A Long-Term Ecological Research Data Set From the Marine Genetic Monitoring Program ARMS-MBON 2018–2020
Bibliographic record
Abstract
Molecular methods such as DNA/eDNA metabarcoding have emerged as useful tools to document the biodiversity of complex commu- nities over large spatio-temporal scales. We established an international Marine Biodiversity Observation Network (ARMS- MBON) combining standardised sampling using autonomous reef monitoring structures (ARMS) with metabarcoding for genetic monitoring of marine hard- bottom benthic communities. Here, we present the data of our first sampling campaign comprising 56 ARMS units deployed in 2018–2019 and retrieved in 2018–2020 across 15 observatories along the coasts of Europe and adjacent regions. We de- scribe the open- access data set (image, genetic and metadata) and explore the genetic data to show its potential for marine biodiversity monitoring and ecological research. Our analysis shows that ARMS recovered more than 60 eukaryotic phyla capturing diversity of up to ~5500 amplicon sequence variants and ~1800 operational taxonomic units, and up to ~250 and ~50 species per observatory using the cytochrome c oxidase subunit I (COI) and 18S rRNA marker genes, respectively. Further, ARMS detected threatened, vulnerable and non- indigenous species often targeted in biological monitoring. We show that while deployment duration does not drive diversity estimates, sampling effort and sequencing depth across observatories do. We recommend that ARMS should be deployed for at least 3–6 months during the main growth season to use resources as efficiently as possible and that post- sequencing curation is applied to enable statistical comparison of spatio- temporal entities. We suggest that ARMS should be used in biological monitoring programs and long- term ecological research and encourage the adoption of our ARMS- MBON protocols.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".