Assessing the trophic impacts of marine mammals: From metabolism to food web indices
Bibliographic record
Abstract
Abstract Marine mammals are an important part of ecosystems, and their trophic role and potential impact have been increasingly studied. One key question is how these large animals interact with fisheries or compete for similar resources. Consequently, some models once used only for fisheries management are now including pinnipeds and cetaceans. However, fish and marine mammals do not share the same ecology and bioenergetics, and complex ecosystem models may not be the best way to assess the impact of pinnipeds or cetaceans in food webs. Indeed, simpler methods based on thermodynamics might give us reasonable answers with limited amounts of data. Here, we present an assessment of two different approaches to assess the trophic role of marine mammals in the northern Gulf of St. Lawrence (Canada): mixed trophic impacts (MTI) based on ecosystem modeling and surface index (SI) impact based on bioenergetics. Our results show that while modeling represents a good way of getting a holistic view of the role of marine mammals in ecosystems, trophic impact estimates based on fundamental thermodynamics principles can also give us answers requiring less data. The body surface area approach presented here might provide a practical tool for ecologists, who are not necessarily ecosystem modelers, to study this issue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".