Phytochemical diversity impacts herbivory in a tropical rainforest tree community
Data files
Sep 06, 2023 version files 187.44 KB
Abstract
Metabolomics provides an unprecedented window into diverse plant secondary metabolites that represent a potentially critical niche dimension in tropical forests underlying species co-existence. Here, we used untargeted metabolomics to evaluate chemical composition of 358 tree species and its relationship with phylogeny and variation in light environment, soil nutrients, and insect-herbivore leaf damage in a tropical rain forest plot. We report no phylogenetic signal in most compound classes, indicating rapid diversification in tree metabolomes. We found that locally co-occurring species were more chemically dissimilar than random, and that local chemical dispersion and metabolite diversity was associated with lower herbivory, especially that of specialist insect herbivores. Our results highlight the role of secondary metabolites in mediating plant-herbivore interactions and their potential to facilitate niche differentiation in a manner that contributes to species coexistence. Furthermore, our findings suggest that specialist herbivore pressure is an important mechanism promoting phytochemical diversity in tropical forests.
README: Phytochemical diversity impacts herbivory in a tropical rainforest tree community
https://doi.org/10.5061/dryad.n2z34tn32
Description of the data and file structure
1. In the El_submitted file\, qua was plot name; species richness is the species richness in each plot; scale_herbivory.ratio is herbivory ratio data which was scaled; scale(generalized.ratio) was scaled generalized ratio; scale(specialized.ratio) was scaled specialized ratio; soil.pc1\, soil.pc2 and soil.pc3 were the first three axes after the principal component analysis of soil nutrient indicators; scale_PSMshannon was Shannon index of plant secondary metabolites which was scaled; all.classified.nri was the net-relatedness index of all classified compounds; all.chemical.diversity was diversity of all chemical compounds; Alkaloids.diversity\, Amino.diversity\, Carbohydrates.diversity\, Fattyacids.diversity\, Polyketides.diversity\, Shikimates.diversity\, Terpenoids.diversity was diversity of Alkaloids\, Amino\, Carbohydrates\, Fattyacids\, Polyketides\, Shikimates\, Terpenoids.
2. In pathway_percent file\, pathway was different classified chemical compound; number was the number of compounds in different classes; percent was the percentage of different classes of compounds in the total compounds.
3. In 7type_boxplot file\, adj.nri was the net-relatedness index after removing the spatial autocorrelation; in the type\, aap means amino acids and peptides\, alkaloids mean alkaloids\, carbohy means carbohydrates\, fa means fatty acids\, poly means polyketides\, sp means shikimates and phenylpropanoids\, terp means terpenoids.
Sharing/Access information
Data was derived from the following sources: https://datadryad.org/stash/share/TdkGT62wy0UECZQu2Rw08UvtQIIktNmS8jBu7fd7Kuo
Methods
Plant secondary metabolites were extracted and analyzed from leaves using ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS), following Sedio et al. (2021) with slight modifications. Three branches were taken from different directions, and 10 leaves per branch were sequentially selected from top to bottom on each branch to avoid overestimating the percent herbivory and ignoring certain types of herbivores (Woodman & Fernandes, 1991). All collected leaves were scanned, and the leaf area was calculated using ImageJ software (Abramoff et al., 2004). The percent herbivory for each leaf was calculated as the ratio of damaged area to estimated undamaged area, with higher percentages indicating greater herbivore damage (Kurokawa & Nakashizuka, 2008). We classified herbivore damage into broad categories (e.g., hole feeding, margin feeding) according to Labandeira et al. (2007) and further divided them into three diet-breadth categories: generalized, intermediate, or specialized (Labandeira et al., 2007; Wang et al., 2022). We used published data from our laboratory for 9 soil nutrient variables in the plot (Hu et al., 2012, Yang et al., 2014), including pH, total N, total P, total K, available N, extractable P, extractable K, total C and bulk density. Light availability was measured using hemispherical photographs taken with a digital camera and analyzed using the Gap Light Analyzer Version 2.0 software to calculate the canopy gap fraction, representing the non-vegetated portion of the image (Frazer et al., 2000). To quantify variation in percent herbivory and chemical diversity in different habitats, the plot was divided into slope, ridge and valley habitats from Yang et al.