Environmental drivers of persistent humpback whale Megaptera novaeangliae feeding events in a Mexican breeding area
Bibliographic record
Abstract
Humpback whales Megaptera novaeangliae typically fast for several months in low-latitude breeding areas. Here we report on persistent feeding events during 5 wintering seasons between 2013 and 2020 in a known upwelling region of Banderas Bay of the mainland Mexico breeding area. In total, there were 76 unique feeding events documented (group size = 1 to ~100 individuals), involving 201 photo-identified whales, of which 18 were documented feeding in multiple years. The most prolific years of documented feeding in 2017 and 2018 (based on number of reports/individuals photo-identified feeding) followed the strongest marine heatwave ever recorded in the North Pacific. Whales documented feeding in Banderas Bay had significantly shorter mean sighting histories (2.3 yr) than a non-feeding sample (8.7 yr) and were reported to be of small size, suggesting they were predominantly younger whales. Most high-latitude recaptures of Banderas Bay feeding whales were in more northern North Pacific feeding grounds (50.8% were resighted in Russia, Alaska, and northern British Colombia, Canada). A binomial general linear model revealed a significant relationship between the probability of whales feeding in Banderas Bay and sea surface temperature (SST). Specifically, feeding consistently occurred in years of lower than average winter SST (<25°C), associated with La Niña years of the El Niño Southern Oscillation (ENSO). We conclude that feeding of humpback whales is now a predictable occurrence in the upwelling region of Banderas Bay in years that ENSO fluctuations lead to lower regional SST. The magnitude of several years of low-latitude feeding events reported here was likely influenced by climate change induced marine heatwaves that occurred during the study period.
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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 | 0.000 | 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 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".