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DNA-based biodiversity surveys involve collecting physical samples fromsurvey sites and assaying the contents in the laboratory to detect species viatheir diagnostic DNA sequences. DNA-based surveys are increasingly beingadopted for biodiversity monitoring. The most commonly employed method ismetabarcoding, which combines PCR with high-throughput DNA sequencing toamplify and then read `DNA barcode' sequences. This process generates countdata indicating the number of times each DNA barcode was read. However,DNA-based data are noisy and error-prone, with several sources of variation. Inthis paper, we present a unifying modelling framework for DNA-based dataallowing for all key sources of variation and error in the data-generatingprocess. The model can estimate within-species biomass changes across sites andlink those changes to environmental covariates, while accounting for speciesand sites correlation. Inference is performed using MCMC, where we employ Gibbsor Metropolis-Hastings updates with Laplace approximations. We also implement are-parameterisation scheme, appropriate for crossed-effects models, leading toimproved mixing, and an adaptive approach for updating latent variables,reducing computation time. We discuss study design and present theoretical andsimulation results to guide decisions on replication at different stages and onthe use of quality control methods. We demonstrate the new framework on adataset of Malaise-trap samples. We quantify the effects of elevation anddistance-to-road on each species, infer species correlations, and produce mapsidentifying areas of high biodiversity, which can be used to rank areas byconservation value. We estimate the level of noise between sites and withinsample replicates, and the probabilities of error at the PCR stage, which areclose to zero for most species considered, validating the employed laboratoryprocessing.

eDNAPlus: A unifying modelling framework for DNA-based biodiversity monitoring