Tree growth and mortality after small-group harvesting in New Zealand old-growth <i>Nothofagus</i> forests
Bibliographic record
Abstract
In New Zealand uncertainty about how silvicultural systems influence growth and mortality of residual beech trees has caused controversy surrounding sustainable beech management. We assess the impact of group-selection harvesting on residual-tree growth and mortality in old-growth hard beech (Nothofagus truncata (Col.) Ckn.) and mixed red beech silver beech (Nothofagus fusca (Hook. F.) Oerst. Nothofagus menziesii (Hook. F.) Oerst.) forests. Proximity to cuts had a major influence on growth that varied with both species and initial tree diameter. For all three beech species, diameter increment of small trees (<60 cm DBH) in intact forest was less than that of large trees, but smaller trees grew two- to four-fold faster along cut edges than in intact forest. In contrast, growth of large hard beech and silver beech and intermediate-sized red beech trees did not vary with cut proximity, suggesting that these main canopy trees were growing at a maximum potential rate. Edge trees were more likely to die as their level of Platypus beetle infection increased, but overall mortality rates were unrelated to harvest proximity. These results demonstrate that complex mortality and growth responses to harvesting should be incorporated into forest growth models upon which silvicultural systems that sustain the structural, compositional, and functional characteristics of forests are based.
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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".