projections of global VI fluxes and carbon dynamics in light of climate change. 2. Material and Methods 2.1 Study site and experimental design Data were collected at an upland forest (locally called terra firme ) permanent plot located in the Amazon Tall Tower Observatory (ATTO) site in central Amazonia. The ATTO site is located about 150 km northeast of Manaus (02° 08.9’ S, 59° 00.2’ W), at the Uatumã Sustainable Development Reserve. The climate is humid tropical, with mean annual temperature of 26.7 ºC and precipitation of 2376 mm, being characterized by a pronounced wet season from December to May and a drier season from July to October, with transitions in between (Botía et al., 2022 ). Vegetation in the terra firme plot is dense (leaf area index of 5.3 m 2 m − 2 ), mature, and non-flooded, with a mean canopy height of 35 m (Gomes Alves et al., 2023 ). The soil is a highly weathered and well-drained ferralsol (Chauvel et al., 1987 ). For more details on the experimental site see Andreae et al. ( 2015 ). We sampled trees and performed measurements between November 29 - December 6, 2022. This period corresponds to the beginning of the wet season, when canopies mostly contain mature leaves (Alves et al., 2018 ; Gomes Alves et al., 2023 ), and variation in leaf age is expected to be low. Measurements were performed in 12 selected trees from 12 species of angiosperms (one tree/species, Table 1 ), including six isoprene emitters, and six non-emitters. Each group of isoprene emitters and non-emitters contained three evergreen and three brevideciduous trees. Detailed leaf phenological type classifications and isoprene emission factor measurements are in Robin et al. ( 2024 ). Due to a lack of replicates within species, the statistical comparisons were made among volatile emission groups (isoprene emitter/non-emitter) and leaf phenology groups (brevideciduous/evergreen). Table 1 List of species measured in this study; leaf phenological types; capacity to emit isoprene (Emitter) or not (Non-emitter); isoprene emission factors ( ε 0 , µg C g − 1 h − 1 ); and their position in the regional biomass rank. Isoprene ε 0 is from Robin et al. ( 2024 ). The biomass rank of each species was derived from values of aboveground biomass for central Amazon Forest species presented in Fauset et al. ( 2015 ). Species Leaf phenological type Isoprene emission Isoprene ε 0 (µg C g − 1 h − 1 ) Biomass rank Brosimum parinarioides Brevideciduous Emitter 1.7 160 Endopleura uchi Brevideciduous Emitter 2.4 53 Peltogyne catingae Brevideciduous Emitter 2.0 80 Cariniana decandra Brevideciduous Non-emitter 0 275 Manilkara bidentata Brevideciduous Non-emitter 0.2 17 Pouteria guianensis Brevideciduous Non-emitter 0 5 Eschweilera cyathiformis Evergreen Emitter 1.6 21 Minquartia guianensis Evergreen Emitter 3.9 7 Protium spruceanum Evergreen Emitter 5.5 224 Croton matourensis Evergreen Non-emitter 0 408 Dinizia excelsa Evergreen Non-emitter 0 2 Scleronema micranthum Evergreen Non-emitter 0 4 Isoprene emission factor = isoprene emission rate measured at standard conditions of incident photosynthetic photon flux density (PPFD, 1000 µmol m − 2 s − 1 ) and leaf temperature (30°C). All trees occupied the upper canopy layer of the plot. Hence, given the logistical challenges of measuring intact leaves from trees exceeding 30 m in height, we performed all measurements on leaf samples collected from cut branches immediately placed in water. This method provides a practical solution for conducting VI emissions and gas exchange measurements without compromising leaf viability (Penuelas et al., 2010 ; Llusia et al., 2014 ; Albert et al., 2018 ; Jardine et al., 2020 ; Taylor et al., 2021 ; Gomes Alves et al., 2022 ; Robin et al., 2025 ), and cutting branches does not significantly compromise gas exchange or VI measurements (Monson et al., 1994 , 2016 ; Keller & Lerdau, 1999 ; Ghirardo et al., 2016 ). With the help of a tree climber, one branch of at least 2 cm in diameter was collected from a sun-exposed area of the canopy. After collection, the branch was immediately re-cut under water to prevent embolism, stored in a water bottle for transport, and re-cut again under water at the field camp before VI emission and gas exchange measurements. We selected one visibly mature and healthy leaf of the branch to measure the responses of VI emissions and characteristics of photochemical activity to changes in light and temperature conditions. 2.2 Measurements of light and temperature response curves of isoprene emission and characteristics of photochemical activity For each tree, we measured the responses of VI emissions (section 2.4 ) and characteristics of photochemical activity (section 2.3 ) to changes in light at ambient (21%) and reduced (2%) O 2 (to suppress photorespiration), and to changes in temperature. We performed gas exchange and chlorophyll fluorescence measurements with a LI-6800 (LI-6400XT for Cariniana decandra ) portable gas exchange system (LiCor Inc., USA). Before each response curve measurement, we separately enclosed the leaf (for compound leaves we considered a leaflet as the equivalent of a simple leaf lamina) in the leaf chamber with the following environmental conditions: photosynthetic photon flux density (PPFD) of 1000 µmol m − 2 s − 1 , leaf temperature of 30°C, flow rate of air going into the leaf chamber of 500 µmol s − 1 , CO 2 and H 2 O concentrations of 420 µmol mol − 1 and 21 mmol mol − 1 , and relative humidity of ~ 60%. We began our measurements after acclimating the leaf to these conditions for at least 20 min, or until net assimilation ( A n ), stomatal conductance ( g s ), and internal CO 2 concentration ( C i ) reached a stable, positive plateau. If stability was not reached, the leaf was replaced, or a new branch was sampled. Ambient (21%) and reduced (2%) O 2 light response curves were performed under decreasing light intensity steps: 2000, 1500, 1000, 750, 500, 250, 100, 50, and 0 µmol m − 2 s − 1 ; while leaf temperature (30°C), CO 2 (420 µmol mol − 1 ), and relative humidity (~ 60%) were fixed. At each light step, we measured gas exchange characteristics every 30 s for 2.5 min. We used the same method to perform the light curves with reduced (2%) O 2 conditions. Low O 2 was achieved by injecting a controlled mix of ultrahigh-purity nitrogen at a rate of 450 ml min − 1 and ambient air at 50 ml min − 1 into the leaf chamber. Temperature curves were performed under increasing leaf temperature steps: 30, 35, 37.5, 40, 42.5, and 45°C; while PPFD (1000 µmol m − 2 s − 1 ), CO 2 (420 µmol mol − 1 ), and relative humidity (~ 60%) were fixed. At each temperature step, we measured gas exchange characteristics every 30 s for 5 min. We used a leaf chamber fluorometer to simultaneously quantify chlorophyll fluorescence variables during all response curves. At the final log of each successive light and temperature step, an actinic light pulse of 10000 µmol m − 2 s − 1 (10% blue light and 90% red light), modulated at 20 kHz, was applied for 1 s, and steady-state ( F s ), light-adapted maximal ( F m’ ) and light-adapted minimal ( F o’ ) fluorescence yields were recorded. After measuring all light response curves, the leaves were scanned with a table scanner to obtain leaf area, dried in an oven at 60°C for 72 hrs, and weighed to obtain leaf dry mass. We analyzed the images of scanned leaves with ImageJ software (Schneider et al., 2012 ) to obtain leaf area, and calculated specific leaf area (SLA) as the ratio of leaf area to leaf dry mass. 2.3 Characteristics of photochemical activity Quantum efficiency of photosystem II (ϕ PSII , Eq. 1) and photochemical quenching ( qP , Eq. 2) were calculated as (Genty et al., 1989 ): $$\:{\varphi\:}_{PSII}\:=\:\frac{({F}_{m’}-{F}_{s})}{{F}_{m’}}\:\left(1\right)$$ $$\:qP\:=\:\frac{({F}_{m’}-{F}_{s})}{{(F}_{m’}-\:{F}_{o’})}\:\left(2\right)$$ Ambient (21%) and reduced (2%) O 2 light response curves were fitted to a hyperbolic function (Huang et al., 2021 ) (Fig. S1 ,2). Ambient O 2 curves were used to define the light saturation point ( LSP ) and estimate the photosynthetic capacity ( A sat ) of each species. LSP was defined as the first PPFD (above the inflection point) where the rate of increase in mean A n (i.e., the derivative of the logistic function) fell below 5% of maximum A n , and we estimated A sat as the predicted A n at LSP (Fig. S1 ). For each species, we calibrated the electron transport rate ( J , µmol e − m − 2 s − 1 ) as: $$\:J\:=\:k\:\:PPFD\:\:{\varphi\:}_{PSII}\:\left(3\right)$$ where the lumped parameter k is the slope of the linear regression between A n and PPFD ϕ PSII / 4 at 2% O 2 (Fig. S3), derived from the linear part of the light response curve, just before the inflection point of the curve (Fig. S1 ,2) (Yin et al., 2009 , 2011 ). Due to limitations in the number of observations in low-light intensities (0–100 µmol m − 2 s − 1 ), we performed the linear regression using values obtained from the fitted hyperbolic curves. Then, we fitted a quadratic function to the relationship between A n and leaf temperature for each species (Fig. S4) (Kumarathunge et al., 2024 ), and estimated photosynthesis temperature optimum ( T opt °C) based on the fitted model parameters. Since observed data for Peltogyne catingae and Scleronema micranthum did not follow a quadratic function, photosynthesis T opt for these species was taken as the leaf temperature where the highest A n was observed. For the species Cariniana decandra and Manilkara bidentata , the estimated T opt fell before the first observed data point (Fig. S4). 2.4 Identification and quantification of volatile isoprenoid emissions Real-time VI emission measurements were obtained by redirecting the air exiting the gas analyzer leaf chamber to a proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS, IONICON, Analytik, Innsbruck, Austria). The PTR-QMS operated in standard conditions with a drift tube voltage of 600 V, drift tube pressure of 2.2 mbar, and E/N 120 Td. This system allows obtaining real-time VI emission measurements under the controlled environmental conditions of the gas analyzer leaf chamber. At the beginning of each day and before measuring each species, we obtained a chamber blank sample from the empty leaf chamber. A hydrocarbon filter (Restek Pure Chromatography, Restek Corporations, USA) was installed at the air inlet of the gas analyzer to remove VIs from incoming ambient air, and all tubing in contact with the sampling air was PTFE - a material inert to VIs. The flow rate of air going inside the PTR-QMS was 200 ml min − 1 , and measurements were performed for 2.5 min and 5 min at each light and temperature step, respectively. During each PTR-QMS measurement cycle, the following mass-to-charge ratios (m/z) were monitored: 21 + (H 3 18 O + ), 32 + (O 2 + ), and 37 + (H 2 O-H 3 O + ) with a dwell time of 500 ms each; 41 + (isoprene fragment), 69 + (isoprene), 81 + (monoterpene fragment), 137 + (monoterpenes), 149 + (sesquiterpene fragment) and 205 + (sesquiterpenes) with a dwell time of 1 s each. Humidity-dependent calibrations (using water-bubbled nitrogen to dilute standard gas, simulating ambient relative humidity) were performed with a certified standard gas provided by Apel-Riemer Environmental, Inc. (Table S1 ), at the beginning and end of the measurement campaign. The mixing ratios of VIs were calculated from the calibration curves (R 2 ≈ 0.99). PTR-QMS detection limits were calculated as three times the standard deviation of isoprene, monoterpenes, and sesquiterpenes (ppb) detected in the water-bubbled nitrogen background of the calibration curves and were equal to 0.93, 2.14, and 2.83 ppb, respectively. Once the mixing ratios of isoprene, monoterpenes and sesquiterpenes (ppb) from the samples were obtained, fluxes per area were determined using the equation ( F = Rppb × Q/S), where F (nmol m − 2 s − 1 ) is the VI leaf flux; Rppb (nmol mol − 1 ) is VI concentration of the outgoing air (ppb); Q is the flow rate of air into the leaf chamber (500 µmol s − 1 ); and S is the area of leaf within the chamber (0.0002 m²). Since individual monoterpene and sesquiterpene compounds cannot be identified and quantified by the PTR-QMS, air exiting the leaf chamber during the temperature curves was also routed to fill adsorbent cartridges (stainless steel tubes filled with Tenax TA and Carbograph 5 TD adsorbents) at a rate of 200 ml min − 1 for 5 min, resulting in collection of VIs from 1 L chamber air for compound identification and quantification in the lab. VIs accumulated in the adsorbent cartridges were determined by gas chromatography-time of flight-mass spectrometry (GC-ToF-MS), at the Atmospheric Chemistry Department of the Max Planck Institute for Chemistry (Mainz, Germany). Sample desorption of the VIs accumulated in adsorbent cartridges was achieved with a two-stage automated thermal desorber (TD100-xr, MARKES International, UK), with helium 5.0 as the carrier gas. The adsorbent cartridge was purged with carrier gas for 5 min at a flow of 50 ml min − 1 , followed by sample desorption at a temperature of 250°C and a flow of 50 ml min − 1 of helium for 5 min onto a focusing cold trap (materials emissions, MARKES International, UK) for pre-concentration at 30°C. The cold trap was purged with carrier gas for 1 min with a flow of 50 ml min − 1 , then rapidly heated to 250°C. The sample was removed from the cold trap with a He flow of 2 ml min − 1 and injected into the GC column. The sampled compounds were separated using a 60 m DB-1 column (0.25 mm internal diameter, film thickness 1 µm, Agilent Technologies, UK). The temperature program used was as follows: 50°C to 150°C at 4°C min − 1 , and 150°C to 250°C at 8°C min − 1 , the temperature was then held for 5 min. The column flow was set to 2 ml min − 1 . Detection was achieved using a time-of-flight mass spectrometer (Bench TOF-Select, MARKES International, UK). VI calibration and identification were achieved using a standard BVOC gas mixture (Apel-Riemer, 2019). All sesquiterpenes were identified with the NIST library and headspace tests from liquid standards when liquid standards were available. Sesquiterpenes were first quantified using the calibration factor of α-pinene and then back-calibrated using relative response factors derived from the ratio of the gradients of the calibration curves from the liquid standards of α-pinene and the individual sesquiterpene. When a liquid standard was unavailable, an average of response factors of other sesquiterpenes was used. Most monoterpenes were quantified with their own calibration factors; the calibration factor for α-pinene was used for cis-/trans-β-ocimene, α-thujene, and eucalyptol. 2.5 VI emission light and temperature responses models We modeled light responses of isoprene and light-dependent monoterpene emissions using the light response algorithm from Guenther et al. ( 1999 ): $$\:emission\:rate\:\left({\gamma\:}_{P,i}\right)=\:{\epsilon\:}_{0\:}\frac{\alpha\:\:{C}_{L1}L}{\sqrt{1+\:\frac{{\alpha\:}^{2}*\:{L}^{2}}{{C}_{p5}^{2}}}}\:\left(4\right)$$ where the emission factor ε 0 is the observed compound emission rate at standard conditions (PPFD of 1000 µmol m − 2 s − 1 and leaf temperature of 30°C), L is incident PPFD (µmol m − 2 s − 1 ), and C p5 = 1.0 (Monson et al., 2012 ). We estimated empirical coefficients α and C L1 based on non-linear least-square fits to the observed data (Fig. S5). We defined isoprene LSP as the first PPFD where the rate of increase in mean isoprene emission rates fell below 5% of maximum isoprene emission rates. We modeled the temperature response of normalized isoprene emissions using the temperature response algorithm from Guenther et al. ( 1999 ): $$\:emission\:rate\:\left({\gamma\:}_{T,i}\right)=\:\frac{{E}_{opt}\:{C}_{T2}\:{e}^{{C}_{T1}x}}{{C}_{T2}-\:{C}_{T1}\:(1-{e}^{{C}_{T2}x})}\:\left(5\right)$$ where $$\:x=\:\frac{\frac{1}{{T}_{opt}}-\:\frac{1}{{T}_{L}}}{R}\:\left(6\right)$$ R is the gas constant (0.008314 kͿ K − 1 mol − 1 ), T opt (K) is the leaf temperature at the highest observed isoprene emission rate ( E opt ), and T L (K) is leaf temperature. We estimated empirical coefficients C T1 and C T2 (activation and deactivation energy parameters, respectively) based on non-linear least-square fits to the observed data (Fig. S6). We calculated the empirically derived β parameter of monoterpene and sesquiterpene emission responses to temperature as (Guenther et al., 1993 ): $$\:\beta\:=\:\frac{{log}_{e}\frac{{E}_{T1}}{{E}_{T2}}}{{T}_{1}-\:{T}_{1}}\:\left(7\right)$$ where E T 1 and E T 2 are monoterpene and sesquiterpene emission rates at leaf temperatures T 1 = 30°C and T 2 = 45°C. We modeled isoprene emission rates from 175 canopy-dominant trees measured in Robin et al. ( 2024 ) under different conditions of average air temperature (°C) and PPFD (µmol m − 2 s − 1 ) at different hours of the day (09:00, 13:00, and 17:00) in October, November, and December/2022 using empirically derived light and temperature response parameters (α, C L1 , C T1 , C T2 ) from this study and compared values with those modeled with parameters from Guenther et al. ( 1999 ). Following that, we compared canopy-level isoprene fluxes estimated at different hours of the day during the same months using combinations of: i) average emission factors from Robin et al. ( 2024 ) and empirically derived parameters from this study (Model 1); ii) average emission factors from Robin et al. ( 2024 ) and reference parameters from Guenther et al. ( 1999 ) (Model 2); and iii) emission factors suggested for evergreen and deciduous tropical forest trees in Guenther et al. ( 2012 ) and reference parameters from Guenther et al. ( 1999 ) (Model 3). Air temperature and PPFD values were recorded at 36 m above the ground, corresponding to the plot’s average canopy height (Table S2). Fluxes were modeled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al., 2012 ): $$\:{\gamma\:}_{i}=\:{C}_{CE}\:LAI\:{\gamma\:}_{P,i}\:\:{\gamma\:}_{T,i}\:\:{\gamma\:}_{A,i}\:\:{\gamma\:}_{SM,i}\:\:{\gamma\:}_{C,i}\:\left(8\right)$$ where isoprene fluxes (γ i ) were estimated as a function of a canopy environmental coefficient ( C CE ), canopy leaf area index (LAI), and responses to light (γ P,i ), temperature (γ T,i ), leaf age (γ A,i ), soil moisture (γ SM,i ), and environmental CO 2 concentration (γ C,i ). For each month, we assumed γ SM,i and γ C,i were constant and equal to 1. Similarly, we assumed that, during this period, canopies were mostly composed of mature leaves and γ A,i would be equal to 1. γ P,i and γ T,i were estimated using equations 7 and 8, respectively, and C CE = 0.57 and LAI = 5.3 m 2 m − 2 (Gomes Alves et al., 2023 ). Individual variations in isoprene emission rates were estimated by multiplying γ P,i and γ T,i . 2.6 Measurements of post-illumination isoprene emissions We measured post-illumination isoprene emissions according to Rasulov et al. ( 2009 ). Following light and temperature response curves, leaves were again acclimated to the standard environmental conditions described in section 2.2 for at least 20 min, or until net assimilation ( A n ), stomatal conductance ( g s ), internal CO 2 concentration ( C i ), and isoprene emission rates reached a stable, positive plateau. Once steady-state isoprene emission rates were achieved, we rapidly switched off the light and monitored the decay of isoprene emissions until emission rates reached zero. For each species, we monitored the first dark-decay peak of isoprene emissions, which corresponds to the dimethylallyl diphosphate (DMADP) pool that was formed before the light was switched off and is immediately available for isoprene synthesis (Rasulov et al., 2009 ). We estimated species DMADP pool sizes by integrating the first dark-decay of isoprene emission rates after the light was switched off using the Peak Analyzer module from OriginPro 2019b (OriginLab) (Fig. S7). We also integrated DMADP pool sizes at different moments of the first dark-decay of isoprene emissions, obtained paired values of isoprene emission rate vs. DMADP pool size, and estimated isoprene synthase ( IspS ) activity as the slope of the linear regression between isoprene emissions and DMADP pool sizes (Rasulov et al., 2010 ). Although the air exiting the gas analyzer leaf chamber was directly redirected to the PTR-QMS, and the leaf chamber can quickly reach a steady state after rapid changes in gas concentration (~ 4 s) (Niinemets, 2012 ), we also applied a chamber finite time response correction according to Rasulov et al. ( 2009 ). In a previous experiment (Souza et al., 2025 ), we estimated the chamber time response by injecting a small stream of isoprene standard of known concentration into the empty leaf chamber using a needle. After quickly removing the needle, isoprene levels were monitored with a proton-transfer-reaction time-of-flight mass-spectrometer (PTR-ToF-MS) until they reached background values. Thus, parameters estimated from post-illumination isoprene emission responses were calculated after subtracting the chamber-clearing trace from the observed isoprene emission values. 2.7 Statistical analyses To evaluate how C partitioning among different VIs varied as a function of leaf temperature in different leaf phenological types (Brevideciduous, Evergreen), we calculated an emission rate mean weighted by the mass of each emitted compound (i.e., isoprenoid emission metric; Gomes Alves et al., 2022 ). At each measured temperature, we calculated the isoprenoid emission metric by multiplying the mass-based emission rate (µg C g⁻¹ h⁻¹) of each observed compound by the number of carbon atoms in its molecule. We then summed these carbon-weighted values and divided the result by the total sum of the mass-based emission rates. Values close to 5, 10, or 15 indicate that isoprene, monoterpenes, or sesquiterpenes, respectively, are predominant in the emission profile. Then, we performed general linear regression models of the following independent variables ( y ): isoprene, monoterpene, and sesquiterpene emission rates, and isoprenoid emission metric; varying as a function of leaf phenological types at each leaf temperature step (T i ) ( y at T i ~ pheno.type; p (T i )), and varying as a function of leaf temperature ( y ~ temp; p (temp)), of leaf phenological types ( y ~ pheno.type; p (pheno.type)), and of the interaction between leaf temperature and leaf phenological types ( y ~ temp * pheno.type; p (temp:pheno)). We also estimated the percentage of photosynthetic carbon (%C) loss to VI emissions for each species at each leaf temperature step by dividing each compound’s observed mass-based emission rate (µg C g⁻¹ h⁻¹) - adjusted for biosynthetic carbon cost (i.e., multiplied by 1.2 to account for 6/5 for isoprene, 12/10 for monoterpenes, and 18/15 for sesquiterpenes) - by the corresponding mass-based net photosynthetic assimilation rate (µg C g⁻¹ h⁻¹), and multiplying by 100. To examine whether characteristics of photochemical activity differed between isoprene emitters and non-emitters from different leaf phenological types, we performed general linear regression models of the following independent variables ( y ): LSP , A sat , J , A n at 45°C, qP , ϕ PSII , g s at A sat ; varying as a function of isoprene emissions (Emitter, Non-emitter; y ~ isoprene; p (isoprene)), of leaf phenological types ( y ~ pheno.type; p (pheno.type)), and of the interaction between isoprene emissions and leaf phenological types ( y ~ isoprene * pheno.type; p (isop:pheno)). To evaluate how characteristics of photochemical activity varied as a function of leaf temperature in isoprene emitters and non-emitters across different phenological types, we modeled the temperature responses of J , qP , ϕ PSII , and A n using a non-linear regression framework. We fitted a peaked Arrhenius temperature response model to each variable: $$\:f\left(T\right)=\:{k}_{25}\:{e}^{\frac{{E}_{\alpha\:\:(T-\:{T}_{ref})}}{R\:T\:{T}_{ref}}}\:\left[\frac{1+{e}^{\frac{{T}_{ref}\:\varDelta\:S-\:Hd}{R\:{T}_{ref}}}}{1+{e}^{\frac{T\varDelta\:S-\:Hd}{RT}}}\right]\left(9\right)$$ where T is leaf temperature (K), k 25 is the modeled rate at 25°C, E α is the activation energy (J mol⁻¹), ΔS is the entropy term (J mol⁻¹ K⁻¹), Hd is the deactivation energy (J mol⁻¹), T ref = 298.15 K (25°C), and R is the universal gas constant. Although our measurements began at 30°C, we used 25°C as a reference because k 25 serves as a standardized model-derived parameter widely used to compare physiological baseline performance across studies and conditions (Medlyn et al., 2002 ). We fitted this model separately for each combination of isoprene emission (emitter vs. non-emitter) and leaf phenological type (brevideciduous vs. evergreen). To account for within-group variability and obtain confidence intervals for parameter estimates, we performed bootstrap resampling (n = 300) within each group, recording the distributions of the fitted parameters ( k 25 , E α , ΔS , Hd ). From these bootstrapped fits, we calculated and compared means and 95% confidence intervals for each parameter and group. To test whether empirically derived parameters from isoprene light (α and C L1 ) and temperature ( C T1 and C T2 ) response algorithms, and observed isoprene T opt , LSP , DMADP pool size, and IspS activity rate significantly varied between leaf phenological types, we performed general linear regression models of each independent variable ( y ) varying as a function of leaf phenological types ( y ~ pheno.type). Lastly, we performed Kruskal-Wallis pairwise comparisons between isoprene emission rates estimated with empirically derived and reference parameters (see section 2.5 ) at different hours of the day. All statistical analyses were performed in Python 3 with Jupyter Notebook as the primary environment. The Python libraries pandas , NumPy , and SciPy , were used for data handling, fitting, and model construction; math for mathematical equations; statsmodels for general linear regression models; and matplotlib and seaborn for visualizing results. 3. Results 3.1 VI emission responses to changes in light and temperature in different leaf phenological types All volatile compounds showed increases in emission rates with rising leaf temperature (Fig. 1 ). This effect was particularly stronger for monoterpenes and sesquiterpenes, which showed average 10 and 12.5-fold emission increases from 30 to 45°C (Table 2 ). Isoprene and monoterpene emissions did not differ between leaf phenological types (Fig. 1 A,B). However, sesquiterpene emissions from brevideciduous trees had higher increases with leaf temperature compared to evergreen trees ( p (temp:pheno) = 0.04, Fig. 1 C), as well as higher emission rates at 45°C ( p (T 45 ) = 0.02, Fig. 1 C). Finally, increases in isoprenoid emission metric with temperature indicated a trend toward a shift from “lighter” to “heavier” compounds (as characterized by isoprenoid emission metrics) with leaf temperature ( p (temp) = 0.06; Fig. 1 D). Table 2 Values of isoprene, monoterpene, and sesquiterpene emission rates (µg C g − 1 h − 1 ) at each leaf temperature (°C) for each species measured in this study, and average values and ± standard errors for brevideciduous, evergreen and all species (total average). The 45°C / 30°C ratio was obtained by dividing the observed emission value at 45°C by the observed emission value at 30°C. 30°C 35°C 37.5°C 40°C 42.5°C 45°C 45°C / 30°C Isoprene Brosimum parinarioides 0.70 2.75 5.17 7.37 8.72 6.11 9 Endopleura uchi 17.20 38.28 45.54 57.14 64.67 46.41 3 Peltogyne catingae 25.57 37.33 57.38 67.77 71.33 53.14 2 Cariniana decandra 1.78 0.83 0.90 1.08 2.27 3.39 2 Manilkara bidentata 0.94 0.59 1.59 1.49 2.00 4.36 5 Pouteria guianensis 1.29 0.21 1.87 3.61 2.18 5.73 4 Eschweilera cyathiformis 8.20 18.35 26.28 29.16 27.82 20.20 2 Minquartia guianensis 9.58 19.57 23.99 32.36 32.28 23.15 2 Protium spruceanum 71.60 149.85 187.88 221.02 255.70 252.56 4 Croton matourensis 1.98 1.67 4.56 6.81 8.97 10.18 5 Dinizia excelsa 0.53 0.90 0.54 0.91 0.60 1.23 2 Scleronema micranthum 0.96 0.68 1.80 0.86 2.29 2.81 3 Brevideciduous 7.91 ± 4.40 13.33 ± 7.75 18.74 ± 10.48 23.08 ± 12.56 25.19 ± 13.60 19.86 ± 9.51 4 Evergreen 15.47 ± 11.33 31.84 ± 23.87 40.84 ± 29.77 48.52 ± 34.96 54.61 ± 40.58 51.68 ± 40.34 3 Total average 11.69 ± 5.91 22.58 ± 12.29 29.79 ± 15.41 35.80 ± 18.12 39.90 ± 20.88 35.77 ± 20.33 3.5 Monoterpenes Brosimum parinarioides 16.73 53.66 72.51 72.35 50.54 36.13 2 Endopleura uchi 10.44 8.92 18.61 23.11 35.47 42.24 4 Peltogyne catingae 0.50 2.58 9.88 16.03 15.35 20.60 42 Cariniana decandra 22.93 31.42 46.71 47.16 38.71 37.37 2 Manilkara bidentata 3.98 5.63 7.47 13.78 19.63 32.64 8 Pouteria guianensis 3.02 6.94 24.87 30.82 35.93 49.44 16 Eschweilera cyathiformis 1.15 3.98 13.72 20.37 19.64 21.84 19 Minquartia guianensis 3.26 7.76 13.44 20.64 23.17 39.28 12 Protium spruceanum 13.17 18.77 27.62 44.63 52.56 74.15 6 Croton matourensis 58.71 134.76 224.60 243.55 254.61 125.73 2 Dinizia excelsa 2.01 4.65 0.42 1.85 5.52 6.01 3 Scleronema micranthum 25.66 27.17 54.82 73.05 81.91 88.41 3 Brevideciduous 9.60 ± 3.59 18.19 ± 8.26 30.01 ± 10.26 33.88 ± 9.14 32.60 ± 5.30 36.40 ± 3.95 12 Evergreen 17.33 ± 9.12 32.85 ± 20.72 55.77 ± 34.60 67.35 ± 36.64 72.90 ± 38.02 59.24 ± 18.37 8 Total average 13.46 ± 4.81 25.52 ± 10.86 42.89 ± 17.64 50.61 ± 18.70 52.75 ± 19.28 47.82 ± 9.60 10 Sesquiterpenes Brosimum parinarioides 2.38 2.09 5.97 8.47 13.19 15.12 6 Endopleura uchi 7.81 3.80 7.28 11.70 13.46 26.61 3 Peltogyne catingae 0.65 3.71 5.46 4.23 9.94 12.04 19 Cariniana decandra 2.99 0.00 2.63 10.36 6.19 14.03 5 Manilkara bidentata 2.69 2.00 6.64 17.98 12.36 28.07 10 Pouteria guianensis 3.11 3.24 13.61 12.57 14.83 21.46 7 Eschweilera cyathiformis 0.41 1.48 4.53 5.40 12.77 13.99 34 Minquartia guianensis 1.30 4.26 6.76 9.83 11.96 15.57 12 Protium spruceanum 0.69 10.03 15.17 15.00 26.61 24.74 36 Croton matourensis 0.00 4.73 8.17 4.47 24.51 17.01 17 Dinizia excelsa 4.13 1.72 4.20 1.84 3.25 2.65 1 Scleronema micranthum 14.38 8.87 13.33 23.15 22.19 21.78 2 Brevideciduous 3.27 ± 0.98 2.47 ± 0.59 6.93 ± 1.49 10.88 ± 1.86 11.66 ± 1.28 19.55 ± 2.78 8 Evergreen 3.48 ± 2.26 5.18 ± 1.46 8.69 ± 1.87 9.95 ± 3.25 16.88 ± 3.69 15.96 ± 3.13 17 Total average 3.38 ± 1.18 3.83 ± 0.85 7.81 ± 1.17 10.42 ± 1.79 14.27 ± 2.02 17.76 ± 2.07 12.5 Isoprene non-emitters emitted proportionally higher amounts of monoterpenes and sesquiterpenes than isoprene emitters. Changes in monoterpene composition in response to temperature suggested that Brosimum parinarioides , Cariniana decandra , and Croton matourensis emitted monoterpenes in a light-dependent manner (Fig. 2 A,D,J) since their emissions followed an enzymatic activity response curve (Fischbach et al., 2000 ; Niinemets et al., 2002 ). Trans -β-ocimene dominated monoterpene emissions in B. parinarioides , but smaller amounts of cis -β-ocimene were also detected (Fig. 2 A). Monoterpene emissions of C. decandra and C. matourensis were dominated by α-pinene, but C. decandra also showed some β-pinene emissions, and smaller emissions of camphene, myrcene, sabinene, and tricyclene (Fig. 2 D). Croton matourensis showed small emissions of sabinene, camphene, and myrcene starting from 37.5°C (Fig. 2 J). Species showed unique patterns of changes in the percentage of photosynthetic carbon (%C) loss to VI emissions with rising leaf temperature, regardless of isoprene emissions or leaf phenological types (Table 3 , Fig. S8). For many species, %C loss to isoprene emissions became negative at a given leaf temperature, which indicates that photosynthesis had ceased and that the compound was likely being produced from alternative carbon pools (carbon storage, CS; Table 3 ) (Loreto et al., 2004 ; de Souza et al., 2018 ). There were also no significant differences in species’ photosynthetic thermal limits (i.e., leaf temperature where photosynthesis became negative) between isoprene emitters and non-emitters from different leaf phenological types (Fig. S9). Table 3 Values of percentages of photosynthetic carbon (%C) loss to emissions of isoprene, monoterpenes, and sesquiterpenes at each leaf temperature (°C) for each species measured in this study, average values for brevideciduous and evergreen species, and total average values for all species. CS = carbon storage; indicates that emissions continued after photosynthesis had ceased and derived solely from stored carbon pools. 30°C 35°C 37.5°C 40°C 42.5°C 45°C Isoprene Brosimum parinarioides 0.04 0.1 0.3 0.9 CS CS Endopleura uchi 1.1 2.5 3.4 5.4 17.3 2.7 Peltogyne catingae 1.1 2 3.7 5.8 8.6 10.9 Cariniana decandra 0.1 0.05 0.1 1.3 CS CS Manilkara bidentata 0.1 0.1 0.2 0.5 CS CS Pouteria guianensis 0.1 0.02 0.2 0.4 0.5 11.8 Eschweilera cyathiformis 1 1.6 3.5 9.7 CS CS Minquartia guianensis 1 2.2 2.6 4.1 6.2 11.7 Protium spruceanum 2.1 4.5 5.5 7.2 10.6 14.7 Croton matourensis 0.03 0.02 0.1 0.1 0.4 2.7 Dinizia excelsa 0.1 0.1 0.1 0.2 0.8 CS Scleronema micranthum 0.05 0.1 0.1 0.1 0.2 0.6 Monoterpenes Brosimum parinarioides 0.9 2.8 4.1 8.9 CS CS Endopleura uchi 0.6 0.6 1.4 2.2 9.5 2.4 Peltogyne catingae 0.02 0.1 0.6 1.4 1.9 4.2 Cariniana decandra 1.6 1.8 4.8 58 CS CS Manilkara bidentata 0.4 0.6 1 4.2 CS CS Pouteria guianensis 0.3 0.6 2.3 3.5 8.7 102.1 Eschweilera cyathiformis 0.1 0.4 1.8 6.8 CS CS Minquartia guianensis 0.3 0.9 1.5 2.6 4.5 19.8 Protium spruceanum 0.4 0.6 0.8 1.4 2.2 4.3 Croton matourensis 0.8 1.9 3.4 4.4 11.9 32.8 Dinizia excelsa 0.3 0.7 0.1 0.4 7.3 CS Scleronema micranthum 1.3 2.8 3.4 5.2 7.7 18.3 Sesquiterpenes Brosimum parinarioides 0.1 0.1 0.3 1 CS CS Endopleura uchi 0.5 0.2 0.5 1.1 3.6 1.5 Peltogyne catingae 0.03 0.2 0.4 0.4 1.2 2.5 Cariniana decandra 0.2 0 0.3 12.7 CS CS Manilkara bidentata 0.3 0.2 0.9 5.5 CS CS Pouteria guianensis 0.3 0.3 1.3 1.4 3.6 44.3 Eschweilera cyathiformis 0.05 0.1 0.6 1.8 CS CS Minquartia guianensis 0.1 0.5 0.7 1.2 2.3 7.8 Protium spruceanum 0.02 0.3 0.4 0.5 1.1 1.4 Croton matourensis 0 0.1 0.1 0.1 1.2 4.4 Dinizia excelsa 0.6 0.3 0.8 0.4 4.3 CS Scleronema micranthum 0.7 0.9 0.8 1.7 2.1 4.5 3.2 Characteristics of photochemical activity of isoprene emitters and non-emitters from different leaf phenological types None of the characteristics of photochemical activity measured in our study varied significantly between isoprene emitters and non-emitters from different leaf phenological types, though results suggested that brevideciduous isoprene emitters showed higher LSP (p (isop:pheno) = 0.08; Fig. 3 A). Parallel to this, J , qP , ϕ PSII , and A n all decreased with rising leaf temperature (Fig. 4 ). However, non-linear regression models revealed differences in temperature response parameters across isoprene emitters and non-emitters within each phenological group (Table 4 ). For net photosynthesis ( A n ), brevideciduous isoprene emitters exhibited the highest k 25 values, suggesting enhanced baseline photosynthetic performance. In contrast, for J ₜ, ϕ PSII , and qP , the highest k 25 values were observed in either evergreen non-emitters or split between non-emitters and emitters, without a consistent pattern. Across all groups and variables, the bootstrapped confidence intervals (CIs) for k 25 did not include zero, which supports the reliability of these baseline performance estimates. Estimates for activation energy ( E α ) showed high uncertainty, as CIs included zero in nearly all cases. For J , qP , and ϕ PSII , entropy values ( ΔS ) tended to be higher in non-emitters, particularly among evergreen trees. However, many ΔS CIs approached the model’s upper constraint (2000 J mol⁻¹ K⁻¹), indicating uncertainty in upper-bound estimates. Deactivation energy ( Hd ) was consistently higher in evergreen non-emitters across most variables, but brevideciduous non-emitters showed the highest Hd for A n . Confidence intervals for Hd estimates were broad but did not reach model bounds, supporting a trend of greater thermal stability in non-emitters, particularly evergreens. Table 4 Temperature response model parameters for net photosynthesis ( A n ), electron transport rate ( J ), quantum efficiency of photosystem II (ϕ PSII ), and photochemical quenching ( qP ), grouped by isoprene emission status (EM = emitter, NE = non-emitter) and leaf phenological type (BD = brevideciduous, EV = evergreen). For each variable and group, the fitted values of base activity at 25°C ( k 25 ), activation energy ( E α ), entropy ( ΔS ), and deactivation energy ( Hd ) are reported, along with their 95% bootstrapped confidence intervals (CI). k 25 reflects modeled activity under moderate temperature; E α indicates thermal sensitivity near baseline; Hd reflects thermal tolerance at high temperatures; and ΔS describes the shape of the deactivation curve. Modeled values are derived from non-linear regression fits using the peaked Arrhenius function. Group k 25 k 25 CI E α E α CI ΔS ΔS CI Hd Hd CI A n EM BD 7.49 0.53–12.42 48305.47 0–451963.16 1439.97 571.92–2000 447996.67 175174.48–627908.33 EM EV 3.34 0.19–8.95 161538.03 0–537463.69 1526.58 510.52–2000 471559.24 159017.24–633753.62 NE BD 3.35 0.48–5.23 63444.06 0–352622.45 1759.01 1139.87–2000 546957.13 353998.97–628015.26 NE EV 6.29 0.16–25.41 144391.92 0–469796.13 1642.16 508.77–2000 501196.46 152731.53–632673.28 J EM BD 3.33 0.04–7.59 181637.61 0–552209.76 1271.01 521.78–1999.9 392503.06 151417.5–627406.09 EM EV 3.28 0.11–8.28 166813.43 0–582195.67 1412.02 175.27–2000 432946.99 82031.26–638435.42 NE BD 2.9 0.2–5.06 113375.71 0–527639.77 1381.8 656.52–2000 431393.14 196559.12–634798.95 NE EV 3.64 0.13–12.4 168185.14 0–539373.84 1562.64 669.1–2000 480646.86 168918.67–635364.76 ϕ PSII EM BD 0.12 0.01–0.27 166814.43 0–553722.72 1274.32 343.33–1999.9 393664.72 107575.03–634680.95 EM EV 0.11 0.01–0.21 153554.4 0–583658.85 1427.61 236.11–2000 448178.26 106653.22–638343.23 NE BD 0.11 0.01–0.19 114216.22 0–541867.48 1354.2 636.93–2000 422725.17 192540.22–634997.78 NE EV 0.14 0.005–0.36 138841.58 0–541637.82 1526.81 628.59–2000 472402.47 163964.16–635347.55 qP EM BD 0.29 0.01–0.54 165268.8 0–571204.52 1193.95 360.82–1999.9 370427.88 114070.17–635815.47 EM EV 0.31 0.02–0.53 148545.09 0.000003–591400.47 1225.07 157.13–2000 385370.74 49769.9–640294.53 NE BD 0.31 0.02–0.53 130643.87 0–566028.93 1380.47 557.3–2000 429735.7 167521.58–636979.33 NE EV 0.3 0.01–0.62 157185.65 0–566731.46 1414.94 216.44–2000 439506.63 66931.47–637419.96 3.3 Light and temperature response parameters of isoprene emissions from different leaf phenological types and canopy-level flux projections Estimated model parameters α (initial light-response slope), C L1 (light-saturation coefficient), C T1 (temperature activation energy), and C T2 (temperature deactivation energy) from isoprene light and temperature response models for each species and average values for each leaf phenological type are presented in Table 5 . Results showed that brevideciduous isoprene emitters had higher C T2 (p = 0.001; Fig. 5 D) with a trend toward higher isoprene LSP (p = 0.09; Fig. 5 E). Still, dimethylallyl diphosphate (DMADP) pool sizes (Fig. S10A) and isoprene synthase ( IspS ) activity rates (Fig. S10B) were not significantly different between brevideciduous and evergreen trees. Table 5 Values of parameters α (initial light-response slope), C L1 (light-saturation coefficient), C T1 (temperature activation energy), and C T2 (temperature deactivation energy) empirically estimated based on non-linear least-square fits of light and temperature emission response curves (Guenther et al., 1999 ) to the observed data for each isoprene-emitting species measured in this study; average values for brevideciduous and evergreen trees; and values from Guenther et al. ( 1999 ). α C L1 C T1 C T2 Brosimum parinarioides 0.00365 0.98 150 562 Endopleura uchi 0.00883 0.88 81 658 Peltogyne catingae 0.00328 1.05 71 625 Eschweilera cyathiformis 0.00961 0.97 165 305 Minquartia guianensis 0.01714 1.04 178 311 Protium spruceanum 0.00848 1.03 123 254 BD 0.00525 0.97 101 615 EV 0.01174 1.01 155 290 Guenther et al ( 1999 ) 0.00142 1.22 95 230 Individual variation in isoprene emission rates estimated using empirically derived parameters showed that emissions were generally above 2000 µg m − 2 h − 1 at 09:00, peaked at 13:00 and decreased again at 17:00 for all trees (Fig. 6 ). Meanwhile, differences between values estimated with empirically derived and reference parameters (Guenther et al., 1999 ) indicated that the latter consistently overestimated isoprene emissions for both brevideciduous and evergreen trees (Fig. 7 ). It appeared that reference parameters underestimated emissions at 17:00 in October and November for brevideciduous trees (Fig. 7 A), and in October for evergreen trees (Fig. 7 B). However, Kruskal-Wallis pairwise comparisons showed that differences in emission rates at 17:00 were not statistically significant (Fig. 8 ), while reference parameter estimates were significantly higher at 09:00 and 13:00 for evergreen trees (Fig. 8 B), with a similar trend for brevideciduous trees (Fig. 8 A). Finally, we compared percentages of variation in canopy-level isoprene fluxes from brevideciduous and evergreen trees estimated using average emission factors from Robin et al. ( 2024 ) and empirically derived parameters from this study (Model 1), average emission factors from Robin et al. ( 2024 ) and reference parameters from Guenther et al. ( 1999 ) (Model 2), and emission factors from Guenther et al. ( 2012 ) and reference parameters from Guenther et al. ( 1999 ) (Model 3). Comparisons between models (Table 6 ) showed that Model 2 estimated 98.4 and 256.4% higher fluxes for brevideciduous and evergreen trees, respectively, compared to Model 1; and Model 3 fluxes were 794.8 and 1565.9% higher for brevideciduous and evergreen trees, respectively, compared to Model 1. Table 6 Values of (A) canopy-level isoprene fluxes (µg m − 2 h − 1 ) estimated at different hours of the day (09:00, 13:00, and 17:00) in October, November, and December/2022 using average emission factors fromRobin et al. ( 2024 ) and empirically derived parameters from this study (Model 1), average emission factors fromRobin et al. ( 2024 ) and reference parameters fromGuenther et al. ( 1999 ) (Model 2), and emission factors fromGuenther et al. ( 2012 ) and reference parameters fromGuenther et al. ( 1999 ) (Model 3); and (B) percentages of variation (%) between values estimated using each model. The average emission factor fromRobin et al. ( 2024 ) is 1552.2 µg m − 2 h − 1 for brevideciduous (BD) and 1497.4 µg m − 2 h − 1 for evergreen (EV); and from Guenther et al. ( 2012 ), the emission factor is 7000 µg m − 2 h − 1 , which is the value assigned for the plant functional types broadleaf evergreen tropical tree and broadleaf deciduous tropical tree. Model 1 Model 2 Model 3 (A) BD EV BD EV BD EV October 9:00 5170.7 2538.6 13938.4 13445.9 62856.6 62856.6 13:00 6888.2 4022.5 15099.6 14566.1 68093.1 68093.1 17:00 2606.9 2366.7 2340.6 2257.9 10555.1 10555.1 November 9:00 4813.1 2278.1 12999.1 12539.8 58620.7 58620.7 13:00 6847.9 3967.9 15417.3 14872.5 69525.5 69525.5 17:00 2583.8 2161.2 2380.3 2296.2 10734.2 10734.2 December 9:00 4323.1 1943.7 11381.9 10979.8 51327.9 51327.9 13:00 5614.6 2894.2 14363.2 13855.7 64772.1 64772.1 17:00 2636.0 1766.9 2655.1 2561.3 11973.6 11973.6 Model 1 vs. 2 (%) Model 1 vs. 3 (%) Model 2 vs. 3 (%) (B) BD EV BD EV BD EV October 9:00 169.6 429.7 1115.6 2376.0 351.0 367.5 13:00 119.2 262.1 888.6 1592.8 351.0 367.5 17:00 -10.2 -4.6 304.9 346.0 351.0 367.5 November 9:00 170.1 450.4 1117.9 2473.2 351.0 367.5 13:00 125.1 274.8 915.3 1652.2 351.0 367.5 17:00 -7.9 6.2 315.4 396.7 351.0 367.5 December 9:00 163.3 464.9 1087.3 2540.7 351.0 367.5 13:00 155.8 378.7 1053.6 2138.0 351.0 367.5 17:00 0.7 45.0 354.2 577.7 351.0 367.5 Average 9:00 167.6 448.3 1107.0 2463.3 351.0 367.5 13:00 133.4 305.2 952.5 1794.3 351.0 367.5 17:00 -5.8 15.5 324.9 440.1 351.0 367.5 Total average 98.4 256.4 794.8 1565.9 351.0 367.5 4. Discussion The purpose of our study was to evaluate i) how VI emissions varied in response to light and temperature changes in different leaf phenological types; ii) how characteristics of photochemical activity varied between isoprene emitters and non-emitters from different leaf phenological types; and iii) how the interaction of leaf phenological types and light and temperature changes affected canopy isoprene emission estimates. We observed that i) emission rates of all volatile compounds increased with rising leaf temperature, with a stronger effect for monoterpene and sesquiterpene emissions, which increased 10 and 12.5-fold from 30 to 45°C, respectively. While isoprene and monoterpene emissions did not differ between leaf phenological types, brevideciduous trees had higher increases in sesquiterpene emissions with leaf temperature and higher emission rates at 45°C. Moreover, we observed that ii) characteristics of photochemical activity varied across isoprene emitters and non-emitters from brevideciduous and evergreen trees, with brevideciduous isoprene emitters showing the highest baseline photosynthetic performance and a trend toward higher photosynthesis and isoprene light saturation points ( LSP ). Lastly, we saw that iii) brevideciduous isoprene emitters had higher isoprene temperature deactivation energy ( C T2 ) and a trend toward higher isoprene LSP , and that canopy-level isoprene fluxes estimated using reference emission factors and parameters (Guenther et al., 1999 , 2012 ) highly overestimated fluxes when compared to estimates using emission factors from Robin et al. ( 2024 ) and empirically derived parameters from our light and temperature response curves. 4.1 VI emission responses to changes in light and temperature in different leaf phenological types Temperature response curves of VI emissions revealed that emission rates of all compounds increased with leaf temperature. Isoprene emissions increased on average 3.5 times from 30 to 45°C for both brevideciduous and evergreen trees, and even species that were not previously classified as isoprene emitters (Table 1 ) showed such increases. This suggests that the Amazon Forest possibly harbors a much larger percentage of isoprene emitters (Jardine et al., 2020 ; Mu et al., 2022 ) than previously thought (Harley et al., 2004 ; Loreto & Fineschi, 2015 ). Furthermore, all species measured showed significantly higher isoprene emission factors (emission measured at photosynthetic photon flux density of 1000 µmol m − 2 s − 1 and leaf temperature of 30°C) in this study compared to Robin et al. ( 2024 ) (Fig. S11). We argue that this might be because here we performed measurements in early December - compared to October/November (Robin et al., 2024 ) - when leaves were probably overall older and thus more photosynthetically active, with higher isoprene synthase ( IspS ) activity and isoprene emission rates (Schnitzler et al., 1997 ; Alves et al., 2014 , 2018 ). Our results also corroborate studies showing that the highest isoprene fluxes are not observed in the warmest months, but when canopies have proportionally larger fractions of mature leaves (Gomes Alves et al., 2023 ). We also saw that monoterpene and sesquiterpene emissions increased on average 10 to 12.5 times, respectively, between 30 and 45°C. Brevideciduous trees had higher increases in sesquiterpene emissions with temperature, as well as higher sesquiterpene emission rates at 45°C. Likewise, even though isoprenoid mass investments showed a trend to shift from “lighter” (i.e., isoprene) to “heavier” (i.e., monoterpenes and sesquiterpenes) compounds at higher temperatures, this effect was potentially stronger in brevideciduous trees. Moreover, monoterpene emissions reached higher magnitudes (52.75 ± 19.28 µg C g − 1 h − 1 ) than isoprene (39.9 ± 20.88 µg C g − 1 h − 1 ) and sesquiterpenes (14.3 ± 2.02 µg C g − 1 h − 1 ) at 42.5°C. Similarly, empirically derived β coefficients (Guenther et al., 1993 ) for most monoterpenes and sesquiterpenes measured fell above the ± 20 % range values adoptedin MEGAN v2.1 (Guenther et al., 2012 ) (Fig. S12). The exceptions were α- and β-pinene and trans -β-ocimene, and this could be because these were likely emitted in a light-dependent manner. Nevertheless, this indicates that most monoterpenes and sesquiterpenes showed higher temperature sensitivities than previously thought (Nagalingam et al., 2023 ; Bourtsoukidis et al., 2024 ). Temperature response curves suggested that Brosimum parinarioides and Croton matourensis emitted light-dependent monoterpenes, as they showed emissions of trans -β-ocimene ( B. parinarioides ) and α-pinene ( C. matourensis ) that do not follow the typical exponential increase of storage pool emissions (Guenther et al., 1993 ), but follow that of enzymatic activity temperature responses, with a T opt around 40°C (Fischbach et al., 2000 ; Niinemets et al., 2002 ). This is further supported by the monoterpene light response curves in these species (Fig. S5). Temperature curves also suggested the presence of light-dependent monoterpene emissions in Cariniana decandra , since the species showed similar emission patterns for α-pinene. Trans -β-ocimene dominated monoterpene emissions in B. parinarioides . This is quite relevant given that 13 C-labeling has demonstrated that Amazon Forest trees emit this compound in a light-dependent manner and that trans -β-ocimene reacts more rapidly to NO x and contributes to higher O 3 formation in polluted atmospheres (Jardine et al., 2017 ). Regardless of leaf phenological types, all species showed unique patterns of changes in percentages of carbon (%C) loss to VI emissions with temperature, as well as unique photosynthetic thermal limits (i.e., leaf temperature where photosynthesis ceased). Whether they emitted isoprene or not, some species were able to sustain photosynthesis at 45°C while others ceased between 40 and 42.5°C. Still, %C loss to isoprene emissions reached up to 14.4% before photosynthesis ceased, which represents much higher losses compared to the percentages generally assumed under non-stress conditions (1–2%; Sharkey & Loreto, 1993 ; Kesselmeier et al., 2002 ). Also, in many cases, isoprene emissions continued even after photosynthesis ceased, signaling the use of alternative carbon sources for isoprene production (Jardine et al., 2014 ; de Souza et al., 2018 ). Moreover, %C loss to monoterpene and sesquiterpene emissions reached values as high as 85.1 and 36.9%, respectively. While sesquiterpenes and some monoterpenes are emitted from storage pools and do not rely directly on photosynthetic carbon, these values still represent major losses to species’ overall carbon budgets under high-temperature stress. Observed %C loss to monoterpene emissions in B. parinarioides and C. decandra also suggested shifts from light-dependent emissions to storage pool emissions or use of stored carbon reserves at 40°C, when photosynthesis became negative. Given that Amazon Forest canopies are likely to experience temperatures exceeding 40°C (Jardine et al., 2017 ; Manzi et al., 2024 ), and that many of the species measured here are highly positioned in the regional biomass rank (Fauset et al., 2015 ), our findings suggest that current models probably underestimate monoterpene and sesquiterpene global fluxes (Kuhn et al., 2007 ; Guenther et al., 2012 ; Jardine et al., 2015 ). Monoterpenes and sesquiterpenes not only incur higher carbon losses but also contribute to two and 10 times more particle formation than isoprene, respectively (Griffin et al., 1999b ; Kroll et al., 2005 ; Xu et al., 2014 ). Therefore, our findings are critical considering that warmer and drier climates will possibly favor the selection of brevideciduous trees (Aleixo et al., 2019 ) and that increasingly more frequent and intense stressors (Gatti et al., 2021 ) are expected to promote stronger stress-induced emissions of these “heavier” and more chemically reactive compounds - which were also seen here as significantly associated with brevideciduity. 4.2 Physiological changes in isoprene emitters and non-emitters across different leaf phenological types Our results showed that net photosynthesis ( A n ), electron transfer rates ( J ), photochemical quenching ( qP ), and quantum efficiency of photosystem II (ϕ PSII ) all decreased with leaf temperature. However, we observed that the modeled temperature responses of these variables varied differently across isoprene emitters and non-emitters from different leaf phenological types. While brevideciduous isoprene emitters showed elevated photosynthetic performance at moderate temperatures, no consistent thermal advantage was observed across other characteristics of photochemical activity in isoprene emitters. Moreover, entropy and deactivation energy patterns pointed toward greater thermal stability in isoprene non-emitters, particularly evergreens. Nonetheless, trends in light saturation points and the high photosynthesis k 25 value suggest that brevideciduous isoprene emitters may exhibit functional advantages under moderately high light and temperature conditions. These findings challenge the notion of a uniform stress-tolerance benefit from isoprene emission (Singsaas et al., 1997 ; Hanson & Sharkey, 2001 ; Behnke et al., 2007 ; Taylor et al., 2019 ; Rodrigues et al., 2020 ), and instead suggest that light and temperature stress responses in central Amazon Forest trees may emerge from interactions between isoprene emissions and leaf turnover strategies (Robin et al., 2024 ). The high baseline photosynthetic performance and observed trends in light saturation points in brevideciduous isoprene emitters are likely explained by isoprene-associated mechanisms, such as reactive oxygen species (ROS) scavenging and modulation of stress signaling (Zuo et al., 2025 ). These mechanisms have been proposed to mitigate photoinhibition under high irradiance conditions (Vickers et al., 2009 ; Pollastri et al., 2014 ). Concurrently, brevideciduous trees also showed stronger increases in sesquiterpene emissions and a trend toward shifting from “lighter” to “heavier” compound emissions. Sesquiterpene emission incurs higher carbon losses, and it had been previously suggested that resource-acquisitive (Wright et al., 2004 ) brevideciduous leaves were not likely to favor more carbon “costly” compounds (Harrison et al., 2013 ). Nonetheless, sesquiterpenes may highly benefit brevideciduous trees, as these compounds are deeply involved in herbivore deterrence and plant communication (Pichersky & Gershenzon, 2002 ; Fineschi & Loreto, 2012 ). This suggests that brevideciduous trees might be equipped with sesquiterpene emissions to cope with biotic stressors. It has been hypothesized that central Amazon Forest trees typically flush leaves during the dry season as protection against drought stress and herbivory (Lopes et al., 2016 ); and brevideciduous trees annually renew major fractions - if not all - of their canopies in a synchronous manner (Lopes et al., 2016 ; Gonçalves et al., 2020 ). Thus, we suggest that increased sesquiterpene production is a considerable advantage for brevideciduous trees since it would also benefit the defenses from nearby plants, and potentially provide a stronger “community-level” shield for cohorts of vulnerable young leaves in the dry season (Coley & Barone, 1996 ; Robin et al., 2024 ). This idea is supported by evidence that isoprene exposure can upregulate terpene synthase gene expression in Arabidopsis (Harvey & Sharkey, 2016 ). Moreover, studies have demonstrated that isoprene emissions were positively correlated with emissions of sesquiterpene compounds α-copaene, α-humulene, alloaromadendrene, and β-caryophyllene (Zeng et al., 2025 ), and that brevideciduous central Amazon Forest trees showed an increased diversity of stored sesquiterpene compounds associated with higher isoprene emission factors (Robin et al., 2024 ). Taken together, these findings suggest that brevideciduous trees may rely on a coordinated strategy that combines abiotic stress tolerance via isoprene with enhanced chemical defense through sesquiterpenes, optimized for synchronous canopy renewal during the dry season. 4.3 Canopy-level variations in isoprene fluxes from brevideciduous and evergreen trees Comparisons of isoprene emission rates estimated for 175 canopy-dominant trees measured in Robin et al. ( 2024 ) using empirically derived and reference parameters (Guenther et al., 1999 ) showed that the latter consistently overestimated isoprene emissions at 09:00 and 13:00 and that the effect was significantly stronger for evergreen trees. Our findings also demonstrated that a high degree of spatial heterogeneity in leaf-level isoprene emission rates can be found even in considerably small areas (~ 4 ha). Similarly, canopy-level isoprene fluxes estimated using reference emission factors and light and temperature response parameters were extremely higher than fluxes estimated with observed emission factors and empirically derived parameters, particularly for evergreen trees. This suggests that parameter values currently used in global isoprene flux models (Guenther et al., 1999 , 2012 ) still carry high uncertainty for the Amazon Forest. Based on our results, we suggest that current model estimates will improve if models are inputted by gridded emission maps based on observed emission factors and empirically derived parameters, rather than relying on single values assigned for simplified plant functional type (PFT) classifications. Furthermore, reference emission factors and parameters currently used in models overall derive from flux tower measurements, which are still scarce across the Amazon basin and therefore tend to smooth spatial variability. Hence, these findings also emphasize the importance of leaf-level measurements for obtaining more accurate isoprene emission factors and estimating parameters that truly reflect variation in emission responses to light and temperature in different leaf phenological types. 4.4 Implications and summary Conducting field experiments in remote and isolated locations of the Amazon Forest - particularly when sampling from very tall trees (> 30 m) - is inherently challenging, and often restricts the number of available observations and contributes to high data variability. These limitations become especially pronounced when numerous variables are investigated simultaneously, impacting statistical significance. Nevertheless, despite these constraints, our results revealed significant relationships and clear trends consistent with physiological expectations. These findings establish an important foundation for future studies exploring ecophysiological differences between isoprene emitters and non-emitters from different leaf phenological types and emphasize the importance of linking leaf-level observations to broader biogeochemical processes. In sum, our findings provide robust empirical evidence that light and temperature responses of VI emissions vary not only with species physiology but with leaf phenological strategy - a dimension largely overlooked for tropical tree species in current global models (e.g., Guenther et al., 2012 ). We demonstrated that standard parameterizations based on coarse functional types systematically misrepresent emission dynamics, particularly in evergreen Amazonian canopies, and that brevideciduous trees may engage in a coordinated defense strategy involving both isoprene and sesquiterpenes. As climate change continues to alter thermal and hydrological regimes across the Amazon (Malhi et al., 2008 ; Gloor et al., 2013 ; Flores et al., 2024 ), these shifts in emission chemistry and canopy phenology could drive changes in atmospheric reactivity and feedback processes (Yáñez-Serrano et al., 2020 ). Given that the Amazon Forest harbors the largest area of tropical forest globally (Nobre et al., 2021 ) and is the largest contributor to global volatile isoprenoid fluxes (Guenther et al., 2012 ; Gomes Alves et al., 2023 ), understanding these emission dynamics is critical. Our study, therefore, lays a foundation for incorporating ecophysiological nuance into global BVOC emission models and highlights the urgent need for gridded, trait-based parameter maps rooted in in-situ leaf-level measurements. This approach will be critical for improving the accuracy of predictions related to atmospheric composition, biosphere resilience, and climate–vegetation feedbacks in tropical forests. Declarations Competing interests The authors declare no conflicts of interest. Funding This study was funded by the German-Brazilian project ATTO (Amazon Tall Tower Observatory), supported by the German Federal Ministry of Education and Research (BMBF, funds 01LB1001A and 01LK2101D) and by the Brazilian Ministry of Science, Technology and Innovation (FINEP/MCTI, contract 01.11.01248.00). MR was supported by the International Max Planck Research School for global biogeochemical cycles (IMPRS-gBGC). Additional support was provided by the Ministry of Education and Research of Estonia (Center of Excellence AgroCropFuture, project TK200), and the Estonian Research Council (grants MOBJD696 and PRG2207). Authors' contributions Michelle Robin contributed to the development and sampling design of the study; the collection of volatile isoprenoid, gas exchange, and chlorophyll fluorescence data; and the statistical analysis of datasets. Vinícius F. de Souza contributed to the collection of volatile isoprenoid, gas exchange, and chlorophyll fluorescence data; and the analysis of first dark-decay kinetics of isoprene emissions. Joseph Byron contributed to the identification and quantification of volatile isoprenoids accumulated in adsorbent cartridges. Ülo Niinemets contributed to the provision of equipment; and the statistical analysis of datasets. Christine Römermann contributed to the development and sampling design of the study. Flávio A. F. D’Oliveira and Cléo Quaresma Dias-Junior contributed to the micrometeorological tower data from the upland forest plot. Jonathan Williams and José Francisco C. Gonçalves contributed to the provision of equipment. Eliane Gomes Alves contributed to the development and sampling design of the study; the funding acquisition; the provision of equipment; and the statistical analysis of datasets. All authors contributed to the writing of the manuscript. Acknowledgements We acknowledge the support of the ATTO project, LBA/INPA, and SDS/CEUC/RDS-Uatumã. We truly thank Prof. Juliana Schietti for the assistance with field equipment and processing of leaf material. We would also like to thank all the field assistants, Jose Raimundo Ferreira Nunes, Jardel Valente Nunes, Jardison Valente Nunes, Alessandra Peixoto, and Gabriela Ushida Neves; and all the people involved in the logistic support of the ATTO project, especially Roberta de Souza, who were all imperative for the development of this study. Data availability statement The entire dataset generated and analyzed for this study can be found in the ATTO Data Portal ( https://www.attodata.org/ddm/data/Showdata/519 ) and is available upon request. References Affek HP, Yakir D . 2003 . Natural Abundance Carbon Isotope Composition of Isoprene Reflects Incomplete Coupling between Isoprene Synthesis and Photosynthetic Carbon Flow. Plant Physiology 131 : 1727–1736. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7270146","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503844382,"identity":"ca30fed9-a1e4-4cff-a6fc-2a9959014418","order_by":0,"name":"Michelle Robin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYPACCQYG9h4Qgw0uxNhAUAvPGdK0gHTloPJxatFtP/zsc0GFRT6/5NvDL34w8MmZsx9g3Vzwi0G2H4cWszNpxrNnnJGwnDk7L82yh4HN2LInge32zD4G45k4rDG7wWDMzNsmYWBwO8fMmPEfW+KGA0AtvD0MQAYuLeyfmXn/AbXcPGNmDPR+/YbzDyBa9uPUwgO0pQGoBch4DNSSYHADaAvPD6AtOP2SU8zMc0zCQLInL40R6BfDDTcett2e2SBhPAOXLcePb2bmqakz4Gc/e/jDD4Zj8gbnk4/dLvhjI9uPw/vIgA0YoccYQDHCzNgmQVg9EDB/YGCogbAY/hClYxSMglEwCkYGAACN6VlFHWhcxQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2280-9436","institution":"Biogeochemical Processes Department, Max Planck Institute for Biogeochemistry, Jena, Germany","correspondingAuthor":true,"prefix":"","firstName":"Michelle","middleName":"","lastName":"Robin","suffix":""},{"id":503844383,"identity":"f4f36b00-fc2f-4d53-96f4-ea432851b517","order_by":1,"name":"Vinícius de Souza","email":"","orcid":"","institution":"Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia","correspondingAuthor":false,"prefix":"","firstName":"Vinícius","middleName":"","lastName":"de Souza","suffix":""},{"id":503844384,"identity":"878f62b8-4768-4761-8862-1f068fd6bc75","order_by":2,"name":"Joseph Byron","email":"","orcid":"https://orcid.org/0000-0001-9452-0186","institution":"Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany​","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Byron","suffix":""},{"id":503844385,"identity":"a96605f2-1dd9-4c07-a953-e8209361eda9","order_by":3,"name":"Ülo Niinemets","email":"","orcid":"","institution":"Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia","correspondingAuthor":false,"prefix":"","firstName":"Ülo","middleName":"","lastName":"Niinemets","suffix":""},{"id":503844386,"identity":"a8542c5b-94e6-4ae5-af29-86dd86748e41","order_by":4,"name":"Jonathan Williams","email":"","orcid":"https://orcid.org/0000-0001-9421-1703","institution":"Atmospheric Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany","correspondingAuthor":false,"prefix":"","firstName":"Jonathan","middleName":"","lastName":"Williams","suffix":""},{"id":503844387,"identity":"21ee789f-af1e-4c71-bb79-d74b82421a45","order_by":5,"name":"Christine Römermann","email":"","orcid":"","institution":"Institute for Biodiversity, Ecology and Evolution, Friedrich-Schiller University, Jena, Germany; Senckenberg Institute for Plant Form and Function Jena (SIP), Jena, Germany; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Germany","correspondingAuthor":false,"prefix":"","firstName":"Christine","middleName":"","lastName":"Römermann","suffix":""},{"id":503844388,"identity":"ced88c58-6a3a-4dd9-bc81-dc990e57ec04","order_by":6,"name":"Flávio D’Oliveira","email":"","orcid":"https://orcid.org/0000-0003-1123-6441","institution":"Department of Physics, Federal Institute of Pará (IFPA), Belém, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Flávio","middleName":"","lastName":"D’Oliveira","suffix":""},{"id":503844389,"identity":"c735b5be-62f0-4ce3-affe-2e5954673fed","order_by":7,"name":"Cléo Dias-Junior","email":"","orcid":"https://orcid.org/0000-0003-4783-4689","institution":"Department of Physics, Federal Institute of Pará (IFPA), Belém, Brazil; Department of Climate and Environment, National Institute of Amazonian Research, Manaus, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Cléo","middleName":"","lastName":"Dias-Junior","suffix":""},{"id":503844390,"identity":"a2b09608-f2ca-473f-857f-40a02401c89b","order_by":8,"name":"José Francisco Gonçalves","email":"","orcid":"","institution":"Coordination of Environmental Dynamics, National Institute of Amazonian Research, Manaus, Brazil","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Francisco","lastName":"Gonçalves","suffix":""},{"id":503844391,"identity":"cb877537-6a3c-462a-b257-364af41f9efa","order_by":9,"name":"Eliane Gomes Alves","email":"","orcid":"https://orcid.org/0000-0001-5245-1952","institution":"Biogeochemical Processes Department, Max Planck Institute for Biogeochemistry, Jena, Germany; Department of Climate and Environment, National Institute of Amazonian Research, Manaus, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Eliane","middleName":"Gomes","lastName":"Alves","suffix":""}],"badges":[],"createdAt":"2025-08-01 10:16:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7270146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7270146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90492720,"identity":"479a56af-f42f-426f-97a7-fc7e42aba6b8","added_by":"auto","created_at":"2025-09-03 09:57:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54316,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of (A) isoprene (\u003cem\u003en\u003c/em\u003e = 6), (B) monoterpene (\u003cem\u003en\u003c/em\u003e = 12), and (C) sesquiterpene (\u003cem\u003en\u003c/em\u003e = 12) emission responses (nmol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) and (D) isoprenoid emission metric (\u003cem\u003en\u003c/em\u003e = 12) responses to changes in leaf temperature (°C) between brevideciduous (brown) and evergreen (green) trees. The isoprenoid emission metric was calculated for each leaf temperature by multiplying the mass-based emission rate (µg C g\u003csup\u003e-1 \u003c/sup\u003eh\u003csup\u003e-1\u003c/sup\u003e) of each compound by their respective number of C atoms and dividing the summed carbon-weighted values by the sum of the mass-based emission rates. Values close to 5, 10, and 15 indicate how much of the emission profile is dominated by isoprene, monoterpenes, or sesquiterpenes, respectively. Boxplots show the median and 25th and 75th percentiles, whiskers show the maximum and minimum acquired data points that were not considered outliers, black circles represent the observed data points, and average values are indicated by the white squares. The \u003cem\u003ep\u003c/em\u003e values were extracted from general linear regression models of each \u003cem\u003ey\u003c/em\u003e variable varying as a function of leaf phenological types (Brevideciduous, Evergreen) at each leaf temperature step (T\u003csub\u003ei\u003c/sub\u003e) (\u003cem\u003ey\u003c/em\u003e at T\u003csub\u003ei\u003c/sub\u003e ~ pheno.type; \u003cem\u003ep\u003c/em\u003e (T\u003csub\u003ei \u003c/sub\u003e°C)), and varying as a function of leaf temperature (\u003cem\u003ey\u003c/em\u003e ~ temp; \u003cem\u003ep\u003c/em\u003e (temp)), of leaf phenological types (\u003cem\u003ey\u003c/em\u003e ~ pheno.type; \u003cem\u003ep\u003c/em\u003e (pheno.type)), and of the interaction between temperature and leaf phenological type (\u003cem\u003ey\u003c/em\u003e ~ temp * pheno.type; \u003cem\u003ep\u003c/em\u003e (temp:pheno)).\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/f847a7b1322a9feaef53d3aa.png"},{"id":90492721,"identity":"4d29ac32-3bba-4fd4-a68e-e3fdd3abf7bf","added_by":"auto","created_at":"2025-09-03 09:57:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":83548,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the composition of emitted volatile isoprenoids (VI; µg C g\u003csup\u003e-1\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) as a response to increases in leaf temperature (°C) for each species measured in this study. The font color of the species names corresponds to their leaf phenological types (Brevideciduous - brown; Evergreen - green). The symbol “*” indicates that the species was classified as an isoprene emitter in Robin et al. (2024). Isoprene data is derived from PTR-QMS measurements and shown in gray. Monoterpene and sesquiterpene data are derived from absorbent cartridge measurements and shown in blue and red, respectively.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/0f80f03e6a96dcbe2c2f7730.png"},{"id":90492993,"identity":"8af2ffb4-183b-420f-9982-6147b6ad9de2","added_by":"auto","created_at":"2025-09-03 10:05:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":75285,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of (A) photosynthesis light saturation point (\u003cem\u003eLSP\u003c/em\u003e, µmol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e), (B) photosynthetic capacity (\u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e, µmol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e), (C) electron transport rate (\u003cem\u003eJ\u003c/em\u003e, µmol e\u003csup\u003e-\u003c/sup\u003e m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e), (D) net assimilation rate (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e, µmol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) at 45 °C, (E) photosynthesis temperature optimum (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e, °C), (F) quantum efficiency of photosystem II (ϕ\u003csub\u003ePSII\u003c/sub\u003e), and (G) stomatal conductance (\u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e, mol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) measured at \u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e, between brevideciduous (brown) and evergreen (green) isoprene emitters and non-emitters (\u003cem\u003en\u003c/em\u003e = 12). Boxplots show the median and 25th and 75th percentiles, whiskers show the maximum and minimum acquired data points that were not considered outliers, black circles represent the observed data points, and average values are indicated by the white squares. The \u003cem\u003ep\u003c/em\u003e values were extracted from general linear regression models of each \u003cem\u003ey\u003c/em\u003e varying as a function of isoprene emissions (Emitter, Non-emitter; \u003cem\u003ey\u003c/em\u003e ~ isoprene; \u003cem\u003ep\u003c/em\u003e (isoprene)), of leaf phenological types (Brevideciduous, Evergreen; \u003cem\u003ey\u003c/em\u003e ~ pheno.type; \u003cem\u003ep\u003c/em\u003e (pheno.type)), and of the interaction between isoprene emissions and leaf phenological types (\u003cem\u003ey\u003c/em\u003e ~ isoprene * pheno.type; \u003cem\u003ep\u003c/em\u003e (isop:pheno)).\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/75afb2b498781d64d7154e12.png"},{"id":90492722,"identity":"d4152da6-b2c3-4527-8d1f-2e0c5a133214","added_by":"auto","created_at":"2025-09-03 09:57:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":63863,"visible":true,"origin":"","legend":"\u003cp\u003eResponses of (A) electron transport rate (\u003cem\u003eJ\u003c/em\u003e, µmol e\u003csup\u003e-\u003c/sup\u003e m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e), (B) photochemical quenching (\u003cem\u003eqP\u003c/em\u003e), (C) quantum efficiency of photosystem II (ϕ\u003csub\u003ePSII\u003c/sub\u003e), and (D) net assimilation rate (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e, µmol CO\u003csub\u003e2\u003c/sub\u003e m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) with increases in leaf temperature (°C) between brevideciduous (brown) and evergreen (green) isoprene emitters (solid lines) and non-emitters (dashed lines) (\u003cem\u003en\u003c/em\u003e = 12).\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/10720a31e9de90b38e1b7aac.png"},{"id":90493883,"identity":"88d26c92-74bf-4722-9f76-558669940c75","added_by":"auto","created_at":"2025-09-03 10:13:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":42832,"visible":true,"origin":"","legend":"\u003cp\u003eComparisons of parameters (A) α (initial light-response slope), (B) \u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e (light-saturation coefficient), (C) \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e (temperature activation energy), and (D) \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e (temperature deactivation energy) empirically estimated based on non-linear least-square fits of light and temperature emission response curves (Guenther et al., 1999) to the observed data; and isoprene emission (E) light saturation point (\u003cem\u003eLSP\u003c/em\u003e), and (F) temperature optimum (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e, °C) between brevideciduous (brown) and evergreen (green) isoprene emitters (\u003cem\u003en\u003c/em\u003e = 6). Boxplots show the median and 25th and 75th percentiles, whiskers show the maximum and minimum acquired data points that were not considered outliers, black circles represent the observed data points, and average values are indicated by the white squares. The \u003cem\u003ep\u003c/em\u003e values were extracted from general linear regression models of each \u003cem\u003ey\u003c/em\u003e varying as a function of leaf phenological types (Brevideciduous, Evergreen; \u003cem\u003ey\u003c/em\u003e ~ pheno.type). Dashed horizontal gray lines in A-D indicate parameter values from Guenther et al. (1999) and the shaded area represents the ±20% range around each suggested value.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/118fa73e3a70a7448ff4b947.png"},{"id":90492725,"identity":"987589e1-1a3f-4e98-8e79-c5be547f375d","added_by":"auto","created_at":"2025-09-03 09:57:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23225,"visible":true,"origin":"","legend":"\u003cp\u003eIsoprene emission rates (µg m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) estimated using empirically derived parameters from this study and isoprene emission factors measured from 175 canopy-dominant trees in an upland forest plot located at the Amazon Tall Tower Observatory (ATTO) site (Robin \u003cem\u003eet al.\u003c/em\u003e, 2024). (A) brevideciduous emitting isoprene (\u003cem\u003en\u003c/em\u003e = 25) trees and (B) evergreen emitting isoprene (\u003cem\u003en\u003c/em\u003e = 63) trees. Gray cells in all subplots indicate brevideciduous (\u003cem\u003en\u003c/em\u003e = 19) and evergreen (\u003cem\u003en\u003c/em\u003e = 68) trees for which isoprene emission was not detected in Robin et al. (2024). Fluxes were estimated using air temperature (°C) and incident photosynthetic photon flux density (PPFD, µmol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) recorded at different hours of the day in October, November, and December/2022.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/c2e671d2957f02481d1dff2a.png"},{"id":90492731,"identity":"19b7e992-3fc0-4709-9a7e-8adfecb771f8","added_by":"auto","created_at":"2025-09-03 09:57:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":25883,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized differences between isoprene emission rates (µg m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) estimated using empirically derived parameters from this study and reference parameters from Guenther et al. (1999). All emission rates were modeled using isoprene emission factors measured for 175 canopy-dominant trees in an upland forest plot located at the Amazon Tall Tower Observatory (ATTO) site (Robin \u003cem\u003eet al.\u003c/em\u003e, 2024). (A) brevideciduous emitting isoprene (\u003cem\u003en\u003c/em\u003e = 25) trees and (B) evergreen emitting isoprene (\u003cem\u003en\u003c/em\u003e = 63) trees. Gray cells in all subplots indicate brevideciduous (\u003cem\u003en\u003c/em\u003e = 19) and evergreen (\u003cem\u003en\u003c/em\u003e = 68) trees for which isoprene emission was not detected in Robin et al. (2024). Fluxes were estimated using air temperature (°C) and incident photosynthetic photon flux density (PPFD, µmol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) recorded at different hours of the day in October, November, and December/2022.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/ba1c8c5843c6f7da80ea4719.png"},{"id":90492724,"identity":"579d0a9f-505f-4668-8f11-ded9c88c949e","added_by":"auto","created_at":"2025-09-03 09:57:22","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":59211,"visible":true,"origin":"","legend":"\u003cp\u003ePairwise comparisons of isoprene emission rates (µg m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) estimated for 175 canopy-dominant (A) brevideciduous (\u003cem\u003en\u003c/em\u003e = 25) and (B) evergreen (\u003cem\u003en\u003c/em\u003e = 63) trees at different hours of the day in an upland forest plot located at the Amazon Tall Tower Observatory (ATTO) site using empirically derived (diagonal, Model 1) and reference (solid, Model 2) parameters. Fluxes were estimated using air temperature and incident photosynthetic photon flux density (PPFD, µmol m\u003csup\u003e-2\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e) at different hours of the day in October, November, and December/2022. Boxplots show the median and 25th and 75th percentiles, whiskers show the maximum and minimum acquired data points that were not considered outliers, black circles represent the observed data points, and average values are indicated by the white squares. The \u003cem\u003ep\u003c/em\u003e values were extracted from Kruskal-Wallis pairwise comparisons between isoprene emission rates estimated using empirically derived and reference parameters at each hour of the day.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/83cecbdf0550b4502c4004b7.png"},{"id":91148182,"identity":"89f90826-5b79-41c1-a3da-f2f72e58acd4","added_by":"auto","created_at":"2025-09-12 06:43:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6744380,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/a36e928b-d95a-4c1f-87b2-89efa9769061.pdf"},{"id":90492999,"identity":"a9da6d5b-79fa-4fd0-aae4-e03dd628d836","added_by":"auto","created_at":"2025-09-03 10:05:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5372852,"visible":true,"origin":"","legend":"Supplementary figures and tables","description":"","filename":"SupplementarymaterialCEE.docx","url":"https://assets-eu.researchsquare.com/files/rs-7270146/v1/7a6b0910535fa4c43c987e1a.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Coordinated volatile isoprenoid production and leaf turnover protect central Amazon Forest trees against stress","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Amazon Forest covers more than half of the tropical forest area in the world and stores up to 200 Pg of carbon (Malhi \u0026amp; Grace, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Nobre et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Yet, climate stress (e.g., droughts, heatwaves, floods, elevated atmospheric CO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e) (Calvin et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) is slowly turning it into a net carbon source due to rising tree vulnerability to disturbances like windthrows, fires, insect outbreaks, and pathogens (Gatti et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Hence, understanding the ecophysiological mechanisms driving stress responses of Amazonian trees is both timely and critical. Such mechanisms include leaf turnover (Aleixo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the production of volatile isoprenoids (VIs) like isoprene (C\u003csub\u003e5\u003c/sub\u003eH\u003csub\u003e8\u003c/sub\u003e), monoterpenes (C\u003csub\u003e10\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003e) and sesquiterpenes (C\u003csub\u003e15\u003c/sub\u003eH\u003csub\u003e24\u003c/sub\u003e) (Loreto \u0026amp; Schnitzler, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLeaf turnover in the central Amazon Forest is driven by precipitation seasonality, with co-occurring brevideciduous and evergreen trees - though evergreens predominate (Wu et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lopes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Aleixo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). During the driest months, brevideciduous trees shed large fractions (or all) of their leaves synchronously, while evergreen trees exhibit fewer, more irregular flushing events (Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mesquita Pinho, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bot\u0026iacute;a et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Drought stress and herbivore avoidance are proposed evolutionary drivers of these patterns (Lopes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Since young leaves are softer and more vulnerable to herbivory, studies suggest dry-season flushing evolved to avoid wet-season herbivore pressure (Wright \u003cem\u003eet al.\u003c/em\u003e, 1994; Coley \u0026amp; Barone, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Lopes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Other studies argue it helps mitigate drought stress, as younger leaves may regulate water loss more effectively and brevideciduous trees may be more prone to hydraulic failure (Reich \u0026amp; Borchert, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e1988\u003c/span\u003e; Aleixo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Mesquita Pinho, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Leaf turnover patterns are also not strictly conserved at the species level and can shift due to stress events and disturbances (Borchert, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Cleland et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIsoprene - and certain light-dependent monoterpenes (Loreto et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Jardine et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) - are rapidly produced from newly fixed photosynthetic carbon and emitted immediately (Delwiche \u0026amp; Sharkey, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Affek \u0026amp; Yakir, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). They are associated with abiotic stress mitigation and proposed to act by either directly scavenging reactive oxygen species (ROS) (Velikova, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), acting as sinks for excessive reducing power (Morfopoulos et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), enhancing photochemical efficiency (Pollastri et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), or possibly stabilizing thylakoid membranes (Velikova et al., \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) - though evidence for membrane stabilization is limited (Harvey et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Recent studies also emphasize that isoprene engages in signaling networks linked to growth and defense responses, regulating carbon allocation under stress (Behnke et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Lantz et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Frank et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Monson et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In contrast, sesquiterpenes and most monoterpenes accumulate in specialized structures (e.g., resin ducts or glandular trichomes) and are emitted gradually under normal conditions or rapidly upon structural damage (Arneth \u0026amp; Niinemets, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Niinemets et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rasulov et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Nagalingam et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These stored compounds are mostly known as herbivore deterrents and plant signaling molecules (Pichersky \u0026amp; Gershenzon, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Fineschi \u0026amp; Loreto, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Still, increased monoterpene and sesquiterpene emissions under abiotic stress conditions have also been observed and could reflect an active protective mechanism, or simply derive from increased diffusion due to elevated vapor pressure deficit (VPD) (Jardine et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Byron et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nagalingam et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bourtsoukidis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn addition to their roles in plant stress responses, VIs also significantly impact atmospheric processes. Upon entering the atmosphere, they rapidly oxidize and decompose in the presence of atmospheric radicals (OH) (Lelieveld et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Pfannerstill et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and contribute to the formation and growth of secondary organic aerosols and cloud condensation nuclei - influencing light scattering, precipitation, and the radiative balance of the atmosphere (Griffin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999a\u003c/span\u003e; P\u0026ouml;schl et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Curtius et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). They also contribute to the formation of tropospheric O\u003csub\u003e3\u003c/sub\u003e in the presence of nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e) - intensifying the radiative forcing of greenhouse gases (Y\u0026aacute;\u0026ntilde;ez-Serrano et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, although isoprene dominates VI fluxes (Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), monoterpenes and sesquiterpenes incur higher carbon losses (Gomes Alves et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and are considerably more chemically reactive, yielding 2- and 10-times more particle formation than isoprene, respectively (Griffin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e; Kroll et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith its massive plant biomass and species diversity (Fauset et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; ter Steege et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the Amazon Forest is estimated as the largest and most chemically diverse source of VIs to the atmosphere (Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Y\u0026aacute;\u0026ntilde;ez-Serrano et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gomes Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yet, VI fluxes predicted from current emission models (e.g., Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) are highly uncertain, as these models rely on coarse emission factors tied to generalized plant functional types (e.g., CLM4 model, Oleson et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This mechanistic gap is compounded by the use of generalized parameters derived from temperate forest species responses and the limited flux tower data, masking the variability in light and temperature sensitivity across tropical forest species (Mu et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These simplified assumptions overlook species-specific physiology and leaf phenological types, rarely accounting for the carbon costs of VI emissions under high light and temperature stress. Furthermore, the production of VIs relies on the activity of isoprenoid-specific synthase enzymes and the availability of their common precursor dimethylallyl diphosphate (DMADP), being tightly coupled to photosynthetic activity and varying between species, leaf developmental stages (Schnitzler et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Niinemets et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Souza et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) - and possibly leaf phenological types.\u003c/p\u003e\u003cp\u003eWarmer and drier climates are expected to favor the selection of brevideciduous trees (Aleixo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), as well as of isoprene and light-dependent monoterpene emitters - given that emitters often sustain higher photosynthetic rates under heat than non-emitters (Singsaas et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Pollastri et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Byron et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At the same time, more extreme and frequent stress events will likely promote substantial stress-induced VI emissions, especially from heavier and more reactive stored monoterpenes and sesquiterpenes (Byron et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Bourtsoukidis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Indeed, such shifts have been observed in the Amazon during El Ni\u0026ntilde;o years, when reductions in photosynthesis and isoprene emissions coincide with increased release of more reactive and temperature-sensitive monoterpenes and sesquiterpenes (Jardine et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pfannerstill et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gomes Alves et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Still, it is not entirely clear how the combined effects of global climate change will affect forest-atmosphere emission feedbacks (Y\u0026aacute;\u0026ntilde;ez-Serrano et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Satake et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), particularly considering the potential for differential emission responses to abiotic stress across leaf phenological types. Hence, in this study, we asked: i) how do VI emissions vary in response to light and temperature changes in different leaf phenological types; ii) how do characteristics of photochemical activity vary between isoprene emitters and non-emitters from different leaf phenological types; and iii) how may the interaction of leaf phenological types and light and temperature changes affect canopy isoprene emission estimates?\u003c/p\u003e\u003cp\u003eTo answer these questions, we measured the responses of VI emissions, and key leaf gas exchange and chlorophyll fluorescence characteristics - net photosynthesis (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e), stomatal conductance (\u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e), photosynthesis temperature optimum (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e), light saturation point (\u003cem\u003eLSP\u003c/em\u003e), photosynthetic capacity (\u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e), electron transport rate (\u003cem\u003eJ\u003c/em\u003e), quantum efficiency of photosystem II (ϕ\u003csub\u003ePSII\u003c/sub\u003e), and photochemical quenching (\u003cem\u003eqP\u003c/em\u003e) - to changes in light and temperature conditions in 12 angiosperm species at a central Amazon Forest. Among the species, six were isoprene emitters and six non-emitters, with each group containing three evergreen and three brevideciduous trees. By answering the questions, our study aims to provide deeper ecophysiological knowledge and a better mechanistic understanding of VI emissions across different leaf phenological types. This is fundamental for advancing model parametrization and improving future projections of global VI fluxes and carbon dynamics in light of climate change.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study site and experimental design\u003c/h2\u003e\u003cp\u003eData were collected at an upland forest (locally called \u003cem\u003eterra firme\u003c/em\u003e) permanent plot located in the Amazon Tall Tower Observatory (ATTO) site in central Amazonia. The ATTO site is located about 150 km northeast of Manaus (02\u0026deg; 08.9\u0026rsquo; S, 59\u0026deg; 00.2\u0026rsquo; W), at the Uatum\u0026atilde; Sustainable Development Reserve. The climate is humid tropical, with mean annual temperature of 26.7 \u0026ordm;C and precipitation of 2376 mm, being characterized by a pronounced wet season from December to May and a drier season from July to October, with transitions in between (Bot\u0026iacute;a et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Vegetation in the \u003cem\u003eterra firme\u003c/em\u003e plot is dense (leaf area index of 5.3 m\u003csup\u003e2\u003c/sup\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), mature, and non-flooded, with a mean canopy height of 35 m (Gomes Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The soil is a highly weathered and well-drained ferralsol (Chauvel et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). For more details on the experimental site see Andreae et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe sampled trees and performed measurements between November 29 - December 6, 2022. This period corresponds to the beginning of the wet season, when canopies mostly contain mature leaves (Alves et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Gomes Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and variation in leaf age is expected to be low. Measurements were performed in 12 selected trees from 12 species of angiosperms (one tree/species, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), including six isoprene emitters, and six non-emitters. Each group of isoprene emitters and non-emitters contained three evergreen and three brevideciduous trees. Detailed leaf phenological type classifications and isoprene emission factor measurements are in Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to a lack of replicates within species, the statistical comparisons were made among volatile emission groups (isoprene emitter/non-emitter) and leaf phenology groups (brevideciduous/evergreen).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of species measured in this study; leaf phenological types; capacity to emit isoprene (Emitter) or not (Non-emitter); isoprene emission factors (\u003cem\u003eε\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e, \u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); and their position in the regional biomass rank. Isoprene \u003cem\u003eε\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e is from Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The biomass rank of each species was derived from values of aboveground biomass for central Amazon Forest species presented in Fauset et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeaf phenological type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIsoprene emission\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eIsoprene \u003cem\u003eε\u003c/em\u003e\u003csub\u003e0\u003c/sub\u003e (\u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBiomass rank\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrevideciduous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e160\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrevideciduous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrevideciduous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrevideciduous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-emitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrevideciduous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-emitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrevideciduous\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-emitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvergreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvergreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvergreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEmitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvergreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-emitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e408\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvergreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-emitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEvergreen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-emitter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIsoprene emission factor\u0026thinsp;=\u0026thinsp;isoprene emission rate measured at standard conditions of incident photosynthetic photon flux density (PPFD, 1000 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and leaf temperature (30\u0026deg;C).\u003c/p\u003e\u003cp\u003eAll trees occupied the upper canopy layer of the plot. Hence, given the logistical challenges of measuring intact leaves from trees exceeding 30 m in height, we performed all measurements on leaf samples collected from cut branches immediately placed in water. This method provides a practical solution for conducting VI emissions and gas exchange measurements without compromising leaf viability (Penuelas et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Llusia et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Albert et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Jardine et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gomes Alves et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Robin et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and cutting branches does not significantly compromise gas exchange or VI measurements (Monson et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1994\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Keller \u0026amp; Lerdau, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Ghirardo et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). With the help of a tree climber, one branch of at least 2 cm in diameter was collected from a sun-exposed area of the canopy. After collection, the branch was immediately re-cut under water to prevent embolism, stored in a water bottle for transport, and re-cut again under water at the field camp before VI emission and gas exchange measurements. We selected one visibly mature and healthy leaf of the branch to measure the responses of VI emissions and characteristics of photochemical activity to changes in light and temperature conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Measurements of light and temperature response curves of isoprene emission and characteristics of photochemical activity\u003c/h2\u003e\u003cp\u003eFor each tree, we measured the responses of VI emissions (section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e2.4\u003c/span\u003e) and characteristics of photochemical activity (section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e) to changes in light at ambient (21%) and reduced (2%) O\u003csub\u003e2\u003c/sub\u003e (to suppress photorespiration), and to changes in temperature. We performed gas exchange and chlorophyll fluorescence measurements with a LI-6800 (LI-6400XT for \u003cem\u003eCariniana decandra\u003c/em\u003e) portable gas exchange system (LiCor Inc., USA). Before each response curve measurement, we separately enclosed the leaf (for compound leaves we considered a leaflet as the equivalent of a simple leaf lamina) in the leaf chamber with the following environmental conditions: photosynthetic photon flux density (PPFD) of 1000 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, leaf temperature of 30\u0026deg;C, flow rate of air going into the leaf chamber of 500 \u0026micro;mol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO concentrations of 420 \u0026micro;mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 21 mmol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and relative humidity of ~\u0026thinsp;60%. We began our measurements after acclimating the leaf to these conditions for at least 20 min, or until net assimilation (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e), stomatal conductance (\u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e), and internal CO\u003csub\u003e2\u003c/sub\u003e concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e) reached a stable, positive plateau. If stability was not reached, the leaf was replaced, or a new branch was sampled.\u003c/p\u003e\u003cp\u003eAmbient (21%) and reduced (2%) O\u003csub\u003e2\u003c/sub\u003e light response curves were performed under decreasing light intensity steps: 2000, 1500, 1000, 750, 500, 250, 100, 50, and 0 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; while leaf temperature (30\u0026deg;C), CO\u003csub\u003e2\u003c/sub\u003e (420 \u0026micro;mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and relative humidity (~\u0026thinsp;60%) were fixed. At each light step, we measured gas exchange characteristics every 30 s for 2.5 min. We used the same method to perform the light curves with reduced (2%) O\u003csub\u003e2\u003c/sub\u003e conditions. Low O\u003csub\u003e2\u003c/sub\u003e was achieved by injecting a controlled mix of ultrahigh-purity nitrogen at a rate of 450 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and ambient air at 50 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e into the leaf chamber. Temperature curves were performed under increasing leaf temperature steps: 30, 35, 37.5, 40, 42.5, and 45\u0026deg;C; while PPFD (1000 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), CO\u003csub\u003e2\u003c/sub\u003e (420 \u0026micro;mol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and relative humidity (~\u0026thinsp;60%) were fixed. At each temperature step, we measured gas exchange characteristics every 30 s for 5 min. We used a leaf chamber fluorometer to simultaneously quantify chlorophyll fluorescence variables during all response curves. At the final log of each successive light and temperature step, an actinic light pulse of 10000 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (10% blue light and 90% red light), modulated at 20 kHz, was applied for 1 s, and steady-state (\u003cem\u003eF\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e), light-adapted maximal (\u003cem\u003eF\u003c/em\u003e\u003csub\u003em\u0026rsquo;\u003c/sub\u003e) and light-adapted minimal (\u003cem\u003eF\u003c/em\u003e\u003csub\u003eo\u0026rsquo;\u003c/sub\u003e) fluorescence yields were recorded. After measuring all light response curves, the leaves were scanned with a table scanner to obtain leaf area, dried in an oven at 60\u0026deg;C for 72 hrs, and weighed to obtain leaf dry mass. We analyzed the images of scanned leaves with ImageJ software (Schneider et al., \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) to obtain leaf area, and calculated specific leaf area (SLA) as the ratio of leaf area to leaf dry mass.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Characteristics of photochemical activity\u003c/h2\u003e\u003cp\u003eQuantum efficiency of photosystem II (ϕ\u003csub\u003ePSII\u003c/sub\u003e, Eq.\u0026nbsp;1) and photochemical quenching (\u003cem\u003eqP\u003c/em\u003e, Eq.\u0026nbsp;2) were calculated as (Genty et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1989\u003c/span\u003e):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varphi\\:}_{PSII}\\:=\\:\\frac{({F}_{m\u0026rsquo;}-{F}_{s})}{{F}_{m\u0026rsquo;}}\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:qP\\:=\\:\\frac{({F}_{m\u0026rsquo;}-{F}_{s})}{{(F}_{m\u0026rsquo;}-\\:{F}_{o\u0026rsquo;})}\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAmbient (21%) and reduced (2%) O\u003csub\u003e2\u003c/sub\u003e light response curves were fitted to a hyperbolic function (Huang et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e,2). Ambient O\u003csub\u003e2\u003c/sub\u003e curves were used to define the light saturation point (\u003cem\u003eLSP\u003c/em\u003e) and estimate the photosynthetic capacity (\u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e) of each species. \u003cem\u003eLSP\u003c/em\u003e was defined as the first PPFD (above the inflection point) where the rate of increase in mean \u003cem\u003eA\u003c/em\u003e\u003csub\u003e\u003cem\u003en\u003c/em\u003e\u003c/sub\u003e (i.e., the derivative of the logistic function) fell below 5% of maximum \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e, and we estimated \u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e as the predicted \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e at \u003cem\u003eLSP\u003c/em\u003e (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFor each species, we calibrated the electron transport rate (\u003cem\u003eJ\u003c/em\u003e, \u0026micro;mol e\u003csup\u003e\u0026minus;\u003c/sup\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:J\\:=\\:k\\:\\:PPFD\\:\\:{\\varphi\\:}_{PSII}\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere the lumped parameter \u003cem\u003ek\u003c/em\u003e is the slope of the linear regression between \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e and PPFD ϕ\u003csub\u003ePSII\u003c/sub\u003e / 4 at 2% O\u003csub\u003e2\u003c/sub\u003e (Fig. S3), derived from the linear part of the light response curve, just before the inflection point of the curve (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e,2) (Yin et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Due to limitations in the number of observations in low-light intensities (0\u0026ndash;100 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), we performed the linear regression using values obtained from the fitted hyperbolic curves.\u003c/p\u003e\u003cp\u003eThen, we fitted a quadratic function to the relationship between \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e and leaf temperature for each species (Fig. S4) (Kumarathunge et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and estimated photosynthesis temperature optimum (\u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e \u0026deg;C) based on the fitted model parameters. Since observed data for \u003cem\u003ePeltogyne catingae\u003c/em\u003e and \u003cem\u003eScleronema micranthum\u003c/em\u003e did not follow a quadratic function, photosynthesis \u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e for these species was taken as the leaf temperature where the highest \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e was observed. For the species \u003cem\u003eCariniana decandra\u003c/em\u003e and \u003cem\u003eManilkara bidentata\u003c/em\u003e, the estimated \u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e fell before the first observed data point (Fig. S4).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Identification and quantification of volatile isoprenoid emissions\u003c/h2\u003e\u003cp\u003eReal-time VI emission measurements were obtained by redirecting the air exiting the gas analyzer leaf chamber to a proton-transfer-reaction quadrupole mass spectrometer (PTR-QMS, IONICON, Analytik, Innsbruck, Austria). The PTR-QMS operated in standard conditions with a drift tube voltage of 600 V, drift tube pressure of 2.2 mbar, and E/N 120 Td. This system allows obtaining real-time VI emission measurements under the controlled environmental conditions of the gas analyzer leaf chamber. At the beginning of each day and before measuring each species, we obtained a chamber blank sample from the empty leaf chamber. A hydrocarbon filter (Restek Pure Chromatography, Restek Corporations, USA) was installed at the air inlet of the gas analyzer to remove VIs from incoming ambient air, and all tubing in contact with the sampling air was PTFE - a material inert to VIs.\u003c/p\u003e\u003cp\u003eThe flow rate of air going inside the PTR-QMS was 200 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and measurements were performed for 2.5 min and 5 min at each light and temperature step, respectively. During each PTR-QMS measurement cycle, the following mass-to-charge ratios (m/z) were monitored: 21\u003csup\u003e+\u003c/sup\u003e (H\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e18\u003c/sup\u003eO\u003csup\u003e+\u003c/sup\u003e), 32\u003csup\u003e+\u003c/sup\u003e (O\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), and 37\u003csup\u003e+\u003c/sup\u003e (H\u003csub\u003e2\u003c/sub\u003eO-H\u003csub\u003e3\u003c/sub\u003eO\u003csup\u003e+\u003c/sup\u003e) with a dwell time of 500 ms each; 41\u003csup\u003e+\u003c/sup\u003e (isoprene fragment), 69\u003csup\u003e+\u003c/sup\u003e (isoprene), 81\u003csup\u003e+\u003c/sup\u003e (monoterpene fragment), 137\u003csup\u003e+\u003c/sup\u003e (monoterpenes), 149\u003csup\u003e+\u003c/sup\u003e (sesquiterpene fragment) and 205\u003csup\u003e+\u003c/sup\u003e (sesquiterpenes) with a dwell time of 1 s each. Humidity-dependent calibrations (using water-bubbled nitrogen to dilute standard gas, simulating ambient relative humidity) were performed with a certified standard gas provided by Apel-Riemer Environmental, Inc. (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), at the beginning and end of the measurement campaign. The mixing ratios of VIs were calculated from the calibration curves (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026asymp;\u0026thinsp;0.99). PTR-QMS detection limits were calculated as three times the standard deviation of isoprene, monoterpenes, and sesquiterpenes (ppb) detected in the water-bubbled nitrogen background of the calibration curves and were equal to 0.93, 2.14, and 2.83 ppb, respectively. Once the mixing ratios of isoprene, monoterpenes and sesquiterpenes (ppb) from the samples were obtained, fluxes per area were determined using the equation (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u003cem\u003eRppb\u003c/em\u003e \u0026times; Q/S), where \u003cem\u003eF\u003c/em\u003e (nmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is the VI leaf flux; \u003cem\u003eRppb\u003c/em\u003e (nmol mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is VI concentration of the outgoing air (ppb); Q is the flow rate of air into the leaf chamber (500 \u0026micro;mol s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e); and S is the area of leaf within the chamber (0.0002 m\u0026sup2;).\u003c/p\u003e\u003cp\u003eSince individual monoterpene and sesquiterpene compounds cannot be identified and quantified by the PTR-QMS, air exiting the leaf chamber during the temperature curves was also routed to fill adsorbent cartridges (stainless steel tubes filled with Tenax TA and Carbograph 5 TD adsorbents) at a rate of 200 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for 5 min, resulting in collection of VIs from 1 L chamber air for compound identification and quantification in the lab. VIs accumulated in the adsorbent cartridges were determined by gas chromatography-time of flight-mass spectrometry (GC-ToF-MS), at the Atmospheric Chemistry Department of the Max Planck Institute for Chemistry (Mainz, Germany). Sample desorption of the VIs accumulated in adsorbent cartridges was achieved with a two-stage automated thermal desorber (TD100-xr, MARKES International, UK), with helium 5.0 as the carrier gas. The adsorbent cartridge was purged with carrier gas for 5 min at a flow of 50 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, followed by sample desorption at a temperature of 250\u0026deg;C and a flow of 50 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of helium for 5 min onto a focusing cold trap (materials emissions, MARKES International, UK) for pre-concentration at 30\u0026deg;C. The cold trap was purged with carrier gas for 1 min with a flow of 50 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, then rapidly heated to 250\u0026deg;C. The sample was removed from the cold trap with a He flow of 2 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and injected into the GC column. The sampled compounds were separated using a 60 m DB-1 column (0.25 mm internal diameter, film thickness 1 \u0026micro;m, Agilent Technologies, UK). The temperature program used was as follows: 50\u0026deg;C to 150\u0026deg;C at 4\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and 150\u0026deg;C to 250\u0026deg;C at 8\u0026deg;C min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, the temperature was then held for 5 min. The column flow was set to 2 ml min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Detection was achieved using a time-of-flight mass spectrometer (Bench TOF-Select, MARKES International, UK). VI calibration and identification were achieved using a standard BVOC gas mixture (Apel-Riemer, 2019). All sesquiterpenes were identified with the NIST library and headspace tests from liquid standards when liquid standards were available. Sesquiterpenes were first quantified using the calibration factor of α-pinene and then back-calibrated using relative response factors derived from the ratio of the gradients of the calibration curves from the liquid standards of α-pinene and the individual sesquiterpene. When a liquid standard was unavailable, an average of response factors of other sesquiterpenes was used. Most monoterpenes were quantified with their own calibration factors; the calibration factor for α-pinene was used for cis-/trans-β-ocimene, α-thujene, and eucalyptol.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 VI emission light and temperature responses models\u003c/h2\u003e\u003cp\u003eWe modeled light responses of isoprene and light-dependent monoterpene emissions using the light response algorithm from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e):\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:emission\\:rate\\:\\left({\\gamma\\:}_{P,i}\\right)=\\:{\\epsilon\\:}_{0\\:}\\frac{\\alpha\\:\\:{C}_{L1}L}{\\sqrt{1+\\:\\frac{{\\alpha\\:}^{2}*\\:{L}^{2}}{{C}_{p5}^{2}}}}\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere the emission factor ε\u003csub\u003e0\u003c/sub\u003e is the observed compound emission rate at standard conditions (PPFD of 1000 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and leaf temperature of 30\u0026deg;C), \u003cem\u003eL\u003c/em\u003e is incident PPFD (\u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and \u003cem\u003eC\u003c/em\u003e\u003csub\u003ep5\u003c/sub\u003e = 1.0 (Monson et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We estimated empirical coefficients α and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e based on non-linear least-square fits to the observed data (Fig. S5). We defined isoprene \u003cem\u003eLSP\u003c/em\u003e as the first PPFD where the rate of increase in mean isoprene emission rates fell below 5% of maximum isoprene emission rates.\u003c/p\u003e\u003cp\u003eWe modeled the temperature response of normalized isoprene emissions using the temperature response algorithm from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e):\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:emission\\:rate\\:\\left({\\gamma\\:}_{T,i}\\right)=\\:\\frac{{E}_{opt}\\:{C}_{T2}\\:{e}^{{C}_{T1}x}}{{C}_{T2}-\\:{C}_{T1}\\:(1-{e}^{{C}_{T2}x})}\\:\\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:x=\\:\\frac{\\frac{1}{{T}_{opt}}-\\:\\frac{1}{{T}_{L}}}{R}\\:\\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e is the gas constant (0.008314 kͿ K\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e (K) is the leaf temperature at the highest observed isoprene emission rate (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e), and \u003cem\u003eT\u003c/em\u003e\u003csub\u003eL\u003c/sub\u003e (K) is leaf temperature. We estimated empirical coefficients \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e (activation and deactivation energy parameters, respectively) based on non-linear least-square fits to the observed data (Fig. S6).\u003c/p\u003e\u003cp\u003eWe calculated the empirically derived β parameter of monoterpene and sesquiterpene emission responses to temperature as (Guenther et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1993\u003c/span\u003e):\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:\\beta\\:=\\:\\frac{{log}_{e}\\frac{{E}_{T1}}{{E}_{T2}}}{{T}_{1}-\\:{T}_{1}}\\:\\left(7\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e1\u003c/sub\u003e and \u003cem\u003eE\u003c/em\u003e\u003csub\u003e\u003cem\u003eT\u003c/em\u003e2\u003c/sub\u003e are monoterpene and sesquiterpene emission rates at leaf temperatures \u003cem\u003eT\u003c/em\u003e\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;30\u0026deg;C and \u003cem\u003eT\u003c/em\u003e\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;45\u0026deg;C.\u003c/p\u003e\u003cp\u003eWe modeled isoprene emission rates from 175 canopy-dominant trees measured in Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) under different conditions of average air temperature (\u0026deg;C) and PPFD (\u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at different hours of the day (09:00, 13:00, and 17:00) in October, November, and December/2022 using empirically derived light and temperature response parameters (α, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e, \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e) from this study and compared values with those modeled with parameters from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Following that, we compared canopy-level isoprene fluxes estimated at different hours of the day during the same months using combinations of: i) average emission factors from Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and empirically derived parameters from this study (Model 1); ii) average emission factors from Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and reference parameters from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Model 2); and iii) emission factors suggested for evergreen and deciduous tropical forest trees in Guenther et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and reference parameters from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Model 3). Air temperature and PPFD values were recorded at 36 m above the ground, corresponding to the plot\u0026rsquo;s average canopy height (Table S2). Fluxes were modeled using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) (Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e):\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:{\\gamma\\:}_{i}=\\:{C}_{CE}\\:LAI\\:{\\gamma\\:}_{P,i}\\:\\:{\\gamma\\:}_{T,i}\\:\\:{\\gamma\\:}_{A,i}\\:\\:{\\gamma\\:}_{SM,i}\\:\\:{\\gamma\\:}_{C,i}\\:\\left(8\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere isoprene fluxes (γ\u003csub\u003ei\u003c/sub\u003e) were estimated as a function of a canopy environmental coefficient (\u003cem\u003eC\u003c/em\u003e\u003csub\u003eCE\u003c/sub\u003e), canopy leaf area index (LAI), and responses to light (γ\u003csub\u003eP,i\u003c/sub\u003e), temperature (γ\u003csub\u003eT,i\u003c/sub\u003e), leaf age (γ\u003csub\u003eA,i\u003c/sub\u003e), soil moisture (γ\u003csub\u003eSM,i\u003c/sub\u003e), and environmental CO\u003csub\u003e2\u003c/sub\u003e concentration (γ\u003csub\u003eC,i\u003c/sub\u003e). For each month, we assumed γ\u003csub\u003eSM,i\u003c/sub\u003e and γ\u003csub\u003eC,i\u003c/sub\u003e were constant and equal to 1. Similarly, we assumed that, during this period, canopies were mostly composed of mature leaves and γ\u003csub\u003eA,i\u003c/sub\u003e would be equal to 1. γ\u003csub\u003eP,i\u003c/sub\u003e and γ\u003csub\u003eT,i\u003c/sub\u003e were estimated using equations 7 and 8, respectively, and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eCE\u003c/sub\u003e = 0.57 and LAI\u0026thinsp;=\u0026thinsp;5.3 m\u003csup\u003e2\u003c/sup\u003e m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e (Gomes Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Individual variations in isoprene emission rates were estimated by multiplying γ\u003csub\u003eP,i\u003c/sub\u003e and γ\u003csub\u003eT,i\u003c/sub\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Measurements of post-illumination isoprene emissions\u003c/h2\u003e\u003cp\u003eWe measured post-illumination isoprene emissions according to Rasulov et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Following light and temperature response curves, leaves were again acclimated to the standard environmental conditions described in section \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e for at least 20 min, or until net assimilation (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e), stomatal conductance (\u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e), internal CO\u003csub\u003e2\u003c/sub\u003e concentration (\u003cem\u003eC\u003c/em\u003e\u003csub\u003ei\u003c/sub\u003e), and isoprene emission rates reached a stable, positive plateau. Once steady-state isoprene emission rates were achieved, we rapidly switched off the light and monitored the decay of isoprene emissions until emission rates reached zero. For each species, we monitored the first dark-decay peak of isoprene emissions, which corresponds to the dimethylallyl diphosphate (DMADP) pool that was formed before the light was switched off and is immediately available for isoprene synthesis (Rasulov et al., \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). We estimated species DMADP pool sizes by integrating the first dark-decay of isoprene emission rates after the light was switched off using the Peak Analyzer module from OriginPro 2019b (OriginLab) (Fig. S7). We also integrated DMADP pool sizes at different moments of the first dark-decay of isoprene emissions, obtained paired values of isoprene emission rate vs. DMADP pool size, and estimated isoprene synthase (\u003cem\u003eIspS\u003c/em\u003e) activity as the slope of the linear regression between isoprene emissions and DMADP pool sizes (Rasulov et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAlthough the air exiting the gas analyzer leaf chamber was directly redirected to the PTR-QMS, and the leaf chamber can quickly reach a steady state after rapid changes in gas concentration (~\u0026thinsp;4 s) (Niinemets, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), we also applied a chamber finite time response correction according to Rasulov et al. (\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In a previous experiment (Souza et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), we estimated the chamber time response by injecting a small stream of isoprene standard of known concentration into the empty leaf chamber using a needle. After quickly removing the needle, isoprene levels were monitored with a proton-transfer-reaction time-of-flight mass-spectrometer (PTR-ToF-MS) until they reached background values. Thus, parameters estimated from post-illumination isoprene emission responses were calculated after subtracting the chamber-clearing trace from the observed isoprene emission values.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 Statistical analyses\u003c/h2\u003e\u003cp\u003eTo evaluate how C partitioning among different VIs varied as a function of leaf temperature in different leaf phenological types (Brevideciduous, Evergreen), we calculated an emission rate mean weighted by the mass of each emitted compound (i.e., isoprenoid emission metric; Gomes Alves et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). At each measured temperature, we calculated the isoprenoid emission metric by multiplying the mass-based emission rate (\u0026micro;g C g⁻\u0026sup1; h⁻\u0026sup1;) of each observed compound by the number of carbon atoms in its molecule. We then summed these carbon-weighted values and divided the result by the total sum of the mass-based emission rates. Values close to 5, 10, or 15 indicate that isoprene, monoterpenes, or sesquiterpenes, respectively, are predominant in the emission profile.\u003c/p\u003e\u003cp\u003eThen, we performed general linear regression models of the following independent variables (\u003cem\u003ey\u003c/em\u003e): isoprene, monoterpene, and sesquiterpene emission rates, and isoprenoid emission metric; varying as a function of leaf phenological types at each leaf temperature step (T\u003csub\u003ei\u003c/sub\u003e) (\u003cem\u003ey\u003c/em\u003e at T\u003csub\u003ei\u003c/sub\u003e ~ pheno.type; \u003cem\u003ep\u003c/em\u003e (T\u003csub\u003ei\u003c/sub\u003e)), and varying as a function of leaf temperature (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;temp; \u003cem\u003ep\u003c/em\u003e (temp)), of leaf phenological types (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;pheno.type; \u003cem\u003ep\u003c/em\u003e (pheno.type)), and of the interaction between leaf temperature and leaf phenological types (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;temp * pheno.type; \u003cem\u003ep\u003c/em\u003e (temp:pheno)). We also estimated the percentage of photosynthetic carbon (%C) loss to VI emissions for each species at each leaf temperature step by dividing each compound\u0026rsquo;s observed mass-based emission rate (\u0026micro;g C g⁻\u0026sup1; h⁻\u0026sup1;) - adjusted for biosynthetic carbon cost (i.e., multiplied by 1.2 to account for 6/5 for isoprene, 12/10 for monoterpenes, and 18/15 for sesquiterpenes) - by the corresponding mass-based net photosynthetic assimilation rate (\u0026micro;g C g⁻\u0026sup1; h⁻\u0026sup1;), and multiplying by 100.\u003c/p\u003e\u003cp\u003eTo examine whether characteristics of photochemical activity differed between isoprene emitters and non-emitters from different leaf phenological types, we performed general linear regression models of the following independent variables (\u003cem\u003ey\u003c/em\u003e): \u003cem\u003eLSP\u003c/em\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e, \u003cem\u003eJ\u003c/em\u003e, \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e at 45\u0026deg;C, \u003cem\u003eqP\u003c/em\u003e, ϕ\u003csub\u003ePSII\u003c/sub\u003e, \u003cem\u003eg\u003c/em\u003e\u003csub\u003es\u003c/sub\u003e at \u003cem\u003eA\u003c/em\u003e\u003csub\u003esat\u003c/sub\u003e; varying as a function of isoprene emissions (Emitter, Non-emitter; \u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;isoprene; \u003cem\u003ep\u003c/em\u003e (isoprene)), of leaf phenological types (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;pheno.type; \u003cem\u003ep\u003c/em\u003e (pheno.type)), and of the interaction between isoprene emissions and leaf phenological types (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;isoprene * pheno.type; \u003cem\u003ep\u003c/em\u003e (isop:pheno)).\u003c/p\u003e\u003cp\u003eTo evaluate how characteristics of photochemical activity varied as a function of leaf temperature in isoprene emitters and non-emitters across different phenological types, we modeled the temperature responses of \u003cem\u003eJ\u003c/em\u003e, \u003cem\u003eqP\u003c/em\u003e, ϕ\u003csub\u003ePSII\u003c/sub\u003e, and \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e using a non-linear regression framework. We fitted a peaked Arrhenius temperature response model to each variable:\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$\\:f\\left(T\\right)=\\:{k}_{25}\\:{e}^{\\frac{{E}_{\\alpha\\:\\:(T-\\:{T}_{ref})}}{R\\:T\\:{T}_{ref}}}\\:\\left[\\frac{1+{e}^{\\frac{{T}_{ref}\\:\\varDelta\\:S-\\:Hd}{R\\:{T}_{ref}}}}{1+{e}^{\\frac{T\\varDelta\\:S-\\:Hd}{RT}}}\\right]\\left(9\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003eT\u003c/em\u003e is leaf temperature (K), \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e is the modeled rate at 25\u0026deg;C, \u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e is the activation energy (J mol⁻\u0026sup1;), \u003cem\u003eΔS\u003c/em\u003e is the entropy term (J mol⁻\u0026sup1; K⁻\u0026sup1;), \u003cem\u003eHd\u003c/em\u003e is the deactivation energy (J mol⁻\u0026sup1;), \u003cem\u003eT\u003c/em\u003e\u003csub\u003eref\u003c/sub\u003e = 298.15 K (25\u0026deg;C), and \u003cem\u003eR\u003c/em\u003e is the universal gas constant. Although our measurements began at 30\u0026deg;C, we used 25\u0026deg;C as a reference because \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e serves as a standardized model-derived parameter widely used to compare physiological baseline performance across studies and conditions (Medlyn et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). We fitted this model separately for each combination of isoprene emission (emitter vs. non-emitter) and leaf phenological type (brevideciduous vs. evergreen). To account for within-group variability and obtain confidence intervals for parameter estimates, we performed bootstrap resampling (n\u0026thinsp;=\u0026thinsp;300) within each group, recording the distributions of the fitted parameters (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e, \u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e, \u003cem\u003eΔS\u003c/em\u003e, \u003cem\u003eHd\u003c/em\u003e). From these bootstrapped fits, we calculated and compared means and 95% confidence intervals for each parameter and group.\u003c/p\u003e\u003cp\u003eTo test whether empirically derived parameters from isoprene light (α and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e) and temperature (\u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e) response algorithms, and observed isoprene T\u003csub\u003eopt\u003c/sub\u003e, \u003cem\u003eLSP\u003c/em\u003e, DMADP pool size, and \u003cem\u003eIspS\u003c/em\u003e activity rate significantly varied between leaf phenological types, we performed general linear regression models of each independent variable (\u003cem\u003ey\u003c/em\u003e) varying as a function of leaf phenological types (\u003cem\u003ey\u003c/em\u003e\u0026thinsp;~\u0026thinsp;pheno.type). Lastly, we performed Kruskal-Wallis pairwise comparisons between isoprene emission rates estimated with empirically derived and reference parameters (see section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e2.5\u003c/span\u003e) at different hours of the day. All statistical analyses were performed in Python 3 with Jupyter Notebook as the primary environment. The Python libraries \u003cem\u003epandas\u003c/em\u003e, \u003cem\u003eNumPy\u003c/em\u003e, and \u003cem\u003eSciPy\u003c/em\u003e, were used for data handling, fitting, and model construction; \u003cem\u003emath\u003c/em\u003e for mathematical equations; \u003cem\u003estatsmodels\u003c/em\u003e for general linear regression models; and \u003cem\u003ematplotlib\u003c/em\u003e and \u003cem\u003eseaborn\u003c/em\u003e for visualizing results.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.1 VI emission responses to changes in light and temperature in different leaf phenological types\u003c/h2\u003e\u003cp\u003eAll volatile compounds showed increases in emission rates with rising leaf temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This effect was particularly stronger for monoterpenes and sesquiterpenes, which showed average 10 and 12.5-fold emission increases from 30 to 45\u0026deg;C (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Isoprene and monoterpene emissions did not differ between leaf phenological types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA,B). However, sesquiterpene emissions from brevideciduous trees had higher increases with leaf temperature compared to evergreen trees (\u003cem\u003ep\u003c/em\u003e (temp:pheno)\u0026thinsp;=\u0026thinsp;0.04, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), as well as higher emission rates at 45\u0026deg;C (\u003cem\u003ep\u003c/em\u003e (T\u003csub\u003e45\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;0.02, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Finally, increases in isoprenoid emission metric with temperature indicated a trend toward a shift from \u0026ldquo;lighter\u0026rdquo; to \u0026ldquo;heavier\u0026rdquo; compounds (as characterized by isoprenoid emission metrics) with leaf temperature (\u003cem\u003ep\u003c/em\u003e (temp)\u0026thinsp;=\u0026thinsp;0.06; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValues of isoprene, monoterpene, and sesquiterpene emission rates (\u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at each leaf temperature (\u0026deg;C) for each species measured in this study, and average values and \u0026plusmn;\u0026thinsp;standard errors for brevideciduous, evergreen and all species (total average). The 45\u0026deg;C / 30\u0026deg;C ratio was obtained by dividing the observed emission value at 45\u0026deg;C by the observed emission value at 30\u0026deg;C.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.5\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.5\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e45\u0026deg;C / 30\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIsoprene\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e57.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e46.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e67.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e71.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e53.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e5.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e32.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e32.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e23.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e149.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e187.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e221.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e255.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e252.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrevideciduous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e7.91\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;4.40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e13.33\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;7.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e18.74\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;10.48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e23.08\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;12.56\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e25.19\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;13.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e19.86\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;9.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEvergreen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e15.47\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;11.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e31.84\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;23.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e40.84\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;29.77\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e48.52\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;34.96\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e54.61\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;40.58\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e51.68\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;40.34\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal average\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e11.69\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;5.91\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e22.58\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;12.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e29.79\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;15.41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e35.80\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;18.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e39.90\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;20.88\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e35.77\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;20.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e3.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonoterpenes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e72.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e72.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e36.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e42.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e20.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e46.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e37.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e30.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e49.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e39.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e52.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e74.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e134.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e224.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e243.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e254.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e125.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e54.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e73.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e81.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e88.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrevideciduous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e9.60\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;3.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e18.19\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;8.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e30.01\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;10.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e33.88\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;9.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e32.60\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;5.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e36.40\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;3.95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEvergreen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e17.33\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;9.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e32.85\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;20.72\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e55.77\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;34.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e67.35\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;36.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e72.90\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;38.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e59.24\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;18.37\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal average\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e13.46\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;4.81\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e25.52\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;10.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e42.89\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;17.64\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e50.61\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;18.70\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e52.75\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;19.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e47.82\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;9.60\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSesquiterpenes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e12.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e15.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e24.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e24.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e21.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eBrevideciduous\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.27\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;0.98\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e2.47\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;0.59\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e6.93\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e10.88\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.86\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e11.66\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.28\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e19.55\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;2.78\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eEvergreen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.48\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;2.26\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e5.18\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e8.69\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.87\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e9.95\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;3.25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e16.88\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;3.69\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e15.96\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;3.13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eTotal average\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.38\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.83\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;0.85\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e7.81\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e10.42\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;1.79\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e14.27\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;2.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e17.76\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e\u0026plusmn;\u0026thinsp;2.07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e12.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIsoprene non-emitters emitted proportionally higher amounts of monoterpenes and sesquiterpenes than isoprene emitters. Changes in monoterpene composition in response to temperature suggested that \u003cem\u003eBrosimum parinarioides\u003c/em\u003e, \u003cem\u003eCariniana decandra\u003c/em\u003e, and \u003cem\u003eCroton matourensis\u003c/em\u003e emitted monoterpenes in a light-dependent manner (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA,D,J) since their emissions followed an enzymatic activity response curve (Fischbach et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Niinemets et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). \u003cem\u003eTrans\u003c/em\u003e-β-ocimene dominated monoterpene emissions in \u003cem\u003eB. parinarioides\u003c/em\u003e, but smaller amounts of \u003cem\u003ecis\u003c/em\u003e-β-ocimene were also detected (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Monoterpene emissions of \u003cem\u003eC. decandra\u003c/em\u003e and \u003cem\u003eC. matourensis\u003c/em\u003e were dominated by α-pinene, but \u003cem\u003eC. decandra\u003c/em\u003e also showed some β-pinene emissions, and smaller emissions of camphene, myrcene, sabinene, and tricyclene (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). \u003cem\u003eCroton matourensis\u003c/em\u003e showed small emissions of sabinene, camphene, and myrcene starting from 37.5\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSpecies showed unique patterns of changes in the percentage of photosynthetic carbon (%C) loss to VI emissions with rising leaf temperature, regardless of isoprene emissions or leaf phenological types (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig. S8). For many species, %C loss to isoprene emissions became negative at a given leaf temperature, which indicates that photosynthesis had ceased and that the compound was likely being produced from alternative carbon pools (carbon storage, CS; Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) (Loreto et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; de Souza et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There were also no significant differences in species\u0026rsquo; photosynthetic thermal limits (i.e., leaf temperature where photosynthesis became negative) between isoprene emitters and non-emitters from different leaf phenological types (Fig. S9).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValues of percentages of photosynthetic carbon (%C) loss to emissions of isoprene, monoterpenes, and sesquiterpenes at each leaf temperature (\u0026deg;C) for each species measured in this study, average values for brevideciduous and evergreen species, and total average values for all species. CS\u0026thinsp;=\u0026thinsp;carbon storage; indicates that emissions continued after photosynthesis had ceased and derived solely from stored carbon pools.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e37.5\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e42.5\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e45\u0026deg;C\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIsoprene\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMonoterpenes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e102.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e19.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSesquiterpenes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCariniana decandra\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eManilkara bidentata\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ePouteria guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e44.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eCroton matourensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eDinizia excelsa\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eScleronema micranthum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Characteristics of photochemical activity of isoprene emitters and non-emitters from different leaf phenological types\u003c/h2\u003e\u003cp\u003eNone of the characteristics of photochemical activity measured in our study varied significantly between isoprene emitters and non-emitters from different leaf phenological types, though results suggested that brevideciduous isoprene emitters showed higher \u003cem\u003eLSP\u003c/em\u003e (p (isop:pheno)\u0026thinsp;=\u0026thinsp;0.08; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eParallel to this, \u003cem\u003eJ\u003c/em\u003e, \u003cem\u003eqP\u003c/em\u003e, ϕ\u003csub\u003ePSII\u003c/sub\u003e, and \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e all decreased with rising leaf temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). However, non-linear regression models revealed differences in temperature response parameters across isoprene emitters and non-emitters within each phenological group (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For net photosynthesis (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e), brevideciduous isoprene emitters exhibited the highest \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e values, suggesting enhanced baseline photosynthetic performance. In contrast, for \u003cem\u003eJ\u003c/em\u003eₜ, ϕ\u003csub\u003ePSII\u003c/sub\u003e, and \u003cem\u003eqP\u003c/em\u003e, the highest \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e values were observed in either evergreen non-emitters or split between non-emitters and emitters, without a consistent pattern. Across all groups and variables, the bootstrapped confidence intervals (CIs) for \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e did not include zero, which supports the reliability of these baseline performance estimates. Estimates for activation energy (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e) showed high uncertainty, as CIs included zero in nearly all cases. For \u003cem\u003eJ\u003c/em\u003e, \u003cem\u003eqP\u003c/em\u003e, and ϕ\u003csub\u003ePSII\u003c/sub\u003e, entropy values (\u003cem\u003eΔS\u003c/em\u003e) tended to be higher in non-emitters, particularly among evergreen trees. However, many \u003cem\u003eΔS\u003c/em\u003e CIs approached the model\u0026rsquo;s upper constraint (2000 J mol⁻\u0026sup1; K⁻\u0026sup1;), indicating uncertainty in upper-bound estimates. Deactivation energy (\u003cem\u003eHd\u003c/em\u003e) was consistently higher in evergreen non-emitters across most variables, but brevideciduous non-emitters showed the highest \u003cem\u003eHd\u003c/em\u003e for \u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e. Confidence intervals for \u003cem\u003eHd\u003c/em\u003e estimates were broad but did not reach model bounds, supporting a trend of greater thermal stability in non-emitters, particularly evergreens.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTemperature response model parameters for net photosynthesis (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e), electron transport rate (\u003cem\u003eJ\u003c/em\u003e), quantum efficiency of photosystem II (ϕ\u003csub\u003ePSII\u003c/sub\u003e), and photochemical quenching (\u003cem\u003eqP\u003c/em\u003e), grouped by isoprene emission status (EM\u0026thinsp;=\u0026thinsp;emitter, NE\u0026thinsp;=\u0026thinsp;non-emitter) and leaf phenological type (BD\u0026thinsp;=\u0026thinsp;brevideciduous, EV\u0026thinsp;=\u0026thinsp;evergreen). For each variable and group, the fitted values of base activity at 25\u0026deg;C (\u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e), activation energy (\u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e), entropy (\u003cem\u003eΔS\u003c/em\u003e), and deactivation energy (\u003cem\u003eHd\u003c/em\u003e) are reported, along with their 95% bootstrapped confidence intervals (CI). \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e reflects modeled activity under moderate temperature; \u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e indicates thermal sensitivity near baseline; \u003cem\u003eHd\u003c/em\u003e reflects thermal tolerance at high temperatures; and \u003cem\u003eΔS\u003c/em\u003e describes the shape of the deactivation curve. Modeled values are derived from non-linear regression fits using the peaked Arrhenius function.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eE\u003c/em\u003e\u003csub\u003eα\u003c/sub\u003e CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eΔS\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eΔS\u003c/em\u003e CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eHd\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eHd\u003c/em\u003e CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eA\u003c/b\u003e\u003csub\u003e\u003cb\u003en\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEM BD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.53\u0026ndash;12.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e48305.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;451963.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1439.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e571.92\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e447996.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e175174.48\u0026ndash;627908.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEM EV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.19\u0026ndash;8.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e161538.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;537463.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1526.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e510.52\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e471559.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e159017.24\u0026ndash;633753.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNE BD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.48\u0026ndash;5.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e63444.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;352622.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1759.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1139.87\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e546957.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e353998.97\u0026ndash;628015.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNE EV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u0026ndash;8.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e166813.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;582195.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1412.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e175.27\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e432946.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e82031.26\u0026ndash;638435.42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNE BD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u0026ndash;5.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e113375.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;527639.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1381.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e656.52\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e431393.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e196559.12\u0026ndash;634798.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNE EV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.13\u0026ndash;12.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e168185.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;539373.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1562.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e669.1\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e480646.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e168918.67\u0026ndash;635364.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c9\"\u003e\u003cp\u003e370427.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e114070.17\u0026ndash;635815.47\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEM EV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u0026ndash;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e148545.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.000003\u0026ndash;591400.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1225.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e157.13\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e385370.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e49769.9\u0026ndash;640294.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNE BD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u0026ndash;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e130643.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;566028.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1380.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e557.3\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e429735.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e167521.58\u0026ndash;636979.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNE EV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u0026ndash;0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e157185.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u0026ndash;566731.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1414.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e216.44\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e439506.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e66931.47\u0026ndash;637419.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e3.3 Light and temperature response parameters of isoprene emissions from different leaf phenological types and canopy-level flux projections\u003c/b\u003e\u003c/p\u003e\u003cp\u003eEstimated model parameters α (initial light-response slope), \u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e (light-saturation coefficient), \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e (temperature activation energy), and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e (temperature deactivation energy) from isoprene light and temperature response models for each species and average values for each leaf phenological type are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Results showed that brevideciduous isoprene emitters had higher \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) with a trend toward higher isoprene \u003cem\u003eLSP\u003c/em\u003e (p\u0026thinsp;=\u0026thinsp;0.09; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE). Still, dimethylallyl diphosphate (DMADP) pool sizes (Fig. S10A) and isoprene synthase (\u003cem\u003eIspS\u003c/em\u003e) activity rates (Fig. S10B) were not significantly different between brevideciduous and evergreen trees.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValues of parameters α (initial light-response slope), \u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e (light-saturation coefficient), \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e (temperature activation energy), and \u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e (temperature deactivation energy) empirically estimated based on non-linear least-square fits of light and temperature emission response curves (Guenther et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) to the observed data for each isoprene-emitting species measured in this study; average values for brevideciduous and evergreen trees; and values from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eα\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eL1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eT1\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eBrosimum parinarioides\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00365\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e562\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEndopleura uchi\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e658\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePeltogyne catingae\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e625\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eEschweilera cyathiformis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00961\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e165\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e305\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMinquartia guianensis\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e311\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eProtium spruceanum\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00848\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e254\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.00525\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e101\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e615\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.01174\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e155\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e290\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuenther et al (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.00142\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e1.22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e95\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e230\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIndividual variation in isoprene emission rates estimated using empirically derived parameters showed that emissions were generally above 2000 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at 09:00, peaked at 13:00 and decreased again at 17:00 for all trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Meanwhile, differences between values estimated with empirically derived and reference parameters (Guenther et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) indicated that the latter consistently overestimated isoprene emissions for both brevideciduous and evergreen trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). It appeared that reference parameters underestimated emissions at 17:00 in October and November for brevideciduous trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), and in October for evergreen trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). However, Kruskal-Wallis pairwise comparisons showed that differences in emission rates at 17:00 were not statistically significant (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e), while reference parameter estimates were significantly higher at 09:00 and 13:00 for evergreen trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB), with a similar trend for brevideciduous trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).\u003c/p\u003e\u003cp\u003eFinally, we compared percentages of variation in canopy-level isoprene fluxes from brevideciduous and evergreen trees estimated using average emission factors from Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and empirically derived parameters from this study (Model 1), average emission factors from Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and reference parameters from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Model 2), and emission factors from Guenther et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and reference parameters from Guenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Model 3). Comparisons between models (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) showed that Model 2 estimated 98.4 and 256.4% higher fluxes for brevideciduous and evergreen trees, respectively, compared to Model 1; and Model 3 fluxes were 794.8 and 1565.9% higher for brevideciduous and evergreen trees, respectively, compared to Model 1.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eValues of (A) canopy-level isoprene fluxes (\u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) estimated at different hours of the day (09:00, 13:00, and 17:00) in October, November, and December/2022 using average emission factors fromRobin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and empirically derived parameters from this study (Model 1), average emission factors fromRobin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and reference parameters fromGuenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Model 2), and emission factors fromGuenther et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and reference parameters fromGuenther et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) (Model 3); and (B) percentages of variation (%) between values estimated using each model. The average emission factor fromRobin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) is 1552.2 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for brevideciduous (BD) and 1497.4 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for evergreen (EV); and from Guenther et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), the emission factor is 7000 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which is the value assigned for the plant functional types broadleaf evergreen tropical tree and broadleaf deciduous tropical tree.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(A)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eEV\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOctober\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5170.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2538.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13938.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13445.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e62856.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62856.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6888.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4022.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15099.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14566.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e68093.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e68093.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2606.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2366.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2340.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2257.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10555.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10555.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNovember\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4813.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2278.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12999.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12539.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e58620.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e58620.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6847.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3967.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15417.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14872.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e69525.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e69525.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2583.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2161.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2380.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2296.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10734.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e10734.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDecember\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4323.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1943.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11381.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10979.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51327.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e51327.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5614.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2894.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14363.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13855.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e64772.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64772.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2636.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1766.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2655.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2561.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11973.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11973.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003e\u003cb\u003eModel 1 vs. 2 (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e\u003cb\u003eModel 1 vs. 3 (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u003cp\u003e\u003cb\u003eModel 2 vs. 3 (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e(B)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eEV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eEV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eEV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOctober\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e169.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e429.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1115.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2376.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e262.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e888.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1592.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e304.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e346.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNovember\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e450.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1117.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2473.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e274.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e915.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1652.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-7.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e315.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e396.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDecember\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e163.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e464.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1087.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2540.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e155.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e378.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1053.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2138.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e354.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e577.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAverage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e167.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e448.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1107.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2463.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e305.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e952.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1794.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17:00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e324.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e440.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal average\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e794.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1565.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e351.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e367.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe purpose of our study was to evaluate i) how VI emissions varied in response to light and temperature changes in different leaf phenological types; ii) how characteristics of photochemical activity varied between isoprene emitters and non-emitters from different leaf phenological types; and iii) how the interaction of leaf phenological types and light and temperature changes affected canopy isoprene emission estimates. We observed that i) emission rates of all volatile compounds increased with rising leaf temperature, with a stronger effect for monoterpene and sesquiterpene emissions, which increased 10 and 12.5-fold from 30 to 45\u0026deg;C, respectively. While isoprene and monoterpene emissions did not differ between leaf phenological types, brevideciduous trees had higher increases in sesquiterpene emissions with leaf temperature and higher emission rates at 45\u0026deg;C. Moreover, we observed that ii) characteristics of photochemical activity varied across isoprene emitters and non-emitters from brevideciduous and evergreen trees, with brevideciduous isoprene emitters showing the highest baseline photosynthetic performance and a trend toward higher photosynthesis and isoprene light saturation points (\u003cem\u003eLSP\u003c/em\u003e). Lastly, we saw that iii) brevideciduous isoprene emitters had higher isoprene temperature deactivation energy (\u003cem\u003eC\u003c/em\u003e\u003csub\u003eT2\u003c/sub\u003e) and a trend toward higher isoprene \u003cem\u003eLSP\u003c/em\u003e, and that canopy-level isoprene fluxes estimated using reference emission factors and parameters (Guenther et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) highly overestimated fluxes when compared to estimates using emission factors from Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and empirically derived parameters from our light and temperature response curves.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 VI emission responses to changes in light and temperature in different leaf phenological types\u003c/h2\u003e\u003cp\u003eTemperature response curves of VI emissions revealed that emission rates of all compounds increased with leaf temperature. Isoprene emissions increased on average 3.5 times from 30 to 45\u0026deg;C for both brevideciduous and evergreen trees, and even species that were not previously classified as isoprene emitters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed such increases. This suggests that the Amazon Forest possibly harbors a much larger percentage of isoprene emitters (Jardine et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mu et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) than previously thought (Harley et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Loreto \u0026amp; Fineschi, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Furthermore, all species measured showed significantly higher isoprene emission factors (emission measured at photosynthetic photon flux density of 1000 \u0026micro;mol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and leaf temperature of 30\u0026deg;C) in this study compared to Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) (Fig. S11). We argue that this might be because here we performed measurements in early December - compared to October/November (Robin et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) - when leaves were probably overall older and thus more photosynthetically active, with higher isoprene synthase (\u003cem\u003eIspS\u003c/em\u003e) activity and isoprene emission rates (Schnitzler et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Alves et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our results also corroborate studies showing that the highest isoprene fluxes are not observed in the warmest months, but when canopies have proportionally larger fractions of mature leaves (Gomes Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWe also saw that monoterpene and sesquiterpene emissions increased on average 10 to 12.5 times, respectively, between 30 and 45\u0026deg;C. Brevideciduous trees had higher increases in sesquiterpene emissions with temperature, as well as higher sesquiterpene emission rates at 45\u0026deg;C. Likewise, even though isoprenoid mass investments showed a trend to shift from \u0026ldquo;lighter\u0026rdquo; (i.e., isoprene) to \u0026ldquo;heavier\u0026rdquo; (i.e., monoterpenes and sesquiterpenes) compounds at higher temperatures, this effect was potentially stronger in brevideciduous trees. Moreover, monoterpene emissions reached higher magnitudes (52.75\u0026thinsp;\u0026plusmn;\u0026thinsp;19.28 \u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) than isoprene (39.9\u0026thinsp;\u0026plusmn;\u0026thinsp;20.88 \u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and sesquiterpenes (14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02 \u0026micro;g C g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) at 42.5\u0026deg;C. Similarly, empirically derived β coefficients (Guenther et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) for most monoterpenes and sesquiterpenes measured fell above the \u0026plusmn;\u0026thinsp;20 % range values adoptedin MEGAN v2.1 (Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) (Fig. S12). The exceptions were α- and β-pinene and \u003cem\u003etrans\u003c/em\u003e-β-ocimene, and this could be because these were likely emitted in a light-dependent manner. Nevertheless, this indicates that most monoterpenes and sesquiterpenes showed higher temperature sensitivities than previously thought (Nagalingam et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Bourtsoukidis et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTemperature response curves suggested that \u003cem\u003eBrosimum parinarioides\u003c/em\u003e and \u003cem\u003eCroton matourensis\u003c/em\u003e emitted light-dependent monoterpenes, as they showed emissions of \u003cem\u003etrans\u003c/em\u003e-β-ocimene (\u003cem\u003eB. parinarioides\u003c/em\u003e) and α-pinene (\u003cem\u003eC. matourensis\u003c/em\u003e) that do not follow the typical exponential increase of storage pool emissions (Guenther et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), but follow that of enzymatic activity temperature responses, with a \u003cem\u003eT\u003c/em\u003e\u003csub\u003eopt\u003c/sub\u003e around 40\u0026deg;C (Fischbach et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Niinemets et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This is further supported by the monoterpene light response curves in these species (Fig. S5). Temperature curves also suggested the presence of light-dependent monoterpene emissions in \u003cem\u003eCariniana decandra\u003c/em\u003e, since the species showed similar emission patterns for α-pinene. \u003cem\u003eTrans\u003c/em\u003e-β-ocimene dominated monoterpene emissions in \u003cem\u003eB. parinarioides\u003c/em\u003e. This is quite relevant given that \u003csup\u003e13\u003c/sup\u003eC-labeling has demonstrated that Amazon Forest trees emit this compound in a light-dependent manner and that \u003cem\u003etrans\u003c/em\u003e-β-ocimene reacts more rapidly to NO\u003csub\u003ex\u003c/sub\u003e and contributes to higher O\u003csub\u003e3\u003c/sub\u003e formation in polluted atmospheres (Jardine et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRegardless of leaf phenological types, all species showed unique patterns of changes in percentages of carbon (%C) loss to VI emissions with temperature, as well as unique photosynthetic thermal limits (i.e., leaf temperature where photosynthesis ceased). Whether they emitted isoprene or not, some species were able to sustain photosynthesis at 45\u0026deg;C while others ceased between 40 and 42.5\u0026deg;C. Still, %C loss to isoprene emissions reached up to 14.4% before photosynthesis ceased, which represents much higher losses compared to the percentages generally assumed under non-stress conditions (1\u0026ndash;2%; Sharkey \u0026amp; Loreto, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Kesselmeier et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Also, in many cases, isoprene emissions continued even after photosynthesis ceased, signaling the use of alternative carbon sources for isoprene production (Jardine et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; de Souza et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moreover, %C loss to monoterpene and sesquiterpene emissions reached values as high as 85.1 and 36.9%, respectively. While sesquiterpenes and some monoterpenes are emitted from storage pools and do not rely directly on photosynthetic carbon, these values still represent major losses to species\u0026rsquo; overall carbon budgets under high-temperature stress. Observed %C loss to monoterpene emissions in \u003cem\u003eB. parinarioides\u003c/em\u003e and \u003cem\u003eC. decandra\u003c/em\u003e also suggested shifts from light-dependent emissions to storage pool emissions or use of stored carbon reserves at 40\u0026deg;C, when photosynthesis became negative.\u003c/p\u003e\u003cp\u003eGiven that Amazon Forest canopies are likely to experience temperatures exceeding 40\u0026deg;C (Jardine et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Manzi et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and that many of the species measured here are highly positioned in the regional biomass rank (Fauset et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), our findings suggest that current models probably underestimate monoterpene and sesquiterpene global fluxes (Kuhn et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Jardine et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Monoterpenes and sesquiterpenes not only incur higher carbon losses but also contribute to two and 10 times more particle formation than isoprene, respectively (Griffin et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1999b\u003c/span\u003e; Kroll et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, our findings are critical considering that warmer and drier climates will possibly favor the selection of brevideciduous trees (Aleixo et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and that increasingly more frequent and intense stressors (Gatti et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) are expected to promote stronger stress-induced emissions of these \u0026ldquo;heavier\u0026rdquo; and more chemically reactive compounds - which were also seen here as significantly associated with brevideciduity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Physiological changes in isoprene emitters and non-emitters across different leaf phenological types\u003c/h2\u003e\u003cp\u003eOur results showed that net photosynthesis (\u003cem\u003eA\u003c/em\u003e\u003csub\u003en\u003c/sub\u003e), electron transfer rates (\u003cem\u003eJ\u003c/em\u003e), photochemical quenching (\u003cem\u003eqP\u003c/em\u003e), and quantum efficiency of photosystem II (ϕ\u003csub\u003ePSII\u003c/sub\u003e) all decreased with leaf temperature. However, we observed that the modeled temperature responses of these variables varied differently across isoprene emitters and non-emitters from different leaf phenological types. While brevideciduous isoprene emitters showed elevated photosynthetic performance at moderate temperatures, no consistent thermal advantage was observed across other characteristics of photochemical activity in isoprene emitters. Moreover, entropy and deactivation energy patterns pointed toward greater thermal stability in isoprene non-emitters, particularly evergreens. Nonetheless, trends in light saturation points and the high photosynthesis \u003cem\u003ek\u003c/em\u003e\u003csub\u003e25\u003c/sub\u003e value suggest that brevideciduous isoprene emitters may exhibit functional advantages under moderately high light and temperature conditions. These findings challenge the notion of a uniform stress-tolerance benefit from isoprene emission (Singsaas et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hanson \u0026amp; Sharkey, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Behnke et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Taylor et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rodrigues et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and instead suggest that light and temperature stress responses in central Amazon Forest trees may emerge from interactions between isoprene emissions and leaf turnover strategies (Robin et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe high baseline photosynthetic performance and observed trends in light saturation points in brevideciduous isoprene emitters are likely explained by isoprene-associated mechanisms, such as reactive oxygen species (ROS) scavenging and modulation of stress signaling (Zuo et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). These mechanisms have been proposed to mitigate photoinhibition under high irradiance conditions (Vickers et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Pollastri et al., \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Concurrently, brevideciduous trees also showed stronger increases in sesquiterpene emissions and a trend toward shifting from \u0026ldquo;lighter\u0026rdquo; to \u0026ldquo;heavier\u0026rdquo; compound emissions. Sesquiterpene emission incurs higher carbon losses, and it had been previously suggested that resource-acquisitive (Wright et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) brevideciduous leaves were not likely to favor more carbon \u0026ldquo;costly\u0026rdquo; compounds (Harrison et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nonetheless, sesquiterpenes may highly benefit brevideciduous trees, as these compounds are deeply involved in herbivore deterrence and plant communication (Pichersky \u0026amp; Gershenzon, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Fineschi \u0026amp; Loreto, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This suggests that brevideciduous trees might be equipped with sesquiterpene emissions to cope with biotic stressors.\u003c/p\u003e\u003cp\u003eIt has been hypothesized that central Amazon Forest trees typically flush leaves during the dry season as protection against drought stress and herbivory (Lopes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); and brevideciduous trees annually renew major fractions - if not all - of their canopies in a synchronous manner (Lopes et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Gon\u0026ccedil;alves et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, we suggest that increased sesquiterpene production is a considerable advantage for brevideciduous trees since it would also benefit the defenses from nearby plants, and potentially provide a stronger \u0026ldquo;community-level\u0026rdquo; shield for cohorts of vulnerable young leaves in the dry season (Coley \u0026amp; Barone, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Robin et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This idea is supported by evidence that isoprene exposure can upregulate terpene synthase gene expression in \u003cem\u003eArabidopsis\u003c/em\u003e (Harvey \u0026amp; Sharkey, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Moreover, studies have demonstrated that isoprene emissions were positively correlated with emissions of sesquiterpene compounds α-copaene, α-humulene, alloaromadendrene, and β-caryophyllene (Zeng et al., \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and that brevideciduous central Amazon Forest trees showed an increased diversity of stored sesquiterpene compounds associated with higher isoprene emission factors (Robin et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Taken together, these findings suggest that brevideciduous trees may rely on a coordinated strategy that combines abiotic stress tolerance via isoprene with enhanced chemical defense through sesquiterpenes, optimized for synchronous canopy renewal during the dry season.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Canopy-level variations in isoprene fluxes from brevideciduous and evergreen trees\u003c/h2\u003e\u003cp\u003eComparisons of isoprene emission rates estimated for 175 canopy-dominant trees measured in Robin et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) using empirically derived and reference parameters (Guenther et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) showed that the latter consistently overestimated isoprene emissions at 09:00 and 13:00 and that the effect was significantly stronger for evergreen trees. Our findings also demonstrated that a high degree of spatial heterogeneity in leaf-level isoprene emission rates can be found even in considerably small areas (~\u0026thinsp;4 ha). Similarly, canopy-level isoprene fluxes estimated using reference emission factors and light and temperature response parameters were extremely higher than fluxes estimated with observed emission factors and empirically derived parameters, particularly for evergreen trees. This suggests that parameter values currently used in global isoprene flux models (Guenther et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1999\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) still carry high uncertainty for the Amazon Forest.\u003c/p\u003e\u003cp\u003eBased on our results, we suggest that current model estimates will improve if models are inputted by gridded emission maps based on observed emission factors and empirically derived parameters, rather than relying on single values assigned for simplified plant functional type (PFT) classifications. Furthermore, reference emission factors and parameters currently used in models overall derive from flux tower measurements, which are still scarce across the Amazon basin and therefore tend to smooth spatial variability. Hence, these findings also emphasize the importance of leaf-level measurements for obtaining more accurate isoprene emission factors and estimating parameters that truly reflect variation in emission responses to light and temperature in different leaf phenological types.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Implications and summary\u003c/h2\u003e\u003cp\u003eConducting field experiments in remote and isolated locations of the Amazon Forest - particularly when sampling from very tall trees (\u0026gt;\u0026thinsp;30 m) - is inherently challenging, and often restricts the number of available observations and contributes to high data variability. These limitations become especially pronounced when numerous variables are investigated simultaneously, impacting statistical significance. Nevertheless, despite these constraints, our results revealed significant relationships and clear trends consistent with physiological expectations. These findings establish an important foundation for future studies exploring ecophysiological differences between isoprene emitters and non-emitters from different leaf phenological types and emphasize the importance of linking leaf-level observations to broader biogeochemical processes.\u003c/p\u003e\u003cp\u003eIn sum, our findings provide robust empirical evidence that light and temperature responses of VI emissions vary not only with species physiology but with leaf phenological strategy - a dimension largely overlooked for tropical tree species in current global models (e.g., Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). We demonstrated that standard parameterizations based on coarse functional types systematically misrepresent emission dynamics, particularly in evergreen Amazonian canopies, and that brevideciduous trees may engage in a coordinated defense strategy involving both isoprene and sesquiterpenes. As climate change continues to alter thermal and hydrological regimes across the Amazon (Malhi et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Gloor et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Flores et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), these shifts in emission chemistry and canopy phenology could drive changes in atmospheric reactivity and feedback processes (Y\u0026aacute;\u0026ntilde;ez-Serrano et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Given that the Amazon Forest harbors the largest area of tropical forest globally (Nobre et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and is the largest contributor to global volatile isoprenoid fluxes (Guenther et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gomes Alves et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), understanding these emission dynamics is critical. Our study, therefore, lays a foundation for incorporating ecophysiological nuance into global BVOC emission models and highlights the urgent need for gridded, trait-based parameter maps rooted in in-situ leaf-level measurements. This approach will be critical for improving the accuracy of predictions related to atmospheric composition, biosphere resilience, and climate\u0026ndash;vegetation feedbacks in tropical forests.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was funded by the German-Brazilian project ATTO (Amazon Tall Tower Observatory), supported by the German Federal Ministry of Education and Research (BMBF, funds 01LB1001A and 01LK2101D) and by the Brazilian Ministry of Science, Technology and Innovation (FINEP/MCTI, contract 01.11.01248.00). MR was supported by the International Max Planck Research School for global biogeochemical cycles (IMPRS-gBGC). Additional support was provided by the Ministry of Education and Research of Estonia (Center of Excellence AgroCropFuture, project TK200), and the Estonian Research Council (grants MOBJD696 and PRG2207).\u003c/p\u003e\u003ch2\u003eAuthors' contributions\u003c/h2\u003e\u003cp\u003eMichelle Robin contributed to the development and sampling design of the study; the collection of volatile isoprenoid, gas exchange, and chlorophyll fluorescence data; and the statistical analysis of datasets. Vin\u0026iacute;cius F. de Souza contributed to the collection of volatile isoprenoid, gas exchange, and chlorophyll fluorescence data; and the analysis of first dark-decay kinetics of isoprene emissions. Joseph Byron contributed to the identification and quantification of volatile isoprenoids accumulated in adsorbent cartridges. \u0026Uuml;lo Niinemets contributed to the provision of equipment; and the statistical analysis of datasets. Christine R\u0026ouml;mermann contributed to the development and sampling design of the study. Fl\u0026aacute;vio A. F. D\u0026rsquo;Oliveira and Cl\u0026eacute;o Quaresma Dias-Junior contributed to the micrometeorological tower data from the upland forest plot. Jonathan Williams and Jos\u0026eacute; Francisco C. Gon\u0026ccedil;alves contributed to the provision of equipment. Eliane Gomes Alves contributed to the development and sampling design of the study; the funding acquisition; the provision of equipment; and the statistical analysis of datasets. All authors contributed to the writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eWe acknowledge the support of the ATTO project, LBA/INPA, and SDS/CEUC/RDS-Uatum\u0026atilde;. We truly thank Prof. Juliana Schietti for the assistance with field equipment and processing of leaf material. We would also like to thank all the field assistants, Jose Raimundo Ferreira Nunes, Jardel Valente Nunes, Jardison Valente Nunes, Alessandra Peixoto, and Gabriela Ushida Neves; and all the people involved in the logistic support of the ATTO project, especially Roberta de Souza, who were all imperative for the development of this study.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e\u003cp\u003eThe entire dataset generated and analyzed for this study can be found in the ATTO Data Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.attodata.org/ddm/data/Showdata/519\u003c/span\u003e\u003cspan address=\"https://www.attodata.org/ddm/data/Showdata/519\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and is available upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eAffek HP, Yakir D\u003c/strong\u003e. \u003cstrong\u003e2003\u003c/strong\u003e. 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How volatile isoprenoids improve plant thermotolerance. \u003cem\u003eTrends in Plant Science\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"isoprene, monoterpenes, sesquiterpenes, BVOCs, brevideciduous, evergreen, leaf-out phenology","lastPublishedDoi":"10.21203/rs.3.rs-7270146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7270146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate stress is shifting the Amazon Forest from a carbon sink to a source, highlighting the need to understand tree resilience strategies, including leaf turnover and volatile isoprenoid (VI) production. Dry-season leaf turnover in Amazonian trees is hypothesized as a strategy to avoid drought and herbivory, and VIs protect trees against abiotic and biotic stresses. We measured temperature- and light-driven changes in VI emissions and characteristics of photochemical activity in 12 brevideciduous and evergreen central Amazon woody species. Brevideciduous trees showed greater increases in sesquiterpene emissions with rising temperature. Among these, isoprene emitters showed superior baseline photosynthetic performance, suggesting coordinated VI production and leaf turnover strategies. Moreover, current global VI models consistently overestimated isoprene fluxes by neglecting leaf phenological variability. These findings reveal overlooked phenological controls on Amazonian VI emissions, challenging standard model parametrization and emphasizing leaf-level data to improve predictions of atmospheric chemistry and climate-vegetation feedbacks.\u003c/p\u003e","manuscriptTitle":"Coordinated volatile isoprenoid production and leaf turnover protect central Amazon Forest trees against stress","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-03 09:57:18","doi":"10.21203/rs.3.rs-7270146/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"84801772-973c-418e-bb99-905782e1fe1e","owner":[],"postedDate":"September 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53531158,"name":"Biological sciences/Plant sciences/Plant stress responses/Heat"},{"id":53531159,"name":"Biological sciences/Ecology/Ecophysiology"},{"id":53531160,"name":"Biological sciences/Ecology/Climate-change ecology/Phenology"}],"tags":[],"updatedAt":"2026-04-30T14:20:13+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-03 09:57:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7270146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7270146","identity":"rs-7270146","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}