Dive behaviour can predict metabolic expenditure in Steller sea lions
Bibliographic record
Abstract
Quantification of costs associated with foraging contributes to understanding the energetic impact that changes in prey availability have on the energy balance of an animal and the fitness of populations. However, estimating the costs of foraging is difficult for breath-hold divers, such as Steller sea lions, that feed underwater. We developed models parameterized with data from free-diving captive Steller sea lions to estimate the costs incurred by wild animals while foraging. We measured diving metabolic rate of trained sea lions performing four types of dives to 10 and 40 m in the open ocean and estimated the separate costs of different dive components: surface time; bottom time; and transiting to and from depth. We found that the sea lions' diving metabolic rates were higher while transiting (20.5 ± 13.0 ml O2 min(-1) kg(-1)) than while swimming at depth (13.5 ± 4.1 ml O2 min(-1) kg(-1)), and both were higher than metabolism at the surface (9.2 ± 1.6 ml O2 min(-1) kg(-1)). These values were incorporated into an energetic model that accurately predicted oxygen consumption for dives only (within 9.5%) and dive cycles (within 7.7%), although it consistently overestimated costs by 5.9% for dives and 21.8% for dive cycles. Differences in the costs of individual components of dives also explained differences in the efficiency of different dive strategies. Single dives were energetically less costly than bout dives; however, sea lions were more efficient at replenishing oxygen stores after bout dives and could therefore spend a greater portion of their time foraging than when undertaking single dives. The metabolic rates we measured for the different behavioural components of diving can be applied to time-depth recordings from wild Steller sea lions to estimate the energy expended while foraging. In turn, this can be used to understand how changes in prey availability affect energy balance and the health of individuals in declining populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".