\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,greek,english]{babel}



\usepackage{float}








\begin{document}

\title{Effects of far-red light on the behaviour and reproduction of the
zoophytophagous predator Macrolophus pygmaeus and its interaction with a
whitefly herbivore}



\author[1]{Davy Meijer}%
\author[1]{Syb Hopkoper}%
\author[1]{Berhane Weldegergis}%
\author[2]{Wendy van ’t Westende}%
\author[1]{Joop J.A. van Loon}%
\author[1]{Marcel Dicke}%
\affil[1]{Wageningen University \& Research Laboratorium voor Entomologie}%
\affil[2]{Wageningen University \& Research Laboratorium voor Plantenveredeling}%


\vspace{-1em}



  
  \date{May 19, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Plants can detect neighbouring plants through a reduction in the ratio
between red and far-red light (R:FR). This provides a signal of
plant-plant competition and induces rapid plant growth while inhibiting
defence against biotic stress, two interlinked responses designated as
the shade avoidance syndrome (SAS). Consequently, the SAS can influence
plant-herbivore interactions that could cascade to higher trophic
levels. However, little is known on how the expression of the SAS can
influence tritrophic interactions. We investigated whether changes in
R:FR affect the emission of herbivore-induced plant volatiles (HIPVs),
and whether these changes influence the attraction of the
zoophytophagous predator \emph{Macrolophus pygmaeus}. We also studied
how the expression of the SAS and subsequent inhibition of plant
defences affects the reproduction of \emph{M. pygmaeus} in both the
presence and absence of the greenhouse whitefly ( \emph{Trialeurodes
vaporariorum}) as arthropod prey. The results show that changes in R:FR
have little effect on HIPV emissions and predator attraction. However, a
reduction in R:FR leads to increased reproduction of both the predator
and the whiteflies. We conclude that shade avoidance responses can
increase the population development of \emph{M. pygmaeus} directly by
reducing plant defences, and indirectly by supporting higher herbivore
densities.%
\end{abstract}%



\sloppy


Effects of far-red light on the behaviour and reproduction of the
zoophytophagous predator \emph{Macrolophus pygmaeus} and its interaction
with a whitefly herbivore

Authors

Davy Meijer\textsuperscript{1}, Syb Hopkoper\textsuperscript{1}, Berhane
Tekle Weldegergis\textsuperscript{1}, Wendy van 't
Westende\textsuperscript{2}, Joop Johannes Antonius van
Loon\textsuperscript{1}, Marcel Dicke\textsuperscript{1}

\textsuperscript{1}Laboratory of Entomology, Wageningen University, PO
Box 16, NL-6700, AA Wageningen, the
Netherlands;\textsuperscript{2}Laboratory of Plant Breeding, Wageningen
University, PO Box 16, NL-6700, AA Wageningen, the Netherlands

Abstract

Plants can detect neighbouring plants through a reduction in the ratio
between red and far-red light (R:FR). This provides a signal of
plant-plant competition and induces rapid plant growth while inhibiting
defence against biotic stress, two interlinked responses designated as
the shade avoidance syndrome (SAS). Consequently, the SAS can influence
plant-herbivore interactions that could cascade to higher trophic
levels. However, little is known on how the expression of the SAS can
influence tritrophic interactions. We investigated whether changes in
R:FR affect the emission of herbivore-induced plant volatiles (HIPVs),
and whether these changes influence the attraction of the
zoophytophagous predator \emph{Macrolophus pygmaeus} . We also studied
how the expression of the SAS and subsequent inhibition of plant
defences affects the reproduction of \emph{M. pygmaeus} in both the
presence and absence of the greenhouse whitefly (\emph{Trialeurodes
vaporariorum} ) as arthropod prey. The results show that changes in R:FR
have little effect on HIPV emissions and predator attraction. However, a
reduction in R:FR leads to increased reproduction of both the predator
and the whiteflies. We conclude that shade avoidance responses can
increase the population development of \emph{M. pygmaeus} directly by
reducing plant defences, and indirectly by supporting higher herbivore
densities.

Keywords: shade avoidance, volatiles, tritrophic interactions,
predator-prey interactions, arthropod performance, \emph{Trialeurodes
vaporariorum}

Introduction

Plants possess elaborate mechanisms to sense and respond to changes in
their dynamic environment. These mechanisms help plants to
differentially allocate their resources to many physiological and
developmental processes in order to adequately respond to the risks and
challenges posed by their environment. These allocation patterns often
come down to trade-offs between growth and defence (Zust and Agrawal,
2017). An example of such a trade-off is the shade avoidance syndrome
(SAS). The SAS is aimed at avoiding shade and light competition from
neighbouring plants and consists of morphological and physiological
adaptations that stimulate growth and reproduction (Casal, 2012), but it
is also associated with reduced defensive responses towards biotic
stress (Ballar\selectlanguage{ngerman}é, 2014). Over the past decades, there has been increasing
interest in understanding how plants perceive and respond to shading by
neighbouring plants (Ballaré \& Pierik, 2017; Fernández-Milmanda \&
Ballaré, 2021), and how these responses interact with responses to other
biotic and abiotic stresses (Courbier \& Pierik, 2019; Lazzarin \emph{et
al.} , 2021). These studies have resulted in valuable insights into how
plants balance responses between different stressors, and thereby
provide opportunities to improve plant resilience in agricultural
settings.

Shade avoidance depends on the detection of far-red light, which is
reflected from vegetative plant tissue and therefore provides a signal
of plant density and the degree of competition for light
(Ballaré\emph{et al.} , 1990). Reflection of far-red light lowers the
ratio between red and far-red light (R:FR), which is detected by the
phytochrome B (phyB) photoreceptor. Red light activates phyB, which is
then transported to the nucleus and inhibits SAS responses. With
increasing far-red light, the R:FR drops and phyB is inactivated,
allowing for the expression of SAS (Ballaré and Pierik, 2017). The SAS
is characterised by morphological adaptations such as stem elongation,
upward leaf movement and reduced branching, which serve to increase
light capture in crowded canopies (Casal, 2012). These adaptations are
mediated through far-red induced stimulation of the growth-promoting
phytohormones auxin and gibberellin (Ballaré and Pierik, 2017). At the
same time, inactivation of phyB inhibits the signalling of jasmonic acid
(JA) and salicylic acid (SA), the two principal phytohormones involved
in defence against biotic stress (Ballaré, 2014; Fernández-Milmanda \&
Ballaré, 2021). Consequently, plants exposed to far-red light are more
susceptible to biotic stress, leading to the increased performance of
arthropod herbivores (Izzaguirre \emph{et al.} , 2006; Moreno \emph{et
al.} , 2009; Cortés \emph{et al.} , 2016; Meijer \emph{et al.} , 2022).

Both JA and SA are known to inhibit the signalling of growth-promoting
phytohormones (Wasternack and Feussner, 2018; van Butselaar and van den
Ackerveken, 2020). Downregulation of defensive signalling is therefore
required for the full and rapid expression of SAS, and serves to
prioritize SAS responses over defensive responses when experiencing
competition for light (Ballaré and Austin, 2019). There are also
indications that plants can compensate for the reduced direct defences
against arthropod herbivores by stimulating interactions with the
natural enemies of the herbivores (Cortés \emph{et al.} , 2016). Plants
that are attacked by herbivores emit herbivore-induced plant volatiles
(HIPVs), a blend of volatile organic compounds (VOCs) that attract the
natural enemies of the attacking herbivores (Dicke and Baldwin, 2010).
These volatile blends are highly specific and can contain information on
the herbivore species, density and on the abiotic conditions of the
plant (Aartsma \emph{et al.} , 2017; Cabedo-López \emph{et al.} , 2019).
Exposure to far-red light can influence VOC emissions, with consequences
for plant-arthropod interactions (Kegge \emph{et al.} , 2013;
Cortés\emph{et al.} , 2016). Plants treated with methyl jasmonate
(MeJA), a volatile form of the stress hormone JA commonly used to induce
anti-herbivore defences, became more attractive to the predatory
bug\emph{Macrolophus pygmaeus} after exposure to far-red light
(Cortés\emph{et al.} , 2016). These results indicate that plants might
balance their direct and indirect defences in response to signals of
competition.

\emph{Macrolophus pygmaeus} is a generalist predator that can feed on a
broad range of herbivore species (Messelink \emph{et al.} , 2011;
Bouagga\emph{et al.} , 2018; Leman \emph{et al.} , 2020; Kenway \emph{et
al.} , 2022). However, \emph{M. pygmaeus} is a zoophytophagous predator,
meaning that it is also able to sustain itself on plant material in the
absence of arthropod prey. At higher densities, \emph{M. pygmaeus} can
itself cause plant damage (Castañé \emph{et al.} , 2011; Sanchez
\emph{et al.} , 2018). The extent of plant-feeding by \emph{M. pygmaeus}
is dependent on the availability of arthropod prey and on the defensive
status of the plants. In the absence of prey, induction of plant
defences by a non-pathogenic strain of \emph{Fusarium}
\emph{oxysporum}reduced \emph{M. pygmaeus} reproduction (Eschweiler
\emph{et al.} , 2019). In the presence of arthropod prey, inoculation
with \emph{F. oxysporum} did not affect \emph{M. pygmaeus} reproduction,
but did lead to decreased prey abundance compared to non-inoculated
plants, while prey abundance was not influenced by \emph{F. oxysporum}
inoculation alone (Eschweiler \emph{et al.} , 2019). Together, these
results indicate a shift toward more prey consumption after induction of
plant defences. Reversely, reduced plant defences in low R:FR conditions
might make the plant a more readily available food source for \emph{M.
pygmaeus} and might cause a shift toward more plant-feeding.

In this study, we further investigated the effects of changes in R:FR on
the attraction and feeding behaviour of \emph{M. pygmaeus} .
Cortés\emph{et al.} (2016) tested the effects of far-red light on the
VOC emission and attractiveness of plants treated with MeJA. Although
MeJA application can induce anti-herbivore responses, there are no
actual herbivores present. The results reported by Eschweiler \emph{et
al.}(2019) highlight the importance of prey availability for the
interaction between plants and \emph{M. pygmaeus} . We therefore studied
the effect of R:FR on the HIPV emission of plants infested with the
greenhouse whitefly (\emph{Trialeurodes vaporariorum} ), and how this
influences the attraction of \emph{M. pygmaeus} . We tested both an
increase and decrease in R:FR compared to sunlight levels, as previous
work indicated that both changes in R:FR could influence plant-whitefly
interactions (Shibuya \emph{et al.} , 2010; Meijer \emph{et al.} ,
2022), with possible consequences for plant-predator interactions. We
also investigated how supplemental far-red light influenced the
reproduction of \emph{M. pygmaeus} in both the presence and absence of
\emph{T. vaporariorum} as prey. We expect that a reduction in R:FR
influences the emission of HIPVs and increases the attraction of
\emph{M. pygmaeus} to whitefly-infested plants. An increase in R:FR is
expected to have little or no effect, as an increase in R:FR also did
not influence the performance of \emph{T. vaporariorum} (Meijer \emph{et
al.} , 2022). We further expect that supplemental far-red light
increases \emph{M. pygmaeus} reproduction in both the absence and
presence of prey, related to reduced plant defences in low R:FR
conditions.

Materials \& Methods

Plants and arthropods

Tomato plants (\emph{Solanum lycopersicum} cv. Moneymaker) were used in
all experiments. Greenhouse whiteflies, \emph{Trialeurodes
vaporariorum}Westwood (Hemiptera: Aleyrodide), were obtained from the
stock colony of the Laboratory of Plant Breeding (Wageningen University,
the Netherlands). The \emph{T. vaporariorum} colony is maintained on
tomato (Moneymaker) at 21/17\selectlanguage{english}@C, 60\% RH and 16h/8h L/D. The predatory
bugs,\emph{Macrolophus pygmaeus} (Hemiptera: Miridae), were obtained
from a commercial strain of Biobest Biological Systems (Westerlo,
Belgium).

Experimental design

Laboratory experiments were performed to investigate the effects of R:FR
on tomato HIPV emissions and the attraction of the predatory bug\emph{M.
pygmaeus} . Tomato seeds were sown in rockwool blocks (7.5 x 7.5 cm)
soaked in Tomato 2.0 nutrient solution (Unifarm, Wageningen, the
Netherlands). Two weeks after germination, the plants were transferred
to a climate-controlled growth chamber (25/18@\selectlanguage{english}C day/night, 70\selectlanguage{ngerman}±3\% RH and
16h/8h L/D) and divided over three separate compartments with different
light treatments. Each treatment consisted of 150
\selectlanguage{greek}μ\selectlanguage{english}mol/m\textsuperscript{2}/s white light (5700K) supplemented with 60
(+FR), 30 (CL) or 0 (-FR) \selectlanguage{greek}μ\selectlanguage{english}mol/m\textsuperscript{2}/s of far-red light
(735 nm). The control light treatment (CL) was set at a R:FR of 1.2,
which is equivalent to sunlight, and was reduced (+FR) or increased
(-FR) to 0.6 and 8.2, respectively. Both white and far-red light were
provided by Dyna LED-modules (Heliospectra, Gothenburg, Sweden). The
different compartments were separated with white reflective plastic to
prevent light contamination between treatments. Plants were exposed to
the light treatments for one week before being exposed to \emph{T.
vaporariorum} whiteflies (WF). Plants were individually covered with
mesh bags, tightly fastened at the base of the rockwool cube. One
hundred adult whiteflies were released inside the mesh bags and left to
feed freely for five days before plants were used in further
experiments. Other plants remained uninfested (U) for five days under
the same conditions, resulting in six treatment combinations
(+FR\textsubscript{WF}, +FR\textsubscript{U}, CL\textsubscript{WF},
CL\textsubscript{U}, -FR\textsubscript{WF} and -FR\textsubscript{U}).

Y-tube olfactometer

The response of \emph{M. pygmaeus} to plant volatiles was observed in a
two-choice Y-tube olfactometer as described previously (Lins \emph{et
al.} , 2014). A Y-shaped Pyrex tube, formed by an entry arm (20 cm) and
two side arms (13 cm, 80\selectlanguage{ngerman}° angle), was positioned vertically. The two
side arms were each connected to 15 L glass jars containing tomato
plants as odour sources. Compressed air was passed through the jars at a
regulated flow of 2.5 L/min to carry the plant volatiles into the arms
of the Y-tube. Before reaching the jars, the air was filtered by passing
it through active charcoal. The glass jars were isolated in light-tight
cabinets, equipped with a Dyna LED module, to provide plants with their
respective light treatment throughout the choice assays while preventing
visual detection of the plants by the predators. A single plant was
introduced in each glass jar. The rockwool base of the plants was
wrapped in aluminium foil before placement into the jar and the plants
were left to acclimate for 15 min before starting choice assays.
Individual predators were released at the downwind arm of the Y-tube,
and their choice for either odour source was recorded when they passed
at least 10 cm into one of the side arms, or no choice was recorded if
they did not pass this mark within 10 min after their release. Pair-wise
comparisons were made between whitefly-infested plants from the three
light treatments (+FR\textsubscript{WF}, CL\textsubscript{WF} and
-FR\textsubscript{WF}). Because \emph{M. pygmaeus} is known to prefer
whitefly-infested plants over undamaged plants (Ingegno \emph{et al.} ,
2011; Leman \emph{et al.} , 2020), choice assays between uninfested and
infested plants of the control-light treatment (CL\textsubscript{U} and
CL\textsubscript{WF}) were added as a positive control. For each
pair-wise comparison, the response of 10 female \emph{M.
pygmaeus}predators of 7-10 days old was tested daily for a total of 13
days (130 predators per comparison). After each set of five predators,
the position of the odour sources was switched between the left and
right side arms to prevent positional bias. After every 10 predators,
the plants were removed and the Y-tube system was flushed with clean air
for 10 min before introducing new plants.

Collection of headspace VOCs

Volatiles were collected from plants of all six treatment combinations.
The rockwool base of plants was wrapped in aluminium foil before plants
were placed in 15 L glass jars. They were left to acclimate for 15 min
before starting the headspace collection. Air was filtered through
active charcoal before reaching the jars, and volatiles were collected
by drawing air with a suction pump through a stainless steel cartridge
containing 200 mg of Tenax TA (20/35 mesh, CAMSCO, Houston, Texas, USA)
at 150 mL/min for two hours. During volatile collection, plants remained
exposed to their respective light treatments. Samples were collected
from at least 10 plants for each of the six treatments. Volatile samples
of empty rockwool blocks soaked with Tomato 2.0 nutrient solution and
wrapped in aluminium foil were also collected to correct for background
odours. The Tenax cartridges with VOC samples were dry purged under a
stream of helium (50 mL/min) for 15 min to remove excess moisture.

The collected volatiles were thermally released from the Tenax TA
adsorbent using an Ultra 50:50 thermal desorption unit (Markes,
Llantrisant, Glamorgan, UK) at 250 °C for 10 min under a 20 mL/min
helium flow, while the volatiles were simultaneously re-collected in a
thermally cooled universal solvent trap: Unity (Markes) at 0 °C. When
desorption was completed, the volatile compounds were released from the
cold trap by ballistic heating at 40 °C/s to 280 °C, which was then kept
for 10 min, while all the volatiles were transferred to a ZB-5 MS
analytical column (30 m x 0.25 mm ID x 1 mm F.T. with 10 m built-in
guard column (Phenomenex, Torrance, CA, USA), placed inside the oven of
a Thermo Trace GC Ultra (Thermo Fisher Scientific, Waltham, MA, USA) for
further separation of the plant volatiles. The gas chromatograph (GC)
oven temperature was initially held at 40 °C for 2 min and was then
raised at 6 °C/min to a final temperature of 280 °C, where it was kept
for 4 min under a constant helium flow of 1 mL/min. A Thermo Trace DSQ
quadrupole mass spectrometer (Thermo Fisher Scientific) coupled to the
GC was operated in an electron impact ionization (EI) mode at 70eV in a
full scan with a mass range of 35--400 amu at 4.70 scans/s. The mass
spectrometer (MS) transfer line and ion source were set at 275 °C and
250 °C, respectively. Automated baseline correction, peak selection (S/N
\textgreater{} 3) and alignments of all extracted mass signals of the
raw data were processed following an untargeted metabolomic workflow
using MetAlign software, producing detailed information on the relative
abundance of mass signals representing the available metabolites
(Lommen, 2009). This is followed by reconstruction of the extracted mass
features into potential compounds using the MSClust software through
data reduction by means of unsupervised clustering and extraction of
putative metabolite mass spectra (Tikunov \emph{et al.} , 2012).
Tentative identification of volatile metabolites was based on comparison
of the reconstructed mass spectra with those in the NIST 2008 and
Wageningen Mass Spectral Database of Natural Products MS libraries, as
well as experimentally obtained linear retention indices (LRIs).

\emph{Macrolophus pygmaeus} reproduction

A greenhouse experiment was conducted to study the effects of
supplemental FR light on the reproduction of \emph{M. pygmaeus} , in
both the absence and presence of \emph{T. vaporariorum} as arthropod
food source. Tomato seeds were sown in rockwool blocks as described
above and transported to a greenhouse compartment (22/18\selectlanguage{english}@C, 70\% RH and
16h/8h L/D) three weeks after germination. Plants were individually
placed in mesh cages (60 x 40 x 40 cm; BugDorm, Taiwan) and divided over
two tables, both illuminated with 150 \selectlanguage{greek}μ\selectlanguage{english}mol/m\textsuperscript{2}/s broad
spectrum white light provided by VYPR series LEDs (model VR-3X-BW4,
Fluence, Rotterdam, the Netherlands) as supplement to natural daylight.
One table was further supplemented with 60 \selectlanguage{greek}μ\selectlanguage{english}mol/m\textsuperscript{2}/s
far-red light (Philips Greenpower LEDs, Eindhoven, the Netherlands),
creating a R:FR of 0.8 (+FR) compared to 1.6 in the control (CL). Ten
plants were placed on either side of the greenhouse and were exposed to
the light conditions for one week. After four weeks, 100 adult
whiteflies of mixed age and sex were introduced to half of the cages,
five in either light treatment, creating four treatments based on the
supplementation of FR light and the presence of whiteflies (CL/-WF,
CL/+WF, +FR/-WF and +FR/+WF). The introduction of 100 adult whiteflies
occurred weekly to maintain a viable population and food source
for\emph{M. pygmaeus} throughout the experiment. At six weeks, 12
adult\emph{M. pygmaeus} of approximately 5-10 days old (six males and
six females) were introduced to each cage. After 25 days, all \emph{M.
pygmaeus} were collected and counted per cage. The experiment was
repeated three times between April and September 2022, resulting in 15
replicates per treatment. During the second and third run, leaflets from
the 6\textsuperscript{th} and 9\textsuperscript{th} leaf in the C/+WF
and +FR/+WF treatments (10 plants per treatment) were collected to
provide estimates of the whitefly population within the cage. Leaf discs
of 2.5 cm\textsuperscript{2} were excised from the three most terminal
leaflets per leaf (30 leaf discs per leaf per treatment) and the number
of whitefly eggs and nymphs was counted. The 9\textsuperscript{th} leaf
was the youngest fully developed leaf.

Statistical analysis

Choice responses of \emph{M. pygmaeus} in the two-choice Y-tube
experiment were analysed with a two-sided binominal test with \selectlanguage{greek}α \selectlanguage{english}= 0.05,
for each pair-wise comparison separately. Data are presented as the
total number of \emph{M. pygmaeus} choosing either odour source in each
pair-wise comparison.

The volatile emission data, expressed as peak heights, were imported
into SIMCA-P 17 statistical software (Umetrics, Umea, Sweden), followed
by log-transformation, mean-centering and unit-variance scaling before
being subjected to multivariate data analysis. Unsupervised principal
component analysis (PCA) and/or supervised partial least
squares-discriminant analysis (PLS-DA), and its extension orthogonal
partial least squares-discriminant analysis (OPLS-DA) were used as tools
to compare and correlate treatment groups. The results of the analysis
are visualized in score plots, which reveal the sample structure
according to model components, and in loading plots, which display the
contribution of the variables (individual VOCs) to these components as
well as the relationships among the variables. R\textsuperscript{2} and
Q\textsuperscript{2} metrics are provided for PLS-DA or OPLS-DA
analysis. These metrics describe the explained variation within the data
set and the predictability of the model, respectively, and were
calculated based on the averages of the sevenfold cross-validation.
R\textsuperscript{2} and Q\textsuperscript{2} values range between 0--1,
and the closer these metrics are to 1, the higher the variance explained
by the model and the more reliable the predictive power of the model.
Significant differences in the total emission of individual VOCs between
light treatments of either infested or uninfested plants were analysed
using Kruskal-Wallis analysis. Pair-wise comparisons, as performed in
the Y-tube choice assays, were analysed using Mann-Whitney U analyses
for comparisons showing significant separation in multivariate data
analysis.

\emph{Macrolophus pygmaeus} reproduction, expressed as the total number
of nymphs produced per six females, were analysed using a univariate
general linear model (GLM) with light treatment and whitefly presence as
fixed factors, and the experimental replicates as random factor.
Whitefly numbers are expressed as the total number of eggs, crawlers
(1\textsuperscript{st} instar nymphs) and nymphs per leaf disc. A
separation between 1\textsuperscript{st} instar nymphs and older stages
is made because crawlers are more easily distinguished from other
nymphal stages. Data were analysed using multivariate GLM with light
treatment and leaf number as fixed factors, and plant ID and the
experimental replicates as random factors.

Results

Y-tube choice assays

In the positive control, \emph{M. pygmaeus} significantly preferred
whitefly-infested plants over uninfested plants (Fig. 1). No preference
was observed when \emph{M. pygmaeus} were offered the choice between
whitefly-infested plants exposed to different R:FR light ratios in
pair-wise comparisons. This indicates that changes in R:FR did not
influence the attraction of \emph{M. pygmaeus} to whitefly-infested
plants.

Headspace VOC emissions

We identified 70 VOCs across the six different treatment combinations
(Table S1). These compounds were detected in at least 50\% of the
samples from at least one of the treatment combinations. Multivariate
analysis (OPLS-DA) comparing the volatile blends from uninfested plants
of all three light treatments (+FR\textsubscript{U},
CL\textsubscript{U}and -FR\textsubscript{U}) did not result in a
significant separation between volatile blends (permutation test:
R\textsuperscript{2} = 0.287, Q\textsuperscript{2} = 0.024;
p\textsubscript{cv-ANOVA} = 1.000), indicating that changes in R:FR did
not influence the composition of constitutive volatile blends (Fig. 2A
and B). Similarly, OPLS-DA including the whitefly-infested plants of all
three light treatments (+FR\textsubscript{WF}, CL\textsubscript{WF} and
-FR\textsubscript{WF}) also did not result in significant separation
(permutation test: R\textsuperscript{2} = 0.320, Q\textsuperscript{2} =
0.056; p\textsubscript{cv-ANOVA} = 0.998), indicating that HIPV emission
is also not affected by changes in R:FR (Fig. 2C and D). In uninfested
plants, nine VOCs were emitted in significantly different quantities by
plants exposed to different R:FR ratios, all showing a gradient from
highest emission in +FR to lowest in -FR, with the exception of
(\emph{Z} )-3-hexen-1-ol (Table S1). In whitefly-infested plants, only
four VOCs showed significant differences, following the same gradient
from +FR to -FR, with the exception of 2-methylbutanal-O-methyloxime
(Table S1).

We also compared volatile blends pair-wise following the pair-wise
comparisons performed in the Y-tube choice essays. Surprisingly,
multivariate analysis (PCA) did not show a separation between the
volatile blends of CL\textsubscript{U} and CL\textsubscript{WF} (Fig.
S1). Similarly, no separation was observed between the volatile blends
of CL\textsubscript{WF} and +FR\textsubscript{WF}, or between VOC blends
of CL\textsubscript{WF} and -FR\textsubscript{WF} (Fig. S2). We did
observe significant separation between the blends of
+FR\textsubscript{WF} and -FR\textsubscript{WF} using PLS-DA analysis
(permutation test: R\textsuperscript{2} = 0.967, Q\textsuperscript{2} =
0.731; p\textsubscript{cv-ANOVA} = 0.011) (Fig. S3). Twenty-three
compounds contributed most to the separation, with five VOCs showing
significantly higher emission in +FR\textsubscript{WF} and one compound
showing significantly lower emission in +FR\textsubscript{WF} (Table
S2).

\emph{Macrolophus pygmaeus} reproduction

The total number of \emph{M. pygmaeus} nymphs collected after the 25-day
period was significantly higher in the presence of whitefly prey
compared to when no prey was available (Fig. 3). Furthermore, the number
of nymphs was significantly higher on plants exposed to +FR compared to
CL treatments. No significant interaction between the presence of prey
and the light treatments was found, indicating that also in the absence
of prey, supplemental FR light increased \emph{M. pygmaeus}reproduction.

\emph{Trialeurodes vaporariorum} density estimate

Both light treatment and leaf number had a significant effect on the
number of whiteflies of all three life stages (Fig. 4). +FR
significantly increased whitefly numbers compared to CL. The effect of
leaf number was related to the leaf age; younger leaves
(9\textsuperscript{th} leaf) contained more eggs and crawlers while
older leaves (6\textsuperscript{th} leaf) had more nymphs. A significant
interaction between light treatment and leaf number was only observed
for nymphs (Fig. 4).

Discussion

Shade avoidance responses are part of a complex network of ecological
interactions within the canopy that together shape the growth-defence
dynamics in plants (de Vries \emph{et al.} , 2017). The prioritization
of shade avoidance responses over defensive responses leads to changes
in plant-arthropod interactions that span across trophic levels
(Lazzarin\emph{et al.} , 2021). Previous work indicated that plants
experiencing competition might trade direct defences for indirect
defences by showing that plants expressing the SAS are more attractive
to the predatory bug\emph{M. pygmaeus} (Cort\selectlanguage{ngerman}és \emph{et al.} , 2016). In
this paper, we further examined the effects of shade avoidance on the
interaction between \emph{M. pygmaeus} and tomato plants, thereby taking
into account the zoophytophagous nature of this predator. We show that
the volatile-mediated attraction of this predator to whitefly-infested
plants is not affected by changes in R:FR, but that far-red light does
influence the reproduction of \emph{M. pygmaeus} and whiteflies.

Our results indicate that changes in R:FR do not strongly affect the
emission of tomato volatiles and also do not influence the attraction
of\emph{M. pygmaeus} to whitefly-infested plants. Cortés \emph{et
al.}(2016) previously reported that exposure to far-red light increased
the attraction of this predator to MeJA-treated tomato plants.
Whiteflies are known to induce SA-dependent defences in plants (Elzinga
\emph{et al.} , 2014; Xu \emph{et al.} , 2019). SA- and MeJA-induced
volatiles can be differentially affected by changes in R:FR, resulting
in differences in predator behaviour in response to these volatiles.
However, previous work showed that the attraction of predatory mites to
plants infested with JA-inducing spider mites (\emph{Tetranychus
urticae} ) was also not affected by changes in R:FR (Meijer \emph{et
al.} , 2023), indicating that changes in R:FR do not influence the
emission of both SA- and JA-induced HIPVs. The observed results by
Cortés \emph{et al.} (2016) might therefore result from the use of MeJA
instead of actual herbivory. Although application of MeJA is an
effective method to induce anti-herbivore responses in plants, there are
differences in the responses induced by MeJA application and true
herbivory (Dicke \emph{et al.} 1999; Kappers \emph{et al.} , 2010;
Lortzing \emph{et al.} , 2017; Papazian \emph{et al.} , 2019), which can
affect predator preference behaviour. For example, volatiles emitted by
spider mite-infested plants were more attractive to a predatory mite
than volatiles from JA-treated plants (Dicke \emph{et al.} , 1999). VOC
blends emitted by hormone-induced plants are missing the specific
information pertaining to herbivore identity and density and might
therefore provide a more general stress signal (Kappers \emph{et al.} ,
2010). Generalist predators are proposed to be more sensitive to general
stress indicators compared to specific HIPVs (Silva \emph{et al.} ,
2021). Due to the zoophytophagous nature of \emph{M. pygmaeus} , it is
also possible that the presence or absence of actual herbivores can
change the informational value of the volatile blends. Far-red light
might alter the information provided by MeJA-induced volatile blends as
it provides information on the defensive status of the plant and the
accessibility of plant material, while changes in R:FR might not change
the attractiveness of herbivore-infested plants as the presence of prey
is the overriding element. It would be interesting to perform
comparative studies between MeJA-treated and herbivore-infested plants
to determine whether the presence of prey is a determining factor for
the attraction of predators in plants experiencing competition.

The lack of preference between whitefly-infested plants exposed to
different R:FR light ratios largely corresponds with the lack of
differences in volatile blend composition, with the exception of the
pair-wise comparison between +FR\textsubscript{WF} and
-FR\textsubscript{WF}. A significant separation between the volatile
blends of these treatments was observed, although \emph{M. pygmaeus} did
not show a preference for plants exposed to either
+FR\textsubscript{FW}or -FR\textsubscript{WF}. Reversely, we did not
find a significant separation between volatile blends of
whitefly-infested or uninfested plants grown under control light
conditions (CL\textsubscript{WF} and CL\textsubscript{U}), while
\emph{M. pygmaeus} showed a clear preference for CL\textsubscript{WF}
plants. A possible explanation is that compounds emitted by
whitefly-infested tomato plants that are relevant for attracting
\emph{M. pygmaeus} were emitted at levels below the detection ability of
the analytical instruments used. The volatiles detected in the headspace
of tomato plants in this study primarily consisted of terpenoids (60 out
of 70 compounds), which play a minor role in the attraction of mirid
predators (Silva \emph{et al.} , 2021). Feeding by \emph{T.
vaporariorum} is also known to induce the emission of the ester methyl
salicylate (MeSA) by tomato plants (López \emph{et al.} , 2012; Conboy
\emph{et al.} , 2020), which was not detected in the headspace analysis
of the current study. MeSA is an important compound for the attraction
of mirids (Silva \emph{et al.} , 2021), and its emission can be
modulated after exposure to far-red light (Cortés\emph{et al.} , 2016).

The results of this study also show that exposure to supplemental
far-red light increases the reproduction of \emph{M. pygmaeus} in both
the presence and absence of prey. These results support the hypothesis
that exposure to low R:FR and the subsequent downregulation of defences
increases plant quality for \emph{M. pygmaeus} . Although no significant
interaction between light treatment and whitefly presence was found, the
effect of far-red light on predator reproduction appears to be stronger
in the presence of whitefly prey, which might be related to increased
prey availability. Along with the increased reproduction of \emph{M.
pygmaeus} , we also found increased numbers of \emph{T. vaporariorum} in
the +FR treatment, which corresponds with previous findings
(Meijer\emph{et al.} , 2022). This indicates that supplemental far-red
light can stimulate \emph{M. pygmaeus} population development both
directly through changes in plant quality and indirectly by supporting
higher herbivore densities. In previous studies, supplemental far-red
light increased the population growth of spider mites (\emph{T. urticae}
), allowing a more rapid population growth of the predatory
mite\emph{Phytoseiulus persimilis} and leading to a stronger suppression
of the herbivore (Meijer \emph{et al.} , 2023). Unfortunately, the time
frame of the current study was too short to study population development
in \emph{M. pygmaeus} and to make a proper assessment on the
effectiveness of whitefly control by \emph{M. pygmaeus} . However, the
observed results provide promising opportunities for the use of far-red
light for biological control in greenhouses.

The use of far-red LEDs is an application in horticulture to manipulate
plant morphology and reproductive development and thereby increase crop
yield (Demotes-Mainard \emph{et al.} , 2016), but at the same time, it
can lead to an increased pest pressure (e.g. Meijer \emph{et al.} ,
2022). Cortés \emph{et al.} (2016) suggested that far-red light might
have a role in biological control by increasing the attraction of
natural enemies. Although our current results indicate that changes in
R:FR neither enhance nor interfere with the attraction of the predatory
bug \emph{M. pygmaeus} , supplemental far-red light might still
stimulate biological control by allowing a more rapid build-up of the
predator population, both in the presence and absence of pests.
\emph{Macrolophus pygmaeus} is often used in preventative release
strategies by maintaining predator populations on banker plants, but its
successful establishment depends on the species of banker plant, the
crop and the availability of supplemental food (Bresch \emph{et al.} ,
2014; Messelink\emph{et al.} , 2015; Sanchez \emph{et al.} , 2021).
Supplemental far-red light might contribute to increased establishment
of \emph{M. pygmaeus}during preventative release.

We conclude that exposure to low R:FR conditions could indirectly lead
to increased plant protection against arthropod herbivores through
changes in plant defences and predator-prey dynamics, with possible
applications to stimulate biological control in horticulture. However,
the tritrophic interactions between plants, arthropod herbivores
and\emph{M. pygmaeus} are complex, and more research is required to
understand how far-red light shapes the ecological interactions between
trophic levels. For example, plant feeding by mirid predators can induce
plant defences and increase resistance to herbivores (Pappas \emph{et
al.} , 2015; Bouagga \emph{et al.} , 2019; Pérez-Hedo \emph{et al.} ,
2022; Silva \emph{et al.} , 2022). Herbivores also avoid plants that
have been exposed to \emph{M. pygmaeus} (Zhang \emph{et al.} , 2019). On
the other hand, too high densities of \emph{M. pygmaeus} can cause plant
damage through direct feeding or the transmission of viruses (Castañé
\emph{et al.} , 2011; Moerkens \emph{et al.} , 2016; Moerkens \emph{et
al.} , 2017; Sanchez \emph{et al.} , 2018). As far-red light exerts
strong effects on plant defensive signalling, it has the potential to
influence both direct and indirect interactions between all three
trophic levels. A proper understanding of how these interactions are
affected by changes in R:FR is required to adequately assess the
efficiency of supplemental far-red light to stimulate biological
control.

Acknowledgements

This research was funded by the Dutch Research Council (NWO), project
number ALWGR.2017.004 with financial contributions by Biobest Biological
Systems,\selectlanguage{english} Enza Seeds,\selectlanguage{english} Florensis,\selectlanguage{english} Schoneveld Breeding,\selectlanguage{english} WPK Vegetable
Plants, and Heliospectra\selectlanguage{english} through the NWO.

References

Aartsma, Y., Bianchi, F.J.J.A., van der Werf, W., Poelman, E.H. and
Dicke, M. (2017). Herbivore-induced plant volatiles and tritrophic
interactions across spatial scales. \emph{New Phytologist} , 216,
1054-1063.

Ballare, C.L. (2014). Light regulation of plant defense. \emph{Annual
Review of Plant Biology} , 65, 335-363.

Ballare, C.L. and Austin, A.T. (2019). Recalculating growth and defense
strategies under competition: key roles for photoreceptors and
jasmonates. \emph{Journal of Experimental Botany} , 70(13), 3452-3436.

Ballare, C.L. and Pierik, R. (2017) The shade-avoidance syndrome:
multiple signals and ecological consequences. \emph{Plant, Cell and
Environment} , 40(11), 2530-2543.

Ballare, C.L., Scopel, A.L. and Samchez, R.A. (1990) Far-red radiation
reflected from adjacent leaves: an early signal of competition in plant
canopies. \emph{Science} , 247(4940), 329-332.

Bouagga, S., Urbaneja, A. and Perez-Hedo, M. (2018). Comparative
biocontrol potential of three predatory mirids when preying on sweet
pepper key pests. \emph{Biological Control} , 121, 168-174.

Bouagga, S., Urbaneja, A., Depalo, L., Rubio, L. Perez-Hedo, M. (2019).
Zoophytophagous predator-induced defences restrict accumulation of the
tomato spotted wilt virus. \emph{Pest Management Science} , 76, 561-567.

Bresch, C., Ottenwalder, l., Poncet, C. and Parolin, P. (2014). Tobacco
as banker plant for \emph{Macrolophus pygmaeus} to
control\emph{Trialeurodes vaporariorum} in tomato crops. \emph{Universal
Journal of Agricultural Research} , 2(8), 297-304.

Cabedo-Lopez, M., Cruz-Miralles, J., Vacas, S., Navarro-Llopis, V.,
Perz-Hedo, M., Flors, V. and Jaques, J.A. (2019). The olfactive response
of \emph{Tetranychus urticae} natural enemies in citrus depend om
genotype, prey presence, and their diet specialization. \emph{Journal of
Pest Science} , 92, 1165-1177.

Casal, J.J. (2012) Shade avoidance. \emph{The Arabidopsis book /
American Society of Plant Biologists} , 10.

Castane, C., Arno, J., Gabarra, R. and Alomar, O. (2011). Plant damage
to vegetable crops by zoophytophagous mirid predators. \emph{Biological
Control} , 59, 22-29.

Conboy, N.J.A., McDaniel, T., George, D., Ormerod, A., Edwards, M.,
Donohoe, P., Gatehouse, A.M.R. and Tosh, C.R. (2020). Volatile organic
compounds as insect repellents and plant elicitors: an integrated pest
management (IPM) strategy for glasshouse whitefly (\emph{Trialeurodes
vaporariorum} ). \emph{Journal of Chemical Ecology} , 46, 1090-1104.

Cortes, L.E., Weldegergis, B.T., Boccalandro, H.E., Dicke, M. and
Ballare, C.L. (2016). Trading direct for indirect defense? Phytochrome B
inactivation in tomato attenuates direct anti-herbivore defenses whilst
enhancing volatile-mediated attraction of predators. \emph{New
Phytologist} , 212, 1057-1071.

Courbier, S. and Pierik, R. (2019). Canopy light quality modulates
stress responses in plants. \emph{iScience} , 22, 441-452.

De Vries, J., Evers, J.B. and Poelman, E.H. (2017). Dynamic
plant-plant-herbivore interactions govern plant growth-defence
integration. \emph{Trends in Plant Science} , 22(4), 329-337.

Demotes-Mainard, S., Peron, T., Corot, A., Bertheloot, J., le
Gourrierec, J., Pelleschi-Travier, S., Crespel, L., Morel, P.,
Huche-Thelier, L., Boumaza, R., Vian, A., Guerin, V., Leduc, N. and
Sakr, S. (2016). Plant responses to red and far-red lights, applications
in horticulture. \emph{Environmental and Experimental Botany} , 121,
4-21.

Dicke, M. and Baldwin, I.T. (2010). The evolutionary context for
herbivore-induced plant volatiles: beyond the `cry for help'.\emph{Trend
in Plant Science} , 15(3), 167-175.

Dicke, M., Gols, R., Ludeking, D. and Posthumus, M.A. (1999), Jasmonic
acid and herbivory differentially induce carnivore attracting plant
volatiles in lima bean plants. \emph{Journal of Chemical Ecology} ,
25(8), 1907-1922.

Elzinga, D.A., de Vos, M. and Jander, G. (2014). Suppression of plant
defense by a \emph{Myzus persicae} (green peach aphid) salivary effector
protein. \emph{Molecular Plant-Microbe Interactions} , 27, 747-756.

Eschweiler, J., van Holstein-Saj, R., Kruidhof, H.M., Schouten, A. and
Messelink, G.J. (2019). Tomato inoculation with a non-pathogenic strain
of \emph{Fusarium oxysporum} enhances pest control by changing the
feeding preference of an omnivorous predator. \emph{Frontiers in Ecology
and Evolution} , 7, 213.

Fernandez-Milmanda, G.L. and Ballare, C.L. (2021) Shade avoidance:
expanding the color and hormone palette. \emph{Trends in Plant Science}
, 26(5), 509-523.

Ingegno, B.L., Pansa, M.G. and Tavella, L. (2011). Plant preference in
the zoophytophagous generalist predator \emph{Macrolophus
pygmaeus}(Heteroptera: Miridae). \emph{Biological Control} , 58,
174-181.

Izaguirre, M.M., Mazza, C.A., Biondini, M., Baldwin, I.T. and Ballare,
C.L. (2006). Remote sensing of future competition: impacts on plant
defenses. \emph{Proceedings of the National Academy of Sciences} ,
103(18), 7170-7174.

Kappers, I.F., Verstappen, F.W.A., Luckerhoff, L.L.P., Bouwmeester, H.J.
and Dicke, M. (2010). Genetic variation in jasmonic acid- and spider
mite-induced plant volatile emission in cucumber accessions and
attraction of the predator \emph{Phytoseiulus persimilis} .
\emph{Journal of Chemical Ecology} , 36, 500-512.

Kegge, W., Weldegergis, B.T., Soler, R., Vergeer-van Eijk, M., Dicke,
M., Voesenek, L.A.C.J. and Pierik, R. (2013). Canopy light cues affect
emission of constitutive and methyl jasmonate-induced volatile organic
compounds in \emph{Arabidopsis thaliana} . \emph{New Phytologist} , 200,
861-874.

Kenway, A.H.E., El-Sheikh, W.E.A. and Mohamed, M.A. (2022). Evaluation
of \emph{Chrysoperla carnea} and \emph{Macrolophus pygmaeus} as
biological control agents of \emph{Frankliniella occidentalis} on
Batavia lettuce under hydroponic cultivation. \emph{Journal of Crop
Protection} , 11(2), 269-278.

Lazzarin, M., Meisenburg, M., Meijer, D., van Ieperen, W., Marcelis,
L.F.M., Kappers, I.F., van der Krol, A.R., van Loon, J.J.A. and Dicke,
M. (2021). LEDs make it resilient: effects on plant growth and
defense.\emph{Trends in Plant Science} , 26(5), 496-508.

Leman, A., Ingegno, B.L., Tavella, L., Janssen, A. and Messelink, G.J.
(2020). The omnivorous predator \emph{Macrolophus pygmaeus} , a good
candidate for the control of both greenhouse whitefly and poinsettia
thrips on gerbera plants. \emph{Insect Science} , 27, 510-518.

Lins Jr, J.C., van Loon, J.J.A., Bueno, V.H.P., Lucas-Barbosa, D.,
Dicke, M. and van Lenteren, J.C. (2014). Response of the zoophytophagous
predators \emph{Macrlophus pygmaeus} and \emph{Nesidiocorus tenuis} to
volatiles of uninfested plants and to plants infested by prey or
conspecifics. \emph{BioControl} , 59(6), 707-718.

Lommen, A. (2009). MetAlign: interface-driven versatile metabolomics
tool for hyphenated full-scan mass spectrometry data
processing.\emph{Analytical Chemistry} , 81, 3079-3086.

Lopez, Y.I.A., Martinez-Gallardo, N.A., Ramirez-Romero, R., Lopez, M.G.,
Sanchez-Hernandez, C. and Delano-Frier, J.P. (2012). Cross-kingdom
effects of plant-plant signaling via volatile organic compounds emitted
by tomato (\emph{Solanum lycopsersicum} ) plants infested by the
greenhouse whitefly () plants infested by the greenhouse whitefly
(\emph{Trialeurodes vaporariorum)} . \emph{Journal of Chemical Ecology}
, 38, 1376-1386.

Lortzing, T., Firtzlaff, V., Nguyen, D., Rieu, I., Stelzer, S., Schad,
M., Kallarackal, J. and Steppuhn, A. (2017). Transcriptomic responses
of\emph{Solanum dulcamara} to natural and simulated
herbivory.\emph{Molecular Ecology Resources} , 17(6), e196-e211.

Meijer, D., Meisenburg, M., van Loon, J.J.A. and Dicke, M. (2022).
Effects of low and high red to far-red light ratio on tomato plant
morphology and performance of four arthropod herbivores. \emph{Scientia
Horticulturae} , 292, 110645.

Meijer, D., van der Vleut, J., Weldegergis, B.T., Costaz, T., Duarte,
M.V.A., Pekas, A., van Loon, J.J.A. and Dicke, M. (2023). Effects of
far-red light on tritrophic interactions between the two-spotted spider
mite (\emph{Tetranychus urticae} ) and the predatory
mite\emph{Phytoseiulus persimilis} on tomato. Submitted.

Messelink, G.J., Bloemhard, C.M.J., Hoogerbrugge, H., van Schelt, J.,
Ingegno, B.L. and Tavella, L. (2015). Evaluation of mirid predatory bugs
and release strategies for aphid control in sweet pepper. \emph{Journal
of Applied Entomology} , 139(5), 333-341.

Messelink, G.J., Bloemhard, C.M.J., Kok, L. and Janssen, A. (2011).
Generalist predatory bugs control aphids in sweet pepper.\emph{IOBC/wprs
Bull} , 68, 115-118.

Moerkens, R., Berckmoes, E., van Damme, V., Ortega-Parra, N., Hanssen,
I., Wuytack, M., Wittemans, L., Casteels, H., Tirry, L., de Clerq, P.
and de Vis, R. (2016). High population densities of \emph{Macrolophus
pygmaeus} on tomato plants can cause economic fruit damage: interaction
with \emph{Pepino mosaic virus} ? \emph{Pest Management Science} , 76,
1350-1358.

Moerkens, R., Berckmoes, E., van Damme, V., Wittemans, L., Tirry, L.,
Casteels, H., de Clerq, P. and de Vis, R. (2017). Innoculative release
strategies of \emph{Macrolophus pygmaeus} Rambur (Hemiptera: Miridae) in
tomato crops: population dynamics and dispersal. \emph{Journal of Plant
Disease and Protection} , 124(3), 295-303.

Moreno, J.E., Tao. Y., Chory, J. and Ballare, C.L. (2009). Ecological
modulation of plant defense via phytochrome control of jasmonate
sensitivity. \emph{Proceedings of the National Academy of Sciences} ,
106(12), 4935-4940.

Papazian, S., Girdwood, T., Wessels, B.A., Poelman, E.H., Dicke, M.,
Moritz, T. and Albrectsen, B.R. (2019). Leaf metabolomic signatures
induced by real and simulated herbivory in black mustard (\emph{Brassica
nigra)} . \emph{Metabolomics} , 15(10), 1-16.

Pappas, M.L., Steppuhn, A., Geuss, D., Topalidou, N., Zografou, A.,
Sabelis, M.W. and Broufas, G.D. (2015). Beyond predation: the
zoophytophagous predator \emph{Macrolophus pygmaeus} induced tomato
resistance against spider mites. \emph{PLoS One} , 10(5), e0127251.

Perez-Hedo, M., Bouagga, S., Zhang, N.X., Moerkens, R., Messelink, G.,
Jaques, J.A., Flors, V., Broufas, G. Urbaneja, A. and Pappas, M.L.
(2022). Induction of plants defenses: the added value of zoophytophagous
predators. \emph{Journal of Pest Science} , 95, 1501-1517.

Sanchez, J.A., Lopez-Gallego, E., Perez-Marcos, M. and Perera-Fernandez
L. (2021). The effects of banker plants and pre-release on the
establishment and pest control of \emph{Macrolophus pygmaeus} in tomato
greenhouses. \emph{Journal of Pest Science} , 94, 297-307.

Sanchez, J.A., Lopez-Gallego, E., Perez-Marcos, M., Perera-Fernandez,
L.G. and Ramirez-Soria, M.J. (2018). How safe is it to rely
on\emph{Macrolophus pygmaeus} (Hemiptera: Miridae) as a biocontrol agent
in tomato crops? \emph{Frontiers in Ecology and Evolution} , 6, 132.

Shibuya, T., Komuro, J., Hirai, N., Sakamoto, Y., Endo, R. and Kitaya,Y.
(2010). Preference of sweetpotato whitefly adults to cucumber seedlings
grown under two different light sources. \emph{HortTechnology} , 20(5),
873-876.

Silva, D.B., Hanel, A., Franco, F.P., de Castro Silva-Filho, M. and
Bento, J.M.S. (2022). Two in one: the neotropical mirid
predator\emph{Macrolophus basicornis} increased pest control by feeding
on plants. \emph{Pest Management Science} , 78, 3314-3323.

Silva, D.B., Urbaneja, A. and Perez-Hedo, M. (2021). Response of mirid
predators to synthetic herbivore-induced plant
volatiles.\emph{Entomologia Experimentalis et Applicata} , 169, 125-132.

Tikunov, Y.M., Laptenok, S., Hall, R.D., Bovy, A. and de Vos, R.C.H.
(2012). MSClust: a tool for unsupervised mass sepctra extraction of
chromatography-mass-spectrometry ion-wise aligned
data.\emph{Metabolomics} , 8, 714-718.

Van Butselaar, T. and van den Ackerveken, G. (2020) Salicylic acid
steers the growth-immunity tradeoff. \emph{Trends in Plant Science} ,
25(6), 566-576.

Wasternack, C. and Feussner, I. (2018) The oxylipin pathways:
biochemistry and function. \emph{Annual Reviews of Plant Biology} , 69,
363-386.

Xu, H., Qian, L., Wang, X., Shao, R., Hong, Y., Liu, S. and Wang, X.
(2019). A salivary effector enables whitefly to feed on host plants by
eliciting salicylic acid-signaling pathway. \emph{PNAS} , 116(2),
490-495.

Zhang, N.X., van Wieringen, D., Messelink, G.J. and Janssen, A. (2019).
Herbivores avoid host plants previously exposed to their omnivorous
predator \emph{Macrolophus pygmaeus} . \emph{Journal of Pest Science} ,
92, 737-745.

Zust, T. and Agrawal, A.A. (2017). Trade-offs between plant growth and
defense against insect herbivory: an emerging mechanistic
synthesis.\emph{Annual Review of Plant Biology} , 68, 513-534.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1: Total number of M. pygmaeus choosing either odour source in
pair-wise comparisons between plants exposed to different R:FR light
ratios (+FR = 0.6, CL = 1.2 and -FR = 8.2) and either infested with T.
vaporariorum whiteflies (WF) or uninfested (U). P-values in bold
indicate a significant preference for either odour source (two-sided
binominal test). For each pair-wise comparison, 130 predators were
tested. NC = no choice.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

Figure 2: Effects of exposure to different R:FR light ratios on the
blend of volatile organic compounds (VOCs) collected from either
uninfested or whitefly-infested tomato (Solanum lycopersicum cv
Moneymaker) plants. (A) Multivariate comparison using orthogonal
projection to latent structures-discriminant analysis (OPLS-DA) between
uninfested plants exposed to different R:FR light ratios
(+FR\textsubscript{U} = 0.6, n = 11; CL\textsubscript{U} = 1.2, n = 13;
-FR\textsubscript{U} = 8.2, n = 13), displayed in a two-dimensional
score plot. (B) Loading plot indicating the contribution of each VOC to
the separation between uninfested plants exposed to different R:FR light
ratios. (C) Same analysis as described for (A) but now comparing plants
infested with Trialeurodes vaporariorum whiteflies and exposed to
different R:FR light ratios (+FR\textsubscript{WF} n = 13;
CL\textsubscript{WF} n = 11; -FR\textsubscript{WF} n = 14). (D) Loading
plot indicating the contribution of each VOC to the separation between
whitefly-infested plants exposed to different R:FR light ratios. For VOC
identification: the numbers in the loading plots correspond to the
numbers in Table S1.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

Figure 3: Average number of nymphs (\selectlanguage{ngerman}±SE) produced by 12 M. pygmaeus (6
males and 6 females) in the presence (+WF) or absence (-WF) of whitefly
prey on tomato plants grown under control light conditions (CL) or CL
with supplemental far-red light (+FR) during 25 days. There was a
significant effect of both the presence of prey (df = 1, F = 118.48, p =
0.008) and the light treatment (df = 1, F = 23.18, p = 0.038) on the
total number of nymphs produced. There were no significant differences
between experimental replicates (df = 2, F= 3.54, p = 0.358), nor was
there an interaction effect between prey presence and light treatment
(df = 1, F = 5.15, p = 0.150). Numbers in bars represent the averages.
CL/-WF n = 15, CL/+WF n = 15, +FR/-WF n = 15, +FR/+WF n = 13.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}

Figure 4: Average number of whitefly eggs, crawlers or nymphs (\selectlanguage{ngerman}±SE)
counted on leaf discs from the 6\textsuperscript{th} or
9\textsuperscript{th} leaf of tomato plants grown under control light
conditions (CL) or CL with supplemental far-red light (+FR). Whitefly
numbers in all three life stages were significantly affected by light
treatment (Eggs: df = 1, F = 38.44, p \textless{} 0.001; Crawlers: df =
1, F = 11.72, p \textless{} 0.001; Nymphs: df = 1, F = 14.41, p
\textless{} 0.001) and leaf number (Eggs: df = 1, F = 97.70, p
\textless{} 0.001; Crawlers: df = 1, F = 86.01, p \textless{} 0.001;
Nymphs: df = 1, F = 172.78, p \textless{} 0.001). A significant
interaction between light treatment and leaf number was only observed
for nymphs (Eggs: df = 1, F = 2.15, p = 0.146; Crawlers: df = 1, F =
0.16, p = 0.690; Nymphs: df = 1, F = 10.46, p = 0.002). Results were not
affected by the experimental replicates (Eggs: df = 1, F = 0.02, p =
0.880; Crawlers: df = 1, F = 0.53, p = 0.470; Nymphs: df = 1, F = 0.04,
p = 0.835) or plant ID (Eggs: df = 1, F = 0.01, p = 0.933; Crawlers: df
= 1, F = 0.14, p = 0.706; Nymphs: df = 1, F = 0.22, p = 0.63). Leaf 6:
CL n = 30, +FR n = 30; Leaf 9: CL n = 30, +FR n = 27.

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

