Differential reproductive plasticity under thermal variability in a freshwater fish (Danio rerio)
Bibliographic record
Abstract
Human-driven increases in global mean temperatures are associated with concomitant increases in thermal variability. Yet, few studies have explored the impacts of thermal variability on fitness-related traits, limiting our ability to predict how organisms will respond to dynamic thermal changes. Among the myriad organismal responses to thermal variability, one of the most proximate to fitness – and, thus, a population’s ability to persist - is reproduction. Here, we examine how a model freshwater fish (Danio rerio) responds to diel thermal fluctuations that span the species’ viable developmental range of temperatures. We specifically investigate reproductive performance metrics including spawning success, fecundity, egg provisioning, and sperm concentration. Notably, we apply thermal variability treatments during two ontogenetic timepoints to disentangle the relative effects of developmental plasticity and reversible acclimation. We found evidence of direct, negative effects of thermal variability during later ontogenetic stages on reproductive performance metrics. We also found complex interactive effects of early and late-life exposure to thermal variability, with evidence of beneficial acclimation of spawning success and modification of the relationship between fecundity and egg provisioning. Our findings illuminate the plastic life-history modifications that fish may undergo as their thermal environments become increasingly variable.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".