Unique seasonal forage bases within a local population of bottlenose dolphin (<i>Tursiops truncatus</i>)
Bibliographic record
Abstract
Abstract Using photo‐identification data, bottlenose dolphin (Tursiops truncatus) populations can be differentiated based on their use of particular estuaries or coastal habitats. Questions remain, however, about the validity of such fine‐scale population partitioning and whether the resulting assemblages utilize unique forage bases. To address the issue of forage base use, stable isotopes of carbon (δ13C), nitrogen (δ15N) and sulfur (δ34S) were analyzed from skin tissues (n= 74) of bottlenose dolphins sampled seasonally along the coast and in three estuaries near Charleston, South Carolina. Autumn values of δ34S, δ15N, and δ13C and summer values of δ34S indicated that dolphins sampled from these four assemblages utilized unique forage bases, despite limited sample sizes. Likewise, autumn and spring differences in δ15N and δ13C values were evident in the North Edisto River, and in δ34S from dolphins sampled from all three estuarine assemblages; no seasonal differences were identified in the coastal assemblage. Results demonstrate the importance of considering spatial and temporal variation in forage base when developing local management plans for bottlenose dolphin and highlight the discriminatory power of δ34S for estuarine and coastal marine mammals. These results also suggest that stable isotopes could be developed as a complementary tool for photo‐identification based partitioning of bottlenose dolphin populations.
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 | 0.000 |
| 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".