The strength of assortative mating for flowering date and its basis in individual variation in flowering schedule
Bibliographic record
Abstract
Although it has been widely asserted that plants mate assortatively by flowering time, there is virtually no published information on the strength or causes of phenological assortment in natural populations. When strong, assortative mating can accelerate the evolution of plant reproductive phenology through its inflationary effect on genetic variance. We estimated potential assortative mating for flowering date in 31 old-field species in Ontario, Canada. For each species, we constructed a matrix of pairwise mating probabilities from the individual flowering schedules, that is the number of flower deployed on successive dates. The matrix was used to estimate the phenotypic correlation between mates, ρ, for flowering date. We also developed a measure of flowering synchrony within species, S, based upon the eigenstructure of the mating matrix. The mean correlation between pollen recipients and potential donors for flowering date was ρ=0.31 (range: 0.05-0.63). A strong potential for assortative mating was found among species with high variance in flowering date, flowering schedules of short duration and skew towards early flower deployment. Flowering synchrony, S, was negatively correlated with potential assortment (r= -0.49), but we go on to show that although low synchrony is a necessary condition for phenological assortative mating, it may not be sufficient to induce assortment for a given phenological trait. The potential correlation between mates showed no seasonal trend; thus, as climate change imposes selection on phenology through longer growing seasons, spring-flowering species are no more likely to experience an accelerated evolutionary response than summer species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".