Humpback whale (<i>Megaptera novaeangliae</i>) winter distribution and core habitats in relation to El Ninõ Southern Oscillation and depth in coastal and oceanic waters off Ecuador
Bibliographic record
Abstract
Abstract Southern Pacific humpback whales (Megaptera novaeangliae) breed in subtropical and tropical waters off Peru in the south to Nicaragua in the north. The effect of warming oceans on humpback whale distribution in breeding areas remains unclear. We modeled the spatial distribution of humpback whales off the coast of Ecuador in relation to environmental variables. We analyzed the temporal variability in humpback whale sighting rates (animals/hour) in a subtropical (Manabí, 1996–1999) and tropical (Esmeraldas, 2001–2019) breeding ground. At the regional scale, we found humpback whale presence was more likely in shallow waters over the continental shelf. Esmeraldas and Manabí breeding grounds are core wintering habitats with most humpback whale sightings along Ecuador. Within breeding grounds, individual sighting rates varied between and within years and in relation to local sea surface temperature anomalies (SSTa). More animals were sighted in years with cooler waters in the Esmeraldas breeding ground, while the opposite was true in Manabí. Our findings suggest that during ENSO conditions, humpback whales may reach their temperature niche limit in the warm tropical waters near Esmeraldas, while during La Niña conditions, cooler areas such as Peru and Manabi become less suitable, and whales move further north.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".