Meeting seed demand for landscape-scale restoration sustainably: the influence of seed harvest intensity and site management
Bibliographic record
Abstract
Native seed is often collected en masse from remnant ecosystems to supply landscape-scale restoration. Successful large-scale restoration depends on sustained seed yields but also on donor population persistence. Native plants that reproduce solely by seed are especially sensitive to harvesting practices. We addressed the challenge of procuring sufficient seed from remnant sources to restore landscapes while also maintaining remnant populations of native plants. We evaluated: 1) the sustainability of seed harvest at varying intensities in Rudbeckia hirta, a seed-reliant plant; and 2) the contribution of fire in promoting sustainability of seed donor populations. We planted seedlings of R. hirta in a field experiment that manipulated management type (burned or unburned) and harvest intensity (0, 50%, or 100% seed removed), and measured changes in seedling recruitment and seed production among treatments. Moderate intensity harvest and burning did not significantly reduce seedling recruitment, but high intensity harvest with burning reduced recruitment by 95% compared to controls. Seed production nearly doubled in burned treatments. In unburned prairie, recruitment is negligible, and harvest intensity does not have an effect on recruitment. For harvest-sensitive prairie species, a strategy incorporating moderate intensity seed harvest with burning is most likely to provide seed for large-scale restoration sustainably.
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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".