The Role of Plant Abiotic Factors on the Interactions Between Cnaphalocrocis medinalis (Lepidoptera: Crambidae) and its Host Plant

The Role of Plant Abiotic Factors on the Interactions Between Cnaphalocrocis medinalis... Abstract Atmospheric temperature increases along with increasing atmospheric CO2 concentration. This is a major concern for agroecosystems. Although the impact of an elevated temperature or increased CO2 has been widely reported, there are few studies investigating the combined effect of these two environmental factors on plant–insect interactions. In this study, plant responses (phenological traits, defensive enzyme activity, secondary compounds, defense-related gene expression and phytohormone) of Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Pyralidae) -susceptible and resistant rice under various conditions (environment, soil type, variety, C. medinalis infestation) were used to examine the rice–C. medinalis interaction. The results showed that leaf chlorophyll content and trichome density in rice were variety-dependent. Plant defensive enzyme activities were affected environment, variety, or C. medinalis infestation. In addition, total phenolic content of rice leaves was decreased by elevated CO2 and temperature and C. medinalis infestation. Defense-related gene expression patterns were affected by environment, soil type, or C. medinalis infestation. Abscisic acid and salicylic acid content were decreased by C. medinalis infestation. However, jasmonic acid content was increased by C. medinalis infestation. Furthermore, under elevated CO2 and temperature, rice plants had higher abscisic acid content than plants under ambient conditions. The adult morphological traits of C. medinalis also were affected by environment. Under elevated CO2 and temperature, C. medinalis adults had greater body length in the second and third generations. Taken together these results indicated that elevated CO2 and temperature not only affects plants but also the specialized insects that feed on them. elevated temperature with carbon dioxide, multiple factor effect, phytohormone, plant–insect interaction Climate change is one of the most important issues affecting the planet, with large impacts predicted worldwide for economies, agriculture, and ecosystems. Rising atmospheric temperature and CO2 concentration are the two key factors driving climate change. The atmospheric CO2 level is predicted to increase from the current 410 ppm to 1,000 ppm by the end of this century (Meehl et al. 2007), leading to a predicted increase in the atmospheric temperature of up to 5°C (IPCC 2013). Plants could benefit from an increased growth rate, biomass, and higher photosynthetic rates due to elevated CO2 (Ainsworth and Long 2005, Ainsworth et al. 2008). In addition, plants could synthesize more carbohydrates and dilute their foliar nitrogen concentration, leading to a higher C/N ratio in them under elevated CO2 conditions (Pritchard et al. 1999, Coley et al. 2002, Ainsworth and Long 2005, Taub and Wang 2008, Murray et al. 2013, Oehme et al. 2013, Manimanjari et al. 2014, Ryan et al. 2014, Dáder et al. 2016). Furthermore, elevated CO2 levels would lead plants to reallocate limited resources and to changes in their chemistry, such as in the composition of their secondary metabolites (Peñuelas and Estiarte 1998, Awmack and Leather 2002, Salazar-Parra et al. 2015). However, insect herbivory may not be affected directly by elevated CO2 (Fajer et al. 1991, Murray et al. 2013). Indirectly, however, the increased C/N ratio and changing chemical components in plants could influence insect herbivory performance and feeding behavior (Hughes and Bazzaz 2001, Himanen et al. 2008, Sun et al. 2009, Ge et al. 2010, Cornelissen 2011, Oehme et al. 2013, Scherber et al. 2013, Stiling et al. 2013, Dáder et al. 2016). There is no general consistent trend in how plant–insect interactions are effected by elevated CO2. For example, Rhopalosiphum padi L. (Hemiptera: Aphididae) showed an increased growth rate under elevated CO2 (Sun et al. 2009, Oehme et al. 2012, Oehme et al. 2013) while R. maidis Fitch increased their portion of alates (Xie et al. 2014). Leaf-chewing insects, such as grasshoppers and caterpillars, consumed more leaf tissues under elevated CO2 conditions (Johnson and Lincoln 1990, Johnson and Lincoln 1991, Lindroth et al. 1993, Lindroth et al. 1995, Wu et al. 2006). However, not all insects respond to elevated CO2 in the same way. Under elevated CO2 condition, the fecundity of Sitobion avenae F. (Hemiptera: Aphididae) increased (Awmack et al. 1996) while that of Brevicoryne brassicae L. (Hemiptera: Aphididae) and Neophilaenus lineatus L. (Hemiptera: Cercopidae) went unchanged (Smith and Jones 1998, Brooks and Whittaker 1999); though N. lineatus did have a lower survival rate in response to the elevated CO2 conditions (Brooks and Whittaker 1999). In a different species, elevated CO2 had a negative impact on the developmental period of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae) (Wu et al. 2006). In addition, a CO2 concentration of 600 ppm had no impact on Bemisia tabaci population dynamics (Butler et al. 1986), whereas 1,000 ppm was capable of decreasing the numbers of Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) (Tripp et al. 1992). These varied findings suggest that to better understand CO2 concentration-dependent effects on insect herbivores, more of them should be investigated. Under elevated CO2, plant phytohormone signaling may be re-configured (Casteel et al. 2008, Zavala et al. 2008, Casteel et al. 2012a). Elevated CO2 was found to increase the salicylic acid (SA) content of soybean (Glycine max)(Casteel et al. 2012b); while in tomato it increased the SA-dependent signaling pathway and decreased the incidence of Tomato Yellow Leaf Curl Virus (TYLCV) disease (Guo et al. 2016). However, this effect was reversed in the tomato genotype carrying the resistance gene, Mi-1.2 (Guo et al. 2016). Thus, plant genotypes within species can respond differently in terms of their disease resistance and phytohormone regulation under elevated CO2. An elevated temperature may increase the leaf biomass and foliar nitrogen concentration in plants (Way and Oren 2010, Murray et al. 2013). In contrast to CO2, warming is predicted to have a direct effect on the developmental period of insects by increasing their metabolic rates (Bale et al. 2002). The combined effect of elevated CO2 and temperature has been studied in plants (Morison and Lawlor 1999, Lumoala et al. 2005, Wan et al. 2014). The plant assimilation rate under elevated CO2 would be more sensitive to a high temperature when compared with that under ambient CO2 (Wang et al. 2016). However, very few studies have investigated plant–insect interactions as affected by both atmospheric temperature and CO2. Aphids had an enhanced fecundity and longevity under elevated CO2, but did not benefit in this way under combined increases of CO2 and temperature (Ryalls et al. 2017). Thus, with elevated CO2 and temperature, plant and insect phenotypes may be affected at the same time, which can alter the plant–insect relationships. Many studies have evaluated plant–insect interactions under either CO2 or temperature condition (Karowe and Migliaccio 2011, Casteel et al. 2012a, Bauerfeind and Fischer 2013, Dyer et al. 2013, Manimanjari et al. 2014, Dáder et al. 2016, Sharma et al. 2016, Block et al. 2017, ShuQi et al. 2017). In these studies, the insects tested under the elevated conditions were previously reared under ambient conditions. In addition, under elevated CO2 condition, experiments beyond one generation indicated different responses between generations which may depend on the period of plant growth (Brooks and Whittaker 1998, Wu et al. 2006). Hence, a better understanding of the effect of elevated CO2 in tandem with temperature on insect herbivores is now needed. Rice (Oryza sativa L.) is one of the most globally important major crops. It is the main food source in Asia, namely China, India, and Taiwan. Current estimates for world rice production stand at more than 700 million tons per year (FAO 2014). However, rice production and its quality has been affected by multiple stresses, including abiotic and biotic stresses. It is estimated that losses of 13–26% in rice yield are caused by pests (Oerke 2006). Climate change could exacerbate threats to rice production via the increased atmospheric temperature and CO2. Yet a recent meta-analysis of rice studies suggested that elevated CO2 would enhance rice yields by up to 20% (Wang et al. 2015). However, this enhanced effect on rice yield and spikelet fertility would be diminished by the combined effect of warming with elevated CO2 (Wang et al. 2015). Therefore, it is crucial that we better understand how the interplay of elevated CO2 and temperature may impact rice–insect enemy interactions. In Taiwan and neighboring countries, Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Pyralidae) is one of the major pests of rice crops. The C. medinalis larvae fold rice leaves and can cause a huge reduction in rice yields. The objective of this study was to assess the dual impact of both temperature and CO2 on the rice–C. medinalis interaction. First, we reared C. medinalis colonies in the corresponding environments over one generation before the study and further measure of insect morphological traits. Furthermore, we examined the rice–C. medinalis interactions under elevated CO2 with temperature by assessing the traits and gene expression of plants. Going further, we also examined the influence of soil on the rice–C. medinalis interaction by using soil taken from organic rice and conventional rice fields. Finally, the phytohormone abundances and related direct defensive metabolites in rice were also examined in this study. Materials and Methods Plant Materials Two rice (O. sativa L.) varieties, Taichung Native 1 (TN1; indica) and QingLiu (QL; indica), were chosen for use in this study. The TN1 seeds were obtained from Dr. Shu-Jen Wang at National Taiwan University, Taiwan. The QingLiu seeds were obtained from the Taichung District Agricultural Research and Extension Station, COA, Changhua, Taiwan. These varieties were used because of their differences in resistance to C. medinalis. TN1 is susceptible to C. medinalis herbivory whereas QL is the resistant variety. Seeds were first sterilized by 2% (v/v) NaOCl for 30 min and then further washed under sterile water. The sterilized seeds were put on a wet paper towel on a petri dish and incubated under dark conditions at 37°C for 2 d. Germinated seeds similar in size were selected for planting within a peatmoss mix (Da Chiang Chun Horticulture Material Co. LTD., Nantou County, Taiwan). After reaching the two-leaf stage, seedlings were transferred to plastic pots (8.5 cm diameter, 16 cm height) with assigned soil (one seedling per pot). The conventional and organic soils were both obtained from the rice fields at the Taoyuan District Agricultural Research and Extension Station, COA, Taoyuan, Taiwan. The organic rice field had been planted with organic rice for 15 yr. In this study, all plants were fertilized with an organic fertilizer (No.1 Biotec Organic Fertilizer, Taiwan Fertilizer, Taipei, Taiwan). Plants were used in the experiment at 30 d after they had germinated (i.e., the six-leaf stage). Growth Chambers Two growth chamber for rice plants and C. medinalis were set in this study. One growth chamber was set to 30°C/25°C for day and night, with the CO2 concentration in the growth chamber set to 500 ppm; this treatment was designated ‘Ambient’. Another growth chamber was set to 35°C/30°C for day and night, with a CO2 concentration in the growth chamber of 1,000 ppm; this treatment was designated ‘HCHT’ (high CO2 and high temperature). Both growth chambers were set to12:12 (L:D) h cycle under a relative humidity of 55 ± 5%. Insect Rearing C. medinalis Guenée individuals were obtained from the Taichung District Agricultural Research and Extension Station, COA, Changhua, Taiwan, which originally had collected them from the rice field near Taichung, Taiwan. The C. medinalis colonies were reared on corn seedlngs (White pearl, Known-You Seed, Kaohsiung, Taiwan) by the corn seedling method (Shono and Hirano 1989) in insect cages (BugDorm-4, MegaView, Taichung, Taiwan). Corn seedlings were planted in vermiculite for 1 wk before use with the C. medinalis larvae. The C. medinalis moths were fed a 10% (v/v) sucrose solution. To foster the acclimation of plants and insects to the set environmental conditions, the insect cages were put into the two growth chambers (Ambient and HCHT) described earlier. Ambient and HCHT colonies were maintained in the corresponding environments for at least one generation prior to the experiment. C. medinalis Morphological Traits C. medinalis larvae were successfully reared in the two growth environments (Ambient and HCHT), under which conditions their insect morphological traits were measured during the second and third generations. Thirty random moths that emerged within 5 d in each environment were selected to measure their wing length, body size, and body weight, as well as the sample’s sex ratio. Leaf Chlorophyll Content Leaf chlorophyll content was measured with the Soil Plant Analysis Development chlorophyll meter (SPAD-502, Konica Minolta, Osaka, Japan) on treatments [Environment (Ambient and HCHT) × Soil (conventional soil and organic soil) × Variety (TN1 and QL)]. The SPAD meter provides a non-destructive approach to measure chlorophyll content. Each treatment has three un-infested plants. Readings from the tip, middle, and base of the youngest expanded leaf per plant (at the six-leaf stage of development) were averaged to yield a single value for chlorophyll content. The experiment was repeated three times. Leaf Toughness Leaf toughness was measured on the middle of the youngest expanded leaf (at the six-leaf stage of development) with a portable penetrometer (Chatillon DFEII, Ametek, Largo, FL) on treatments [Environment (Ambient and HCHT) × Soil (conventional soil and organic soil) × Variety (TN1 and QL)]. Each treatment has three un-infested plants. The experiment was repeated three times. Leaf Trichome Density The number of trichomes of the youngest expanded leaf at the six-leaf stage was counted by the photo photographed by dissection microscopy (Nikon SMZ1500, Kanagawa, Japan) under a high resolution (MediaCybernetics-Evolution MP COLOR, Silver Spring, MD) with treatments [Environment (Ambient and HCHT) × Soil (conventional soil and organic soil) × Variety (TN1 and QL)]. Each treatment has three un-infested plants. The experiment was repeated three times. Plant Phytohormone-Related Gene Expression, Enzyme Activity, and Secondary Metabolic Profiles Under Larval Infestation The third instar C. medinalis larvae were transferred to the newly expanded leaves of rice plants (in the six-leaf stage). The larvae were starved 4 h before placing them upon the plants (one larva per plant). Each plant was covered with a plastic cover with mesh cloth to prevent the insects from escaping. The plastic cover was 58 cm tall with four windows (6.5 × 6.5 cm). Each window was covered by mesh cloth to avoid the water condensation in the plastic cage. Uninfested plants covering plastic cover with mesh cloth served as the controls. In these experiments investigating gene expression, enzyme activity, and secondary metabolic profiles, each treatment consisted of three individual plants (one larva per plant, one plant per pot). The experiment was repeated three times. The infested leaf tissues were collected after 1 and 3 d after C. medinalis infestation (1DAI and 3DAI) for the gene expression and enzyme activity experiments, but only 3DAI for secondary metabolic experiment; in all experiments, the leaf samples were immediately frozen with liquid nitrogen and stored at −80°C. Leaf tissues were further homogenized in liquid nitrogen by using the 2010 Geno/Grinder (SPEX SamplePrep, Metuchen, NJ). Defense Enzyme Activity Assay Protein concentrations were determined by the RC-DC Protein assay (Bio-Rad, Hercules, CA). The measurements of phenylalanine lyase (PAL) and polyphenoloxidase (PPO) enzyme activity were modified from Duan et al. (2014). For the PAL activity analysis, 50 mg of leaf powder was collected and mixed with 600 µl of a 50 mM sodium borated buffer (pH 8.8 with 2% PVP) and then centrifuged at 13,000 rpm for 20 min at 4°C. The supernatant was centrifuged again to remove any debris. The ensuing supernatant was collected for the PAL activity analysis. Two-hundred microliters of a 500 µg/ml-leaf protein extract was incubated with 200 µl of a 50 mM sodium borated buffer for 10 min at 37°C in an incubator. To the mixture was added 200 µl of 20 mM L-phenylalanine and then further mixed and centrifuged to get the supernatant. The PAL activity was calculated by the observed change at 290 nm with a spectrophotometer (U1800, Hitachi, Tokyo, Japan). For the PPO activity analysis, 20 mg of ground leaf powder was mixed with 400 µl of a 0.1M sodium phosphate buffer (pH 7.0 with 2% PVP) and centrifuged at 13,000 rpm for 20 min at 4°C. The supernatant was centrifuged again to remove debris, after which the supernatant was collected for the PPO activity analysis. From this supernatant, 100 µl was drawn and added to 100 µl of 200 mM catechol to measure the absorbance change at 398 nm. For the peroxidase (POD) analysis, the measurement of POD enzyme activity was modified from the method of Qin and Tian (2005). Briefly, 20 mg of ground leaf powder was mixed with 400 µl of 0.1M sodium phosphate buffer (pH 7.0 with 2% PVP), centrifuged at 13,000 rpm for 20 min at 4°C, then centrifuged again to remove debris. A total of 50 µl of the supernatant was mixed with 25 µl of a 0.1M sodium borated buffer (pH 7.0), then mixed with 25 µl of 10 mM guaiacol for 30 min at 30°C. To this mixture was added 25 µl of 10 mM H2O2 to measure the absorbance change at 460 nm. Real-Time RT-PCR Plant RNA was extracted with Direct-zol RNA Kits (Zymo Research, Irvine, CA) following the manufacturer’s instructions; likewise, the genomic DNA was removed according to the manufacturer’s suggestion. The genomic DNA-free RNA was quantified by using Nanodrop (Thermo-Fisher Scientific, Waltham, MA). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed with the One Step SYBR PrimeScriptTM RT-PCR Kit II (Takara Bio USA, Mountain View, CA), with OsUbiquitin used as the reference gene. Gene-specific primer sequences are detailed in Supp. Table S1 (Ye et al. 2013, Duan et al. 2014, Li et al. 2015). The qRT-PCR was performed in a Stratagene MX3000PTM PCR system. The PCR was conducted by using the default conditions: step 1, 42°C for 5 min and 95°C for 30 s; step 2, 95°C for 5 s and 60°C for 30 s, repeated for 40 cycles. RNA extraction and qRT-PCR were performed for each biological replicate separately, with each treatment represented by three biological replicates. Relative quantification of gene expression was analyzed with the software MxPro QPCR v3.00 (Agilent, Santa Clara, CA). Plant Phenolic Compound Assay Leaf samples were vacuum frozen using a freeze dryer (FD-5N, Eyela, Tokyo, Japan) under –25 pa for 2 d. The dried samples (20 mg) was mixed with 2 ml of an extracting liquid solution (3% HCL + 60% MeOH) for 30 min and then centrifuged at 3,000 rpm for 15 min at 4°C. The supernatant was collected for further analysis and stored at –20°C in a freezer. The total phenolic content measurement was modified from Singleton et al. (1965). Specifically, 100 µl of the extracted supernatant was mixed with 500 µl Folin-Ciocalteu reagent and later added to 500 µl of 4% (w/v) Na2CO3 for 1 h. During the reaction, it was necessary to open the lip of the tubes to release CO2. The absorbance was measured at 750 nm to determine the total phenolic content using a gallic acid calibration curve. The flavonoid measurements were modified from Zhishen et al. (1999). From the extracted supernatant, 100 µl was mixed with 300 µl of an extracting liquid solution (3% HCl + 60% MeOH), 100 µl of 1%AlCl3, 100 µl of 1M potassium acetate, and 500 µl of sterile water. The absorbance was measured at 415 nm for the content of flavonoids. The anthocyanin measurement was followed by Huang et al. (2014). Phytohormone Extraction and Quantitation The third instar C. medinalis larvae were transferred to the newly expanded leaves of rice plants (in the six-leaf stage). The larvae were starved for 4 h before placing them upon the plants, which were then covered with a plastic cover to avoid insect escape. Uninfested plants were used as the controls. In this experiment, each treatment had four individual plants (i.e., one larva per plant, one plant per pot). The infested leaf tissues were collected after 1 h of infestation, then frozen with liquid nitrogen and stored at –80°C. Plant tissues (200 mg) from around the feeding sites were used to extract the phytohormones, which followed the method described by Chen et al. (2014). The phytohormone quantitations were analyzed by a linear ion trap-orbitrap mass spectrometer (Orbitrap Elite; Thermo Fisher Scientific) coupled online to a UHPLC system (ACQUITY UPLC; Waters, Milford, CT). The amounts of SA, abscisic acid (ABA), jasmonic acid (JA), and JA-Isoleucine (JA-Ile, the bioactive compound of JA) were quantified by comparison to internal standards of the deuterated isotopes of these hormones (Chen et al. 2014). The fragmentation reactions of m/z 137.02 to 93.03 for SA; 263.13 to 153.10 for ABA; 209.12 to 59.01 for JA; 322.20 to 130.09 for JA-Ile; 141.05 to 97.06 for d6-SA; 269.20 to 159.13 for d6-ABA; and 211.13 to 59.01 for H2JA were selected for quantitation. Phytohormones were extracted from four plants per treatment per time point. Statistical Analyses The responses in terms of plant phenological traits, defensive enzymes, chemical traits, gene expression, and phytohormone experiments assumed that all three factors (environment, insect infestation, and rice variety; each with two levels) were fixed effects. The factorial design: 1) Environmental factor: Ambient and HCHT, 2) Soil factor: conventional soil and organic soil, 3) Rice variety factor: TN1 and QL, and 4) Insect infestation factor: non-infestation control and infestation. For the phenological traits (SPAD, trichome density, and toughness), there were eight treatment combinations (environmental × soil × variety). For the plant defensive enzyme, chemical trait, gene expression experiments, there were 16 treatment combinations (environmental × soil × variety × infestation). The experimental materials were prepared in three different batches, due to the capacity of the growth chambers, and the treatment combinations were allocated randomly to the units in each batch. In order to take the variation among batches into account, the batch effects were treated as fixed block effects in our data analysis. For the phytohormone experiment, there were 16 treatment combinations (environmental × soil × variety × infestation) but have one batch. The experimental responses were fitted by linear models, and the effects of different factors were evaluated by ANOVA. For the insect morphological traits, the data were analyzed by one-way ANOVA. All the data were analyzed in the free statistical software platform, R (https://www.r-project.org). Results Phenological Traits The SPAD value and trichome density of the experimental plants were affected by rice variety (Table 1). Within varieties, TN1 had a significantly higher SPAD value than QL (Supp. Fig. S1A). For trichome density, TN1 had fewer trichomes than QL (Supp. Fig. S1B). However, the statistical analysis showed that neither environment nor soil type had any significant effects, nor did any interaction among the factors, for all the tested parameters (Table 1). Table 1. P-values of three-way ANOVA on plant phenological trait (SPAD valuea, trichome density, and leaf toughness) responses to multiple factors SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 aSPAD value: value of relative proportion of leaf chlorophyll concentration. bAmbient versus HCHT. cConventional soil versus organic soil. dTN1 versus QL. *P < 0.01; †P < 0.001. View Large Table 1. P-values of three-way ANOVA on plant phenological trait (SPAD valuea, trichome density, and leaf toughness) responses to multiple factors SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 aSPAD value: value of relative proportion of leaf chlorophyll concentration. bAmbient versus HCHT. cConventional soil versus organic soil. dTN1 versus QL. *P < 0.01; †P < 0.001. View Large Plant Defensive Enzyme Activity For 1 d after C. medinalis larvae infestation (1DAI), PAL was affected by insect infestation; PPO and POD were affected by rice variety (Table 2). PAL enzyme activity was higher in the control than under the C. medinalis infestation treatment (Supp. Fig. S2A), while TN1 had lower PPO and POD activity than did QL at 1DAI (Supp. Fig. S2B and C). In addition, POD was affected by the environmental factor at 1DAI. Plants had higher POD activity under HCHT than under Ambient treatment (Supp. Fig. S2D). The interaction among environment, rice variety, and soil type significantly affected the POD activity. At 3DAI, PAL activity was affected by rice variety, while TN1 had lower PAL activity than did QL (Supp. Fig. S2E). The interaction among environment, soil type, and C. medinalis infestation had significant effects on the activity of both PAL and POD. Table 2. P-values of four-way ANOVA on plant defensive enzyme (PAL, PPO, POD) responses to multiple factors Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01. View Large Table 2. P-values of four-way ANOVA on plant defensive enzyme (PAL, PPO, POD) responses to multiple factors Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01. View Large Secondary Metabolite Profiles Total phenolic content was affected by the environment and insect infestation (Table 3). Plants had lower total phenolic content under HCHT than under Ambient treatment (Supp. Fig. S3A). In addition, the total phenolic content was lower in the insect infestation treatment than in the control plants (Supp. Fig. S3B). However, the statistical analysis showed no main effect of any factor on either anthocyanins or flavonoids (Table 3). Anthocyanins were, however, affected by the interaction between environment and soil (Supp. Fig. S3C) and that among environment, insect infestation, and rice variety. Table 3. P-values of four-way ANOVA on plant secondary compounds (total phenolics, anthocyanins, flavonoids) responses to multiple factors Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05. View Large Table 3. P-values of four-way ANOVA on plant secondary compounds (total phenolics, anthocyanins, flavonoids) responses to multiple factors Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05. View Large Plant Phytohormone Related Gene Expression JA and SA are two keys phytohormones that regulate plant defense responses against attacking insect herbivores. AOS and LOX are involved in the JA biosynthesis pathway. COI1 acts as receptor in the JA signaling pathway. ICS participates in the SA biosynthesis pathway. Two time points (1DAI and 3DAI) were measured for OsAOS1, OsLOX, OsCOI1, and OsICS1. At 1DAI, the gene expression of OsAOS1, OsLOX, and OsCOI1 were affected by the environment (Table 4). These three genes had a higher expression level under HCHT than under the Ambient treatment (Supp. Fig. S4A–C). OsCOI1 gene expression was also affected by soil type: OsCOI1 had a higher expression level in plants growing in organic soil than those in conventional soil (Supp. Fig. S4D). OsICS1 gene expression was affected by C. medinalis infestation at 1DAI: OsICS1 had higher expression level in the non-infested control than under infestation treatment (Supp. Fig. S4E). Two interactions (environment with variety on OsCOI1, and environment with soil type on OsICS1) were statistically significant (Table 4, Supp. Fig. S5A and B). At 3DAI, the only significant effect detected was for OsLOX gene expression by soil type. Plants in conventional soil had a higher expression level of OsLOX than did those in organic soil (Supp. Fig. S4F). The statistical analysis found no significant interactions among the factors for all the tested parameters (Table 4). Table 4. P-values of four-way ANOVA on plant phytohormone related gene expression (OsAOS1, OsLOX, OsCOI1, OsICS1) responses to multiple factors OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Table 4. P-values of four-way ANOVA on plant phytohormone related gene expression (OsAOS1, OsLOX, OsCOI1, OsICS1) responses to multiple factors OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Phytohormones Plant phytohormones (ABA, SA, JA, JA-Ile) were analyzed for 1 h after C. medinalis larvae infestation. These four phytohormones were affected by insect infestation (Table 5). When compared with the control, plants had lower ABA and SA when under insect infestation; however, plants had more JA and JA-Ile under the insect infestation treatment (Supp. Fig. S6A–D). Additionally, ABA was affected by the environmental factor. Plants had higher SA under HCHT than the Ambient treatment (Supp. Fig. S6E). The interaction between environment and insect infestation significantly affected ABA and SA contents in plants (Supp. Fig. S7A and B). Table 5. P-values of four-way ANOVA on plant phytohormone (ABA, SA, JA, JA-Ile) responses to multiple factors ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Table 5. P-values of four-way ANOVA on plant phytohormone (ABA, SA, JA, JA-Ile) responses to multiple factors ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Insect Morphological Traits The C. medinalis moth morphological traits were significantly affected by the growth environment (Supp. Table S2). The male moths under HCHT had a greater wing length, body length, and body weight in the second generations than did their counterparts under the Ambient conditions (Supp. Table S2). In the third generations, male moths under HCHT had a greater body length (Supp. Table S2). The female moths followed similar trend as the males, except for wing length in the second generation. In the third generation, female moths under HCHT had a greater wing length, body length, and body weight (Supp. Table S2). Discussion One way by which climate change may affect terrestrial vegetation ecosystems is if its two key factors, elevated temperature and CO2, impact plant–insect interactions. In this study, we used the rice–C. medinalis interaction to dissect how various factors (environment, soil type, variety, insect infestation, and their interactions) might affect the traits or molecular responses of host plants. Our results suggest that each plant trait responds to the growth environment somewhat differently, though this environmental factor (i.e., HCHT) would influence several plant traits at once (POD activity, JA-related gene expression, and ABA content). Furthermore, there may be specific-regulation of a given trait associated with each environmental factor. Besides, plant traits may have unique responses to the interactions among factors. Under the elevated temperature and CO2, plants had higher POD activity, OsAOS1, OsLOX, OsCOI1 gene expression, and ABA content than under the Ambient condition. Rice POD activity would be considerably increased under insect infestation (Rani and Jyothsna 2010, Punithavalli et al. 2013, Ye et al. 2013, Duan et al. 2014). Since OsAOS1, OsLOX, and OsCOI1 all function in the JA biosynthesis and signaling pathway, this may indicate JA content is susceptible to climate change effects. Yet, we found no significant difference on the JA and JA-Ile responses to the environmental factor (Table 5). Furthermore, ABA was induced under elevated temperature and CO2 in our plant-herbivore system. A recent study showed that rice plants under elevated temperature has a higher ABA content (Wu et al. 2016), which supports our findings. In addition, elevated CO2 would induce the ABA-induced stomatal closure in soybean (Levine et al. 2009). These studies suggest that plants would have the drought stress responses under elevated CO2 and temperature condition. This inconsistency between the gene expression and phytohormone results may due to the different time points of the phytohormone experiment (1 h after infestation) and the plant defensive related gene expression experiment (1 and 3 d after infestation). However, the plants in this study were grown under the same two environments; hence, further study to understand the regulatory mechanism between gene expression and phytohomones is needed. In addition, the total phenolic content was affected by the environmental factor (Table 3), as it was lowered by the HCHT treatment (vs. Ambient). The plant secondary metabolites affected by global climate change would likely be plant species-specific and chemical type-specific (Bidart-Bouzat and Imeh-Nathaniel 2008). In a survey of 343 publications, nearly half of the studies found that plant phenolic content would be increased under elevated CO2 (Ryan et al. 2010). However, a small proportion of studies do exist that report a decreased phenolic content under elevated CO2 (Ryan et al. 2010), which is consistent with our results. Total phenolic content represents a group of similar functional chemicals, but we need to further analyze the content of each specific chemical in the future. With the above data, our results suggest that environmental change would have an impact on plant physiology, ranging from gene expression to enzyme activity and phytohormone content. We found that QL had a lower SPAD value than did TN1 under all tested conditions (Table 1). The SPAD value can be used as an indirect indicator of leaf nitrogen content, which is one of the limiting factors in the development of herbivore populations (Arregui et al. 2006, Ziadi et al. 2008, Yuan et al. 2016a, Yuan et al. 2016b). Thus, most insect herbivores would prefer plants with a higher leaf nitrogen content. In Asia, the excessive application of nitrogen fertilizer to rice fields has increased the size of several major insect pest populations (Lu et al. 2007). Other work has shown that TN1 has the highest SPAD value among genotypes, with SPAD being positively correlated to corrected damage ratings caused by C. medinalis infestation (Xu et al. 2010). Nonetheless, QL, the rice variety resistant to C. medinalis, had a higher trichome density than did TN1 under all the tested conditions (Table 1); moreover, QL showed greater PAL, PPO, and POD activity than did TN1 (Table 2). POD and PAL are two candidate markers associated with C. medinalis resistance in rice (Sinha et al. 2005). PPO oxidizes phenolics to quinones involved in the formation of plant defensive barriers. In tomato (Solanum lycopersicum), PPO plays a defensive role in resistance to the beet armyworm (Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae)) and cotton bollworm (Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)) by decreasing the rates of weight gain in larvae and their foliar consumption (Bhonwong et al. 2009). PAL is the key enzyme in the biosynthetic phenylpropanoids that participate in plant responses to UV irradiation, mechanical wounding, and pathogen attack (Dixon and Paiva 1995, Weisshaar and Jenkins 1998). Furthermore, PAL is also involved in the SA biosynthetic pathway. Hence, the C. medinalis resistance of QL may have multiple components, and not be all that surprising. Organic farming is gaining more attention with the concerned public. Soil microbes could help plants to grow faster and resist external stresses. In our study, soil type only affected the expression of two JA-related genes, indicating that soil type likely would not have strong impact on plant defensive enzymes and phytohormones. Furthermore, it did not interact with environmental factors (i.e., temperature and CO2) in this study system. A possible explanation for this result is that we did not put the soil into the corresponding environments before the experiment, as we did for the insect colonies. The soil microbes in the environment may not have had enough time to adapt to their new growing conditions. However, it is difficult to know how long it will environmental acclimation. In this study, we conducted gene expression and defensive enzyme experiments at 1 and 3 d after insect infestation. Their purpose was to enhance our understanding of a later response in plant defense after insect feeding. However, the responses for gene expression and defensive enzymes may occur before these two sampling time points. In addition, it may even follow a different pattern during the earlier plant response. Further investigations are required to discern and understand the whole picture of plant defense in response to insect feeding under climate change scenarios. Insect morphological traits were also affected by the environmental factor (temperature with CO2) (Supp. Table S2), in that female moths had a greater body weight under HCHT than under Ambient conditions. Larger female moths produced more eggs in Pararge aegeria under elevated temperature (Berger et al. 2008). In addition, the quantity of food affected the size of horns in male Onthophagus acuminatus (Emlen 1994). Thus, the changes in insect morphological traits in our study may due to the either food quantity or quality differences between the two environments tested. In addition, the corn seedling rearing method may have impacted the C. medinalis colonies. This method was used instead of rearing C. medinalis on rice plants prior to our experiments for two reasons. First, the corn seedling rearing method has been proved to be effective for the mass rearing of C. medinalis (Shono and Hirano 1989). In this study, we needed many same-age and -sized C. medinalis larvae for the experiments. Second, we wanted unbiased C. medinalis larvae lacking any preference and experience on rice plants before the experiments began. Using such ‘naive’ larvae guarded against the results being unduly influenced by insect experiences. However, only further experimentation will improve our understanding of how environmental factor affects C. medinalis body size when the C. medinalis colonies are first reared on rice plants. In addition, it will be helpful to know how later generations of C. medinalis are possibly affected under elevated temperature with CO2. Supplementary Data Supplementary Data are available at Environmental Entomology online. Acknowledgments We are grateful for the funding provided by the Ministry of Science and Technology, Taiwan (104-2313-B-002-001-MY2, 106-2313-B-002-001, and 106-2311-B-002-025). We thank Dr. Shu-Jen Wang for generously providing the TN1 seeds and Dr. Chi-Te Liu for kindly providing the vacuum equipment used in this study. We also thank Dr. Yet-Ran Chen for the supporting phytohormone analysis done at the Metabolomics Core Facility (Academia Sinica, Taiwan) and Ms. Po-Ya Wu for the statistical analysis, and Drs. Yun-Fen Huang and Hsiang-Chin Chen for their helpful comments on this paper. References Cited Ainsworth , E. A. , and S. P. Long . 2005 . What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2 . New Phytol . 165 : 351 – 371 . Google Scholar CrossRef Search ADS PubMed Ainsworth , E. A. , A. D. Leakey , D. R. Ort , and S. P. Long . 2008 . FACE-ing the facts: inconsistencies and interdependence among field, chamber and modeling studies of elevated [CO2] impacts on crop yield and food supply . New Phytol . 179 : 5 – 9 . Google Scholar CrossRef Search ADS PubMed Arregui , L. , B. Lasa , A. Lafarga , I. Irañeta , E. Baroja , and M. Quemada . 2006 . Evaluation of chlorophyll meters as tools for N fertilization in winter wheat under humid Mediterranean conditions . Eur. J. Agron . 24 : 140 – 148 . Google Scholar CrossRef Search ADS Awmack , C. S. , and S. R. Leather . 2002 . Host plant quality and fecundity in herbivorous insects . Annu. Rev. Entomol . 47 : 817 – 844 . Google Scholar CrossRef Search ADS PubMed Awmack , C. , R. Harrington , S. Leather , and J. Lawton . 1996 . The impacts of elevated CO2 on aphid-plant interactions . Asp. Appl. Biol . 45 : 317 – 322 . Bale , J. S. , G. J. Masters , I. D. Hodkinson , C. Awmack , T. M. Bezemer , V. K. Brown , J. Butterfield , A. Buse , J. C. Coulson , and J. Farrar . 2002 . Herbivory in global climate change research: direct effects of rising temperature on insect herbivores . Glob. Change Biol . 8 : 1 – 16 . Google Scholar CrossRef Search ADS Bauerfeind , S. S. , and K. Fischer . 2013 . Increased temperature reduces herbivore host-plant quality . Glob. Chang. Biol . 19 : 3272 – 3282 . Google Scholar PubMed Berger , D. , R. Walters , and K. Gotthard . 2008 . What limits insect fecundity? Body size-and temperature-dependent egg maturation and oviposition in a butterfly . J. Functional Ecology . 22 : 523 – 529 . Google Scholar CrossRef Search ADS Bhonwong , A. , M. J. Stout , J. Attajarusit , and P. Tantasawat . 2009 . Defensive role of tomato polyphenol oxidases against cotton bollworm (Helicoverpa armigera) and beet armyworm (Spodoptera exigua) . J. Chem. Ecol . 35 : 28 – 38 . Google Scholar CrossRef Search ADS PubMed Bidart-Bouzat , M. G. , and A. Imeh-Nathaniel . 2008 . Global change effects on plant chemical defenses against insect herbivores . J. Integr. Plant Biol . 50 : 1339 – 1354 . Google Scholar CrossRef Search ADS PubMed Block , A. , M. M. Vaughan , S. A. Christensen , H. T. Alborn , and J. H. Tumlinson . 2017 . Elevated carbon dioxide reduces emission of herbivore-induced volatiles in Zea mays . Plant. Cell Environ . 40 : 1725 – 1734 . Google Scholar CrossRef Search ADS PubMed Brooks , G. , and J. Whittaker . 1998 . Responses of multiple generations of Gastrophysa viridula, feeding on Rumex obtusifolius, to elevated CO2 . Glob. Chang. Biol . 4 : 63 – 75 . Google Scholar CrossRef Search ADS Brooks , G. , and J. Whittaker . 1999 . Responses of three generations of a xylem-feeding insect, Neophilaenus lineatus (Homoptera), to elevated CO2 . Glob. Chang. Biol . 5 : 395 – 401 . Google Scholar CrossRef Search ADS Butler , G. , B. Kimball , and J. Mauney . 1986 . Populations of Bemisia tabaci (Homoptera: Aleyrodidae) on cotton grown in open-top field chambers enriched with CO2 . Environ. Entomol . 15 : 61 – 63 . Google Scholar CrossRef Search ADS Casteel , C. L. , B. F. O’Neill , J. A. Zavala , D. D. Bilgin , M. R. Berenbaum , and E. H. Delucia . 2008 . Transcriptional profiling reveals elevated CO2 and elevated O3 alter resistance of soybean (Glycine max) to Japanese beetles (Popillia japonica) . Plant. Cell Environ . 31 : 419 – 434 . Google Scholar CrossRef Search ADS PubMed Casteel , C. L. , O. K. Niziolek , A. D. Leakey , M. R. Berenbaum , and E. H. DeLucia . 2012a . Effects of elevated CO2 and soil water content on phytohormone transcript induction in Glycine max after Popillia japonica feeding . Arthropod Plant Interact . 6 : 439 – 447 . Google Scholar CrossRef Search ADS Casteel , C. L. , L. M. Segal , O. K. Niziolek , M. R. Berenbaum , and E. H. DeLucia . 2012b . Elevated carbon dioxide increases salicylic acid in Glycine max . Environ. Entomol . 41 : 1435 – 1442 . Google Scholar CrossRef Search ADS Chen , Y. L. , C. Y. Lee , K. T. Cheng , W. H. Chang , R. N. Huang , H. G. Nam , and Y. R. Chen . 2014 . Quantitative peptidomics study reveals that a wound-induced peptide from PR-1 regulates immune signaling in tomato . Plant Cell . 26 : 4135 – 4148 . Google Scholar CrossRef Search ADS PubMed Coley , P. , M. Massa , C. Lovelock , and K. Winter . 2002 . Effects of elevated CO2 on foliar chemistry of saplings of nine species of tropical tree . Oecologia . 133 : 62 – 69 . Google Scholar CrossRef Search ADS PubMed Cornelissen , T . 2011 . Climate change and its effects on terrestrial insects and herbivory patterns . Neotrop. Entomol . 40 : 155 – 163 . Google Scholar CrossRef Search ADS PubMed Dáder , B. , A. Fereres , A. Moreno , and P. Trębicki . 2016 . Elevated CO2 impacts bell pepper growth with consequences to Myzus persicae life history, feeding behaviour and virus transmission ability . Sci. Rep . 6 : srep19120 . Google Scholar CrossRef Search ADS Dixon , R. A. , and N. L. Paiva . 1995 . Stress-induced phenylpropanoid metabolism . Plant Cell . 7 : 1085 – 1097 . Google Scholar CrossRef Search ADS PubMed Duan , C. , J. Yu , J. Bai , Z. Zhu , and X. Wang . 2014 . Induced defense responses in rice plants against small brown planthopper infestation . Crop J . 2 : 55 – 62 . Google Scholar CrossRef Search ADS Dyer , L. A. , L. A. Richards , S. A. Short , and C. D. Dodson . 2013 . Effects of CO2 and temperature on tritrophic interactions . PLoS One . 8 : e62528 . Google Scholar CrossRef Search ADS PubMed Emlen , D. J . 1994 . Environmental control of horn length dimorphism in the beetle Onthophagus acuminatus (Coleoptera: Scarabaeidae) . Proc. Royal Soc. London B: Biol. Sci . 256 : 131 – 136 . Google Scholar CrossRef Search ADS Fajer , E. D. , M. D. Bowers , and F. A. Bazzaz . 1991 . The effects of enriched CO2 atmospheres on the buckeye butterfly, Junonia Coenia . J. Ecology . 72 : 751 – 754 . Google Scholar CrossRef Search ADS FAO . 2014 . FAOSTAT online statistical service . FAO , Rome, Italy . Ge , F. , F. Chen , G. Wu , and Y. Sun . 2010 . Research advance on the response of insects to elevated CO2 in China . Chinese Bulletin of Entomology . 47 : 229 – 235 . Guo , H. , L. Huang , Y. Sun , H. Guo , and F. Ge . 2016 . The contrasting effects of elevated CO2 on TYLCV infection of tomato genotypes with and without the resistance gene, Mi-1.2 . Front. Plant Sci . 7 : 1680 . Google Scholar PubMed Himanen , S. J. , A. Nissinen , W. X. Dong , A. Nerg , C. Stewart , G. M. Poppy , and J. K. Holopainen . 2008 . Interactions of elevated carbon dioxide and temperature with aphid feeding on transgenic oilseed rape: are Bacillus thuringiensis (Bt) plants more susceptible to nontarget herbivores in future climate ? Glob. Chang. Biol . 14 : 1437 – 1454 . Google Scholar CrossRef Search ADS Huang , W. D. , K. H. Lin , M. H. Hsu , M. Y. Huang , Z. W. Yang , P. Y. Chao , and C. M. Yang . 2014 . Eliminating interference by anthocyanin in chlorophyll estimation of sweet potato (Ipomoea batatas L.) leaves . Bot. Stud . 55 : 11 . Google Scholar CrossRef Search ADS PubMed Hughes , L. , and F. A. Bazzaz . 2001 . Effects of elevated CO2 on five plant-aphid interactions . Entomologia Experimentalis et Applicata . 99 : 87 – 96 . Google Scholar CrossRef Search ADS IPCC . 2013 . Climate change 2013: the physical science basis , pp. 1535 . In T. F. Stocker , D. Qin , G-K. Plattner , M. Tignor , S. K. Allen , J. Boschung , A. Nauels , Y. Xia , B. Bex , and B. Midgley (eds.), Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change . Cambridge University Press , Cambridge, UK . Johnson , R. H. , and D. E. Lincoln . 1990 . Sagebrush and grasshopper responses to atmospheric carbon dioxide concentration . Oecologia . 84 : 103 – 110 . Google Scholar CrossRef Search ADS PubMed Johnson , R. H. , and D. E. Lincoln . 1991 . Sagebrush carbon allocation patterns and grasshopper nutrition: the influence of CO2 enrichment and soil mineral limitation . Oecologia . 87 : 127 – 134 . Google Scholar CrossRef Search ADS PubMed Karowe , D. N. , and A. Migliaccio . 2011 . Performance of the legume-feeding herbivore, Colias philodice (Lepidoptera: Pieridae) is not affected by elevated CO2 . Arthropod Plant Interact . 5 : 107 – 114 . Google Scholar CrossRef Search ADS Levine , L. H. , J. T. Richards , and R. M. Wheeler . 2009 . Super-elevated CO2 interferes with stomatal response to ABA and night closure in soybean (Glycine max) . J. Plant Physiol . 166 : 903 – 913 . Google Scholar CrossRef Search ADS PubMed Li , R. , J. Zhang , J. Li , G. Zhou , Q. Wang , W. Bian , M. Erb , and Y. Lou . 2015 . Prioritizing plant defence over growth through WRKY regulation facilitates infestation by non-target herbivores . Elife . 4 : e04805 . Google Scholar PubMed Lindroth , R. L. , K. K. Kinney , and C. L. Platz . 1993 . Responses of diciduous trees to elevated atmospheric CO2: productivity, phytochemistry, and insect performance . J. Ecology . 74 : 763 – 777 . Google Scholar CrossRef Search ADS Lindroth , R. , G. Arteel , and K. Kinney . 1995 . Responses of three saturniid species to paper birch grown under enriched CO2 atmospheres . Funct. Ecol . 9 : 306 – 311 . Google Scholar CrossRef Search ADS Lu , Z-X. , X-P. Yu , K-l. Heong , and H. Cui . 2007 . Effect of nitrogen fertilizer on herbivores and its stimulation to major insect pests in rice . J. Rice Science . 14 : 56 – 66 . Google Scholar CrossRef Search ADS Lumoala , E. , K. Laitinen , S. Sutinen , S. Kellomäki , and E. Vapaavuori . 2005 . Stomatal density, anatomy and nutrient concentrations of Scots pine needles are affected by elevated CO2 and temperature . Plant Cell Environ . 28 : 733 – 749 . Google Scholar CrossRef Search ADS Manimanjari , D. , M. Srinivasa Rao , P. Swathi , C. Rama Rao , M. Vanaja , and M. Maheswari . 2014 . Temperature-and CO2-dependent life table parameters of Spodoptera litura (Noctuidae: Lepidoptera) on sunflower and prediction of pest scenarios . J. Insect Sci . 14 : 297 . Google Scholar CrossRef Search ADS PubMed Meehl , G. A. , C. Covey , K. E. Taylor , T. Delworth , R. J. Stouffer , M. Latif , B. McAvaney , and J. F. Mitchell . 2007 . The WCRP CMIP3 multimodel dataset: a new era in climate change research . Bull. Am. Meteorol. Soc . 88 : 1383 – 1394 . Google Scholar CrossRef Search ADS Morison , J. , and D. Lawlor . 1999 . Interactions between increasing CO2 concentration and temperature on plant growth . Plant Cell Environ . 22 : 659 – 682 . Google Scholar CrossRef Search ADS Murray , T. J. , D. S. Ellsworth , D. T. Tissue , and M. Riegler . 2013 . Interactive direct and plant-mediated effects of elevated atmospheric [CO2] and temperature on a eucalypt-feeding insect herbivore . Glob. Chang. Biol . 19 : 1407 – 1416 . Google Scholar CrossRef Search ADS PubMed Oehme , V. , P. Högy , J. Franzaring , C. Zebitz , and A. Fangmeier . 2012 . Response of spring crops and associated aphids to elevated atmospheric CO2 concentrations . J. Appl. Bot. Food Qual . 84 : 151 . Oehme , V. , P. Högy , C. P. Zebitz , and A. Fangmeier . 2013 . Effects of elevated atmospheric CO2 concentrations on phloem sap composition of spring crops and aphid performance . J. Plant Interact . 8 : 74 – 84 . Google Scholar CrossRef Search ADS Oerke , E-C . 2006 . Crop losses to pests . J. Agric. Sci . 144 : 31 – 43 . Google Scholar CrossRef Search ADS Peñuelas , J. , and M. Estiarte . 1998 . Can elevated CO2 affect secondary metabolism and ecosystem function ? Trends Ecol. Evol . 13 : 20 – 24 . Google Scholar CrossRef Search ADS PubMed Pritchard , S. , H. Rogers , S. A. Prior , and C. Peterson . 1999 . Elevated CO2 and plant structure: a review . Glob. Chang. Biol . 5 : 807 – 837 . Google Scholar CrossRef Search ADS Punithavalli , M. , N. Muthukrishnan , and M. B. Rajkuma . 2013 . Defensive responses of rice genotypes for resistance against rice leaffolder Cnaphalocrocis medinalis . J. Rice Science . 20 : 363 – 370 . Google Scholar CrossRef Search ADS Qin , G. Z. , and S. P. Tian . 2005 . Enhancement of biocontrol activity of cryptococcus laurentii by Silicon and the possible mechanisms involved . Phytopathology . 95 : 69 – 75 . Google Scholar CrossRef Search ADS PubMed Rani , P. U. , and Y. Jyothsna . 2010 . Biochemical and enzymatic changes in rice plants as a mechanism of defense . Acta Physiologiae Plantarum . 32 : 695 – 701 . Google Scholar CrossRef Search ADS Ryalls , J. M. , B. D. Moore , M. Riegler , L. M. Bromfield , A. A. Hall , and S. N. Johnson . 2017 . Climate and atmospheric change impacts on sap-feeding herbivores: a mechanistic explanation based on functional groups of primary metabolites . Funct. Ecol . 31 : 161 – 171 . Google Scholar CrossRef Search ADS Ryan , G. D. , S. Rasmussen , and J. A. Newman . 2010 . Global atmospheric change and trophic interactions: are there any general responses ?, pp. 179 – 214 . In F. Baluška and V. Ninkovic (eds.), Plant communication from an ecological perspective . Springer , Berlin, Heidelberg, Germany . Ryan , G. D. , S. Rasmussen , H. Xue , A. J. Parsons , and J. A. Newman . 2014 . Metabolite analysis of the effects of elevated CO2 and nitrogen fertilization on the association between tall fescue (Schedonorus arundinaceus) and its fungal symbiont Neotyphodium coenophialum . Plant. Cell Environ . 37 : 204 – 212 . Google Scholar CrossRef Search ADS PubMed Salazar-Parra , C. , I. Aranjuelo , I. Pascual , G. Erice , Á. Sanz-Sáez , J. Aguirreolea , M. Sánchez-Díaz , J. J. Irigoyen , J. L. Araus , and F. Morales . 2015 . Carbon balance, partitioning and photosynthetic acclimation in fruit-bearing grapevine (Vitis vinifera L. cv. Tempranillo) grown under simulated climate change (elevated CO2, elevated temperature and moderate drought) scenarios in temperature gradient greenhouses . J. Plant Physiol . 174 : 97 – 109 . Google Scholar CrossRef Search ADS PubMed Scherber , C. , D. J. Gladbach , K. Stevnbak , R. J. Karsten , I. K. Schmidt , A. Michelsen , K. R. Albert , K. S. Larsen , T. N. Mikkelsen , C. Beier et al. 2013 . Multi-factor climate change effects on insect herbivore performance . Ecol. Evol . 3 : 1449 – 1460 . Google Scholar CrossRef Search ADS PubMed Sharma , H. C. , A. R. War , M. Pathania , S. P. Sharma , S. M. Akbar , and R. S. Munghate . 2016 . Elevated CO2 influences host plant defense response in chickpea against Helicoverpa armigera . Arthropod Plant Interact . 10 : 171 – 181 . Google Scholar CrossRef Search ADS Shono , Y. , and M. Hirano . 1989 . Improved mass-rearing of the rice leaffolder, cnaphalocrocis medinalis (GUENEE) (Lepidoptera: Pyralidae) using corn seedlings . J. Appl. Entomol. Zool . 24 : 258 – 263 . Google Scholar CrossRef Search ADS ShuQi , H. , L. Ying , Q. Lei , L. ZhiHua , X. Chao , Y. Lu , and G. FuRong . 2017 . The influence of elevated CO2 concentration on the fitness traits of frankliniella occidentalis and frankliniella intonsa (Thysanoptera: Thripidae) . Environ. Entomol . 46 : 722 – 728 . Google Scholar CrossRef Search ADS PubMed Singleton , V. L. , and J. A. Rossi . 1965 . Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents . Am. J. Enol. Vitic . 16 : 144 – 158 . Sinha , S. , R. Balasaraswathi , K. Selvaraju , and P. Shanmugasundaram . 2005 . Molecular and biochemical markers associated with leaffolder (Cnaphalocrocis medinalis G.) resistance in rice (Oryza sativa L.) . Indian J. Biochem. Biophys . 42 : 228 – 232 . Google Scholar PubMed Smith , P. , and T. Jones . 1998 . Effects of elevated CO2 on the chrysanthemum leaf-miner, Chromatomyia syngenesiae: a greenhouse study . Glob. Chang. Biol . 4 : 287 – 291 . Google Scholar CrossRef Search ADS Stiling , P. , D. Moon , A. Rossi , R. Forkner , B. A. Hungate , F. P. Day , R. E. Schroeder , and B. Drake . 2013 . Direct and legacy effects of long-term elevated CO2 on fine root growth and plant-insect interactions . New Phytol . 200 : 788 – 795 . Google Scholar CrossRef Search ADS PubMed Sun , Y. C. , F. J. Chen , and F. Ge . 2009 . Elevated CO2 changes interspecific competition among three species of wheat aphids: sitobion avenae, Rhopalosiphum padi, and Schizaphis graminum . Environ. Entomol . 38 : 26 – 34 . Google Scholar CrossRef Search ADS PubMed Taub , D. R. , and X. Wang . 2008 . Why are nitrogen concentrations in plant tissues lower under elevated CO2? A critical examination of the hypotheses . J. Integr. Plant Biol . 50 : 1365 – 1374 . Google Scholar CrossRef Search ADS PubMed Tripp , K. E. , W. K. Kroen , M. M. Peet , and D. H. Willits . 1992 . Fewer whiteflies found on CO2-enriched greenhouse tomatoes with high C: N ratios . J. HortScience . 27 : 1079 – 1080 . Wan , G. , Z. Dang , G. Wu , M. N. Parajulee , F. Ge , and F. Chen . 2014 . Single and fused transgenic Bacillus thuringiensis rice alter the species-specific responses of non-target planthoppers to elevated carbon dioxide and temperature . Pest Manag. Sci . 70 : 734 – 742 . Google Scholar CrossRef Search ADS PubMed Wang , J. , C. Wang , N. Chen , Z. Xiong , D. Wolfe , and J. Zou . 2015 . Response of rice production to elevated [CO2] and its interaction with rising temperature or nitrogen supply: a meta-analysis . Clim. Change 130 : 529 – 543 . Google Scholar CrossRef Search ADS Wang , D. R. , J. A. Bunce , M. B. Tomecek , D. Gealy , A. McClung , S. R. McCouch , and L. H. Ziska . 2016 . Evidence for divergence of response in Indica, Japonica, and wild rice to high CO2 × temperature interaction . Glob. Chang. Biol . 22 : 2620 – 2632 . Google Scholar CrossRef Search ADS PubMed Way , D. A. , and R. Oren . 2010 . Differential responses to changes in growth temperature between trees from different functional groups and biomes: a review and synthesis of data . Tree Physiol . 30 : 669 – 688 . Google Scholar CrossRef Search ADS PubMed Weisshaar , B. , and G. I. Jenkins . 1998 . Phenylpropanoid biosynthesis and its regulation . Curr. Opin. Plant Biol . 1 : 251 – 257 . Google Scholar CrossRef Search ADS PubMed Wu , G. , F. J. Chen , and F. Ge . 2006 . Response of multiple generations of cotton bollworm Helicoverpa armigera Hübner, feeding on spring wheat, to elevated CO2 . J. Appl. Entomol . 130 : 2 – 9 . Google Scholar CrossRef Search ADS Wu , C. , K. Cui , W. Wang , Q. Li , S. Fahad , Q. Hu , J. Huang , L. Nie , and S. Peng . 2016 . Heat-induced phytohormone changes are associated with disrupted early reproductive development and reduced yield in rice . Sci. Rep . 6 : 34978 . Google Scholar CrossRef Search ADS PubMed Xie , H. , L. Zhao , W. Wang , Z. Wang , X. Ni , W. Cai , and K. He . 2014 . Changes in life history parameters of Rhopalosiphum maidis (Homoptera: Aphididae) under four different elevated temperature and CO2 combinations . J. Econ. Entomol . 107 : 1411 – 1418 . Google Scholar CrossRef Search ADS PubMed Xu , J. , Q. X. Wang , and J. C. Wu . 2010 . Resistance of cultivated rice varieties to Cnaphalocrocis medinalis (Lepidoptera: Pyralidae) . J. Econ. Entomol . 103 : 1166 – 1171 . Google Scholar CrossRef Search ADS PubMed Ye , M. , Y. Song , J. Long , R. Wang , S. R. Baerson , Z. Pan , K. Zhu-Salzman , J. Xie , K. Cai , and S. Luo . 2013 . Priming of jasmonate-mediated antiherbivore defense responses in rice by silicon . Proc. Natl Acad Sci . 110 : E3631 – E3639 . Google Scholar CrossRef Search ADS Yuan , Z. , S. T. Ata-Ul-Karim , Q. Cao , Z. Lu , W. Cao , Y. Zhu , and X. Liu . 2016a . Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readings . Field Crops Res . 185 : 12 – 20 . Google Scholar CrossRef Search ADS Yuan , Z. , Q. Cao , K. Zhang , S. T. Ata-Ul-Karim , Y. Tian , Y. Zhu , W. Cao , and X. Liu . 2016b . Optimal leaf positions for SPAD meter measurement in rice . Front. Plant Sci . 7 : 719 . Zavala , J. A. , C. L. Casteel , E. H. DeLucia , and M. R. Berenbaum . 2008 . Anthropogenic increase in carbon dioxide compromises plant defense against invasive insects . Proc. Natl Acad Sci . 105 : 5129 – 5133 . Google Scholar CrossRef Search ADS Zhishen , J. , T. Mengcheng , and W. Jianming . 1999 . The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals . Food Chem . 64 : 555 – 559 . Ziadi , N. , M. Brassard , G. Bélanger , A. Claessens , N. Tremblay , A. N. Cambouris , M. C. Nolin , and L-É. Parent . 2008 . Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status . Agron. J . 100 : 1264 – 1273 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental Entomology Oxford University Press

The Role of Plant Abiotic Factors on the Interactions Between Cnaphalocrocis medinalis (Lepidoptera: Crambidae) and its Host Plant

Loading next page...
 
/lp/ou_press/the-role-of-plant-abiotic-factors-on-the-interactions-between-Vo5mBXfftf
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
ISSN
0046-225X
eISSN
1938-2936
D.O.I.
10.1093/ee/nvy066
Publisher site
See Article on Publisher Site

Abstract

Abstract Atmospheric temperature increases along with increasing atmospheric CO2 concentration. This is a major concern for agroecosystems. Although the impact of an elevated temperature or increased CO2 has been widely reported, there are few studies investigating the combined effect of these two environmental factors on plant–insect interactions. In this study, plant responses (phenological traits, defensive enzyme activity, secondary compounds, defense-related gene expression and phytohormone) of Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Pyralidae) -susceptible and resistant rice under various conditions (environment, soil type, variety, C. medinalis infestation) were used to examine the rice–C. medinalis interaction. The results showed that leaf chlorophyll content and trichome density in rice were variety-dependent. Plant defensive enzyme activities were affected environment, variety, or C. medinalis infestation. In addition, total phenolic content of rice leaves was decreased by elevated CO2 and temperature and C. medinalis infestation. Defense-related gene expression patterns were affected by environment, soil type, or C. medinalis infestation. Abscisic acid and salicylic acid content were decreased by C. medinalis infestation. However, jasmonic acid content was increased by C. medinalis infestation. Furthermore, under elevated CO2 and temperature, rice plants had higher abscisic acid content than plants under ambient conditions. The adult morphological traits of C. medinalis also were affected by environment. Under elevated CO2 and temperature, C. medinalis adults had greater body length in the second and third generations. Taken together these results indicated that elevated CO2 and temperature not only affects plants but also the specialized insects that feed on them. elevated temperature with carbon dioxide, multiple factor effect, phytohormone, plant–insect interaction Climate change is one of the most important issues affecting the planet, with large impacts predicted worldwide for economies, agriculture, and ecosystems. Rising atmospheric temperature and CO2 concentration are the two key factors driving climate change. The atmospheric CO2 level is predicted to increase from the current 410 ppm to 1,000 ppm by the end of this century (Meehl et al. 2007), leading to a predicted increase in the atmospheric temperature of up to 5°C (IPCC 2013). Plants could benefit from an increased growth rate, biomass, and higher photosynthetic rates due to elevated CO2 (Ainsworth and Long 2005, Ainsworth et al. 2008). In addition, plants could synthesize more carbohydrates and dilute their foliar nitrogen concentration, leading to a higher C/N ratio in them under elevated CO2 conditions (Pritchard et al. 1999, Coley et al. 2002, Ainsworth and Long 2005, Taub and Wang 2008, Murray et al. 2013, Oehme et al. 2013, Manimanjari et al. 2014, Ryan et al. 2014, Dáder et al. 2016). Furthermore, elevated CO2 levels would lead plants to reallocate limited resources and to changes in their chemistry, such as in the composition of their secondary metabolites (Peñuelas and Estiarte 1998, Awmack and Leather 2002, Salazar-Parra et al. 2015). However, insect herbivory may not be affected directly by elevated CO2 (Fajer et al. 1991, Murray et al. 2013). Indirectly, however, the increased C/N ratio and changing chemical components in plants could influence insect herbivory performance and feeding behavior (Hughes and Bazzaz 2001, Himanen et al. 2008, Sun et al. 2009, Ge et al. 2010, Cornelissen 2011, Oehme et al. 2013, Scherber et al. 2013, Stiling et al. 2013, Dáder et al. 2016). There is no general consistent trend in how plant–insect interactions are effected by elevated CO2. For example, Rhopalosiphum padi L. (Hemiptera: Aphididae) showed an increased growth rate under elevated CO2 (Sun et al. 2009, Oehme et al. 2012, Oehme et al. 2013) while R. maidis Fitch increased their portion of alates (Xie et al. 2014). Leaf-chewing insects, such as grasshoppers and caterpillars, consumed more leaf tissues under elevated CO2 conditions (Johnson and Lincoln 1990, Johnson and Lincoln 1991, Lindroth et al. 1993, Lindroth et al. 1995, Wu et al. 2006). However, not all insects respond to elevated CO2 in the same way. Under elevated CO2 condition, the fecundity of Sitobion avenae F. (Hemiptera: Aphididae) increased (Awmack et al. 1996) while that of Brevicoryne brassicae L. (Hemiptera: Aphididae) and Neophilaenus lineatus L. (Hemiptera: Cercopidae) went unchanged (Smith and Jones 1998, Brooks and Whittaker 1999); though N. lineatus did have a lower survival rate in response to the elevated CO2 conditions (Brooks and Whittaker 1999). In a different species, elevated CO2 had a negative impact on the developmental period of Helicoverpa armigera (Hubner) (Lepidoptera: Noctuidae) (Wu et al. 2006). In addition, a CO2 concentration of 600 ppm had no impact on Bemisia tabaci population dynamics (Butler et al. 1986), whereas 1,000 ppm was capable of decreasing the numbers of Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) (Tripp et al. 1992). These varied findings suggest that to better understand CO2 concentration-dependent effects on insect herbivores, more of them should be investigated. Under elevated CO2, plant phytohormone signaling may be re-configured (Casteel et al. 2008, Zavala et al. 2008, Casteel et al. 2012a). Elevated CO2 was found to increase the salicylic acid (SA) content of soybean (Glycine max)(Casteel et al. 2012b); while in tomato it increased the SA-dependent signaling pathway and decreased the incidence of Tomato Yellow Leaf Curl Virus (TYLCV) disease (Guo et al. 2016). However, this effect was reversed in the tomato genotype carrying the resistance gene, Mi-1.2 (Guo et al. 2016). Thus, plant genotypes within species can respond differently in terms of their disease resistance and phytohormone regulation under elevated CO2. An elevated temperature may increase the leaf biomass and foliar nitrogen concentration in plants (Way and Oren 2010, Murray et al. 2013). In contrast to CO2, warming is predicted to have a direct effect on the developmental period of insects by increasing their metabolic rates (Bale et al. 2002). The combined effect of elevated CO2 and temperature has been studied in plants (Morison and Lawlor 1999, Lumoala et al. 2005, Wan et al. 2014). The plant assimilation rate under elevated CO2 would be more sensitive to a high temperature when compared with that under ambient CO2 (Wang et al. 2016). However, very few studies have investigated plant–insect interactions as affected by both atmospheric temperature and CO2. Aphids had an enhanced fecundity and longevity under elevated CO2, but did not benefit in this way under combined increases of CO2 and temperature (Ryalls et al. 2017). Thus, with elevated CO2 and temperature, plant and insect phenotypes may be affected at the same time, which can alter the plant–insect relationships. Many studies have evaluated plant–insect interactions under either CO2 or temperature condition (Karowe and Migliaccio 2011, Casteel et al. 2012a, Bauerfeind and Fischer 2013, Dyer et al. 2013, Manimanjari et al. 2014, Dáder et al. 2016, Sharma et al. 2016, Block et al. 2017, ShuQi et al. 2017). In these studies, the insects tested under the elevated conditions were previously reared under ambient conditions. In addition, under elevated CO2 condition, experiments beyond one generation indicated different responses between generations which may depend on the period of plant growth (Brooks and Whittaker 1998, Wu et al. 2006). Hence, a better understanding of the effect of elevated CO2 in tandem with temperature on insect herbivores is now needed. Rice (Oryza sativa L.) is one of the most globally important major crops. It is the main food source in Asia, namely China, India, and Taiwan. Current estimates for world rice production stand at more than 700 million tons per year (FAO 2014). However, rice production and its quality has been affected by multiple stresses, including abiotic and biotic stresses. It is estimated that losses of 13–26% in rice yield are caused by pests (Oerke 2006). Climate change could exacerbate threats to rice production via the increased atmospheric temperature and CO2. Yet a recent meta-analysis of rice studies suggested that elevated CO2 would enhance rice yields by up to 20% (Wang et al. 2015). However, this enhanced effect on rice yield and spikelet fertility would be diminished by the combined effect of warming with elevated CO2 (Wang et al. 2015). Therefore, it is crucial that we better understand how the interplay of elevated CO2 and temperature may impact rice–insect enemy interactions. In Taiwan and neighboring countries, Cnaphalocrocis medinalis (Guenée) (Lepidoptera: Pyralidae) is one of the major pests of rice crops. The C. medinalis larvae fold rice leaves and can cause a huge reduction in rice yields. The objective of this study was to assess the dual impact of both temperature and CO2 on the rice–C. medinalis interaction. First, we reared C. medinalis colonies in the corresponding environments over one generation before the study and further measure of insect morphological traits. Furthermore, we examined the rice–C. medinalis interactions under elevated CO2 with temperature by assessing the traits and gene expression of plants. Going further, we also examined the influence of soil on the rice–C. medinalis interaction by using soil taken from organic rice and conventional rice fields. Finally, the phytohormone abundances and related direct defensive metabolites in rice were also examined in this study. Materials and Methods Plant Materials Two rice (O. sativa L.) varieties, Taichung Native 1 (TN1; indica) and QingLiu (QL; indica), were chosen for use in this study. The TN1 seeds were obtained from Dr. Shu-Jen Wang at National Taiwan University, Taiwan. The QingLiu seeds were obtained from the Taichung District Agricultural Research and Extension Station, COA, Changhua, Taiwan. These varieties were used because of their differences in resistance to C. medinalis. TN1 is susceptible to C. medinalis herbivory whereas QL is the resistant variety. Seeds were first sterilized by 2% (v/v) NaOCl for 30 min and then further washed under sterile water. The sterilized seeds were put on a wet paper towel on a petri dish and incubated under dark conditions at 37°C for 2 d. Germinated seeds similar in size were selected for planting within a peatmoss mix (Da Chiang Chun Horticulture Material Co. LTD., Nantou County, Taiwan). After reaching the two-leaf stage, seedlings were transferred to plastic pots (8.5 cm diameter, 16 cm height) with assigned soil (one seedling per pot). The conventional and organic soils were both obtained from the rice fields at the Taoyuan District Agricultural Research and Extension Station, COA, Taoyuan, Taiwan. The organic rice field had been planted with organic rice for 15 yr. In this study, all plants were fertilized with an organic fertilizer (No.1 Biotec Organic Fertilizer, Taiwan Fertilizer, Taipei, Taiwan). Plants were used in the experiment at 30 d after they had germinated (i.e., the six-leaf stage). Growth Chambers Two growth chamber for rice plants and C. medinalis were set in this study. One growth chamber was set to 30°C/25°C for day and night, with the CO2 concentration in the growth chamber set to 500 ppm; this treatment was designated ‘Ambient’. Another growth chamber was set to 35°C/30°C for day and night, with a CO2 concentration in the growth chamber of 1,000 ppm; this treatment was designated ‘HCHT’ (high CO2 and high temperature). Both growth chambers were set to12:12 (L:D) h cycle under a relative humidity of 55 ± 5%. Insect Rearing C. medinalis Guenée individuals were obtained from the Taichung District Agricultural Research and Extension Station, COA, Changhua, Taiwan, which originally had collected them from the rice field near Taichung, Taiwan. The C. medinalis colonies were reared on corn seedlngs (White pearl, Known-You Seed, Kaohsiung, Taiwan) by the corn seedling method (Shono and Hirano 1989) in insect cages (BugDorm-4, MegaView, Taichung, Taiwan). Corn seedlings were planted in vermiculite for 1 wk before use with the C. medinalis larvae. The C. medinalis moths were fed a 10% (v/v) sucrose solution. To foster the acclimation of plants and insects to the set environmental conditions, the insect cages were put into the two growth chambers (Ambient and HCHT) described earlier. Ambient and HCHT colonies were maintained in the corresponding environments for at least one generation prior to the experiment. C. medinalis Morphological Traits C. medinalis larvae were successfully reared in the two growth environments (Ambient and HCHT), under which conditions their insect morphological traits were measured during the second and third generations. Thirty random moths that emerged within 5 d in each environment were selected to measure their wing length, body size, and body weight, as well as the sample’s sex ratio. Leaf Chlorophyll Content Leaf chlorophyll content was measured with the Soil Plant Analysis Development chlorophyll meter (SPAD-502, Konica Minolta, Osaka, Japan) on treatments [Environment (Ambient and HCHT) × Soil (conventional soil and organic soil) × Variety (TN1 and QL)]. The SPAD meter provides a non-destructive approach to measure chlorophyll content. Each treatment has three un-infested plants. Readings from the tip, middle, and base of the youngest expanded leaf per plant (at the six-leaf stage of development) were averaged to yield a single value for chlorophyll content. The experiment was repeated three times. Leaf Toughness Leaf toughness was measured on the middle of the youngest expanded leaf (at the six-leaf stage of development) with a portable penetrometer (Chatillon DFEII, Ametek, Largo, FL) on treatments [Environment (Ambient and HCHT) × Soil (conventional soil and organic soil) × Variety (TN1 and QL)]. Each treatment has three un-infested plants. The experiment was repeated three times. Leaf Trichome Density The number of trichomes of the youngest expanded leaf at the six-leaf stage was counted by the photo photographed by dissection microscopy (Nikon SMZ1500, Kanagawa, Japan) under a high resolution (MediaCybernetics-Evolution MP COLOR, Silver Spring, MD) with treatments [Environment (Ambient and HCHT) × Soil (conventional soil and organic soil) × Variety (TN1 and QL)]. Each treatment has three un-infested plants. The experiment was repeated three times. Plant Phytohormone-Related Gene Expression, Enzyme Activity, and Secondary Metabolic Profiles Under Larval Infestation The third instar C. medinalis larvae were transferred to the newly expanded leaves of rice plants (in the six-leaf stage). The larvae were starved 4 h before placing them upon the plants (one larva per plant). Each plant was covered with a plastic cover with mesh cloth to prevent the insects from escaping. The plastic cover was 58 cm tall with four windows (6.5 × 6.5 cm). Each window was covered by mesh cloth to avoid the water condensation in the plastic cage. Uninfested plants covering plastic cover with mesh cloth served as the controls. In these experiments investigating gene expression, enzyme activity, and secondary metabolic profiles, each treatment consisted of three individual plants (one larva per plant, one plant per pot). The experiment was repeated three times. The infested leaf tissues were collected after 1 and 3 d after C. medinalis infestation (1DAI and 3DAI) for the gene expression and enzyme activity experiments, but only 3DAI for secondary metabolic experiment; in all experiments, the leaf samples were immediately frozen with liquid nitrogen and stored at −80°C. Leaf tissues were further homogenized in liquid nitrogen by using the 2010 Geno/Grinder (SPEX SamplePrep, Metuchen, NJ). Defense Enzyme Activity Assay Protein concentrations were determined by the RC-DC Protein assay (Bio-Rad, Hercules, CA). The measurements of phenylalanine lyase (PAL) and polyphenoloxidase (PPO) enzyme activity were modified from Duan et al. (2014). For the PAL activity analysis, 50 mg of leaf powder was collected and mixed with 600 µl of a 50 mM sodium borated buffer (pH 8.8 with 2% PVP) and then centrifuged at 13,000 rpm for 20 min at 4°C. The supernatant was centrifuged again to remove any debris. The ensuing supernatant was collected for the PAL activity analysis. Two-hundred microliters of a 500 µg/ml-leaf protein extract was incubated with 200 µl of a 50 mM sodium borated buffer for 10 min at 37°C in an incubator. To the mixture was added 200 µl of 20 mM L-phenylalanine and then further mixed and centrifuged to get the supernatant. The PAL activity was calculated by the observed change at 290 nm with a spectrophotometer (U1800, Hitachi, Tokyo, Japan). For the PPO activity analysis, 20 mg of ground leaf powder was mixed with 400 µl of a 0.1M sodium phosphate buffer (pH 7.0 with 2% PVP) and centrifuged at 13,000 rpm for 20 min at 4°C. The supernatant was centrifuged again to remove debris, after which the supernatant was collected for the PPO activity analysis. From this supernatant, 100 µl was drawn and added to 100 µl of 200 mM catechol to measure the absorbance change at 398 nm. For the peroxidase (POD) analysis, the measurement of POD enzyme activity was modified from the method of Qin and Tian (2005). Briefly, 20 mg of ground leaf powder was mixed with 400 µl of 0.1M sodium phosphate buffer (pH 7.0 with 2% PVP), centrifuged at 13,000 rpm for 20 min at 4°C, then centrifuged again to remove debris. A total of 50 µl of the supernatant was mixed with 25 µl of a 0.1M sodium borated buffer (pH 7.0), then mixed with 25 µl of 10 mM guaiacol for 30 min at 30°C. To this mixture was added 25 µl of 10 mM H2O2 to measure the absorbance change at 460 nm. Real-Time RT-PCR Plant RNA was extracted with Direct-zol RNA Kits (Zymo Research, Irvine, CA) following the manufacturer’s instructions; likewise, the genomic DNA was removed according to the manufacturer’s suggestion. The genomic DNA-free RNA was quantified by using Nanodrop (Thermo-Fisher Scientific, Waltham, MA). Quantitative real-time polymerase chain reaction (qRT-PCR) was performed with the One Step SYBR PrimeScriptTM RT-PCR Kit II (Takara Bio USA, Mountain View, CA), with OsUbiquitin used as the reference gene. Gene-specific primer sequences are detailed in Supp. Table S1 (Ye et al. 2013, Duan et al. 2014, Li et al. 2015). The qRT-PCR was performed in a Stratagene MX3000PTM PCR system. The PCR was conducted by using the default conditions: step 1, 42°C for 5 min and 95°C for 30 s; step 2, 95°C for 5 s and 60°C for 30 s, repeated for 40 cycles. RNA extraction and qRT-PCR were performed for each biological replicate separately, with each treatment represented by three biological replicates. Relative quantification of gene expression was analyzed with the software MxPro QPCR v3.00 (Agilent, Santa Clara, CA). Plant Phenolic Compound Assay Leaf samples were vacuum frozen using a freeze dryer (FD-5N, Eyela, Tokyo, Japan) under –25 pa for 2 d. The dried samples (20 mg) was mixed with 2 ml of an extracting liquid solution (3% HCL + 60% MeOH) for 30 min and then centrifuged at 3,000 rpm for 15 min at 4°C. The supernatant was collected for further analysis and stored at –20°C in a freezer. The total phenolic content measurement was modified from Singleton et al. (1965). Specifically, 100 µl of the extracted supernatant was mixed with 500 µl Folin-Ciocalteu reagent and later added to 500 µl of 4% (w/v) Na2CO3 for 1 h. During the reaction, it was necessary to open the lip of the tubes to release CO2. The absorbance was measured at 750 nm to determine the total phenolic content using a gallic acid calibration curve. The flavonoid measurements were modified from Zhishen et al. (1999). From the extracted supernatant, 100 µl was mixed with 300 µl of an extracting liquid solution (3% HCl + 60% MeOH), 100 µl of 1%AlCl3, 100 µl of 1M potassium acetate, and 500 µl of sterile water. The absorbance was measured at 415 nm for the content of flavonoids. The anthocyanin measurement was followed by Huang et al. (2014). Phytohormone Extraction and Quantitation The third instar C. medinalis larvae were transferred to the newly expanded leaves of rice plants (in the six-leaf stage). The larvae were starved for 4 h before placing them upon the plants, which were then covered with a plastic cover to avoid insect escape. Uninfested plants were used as the controls. In this experiment, each treatment had four individual plants (i.e., one larva per plant, one plant per pot). The infested leaf tissues were collected after 1 h of infestation, then frozen with liquid nitrogen and stored at –80°C. Plant tissues (200 mg) from around the feeding sites were used to extract the phytohormones, which followed the method described by Chen et al. (2014). The phytohormone quantitations were analyzed by a linear ion trap-orbitrap mass spectrometer (Orbitrap Elite; Thermo Fisher Scientific) coupled online to a UHPLC system (ACQUITY UPLC; Waters, Milford, CT). The amounts of SA, abscisic acid (ABA), jasmonic acid (JA), and JA-Isoleucine (JA-Ile, the bioactive compound of JA) were quantified by comparison to internal standards of the deuterated isotopes of these hormones (Chen et al. 2014). The fragmentation reactions of m/z 137.02 to 93.03 for SA; 263.13 to 153.10 for ABA; 209.12 to 59.01 for JA; 322.20 to 130.09 for JA-Ile; 141.05 to 97.06 for d6-SA; 269.20 to 159.13 for d6-ABA; and 211.13 to 59.01 for H2JA were selected for quantitation. Phytohormones were extracted from four plants per treatment per time point. Statistical Analyses The responses in terms of plant phenological traits, defensive enzymes, chemical traits, gene expression, and phytohormone experiments assumed that all three factors (environment, insect infestation, and rice variety; each with two levels) were fixed effects. The factorial design: 1) Environmental factor: Ambient and HCHT, 2) Soil factor: conventional soil and organic soil, 3) Rice variety factor: TN1 and QL, and 4) Insect infestation factor: non-infestation control and infestation. For the phenological traits (SPAD, trichome density, and toughness), there were eight treatment combinations (environmental × soil × variety). For the plant defensive enzyme, chemical trait, gene expression experiments, there were 16 treatment combinations (environmental × soil × variety × infestation). The experimental materials were prepared in three different batches, due to the capacity of the growth chambers, and the treatment combinations were allocated randomly to the units in each batch. In order to take the variation among batches into account, the batch effects were treated as fixed block effects in our data analysis. For the phytohormone experiment, there were 16 treatment combinations (environmental × soil × variety × infestation) but have one batch. The experimental responses were fitted by linear models, and the effects of different factors were evaluated by ANOVA. For the insect morphological traits, the data were analyzed by one-way ANOVA. All the data were analyzed in the free statistical software platform, R (https://www.r-project.org). Results Phenological Traits The SPAD value and trichome density of the experimental plants were affected by rice variety (Table 1). Within varieties, TN1 had a significantly higher SPAD value than QL (Supp. Fig. S1A). For trichome density, TN1 had fewer trichomes than QL (Supp. Fig. S1B). However, the statistical analysis showed that neither environment nor soil type had any significant effects, nor did any interaction among the factors, for all the tested parameters (Table 1). Table 1. P-values of three-way ANOVA on plant phenological trait (SPAD valuea, trichome density, and leaf toughness) responses to multiple factors SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 aSPAD value: value of relative proportion of leaf chlorophyll concentration. bAmbient versus HCHT. cConventional soil versus organic soil. dTN1 versus QL. *P < 0.01; †P < 0.001. View Large Table 1. P-values of three-way ANOVA on plant phenological trait (SPAD valuea, trichome density, and leaf toughness) responses to multiple factors SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 SPAD value Trichome density Leaf toughness Environmentb 0.4956 0.5294 0.3108 Soilc 0.9420 0.6202 0.2835 Varietyd 0.0010† 0.0025* 0.4884 Environment × soil 0.0985 0.6521 0.8921 Environment × variety 0.9844 0.8920 0.8921 Soil × variety 0.7698 0.3725 0.9252 Environment × variety × soil 0.9674 0.5008 0.0978 aSPAD value: value of relative proportion of leaf chlorophyll concentration. bAmbient versus HCHT. cConventional soil versus organic soil. dTN1 versus QL. *P < 0.01; †P < 0.001. View Large Plant Defensive Enzyme Activity For 1 d after C. medinalis larvae infestation (1DAI), PAL was affected by insect infestation; PPO and POD were affected by rice variety (Table 2). PAL enzyme activity was higher in the control than under the C. medinalis infestation treatment (Supp. Fig. S2A), while TN1 had lower PPO and POD activity than did QL at 1DAI (Supp. Fig. S2B and C). In addition, POD was affected by the environmental factor at 1DAI. Plants had higher POD activity under HCHT than under Ambient treatment (Supp. Fig. S2D). The interaction among environment, rice variety, and soil type significantly affected the POD activity. At 3DAI, PAL activity was affected by rice variety, while TN1 had lower PAL activity than did QL (Supp. Fig. S2E). The interaction among environment, soil type, and C. medinalis infestation had significant effects on the activity of both PAL and POD. Table 2. P-values of four-way ANOVA on plant defensive enzyme (PAL, PPO, POD) responses to multiple factors Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01. View Large Table 2. P-values of four-way ANOVA on plant defensive enzyme (PAL, PPO, POD) responses to multiple factors Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 Phenylalanine lyase (PAL) Polyphenoloxidase (PPO) Peroxidase (POD) 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.8973 0.3365 0.0921 0.7988 0.0279* 0.5492 Soilb 0.8367 0.0714 0.7156 0.1160 0.6586 0.8290 Varietyc 0.2675 0.0200* 0.0309* 0.4745 0.0011† 0.5452 Insect infestationd 0.0310* 0.5551 0.3890 0.7226 0.3067 0.9846 Environment × soil 0.3793 0.5264 0.2161 0.5855 0.1425 0.1877 Environment × variety 0.5327 0.4485 0.8191 0.7431 0.4186 0.5459 Soil × variety 0.0508 0.1926 0.4763 0.7697 0.0540 0.6278 Environment × insect infestation 0.6759 0.4114 0.6826 0.7139 0.4818 0.4991 Variety × insect infestation 0.5649 0.1168 0.4554 0.6814 0.8337 0.5588 Soil × insect infestation 0.1129 0.4000 0.2065 0.8472 0.6721 0.0513 Environment × variety × soil 0.0745 0.5591 0.9884 0.3408 0.0417* 0.2372 Soil × insect infestation × variety 0.3443 0.7426 0.4425 0.8200 0.0648 0.4186 Environment × soil × insect infestation 0.1129 0.0276* 0.5419 0.2303 0.8038 0.0091† Environment × insect infestation × variety 0.3162 0.3037 0.9687 0.2403 0.7803 0.2766 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01. View Large Secondary Metabolite Profiles Total phenolic content was affected by the environment and insect infestation (Table 3). Plants had lower total phenolic content under HCHT than under Ambient treatment (Supp. Fig. S3A). In addition, the total phenolic content was lower in the insect infestation treatment than in the control plants (Supp. Fig. S3B). However, the statistical analysis showed no main effect of any factor on either anthocyanins or flavonoids (Table 3). Anthocyanins were, however, affected by the interaction between environment and soil (Supp. Fig. S3C) and that among environment, insect infestation, and rice variety. Table 3. P-values of four-way ANOVA on plant secondary compounds (total phenolics, anthocyanins, flavonoids) responses to multiple factors Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05. View Large Table 3. P-values of four-way ANOVA on plant secondary compounds (total phenolics, anthocyanins, flavonoids) responses to multiple factors Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 Total phenolics Anthocyanins Flavonoids Environmenta 0.0135* 0.1672 0.0869 Soilb 0.7414 0.0853 0.3337 Varietyc 0.1468 0.4454 0.4577 Insect infestationd 0.0102* 0.2829 0.7158 Environment × soil 0.8856 0.0205* 0.2542 Environment × variety 0.6593 0.0631 0.9704 Soil × variety 0.4477 0.8783 0.6854 Environment × insect infestation 0.4101 0.1219 0.9223 Variety × insect infestation 0.8948 0.6977 0.7923 Soil × insect infestation 0.8645 0.0669 0.9927 Environment × variety × soil 0.0678 0.5986 0.1577 Soil × insect infestation × variety 0.9830 0.8694 0.4914 Environment × soil × insect infestation 0.7444 0.2648 0.4561 Environment × insect infestation × variety 0.7141 0.0413* 0.8055 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05. View Large Plant Phytohormone Related Gene Expression JA and SA are two keys phytohormones that regulate plant defense responses against attacking insect herbivores. AOS and LOX are involved in the JA biosynthesis pathway. COI1 acts as receptor in the JA signaling pathway. ICS participates in the SA biosynthesis pathway. Two time points (1DAI and 3DAI) were measured for OsAOS1, OsLOX, OsCOI1, and OsICS1. At 1DAI, the gene expression of OsAOS1, OsLOX, and OsCOI1 were affected by the environment (Table 4). These three genes had a higher expression level under HCHT than under the Ambient treatment (Supp. Fig. S4A–C). OsCOI1 gene expression was also affected by soil type: OsCOI1 had a higher expression level in plants growing in organic soil than those in conventional soil (Supp. Fig. S4D). OsICS1 gene expression was affected by C. medinalis infestation at 1DAI: OsICS1 had higher expression level in the non-infested control than under infestation treatment (Supp. Fig. S4E). Two interactions (environment with variety on OsCOI1, and environment with soil type on OsICS1) were statistically significant (Table 4, Supp. Fig. S5A and B). At 3DAI, the only significant effect detected was for OsLOX gene expression by soil type. Plants in conventional soil had a higher expression level of OsLOX than did those in organic soil (Supp. Fig. S4F). The statistical analysis found no significant interactions among the factors for all the tested parameters (Table 4). Table 4. P-values of four-way ANOVA on plant phytohormone related gene expression (OsAOS1, OsLOX, OsCOI1, OsICS1) responses to multiple factors OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Table 4. P-values of four-way ANOVA on plant phytohormone related gene expression (OsAOS1, OsLOX, OsCOI1, OsICS1) responses to multiple factors OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 OsAOS1 OsLOX OsCOI1 OsICS1 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI 1DAI 3DAI Environmenta 0.0016† 0.2356 0.0001‡ 0.1862 0.0000‡ 0.3978 0.9620 0.7462 Soilb 0.0831 0.5845 0.4308 0.0257* 0.0399* 0.7962 0.4935 0.9659 Varietyc 0.0844 0.5583 0.4748 0.9036 0.7949 0.6825 0.8013 0.6106 Insect infestationd 0.5351 0.4785 0.6882 0.3432 0.1583 0.4597 0.0161* 0.6828 Environment × soil 0.0771 0.4523 0.6122 0.1789 0.2190 0.9133 0.0472* 0.6040 Environment × variety 0.4651 0.6607 0.8099 0.7890 0.0217* 0.8637 0.6152 0.3745 Soil × variety 0.8963 0.4381 0.6885 0.9165 0.8820 0.7460 0.7600 0.4458 Environment × insect infestation 0.8417 0.3802 0.8232 0.5763 0.0515 0.8817 0.0512 0.3390 Variety × insect infestation 0.7245 0.5448 0.9940 0.4795 0.6410 0.6044 0.4299 0.9498 Soil × insect infestation 0.1393 0.1985 0.0982 0.7365 0.7982 0.2562 0.1176 0.6175 Environment × variety × soil 0.5176 0.3157 0.3643 0.8800 0.2286 0.2826 0.1311 0.4842 Soil × insect infestation × variety 0.6004 0.7584 0.4932 0.7866 0.1295 0.4742 0.1382 0.3096 Environment × soil × insect infestation 0.3636 0.2565 0.2534 0.5650 0.1416 0.6963 0.6249 0.3825 Environment × insect infestation × variety 0.6917 0.3458 0.7389 0.8192 0.5244 0.8473 0.4716 0.1507 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Phytohormones Plant phytohormones (ABA, SA, JA, JA-Ile) were analyzed for 1 h after C. medinalis larvae infestation. These four phytohormones were affected by insect infestation (Table 5). When compared with the control, plants had lower ABA and SA when under insect infestation; however, plants had more JA and JA-Ile under the insect infestation treatment (Supp. Fig. S6A–D). Additionally, ABA was affected by the environmental factor. Plants had higher SA under HCHT than the Ambient treatment (Supp. Fig. S6E). The interaction between environment and insect infestation significantly affected ABA and SA contents in plants (Supp. Fig. S7A and B). Table 5. P-values of four-way ANOVA on plant phytohormone (ABA, SA, JA, JA-Ile) responses to multiple factors ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Table 5. P-values of four-way ANOVA on plant phytohormone (ABA, SA, JA, JA-Ile) responses to multiple factors ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 ABA SA JA JA-Ile Environmenta 0.0050† 0.0940 0.4981 0.7340 Soilb 0.6271 0.4071 0.2508 0.1180 Varietyc 0.8784 0.1374 0.2359 0.7809 Insect infestationd 0.0042† 0.0296* <0.0001‡ <0.0001‡ Environment × soil 0.6043 0.4476 0.9738 0.9874 Environment × variety 0.7438 0.2982 0.2140 0.1994 Soil × variety 0.4229 0.9565 0.1597 0.1240 Environment × insect infestation 0.0032† 0.0017† 0.8679 0.9399 Variety × insect infestation 0.7846 0.4363 0.3828 0.6537 Soil × insect infestation 0.6724 0.3457 0.2441 0.1905 Environment × variety × soil 0.4237 0.4314 0.8763 0.4465 Soil × insect infestation × variety 0.3534 0.5846 0.1174 0.1617 Environment × insect infestation × variety 0.7496 0.6640 0.1101 0.2189 Soil × variety × environment × insect infestation 0.4883 0.8014 0.9397 0.5717 aAmbient versus HCHT. bConventional soil versus organic soil. cTN1 versus QL. dC. medinalis larvae infestation versus no-feeding control. *P < 0.05; †P < 0.01; ‡P < 0.001. View Large Insect Morphological Traits The C. medinalis moth morphological traits were significantly affected by the growth environment (Supp. Table S2). The male moths under HCHT had a greater wing length, body length, and body weight in the second generations than did their counterparts under the Ambient conditions (Supp. Table S2). In the third generations, male moths under HCHT had a greater body length (Supp. Table S2). The female moths followed similar trend as the males, except for wing length in the second generation. In the third generation, female moths under HCHT had a greater wing length, body length, and body weight (Supp. Table S2). Discussion One way by which climate change may affect terrestrial vegetation ecosystems is if its two key factors, elevated temperature and CO2, impact plant–insect interactions. In this study, we used the rice–C. medinalis interaction to dissect how various factors (environment, soil type, variety, insect infestation, and their interactions) might affect the traits or molecular responses of host plants. Our results suggest that each plant trait responds to the growth environment somewhat differently, though this environmental factor (i.e., HCHT) would influence several plant traits at once (POD activity, JA-related gene expression, and ABA content). Furthermore, there may be specific-regulation of a given trait associated with each environmental factor. Besides, plant traits may have unique responses to the interactions among factors. Under the elevated temperature and CO2, plants had higher POD activity, OsAOS1, OsLOX, OsCOI1 gene expression, and ABA content than under the Ambient condition. Rice POD activity would be considerably increased under insect infestation (Rani and Jyothsna 2010, Punithavalli et al. 2013, Ye et al. 2013, Duan et al. 2014). Since OsAOS1, OsLOX, and OsCOI1 all function in the JA biosynthesis and signaling pathway, this may indicate JA content is susceptible to climate change effects. Yet, we found no significant difference on the JA and JA-Ile responses to the environmental factor (Table 5). Furthermore, ABA was induced under elevated temperature and CO2 in our plant-herbivore system. A recent study showed that rice plants under elevated temperature has a higher ABA content (Wu et al. 2016), which supports our findings. In addition, elevated CO2 would induce the ABA-induced stomatal closure in soybean (Levine et al. 2009). These studies suggest that plants would have the drought stress responses under elevated CO2 and temperature condition. This inconsistency between the gene expression and phytohormone results may due to the different time points of the phytohormone experiment (1 h after infestation) and the plant defensive related gene expression experiment (1 and 3 d after infestation). However, the plants in this study were grown under the same two environments; hence, further study to understand the regulatory mechanism between gene expression and phytohomones is needed. In addition, the total phenolic content was affected by the environmental factor (Table 3), as it was lowered by the HCHT treatment (vs. Ambient). The plant secondary metabolites affected by global climate change would likely be plant species-specific and chemical type-specific (Bidart-Bouzat and Imeh-Nathaniel 2008). In a survey of 343 publications, nearly half of the studies found that plant phenolic content would be increased under elevated CO2 (Ryan et al. 2010). However, a small proportion of studies do exist that report a decreased phenolic content under elevated CO2 (Ryan et al. 2010), which is consistent with our results. Total phenolic content represents a group of similar functional chemicals, but we need to further analyze the content of each specific chemical in the future. With the above data, our results suggest that environmental change would have an impact on plant physiology, ranging from gene expression to enzyme activity and phytohormone content. We found that QL had a lower SPAD value than did TN1 under all tested conditions (Table 1). The SPAD value can be used as an indirect indicator of leaf nitrogen content, which is one of the limiting factors in the development of herbivore populations (Arregui et al. 2006, Ziadi et al. 2008, Yuan et al. 2016a, Yuan et al. 2016b). Thus, most insect herbivores would prefer plants with a higher leaf nitrogen content. In Asia, the excessive application of nitrogen fertilizer to rice fields has increased the size of several major insect pest populations (Lu et al. 2007). Other work has shown that TN1 has the highest SPAD value among genotypes, with SPAD being positively correlated to corrected damage ratings caused by C. medinalis infestation (Xu et al. 2010). Nonetheless, QL, the rice variety resistant to C. medinalis, had a higher trichome density than did TN1 under all the tested conditions (Table 1); moreover, QL showed greater PAL, PPO, and POD activity than did TN1 (Table 2). POD and PAL are two candidate markers associated with C. medinalis resistance in rice (Sinha et al. 2005). PPO oxidizes phenolics to quinones involved in the formation of plant defensive barriers. In tomato (Solanum lycopersicum), PPO plays a defensive role in resistance to the beet armyworm (Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae)) and cotton bollworm (Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)) by decreasing the rates of weight gain in larvae and their foliar consumption (Bhonwong et al. 2009). PAL is the key enzyme in the biosynthetic phenylpropanoids that participate in plant responses to UV irradiation, mechanical wounding, and pathogen attack (Dixon and Paiva 1995, Weisshaar and Jenkins 1998). Furthermore, PAL is also involved in the SA biosynthetic pathway. Hence, the C. medinalis resistance of QL may have multiple components, and not be all that surprising. Organic farming is gaining more attention with the concerned public. Soil microbes could help plants to grow faster and resist external stresses. In our study, soil type only affected the expression of two JA-related genes, indicating that soil type likely would not have strong impact on plant defensive enzymes and phytohormones. Furthermore, it did not interact with environmental factors (i.e., temperature and CO2) in this study system. A possible explanation for this result is that we did not put the soil into the corresponding environments before the experiment, as we did for the insect colonies. The soil microbes in the environment may not have had enough time to adapt to their new growing conditions. However, it is difficult to know how long it will environmental acclimation. In this study, we conducted gene expression and defensive enzyme experiments at 1 and 3 d after insect infestation. Their purpose was to enhance our understanding of a later response in plant defense after insect feeding. However, the responses for gene expression and defensive enzymes may occur before these two sampling time points. In addition, it may even follow a different pattern during the earlier plant response. Further investigations are required to discern and understand the whole picture of plant defense in response to insect feeding under climate change scenarios. Insect morphological traits were also affected by the environmental factor (temperature with CO2) (Supp. Table S2), in that female moths had a greater body weight under HCHT than under Ambient conditions. Larger female moths produced more eggs in Pararge aegeria under elevated temperature (Berger et al. 2008). In addition, the quantity of food affected the size of horns in male Onthophagus acuminatus (Emlen 1994). Thus, the changes in insect morphological traits in our study may due to the either food quantity or quality differences between the two environments tested. In addition, the corn seedling rearing method may have impacted the C. medinalis colonies. This method was used instead of rearing C. medinalis on rice plants prior to our experiments for two reasons. First, the corn seedling rearing method has been proved to be effective for the mass rearing of C. medinalis (Shono and Hirano 1989). In this study, we needed many same-age and -sized C. medinalis larvae for the experiments. Second, we wanted unbiased C. medinalis larvae lacking any preference and experience on rice plants before the experiments began. Using such ‘naive’ larvae guarded against the results being unduly influenced by insect experiences. However, only further experimentation will improve our understanding of how environmental factor affects C. medinalis body size when the C. medinalis colonies are first reared on rice plants. In addition, it will be helpful to know how later generations of C. medinalis are possibly affected under elevated temperature with CO2. Supplementary Data Supplementary Data are available at Environmental Entomology online. Acknowledgments We are grateful for the funding provided by the Ministry of Science and Technology, Taiwan (104-2313-B-002-001-MY2, 106-2313-B-002-001, and 106-2311-B-002-025). We thank Dr. Shu-Jen Wang for generously providing the TN1 seeds and Dr. Chi-Te Liu for kindly providing the vacuum equipment used in this study. We also thank Dr. Yet-Ran Chen for the supporting phytohormone analysis done at the Metabolomics Core Facility (Academia Sinica, Taiwan) and Ms. Po-Ya Wu for the statistical analysis, and Drs. Yun-Fen Huang and Hsiang-Chin Chen for their helpful comments on this paper. References Cited Ainsworth , E. A. , and S. P. Long . 2005 . What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2 . New Phytol . 165 : 351 – 371 . Google Scholar CrossRef Search ADS PubMed Ainsworth , E. A. , A. D. Leakey , D. R. Ort , and S. P. Long . 2008 . FACE-ing the facts: inconsistencies and interdependence among field, chamber and modeling studies of elevated [CO2] impacts on crop yield and food supply . New Phytol . 179 : 5 – 9 . Google Scholar CrossRef Search ADS PubMed Arregui , L. , B. Lasa , A. Lafarga , I. Irañeta , E. Baroja , and M. Quemada . 2006 . Evaluation of chlorophyll meters as tools for N fertilization in winter wheat under humid Mediterranean conditions . Eur. J. Agron . 24 : 140 – 148 . Google Scholar CrossRef Search ADS Awmack , C. S. , and S. R. Leather . 2002 . Host plant quality and fecundity in herbivorous insects . Annu. Rev. Entomol . 47 : 817 – 844 . Google Scholar CrossRef Search ADS PubMed Awmack , C. , R. Harrington , S. Leather , and J. Lawton . 1996 . The impacts of elevated CO2 on aphid-plant interactions . Asp. Appl. Biol . 45 : 317 – 322 . Bale , J. S. , G. J. Masters , I. D. Hodkinson , C. Awmack , T. M. Bezemer , V. K. Brown , J. Butterfield , A. Buse , J. C. Coulson , and J. Farrar . 2002 . Herbivory in global climate change research: direct effects of rising temperature on insect herbivores . Glob. Change Biol . 8 : 1 – 16 . Google Scholar CrossRef Search ADS Bauerfeind , S. S. , and K. Fischer . 2013 . Increased temperature reduces herbivore host-plant quality . Glob. Chang. Biol . 19 : 3272 – 3282 . Google Scholar PubMed Berger , D. , R. Walters , and K. Gotthard . 2008 . What limits insect fecundity? Body size-and temperature-dependent egg maturation and oviposition in a butterfly . J. Functional Ecology . 22 : 523 – 529 . Google Scholar CrossRef Search ADS Bhonwong , A. , M. J. Stout , J. Attajarusit , and P. Tantasawat . 2009 . Defensive role of tomato polyphenol oxidases against cotton bollworm (Helicoverpa armigera) and beet armyworm (Spodoptera exigua) . J. Chem. Ecol . 35 : 28 – 38 . Google Scholar CrossRef Search ADS PubMed Bidart-Bouzat , M. G. , and A. Imeh-Nathaniel . 2008 . Global change effects on plant chemical defenses against insect herbivores . J. Integr. Plant Biol . 50 : 1339 – 1354 . Google Scholar CrossRef Search ADS PubMed Block , A. , M. M. Vaughan , S. A. Christensen , H. T. Alborn , and J. H. Tumlinson . 2017 . Elevated carbon dioxide reduces emission of herbivore-induced volatiles in Zea mays . Plant. Cell Environ . 40 : 1725 – 1734 . Google Scholar CrossRef Search ADS PubMed Brooks , G. , and J. Whittaker . 1998 . Responses of multiple generations of Gastrophysa viridula, feeding on Rumex obtusifolius, to elevated CO2 . Glob. Chang. Biol . 4 : 63 – 75 . Google Scholar CrossRef Search ADS Brooks , G. , and J. Whittaker . 1999 . Responses of three generations of a xylem-feeding insect, Neophilaenus lineatus (Homoptera), to elevated CO2 . Glob. Chang. Biol . 5 : 395 – 401 . Google Scholar CrossRef Search ADS Butler , G. , B. Kimball , and J. Mauney . 1986 . Populations of Bemisia tabaci (Homoptera: Aleyrodidae) on cotton grown in open-top field chambers enriched with CO2 . Environ. Entomol . 15 : 61 – 63 . Google Scholar CrossRef Search ADS Casteel , C. L. , B. F. O’Neill , J. A. Zavala , D. D. Bilgin , M. R. Berenbaum , and E. H. Delucia . 2008 . Transcriptional profiling reveals elevated CO2 and elevated O3 alter resistance of soybean (Glycine max) to Japanese beetles (Popillia japonica) . Plant. Cell Environ . 31 : 419 – 434 . Google Scholar CrossRef Search ADS PubMed Casteel , C. L. , O. K. Niziolek , A. D. Leakey , M. R. Berenbaum , and E. H. DeLucia . 2012a . Effects of elevated CO2 and soil water content on phytohormone transcript induction in Glycine max after Popillia japonica feeding . Arthropod Plant Interact . 6 : 439 – 447 . Google Scholar CrossRef Search ADS Casteel , C. L. , L. M. Segal , O. K. Niziolek , M. R. Berenbaum , and E. H. DeLucia . 2012b . Elevated carbon dioxide increases salicylic acid in Glycine max . Environ. Entomol . 41 : 1435 – 1442 . Google Scholar CrossRef Search ADS Chen , Y. L. , C. Y. Lee , K. T. Cheng , W. H. Chang , R. N. Huang , H. G. Nam , and Y. R. Chen . 2014 . Quantitative peptidomics study reveals that a wound-induced peptide from PR-1 regulates immune signaling in tomato . Plant Cell . 26 : 4135 – 4148 . Google Scholar CrossRef Search ADS PubMed Coley , P. , M. Massa , C. Lovelock , and K. Winter . 2002 . Effects of elevated CO2 on foliar chemistry of saplings of nine species of tropical tree . Oecologia . 133 : 62 – 69 . Google Scholar CrossRef Search ADS PubMed Cornelissen , T . 2011 . Climate change and its effects on terrestrial insects and herbivory patterns . Neotrop. Entomol . 40 : 155 – 163 . Google Scholar CrossRef Search ADS PubMed Dáder , B. , A. Fereres , A. Moreno , and P. Trębicki . 2016 . Elevated CO2 impacts bell pepper growth with consequences to Myzus persicae life history, feeding behaviour and virus transmission ability . Sci. Rep . 6 : srep19120 . Google Scholar CrossRef Search ADS Dixon , R. A. , and N. L. Paiva . 1995 . Stress-induced phenylpropanoid metabolism . Plant Cell . 7 : 1085 – 1097 . Google Scholar CrossRef Search ADS PubMed Duan , C. , J. Yu , J. Bai , Z. Zhu , and X. Wang . 2014 . Induced defense responses in rice plants against small brown planthopper infestation . Crop J . 2 : 55 – 62 . Google Scholar CrossRef Search ADS Dyer , L. A. , L. A. Richards , S. A. Short , and C. D. Dodson . 2013 . Effects of CO2 and temperature on tritrophic interactions . PLoS One . 8 : e62528 . Google Scholar CrossRef Search ADS PubMed Emlen , D. J . 1994 . Environmental control of horn length dimorphism in the beetle Onthophagus acuminatus (Coleoptera: Scarabaeidae) . Proc. Royal Soc. London B: Biol. Sci . 256 : 131 – 136 . Google Scholar CrossRef Search ADS Fajer , E. D. , M. D. Bowers , and F. A. Bazzaz . 1991 . The effects of enriched CO2 atmospheres on the buckeye butterfly, Junonia Coenia . J. Ecology . 72 : 751 – 754 . Google Scholar CrossRef Search ADS FAO . 2014 . FAOSTAT online statistical service . FAO , Rome, Italy . Ge , F. , F. Chen , G. Wu , and Y. Sun . 2010 . Research advance on the response of insects to elevated CO2 in China . Chinese Bulletin of Entomology . 47 : 229 – 235 . Guo , H. , L. Huang , Y. Sun , H. Guo , and F. Ge . 2016 . The contrasting effects of elevated CO2 on TYLCV infection of tomato genotypes with and without the resistance gene, Mi-1.2 . Front. Plant Sci . 7 : 1680 . Google Scholar PubMed Himanen , S. J. , A. Nissinen , W. X. Dong , A. Nerg , C. Stewart , G. M. Poppy , and J. K. Holopainen . 2008 . Interactions of elevated carbon dioxide and temperature with aphid feeding on transgenic oilseed rape: are Bacillus thuringiensis (Bt) plants more susceptible to nontarget herbivores in future climate ? Glob. Chang. Biol . 14 : 1437 – 1454 . Google Scholar CrossRef Search ADS Huang , W. D. , K. H. Lin , M. H. Hsu , M. Y. Huang , Z. W. Yang , P. Y. Chao , and C. M. Yang . 2014 . Eliminating interference by anthocyanin in chlorophyll estimation of sweet potato (Ipomoea batatas L.) leaves . Bot. Stud . 55 : 11 . Google Scholar CrossRef Search ADS PubMed Hughes , L. , and F. A. Bazzaz . 2001 . Effects of elevated CO2 on five plant-aphid interactions . Entomologia Experimentalis et Applicata . 99 : 87 – 96 . Google Scholar CrossRef Search ADS IPCC . 2013 . Climate change 2013: the physical science basis , pp. 1535 . In T. F. Stocker , D. Qin , G-K. Plattner , M. Tignor , S. K. Allen , J. Boschung , A. Nauels , Y. Xia , B. Bex , and B. Midgley (eds.), Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change . Cambridge University Press , Cambridge, UK . Johnson , R. H. , and D. E. Lincoln . 1990 . Sagebrush and grasshopper responses to atmospheric carbon dioxide concentration . Oecologia . 84 : 103 – 110 . Google Scholar CrossRef Search ADS PubMed Johnson , R. H. , and D. E. Lincoln . 1991 . Sagebrush carbon allocation patterns and grasshopper nutrition: the influence of CO2 enrichment and soil mineral limitation . Oecologia . 87 : 127 – 134 . Google Scholar CrossRef Search ADS PubMed Karowe , D. N. , and A. Migliaccio . 2011 . Performance of the legume-feeding herbivore, Colias philodice (Lepidoptera: Pieridae) is not affected by elevated CO2 . Arthropod Plant Interact . 5 : 107 – 114 . Google Scholar CrossRef Search ADS Levine , L. H. , J. T. Richards , and R. M. Wheeler . 2009 . Super-elevated CO2 interferes with stomatal response to ABA and night closure in soybean (Glycine max) . J. Plant Physiol . 166 : 903 – 913 . Google Scholar CrossRef Search ADS PubMed Li , R. , J. Zhang , J. Li , G. Zhou , Q. Wang , W. Bian , M. Erb , and Y. Lou . 2015 . Prioritizing plant defence over growth through WRKY regulation facilitates infestation by non-target herbivores . Elife . 4 : e04805 . Google Scholar PubMed Lindroth , R. L. , K. K. Kinney , and C. L. Platz . 1993 . Responses of diciduous trees to elevated atmospheric CO2: productivity, phytochemistry, and insect performance . J. Ecology . 74 : 763 – 777 . Google Scholar CrossRef Search ADS Lindroth , R. , G. Arteel , and K. Kinney . 1995 . Responses of three saturniid species to paper birch grown under enriched CO2 atmospheres . Funct. Ecol . 9 : 306 – 311 . Google Scholar CrossRef Search ADS Lu , Z-X. , X-P. Yu , K-l. Heong , and H. Cui . 2007 . Effect of nitrogen fertilizer on herbivores and its stimulation to major insect pests in rice . J. Rice Science . 14 : 56 – 66 . Google Scholar CrossRef Search ADS Lumoala , E. , K. Laitinen , S. Sutinen , S. Kellomäki , and E. Vapaavuori . 2005 . Stomatal density, anatomy and nutrient concentrations of Scots pine needles are affected by elevated CO2 and temperature . Plant Cell Environ . 28 : 733 – 749 . Google Scholar CrossRef Search ADS Manimanjari , D. , M. Srinivasa Rao , P. Swathi , C. Rama Rao , M. Vanaja , and M. Maheswari . 2014 . Temperature-and CO2-dependent life table parameters of Spodoptera litura (Noctuidae: Lepidoptera) on sunflower and prediction of pest scenarios . J. Insect Sci . 14 : 297 . Google Scholar CrossRef Search ADS PubMed Meehl , G. A. , C. Covey , K. E. Taylor , T. Delworth , R. J. Stouffer , M. Latif , B. McAvaney , and J. F. Mitchell . 2007 . The WCRP CMIP3 multimodel dataset: a new era in climate change research . Bull. Am. Meteorol. Soc . 88 : 1383 – 1394 . Google Scholar CrossRef Search ADS Morison , J. , and D. Lawlor . 1999 . Interactions between increasing CO2 concentration and temperature on plant growth . Plant Cell Environ . 22 : 659 – 682 . Google Scholar CrossRef Search ADS Murray , T. J. , D. S. Ellsworth , D. T. Tissue , and M. Riegler . 2013 . Interactive direct and plant-mediated effects of elevated atmospheric [CO2] and temperature on a eucalypt-feeding insect herbivore . Glob. Chang. Biol . 19 : 1407 – 1416 . Google Scholar CrossRef Search ADS PubMed Oehme , V. , P. Högy , J. Franzaring , C. Zebitz , and A. Fangmeier . 2012 . Response of spring crops and associated aphids to elevated atmospheric CO2 concentrations . J. Appl. Bot. Food Qual . 84 : 151 . Oehme , V. , P. Högy , C. P. Zebitz , and A. Fangmeier . 2013 . Effects of elevated atmospheric CO2 concentrations on phloem sap composition of spring crops and aphid performance . J. Plant Interact . 8 : 74 – 84 . Google Scholar CrossRef Search ADS Oerke , E-C . 2006 . Crop losses to pests . J. Agric. Sci . 144 : 31 – 43 . Google Scholar CrossRef Search ADS Peñuelas , J. , and M. Estiarte . 1998 . Can elevated CO2 affect secondary metabolism and ecosystem function ? Trends Ecol. Evol . 13 : 20 – 24 . Google Scholar CrossRef Search ADS PubMed Pritchard , S. , H. Rogers , S. A. Prior , and C. Peterson . 1999 . Elevated CO2 and plant structure: a review . Glob. Chang. Biol . 5 : 807 – 837 . Google Scholar CrossRef Search ADS Punithavalli , M. , N. Muthukrishnan , and M. B. Rajkuma . 2013 . Defensive responses of rice genotypes for resistance against rice leaffolder Cnaphalocrocis medinalis . J. Rice Science . 20 : 363 – 370 . Google Scholar CrossRef Search ADS Qin , G. Z. , and S. P. Tian . 2005 . Enhancement of biocontrol activity of cryptococcus laurentii by Silicon and the possible mechanisms involved . Phytopathology . 95 : 69 – 75 . Google Scholar CrossRef Search ADS PubMed Rani , P. U. , and Y. Jyothsna . 2010 . Biochemical and enzymatic changes in rice plants as a mechanism of defense . Acta Physiologiae Plantarum . 32 : 695 – 701 . Google Scholar CrossRef Search ADS Ryalls , J. M. , B. D. Moore , M. Riegler , L. M. Bromfield , A. A. Hall , and S. N. Johnson . 2017 . Climate and atmospheric change impacts on sap-feeding herbivores: a mechanistic explanation based on functional groups of primary metabolites . Funct. Ecol . 31 : 161 – 171 . Google Scholar CrossRef Search ADS Ryan , G. D. , S. Rasmussen , and J. A. Newman . 2010 . Global atmospheric change and trophic interactions: are there any general responses ?, pp. 179 – 214 . In F. Baluška and V. Ninkovic (eds.), Plant communication from an ecological perspective . Springer , Berlin, Heidelberg, Germany . Ryan , G. D. , S. Rasmussen , H. Xue , A. J. Parsons , and J. A. Newman . 2014 . Metabolite analysis of the effects of elevated CO2 and nitrogen fertilization on the association between tall fescue (Schedonorus arundinaceus) and its fungal symbiont Neotyphodium coenophialum . Plant. Cell Environ . 37 : 204 – 212 . Google Scholar CrossRef Search ADS PubMed Salazar-Parra , C. , I. Aranjuelo , I. Pascual , G. Erice , Á. Sanz-Sáez , J. Aguirreolea , M. Sánchez-Díaz , J. J. Irigoyen , J. L. Araus , and F. Morales . 2015 . Carbon balance, partitioning and photosynthetic acclimation in fruit-bearing grapevine (Vitis vinifera L. cv. Tempranillo) grown under simulated climate change (elevated CO2, elevated temperature and moderate drought) scenarios in temperature gradient greenhouses . J. Plant Physiol . 174 : 97 – 109 . Google Scholar CrossRef Search ADS PubMed Scherber , C. , D. J. Gladbach , K. Stevnbak , R. J. Karsten , I. K. Schmidt , A. Michelsen , K. R. Albert , K. S. Larsen , T. N. Mikkelsen , C. Beier et al. 2013 . Multi-factor climate change effects on insect herbivore performance . Ecol. Evol . 3 : 1449 – 1460 . Google Scholar CrossRef Search ADS PubMed Sharma , H. C. , A. R. War , M. Pathania , S. P. Sharma , S. M. Akbar , and R. S. Munghate . 2016 . Elevated CO2 influences host plant defense response in chickpea against Helicoverpa armigera . Arthropod Plant Interact . 10 : 171 – 181 . Google Scholar CrossRef Search ADS Shono , Y. , and M. Hirano . 1989 . Improved mass-rearing of the rice leaffolder, cnaphalocrocis medinalis (GUENEE) (Lepidoptera: Pyralidae) using corn seedlings . J. Appl. Entomol. Zool . 24 : 258 – 263 . Google Scholar CrossRef Search ADS ShuQi , H. , L. Ying , Q. Lei , L. ZhiHua , X. Chao , Y. Lu , and G. FuRong . 2017 . The influence of elevated CO2 concentration on the fitness traits of frankliniella occidentalis and frankliniella intonsa (Thysanoptera: Thripidae) . Environ. Entomol . 46 : 722 – 728 . Google Scholar CrossRef Search ADS PubMed Singleton , V. L. , and J. A. Rossi . 1965 . Colorimetry of Total Phenolics with Phosphomolybdic-Phosphotungstic Acid Reagents . Am. J. Enol. Vitic . 16 : 144 – 158 . Sinha , S. , R. Balasaraswathi , K. Selvaraju , and P. Shanmugasundaram . 2005 . Molecular and biochemical markers associated with leaffolder (Cnaphalocrocis medinalis G.) resistance in rice (Oryza sativa L.) . Indian J. Biochem. Biophys . 42 : 228 – 232 . Google Scholar PubMed Smith , P. , and T. Jones . 1998 . Effects of elevated CO2 on the chrysanthemum leaf-miner, Chromatomyia syngenesiae: a greenhouse study . Glob. Chang. Biol . 4 : 287 – 291 . Google Scholar CrossRef Search ADS Stiling , P. , D. Moon , A. Rossi , R. Forkner , B. A. Hungate , F. P. Day , R. E. Schroeder , and B. Drake . 2013 . Direct and legacy effects of long-term elevated CO2 on fine root growth and plant-insect interactions . New Phytol . 200 : 788 – 795 . Google Scholar CrossRef Search ADS PubMed Sun , Y. C. , F. J. Chen , and F. Ge . 2009 . Elevated CO2 changes interspecific competition among three species of wheat aphids: sitobion avenae, Rhopalosiphum padi, and Schizaphis graminum . Environ. Entomol . 38 : 26 – 34 . Google Scholar CrossRef Search ADS PubMed Taub , D. R. , and X. Wang . 2008 . Why are nitrogen concentrations in plant tissues lower under elevated CO2? A critical examination of the hypotheses . J. Integr. Plant Biol . 50 : 1365 – 1374 . Google Scholar CrossRef Search ADS PubMed Tripp , K. E. , W. K. Kroen , M. M. Peet , and D. H. Willits . 1992 . Fewer whiteflies found on CO2-enriched greenhouse tomatoes with high C: N ratios . J. HortScience . 27 : 1079 – 1080 . Wan , G. , Z. Dang , G. Wu , M. N. Parajulee , F. Ge , and F. Chen . 2014 . Single and fused transgenic Bacillus thuringiensis rice alter the species-specific responses of non-target planthoppers to elevated carbon dioxide and temperature . Pest Manag. Sci . 70 : 734 – 742 . Google Scholar CrossRef Search ADS PubMed Wang , J. , C. Wang , N. Chen , Z. Xiong , D. Wolfe , and J. Zou . 2015 . Response of rice production to elevated [CO2] and its interaction with rising temperature or nitrogen supply: a meta-analysis . Clim. Change 130 : 529 – 543 . Google Scholar CrossRef Search ADS Wang , D. R. , J. A. Bunce , M. B. Tomecek , D. Gealy , A. McClung , S. R. McCouch , and L. H. Ziska . 2016 . Evidence for divergence of response in Indica, Japonica, and wild rice to high CO2 × temperature interaction . Glob. Chang. Biol . 22 : 2620 – 2632 . Google Scholar CrossRef Search ADS PubMed Way , D. A. , and R. Oren . 2010 . Differential responses to changes in growth temperature between trees from different functional groups and biomes: a review and synthesis of data . Tree Physiol . 30 : 669 – 688 . Google Scholar CrossRef Search ADS PubMed Weisshaar , B. , and G. I. Jenkins . 1998 . Phenylpropanoid biosynthesis and its regulation . Curr. Opin. Plant Biol . 1 : 251 – 257 . Google Scholar CrossRef Search ADS PubMed Wu , G. , F. J. Chen , and F. Ge . 2006 . Response of multiple generations of cotton bollworm Helicoverpa armigera Hübner, feeding on spring wheat, to elevated CO2 . J. Appl. Entomol . 130 : 2 – 9 . Google Scholar CrossRef Search ADS Wu , C. , K. Cui , W. Wang , Q. Li , S. Fahad , Q. Hu , J. Huang , L. Nie , and S. Peng . 2016 . Heat-induced phytohormone changes are associated with disrupted early reproductive development and reduced yield in rice . Sci. Rep . 6 : 34978 . Google Scholar CrossRef Search ADS PubMed Xie , H. , L. Zhao , W. Wang , Z. Wang , X. Ni , W. Cai , and K. He . 2014 . Changes in life history parameters of Rhopalosiphum maidis (Homoptera: Aphididae) under four different elevated temperature and CO2 combinations . J. Econ. Entomol . 107 : 1411 – 1418 . Google Scholar CrossRef Search ADS PubMed Xu , J. , Q. X. Wang , and J. C. Wu . 2010 . Resistance of cultivated rice varieties to Cnaphalocrocis medinalis (Lepidoptera: Pyralidae) . J. Econ. Entomol . 103 : 1166 – 1171 . Google Scholar CrossRef Search ADS PubMed Ye , M. , Y. Song , J. Long , R. Wang , S. R. Baerson , Z. Pan , K. Zhu-Salzman , J. Xie , K. Cai , and S. Luo . 2013 . Priming of jasmonate-mediated antiherbivore defense responses in rice by silicon . Proc. Natl Acad Sci . 110 : E3631 – E3639 . Google Scholar CrossRef Search ADS Yuan , Z. , S. T. Ata-Ul-Karim , Q. Cao , Z. Lu , W. Cao , Y. Zhu , and X. Liu . 2016a . Indicators for diagnosing nitrogen status of rice based on chlorophyll meter readings . Field Crops Res . 185 : 12 – 20 . Google Scholar CrossRef Search ADS Yuan , Z. , Q. Cao , K. Zhang , S. T. Ata-Ul-Karim , Y. Tian , Y. Zhu , W. Cao , and X. Liu . 2016b . Optimal leaf positions for SPAD meter measurement in rice . Front. Plant Sci . 7 : 719 . Zavala , J. A. , C. L. Casteel , E. H. DeLucia , and M. R. Berenbaum . 2008 . Anthropogenic increase in carbon dioxide compromises plant defense against invasive insects . Proc. Natl Acad Sci . 105 : 5129 – 5133 . Google Scholar CrossRef Search ADS Zhishen , J. , T. Mengcheng , and W. Jianming . 1999 . The determination of flavonoid contents in mulberry and their scavenging effects on superoxide radicals . Food Chem . 64 : 555 – 559 . Ziadi , N. , M. Brassard , G. Bélanger , A. Claessens , N. Tremblay , A. N. Cambouris , M. C. Nolin , and L-É. Parent . 2008 . Chlorophyll measurements and nitrogen nutrition index for the evaluation of corn nitrogen status . Agron. J . 100 : 1264 – 1273 . Google Scholar CrossRef Search ADS © The Author(s) 2018. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Environmental EntomologyOxford University Press

Published: Aug 1, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off