Leaf size determines damage- and herbivore-induced volatile emissions in maize

Leaf size determines damage- and herbivore-induced volatile emissions in maize | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Leaf size determines damage- and herbivore-induced volatile emissions in maize View ORCID Profile Jamie M. Waterman , View ORCID Profile Tristan M. Cofer , Ophélie M. Von Laue , View ORCID Profile Pierre Mateo , View ORCID Profile Lei Wang , View ORCID Profile Matthias Erb doi: https://doi.org/10.1101/2024.11.14.623649 Jamie M. Waterman 1 Institute of Plant Sciences, University of Bern , Bern, Switzerland 2 Discipline of Botany, School of Natural Sciences, Trinity College Dublin , Dublin 2, Ireland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jamie M. Waterman For correspondence: watermaj{at}tcd.ie matthias.erb{at}unibe.ch Tristan M. Cofer 1 Institute of Plant Sciences, University of Bern , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Tristan M. Cofer Ophélie M. Von Laue 1 Institute of Plant Sciences, University of Bern , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site Pierre Mateo 1 Institute of Plant Sciences, University of Bern , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pierre Mateo Lei Wang 1 Institute of Plant Sciences, University of Bern , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Lei Wang Matthias Erb 1 Institute of Plant Sciences, University of Bern , Bern, Switzerland Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Matthias Erb For correspondence: watermaj{at}tcd.ie matthias.erb{at}unibe.ch Abstract Full Text Info/History Metrics Preview PDF Abstract Stress-induced plant volatiles play an important role in mediating ecological interactions between plants and their environment. The timing and location of the inflicted damage is known to influence the quality and quantity of induced volatile emissions. However, how leaf characteristics and herbivore feeding behavior interact to shape volatile emissions is not well understood. Using a high-throughput volatile profiling system with high temporal resolution, we examined how mechanical damage and herbivore feeding on different leaves shapes plant-level volatile emission patterns in maize. We then tested feeding patterns and resulting consequences on volatile emissions with two generalist herbivores ( Spodoptera exigua and Spodoptera littoralis ), and assessed whether feeding preferences are associated with enhanced herbivore performance. We found maize seedlings emit more volatiles when larger leaves are damaged. Larger leaves emitted more volatiles locally, which was the determining factor for higher plant-level emissions. Surprisingly, both S. exigua and S. littoralis preferentially consumed larger leaves, and thus maximize plant volatile emission without apparent growth benefits. Together, these findings provide an ecophysiological and behavioral mechanism for plant volatile emission patterns, with potentially important implications for volatile-mediated plant-environment interactions. Introduction Time and space are important factors that shape plant defense expression and plant-environment interactions ( Waterman et al ., 2019 ; Hunziker et al ., 2021 ). It is well known that discrete tissues, especially those with temporally separated development, exhibit specific defense capacities ( Köhler et al ., 2015 ; Maag et al ., 2015 ; Maag et al ., 2016 ; Hunziker et al ., 2021 ). In general, evidence suggests that younger plant tissues are typically more well-defended ( Ohnmeiss & Baldwin, 2000 ; Barton et al ., 2010 ). While this is often true for inducible defenses, in some plants, such as maize, older tissues tend to have higher levels of constitutive chemical defenses ( Köhler et al ., 2015 ). As such, the specific defense, as well as the nature of its metabolism, are critical considerations. Additionally, spatiotemporal differences in inducible plant defense chemistry often depend on the specific mode of induction. In maize treated with ZmPep3, a peptide associated with regulating responses to wounding, older maize leaves respond more strongly, however, when exposed to herbivore-induced plant volatiles, younger leaves are more responsive ( Wang et al ., 2023 ). Even within the same leaf and over relatively short temporal time scales (hours), the timing and position of damage can strongly influence both qualitative and quantitative features of damage-induced responses ( Mithöfer et al ., 2005 ; Maag et al ., 2016 ). Tissue-specific patterns may also be temporally dependent. Specifically, in cotton, young leaves from younger plants produced more terpenoids when mechanically damaged in comparison to young leaves in older plants ( Eisenring et al ., 2017 ). Similarly, damage to plants at different ontogenetic stages is also an important consideration. In lima bean, tolerance to herbivory through compensatory growth was observed in plants that were previously damaged (primed) during the seedling stages of development and not observed if plants were primed during juvenile and reproductive stages ( Bustos-Segura et al ., 2022 ). An important damage-induced plant defense is the production of damage-induced volatiles. These volatiles are particularly versatile, as they are biosynthesized within tissues, where they can act as a direct defense against biotic stress ( Maurya et al ., 2020 ; Chen et al ., 2021 ; Chen et al ., 2023 ). These volatiles are also emitted into the environment, where they have broad ecological relevance ( Escobar-Bravo et al ., 2023 ). Plant-derived volatiles can serve as within-plant signaling molecules ( Heil & Silva Bueno, 2007 ), attract herbivore natural enemies ( Schnee et al ., 2006 ; Turlings & Erb, 2018 ), influence herbivore oviposition ( Hajdu et al ., 2024 ), mediate plant-pollinator interactions ( Groen et al ., 2016 ) and mediate plant-plant interactions ( Wang et al ., 2023 ; Waterman et al ., 2024 ). Damage-induced volatiles can be manipulated by herbivores, which employ several strategies to reduce plant volatile emissions, potentially minimizing detection from natural enemies such as predators and parasitoids. For example, the saliva of the caterpillar Helicoverpa zea , which can be secreted onto plant tissues during feeding, contains the enzyme glucose oxidase, which causes stomatal closure and reduced the emission of some volatiles ( Lin et al ., 2021 ). Conversely, herbivores can also betray themselves by attracting natural enemies, for example Manduca sexta oral secretions decrease the (Z) / (E) ratio of green leaf volatiles emitted by plants during feeding, which increases herbivore attractiveness to predators ( Allmann & Baldwin, 2010 ). While herbivore feeding patterns can influence plant phenotypes, within-plant spatiotemporal phenotypic variation can also influence herbivore feeding behavior, and in turn shape wider plant-environment interactions ( Bellec et al ., 2022 ; Orrock et al ., 2022 ). While plant traits have been shown to influence herbivore behavior, broadly applicable patterns are difficult to identify and many herbivore-feeding patterns may be explained by processes and environmental features independent of the plant ( The Herbivory Variability Network* † et al ., 2023 ; Pan & Wetzel, 2024 ). Although volatile emissions have been shown to positively correlate with damage extent ( Schmelz et al ., 2003 ; Rodriguez-Saona et al ., 2009 ), qualitative damage patterns are also known to shape volatile emission profiles ( Mithöfer et al ., 2005 ; Lange et al ., 2020 ). Importantly, the degree to which herbivore feeding patterns and preferences align with inherent spatiotemporal differences in volatile induction is unclear. The aim of our study was to test how damage done to different leaves affects the emission of ecologically relevant induced plant volatiles at the plant level, and to identify the mechanistic basis for these differences. Further, to better understand the potential implications of these differences, we investigated the feeding patterns of two lepidopteran herbivore species, and the resulting impact on herbivore-induced volatile emissions. We tested a number of biochemical, chemical and morphological leaf traits across developmental stages, and manipulated leaf size to build a mechanistic understanding of what drives differences in emission between leaves. Additionally, we determined that herbivores preferentially fed on larger leaves without any apparent performance benefit, and thus maximized plant volatile emissions. Together, these findings reveal leaf size as an important determinant of plant-level volatile emissions and demonstrate that two generalist herbivores feed in a way that maximizes rather than minimizes leaf volatile emissions, without apparent direct benefits. Materials and Methods Plant and insect growth V2- and V3-stage maize seedlings ( Zea mays , B73) were used throughout this study. At the V2 stage, maize has four leaves: two are fully developed, one is expanding and one is emerging. At the V3 stage maize has 5 leaves: three are fully developed, one is expanding and one is emerging. At these growth stages, maize plants are often attacked by herbivores such as Spodoptera spp. ( van den Berg et al ., 2021 ). Maize plants were grown in commercial potting soil (Selmaterra, BiglerSamen, Switzerland) in 180 ml pots. Plants were grown in greenhouse conditions and supplemented with artificial lights ( ca . 300 µmol m −2 s −1 ). The greenhouse was kept at 22 ± 2 °C, 40–60% relative humidity, with a 14 h : 10 h, light : dark cycle. Spodoptera exigua (Frontier Agricultural Sciences, USA) and Spodoptera littoralis (University of Neuchatel, Switzerland) were reared from eggs on artificial diet ( Maag et al ., 2014 ) and used for experiments when they reached the 4 th instar stage. All experiments were conducted between the hours of 10:00 and 20:00 under full-light conditions. Plant damage To mimic dynamic patterns, we damaged plants three times in ca. 30 min intervals with a hemostat. Each wound was ca. 10 mm 2 . Each damage event consisted of two wounds, one on each side of the midvein. We chose damage treatments without application of larval oral secretions to obtain generalizable induction patterns without species-specific induction and suppression of volatiles ( Lange et al ., 2020 ). The first damage event was at the base of the leaf, the second in the middle, and the third at the tip. The extent and relative spacing of damage was identical across leaves, so that for each leaf the tip, middle and base of each leaf was damaged. Damage was either inflicted on leaves of intact plants or detached leaves as specified in the text and figure legends. Detached leaf assay Leaves were dissected following the protocol of Wang et al., 2023 . In brief, leaves were cut at the base from maize seedlings while submerged in Milli-Q water. The cut base of leaves remained submerged in water for 2 hr to allow for tissues to recover from dissections, prior to damage treatments. Volatile emissions from individual leaves were measured the same way as intact seedlings. Volatile sampling Entire seedlings or detached leaves were placed in transparent glass chambers (Ø×H 12 × 45 cm) that were sealed other than an clean airflow inlet and an outlet. Clean air was supplied at a flow-rate of 0.8 L min −1 . Volatiles were measured with a high-throughput platform comprising of a proton transfer reaction time-of-flight mass spectrometer (PTR-ToF-MS; Tofwerk, Switzerland) and a custom-made automated headspace sampling system ( Gonin et al ., 2018 ). The outlet of the chamber was accessible to the autosampler/PTR-ToF-MS system. The PTR-ToF-MS system drew air at 0.1 L min −1 . Between samples, a zero-gas measurement was performed for 3 s to flush the system. At each time point (as indicated by the x-axis of the respective figure), volatiles were continuously measured for 25-30 s and averaged to a single mean per sample. When total emission over the experimental period is reported, the frequency of each measurement is specified in the respective figure legend. Complete mass spectra (0-500 m/z) were recorded in positive mode at ca. 10 Hz. The PTR was operated at 100 °C and an E/N of approximately 120 Td. The volatile data extraction and processing were conducted using Tofware software package v3.2.2 (Tofwerk, Switzerland). Protonated compounds were identified based on their molecular weight + 1. During volatile collection, LED lights (DYNA, heliospectra) were placed ca. 80 cm above the glass cylinders and provided light at 300 μmol m −2 s −1 . Identical light : dark cycle timing as in the greenhouse for plant growth was used. To calculate ‘total’ volatile emissions, we took the integral of the emission curve over time, in other words, the sum of each individual volatile measurement time point over the experimental period. Gene expression 1.5 hr after the first leaf damage, each leaf (leaf 2, 3 and 4) of each plant was harvested and flash frozen on liquid nitrogen. Total RNA extraction and purification, genomic DNA removal, cDNA synthesis and quantification of gene expression were conducted identically to Waterman et al., 2024 . Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was performed using ORA SEE qPCR Mix (highQu GmbH, Germany) on an Applied Biosystems QuantStudio 5 Real-Time PCR system. The normalized expression (NE) values were calculated as in Waterman et al., 2024 using ubiquitin (UBI1) as the reference gene. Gene identifiers and primer sequences are listed in Supplementary Table 1 . HDMBOA-Glc quantification 24 hr after the first leaf damage, each leaf (leaf 2, 3 and 4) of each plant was harvested and flash frozen on liquid nitrogen. Approximately 100 mg of fresh frozen, finely ground leaf was extracted in 1 mL acidified MeOH/H 2 O 70:30 v/v spiked with 0.1% formic acid. Extracts were vortexed for 10 seconds and centrifuged at 13000 rpm for 20 min at 10 °C. The supernatant was removed and used for subsequent quantification. HDMBOA-Glc profiling was performed with an Acquity i-Class UPLC system equipped with a photodiode array detector (PDA) and a single quadrupole detector (QDa) with electrospray source (Waters, USA). Gradient elution was performed on an Acquity BEH C18 column (1.7 μm, 2.1 × 100 mm, Waters, USA).The elution conditions were as follows: 90-70% A over 3 min, 70-60% A over 1 min, 40-100% B over 1 min, holding at 100% B for 2.5 min, holding at 90% A for 1.5 min where A = 0.1% formic acid/water and B = 0.1% formic acid/acetonitrile. The flow rate was 0.4 mL/min. The temperature of the column was maintained at 40 °C, and the injection volume was 1 μL. Absolute quantities were determined using standard curves of the corresponding pure HDMBOA-Glc. Stomatal density Clear nail polish was painted onto either the base, middle or tip of the abaxial and adaxial side of each leaf. Once hardened, the polish was removed with double-sided sticky tape, creating an epidermal imprint. Imprints were mounted onto glass microscope slides and imaged with a Leica DM2500 optical microscope coupled to an HD digital microscope camera (Leica MC170 HD; Leica Microsystems, Germany). The density of stomata on each imprint was calculated based on the average of three distinct and non-overlapping fields of view. Herbivore feeding on intact plants To assess damage patterns on intact V2-stage maize seedlings, a single S. exigua or S. littoralis larva was placed on either leaf 2 or leaf 3. Herbivores were allowed to feed for 6 hr. At the end of the feeding period, damaged area (percentage of total leaf) of each leaf was estimated visually. To convert this estimated value on a per-leaf basis into the percentage of total plant damage, a subset of plants was kept undamaged. Using ImageJ (National Institutes of Health, USA), the average surface area of each leaf on these control plants was used as a reference, and the percent area contributed by each individual leaf to the total surface area of the plant was calculated. In other words, the estimated percentage of a given leaf removed by herbivores was multiplied the percentage of the total area of all leaves made up by said leaf. Herbivore feeding and performance on individual leaves To assess herbivore feeding on detached leaf 2 and leaf 3, leaves were dissected as previously described (see detached leaf assay ). One 4 th instar larva was placed at the base of each leaf and allowed to feed for 6 hr. The area of tissue damage was calculated using ImageJ and methods described in ( Waterman et al ., 2021 ). To calculate the biomass of tissue consumed we multiplied the area of damage by the leaf mass per area (LMA), which is descried as the ratio of mass per unit area ( KATTGE et al ., 2011 ). This was calculated for leaf 2 (LMA = 0.0031, n = 6) and leaf 3 (LMA = 0.0025, n = 6). To assess the relative growth rate of herbivores on detached leaves, larvae were placed on leaves in an identical fashion to described above, however were allowed to feed for 48 hr, with leaves being replaced at the 24 hr mark to prevent desiccation. Relative growth rate was calculated as: ((final larval weight – initial larval weight) / initial larval weight) / # of days of feeding, as previously described ( Waterman et al ., 2021 ). 48 hr feeding periods were chosen to approximate the time it would take a 4 th instar Spodoptera larva to consume leaf 2 or leaf 3, while also allowing robust inferences regarding the quality of plant material for larval development ( Himanen et al ., 2009 ; Paul-Victor et al ., 2010 ; Underwood, 2012 ; Lin et al ., 2020 ; Waterman et al ., 2021 ). Statistical analyses All statistical analyses were conducted in R version 4.2.2 ( R Core Team, 2022 ). Gene expression and stomatal density were analyzed using two-way ANOVA (Type = II). Gene expression was analyzed on log-transformed normalized expression values. HDMBOA-Glc was analyzed with one-way ANOVA within each leaf for plants where leaf 2 and leaf 3 were damaged. As data from plants with leaf 4 damage did not meet the assumptions of ANOVA, differences in HDMBOA-Glc between leaves of these plants were determined using a Kruskal-Wallis test. Differences in detached leaf biomass were also analyzed with one-way ANOVA. Where necessary, to obtain heteroscedasticity-consistent standard errors, White-adjusted ANOVAs were used ( White, 1980 ). To determine leaf-damage area by herbivores in intact seedlings, as the assumptions of ANOVA were not met, Kruskal-Wallis tests were used. For herbivore feeding and performance assays in detached leaves, Welch’s two-sample t-tests were used to compare leaf 2 and leaf 3. Statistical test summaries are included as supplemental information ( Supplemental Tables 2 and 3 ). Results Plant-level volatile emissions are determined by damage position In order to determine how damage position impacts induced plant-level volatile emissions, we damaged each leaf of a V2-stage maize seedling individually (one damaged leaf per plant; Fig 1A ) and measured induced volatile emissions. Green leaf volatile emissions, which are strongly localized to wound sites ( Matsui, 2006 ), were comparable across treatments, with the exception of plants where the youngest leaf (leaf 4) was damaged, which resulted in, generally, lower green leaf volatile emissions ( Fig 1B ). Plant-level emissions of terpenes and indole were strongly influenced by damage location. Plants damaged on leaf 3 had the highest emission levels of monoterpenes [C 10 H 17 + , m/z = 137.13], sesquiterpenes [C 15 H 25 + , m/z = 205.20], and 4,8,12-trimethyltrideca-1,3,7,11-tetraene (TMTT) [C 16 H 27 + , m/z = 219.21], 4,8-dimethylnona-1,3,7-triene (DMNT) [C 11 H 19 + , m/z = 151.15] and indole [C 8 H 8 N + , m/z = 118.07], followed by leaf 2 and leaf 4 ( Fig 1C ). Thus, which leaf is damaged is a strong determinant of plant-level volatile emissions. Download figure Open in new tab Figure 1. Plant-level volatile emissions depend on which leaves are damaged. A) The different leaves and respective sizes of intact V2-stage maize seedlings. Emission kinetics of B) green leaf volatiles and C) terpenes and indole are shown. Perforated vertical lines depict the timing of each mechanical damage entry. Abbreviations: HAC, Hexenyl acetate; DMNT, 4,8-dimethylnona-1,3,7-triene; TMTT, 4,8,12-trimethyltrideca-1,3,7,11-tetraene. Compounds were identified based on their molecular weight + 1, as all compounds were protonated. HAC: m/z = 143.11, Hexenal: m/z = 99.08, Hexenol: m/z = 101.0, DMNT: m/z = 151.15, Indole: m/z = 118.07, Monoterpenes: m/z = 137.13, Sesquiterpenes: m/z = 205.20, TMTT: m/z = 219.21. Colored points = mean ± SE. n = 5. Minimal differences in local damage inducibility of volatile biosynthesis genes and direct defenses To test whether damage to the different leaves leads to differential local and systemic defense induction, which may determine leaf- and plant-level volatile emissions, we measured terpene and indole biosynthesis gene expression. Terpene synthase 2 and 10 (TPS2 and TPS10), as well as dimethylnonatriene/trimethyletetradecatetraene synthase (CYP92C5) are rate limiting for terpene production, and indole-3-glycerol phosphate lyase (IGL) is rate limiting for volatile indole biosynthesis ( Frey et al ., 2000 ; Richter et al ., 2016 ; Block et al ., 2019 ). We also measured HDMBOA-Glc levels as a major marker of non-volatile defense induction in maize to test whether spatial defense patterns may be generalizable ( Li et al ., 2018 ). Following damage to individual leaves, all tested genes were significantly induced both locally and systemically ( Fig 2 ). Local induction was generally highest in leaf 2 and leaf 3 and lower in leaf 4 ( Fig 2 ). Systemic induction was similar across treatments. HDMBOA-Glc was induced locally, but not systemically across all leaves at comparable concentrations, although the effect was only significant for leaf 2 and leaf 3 ( Supplemental Fig 1 ). Thus, the differences in plant volatile emissions upon damage of cannot be explained by differential local and systemic inducibility. Download figure Open in new tab Supplemental Figure 1. HDMBOA-Glc is similarly induced across leaves in intact V2-stage maize seedlings. Lowercase letters indicate significant differences between damage sites within a given leaf based on multiple comparisons tests. Grey points represent biological replicates. Bars = mean ± SE. n = 5-6. Download figure Open in new tab Figure 2. Minimal differences in volatile biosynthesis inducibility of different leaves in intact plants. Expression of terpene and indole biosynthesis genes are shown. Abbreviations: TPS2, terpene synthase 2; TPS10, terpene synthase 10; CYP92C5, Dimethylnonatriene/trimethyltetradecatetraene synthase, IGL, indole-3-glycerol phosphate lyase. Lowercase letters indicate significant differences between damage sites within a given leaf and uppercase letters indicate overall differences between leaves, regardless of which left was damaged (based on multiple comparisons tests). Grey points represent biological replicates. Bars = mean ± SE. n = 5. Plant volatile emissions strongly correlate with the size of the damaged leaf One apparent difference between the leaves is their size, both in terms of biomass and surface area ( Fig 1A ). Thus, we hypothesized that larger leaves may result in higher volatile release upon damage. We used biomass as the principal metric of size, which is also highly correlated with leaf surface area across all leaves, and this correlation is maintained across developmental stages ( Supplemental Fig 2 ). We found strong and significant correlations between the biomass of the damaged leaf and plant-level volatile emissions ( Fig 3A ). We also found similar correlations for leaf-level emissions in individual detached damaged leaves of V2- and V3-stage seedlings ( Fig 3B ; Supplemental Fig 3 ). There was also a significant linear relationship between volatile emissions and the surface area of the damaged leaf in both seedling stages (albeit slightly weaker than the relationship with damaged leaf mass; Supplemental Fig 4 ). Immature leaves (leaf 3 and 4 for V2-stage seedlings and leaf 4 and 5 for V3-stage seedlings) showed the strongest relationship between emission and size across the different leaves ( Fig 3 ; Supplemental Fig 4 ). Interestingly, leaf 2 consistently showed higher volatile emissions than expected for its size in V2-stage seedlings, as did leaf 3 in V3-stage seedlings ( Fig 3 ). Download figure Open in new tab Supplemental Figure 2. Strong positive relationship between leaf mass and leaf area in both V2-stage and V3-stage maize seedlings. Black lines represent linear regression for significant linear relationships for each leaf. Colored points depict biological replicates of different leaves. Plants used were undamaged. n = 12-16. Download figure Open in new tab Figure 3. Whole-plant- and leaf-level volatile emissions strongly correlate with the mass of the damaged leaf. Colored points represent biological replicates and depict the relationship between total volatile emission (sum of individual measurement time points over 9 hr; see Materials and Methods, Volatile sampling ) and leaf biomass in A) intact V2-stage seedlings and B) detached leaves of V3-stage seedlings. Individual measurements were taken every ca. 10 or 20 min for V2 and V3, respectively. Black lines represent linear regression for significant linear relationships between all leaves. Grey lines represent linear regression for significant linear relationships between immature leaves (leaf 3 and leaf 4 for V2-stage; leaf 4 and leaf 5 for V3-stage). Abbreviations: DMNT, 4,8-dimethylnona-1,3,7-triene; TMTT, 4,8,12-trimethyltrideca-1,3,7,11-tetraene. Compounds were identified based on their molecular weight + 1, as all compounds were protonated. DMNT: m/z = 151.15, Indole: m/z = 118.07, Monoterpenes: m/z = 137.13, Sesquiterpenes: m/z = 205.20, TMTT: m/z = 219.21. n = 5 for V2-stage seedlings, n = 4 for V3-stage leaves. Download figure Open in new tab Supplemental Figure 3. Detached maize leaves from V2-stage seedlings maintain the same emission-leaf size relationships as when leaves of intact plants are damaged. Colored points represent biological replicates and depict the relationship between total volatile emission (sum of individual measurement time points over 9 hr; see Materials and Methods, Volatile sampling ) and leaf biomass. Individual measurements were taken every ca. 10 min. Black lines represent linear regression for significant correlations between all leaves. Grey lines represent linear regression for significant linear relationships between immature leaves (leaf 3 and leaf 4). Abbreviations: DMNT, 4,8-dimethylnona-1,3,7-triene; TMTT, 4,8,12-trimethyltrideca-1,3,7,11-tetraene. Compounds were identified based on their molecular weight + 1, as all compounds were protonated. DMNT: m/z = 151.15, Indole: m/z = 118.07, Monoterpenes: m/z = 137.13, Sesquiterpenes: m/z = 205.20, TMTT: m/z = 219.21. Error bars = SE. n = 6. Download figure Open in new tab Supplemental Figure 4. Relationships are maintained between volatile emissions and the area of damaged leaves across developmental stages. Colored points represent biological replicates and depict the relationship between total emission (sum of individual measurement time points over 9 hr; see Materials and Methods, Volatile sampling ) of plant volatiles and leaf surface area in A) intact V2-stage seedlings and B) detached leaves of V3-stage seedlings. Individual measurements were taken every ca. 10 or 20 min for V2 and V3, respectively. Black lines represent linear regression for significant correlations between all leaves. Grey lines represent linear regression for significant linear relationships between immature leaves (leaf 3 and leaf 4 for V2-stage; leaf 4 and leaf 5 for V3-stage). Abbreviations: DMNT, 4,8-dimethylnona-1,3,7-triene; TMTT, 4,8,12-trimethyltrideca-1,3,7,11-tetraene. Compounds were identified based on their molecular weight + 1, as all compounds were protonated. DMNT: m/z = 151.15, Indole: m/z = 118.07, Monoterpenes: m/z = 137.13, Sesquiterpenes: m/z = 205.20, TMTT: m/z = 219.21. Error bars = SE. n = 5 for V2-stage seedlings, n = 4 for V3-stage leaves. Standardizing leaf area results in similar induced volatile emissions In order to investigate whether leaf size is the driving factor for induced volatile emission, we compared entire detached leaves with leaves that were all cut to the biomass of leaf 4 ( Fig 4A-C ). Entire detached leaves showed similar emission kinetics to entire plants, where leaf 3 emitted the most volatiles ( Fig 4D ). When cut to equal size, emissions of damage-induced volatiles were similar between leaf 2 and 3 ( Fig 4E ). For all compounds except TMTT, both leaf 2 and leaf 3 emitted more volatiles than leaf 4 when cut to equal biomass. As terpene and indole emission are controlled by stomata, we measured stomatal densities between leaf 2 and leaf 3 and found no differences ( Supplemental Fig 5 ). Maize-leaf development occurs on a gradient from the tip to base, with the tip being most developed and the base being least developed ( Raissig et al ., 2017 ; Wang et al ., 2019 ). Accordingly, the base of leaves has fewer developed stomata than the other sections ( Supplemental Fig 5 ). When cutting leaves to similar sizes, we only removed the bottom portion (with fewer on stomata for both leaves). There were no differences stomatal density between the middle and tip in leaves 2 and 3, and no differences in stomatal density were observed between leaf 2 and leaf 3 ( Supplemental Fig 5 ), thus ruling out stomatal density as a driver of differential volatile emissions between these two leaves. Download figure Open in new tab Figure 4. Standardizing leaf biomass results in similar induced volatile emissions between detached leaves of V2-stage seedlings. Biomass of A) entire detached leaves of V2-stage seedlings and B) V2-stage leaves 2 and 3 cut to the biomass of Leaf 4. C) V2-stage leaves 2-4 when standardized to size. Emission kinetics of D) entire detached V2-stage leaves and E) V2-stage leaves cut to leaf 4 biomass. Perforated vertical lines depict the timing of each mechanical damage entry. Lowercase letters indicate significant differences between leaves based on multiple comparisons tests. Abbreviations: DMNT, 4,8-dimethylnona-1,3,7-triene; TMTT, 4,8,12-trimethyltrideca-1,3,7,11-tetraene. Compounds were identified based on their molecular weight + 1, as all compounds were protonated. DMNT: m/z = 151.15, Indole: m/z = 118.07, Monoterpenes: m/z = 137.13, Sesquiterpenes: m/z = 205.20, TMTT: m/z = 219.21. Grey points represent biological replicates. Bars = mean ± SE. n = 6. Download figure Open in new tab Supplemental Figure 5. Stomatal patterns are similar between leaf 2 and leaf 3 in V2-stage maize seedlings. Stomatal density (abaxial and adaxial sides combined) of the sections of leaf 2 and leaf 3. Differing lowercase letters indicate significant differences between leaf and leaf section based on multiple comparisons tests. Grey points represent biological replicates. Bars = mean ± SE. n = 6. Generalist herbivore feeding patterns maximize volatile emissions To test how herbivore feeding interacts with spatiotemporal differences in volatile emission, we placed a single Spodoptera exigua or Spodoptera littoralis larva on either leaf 2 or leaf 3 of V2-stage maize seedlings and tracked their feeding patterns. For S. exigua , we found that, regardless of starting position, herbivores consistently eat mostly leaf 3 ( Fig 5 ). When placed on leaf 3, only 6.3% of plants had feeding on leaf 2 and 84.4% of plants had feeding damage on leaf 3 ( Table 1 ). Even when placed on leaf 2, only 40.6% of plants had feeding damage on leaf 2 and 81.3% of plants had feeing on leaf 3. When herbivores were placed on leaf 2, damage area on leaf 4 was similar to leaf 2, and when placed on leaf 3, leaf 4 showed comparable damage levels to leaf 3. The trends for S. littoralis were similar to S. exigua with some key differences. Firstly, all S. exigua larvae placed on plants fed ( N = 64). For S. littoralis only 36/64 fed (56%). S. littoralis also appeared far more mobile; many dropped off of into the soil at the base of the plant and did not eat. For S. littoralis larvae placed on leaf 2, although trends matched S.exigua patterns, differences in feeding area between leaves were not significant ( Fig 5 ). In addition to damage area, for leaf 2-placed S. littoralis , the number of larvae feeding on each leaf was similar between leaves, whereas this was not the case for S. exigua placed on leaf 2 ( Table 1 ). However, for larvae placed on leaf 3, the S. littoralis patterns matched those of S. exigua , whereby leaf 3 and leaf 4 had significantly more damage than the other leaves ( Fig 5 ). Thus indicating that generalist herbivores preferentially feed on the leaves that result in the greatest plant-level damaged-induced volatile emissions. Download figure Open in new tab Figure 5. Generalist herbivores consistently feed heavily on leaf 3 regardless of starting position. The percentages of total plant damage (on V2-stage seedlings) done on each leaf for both Spodoptera exigua and S. littoralis . Larvae were either placed on leaf 2 or leaf 3 (starting position). For each herbivore species, lowercase letters indicate significant differences between leaves within a given herbivore starting position based on pairwise Wilcox tests. Grey points represent biological replicates. Bars = mean ± SE. For S. exigua , n = 32 and for S. littoralis , n = 14-22. View this table: View inline View popup Download powerpoint Table 1. Intact maize seedling herbivore feeding information table. Similar to mechanical damage patterns, we found, under authentic herbivory conditions, leaf 3 emitted higher amounts of all measured volatiles compared to leaf 2 in detached leaves ( Fig 6A ). In intact seedlings, total emissions from S. exigua -infested plants were positively correlated with leaf 3 damage for all measured compounds ( Fig 6B ): This was not the case for leaf 2, where no positive correlations were observed, and TMTT emission even showed a significant negative relationship with leaf 2 damage ( Fig 6C ). Thus confirming that, like mechanical damage, herbivory on leaf 3 is of the greatest consequence to total volatile emissions in maize seedlings. Download figure Open in new tab Figure 6. Leaf 3 plays a determinant role in overall herbivore-induced volatile emission during Spodoptera exigua herbivory. A) Emission from S. exigua -fed detached leaves from V2-stage seedlings. B) Correlation of total plant-level emission with total leaf 3 damage after 6 hr of herbivory. C) Correlation of total volatile emission with total leaf 2 damage after 6 hr of herbivory. B and C depict the same individual plants. For B and C, total emissions are the sum of individual measurement time points (taken every ca. 6 min) over 6 hr (see Materials and Methods, Volatile sampling ). Colored points represent biological replicates and black lines represent significant linear relationships between emission and damage. In addition to monitoring how herbivores mediate volatile emission patterns, we aimed to investigate how maize leaves differentially impact herbivore performance. Both S. exigua and S. littoralis damaged leaf 3 to a greater extent than leaf 2 in detached leaves ( Fig 7A ). After 48 hr of feeding, there were no clear differences in relative growth rate for either species between leaf 2- and leaf 3-fed larvae ( Fig 7C ). Download figure Open in new tab Figure 7. Generalist herbivores damage a greater area of leaf 3 with no apparent performance benefit. A) Area of leaf tissue consumed by 4th instar Spodoptera exigua and S. littoralis after 6 hr feeding on detached leaves from V2-stage seedlings, B) calculated biomass consumption based on leaf mass per area (LMA) of leaf 2 (0.0031) and leaf 3 (0.0025), and C) relative growth rate of 4th S. exigua and S. littoralis after 48 hr of feeding. * = p < 0.05, *** = p < 0.001 as determined by Welch’s two-sample t- test. Grey points represent biological replicates. Bars = mean ± SE. For S. exigua n = 13-19, for S. littoralis n = 9-17. Discussion Spatiotemporal and developmental patterns have been shown influence volatile emissions in ecologically important ways ( Köllner et al ., 2013 ; Wang et al ., 2023 ; Waterman et al ., 2024 ). We show that size of the damaged maize leaves plays a determinant role in the overall plant-level volatile emissions and that generalist herbivores preferentially feed on the leaves that result in the highest volatile emissions. Here we discuss the mechanisms and potential implications of these patterns. Within-plant spatiotemporal patterns are known to be important factors mediating stress responses ( Barton et al ., 2010 ; Köhler et al ., 2015 ; Wang et al ., 2023 ). Given maize seedlings differentially emit volatiles depending on the location of damage, one possible explanation for this variability could be differing induction capacities or systemic signaling capabilities between foliar tissues ( Köllner et al ., 2013 ; Irmisch et al ., 2014 ). However, our data show minimal differences in induction between leaves; volatile biosynthesis genes are not only comparable across locally-damaged leaves, but the capacity for induction in undamaged, systemic leaves are consistent regardless of where damage occurs. A similar pattern was found for HDMBOA-Glc, which is a compound known to play an important role in direct defense against chewing herbivores such as Spodoptera spp. ( Glauser et al ., 2011 ; Tzin et al ., 2017 ; Li et al ., 2018 ). As such, per unit tissue, induction capacity is similar between V2-stage maize seedling leaves, most clearly shown between leaf 2 and leaf 3. Nevertheless, it is possible that underdeveloped defense machinery and gas exchange capacity in the emerging leaf 4 contributes to the apparent reductions in volatile emissions in leaf 4 ( Wang et al ., 2019 ; Wang et al ., 2023 ). Based on these findings, it is clear that the observed differences in emission are not driven by differences in defense inducibility between leaves. Leaf size seems to be the determinant factor controlling volatile emissions in maize seedlings, as there are very strong and consistent relationships between emission and both damaged-leaf mass and area across developmental stages. While there is generally a strong relationship between emission and area, it is less robust as the relationship between emission and biomass. As such, it is likely that emission is a function of both area, whereby greater area equates to more points of exit for volatiles (e.g., more stomata), and thickness, whereby thicker leaves may have more tissue capable of producing these volatiles. Biomass is a function of both area and thickness, which likely explains why this relationship is strongest. The relationship between emission and leaf size is also clearly strongest for leaves that are actively developing. As developing leaves show greater variation in mass and area than fully-developed leaves ( Powell & Lenhard, 2012 ), the close relationship between size and emission is apparent. Once leaves are fully developed there is much lower variation in size between leaves, however there are still differences in emission between individual leaves. We have previously identified leaf development as a factor that influences volatile emissions ( Wang et al ., 2023 ), and our findings in the present study support this; when immature and mature leaves are comparable in size, mature leaves emit more volatiles given equal damage. While green leaf volatiles are formed in, and emitted directly from, wounded tissues ( Hatanaka, 1993 ; Matsui et al ., 2000 ; Escobar-Bravo et al ., 2023 ), other volatiles such as terpenes and indole are produced de novo and systemically over time following stress ( Erb et al ., 2015 ), and emissions of these compounds are likely regulated by stomatal aperture ( Niinemets, 2003 ; Seidl-Adams et al ., 2015 ; Maleki et al ., 2024 ). Some herbivores have been shown to excrete enzymes, such as glucose oxidase, which close stomata and minimize volatile emissions during feeding ( Lin et al ., 2021 ). Nevertheless, the relationship between herbivore feeding behavior and volatile emissions is not well understood. We find that two generalist herbivores preferentially feed on the leaves that result in the highest local and plant-level volatile emissions. As herbivores feed more heavily on leaf 3, which emits the most volatiles, we reasoned that there would be a clear benefit for herbivores to eat this leaf. However, surprisingly, this was not the case. In fact, what we observed was that, in order to reach the same level of growth, herbivores must consume more leaf 3 tissue compared to leaf 2, resulting in even greater volatile emissions. Even in intact plants, where herbivores can move freely over the entire plant, herbivores chose to consume leaf 3 most, which has a determinant impact on overall volatile emissions in maize seedlings. As a result, Spodoptera larvae may reveal themselves to the environment by feeding on the tissues that release the most volatiles. Herbivore-induced volatile emissions can be disadvantageous for herbivores through direct toxicity ( Veyrat et al ., 2016 ; Chen et al ., 2021 ; Chen et al ., 2023 ), enhanced attraction of natural enemies ( Michereff et al ., 2019 ) and population and community resistance by enhancing neighboring plant defenses ( Escobar-Bravo et al ., 2023 ; Waterman et al ., 2024 ). On the other hand, they can also be advantageous for herbivores by confusing natural enemies and can alter competition between herbivores by enhancing host location and increasing oviposition ( Poelman et al ., 2012 ; Signoretti et al ., 2012 ; Zu et al ., 2020 ). Additionally, it has been shown that relative ratios and blends of emitted compounds, beyond absolute amounts can shape volatile-mediated interactions between plants and the environment ( Allmann & Baldwin, 2010 ; Fouchier et al ., 2018 ). More research, including longer-term feeding experiments under natural conditions, is required to understand to the consequences of increased volatile emissions in the atmosphere, as well as the potential fitness costs and benefits of herbivore feeding behavior in this context. In conclusion, our work shows that the size of damaged leaves determines the extent of damage-induce volatiles emitted from maize seedlings. While tissue damage can be inflicted by many abiotic and biotic stressors, it is particularly noteworthy that, in addition to mechanical damage, patterns are consistent with real herbivore feeding. Our work provides a basis for developing a deeper understanding of the precise nature of herbivore feeding patterns and defense phenotypes, and how the interaction of the two can yield potentially important ecological outcomes for both plants and herbivores. Supplemental Tables View this table: View inline View popup Download powerpoint Supplemental Table 1. Gene identifiers and qRT-PCR primer sequences used in this study. View this table: View inline View popup Download powerpoint Supplemental Table 2. ANOVA and Kruskal-Wallis test results from data presented in the main text. Bold values: p < 0.05. Abbreviations: TPS2 = terpene synthase 2, TPS10 = terpene synthase 10, CYP92C5 = Dimethylnonatriene/trimethyltetradecatetraene synthase, IGL = indole-3-glycerol phosphate lyase, DMNT = 4,8-dimethylnona-1,3,7-triene, MNT = monoterpenes, SQT = sesquiterpenes, TMTT = 4,8,12-trimethyltrideca-1,3,7,11-tetraene, S.e = Spodoptera exigua , S.l = Spodoptera littoralis View this table: View inline View popup Download powerpoint Supplemental Table 3. Welch’s two-sample t-test results for herbivore performance parameters between detached leaf 2 and leaf 3 Author Contributions JMW conceived the ideas and designed experiments with critical inputs and support from ME. JMW, TMC, OMVL, PM and LW conducted experiments. JMW wrote the first draft of the manuscript and JMW and ME wrote the final version of the manuscript. All authors gave final approval of the manuscript. The authors have no conflicts of interest to declare. Data availability statement All data will be made available (either as supplemental material or on a data repository) upon publishing the final version, or beforehand upon request. Acknowledgements We would like to thank Sarah Holder for assistance with plant growth and all members of the Biotic Interactions and Chemical Ecology groups at the University of Bern for helpful discussions. This work was supported by the Swiss National Science Foundation (Grants Nr. 210651 and 200355), the State Secretariat for Education, Research, and Innovation SERI (Project CANWAS), as well as the University of Bern. Footnotes Minor adjustments to figures/figure axes. Interpretations remain the same. References ↵ Allmann S , Baldwin IT . 2010 . 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