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\begin{document}

\title{Mapping seasonal and interannual Non-Structural Carbohydrate variation
to drought-resistance strategies in eastern Amazon tree species}



\author[1]{Mauro Brum}%
\author[1]{Scott Saleska}%
\author[2]{Luciana F Alves}%
\author[3]{Deliane Penha}%
\author[4]{Valeriy Ivanov}%
\author[1]{Natalia Restrepo-Coupe}%
\author[5]{Loren Albert}%
\author[6]{Raimundo  Oliveira-Junior}%
\author[3]{José Mauro Moura}%
\author[7]{Sarah Mião}%
\author[7]{Caroline Signori-Müller}%
\author[1]{Neill Prohaska}%
\author[8]{Luiz Aragão}%
\author[9]{Rafael Oliveira}%
\affil[1]{The University of Arizona Department of Ecology and Evolutionary Biology}%
\affil[2]{University of California Los Angeles}%
\affil[3]{Universidade Federal do Oeste do Para}%
\affil[4]{University of Michigan}%
\affil[5]{Brown University}%
\affil[6]{EMBRAPA Amazônia Oriental}%
\affil[7]{UNICAMP IB}%
\affil[8]{Instituto Nacional de Pesquisas Espaciais}%
\affil[9]{University of Campinas}%


\vspace{-1em}



  
  \date{June 15, 2024}


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\selectlanguage{english}
\begin{abstract}
Carbon allocation to non-structural carbohydrates (NSC) is essential for
plant metabolism playing an important role in tree responses to drought.
It is still unclear if and how interspecific hydraulic trait variation
modulates NSC concentration dynamics in different plant organs,
particularly in tropical tree species. We investigated whether
drought-resistance strategies (inferred from hydraulic traits) explain
seasonal and interannual NSC dynamics in leaves, branches, trunks, and
roots in seasonal eastern Amazon tree species in Brazil. We measured NSC
concentration in eight abundant species during three years, including
the end of the wet and dry seasons of the typical regular years
(2013-2014) and the extreme drought induced by El Ni\selectlanguage{ngerman}ño--Southern
Oscillation in 2015 (ENSO). Organs have an important contribution to
explain the starch (ST), soluble sugar (SS), and NSC variance among
trees. We showed seasonal and year-to-year homeostasis in ST and SS
concentrations in a majority of organs during 2013 and 2014, but SS
increased in all organs during the extreme ENSO drought, while the ST
concentration did not. The increase in SS concentration was more evident
in woody organs from species with intermediate and tolerant drought
strategies. The drought-tolerant species maintain higher root starch
concentrations and mobilize more SS during extreme drought.%
\end{abstract}\selectlanguage{ngerman}%



\sloppy


\textbf{Introduction}

Understanding how functional trait diversity modulates ecosystem
processes is crucial for improving predictions of tropical forest
responses to climate change (Sterck, Markesteijn, Schieving \& Poorter
2011; Allen, Breshears \& McDowell 2015; Barros \emph{et al.} 2019).
Most of evergreen Amazon forests annually experience a distinct dry
season, taken to be the period when monthly precipitation falls below
100 mm (Sombroek 2001, Restrepo-Coupe \emph{et al} . 2016). Further,
prolonged water stress periods caused by El Ni\selectlanguage{ngerman}ño Southern Oscillation
(ENSO) events are also becoming more frequent in this region (Marengo,
Tomasella, Alves, Soares \& Rodriguez 2011; Jiménez-Muñoz \emph{et
al.}2016; Brum \emph{et al} . 2018), inducing changes in forest
metabolism and nutrient cycling (Doughty \emph{et al} . 2015; Hartmann
\emph{et al} . 2018, Oliveira-Junior \emph{et al} . 2015, Yang \emph{et
al} . 2018) and accelerating tree mortality events (Phillips \emph{et
al} . 2010, Brienen\emph{et al} . 2015). Previous research suggests that
the regulation and mobilization of non-structural carbohydrates (NSC)
across plant organs is key to preventing the collapse of the plant's
water transport system when faced with increasing drought stress
(McDowell \emph{et al} . 2011). However, the mechanisms of tropical
forest drought-resilience are still under debate (McDowell, Brodribb \&
Nardini 2019, Hirota \& Oliveira 2020; Janssen \emph{et al} . 2020;
Oliveira \emph{et al} . 2021), and a comprehensive understanding of the
coordination between tree carbon (C) allocation patterns in different
tree organs with tree hydraulic strategies is essential to understand
the response of seasonal Amazon forests to prolonged drought (Saleska
\emph{et al} . 2003; Longo \emph{et al} . 2018, Barros \emph{et al} .
2019).

The NSC pool maintenance can lead to higher survival rates during
episodes of environmental stress (Chapin, Schulze \& Mooney 1990;
Niinemets, 2010). The final balance of NSC concentration in each organ
is determined by the process that regulate the C-source (i.e.,
photosynthesis) and the components of C-sink, where in the latter case
growth, respiration, secondary metabolites, and osmoregulation compete
with carbon storage for future use (Chapin, Schulze \& Mooney 1990;
Hoch, Richter \& Körner 2003; Würth, Peláez-Riedl, Wright \& Körner
2005; Sala \emph{et al.} 2010; Körner 2015). The total NSC can be
partitioned into two major components: i) soluble sugar (SS), which has
an immediate use in the processes of primary and secondary metabolism,
or controlling the cell osmotic potential as an osmoregulator substance;
and ii) starch (ST), which is an insoluble molecule mainly used as
long-term C-storage reserve (Martínez-Vilalta \emph{et al} . 2016), as
transient molecules in leaves (Pelleschi \emph{et al} . 1997; Thalmann
and Santelia, 2017), and also may act as an integrator of plant
metabolism and growth (Sulpice \emph{et al} . 2009).

The concentration of the total NSC, and its components, SS and ST, is
different among plant organs (i.e. leaf, branch, trunk or roots) across
a variety of biomes (Würth \emph{et al} . 2004; Martínez-Vilalta
\emph{et al} . 2016; Furze \emph{et al} . 2018). As a less accessible
C-pool, the stored NSC can remain for decades in stemwood and roots, but
still represents a large portion of the total C budget (Richardson
\emph{et al} . 2013; Carbone \emph{et al} . 2013). On the other hand, in
organs such as leaves and branches, the dynamics of NSC can be highly
transient with NSC storage displaying daily, seasonal or year-to-year
variation in concentrations on a global scale (Martínez-Vilalta \emph{et
al} . 2016). However, a homeostatic NSC variance (synonymous to
low-variability over time) has been described for leaves and branches of
trees in a tropical forest in Panama during an extreme drought event
induced by ENSO (Dickman \emph{et al} . 2018). Also, in a throughfall
exclusion experiment in Eastern Amazonia, the NSC reserves of trees
subjected to long-term drought did not differ from those in unstressed
trees (Rowland\emph{et al} . 2015). Despite that, empirical studies of
NSC dynamics at the whole-tree scale in tropical forests are rare and
limited to a small number of sites, species, and tree organs (\emph{see}
Würth \emph{et al} . 2004; O'Brien \emph{et al} . 2015; Rowland \emph{et
al} . 2015; Dickman \emph{et al} . 2018; Rowland \emph{et al} . 2021;
Signori-Müller\emph{et al} . \emph{in press} ). Therefore, to gain a
better understanding of NSC within each organ, it is necessary to
identify environmental controls and species-specific differences
determining physiological constraints on NSC storage in tropical
forests, as well as the different roles played by each fraction of NSC
in plant metabolism (Hartmann \& Trumbore 2016).

Monitoring NSC dynamics in tree species representing a wide range of
hydraulic strategies is an opportunity to understand how tropical trees
respond to water stress. First, it is particularly important to link the
NSC allocation pattern with plant hydraulic traits and the
hydrometeorological conditions that define physiological drought
experienced by the plant (McDowell \emph{et al} . 2011, Sevanto \emph{et
al} . 2014; Anderegg \emph{et al} . 2016; Kannenberg \emph{et al} .
2020). Jointly, this framework may contribute to a mechanistic
understanding of drought-induced tree mortality and trait selection for
different aridity regimes in tropical forests (McDowell \emph{et al} .
2018; Barros\emph{et al} . 2019). Specifically, physiological drought is
a response to plant water-use regulation that depends on the declining
soil water and that determines thresholds associated with hydraulic or
carbohydrate-mediated mortality (Mitchell \emph{et al} . 2013). The link
between drought-resistance strategies and C-source-sink balance is
regulated by mechanisms that minimize water loss or avoid drought
stress, and strategies that maximize the C-uptake to ensure reproductive
success (Niinemets, 2010; Tomasella \emph{et al} . 2020). For instance,
if during a drought event water stress is too high, stomatal regulation
and excessive xylem embolism disrupting transpiration flux could limit
photosynthetic rates with a reduction in C-source activity; this, in
turn, will lead to a negative C-balance over time that will be reflected
in NSC stores (Sperry, Hack, Oren \& Comstock 2002; McDowell \emph{et
al} . 2008, Sala, Piper \& Hoch 2010; ; Oliveira \emph{et al} . 2014;
Rowland \emph{et al} . 2015). In fact, the hydraulic architecture of
plants appears to be well coordinated with photosynthetic capacity at
the whole plant scale in tropical forest (Santiago \emph{et al.} 2004).
In addition, a hydraulically mediated process to maintain cell turgor
plays an important role in regulating tree growth that often declines in
response to drought before reductions in leaf photosynthesis (Körner,
2015). Different rooting depths also contribute to the differences in
the drought-resistance within tropical regions (Ivanov \emph{et al} .
2012, Brum \emph{et al} . 2017; Brum et al. 2019a) and might affect the
NSC allocation pattern in different tree organs. Root depth ultimately
interacts with the seasonal progression of soil water status, gas
exchange, and NSC concentration and therefore emerges as a key trait
determining tree resistance to extreme drought, and might be one of the
most important traits preserving some tree species from drought-induced
mortality (Nardini \emph{et al} . 2016).

Here we aimed to understand if and how plant hydraulic strategies
inferred from a suite of hydraulic traits can explain the interspecific
seasonal and interannual NSC variation of trees. For the first time, we
investigated the NSC concentration in different organs across a diverse
number of dominant tree species (experiencing a long dry season) of an
Amazonian forest in Brazil and evaluated its climate-induced variations.
Specifically, we investigated how extreme drought events affect more
typical seasonal dynamics of NSC and water regulation in tropical trees
by sampling seasonal and interannual NSC dynamics over a three-year
period, including one extreme drought period induced by the 2015 El
Niño--Southern Oscillation (ENSO). The precipitation anomaly caused by
the 2015 ENSO resulted in severe water stress across the eastern Amazon
basin, including our study site (Jiménez-Muñoz \emph{et al} . 2016,
Brum\emph{et al} . 2018). Specifically, we addressed two key questions:
(\textbf{Q1} ) How does the seasonal and interannual hydrometeorological
variability affect the concentrations of starch, soluble sugar, and
total NSC among organs of tree species from a seasonal Amazonian
forest?\textbf{(Q2)} How do tree drought hydraulic resistance strategies
affect the pattern of NSC concentration and variation in different
organs across seasons and years? The overarching hypothesis in this
study was that organs (leaf, branch, trunk, root) would have the highest
contribution to explain the NSC, SS, and ST variability in trees, while
NSC, SS and ST would differ in response to seasonality (dry and wet
season) and due year-to-year variation including an extreme drought year
induced by ENSO. Furthermore, the species-specific differences in
drought hydraulic resistance strategies would influence the direction of
change in NSC, SS, and ST concentrations in response to regular dry
season and extreme drought conditions.

\textbf{Methods}

Site description

We performed this study at the Large-Scale Biosphere-Atmosphere
Experiment in Amazonia (LBA-ECO) flux tower site in the Tapajós National
Forest (BR-163 road, km67; 54°58'W, 2°51'S, Brazil). The area is located
in the region of deep soil of the Barreiras formation, characterized as
Dystrophic Yellow Latosol with a high fraction of clay, well-drained,
presenting a moderate surface horizon (Oliveira-Junior \& Correa 2001).

The region has a mean annual temperature and precipitation of 25°C and
2037 mm year\textsuperscript{-1}, respectively, and the climate is
strongly seasonal with a dry season (DS) characterized by up to
\textasciitilde{}5 consecutive months (August through December) with
strong reduction in monthly precipitation (Restrepo-Coupe \emph{et al} .
2016). The forest is mostly evergreen and displays a closed canopy with
a mean height of 40-45 m and is vertically structured with a high
density of smaller understory trees (Rice \emph{et al.} 2004;
Stark\emph{et al.} 2012; Smith \emph{et al.} 2019). There is an increase
in cloud cover during the wet season (WS) that reduces incoming solar
radiation, while increasing the fraction of diffuse radiation
(Brando\emph{et al} . 2010). The forest phenology is driven by
seasonality, typically showing a subtle but significant increase in leaf
area index (LAI) from the beginning to the end of the dry season (Wu
\emph{et al} . 2016; Smith \emph{et al} . 2019). Peak leaf-flush occurs
in September, and the seasonality of leaf productivity has a negative
correlation with trunk wood growth (Brando \emph{et al} . 2010,
Restrepo-Coupe \emph{et al} . 2013, Restrepo-Coupe \emph{et al} . 2016).

Characterization of interannual and seasonal drought stress level

A third of the Amazon rainforest is typically affected by moderate
(Palmer Drought SeverityIndex - scPDSI \textless{} -2) to severe drought
(scPDSI \textless{} -3), and the Tapajós forest area corresponds to the
range of moderate to severe drought index (Jiménez-Muñoz \emph{et al} .
2016). The weather is seasonal, and the wet season occurs from January
to July, while the dry season starts from August to December. Therefore,
August and December represent transition periods. During our sampling
effort, the water stress levels ranged from a very humid year in 2013
(CWD\textsubscript{max}= 123 mm), through an intermediate intensity of
water stress in 2014 (CWD\textsubscript{max}= 289 mm), to an extreme
drought in 2015 caused by the ENSO event (CWD\textsubscript{max}= 400
mm) (Figure 1). During the 2015-2016 ENSO, the eastern Amazon exhibited
extreme drought severity (scPDSI\textless{} -3) (Jiménez-Muñoz \emph{et
al} . 2016). Barros \emph{et al} . (2019) noticed for the Tapajós forest
a large variation in water availability measured by CWD during 2015-2016
ENSO. The drought had a record-breaking mean monthly temperature, with
1.5 and 2ºC higher than what was observed during previous ENSO events,
October 1997 and January 1983, respectively (Jiménez-Muñoz \emph{et al}
. 2016).

Species selection

We selected six dominant tree species (\textasciitilde{}35\% of total
forest basal area) from a long-term tree inventory database (stems \selectlanguage{english}[?]10
cm dbh) of permanent transects located at LBA research station, Tapajos
National Forest (Pyle \emph{et al} . 2008 updated by Longo, 2013, see
Table 1). We also selected two abundant small-sized understory species
(mostly with stems \textless{} 10 cm dbh), which were not included in
the original forest database (Table 1). We categorized each species
according to its adult tree height (H\textsubscript{max}, calculated as
the 95th percentile of the height values in the population) as overstory
(H\textsubscript{max} \textgreater{} 35m; trees with top canopy leaves
exposed to high-light environments); midstory
(H\textsubscript{max}\textgreater{} 15-35m; trees with canopies
occurring at the intermediary position along the vertical forest
profile); and understory (H\textsubscript{max} \textless{} 10m; trees
with shaded canopy leaves rarely or never reaching the midstory) (Table
1) (Smith \emph{et al} . 2019).

For three consecutive years (2013, 2014, and 2015) we sampled individual
trees at the end of the wet season (WS; August) and dry season (DS; last
week of November and the first week of December). We marked and
re-visited the same individual trees across the years to sample for NSC
concentration (repeated measurement, see NSC analysis below). Most of
the individuals were selected and measured in our first sampling
campaign (2013), however, some individuals were added in subsequent
campaigns when we were able to reach the canopy of the overstory trees.
We sampled 3-5 individuals of each species; a complete sampling size per
species can be accessed in the supplementary SM-Table 1. None of the
sampled individuals suffered noticeably damage from sampling, nor did
they die during the three year sampling period

Hydraulic traits and drought-resistance strategies

We used the tree hydraulic traits together to form a single axis of
drought resistance strategies (Figure 2a). All data and specific
methodological explanations on how these hydraulic traits were measured
can be accessed in Brum \emph{et al} . (2019b). Importantly, we
performed the NSC measurement for the same tree individuals and species
during the same fieldwork as described in Brum \emph{et al} . (2019a),
only adding additional trees paired to the tagged ones to increase the
sampling effort (SM-Table 1). Here, we used the species-specific
vulnerability to xylem embolism assessed as the relationship between the
percentage loss of xylem conductivity (PLC) and xylem water potential
(\selectlanguage{greek}Ψ\selectlanguage{english}). The vulnerability curve can be used to estimate the water
potentials at which the tissue loses 50\% (P50) and 88\% (P88) of its
hydraulic conductivity (Sperry \emph{et al.} 2002). (Sperry, Hacke, Oren
\& Comstock 2002); higher P50 and P88 indicates greater vulnerability to
xylem embolism. We also used the leaf water potential measured in the
field at the peak of the dry season during a non- ENSO year (December
2014) and during the ENSO drought year (December 2015) to calculate
interannual differences in the midday (12 AM - 2 PM) minimal leaf water
potential (\selectlanguage{greek}Δ\selectlanguage{english}LWP) measured in the field. The increase in \selectlanguage{greek}Δ\selectlanguage{english}LWP represents
a proxy of the interannual isohydric to anisohydric status of trees
(Tardieu \& Simonneau 1998), as the year-to-year minimal leaf water
potential declines due to drought induced by ENSO. In addition, we used
the natural-abundance of oxygen stable isotopes (\selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O)
from xylem and soil water as a proxy to determining the depths from
which plants acquire water in the soil (Dawson \emph{et al} .
2002)\emph{.} According to Brum \emph{et al} . (2019a), a fractioned
evaporative gradient due an extreme drought induced by ENSO allowed us
to determine a negative vector to describe the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O
(%0) range in xylem and soil water. Less negative \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O
indicates that roots uptake enriched water from shallow soil, whereas
deeper-rooted trees tend to present less enriched water and more
negative value \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O as also observed in deeper soil.

We used this combination of functional hydraulic traits to create an
index of drought-resistance and define \emph{a posteriori} functional
groups (Figure 2b). Using the matrix of species average functional
hydraulic traits, we performed a principal components analysis (PCA) to
create an index of drought-resistance and define a posteriori functional
groups. Before running the PCA, we standardized the variables to have
zero mean and unit variance. We used the scores of the first axis
(PCA1), which represents the continuum of drought resistance from more
negative PCA1 that summarizes the tolerance to more negative water
potential, shallow root depth, and higher tolerance to xylem embolism,
and to positive PCA1 describing taller drought-avoidance trees, with
deeper water access and inter-annual isohydric (Figure S1). Because we
did not have information on \selectlanguage{greek}Δ\selectlanguage{english}LWP for \emph{M. elata} , to perform the
PCA, we replaced gaps with the average values across all species. In
order to detect general groups based on hydraulic traits, we submitted
the traits matrix to a hierarchical cluster analysis based on Euclidean
distances, which measures pairwise dissimilarity of traits in the set
for all studied species. We derived three \emph{a posteriori} groups of
species in accordance with species average functional hydraulic traits
related to drought-resistance strategies (Table 1, Figure 2b). A
tolerant group with \emph{R. pubiflora} , \emph{Miconia sp.} , \emph{C.
paniculata} with a more negatives values in PCA1 axis (Figure 2), an
intermediate group with \emph{P. apiculatum} , \emph{C. scleroxylon}
,\emph{A. longifolia} with positive value in PCA1 axis close to zero,
and an avoidant group with \emph{M. elata} and \emph{E. uncinatum}
higher values in PCA1. Table 1 shows that a priori canopy position
height categorization, given by canopy position, not necessarily
represents the drought resistance strategies in this site, mainly due
the emergence of the intermediate group. We performed the PCA and
cluster analysis using a basic package from R software (R Core Team
2018).

NSC analysis

For each tree, we collected samples of (a) coarse roots, by excavating
close to the junction between the roots and the main trunk, up to a
maximum of 30 cm depth; (b) trunk-wood tissue from 1.30 m height (up to
5 cm depth) using a 5 mm increment borer; (c) first-order branches close
to the leaves, trying to sample the highest branch possible, or the most
sunlight-exposed branch and leaves. We obtained branch samples using
tree climbing, ladders, or pole pruners depending on the height and
accessibility of the sampled trees (Table 1). In general, most of our
samples were obtained at the bottom and at intermediary layers within
the canopy of each individual tree. Based on previous studies showing
that samples taken from full sun exposure compared to shade within the
canopy had no significant effect on NSC concentrations in tropical
forest trees (W\selectlanguage{ngerman}ürth \emph{et al} . 2005 in Panama; and Martin \emph{et
al} . 2020 across a tropical forest elevation gradient in southern
Peru). We assumed that leaves and branches from the bottom and
intermediate layers would approximate the full canopy NSC storage. In
addition, none of the individuals suffered noticeable damage from
sampling or died during the three-year period. For more sample
information and sample size see the SM-Table 1 in the supplementary
material.

All samples were kept cool during the fieldwork in a cooler with ice
and, on the same day, as soon as we arrived at the basecamp (lab field
\textasciitilde{}20 km from research site), we microwaved the samples
for 90 seconds at 400 W to denature enzymes that would otherwise affect
NSC levels. We then dried the leaves at 65 ºC for 36 hours, starting in
the evening of the sampling day (Höch \emph{et al} . 2003).

We performed the NSC extraction in the laboratory of plant functional
ecology at the University of Campinas (Campinas, SP, Brazil). We used
the enzymatic method to quantify the concentrations of NSC fractions of
soluble sugar and starch (see Quentin \emph{et al} . 2015 for further
discussion about NSC quantification see Höch \emph{et al} . 2003,
Dickman, Mcdowell, Sevanto, Pangle \& Pockman 2015), using the following
protocol: (a) we grounded leaves, wood, and root samples with their bark
removed into a fine powder using the 2010 Geno/Grinder® SPEX SamplePrep.
Ground samples were stored at -20 ºC until further processing. (b) We
weighed approximately 14-15 mg of powdered plant material in 2 ml vials
and added 1.6 mL of distilled water to each vial. (c) We incubated the
vials in a water bath at 90-100 ºC for 60 minutes to solubilize sugars.
Then an aliquot (700 uL) from each sample was incubated overnight to
react with Amyloglucosidase from \emph{Aspergillus niger}(Sigma-Aldrich,
St. Louis, MO, USA) to breakdown the total NSC to glucose. We used the
remaining aliquot volumes to determine the soluble sugar concentration
using invertase from \emph{Saccharomyces cerevisiae}(Sigma-Aldrich, St.
Louis, MO, USA) to break down sucrose to fructose and glucose.
Additionally, for both reaction routines, we used GAHK (Glucose Assay
Hexokinase Kit - Sigma-Aldrich, St. Louis, MO, USA) together with
phosphoglucose isomerase from \emph{Saccharomyces cerevisiae}
(Sigma-Aldrich, St. Louis, MO, USA). (d) We measured the concentration
of free glucose photometrically in a 96-well microplate
spectrophotometer at 340 nm (EPOCH - Biotek Instruments INC - Winooski,
VT - USA). (e) Finally, we calculated the starch concentration as total
NSC minus soluble sugars concentration. All NSC values are expressed as
the percentage of dry weight. In our analysis, we distinguished between
NSC fractions by separately analyzing soluble sugar and non-soluble
starch, due to their distinct role in tree physiological metabolism
(Hartmann \& Trumbore 2016).

Statistical analysis

In all analyses, we fitted a model for soluble sugar, starch, and NSC
separately because we were interested in understanding the role of each
soluble sugar separately. In addition, we calculated the fraction of
average soluble sugar and starch concentration to average total NSC
concentration (SS\%NSC, ST\%NSC). In order to answer questions 1 and 2
and identify how the different NSC, SS, and ST concentrations vary as a
function of fixed effect, we performed a linear model using generalized
least square (GLS) model using the function \emph{gls} from
\emph{nlme}package (Pinheiro, Bates, DebRoy \& Sakar, 2018). In this
model, the errors are allowed to be correlated, present unequal variance
and sample size in fixed terms. In addition, the model processes
longitudinal data with repeated measurement across time, by considering
a within-group correlation structure. Here, we used the compound
symmetry correlation structure that corresponds to uniform correlation
(\emph{corCompSymm}function - Pinheiro, Bates, DebRoy \& Sakar, 2018).
For that, we considered the year of measurement as the time that
co-covariate, and individuals nested within the year as the repeated
measurement. We used the GLS because the starch and NSC concentration
data presented a skewed distribution and in any model we tested the
variance was not homogeneous. In all models evaluated we used the
response variable in log scale because that reduced the AIC of the final
model in contrast to the model fitted to non- log-transformed data.
Therefore, in all output tables, coefficients and errors are described
in log scale.

In order to answer Q1 , we first fit the GLS model using organ (leaf,
branch, trunk, root) as the main fixed effect to identify the degree of
difference in NSC, ST and SS allocation in each organ. After that, in
order to identify the degree of difference in sugar allocation in each
organ between the seasons and across the years, we fitted a model
considering the interaction of season (wet, dry) and year (2013, 2014,
2015) as the fixed effect. In Q2 , we first performed a Peason
correlation test between the average NSC, ST, SS, SS\%NSC and ST\%NSC as
a function of each hydraulic trait and PCA1 axis. Our goal was to
understand the overall NSCs response to the specific hydraulic traits
and drought-resistance index, independently of the period sampled.
Second, in order to understand the seasonal and year-to-year variability
in NSC, ST, and SS concentration variance, we fitted another GLS model
using drought-resistance strategy groups as the main effect (avoidant,
intermediate, tolerant; Figure S1), and season and year as a fixed
effect (Figure 1). We used the drought-resistance strategy groups as
fixed effect in order to simplify the interaction with seasonality
within each year, and variability across the years.

In order to identify the effect size and the power of results for each
GLS model fitted we compare the model with the \emph{Anova} function
from the \emph{car} package (Fox \& Weisberg 2011). The function returns
an analysis of deviance table (type-II test) that reports the
likelihood-ratio Chi Square (hereafter denoted as \emph{X²} ) and gives
the effect size of the fitted model and p-value (for p\textless{}0.05 we
accepted as statistically significant). In addition to that, for every
fitted model, we performed a PostHoc test using the
\emph{emmeans}package (Lenth 2019) that estimates the marginal means
(least-squared means) for specific factors or factor combinations. We
used a pairwise comparison to determine specific mean differences where
the p-value is adjusted in accordance with the Tukey method (hereafter
denoted as PostHoc\textsubscript{test}).

\textbf{Results}

Seasonal and interannual variability of non-structural carbohydrates

The mean soluble sugar to starch ratio independent of season and year
was 6.4:1 in leaves, 0.9:1 in branches, 1.1:1 in trunks, and 0.5:1 in
roots (Table S1). The computed estimated marginal mean of total NSC
ranged 25.2 to 37.5 mg g\textsuperscript{-1} of dry matter for all
organs sampled. There was a significant difference in SS (GLS
model;\emph{X²} =164.10, Df=3, p\textless{}0.001), ST (GLS
model;\emph{X²} =144.34, Df=3, p\textless{}0.001), and NSC (GLS
model;\emph{X²} =61.50, Df=3, p\textless{}0.001) concentrations when we
evaluated organ as the unique fixed effect (Figure 3a). All organs
differed in the average of SS and NSC concentrations (Table S1; Figure
3a; PostHoc\textsubscript{test}; p \textless{}0.001). But, the
concentration of ST\textsubscript{leaf} was on average 5.27 mg
g\textsuperscript{-1} lower than all other organs
(PostHoc\textsubscript{test}; p\textless{}0.001). Here, we did not
observe significant pairwise differences in starch concentration among
branches, trunks, and roots (PostHoc test; p\textless{}0.001). The
NSC\textsubscript{leaf} differed from all other organs and were an
average 4.79 mg g\textsuperscript{-1} lower than branch and root, and
4.60 mg g\textsuperscript{-1} higher than trunk (PostHoc test;
p\textless{}0.001). Branch and root showed similar NSC concentrations
(PostHoc test; p\textgreater{}0.05), whereas the
NSC\textsubscript{trunk} concentration was on average 12.3 mg
g\textsuperscript{-1} lower than leaves, branches, and roots (PostHoc
test; p\textgreater{}0.05).

There was a seasonal effect modulating the concentration of SS in leaves
(GLS model; \emph{X²} =5.7, Df=1, p\textless{}0.05) and branches (GLS
model; \emph{X²} =25.7, Df=1, p\textless{}0.05) but not in trunks and
roots (SM-Table 2). The interannual variability affected significantly
the concentration of SS only in trunks (GLS model; \emph{X²} =27.7,
Df=2, p\textless{}0.05) and branches (GLS model; \emph{X²} =8.1, Df=2,
p\textless{}0.05), while the interaction of seasonality and interannual
variability affected the concentration of SS from all organs. Despite
that, the SS from all organs presented a significant variation including
the interaction with seasonality and interannual variability (GLS model;
Df=1, p\textless{}0.05). The SS\textsubscript{leaf} was similar during
the dry season and wet season of 2013 and 2014, but during ENSO year the
estimated marginal mean of SS\textsubscript{leaf} was 12.5 mg
g\textsuperscript{-1} higher during dry season in contrast to wet season
(PostHoc test= 12.50; SE=3.41, df=693; T ratio=-3.66; p =0.003; Figure
3b). Similarly, the main seasonal difference in
SS\textsubscript{branch}was observed during the dry season of the ENSO
year (PostHoc test= -13.08; SE=2.70, df=693; T ratio=-4.48; p =0.003).
Considering the inter-annual variability and comparing seasons in
different years, the concentration of SS\textsubscript{leaf} and
SS\textsubscript{branch}during the rainy season was similar during the
three studied years (PostHoc test; p\textgreater{}0.05). However, the SS
concentration during the dry season was on average 9.15 and 9 mg
g\textsuperscript{-1}higher in leaf and branch during the ENSO year in
contrast to dry season of 2013 and 2014 together (PostHoc test;
p\textless{}0.001).

Trunks and roots presented the strongest interaction between season and
year to explain the SS variability (SM-Table 2). There was an overall
tendency in wet to dry decreases in SS\textsubscript{root} and
SS\textsubscript{trunk} during 2013 and 2014 (PostHoc test;
p\textless{}0.001), however, this pattern inverted during ENSO year
(Figure 3b). The SS\textsubscript{trunk} and SS\textsubscript{root} were
8.84 and 10.09 mg g\textsuperscript{-1} higher in the dry season in
contrast to the wet season in 2015, respectively (PostHoc test;
p\textless{}0.001). The trunks and root present, respectively, on
average 10.3 and 9.3 mg g\textsuperscript{-1} lower in the wet season of
2015 in contrast to the wet season from 2013 and 2014. The
SS\textsubscript{root} also increased in the dry season of ENSO year,
there were on average 7.32 mg g\textsuperscript{-1} higher than the dry
season of the previous year (Figure 3b).

Only the trunks presented a seasonal difference in starch concentration
(GLS model; \emph{X²} =5.7, Df=1, p\textless{}0.05), although this
effect was not too strong (low X²). In addition, both leaves (GLS
model;\emph{X²} =29.7, Df=1, p\textless{}0.05) and branches (GLS
model;\emph{X²} =12.9, Df=1, p\textless{}0.05) showed differences in
starch concentration across the years. There was no significant
interaction between seasons and years in all organs (Figure 3b; GLS
model; p\textgreater{}0.05; SM-Table 2). Considering the inter-annual
variability and comparing seasons among years, the ST concentration was
always similar in different years (PostHoc test; p\textgreater{}0.05).

The variance of total NSC concentration was not explained by seasonality
in all organs (SM2-Table 2). Whereas trunks (GLS model; \emph{X²} =8.7,
Df=2, p\textless{}0.05) and roots (GLS model; \emph{X²} =11.4, Df=2,
p\textless{}0.05) exhibited interannual differences in NSC
concentration, only leaves (GLS model; \emph{X²} =23.4, Df=2,
p\textless{}0.05) and trunks (GLS model; \emph{X²} =16.5, Df=2,
p\textless{}0.05) presented an interaction effect between seasons and
years (SM2-Table 2). During the ENSO year, the
NSC\textsubscript{leaf}concentration increased 11.4 mg
g\textsuperscript{-1} during dry season in relation to wet season
(PostHoc test= -11.4; SE=4.27, df=693; T ratio=-2.67; p\textless{}0.01),
whereas there was no seasonal difference in NSC\textsubscript{leaf}
concentrations during 2013 and 2014 (PostHoc test; p\textgreater{}0.05).
All the other organs showed no seasonal differences in NSC concentration
during 2013, 2014, and 2015. In addition, considering the inter-annual
variability and comparing seasons among years, the NSC concentration was
always similar in different years (PostHoc test; p\textgreater{}0.05).

Drought-resistance strategies and non-structural carbohydrates variation

The PCA1 explained 58\% the proportion of variance of the species
functional hydraulic traits (Figure 2a). The drought-tolerant group is
formed by shallow-rooted species, high embolism-resistance plants that
also showed sharp declines (towards more negative values) in the annual
minimum leaf water potential during ENSO (Figure 2b). The intermediate
group included either shallow-rooted and vulnerable to xylem embolism or
deep-rooted with higher resistance to xylem embolism. The
drought-avoidant group was formed by overstory trees with very deep
roots and high vulnerability to xylem embolism used as fixed effect
(Figure 2b; S1).

The continuous PCA1 axis summarizing the drought-resistance axis was
81\% and 82\% negatively correlated with the average
ST\textsubscript{root} and total NSC\textsubscript{root}, respectively
(Figure 4a). Additionally, the PCA1 explained 81\% and 73\% of variation
in the mean fraction of SS and ST with respect to total NSC in roots
(Pearson correlation; p\textless{}0.05; SM-Table 3; Figure 4b). Among
all hydraulic traits, only the \selectlanguage{greek}Δ\selectlanguage{english}LWP alone presented a significant and
strong relationship with SS\%NSC and ST\%NSC in branches and roots
(SM-Table 4). Here, it is important to stress out that the sample size
of this relationship was lower (n=7) than any other hydraulic traits
(n=8) because we do not have \selectlanguage{greek}Δ\selectlanguage{english}LWP for \emph{Manilkara elata} . The tree
maximum DBH was also negatively correlated with ST concentration in
roots (SM-Table 3). In addition, we showed a significant effect of
drought resistance strategy groups to explain the variation of starch
and total NSC concentrations in branch, trunk and root (GLS model;
p\textgreater{}0.05; SM-Table 4), but only in branch we observed a more
variable concentration of ST and NSC considering the interaction between
drought resistance strategy, seasonally and the year studied (GLS model;
p\textless{}0.05).

The variance of soluble sugar in leaves, branches, trunks or roots did
not differ among the drought-resistance strategy groups (GLS model;
p\textgreater{}0.05; SM-Table 4; Figure 5). Only
SS\textsubscript{branch} presented significant change across the year
(GLS model; X\selectlanguage{ngerman}²=11.86, Df=4, p\textless{}0.05; SM-Table 4), and the
SS\textsubscript{trunk} showed a high variability determined by the
interaction between drought-resistance strategy, seasonality, and years
(GLS model; X²=15.17, Df=4, p\textless{}0.05). In particular, we did not
observe seasonal differences in SS\textsubscript{leaf} in the avoidant
group in all studied years (PostHoc test; p\textgreater{}0.05), however,
the SS\textsubscript{leaf} concentration increased 10.3 mg
g\textsuperscript{-1} in intermediate and 15.63 mg
g\textsuperscript{-1}in the tolerant groups from wet to dry season of
the ENSO year (PostHoc test; p\textless{}0.01). Also, both intermediate
and tolerant groups presented, respectively, 8.2 and 18 mg
g\textsuperscript{-1} of increase in SS\textsubscript{branch} from wet
to dry season during the ENSO year (PostHoc test; p\textless{}0.01). For
trunks, the avoidant and intermediate trees showed higher seasonal
variability in soluble sugar concentration during the dry season among
all years (PostHoc; p\textless{}0.01). However, all groups increased on
average 8.45 mg g\textsuperscript{-1} of the SS\textsubscript{trunk}
from the dry to wet season of the ENSO year (PostHoc; p\textless{}0.01).
Trees from the intermediate group increased the SS\textsubscript{root}
by 13.57 mg g\textsuperscript{-1} from wet to dry season during the ENSO
year (PostHoc; T-ratio=-4.28,p\textless{}0.01). In particular, during
2013 and 2014, there was an average of 7.8 mg g\textsuperscript{-1}
decline from wet to dry season in SS\textsubscript{root} (PostHoc;
p\textless{}0.05). However, in 2015 this pattern reversed and
SS\textsubscript{root} increased 10.69 mg g\textsuperscript{-1} during
the dry season in contrast to wet season (PostHoc;
T-ratio=-3.24,p\textless{}0.001).

The ST\textsubscript{leaf} and NSC\textsubscript{leaf} concentrations
did not differ among drought-resistance strategies, and with
interactions among seasonality, drought-resistance strategy and year
(GLS model; p\textgreater{}0.38; SM-Table 4). There was no particular
seasonal difference in ST\textsubscript{leaf} among the years
(PostHoc\textsubscript{test;} p\textgreater{}0.05). But, during the dry
season of 2015 (ENSO year), the NSC\textsubscript{leaf} concentration
increased in intermediate (9.6 mg g\textsuperscript{-1}) and in tolerant
(17.9 mg g\textsuperscript{-1}) drought-resistance groups in contrast to
the wet season of the same year (PostHoc test; p\textless{}0.01). The
ST\textsubscript{branch} was on average 13.5 mg
g\textsuperscript{-1}higher in the drought tolerant than the average of
avoidant and intermediated ST\textsubscript{branch} concentration
(PostHoc test; p\textless{}0.01). The tolerant group presented higher
NSC concentration in branches than the intermediate group (PostHoc;
p\textless{}0.001). In addition, the avoidant group contains 2.7 times
higher NSC\textsubscript{branch} concentrations during dry as compared
to wet season, independently of the year (PostHoc; T ration=-2.97;
\textless{}0.01). The avoidant group exhibited about 24.8 mg
g\textsuperscript{-1} higher NSC\textsubscript{branch} during the dry
season in contrast to the wet season when we included all years together
(PostHoc; p\textless{}0.001). In trunks, the concentration of starch and
NSC was very similar among drought-resistance groups, and presented a
nearly homeostatic pattern across the seasons and years.

On the other hand, the variations of ST\textsubscript{root} (GLS
model;\emph{X²} =62.68, df=2,p\textless{}0.001) and
NSC\textsubscript{root}(GLS model; \emph{X²} =62.68,
df=2,p\textless{}0.001) were strongly determined by the
drought-resistance groups (Figure 5). The tolerant group showed about
45.3 mg g\textsuperscript{-1} higher ST\textsubscript{root}
concentration in contrast to the avoidant group (PostHoc; T-ratio=-7.54,
p\textless{}0.01) and 34.5 mg g\textsuperscript{-1} higher than
intermediate groups (PostHoc; T-ratio=-2.82,p\textless{}0.01). In
addition, the intermediate group exhibited 10.7 mg g\textsuperscript{-1}
higher ST\textsubscript{root}than the avoidant group (PostHoc;
T-ratio=-3.52 p\textless{}0.01). Similarly, all groups showed
differences in contrast to NSC\textsubscript{root} concentration
(PostHoc; p\textless{}0.01). Even though the root system presented a
rather seasonal homeostatic pattern and did not show differences across
the seasons and years in ST and NSC concentration (Figure 6).

\textbf{4. Discussion}

We demonstrate that the concentration of SS, ST and NSC varies
considerably among organs in seasonal Amazon tree species (\textbf{Q1}
), confirming our initial hypothesis. Our results are in line with
previous studies showing that tree organs have the highest contribution
to explain the NSC variance across a diversity of biomes (Würth \emph{et
al.} 2004; Martínez-Vilalta \emph{et al} . 2016; Furze \emph{et al} .
2018). We also showed that the dynamics of seasonal and interannual SS
concentrations in these organs was strongly affected by the extreme
drought event induced by ENSO in 2015 (\textbf{Q1} ; Figure 3a). Indeed,
we showed a seasonal and year-to-year homeostasis in ST and SS
concentrations as observed in all organs during 2013 and 2014 (with the
exception to soluble sugar in roots) (\textbf{Q1;} Figure 3b). However,
the SS increased in all organs during the extreme ENSO drought, which
disrupted the NSC homeostasis, while the ST concentration did not
(\textbf{Q1} ). An important novelty in our study is the effect that the
drought-resistance strategies have on the variation of NSC concentration
in some organs (\textbf{Q2} ). Indeed, according to our overarching
hypothesis species-specific differences in drought hydraulic resistance
strategies did influence the direction of change in SS concentrations in
response to regular dry season and extreme drought conditions, but not
ST. The increase in SS concentration was more important in woody organs
of intermediate and tolerant drought strategies during the extreme
drought event. In addition, drought-resistant strategies exert a strong
effect on starch and NSC concentration in roots (\textbf{Q2} ; Figure 4
and 5). These patterns suggest that contrasting drought survival
strategies selected over evolutionary time scales explain part of the
NSC variations. Therefore, we show for the first time that hydrological
niche segregation, which determines the drought avoidance-tolerance axis
of coexisting trees in Tapajós forest (Brum \emph{et al} . 2019a), also
has an important effect in determining the NSC allocation pattern in
different organs and the variation in response to an extreme drought
event.

NSC concentration at organ level across seasons and years

At the organ level, the average ST concentrations on leaves were lower
as compared to branches, trunks and roots that showed similar average ST
concentration. The ST presented more depletion than did SS, mainly in
leaves and branches across the years (Figure 3; Figure S2). Moreover,
the SS concentration presented the highest concentration in leaves,
followed by branches, roots and trunk. The observed higher
SS\textsubscript{leaf} and low ST\textsubscript{leaf} in our study might
being reflecting the functional role of leaves that: \textbf{i} )
present higher proportion of phloem tissue loading sugars towards
branches as a carbon source organ, and \textbf{ii} ) sustain a higher
metabolic rate compared to other organs (Sala, Woodruff, Meinzer 2012;
Sala and Mencuccini, 2014; Martínez-Vilalta \emph{et al} . 2016; Guo
\emph{et al} . 2020). As starch is synthesized and degraded in
chloroplasts within photosynthetic tissue at hourly basis (Santelia \&
Lunn 2017; Thalmann \& Santelia, 2017, MacNeill \emph{et al} . 2017), it
is likely that unmeasured diurnal rhythms may affect our understanding
of what determines the ST concentration in leaves in our survey, because
leaves were sampled only once (at a roughly random time between 8 am to
3 pm) within each season. However, another study with 82 Amazonian
species, in which the sample time was standardized, also shows higher SS
concentrations in leaves than in branches (Signori-Müller \emph{et al} .
in press). Therefore, our understanding of what drives
ST\textsubscript{leaf} in the seasonal Amazon forest still needs
increase the sampling frequency, include observations of phenological
development and age-dependent leaf physiology (Albert \emph{et al} ,
2018), and perform a diurnal cycle in contrasting seasons (Gent 2018;
Tixier \emph{et al} ., 2018). We also speculate that species-specific
leaf traits could also explain the differences observed in NSC among
species in our site, such as observed in a tropical forest in Panama
where the leaf mass per area from 23 species explained 31\% of the
variation of leaf total NSC concentration (Dickman \emph{et al} . 2018);
or the net leaf photosynthesis that might explain a large variation of
the total ST\textsubscript{leaf} concentrations (Guo \emph{et al} .
2020).

Roots, followed by branches maintained the higher average NSC
concentration, a difference mainly related to a higher allocation to ST
concentration. It is well known that roots are also specialized as
storage organs (Kozlowski, 1992; Aubrey \& Teskey, 2018). This pattern
was true for the tree species from seasonal Amazon as the coarse root
system represents the main ST and NSC storage organ in our site (Table
2). Even though two species (canopy tree \emph{Erisma uncinatum} and the
understory \emph{Amphirrhox longifolia} ) present very little ST
concentration in the root (Figure S3), the reasons for this observed
difference is still unclear. In our study, we also showed that branches,
in addition to roots have a similar average of ST concentration. A
similar pattern was also described in species from a mixed temperate
forest in North America (Furze \emph{et al} . 2018), indicating the
importance of branches to regulate the starch pool even in a contrasting
ecosystem such as tropical forest. Also, branches are the woody organs
that present the highest SS concentrations, which may indicate that
branches sustain a higher metabolic rate compared to the other woody
organs. In fact, branches represent the major buffer related to NSC
fluctuation according to the tree water status (Woodruff \& Meinzer,
2011; Dickman \emph{et al} . 2015; Signori-Müller \emph{et al} . in
press), but also according to climatic temperature variation, leaf
phenology, and changes in leaf photosynthesis rates of overstory trees
in a tropical forest from Panama (Newell, Mulkey \& Wright, 2002,
Würth\emph{et al} . 2005; Dickman \emph{et al} . 2019). Compared to
roots and branches, trunks have the lowest mean concentration of
ST\textsubscript{trunk} and SS\textsubscript{trunk} (each
\textasciitilde{}50\% of the total NSC), but the percentage of SS and ST
from total NSC is about 50\% such as those observed in branches (Figure
6). Low concentration in NSC\textsubscript{trunk} compared to the other
organs reflects the relatively higher proportion of lignified and
non-living tissues in trunks and sapwood as opposed to the other plant
organs (Plavcová \emph{et al} . 2016, Martínez-Vilalta \emph{et al} .
2016; Morris \emph{et al} . 2016). Although we measured
NSC\textsubscript{trunk} at a maximum of 5 cm depth in sapwood, tropical
forest trees can store NSC up to 15 cm or deeper into the higher
lignified sapwood (Würth \emph{et al} . 2005) and we would expect a
similar pattern in trees found in seasonal Amazon forest. In addition,
the trunk is the largest fraction of tree biomass (Chave \emph{et al} .
2014), therefore the low NSC, SS and ST concentration might be related
to the trunk size relative to the rest of the plant and sugars could be
more diluted along to the organ. In addition, if NSC decreases radially
and such pattern is species-specific, the 5 cm deep sample could also
skew the results contributing to the diverse dynamics observed in our
study.

The strong drought event induced by the ENSO in 2015 disrupted the
seasonal homeostasis of soluble sugar in all organs, but not the starch
concentration. Overall ST concentration variation in leaves, branches
and roots was not explained by seasonal and interannual variability in
climatic conditions, although a weak effect was observed in the trunk.
The year-to-year differences in ST concentration in leaf and branch are
reflected in the prioritization of SS concentration in detriment of ST
during 2015. In fact, the homeostatic pattern in ST concentration was
not observed for SS, that was more variable among seasons and was driven
mainly due the strong dry season during ENSO 2015 (Figure 3b). One
mechanism that has been proposed to explain how plants avoid dehydration
is via osmoregulation, which helps plants maintain lower turgor
potentials and avoid cell water loss, reduce xylem vulnerability to
cavitation, improving water uptake and allowing the plant to maintain
functional metabolism during extreme drought (Morgan, 1983; Tardieu,
1996; Boyer, 1996; Sala \& Mencuccini 2014; De Baerdemaeker \emph{et al}
. 2017). Indeed, during periods of water stress, changes in leaf
hexose/sucrose ratios cause transient starch depletion and this shift
contributes to increased osmoregulation in leaves (Pelleschi \emph{et
al} . 1997). Previous studies show that starch concentration in leaves
usually decreases in response to water stress for a variety of species
(Thalmann \& Santelia, 2017; He \emph{et al} . 2019; Du \emph{et al} .
2020). In addition, as recently demonstrated by Signori-Müller \emph{et
al} . (\emph{in press} ) there is a prioritization of SS relative to ST
in leaves in a broad range of taxa in the Amazon, which is coupled to
tree hydraulic status, indicating the involvement of SS in the
maintenance of the hydraulic function in adult tropical trees. In fact,
in our dataset the proportion of ST from total NSC in leaves was the
minimum during the dry season of 2015. Similarly, depletion in starch
storage and increase in SS during the 2015 ENSO (Figure 1), suggests
that branches are the woody organ with higher sensitivity to seasonal
and year-to-year C-balance. In contrast to the other organs, branches
contributed to the highest C turnover for larger scale responses over
longer timescales (Trumbore 1997; Carbone \emph{et al} . 2013; Carbone
\emph{et al} . 2016).

We described an overall tendency of the roots and trunks to decrease SS
concentration from wet to dry season in 2013 and 2014, but during the
ENSO drought this pattern was inverted, and SS increased from wet to dry
season in 2015. In the trunk, it is expected that endogenous and
phenological rhythms might define the prioritization in carbon
allocation to growth and osmoregulation might covariate with NSC cycle
seasonality (Richardson \emph{et al} ., 2013; Wagner \emph{et al} .,
2016; Fang \emph{et al} ., 2019). Considering that Tapajós trees exhibit
low wood increment during dry season in relation to wet season at
community level (Rice \emph{et al} . 2004, Vieira \emph{et al} . 2004),
and that under water limitation the cambial activity is inhibited faster
than photosynthesis during droughts (Fatichi, Leuzinger \& Körner 2014,
Körner, 2015), we expected that such C-sink limitation would increase
ST\textsubscript{trunk} as a result of the balance between reduced
growth and increase storage during dry season (Sala \emph{et al} . 2012,
Wiley \& Helliker, 2012, Körner, 2015; Wagner \emph{et al} ., 2016).
Also, an increase in heterotrophic trunk respiration (higher
CO\textsubscript{2} efflux) due water deficit (Rowland \emph{et al} .,
2018) could also down-regulate the starch storage in the trunk of
Tapajós tree species. Instead, we observed a homeostatic pattern in ST
concentration in the trunk, while the total NSC and soluble sugar varied
with seasons and across years, but this difference was related to the
prioritization of SS during the extreme drought event. To better
understand all the processes that affect SS\textsubscript{trunk} and
ST\textsubscript{trunk} dynamics, future studies should focus on the
interplay between cambium phenology, wood increment rates, heterotrophic
trunk respiration and changes in wood water content and NSC balance
(Scartazza \emph{et al.,} 2013, Hartmann \& Trumbore 2016). In roots, we
observed the lowest year-to-year seasonal changes in starch
concentration, but the increase in SS in roots during the dry season
might be related to water stress caused by water depletion in the clay
soil matrix. Trees in our study site grow over clay soils with high
water holding capacity (Oliveira-Junior \& Correa, 2001, Ivanov \emph{et
al} ., 2012). If soil-root exchange is not interrupted due to increase
in air gaps between roots and drying soil (Liu \emph{et al.,} 2015), low
soil water potential might induce root-to-soil water loss during the dry
season (Oliveira \emph{et al} ., 2005). An increase in
SS\textsubscript{roots} may be a complementary part of a mechanism
exhibited by these trees to overcome the reduced soil water potential
and avoid water loss to shallow dry soil during the extreme drought
event of 2015. Furthermore, there is a higher interaction between soil
microorganisms and root exudation production at the cost of C that may
also affect the dynamics of soluble sugar (Canarini \emph{et al} .,
2018). However, as many variables drive the strength of the
C-source-sink, the feedback between climate and vegetation traits, and
the NSC-balance, any attempt to explain these processes not including
direct measurements of NSC falls into an unwished circularity (see
Hartmann \& Trumbore 2016).

Drought-resistance strategies effect in the seasonal and interannual
patterns of NSC concentration

The drought-resistance strategies summarize the previous finding that
distribution of leaf-area and light-environments in seasonal Amazon
forests is integrally connected to \textbf{i} ) tree diameter
distribution (Stark \emph{et al.,} 2015), \textbf{ii} ) the effective
rooting depth (Nepstad \emph{et al} ., 1994; Markewitz \emph{et al} .,
2010; Ivanov \emph{et al} ., 2012), and \textbf{iii} ) the coordination
between rooting depth and embolism resistance suggesting a trade-off
between drought avoidance (i.e. deep rooting) and drought tolerance
(i.e. embolism-resistance) (Brum \emph{et al} ., 2019a; Figure 2a).
Indeed, canopy trees with greater canopy exposure to light exhibit
drought-avoiding traits such as a deeper rooting which buffer the water
uptake during dry season and allow a reduced investment to xylem
embolism resistance and less year-to-year variation in minimal water
potential during the peak of the dry season (Figure 2a). In contrast,
small, shallow-rooted trees in the light-limited understory habitat
exhibited the lower P50 and P88 and higher year-to-year variability in
xylem water potential (Figure 2a). Here we add a novel and interesting
finding to this discussion: the hydrological niche segregation
(expressed by contrasting coexisting drought strategies) is also related
to contrasting NSC allocation patterns. Intermediate and tolerant
species exhibit higher ST storage in roots and higher remobilization to
SS in all organs during water periods induced by ENSO, while the
avoidant species did not show the same pattern.

Climatic regimes characterized by seasonal droughts likely select for
higher NSC storage in roots of shallow-rooted trees that experience
frequent reductions in soil water availability. A certain homeostasis of
ST\textsubscript{root} concentrations from drought-tolerant trees
indicates the importance of maintaining higher starch reserves as a key
component of their drought resistance strategies. Indeed, an increase in
ST/NSC ratio also increases the resilience to drought-tolerant trees to
less reliable water availability during dry season or less C-uptake
during the rainy season due lower light availability (Huete \emph{et al}
. 2006, Restrepo-Coupe \emph{et al} . 2016, Smith \emph{et al} . 2019).
This pattern is also associated with resprouting ability in the case of
overstory branches falling and causing stem or canopy damage to the
understory species (Poorter \emph{et al} . 2010). In addition, the
stored root carbohydrates can maintain root respiration for extended
periods of C-source limitation (Aubrey \& Teskey, 2017) which might be
an important physiological strategy for drought-tolerant species to
survive in seasonal forests. These findings point to rooting depth as a
key trait underlying tree resistance and resilience to extreme drought
events, and is likely one of the most important traits protecting some
tree species from drought-induced mortality (Nardini \emph{et al} .
2015; Silvertown\emph{et al.} 2015; Chitra-Tarak \emph{et al.} 2017), or
selecting for drought-persistence in Tapajós forest (Brum \emph{et al.}
2019a). But also, the higher ST and NSC concentration in branches of
drought tolerant species that support more decrease in leaf water
potential (SM-Table 3) represent a new perspective to understand the
covariation effect between C-allocation and drought-resistance
strategies in branches also. It seems that the survival of species
relying only on shallow soil water resources requires mechanisms to
buffer the environmental stresses by guaranteeing higher ST storage.
Indeed, shallow-rooted drought-tolerant species experience seasonal root
water stress, low radiation availability inhibiting the high
accumulation of photosynthates.

The temporal variation in water stress induced by ENSO determines higher
remobilization to SS in all organs in species of intermediate and high
drought tolerance. The seasonal changes in soluble sugar from the
intermediate and tolerant plants during an extreme drought, also reveal
ecological implications related to the ecohydrological niches of species
and the need for species to present short-term physiological adjustments
to survive during an extreme drought event. While the deeply rooted and
drought-avoidant trees can buffer water stress, trees whose roots are
limited to shallow soil, where the effect of drought can be stronger,
needed a whole plant osmotic adjustment reflecting the SS increase in
all organs. In fact, the shallow rooted drought-tolerant species that
face lower water variability during the extreme drought also prioritize
SS relative to ST in the whole plant, reflecting a mechanism that
increases the osmoregulation of these trees (Sala \emph{et al} . 2012;
He\emph{et al} . 2020). These results suggest that an increase in C-pool
of stored reserves represent a key drought-resistance trait as it
provides the necessary SS for the maintenance of whole plant cell turgor
and vascular integrity under adverse environmental conditions (Morgan,
1984; Turner, 2018, Sala \emph{et al} . 2012; Dickman \emph{et al} .
2015; Guo\emph{et al} . 2019). These results also suggest that species
that are more adapted to seasonal and year-to-year variation in water
deficit may have higher ability to rapidly adjust the whole plant SS
concentration under extreme water stress.

\textbf{5.1 Conclusion}

The high interspecific variation in NSC fluctuations observed in this
study likely represents the diversity of evolutionary adaptation to cope
with drought within this taxonomically diverse tropical forest.
Interestingly, no strong depletion of NSC was observed at any time, most
likely because an increased investment in soluble sugars was a uniform
response in all organs during the 2015 ENSO extreme drought. This event
disrupted the regular year-to-year SS homeostasis observed in regular
years. The link between soluble sugar and water stress was especially
important for branches, trunks, and roots of species of intermediate and
high drought tolerance. A final important finding was the consistent
pattern of starch storage exhibited by drought-tolerant species. It
seems that the persistence in seasonal tropical forest understories
requires high tolerance of environmental stresses, obtained in part by
increasing soluble sugar during water stress, and maintaining high
starch concentration in roots. Indeed, shallow-rooted drought-tolerant
species experience seasonal root water stress, low radiation
availability inhibiting the high accumulation of photosynthates. In
general, when abiotic conditions constrain growth due to high C-source
limitation, NSC responses depend on hydraulic traits and physiological
regulation patterns which have been selected to increase survival under
reduced productivity.

\textbf{Acknowledgement}

We are grateful to the LBA-INPA assistance in Santarém with special
respect to Louro Lima. We would also like to thank Sky Dominguez for the
manager's support. The authors confirm they do not have any conflict of
interest.

\textbf{Funding}

This study was financed in part by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001 for MB,
CS-M and DP PhD project, by the Go-Amazon project (FAPESP 2013/50533-5
and U.S. DOE grant \# DE. SC0008383), and by U.S. National Science
Foundation (NSF) award \#1754183. CS-M thanks the Brazilian National
Council for Scientific and Technological Development for a scholarship
(CNPq 140353/2017-8) and R.S.O for a CNPq productivity scholarship.
R.S.O thanks FAPESP/Microsoft research (grant 11/52072-0). VI
acknowledges the support of the NSF DEB Grant 1754163 and U.S.
Department of Energy OBER Grant DESC0011078.

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\textbf{List of Table}

\textbf{Table 1} Biological and structural attributes of the species
studied at the Tapajos Forest km 67 LBA study area, Brazil. Values for
basal area abundance are averages across a 4-ha survey area of all trees
larger than 10 cm in diameter at breast height. Two understory species
(\emph{A. longifolia} and \emph{R. pubiflora} ) were recorded in five
0.05-ha plots. Species are listed from the deepest rooted species at the
top to the shallowest rooted species at the bottom, as determined in
Brum \emph{et al} . (2019). The maximum height attained
(H\textsubscript{max}, m) per species was calculated as the
95\textsuperscript{th} percentile of the distribution of heights
generated by a model fitting a height-stem diameter relationship at the
study site (Longo \emph{et al} . 2020). For the understory species,
H\textsubscript{max} was calculated from direct height measurements (M.
Brum, unpublished).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Species*}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Family}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Hmax} \textbf{(m)}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Canopy Position}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Drought resistance group}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\selectlanguage{ngerman}\textbf{Basal Area (m².ha\textsuperscript{-1})}\strut
\end{minipage} & \begin{minipage}[b]{0.14\columnwidth}\raggedright\strut
\textbf{Abundance} \textbf{(ind.ha\textsuperscript{-1})}\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Manilkara elata} (Allem\selectlanguage{ngerman}ão ex Miq.) Monach\textsuperscript{1}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Sapotaceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
41.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
overstory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
avoidant\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.19\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
10.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Erisma uncinatum} Warm.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Vochysiaceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
48.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
overstory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
avoidant\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3.64\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
11.0\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Chamaecrista scleroxylon} (Ducke) H.S.Irwin and
Barneby\textsuperscript{2}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Leguminosae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
35.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
overstory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
intermediate\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.01\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
15.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Protium apiculatum} Swart.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Burseraceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
25.8\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
midstory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
intermediate\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.65\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
24.3\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Coussarea paniculata} (Vahl) Standl\textsuperscript{3}\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Rubiaceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17.1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
midstory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
tolerant\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.48\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
92.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Miconia} sp.\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Melastomataceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
17.2\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
midstory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
tolerant\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.08\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.5\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Amphirrhox longifolia} (A.St.-Hil.) Spreng\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Violaceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
5.5\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
understory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
intermediate\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
0.35\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
908\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\emph{Rinorea pubiflora} (Benth.) Sprague and Sandwith\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Violaceae\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
4.3\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
understory\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
tolerant\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
2.45\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
3104\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

*Species names updated since Brum \emph{et al} . (2019).

\textsuperscript{1} Identified in KM 67 species inventory
as\emph{Manilkara huberi} , but updated after a new floristic inventory
(Herbario IAN, EMBRAPA Belem) as accepted name \emph{Manilkara elata} .

\textsuperscript{2} Identified in KM 67 species inventory
as\emph{Chamaecrista xinguensis} but updated after a new floristic
inventory (Herbario IAN, EMBRAPA Belem) as accepted
name\emph{Chamaecrista scleroxylon} .

\textsuperscript{3} Identified in KM 67 species inventory
as\emph{Coussarea albescens} but updated after a new floristic inventory
(Herbario IAN, EMBRAPA Belem) as accepted name \emph{Coussarea
paniculata.}

\textbf{List of Figures}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 1} Monthly cumulative water deficit (mm) from 1999 to
2016 for km67 in Tapaj\selectlanguage{ngerman}ós National Forest, Belterra-PA. NSC measurements
were performed in 2013 (blue line), 2014 (orange line), and 2015 (red
line). The strongest increase in CWD was observed from October 2015 to
February 2016. The square dots on each blue, orange and red lines denote
the months during which the NSC samples were collected. The black line
and the grey shaded curve show the average monthly CWD including all
years together and the ±SD around the mean. The individual grey lines
show the variability of each year studied. The grey shaded square shows
the period of intensification of the dry season. Details on how the CWD
was computed can be found at the supplementary material.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 2 a)} Scales of drought-resistance axis (PCA1) defined by
functional hydraulic traits from eight seasonal Amazon tree species used
to contrast the interannual changes of non-structural carbohydrates in
leaves, branches, trunks, and roots in trees. P50 and P88 show the water
potentials at which the tissue loses 50\% and 88\% of xylem conductance;
the \selectlanguage{greek}δ\selectlanguage{english}\textsuperscript{18}O gives the natural-abundance stable isotopes
of xylem water used as proxy to determining the depths from which plants
uptake soil water; the \selectlanguage{greek}Δ\selectlanguage{english}LWP represents the interannual differences in
the midday (12 AM - 2 PM) minimal leaf water potential measured in the
field at the peak of the dry season during a non-ENSO year (December
2014) and during the ENSO drought year (December 2015); the tree DBH is
the maximum diameter at breast height. We used this matrix of average
values of each transformed functional hydraulic traits into a principal
components analysis and obtained the PCA1. \textbf{b} ) Hierarchical
clustering dendrogram based on Euclidean distance showing similarities
between species based on five functional hydraulic traits. We grouped
species based on scaled functional hydraulic traits similarities into
strategies groups: a tolerant group (green group; negative value in PCA1
axis), an intermediate group (red group, positive value in PCA1 axis
close to zero and below 1) and an avoidant group (yellow group\emph{and}
higher values in PCA1).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3} . \textbf{a} ) Density plot of the non-structural
carbohydrates (NSC) concentrations including all species together for
each organ (Table 1). Each point under the curve represents one
measurement, the vertical black line in each density plot shows the
median values for each tree organ, and the vertical red line represents
the median value for each NSC, Soluble Sugar and Starch. The upper
letters in the right side represent a significant difference between
average computed from PostHoc Tukey test. \textbf{b} ) Box plot of the
seasonal and interannual NSC concentration including all species
together (Table 1). The vertical black line in each panel shows the
median value of each NSC type for that organ. The symbol of a plus
represents significant differences between seasons within each year. The
open circle symbol represents significant differences of each specific
season among years (PostHoc test; p\textless{}0.05). The amplitude of
NSC is different between each panel because we intend to highlight the
seasonal differences.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

\textbf{Figure 4} \textbf{a} ) Mean (black points) and standard
deviation (SD whiskers) of NSC and Starch concentration (mg
g\textsuperscript{-1}), and \textbf{b} ) the Soluble Sugar (SS) and
Starch (ST) fraction of total NSC (\%) of each species studied including
all seasons and years together as a function of PCA1. Note that Y axis
is represented in log scale. The grey line represents the linear
regression line of Pearson correlation analysis. We showed only the data
for starch and NSC in root because it was the unique significant
relationship (see SM-Table 3). Symbols represent the
species:\emph{Manilkara elata} : square, \emph{Erisma uncinatum} : the x
symbol;\emph{Protium apiculatum} : diamond; \emph{Chamaecrista
scleroxylon} : circle; \emph{Amphirrhox longifolia} : cross;
\emph{Miconia sp} : triangle; \emph{Coussarea paniculata} : inverted
triangle; \emph{Rinorea pubiflora} : asterisk.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}

\textbf{Figure 5} . Mean (columns) and confidence interval (95\%,
whiskers) of seasonal and interannual non-structural carbohydrates (NSC)
concentration for the drought resistance groups (Table 1). The ST
represents Starch, and SS represents soluble sugar. We performed the
measurements at the end of the wet season (blue) and the end of the dry
season (orange) during 2013, 2014, and 2015 (ENSO year). Asterisks
represent differences in mean SS and ST between dry and wet seasons
(PostHoc test; p\textless{}0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}

\textbf{Figure 6} Fractions of soluble sugar (ligh tgrey) and starch
(dark grey) with respect to total non-structural carbohydrates (NSC) in
different organs and contrasting the drought-resistance groups during
2013, 2014, and 2015 grouped as wet and dry seasons.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-1/Fig-1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-2/Fig-2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-3/Fig-3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-4/Fig-4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-5/Fig-5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Fig-6/Fig-6}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

