Isotopic niche overlap of four large pelagic predatory fish species in the Northwest Atlantic Ocean
Bibliographic record
Abstract
Understanding trophic dynamics and the potential for competition among pelagic predators is critical to managing multi-species fisheries. We estimated trophic position and isotopic niche overlap for albacore ( Thunnus alalunga; n = 63), Atlantic bluefin tuna ( Thunnus thynnus; n = 16), bigeye tuna ( Thunnus obesus; n = 70), and swordfish ( Xiphias gladius; n = 20) collected from the northwest Atlantic in 2022 using stable isotope ratios of carbon (δ13C) and nitrogen (δ15N) in muscle. Atlantic bluefin tuna showed low overlap with other species, while bigeye tuna had high overlap with albacore and swordfish (particularly swordfish ≤ 175 cm in length). Large swordfish may avoid competition with other predators by foraging on deeper prey based on higher δ13C values. Different size incorporation methods differentially affected overlap estimates, necessitating careful consideration of methodology. Trophic position estimates were similar to results from 14 to 21 years ago (except for bigeye tuna), despite ecological changes in the region. Understanding trophic dynamics among pelagic predators is essential to managing a system expected to undergo further shifts caused by climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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".