Experimental and genetic analyses reveal that inbreeding depression declines with increased self‐fertilization among populations of a coastal dune plant
Bibliographic record
Abstract
Theory predicts that inbreeding depression (ID) should decline via purging in self-fertilizing populations. Yet, intraspecific comparisons between selfing and outcrossing populations are few and provide only mixed support for this key evolutionary process. We estimated ID for large-flowered (LF), predominantly outcrossing vs. small-flowered (SF), predominantly selfing populations of the dune endemic Camissoniopsis cheiranthifolia by comparing selfed and crossed progeny in glasshouse environments differing in soil moisture, and by comparing allozyme-based estimates of the proportion of seeds selfed and inbreeding coefficient of mature plants. Based on lifetime measures of dry mass and flower production, ID was stronger in nine LF populations [mean δ = 1-(fitness of selfed seed/fitness of outcrossed seed) = 0.39] than 16 SF populations (mean δ = 0.03). However, predispersal ID during seed maturation was not stronger for LF populations, and ID was not more pronounced under simulated drought, a pervasive stress in sand dune habitat. Genetic estimates of δ were also higher for four LF (δ = 1.23) than five SF (δ = 0.66) populations; however, broad confidence intervals around these estimates overlapped. These results are consistent with purging, but selective interference among loci may be required to maintain strong ID in partially selfing LF populations, and trade-offs between selfed and outcrossed fitness are likely required to maintain outcrossing in SF 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".