Andruszkiewicz Allan, E. | bioRxiv Science

bioRxiv2026

Depth-dependent eDNA abundances across ecosystems inform deep-sea sampling strategies

Environmental DNA (eDNA) surveys are increasingly used to assess marine biodiversity and inform deep-sea environmental decision-making, including mineral resource management and fisheries oversight. Yet standard low-volume protocols inherited from coastal work may be inadequate at depth, and recent findings indicate that depth- and productivity-aware sampling, often requiring high-volume in situ filtration, is needed, but no quantitative framework links depth and ecosystem context to defensible filtration volume targets. We compiled 841 eDNA samples from eight expeditions across the North Atlantic, Wider Caribbean, and Pacific (surface to 4000 m) to quantify how recoverable eDNA scales with depth and surface productivity, and to derive depth- and productivity-aware sampling targets. Total eDNA concentration declined with depth as a power law, with attenuation exponents (b) modulated by surface productivity: most gradual in eutrophic waters (b = 0.67), intermediate in mesotrophic (b = 0.90), and steepest in oligotrophic systems (b = 1.25); volume-weighted models explained 66 to 88% of the variance. At a fixed extract-concentration target, required filtration volumes diverged ~7-fold between oligotrophic and eutrophic systems at 200 m and ~38-fold at 4000 m. Conventional Niskin sampling therefore undersamples deep-sea biodiversity, particularly in mid- to low-productivity systems. Among laboratory parameters, the assay-specific extract-concentration target exerted greater leverage on required volume than extraction efficiency or elution volume. Volume-aware sampling paired with optimized recovery should be routine in deep-sea eDNA surveys.

ecology↗

bioRxiv2022

Distinguishing Signal from Noise: Understanding Patterns of Non-Detections to Inform Accurate Quantitative Metabarcoding

Correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data is the presence of non-detections, where a technical PCR replicate fails to detect a species observed in other replicates. Such non-detections are an important special case of variability among technical replicates in metabarcoding data, particularly in environmental samples. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) develop a qualitative understanding of how non-detections arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show in both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing -- both of which we can model -- but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

molecular biology↗