Data from: Meroplankton diversity, seasonality and life-history traits across the Barents Sea Polar Front revealed by high-throughput DNA barcoding
Bibliographic record
Abstract
In many species of marine benthic invertebrates, a planktonic larval phase plays a critical role in dispersal. Very little is known about the larval biology of most species, however, in part because species identification has historically been hindered by the microscopic size and morphological similarity among related taxa. This study aimed to determine the taxonomic composition and seasonal distribution of meroplankton in the Barents Sea, across the Polar Front. We collected meroplankton during five time points seasonally and used high-throughput DNA barcoding of individual larvae to obtain species-level information on larval seasonality. We found that meroplankton was highly diverse (72 taxa from eight phyla) and present in the Barents Sea year-round with a peak in abundance in August and November, defying the conventional wisdom that peak abundance would coincide with the spring phytoplankton bloom. Ophiuroids, bivalves and polychaetes dominated larval abundance while gastropods and polychaetes accounted for the bulk of the taxon diversity. Community structure varied seasonally and total abundance was generally higher south of the Polar Front while taxon richness was overall greater to the north. Of the species identified, most were known inhabitants of the Barents Sea. However, the nemertean Cephalothrix iwatai and the brittle star Ophiocten gracilis were abundant in the meroplankton despite never having been previously recorded in the northern Barents Sea. The new knowledge on seasonal patterns of individual meroplanktonic species has implications for understanding environment-biotic interactions in a changing Arctic and provides a framework for early detection of potential newcomers to the system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".