Stable Carbon and Nitrogen Isotopes Show Evidence of Resource Partitioning Between Spinner Dolphins ( <scp> <i>Stenella longirostris</i> </scp> ) and Large Pelagic Fishes From the Fernando de Noronha Archipelago
Bibliographic record
Abstract
ABSTRACT We applied stable carbon and nitrogen isotopes to investigate the trophic ecology of four large pelagic predators from the Fernando de Noronha Archipelago (FNA), northeastern Brazil: spinner dolphin ( Stenella longirostris ), yellowfin tuna ( Thunnus albacares ), wahoo ( Acanthocybium solandri ), and great barracuda ( Sphyraena barracuda ). We found no significant divergences in δ 13 C and δ 15 N values or trophic positions between male and female spinner dolphins; and the high isotopic niche overlap indicated limited sexual segregation. Our data indicate overlap in the isotopic niches of yellowfin tuna, wahoo, and great barracuda, but significant segregation between these pelagic predators and spinner dolphins. While spinner dolphis occupy higher trophic positions, reflecting their consumption of mesopleagic squids, the fishes seem to target a broader prey spectrum, with yellowfin tuna showing a more specialized diet. This partitioning likely reduces interspecific competition for prey in the FNA, with different species occupying distinct ecological niches. The results presented here provide important information about the trophic relationships among pelagic predators from the FNA, which is crucial for their conservation in a region where they are vulnerable to local anthropogenic activities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".