Compensatory foraging in Trinidadian guppies: Effects of acute and chronic predation threats
Bibliographic record
Abstract
Abstract In response to acute predation threats, prey may sacrifice foraging opportunities in favour of increased predator avoidance. Under conditions of high or frequent predation risk, such trade-offs may lead to reduced fitness. Here, we test the prediction that prey reduce the costs associated with lost opportunities following acute predation threats by exhibiting short-term compensatory foraging responses. Under semi-natural conditions, we exposed female guppies Poecilia reticulate from high and low predation risk sites to one of three levels of acute predation threat (high, intermediate or low concentrations of conspecific alarm cues). Our results confirm previous reports, demonstrating that guppies from a high predation site were consistently ‘bolder’ (shorter escape latencies) and exhibited graded threat-sensitive responses to different simulated threat levels while those from the low predation site were ‘shyer’ and exhibited non-graded responses. Most importantly, we found that when guppies from low predation sites resumed foraging, they did so at rates significantly lower than baseline rates. However, guppies from high predation sites resumed foraging either at rates equal to baseline (in response to low or intermediate risk stimuli) or significantly increased relative to baseline rates (in response to high risk stimuli). Together, these results highlight a complex compensatory behavioral mechanism that may allow prey to reduce the long-term costs associated with predator avoidance.
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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".