Local predation risk shapes spatial and foraging neophobia patterns in Trinidadian guppies
Bibliographic record
Abstract
The "dangerous niche" hypothesis posits that neophobia functions to reduce the cost of habitat use among animals exposed to unknown risks. For example, more dangerous foraging or higher competition may lead to increased spatial neophobia. Likewise, elevated ambient predation threats have been shown to induce phenotypically plastic neophobic predator avoidance. In both cases, neophobia is argued to reduce the cost of living associated with ecological uncertainty. Here, we test the hypothesis that ambient predation shapes both neophobic predator avoidance and spatial and foraging neophobia in Trinidadian guppies. Guppies were exposed to a novel foraging arena paired with a known cue (conspecific alarm cue), a novel cue (lemon odor), or a stream water control in three streams differing in ambient predation risk. We demonstrate that guppies from a high-predation-risk stream exhibited risk-averse foraging patterns regardless of the chemical stimulus presented (high spatial neophobia) and that those from a low-predation-risk stream were only risk-averse when the foraging arenas were paired with conspecific alarm cue (lower spatial neophobia). Those tested in the intermediate-predation-risk stream were consistently intermediate to the high-risk vs. low-risk populations. Our study suggests that ambient predation risk shapes both neophobic predator avoidance and space-use patterns and that neophobia may function as a "generalized" response to ecological uncertainty.
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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".