Fine-scale hunting strategies in Australian fur seals
Bibliographic record
Abstract
Introduction Knowledge of the hunting strategies of top predators can provide insights into the cost-benefit trade-offs of their foraging activities. Air-breathing marine predators are constrained in their foraging activities due to their metabolic expenditure at depth being supported by limited body oxygen stores. Understanding how these species adapt their behaviours to maximise foraging success is of importance in view of the anticipated alterations to marine ecosystems in response to global change. The Australian fur seal (Arctocephalus pusillus doriferus), the largest fur seal species, has a distribution restricted to south-eastern Australia, which is one of the fastest warming oceanic regions and where the abundance, distribution and diversity of prey species is expected to change in coming decades. Methods In the present study, combined IMU (acceleration, magnetometer, gyroscope), depth and GPS data logger information was used to reconstruct 3-dimensional tracks during diving, assess energy expenditure and quantify prey capture events in adult female Australian fur seals during benthic foraging. Results The results revealed that individuals ascended at steeper pitches (to reduce transit time), remained for shorter durations and travelled shorter distances at the surface, and then descended at steeper pitches on subsequent dives after predatory events on the seafloor. Higher travel speeds and more directional changes during searching for prey along the seafloor, while requiring greater energy expenditure, were associated with more prey captures. Interestingly, individuals did not display conventional Area Restricted Search, with the heading between dives not influenced by prey encounters. Discussion Together, these results suggest Australian fur seals undertake rapid searching along the seafloor to surprise cryptic prey and, if prey is encountered, undertake rapid surfacing (to reload body oxygen stores) and return to nearby seafloor habitat with a similar but undisturbed prey field.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".