Effects of overstory competition on canopy recruitment patterns of naturally regenerated longleaf pine on two site types
Bibliographic record
Abstract
Recent interest in continuous cover forest management of longleaf pine (Pinus palustris Mill.) ecosystems raises questions of long-term sustainability because of uncertainty in rates of canopy recruitment of longleaf pine trees. We destructively sampled 130 naturally regenerated, midstory longleaf pines across an 11 300 ha, second-growth longleaf pine landscape in southwestern Georgia, United States, to reconstruct individual tree height growth patterns. We tested effects of stand density (using a competition index) and site quality (based on two site classifications: mesic and xeric) on height growth and demographics of midstory trees. We also compared height growth of paired midstory and overstory trees to infer stand regeneration and recruitment dynamics. In low-density stands, midstory trees were younger and grew at greater rates than trees within high-density stands. Midstory trees in low-density stands were mostly from a younger regeneration cohort than their paired overstory trees, whereas midstory–overstory pairs in high-density stands were mostly of the same cohort. Our results highlight the importance of releasing midstory longleaf pine trees from local competition for sustained height growth in partial-harvesting management systems. They also demonstrate patterns of long-term persistence in high-density stands, indicating flexibility in the canopy recruitment process of this shade-intolerant tree species.
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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 | 0.000 | 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".