DOI: 10.5061/dryad.1g1jwsv8r
title: Data and code from: Volatile organic compounds diversity mediates tree diversity-insect herbivory relationships

description: The effects of plant diversity on insect herbivory are inconsistent in the
 literature, highlighting the need for further mechanistic understanding.
 Plant volatile organic compounds (VOCs) are influenced by plant diversity
 and related to insect herbivory. However, the role of VOCs in mediating
 plant diversity effects on insect herbivory remains poorly explored. Here,
 we investigated chewing insect herbivory of 128,818 leaves from 2606
 trees, measured leaf VOCs from 137 tree individuals, and assessed tree
 growth across a tree species richness gradient in a Biodiversity-Ecosystem
 Functioning experiment in southern China. We aimed to explore the
 mechanisms underlying tree diversity effects on insect herbivory by
 examining the roles of plant VOCs.
description: Based on the VOC profiles of the 137 samples [VOC_abundance_data.
 csv; group_info. csv], variations in the composition and concentration of
 samples’ VOCs across different plant diversity levels were evaluated by
 the permutational multivariate analysis of variance (PERMANOVA) and the
 principal coordinate analysis (PCoA). We first constructed a sample-VOC
 abundance matrix for all samples, where peak areas from chromatograms
 represented the abundance of each compound (Massad <em>et
 al.</em>, 2017). Before analysis, we standardized the VOC abundance
 by dividing each emission by the row sum, converting the data to relative
 abundance. In the PERMANOVA, the standardized VOC matrix was considered as
 the response variable, while plant species richness and species identity
 as independent variables. The analysis was based on the Bray-Curtis
 dissimilarity and we set the number of permutations to 999. We used
 “vegan”  (Oksanen <em>et al.</em>, 2022) and “ape” (Paradis
 & Schliep, 2019) packages to perform PERMANOVA and PCoA analyses
 separately. Additionally, we used a Mantel test to evaluate whether
 differences in VOCs between individuals were responsible for differences
 in their insect herbivory or RGR, using the same permutation number and
 distance matrix as in the PERMANOVA. To test whether trees with more
 distinct VOCs suffer greater insect herbivory, for each of the 137 sampled
 trees, we quantified its VOC distinctiveness as the mean Bray-Curtis
 distance between its VOC profiles and those of its neighbors, using the
 “vegdist” function in the “vegan” package. This metric represents the
 degree of VOC differentiation of an individual from its surroundings
 (Gaüzère <em>et al.</em>, 2023). Finally, we used the
 generalized linear mixed effects model (GLMM) with a beta distribution and
 logit link to examine the relationships between insect herbivory and VOC
 distinctiveness, as the insect herbivory was proportional data. The model
 was fitted using the “glmmTMB” function in the “glmmTMB” package (Brooks
 <em>et al.</em>, 2017). To account for spatial autocorrelation
 and the non-independence of tree individuals originating from the same
 plot or species, we included plot nested in block and species as random
 intercepts. We constructed a binary species-VOC matrix
 by combining conspecific samples [VOC_SP_01_data. csv], where each row
 represents a species and each column a unique VOC. The matrix denotes the
 presence (1) or absence (0) of each VOC in a given species. Based on this
 matrix, we quantified the dissimilarity of VOCs between each pair of
 species using the Jaccard distance. We also calculated the relative
 abundance of each species within each plot [species distribution. csv].
 Using these data, we calculated the community-level VOC diversity for all
 plots as Rao’s quadratic entropy index (Ricotta & Moretti, 2011;
 Salazar <em>et al.</em>, 2016) with the “dbFD” function in the
 “FD” package (Grenié & Gruson, 2023). To
 examine the effects of tree species richness, community VOC diversity and
 RGR on insect herbivory, we fitted a GLMM with a beta distribution and
 logit link [LAR and RGR data. csv]. In the model, we also included plot
 nested in block and species as random intercepts. This analysis included
 the insect herbivory and growth data of all blocks and plots. Insect
 herbivory was considered as the response variable, where tree species
 richness, community VOC diversity, their interaction term and RGR were
 included as independent variables. In addition, we fitted a linear mixed
 effects model (LMM) to investigate the effects of tree species richness
 and community VOC diversity on RGR  by the “lmer” function in the “lme4”
 package (Bates <em>et al.</em>, 2015), which also included
 plot nested in block and species as random intercepts. Lastly, the
 relationship between plant species richness and community VOC diversity
 was also tested by an LMM, with VOCs diversity as the response variable
 and block as random intercepts. We constructed
 structural equation modeling (SEM) using the “psem” function in the
 “piecewiseSEM” package (Lefcheck, 2016) to further elucidate the direct
 and indirect relationships among tree species richness, community VOC
 diversity, RGR, and insect herbivory [LAR and RGR data. csv]. We
 hypothesized that tree species richness would directly reduce insect
 herbivory, with these effects mediated by the changes in community VOC
 diversity and RGR along richness gradients (Appendix: Figure S2). In the
 SEM, we used GLMMs with a beta distribution and logit link to construct
 the pathways of tree species richness, community VOC diversity and RGR on
 insect herbivory, with plot nested in block and species as random
 intercepts. LMMs were used to construct the pathways of community VOC
 diversity on RGR, with random effects structured as in the GLMMs. We also
 used an LMM to analyze the effects of tree species richness on community
 VOC diversity, with block being random intercepts. All independent
 variables were z-scored standardized before model construction.
 References Bates D, Mächler M, Bolker B,
 Walker S. 2015. Fitting linear mixed-effects models using lme4.
 <em>Journal of Statistical Software</em> 67: 1–48.
 Brooks M E, Kristensen K, Benthem K J ,van, Magnusson A, Berg C
 W, Nielsen A, Skaug H J, Mächler M, Bolker B M. 2017. glmmTMB balances
 speed and flexibility among packages for zero-inflated generalized linear
 mixed modeling. <em>The R Journal</em> 9: 378.
 Gaüzère P, Blonder B, Denelle P, Fournier B, Grenié M, Delalandre
 L, Münkemüller T, Munoz F, Violle C, Thuiller W. 2023. The functional
 trait distinctiveness of plant species is scale dependent.
 <em>Ecography</em> 2023: e06504. Grenié M,
 Gruson H. 2023. fundiversity: a modular R package to compute functional
 diversity indices. <em>Ecography</em> 2023: e06585.
 Lefcheck JS. 2016. piecewiseSEM: piecewise structural equation
 modeling in R for ecology, evolution, and systematics. <em>Methods
 in Ecology and Evolution</em> 7: 573–579. Massad
 TJ, Martins de Moraes M, Philbin C, Oliveira C, Cebrian Torrejon G, Fumiko
 Yamaguchi L, Jeffrey CS, Dyer LA, Richards LA, Kato MJ. 2017. Similarity
 in volatile communities leads to increased herbivory and greater tropical
 forest diversity. <em>Ecology</em> 98: 1750–1756.
 Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin
 PR, O’Hara RB, Solymos P, Stevens MHH, Szoecs E, <em>et
 al.</em> 2022. <em>vegan: community ecology
 package</em>. Paradis E, Schliep K. 2019. ape
 5.0: an environment for modern phylogenetics and evolutionary analyses in
 R. <em>Bioinformatics</em> 35: 526–528.
 Ricotta C, Moretti M. 2011. CWM and Rao’s quadratic diversity: a
 unified framework for functional ecology. <em>Oecologia</em>
 167: 181–188. Salazar D, Jaramillo A, Marquis RJ. 2016.
 The impact of plant chemical diversity on plant–herbivore interactions at
 the community level. <em>Oecologia</em> 181:
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description: # Data and code from: Volatile organic compounds diversity mediates tree
 diversity-insect herbivory relationships Dataset DOI:
 [10.5061/dryad.1g1jwsv8r](https://doi.org/10.5061/dryad.1g1jwsv8r) ##
 Description of the data and file structure ### Files and variables ####
 Files: * LAR_and_RGR_data.csv * species_distribution.csv * VOC_01_data.csv
 * VOC_abundance_data.csv * VOC_SP_01_data.csv **Description:** 
 **LAR_and_RGR_data.csv: data for GLMM, LMM and SEM, including raw data** *
 Label: Unique sample identifier in the study site * Block: Block number
 (1-8) * Plot: Plot number (1-40) * BP: Block and Plot combination code *
 SP: species * SR: species richness (1,2,4,8) * Compo: species composition
 of the plot * LAR: insect herbivory (%) * LARp: insect herbivory + 0.00001
 (%) * D_4: diameter in 2020 (mm) * D_5: diameter in 2021 (mm) * RGR_D:
 relative growth rate based on diameter * raoQ_137: community VOC diversity
 (Rao's) based on VOC abundance * raoQ_137_01: community VOC diversity
 (Rao's) * SR_std: Standardized Species Richness *
 raoQ_137_01_std: Standardized community VOC diversity (Rao's) *
 RGR_D_std: Standardized RGR value * raoQ_137_std: Standardized community
 VOC diversity (Rao's) based on VOC abundance
 **species_distribution.csv: data for helping to calculate community VOC
 diversity (Rao's)** * BP: Block and Plot combination code * Plot:
 Plot number (1-40) * SR: species richness * SR_re: corrected species
 richness * CACA-SCSU: the eight species name **VOC_SP_01_data.csv: for
 calculating community VOC diversity (Rao's)** SP: species name
 C1-C137: the detected VOC code **VOC_01_data.csv: VOC 0-1 matrix data**  *
 ID: 137 individuals * Label: Unique sample identifier in the study site *
 LAR: insect herbivory (%) * Plot: Plot number * SR: species richness
 (1,2,4,8) * SP: species * Compo: species composition of the plot * D_4:
 diameter in 2020 (mm) * D_5: diameter in 2021 (mm) * RGR: relative growth
 rate based on diameter * C1-C137: the detected VOC code
 **VOC_abundance_data.csv: VOC abundance matrix data, for PERMANOVA and
 Mantel test** * ID: 137 individuals * Label: Unique sample identifier in
 the study site * LAR: insect herbivory (%) * BP: Block and Plot
 combination code * Position: location of the individual in its plot *
 Block: Block number * Plot: Plot number * SR: species richness (1,2,4,8) *
 SP: species * Compo: species composition of the plot * D_4: diameter in
 2020 (mm) * D_5: diameter in 2021 (mm) * RGR: relative growth rate based
 on diameter * aver_dist: VOC distinctiveness of the focal individuals *
 Block: Block number * LARp: LAR+0.00001 (%) * C1-C137: the detected VOC
 code ### Code/Software * Code.R All analyses were conducted in R ver.
 4.4.3