Avian predators transmit fear along the air–water interface influencing prey and their parental care
Bibliographic record
Abstract
The nonconsumptive consequences of predators on prey behavior, survival, and demography have recently garnered significant attention by ecologists. However, the impacts of top predators on free-ranging prey are challenging to evaluate because the most common fright response for prey is to leave the area of risk. Additionally, the top-down impacts of avian predators on aquatic environments are surprisingly overlooked. Here we investigated the nonconsumptive effects of avian predators on parental care in pumpkinseed (Lepomis gibbosus (L., 1758)) through use of a realistic model of a predatory bird, the Osprey (Pandion haliaetus (L., 1758)). Our predator model exacted dramatic metabolic fright responses and inducible defenses in experimental fish resulting in significant behavioral changes with respect to their parental care. Key parental behaviors including in-nest rotations and egg and nest maintenance were noticeably altered by predator treatments demonstrating as much as an order of magnitude difference in parental performance, suggesting that even transient predation risk might decrease reproductive fitness. Our data provide important new insights on how the landscape of fear operates along the air–water interface and suggests that avian predators may have greater controlling effects on fish populations than previously thought.
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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 | 0.000 |
| 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 | 0.000 |
| 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".