Does habitat complexity influence the ability of fathead minnows to learn heterospecific chemical alarm cues?
Bibliographic record
Abstract
Numerous aquatic animals release chemical cues when attacked by a predator. These cues "warn" other individuals of danger and have been termed alarm cues. Cross-species responses to alarm cues are common and in some cases result from learned recognition. However, little is known about the ecological factors that could influence this learned recognition. The current study focuses on the role of habitat complexity in the learning of heterospecific alarm cues. We introduced brook stickleback (Culaea inconstans) into outdoor pools containing fathead minnows (Pimephales promelas) naïve to stickleback. The pools all contained a predatory fish (northern pike, Esox lucius) but varied in habitat characteristics. Pools representing high-complexity habitats had a large amount of structure to obscure the visual environment, while pools representing low-complexity habitats had minimal structure. After 8 days, fish were removed from the pools and behavioural assays were conducted in the laboratory. We tested the minnows for a response to either stickleback skin extract (experimental) or swordtail (Xiphophorus helleri) skin extract (control) and found that minnows conditioned in pools with little structure had learned to recognize stickleback alarm cues, while those from pools with complex structure did not recognize stickleback alarm cues.
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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 | 0.000 | 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".