‘Whales eat fish’? Demystifying the myth in the Caribbean marine ecosystem
Bibliographic record
Abstract
Abstract There has been much recent discussion about the idea that large whales are potential competitors with fisheries for available marine resources. Based on this idea, often referred to as the ‘whales eat fish’ conflict, culling whales has been proposed as a way to increase resources available for human consumption and thereby ensuring global food security. However, the scientific basis for such arguments remains unclear, especially in the Caribbean waters where baleen whales generally do not feed. In this article, we (i) develop an ecosystem model describing the trophic interactions between whales, fish and fisheries in the Caribbean waters, (ii) calculate the level of overlap between cetaceans and fisheries for food resources, and (iii) simulate the removal of cetaceans from the Caribbean waters in order to quantify the potential increase in available biomass of commercially important fish. Ten groups of cetaceans are considered in the model, including baleen whales, toothed whales and small cetaceans. Our results suggest that baleen whales are not a threat to fisheries in Caribbean waters, while toothed cetaceans seem to be more impacted by fisheries than they actually impacting them. Whales target different types of food resources and consume significantly less than what is taken by fisheries. Moreover, simulated reductions in large whale abundance do not produce any appreciable increase in biomass of the commercially important fish species. In some cases, the presence of some whales actually improves fishery yield as a result of indirect predation effects.
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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 | 0.001 |
| 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 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".