Sender and receiver experience alters the response of fish to disturbance cues
Bibliographic record
Abstract
Abstract Predation is a pervasive selection pressure, shaping morphological, physiological, and behavioral phenotypes of prey species. Recent studies have begun to examine how the effects of individual experience with predation risk shapes the use of publicly available risk assessment cues. Here, we investigated the effects of prior predation risk experience on disturbance cue production and use by Trinidadian guppies Poecilia reticulata under laboratory conditions. In our first experiment, we demonstrate that the response of guppies from a high predation population (Lopinot River) was dependent upon the source of disturbance cue senders (high vs. low predation populations). However, guppies collected from a low predation site (Upper Aripo River) exhibited similar responses to disturbance cues, regardless of the sender population. In our second experiment, we used laboratory strain guppies exposed to high versus low background risk conditions. Our results show an analogous response patterns as shown for our first experiment. Guppies exposed to high background risk conditions exhibited stronger responses to the disturbance cues collected from senders exposed to high (vs. low) risk conditions and guppies exposed to low risk conditions were not influenced by sender experience. Combined, our results suggest that experience with background predation risk significantly impacts both the production of and response to disturbance cues in guppies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".