Metre-scale vertical zonation of corals and sponges on a deep-marine cliff reflects trophic resource partitioning
Bibliographic record
Abstract
Corals and sponges are considered foundational species and can create biodiversity hotspots in the deep sea, yet little is known of their competitive interactions, particularly with respect to resource partitioning among benthic fauna. Here we report on the feeding ecology of deep-water corals, sponges, ascidians, and anemones from a ~ 450 m deep submarine canyon wall off Nova Scotia, Canada. Analysis of bulk stable isotopes of carbon and nitrogen confirms isotopic niche partitioning between species despite their physical proximity. Compound-specific nitrogen isotopes of amino acids (δ 15 N-AA) separated the taxa along continua of trophic position and benthic-pelagic coupling and resolve the conspicuously enriched bulk nitrogen patterns commonly observed in sponges. Radiocarbon dating (as Δ 14 C) of tissue samples, particulate organic matter (POM) and dissolved inorganic carbon (DIC) from the Scotian Slope sheds light on food provenance and distinguishes diets dominated by older, recalcitrant forms of organic matter versus surface-derived POM. Our results reveal significant differences in resource utilisation among sympatric corals, sponges, ascidians, and anemones and highlight that organisms capable of feeding on more recalcitrant resources will likely play a greater role in supporting deep-water habitats where the quality and flux of fresh POM may be diminished.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".