Microhabitat complexity influences fear acquisition in fathead minnows
Bibliographic record
Abstract
Abstract Habitat varies in structure, with animals often preferring a certain degree of microhabitat complexity that facilitates fitness-related activities such as predator avoidance. Environments with high predation risk can induce elevated baseline fear and neophobia in prey, but whether microhabitat complexity influences the acquisition of neophobia has yet to be reported. Here, we tested whether exposure to predation risk induces different levels of fear in microhabitats that differed in complexity. We exposed fathead minnows, Pimephales promelas, to predation risk repeatedly (12 times over 4 days) in the form of damage-released chemical alarm cues (compared to water control) in tanks with vertical plant structure distributed either throughout the tank (complex habitat) or clumped together (simple habitat). Then, we tested minnows before and after exposure to a novel odor in tanks with either the same microhabitat complexity (i.e., familiar habitats) or in tanks with novel habitat that had different substrate structure and no vertical structure. Minnows in the complex habitat showed less overall movement one day after the background risk period, whereas individuals in the simple habitat showed reduced movement regardless of prior risk exposure. We observed stronger effects in the novel habitat, where background risk in both simple and complex habitats caused neophobia. However, individuals from the simple background habitat showed higher baseline fear behaviors. Hence, for minnows, low microhabitat complexity appears to lead to elevated fear, which remains even after a habitat change.
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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 | 0.000 |
| 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".