Planting time, first‐year mowing, and seed mix design influence ecological outcomes in agroecosystem revegetation projects
Bibliographic record
Abstract
The conversion of tallgrass prairie to agriculture has negatively affected provisioning of ecosystem services. Successful restoration of ecosystem services could depend on management decisions applied during revegetation projects. We examined the effects of three management decisions (seed mix design, planting time, and first‐year mowing) on targeted ecosystem services (erosion control, weed resistance, and pollinator resources). We tested three seed mixes of varying diversity and grass‐to‐forb seeding ratios: Economy mix (21 species, 3:1 grass:forb), Pollinator mix (38 species, 1:3), and Diversity mix (71 species, 1:1). We established plots at two planting times (dormant‐season and spring) with or without first‐year mowing. To assess ecosystem services, we measured stem density, canopy cover, and floral density and richness of sown species in the second year after planting. The Economy mix had the highest stem density and cover but lowest floral density and richness. The Pollinator mix had the lowest stem density and cover but highest floral density. The Diversity mix had comparable stem density and cover to the Economy mix and comparable floral density and richness to the Pollinator mix. Mowing accelerated native plant establishment in all seed mixes. Dormant‐season planting improved establishment of spring and fall forbs and favored cool‐season graminoids over warm‐season grasses. All three management decisions influenced ecosystem outcomes, and comparison to a previous study revealed these effects to be robust to variation in site and climatic conditions. We recommend a diverse, balanced seed mix design, first‐year mowing, and dormant‐season planting to improve multifunctionality of conservation projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".