Global Change Biology Global Change Biology (2014) 20, 3159–3176, doi: 10.1111/gcb.12633

Will chemical defenses become more effective against specialist herbivores under elevated CO2? J O H N M . L A N D O S K Y and D A V I D N . K A R O W E Western Michigan University, Kalamazoo, MI 49008-5410, USA

Abstract Elevated atmospheric CO2 is known to affect plant–insect herbivore interactions. Elevated CO2 causes leaf nitrogen to decrease, the ostensible cause of herbivore compensatory feeding. CO2 may also affect herbivore consumption by altering chemical defenses via changes in plant hormones. We considered the effects of elevated CO2, in conjunction with soil fertility and damage (simulated herbivory), on glucosinolate concentrations of mustard (Brassica nigra) and collard (B. oleracea var. acephala) and the effects of leaf nitrogen and glucosinolate groups on specialist Pieris rapae consumption. Elevated CO2 affected B. oleracea but not B. nigra glucosinolates; responses to soil fertility and damage were also species-specific. Soil fertility and damage also affected B. oleracea glucosinolates differently under elevated CO2. Glucosinolates did not affect P. rapae consumption at either CO2 concentration in B. nigra, but had CO2-specific effects on consumption in B. oleracea. At ambient CO2, leaf nitrogen had strong effects on glucosinolate concentrations and P. rapae consumption but only gluconasturtiin was a feeding stimulant. At elevated CO2, direct effects of leaf nitrogen were weaker, but glucosinolates had stronger effects on consumption. Gluconasturtiin and aliphatic glucosinolates were feeding stimulants and indole glucosinolates were feeding deterrents. These results do not support the compensatory feeding hypothesis as the sole driver of changes in P. rapae consumption under elevated CO2. Support for hormone-mediated CO2 response (HMCR) was mixed; it explained few treatment effects on constitutive or induced glucosinolates, but did explain patterns in SEMs. Further, the novel feeding deterrent effect of indole glucosinolates under elevated CO2 in B. oleracae underscores the importance of defensive chemistry in CO2 response. We speculate that P. rapae indole glucosinolate detoxification mechanisms may have been overwhelmed under elevated CO2 forcing slowed consumption. Specialists may have to contend with hosts with poorer nutritional quality and more effective chemical defenses under elevated CO2. Keywords: Brassica nigra, Brassica oleracea, compensatory feeding hypothesis, elevated CO2, feeding deterrent, glucosinolates, hormone-mediated CO2 response (HMCR), Pieris rapae, soil fertility, structural equation model (SEM) Received 18 December 2013 and accepted 10 April 2014

Introduction Human perturbations are changing the context of ecological interactions. Some anthropogenic disturbances, such as oil spills and wildfires, are regional and potentially ephemeral in nature. This is in contrast to global human perturbations to ecosystems, such as elevated atmospheric CO2 and climate change, the effects of which will be ubiquitous and long-lasting. Understanding the effects of global ecological perturbations is synonymous with understanding ‘new normal’ ecological interactions. Since the Industrial Revolution, atmospheric CO2 concentrations have risen from 270 to 393 ppm today (Bala, 2013). CO2 concentrations will likely reach or exceed 700 ppm by the end of the century and remain elevated for centuries to millennia (Solomon et al., 2009). Because natural and agricultural ecosystems will function in this elevated CO2 state for Correspondence: John Landosky, tel. (269) 370-1019, fax (269) 387-5609, e-mail: [email protected]

© 2014 John Wiley & Sons Ltd

some time, it is important to determine how elevated CO2 will affect the ecology of these systems. Elevated CO2 has predictable, direct effects on plant chemistry including increased photosynthetic rates and decreased leaf nitrogen concentrations (Robinson et al., 2012). Because phytophagous insect performance increases with leaf nitrogen content, elevated CO2 typically decreases plant nutritional quality (Mattson, 1980). Insects typically increase their feeding rates under elevated CO2, a response widely presumed to be an effort to mitigate the effects of decreased food quality (Lincoln et al., 1986). Still, growth rates and ultimate insect biomass frequently decrease under elevated CO2 (Robinson et al., 2012). Even given these broad trends, insect herbivore responses to elevated CO2 are highly variable. One reason for these disparities may be the role of environmental variation on CO2 response. Studies typically do not consider the effects of elevated CO2 in conjunction with environmental variation, but plant and herbivore responses to elevated CO2 often depend on conditions 3159

3160 J . M . L A N D O S K Y & D . N . K A R O W E such as soil fertility and temperature (Robinson et al., 2012). While this underscores the necessity of multiple factors in elevated CO2 studies, it may also explain variation in herbivore response in the literature. Especially if abiotic and biotic variables are controlled within experiments, differences in these variables between experiments could explain the overall variation in herbivore response to elevated CO2. Another potential explanation for variation in herbivore response may be variation in plant secondary chemistry response to elevated CO2 (Zavala et al., 2013). Elevated CO2 tends to increase phenolics and decrease nitrogen-containing plant defenses, but the responses of these and other secondary compounds are highly variable (Robinson et al., 2012). While these trends are consistent with the resource-based carbonnutrient balance hypothesis (CNB) (Bryant et al., 1983), the variation in response to elevated CO2 suggests there are additional drivers. For instance, new evidence suggests that elevated CO2 affects plant hormones responsible for regulation of constitutive and enemy-induced secondary chemistry (Zavala et al., 2013). Hormonal regulation of secondary chemistry is complex and not fully understood, but jasmonic acid (JA) and salicylic acid (SA) play important roles in promoting compounds responsible for herbivore and pathogen defense respectively (Vos et al., 2013). Elevated CO2 increases salicylic acid (SA) concentrations and decreases jasmonic acid (JA) and ethylene (ET) concentrations. These changes in plant hormone concentrations may cause hormone-mediated CO2 response (HMCR) of constitutive defenses (Zavala et al., 2013). HMCR may also affect defense induction following herbivory through SA suppression of the JA pathway and direct inhibition of protein kinase activation (which directly inhibits the transcription of defense-related genes) (Zavala et al., 2013). Plants can target induced defenses following attack to the attacking herbivore species to maximize benefits and minimize costs of response (Vos et al., 2013). Elevated CO2 may also interfere with synergistic coordination of hormones to elicit specific defensive suites following attack. Insect herbivores are often categorically divided into ‘generalists’ that consume several plant families and ‘specialists’ that consume only one plant family, though Ali & Agrawal (2012) make a strong case to consider specialization along a continuum. Generalists are more negatively affected by plant chemical defenses than are specialists, which have developed effective ways of handling their hosts’ defenses through detoxification, excretion, or even sequestration for their own defense. Specialists often utilize the defenses of their host plant family as oviposition and feeding stimulants (F€ urstenberg-H€ agg et al., 2013). Specialists are widely but

incorrectly assumed to be completely immune to the chemical defenses of their hosts (Ali & Agrawal, 2012), though studies demonstrating a negative effect of host chemistry to a specialist are rare. To better understand how plant-insect interactions will function in future environments, we studied the effects of elevated CO2 on the individual glucosinolates of mustard (Brassica nigra) and collard (B. oleraceae var. acephala) in the context of variation in soil nutrient availability and mechanical damage (to simulate herbivory). We also considered whether elevated CO2 will alter the response of the specialist cabbage white butterfly (Pieris rapae) to glucosinolates. Glucosinolates are a class of over 120 nitrogen-containing chemical defenses exclusively found in the Capparales tribe often subdivided into aliphatic, indole, and aromatic structural groups based on the amino acid from which they are derived. Glucosinolates are not themselves toxic, but are hydrolyzed by myrosinase to form one of several breakdown products including isothiocyanates, nitriles, epithionitriles, and thiocyanates (Bones & Rossiter, 1996). Plant hormones can influence relative and absolute concentrations of individual glucosinolates in Brassica species. Constitutive concentrations of aliphatic and indole glucosinolates are promoted by SA and JA, respectively. Because elevated CO2 increases SA and decreases JA concentrations, HMCR would predict increased concentrations of aliphatic glucosinolates and decreased concentrations of indole glucosinolates under elevated CO2 (Zavala et al., 2013). We are aware of seven studies considering the effects of elevated CO2 on individual glucosinolate concentrations of seven cultivars in the Brassicaceae; their overall pattern shows weak evidence of HMCR with regard to constitutive aliphatic glucosinolates but not indole glucosinolates (Table 1). Glucosinolates contain nitrogen (N) and sulfur (S); the relative and absolute amounts of these nutrients available in the soil have strong effects on glucosinolate concentrations (Martınez-Ballesta et al., 2013). The relative soil availabilities of N and S are important because a deficiency in one can repress the assimilation pathway of the other, resulting in reduced synthesis of amino acid glucosinolate precursors (Martınez-Ballesta et al., 2013). Nutrient availability, especially N, can also affect plant growth; therefore soil N could increase plant growth and thereby dilute glucosinolate concentrations or could increase nitrogen-containing glucosinolate concentrations depending on other limits to plant growth (Herms & Mattson, 1992). Elevated CO2 decreases leaf nitrogen concentrations because of dilution by nonstructural photosynthates and because of reduced needs for high concentrations of © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

D E F E N S E S M O R E E F F E C T I V E U N D E R E L E V A T E D C O 2 ? 3161 Table 1 The response of individual glucosinolates to elevated CO2 by group (aliphatic, indole, and aromatic) Aliphatic Study

Species

Reddy et al. (2004) Reddy et al. (2004) Reddy et al. (2004) Reddy et al. (2004) Bidart-Bouzat et al. (2005)

Cabbage Brassica oleracea subsp. capitata cv. Lennox Cabbage Brassica oleracea subsp. capitata cv. Rinda Oilseed rape Brassica rapa subsp. oleifera cv. Valo Oilseed rape Brassica rapa subsp. oleifera cv. Tuli Mouse ear cress Arabidopsis thaliana (93 genotypes)

Schonhof et al. (2007) Himanen et al. (2008) Himanen et al. (2008) La et al. (2009)

Broccoli Brassica oleracea var. italica cv. Marathon Oilseed rape Brassica napus ssp. oleifera cv. Westar Oilseed rape Brassica napus ssp. oleifera Bt-transgenic Chinese kale Brassica alboglabra var. Sijicutiao Brussels sprouts Brassica oleracea var. gemmifera Brussels sprouts Brassica oleracea var. gemmifera

Klaiber et al. (2013b) Klaiber et al. (2013a)

Up

Down

Indole No D

Up

Down

Aromatic No D

Up

Down a

No D

Comments a

0

0

6

0

0

3

0

1

0

0

0

6

0

0

3

0

0

1

0

0

9

0

2

2

0

0

2

0

0

9

1

0

3

1

0

1

0–2b

0–2b

2

0

0

3

0

0

0

2

0

0

0

0

4

0

0

0

0

0

3

0

0

4

0

0

1

0

0

3

0

0

4

0

0

1

5

1

1

0

0

4

0

0

0

1

0

5

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2

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0

ribulose-1,5-bisphospate due to reduced photorespiration (Drake et al., 1997). Elevated CO2 decreased leaf sulfur concentrations of Chinese kale B. alboglabra, but little is known about effects in other species (La et al., 2009). Brassicaceae typically maintain low constitutive concentrations of glucosinolates but rapidly induce higher concentrations following herbivory (Badenes-Perez et al., 2013). This strategy may be favored over maintaining high constitutive concentrations because of their high allocation costs (Bekaert et al., 2012). Glucosinolates are induced by herbivory through stimulation of the JA pathway. Though this pathway primarily induces indole glucosinolates, herbivory can also induce aliphatic glucosinolates (Textor & Gershenzon, 2009; Kos et al., 2012). HMCR predicts elevated CO2 will mute glucosinolate induction following herbivory due to SA suppression of the JA defensive pathway. Consistent with this hypothesis, induction of indole glucosinolates following herbivory was inhibited in an Arabidopsis SA-overproducing mutant (Mikkelsen et al., 2003). We also considered whether elevated CO2 will change the effects of glucosinolates on P. rapae consumption. Gluconasturtiin (an aromatic glucosinolate), © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

trend (P = 0.064)

b

genotype-specific CO2 response, direction unknown

Longest of three CO2 exposure times

aliphatic, and indole glucosinolates can all be feeding stimulants for P. rapae under current CO2, and their stimulatory effects may increase with dose; however elevated CO2 may change the specialist’s relationship with these glucosinolates (Renwick & Lopez, 1999; Miles et al., 2005; M€ uller et al., 2010). Elevated CO2 could alter P. rapae’s perception of glucosinolates, detoxification costs, or detoxification efficacy, any of which could affect consumption rates. Pierid larvae use taste sensilla on their mouthparts to detect the presence of glucosinolates (Miles et al., 2005). Until recently, trace amounts of glucosinolate were thought to be located in Brassicaceae cuticles, but current evidence suggests that herbivores either penetrate the cuticle to perceive glucosinolates or glucosinolates escape the leaf interior through stomata (Hopkins et al., 2009). Elevated CO2 could interfere with glucosinolate perception in either case. The cuticle can be a formidable barrier for newly hatched Lepidoptera (Peeters, 2002). Elevated CO2 may increase cuticle thickness thereby increasing the difficulty of glucosinolate perception (Vanhatalo et al., 2001). Elevated CO2 also decreases stomatal conductance, which may reduce glucosinolate perception by P. rapae (Ainsworth & Rogers, 2007).

3162 J . M . L A N D O S K Y & D . N . K A R O W E Additionally, elevated CO2 could affect the costs or efficacy of glucosinolate detoxification, which could feedback to alter consumption rates. P. rapae reduces glucosinolate toxicity by promoting formation of the less toxic nitrile over isothiocyanate hydrolysis products via the nitrile specifier protein (NSP) released in the gut (Hopkins et al., 2009). Elevated CO2 could increase the costs of nitrogenous investment into NSP synthesis due to nitrogen deficient food and/or increase the amount of NSP necessary due to increased consumption rates (and therefore increased glucosinolate exposure rates). Elevated CO2 could also alter the efficacy of NSP and/or alter the background ratio of nitrile to isothiocyanate hydrolysis (i.e. without NSP influence) by altering pH or other reaction conditions (Bones & Rossiter, 1996). Decreased effectiveness of the NSP system could force P. rapae to slow consumption rates in the presence of high concentrations of one or more glucosinolate groups to avoid harmful isothiocyanate exposure (Agrawal & Kurashige, 2003).

Materials and methods The field portion of this study took place at the University of Michigan Biological Station during the summer of 1997. B. nigra and B. oleracea var. acephala seeds were grown in 6-inch pots filled with Hyponex topsoil, one plant per pot. When their first true leaves began to form, they were randomly assigned to an outdoor growth chamber facility, 20 plants per chamber. Twenty-six open-topped, 0.5 m3 chambers were used to apply CO2 treatments, as described by Drake et al. (1989), with the exception that our chambers were rectangular cuboids and frames were constructed with PVC pipe (e.g., Karowe & Grubb, 2011). Thirteen chambers were randomly assigned elevated CO2 and the remaining assigned ambient CO2 (360 ppm). A fan (Dayton model 4C443A, 100 ft3 min 1) connected to each chamber delivered constant airflow to moderate chamber temperature (Drake et al., 1989). For elevated CO2 chambers, 100% CO2 was dispensed into fan airflow to maintain 720 ppm (CO2 concentration predicted for late 21st century). CO2 levels were monitored by pumping air from one ambient and all elevated chambers to an infrared gas analyzer. CO2 flow to each elevated chamber was adjusted as appropriate via a manual flowmeter. Plants were randomly assigned high or low nutrient treatments when they were placed into chambers; nutrient treatments were independently applied to each plant. High nutrient treatments were given 300 ml of 1.48 g l 1 Scotts 20 : 20 : 20 (N : P : K) once per week (the recommended concentration for potted outdoor plants) and low nitrogen treatments were given 300 ml of 0.74 g l 1 Scotts 20 : 20 : 20 (N : P : K) every 2 weeks. Half of the plants in each CO2 and nutrient treatment combination were randomly assigned to damage treatments. For damaged plants, the proximal third of the first, second, and third fully expanded leaves were

crushed once per side using a garlic press 48 h prior to harvest avoiding the midrib. B. nigra and B. oleracea plants were therefore subjected to one of two levels of CO2 (ambient or elevated), nutrient (low or high), and damage (damaged or not damaged), creating eight treatment combinations per plant species. Three hundred and twenty-eight plants were haphazardly selected for harvest, avoiding plants with nontreatment associated damage, 14–29 plants per species per treatment combination.

Plant harvest and P. rapae feeding trials During plant harvest, 3–18 plants per species per treatment combination were haphazardly chosen for CHN analysis; a subset of plants was analyzed to reduce costs. For these plants, a 3.7 cm2 leaf disk was taken from the proximal portion of the 3rd fully expanded leaf of each plant and dried for 72 h at 50 °C. Disks were ground using a Wiley mill (Thomas Scientific, Swedesboro, NJ, USA) and analyzed for carbon, hydrogen, and nitrogen concentrations using a Perkin-Elmer CHN Elemental Analyzer. Missing data is both common and problematic in ecological datasets; appropriate resolutions to missing data depend on why data are missing (Schafer & Graham, 2002). Plants without percent nitrogen data are considered to be missing completely at random in this study (Ruben, 1976). P. rapae larvae were obtained from gravid females collected in the field in Cheboygan County, MI. After the eggs hatched, larvae were maintained on cabbage looper artificial diet through the fourth instar (Bioserv Inc., Frenchtown, NJ, USA) to avoid feeding preference induction (Karowe, 1989). Upon molting into the fifth (final) instar, larvae were sexed and randomly assigned the distal 2/3 of the second fully expanded leaf (excluding the midrib) from either B. nigra or B. oleracea plants subjected to different CO2, nutrient, and damage treatments. Feeding trials were conducted in 100 mm diameter petri dishes with moist Whatman filter paper lining the bottom to maintain humidity and lasted 48 h. Trials were conducted in an environmental chamber with a 16 : 8 light : dark cycle at 25 °C (light) and 20 °C (dark). Larval consumption was measured relative to larval size (RCR) (Waldbauer, 1968). This standard gravimetric index was used to quantify insect behavior and performance. Following feeding trials, larvae were weighed and frozen at 20 °C. To obtain dry weights, larvae, remaining leaf material, and frass were dried (72 h, 50 °C) and weighed. To estimate the initial dry weight of test larvae, upon molting into their fifth instar, 11 larvae were weighed, dried (72 h, 50 °C), and weighed again to obtain average percent dry weight (Waldbauer, 1968). Feeding trials were initiated whenever a newly molted 5th instar P. rapae larva was available at the time of plant harvest. Two hundred and twenty-six feeding trials were initiated, 9– 19 trials per treatment combination. Twelve feeding trials were excluded, 11 because larvae didn’t eat and one because of human error. Feeding trials excluded because larvae didn’t eat are not missing at random with regard to RCR and their absence is therefore nonignorable (Ruben, 1976).

© 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

D E F E N S E S M O R E E F F E C T I V E U N D E R E L E V A T E D C O 2 ? 3163

Glucosinolate analysis

Statistical analyses 2

A leaf disk 10.2, 11.0, 14.5, 22.8, or 29.3 cm was cut from the distal portion of the 3rd fully expanded leaves of B. nigra and B. oleracea avoiding the midrib. Disk size was determined by the largest punch that would fit the 3rd leaf. The disk was weighed and immediately placed in boiling water. Disks were boiled in water for 10 min to denature the myrosinase enzyme, ground with a tissue homogenizer, and centrifuged. The supernatant was lyophilized and stored for HPLC analysis at Western Michigan University. Glucosinolate samples were reconstituted in 2 ml of HPLCgrade water and centrifuged to remove nonwater soluble plant constituents. The supernatant was loaded onto a Sephadex column activated by 6 ml of 0.5 M pyridine-acetate buffer (4.03 ml pyridine and 2.85 ml glacial acetic acid, brought to 100 ml with HPLC-grade H2O) and washed with 12 ml HPLC-grade water. After the supernatant was loaded onto the column, 10 ml of HPLC-grade water was run through the column to wash away other plant constituents. A 0.25% sulfatase enzyme solution was then added to the column to cleave the Sephadex-bound sulfur group from the rest of the glucosinolate, freeing the desulfated glucosinolate to be eluted from the column with 5 ml HPLC-grade water. The purified sample was then spun in a Savant SC250DDA SpeedVac Plus to dryness, reconstituted in 1 ml HPLC-grade water, filtered through a 0.45 micron Agrodisk, and loaded into an HLPC vial. Desulphinated glucosinolates were analyzed by HPLC using a Waters 2690 HPLC pump housing a LiChroCART 250–4 25 cm Reverse-Phase-18, 5 lm column maintained at 35 °C. Desulphinated glucosinolates were separated by their polarity using a gradient of water and acetonitrile. The flow rate throughout the run was 1.0 ml per minute. Each run lasted 65 min, with a 15 min equilibration (100% water) between each run. The water : acetonitrile gradient was as follows: 0 min: 100 : 0, 30 min 85 : 15, 55 min 75 : 25, 60 min 0 : 100. A Waters 996 Photodiode Array Detector was used to scan from 205 to 235 nm with a sampling rate of 1.0 and a resolution of 1.2. Desulphinated glucosinolates were quantified at 224.6 nm. The run was controlled and processed using Millennium 3.1 software. Samples were divided into 19 HPLC runs balanced by treatment combination. An external sinigrin standard was run at the beginning, middle, and end of each run to account for variation between runs. Eight glucosinolate standards and 17 plant extracts with identified glucosinolates, generously provided by Richard N. Bennett, were used to identify and quantify glucosinolates in our samples (Table 2). Standards and extracts were used to create a glucosinolate library using Millennium 3.1. The library identified peaks that closely matched the absorbance of a glucosinolate in the library from 205 to 235 nm and quantified match similarity using a match angle and retention time. Standard curves were generated for each standard. To quantify glucosinolates for which we had no standard curve, we pooled data from the eight standard curves to create a generalized linear model for the relationship between chromatogram area and concentration (F1,44 = 1367.58, P < 0.001, r2 = 0.968). © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

Treatment effects on individual glucosinolate concentrations (lmol g 1 dry weight) were evaluated using fully factorial three-way split-plot MANCOVA with damage, nutrient, and CO2 treatments as fixed main effects (PROC GLM, SAS 9.3, 2010). B. nigra and B. oleracea have different suites of glucosinolates and were therefore analyzed in separate MANCOVAs, analyzing each individual glucosinolate as a dependent variable. MANCOVA was necessary rather than univariate ANCOVA because we expected glucosinolates to vary at the level of the plant for trivial reasons such as whole-plant differences in the efficacy of extraction or desulfation. The MANCOVA determined which factors or interactions were significant over the entire suite of glucosinolates. Only those factors or interactions significant in MANCOVA were interpreted when significant in subsequent individual glucosinolate univariate ANCOVAs as a means of reducing Type I error (Scheiner, 2001). Because CO2 was applied at the level of the chamber (whole-plot) and nutrient and damage treatments were applied at the level of the plant (split-plot), the random effect Chamber(CO2) determined chamber effect and tested CO2 treatment over chamber effect (Potvin, 2001). The mean of the three sinigrin external standards in each HPLC run were used as a covariate to account for differences between runs. Type III sums of squares were used to account for the complete but unbalanced dataset. Individual glucosinolate concentrations were cube-root transformed to meet MANCOVA assumptions. Nine plants in the dataset did not have associated glucosinolate values because glucosinolates were never extracted or glucosinolate vials were missing or broken (missing completely at random). The glucosinolates of one plant in the dataset were excluded because the fresh weight of the glucosinolate sample was not properly recorded (missing completely at random). These ten plants were not included in MANCOVA and ANCOVA analyses (listwise deletion). The effect of glucosinolate groups (aliphatic, indole, aromatic) on larval consumption was evaluated for B. nigra and B. oleracea using structural equation modeling (SEM) (AMOS 20.0.0, 2011). Data were grouped by CO2 treatment (multigroup SEM) to determine whether the behavioral effects of leaf nitrogen and glucosinolate groups on P. rapae will change under future CO2 concentrations. Leaf nitrogen content was included to account for the effect of leaf nutritional value on larval consumption and to determine its effects on glucosinolate concentrations at ambient and elevated CO2. In the model, causal pathways connected each glucosinolate group to larval consumption (Fig. 3). The mean of the three sinigrin external standards in each HPLC run were used as a covariate for all glucosinolate groups to account for differences between runs (see also Figure S1). We allowed glucosinolate errors to covary because we expected glucosinolate concentrations to vary at the level of the plant for trivial reasons (e.g., differences in whole-plant glucosinolate extraction yields). Glucosinolate groups were cube-root transformed and leaf nitrogen was Log10-transformed to meet SEM assumptions. Larval consumption was measured as Log10-transformed Relative Consumption Rate (Waldbauer, 1968). Reported standardized path coefficients must be interpreted in the context of these

3164 J . M . L A N D O S K Y & D . N . K A R O W E Table 2 Identification of individual glucosinolates found in B. nigra and B. oleracea. Species indicates the plant species in which the glucosinolate was found (n = Brassica nigra, o = B. oleracae). Type indicates the chemical subclass of each glucosinolate. The retention time (rt) refers to the time (in minutes) at which the glucosinolate was observed using our HPLC methods Semi-systematic name

Trivial name

Species

Type

rt

3-Methylthiopropyl 3-Butenyl 4-Pentenyl 2-Propenyl 3-Methylsulfonylpropyl 4-Methylsulphinylbutyl 3-Methylsulfiniylpropyl 3-Indolylmethyl N-Methoxy-3-indolylmethyl 2-Phenylethyl

Glucoberteroin Gluconapin Glucobrassicanapin Sinigrin Glucocheirolin Glucoraphanin Glucoiberin Glucobrassicin Neoglucobrassicin Gluconasturtiin

n n, o o n, o o o o n, o o n, o

Aliphatic (thioalkyl) Aliphatic (alkenyl) Aliphatic (alkenyl) Aliphatic (alkenyl) Aliphatic (sulfonylalkyl) Aliphatic (sulfinylalkyl) Aliphatic (sulfinylalkyl) Indole Indole Aromatic

39.6 15.4 16.9 9.4 7.0 10.2 5.6 26.7 40.6 31.7

transformations; they are useful for indicating the direction of effect (positive or negative) and their relative strengths when independent and dependent variables are similarly transformed, but do not indicate the absolute magnitude of effects on untransformed variables. We also interpreted how well the entire model predicted each endogenous variable; i.e. squared multiple correlations (R2). There was no significance test for individual endogenous R2 values. Significance of causal paths were specific to CO2 level; if multi-group comparisons were appropriate (see below), we considered the effects of CO2 both by whether or not paths were significant at each CO2 level and by the critical ratios of their pairwise parameter comparisons (difference between the ambient and elevated CO2 estimate divided by an estimate of the standard error of the difference) between CO2 levels. AMOS confirms model identification by establishing that the number of sample moments exceeds the number of estimated parameters, their difference equaling the degrees of freedom for the model (Grace, 2006). Model fit was indicated by a nonsignificant chi-square goodness of fit test, meaning the pattern of the observed data did not significantly differ from the model specified (Grace, 2006). The suitability of multi-group comparisons (i.e. ambient vs. elevated CO2) was determined by comparing model fit between the unconstrained multi-group model and a multi-group model with all paths constrained to be equal between CO2 groups. Model comparison was achieved by subtracting the constrained model chi-square and degrees of freedom from the unconstrained model chi-square (CMIN) and degrees of freedom and considering the significance of the differences (i.e. nested model comparison). A significant difference between the models indicated that the more restricted model (i.e. ambient and elevated paths equal) was a worse fit for the data than the unrestricted model. Of the 148 harvested B. nigra plants, seven did not have associated glucosinolate profiles, 47 did not have associated feeding trials, 109 did not have leaf nitrogen measurements, and one feeding trial was excluded because the larva did not eat (see above). Of the 180 harvested B. oleracea plants, three did not have associated glucosinolate profiles, 67 did not have associated feeding trials, 130 did not have leaf nitrogen measurements, ten feeding trials were excluded because larvae did

not eat, and one because of human error (see above). We used Full Information Maximum Likelihood (FIML) in AMOS to handle these missing data for SEM analyses (Enders & Bandalos, 2001). The dataset met all assumptions of FIML except for the 11 larvae that did not eat (not missing at random with regard to RCR). These excluded data were quantitatively compared to included data to complete consideration of the effects of glucosinolates on RCR (ad hoc statistical analyses below). We conducted two ad hoc statistics to consider potential reasons why eleven larvae did not eat. Because these data are not missing at random regarding consumption rate, we quantitatively considered these data when addressing consumption hypotheses. One hypothesis was that larvae didn’t eat because elevated CO2 increased cuticle thickness or reduced stomatal conductance and thereby reduced initial contact with feeding stimulant glucosinolates (St€ adler & Reifenrath, 2009). While we didn’t quantify either cuticle thickness or stomatal conductance, we ran a one-tailed chi-square test-for-association test to determine if host plants grown under elevated CO2 were more often rejected than hosts grown under ambient CO2. Given the results of our B. oleracea SEM, another hypothesis was that rejected hosts contained high concentrations of feeding deterrent indole glucosinolates. Because indole glucosinolates only functioned as feeding deterrents for B. oleracea under elevated CO2, we ran a one-tailed t-test to determine if seven rejected B. oleracea hosts grown under elevated CO2 had greater concentrations of indole glucosinolates than the 93 accepted B. oleracea hosts grown under elevated CO2.

Results

Treatment effects on glucosinolates Plant damage and nutrient availability affected B. nigra glucosinolate levels (Table 3). Glucoberteroin was induced by damage, and there was weak evidence that glucobrassicin was as well (Table 3; Fig. 1). Sinigrin and glucobrassicin concentrations were higher in plants with nutrient stress, and there was weak evidence that gluconapin was as well. CO2 concentration, damage, © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

D E F E N S E S M O R E E F F E C T I V E U N D E R E L E V A T E D C O 2 ? 3165 Table 3 MANCOVA tests for the fixed effects of damage, nutrient and CO2 treatments and their interactions on the suite of B. nigra glucosinolates. Univariate ANCOVAs test for treatment and interaction effects for each glucosinolate. Only treatments and interactions found to be significant in MANCOVA analysis are interpreted in univariate ANCOVAs. External Standard is the average absorbance of three sinigrin external standards per HPLC run used as a covariate to explain differences between runs

MANCOVA

Univariate ANCOVAs Sinigrin

Glucobrassicin

Glucoberteroin

Gluconapin

Source

df

Wilks’ k

F

P

CO2 Damage Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External standard

5, 104 5, 104 5, 104 5, 104 5, 104 5, 104 5, 104 5, 104

0.9298 0.8647 0.8945 0.9479 0.9506 0.9506 0.9233 0.8857

1.57 3.25 2.45 1.14 1.08 1.08 1.73 2.68

0.1746 0.0090 0.0382 0.3427 0.3756 0.3757 0.1345 0.0253

Source Main plot CO2 Main plot error Subplot Damage Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External standard Subplot error Main plot CO2 Main plot error Subplot Damage Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External standard Subplot error Main plot CO2 Main plot error Subplot Damage Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External Standard Subplot error Main plot CO2 Main plot error Subplot Damage

© 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

df

Type III SS

1 27.43 1 1 1 1 1 1 1 108 1 25.92 1 1 1 1 1 1 1 108 1 26.20 1 1 1 1 1 1 1 108 1 28.07 1

F

P

0.3087 3.9332

2.15

0.1537

0.2024 0.7385 0.4335 0.0711 0.1280 0.0804 0.4068 15.8951

1.38 5.02 2.95 0.48 0.87 0.55 2.76

0.2435 0.0271 0.0890 0.4886 0.3532 0.4616 0.0993

0.0039 0.2256

0.45

0.5105

0.0171 0.0342 0.0074 0.0045 0.0256 0.0193 0.0278 0.5615

3.29 6.57 1.43 0.86 4.92 3.71 5.35

0.0724 0.0117 0.2340 0.3546 0.0286 0.0566 0.0226

0.0347 0.2238

4.07

0.0541

0.0831 0.0022 0.0063 0.0173 0.0004 0.0028 0.0438 0.6273

14.30 0.37 1.09 2.98 0.06 0.48 7.53

0.0003 0.5428 0.2995 0.0872 0.8054 0.4911 0.0071

0.0167 0.2739

1.72

0.2009

0.0057

0.49

0.4863

3166 J . M . L A N D O S K Y & D . N . K A R O W E Table 3 (continued)

Univariate ANCOVAs

Gluconasturtiin

Source

df

Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External Standard Subplot error Main plot CO2 Main plot error Subplot Damage Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External Standard Subplot error

1 1 1 1 1 1 108

nutrient availability, and their three-way interaction had significant effects on B. oleracea glucosinolates (Table 4). Elevated CO2 decreased glucocheirolin but there was weak evidence that it increased glucoraphanin (Table 4; Fig. 2). Plant damage decreased concentrations of glucocheirolin and neoglucobrassicin. Nutrient stress decreased concentrations of glucobrassicin and neoglucobrassicin and possibly gluconasturtiin. There was also evidence of a three-way interaction between treatments on sinigrin, glucocheirolin, and possibly glucoiberin. This interaction appeared to be driven by large concentrations of both sinigrin and glucocheirolin in the ambient CO2, high nutrient, undamaged treatment (Fig. 2).

Glucosinolate effects on larvae The unconstrained B. nigra SEM was identified, i.e. all model parameters (e.g., error correlations, path coefficients, and variances) could be estimated independently for both CO2 groups. The multi-group (ambient vs. elevated) B. nigra model constraining all path coefficients to be equal between CO2 levels was an equal fit to the data as the unconstrained multi-group model (CMIN = 7.760, df = 10, P = 0.65); i.e. there was no significant difference in ambient vs. elevated path coefficients. We therefore analyzed B. nigra without regard to CO2 treatment (v2 = 2.049, df = 2, P = 0.36) (Fig. 3a). Higher leaf nitrogen concentrations caused indole glucosinolates to increase (c = 0.332, P = 0.011), but did not affect aliphatic (c = 0.006, P = 0.97) or aromatic

1 25.56 1 1 1 1 1 1 1 108

Type III SS

F

P

0.0450 0.0393 0.0034 0.0158 0.0053 0.0373 1.2639

3.84 3.36 0.29 1.35 0.45 3.19

0.0526 0.0695 0.5917 0.2483 0.5014 0.0770

0.0117 0.3359

0.89

0.3544

0.0079 0.0036 0.0016 0.0003 0.0010 0.0527 0.0093 0.6966

1.23 0.56 0.25 0.04 0.15 8.18 1.44

0.2699 0.4562 0.6216 0.8411 0.6959 0.0051 0.2333

(c = 0.026, P = 0.86) glucosinolate groups. Larvae consumed more when fed leaf material with low nitrogen concentrations (c = 0.419, P = 0.003). Glucosinolates did not affect larval consumption for any group, aliphatic (c = 0.012, P = 0.92), indole (c = 0.129, P = 0.40), or aromatic (c = 0.161, P = 0.16). The external standard covariate explained variation between HPLC runs for aliphatic (c = 0.186, P = 0.025) and indole (c = 0.181, P = 0.026) glucosinolates, but not aromatic glucosinolates (c = 0.121, P = 0.15) (see also Figure S1a). All glucosinolate errors covaried: aliphatic and indole (φ = 0.016, P < 0.001), aromatic and indole (φ = 0.003, P < 0.001), and aliphatic and aromatic (φ = 0.006, P = 0.026) (see also Figure S1a). The model explained some variation in indole glucosinolates (R2 = 0.143) and larval consumption (R2 = 0.275), but had little power to explain variation in aliphatic (R2 = 0.034) or aromatic (R2 = 0.015) glucosinolates (Fig. 3a). The unconstrained B. oleracea SEM was identified, i.e. all model parameters (e.g., error correlations, path coefficients, and variances) could be estimated independently for both CO2 groups. The multi-group (ambient vs. elevated) B. oleracea model constraining all path coefficients to be equal between CO2 levels was a poorer fit to the data than the unconstrained multi-group model (CMIN = 22.388, df = 10, P = 0.013); i.e. there was a significant difference between ambient vs. elevated path coefficients. We therefore interpreted the unconstrained multi-group model (v2 = 3.268, df = 4, P = 0.51; Fig 3b). Pairwise comparisons between ambient and elevated paths show that © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

D E F E N S E S M O R E E F F E C T I V E U N D E R E L E V A T E D C O 2 ? 3167 AliphaƟc Glucoberteroin

Gluconapin

0.08 0.06 0.04 0.02

N Y N Y High Low Ambient

0.04 0.03 0.02 0.01 0 Damage Nutrient CO2

N Elevated Y N Y High Low Elevated

Indole Glucobrassicin

5 4 3 2 1

N Ambient Y N Y High Low Ambient

N Elevated Y N Y High Low Elevated

0 Damage Nutrient CO2

N Ambient Y N Y High Low Ambient

N Elevated Y N Y High Low Elevated

GluconasturƟin 0.03

0.05

μmol gram–1 dry weight

μmol gram–1 dry weight

6

AromaƟc

0.06

0.04 0.03 0.02 0.01 0 Damage Nutrient CO2

μmol gram–1 dry weight

0.05

μmol gram–1 dry weight

μmol gram–1 dry weight

0.1

0 Damage Nutrient CO2

Sinigrin 7

N Ambient Y N Y High Low Ambient

N Elevated Y N Y High Low Elevated

0.025 0.02 0.015 0.01 0.005 0 Damage Nutrient CO2

N Ambient Y N Y High Low Ambient

N Elevated Y N Y High Low Elevated

Fig. 1 Levels of the individual glucosinolates of mustard by treatment (Damage: N = not damaged, Y = damaged 48 h prior to harvest; Nutrient: High = 0.44 g Scotts 20 : 20 : 20 weekly, Low = 0.22 g Scotts 20 : 20 : 20 biweekly; CO2: Ambient = 360 ppm, Elevated = 720 ppm). Error bars = standard error. See Table 3 for corresponding MANCOVA/ANCOVA results.

the only significantly different path was between percent nitrogen and larval consumption (cA = 0.909, cE = 0.394, critical ratio = 2.968, critical ratiocritical = 1.96) (Denis, 2010); however, we interpret the presence or absence of significant paths in ambient vs. elevated CO2 models. Under ambient CO2, higher leaf nitrogen contents caused glucosinolate concentrations to increase over all groups: aliphatic (c = 0.437, P < 0.001), indole (c = 0.300, P = 0.027), and aromatic (c = 0.415, P = 0.001). Larvae also consumed more leaf material as leaf nitrogen concentrations decreased (c = 0.909, P < 0.001). Gluconasturtiin (the only aromatic glucosinolate) acted as a feeding stimulant (c = 0.223, P = 0.033), but larval consumption was unaffected by aliphatic (c = 0.071, P = 0.60) or indole (c = 0.139, P = 0.27) glucosinolate groups. The external standard covariate explained variation between HPLC runs for aliphatic (c = 0.243, P = 0.012), indole (c = 0.212, P = 0.035), and aromatic (c = 0.219, P = 0.025) glucosinolates. Covariance between the aliphatic and indole glucosinolate errors was significant (φ = 0.036, P < 0.001); covariances between aliphatic and aromatic (φ = 0.002, P = 0.60) and indole and aromatic (φ = 0.001, P = 0.73) glucosinolate errors were not (see also Figure S1b). The model had the following © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

power to explain variation of endogenous variables under ambient CO2: aliphatic glucosinolates (R2 = 0.250), indole glucosinolates (R2 = 0.135), aromatic glucosinolates (R2 = 0.220) and larval consumption (R2 = 0.735). Pairwise comparisons of squared multiple correlations showed no significant difference between ambient and elevated CO2. Under elevated CO2, there was weak evidence that higher leaf nitrogen concentrations caused aliphatic glucosinolates to increase (c = 0.287, P = 0.094; Fig. 3b), but did not affect indole (c = 0.125, P = 0.47) or aromatic (c = 0.112, P = 0.55) glucosinolate groups. Larvae consumed more when fed leaf material with low nitrogen concentrations (c = 0.394, P = 0.024). Glucosinolates had stronger effects on larval consumption under elevated CO2; aliphatic (c = 0.532, P = 0.010) and aromatic (c = 0.268, P = 0.047) glucosinolates acted as feeding stimulants and indole glucosinolates acted as feeding deterrents (c = 0.584, P = 0.003). The external standard covariate explained variation between HPLC runs for aliphatic (c = 0.244, P = 0.019) and indole (c = 0.335, P < 0.001) glucosinolates but not aromatic glucosinolates (c = 0.080, P = 0.46). Covariances between aliphatic and indole (φ = 0.037, P < 0.001) and aromatic and indole (φ = 0.007, P = 0.024) glucosinolate

3168 J . M . L A N D O S K Y & D . N . K A R O W E Table 4 MANCOVA tests for the fixed effects of damage, nutrient and CO2 treatments and their interactions on the suite of B. oleracea glucosinolates. Univariate ANCOVAs test for treatment and interaction effects for each glucosinolate. Only treatments and interactions found to be significant in MANCOVA analysis are interpreted in univariate ANCOVAs. External Standard is the average absorbance of three sinigrin external standards per HPLC run used as a covariate to explain differences between runs

MANCOVA

Univariate ANCOVAs Sinigrin

Glucobrassicin

Gluconapin

Gluconasturtiin

Source

df

Wilks’ k

F

P

CO2 Damage Nutrient Damage 9 Nutrient Damage 9 CO2 Nutrient 9 CO2 Damage 9 Nutrient 9 CO2 External standard

9, 135 9, 135 9, 135 9, 135 9, 135 9, 135 9, 135 9, 135

0.8817 0.8848 0.7932 0.9446 0.9122 0.9057 0.8640 0.7830

2.01 1.95 3.91 0.88 1.44 1.56 2.36 4.16

0.0425 0.0496 0.0002 0.5457 0.1753 0.1327 0.0165 0.05), and number followed by ‘ns’ superscript represents a nonsignificant path. Data transformations must be considered when interpreting standardized path coefficients. Modeled but not included in the figure for simplicity are HPLC external standard as a covariate for glucosinolate groups and glucosinolate errors which were allowed to covary (see Figure S1a and b for full models).

one treatment, elevated CO2, high nutrient, no damage may have influenced the effect of damage in the model (Fig. 2). While we were interested in the effects of elevated CO2, nutrient stress, and damage on the glucosinolates of B. nigra and B. oleracea, we were particularly interested in whether the effects of nutrient stress and induction on glucosinolates change in an elevated CO2 environment. We are aware of only one study to consider how atmospheric CO2 influences the effects of soil fertilization on glucosinolate concentrations. La et al. (2009) considered glucosinolate concentrations at two CO2 levels and three soil N fertilization regimes in Chinese kale B. alboglabra. They reported significant

CO2 9 N interactions for six of seven aliphatic glucosinolates and one of four indole glucosinolates. We are aware of two studies that have considered the interaction between atmospheric CO2 concentration and herbivory on glucosinolate concentrations. Himanen et al. (2008) showed induction of one aliphatic glucosinolate was suppressed by elevated CO2 but also provided weak evidence that induction of two indole glucosinolates was enhanced by elevated CO2 in oilseed rape B. napus. Bidart-Bouzat et al. (2005) found little evidence of induction under ambient CO2, but induction did occur in two of four aliphatic glucosinolates and two of three indole glucosinolates under elevated CO2 in Arabidopsis. © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

D E F E N S E S M O R E E F F E C T I V E U N D E R E L E V A T E D C O 2 ? 3173 We found no evidence of two-way interactions for B. nigra or B. oleracea, but did detect a three-way interaction for B. oleracea glucosinolates, specifically sinigrin, glucocheirolin, and possibly gluociberin (Table 4; Fig. 2). For sinigrin and gluococheirolin, this interaction appeared to be driven by large concentrations in the ambient CO2, high nutrient, undamaged treatment, i.e. at ambient CO2, damage strongly decreased two aliphatic glucosinolates under high nutrient conditions but did not affect concentrations under low nutrient conditions and this pattern was not seen at elevated CO2. Because of the importance of Arabidopsis as a model plant, much of the work of mapping the pathways and enzymatic machinery responsible for glucosinolate biosynthesis has been discovered (Sønderby et al., 2010). This foundation could be utilized to determine whether common substrate limitations, regulation at the steps of elongation, reconfiguration, or modification, or effects of sulfur limitations are responsible for coordinated plasticity of sinigin and glucocheirolin but not other aliphatic glucosinolates (Li et al., 2013). It is presently clear, however, that the predictions of HMCR on glucosinolate induction are not supported here or in previous work (Bidart-Bouzat et al., 2005; Himanen et al., 2008; Zavala et al., 2013). We also considered the effects of leaf nitrogen and glucosinolate concentrations on P. rapae consumption. Klaiber et al. (2013a) report that elevated CO2 increased glucosinolate concentrations in B. oleracea and consumption rates of P. brassicae, but were unable to separate whether decreased leaf nutritional quality or increased feeding stimulant was driving increased consumption rates. Our SEM was able to isolate the direct effect of leaf nitrogen on consumption from its indirect effects mediated through glucosinolates, consider the effects of leaf nitrogen on glucosinolate concentrations, and determine if those relationships differed at different CO2 concentrations. Overall, B. nigra leaf nitrogen had a direct effect on larval consumption but no indirect effect mediated through glucosinolates (Fig. 3a). Interestingly, B. nigra leaf nitrogen was positively correlated with glucobrassicin even though B. nigra glucobrassicin concentrations were lower in the high nutrient treatment. One possible explanation for this pattern is that the difference in glucosinolates among nutrient treatments was driven by an interaction between K and N; in the low nutrient treatment K limitation stimulated the JA pathway to increase glucosinolate synthesis but the acclimation of glucosinolates was ultimately regulated by N availability. Alternatively, this pattern could be due to the interrelated regulation of N and S assimilation and the importance of the N : S ratio in glucosinolate synthesis (Martınez-Ballesta et al., 2013). N limitation can repress © 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

S assimilation and S limitation can repress N assimilation (Martınez-Ballesta et al., 2013). Because our fertilizer contained N but not S, S availability was limited to its initial presence in the topsoil. It is possible that high nutrient treatment induced S assimilation earlier in the growing season, thus depleting it in the soil such that at harvest, S limitation repressed N assimilation in the high nutrient treatment and S and N limitations decreased glucobrassicin concentration. Brassicas have a large range of optimal N : S ratios, from 4 : 1 to 8 : 1, which could explain why this pattern was observed in B. nigra but not B. oleracea (Clark, 2007). The B. nigra SEM showed no other effects of leaf nitrogen on glucosinolate concentrations and no evidence of dose-dependent feeding stimulation of any glucosinolate group. Unlike B. nigra, the B. oleracea SEM differed between ambient and elevated CO2 groups. Though individual pairwise comparisons were nonsignificant, uniform reduction in squared multiple correlations (R2) suggests the SEM may have less power to predict endogenous variables under elevated CO2. This suggests elevated CO2 may alter the ecology of this system (because exogenous and endogenous variables included in our model are less able to explain concentrations of glucosinolate groups and P. rapae consumption under elevated CO2). In addition to potentially weakening relationships among leaf nitrogen, glucosinolate groups, and P. rapae consumption, elevated CO2 also changed those relationships. At ambient CO2, leaf nitrogen had significant, positive effects on all glucosinolate groups while, under elevated CO2, leaf nitrogen only affected aliphatic glucosinolates. HMCR may explain this pattern (Zavala et al., 2013). Because ambient CO2 does not stimulate the JA or SA pathways, concentrations of all groups are passively and equally regulated by excess N availability (Herms & Mattson, 1992). Under elevated CO2, N is scarcer and therefore not passively regulating glucosinolate concentrations. SA signals increased aliphatic concentrations under elevated CO2, but the response is ultimately nitrogen-limited. B. oleracea leaf nitrogen also had strong effects on consumption, directly under ambient CO2 and through glucosinolates under elevated CO2. Glucosinolates did not have strong effects as dose-dependent feeding stimulants under ambient CO2; there was weak evidence that gluconasturtiin alone stimulated feeding, consistent with Miles et al. (2005). Glucosinolates had much stronger effects on consumption rates at elevated CO2. Aliphatic and aromatic glucosinolates both acted as feeding stimulants. The compensatory feeding hypothesis states that insects increase feeding rates under elevated CO2 to mitigate for poorer food quality (Lincoln et al., 1986); however our study shows that,

3174 J . M . L A N D O S K Y & D . N . K A R O W E under elevated CO2, leaf nitrogen (the putative cause of CO2-induced nutritional decline) actually decreased in importance as a direct predictor for larval consumption and the importance of aliphatic and aromatic glucosinolates increased. These results support Klaiber et al. (2013a)’s interpretation that increased P. brassicae consumption rates on B. oleracea var. gemmifera under elevated CO2 was driven by increased concentrations of glucosinolate feeding stimulants. Our model also suggests that indole glucosinolates act as feeding deterrents under elevated CO2. To our knowledge, no glucosinolate has been reported to act as a feeding deterrent for P. rapae or any specialist of the Brassicaceae. We suspect the capacity of P. rapae’s NSP detoxification system was overwhelmed for indole glucosinolates under elevated CO2 and they decreased consumption rates to match their NSP detoxification capacities. Consistent with this interpretation, when Klaiber et al. (2013a) found elevated CO2 did not affect leaf nitrogen content but increased glucosinolate concentrations of B. oleracea, they attributed reduced P. brassicae performance under elevated CO2 to increased glucosinolate concentrations (though they had no mechanism for separating the effects of glucosinolates to other CO2-induced phenotypic changes). We did not observe increased concentrations of aliphatic glucosinolates as predicted by HMCR; however, P. rapae could encounter more glucosinolates per unit time due to CO2-induced consumption rate increases. CO2-induced changes in plant chemistry may also compromise the efficacy of NSF to promote indole hydrolysis of nitriles over isothiocyanates. Glucosinolate hydrolysis products are strongly influenced by the reaction environment. A neutral pH environment promotes isothiocyanates hydrolysis while an acidic pH promotes nitrile hydrolysis (Bones & Rossiter, 1996). If elevated CO2 causes P. rapae guts to become more alkaline, the propensity of hydrolysis to isothiocyanates will increase and NSF detoxification may become more challenging. Changes in pH or other gut conditions could directly alter the functionality of the NSF enzyme as well. Finally, resource limitations under elevated CO2 could cause decreased investment in the NSF detoxification system and thereby increase glucosinolate toxicity. Indole glucosinolates did not affect P. rapae consumption at either CO2 level when fed B. nigra. Further work is required to determine for which Brassicacea indole glucosinolates deter P. rapae consumption under elevated CO2 and whether defenses become more effective against specialists in other systems. The generality of this phenomenon will likely depend on its cause. If glucosinolates are more toxic because of hydrolysis conditions (i.e. higher P. rapae gut pH causing a greater

proportion of isothocyanates), then this could be limited to the Brassicacea and its specialists. If specialist enzymatic detoxification is less efficient because the enzymes must function in a novel environment, because elevated CO2-induced nitrogen limitation causes decreased investment in detoxification enzymes, or because elevated CO2-induced increased consumption rate results in greater exposure rates to defensive chemistry, this phenomenon may be more general. We propose investigation of P. rapae gut pH under elevated CO2, investigation of the effects of elevated CO2 on Brassicacea specialist consumption rates using Arabidopsis lines with varying levels of indole glucosinolate production, and investigation of the relationship between leaf nutrition, defensive chemistry, and consumption rates of specialists in other systems under elevated CO2. SEM can help parse the effects of leaf nitrogen and defensive chemistry in these studies. While further work is needed to both confirm this conclusion in our system and determine how general this result may be, these findings suggest that chemical defenses may become more effective against specialists under elevated CO2. HMCR is a novel way to predict plant secondary chemistry and insect herbivore response to elevated CO2 (Zavala et al., 2013). While there are few studies in the Brassicaceae system that can address its predictions for constitutive and induced defenses, we surmise that this and other studies provide weak support for increased constitutive concentrations of aliphatic glucosinolates under elevated CO2, but little support for decreased constitutive concentrations of indole glucosinolates or suppressed ability to induce glucosinolates following herbivory under elevated CO2. Our strongest support for HMCR was the reaction of B. oleracea glucosinolate groups to leaf nitrogen under ambient and elevated CO2. Decreased importance of leaf nitrogen and increased importance of glucosinolate concentration to predict larval consumption under elevated CO2 also supports the general conclusion that the compensatory feeding hypothesis alone may not be sufficient to address herbivore response to future plant chemistry and may be enhanced by considering the role of elevated CO2 on secondary chemistry (Zavala et al., 2013). The importance secondary chemistry in predicting herbivore response to elevated CO2 is underscored by our observation that elevated CO2 caused a specialist herbivore to respond to the defensive chemistry of its host plant as a feeding deterrent.

Acknowledgements We thank Russell and Penny Landosky, Shelley Martin Gillman, Michael Grant, and Angie Migliaccio for their help with field

© 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176

D E F E N S E S M O R E E F F E C T I V E U N D E R E L E V A T E D C O 2 ? 3175 data collection, Robert Vande Kopple and Christoph Vogel for their help with logistics and the CO2 monitoring system, and the faculty and staff of the University of Michigan Biological Station for their support services. We also thank R. Alan Renwick and Richard Bennett for their help with glucosinolate processing methods and standards and Mark Hammond, Kevin Mulherin, Santiago Navarro, and Steven Malcolm for their help with HPLC methods development. We would also like to thank Don Schoolmaster, Steve Kohler, and Julie Ryan for statistical consultations and thoughtful manuscript reviews. We thank James Grace for his continued work to establish the powerful and flexible SEM technique into the ecological literature. We would like to thank NSF grant #BBS-9100525 to D. Karowe and J. Teeri as well as NSF grant #DEB-9796250 and USDA grant #9706410 to D. Karowe for funding this work.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Figure S1. Structural equation models for the causal relationships between leaf nitrogen, external standard (HPLC covariate), glucosinolate groups, and P. rapae consumption showing all paths.

© 2014 John Wiley & Sons Ltd, Global Change Biology, 20, 3159–3176