Marine Mammal Impacts in Exploited Ecosystems: Would Large Scale Culling Benefit Fisheries?
Bibliographic record
Abstract
Competition between marine mammals and fisheries for marine resources-whether real or perceived-has become a major issue for several countries and in international fora. We examined trophic interactions between marine mammals and fisheries based on a resource overlap index, using seven Ecopath models including marine mammal groups. On a global scale, most food consumed by marine mammals consisted of prey types that were not the main target of fisheries. For each ecosystem, the primary production required (PPR) to sustain marine mammals was less than half the PPR to sustain fisheries catches. We also developed an index representing the mean trophic level of marine mammal's consumption (TL(Q)) and compared it with the mean trophic level of fisheries' catches (TL(C)). Our results showed that overall TL(Q) was lower than TL(C) (2.88 versus 3.42). As fisheries increasingly exploit lower-trophic level species, the competition with marine mammals may become more important. We used mixed trophic impact analysis to evaluate indirect trophic effects of marine mammals, and in some cases found beneficial effects on some prey. Finally, we assessed the change in the trophic structure of an ecosystem after a simulated extirpation of marine mammal populations. We found that this lead to alterations in the structure of the ecosystems, and that there was no clear and direct relationship between marine mammals' predation and the potential catch by fisheries. Indeed, total biomass, with no marine mammals in the ecosystem, generally remained surprisingly similar, or even decreased for some species.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".