Stand density, drought, and herbivory constrain ponderosa pine regeneration pulse
Bibliographic record
Abstract
Trees in dry forests often regenerate in episodic pulses when wet periods coincide with ample seed production. Factors leading to success or failure of regeneration pulses are poorly understood. We investigated the impacts of stand thinning on survival and growth of the 2013 cohort of ponderosa pine (Pinus ponderosa Douglas ex P. Lawson & C. Lawson) seedlings in northern Arizona, United States. We measured seedling survival and growth over the first five growing seasons after germination in six stand basal areas (BAs; 0, 7, 14, 23, 34, and 66 (unthinned) m2·ha−1) produced by long-term experimental thinnings. Five-year survival averaged 2.5% and varied among BAs. Mean survival duration was longer in intermediate BAs (11 to 16 months) than in clearings and high BAs (5 months). The BAs of 7, 14, and 23 m2·ha−1 had >2600 5-year-old seedlings·ha−1. In contrast, regeneration was lower in the clearing (666 seedlings·ha−1) and failed completely in the 34 m2·ha−1 and unthinned treatments. Seedling survival was highest during wet years and lowest during drought years. Many surviving seedlings had no net height growth between years 4 and 5 because of stem browsing. Results indicate that natural regeneration of ponderosa pine is influenced by stand BA, drought, herbivory, and interactions between extreme climatic events.
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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".