Shoaling in the Trinidadian guppy: costs, benefits, and plasticity in response to an ambush predator
Bibliographic record
Abstract
Abstract Shoaling, the formation of social groupings in fish, can provide benefits including reduced predation risk. However, it can also inflict costs including increased competition for resources, transmission of parasites, and salience to predators. Trinidadian guppies exhibit inter-population variation in shoaling behavior where individuals coexisting with large piscivorous predators (high predation localities) spend most of their time in shoals and those coexisting with an ambush predator, Rivulus hartii (recently, Anablepsoides hartii), that preys primarily on smaller guppies (low predation localities) do not. It has been suggested that this predator selects for reduced shoaling because doing so reduces salience to the predator. Here, as far as we know, we perform the first test of this idea. First, we investigated the effectiveness of shoaling in encounters with this predator. In survival trials, where one rivulus interacted with a group of guppies, we found that the predator was more likely to attack individuals in shoals than singletons. However, we also found that attacks directed at shoals were less likely to succeed. This suggests that the optimal strategy for guppies co-existing with this predator is to reduce shoaling to reduce the probability of being attacked, and to form shoals when an attack is initiated. We then asked if guppies modified their shoaling behavior in response to visual and olfactory cues from this predator during development. We found changes in guppy behavior in response to the treatment: guppies increased shoaling behavior when there was heightened risk of predation.
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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 | 0.001 |
| 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".