Malaise-trap metabarcoding dataset from temperate-zone forest Oregon, USA

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.