Rodent-mediated seed limitation affects woody seedling establishment more than invasive shrubs and downed woody debris
Bibliographic record
Abstract
Seedling establishment is crucial for the development of self-regenerating tree populations. Determinants of tree establishment vary widely and may compound in their effects. Using a factorial experiment, we manipulated invasive shrubs, downed woody debris (DWD), and rodent access to evaluate factors limiting the establishment of six woody species (five native trees, one invasive shrub). Our results suggest these factors independently contribute to tree seedling establishment. Exclusion of rodents increased establishment threefold. Invasive shrub removal ( Elaeagnus umbellata; Lonicera maackii) and the presence of DWD promoted establishment of two native trees ( Pinus strobus; Sassafras albidum). Notably, the presence of DWD halved L. maackii establishment. In identifying rodents as drivers of seed limitation, our results support findings that seed additions will likely promote woody seedling establishment when rodents are not abundant (e.g., low populations) or when seeds are physically or chemically protected (e.g., via taste deterrents). Management plans vary in DWD retention; results from our experimental cohort indicate retaining or introducing DWD promotes native tree recruitment and limits invasive shrub establishment. Future studies exploring the species-specific effects of invasive shrub removal and DWD amendments across multiple cohorts will help determine which woody species benefit most from these management practices.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".