Dataset for: Interactive effects of tree species composition and water availability on growth and direct and indirect defences in Quercus ilex
Galmán, Andrea; Vázquez-González, Carla; Röder, Gregory; Castagneyrol, Bastien (2022), Dataset for: Interactive effects of tree species composition and water availability on growth and direct and indirect defences in Quercus ilex, Dryad, Dataset, https://doi.org/10.5061/dryad.rjdfn2zcw
Plant diversity has often been reported to decrease insect herbivory in plants. Of the numerous mechanisms that have been proposed to explain this phenomenon, how plant diversity influences plant defences via effects on growth has received little attention. In addition, plant diversity effects may be contingent on abiotic conditions (e.g., resource and water availability). Here, we used a long-term experiment to explore the interactive effects of tree species composition and water availability on growth, direct (i.e. phenolics) and indirect (i.e. Volatile Organic Compounds – VOCs) defences and leaf herbivory in Quercus ilex. We quantified herbivory by chewing insects, phenolic compounds and VOCs in Q. ilex trees growing in stands differing in tree species composition (Q. ilex, Q. ilex + Betula Pendula, Q. ilex + Pinus pinaster and Q. ilex + B. pendula + P. pinaster) and water availability (irrigated vs control). Both direct and indirect defences were affected by tree species composition, but such changes were not mediated by changes in tree stem diameter. Q. ilex trees growing in stands with P. pinaster had the lowest concentration of both direct and indirect defences. Importantly, the effects of tree species composition on VOCs were exacerbated on irrigated blocks. Despite variation in defences, tree species composition did not affect herbivory in Q. ilex. Accordingly, we did not find any association between defences and insect herbivory. Our results suggest that changes in the micro-environment rather than growth-defence associations may mediate tree diversity effects on defences. In addition, reduced defensive investment in more diverse stands could negatively impact tree resistance masking the beneficial effects of species diversity at reducing insect herbivory.
This study was conducted in the ORPHEE experimental trial established in 2008 in South-West France (44°440 N, 00°460 W). The experimental design consisted of eight blocks and 32 plots within each block. Each plot represented a tree species composition treatment, corresponding to 31 possible combinations of one to five tree species (Betula pendula, Quercus robur, Q. pyrenaica, Q. ilex, and Pinus pinaster) and an additional plot replicate of the five species mixture. Each plot contained 10 rows of 10 trees planted 2 m apart (100 trees on 400 m²). Tree species mixtures were established according to a substitutive design, keeping tree density of tree neighbours equal across plots. Within plots, individual trees from different species were planted in a regular alternate pattern, such that a tree from a given species had at least one neighbour from each of the other species within a 2-m radius. From 2015 four out of the eight experimental blocks were allocated to an irrigation treatment consisting of sprinkling the equivalent of 3 mm precipitation from a 2 m height pole in the centre of each irrigated plot. Blocks were irrigated on a daily basis, at night, from May to October. The four remaining blocks were kept as controls.
This datasets collects data for Q. ilex. In particular, we focused on Quercus ilex as target species and selected six blocks (three irrigated and three control) and four plots (tree species composition treatments) in each block corresponding to the monoculture of Q. ilex and its combinations with B. pendula and P. pinaster (Q. ilex + B. pendula, Q. ilex + P. pinaster and Q. ilex + B. pendula + P. pinaster). Therefore, a total of 24 experimental plots (4 tree species composition treatments × 2 irrigation treatments × 3 blocks) were included in the study.
Sampling and measurements
At the end of the growing season (September 2019), we haphazardly selected four Q. ilex trees in each of the 24 plots (N = 96 trees). Trees in the plot margins were not selected to avoid border effects. First, we estimated total height and basal diameter (± 30 cm aboveground) in all experimental trees with a tape-measure and a digital caliper respectively. After tree growth measurements, we collected VOCs for each tree. Briefly, we bagged one branch of each tree with a 1L nalophan bag and we trapped the compounds on a charcoal filter by pulling air through the filter using an air-sampling pump for 2 h at a rate of 250 ml min-1. Importantly, we sampled air VOCs in empty bags (one bag placed in the middle of each plot within each block) as controls, in order to identify compounds that may contaminate the blend of VOCs taken from the focal trees (e.g., VOCs emitted by neighbour species). After collecting the VOCs, we stored the filters at -80ºC until chemical analyses. Right after VOCs collection, we haphazardly collected 20 fully expanded and developed leaves throughout the tree’s canopy. Importantly, because Q. Ilex is an evergreen species, sampled leaves may have consisted of one to three cohorts of leaves (i.e. produced between 2017 and 2019; up to two-years old). For each leaf, we visually estimated the percentage of leaf area removed by insect herbivores (mostly leaf chewers) using the following scale: 0 = no damage; 1 = 1–5% damaged; 2 = 6–10% damaged; 3 = 11–25% damaged; 4 = 26–50% damaged; 5 = 51–75% damaged; 6 = >75% damaged (“leaf herbivory” hereafter). We averaged class values across all leaves to obtain a mean value per tree for statistical analyses. We selected a subset of 4-5 leaves with little or no evidence of herbivory for further chemical analyses of phenolic compounds. Leaves were oven-dried for 48 h at 40ºC.
Quantification of volatile organic compounds (VOCs)
To analyse VOCs, we performed gas chromatography and mass spectrometry analyses. To extract the compounds from the charcoal traps, we first added 5 μl of naphthalene (20 ng ml−1) as an internal standard to the traps (Pellissier et al., 2016), and then eluted their contents with 400 μl of dichloromethane. We then injected 2 μl of the extract for each sample into a gas chromatograph (GC) coupled with a mass selective detector (MSD) fitted with a 30 m × 0.25 mm × 0.25 mm film thickness HP-5MS fused silica column. We operated the GC in splitless mode with helium as the carrier gas (constant flow rate 0.9 ml min−1). The GC oven temperature program was: 1 min hold at 40°C, and then 10°C min−1 ramp to 240°C. We identified individual volatile compounds (i.e., terpenes) using Kovats retention index from published work, the NIST Standard Reference Database 1A v17, and by comparison with commercial standards when available. Volatile emissions are reported as nanograms naphthalene equivalents. For subsequent analyses, we selected VOCs identified as either monoterpenes or sesquiterpenes. We quantified individual monoterpenes and sesquiterpenes relative to the internal standard and used for statistical analyses those exhibiting a relative abundance higher than 1%. Importantly, for those compounds present in both the samples and the corresponding control, we only consider those which intensity in the sample was at least double than in the control. Finally, we quantified the total concentration of VOCs as the sum of concentrations of all individual compounds.
Quantification of phenolic compounds
We extracted phenolic compounds from 20 mg of dry leaf tissue with 1 ml of 70% methanol in an ultrasonic bath for 15 min, followed by centrifugation (Moreira et al., 2020) and transferred the extracts to chromatographic vials. To analyse the phenolic compounds, we performed chromatographic analyses using ultra-high performance liquid chromatography equipped with a Nexera SIL-30AC injector and one SPD-M20A UV/VIS photodiode array detector. The compound separation was carried out on a Kinetex 2.6 μm C18 82–102 Å, LC Column 100 × 4.6 mm, protected with a C18 guard cartridge. The flow rate was 0.4 ml min−1 and the oven temperature was set at 25°C. The mobile phase consisted of two solvents: water–formic acid (0.05%) (A) and acetonitrile–formic acid (0.05%) (B), starting with 5% B and using a gradient to obtain 30% B at 4 min, 60% B at 10 min, 80% B at 13 min and 100% B at 15 min. The injection volume was 15 μl. For phenolic compound identification, we used an ultra-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry. We identified four groups of phenolic compounds: flavonoids, ellagitannins and gallic acid derivates (‘hydrolysable tannins’ hereafter), proanthocyanidins (‘condensed tannins’ hereafter) and hydroxycinnamic acid precursors to lignins (‘lignins’ hereafter). We quantified flavonoids as rutin equivalents, condensed tannins as catechin equivalents, hydrolysable tannins as gallic acid equivalents, and lignins as ferulic acid equivalents . The quantification of these was conducted by external calibration using the corresponding calibration curve at 0.25, 0.5, 1, 2 and 5 μg ml−1 for each of the four standards used (rutin, catechin, gallic acid and ferulic acid). We expressed phenolic compound concentrations in mg g−1 tissue on a dry weight basis.
Consejo Superior de Investigaciones Científicas, Award: I-LINK12212018-2019