Maternal meddling in neonatal sharks: implications for interpreting stable isotopes in young animals
Bibliographic record
Abstract
Stable isotopes of neonatal vertebrates reflect those of their mother's diet and foraging location. Evaluating feeding strategies and habitat use of neonates is consequently complicated by the maternal isotopic signal and its subsequent elimination with growth. Thus, methods that measure the loss of the maternal signal, i.e. when the isotopic signal of a neonate reflects its own diet, are needed. Values of δ(13)C and δ(15)N were measured in liver and muscle tissues of <1 year old bull (Carcharhinus leucas) and Atlantic sharpnose (Rhizoprionodon terraenovae) sharks and related to age using, total length, date sampled and umbilical scar stage (USS). We observed a decline in δ(13)C and δ(15)N values with age that was different among species, similar among isotopes, and greater in liver than in muscle; highlighting that retention of the maternal signal is dependent on species-specific life history and tissue characteristics. USS was most effective for assessing the loss of the maternal isotopic signal in the faster growing Atlantic sharpnose shark, but was less effective for the slower growing bull shark. Total length and date sampled were overall less effective and may be more informative for slower growing species when coupled with USS, as variable size at birth and misclassification of animals >1 year old, which remain in nursery habitats, increase the variability of the isotopic values. Consideration of the maternal signal and measuring its loss are thus necessary when analyzing the stable isotopes of young animals, as there is potential to misinterpret feeding strategies, over-estimate trophic position and incorrectly assign carbon source.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".