The inbreeding depression cost of selfing: Importance of flower size and population size in <i>Collinsia parviflora</i> (Veronicaceae)
Bibliographic record
Abstract
Inbreeding depression should evolve with selfing rate when frequent inbreeding results in exposure of and selection against deleterious alleles. The selfing rate may be modified by plant traits such as flower size, or by population characteristics such as census size that can affect the probability of biparental inbreeding. Here we quantify inbreeding depression (δ) among different population sizes of Collinsia parviflora, a wildflower with interpopulation variation in flower size, by comparing fitness components and multiplicative fitness of experimentally produced selfed and outcrossed offspring. Selfed offspring had reduced multiplicative fitness compared to outcrossed offspring, but inbreeding depression was low in all combinations of population size and flower size (δ ≤ 0.05) except in large populations of large-flowered plants (δ = 0.45). The decrement to multiplicative fitness with inbreeding was not affected by population size nested within flower size, but differed between small- and large-flowered plants: small-flowered populations had lower overall inbreeding depression (δ = 0.04) compared to large-flowered populations (δ = 0.25). The difference in load with flower size suggests that either selection has removed deleterious recessive alleles or these alleles have become fixed in small-flowered, potentially more selfing populations, but that purging has not occurred to the same extent in presumably outcrossing large-flowered populations.
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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".