Interactive effects of reproductive assets and ambient predation risk on the threat-sensitive decisions of Trinidadian guppies
Bibliographic record
Abstract
Threat-sensitive behavioral trade-offs allow prey animals to balance the conflicting demands of successful predator detection and avoidance and a suite of fitness-related activities such as foraging, mating, and territorial defense. Here, we test the hypothesis that background predation level and reproductive status interact to determine the form and intensity of threat-sensitive behavioral decisions of wild-caught female Trinidadian guppies Poecilia reticulata. Gravid and nongravid guppies collected from high- and low-predation pressure populations were exposed to serial dilutions of conspecific chemical alarm cues. Our results demonstrate that there was `no effect of reproductive status on the response of females originating from a low-predation population, with both gravid and nongravid guppies exhibiting strong anti-predator responses to the lowest concentration of alarm cues tested. Increasing cue concentrations did not result in increases in response intensity. Conversely, we found a significant effect of reproductive status among guppies from a high-predation population. Nongravid females from the high-predation population exhibited a strong graded (proportional) response to increasing concentrations of alarm cue. Gravid females from the same high-predation population, however, shifted to a nongraded response. Together, these results demonstrate that accrued reproductive assets influence the threat-sensitive behavioral decisions of prey, but only under conditions of high-ambient predation risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".