Spatial distribution of fishes in a Northwest<scp>A</scp>tlantic ecosystem in relation to risk of predation by a marine mammal
Bibliographic record
Abstract
1. Numerous studies have shown that, at spatial scales of metres to several kilometres, animals balance the trade-off between foraging success and predation mortality by increasing their use of safer but less profitable habitats as predation risk increases. However, it is less clear whether prey respond similarly at the larger spatiotemporal scales of many ecosystems. 2. We determine whether this behaviour is evident in a large marine ecosystem, the southern Gulf of St. Lawrence (sGSL, 75 000 km(2) ) over a 42-year period. This ecosystem is characterized by a recent increase in the abundance of a large marine predator, the grey seal (Halichoerus grypus Fabricius), by more than an order of magnitude. 3. We compared changes in spatial distribution over the 1971-2012 period between important prey of grey seals (Atlantic cod, Gadus morhua L.; white hake, Urophycis tenuis Mitchill; and thorny skate, Amblyraja radiata Donovan) and non-prey fishes. 4. Distribution was modelled using generalized additive models incorporating spatially variable effects of predation risk, density dependence and water temperature. Distributions of cod, hake and skate were strongly related to risk of predation by seals, with distribution shifting into lower risk areas as predation risk increased. Non-prey species did not show similar changes in habitat use. Spatial variation in fish condition suggests that these low-risk areas are also less profitable for cod and skate in terms of food availability. The effects of density dependence and water temperature were also important in models, but did not account for the changes in habitat use as the risk of predation increased. 5. These results indicate that these fish are able to assess and respond to spatial variation in predation risk at very large spatial scales. They also suggest that non-consumptive 'risk' effects may be an important component of the declines in productivity of seal prey in this ecosystem, and of the indirect effects at lower trophic levels.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".