DOI: 10.5061/dryad.ffbg79d8m
title: Data from: Tracking the phenology of riverine insect communities using environmental DNA

description: The data contains three datasets and one R scripts used in the article
 "Tracking the phenology of riverine insect communities using
 environmental DNA". The three datasets contain the
 following:  Presence/absence of aquatic insects based on eDNA
 sampling Information on the sampling sites Permutation tests Aquatic
 insects are iconic and ecologically highly relevant inhabitants of
 riverine ecosystems. They are also often the target of monitoring programs
 to assess the ecological status of these lotic habitats. Environmental DNA
 (eDNA) techniques have been widely and successfully implemented to
 investigate freshwater insects and other macroinvertebrates. Commonly,
 such monitoring is conducted at one or two timepoints per year, despite
 the known phenology of aquatic insects' life history strongly
 affecting species presence—or detection—and population dynamics through
 the seasons. Here, we assessed if and to which extent eDNA can capture the
 temporal changes of the orders Ephemeroptera (mayflies), Plecoptera
 (stoneflies), Trichoptera (caddisflies), and Diptera (true flies). We
 carried out eDNA sampling at roughly monthly intervals from April to
 October at 25 sites across a whole river catchment in the northeastern
 part of Switzerland. We found pronounced, cyclic phenological trends in
 all orders but Trichoptera: the communities diverged from spring to summer
 and then in fall gradually returned closer to the spring state. The four
 orders exhibited different predominance in gains or losses of species
 detection throughout this time interval. Lastly, we found that field
 replicates, despite showing a relatively high local stochasticity, were
 able to provide a more complete assessment of aquatic communities. Field
 replicates, when used as a proxy for the frequency of observation of a
 species through the seasons, yielded comparable temporal patterns to the
 ones extracted from the Global Biodiversity Information Facility (GBIF)
 for about 35% of the investigated species. Overall, our findings
 demonstrate that eDNA techniques can be used to reveal intra-annual
 dynamics of aquatic insects. Given the current necessity to assess and
 monitor the biodiversity status of ecosystems, we therefore show that eDNA
 methods are a viable option to obtain a deeper understanding of the
 structuring of freshwater communities over time.
description: # Data from: Tracking the phenology of riverine insect communities using
 environmental DNA Dataset DOI:
 [10.5061/dryad.ffbg79d8m](https://doi.org/10.5061/dryad.ffbg79d8m) ##
 Description of the data and file structure This
 folder, Data_and_Script.zip, contains all the datasets and R scripts used
 in the study "Tracking the phenology of riverine insect communities
 using environmental DNA" 0_data folder contains the following
 datasets: * EPTD_long_presence.txt * sites_Necker.txt *
 median_jaccard_permutation.rda See below information on the dataset
 metadata. 1_R_script folder contains the R script used to run the data
 analysis. In order for the R script to run without issues, do not rename
 or move the two folders. \############### EPTD_long_presence.txt Dataset
 containing information on the insect communities at the Necker. Contains
 the following variables: * taxon: ID of the organism at the highest
 taxonomic resolution available * order: taxonomic rank, order * family:
 taxonomic rank, family * genus: taxonomic rank, genus * species: taxonomic
 rank, species * replicate: eDNA field replicate ID * date: sampling date *
 site_ID: unique site ID * week_number: week number associated to the
 sampling date * readnr: number of reads * presence: presence (= 1) or
 absence (= 0) of the speciment. Only presences are reported here.
 \############### sites_Necker.txt Dataset containing information on the
 sampling sites at the Necker river. Contains the following variables: *
 site_ID: unique site ID * Location: name of the location associated with
 the site_ID * x: longitudinal coordinates of the site (coordinate system
 CH1903) * y: latitudinal coordinates of the site (coordinate system
 CH1903) \############### median_jaccard_permutation.rda Dataset contains
 the output of the Jaccard permutations run at L231-249 of the R script ##
 Code/software Analysis was performed in R version 4.3.3. The analysis
 steps are described in the script.  The following packages and versions
 were used: \ \# RColorBrewer 1.1.3 \# rstudioapi 0.16.0 \# jsonlite 1.8.8
 \# wk 0.9.1 \# magrittr 2.0.3 \# farver 2.1.1 \# adegraphics 1.0.21 \#
 fields 15.2 \# vctrs 0.6.5 \# spdep 1.3.3 \# base64enc 0.1.3 \# terra
 1.7.71 \# htmltools 0.5.8.1 \# forcats 1.0.0 \# progress 1.2.3 \#
 itertools 0.1.3 \# plotrix 3.8.4 \# betapart 1.6 \# raster 3.6.26 \# s2
 1.1.6 \# adegenet 2.1.10 \# spData 2.3.0 \# KernSmooth 2.23.22 \#
 htmlwidgets 1.6.4 \# adephylo 1.1.16 \# plyr 1.8.9 \# lubridate 1.9.3 \#
 uuid 1.2.0 \# traudem 1.0.3 \# igraph 2.0.3 \# mime 0.12 \# lifecycle
 1.0.4 \# minpack.lm 1.2.4 \# iterators 1.0.14 \# pkgconfig 2.0.3 \#
 elevatr 0.99.0 \# Matrix 1.6.5 \# R6 2.5.1 \# fastmap 1.1.1 \# shiny
 1.8.1.1 \# magic 1.6.1 \# digest 0.6.35 \# colorspace 2.1.0 \# patchwork
 1.2.0 \# phylobase 0.8.12 \# vegan 2.6.4 \# labeling 0.4.3 \# progressr
 0.14.0 \# fansi 1.0.6 \# timechange 0.3.0 \# httr 1.4.7 \# abind 1.4.5 \#
 mgcv 1.9.1 \# compiler 4.3.3 \# proxy 0.4.27 \# withr 3.0.0 \# DBI 1.2.2
 \# maps 3.4.2 \# MASS 7.3.60.0.1 \# base 4.3.3 \# stats 4.3.3 \# classInt
 0.4.10 \# permute 0.9.7 \# tools 4.3.3 \# units 0.8.5 \# rncl 0.8.7 \# ape
 5.8 \# httpuv 1.6.15 \# glue 1.7.0 \# graphics 4.3.3 \# rcdd 1.6 \# nlme
 3.1.164 \# promises 1.3.0 \# grid 4.3.3 \# sf 1.0.16 \# cluster 2.1.6 \#
 reshape2 1.4.4 \# ade4 1.7.22 \# generics 0.1.3 \# snow 0.4.4 \# seqinr
 4.2.36 \# gtable 0.3.4 \# tzdb 0.4.0 \# class 7.3.22 \# tidyr 1.3.1 \# hms
 1.1.3 \# sp 2.1.3 \# xml2 1.3.6 \# utf8 1.2.4 \# foreach 1.5.2 \# pillar
 1.9.0 \# adespatial 0.3.23 \# stringr 1.5.1 \# spam 2.10.0 \# later 1.3.2
 \# splines 4.3.3 \# dplyr 1.1.4 \# lattice 0.22.5 \# deldir 2.0.4 \#
 tidyselect 1.2.1 \# knitr 1.46 \# xfun 0.43 \# datasets 4.3.3 \# stringi
 1.8.3 \# boot 1.3.29 \# codetools 0.2.19 \# interp 1.1.6 \# tibble 3.2.1
 \# cli 3.6.2 \# xtable 1.8.4 \# geometry 0.4.7 \# munsell 0.5.1 \# OCNet
 1.2.2 \# Rcpp 1.0.12 \# doSNOW 1.0.20 \# rnaturalearth 1.0.1 \# tidyverse
 2.0.0 \# utils 4.3.3 \# png 0.1.8 \# XML 3.99.0.16.1 \# parallel 4.3.3 \#
 picante 1.8.2 \# RNeXML 2.4.11 \# rgl 1.3.1 \# ggplot2 3.5.0 \# readr
 2.1.5 \# methods 4.3.3 \# prettyunits 1.2.0 \# dotCall64 1.1.1 \#
 latticeExtra 0.6.30 \# jpeg 0.1.10 \# viridisLite 0.4.2 \# rivnet 0.4.2 \#
 scales 1.3.0 \# grDevices 4.3.3 \# e1071 1.7.14 \# purrr 1.0.2 \# crayon
 1.5.2 \# rlang 1.1.3 \# cowplot 1.1.3 \# fastmatch 1.1.4 ## Access
 information GBIF data was derived from the following sources: * GBIF.org.
 (15 November 2024) GBIF Occurrence Download.
 [https://doi.org/10.15468/dl.gfqxj9](https://doi.org/10.15468/dl.gfqxj9)