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\begin{document}

\title{Seasonal shifts in isoprenoid emission compositions from three
hyperdominant tree species in central Amazonia}



\author[1]{Eliane Gomes Alves}%
\author[2]{Tyeen Taylor}%
\author[1]{Michelle  Robin }%
\author[3]{Debora Pinheiro de Oliveira}%
\author[4]{Juliana Schietti}%
\author[5]{Sergio Duvoisin Júnior}%
\author[6]{Nora Zannoni}%
\author[6]{Christoph  Hartmann}%
\author[6]{Jonathan. Williams}%
\author[3]{José Francisco Gonçalves}%
\author[3]{Jochen  Schöngart}%
\author[7]{ Florian Wittmann}%
\author[3]{Maria  Piedade}%
\affil[1]{Max Planck Institute for Biogeochemistry}%
\affil[2]{University of Miami}%
\affil[3]{National Institute of Amazonian Research}%
\affil[4]{Federal University of Amazonas}%
\affil[5]{University of Amazonas State}%
\affil[6]{Max Planck Institute for Chemistry}%
\affil[7]{Karlsruhe Institute of Technology}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\selectlanguage{english}
\begin{abstract}
Volatile isoprenoids regulate plant performance and atmospheric
processes, and Amazon forests comprise the dominant source to the global
atmosphere. Still, there is a poor understanding of how isoprenoid
emission capacities vary in response to ecophysiological and
environmental controls in Amazonian ecosystems. We measured isoprenoid
emission capacities of Amazonian hyperdominant tree species---Protium
hebetatum, Eschweilera grandiflora, Eschweilera coriacea --- across
seasons and along a topographic and edaphic environmental gradient in
the central Amazon. From wet to dry season, both photosynthesis and
isoprene emission capacities strongly declined, while emissions
increased among the heavier isoprenoids---monoterpenes and
sesquiterpenes. Plasticity across habitats was most evident in P.
hebetatum, which emitted sesquiterpenes only in the dry season, at rates
that significantly increased along the hydro-topographic gradient from
white sands (shallow root water access) to upland (deep water table). We
suggest that emission composition shifts are part of a plastic response
to increasing abiotic stress (e.g., heat and drought) and reduced
photosynthetic supply of substrates for isoprenoid synthesis. Our
comprehensive measurements suggest that more emphasis should be placed
on other isoprenoids besides isoprene in the context of abiotic stress
responses. Shifting emission compositions have implications for
atmospheric responses due to the strong variation in reactivity among
isoprenoid compounds.%
\end{abstract}%



\sloppy


\textbf{1. Introduction}

Isoprenoids are volatile organic compounds (VOCs) that are emitted to
the atmosphere mostly by plants. They have diverse functional roles at
multiple scales, from cellular protection and defense at the foliar
level, through chemical signaling within and among plants, up to the
regulation of large-scale biogeochemical processes, such as the effect
on atmospheric chemical composition and contribution to aerosol
formation (Laothawornkitkul, Taylor, Paul \& Hewitt 2009). Volatile
isoprenoids are represented by isoprene
(C\textsubscript{5}H\textsubscript{8}), monoterpenes
(C\textsubscript{10}H\textsubscript{16}), and sesquiterpenes
(C\textsubscript{15}H\textsubscript{24}), and their largest source is
from tropical trees that contribute ca. 80\% of global emissions
(Guenther \emph{et al.} 2012). With half of the world's tropical
forests, Amazonia is recognized as the most important global source of
isoprenoids to the atmosphere (Sindelarova \emph{et al.} 2014). Since
the early 1980s, multiple investigations have studied canopy flux,
canopy concentrations, and to a lesser degree, leaf-level emissions of
isoprenoids. These studies have reported meaningful insights into the
emission drivers and how these compounds are involved in subsequent
atmospheric processes (Y\selectlanguage{ngerman}áñez\selectlanguage{english}-Serrano \emph{et al.} 2020). These combined
findings contributed to developing and optimizing an isoprenoid emission
model (Guenther \emph{et al.} 2012). Despite this effort, emission
estimates from tropical vegetation still carry a high uncertainty due to
a poor understanding of the biological controls that determine
the\emph{capacity} of emission and its plasticity in response to
ecological and environmental conditions, as well as of the environmental
controls that determine \emph{rates} of emissions (Alves \emph{et al.}
2018).

The constitutive emission capacity of isoprenoids is determined by the
emission at leaf standard conditions (1000 \selectlanguage{english}?mol
m\textsuperscript{-2}s\textsuperscript{-1}photosynthetically active
radiation, 30 \selectlanguage{english}@C), and this is a physiological property that may vary
plastically (across individuals) under different ecological and
physiological conditions. Actual emission rates are a function of
emission capacity and variation in light and temperature (Niinemets
\emph{et al.} 2011). This implies that, for modeling isoprenoid
emissions, first it is necessary to quantify emission capacities across
species, and their plasticity across individuals, to then quantify the
emission variation driven by environmental factors such as light and
temperature (Duhl, Helmig \& Guenther 2008; Niinemets \emph{et al.}
2011; Guenther \emph{et al.}2012). For several practical reasons, such
as the remoteness and high plant species diversity of Amazonia, most
studies so far have only investigated isoprenoid emission rates at the
ecosystem level and how they vary with environmental factors. The
majority of these studies measured canopy concentration or flux of
isoprenoids, focusing on isoprene, known to be the strongest emitted
compound (Eerdekens \emph{et al.} 2009). Only a few studies measured
monoterpenes, and very few studies quantified sesquiterpenes
(Y\selectlanguage{ngerman}áñez\selectlanguage{english}-Serrano \emph{et al.} 2020).

A first attempt to address isoprene emission capacities at the leaf
level and the upscaling to ecosystems was made by Harley \emph{et
al.}(2004). Knowing that not all plant species emit isoprene (Monson,
Jones, Rosenstiel \& Schnitzler 2013), the aforementioned study aimed at
quantifying the isoprene emission capacity for multiple plant species
from different Amazonian regions, and a method was created to impute the
isoprene trait to other non-measured trees by using species
identification and phylogenetic proximity; then, results were used to
upscale the isoprene emission capacity to the ecosystem level based on
the trees fraction of isoprene emitters. Subsequent work has further
expanded the number of species measured (Jardine \emph{et al.} 2020;
Taylor et al. 2021). Recent work has derived more mechanistic approaches
to scaling isoprene emission across the landscape by determining how the
fraction of emitters relates to mean climate conditions (Taylor \emph{et
al.} 2018) due to differential performance between isoprene-emitting and
non-emitting species (Taylor, Smith, Slot \& Feeley 2019). These studies
were important as they identified the emission capacity of isoprene, and
in a few cases also of monoterpenes, and certainly contributed to the
overall body of work from nearly 30 years of research on modeling
isoprenoid emission in Amazonia.

Yet, it is also known that isoprenoid composition is conserved within
plant species, but that these compounds' emission capacity may vary
significantly within species and individuals with photosynthetic
capacity, carbon and nutrient investment tradeoffs, habitat, and the
environment (Harrison \emph{et al.} 2013). This variability in emission
capacities is an essential factor to explain why we observe seasonal
variation in isoprenoid emission (see a synthesis of studies in
Yanez\selectlanguage{english}-Serrano \emph{et al.} 2020), which does not entirely follow the
seasonal variation in solar radiation and temperature in central
Amazonia (Alves \emph{et al.} 2016, 2018).

Seasonal factors such as leaf demography and phenology are important
drivers of variability in leaf emission capacities and the composition
of emitted isoprenoids. During early leaf development, young leaves
synthesize less isoprene and more monoterpenes and sesquiterpenes
(Gershenzon \& Croteau 1991; Kuhn \emph{et al.} 2004b), and the opposite
occurs with leaf maturation (Alves, Harley, Goncalves, Silva \& Jardine
2014). This shift in emission composition results from physiological and
ecological factors, which cannot be explained by atmospheric
observations and direct abiotic effects alone. Variation in leaf
physiology with ontogeny scales up through leaf age distributions ---
with a higher proportion of young leaves during the dry season
(Lopes\emph{et al.} 2016; Wu \emph{et al.} 2016 ) --- to influence
seasonal variation in isoprenoid emission capacities and total ecosystem
emissions (Alves, Harley, Goncalves, Silva \& Jardine 2014;
Alves\emph{et al.} 2016, 2018).

Faced with these reports, we can infer that most of the studies in
Amazonia were focused on emission \emph{rates} --- canopy-level
sensitivity concerning environmental factors and landscape-level
sensitivity to leaf quantity and emitter fraction. Relatively little
work has focused on mechanisms of variation in emission
\emph{capacity,}either within or between species, and these have focused
exclusively on leaf age, and primarily on isoprene (Alves \emph{et al.}
2014, 2016, 2018). There is still a lack of understanding of the
different physiological roles of the other light-dependent isoprenoids,
and, by measuring all of them across habitats and seasons within
species, we can begin to infer conditions under which one or the other
compound is favored and how this can be more accurately scaled to the
ecosystem. Therefore, the determination of intraspecific variation in
emission capacities of different isoprenoids is a critical knowledge gap
that this study intends to address.

In this study, we present a uniquely comprehensive set of leaf-level
isoprenoid emission measurements to characterize variation in emission
capacities and chemical compositions within species, across habitats,
and across seasons. We performed our measurements on trees of three
hyperdominant species from central Amazonia --- \emph{Protium hebetatum,
Eschweilera grandiflora,} and \emph{Eschweilera} \emph{coriacea} (ter
Steege \emph{et al.} 2013) --- distributed along a topographic and
edaphic environmental gradient at the Amazon Tall Tower Observatory
(ATTO) site, during the wet and the dry seasons. Simultaneous
measurement of photosynthesis and emissions allowed us to assess
shifting isoprenoid investments in the context of the leaf carbon
balance and the inferred availability of photosynthetic substrates for
isoprenoid synthesis.

\textbf{2. Methods~}

\textbf{2.1. Study site}

The study was conducted at the Amazon Tall Tower Observatory (ATTO)
within the PELD-MAUA (PELD is the acronym in Portuguese for Long-term
Ecological Research) experimental plots. This experimental site is in
the Uatuma Sustainable Development Reserve (USDR), about 150 km
northeast of the city of Manaus (S 02 08.9deg W 059 00.2deg), in central
Amazonia (Fig. 1). The climate is tropical humid, with mean annual
temperature and precipitation of 28oC and 2376 mm, respectively, and is
marked by a pronounced rainy season from November to May and a drier
season from June to October (Andreae \emph{et al.} 2015). The reserve
covers 4244 km2 with a mosaic of dense non-flooded upland forest
vegetation, dense non-flooded forest upon ancient river terraces, and
shrubland/closed-canopy vegetation on white sands (Yanez-Serrano\emph{et
al.} 2015). These three forest types are characterized with differences
in soil and vegetation attributes. In the upland forest, soils are
classified as ferralsols, which are highly weathered and well-drained
(Chauvel, Lucas \& Boulet 1987). Soils in the white-sand forests are
classified as arenosols, with characteristic properties of high water
permeability, low water holding capacity, low specific heat capacity,
and often low nutrient contents that are mostly held on organic matter
(Quesada \emph{et al.} 2011). Also, white-sand forests can be subject to
extremes of flooding and drought at different times of the year. Through
intense leaching, Fe, Al, Mg and other compounds are deposited in the
lower layers of the soil forming a hard layer that can block water
drainage. Thus, in the dry season the vegetation can suffer severe water
deficit, and in the rainy season water-logged soils or even superficial
inundation of the root system for months (Kubitzki 1989). Soils in the
ancient river terrace forests are classified as alisols, which represent
a more recent pedogenetic status compared to the ferralsols from the
upland forest and therefore have greater capacity to supply nutrients,
as it was observed with higher total phosphorus and higher total reserve
bases (Andreae \emph{et al.} 2015). However, some ancient river terrace
soils show signs of anoxia (mottling) in deeper horizons, which may have
an influence on forest structure (Quesada\emph{et al.} 2012; Emilio
\emph{et al.} 2013) and dynamics (Cintra\emph{et al.} 2013), and
possibly restrict tree height and individual biomass storage (Martins
\emph{et al.} 2015) compared to upland forests. The vegetation across
the three forest types presents differences in tree species richness,
with the highest number of species on upland forest (137+-5), followed by
ancient river terrace forest (127+-8) and white-sand forest (64+-18)
(Andreae \emph{et al.} 2015). Carbon stocks in above-ground biomass
followed the same pattern as species richness, by increasing from 79 +-
26 Mg ha\selectlanguage{english}\textsuperscript{-1} in the white-sand and 101 +- 13 Mg
ha\selectlanguage{english}\textsuperscript{-1} in the ancient river terrace to a maximum of 170
+- 13 Mg ha\selectlanguage{english}\textsuperscript{-1} in the upland forests.~ The ATTO site
combines high alpha diversity with high beta diversity within a small
geographic scale, where tree species diverge mostly to local edaphic
conditions (Andreae \emph{et al.} 2015).

\textbf{2.2. Sampling}

We sampled isoprenoid emissions from 30 trees across two permanent plots
for each forest type: upland forest\emph{,} white-sand forest and
ancient river terrace forest. Plots were distributed at least one
kilometer from each other along the ATTO access road (Fig. 1). Tree
species were previously identified (with individual vouchers collected)
and confirmed taxonomically. Two species were chosen from a preliminary
selection based on (i) their abundance in the PELD MAUA tree species
inventory (Andreae \emph{et al.} 2015), (ii) their contribution to a
wider distribution in central Amazonia (ter Steege \emph{et al} . 2013;
Fauset \emph{et al.} 2015), and (iii) their occurrence in at least two
of the forest types of the study experimental site. \emph{Protium
hebetatum} (Burseraceae) occurred across the three forest types: upland
forest, white-sand forest and ancient river terrace
forest.\emph{Eschweilera grandiflora} (Lecythidaceae) was present in two
forest types: upland forest and ancient river terrace forest. In
addition, we selected the second most dominant species in stem
abundance, biomass and productivity in central Amazonia -
\emph{Eschweilera coriacea}(Lecythidaceae) (ter Steege \emph{et al} .
2013; Fauset \emph{et al.}2015), but this species was only present in
the plots of white-sand forest. For each species we selected and sampled
five trees with similar diameters in each forest type where the species
occured. Their height and position in the canopy varied with species
life history. Leaf traits and isoprenoid emission were repeatedly
sampled in May 2019 (wet season) and September 2019 (dry season). In
addition, a smaller subset of samples were previously taken in December
2018 (dry-to-wet transition season). See table 1 for more details on the
weather and climatic conditions for each campaign.

\textbf{2.3. Isoprenoid emission}

Leaf-level gas fluxes were measured on site from one leaf per tree and
repeated in the same tree across seasons. Measurements were made using a
commercial, portable gas exchange system with infrared gas analyzer
(IRGA), the LI6400XT (LiCor, U.S.A.). A hydrocarbon filter (Restek Pure
Chromatography, Restek Corporations, U.S.A.) was installed at the air
inlet of the IRGA to remove hydrocarbons from incoming ambient air. All
tubing in contact with the sampling air was PTFE (a material that is
non-reactive to hydrocarbons). Before each measurement, a blank was
sampled from the empty leaf chamber (see more details on blank samples
in Table S1, Supporting Information). One leaf was separately enclosed
in the leaf chamber with standard conditions --- photosynthetic photon
flux density (PPFD) at 1000 \selectlanguage{greek}μ\selectlanguage{english}mol
m\textsuperscript{-2}s\textsuperscript{-1} and leaf temperature at 30\selectlanguage{ngerman}ºC
--- until net assimilation (A\textsubscript{n}), stomatal conductance
(g\textsubscript{s}) and internal CO\textsubscript{2} concentration
(C\textsubscript{i}) were stable. The stability criterion for
measurements was assigned as up to one standard deviation of the mean
A\textsubscript{n}. The flow rate of air going into the leaf chamber was
400 \selectlanguage{greek}μ\selectlanguage{english}mol s\textsuperscript{-1}, CO\textsubscript{2} and
H\textsubscript{2}O concentrations were 400 \selectlanguage{greek}μ\selectlanguage{english}mol
mol\textsuperscript{-1}and 21 mmol mol\textsuperscript{-1} (relative
humidity of \textasciitilde{} 60\%), respectively. Air exiting the IRGA
leaf chamber was routed to flow through adsorbent cartridges (stainless
silico steel tubes filled with Tenax TA and Carbograph 5 TD adsorbents)
at a rate of 200 sccm for 10 min, which resulted in 2 L air samples for
isoprenoid chemical analysis. The isoprenoids accumulated in the
adsorbent cartridges were determined subsequently by laboratory
analysis. Samples from December 2018 and May 2019 were analysed in the
State University of Amazonas (UEA, Brazil), and samples from September
2019 were analysed in the Max Planck Institute for Chemistry (MPIC,
Germany).

In UEA, cartridges were analyzed with a thermal desorption system (TD;
Markes International, U.K.) interfaced with gas chromatograph-mass
spectrometer and flame ionization detectors (GC-MS-FID; 7890B-GC and
5977A-MSD series, Agilent Technologies, U.S.A.). The cartridges were
loaded in the TD automatic sampler (TD-100, Markes International, U.K.),
which connects to the thermal desorption system. Then, samples were
dried by purging for 5 min with 50 sccm of ultrahigh-purity helium (all
flow vented out of the split vent) and transferred (300\selectlanguage{ngerman}ºC for 10 min
with 50 sccm of ultrapure nitrogen) to the thermal desorption cold trap
held at -10ºC (Unity Series 1, Markes International, U.K.). During GC
injection, the trap was heated to 300ºC for 3 min while backflushing
with carrier gas (helium) at a flow rate of 6.0 sccm directed into the
column (Agilent HP-5, 5\% phenyl methyl siloxane capillary, 30.0 m x 320
\selectlanguage{greek}μ\selectlanguage{english}m x 0.25 \selectlanguage{greek}μ\selectlanguage{english}m). The oven ramp temperature was programmed with an initial
hold of 6 min at 27\selectlanguage{ngerman}ºC, followed by an increase to 85ºC at 6ºC
min\textsuperscript{-1}, followed by a hold at 200 ºC for 6 min. We
confirmed the identification of emitted isoprenoids from the samples by
comparison of retention time with a solution of authentic liquid
standards in methanol (Sigma-Aldrich, U.S.A.) and comparison to the
library of the National Institute of Standards and Technology (NIST).
The GC-MS-FID was calibrated at least three times before the analysis of
the sample cartridges; with calibration curves that were generated by
injecting different amounts of gas standard (27 biogenic VOCs gas
mixture, by Apel \& Riemer Environmental Inc., U.S.A.) into separate
cartridges, a mean correlation coefficient \textgreater{}= 0.98 was
obtained, and the LOD quantified as 27 pptv.

In MPIC, the cartridges were analyzed through thermal-desorption gas
chromatography time of flight mass spectrometry (TD-GC-TOF-MS, Bench ToF
Tandem Ionisation from Markes International, U.K.). The analysis
consisted of three main steps: desorption of the analytes from the
cartridges (TD), separation of the analytes through gas chromatography
(GC), and quantification and identification of the analytes through
time-of-flight mass spectrometry (ToF-MS). Samples were first dried by
purging them for 5 min with a flow of ultrapure N\textsubscript{2} at 50
ml min\textsuperscript{-1}, then transferred to the thermal-desorption
unit. Thermal-desorption was carried out in two stages - tube desorption
and trap desorption, both performed at 250°C for 10 minutes through a TD
100xr (Markes International, U.K.). The desorbed components were carried
in a flow of Helium into the GC column (dimethyl T.B.S. \selectlanguage{greek}β-\selectlanguage{english}cyclodextrin
0.15\selectlanguage{greek}µ\selectlanguage{english}m, 0.15mm ID, 25m L, from MEGA, Italy). The temperature ramp
consisted of an initial 5 min at 40\selectlanguage{ngerman}°C, after which the temperature was
increased at a rate of 1.5 °C/min from 40°C to 150°C, and further
increased at a rate of 30°C/min from 150°C to 200°C. After the GC
column, the analytes were fragmented through electron impact ionization
at -70 eV in the ToF. Identification was obtained by comparing the MS
spectra with the MS NIST library for the same ionization energy and by
injection of gas mixtures (162 VOCs gas mixture and 25 biogenic VOCs gas
mixture, by Apel \& Riemer Environmental Inc., U.S.A.) and liquid
standards. The obtained chromatograms were integrated with TOF-DS
(Markes International, U.K.). Gas standard cartridges were used to
calibrate the instrument, determine the precision and LOD of the
analysis, which was quantified as 23\% and \textasciitilde{}1 pptv,
respectively. More information on the material and method used can be
found in Zannoni \emph{et al.} (2020).

For the final flux calculation, isoprenoid concentrations were
determined using the sample volume that was passed through each
cartridge. This volume is the integration of the mass flow rate measured
and controlled by the pump used to suck the air coming out from the IRGA
leaf chamber. Once the volume mixing ratios of isoprenoids (ppbv) were
obtained, leaf emission fluxes were determined using the equation
(\emph{F = R} ppbv × Q/A), where \emph{F} (nmol
m\textsuperscript{-2}s\textsuperscript{-1}) is leaf flux of isoprenoid
emission; \emph{Rppbv}(nmol mol\textsuperscript{-1}) is isoprenoid
concentration of the sample; \emph{Q} is flow rate of air into the leaf
chamber (400 x 10\textsuperscript{-6} mol s\textsuperscript{-1}); and
\emph{A} is the area of leaf within the chamber (0.06 m²).~In order to
calculate isoprenoid emission on a mass basis, we measured Leaf Mass per
Area (LMA). LMA was calculated as the ratio of leaf dry weight to leaf
area. We did not include petioles in the LMA calculation since they can
be quite large for rainforest species and are usually more related to
leaf positioning rather than biomass efficiency (Poorter, Castilho,
Schietti, Oliveira \& Costa 2018). With LMA, isoprenoid emissions were
then calculated to \selectlanguage{greek}μ\selectlanguage{english}gC g\textsuperscript{-1} h\textsuperscript{-1}.

\textbf{2.4. Emission Metrics and Statistical Analysis~}

Isoprenoid emission and photosynthetic rates were analyzed in common
units of \selectlanguage{greek}μ\selectlanguage{english}gC g\textsuperscript{-1} h\textsuperscript{-1}. This enabled
us to produce integrated metrics related to the leaf carbon balance. We
analyzed total isoprenoid emissions both as the sum of all C emitted in
the form of isoprenoids, and as a percentage of photosynthetic C
assimilation rates. The three compound classes vary in mass (5C
isoprene, 10C monoterpenes, 15C sesquiterpenes). We developed an
`isoprenoid mass investment' metric to assess the partitioning of C
among the different classes of emitted isoprenoids, calculated by
multiplying the emission rates of each compound (in \selectlanguage{greek}μ\selectlanguage{english}gC
g\textsuperscript{-1} h\textsuperscript{-1}) by their respective masses
(number of C atoms), and dividing by the combined total emission rate
(i.e. the emission-rate weighted mean mass of emitted compounds). The
mass investment metric reflects how much carbon is allocated to lighter
versus heavier compounds. For example, the average mass investment of
pure isoprene emission is 5C. Since sesquiterpenes contain three times
as much C as isoprene, a 2:1 molar ratio of sesquiterpenes to isoprene
yields an average mass investment of (2(15C \selectlanguage{ngerman}× 3) + 1(5C × 1)) / 7 =
13.6C. Variation in metrics among habitats within species and seasons
was analyzed by Tukey Honestly Significant Difference (HSD) test (alpha
= 0.05). Variation between dry and wet seasons was analyzed by paired
t-test (alpha = 0.05) on measurements taken from the same individuals in
both seasons.

\textbf{3. Results}

Here we present results of isoprenoid emission capacity (including
isoprene, monoterpenes, and sesquiterpenes), photosynthesis, and carbon
allocation strategies across habitats and seasons for three tree species
-- \emph{P. hebetatum, E. coriacea} and~ \emph{E. grandiflora} .

\textbf{3.1. Isoprenoid emission variation among habitats and seasons}

Isoprenoid emissions and their habitat and seasonal associations varied
among plant species (Fig. 2, Table 2). Isoprene emissions were mostly
indistinguishable among habitats, except that emission capacities were
higher from \emph{P. hebetatum} in the ancient river terrace (AR) and
upland (Up) forest habitats than white-sand (WS) forest in the dry
season. Isoprene emission capacities were generally lower in the dry
than wet season (Fig. 2), though only significantly so for \emph{P.
hebetatum} in two habitats. When aggregating all trees together,
isoprene emissions were significantly lower in the dry than wet season
(a factor of 0.33, paired T-test p \textless{} 0.05, Fig. S1, Supporting
Information). Among the subset of trees sampled during the dry-to-wet
transition season (December 2018), isoprene emissions were significantly
higher than in the dry or wet seasons (Tukey HSD, p \textless{} 0.05),
averaging 3.3 and 2.4 times higher than wet season emissions for\emph{E.
coriacea} (n = 5 trees) and \emph{P. hebetatum} (n = 3 trees) (Fig. S2).

Monoterpene emission capacities were highly variable in magnitude and
chemical diversity among individuals within species, even in the same
habitat and season (Table 3). Total monoterpene emission capacities were
mostly indistinguishable among habitats, except for higher rates
from\emph{E. grandiflora} in AR than Up in the wet season, tracking
patterns of isoprene emission. The species that emitted most
monoterpenes was\emph{P. hebetatum} , with emissions frequently
exceeding 15 \selectlanguage{greek}μ\selectlanguage{english}gC g\textsuperscript{-1} h\textsuperscript{-1}.~ Comparing
monoterpene emission capacities between seasons, we observed that
\emph{E. coriacea}only emitted monoterpenes during the dry season. No
significant seasonal differences were detected within habitats (Fig. 2)
or when aggregating by trees and species (Fig. S1). However, the number
of chemical species of monoterpenes increased from the wet season to the
dry season, both in WS and AR habitats (Table 3). During the dry-to-wet
transition season, no monoterpene emissions were detected from \emph{E.
coriacea} or\emph{P. hebetatum} .

Sesquiterpene emissions were only detected from \emph{P. hebetatum} ,
reaching rates comparable to isoprene (when analyzed in units of C
emitted, but not moles emitted). These emissions only occurred during
the dry season, and significantly increased from WS to AR and Up
habitats (Fig. 2). No sesquiterpene emissions were detected during the
dry-to-wet transition season. For the chemical species of monoterpenes
and sesquiterpenes, see Table 3.

\textbf{3.2. Photosynthesis and carbon investment in isoprenoid
emissions}

Photosynthesis significantly decreased in most species and habitats
during the dry season, while trends varied among species in total
isoprenoid emissions and their associated carbon investments (Fig. 3).
Photosynthesis was significantly lower in the dry than wet season in
most habitats for \emph{E. grandiflora} and \emph{P. hebetatum} , but
not for \emph{E. coriacea} . Total C emitted in the form of isoprenoids
varied among habitats, being significantly greater from \emph{E.
grandiflora} in AR than Up habitats in the dry season, and greater
from\emph{P. hebetatum} in AR and Up than WS habitats in the wet season
(mainly due to the trend in sesquiterpene emissions shown in Fig. 2).
Total C investment in emissions as a percent of photosynthesis was
mostly \textless{} 2\% and not distinguishable between habitats in
either season, though it reached an average 6.1\% in \emph{P.
hebetatum}in the Up habitat in the dry season. In \emph{P. hebetatum} ,
isoprenoid mass investment was significantly greater in AR and Up than
WS habitats in both wet season (attributable to greater monoterpene
emissions, Fig. 2) and dry season (attributable to greater sesquiterpene
emissions, Fig. 2), and was significantly greater in the dry than wet
season in AR and Up habitats (Fig. 3).

When aggregated across forest habitats and species, seasonal variation
was strongest in photosynthesis and isoprenoid mass investment, and less
pronounced in total isoprenoid emissions and carbon emitted as a percent
of photosynthesis (Fig. 4). Statistical significance reflects paired
t-tests for individuals measured in both seasons (alpha = 0.05), while
Figure 4 visualizes the dry season values as a factor of wet season
values measured from the same individuals (`dry/wet factor').
Photosynthesis was non-significantly lower in the dry season in \emph{E.
coriacea} (mean dry/wet factor = 0.94), significantly lower in \emph{E.
grandiflora} (mean dry/wet factor = 0.73) and \emph{P. hebetatum} (mean
dry/wet factor = 0.43), and in all species combined (mean dry/wet factor
= 0.62). Total emission was non-significantly higher in the dry season
(mean dry/wet factor for all species combined = 1.47), even though
emissions were strongly suppressed in many individual trees, except
in\emph{E. coriacea} which showed significantly higher emissions (mean
dry/wet factor = 2.97). Carbon emitted as a percent of photosynthesis
was non-significantly higher in the dry season overall (mean dry/wet
factor for all species combined = 3.22) but highly variable among trees,
except in \emph{E. coriacea} which showed a significantly higher
percentage (mean dry/wet factor = 3.43). Isoprenoid emission mass
investment in the dry compared to wet season was non-significantly
greater in \emph{E. coriacea} (mean dry/wet factor = 1.41) and \emph{E.
grandiflora} (mean dry/wet factor = 1.07), significantly greater
in\emph{P. hebetatum} (mean dry/wet factor = 1.65), and significantly
greater in all species combined (mean dry/wet factor = 1.41). Total
emissions were positively correlated with photosynthesis in \emph{E.
grandiflora} (linear regression on all measurements combined, p
\textless{} 0.01, r\textsuperscript{2} = 0.37), while there was no
detectable correlation in the other two species (Fig. S3).

\textbf{4. Discussion}

Past studies have examined constitutive leaf isoprenoid emission from
tropical trees with a focus on isoprene (e.g. Keller \& Lerdau 1999;
Harley \emph{et al.} 2004; Kuhn \emph{et al.} 2004b; Pegoraro, Rey,
Abrell, Haren \& Lin 2006; Alves, Harley, Gon\selectlanguage{ngerman}çalves, Silva \& Jardine
2014; Jardine \emph{et al.} 2014, 2016; Taylor, Smith, Slot \& Feeley
2019), followed by monoterpenes (e.g. Kuhn \emph{et al.} 2004a;
Jardine\emph{et al.} 2017, 2020). These past studies offered meaningful
insights on how isoprene and monoterpenes respond to light, temperature
and drought, and how they might be related to plant stress tolerance.
Still, constitutive sesquiterpene emission has only been shown from
tropical species in one study in Borneo (Llusia, Sardans, Niinemets,
Owen \& Peñuelas 2014), and therefore most of our scarce knowledge on
sesquiterpenes produced by tropical species comes from the content of
these compounds either in leaf or resins (e.g. Salazar \emph{et
al.}2018), which only configures the potential for induced emissions. In
this study, we observed that Amazonian hyperdominant tree species emit
isoprene, monoterpenes and sesquiterpenes in a light-dependent manner
(constitutively), and that the amount and proportion of emissions change
seasonally. The plasticity of emission capacity and chemical composition
of isoprenoids emitted by trees distributed across different forest
types, their seasonal behavior, and the significance of our findings are
discussed in the following sections.

\textbf{4.1. Isoprenoid emission capacity from hyperdominant tree
species distributed along a topographic and edaphic environmental
gradient}

We found that on average, isoprene emission capacities did not
significantly vary across tree populations of \emph{E. grandiflora} in
different habitats, but did vary among populations of \emph{P.
hebetatum} . Although it is known that the isoprene trait is conserved
within plant species, emission quantities may vary significantly with
photosynthetic capacity, carbon and nutrient investment tradeoffs,
habitat and the environment (Harrison \emph{et al.} 2013). The species
investigated here occur in forest types that vary in edaphic properties,
soil water holding capacity, species richness and below- and
above-ground biomass (Andreae \emph{et al.} 2015). These changes in soil
and vegetation attributes can influence plant performance and, in
intraspecific processes, it is common to observe increases in both plant
growth and defense secondary metabolites as resource availability
increases (e.g. greater nutrient availability in uplands) (Agrawal
2020). Yet, although we observed the highest isoprene emission
from\emph{P. hebetatum} at upland forest,~ this was not observed in
\emph{E. grandiflora} and that was probably due to the large variability
within individuals at the same forest type.

The plant intraspecific variability was even more pronounced within
emissions of monoterpenes -- either in diversity of chemical compounds
or in total amount of monoterpenes -- when comparing individuals
distributed in the different forest types. This group of compounds is
known to have two different processes of emission. After being
synthesized, many monoterpenes are commonly stored in leaves and their
inducible emission results mostly from biotic stress; nonetheless, when
plant species lack storage structures, most hydrophobic monoterpenes
that were synthesized can accumulate in the leaf lipid phase and be
constitutively emitted to the atmosphere (Ormeño, Goldstein \& Niinemets
2011).~ Although the functional role of the constitutive emission of
monoterpenes has been reported as being similar to isoprene emission
(Loreto \emph{et al.} 1996) -- namely, leaf thermal protection and leaf
excess energy dissipation (Rosenstiel, Ebbets, Khatri, Fall \& Monson
2004; Sanadze 2004; Sharkey \& Monson 2017)~ --~ the main functional
role of monoterpenes is attributed to plant defense against pathogens
and herbivores (Fineschi \& Loreto 2012), and that may result in better
success in plant competition and colonization (Salazar \emph{et
al.}2018). Thus, the high variability of monoterpene emission within
species may be related to a plant defense strategy.

In contrast to isoprene and monoterpenes, sesquiterpene emission was
only observed from \emph{P. hebetatum} , from trees in all three forest
types. To the best of our knowledge, this is the first time that
constitutive emission of sesquiterpenes has been observed for an
Amazonian tree species that also presented variation across
populations.\emph{P. hebetatum} was measured for isoprenoid emission in
another Amazonian forest site, but the authors only found emission of
isoprene and the monoterpene cis-\selectlanguage{greek}β-\selectlanguage{english}ocimene (Jardine \emph{et al.}
2020).\emph{Protium} is a genus that is widespread across the whole
Amazon basin (ter Steege \emph{et al.} 2013; Fauset \emph{et al.} 2015)
and it has been hypothesized that its great abundance is probably
related to its high diversity of chemical defenses, which provides a
strategy against a large number of enemies that consistently attack
plant species (Salazar \emph{et al.} 2018). This hypothesis is indeed
reinforced by the fact that \emph{P. hebetatum} occurs in all three
forest types of this study, in contrast to the other two hyperdominant
species in our study, which occurred in only one or two forest types.
Furthermore, given the fact that the upland forest has the highest plant
species richness in this experimental site (Andreae \emph{et al.} 2015),
a high demand for chemical compounds for herbivory defense is expected~
in this environment. Habitats with high plant richness are commonly
followed by high richness of herbivores, meaning that constant and high
potential plant losses to herbivores are diminished with high
investments in defenses (Bixenmann, Coley, Weinhold \& Kursar 2016).
This could explain why emission rates and chemical diversity of
sesquiterpenes peaked in the upland forest (Table 3).

\textbf{4.2. Investment of C in isoprenoid emissions shifts to heavier
compounds when photosynthesis is reduced in the dry season}

From the wet to dry season, we found a general trend of decreasing
photosynthesis rates, coupled with relatively constant total isoprenoid
emission rates (Fig. 4). This combination led to a three-fold average
increase in C allocation to isoprenoid emissions relative to
photosynthesis (though this was variable and non-significant, Fig. 4).
Even so, the C sink to isoprenoid emissions was less than 2\% of
photosynthesis in most plants, except in strongly
sesquiterpene-emitting\emph{P. hebetatum} , whose emissions averaged
6.1\% of photosynthesis (occasionally \textgreater{} 10\%) in the upland
in the dry season. Associated with this increasing investment in
emissions, plants also allocated more C toward heavier isoprenoid
compounds (Fig. 4). Increasing `isoprenoid mass investment' was a result
of reduced isoprene emissions and increased monoterpene and
sesquiterpene emissions in the dry season (Figs. S2).

Previous studies indicated that seasonality in isoprenoid emissions from
Amazonia is controlled by changes in solar radiation, temperature and
leaf demography and phenology (Kuhn \emph{et al.} 2004b; Alves \emph{et
al.} 2016, 2018). It is well known that the central Amazon dry season
reaches maxima in solar radiation, temperature and leaf flushing, and
the latter translates into a higher proportion of canopy young leaves
during this season (Lopes \emph{et al.} 2016; Wu \emph{et al.} 2016;
Gon\selectlanguage{ngerman}çalves \emph{et al.} 2020). In terms of changes in chemical
composition of isoprenoid emission capacities, leaf age is an important
factor because the synthesis of these compounds changes with leaf
ontogeny. Isoprene and monoterpenes are produced from dimethylallyl
diphosphate (DMADP), which is catalyzed by isoprene synthase and
monoterpene synthases, in the methylerythritol 4-phosphate (MEP) pathway
in chloroplasts. The DMADP pool size varies with leaf ontogeny, meaning
that in early developmental stages, leaves have low DMADP -- due to high
demand for leaf growth and greening, and it increases with leaf
maturation. A low DMADP pool in young leaves may be responsible for a
down-regulation in the isoprene synthesis and up-regulation in the
monoterpene synthesis; this might be in part explained by the fact that
the enzymes involved in the monoterpene synthesis pathway have higher
affinity for DMADP, implying that these enzymes achieve maximum
catalytic efficiency already at low DMADP concentration, which favors
the synthesis of monoterpenes rather than isoprene in young leaves
(Kuhn\emph{et al.} 2004b). As constitutive emission of monoterpenes and
isoprene has been suggested to have similar functional roles to protect
plants against high temperatures and oxidants (Loreto \emph{et
al.}1996), when these stresses - typical of the Amazon dry season - are
imposed to young leaves and isoprene synthesis is constrained by enzyme
deficits or low DMADP concentration (Kuhn \emph{et al.} 2004b), the
production of monoterpenes might be preferable and then confer the high
demand for leaf protection.

Additionally, the plasticity in emission capacities and chemical
compositions may not exclusively be a response to abiotic stress, but
they may signal up-regulation of defenses against herbivores and
pathogens, to which the plant is more susceptible under abiotic stress
(Monson, Weraduwage, Rosenkranz, Schnitzler \& Sharkey 2021).
Monoterpenes and sesquiterpenes have long been recognized as important
compounds for plant defense against herbivores and pathogens (Gershenzon
\& Dudareva 2007). Young leaves have more soft tissues and less physical
and structural defenses that make them more palatable and attractive to
predators than mature leaves. In this sense, the increase in emitted
monoterpenes and sesquiterpenes (in \emph{P. hebetatum} ) during the dry
season might also be interpreted as an increase in the demand for
defense by new leaf tissues of the young leaves.

A higher proportion of young leaves during the dry season is clearly
seen in the study site presented here (Lopes \emph{et al.} 2016;
Gonçalves \emph{et al.} 2020). However, it was also observed that
approximately 30\% of trees do not have a massive leaf flushing in any
time of the year, meaning that they are possibly flushing leaves
continuously and therefore do not have a distinct seasonal leaf age
distribution (Lopes \emph{et al.} 2016). As the pattern of leaf
phenology is plant species-specific and we do not have information on
the leaf age for the individuals that we performed our measurements
with, we cannot exclude other reasons for the shift in composition of
isoprenoid emission capacities rather than leaf age. If significant
changes in leaf age is unaccountable (i.e. specifically for these tree
species and in the moments that our measurements were performed), then
what might have been playing a role is the leaf physiological response
to the environment, which is also affected by the environmental
conditions previous to measurements, and not only to the standard
conditions imposed to leaves during measurements.

The dry and wet season environment in central Amazonia is different for
the leaf - the wet season imposes more light-limitation and the dry
season imposes more vapor pressure deficit and higher temperature
limitation to photosynthesis (Wu \emph{et al.} 2017). The limitations
imposed during the dry season are more stressful for plants, which might
require more or different defense mechanisms. Recent work on the
integration of isoprene, monoterpenes, sesquiterpenes and other
secondary metabolites in signaling networks that mediate growth-defense
tradeoffs (Monson, Weraduwage, Rosenkranz, Schnitzler \& Sharkey 2021),
suggests that plasticity and changes in emission composition (as can be
observed seasonally and/or across habitats) could enhance plant chemical
defense and therefore confer to the physiological system more resistance
against different types and levels of stress - e.g., reducing isoprene
and increasing monoterpene and sesquiterpene in the Amazon dry season
might represent a switch in plant gas composition in order to cope with
stresses such as heat and drought, and to prime the plant to associated
biotic stresses.

While the dry-season increase in `isoprenoid mass investment' was most
significant for \emph{P. hebetatum} , it was mirrored by non-significant
trends within most species and habitats (Fig. 3). We devised the
`isoprenoid mass investment' metric to assess whether changing
environmental conditions are associated with shifts in C allocation to
emissions along the spectrum from lighter to heavier isoprenoids. Many
light dependent isoprenoids are evidenced to play related
metabolic-support roles as components of cellular signalling networks
and antioxidant defenses (Vickers, Gershenzon, Lerdau \& Loreto 2009;
Riedlmeier \emph{et al} . 2017; Frank \emph{et al.} 2021; Zuo \emph{et
al.} 2019; Monson, Weraduwage, Rosenkranz, Schnitzler \& Sharkey 202;
Harrison \emph{et al.} 2013). While the diversity of specific compounds
is high, there are general trends along the mass gradient from 5C
isoprene through 10C monoterpenes to 15C sesquiterpenes. Notably, along
the low-to-high mass gradient, volatile isoprenoid production has a
decreasing dependency on photosynthesis. Compared to monoterpene
synthase enzymes, isoprene synthase has a low reaction affinity for the
primary isoprenoid substrate DMADP (Harrison \emph{et al.} 2013;
Kuhn\emph{et al.} 2004b). Isoprene therefore requires a larger substrate
pool in the chloroplast that is derived from photosynthesis (Sharkey \&
Monson 2014). This variation in emission capacity with variation in the
supply of photosynthetically derived substrates is likely an explanation
for why species either emit monoterpenes or isoprene at high rates, but
not both (Harrison \emph{et al.} 2013). Sesquiterpene biosynthesis is at
least partially localized to the cytosol and use Isopentenyl
Pyrophosphate (IPP) and DMADP from the Mevalonate pathway (MVA) (Sallaud
et al. 2009), which further reduces their dependency on photosynthesis
and DMADP pools in the chloroplast, relative to the smaller isoprenoids.
This implies that seasonal changes in photosynthesis may affect isoprene
and higher isoprenoid production, since the amount of DMADP available
for isoprene synthesis is not only dependent on upstream flux through
the MEP pathway, but also its downstream use for the production of
higher compounds, such as monoterpenes and sesquiterpenes (Monson,
Weraduwage, Rosenkranz, Schnitzler \& Sharkey 2021), that can be
demanded after distinct biotic and abiotic stresses over seasons
(Harrison \emph{et al.} 2013).~ In addition, the heavier compounds tend
to be much more reactive, as evidenced for example by the typical
atmospheric half-lives of isoprene (hours), monoterpenes (minutes), and
sesquiterpenes (minutes) (Atkinson \& Arey 2003). Higher reactivity
during the short residence time of volatiles within the cell may allow
for a stronger effect as signaling molecules or even as direct
antioxidants at lower production rates.

We hypothesize that as photosynthesis declines, an investment shift
toward emissions of heavier isoprenoid compounds may allow sustained
metabolic support due to their increasing reactivity and decreasing
dependency on photosynthesis for production. Consistent with this
hypothesis, our results show that when photosynthesis was reduced under
dry season conditions, there was a tendency among all species to shift
emission compositions toward heavier compounds. Interestingly, the only
species to emit detectable sesquiterpenes, \emph{P. hebetatum} , emitted
them only in the dry season, and showed significantly increasing
emission rates along the soil hydrologic gradient from shallow (wetter)
to deep (drier) water table depths (Fig. 2), suggesting an association
between investment in sesquiterpenes and the seasonal drought conditions
experienced by individual plants. We suggest that our hypothesis is
consistent with our empirical data and general theory, but that further
experimental physiology work is required to demonstrate emission rate
tradeoffs, photosynthetic dependencies, and the relationship between
emission rates and metabolic responses for specific isoprenoid
compounds.

Although there is variability within and among the tree species studied
here and it is not possible to generalize results from three species to
a whole plant community, here we presented results of species that are
widespread in the central Amazon and have meaningful contribution to
plant biomass and productivity (Fauset \emph{et al.} 2015). Furthermore,
the trend of reduction in isoprene emission with proportional increase
in monoterpene and sesquiterpene emission during the dry season has
implications for atmospheric chemical and physical processes. As for
aerosol formation, laboratory-determined secondary organic aerosol (SOA)
yield from isoprene has been reported as \textless{} 6\% (Kroll, Ng,
Murphy, Flagan \& Seinfeld 2005; Xu, Kollman, Song, Shilling \& Ng 2014)
or higher over forested regions when isoprene is emitted in much more
quantities relative to other compounds (Carlton, Wiedinmyer \& Kroll
2009). Nonetheless, if other isoprenoids are more emitted, the
contribution to the SOA formation could be also higher, as shown by SOA
yields of \textasciitilde{}5-10\% for monoterpenes (Griffin, Cocker,
Flagan \& Seinfeld 1999a; Griffin, Cocker, Seinfeld \& Dabdub 1999b) and
\textasciitilde{}20-70\%~ for sesquiterpenes (Hoffmann \emph{et
al.}1997; Griffin \emph{et al.} 1999b; Lee \emph{et al.} 2006a b; Chen,
Li, McKinney, Kuwata \& Martin 2012; Jaoui, Kleindienst, Docherty,
Lewandowski \& Offenberg 2013).

In this light, it is important for future studies to consider a wider
range of volatile organic compounds with their synergetic importance in
plant ecophysiological processes and the subsequent impact on the
atmosphere. Our results suggest that emissions of monoterpenes and
sesquiterpenes might be higher than anticipated and indicate a seasonal
change in the composition of the emitted isoprenoids. In fact, seasonal
shifts in monoterpene composition have been already reported in ambient
air (Jardine \emph{et al.} 2015; Yáñez-Serrano \emph{et al.} 2018); but
sesquiterpenes might have been underestimated given their high
reactivity with ozone and OH and thereby the difficulty to detect in
ambient air (Yee \emph{et al.} 2018), indicating that only leaf level
measurements are likely to give us a true measure of forest
sesquiterpene emissions. Even though our observations do not offer
results to make a clear statement of how isoprene, monoterpenes and
sesquiterpenes work together in the face of abiotic stress such as heat
and drought during the dry season, they supports the recent findings
that isoprene does not confer plant tolerance by itself, but rather it
is likely part of an intricate network that involves other plant
metabolic processes and therefore other compounds (Harvey, Li,
Tjellström, Blanchard \& Sharkey 2015; Monson, Weraduwage, Rosenkranz,
Schnitzler \& Sharkey 2021). This opens more questions in order to
understand what are the processes regulating isoprenoid emission
capacities of Amazonian trees, and the need to consider seasonal shifts
in isoprenoid composition in emission modeling.

\textbf{Acknowledgements}

We thank the National Institute for Amazon Research (INPA) and the Max
Planck Institute for Biogeochemistry (MPI-BGC) for their continuous
support. We acknowledge the support by the ATTO project (German Federal
Ministry of Education and Research, BMBF funds 01LB1001A; Brazilian
Ministry of Science, Technology, Innovation and Communication;
FINEP/MCTIC contract 01.11.01248.00); UEA and F.A.P.E.A.M., LBA/INPA and
SDS/CEUC/RDS-Uatumã. T.C.T. was supported by grant \#NSF-PRFB-1711997,
and \#NSF-1754163. We also truly thank PELD MAUA for providing the
species inventory dataset for initial selection of species, Dr. Daniel
Marra and the Forest Management Lab for the tearing apparatus, Juliane
Menezes for her help in the May/2019 field campaign. We would like to
especially thank the field assistants and all the people involved in the
logistic support of the ATTO project, who were all imperative for the
development of this study.

\textbf{Authors' contributions}

Eliane Gomes Alves, Tyeen Taylor, Michelle Robin and Débora Pinheiro de
Oliveira contributed to the development and sampling design of the
study, the collection and the chemical analysis of isoprenoid samples,
and to the statistical analysis of datasets. Juliana Schietti
contributed to the development and sampling design of the study, and to
the statistical analysis of datasets. Sérgio Duvoisin Júnior, Nora
Zannoni, Jonathan Williams and Christoph Hartmann contributed to the
chemical analysis of isoprenoid samples. José Francisco C. Gonçalves
contributed to the collection of isoprenoid data. Jochen Schöngart,
Florian Wittmann and Maria T. F. Piedade contributed with the dataset
for the initial selection of species. All authors contributed to the
writing of the manuscript.

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\textbf{Tables}

\textbf{Table 1:} Air temperature, relative humidity (RH),
photosynthetic active radiation (PAR) and

precipitation for the days of each intensive campaign and monthly
average from 2013 to 2019\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{year}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{month}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{campaign days}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{season}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{air temp.} \selectlanguage{ngerman}\textbf{(°C)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{RH~} \textbf{(\%)}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{PAR} \selectlanguage{greek}\textbf{(μmol m\textsuperscript{-2}
s\textsuperscript{-1})}\selectlanguage{english}\strut
\end{minipage} & \begin{minipage}[b]{0.12\columnwidth}\raggedright\strut
\textbf{precipitation (mm)}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2018\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
December\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
dry-to-wet transition\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
26.03 (1.9)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
90.3 (8.3)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
742.8 (555.0)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2019\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
May\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
from 1\textsuperscript{st} to 5\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
wet\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25.8 (2.3)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
94.8 (7.9)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
732.2 (626.0)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
23.6\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2019\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
September\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
from 21\textsuperscript{st} to 24\textsuperscript{th}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
dry\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
28.3 (3.1)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
77.3 (12.8)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1060.6 (756.6)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013-2019*\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
December\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
dry-to-wet transition\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
26.4 (2.8)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
87.7 12.6)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
749.3 (574.0)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
170.7 (79.1)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013-2019*\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
May\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
wet\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25.7 (2.4)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
93.6 (8.7)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
722.5 (584.4)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
245.2 (73.9)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2013-2019*\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
September\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
dry\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
27.4 (3.3)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
81.4 (14.3)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
975.3 (680.2)\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
53.6 (21.9)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\emph{Note}: Values within brackets represent one standard deviation of
mean * Monthly average of data provided by the weather station since its
installation at the INSTANT tower

\textbf{Table 2:} Emission capacities of isoprene, total monoterpenes
and total sesquiterpenes; and photosynthesis for the three forest types
-- white-sand forest (WS), ancient river terrace forest (ART) and upland
forest (UP) -- during the dry-to-wet transition season (December 2018),
the wet season (May 2019) and the dry season (September 2019).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{tree species} & \textbf{season} & \textbf{forest type} &
\textbf{isoprene} & \textbf{sum MT} & \textbf{sum SQT} &
\textbf{photosynthesis}\tabularnewline
\midrule
\endhead
& & & \textbf{{[}ugC.g-1.h-1{]}} & \textbf{{[}ugC.g-1.h-1{]}} &
\textbf{{[}ugC.g-1.h-1{]}} & \textbf{{[}ugC.g-1.h-1{]}}\tabularnewline
\emph{Eschweilera coriacea} & \emph{DWT} & WS (\emph{n}=5) & 10.71
(1.75) & & & 1897.45 (372.89)\tabularnewline
& \emph{wet} & WS (\emph{n}=5) & 3.30 (1.24) & 0.75 (0.24) & & 2634.05
(692.45)\tabularnewline
& \emph{dry} & WS (\emph{n}=5) & 2.11 (0.58) & 5.96 (4.85) & & 2283.18
(407.46)\tabularnewline
\emph{Eschweilera grandiflora} & \emph{wet} & ART (\emph{n}=5) & 17.07
(10.37) & 4.26 (1.19) & & 4053.90 (663.38)\tabularnewline
& & UP (\emph{n}=5) & 5.95 (2.87) & 1.29 (0) & & 2429.67
(821.09)\tabularnewline
& \emph{dry} & ART (\emph{n}=5) & 2.89 (0.25) & 2.74 (5.85) & & 2321.95
(800.14)\tabularnewline
& & UP (\emph{n}=5) & 2.10 (1.06) & 0.04 (0.05) & & 1983.25
(707.86)\tabularnewline
\emph{Protium hebetatum} & & WS (\emph{n}=5) & 11.49 (4.48) & 3.19
(2.63) & & 1977.45 (533.87)\tabularnewline
& \emph{wet} & ART (\emph{n}=5) & 10.30 (7.14) & 15.61 (19.28) & &
2065.45 (256.60)\tabularnewline
& & UP (\emph{n}=5) & 16.32 (5.69) & 9.86 (10.56) & & 2195.56
(371.22)\tabularnewline
& & WS (\emph{n}=5) & 1.86 (0.46) & 13.22 (10.16) & 2.7 & 655.03
(487.07)\tabularnewline
& \emph{dry} & ART (\emph{n}=5) & 3.90 (1.60) & 7.20 (4.92) & 7.32
(4.64) & 1412.10 (349.11)\tabularnewline
& & UP (\emph{n}=5) & 4.61 (1.18) & 8.21 (6.76) & 15.90 (3.16) & 635.14
(281.49)\tabularnewline
\bottomrule
\end{longtable}

\subsection*{\texorpdfstring{\emph{Note}: Values in parenthesis represent
one standard deviation of
mean.}{Note: Values in parenthesis represent one standard deviation of mean.}}

{\label{note-values-in-parenthesis-represent-one-standard-deviation-of-mean.}}

\textbf{Table 3:} Chemical species of monoterpenes and sesquiterpenes
emitted by \emph{Eschweilera coriacea} , \emph{Eschweilera
grandiflora}and \emph{Protium hebetatum} measured in three forest types
-- white-sand forest (WS), ancient river terrace forest (ART) and upland
forest (UP), during the wet season (May 2019) and the dry season
(September 2019). Emissions are in \selectlanguage{greek}μ\selectlanguage{english}gC
g\textsuperscript{-1}h\textsuperscript{-1}\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& & & \textbf{wet season} & & & \textbf{dry season} &\tabularnewline
\midrule
\endhead
\textbf{tree species} & \textbf{compound} & \textbf{WS} & \textbf{ART} &
\textbf{UP} & \textbf{WS} & \textbf{ART} & \textbf{UP}\tabularnewline
\emph{Eschweilera coriacea*} & \selectlanguage{greek}α-\selectlanguage{english}Pinene & 0.34; n=1 & & & 1.17; n=1 &
&\tabularnewline
& \selectlanguage{greek}β-\selectlanguage{english}Pinene & & & & 0.36; n=1 & &\tabularnewline
& Camphene & 0.53; n=1 & & & 0.13; n=1 & &\tabularnewline
& 3-Carene & & & & 3.14; n=1 & &\tabularnewline
& Limonene & & & & 1.53 (1.54); n=2 & &\tabularnewline
& \selectlanguage{greek}β-\selectlanguage{english}Phellandrene & 0.91; n=1 & & & & &\tabularnewline
& \selectlanguage{greek}α-\selectlanguage{english}Terpinene & & & & 3.285 (1.92); n=2 & &\tabularnewline
& \selectlanguage{greek}γ-\selectlanguage{english}Terpinene & 0.47; n=1 & & & & &\tabularnewline
\emph{Eschweilera grandiflora**} & \selectlanguage{greek}α-\selectlanguage{english}Pinene & & 0.24 (0.15); n=4 & & &
0.04 (0.51); n=3 & 0.03 (0.02); n=2\tabularnewline
& \selectlanguage{greek}β-\selectlanguage{english}Pinene & & & & & 0.01 (0.004); n=4 & 0.01 (0.0005);
n=2\tabularnewline
& Camphene & & & & & 0.26 (0.12); n=2 & 0.04 (0.03); n=2\tabularnewline
& 3-Carene & & & & & 3.20; n=1 &\tabularnewline
& D-Limonene & & 1.78 (0.15); n=5 & & & &\tabularnewline
& Limonene & & & & & 3.63 (3.60); n=2 &\tabularnewline
& \selectlanguage{greek}α-\selectlanguage{english}Phellandrene & & 0.26 (0.07); n=4 & & & &\tabularnewline
& \selectlanguage{greek}β-\selectlanguage{english}Ocymene & & & 1.29; n=1 & & &\tabularnewline
& \selectlanguage{greek}ο-\selectlanguage{english}Cymene & & 2.04 (0.65); n=5 & & & &\tabularnewline
& \selectlanguage{greek}γ-\selectlanguage{english}Terpinene & & 0.49; n=1 & & & &\tabularnewline
\emph{Protium hebetatum} & \selectlanguage{greek}α-\selectlanguage{english}Pinene & 0.24; n=1 & 4.56 (7.21); n=4 &
0.41 (0.19); n=3 & 1.57 (0.15); n=2 & 0.83 (3.43); n=5 & 2.13 (1.49);
n=5\tabularnewline
& \selectlanguage{greek}β-\selectlanguage{english}Pinene & & 7.24 (4.05); n=2 & & 0.67 (0.65); n=2 & 0.17 (0.76); n=5
& 0.36 (0.14); n=5\tabularnewline
& Camphene & 0.35 (0.01); n=2 & & 0.16; n=1 & 0.18 (0.15); n=2 & 0.23
(0.45); n=5 & 0.15 (0.16); n=5\tabularnewline
& 3-Carene & & & 5.27 (7.59); n=5 & 2.51 (2.46); n=2 & 1.57 (3.12); n=3
& 0.06 (0.02); n=3\tabularnewline
& D-Limonene & & 6.59 (12.57); n=4 & 0.48 (0.63); n=3 & &
&\tabularnewline
& Limonene & & & & 8.27 (3.75); n=2 & 1.05 (1.23); n=5 & 1.01 (1.21);
n=5\tabularnewline
& \selectlanguage{greek}α-\selectlanguage{english}Phellandrene & & & 0.26 (0.07); n=2 & & &\tabularnewline
& \selectlanguage{greek}β-\selectlanguage{english}Phellandrene & & 1.34; n=1 & & & &\tabularnewline
& \selectlanguage{greek}ο-\selectlanguage{english}Cymene & 4.32 (0.62); n=2 & & 0.30 (0.20); n=3 & & &\tabularnewline
& \selectlanguage{greek}α-\selectlanguage{english}Terpinene & & & & 0.01; n=1 & & 1.53 (1.31); n=4\tabularnewline
& \selectlanguage{greek}γ-\selectlanguage{english}Terpinene & & & 0.05; n=1 & & 0.03 (0.48); n=3 & 1.46 (1.29);
n=4\tabularnewline
& \selectlanguage{greek}α-\selectlanguage{english}Cubene & & & & & & 1.10 (0.42); n=5\tabularnewline
& \selectlanguage{greek}α-\selectlanguage{english}Copaene & & & & & & 7.89 (1.34); n=5\tabularnewline
& Caryophyllene & & & & 2.70; n=1 & 5.17 (6.48); n=5 & 7.71 (1.29);
n=5\tabularnewline
\bottomrule
\end{longtable}

\emph{Note:} * \emph{Eschweilera coriacea} was only found in the
white-sand forest type

\emph{** Eschweilera grandiflora was} not found in the white-sand forest
type\selectlanguage{english}
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