Can’t see the flowers for the trees: factors driving floral abundance within early-successional forests in the central Appalachian Mountains
Bibliographic record
Abstract
Silviculture can be a powerful tool for restoring and enhancing habitat for forest-dependent wildlife. In eastern North America, regenerating timber harvests support abundant wildflowers that provide essential forage for native pollinators. Factors driving floral resource availability within regenerating forests remain almost entirely unstudied. Recent efforts to increase the area of regenerating forests (<10 years old) through overstory removal harvest in the central Appalachian Mountains provide an opportunity to investigate the development of forest wildflower communities following canopy removal. We conducted 1208 surveys of blooming plants across 143 harvests, recording 1 525 245 flowers representing 220 taxa spanning 47 families. The number of flowers within recently harvested stands was negatively associated with fern and sapling cover but positively associated with grass and bramble ( Rubus spp.) cover. Early in the growing season, more flowers bloomed in older regenerating stands (e.g., >5 years old), but this pattern reversed by the end of the growing season. Ultimately, our study demonstrates that the abundance of flowers available to pollinators within regenerating hardwood stands varies with factors associated with advancing succession. Recognizing the potential trade-off between woody regeneration (i.e., saplings) and pollinator forage availability may benefit forest managers who intend to provide floral resources to flower-dependent wildlife like pollinators via silviculture.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".