induced plant volatiles accurately predict history ... - Wiley Online Library

Research

Herbivore-induced plant volatiles accurately predict history of coexistence, diet breadth, and feeding mode of herbivores Holger Danner1, Gaylord A. Desurmont2,3, Simona M. Cristescu4 and Nicole M. van Dam1,5,6 1

Molecular Interaction Ecology, Institute for Water and Wetland Research (IWWR), Radboud University, PO Box 9010, 6500 GL Nijmegen, the Netherlands; 2Institute of Biology, University

of Neuch^atel, Neuch^atel 2000, Switzerland; 3European Biological Control Laboratory, USDA-ARS, CS 90013 Montferrier-sur-Lez, France; 4Life Science Trace Gas Facility, Institute for Molecules and Materials, Radboud University, 6500 GL Nijmegen, the Netherlands; 5German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig 04103, Germany; 6Institute of Ecology, Friedrich Schiller University Jena, Dornburger-Str. 159, Jena 07743,Germany

Summary Author for correspondence: Nicole M. van Dam Tel: +49 3419733165 Email: [email protected] Received: 1 October 2016 Accepted: 8 December 2016

New Phytologist (2017) doi: 10.1111/nph.14428

Key words: aphids, Brassicaceae, caterpillars, invasive herbivores, isothiocyanates, plant– herbivore interactions, slugs, terpenes.

Herbivore-induced plant volatiles (HIPVs) serve as specific cues to higher trophic levels. Novel, exotic herbivores entering native foodwebs may disrupt the infochemical network as a result of changes in HIPV profiles. Here, we analysed HIPV blends of native Brassica rapa plants infested with one of 10 herbivore species with different coexistence histories, diet breadths and feeding modes. Partial least squares (PLS) models were fitted to assess whether HIPV blends emitted by Dutch B. rapa differ between native and exotic herbivores, between specialists and generalists, and between piercing–sucking and chewing herbivores. These models were used to predict the status of two additional herbivores. We found that HIPV blends predicted the evolutionary history, diet breadth and feeding mode of the herbivore with an accuracy of 80% or higher. Based on the HIPVs, the PLS models reliably predicted that Trichoplusia ni and Spodoptera exigua are perceived as exotic, leafchewing generalists by Dutch B. rapa plants. These results indicate that there are consistent and predictable differences in HIPV blends depending on global herbivore characteristics, including coexistence history. Consequently, native organisms may be able to rapidly adapt to potentially disruptive effects of exotic herbivores on the infochemical network.

Introduction In response to herbivory, plants emit complex blends of herbivore-induced plant volatiles (HIPVs) (Reymond et al., 2000; Unsicker et al., 2009; Dudareva et al., 2013). Plants are able to recognize herbivore species and tailor their response accordingly, based on, for example, chemical elicitors present in the herbivores’ saliva (Mithofer & Boland, 2008; Bonaventure, 2012). Because of this, HIPVs have been hypothesized to be a rich source of information for organisms associated with plants, and thus are considered a major component of the so-called ‘infochemical network’ (Vet & Dicke, 1992; Shiojiri et al., 2001; Dicke & Baldwin, 2010; Desurmont et al., 2014). For example, herbivorous insects use HIPVs to avoid plants that are already occupied by herbivores (De Moraes et al., 2001; Kessler & Baldwin, 2001; Zakir et al., 2013). Higher trophic levels, such as predators and parasitoids of arthropod herbivores, use HIPVs to locate their host or prey (Vinson, 1976; Dicke & Sabelis, 1988; Dicke & Baldwin, 2010; Amo et al., 2013). The adaptive value of exploiting HIPVs to enhance host location efficiency has been well established for parasitic wasps, which may also learn specific compounds in HIPV blends to increase their foraging efficiency Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

(Meiners et al., 2003; Hoedjes et al., 2011; Kruidhof et al., 2015). From the plant’s perspective, it has been hypothesized that HIPVs should evolve towards higher specificity in order to become a reliable signal for specialized natural enemies (Vet & Dicke, 1992). Feeding mode is a main trait on the basis of which plants can recognize herbivores. Leaf-chewing insects are known to trigger specific signalling pathways and to induce different HIPVs from piercing–sucking insects (Howe & Jander, 2008; Bidart-Bouzat & Kliebenstein, 2011; Verheggen et al., 2013). A recent metaanalysis including > 100 studies on HIPVs found that, overall, leaf-chewing herbivores induced more volatiles than sap-feeders (Rowen & Kaplan, 2016). In addition, this meta-analysis identified that the degree of host specialization may be another factor explaining differences in HIPV emissions (Rowen & Kaplan, 2016). Specialists were found to induce similar numbers of compounds to generalists, but in higher quantities. It was mentioned, however, that these conclusions were probably confounded by differences in the representation of sucking herbivores in the specialist (19%) and generalist (c. 50%) classes. Moreover, the authors explicitly mentioned that there are too few studies ‘directly comparing volatile induction by generalist and specialist New Phytologist (2017) 1 www.newphytologist.com

New Phytologist

2 Research

herbivores’ (Rowen & Kaplan, 2016). In addition, the few comparative studies there are often suffer from a low level of replication at the class level; they usually compare HIPVs induced by one specialist to those induced by one generalist species (e.g. Diezel et al., 2009; Chabaane et al., 2015). Finally, the history of coexistence between plants and herbivores may also impact HIPVs (Desurmont et al., 2014). This may be particularly relevant for exotic herbivores invading new environments where they may feed on native plant species that were previously not exposed to these species. Globalization of trade and travel has facilitated the spread of exotic species across the planet (Harvey et al., 2010; Keller et al., 2011). In addition, anthropogenic climate change leads to many species shifting or extending their ranges pole-wards, thereby invading new environments (Chen et al., 2011; Bebber et al., 2013). Whereas the effects of climatic factors, for example CO2, ozone and temperature, on HIPV emissions have received ample attention (Laothawornkitkul et al., 2009; Yuan et al., 2009; Dicke & Loreto, 2010; Penuelas & Staudt, 2010; Unger, 2014), the effects of exotic herbivores on HIPV production by native plant species have been greatly neglected. The specificity of HIPVs induced by different – even closely related – herbivore species (see e.g. Geervliet et al., 1997) suggests that plants have evolved finely tuned responses to their native herbivore communities over evolutionary time. Therefore, it might be expected that HIPVs induced by exotic herbivores will differ compared with those induced by native herbivores even when they have similar feeding strategies or are closely related. However, the question of whether or not these differences are consistent enough for higher trophic levels to discriminate native hosts from exotic herbivores is as yet unanswered. Here, we tested the hypothesis that history of coexistence, diet breadth, and feeding mode affect HIPV blends in Brassica rapa originating from a Dutch natural population. We performed an untargeted analysis of HIPVs emitted after feeding by 10 different native and exotic herbivore species, belonging to four different taxa (Lepidoptera, Hemiptera, Hymenoptera and

Gastropoda; Table 1), with different levels of host specialization and feeding strategies. Even though we strived for a very broad and balanced selection of herbivores in each subclass, our test set was constrained by the availability of experimental herbivores in laboratory cultures and in the wild. Two-class partial least squares (PLS) models were fitted on the HIPV blends to assess the effect of coexistence history (native/exotic), diet breadth (generalist/specialist) and feeding mode (phloem feeding/leaf chewing) of the herbivores. The models were first run with the full set of herbivores, ignoring confounding factors, for example imbalances in the equal representation of diet breadths among native (both specialists and generalists) and exotic (all generalist) herbivores (see Fig. 1). Balanced models may give a more accurate comparison of HIPVs induced by different classes of herbivores (see also Box 2 in Ali & Agrawal, 2012), and therefore we reran the models after pruning the data for representation imbalances within classes. Most interestingly, this multivariate modelling approach allowed us to test whether HIPV blends can also predict a herbivore’s status. In other words, is it possible to predict the characteristics of a given herbivore species based on the HIPVs that are induced after feeding, which is the same issue that organisms at higher trophic levels are facing when hunting for hosts or prey? The latter question was addressed using the HIPV blends of two additional lepidopteran herbivores, Trichoplusia ni and Spodoptera exigua. Both species originate from (sub)tropical regions and are partially naturalized in northwestern Europe, either by spreading from glasshouses every growing season or by yearly northward migrations. Their history of coexistence with natural Dutch B. rapa populations is thus unclear. Therefore, the PLS models were used to predict whether the volatile blends induced by these two herbivores correspond to HIPVs typically induced by native or by exotic herbivores. In brief, our PLS models showed that HIPV blends contain reliable information on coexistence history, diet breadth and feeding mode of the herbivore with an accuracy of 80–100%. Overall, the balanced models showed a higher accuracy in classification. Based on the HIPV blend they induced in B. rapa plants, the

Table 1 Herbivores used for the induction of volatiles, including their classification for co-existence history, diet breadth and feeding mode Species 1

Arion rufus Athalia rosae2 Brevicoryne brassicae1 Mamestra brassicae3 Mamestra configurata4 Myzus persicae1 Pieris brassicae3 Plutella xylostella3 Spodoptera exigua1 Spodoptera frugiperda1 Spodoptera littoralis5 Trichoplusia ni3

Abbr.

n

History

Diet breadth

Feeding mode

Herbivores

Stage

AR SY BB MB MC MP PB PX SE SF SL TN

4 7 4 12 10 4 11 11 8 6 10 9

Native Native Native Native Exotic Native Native Native Uncl. Exotic Exotic Uncl.

Generalist Specialist Specialist Generalist Generalist Generalist Specialist Specialist Generalist Generalist Generalist Generalist

Leaf chewing Leaf chewing Phloem feeding Leaf chewing Leaf chewing Phloem feeding Leaf chewing Leaf chewing Leaf chewing Leaf chewing Leaf chewing Leaf chewing

12 18 100 15 16 100 23 20 22 20 19 19

Adult Larvae Adult L2/3 L2/3 Adult L2/3 L2/3 L2/3 L2/3 L2/3 L2/3

n, number of experimental plants sampled; uncl., unclear; herbivores, average number of herbivores per plant; L2/3, larval instar 2 or 3. Herbivore source: 1 own rearing; 2University of Bielefeld (Germany); 3Wageningen University (the Netherlands); 4Ottawa Research and Development Center (Canada); 5Syngenta AG (Switzerland). New Phytologist (2017) www.newphytologist.com

Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

New Phytologist

Research 3

Fig. 1 Numbers of herbivore species with different coexistence history, diet breadth and feeding mode in: (a) models 1a and 1b, coexistence history; (b) models 2a and 2b, diet breadth; (c) models 3a and 3b, feeding mode. Both the groups in the dark frames and those in the light frames were included in the full models (1–3a), whereas only the groups in the dark frames were included in the balanced models (1–3b). Dashed frames are classes not represented in the experiments.

models predicted that both T. ni and S. exigua are leaf-chewing, exotic generalists, which indicates that these species are not fully naturalized in the Netherlands yet.

Materials and Methods Plant and insect cultivation Brassica rapa L. seeds were batch collected in a natural population in the Netherlands (Maarssen, the Netherlands, in 2009). The seeds were germinated on glass beads in plastic food containers with transparent lids and kept at constant temperature and humidity (24°C; 70% relative humidity (RH)) under long-day conditions (16 h : 8 h, light : dark cycle) in a climate chamber for 1 wk (Snijders Labs, Tilburg, the Netherlands). The seedlings Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

were subsequently transferred to 2.2-l pots (11 9 11 9 21.5 cm) filled with potting soil (Lentse Potgrond no. 4; Horticoop, Bleiswijk, the Netherlands) and covered with a thin layer of plain river sand. The seedlings were placed in the centre of a sand-filled plastic foil ring (height 3 cm) placed on top of the sand layer. The ring and the sand inside were removed before volatile collection to facilitate the assembly of the shoot enclosures around the stem–root interface. Plants were grown in an insect-free glasshouse for 4 wk maintaining the 16 h : 8 h, light : dark cycle with highpressure sodium lamps (Philips, Eindhoven, the Netherlands) when photosynthetically active radiation was < 250 lmol m 2 s 1, until they had at least six fully developed leaves. Lepidopterans were obtained as eggs from different sources, and saw flies as first instars. The insects were reared on a mix of B. rapa and Brassica oleracea L. plants until used in the New Phytologist (2017) www.newphytologist.com

New Phytologist

4 Research

experiments (see Table 1 for numbers and instars used). The slugs used for the experiments were c. 3 cm in body size. Volatile organic compound collection and GC-MS analysis Plants were infested 48 h before the start of volatile organic compound (VOC) collections. The plants with the insects were enclosed in perforated polyethylene bread bags (c. 40 9 30 cm) which were closed around the plant pots with a rubber band to prevent insects from escaping. The soil was covered with aluminium foil and the plants were kept in a room maintained at 21°C 2°C and 60 10% RH with artificial plant lights before being transferred to the climate chambers for VOC collection. The plants were infested with differing numbers of herbivores to obtain approximately equally damaged plants (ANOVA and Tukey’s honest significant difference (HSD) test; P > 0.05) in each treatment (Table 1; Supporting Information Fig. S1). The perforated bread bags and the plastic rings around the stem–root interface were removed and polyethylene terephthalate oven bags (Dumil; ITH Complast, Zwijndrecht, the Netherlands) were attached with a cable tie around the stem–root interface resulting in a semi-tight enclosure. The insects remained on the plants during VOC collection. On the top, one upper corner of the oven bag was sliced open and a Teflon tube, connected via the VOC trap (30 mg of PorapakQ, 80/100 mesh; Sigma-Aldrich, Zwijndrecht, the Netherlands) to the vacuum pump system, was inserted and fixed with another cable tie to a wooden stick outside the cuvette system. Eight plants randomly assigned to one treatment were sampled simultaneously in one climate chamber (Snijders Labs) maintaining constant temperature, humidity and light (24°C, 70 10% RH, and 600 lmol m 2 s 1). The pump system with eight channels was connected to a computer to adjust the flow through each trap to 0.5 l min 1 and flows were monitored for each channel over the whole sampling period of 8–10 h. Depending on the herbivore species, four to 12 individual plants were sampled (Table 1). Undamaged plants were sampled on each day and served as controls (n = 15). At the end of VOC collection, the leaves were detached and the damage was captured with a digital image of the leaves on a white plastic plate with a Plexiglas cover containing a size marker for accurate image analyses. Immediately after the image was taken, the leaves were weighed and instantly flash frozen in liquid nitrogen and kept at 20°C until freeze drying for the determination of dry weights. The damaged leaf areas were determined using IMAGEJ (Schneider et al., 2012). The trapped VOCs were eluted twice with 100 ll of dichloromethane containing 1 ng ll 1 of nonylacetate as an internal standard. Eluted VOCs were stored at 20°C until GCMS analysis. Qualitative and relative quantitative determination of B. rapa volatiles was accomplished using an Agilent 7890 Series gas chromatograph equipped with a DB-5 MS column (Agilent, Santa Clara, CA, USA; 30 m length, 0.25 mm inner diameter, 0.25 micrometer film thickness) coupled to a JEOL accurate mass TOF instrument (JMS T100-GcV; JEOL, Tokyo, Japan; interface temperature 250°C; ion source temperature 200°C; electron energy 70 eV; detector voltage New Phytologist (2017) www.newphytologist.com

1950 V) recording spectra between 33 and 300 Da every 0.4 s. Helium was used as a carrier gas. The injector temperature of the GC was kept constant at 250°C and 1 ll of each sample was injected in splitless mode at a starting oven temperature of 39°C. The temperature was held for 3 min and then increased to 200°C at a rate of 4°C min 1, after which the temperature was increased to 300°C at 10°C min 1 and held for 1.75 min. Mass drift compensation was accomplished using the column bleeding at 207.0329 and 281.0517 Da as a reference to obtain accurate masses with an error smaller than m/z 2 mmu. The raw data were subsequently centroided and exported from the proprietary MASSCENTER software (JEOL) into the open exchange NetCDF format. Deconvoluted spectra (obtained using AMDIS v.2.71; National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA) were compared with spectra of authentic standards (dimethyldisulfide, (Z)-3-hexen-1-ol, (E )-2-hexenol, apinene, b-pinene, 6-methyl-5-hepten-2-one, D-limonene, and b-caryophyllene; Sigma Aldrich), or were identified by comparisons to spectra in the NIST library (2011, National Institute of Standards and Technology, Gaithersburg, MD, USA) including accurate mass and retention index comparisons in the literature (Table S1). Processing of chromatographic peak data The centroided GC-MS data in NetCDF format were further processed using the BIOCONDUCTOR package XCMS (Smith et al., 2006) implemented in the statistical software R (R Core Team, 2014). The peaks in each sample were detected with the CENTWAVE algorithm (peak width 2–20 s; signal to noise threshold 4). The retention time for peak detection was restricted from 3.5 to 40 min. The mass deviation was set to 20 ppm, which is c. 10-fold the mass accuracy of the mass spectrometer as recommended in Tautenhahn et al. (2008). Peak grouping across samples was restricted to peaks present in at least 50% of the samples in at least one treatment group (minfrac = 0.5). Retention time correction was accomplished with the symmetric method and nonlinear loess-smoothing and iterated three times with decreasing bandwidth parameter for the grouping from 10 to 0.2 s. The extracted ion species were grouped according to their parent molecule into pseudospectra with the BIOCONDUCTOR package CAMERA (Kuhl et al., 2012). This resulted in a final feature table containing mass-to-charge ratio (m/z), retention time, peak area and the pseudospectra group for each detected ion species. Groups containing only a single ion species were considered to be artefacts and removed from the final feature table. Furthermore, siloxane derivatives from the GC column were automatically removed from the data set with a custom-written script. The peak areas were all normalized to the peak area of the internal standard (nonylacetate; m/z 126) to avoid variation added during the elution and as a consequence of variations in detector sensitivity. Peak areas were also standardized to plant dry weights and the minor differences in the duration of VOC collection, yielding a value expressed as peak area g 1 Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

New Phytologist h 1 DW and hour for each ion species. From each of the remaining groups the base peak of each pseudospectrum was selected as a quantifier ion for the respective parent molecule. This resulted in a final table containing 293 ion species further referred to as volatile features. Statistical analyses The data were log (x + 1)-transformed and for each feature the average peak area of the control plants (n = 15) was subtracted from the corresponding peak area of each sample to centre the data around the control treatment values and to remove background peaks present in all samples. PLS models were fitted using the PLS algorithm as implemented in the PLS package (Mevik & Wehrens, 2007) for R (version 2016, R Core Team, 2014) with three different classification options. Coexistence history (native versus exotic; models 1a and 1b); diet breadth (generalist versus specialist; models 2a and 2b); and feeding guild (phloem feeding versus leaf chewing; models 3a and 3b) (Fig. 1). For each model, the optimal number of latent variables (LVs) was determined by optimizing the model with the receiver operating characteristic (ROC) as an optimizing criterion between one and 20 LVs. The models were cross-validated 200 times with 10-fold crossvalidation (CV) using the CARET package (Kuhn, 2008). We further compared the model accuracies to those of models calculated with the same data set but with permuted predictors to assess random model performance (200 repeats of a 10-fold CV). The accuracies of the permuted models remained at the expected random accuracy (50% for two-class PLS models) for all fitted models. An example is depicted in Fig. S2. Preprocessing of data (scaling to unit variance) was included in the cross-validation loop as recommended in Kuhn & Johnson (2013). The prediction capacity of the models is expressed as the rate of positive predictions (accuracy) during the 200 repeats of a 10-fold CV. After fitting the three models with all 293 features as the predictor variables, important predictors in the model were selected by calculating the variable importance in projection (VIP) value for each of them (Eriksson et al., 2001). Even though there is no hard threshold for this value, the ‘greater than 1 rule’ is commonly used as a criterion for selecting variables (Chong & Jun, 2005). VIPs were determined for each of the models during 200 repeats of the 10-fold CV and only features with VIP values > 1 in all submodels were selected. The stability of VIP values is reflected by the average VIP calculated for each of the features across the cross-validation procedure. Only features with an average VIP > 1 were used for fitting new models (model 1a: 53 features; model 2a: 34 features; model 3a: 46 features) with the important features only. Using the same methods, we assessed how the models were influenced by imbalances in herbivore classes. For example, the exotic species in our sampling consisted exclusively of generalist leaf-chewing herbivores, whereas the native species consisted of specialist and generalist as well as leaf-chewing and phloemfeeding herbivores (Table 1; Fig. 1). Consequently, exotic herbivores in the diet breadth model only appear in the generalist class and in the feeding mode model only in the leaf-chewing Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

Research 5

herbivore class. Therefore, we fitted three additional PLS models excluding specialists and phloem-feeding herbivores from the first model, and exotic species from the other two models. In the second step, VIP > 1 selected features were used again for fitting the final models (model 1b: 21 features; model 2b: 24 features; model 3b: 41 features). The treatments with the nonnative but partially naturalized species T. ni and S. exigua were excluded from the model fitting. To predict their coexistence history, we used model 1a and the data served as an external test set for the models 2a and 3a. For both procedures, a 10-fold cross-validation was repeated 200 times.

Results Models including all herbivores The untargeted volatile analyses resulted in a data matrix with 293 features. Of these 293 features, we identified a subset of 21 HIPVs including three tentatively identified compounds (Table S1). The PLS model fitted for the coexistence history with all 293 features of the HIPV blends predicted native and exotic species correctly with an accuracy of 90.7% 0.1 (Fig. S3) based on a model with five latent variables (LVs). The first three LVs explained 51.3%, 18.7% and 4.6% of the variance, respectively, and the latter two ≤ 1.5%. Comparing the emission rates between herbivore classes for all 293 volatile features, 10 features (3.4%) were induced more by exotic herbivores, whereas 88 features (30.0%) were more induced by native herbivores. Compared with control levels, 13 features (4.4%) were induced and three features (1%) suppressed by exotics whereas native herbivores induced 43 features (13.7%) and suppressed two features

Fig. 2 Accuracy of the partial least squares (PLS) models (mean based on 200 iterations) for different classifications after feature selection (variable importance projection (VIP) method). Whiskers represent 1.5 times the interquartile range; circles represent values outside the inner quartiles. Either all herbivores were included or only subsets, as indicated on the xaxis. Predictors: coexistence history, native versus exotic species (all (model 1a), seven latent variables (LVs) and 53 features; generalist leaf chewers only (model 1b), two LVs and 21 features); diet breadth, generalist versus specialist herbivores (all (model 2a), six LVs and 34 features; native species only (model 2b), five LVs and 24 features); feeding mode, leaf-chewing versus phloem-feeding herbivores (all (model 3a), two LVs and 46 features; native species only (model 3b), two LVs and 41 features). Letters above the boxplots indicate the results of the ANOVA followed by the Tukey honest significant difference (HSD) post hoc test. New Phytologist (2017) www.newphytologist.com

New Phytologist

6 Research

(0.7%). After selection of robust predictors (53 features with VIP > 1 in all submodels of the cross-validation), the model showed an accuracy of 91.8% 0.1 (seven LVs; explained variance of first five LVs: 41.0%, 16.9%, 15%, 4.1%, and 2.1%, respectively; Fig. 2). Partial least squares discriminant analysis (PLS-DA) plots showed that the volatile profiles of plants damaged by exotic or native herbivores separated on the first and second LV axis, explaining 60% of the total variation (Fig. 3a). Five unknown compounds had the highest VIP values and four of them were differentially expressed in the two treatment groups (Table S2). Among the identified compounds, the two isothiocyanates (ITCs) 3-butenyl-ITC and phenethyl-ITC were the best predictors (Table S2), whereby 3-butenyl-ITC was more induced in plants damaged by exotic herbivores (Fig. S4a). The HIPV blends induced by native and exotic herbivores therefore contain information about the coexistence history of the herbivores with B. rapa (model 1a). The second PLS model fitted for diet breadth including all 293 features discriminated generalist and specialist herbivores with an accuracy of 79.4% 0.2 (17 LVs; explained variance of first three: 59.9%, 7.0% and 7.1%, respectively; Fig. S3). About half of the features (51.2%) were induced more strongly by generalist herbivores, and none were more induced by specialists than by generalists. Compared with control levels, nine features (3.1%) were induced and two features (0.7%) suppressed by specialists, whereas generalists induced 103 features (35.2%) and only one feature (0.3%) was suppressed. After VIP selection, the model based on the remaining 34 features predicted diet breadth with an accuracy of 89.0% 0.1 (Fig. 2; six LVs; explained variance of first five LVs: 22.8%, 26.8%, 6.8%, 9.4% and 5.6%, respectively). Therefore, induced HIPV blends also carry information about the diet breadth of herbivores (model 2a), even though the HIPV profiles induced by generalists and specialists overlapped more in the PLS-DA plot than those induced by exotic and native herbivores (Fig. 3a versus 3c). In the third PLS model based on all features with feeding mode as a classifier, an accuracy of 100% 0 was obtained (two LVs; explained variance: 59.1% and 11.9%, respectively; Fig. S3). Of all 293 volatile features, five features (1.7%) were induced more after phloem-feeding herbivory, whereas 227 features (77.5%) were more induced after leaf-chewing herbivory. Compared with control levels, six features (2%) were induced and 114 features (38.9%) suppressed by phloem feeders whereas leaf chewers induced 47 features (16%) and suppressed two features (0.7%). With 46 VIP-selected features, the model still performed with an accuracy of 100% 0 (two LVs; explained variance: 54.0% and 22.0%, respectively; see Fig. 2). In the PLSDA, the HIPVs induced by sucking herbivores clearly separated on the first LV, whereas the specialist and generalist aphids separated on the second LV axis. VOC profiles are thus highly informative about the feeding mode of herbivores (model 3a; Fig. 3e). Each of the model accuracies was compared with the accuracies of models with randomly permuted classes assigned to the herbivores with 200 repeats of a 10-fold CV. This resulted in accuracies of c. 50% for each of the randomly assigned models, showing New Phytologist (2017) www.newphytologist.com

that the high accuracies obtained in the real models were unlikely to occur randomly (Fig. S2). If we compare the accuracies of the models with VIP selected features (models 1a, 2a and 3a), feeding mode was significantly better predicted than coexistence history, which was again better predicted than diet breadth (Fig. 2; ANOVA P < 0.001; Tukey HSD P < 0.001). Balanced model analyses For the detailed analyses of HIPVs important for the discrimination of coexistence history, diet breadth and feeding mode, models for balanced subsets of all herbivores were used. In the coexistence model (model 1a), specialists as well as phloem feeders were removed as these were all native (Fig. 1a). With the newly fitted PLS model (model 1b), the VIP method selected 21 features with VIP > 1 and the accuracy increased to 92.9% 0.1 (two LVs; explained variance: 43.1% and 24.1%, respectively; see Figs 2, 3b) compared with model 1a. Only 15.6% of the VIP > 1 selected features in model 1a remained in model 1b and none of the identified compounds was a robust predictor in model 1b any more (Tables S2, S3). When we focused only on the subset of compounds that we could identify, some additional differences between native and exotic chewing generalists emerged. A few compounds were induced above control levels to a similar extent by both native and exotic leaf chewers, such as the green leaf volatiles (GLVs) (Z)-3-hexen-1-ol and (Z)-3-hexenyl acetate and the glucosinolate (GSL) breakdown product 3-butenyl-ITC (Fig. 4a). Native herbivores additionally induced the emissions of (E)-2-hexenol, d-3carene, limonene, an unknown monoterpene, ethylbenzene, οcymene, 6-methyl-5-hepten-2-on, indane, decanal and acetophenone. The emissions of these compounds were consistently more induced by native herbivores than by exotic herbivores. Also, bcaryophyllene and nonanal were emitted in significantly higher amounts after native than after exotic herbivore feeding, even though the emissions did not significantly differ from the levels of control plants (Fig. 4a). A comparison of the induced HIPVs between model 1a and 1b reveals that the stronger induction of (Z)-3-hexen-1-ol, (Z)-3hexenyl acetate, and 3-butenyl-ITC emissions by exotic herbivores (Fig. S4a) disappears without specialists and phloem feeders included (Fig. 4a). This is probably attributable to the fact that phloem feeders (all natives) do not induce the emissions of these VOCs and thus lowered the average emission rates in the full model (Figs 4c, S4c). The diet breadth model was balanced by removing the exotic herbivores, which were all generalists, from the data set (Fig. 1b). This analysis (model 2b) resulted in 24 robust features with a VIP > 1 but the accuracy slightly decreased to 83.6% 0.2 (five LVs; explained variance: 20.0%, 26.5%, 16.6%, 7.3% and 4.4%, respectively; see Figs 2, 3d). The new model shared 41.5% VIP selected features with the model containing all herbivores (model 2a). Among the identified compounds, the GLV (E)-2-hexenol, the GSL breakdown products 3-butenyl-ITC and phenethylÓ 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

New Phytologist

Research 7

(a)

(b)

(c)

(d)

(e)

(f)

Fig. 3 Partial least squares–discriminant analysis (PLS-DA) score plots of models with all herbivores included on the left (a, c, e) and subsets of herbivores on the right (b, d, f). Coexistence history: (a) model 1a with all herbivores (seven latent variables (LVs); R2 = 0.85); (b) model 1b with generalist leaf chewers only (two LVs; R2 = 0.76). Diet breadth: (c) model 2a with all herbivores (six LVs; R2 = 0.77); (d) model 2b with only native species (five LVs; R2 = 0.70). Feeding mode: (e) model 3a with all herbivores (two LVs; R2 = 0.79); (f) model 3b with only native species (two LVs; R2 = 0.90). Axis values in brackets are the explained variation of each LV. Two-letter codes are species abbreviations (see Table 1).


New Phytologist (2017) www.newphytologist.com

New Phytologist

8 Research

(a)

(b)

(c)

Fig. 4 Average relative emission rates (+ SE) for identified compounds of herbivore-induced plant volatiles (HIPVs) emitted by Brassica rapa (scaled to unit variance). (a) Induced by native or exotic herbivores without specialist and phloem-feeding species (model 1b; native = 2 species (n = 16 samples); exotic = 3 species (n = 26 samples)). (b) Induced by generalist and specialist herbivores without exotic species (model 2b; generalist = 3 species (n = 20 samples); specialist = 4 species (n = 33 samples)). (c) Induced by leaf-chewing and phloem-feeding herbivores without exotic species (model 3b; leaf chewing = 5 species (n = 45 samples); phloem feeding = 2 species (n = 8 samples)). The mean relative emission rates were calculated as follows: treatment peak area control peak area mean loge mean loge SE:Compounds with VIP > 1 are in red. Letters indicate significant differences g (dw) h g (dw) h compared with control levels and stars indicate significant differences between the classes of herbivores. Depending on normality, results were obtained using ANOVA + Tukey honest significant difference (HSD) or Kruskal–Wallis + pairwise Wilcox test with Bonferroni correction. DMDS, dimethyldisulfide; ITC, isothiocyanate; 6met5hep2one, 6-methyl-5-hepten-2-one; unk., unknown.

ITC, and the monoterpenoid limonene showed a VIP value > 1 (Table S3; Fig. 4b). Limonene, (E)-2-hexenol, and 3-butenyl ITC were also identified as robust predictors in the model New Phytologist (2017) www.newphytologist.com

including all herbivores (model 2a), in which the GLVs (Z)-3hexen-1-ol and (Z)-3-hexenyl acetate were also among the important predictors (Tables S2, S3). Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

New Phytologist

Research 9

Among the identified compounds, generalist and specialist herbivores increased the emission of the GLV (Z)-3-hexenyl acetate to the same extent above control levels (Fig. 4b). Generalist herbivores further induced the GSL breakdown product 3butenyl-ITC, limonene, ethylbenzene, 6-methyl-5-hepten-2-on, acetophenone, and nonanal above control levels (Fig. 4b). Without exception, generalist herbivores induced HIPV emissions more strongly, especially the GSL breakdown products 3butenyl-ITC and phenethyl-ITC, an unknown monoterpene, ethylbenzene, and 6-methyl-5-hepten-2-on (Fig. 4b). A comparison with the results obtained with model 2a shows that the overall stronger induction of HIPVs by generalists was only observed without the exotic herbivores included (Fig. 4b versus Fig. S4b). This suggests that native specialists possess mechanisms that generally reduce the emission of HIPVs, whereas native generalists do not. In the feeding mode model, the exotic herbivores were removed as they were all leaf chewers (Fig. 1c; model 3b). With the VIP method, 41 robust features were selected and the accuracy of the model remained at 100% 0 (two LVs; explained variance: 64.4% and 10.4%, respectively; see Figs 2, 3f); 64.2% of the VIP selected features were identical to the model including exotic herbivores (model 3a). Among the identified compounds, the GLV (E)-2-hexenol was identified as an important feature discriminating HIPVs induced by phloem-feeding and leaf-chewing herbivores (VIP > 1; for details see Table S3c). Also in the full model 3a, (E)-2-hexenol was a robust predictor discriminating phloem feeders from chewers, in addition to nonanal (Tables S2, S3). Among the identified compounds, phloem feeders suppressed the emission of several compounds, such as the GLV (E)-2-hexenol, several monoterpenoids (d-3-carene, a-pinene and b-pinene), indane and decanal, compared with control plants (Fig. 4c). Leaf chewers, in contrast, increased the emissions of the GLVs (Z)-3-hexen-1-ol (a)

and (Z)-3-hexenyl acetate, as well as ethylbenzene and 6-methyl5-hepten-2-one, above control levels (Fig. 4c). Significant differences between the two herbivore classes were observed for all identified compounds except for 3-butenyl-ITC, phenethyl-ITC, limonene, phenylacetaldehyde and nonanal. The HIPV emission pattern in the reduced model 3b was highly similar to the pattern found in the full model (model 3a; Figs 4c, S4c). Only slight differences were found, for example for 3-butenyl-ITC, which was only found to be induced by leaf-chewing insects when exotic herbivores were included, probably as a result of the higher number of generalists (see Fig. 4b). Predictions of herbivore status based on HIPVs The HIPV blends induced after herbivory by S. exigua and T. ni (Fig. S5) were used for predicting the coexistence history of the herbivores using model 1a with the VIP selected features. Based on the volatile profiles S. exigua (n = 8) and T. ni (n = 9), samples were assigned to the class of exotic herbivores with an accuracy of 99.8% 0.03 and 99.9% 0.02, respectively (200 times 10fold cross-validation; Fig. 5a). In the diet breadth model (model 2a), S. exigua was classified as a generalist with an accuracy of 92.1% 0.11 and T. ni with an accuracy of 90.43% 0.09 (Fig. 5b). In the feeding mode model, both chewing herbivores were classified as such with an accuracy of 100% 0 (Fig. 5c). Thus, the models predict that both species induce HIPV profiles that are most similar to exotic leaf-chewing generalists.

Discussion Our study reveals that HIPVs are reliable cues to assess the history of coexistence, diet breadth and feeding mode of herbivores feeding on B. rapa plants. By using multiple representatives of each herbivore class, and by analysing the HIPV data in a series (b)

(c)

Fig. 5 Partial least squares–discriminant analysis (PLS-DA) scores of model predictions for the herbivores Spodoptera exigua and Trichoplusia ni. Scores were predicted with the respective models containing all herbivores (models 1a, 2a and 3a). The circles reflect the T2-hotelling distribution of the respective classes. (a) Coexistence history; (b) diet breadth; (c) feeding mode. The reliability was assessed by predicting the class with a 10-fold cross-validation loop, repeated 200 times. LV, latent variable. Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust


10 Research

of supervised multivariate models, we could show that differences in HIPV blends induced by different classes of herbivores are predictable and robust. This was illustrated by using the HIPVs induced by two additional herbivores, S. exigua and T. ni, whose coevolutionary history with B. rapa could not be unequivocally assessed a priori. Based on the HIPV profiles induced by these caterpillars, the models predicted that B. rapa plants originating from a Dutch population respond to these generalist herbivores similarly to the way in which they respond to other exotic leaf chewers, suggesting that they are not completely naturalized in the Netherlands. Most interestingly, our analyses revealed that HIPVs consistently differ between exotic and native herbivores, both with and without specialists and phloem feeders included in the model. Native generalists more strongly induced the emission of specific volatiles, such as mono- and sesquiterpenes, which are attractive to a wide variety of natural enemies (Mumm et al., 2008). This suggests that B. rapa is able to recognize these native species with which it shares a history of coexistence and responds accordingly to enhance its detectability for natural enemies. It is known that leaf-chewing lepidopteran herbivores contain compounds in their saliva that serve as cues determining the specificity of the volatile response (Mithofer & Boland, 2008). Salivary cues of lepidopteran herbivores were not the only elicitors that played a role in our experiments, as we also included a native slug. The locomotory mucus of slugs may contain salicylic acid (SA) which upregulates Pathogenesis Related protein 1 (PR1), a marker gene for SA-induced responses (Meldau et al., 2014). Elicitation of the SA pathway may suppress induced responses under control of the jasmonic acid (JA) pathway, including the up-regulation of terpenoids in Brassica species (van Dam et al., 2010). However, the emissions of JA-regulated HIPVs – such as terpenoids (Tytgat et al., 2013) – did not change when slugs were removed from the models (Fig. 4 versus Fig. S4), suggesting that the potential effect of mucus-derived SA on JA-regulated responses was weak. Both native and exotic herbivores similarly induced the emissions of more generic damage-induced compounds, such as the GLVs (Z)-3-hexen-1-ol and (Z)-3-hexenyl acetate and the GSL breakdown product 3-butenyl-ITC. It was previously reported that these compounds are involved in the attraction of natural enemies, especially generalist parasitoids (Reddy et al., 2002; Steidle & van Loon, 2003; Penaflor et al., 2011; Mathur et al., 2013; Bruce, 2014). Consequently, exotic herbivores are not expected to escape native generalist natural enemies using these generic HIPVs as cues. In contrast to generalist natural enemies, specialized parasitoids are known to respond to small, specific differences in HIPV profiles in complex olfactory backgrounds (Unsicker et al., 2009; Mumm & Dicke, 2010; Kessler & Heil, 2011). Moreover, the ability of specialist parasitoids to learn and associate differences in HIPV blends with the presence of a suitable host may help them to discriminate between plants infested by a native host and those infested by an exotic nonhost (Giunti et al., 2015; Kruidhof et al., 2015). This is illustrated by experiments with native Pieris brassicae and exotic Spodoptera littoralis feeding on B. rapa plants, alone or New Phytologist (2017) www.newphytologist.com

New Phytologist in combination (Chabaane et al., 2015). In a four-arm olfactometer, the native specialist parasitoid Cotesia glomerata preferred HIPVs from plants infested with their host, P. brassicae, over those infested by the exotic nonhost. This preference for host-induced HIPVs decreased when the exotic herbivore was feeding on the same plant (Chabaane et al., 2015). However, in longer term cage experiments, which provided the parasitoids with sufficient time to associate HIPV blends with the presence of P. brassicae, parasitization rates did not differ between plants with and without an exotic caterpillar (Chabaane et al., 2015). Our models also identified significant differences in HIPV profiles induced by specialist or generalist herbivores across a wide range of taxa. In contrast to a recent meta-analysis (Rowen & Kaplan, 2016), we found that plants responded to generalist herbivory by emitting more HIPVs than after specialist feeding. In particular, the generic damage-induced volatile ITCs, phenethyl-ITC and 3-butenyl-ITC, were more strongly induced by generalists (Fig. 4b). This is in concordance with the fact that several specialist herbivores are able to reduce the production of ITCs by interfering with the breakdown of GSL (Hopkins et al., 2009). The specialist Plutella xylostella produces a sulfatase in its gut cleaving the sulfate moiety from the GSL core structure, thereby preventing its hydrolysis by myrosinase (Ratzka et al., 2002). Pieris species, in contrast, direct GSL hydrolysis towards less toxic nitriles by the action of a nitrile specifier protein (Wittstock et al., 2004; Burow et al., 2006; Wheat et al., 2007). Two of the herbivores used in this study, the aphid Brevicoryne brassicae (Kazana et al., 2007) and the sawfly Athalia rosae, are known to sequester intact GSLs, thereby reducing their availability for myrosinase conversion (Opitz & Muller, 2009). Each of these strategies might contribute to the lower ITC emissions after specialist herbivory. Ethylbenzene, 6-methyl-5-hepten-2-one, and an unknown monoterpenoid were also more strongly induced by generalists than by specialists, which may also aid parasitoids and predators to differentiate between plants infested by generalist or specialists. In addition, we found that HIPV profiles strongly differed between sucking–piercing and chewing herbivores. With the exception of the ITCs, which were equally induced by both classes, chewers overall increased HIPV emissions, whereas sucking insects reduced HIPVs (Fig. 4c). Aphids are considered ‘stealthy feeders’ that avoid cell damage and thus induced responses by manoeuvring their stylets between plant cells. In addition, they manipulate plant induced response signalling for their own benefit via salivary effectors (Z€ ust & Agrawal, 2016). These differences in HIPVs attributable to feeding mode are also recognized by parasitoids. The parasitoid Cotesia rubecula did not distinguish between Arabidopsis plants damaged by the leaf-chewing host Pieris rapae and the two leaf-chewing nonhosts Plutella xylostella and S. exigua, but avoided plants damaged by the phloem-feeding herbivore Myzus persicae (van Poecke et al., 2003). Interestingly, we also found differences in HIPV profiles within the class of phloem feeders; the specialist B. brassicae repressed the emissions of most HIPVs, whereas feeding by the generalist M. persicae Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

New Phytologist specifically increased the emissions of 6-methyl-5-hepta-2-one (Fig. S6). This means that specific aphid parasitoids, such as Diaeretiella rapae for B. brassicae or Aphidius colemani for M. persicae, can also use HIPVs to discriminate hosts from nonhosts (Tariq et al., 2013). Moreover, our data also support the postulate that specialist aphids are better able to suppress induced responses, such as HIPVs, that may attract their natural enemies (Z€ ust & Agrawal, 2016). Finally, the question of whether overall HIPV profiles are herbivore species or class specific has been frequently addressed in several reviews and a recent meta-analysis (Reymond et al., 2004; Diezel et al., 2009; Dicke & Baldwin, 2010; Rowen & Kaplan, 2016). However, our approach is unique because we directly compared volatile induction by different herbivore classes with an unprecedented level of replication per herbivore class, using 10 different herbivores on a single wild B. rapa accession. Moreover, we also showed the full potential of multivariate models by using them to predict the ecological status of two herbivores whose coexistence history with Dutch plants was not fully clear. We argue that the predictive power of multivariate modelling is currently underexploited and might be used to address ecological-evolutionary relevant questions by ‘asking the plant and the herbivore’ about the nature and history of their interactions, for example in the context of reconstructing invasion or rangeexpansion scenarios. As our example shows, this would require well-validated models with sufficient levels of replication, which might best be achieved by joining research efforts of research groups working on the same plant species. The fact that B. rapa plants respond differentially towards herbivores with different coexistence histories, diet breadth and feeding modes would allow higher trophic levels to learn and use differences in HIPV blends as reliable cues informing them about the type of herbivore feeding (Vet & Dicke, 1992). This ability to learn may help to attenuate the potential disruptive effects of exotic herbivores on native infochemical networks (Desurmont et al., 2014). Yet, we realize that in natural environments multiple herbivores feed simultaneously or sequentially on a given plant species, which may cause additional shifts in the HIPV blend. On the one hand, there is experimental evidence that the concurrent feeding by aboveground feeding nonhosts does not affect parasitization levels of suitable herbivores in long-term experiments (Chabaane et al., 2015; de Rijk et al., 2016). Root herbivore feeding, however, causes shifts in Brassica HIPV profiles which reduce parasitization rates both in the field and in spatially explicit experiments (Soler et al., 2007a,b; Pierre et al., 2011; Danner et al., 2015). It was postulated that these parasitoids avoid plants with root herbivores because their offspring’s performance will be reduced as a result of changes in the plant’s metabolome that are trickling up via their herbivorous hosts (Soler et al., 2005; Qiu et al., 2009). This exemplifies the need to take the community context as well as the spatial and temporal scale on which interactions occur into account when studying the effect of shifts in HIPVs by nonhosts. Only then can we fully understand the ecological consequences and the longer term evolutionary implications of novel organisms entering native food webs. Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

Research 11

Acknowledgements We thank Caroline M€ uller (University of Bielefeld, Germany) for providing sawflies, Rita Gols (Wageningen University, the Netherlands) for cabbage loopers, and Tom J. de Jong (Leiden University, the Netherlands) for providing wild B. rapa seeds. Peter van Galen and Rob de Graaf (Radboud University, Nijmegen) supported the GC-MS procedures and the glasshouse team of Radboud University Nijmegen helped with plant cultivation. MSc student Miriam Huth helped with the experimental procedures, and Caspar Hallmann supported the development of the PLS modelling procedures in R. This study was financially supported by the ESF-EuroVOL programme funded by the Netherlands Organisation for Scientific Research (NWO-ALW), grant number 855.01.172 NWO-ALW, and the Swiss National Fund (SNF) FN 31VL30-134413 – EUROCORE project InvaVol. N.M.v.D gratefully acknowledges the support of the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig funded by the German Research Foundation (FZT 118).

Author contributions H.D., G.A.D., S.M.C. and N.M.v.D. planned and designed the research, H.D. and G.A.D. carried out the experiments, H.D. performed the chemical and statistical analyses, and N.M.v.D., H.D., S.M.C. and G.A.D. wrote the manuscript.

References Ali JG, Agrawal AA. 2012. Specialist versus generalist insect herbivores and plant defense. Trends in Plant Science 17: 293–302. Amo L, Jansen JJ, van Dam NM, Dicke M, Visser ME. 2013. Birds exploit herbivore-induced plant volatiles to locate herbivorous prey. Ecology Letters 16: 1348–1355. Bebber DP, Ramotowski MAT, Gurr SJ. 2013. Crop pests and pathogens move polewards in a warming world. Nature Climate Change 3: 985– 988. Bidart-Bouzat MG, Kliebenstein D. 2011. An ecological genomic approach challenging the paradigm of differential plant responses to specialist versus generalist insect herbivores. Oecologia 167: 677–689. Bonaventure G. 2012. Perception of insect feeding by plants. Plant Biology 14: 872–880. Bruce TJA. 2014. Glucosinolates in oilseed rape: secondary metabolites that influence interactions with herbivores and their natural enemies. Annals of Applied Biology 164: 348–353. Burow M, Muller R, Gershenzon J, Wittstock U. 2006. Altered glucosinolate hydrolysis in genetically engineered Arabidopsis thaliana and its influence on the larval development of Spodoptera littoralis. Journal of Chemical Ecology 32: 2333–2349. Chabaane Y, Laplanche D, Turlings TCJ, Desurmont GA. 2015. Impact of exotic insect herbivores on native tritrophic interactions: a case study of the African cotton leafworm, Spodoptera littoralis and insects associated with the field mustard Brassica rapa. Journal of Ecology 103: 109–117. Chen IC, Hill JK, Ohlemuller R, Roy DB, Thomas CD. 2011. Rapid range shifts of species associated with high levels of climate warming. Science 333: 1024–1026. Chong I-G, Jun C-H. 2005. Performance of some variable selection methods when multicollinearity is present. Chemometrics and Intelligent Laboratory Systems 78: 103–112. New Phytologist (2017) www.newphytologist.com

12 Research van Dam NM, Qiu BL, Hordijk CA, Vet LEM, Jansen JJ. 2010. Identification of biologically relevant compounds in aboveground and belowground induced volatile blends. Journal of Chemical Ecology 36: 1006–1016. Danner H, Brown P, Cator EA, Harren FJM, van Dam NM, Cristescu SM. 2015. Aboveground and belowground herbivores synergistically induce volatile organic sulfur compound emissions from shoots but not from roots. Journal of Chemical Ecology 41: 631–640. De Moraes CM, Mescher MC, Tumlinson JH. 2001. Caterpillar-induced nocturnal plant volatiles repel conspecific females. Nature 410: 577–580. Desurmont GA, Harvey J, van Dam NM, Cristescu SM, Schiestl FP, Cozzolino S, Anderson P, Larsson MC, Kindlmann P, Danner H et al. 2014. Alien interference: disruption of infochemical networks by invasive insect herbivores. Plant, Cell & Environment 37: 1854–1865. Dicke M, Baldwin IT. 2010. The evolutionary context for herbivoreinduced plant volatiles: beyond the ‘cry for help’. Trends in Plant Science 15: 167–175. Dicke M, Loreto F. 2010. Induced plant volatiles: from genes to climate change. Trends in Plant Science 15: 115–117. Dicke M, Sabelis MW. 1988. How plants obtain predatory mites as bodyguards. Netherlands Journal of Zoology 38: 148–165. Diezel C, von Dahl CC, Gaquerel E, Baldwin IT. 2009. Different lepidopteran elicitors account for cross-talk in herbivory-induced phytohormone signaling. Plant Physiology 150: 1576–1586. Dudareva N, Klempien A, Muhlemann JK, Kaplan I. 2013. Biosynthesis, function and metabolic engineering of plant volatile organic compounds. New Phytologist 198: 16–32. Eriksson L, Johansson E, Kettaneh-Wold N, Wold S. 2001. Multi-and megavariate data analysis Umetrics. Ume a, Sweden: Umetrics Academy. Geervliet JBF, Posthumus MA, Vet LEM, Dicke M. 1997. Comparative analysis of headspace volatiles from different caterpillar-infested or uninfested food plants of Pieris species. Journal of Chemical Ecology 23: 2935–2954. Giunti G, Canale A, Messing RH, Donati E, Stefanini C, Michaud JP, Benelli G. 2015. Parasitoid learning: current knowledge and implications for biological control. Biological Control 90: 208–219. Harvey JA, Bukovinszky T, van der Putten WH. 2010. Interactions between invasive plants and insect herbivores: a plea for a multitrophic perspective. Biological Conservation 143: 2251–2259. Hoedjes KM, Kruidhof HM, Huigens ME, Dicke M, Vet LEM, Smid HM. 2011. Natural variation in learning rate and memory dynamics in parasitoid wasps: opportunities for converging ecology and neuroscience. Proceedings of the Royal Society B: Biological Sciences 278: 889–897. Hopkins RJ, van Dam NM, van Loon JJA. 2009. Role of glucosinolates in insect–plant relationships and multitrophic interactions. Annual Review of Entomology 54: 57–83. Howe GA, Jander G. 2008. Plant immunity to insect herbivores. Annual Review of Plant Biology 59: 41–66. Kazana E, Pope TW, Tibbles L, Bridges M, Pickett JA, Bones AM, Powell G, Rossiter JT. 2007. The cabbage aphid: a walking mustard oil bomb. Proceedings of the Royal Society B: Biological Sciences 274: 2271–2277. Keller R, Geist J, Jeschke J, K€ uhn I. 2011. Invasive species in Europe: ecology, status, and policy. Environmental Sciences Europe 23: 1–17. Kessler A, Baldwin IT. 2001. Defensive function of herbivore-induced plant volatile emissions in nature. Science 291: 2141–2144. Kessler A, Heil M. 2011. The multiple faces of indirect defences and their agents of natural selection. Functional Ecology 25: 348–357. Kruidhof HM, Roberts AL, Magdaraog P, Munoz D, Gols R, Vet LE, Hoffmeister TS, Harvey JA. 2015. Habitat complexity reduces parasitoid foraging efficiency, but does not prevent orientation towards learned host plant odours. Oecologia 179: 353–361. Kuhl C, Tautenhahn R, B€ ottcher C, Larson TR, Neumann S. 2012. CAMERA: an inegrated srategy for cmpound spctra etraction and anotation of liquid chromatography/mass spectrometry data sets. Analytical Chemistry 84: 283– 289. Kuhn M. 2008. Building predictive models in R using the caret package. Journal of Statistical Software 28: 1–26. Kuhn M, Johnson K. 2013. Applied predictive modeling. New York, NY, USA: Springer. New Phytologist (2017) www.newphytologist.com

New Phytologist Laothawornkitkul J, Taylor JE, Paul ND, Hewitt CN. 2009. Biogenic volatile organic compounds in the Earth system. New Phytologist 183: 27–51. Mathur V, Tytgat TOG, Hordijk CA, Harhangi HR, Jansen JJ, Reddy AS, Harvey JA, Vet LEM, van Dam NM. 2013. An ecogenomic analysis of herbivore-induced plant volatiles in Brassica juncea. Molecular Ecology 22: 6179–6196. Meiners T, Wackers F, Lewis WJ. 2003. Associative learning of complex odours in parasitoid host location. Chemical Senses 28: 231–236. Meldau S, K€a stner J, von Knorre D, Baldwin IT. 2014. Salicylic acid-dependent gene expression is activated by locomotion mucus of different molluscan herbivores. Communicative & Integrative Biology 7: e28728. Mevik B-H, Wehrens R. 2007. The pls package: principal component and partial least squares regression in R. Journal of Statistical Software 18: 1–24. Mithofer A, Boland W. 2008. Recognition of herbivory-associated molecular patterns. Plant Physiology 146: 825–831. Mumm R, Dicke M. 2010. Variation in natural plant products and the attraction of bodyguards involved in indirect plant defense. Canadian Journal of ZoologyRevue Canadienne De Zoologie 88: 628–667. Mumm R, Posthumus MA, Dicke M. 2008. Significance of terpenoids in induced indirect plant defence against herbivorous arthropods. Plant, Cell & Environment 31: 575–585. Opitz SEW, Muller C. 2009. Plant chemistry and insect sequestration. Chemoecology 19: 117–154. Penaflor MFGV, Erb M, Miranda LA, Werneburg AG, Bento JMS. 2011. Herbivore-induced plant volatiles can serve as host location cues for a generalist and a specialist egg parasitoid. Journal of Chemical Ecology 37: 1304–1313. Penuelas J, Staudt M. 2010. BVOCs and global change. Trends in Plant Science 15: 133–144. Pierre PS, Dugravot S, Ferry A, Soler R, van Dam NM, Cortesero AM. 2011. Aboveground herbivory affects indirect defences of brassicaceous plants against the root feeder Delia radicum Linnaeus: laboratory and field evidence. Ecological Entomology 36: 326–334. van Poecke RMP, Roosjen M, Pumarino L, Dicke M. 2003. Attraction of the specialist parasitoid Cotesia rubecula to Arabidopsis thaliana infested by host or non-host herbivore species. Entomologia Experimentalis Et Applicata 107: 229– 236. Qiu BL, Harvey JA, Raaijmakers CE, Vet LEM, van Dam NM. 2009. Nonlinear effects of plant root and shoot jasmonic acid application on the performance of Pieris brassicae and its parasitoid Cotesia glomerata. Functional Ecology 23: 496– 505. R Core Team. 2014. R: a language and environment for statistical computing. Rversion 2016. Vienna, Austria: R Foundation for Statistical Computing. Ratzka A, Vogel H, Kliebenstein DJ, Mitchell-Olds T, Kroymann J. 2002. Disarming the mustard oil bomb. Proceedings of the National Academy of Sciences, USA 99: 11223–11228. Reddy GVP, Holopainen JK, Guerrero A. 2002. Olfactory responses of Plutella xylostella natural enemies to host pheromone, larval frass, and green leaf cabbage volatiles. Journal of Chemical Ecology 28: 131–143. Reymond P, Bodenhausen N, Van Poecke RMP, Krishnamurthy V, Dicke M, Farmer EE. 2004. A conserved transcript pattern in response to a specialist and a generalist herbivore. Plant Cell 16: 3132–3147. Reymond P, Weber H, Damond M, Farmer EE. 2000. Differential gene expression in response to mechanical wounding and insect feeding in Arabidopsis. Plant Cell 12: 707–719. de Rijk M, Yang DW, Engel B, Dicke M, Poelman EH. 2016. Feeding guild of non-host community members affects host-foraging efficiency of a parasitic wasp. Ecology 97: 1388–1399. Rowen E, Kaplan I. 2016. Eco-evolutionary factors drive induced plant volatiles: a meta-analysis. New Phytologist 210: 284–294. Schneider CA, Rasband WS, Eliceiri KW. 2012. NIH Image to ImageJ: 25 years of image analysis. Nature Methods 9: 671–675. Shiojiri K, Takabayashi J, Yano S, Takafuji A. 2001. Infochemically mediated tritrophic interaction webs on cabbage plants. Population Ecology 43: 23–29. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry 78: 779– 787. Ó 2017 The Authors New Phytologist Ó 2017 New Phytologist Trust

New Phytologist Soler R, Bezemer TM, Van der Putten WH, Vet LEM, Harvey JA. 2005. Root herbivore effects on above-ground herbivore, parasitoid and hyperparasitoid performance via changes in plant quality. Journal Of Animal Ecology 74: 1121– 1130. Soler R, Harvey JA, Bezemer TM. 2007a. Foraging efficiency of a parasitoid of a leaf herbivore is influenced by root herbivory on neighbouring plants. Functional Ecology 21: 969–974. Soler R, Harvey JA, Kamp AFD, Vet LEM, Van der Putten WH, Van Dam NM, Stuefer JF, Gols R, Hordijk CA, Martijn Bezemer T. 2007b. Root herbivores influence the behaviour of an aboveground parasitoid through changes in plant-volatile signals. Oikos 116: 367–376. Steidle JLM, van Loon JJA. 2003. Dietary specialization and infochemical use in carnivorous arthropods: testing a concept. Entomologia Experimentalis et Applicata 108: 133–148. Tariq M, Wright DJ, Bruce TJA, Staley JT. 2013. Drought and root herbivory interact to alter the response of above-ground parasitoids to aphid infested plants and associated plant volatile signals. PLoS ONE 8: 12. Tautenhahn R, Bottcher C, Neumann S. 2008. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9: 504. Tytgat TOG, Verhoeven KJF, Jansen JJ, Raaijmakers CE, Bakx-Schotman T, McIntyre LM, van der Putten WH, Biere A, van Dam NM. 2013. Plants know where it hurts: root and shoot jasmonic acid induction elicit differential responses in Brassica oleracea. PLoS ONE 8: e65502. Unger N. 2014. On the role of plant volatiles in anthropogenic global climate change. Geophysical Research Letters 41: 8563–8569. Unsicker SB, Kunert G, Gershenzon J. 2009. Protective perfumes: the role of vegetative volatiles in plant defense against herbivores. Current Opinion in Plant Biology 12: 479–485. Verheggen FJ, Haubruge E, De Moraes CM, Mescher MC. 2013. Aphid responses to volatile cues from turnip plants (Brassica rapa) infested with phloem-feeding and chewing herbivores. Arthropod–Plant Interactions 7: 567– 577. Vet LEM, Dicke M. 1992. Ecology of infochemical use by natural enemies in a tritrophic context. Annual Review of Entomology 37: 141–172. Vinson SB. 1976. Host selection by insect parasitoids. Annual Review of Entomology 21: 109–133. Wheat CW, Vogel H, Wittstock U, Braby MF, Underwood D, MitchellOlds T. 2007. The genetic basis of a plant–insect coevolutionary key innovation. Proceedings of the National Academy of Sciences, USA 104: 20427–20431. Wittstock U, Agerbirk N, Stauber EJ, Olsen CE, Hippler M, Mitchell-Olds T, Gershenson J, Vogel H. 2004. Successful herbivore attack due to metabolic diversion of a plant chemical defense. Proceedings of the National Academy of Sciences, USA 101: 4859–4864. Yuan JS, Himanen SJ, Holopainen JK, Chen F, Stewart CN. 2009. Smelling global climate change: mitigation of function for plant volatile organic compounds. Trends in Ecology & Evolution 24: 323–331. Zakir A, Sadek MM, Bengtsson M, Hansson BS, Witzgall P, Anderson P. 2013. Herbivore-induced plant volatiles provide associational resistance against an ovipositing herbivore. Journal of Ecology 101: 410–417.


Research 13 Z€ ust T, Agrawal AA. 2016. Mechanisms and evolution of plant resistance to aphids. Nature Plants 2: 9.

Supporting Information Additional Supporting Information may be found online in the Supporting Information tab for this article: Fig. S1 Average percentage of leaf area consumed on Brassica rapa by the chewing herbivores. Fig. S2 Validation of the coexistence history classification using permutation and 10-fold cross-validation. Fig. S3 Accuracy of the partial least squares models including all 293 features. Fig. S4 Average emission rates ( SE) of identified volatiles emitted by Brassica rapa using the full partial least squares models. Fig. S5 Emissions of identified volatile compounds by Brassica rapa after herbivory by Spodoptera exigua or Trichoplusia ni. Fig. S6 Emissions of identified volatile compounds by Brassica rapa after infestation with Brevicoryne brassicae or Myzus persicae. Table S1 Retention indices, exact mass and level of identification of volatiles in the headspace of Brassica rapa Table S2 Features and identified volatiles produced by Brassica rapa constituting important class predictors identified using full models (models 1a, 2a and 3a) Table S3 Features and identified volatiles constituting important class predictors identified using balanced models (models 1b, 2b and 3b) Please note: Wiley Blackwell are not responsible for the content or functionality of any Supporting Information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.
