North Atlantic deep-sea benthic biodiversity unveiled through sponge natural sampler DNA
Bibliographic record
Abstract
The deep-sea remains the biggest challenge to biodiversity exploration, and anthropogenic disturbances extend well into this realm, calling for urgent management strategies. One of the most diverse, productive, and vulnerable ecosystems in the deep sea are sponge grounds. Currently, environmental DNA (eDNA) metabarcoding is revolutionising the field of biodiversity monitoring, yet complex deep-sea benthic ecosystems remain challenging to assess even with these novel technologies. Here, we evaluate the effectiveness of whole-community metabarcoding to characterise metazoan diversity in sponge grounds across the North Atlantic by leveraging the natural eDNA sampling properties of deep-sea sponges themselves. We sampled 97 sponge tissues from four species across four North-Atlantic biogeographic regions in the deep sea and screened them using the universal COI barcode region. We recovered unprecedented levels of taxonomic diversity per unit effort, especially across the phyla Chordata, Cnidaria, Echinodermata and Porifera, with at least 406 metazoan species found in our study area. These assemblages identify strong spatial patterns in relation to both latitude and depth, and detect emblematic species currently employed as indicators for these vulnerable habitats. The remarkable performance of this approach in different species of sponges, in different biogeographic regions and across the whole animal kingdom, illustrates the vast potential of natural samplers as high-resolution biomonitoring solutions for highly diverse and vulnerable deep-sea ecosystems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".