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Keywords:

  • host plant quality;
  • insect life-history parameters;
  • multivariate plant phenotype;
  • phytochemistry;
  • plant − insect interactions

Summary

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information
  • 1
    Plants use various strategies to cope with opponents. According to the plant defence theory these traits are predicted to covary across taxa and were shown to be grouped into several syndromes for Apocynaceae. Specialist herbivores tolerate or detoxify components of the plants’ chemical weapons. Their development might mirror the putative defence syndromes of their hosts. This hypothesis was tested by measuring nutritive values and potential defence properties of seven species of Brassicaceae, considering leaf age. Effects of these traits were assessed on various life-history traits of the oligophagous sawfly Athalia rosae.
  • 2
    Positive correlations were found between particular plant traits. A hierarchical cluster analysis assembled plants in three distinct groups with either low nutritional quality or higher nutritional quality together with either only chemical or with chemical and mechanical defences. Although young and old leaves of each species grouped within the same clusters, age was a significant source of variation, demonstrating that ontogenetic changes in plant quality influence associations.
  • 3
    The correlations of several life-history parameters of A. rosae with each other and with plant traits were investigated. Mortality rates, developmental times and adult mass were correlated and important for insect fitness. Preference of adult females was largely influenced by larval performance. Three distinct clusters were determined, with fitness of this specialist highly depending on host plant quality.
  • 4
    Multivariate studies of the long-term performance and preference of the specialist in relation to the plant defence syndromes revealed general implications for plant − insect interactions, namely that insect traits mirror the defence syndromes of their hosts. Larval performance and adult preference were more influenced by general mechanical and chemical than by chemical plant defence traits, to which this specialist is adapted.
  • 5
    The ‘plant defence syndromes hypothesis’ is of general importance, however, as defence strategies and nutritional value change drastically throughout ontogeny, tissue age should be considered, and modifications on single trait associations are needed. Clusters forming plant syndromes mostly matched with clusters determined from the parameters of a specialist herbivore. Further research is needed on generalist performance, which might be differently influenced by the plants’ defence strategies.

Introduction

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Plants employ a wide variety of strategies to repel opponents (Stamp 2003). These multivariate phenotypes may comprise chemical, mechanical/structural defences and avoidance/tolerance mechanisms. The combination of factors that structure plant − herbivore interactions are of major importance for ecologists (e.g. Cornell & Hawkins 2003; Agrawal 2004), especially those factors that influence specialist herbivores which tolerate, circumvent, detoxify or even recruit defence components (for Brassicaceae specialists see Ratzka et al. 2002; Müller & Wittstock 2005).

Recently, a novel multivariate approach across plant taxa was presented which considers defence strategies as groups of traits (Agrawal 2007). The ‘plant defence syndrome hypothesis’ predicts that defence syndromes, for example, tolerance and resistance, can trade off if they are true alternative strategies (Agrawal & Fishbein 2006). Agrawal & Fishbein (2006) grouped defence traits according to edibility and defence status of the plant into three possible syndromes in a ‘defence syndrome triangle’: ‘low nutritional quality’, ‘nutrition and defence’, or ‘tolerance/escape’. The two main types of plant defence, that is, mechanical and chemical, both divert resources from growth processes and thus should force plants to invest primarily in one type of defence (Fine et al. 2006; Hanley et al. 2007), but also between other traits trade-offs were found, for example in direct vs. indirect resistance (Rutgers, Strauss & Wendel 2004). In a multivariate study on 23 milkweed species (Asclepias spp., Apocynaceae; formerly Asclepiadaceae), two defence strategies were expressed as two types of the ‘nutrition and defence’ syndrome: one with chemical defences, the other with mechanical defences (Agrawal & Fishbein 2006). The generality of the described defence syndromes for other plant families had not been tested yet. Furthermore, intra-plant variation of multivariate defence strategies and, in turn, its influence on herbivorous insects is still unclear.

In Brassicaceae, the most prominent chemical defence is the glucosinolate–myrosinase system (Halkier & Gershenzon 2006). Next to this potentially toxic weapon, digestibility reducers, namely proteinase inhibitors (e.g. Broadway & Colvin 1992) and mechanical barriers such as trichomes (Handley, Ekbom & Agren 2005) can be found within this family. The turnip sawfly Athalia rosae L. (Hymenoptera: Tenthredinidae) feeds exclusively on Brassicaceae, using various species within this family as hosts (Liston 1995). The larvae are able to concentrate glucosinolates of their host plants within their haemolymph (Müller et al. 2001) and retain them to adulthood (Müller & Sieling 2006). The sequestered glucosinolates are used by the larvae for their own protection against invertebrate predators, revealing a very close association with their host plants (Müller, Boevé & Brakefield 2002; Müller & Brakefield 2003). Performance on inbred plant lines with different glucosinolate and myrosinase levels differed only slightly (Müller & Sieling 2006). But performance differed significantly between groups fed on either of three species of Brassicaceae, which likely represented a wider range of variation in various defence traits as well as nutrient levels (Müller & Arand 2007). From the insect's perspective, host plant quality is generally determined by a suite of different chemical and morphological traits (Scriber & Slansky 1981; Raubenheimer & Simpson 1999; Awmack & Leather 2002). As A. rosae is well-adapted, chemical defences should be a less severe to no impact on insect development and oviposition compared to mechanical defences. Moreover, being a food specialist, this herbivore should also be forced to be a ‘nutrient generalist’ due to limited accessibility of suitable food in natural environments (Raubenheimer & Simpson 1999).

The aim of this study was to test for the generality of the plant defence syndromes as they had been described for Apocynaceae (Agrawal & Fishbein 2006), and to assess, how particular plant traits or syndromes are associated with insect performance parameters. As model system, seven species of Brassicaceae were investigated, which are potential host plants for the common oligophagous sawfly A. rosae. Several plant traits concerning nutritive values and defensive properties were investigated, correlated and clustered. Furthermore, as plant nutritional value and chemical and mechanical defence state are highly variable within a plant due to leaf age (Boege & Marquis 2005), and leaf age nested within plant species varied significantly for several relevant traits, the ‘defence syndrome triangle’ was tested by investigating young and old leaves separately. The influence of these defence strategies on the performance and preference behaviour of the specialist sawfly A. rosae was assessed. Because host quality can affect larval and adult performance parameters to different degrees, a complete evaluation of plant suitability can only be made by considering many insect life-history parameters and their correlations (Scheirs, De Bruyn & Verhagen 2003; Moreau, Benrey & Thiery 2006). Thus, instead of testing only few target plant and insect parameters, multivariate approaches were taken to determine common resistance mechanisms exerted by plant traits and their importance for the fitness of a specialist herbivore.

Materials and methods

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

plant and insect material

Young and old leaves of pre-flowering to early flowering plants of six wild, that is, non-cultivated plant species were collected every other day from late April to early June 2004 in the Botanic Garden of the University of Würzburg (Table 1). Plants were taken from different near natural display habitats or harvested as weeds. Additionally, glasshouse grown plants of a cultivated cabbage variety, the Chinese cabbage, were used (seeds were obtained from a local garden market). All seven species co-occur to some extent with the sawfly and are either already described (Liston 1995) or could be potentially used as host plants. Neonate larvae and naïve adult females (3–5 days after emergence from pupa, without oviposition experience) of A. rosae were taken from a laboratory culture. This was established from a field collection around Würzburg, Germany, and insects were reared on Sinapis alba L.

Table 1.  Glucosinolates used as internal standards for glucosinolate analysis and as substrate in myrosinase enzyme activity assays for sample processing of the seven plant species. For the former, a glucosinolate not naturally occurring in the plant species was chosen. For the latter, the dominant glucosinolate type of the respective plant species was used. Species codes refer to species names and leaf ages (y = young; o = old)
Plant species (common names)TribeSpecies codeInternal standard for glucosinolate analysisGlucosinolate substrate used for myrosinase activity assay
  1. Notes: Glucosinolates were obtained from:†Merck, Darmstadt, Germany;‡Phytoplan, Heidelberg, Germany;§Glucosinolates.com, Copenhagen, Denmark. Nomenclature of plant species follows the SysTax-Database (http://www.biologie.uni-ulm.de/systax; Wisskirchen & Adolphy 1998; Mansfeld & Hanelt 2001) and tribe associations are given according to (Al-Shehbaz, Beilstein & Kellogg 2006; Beilstein et al. 2006). gls, glucosinolate; n.d., Lunaria species could not be related to any accepted tribe yet.

Alliaria petiolata (M. Bieb) Cavara and Grande (garlic mustard)ThlaspideaeApy/Apobenzyl-gls2-propenyl-gls
Armoracia rusticana P. Gaertn., B. Mey. and Scherb. (horseradish)CardamineaeAry/Arobenzyl-gls2-propenyl-gls
Brassica rapa L. em. Metzg. ssp. chinensis (L.) Hanelt, cultivar Cantonner Witkrop (= Granat) (chinese cabbage)BrassiceaeBry/Bro2-propenyl-glsindol-3-ylmethyl-gls
Bunias orientalis L. (Turkish rocket)AnchonieaeBoy/Boo2-propenyl-glsp-hydroxybenzyl-gls§
Cardamine heptaphylla (Vill.) O. E. Schulz (seven-leaflet bittercress)CardamineaeChy/Cho2-propenyl-gls4-methylsulfinylbutyl-gls§
Cardamine pentaphyllos (L.) Crantz (five-leaflet bittercress)CardamineaeCpy/Cpo2-propenyl-glsp-hydroxybenzyl-gls
Lunaria rediviva L. (perennial honesty)n.d.Lry/Lro2-propenyl-gls4-methylsulfinylbutyl-gls

leaf chemistry

To analyse different parameters of plant chemistry of the seven plant species, samples were taken from young and old leaves of four individuals of each species (Table 1). Leaf discs (diameter 1·8 cm) were cut avoiding the main veins. Samples were weighed, frozen in liquid nitrogen and stored at –80 °C until later analysis. Leaf chemistry parameters focus on fresh wt of tissues as a reference, because insects are faced with the concentrations of molecules thus ingested.

C : N-element-analysis

Frozen samples were freeze-dried, weighed, and pulverized in a mill (Retsch, MM301, Haan, Germany). Total carbon and nitrogen content were analysed by quantitative decomposition of substances by oxidative combustion (CHN-O-Rapid, Heraeus, Hanau, Germany).

Glucosinolate analysis

Glucosinolates were analysed by HPLC after conversion to desulfoglucosinolates as described earlier (Martin & Müller 2007), but using a slightly modified gradient (solvent A: water, solvent B: methanol) of 0–5% B (10 min) and 5–45% B (28 min), followed by a cleaning cycle. Quantification was derived from the peak area relative to the area of the internal standard peak (Table 1), considering response factors for different glucosinolate side chains.

Myrosinase activity and soluble protein concentrations

Myrosinase activity was determined by photometric quantification of released glucose from an externally added substrate (Table 1), following the protocol by Travers-Martin et al. (2008). Myrosinase activity was either only found in the soluble fraction (B. orientalis, C. heptaphylla and L. rediviva), or determined from the mixture of the active soluble and insoluble fractions (A. petiolata, Ar. rusticana and C. pentaphyllos). In Br. rapa ssp. chinensis no myrosinase activity could be detected within 24 h. Soluble protein concentrations were determined according to Bradford (1976).

Proteinase inhibitors

Trypsin inhibitor concentrations were determined from frozen leaf material in a radial-diffusion assay according to Cipollini & Bergelson (2000). Proteinase inhibitor activity was measured in category units per leaf disc (0: no inhibition to 6: inhibition which is equivalent to 0·14 nmol of soybean trypsin inhibitor (TI) applied per well, 7: more than 0·14 nmol of TI).

leaf morphology and water content

Trichome density was determined from transparent nail polish imprints of ab- and adaxial leaf sides. Numbers of trichomes were counted on 9 or 36 mm of surface area under a dissecting microscope. Water contents of tissues were determined after freeze-drying of samples. Specific leaf areas (SLA) were obtained by relating leaf disc area to dry weights of samples.

insect performance parameters

To determine how particular plant traits or defence syndromes influence the performance and preference of a common herbivore on Brassicaceae, bioassays were conducted with the sawfly A. rosae. 40 mated and 40 unmated females of A. rosae were allowed to oviposit in separate groups on S. alba plants for 24 h. Because sex is determined by arrhenotoky in this species (Lee et al. 1998), unfertilized eggs develop into males and fertilized eggs into females. Within 20 h of hatching, larvae were distributed into four groups for each of the seven plant species: (i) male larvae (n = 30) on young leaves, (ii) male larvae (n = 30) on old leaves, (iii) a mixture of male and female larvae (n = 50) on young leaves and (iv) a mixture of male and female larvae (n = 50) on old leaves. Larvae were placed on moist tissue paper in 2 or 3 l plastic containers (200 × 200 × 65 or 95 mm; Gerda, Schwelm, Germany) with gauze ventilation in the lid and fed ad libitum with cut leaves of one of the seven plant species. Leaves were supplied with water by transferring the petiole into floral water tubes and were replaced at least every other day. Insects were kept in a climate chamber at 25 °C, 16:8 h light-dark-cycle, and 70% relative humidity (light source: Osram L 58 W/25 Universal White, 4150 lumens, Osram, Munich, Germany) throughout all trials. Eonymphs (the final, non-feeding instars) were transferred to soil containing cups (diameter 4 cm, height 8 cm) for pupation. Developmental times, body masses and sex of adults were recorded. Male adults were frozen immediately after weighing.

To quantify the body composition of male adults, water content was determined after 24 h of drying at 70 °C in an oven. 10 dried males of each rearing group were subjected to C:N element analysis as described above for plant samples. All other dried males were pulverized in 2 mL Eppendorf tubes in a mill (see above) and fat was extracted in three steps with 1 mL of n-hexane each (Roth, Karlsruhe, Germany). The remaining solvent in the body tissue was allowed to evaporate overnight and the dried tissue was weighed. This tissue was then subjected to glucosinolate analysis as described above for plant samples.

insect preference bioassays

To test for innate preferences of A. rosae for young or old leaves of the seven plant species, unmated, 3 to 5 days-old females, which were reared on S. alba as larvae, were used. Each female was placed separately into a Petri dish (diameter 5·5 cm) lined with moist filter paper. Two leaf squares (2 × 2 cm) of different age from one plant species were offered on small pieces of foam (5 × 5 × 2 mm) to allow access to the leaf edges needed for oviposition (Lee et al. 1998) (n = 17 per plant species). Numbers of deposited eggs on both squares were counted after 3 h.

To test for consequences of larval host plant experience on preferences of adults, 15 to 22 unmated, 3 to 5 days-old females from group (c) (see INSECT PERFORMANCE PARAMETERS) of all plant species were used, where available. From B. orientalis no sufficient numbers of female adults developed. Every female was confronted in no-choice tests with one medium-aged leaf disc (diameter 2·4 cm) of one plant species for 2 h in Petri dishes as described above. Every female was exposed to all seven plant species in random order within 2 days. Leaf squares and discs were used instead of whole plants to mainly test effects of chemical and mechanical cues on oviposition choice but to exclude interrelationships with visual cues of different plant architecture and leaf size.

statistical analyses

All statistical tests were performed using SPSS software (version 14·0, Chicago, IL, USA), except for nested analyses of variances which were conducted using R software (version 2·6·2, Free Software Foundation Inc., Boston, MA, USA). Plant data were obtained from young and old leaves of the seven species (datasets: n = 2age × 7species; each set n = 4replicates). The seven species were chosen as they belong to different tribes according to Al-Shehbaz et al. (2006). Thus, it is assumed that evolutionary constraints due to phylogenetic conservatism should be unlikely and that the obtained data across taxa are statistically independent. For leaves of different age, which share the same genetic background, the dynamic processes of differentiation and growth shape nutrition and defence traits phenotypically in different directions (Kursar & Coley 2003; Hanley et al. 2007). The effect of the factors ‘species’ and ‘leaf age’ on the variation of the various plant traits was tested by a nested anova, nesting leaf age (considered as random factor) within species (considered as fixed factor). As leaf age turned out to be a significant source of variation for at least five highly relevant plant traits (Table S2), further analyses were done for young and old leaves separately. Insect developmental data were obtained from rearing larvae in groups on young and old leaves of seven species (groups: n = 2sex × 2age × 7species; each set n = 30 or n = 50replicates at start). A nested anova on insect parameters, nesting the factor leaf age within species, revealed that leaf age significantly contributed to phenotypic variation for almost all insect traits (Table S5), making a separate further evaluation of data on young and old leaves necessary. Egg numbers laid on different plant species by naïve females were obtained in choice tests between leaves of different age within one species, thus data are not independent. However, periods for oviposition in bioassays were very short (3 h), so that any unnatural egg laying pressure was avoided (Odendaal & Rausher 1990). Moreover, egg numbers differed highly between plant species and between leaves of different age. This allowed for approximation of data independence.

All plant traits and insect parameters per sex were tested for normal distribution of data by Kolmogorov-Smirnov-tests and for homogeneity of variances by Levené. Pair-wise correlations of plant traits and insect parameters were conducted separately for young and old leaves on mean values (plants: n = 7; insects: n = 5–7) using Pearson product moment coefficients (r) with Bonferroni-corrections. Highly correlated plant variables were chosen for a subsequent principal component analysis on the means of young and old leaves for data reduction.

Data means were transformed to Z-scores (mean = 0, SD = 1) to obtain comparable scales for individual variables and principal component factors. Hierarchical cluster analyses were conducted using squared Euklidian distances and Ward's method for linkage to identify which of the datasets either from plants or insects group together. Outliers were tested by single linkage. For a complete cluster analysis on insect data containing all 14 rearing groups, missing developmental data were substituted by mean values of the respective variables for the four groups reared on B. orientalis and one group reared on L. rediviva, where mortality was 100%. Discriminant function analyses were subsequently performed to test for contribution of single variables to effective separation of clusters.

Differences between the clusters in mean individual plant traits and insect parameters were tested with unifactorial anova followed by Tukey HSD tests. T-tests were used on insect data where only two clusters included sufficient rearing groups.

To evaluate preference behaviour, multivariate ancova were used to assess effects of species and leaf age as predictors and female mass as a covariate on egg numbers of naïve females. Similarly, the egg deposition data for experienced females were tested in multivariate ancova to assess effects of larval rearing species as predictor and adult female mass as covariate on the total numbers of eggs laid in the seven consecutive bioassays. Furthermore, the numbers of accepted host plants were compared in multivariate ancova with larval rearing species as predictor and adult female mass and total numbers of eggs as covariates.

Multiple regression analyses were used to assess the effects of single plant traits on sawfly performance using mortality rates and means of plant traits and insect parameters (Z-scores). Probit models were used to describe the relationship between insect mortality rates and plant traits. Linear regression models were applied to means of total duration times and adult masses for male and female insects separately, as well as to body composition parameters of male adults and egg numbers of naïve females depending on plant traits.

Results

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

plant defence syndromes

The observed variation of plant traits was higher across species than within species between leaves of different age. Nevertheless, leaf age nested within species had a significant effect on variation of the highly relevant plant traits water content, SLA, myrosinase activity and abaxial and adaxial trichome densities (see Tables S1 and S2 in Supporting Information). Correlation coefficients for mean values of young or old leaf subsets were very similar (data not shown). There were significant positive correlations between protein and nitrogen content as well as between adaxial and abaxial trichome densities, whereas nitrogen and water content were negatively correlated. Several further coefficients were at least r > 0·7 or higher. Thus, two major groups of correlations with high coefficients (r > 0·7) were used for principal component analysis using mean values of young and old leaf data, respectively. Water, soluble protein and nitrogen content of fresh weight and SLA were combined to the factor ‘nutrition’ (PC-A, n = 14; Eigen value: 3·48; communalities – water: 0·868; protein: 0·899; nitrogen: 0·959; SLA: 0·751). An 87% of original variability between species and leaf ages of the four variables was incorporated in the factor values based on regression (component loadings – water: –0·931; protein: 0·948; nitrogen: 0·979; SLA: –0·867). Trypsin inhibitor activities as digestibility reducers and trichome densities of both leaf sides were combined to obtain a second factor (PC-B, n = 14; Eigen value: 2·69; communalities – trypsin inhibitor activity: 0·914; abaxial trichome density: 0·893; adaxial trichome density: 0·88). A 90% of original variability between species and leaf ages of the three variables was incorporated in the factor values based on regression (component loadings – trypsin inhibitor activity: 0·956; abaxial trichome density: 0·945; adaxial trichome density: 0·938). For reasons of clustering efficiency and to avoid incorporating redundant information, these factor values were used for further analyses.

The hierarchical cluster analysis using Ward's method revealed three clusters (Fig. 1). In the three clusters plant species are represented which have combinations of: (i) high nitrogen content, intermediate C : N ratio together with high glucosinolate concentrations, (ii) low nitrogen content, high C : N ratio and low glucosinolate concentrations, (iii) high nitrogen content, low C : N ratio together with high densities of trichomes and high proteinase inhibitor activities. Unifactorial anova on single variables revealed significant differences for all plant traits except for water content, SLA and myrosinase activity concentration between the clusters (Table S3). In a subsequent discriminant function analysis all traits contributed substantially to the separation of clusters, except for myrosinase activity concentration (Table 2). According to these functions plant clusters were effectively separated with a minimum of 98% correct classification.

image

Figure 1. Plant clustering according to the two discriminant functions; C1, centroid of cluster 1; C2, centroid of cluster 2; C3, centroid of cluster 3; three letter abbreviations refer to plant species means for young and old leaves (see Table 1).

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Table 2.  Coefficients of discriminant function analysis of clustering results using mean plant traits of young and old leaves of seven species of Brassicaceae
Plant traitFunction
12
  1. Notes: For original values and units of plant traits see Table S1.

C : N ratio–0·696–0·077
Glucosinolate concentration0·1630·940
Myrosinase activity concentration0·2230·648
‘Nutrition’ (PC-A)1·168–0·116
Trichomes and digestibility reducers (PC-B)1·391–0·214
Eigenwert of function19·0622·589
Explained variability (%)8812

insect performance and preference

Means of insect developmental parameters varied more distinctly between host plant species than between leaf age classes. Nevertheless, leaf age nested within species contributed significantly to the variation of almost all insect traits (Tables S4 and S5). Particularly, mortality rates of male larvae were on average higher when insects were reared on young leaves compared to old leaves for most species. Larval developmental times of females were a little longer, but their pupal times were about 1 day shorter than those of males. Females were about 30% heavier as eonymphs and as adults compared to males (Table S4). However, male and female mean parameters were highly correlated (Pearson-product moment correlations; mortality rates: r = 0·94, n = 14, P < 0·001; developmental times: r = 0·93, n = 11, P < 0·001; adult masses: r = 0·94, n = 11, P < 0·001).

Correlation coefficients of mean values for insects reared on young or old leaves were very similar (data not shown). Developmental parameters of larval stages determined the adult performance parameters (Table S4). Larval mortality rates were highly correlated with total mortality rates (n = 14; males: r = 0·99, P < 0·001; mixture of sexes: r = 0·99, P < 0·001). Larval developmental times were strongly correlated with total developmental times (males: n = 488, r = 0·86, P < 0·001; females: n = 196, r = 0·90, P < 0·001) and eonymph mass determined adult mass (males: n = 487, r = 0·86, P < 0·001; females: n = 196, r = 0·93, P < 0·001).

To define clusters, again only a subset of mean values of insect parameters was used: overall mortality rate, female adult masses, egg numbers of naïve females, as well as fat content and glucosinolate concentration of males. The hierarchical cluster analysis using Ward's method revealed three clusters (Fig. 2). Cluster 1 was formed from insects with high adult masses and high glucosinolate concentrations and nitrogen contents in males. In contrast cluster 2 included insects with low adult masses and low glucosinolate concentrations and nitrogen contents in males. Insects of both clusters showed low mortality and developmental times. In cluster 3 insects with high mortality rates were grouped. Surviving insects showed prolonged developmental times and lower adult masses compared to insects from clusters 1 and 2. Furthermore, males contained remarkably more fat per mass than in the other two clusters. Unifactorial anova revealed significant differences depending on the clusters for mortality rates, as well as for female developmental times and adult masses (Table S6). T-tests comparing all other traits between clusters 1 and 2 revealed differences for most other parameters except for male fat and non-fat dry matter content and male developmental times. A subsequent discriminant function analysis revealed that all parameters contributed meaningfully to the separation of clusters (Table 3). According to these functions, insect clusters were effectively separated with 100% correct classification of rearing groups into the three clusters.

image

Figure 2. Insect clustering according to the two discriminant functions; C1, centroid of cluster 1; C2, centroid of cluster 2; C3, centroid of cluster 3; three letter abbreviations refer to plant species means for young and old leaves (see Table 1); 1three or 2two missing values were substituted with the parameter mean (n = 11/12) for female mass in two groups (Boo and Boy) and fat content and glucosinolate concentration of males in three groups (Boo, Boy and Lry).

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Table 3.  Coefficients of discriminant function analysis of clustering results using mean parameters of insects reared on young and old leaves of seven species of Brassicaceae
Insect parameterFunction
12
  1. Notes: For original values and units of insect parameters see Table S5.

Overall mortality rates1·444–0·241
Egg numbers of naïve females–1·1100·011
Female adult mass0·1390·861
Male fat content1·9810·320
Male glucosinolate concentration1·0830·240
Eigenwert of function25·4665·443
Explained variability (%)82·417·6

Plant species had a stronger influence on oviposition preference of naïve females than leaf age, that is, 60% of variance was explained by the first and only 6% by the latter. The covariate female mass was not significant as a predictor (n = 238, ancova: d.f. = 8, F = 48·1, η2 = 0·63; species effect d.f. = 6, F = 57·5, P < 0·001, inline image = 0·60; leaf age effect d.f. = 1, F = 13·5, P < 0·001, inline image = 0·06; covariate female mass d.f. = 1, F = 0·2, P = 0·669, inline image = 0·001; variances were not homogenous).

For experienced females, the total numbers of eggs that were laid in seven consecutive bioassays were significantly influenced by the species on which larvae were raised, but not by female mass (n = 123, ancova: d.f. = 6, F = 6·1, η2 = 0·24; rearing species effect d.f. = 5, F = 6·3, P < 0·001, inline image = 0·21; covariate female mass d.f. = 1, F = 0·1, P = 0·752, inline image = 0·001; variances were not homogenous). The numbers of accepted host plants, a measure of host selectivity, were influenced by the species on which larvae were raised, and even five times stronger by the total numbers of eggs laid within the bioassays, but not by female mass (n = 123, ancova: d.f. = 7, F = 30·7, η2 = 0·65; rearing species effect d.f. = 5, F = 2·8, P = 0·020, inline image = 0·11; covariate total numbers of eggs d.f. = 1, F = 124·6, P < 0·001, inline image = 0·52; covariate female mass d.f. = 1, F = 2·8, P = 0·098, inline image = 0·024; variances were homogenous).

multiple regression analyses on plant and insect traits

Conducting probit analyses on total mortality rates of insects revealed that the best results were obtained when the plant factor PC-B was replaced by its individual plant traits, trichome densities and proteinase inhibitors, in a model also including plant C : N ratio, glucosinolate, myrosinase activity concentrations, and the factor ‘nutrition’ (Table 4). The coefficients for adaxial trichome density, trypsin inhibitor activity and the factor ‘nutrition’ were positive. These traits should thus increase the probability of death of an insect. C : N ratio, glucosinolate concentration and abaxial trichome density coefficients were negative and therefore these traits likely lead to a decrease of mortality probability. Myrosinase activity concentration had no significant influence on total mortality rates.

Table 4.  Probit analysis of mortality rates using the sum of dead larvae and pupae of male insects only and in mixture with females in rearing groups depending on plant traits (Z-scores); whole model predictability male χ2 = 12·99, d.f. = 6, P = 0·043; both sexes χ2 = 9·01, d.f. = 6, P = 0·173; Test for parallelism: χ2 = 13·778, d.f. = 1, P < 0·001
Plant traitSlopeSEZ-valueP
  1. Notes: All slopes correlate with each other except for C : N ratio: (a) r = 0·9; (b) r = 0·7.

(a) Males
 C : N ratio–0·400·13–3·010·003
 Glucosinolate concentration–1·070·30–3·60< 0·001
 Myrosinase activity concentration0·100·120·830·406
 Adaxial trichome density2·430·822·960·003
 Abaxial trichome density–1·810·54–3·370·001
 Trypsin inhibitor activity0·500·341·450·147
 ‘Nutrition’1·060·234·58< 0·001
 Constant0·030·210·150·880
(b) Both sexes
 C : N ratio–0·500·09–5·41< 0·001
 Glucosinolate concentration–0·760·13–6·01< 0·001
 Myrosinase activity concentration0·020·070·270·787
 Adaxial trichome density1·480·304·90< 0·001
 Abaxial trichome density–1·630·22–7·28< 0·001
 Trypsin inhibitor activity0·750·213·61< 0·001
 ‘Nutrition’0·520·114·81< 0·001
 Constant–0·220·08–2·860·004

Linear regression models for five important insect parameters were fitted on mean plant traits (Table 5). Longer developmental times could be attributed mostly to higher trichome and proteinase inhibitor levels and a little less to lower C : N ratios of plants for both sexes. Higher myrosinase activity was found to correlate with developmental times at least for males. Adult masses of insects regressed strongly on C : N ratio and also slightly on the factor ‘nutrition’. Whereas for males only the coefficient for plant C : N ratio was significantly different from zero, for females both coefficients revealed significant influences. The multiple linear regression models displayed a highly negative influence of myrosinase activity concentration and a slightly positive one of trichomes and proteinase inhibitors on fat accumulation of male adults. The glucosinolate concentration of adult males was strongly influenced by plant C : N ratio, and a little less by trichomes and proteinase inhibitors as well as ‘nutrition’. Egg numbers of naïve females regressed negatively on myrosinase activity and the factors trichomes and proteinase inhibitors as well as ‘nutrition’. Glucosinolate concentration of the plant tissue never had a significant influence on insect development or egg deposition.

Table 5.  Linear regression for developmental parameters depending on plant traits (Z-scores).
Insect parameterModelR2anovaStandardized coefficients‘Nutrition’
Fd fPC : N ratio MyrosinaseTrichomes and digest. reducers
  1. Notes: Complete models were established on the plant traits C : N ratio, glucosinolate and myrosinase activity concentration and the factors ‘trichomes and digestibility (digest.) reducers’ and ‘nutrition’. Reduced models contained only those plant traits or factors for which regression coefficients are displayed. In complete models no other coefficients were significantly different from zero than those displayed; asterisks indicate levels of significance for standardized regression coefficients: ***P < 0·001; **P < 0·01 and *P < 0·05.

(a) Males
Total developmental timeComplete0·9841·415< 0·001–0·39**0·42**0·67** 
 Reduced0·9787·343< 0·001–0·38**0·43**0·69*** 
Adult massComplete0·732·6350·1560·76 (P = 0·05)  0·27 (P = 0·44)
 Reduced0·719·9820·0070·80**  0·36 (P = 0·10)
Fat content of adultsComplete0·855·7450·039 –0·73*0·36 (P = 0·15) 
 Reduced0·8218·7220·001 –0·76**0·35 (P = 0·05) 
Glucosinolate concentration of adultsComplete0·898·2050·0190·82** 0·49*0·53 (P = 0·05)
 Reduced0·8614·0230·0020·81** 0·50*0·69**
(b) Females
Total developmental timeComplete0·9314·7950·003–0·38*0·29 (P = 0·08)0·68** 
 Reduced0·9127·693< 0·001–0·41*0·26 (P = 0·07)0·67** 
Adult massComplete0·898·1850·0190·77**  0·48 (P = 0·07)
 Reduced0·8726·672< 0·0010·80***  0·55**
Egg numbers of naïve femalesComplete0·683·3250·065 –0·57*–0·43 (P = 0·16)–0·49 (P = 0·14)
 Reduced0·625·4430·018 –0·57*–0·53*–0·35 (P = 0·10)

Discussion

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

plant defence syndromes

Three clusters of plant defence traits were found for Brassicaceae that resembled those determined by Agrawal & Fishbein (2006) for Apocynaceae: high nitrogen containing plants with chemical defences were grouped in cluster 1, whereas those with mechanical defences, associated with digestibility reducers, were found in cluster 3 (Fig. 1, Table S3). In plants of cluster 2, nitrogen content was low and also low levels of mechanical or chemical defences were found. Plant species composition in clusters 2 and 3 was very heterogeneous and seemed not to be influenced by phylogenetic history, that is, tribe association. But in cluster 1 both Cardamine species and Ar. rusticana were grouped. According to Al-Shehbaz et al. (2006) these species belong to one tribe, the Cardamineae, which lack trichomes and mostly occur in wet habitats (Beilstein, Al-Shehbaz & Kellogg 2006). Therefore, the observed clustering behaviour can be either due to phylogenetic constraints or due to adaptive evolution because trichomes can function as light and transpiration protection not needed in wet habitats (Roy, Stanton & Eppley 1999).

Defence levels and nutritional value can change drastically throughout ontogeny (Boege & Marquis 2005). Young and old leaves of one Brassicaceae species were always grouped in the same cluster, but leaf age was a statistically important source for variation (Fig. 1, Table S2). Observed differences between leaves of different age were substantial in all three clusters, especially in cluster 2 where age particularly reinforced the ‘low nutritional quality’ syndrome. Here, young leaves of A. petiolata were more closely associated with leaves of Br. rapa ssp. chinensis than with old leaves of A. petiolata, demonstrating, that ontogenetic changes in plant traits can have a profound effect on the quality of a particular defence syndrome. Because most variation was based on the nutritional characteristics and mechanical defence compared to chemical defence, this bias might have restrained the investigation on leaf age effects. In contrast to chemical defences, which can be remobilized from aging leaves (for glucosinolates see Chen & Andréasson 2001), trichomes once built are not accessible for nutrient recovery or relocation to developing tissue (Hanley et al. 2007). A change in defence strategy due to aging of leaves is probably found in most plant species relying mainly on chemical defences. Thus, developmental stage of plant tissue should be considered in investigations of plant defence syndromes.

Within this subset of Brassicaceae, the nutritious value of a plant tissue was predominantly protected by trichomes and proteinase inhibitors, whereas other chemical defences, that is, glucosinolate and myrosinase activity concentrations, were more or less expressed independently (Table 2). In other Brassicaceae, glucosinolates and trichomes were also not correlated phenotypically and only weakly on the genetic level (Traw 2002; Clauss et al. 2006). Throughout all species, water and nitrogen content were negatively correlated. Low water content is usually attributed to ‘hard to eat’ characteristics of a plant (Scriber & Slansky 1981) and was thus associated with the ‘low nutritional quality’ syndrome by Agrawal & Fishbein (2006). But high water contents per fresh weight (approximately 80–90%) indicate higher dilutions of metabolites which result in compensatory feeding of insects (Slansky & Wheeler 1992). Water content of plants might therefore show a biphasic relation to insect performance with an optimum below which feeding barrier characteristics are of higher importance (Coley, Bateman & Kursar 2006) and above which nutrition dilution rather is the prime cause for insect resistance. Also strong associations of proteinase inhibitor activity with trichome densities and with the nutritional status of a tissue were found. Within the syndrome triangle, these digestibility reducing proteins were hypothetically expected to be associated with the ‘low nutritional quality’ syndrome. But it might be more adaptive for a plant to develop high levels of proteinase inhibitors when there is a lot worthy to be protected, that is, high contents of nitrogen or proteins (Broadway & Colvin 1992). In addition, proteinase inhibitors could also act synergistically with trichomes, because the first might impede actual nitrogen acquisition, whereas the latter might cause tissue damage in the insect gut, whose repair is nitrogen demanding (Raubenheimer & Simpson 1999). Therefore, chemical and mechanical defences are likely to represent two independent strategies comprised of several traits (Koricheva, Nykanen & Gianoli 2004; Hanley et al. 2007). But these associations are not mutually exclusive as found for the connection between trichomes (mechanical defence) and proteinase inhibitors (chemical compounds).

insect performance and preference

Several life-history parameters for A. rosae were inter-correlated on the univariate and multivariate levels. As in the analysis of plant traits, three clusters were found for the development and preference of A. rosae (Fig. 2, Table S6). The first one could be ranked the best with high insect fitness and high chemical defence levels. The second was equal to the first with regard to a quick development and low mortality, but adults were significantly lighter and had low glucosinolate levels. In the third cluster, host plants provided the least insect fitness with high mortality rates. Insects that had developed on young or old leaves of one plant species were always grouped in the same cluster, however, within the clusters, associations were in part closer between rearings on different plant species than between rearings on young and old leaves within a species (Fig. 2), demonstrating again the important influence of tissue age on insect development.

Adult mass was no predictor for a high performance. Specifically, adult mass is frequently cited to correlate positively with female fecundity and/or fertility (Awmack & Leather 2002; Tammaru, Esperk & Castellanos 2002), but these traits can also be independent from each other (Moreau et al. 2006). Adult body mass of A. rosae was not correlated with the total numbers of eggs laid, but the latter was influenced by the larval host plant species. The number of accepted host plants increased concomitantly with total amount of eggs produced by the females as described previously for pipevine swallowtail butterfly females (Odendaal & Rausher 1990), but was also not correlated to body mass. This host plant effect on female fecundity and host selectivity could be mediated by potential fat accumulation. In pine sawflies about half of total body fat is allocated to the egg load (Herz & Heitland 2002). Experienced A. rosae females laid the fewest mean egg numbers, accepted the lowest number of host plants and males accumulated the lowest fat content on C. pentaphyllos (Table S4). In contrast, females reared on L. rediviva with a similar body mass laid about three times more eggs on a medium number of hosts and males accumulated a twice as high percentage of fat. Although exact female fat content was not determined, the overall correlation of male and female performance parameters was remarkably high to assume similar results for both sexes.

The choice of oviposition site determines the female's contribution to the next generation (Moreau et al. 2006) and is for many insect species important because young larval instars are not very mobile (Scheirs & De Bruyn 2002). In A. rosae, an overall correlation of preference of naïve females with performance parameters was found, which diverged only for one plant species: larval development on Br. rapa ssp. chinensis was intermediate, but females readily accepted this plant for egg deposition and egg mortality was lowest on this species (data not shown). Thus adult preference did largely reflect larval performance, but egg performance seemed to be of some additional importance.

matching of plant and insect clusters

Although A. rosae is a specialist on Brassicaceae, it could not develop on all plants within this family equally well. Performance differences of the sawfly were clearly shown in this study. Also, various associations between plant characteristics and insect performance traits could be derived from this multivariate approach. In a previous study, Agrawal & Fishbein (2006) did not find any differences in caterpillar growth of Danaus plexippus (Lepidoptera: Nymphalidae) examined for 5-day periods on Apocynaceae, belonging to three defence syndrome clusters. Thus, either the monarchs are well-adapted to all milkweeds or closer affiliations might have become visible, if more performance and preference data had been determined. Following the complete larval development as well as determining adult traits and preference behaviour enabled us to group insects into different clusters and to match these with plant defence syndromes. Multivariate approaches are therefore highly recommended, as testing only individual target traits of either plants or insects likely results in an incomplete picture of interconnections.

Plant clusters matched with insect clusters for most of the 14 groups in the Brassicaceae × sawfly interaction. Insect development was better in general on plants defended only chemically (plant cluster 1) than on those defended also mechanically (plant cluster 3) as might be predicted for a food specialist. But cluster affiliations of two plant species were interchanged with regard to plant and insect clustering (A. petiolata and C. pentaphyllos). The nutritional needs of A. rosae and the mechanism by which the larvae inactivate myrosinases to sequester intact glucosinolates (Müller & Wittstock 2005) are still unclear. But the poor performance on C. pentaphyllos, which had the highest myrosinase activities, could be due to increased costs for the larvae to inhibit myrosinases or to negative effects of glucosinolate breakdown products. Potential nutritional deficits might be compensated for by the larvae only in the absence of mechanical plant defence, because A. petiolata turned out to be a highly suitable host for the sawflies but Br. rapa ssp. chinensis plants, that are moderately covered by trichomes, provided less valuable food.

The availability of nitrogen for growth can be more important for early instars because of higher metabolic activities and growth rates (Zalucki et al. 2002). Severe deficits in protein could thus be lethal for early instars, but in later instars these effects might be compensated by prolonged growth (Raubenheimer & Simpson 1999). As a by-product of prolonged feeding, carbohydrates and also secondary defensive metabolites are then over-ingested (Slansky & Wheeler 1992). As a consequence for a sequestering specialist, fat as well as glucosinolates accumulate in body tissues.

Next to the above described array of plant defensive traits, Brassicaceae can also contain other defences (Cipollini & Gruner 2007). For example, alkaloids and phenylpropanoids were reported for some species of Brassicaceae, including B. orientalis and L. rediviva (Dietz & Winterhalter 1996; Brock et al. 2006). However, the explained variability expressed as the R2 values in regression analyses (Table 5) was very high for almost all insect parameters. It is therefore very likely that the defensive plant traits chosen for this study were highly important for the performance of A. rosae.

The concept of plant defence syndromes, grouping particular plant trait combinations of edibility and feeding barrier characteristics within a triangle, could be confirmed in Brassicaceae. However, chemical and mechanical defence are not mutually exclusive traits, and a revised association of water content and proteinase inhibitors within the triangle is suggested. Larval performance and adult preference of an oligophagous herbivore were more influenced by general mechanical defence and digestibility reducers than by other characteristic chemical defence parameters, as might be predicted for a specialist that uses these plant chemical defences for its own protection. Nutritional deficits of plant quality seemed to be compensated by the sawfly in the absence of mechanical defence. Thus this food specialist is very likely a nutrition generalist. In summary, defence syndromes of Brassicaceae matched with performance and preference of a specialist herbivore, but displayed constraints which are likely caused by limited capacities for compensation of nutritional deficits and chemical ‘detoxification’ by the insect. It remains to be investigated, how these associations might switch when other, more polyphagous herbivores are investigated.

Acknowledgements

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

The authors thank J. Winkler-Steinbeck for plant cultivation, D. Paltian and J. Fuchs for help in sample processing, M. Riederer for making laboratory space and HPLC equipment available for our studies, and two anonymous referees and the associate editor for helpful advice. The authors received financial support for their work from the Sonderforschungsbereich 567 ‘interspecific interactions’ of the Deutsche Forschungsgemeinschaft.

References

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Supporting Information

  1. Top of page
  2. Summary
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussion
  7. Acknowledgements
  8. References
  9. Supporting Information

Table S1. Plant leaf traits of seven species of Brassicaceae.

Table S2. Nested ANOVA of plant leaf traits depending on species, and leaf age nested within species.

Table S3. Relationship between means of all plant leaf traits and clustering of young and old leaves of seven Brassicaceae species.

Table S4. Performance and preference parameters of insects reared on young and old leaves of seven Brassicaceae species.

Table S5. Nested ANOVA for male and female insects separately depending on host plant species, and leaf age nested within species.

Table S6. Relationship between means of insect parameters and clustering of insects reared on young and old leaves of seven Brassicaceae species.

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