Genotypic Diversity Effects on the Performance of Taraxacum officinale Populations Increase with Time and Environmental Favorability
Bibliographic record
Abstract
Within-population genetic diversity influences many ecological processes, but few studies have examined how environmental conditions may impact these short-term diversity effects. Over four growing seasons, we followed experimental populations of a clonal, ubiquitous weed, Taraxacum officinale, with different numbers of genotypes in relatively favorable fallow field and unfavorable mowed lawn environmental treatments. Population performance (measured as total leaf area, seed production or biomass) clearly and consistently increased with diversity, and this effect became stronger over the course of the experiment. Diversity effects were stronger, and with different underlying mechanisms, in the fallow field versus the mowed lawn. Large genotypes dominated in the fallow field driving overyielding (via positive selection effects), whereas in the mowed lawn, where performance was limited by regular disturbance, there was evidence for complementarity among genotypes (with one compact genotype in particular performing better in mixture than monoculture). Hence, we predict stronger genotypic diversity effects in environments where intense intraspecific competition enhances genotypic differences. Our four-year field experiment plus seedling establishment trials indicate that genotypic diversity effects have far-reaching and context-dependent consequences across generations.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".