The innovation and diffusion of “trap‐feeding,” a novel humpback whale foraging strategy
Bibliographic record
Abstract
Abstract The innovation and diffusion of novel foraging strategies within a population can increase the capacity of individuals to respond to shifts in prey abundance and distribution. Since 2011, some humpback whales ( Megaptera novaeangliae ) off northeastern Vancouver Island (NEVI), Canada, have been documented using a new feeding strategy called “trap‐feeding.” We provide the first description of this foraging innovation and explore the ecological and social variables associated with its diffusion using sightings data, video analysis, and logistic regression modeling. The number of humpback whales confirmed to trap‐feed off NEVI increased from two in 2011 to 16 in 2015. Neither the locations of trap‐feeding sessions nor prey species consumed differed from those documented during lunge‐feeding. However, preliminary results indicate that the schools of fish consumed when individuals trap‐fed were smaller and more diffuse than those consumed when whales lunge‐fed. Top‐ranked models predicting whether an individual would be observed exhibiting trap‐feeding behavior included the following parameters: average number of days per year that the individual was seen off NEVI and proportion of the individual's associations that were with other trap‐feeders. These results suggest that trap‐feeding may be a culturally transmitted foraging innovation that provides an energetically efficient method of feeding on small, diffuse prey patches.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".