DOI: 10.5061/dryad.4mw6m90jh
title: Data from: The quantity of deposited environmental DNA in plant-insect interactions depends on taxon, abundance, and interaction time

description: eDNA metabarcoding holds great promise as a simple and efficient tool to
 detect plant-insect interactions. However, the influence of both abiotic
 and biotic factors on eDNA deposition in plant-insect interactions is not
 well understood. It especially remains to be tested how much eDNA
 abundances reflect the frequency or intensity of interactions of insects
 and plants. We experimentally analyzed the quantitative deposition of eDNA
 from three insect species (the southern green stink bug Nezara viridula,
 the mustard beetle Phaedon cochleariae and the fall armyworm Spodoptera
 frugiperda) on leaves of two host plants of varying attractiveness: savoy
 cabbage (Brassica oleracea var. sabauda, preferred host) and tomato
 (Solanum lycopersicum, less preferred host). We tested for the effects of
 insect taxonomy, host plant, and exposure time and abundances of
 interacting insects on the plant material. Our data shows a clear
 quantitative ecological signal in eDNA deposition. Insect abundance and
 interaction time is reflected in the amount of deposited eDNA. Moreover,
 significantly more eDNA was deposited on cabbage, the preferred host
 plant. Besides these ecological drivers, a very strong taxonomic eDNA
 deposition bias between different insect taxa was observed.  eDNA
 detection is strongly influenced by the ecological interactions of the
 targeted taxa, highlighting the utility of the tool to specifically detect
 trophic interactions between plants and arthropods. However, taxonomic
 biases in eDNA deposition preclude comparative analyses of interactions at
 the community level.
description: # Data from: The quantity of deposited environmental DNA in plant-insect
 interactions depends on taxon, abundance, and interaction time
 [https://doi.org/10.5061/dryad.4mw6m90jh](https://doi.org/10.5061/dryad.4mw6m90jh) Scripts for making the figures and analyses from "The quantity of deposited environmental DNA in plant-insect interactions depends on taxon, abundance, and interaction time" plus the raw reads used for the study. ## Description of the data and file structure ### Sarah-ZBJ\*fastq.gz files Those are the raw reads are, available in several compressed FASTQ files. We processed them through the bioinformatic pipeline described in the methods of the manuscript. ### **Files used for the R analyses** #### **data.csv** It is a comma-delimited CSV file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It has basically all the data used for the analyses regarding the read count. It is split in the following columns: * *species*: The target species in the replicate * *vegetable*: The host plant used in the replicate * *individuals*: Number of target species' individuals used in the replicate * *hours*: How long the individuals were left on the host plant * *reads*: How many reads of the target species were recovered * *exp*: Code that summarize the replicate: e.g., SBcF1 indicate *Nezara viridula* (which has the code SB) on cabbage (c), with few individuals (F) and that was left for 1 hour (1). #### gc\_all.xlsx XLSX file, which can be opened with a spreadsheet software (e.g., Microsoft Excel), a text editor or R. It has the data with the GC content for the COI of the considered species. It is split in the following way: * *species*: The target species * *G*: Guanine percentage * *C*: Cytosine percentage * *GC*: GC content ## Code/Software The R scripts are present in the ".R" files. Specifically: -"1_plotting.R" has the scripts for allowing to make the barplots and the boxplots of the analyses. -"2_statistical_analyses_reads.R" has the scripts for making the ANOVA, Shapiro-Wilks and GLM performed in the study. -"3_GC_analyses.R" has the scripts for the t-test regarding the GC content of the species. The ".R" files can be opened with a text editor or straight with R. For the analyses, we used R (version 4.3.1), the "base" package (version 4.3.1) and the “stats” package (version 4.3.1). For plotting and tables, we used R, the “gtsummary” package (version 1.7.2) and "tidyverse" (version 2.0.0).