Intra-specific Niche Partitioning in Antarctic Fur Seals, Arctocephalus gazella
Bibliographic record
Abstract
Abstract Competition for resources within a population can lead to niche partitioning between sexes, throughout ontogeny and among individuals, allowing con-specifics to co-exist. We aimed to quantify such partitioning in Antarctic fur seals, Arctocephalus gazella, breeding at South Georgia, which hosts ~95% of the world’s population. Whiskers were collected from 20 adult males and 20 adult females and stable isotope ratios were quantified every 5 mm along the length of each whisker. Nitrogen isotope ratios (δ15N) were used as proxies for trophic position and carbon isotope ratios (δ13C) indicated foraging habitat. Sexual segregation was evident: δ13C values were significantly lower in males than females, indicating males spent more time foraging south of the Polar Front in maritime Antarctica. In males δ13C values declined with age, suggesting males spent more time foraging south throughout ontogeny. In females δ13C values revealed two main foraging strategies: 70% of females spent most time foraging south of the Polar Front and had similar δ15N values to males, while 30% of females spent most time foraging north of the Polar Front and had significantly higher δ15N values. This niche partitioning may relax competition and ultimately elevate population carrying capacity with implications for ecology, evolution and conservation.
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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".