Insights into adaptive behavioural plasticity from the guppy model system
Bibliographic record
Abstract
Behavioural plasticity allows organisms to respond to environmental challenges on short time scales. But what are the ecological and evolutionary processes that underlie behavioural plasticity? The answer to this question is complex and requires experimental dissection of the physiological, neural and molecular mechanisms contributing to behavioural plasticity as well as an understanding of the ecological and evolutionary contexts under which behavioural plasticity is adaptive. Here, we discuss key insights that research with Trinidadian guppies has provided on the underpinnings of adaptive behavioural plasticity. First, we present evidence that guppies exhibit contextual, developmental and transgenerational behavioural plasticity. Next, we review work on behavioural plasticity in guppies spanning three ecological contexts (predation, parasitism and turbidity) and three underlying mechanisms (endocrinological, neurobiological and genetic). Finally, we provide three outstanding questions that could leverage guppies further as a study system and give suggestions for how this research could be done. Research on behavioural plasticity in guppies has provided, and will continue to provide, a valuable opportunity to improve understanding of the ecological and evolutionary causes and consequences of behavioural plasticity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".