GENOTYPE‐BY‐TEMPERATURE INTERACTIONS MAY HELP TO MAINTAIN CLONAL DIVERSITY IN <i>ASTERIONELLA FORMOSA</i> (BACILLARIOPHYCEAE)
Bibliographic record
Abstract
Marine and freshwater phytoplankton populations often show large clonal diversity, which is in disagreement with clonal selection of the most vigorous genotype(s). Temporal fluctuation in selection pressures in variable environments is a leading explanation for maintenance of such genetic diversity. To test the influence of temperature as a selection force in continually (seasonally) changing aquatic systems we carried out reaction norms experiments on co-occurring clonal genotypes of a ubiquitous diatom species, Asterionella formosa Hassall, across an environmentally relevant range of temperatures. We report within population genetic diversity and extensive diversity in genotype-specific reaction norms in growth rates and cell size traits. Our results showed genotype by environment interactions, indicating that no genotype could outgrow all others across all temperature environments. Subsequently, we constructed a model to simulate the relative proportion of each genotype in a hypothetical population based on genotype and temperature-specific population growth rates. This model was run with different seasonal temperature patterns. Our modeling exercise showed a succession of two to several genotypes becoming numerically dominant depending on the underlying temperature pattern. The results suggest that (temperature) context dependent fitness may contribute to the maintenance of genetic diversity in isolated populations of clonally reproducing microorganisms in temporally variable environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.002 | 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 teacher head, 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".