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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".