Different developmental environments reveal multitrait plastic responses in South American Anostomidae fish
Bibliographic record
Abstract
Complex phenotypes result from developmental processes integrating genetic, epigenetic, and environmental information. Although changing environments combine several signals that may induce multitrait plastic responses, literature often decodes developmental plasticity into single trait variation as a function of isolated environmental signals. To address the multivariate nature of developmental plasticity, we evaluated how different combinations of environmental signals influence the development of morphological and behavioral traits. We raised Megaleporinus macrocephalus (Anostomidae) in four different developmental environments, and found that foraging position and structural complexity during development induced different morphotypes, which overlapped with behavioral patterns. Foraging position induced distinct patterns of mouth and fin positioning and overall body shape, which were accentuated by structural complexity. Moreover, fish most often chose conditions similar to their developmental environments. Combined signals during development, therefore, revealed environment-specific phenotypic patterns associating morphology and behavior. Such results endorse the ability of developmental processes to influence the variation present in natural populations. Implications of addressing the multivariate essence of developmental plasticity transcend the evolutionary theory and inspire applications in several fields.
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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 | 0.001 | 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".