DOI: 10.5061/dryad.4f4qrfjjb
title: Malaise-trap metabarcoding dataset from temperate-zone forest Oregon, USA

description: DNA-based biodiversity surveys involve collecting physical samples from
 survey sites and assaying the contents in the laboratory to detect species
 via their diagnostic DNA sequences. DNA-based surveys are increasingly
 being adopted for biodiversity monitoring and decision-making. The most
 commonly employed method is metabarcoding, which combines PCR with
 high-throughput DNA sequencing to amplify and then read `DNA barcode'
 sequences. This process generates count data indicating the number of
 times each DNA barcode was read. However, DNA-based data are noisy and
 error-prone, with several sources of variation. In this paper, we present
 a unifying modelling framework for DNA-based survey data, eDNAPlus, for
 the first time simultaneously allowing for key sources of variation, error
 and noise in the data-generating process. As we discuss, metabarcoding
 data alone cannot be used to estimate the species-specific amount of DNA
 present, or DNA concentration, at surveyed sites. Instead, we estimate
 changes in DNA biomass within species, across sites, and link those
 changes to environmental covariates, while accounting for between-species
 and between-sites correlation. Inference is performed using MCMC, where we
 employ Gibbs or Metropolis-Hastings updates with Laplace approximations.
 We further implement a re-parameterisation scheme, appropriate for
 crossed-effects models, leading to improved mixing, and an adaptive
 approach for updating latent variables, which reduces computation time. We
 discuss study design and present theoretical and simulation results to
 guide decisions on replication at different survey stages and on the use
 of quality control methods. Finally, we demonstrate the new framework on a
 dataset of Malaise-trap samples. Specifically, we quantify the effects of
 elevation and distance-to-road on each species, infer species
 correlations, and produce maps identifying areas of high biodiversity and
 species DNA biomass, which can be used to rank areas by conservation
 value. We also estimate the level of noise between sites and within sample
 replicates, and the probabilities of error at the PCR stage, which are
 found to be close to zero for most species considered, validating the
 employed laboratory processing.
description: Sample information: We collected 121 Malaise-trap samples from 89
 sample sites in and around the HJ Andrews Experimental Forest and
 surroundings, Oregon, USA. Each sample was subjected to the Begum
 metabarcoding pipeline (described in `Yang, C.Y., Bohmann, K., Wang, X.Y.,
 Wang, C., Wales, N., Ding, Z.L., Gopalakrishnan, S., Yu, D.W.  (2021)
 Biodiversity Soup II:  A bulk-sample metabarcoding pipeline emphasizing
 error reduction. Methods in Ecology and Evolution 12:1252-1264. doi:
 10.1111/2041-210X.13602`.). In short, each sample was DNA-extracted and
 then PCR-amplified for a 313 base-pair fragment of the COI DNA-barcode
 gene (using the Leray-FolDegenRev primer pair, described in Yang et al.
 2021). In the Begum pipeline, each sample is independently PCRd three
 times and then library prepped and sequenced on an Illumina sequencer
 (amplicon sequencing). Finally, we processed the Illumina output files to
 trim low-quality sequences, merge read pairs, and assign the orginating
 sample name to each read. Unlike in the standard Begum
 pipeline, we did not use the 3 separate PCRs per sample to detect and
 filter out erroneous sequences. Instead, we accepted all reads (i.e. by
 filtering at the no stringency: accepting reads if they appeared in ≥1 PCR
 with ≥1 read). This resulting fasta-format read dataset is the starting
 dataset for this archive
 (`data/seqs/folderhja2/hja_filtered_1pcr_1read.fna.gz`).
 We wrote a custom pipeline to process
 `hja_filtered_1pcr_1read.fna.gz` and generate 3 separate sample x species
 tables (known technically as OTU tables, where OTU means Operational
 Taxonomic Unit), one table per PCR. The 3 OTU tables
 are the raw inputs to eDNAPlus (&lt;https://github.com/alexdiana1992/eDNAplus&gt;).  
description: The README file and the comments within the scripts listed below
 provide step-by-step instructions on how to reconstruct the 3 OTU tables
 from the raw sequencing file, which is also provided.
 The first script
 (1_reformat_and_cluster_Begum_output_pipeline_20230806.sh) is a Unix
 bioinformatic pipeline, and the utility software packages are
 <strong>seqkit</strong> (https://bioinf.shenwei.me/seqkit) and <strong>vsearch</strong> (https://github.com/torognes/vsearch). The first script also calls a sub-script (1.1_sum_reads_for_usearch.R) written in R (https://cran.r-project.org) plus these R packages: <strong>tidyverse, seqinr, here, </strong>and<strong> glue</strong>. The second script (2_parse_uc_file_tidyverse.Rmd) is an R pipeline and uses these R packages: <strong>tidyverse, here, glue, tictoc, beepr, </strong>and<strong> sjmisc</strong>.  These scripts have also been archived on Zenodo:  https://doi.org/10.5281/zenodo.8220863