Geographic variation in reaction norms of phenological traits in the greater duckweed, Spirodela polyrhiza
Bibliographic record
Abstract
Variability is a ubiquitous feature of natural environments.Organisms can adapt to this through several methods such as adaptive phenotypic plasticity, changes in phenotype in response to reliable cues predicting fitness outcomes across environments, and bet hedging, the maximization of geometric mean fitness.The greater duckweed Spirodela polyrhiza is an ideal system to study the evolution of these strategies.Phenology of production of overwintering structures called turions, a phenotypically plastic trait that can prevent reproductive failure, is thus vital to fitness.Despite clonal reproduction, offspring show phenotypic variability in both turion phenology and size, mediated through the order in which they are produced, suggesting the expression of diversification bet hedging.Here I use S. polyrhiza populations collected from a latitudinal gradient to study how life-history traits evolve in response to variable environments.I make two hypotheses; first, I hypothesized that reaction norms in turion formation differ across latitudes due to differences in season length and environmental predictability.Second, I hypothesised that populations from northern latitudes would trade off offspring size for number to allow for greater diversification potential at northern latitudes where environments are expected to be more variable.I found support for the first hypothesis, showing that reaction norms in turion phenology do differ such that turions are produced earlier at higher latitudes under warmer, but not colder experimental treatments.I was unable to evaluate my second hypothesis due to premature frond mortality but did show that, while size was only weakly correlated with latitude, significant differences between some populations were present, suggesting offspring size is affected more by local environmental conditions that those that are correlated with latitude.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".