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\DOI{10.5802/crchim.241}
\datereceived{2023-04-15}
\daterevised{2023-06-22}
\dateaccepted{2023-07-05}
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\dateposted{2023-08-22}
\begin{document}

\begin{noXML}

%\makeatletter
%\def\TITREspecial{\relax}
%\def\cdr@specialtitle@english{Chemical Ecology -- Chemical Mediation in the Environment}
%\def\cdr@specialtitle@french{\'Ecologie chimique -- M\'ediation chimique dans l'environnement}
%\makeatother

\title{Volatile organic compound emissions from almond shoots during
spring---dissociation between reproductive and vegetative organs}

\alttitle{\'Emissions de compos\'es organiques volatils par les pousses
d'amandiers au printemps --- dissociation entre les organes
reproducteurs et v\'eg\'etatifs}

\author{\firstname{Anj\'elica} \lastname{Leconte}\CDRorcid{0000-0002-0182-5827}\IsCorresp}
\address{CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France}
\email[A. Leconte]{anjelica.leconte@gmail.com}

\author{\firstname{Nicolas} \lastname{Barthes}\CDRorcid{0000-0003-2326-5135}}
\addressSameAs{1}{CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France}
\email[N. Barthes]{nicolas.barthes@cefe.cnrs.fr}

\author{\firstname{Bruno} \lastname{Buatois}\CDRorcid{0000-0003-3574-8425}}
\addressSameAs{1}{CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France}
\email[B. Buatois]{bruno.buatois@cefe.cnrs.fr}

\author{\firstname{Eug\'enie} \lastname{Coutagne}}
\address{Compagnie des amandes, Aix-en-Provence, France}
\email[E. Coutagne]{ec@compagniedesamandes.com}

\author{\firstname{Henri} \lastname{Duval}\CDRorcid{0000-0001-9980-8664}}
\address{GAFL, INRAE, Montfavet, France}
\email[H. Duval]{henri.duval@inrae.fr}

\author{\firstname{Philippe} \lastname{Lucas}\CDRorcid{0000-0003-2166-8248}}
\address{iEES Paris, INRAE, Sorbonne Universit\'e, CNRS, UPEC, IRD, 
Universit\'e P7, Paris, France}
\email[P. Lucas]{philippe.lucas@inrae.fr}

\author{\firstname{Michael} \lastname{Staudt}\CDRorcid{0000-0001-8694-507X}}
\addressSameAs{1}{CEFE, CNRS, Univ Montpellier, EPHE, IRD, Montpellier, France}
\email[M. Staudt]{michael.staudt@cefe.cnrs.fr}

\shortrunauthors

\keywords{\kwd{Volatile organic compounds}\kwd{\textit{Prunus
dulcis}}\kwd{\textit{Eurytoma amygdali}}\kwd{Biocontrol}\kwd{GC-MS}}

\altkeywords{\kwd{Compos\'{e} organique volatil}\kwd{\textit{Prunus
dulcis}}\kwd{\textit{Eurytoma
amygdali}}\kwd{Biocontr\^{o}le}\kwd{GC-MS}}

\thanks{This work was funded by ANRT, and the Compagnie des Amandes
with the support of LabEx CeMEB, an ANR ``Investissements d'avenir''
program (ANR-10-LABX04-01).}

\begin{abstract} 
\textit{Prunus dulcis}, a fruit tree of global economic and nutritional
importance, is infested by \textit{Eurytoma amygdali}, the almond wasp,
due to the lack of biocontrol measures. This study characterizes the
volatile organic compounds (VOCs) emitted by almond trees. We
identified and quantified by GC-MS 44 VOCs emitted during spring. VOCs
emitted during flowering were benzaldehyde and{\break}
1,4-dimethoxybenzene, while VOCs emitted during fruit and leaf growth
were mainly sesquiterpenes. Emissions increased with temperature but
light did not affect short-term emissions. VOCs identified in this
study could be used to develop a biocontrol method to prevent
infestation.
\end{abstract}

\begin{altabstract} 
\textit{Prunus dulcis}, un arbre fruitier d'importance nutritionnelle
et \'{e}conomique mondiale, est infest\'{e} par \textit{Eurytoma
amygdali}, la gu\^{e}pe de l'amande, par manque de moyen de
biocontr\^{o}le. Cette \'{e}tude caract\'{e}rise les compos\'{e}s
organiques volatils (COV) de l'amandier. Nous avons collect\'{e} et
identifi\'{e} par GC-MS 44 COV \'{e}mis au printemps. Ceux \'{e}mis
pendant la floraison sont le benzald\'{e}hyde et le
1,4-dimethoxybenzene, et ceux \'{e}mis lors de la croissance des fruits
et des feuilles sont principalement des sesquiterp\`{e}nes. Les
\'{e}missions ont augment\'{e} avec la temp\'{e}rature mais la
lumi\`{e}re n'a pas affect\'{e} les \'{e}missions \`{a} court-terme.
L'objectif est d'utiliser les COV identifi\'{e}s dans cette \'{e}tude
pour d\'{e}velopper une m\'{e}thode de biocontr\^{o}le pour
pr\'{e}venir l'infestation.
\end{altabstract} 

\maketitle
\vspace*{8pt}

\twocolumngrid

\end{noXML}

\section{Introduction} \label{sec1}

The almond tree, \textit{Prunus dulcis} (Mill.) D.A.~Webb (Rosaceae),
was already cultivated in Central Asia 5000 years ago~\cite{1}, then
spread to the Mediterranean basin~\cite{2}, and later to other
countries. The almond tree is well adapted to the Mediterranean
climate, and tolerant to drought and summer heat. Most of the almond
trees have low chilling requirements, but there is a high variability
for the blooming time ranging from February to March. About fifty
varieties are grown worldwide, with a genetic base mainly founded on
three cultivars, Tuono, Cristomorto, and Nonpareil~\cite{3}. In France,
five varieties released from the INRAE breeding program are mainly
cultivated: Lauranne, Mandaline, Ferragn\`{e}s, Ferraduel, and
\mbox{Ferrastar}~\cite{4}.

\vspace*{3pt}

The almond tree represents a global economic stake with an unshelled
almond production of 4~million tons in 2021, for a total crop area of
2.3~million hectares (source: FAO) and 57\% of current production comes
from the United States of America. World production and harvested area
have increased by 30\% in 10 years. French almond consumption has
increased and is continuing. In 2021, 47,111 tons of shelled almonds
were imported by France, while only 1940 tons were produced locally,
representing a cost of 238~million euros (source: French customs). The
country is working towards developing a local and sustainable almond
industry to reduce its dependence on imports.

The almond tree is susceptible to a range of diseases and pests,
including those caused by fungi, bacteria, root-knot nematodes, and
insects like \textit{Eurytoma amygdali} End. (Hymenoptera:
Eurytomidae). Due to a lack of effective control methods and the
absence of biocontrol, \textit{E.~amygdali} is one of the most serious
issues facing almond cultivation in France.

Native to the Middle East~\cite{5}, \textit{E.~amygdali} appeared in
France in the 1980s~\cite{6} and can cause more than 80\% of crop
losses~\cite{7}. This univoltine species has a well-described
biology~\cite{5,8,9,10}, with adults emerging and laying eggs in early
spring (April) on developed fruits. The larva feeds on the kernel until
the end of July and stays in the larval stage until January inside the
shell with two diapause phases~\cite{11}. The pupation begins in
February until March and the insect adult perforates the shell to
emerge. Wasps have a diurnal rhythm and are active between 10~a.m. and
7~p.m. with the highest activity around 3~p.m. when temperature and
light are most important~\cite{12}.

Research has tested different ways of developing biocontrol solutions
in almonds (clay, essential oils, nets) in order to convert
conventional agriculture. So far, there are no approved effective
biocontrol methods. Attract-and-kill devices based on the dispersal of
volatile organic compounds (VOCs) from plants may be a promising
solution, as they have been successfully used to monitor and control
other insect pests~\cite{13,14,15}. Indeed, Kouloussis and
Katsoyannos~\cite{16} observed that flower and fruit emissions
stimulate oviposition and aggregation in \textit{E.~amygdali} females.
By investigating the volatile emissions during the flowering phase, we
aim to uncover any chemical cues that may be involved in attracting the
wasps, even though they are not present at that time. However, the
identity of the compounds responsible for this attraction remains
unknown. In the literature, only a few studies have reported VOC
emissions from almond trees, and not all of them present quantitative
values in terms of emission rates~\cite{17,18,19}. Furthermore, the
results of these studies may differ for many reasons, such as the use
of different analytical and headspace techniques, the control and
measurement of environmental factors, the stage and size of the
measured organ, or the almond variety. Generally, almond tree and other
\textit{Prunus} species have been described as low VOC emitters
releasing mainly terpenes (mono- and sesquiterpenes) along with some
oxygenated low-molecular weight VOCs such as
(\textit{Z})-hex-3-en-1-ol, methyl salicylate, nonanal or{\break}
(\textit{Z})-hex-3-enyl acetate~\cite{17,18,19,20,21,22}.

The objective of the present study was to characterize the composition
and quantities of VOCs emitted by fruits, leaves and flowers of four
varieties of almond trees during spring. We studied short-term and
long-term emission variations in the laboratory under controlled
conditions. The short-term study examined the temperature and light
dependence of emissions to determine which VOCs are emitted during the
diurnal activity of the wasp. The long-term emission variations were
monitored at different stages of fruit and leaf development, to spot
the key VOCs emitted during the first emergence of the wasps in the
spring. These findings will enable further investigation into the
detection of these VOCs by \textit{E.~amygdali} and the impact on their
behavior. The goal is to identify an attractive mixture that can be
utilized in trapping devices for effective pest control
strategies.{\vspace*{-.2pc}}

\begin{table*} %tab1
\caption{\label{tabI}Varieties of \textit{Prunus dulcis} used for VOC
sampling under environmental controlled conditions{\vspace*{-.3pc}}}
\begin{tabular}{ccccccc}
\thead
Origin &amp; Planting year &amp; Code &amp; Variety name &amp; Rootstock &amp; Self-fertile
&amp; Earliness (1--9)$^{3}$\\
\endthead
INRAE$^{1}$ &amp; 2014 &amp; R1004 &amp; Hybrid R1004 &amp; GF677 &amp; Yes &amp; 4\\
INRAE$^{1}$ &amp; 1998 &amp; R1568 &amp; Hybrid R1568 &amp; GF305 &amp; No &amp; 4\\
PHP$^{2}$ &amp; 2018 &amp; R916 &amp; Lauranne &amp; GF677 &amp; Yes &amp; 5\\
INRAE$^{1}$ &amp; 2020 &amp; R1569 &amp; Hybrid R1569 &amp; GF677 &amp; No &amp; 7
\botline
\end{tabular}
\tabnote{$^{1}$~INRAE Avignon, France. $^{2}$~P\'{e}pini\`{e}re
Haute-Provence, France. $^{3}$~Earliness of the variety, ranked from
earliest (1) to the latest variety~(9).}
\end{table*}

\section{Materials and methods}\label{sec2}
{\vspace*{-.2pc}}
\subsection{Plant material}\label{sec2.1}
{\vspace*{-.1pc}}

Four varieties of almond trees (\textit{Prunus dulcis}) were used in
our study (Table~\ref{tabI}) from February to June 2021. By including
varieties with different precocity, we were able to extend the duration
of plant material availability. The trees were grown in individual
plastic pots of 45~L (40~cm diameter, 37~cm height) and were stored
outside on the platform ``Terrain Experimental'' of the research center
in Functional and Evolutionary Ecology in Montpellier, France
(43{\textdegree}$38'19.8''$~N 3{\textdegree}$51'43.4''$~E). On frost
days, the pots were placed in a greenhouse at 20~{\textdegree}C. Trees
were irrigated daily from June to August and weekly during the rest of
the year. To ensure almond development, we manually pollinated flowers.
For the self-fertile varieties, the pollen was not supplemented, and we
used a brush to transfer pollen between flowers on the same tree. In
the case of self-incompatible varieties, we used pollen from another
hybrid variety provided\break by INRAE.

The phenological stages in our study were defined based on the
development of almond tree organs, following the classification system
established by Socias i Company {\etal}~\cite{23}. Our stages~1 to~5
correspond to their~G to H~stages. Stages~1 to~3 represent young
almonds characterized by their smaller size, while stages~4 to~5
represent almonds that have reached their final size. The size varies
among different varieties and the classification was adapted for each
\mbox{variety}.

To ensure an adequate number of replicates for statistical analysis, we
grouped these developmental stages of fruits and leaves into two
categories. The first category, referred to as ``young fruits,''
includes stages~1 to~3. The second category, referred to as ``mature
fruits,'' includes stages~4 and~5 (Supplementary Figure~S1). By
grouping the stages in this way, we aimed to ensure a sufficient sample
size for robust statistical analysis.

\subsection{VOC emissions measurement device} \label{sec2.2}

The terminal part of an intact twig with a length of about 15~cm was
placed in a 125~mL FP double-walled glass thermostatic flask (75~mm
outer \mbox{diameter;} Legallais, Montferriez-sur-Lez, France) with a GL~14
cap and an inner tube (75~mm outer diameter), (Figure~\ref{fig1}).
Depending on the phenological stage (flowering, young fruits and mature
fruits), the twigs were composed of flowers alone or flowers with young
leaves during the flowering period. During the growth period, the twigs
were composed of fruits, fruits and leaves, or leaves alone. During our
experiments, we employed two trees of each variety for all the samples
in random order. While the trees were reused throughout the study, the
branch utilized in each sample was never reused.

\begin{figure*}
\includegraphics{fig01}
{\vspace*{.25pc}}
\caption{\label{fig1}Chamber system used to collect almond tree VOCs.
It is composed of an intact branch, circulation of ultrapure air and
$\text{CO}_2$ (black arrow), and water in the double wall (dotted blue
arrow). The temperature sensor (thermocouple; TH) in the chamber is
connected to the water bath. MFC: Mass Flow Controller. The diagram was
made with \url{https://www.Biorender.com}.}
{\vspace*{.2pc}}
\end{figure*}

The airflow circulating in the system came from a bottle of Alphagaz~2
ultrapure air (Air Liquide, Paris, France) and was regulated by a mass
flow controller (V\"{o}gtlin red-y GSC-B4KA-BB26, Muttenz,
Switzerland). The air was humidified via a bypass holding a washing
bottle (GL~45 central). The $\text{CO}_2$ mixing ratio of the air
chamber was adjusted to 400 ${\pm}$ 100~ppm by injecting pure
$\text{CO}_{2}$ via high-precision mass flow controllers (El-Flow
Select, Bronkhorst France S.A.S., Montigny-l\`{e}s-Cormeilles, France).
The flow rate within the chamber was set to
$300~\text{mL}{\cdot}\text{min}^{-1}$. A gas analyzer (LI-COR 840;
Lincoln, NE, USA) was used to measure the outflows of $\text{CO}_2$ and
$\text{H}_{2}\text{O}$.

A lamp (LX60 Heliospectra AB, G\"{o}teburg, Sweden) was used to control
the light. The air temperature inside the chamber was measured by a
thermocouple (Chrom-Constantan, OMEGA, Biel, Switzerland) connected to
the temperature controller of the water bath to adjust the temperature.

VOCs were sampled through adsorbent cartridges filled with Carbotrap
(40~mg, 20--40~mesh) and Tenax TA (80~mg, 20--35~mesh) at a flow rate
of $100~\text{mL}{\cdot}\text{min}^{-1}$ for 30~min using a pump
(Gillian GilAir Plus, Sensidyne LP, USA). Prior to sampling, the
cartridges were conditioned for 30~min at 250~{\textdegree}C with an
$\text{N}_2$ flow from 20 to $50~\text{mL}{\cdot}\text{min}^{-1}$.
{\vspace*{-.15pc}}

\subsection{VOC analyses} \label{sec2.3}
{\vspace*{-.15pc}}
\subsubsection{Gas chromatography-mass spectrometry{\hfill\break}
(GC-MS)} \label{sec2.3.1}

We used a gas chromatograph (GC, Trace{\texttrademark} 1310, Thermo
Scientific{\texttrademark} Milan, Italy) coupled to a mass spectrometer
(ISQ{\texttrademark} QD Single Quadrupole, Thermo
Scientific{\texttrademark} Milan, Italy) with an Optima 5-MS capillary
column (30~m length, 0.25~mm Internal Diameter, $0.25~\upmu$m film
thickness; Macherey-Nagel, D\"{u}ren, Germany). The VOCs trapped on the
cartridges were desorbed with a double-stage desorption system,
composed of a Thermal Desorption Unit (TDU) and a Cold Injection System
(CIS) (multi-purpose sampler, Gerstell, M\"{u}lheim, Germany). First,
the cartridges were desorbed at a temperature of 250~{\textdegree}C in
splitless mode on the CIS trap cooled at ${-}80$~{\textdegree}C by liquid
nitrogen. Then, the CIS trap was heated to 250~{\textdegree}C with a
1:4 split ratio to inject the compounds in the column. Helium was used
as carrier gas at a rate of $1~\text{mL}{\cdot}\text{min}^{-1}$. The
oven temperature was held at 40~{\textdegree}C for 3~min, increased
from 40~{\textdegree}C to 220~{\textdegree}C at a rate of
5~{\textdegree}$\text{C}{\cdot}\text{min}^{-1}$ and from 220 to
250~{\textdegree}C at 10~{\textdegree}$\text{C}{\cdot}\text{min}^{-1}$,
and finally held for 2~min. The temperature of the transfer line and
the ion source of the mass \mbox{spectrometer} were 250~{\textdegree}C and
200~{\textdegree}C respectively. We acquired masses from 38~m/z to
350~m/z and the ionization energy was set to 70~eV.

\subsubsection{GC-MS data processing} \label{sec2.3.2}

MZMine 2.53 software~\cite{24} was used to detect VOCs and obtain the
area under the curve (AUC) from chromatograms. The parameters chosen to
process chromatograms are listed in Supplementary Table~S1.
XCalibur{\texttrademark} software (Thermo Scientific{\texttrademark}
Milan, Italy) was used to identify VOCs with NIST (2011) and Wiley (9th
edition) databases. Retention indices (RI) were calculated from the
retention times (RT) of a series of \textit{n}-alkanes (alkane standard
solution, 04070, Sigma $\text{Aldrich}^{\text{\textregistered}}$) and
compared with the literature (Adams, 2007).

AUCs were calibrated by means of standard solutions of benzaldehyde,
DMNT, (\textit{Z})-hex-3-enyl acetate, limonene, methyl salicylate, and
${\upbeta}$-caryophyllene dissolved in methanol at three concentrations:
1, 10, and $100~\text{ng}{\cdot}\upmu\text{L}^{-1}$. One $\upmu$L of
each solution was injected into the cartridges ($N=3$), then placed for
5~min under an $\text{N}_2$ flow (Alphagaz~1, Air Liquide) and analyzed
by GC-MS.

Each organ (stem, leaves, flowers, fruits) was scanned to calculate the
emissions to obtain a total area projected with ImageJ
software~\cite{25}. Concentrations were calculated from the AUC of each
VOC, the calibration factor and the volume sampled.

The calculation of emissions corresponds to: 
{\[
E_{\mathrm{VOC}}=\frac{(C_{\mathrm{VOC}}-C_{\mathrm{blank}})}{S}\times F
\]}\unskip
with $E_{\mathrm{VOC}}$: VOC emission in $\text{ng}{\cdot}\text{m}^{-2}
{\cdot}\text{s}^{-1}$; $S$: total projected area in $\text{m}^2$;
$C_{\mathrm{VOC}}$ and $C_{\mathrm{blank}}$: VOC concentration in
$\text{ng}{\cdot}\text{m}^{-3}$ in the chamber with almond tree and in
the empty chamber; $F$: airflow in the chamber in
$\text{m}^{3}{\cdot}\text{s}^{-1}$.

\subsubsection{Protocol of testing temperature and light effects on
emissions} \label{sec2.3.3}

In order to assess how emissions varied during day and night, the
emission responses to temperature and light were investigated using the
following protocol: six temperature levels were tested on the same
branch, ranging from 15 to 40~{\textdegree}C in steps of
5~{\textdegree}C at a light intensity of $750 \pm
50~\upmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$
photosynthetic photon flux density (PPFD). For each step, the plant was
left to adapt for 30~min and then VOC emissions were collected for
30~min. The steps were performed in ascending or descending order. The
effect of temperature on emissions was tested on three stages:
flowering ($N=10$), young fruits ($N=5$), and mature fruits ($N=11$)
for three varieties: R1568, R1569, and R1004. The Lauranne variety
could not be used in the temperature experiments due to technical
constraints (tree disease requiring treatment).

The effect of light was studied at a temperature of 30 ${\pm}$
0.2~{\textdegree}C by increasing or decreasing the incident light level
on the same branch, by 0, 75, 250, 500, 1000, and $2000~\upmu\text{mol}
{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ PPFD only on mature fruit and
leaves at stages~4 ($N=8$) and~5 ($N=5$). The varieties tested were
Lauranne, R1568, R1569, and R1004.

\subsubsection{Statistical analyses} \label{sec2.3.4}

Data processing was done with RStudio~\cite{26} with R~4.2.1 version.
Mean comparisons between flowering and fruit growth stages were made
with permutation Student's test or
Fligner--Policello test for non-homogeneous variances. Two Redundant
Discriminant Analysis (RDA, R~package ``RVAideMemoire'') were performed
to study the effects of temperature and light on VOC emissions. In each
RDA, variety, branch composition (flowers, fruits, leaves),
phenological stage, and their interactions were tested. The effect of
variables was tested by a pairwise factor fit with 999 permutations and
a correction of $p$-values by a false discovery rate method. Before RDA
analyses, raw data was transformed by adding a constant to 0.0001 to
remove zeroes and a quadratic-root transformation~\cite{27}.

\section{Results}\label{sec3}
\subsection{Volatile emissions of almond tree in spring} \label{sec3.1}

Under standard temperature and light conditions, 40 compounds were
identified in almond tree emissions collected between February and May
(Table~\ref{tabII}). The trees' phenological stages were divided into
two categories: ``flowering'' and ``fruit and leaf growth''.
Sesquiterpenes (SQTs) accounted for half of the VOCs emitted during
both the flowering and growth stages. The average emissions from
\textit{Prunus dulcis} were low, with a mean value of $36.3 \pm
9~\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ ($N=19$) during
the development stages, but higher during the flowering amounting to
$167.1 \pm 64~\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$
($N=11$). Among the VOCs detected, nine compounds, including five SQTs
and one monoterpene (MNT), could not be identified. Three compounds
were exclusively detected during flowering, namely
1,4-dimethoxybenzene, unknown compound and unknown MNT, while 10
compounds were identified during the development stages. 

\begin{table*} %tab2
\caption{\label{tabII}Mean emission rates ${\pm}$ standard errors of
almond trees measured under standard conditions (30~{\textdegree}C at
$750~\upmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ PPFD) for
30~min, across four varieties, without distinguishing between fruit and
leaf growth stages}
\fontsize{8.15}{10.15}\selectfont
\tabcolsep4pt
\begin{tabular}{cccccccc}
\thead
\xmorerows{1}{Class$^{\mathrm{e}}$} &amp; \xmorerows{1}{VOC} &amp; 
\morerows{1}{\parbox[t]{2cm}{\centering Retention time (min)}} &amp;
\multicolumn{2}{c}{Retention index} &amp; Database &amp;
\multicolumn{2}{c}{Emissions ($\text{ng}{\cdot}\text{m}^{-2}
{\cdot}\text{s}^{-1}$)$^{\mathrm{d}}$}\\
\cline{4-5}\cline{7-8} &amp;&amp; &amp; Calculated &amp; Database &amp;&amp; Flowering &amp;
\parbox[t]{1.5cm}{\centering Fruit/leaf growth~stages}\\
\endthead
GLV &amp; (\textit{E})-hex-3-en-1-ol &amp; \09.05 &amp; \0858 &amp; \0800 &amp; Adams &amp; 0 &amp; 0.03 ${\pm}$ 0.02\\
MNT &amp; ${\upalpha}$-Pinene &amp; 11.73 &amp; \0938 &amp; \0932 &amp; Adams &amp; 0.12 ${\pm}$ 0.04 &amp; 0.02 ${\pm}$ 0.01\\
Phenolic &amp; Benzaldehyde &amp; 12.1\0 &amp; \0939 &amp; \0935 &amp; NIST &amp; 48.3 ${\pm}$ 34.5 &amp; 2.76 ${\pm}$ 2.48\\
GLV &amp; (\textit{E})-hex-3-en-yl Acetate &amp; 13.92 &amp; 1003 &amp; 1004 &amp; Adams &amp; 0 &amp; \00.2 ${\pm}$ 0.11\\
Phenolic &amp; \textit{p}-methyl Anisole &amp; 14.03 &amp; 1007 &amp; 1015 &amp; Adams &amp; 0 &amp; 0.03 ${\pm}$ 0.02\\
Unknown &amp; Unknown compound &amp; 15.31 &amp; 1038 &amp;&amp;&amp; 0.08 ${\pm}$ 0.06 &amp; 0\\
MNT &amp; (\textit{E})-${\upbeta}$-Ocimene &amp; 15.35 &amp; 1048 &amp; 1044 &amp; Adams &amp; 0.38 ${\pm}$ 0.12 &amp; 2.28 ${\pm}$ 0.64\\
MNT &amp; Unknown MNT &amp; 16.01 &amp; 1060 &amp;&amp;&amp; 0.009 ${\pm}$ 0.008 &amp; 0\\
MNT &amp; Linalool &amp; 16.93 &amp; 1097 &amp; 1095 &amp; Adams &amp; 0 &amp; 0.03 ${\pm}$ 0.01\\
Aldehyde &amp; Nonanal &amp; 17.06 &amp; 1096 &amp; 1100 &amp; Adams &amp; \01.2 ${\pm}$ 0.45 &amp; 2.91 ${\pm}$ 0.96\\
HT &amp; DMNT$^{\mathrm{a}}$ &amp; 17.69 &amp; 1114 &amp; 1115 &amp; PACE &amp; 0.95 ${\pm}$ 0.28 &amp; 2.26 ${\pm}$ 0.49\\
Unknown &amp; Unknown compound 1 &amp; 18.08 &amp; 1127 &amp;&amp;&amp; 0 &amp; 0.005 ${\pm}$ 0.005\\
Phenolic &amp; 1,4-dimethoxyBenzene &amp; 19.1\0 &amp; 1161 &amp; 1161 &amp; Adams &amp; 67.4 ${\pm}$ 50.6 &amp; 0\\
Phenolic &amp; Methyl Salicylate &amp; 20.09 &amp; 1194 &amp; 1190 &amp; Adams &amp; 0 &amp; 0.06 ${\pm}$ 0.03\\
GLV &amp; (\textit{Z})-hex-3-en-yl Valerate &amp; 22.13 &amp; 1228 &amp; 1243 &amp; NIST &amp; 0 &amp; 0.003 ${\pm}$ 0.003\\
SQT &amp; Unknown SQT 1 &amp; 24.18 &amp; 1345 &amp;&amp;&amp; 0.13 ${\pm}$ 0.07 &amp; 0.04 ${\pm}$ 0.01\\
SQT &amp; ${\upalpha}$-Cubebene &amp; 24.58 &amp; 1356 &amp; 1345 &amp; Adams &amp; \00.1 ${\pm}$ 0.04 &amp; 0.04 ${\pm}$ 0.02\\
SQT &amp; Unknown SQT 2 &amp; 24.71 &amp; 1361 &amp;&amp;&amp; 0.05 ${\pm}$ 0.02 &amp; 0.04 ${\pm}$ 0.02\\
SQT &amp; ${\upalpha}$-Copaene &amp; 25.32 &amp; 1383 &amp; 1374 &amp; Adams &amp; \00.5 ${\pm}$ 0.21 &amp; 0.67 ${\pm}$ 0.21\\
SQT &amp; ${\upbeta}$-Bourbonene &amp; 25.58 &amp; 1393 &amp; 1387 &amp; Adams &amp; 1.29 ${\pm}$ 0.6\0 &amp; 4.42 ${\pm}$ 1.42\\
SQT &amp; ${\upalpha}$-Bourbonene &amp; 25.64 &amp; 1351 &amp; 1384 &amp; NIST &amp; 0 &amp; 0.35 ${\pm}$ 0.30\\
SQT &amp; ${\upalpha}$-Gurjunene &amp; 26.24 &amp; 1419 &amp; 1409 &amp; Adams &amp; 0.09 ${\pm}$ 0.04 &amp; 0.04 ${\pm}$ 0.02\\
SQT &amp; (\textit{E})-${\upbeta}$-Caryophyllene &amp; 26.5\0 &amp; 1429 &amp; 1417 &amp; Adams &amp; 1.68 ${\pm}$ 0.53 &amp; \01.4 ${\pm}$ 0.33\\
SQT &amp; ${\upbeta}$-Copaene &amp; 26.73 &amp; 1438 &amp; 1430 &amp; Adams &amp; 0.63 ${\pm}$ 0.29 &amp; 0.74 ${\pm}$ 0.23\\
SQT &amp; Unknown SQT 3 &amp; 26.84 &amp; 1446 &amp;&amp;&amp; 0.32 ${\pm}$ 0.14 &amp; 0.17 ${\pm}$ 0.09\\
MNT &amp; (\textit{E})-geranyl Acetone &amp; 27.18 &amp; 1456 &amp; 1453 &amp; Adams &amp; 0.65 ${\pm}$ 0.5\0 &amp; 0.63 ${\pm}$ 0.4\0\\
SQT &amp; (\textit{Z})-Muurola-3,5-diene &amp; 27.2\0 &amp; 1393 &amp; 1448 &amp; Adams &amp; 0 &amp; 0.04 ${\pm}$ 0.01\\
SQT &amp; (\textit{E})-${\upalpha}$-Bergamotene$^{\mathrm{b}}$ &amp; 27.23 &amp; 1458 &amp; 1430 &amp; Adams &amp; 0.71 ${\pm}$ 0.35 &amp; 0.04 ${\pm}$ 0.04\\
SQT &amp; ${\upalpha}$-Humulene &amp; 27.37 &amp; 1464 &amp; 1452 &amp; Adams &amp; 1.18 ${\pm}$ 0.36 &amp; 0.84 ${\pm}$ 0.23\\
SQT &amp; Allo-Aromadendrene &amp; 27.57 &amp; 1472 &amp; 1458 &amp; Adams &amp; 0.98 ${\pm}$ 0.38 &amp; 0.6 ${\pm}$ 0.2\\
SQT &amp; Unknown SQT 4 &amp; 27.64 &amp; 1475 &amp;&amp;&amp; 0.007 ${\pm}$ 0.006 &amp; 0.04 ${\pm}$ 0.02\\
SQT &amp; (\textit{E})-muurola-4(14),5-diene$^{\mathrm{b}}$ &amp; 27.71 &amp; 1476 &amp; 1465 &amp; Adams &amp; 0.09 ${\pm}$ 0.04 &amp; 0.03 ${\pm}$ 0.02\\
SQT &amp; Unknown SQT 4B &amp; 27.84 &amp; 1483 &amp;&amp;&amp; \00.1 ${\pm}$ 0.09 &amp; 0.09 ${\pm}$ 0.05\\
SQT &amp; Germacrene D &amp; 28.04 &amp; 1490 &amp; 1484 &amp; Adams &amp; 36.2 ${\pm}$ 12.6 &amp; 12.54 ${\pm}$ 5.41\0\\
SQT &amp; \parbox[t]{2.5cm}{\centering BicycloGermacrene/{\ubreak}(\textit{E},\textit{E})-${\upalpha}$-Farnesene$^{\mathrm{c}}$} &amp; 28.45 &amp; 1507 &amp; 1500 &amp; Adams &amp; 2.89 ${\pm}$ 0.98 &amp; 2.43 ${\pm}$ 0.76\\
SQT &amp; ${\delta}$-Cadinene &amp; 28.79 &amp; 1526 &amp; 1513 &amp; Adams &amp; 0.36 ${\pm}$ 0.17 &amp; 0.13 ${\pm}$ 0.05\\
SQT &amp; ${\gamma}$-Cadinene &amp; 29 &amp; 1536 &amp; 1522 &amp; Adams &amp; 0.53 ${\pm}$ 0.21 &amp; 0.23 ${\pm}$ 0.08\\
SQT &amp; ${\upalpha}$-Cadinene &amp; 29.38 &amp; 1553 &amp; 1537 &amp; Adams &amp; 0.19 ${\pm}$ 0.1\0 &amp; 0.06 ${\pm}$ 0.03\\
Phenolic &amp; (\textit{E})-hex-3-en-yl Benzoate &amp; 29.95 &amp; 1578 &amp; 1565 &amp; Adams &amp; 0 &amp; 0.08 ${\pm}$ 0.03\\
Unknown &amp; Unknown compound 2 &amp; 30.15 &amp; 1586 &amp;&amp;&amp; 0 &amp; 0.009 ${\pm}$ 0.007
\botline
\end{tabular}
\xxtabnote{$^{\mathrm{a}}$~(\textit{E})-4,8-dimethyl-1,3,7-nonatriene.
$^{\mathrm{b}}$~Tentative identification of \textit{Z} or \textit{E}
isomers. $^{\mathrm{c}}$~Tentative identification. $^{\mathrm{d}}$~Mean
${\pm}$ SE. $^{\mathrm{e}}$~MNT: monoterpene, SQT: sesquiterpene, HT:
homoterpene, Phenolic: phenolic compounds, GLV: green leaf volatile.}
\end{table*}

These results suggest that the emissions of different classes of VOCs
vary depending on the phenological stage of almond trees. Phenolic
compounds, such as benzaldehyde and 1,4-dimethoxybenzene, two major
compounds, were found to be more emitted during the flowering stage
($P=0.006$, Table~\ref{tabI}). For mean comparisons, DMNT and nonanal
were grouped by their biosynthetic pathway (fatty acid), into a class
called other VOC (OVOC). On the other hand, OVOC, MNTs, and green leaf
volatiles (GLVs) were more emitted during the development of fruits and
leaves (Figure~\ref{fig2}). However, the difference between these
classes was only significant for OVOC ($P=0.03$). There was no
significant difference in emissions between flowering and growth stages
for SQTs and unknown compounds\break ($P&gt;0.1$).

\begin{figure*}
\includegraphics{fig02}
\caption{\label{fig2}VOC class comparisons between flowering ($N=11$)
and fruit and leaf development ($N=31$). Emissions (in
$\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$) were measured in
standard controlled conditions at 30~{\textdegree}C and
$750~\upmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ PPFD.
Fruit and leaf development was grouped without differences in stages.
Data are presented as mean $\pm$ standard errors. SQT: sesquiterpenes,
OVOC: other VOC, MNT: monoterpenes and GLV: green leaf volatilales.}
\end{figure*}

SQTs were identified as the second most prevalent class of compounds
emitted during flowering, comprising $53.4 \pm 17.2~\text{ng}{\cdot}
\text{m}^{-2}{\cdot}\text{s}^{-1}$ out of total emission of $180.8 \pm
63.1~\text{ng}{\cdot}\text{m}^{-2} {\cdot}\text{s}^{-1}$, or
approximately 30\% of total emissions (Figure~\ref{fig2}). During fruit
and leaf growth, SQTs were the primary class of emitted VOCs,
accounting for $25 \pm
8.6~\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ out of total
emission of $36.3 \pm
9~\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$, or approximately
70\%. Germacrene~D was found to be the most abundant SQT emitted by
almond trees across all phenological stages. The
bicycloGermacrene/(\textit{E},\textit{E})-${\upalpha}$-farnesene was
another major compound found at both flowering and growth stages.
Additionally, (\textit{E})-${\upbeta}$-caryophyllene was identified as
the second most abundant compound during fruit and leaf growth
(Figure~\ref{fig3}).
During the flowering stage, two unidentified compounds along with
${\upalpha}$-gurjunene were detected as minor SQT. As for the
developmental stages, (\textit{E})-muurola-4(14),5-diene,
${\upalpha}$-gurjunene, and one unidentified compound were identified as
minor VOCs.

\begin{figure*}
\includegraphics{fig03}
\caption{\label{fig3}Average SQT emissions from almond twigs at the
flowering stage ($N=11$) and the fruit and leaf growth stage ($N=31$)
under standard controlled conditions. (A)~Major SQT and (B)~minor SQT.
Data are presented as mean $+$ standard errors. The compounds are
ranked in ascending order of retention time. Bicyclogermacrene (TI) is
bicyclogermacrene or (\textit{E},\textit{E})-${\upalpha}$-farnesene.}
{\vspace*{.6pc}}
\end{figure*}

\subsection{Impact of temperature and light on VOC emissions}
\label{sec3.2}

We analyzed the effects of light and temperature on VOC emissions. In
the course of this experiment, we identified four new compounds in
addition to the 40 compounds identified under standard conditions
(Table~\ref{tabII}). Three compounds were identified during the first
sampling of temperature or light gradients in mature fruit and leaf.
Among these three VOCs,{\break} (\textit{Z})-hex-3-en-1-ol was the most
abundant VOC emitted, with an average of $3.54 \pm
1.34~\text{ng}{\cdot} \text{m}^{-2}{\cdot}\text{s}^{-1}$.
(\textit{Z})-hex-3-en-yl butanoate and
(\textit{Z})-hex-3-en-yl-2-methyl-butanoate were also detected, with
emissions of $0.71 \pm 0.19$ and $0.29 \pm 0.12~\text{ng}{\cdot}
\text{m}^{-2}{\cdot}\text{s}^{-1}$, respectively. Additionally, an
unidentified SQT was emitted in trace amounts by R1568 flowers between
15 and 30~{\textdegree}C, and at 0.14 and
$0.2~\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ at 35 and
40~{\textdegree}C (Supplementary Figure~S2).

\subsubsection{RDA analysis for the temperature model}\label{sec3.2.1}

The RDA model accounted for 57\% of the constrained variance, with the
first two components of the RDA analysis explaining 81\% of this
variance. As a result, these two components were deemed sufficient to
explain the model. Figure~\ref{fig4} displays a score plot of RDA
analysis for differentiating between temperatures. The results of the
effect of vegetative and reproductive parts of the twig and the variety
are shown in Supplementary Figure~S3.

\begin{figure*}
\includegraphics{fig04}
\caption{\label{fig4}Score plot and correlation plot of the first two
constrained components from RDA analysis for the temperature effect on
VOC emissions. The factor shown is the temperature in correlation with
VOCs. The constrained variance was explained at 62\% by component~1 and
at 19\% by component~2. The composition of the measured twigs, the
varieties and the phenological stages are in Supplemental~S3.}
\end{figure*}

The first RDA analysis demonstrated that temperature had a significant
effect on VOC emissions, with a cluster at 15~{\textdegree}C;
20--25~{\textdegree}C; 30--35~{\textdegree}C and 40~{\textdegree}C. The
analysis revealed a significant effect of temperature on the quantities
of VOC emitted, but this effect was observed only when there was a
temperature difference of 10~{\textdegree}C or more between samples
(Table~\ref{tabIII}, Supplementary Table~S3). The analysis also
identified two significant interactions, including a Temperature
$\times$ Phenology interaction indicating that the temperature effect
depended on the phenological stage (\mbox{Table}~\ref{tabIII},
Figure~\ref{fig5}).
VOC emissions increased with temperature for all classes during fruit
and leaf development, except for phenolic compounds, which remained
stable and were emitted only in low quantity between 0.13 ${\pm}$ 0.08
and $1.88 \pm 1.42~\text{ng}{\cdot} \text{m}^{-2}{\cdot}\text{s}^{-1}$
(\mbox{Figure}~\ref{fig4}, Supplementary Figure~S4). Notably, GLVs were
absent during the flowering stage and were present in greater quantity
at 15~{\textdegree}C or 40~{\textdegree}C during the first sampling.
Moreover, with the exception of GLVs, emissions were very low at
15~{\textdegree}C.

\begin{figure*}
{\vspace*{3pt}}
\includegraphics{fig05}
{\vspace*{4pt}}
\caption{\label{fig5}Effect of temperature on VOC emissions according
to the phenological stage (triangle: flowering, circle: fruit and leaf
growth). Emissions were summed by class for each sample and averaged by
temperature level. Error bars are standard errors.}
{\vspace*{4pt}}
\end{figure*}

\begin{table*} %tab3
\caption{\label{tabIII}Summary of the significant permutation $F$-test
results for the two RDA models on VOC emissions and interactions
between factors}
\tabcolsep8.5pt
\begin{tabular}{cccc}
\thead
Model &amp; Variable$^{\mathrm{a}}$ &amp; $p$-value &amp; Significance\\
\endthead
\morerows{5}{Temperature effect} &amp; Temperature &amp; 0.001 &amp; $***$\\
&amp; Phenology &amp; 0.001 &amp; $***$ \\
&amp; Branch composition &amp; 0.018 &amp; $*$\\
&amp; Variety &amp; 0.001 &amp; $***$\\
&amp; Temperature: phenology &amp; 0.011 &amp; $*$\\
&amp; Phenology: variety &amp; 0.007 &amp; $**${\vspace*{8pt}}\\
\morerows{4}{Light effect} &amp; Light &amp; 0.095 &amp; $\cdot$\\
&amp; Phenology &amp; 0.001 &amp; $***$\\
&amp; Branch composition &amp; 0.001 &amp; $***$\\
&amp; Variety &amp; 0.001 &amp; $***$\\
&amp; Phenology: variety &amp; 0.001 &amp; $***$
\botline
\end{tabular}
\xxxtabnote{$^{\mathrm{a}}$~Phenology was categorized into
``flowering'', ``young'' or ``mature'' fruits and leaves for the
temperature model, and into stage~4 and stage~5 for the fruit and leaf
growth in the light effect model. The composition of branches was
``flowers'', ``flowers $+$ leaves'', ``fruits'', ``leaves'' or ``fruits
$+$ leaves''.}
\end{table*}

Regarding other factors, there was no difference in volatile emissions
between twigs bearing only flowers and those with both flowers and
young leaves. However, there was a significant difference in emissions
between twigs with flowers and those with fruits and leaves ($P=0.01$),
regardless of the development stage. Furthermore, pairwise tests
highlighted a difference between young and mature fruits and leaves
($P=0.01$).

The model was conducted on three varieties with different precocity,
with two trees per variety: R1568 ($N=12$), R1569 ($N=7$), and R1004
($N=8$). We found a significant difference between R1004 and R1568
($P=0.003$, Supplementary Figure~S3 and Table~S2). The analysis of
individual VOC compounds did not reveal any significant differences
among the varieties. However, when considering the overall emissions
per class, variations were observed (Figure~\ref{fig6}). The R1004
variety exhibited higher emissions of phenolics compounds and MNTs
compared to R1568 and R1569, and similar emissions of SQTs compared to
R1568. Both R1004 and R1568 varieties had significantly higher
emissions compared to R1569.{\break} No~significant differences were
observed for OCOVs. The R1569 variety displayed the highest emissions
GLVs among the three varieties. Additionally, the second significant
interaction Phenology $\times$ \mbox{Variety}, indicated that the
chemical profile was variable \mbox{according} to the varieties between
flowering and the fruit and leaf growth.

\begin{figure*}
\includegraphics{fig06}
\caption{\label{fig6}VOC emissions summed by class and averaged by
variety ($\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$). Three
varieties were measured in the temperature experiments: R1004, R1568 and
R1569. The emissions presented here are those at 30~{\textdegree}C but
the pattern is similar for each temperature level. VOC emissions were
averaged for fruit and leaf growth stages. SQT: sesquiterpenes, OVOC:
other VOC, MNT: monoterpenes and GLV: green leaf volatilales.}
{\vspace*{-.4pc}}
\end{figure*}

By examining both the score plots and the correlation plot, we found
that floral emissions were \mbox{positively} correlated with SQTs,
(\textit{E})-${\upalpha}$-bergamotene, and 1,4-dimethoxyBenzene, while no
other cluster was observed. On the other hand, fruit and leaves were
positively correlated with SQTs, which were the variables that best
explained the model. Regarding varieties, the odor profile of R1568 was
primarily composed of SQTs and 1,4-dimethoxyBenzene, while no clear
grouping was observed for R1004. GLVs were common to all three
varieties. Overall, SQTs and 1,4-dimethoxyBenzene were the VOCs that
best explained the model (Figure~\ref{fig4}).

\subsubsection{RDA analysis for the light model} \label{sec3.2.2}

The effect of light was evaluated in stage~4 \mbox{($N=8$)} and stage~5
($N=5$), where fruits with leaves or fruits and leaves separated were
used. For the branch with only fruits, the leaves were not removed
beforehand. The varieties tested were Lauranne ($N=2$), R1568 ($N=2$),
R1569 ($N=4$), and R1004 ($N=4$). The proportion of constrained
variance explained by the model was 62\% with 78\% of this variance
explained by the first four dimensions. The light had no significant
effect on VOC emissions, but there were significant effects of
phenology, variety, and branch composition, as well as a significant
interaction between phenology and variety (Table~\ref{tabIII}). This
interaction indicated that the chemical profile varied among varieties
at different stages of fruit and leaf growth. Each variety had a
distinct odor profile, and emissions from fruits with leaves differed
from those from separated fruits or leaves (Figure~\ref{fig7},
Supplementary \mbox{Table}~S3). While emissions varied with the
phenological stage, there was no difference in profile odor between
stages~4 and~5 ($P=0.14$). SQTs and GLVs played a role in explaining
the emission pattern and were correlated with emissions from R1569 and
R1004 (Figures~\ref{fig7}B and~\ref{fig8}).

\begin{figure*}
\includegraphics{fig07}
\caption{\label{fig7}Score plots of the first two constrained
components from RDA analysis for the light effect. The factors were the
plant parts~(A), and the varieties~(B). The constrained variance was
explained at 51\% by component~1 and at 10\% by component~2. The
correlation plot of these components, and components~3 and~4 are in
Supplementary Figure~S5.}
\end{figure*}

\begin{figure*}
\includegraphics{fig08}
\caption{\label{fig8}VOC emissions summed by class and averaged by
variety ($\text{ng}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$). Four
varieties were measured in the temperature experiments: Lauranne,
R1004, R1568 and R1569. The emissions presented here are those at 
$1000~\upmu\text{mol}{\cdot}\text{m}^{-2}{\cdot}\text{s}^{-1}$ PPFD but
the pattern is similar for each light level. VOC emissions were
averaged for fruit and leaf growth stages. SQT: sesquiterpenes, OVOC:
other VOC, MNT: monoterpenes and GLV: green leaf volatilales.}
{\vspace*{-.15pc}}
\end{figure*}

No significant effect of light was observed on VOC emissions. However,
there was a significant effect of variety on these emissions
(Figure~\ref{fig8}). In the light experiments, R1004 and R1568
varieties were found to be the main emitters of MNTs, OVOCs and SQTs.
On the other hand, the Lauranne variety exhibited lower emissions of
VOCs overall, except for phenolic compounds.

\section{Discussion} \label{sec4}
\subsection{Overview of emissions from almond trees} \label{sec4.1}

To our knowledge, only a few studies have investigated VOC emissions
from almond trees (Table~\ref{tabIV}). Because different analysis and
headspace techniques, units, and almond varieties were used in each
study, a direct comparison of the numerical values is not possible.
Nevertheless, our and previous studies consistently show that
\textit{P.~dulcis} is rather a significant emitter of SQTs than an
emitter of MNTs~\cite{17,18,19}.

\begin{table*} %tab4
\caption{\label{tabIV}Overview of major VOCs of \textit{Prunus dulcis}
reported in the previous and present study}
\fontsize{8.8}{10.3}\selectfont
\tabcolsep3pt
\begin{tabular}{cccccc}
\thead
Compound &amp; Value &amp; Unit$^{\mathrm{b}}$ &amp; Objective &amp; Experimental &amp;
Reference\\
\endthead
Benzaldehyde &amp; 2.8--48.3 &amp; \morerows{3}{$\text{ng}{\cdot}\text{m}^{-2}
{\cdot}\text{s}^{-1}$} &amp; \morerows{3}{\parbox[t]{2.5cm}{\centering
Spring emissions under environmentally controlled conditions}} &amp;
\morerows{3}{\parbox[t]{2.5cm}{\centering Dynamic headspace in a glass
chamber\Lbreak Carbotrap/Tenax cartridges}} &amp; \morerows{3}{Our study}\\
1,4-dimethoxyBenzene &amp; 0--67.4 &amp;&amp;&amp;&amp;\\
Germacrene D &amp; 12.5--36.2 &amp;&amp;&amp;&amp;\\
\parbox[t]{3cm}{\centering
bicycloGermacrene/{\ubreak}(\textit{E},\textit{E})-${\upalpha}$-Farnesene}
&amp; 2.4--2.9 &amp;&amp;&amp;&amp;{\vspace*{8pt}}\\

(\textit{Z})-hex-3-en-yl Acetate$^{\mathrm{a}}$ &amp; {\textless}0.5--400 &amp;
\morerows{3}{\parbox[t]{2cm}{\centering Relative peak area
(${\times}10^6$)}} &amp; \morerows{3}{\parbox[t]{2.5cm}{\centering Seasonal
variation of emissions between March and August}} &amp;
\morerows{3}{\parbox[t]{2.5cm}{\centering Static headspace in Teflon
bag\Lbreak SPME--GC-MS}} &amp; \morerows{3}{\parbox[t]{2cm}{\centering
Beck {\etal}~\cite{18}}}\\
Caryophyllene$^{\mathrm{a}}$ &amp; {\textless}0.5--150 &amp;&amp;&amp;&amp;\\
${\upalpha}$-Humulene$^{\mathrm{a}}$ &amp; 0.5--50 &amp;&amp;&amp;&amp;\\
Butyrolactone &amp; 0.5--50 &amp;&amp;&amp;&amp;{\vspace*{8pt}}\\

MNTs &amp; {\textless}0.05 &amp; \morerows{2}{$\upmu\text{g}{\cdot}
\text{h}^{-1}{\cdot}\text{g}^{-1}$ DW} &amp;
\morerows{2}{\parbox[t]{2.5cm}{\centering Emissions from agricultural
crops}} &amp; \morerows{2}{\parbox[t]{3cm}{\centering Dynamic headspace
in Teflon bag\Lbreak Adsorbent cartridges\Lbreak GC-MS/GC-FID}} &amp;
\morerows{2}{\parbox[t]{1.8cm}{\centering Winer~\mbox{\etal} \cite{17}}}\\
SQTs &amp; No data &amp;&amp;&amp;&amp;\\
\parbox[t]{3cm}{\centering Hex-3-en-1-ol $+$ hex-3-en-yl Acetate} &amp; 2.1
&amp;&amp;&amp;&amp;{\vspace*{8pt}}\\

MNTs &amp; 68 ${\pm}$ 51 &amp; \morerows{2}{$\text{ngC}{\cdot}\text{gDM}^{-1}
{\cdot}\text{h}^{-1}$} &amp; \morerows{2}{\parbox[t]{2.7cm}{\centering
Emissions from plant enclosures in a greenhouse in standard
conditions}} &amp; \morerows{2}{\parbox[t]{3cm}{\centering
Dynamic headspace\Lbreak Tenax/Carbopack cartridges\Lbreak GC/MS-FID}}
&amp; \morerows{2}{\parbox[t]{2cm}{\centering Gentner~\mbox{\etal} \cite{19}}}\\
Oxygenated MNTs &amp; 150 ${\pm}$ 28 &amp;&amp;&amp;&amp;\\
SQTs &amp; 10,000 ${\pm}$ 3300 &amp;&amp;&amp;&amp;{\vspace*{8pt}}\\

Benzaldehyde$^{\mathrm{a}}$ &amp; 573.8--1145.5 &amp; \morerows{3}{$\text{ng}
{\cdot}\text{g}^{-1}$ FW} &amp; \morerows{3}{\parbox[t]{2.5cm}{\centering
Concentration in crushed flowers}} &amp;
\morerows{3}{\parbox[t]{3cm}{\centering Fresh tissues crushed in
liquid nitrogen\Lbreak SPME\Lbreak GC-MS}} &amp;
\morerows{3}{\parbox[t]{2cm}{\centering Nawade~\mbox{\etal} \cite{28}}}\\
(\textit{E})-2-Hexenal &amp; 62.06--414.14 &amp;&amp;&amp;&amp;\\
Hexanal &amp; 15.42--48.82 &amp;&amp;&amp;&amp;\\
1,4-dimethoxyBenzene$^{\mathrm{a}}$ &amp; 2.02--205.9 &amp;&amp;&amp;&amp;{\vspace*{8pt}}\\

\textit{n}-Nonanal$^{\mathrm{a}}$ &amp; 2.42--61.41 &amp;
\morerows{3}{$\text{ng}{\cdot}\text{g}^{-1}$ FW} &amp;
\morerows{3}{\parbox[t]{2.5cm}{\centering Concentration in crushed
young fruits}} &amp;&amp;\\
\textit{n}-Decanal &amp; 2.65--14.59 &amp;&amp;&amp;&amp;\\
\textit{n}-Octanol &amp; 1--9.18 &amp;&amp;&amp;&amp;\\
Undecanal &amp; 0.75--7.52 &amp;&amp;&amp;&amp;{\vspace*{8pt}}\\
Eugenol &amp; 1.58--674.48 &amp; \morerows{3}{$\text{ng}{\cdot}\text{g}^{-1}$
FW} &amp; \morerows{3}{\parbox[t]{2.5cm}{\centering Concentration in
crushed leaves}} &amp;&amp;\\
Phenyl ethyl Alcohol &amp; 9.42--92.81 &amp;&amp;&amp;&amp;\\
Methyl Salicylate$^{\mathrm{a}}$ &amp; 1.97--51.62 &amp;&amp;&amp;&amp;\\
Germacrene D$^{\mathrm{a}}$ &amp; 1.55--37.26 &amp;&amp;&amp;&amp;
\botline
\end{tabular}
\xytabnote{The table lists the ranges or mean values ${\pm}$ standard
deviation of the major VOCs detected in each study. Note that the first
four studies use different units for emission rates and that the last
study~\cite{28} reports tissue concentrations instead of emissions.}
\xytabnote{$^{\mathrm{a}}$~Compounds found in our analyses. See
Table~\ref{tabII}. $^{\mathrm{b}}$~FW: fresh weight, DW: dry weight,
DM: dry matter, C: carbon.}
\end{table*}

Qualitatively, we find several common compounds between the studies,
such as ${\upbeta}$-caryophyllene, ${\upalpha}$-humulene,
${\upbeta}$-(\textit{E})-ocimene, linalool and (\textit{E})-hex-3-enyl
acetate. These VOCs were also detected in our study but not in major
amounts. Some VOCs were not detected in our study as
perillene~\cite{19} or butyrolactone~\cite{18}. The study conducted by
Nawade {\etal}~\cite{28} did not report on VOC emissions but examined
the concentrations of VOCs found in crushed almond tree tissue. While
it is not possible to directly compare this study to ours, we found
that some of the VOCs we detected were similar to the composition of
ground tissues (Table~\ref{tabIV}).

Minor VOCs may be as important as major VOCs in plant--insect
interactions~\cite{29}. Our study allowed us to identify many VOCs
emitted in small quantities, though not all. Moreover, low molecular
weight VOCs may not have been retained by our adsorbent cartridges.
{\vspace*{-.4pc}}

\subsection{Seasonal variation of VOCs} \label{sec4.2}
{\vspace*{-.1pc}}

The results highlight the variability in the composition and quantities
of VOCs emitted during flowering and the growth of fruits and leaves,
with stronger emissions from the flowers. However, there was no
significant difference in VOC emissions between flowering shoots with
or without leaves, which could be attributed to the relatively small
size of leaves appearing towards the end of flowering. The floral
profile of \textit{P.~dulcis} showed similarities with other
\textit{Prunus} species~\cite{30}, particularly the presence of
benzaldehyde, a floral compound commonly found in plants~\cite{31,32}.
The other major VOCs of \textit{P.~dulcis}, 1,4-dimethoxybenzene and
germacrene~D, differed from other \textit{Prunus} species. Germacrene~D
was found in the Rosaceae family such as rose scents~\cite{33}, and
1,4-dimethoxyBenzene was not found in \textit{Prunus} species but
similar phenolic compounds existed in \textit{P.~persica} and
\textit{P.~avium}~\cite{32}. However, 1-4-dimethoxyBenzene is commonly
found among major compounds emitted by the flowers of various
\textit{Salix} species and elicits a behavioral response in
bees~\cite{34,35}. Consequently, the high 1-4-dimethoxyBenzene emission
in flowers of almond trees is probably related to pollinator
attraction.

Due to the small number of measurements on the fruits and leaves at
stages~1 and~2, we grouped these two stages into a ``young'' stage, and
stages~3 to~5 were grouped in a ``mature'' stage. Our analysis of
seasonal emission variations during fruit and leaf growth revealed a
diverse range of VOCs, but the majority were SQTs in young and mature
fruits and leaves. Our findings are consistent with those of Beck
{\etal}~\cite{18},{\break} who investigated seasonal variations in VOC
emissions from March to August. They found that GLVs, particularly
(\textit{Z})-hex-3-enyl acetate, were present from mid-May to mid-July,
which corresponds to the period when the fruit is mature. However, we
identified germacrene D as a major \mbox{compound}, although it was
present in only small quantities in Beck {\etal}'s study.

There seem to be differences between leaf and fruit emissions, but more
replicates are needed to identify these VOCs. Since it is difficult to
have branches with only fruits, leaves could be removed several days
before to avoid a burst of GLVs, but this might create a bias. By
adapting the device to the size of an almond, it would enable targeted
sampling of specific VOCs emitted by almonds. The longer
\mbox{accumulation} time provided by the device would allow for a
greater collection of low-level VOC emissions, facilitating more
accurate identification of the specific compounds present in almonds.

\subsection{Influence of temperature and light on{\hfill\break}
emissions} \label{sec4.3}

As reported in numerous studies, plant VOC emissions are influenced by
temperature~\cite{31,36}. To our knowledge, no study had shown the
impact of temperature on VOC emissions from \textit{P.~dulcis}. Since
wasps have a peak in activity in the middle of the day, VOCs emitted at
higher temperatures should be more likely to attract wasps. Our study
shows a positive correlation between emissions and temperature, with a
continuous increase in emissions up{\break} to 40~{\textdegree}C. GLVs,
such as (\textit{Z})-hex-3-en-1-ol, (\textit{Z})-hex-3-en-yl butanoate,
and (\textit{Z})-hex-3-en-yl-2-methyl-butanoate were detected during
the most advanced stages of fruit and leaf growth. However, these VOCs
were only present during the initial measurement and disappeared
afterwards regardless of variations in temperature or light conditions.
It is likely that these VOCs have been induced by mechanical stress
during the branch positioning in the device and were not
\mbox{influenced} by temperature, as discussed by Winer
\mbox{\etal}~\cite{17}.

Furthermore, a significant temperature effect was observed when the
difference between the two samples was at least 10~{\textdegree}C. All
the VOCs we detected were present within a temperature range of{\break}
15 to 40~{\textdegree}C, which made it impossible to select specific
VOCs.

No study has investigated the impact of light on VOC emissions from
\textit{Prunus} species. Since wasps are active during the day,
discriminating the VOCs \mbox{emitted} according to the time of day would have
allowed better targeting of the compounds to be tested. Yet, the
apparent absence of a light effect did not allow us to exclude any VOCs
on this criterion. Our study was however based on the short-term effect
of light. The continuous monitoring of VOC emissions during the natural
day/night courses could provide further insights into the environmental
factors determining the dial emission rhythm.

The effect of light on emissions could not be assessed during the
flowering stage due to the very short flowering time. Thus, four
compounds were not found: an unknown compound, 1,4-dimethoxyBenzene,
unknown SQT~6, and unknown MNT. (\textit{E})-${\upalpha}$-bergamotene and
linalool were only found in the temperature experiments during the
flowering and fruit/leaf growth stages, but not in the light study. It
is possible that these compounds were not emitted during the mature
stage or were present in such small amounts to be detected. The same
hypothesis applies to unknown compound~1, which was not detected in the
temperature experiments.

\subsection{Impact of variety on VOC emissions} \label{sec4.4}

Temperature and light effect models aimed to select VOCs to identify
the simplest and most attractive volatile mixture of
\textit{E.~amygdali}. Both models demonstrated a significant difference
in odor profiles between varieties. In the light experiments, the
Lauranne variety exhibited the lowest emissions of VOCs, except for
phenolic compounds. This finding can be partially attributed to the
fact that the Lauranne variety produced fewer leaves and fruits
compared to the other varieties. In contrast, R1004 and R1568 were
found to be the two varieties with the highest VOC emissions. To reduce
the variability in emissions, conducting additional replicates per
variety is recommended. Furthermore, in order to compare the different
varieties, we initially hypothesized that the variations observed
between the varieties would follow a similar pattern during plant
growth. However, to further strengthen our findings and account for
interactions between the variety and growth stage factors, it would be
beneficial to increase the number of replicates for each stage and
variety. However, the impact of variety on the chemical profile of
almond trees did not allow the exclusion of VOCs since all varieties
were parasitized by \textit{E.~amygdali} during the spring, indicating
that their behavior was not affected by varietal emission differences.
However, given the abundance of almond varieties, further exploration
of differences in VOC emission profiles may prove interesting. Although
our experiences are not comparable, Nawade {\etal}~\cite{28}
studied the effect of the variety on the compounds present in the
tissues and the expression of the TPS genes responsible for the
synthesis of terpenes. The tissue-derived compounds partially supported
our identification of the effect of variety and phenology on VOC
composition. The expression of TPS genes was found in each tissue and
variety, but in varying quantities, which could explain the variations
in chemical profiles. This variability could potentially impact the
interactions between \textit{P.~dulcis} and \textit{E.~amygdali},
although no study has focused on the almond wasp\break to date.

\section{Conclusion} \label{sec5}

In this study, 44 VOCs emitted by four varieties of almond trees were
identified and quantified. VOC emissions were positively correlated
with \mbox{temperature}, except for GLVs which are emitted under
stressful conditions such as high temperature or rough
handling~\cite{37}. Light conditions hand no significant effect on
emissions. We observed an effect of phenology on the VOC profile though
sesquiterpenes-dominated emissions throughout the spring.\looseness=-1

To our knowledge, our work is the first study that measured almond tree
emissions under environmentally controlled and physiologically normal
conditions and investigated their dependency on light and temperature.
However, several parameters can influence emissions and create
measurement uncertainties~\cite{37}. In our study, we only measured
emissions from a limited section of the branch and not from the whole
tree. Moreover, in our study emissions were measured on potted trees,
which could be different from field-grown trees. Another aspect that we
did not investigate was the effect of air and soil humidity on the
emissions. Given the current climatic context, with more frequent
drought episodes, it would be worthwhile to consider the impact of air
and soil humidity on emissions as observed on other
species~\cite{38,39,40}. Moreover, \textit{E.~amygdali} activity is
negatively correlated with humidity~\cite{12} and a context of drought
would potentially favor infestation. Therefore, identifying emissions
in this context could provide valuable insights. Although our results
did not allow us to identify VOCs that likely do not attract the almond
wasp, the chemical characterization of the 44 VOCs will enable us to
conduct electroantennography tests to evaluate their detection by
\textit{E.~amygdali}. This will help in the development of a biocontrol
method for trapping the almond wasp\break in orchards.

\section*{Conflict of interest}

The authors declare that they have no conflict of interest.

\CDRGrant[ANR]{ANR-10-LABX-04-01}

\section*{Acknowledgement} 

We wish to thank the CEFE experimental field team for their involvement
in our project.

\back{}

\section*{Supplementary data}

Supporting information for this article is available on the journal's
website under \printDOI\ or from the author.

\CDRsupplementaryTwotypes{supplementary-material}{\cdrattach{crchim-241-suppl.pdf}}

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