Stressful environments induce novel phenotypic variation: hierarchical reaction norms for sperm performance of a pervasive invader
Bibliographic record
Abstract
Genetic variation for phenotypic plasticity is ubiquitous and important. However, the scale of such variation including the relative variability present in reaction norms among different hierarchies of biological organization (e.g., individuals, populations, and closely related species) is unknown. Complicating interpretation is a trade-off in environmental scale. As plasticity can only be inferred over the range of environments tested, experiments focusing on fine tuned responses to normal or benign conditions may miss cryptic phenotypic variation expressed under novel or stressful environments. Here, we sought to discern the presence and shape of plasticity in the performance of brown trout sperm as a function of optimal to extremely stressful river pH, and demarcate if the reaction norm varies among genotypes. Our overarching goal was to determine if deteriorating environmental quality increases expressed variation among individuals. A more applied aim was to ascertain whether maintaining sperm performance over a wide pH range could help explain how brown trout are able to invade diverse river systems when transplanted outside of their native range. Individuals differed in their reaction norms of phenotypic expression of an important trait in response to environmental change. Cryptic variation was revealed under stressful conditions, evidenced through increasing among-individual variability. Importantly, data on population averages masked this variability in plasticity. In addition, canalized reaction norms in sperm swimming velocities of many individuals over a very large range in water chemistry may help explain why brown trout are able to colonize a wide variety of habitats.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".