Seascapes of fear: evaluating sublethal predator effects experienced and generated by marine mammals
Bibliographic record
Abstract
Abstract The notion that predators can affect their prey without killing them is widely supported in the ecological literature yet rarely applied by marine mammal studies. We present three case studies in which patterns of time allocation by individual marine mammal foragers were used to index the sublethal effects of predators on bottlenose dolphins (Tursiops sp.), harbor seals (Phoca vitulina), and dugongs (Dugong dugon). In each case, foraging individuals optimized energy gain and safety from predators by spending less time in more profitable but dangerous patches or decreasing their use of risky feeding tactics that would increase net energy gain. By implication, marine mammals are subject to the non consumptive effects of their predators (i.e., to intimidation), and fear can mediate their impacts on their resources. We suggest, therefore, that future studies quantify patterns of time allocation to measure sublethal effects of predators on marine mammals, as well as the capacity of marine mammals to have sublethal effects on their own prey. We argue that such an approach is important because non consumptive effects may be of greater magnitude than lethal effects of predators, and information on sublethal effects of predators can inform conservation plans and studies of community structure.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".