Search bioRxiv⌕ Search

bioRxiv · 10.1101/2024.08.26.609755

Water volume, biological and PCR replicates influence the characterization of deep-sea pelagic fish communities.

Abstract

The pelagic deep sea is challenging to investigate due to logistical constraints regarding access and collection of samples, however environmental DNA (eDNA) can potentially revolutionize our understanding of this ecosystem. Although recent advancements are being made regarding eDNA technology and autonomous underwater vehicles, no investigation has been performed to assess the impact of different experimental designs using gear found on many research vessels (i.e., CTD mounted with Niskin bottles). Here, we investigated the effects of sampled water volume, biological and PCR replicates in characterizing deep-sea pelagic biodiversity at the level of species and exact sequence variants (ESVs, representing intraspecific variation). Samples were collected at 450m depth at night in the northern Gulf of Mexico using Niskin bottles, and we targeted the fish community using the MiFish primer (12S rRNA). Our results show that 1L is insufficient to characterize deep-sea pelagic fish communities. The 5L and 10L treatments detected similar community structure (i.e., the combination of number of species and relative occurrence) and numbers of species per biological replicate, but the 10L treatment detected a higher total number of species, more ESVs, and a different community structure when considering ESVs. We found that five biological replicates can detect up to 80% of the species detected in this study in the water collected in both 5L and 10L treatments. PCR replicates also had an important role in species and ESV detection, which implies increasing PCR replicates if water volume is limited. We suggest that future studies collect at least 5L, 5 or more field replicates, and 5-10 PCR replicates to adequately investigate deep-sea pelagic biodiversity using eDNA, considering resource limitations. Our study provides guidance for future eDNA studies and a potential route to expand eDNA studies at a global scale.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Peres, P. A., Bracken-Grissom, H.. 2024-08-27. Water volume, biological and PCR replicates influence the characterization of deep-sea pelagic fish communities.. https://doi.org/10.1101/2024.08.26.609755

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Floristic composition, phenology, and conservation value of four peat bogs in Bucovina, with the presence of Betula nana

This paper presents a comparative analysis of the floristic composition and site characteristics of four peat bogs in Bucovina, Romania: Poiana Stampei, Romanesti, Saru Dornei, and Gaina-Lucina. The research was based on phytosociological releves on 25 msq plots and direct field phenological observations, on six field visits from May to August 2026. Vegetation was characterised using the Braun Blanquet method, and floristic similarity between sites was assessed with the Sorensen and Bray Curtis indices. All four plots shared a common core of taxa characteristic of peatland vegetation: Sphagnum spp., Carex rostrata, Drosera rotundifolia, Eriophorum vaginatum, and Vaccinium species. Species richness was 13 taxa at Poiana Stampei, Romanesti, and Saru Dornei, and 12 at Gaina-Lucina. Romanesti and Saru Dornei showed the highest floristic similarity (descriptive values, not statistically tested, given a single releve per site), while Gaina-Lucina differed most markedly, not through species richness, which was similar across sites, but through species identity and through the presence of Betula nana, a glacial relict absent from the other sites. The results provide a descriptive basis for future research on the floristic composition and conservation of these habitats.

ecology↗

Long-Term Surveillance Reveals Establishment of Aedes albopictus in Eastern Nebraska, USA

Aedes albopictus (Skuse), the Asian tiger mosquito, is a highly competent arboviral vector whose range has expanded substantially across the United States over the past four decades. Despite predictive models placing Nebraska within the species' climatically suitable range, its establishment status in the state has remained poorly characterized. Here, we report results from a nine-year mosquito surveillance program (2017-2025) conducted across 44 Nebraska counties in collaboration with the Nebraska Department of Health and Human Services. Ae. albopictus was detected in five counties, with sustained, annually increasing populations documented in Richardson, Douglas, and Lancaster counties. Richardson County recorded continuous detections during 2017-2025, with proportional representation rising to 60.50% of collected mosquitoes by 2025. In Douglas and Lancaster counties, temporal advancement of first seasonal detection in 2024 and 2025 provide evidence consistent with successful overwintering rather than annual reintroduction. A cumulative degree-day model predicted adult emergence in mid-May across all county-year combinations, consistently preceding trap deployment by two to seven weeks and revealing a systematic early-season surveillance gap. Generalized linear mixed-effects models indicated that trap-level detection persistence, rather than urban location, was the primary predictor of yearly Ae. albopictus positivity, suggesting that current invasion dynamics are driven by focal source populations. These findings provide strong evidence for the establishment of Ae. albopictus in eastern Nebraska and highlight the need for earlier seasonal surveillance and standardized criteria to define establishment in northward-expanding vector populations.

ecology↗

PlanktonLake-CEREEP- A Freshwater Plankton Image Dataset with Semi-Automated Label Cleaning

Plankton plays a fundamental role in aquatic ecosystems, influencing biogeochemical cycles and serving as a key food source for many organisms. Recent high-throughput imaging technologies enable the rapid acquisition of large volumes of microscopic images, creating new opportunities for monitoring planktonic ecosystems. However, the manual processing and annotation of the vast amounts of data generated by these devices remain time-consuming tasks. In this context, machine learning-based classification models offer a promising solution. In this data paper, we introduce a new labeled freshwater plankton dataset comprising approximately 88,000 images distributed across 43 taxa. We also present the labeling assistance method we used to facilitate dataset annotation. Finally, we present a baseline based on a convolutional neural network (CNN), which achieves a classification accuracy of 93% on our dataset.

ecology↗