Semi‐intensive shrimp farms as experimental arenas for the study of predation risk from falcons to shorebirds
Bibliographic record
Abstract
Abstract Varying environmental conditions and energetic demands can affect habitat use by predators and their prey. Anthropogenic habitats provide an opportunity to document both predation events and foraging activity by prey and therefore enable an empirical evaluation of how prey cope with trade‐offs between starvation and predation risk in environments of variable foraging opportunities and predation danger. Here, we use seven years of observational data of peregrine falcons Falco peregrinus and shorebirds at a semi‐intensive shrimp farm to determine how starvation and predation risk vary for shorebirds under a predictable variation in foraging opportunities. Attack rate (mean 0.1 attacks/hr, equating 1 attack every ten hours) was positively associated with the total foraging area available for shorebirds at the shrimp farm throughout the harvesting period, with tidal amplitude at the adjacent mudflat having a strong nonlinear (quadratic) effect. Hunt success (mean 14%) was higher during low tides and declined as the target flocks became larger. Finally, individual shorebird vigilance behaviors were more frequent when birds foraged in smaller flocks at ponds with poorer conditions. Our results provide empirical evidence of a risk threshold modulated by tidal conditions at the adjacent wetlands, where shorebirds trade‐off risk and rewards to decide to avoid or forage at the shrimp farm (a potentially dangerous habitat) depending on their need to meet daily energy requirements. We propose that semi‐intensive shrimp farms serve as ideal “arenas” for studying predator–prey dynamics of shorebirds and falcons, because harvest operations and regular tidal cycles create a mosaic of foraging patches with predictable food supply. In addition, the relatively low hunt success suggests that indirect effects associated with enhanced starvation risk are important in shorebird life‐history decisions.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".