Assessing long-term risks of prairie seed harvest: what is the role of life-history?
Bibliographic record
Abstract
To meet the demand for more and larger tallgrass prairie restorations, seed is frequently collected en-masse from remnant native plant populations. Overharvesting of seed may lead to population extinctions, but these risks are not well studied. Species’ reproductive strategies may provide a basis for risk assessment. We assessed extinction risks associated with seed harvest for grassland plant species with different reproductive strategies (clonal vs. non-clonal). Using stage-based matrix models, we projected the extinction risk for two clonal and four non-clonal prairie species subjected to five harvest scenarios: (i) no harvest, (ii and iii) annual harvest at low/high intensity (50% and 75% seeds removed), and (iv and v) triennial harvest at low/high intensity. We compared the magnitude of growth or decline (λ) and mean extinction risk among populations during a 25-year modeling period. Non-clonal species were robust to triennial and low-intensity harvest, but susceptible to decreases in population growth (λ) up to 0.5 and elevated extinction risks up to 95% with high intensity annual harvest. Clonal species were unaffected by seed harvest, owing to a compensatory effect of vegetative propagation on growth rates. To maintain populations of non-clonal species in remnant grasslands, high intensity annual harvest should be avoided.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".