The Effects of Density on the Learned Recognition of Heterospecific Alarm Cues
Bibliographic record
Abstract
Abstract Numerous species, both aquatic and terrestrial, use alarm cues to mediate predation risk. These cues may be either intentionally or inadvertently released, and may be received by either conspecifics or heterospecifics. In aquatic systems, alarm cues are often chemical in nature and are released when an organism is disturbed or damaged by a predator. In some cases the recognition of alarm cues from conspecifics, or closely related heterospecifics, is innate, while the recognition of alarm cues from distantly related species must be learned. Many studies have documented the use of heterospecific alarm cues, but few have explored the manner in which these cues come to be recognized as an indication of predation. In the current study, we examined the fathead minnow (Pimephales promelas)/brook stickleback (Culaea inconstans) alarm system. We tested the effect of density on the ability of minnows to learn to recognize stickleback alarm cues as a threat. We hypothesized that the ability of minnows to learn to recognize stickleback alarm cues should increase with increasing stickleback density because there would be more opportunity for minnows to associate the heterospecific alarm cue with the threat. To test this hypothesis we stocked minnows into large outdoor pools with no stickleback, low numbers of stickleback, or high numbers of stickleback. All pools contained a predator (pike, Esox lucius) known to the minnows. Following a 14 d conditioning period, minnows were tested for a response to skin extract from stickleback, minnow, and an unknown heterospecific (swordtail, Xiphophorus helleri). Minnows from pools with large numbers of stickleback learned to respond to stickleback alarm cues while minnows from pools with low numbers of stickleback, or no stickleback, did not.
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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".