Weevil seed damage reduces germination and seedling growth of hybrid American chestnut
Bibliographic record
Abstract
Seed predation by weevils (Coleoptera: Curculionidae) has been implicated as a limiting factor in oak recruitment throughout eastern US forests. We examined the effects of weevil seed predation on American chestnut (Castanea dentata (Marsh.) Borkh.). Although an introduced pathogen eradicated sexually reproducing populations of American chestnut in the early 1900s, the recent development of a blight-resistant hybrid makes reintroduction feasible. We nondestructively assessed the amount of weevil damage to seeds using X-ray imagery and traditional float test methods in American and blight-resistant hybrid chestnut. We quantified the effects of weevil damage on seed germination and seedling growth. The float test method misidentified damage for up to 50% of seeds, whereas the X-ray method misidentified only 3% of the sample. Germination declined with damage: the smallest damage level reduced germination from 94% to 32%. No seeds with >50% damage germinated. Weevil damage reduced seedling growth by 50% compared with undamaged seeds. Seedling size increased with seed size, but seed size had no effect on germination. Our results highlight the importance of orchard and seed processing practices that prevent weevil damage to chestnut seeds. Because they drastically reduce germination rates and seedling growth, weevils have the potential to limit seed regeneration and dampen rates of spread in populations following reintroduction.
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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) | 0.001 | 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".