Seeding tallgrass prairie in monospecific patches promotes native species establishment and cover
Bibliographic record
Abstract
In tallgrass prairie reconstruction, the way desired seeds are arranged on the landscape may affect species establishment, species persistence, and the establishment and persistence of undesired (nonseeded) species from the local propagule pool. To test effects of species seeding pattern on how grasslands develop spatially, we seeded 20—4 × 4–m bare soil plots with 16 tallgrass prairie species. Treatment plots were divided into 16—1 × 1–m subplots, 64—0.5 × 0.5–m subplots, 256—0.25 × 0.25–m subplots, or 1,024—0.125 × 0.125–m subplots. Each species was hand broadcast into separate subplots (1 m2 total area/species) within each plot. An additional treatment included uniformly mixing and broadcasting all seeds across a plot. We recorded species cover at the 0.125 × 0.125–m scale within each plot at the beginning of the second and third growing seasons. While species persistence was greatest within plots seeded with larger subplots, plots with smaller subplots were more spatially diverse and less occupied by nonseeded species over time than larger subplot and mixed plots. As is common in reconstruction efforts, establishment was variable among species and seeding with monospecific subplots enhanced colonization of desired rhizomatous species (e.g., Heliopsis helianthoides, Monarda fistulosa, Elymus virginicus) into unoccupied locations at the expense of species from the local propagule pool. Results suggest that seeding species in smaller, monospecific patches could result in grasslands with a more balanced native species composition than those established with a seed mixture approach.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".