Data from: Developmental arcs of plasticity in whole movement repertoires of a clonal fish
Bibliographic record
Abstract
Developmental plasticity at the behavioral repertoire level allows animals to incrementally adjust their behavioral phenotypes to match their environments through ontogeny, serving as a lynchpin between ecological factors that cue phenotypic adjustments and evolutionary forces that select upon emergent phenotypic variation. Quantifying the continuous arcs of plasticity throughout animals’ development, however, has often been prohibitively challenging. Here, we leverage recent advancements in high-resolution behavioral tracking and analysis to (i) track the behavior of 45 genetically identical fish clones (Poecilia formosa) reared in near-identical environments during their first four weeks of life at 0.2 s resolution and (ii) quantify the continuous arcs of plasticity across entire behavioral repertoires through development. Doing so, we empirically address one of the most fundamental theoretical predictions from Bayesian models of development that, in stable (but initially unknown) environments, behavioral plasticity should gradually decrease as individuals age. Using two approaches to measure plasticity across ontogeny, we first quantify plasticity in individual behavioral metrics before also developing a novel whole-repertoire approach that calculates plasticity as the degree of ‘behavioral entropy’ across a multi-dimensional behavioral phenotype space. We robustly find – despite experimentally matching as best as possible the assumptions of models that predict decreasing plasticity – a ~two-week initial increase in plasticity in movement behaviors before plasticity subsequently decreased. Our results help address one of the most widespread intuitions about the optimal developmental course of plasticity through early ontogeny, thereby also demonstrating the value of long-term behavioral tracking approaches for testing fundamental predictions on phenotypic development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".