The Abundance and Structure of Deadwood: A Comparison of Mixed and Thinned Chinese Fir Plantations
Bibliographic record
Abstract
The sustainability of coniferous monoculture plantations is facing challenges with respect to yields, ecology, and biodiversity. Conversion of monocultural coniferous plantations into mixed stands using thinning or direct mixed planting is widely considered to be a key strategy for overcoming these challenges and transforming the characteristics of plantations on a regional scale. Substantial amounts of deadwood may be produced in mixed forests (MFs); this material is important for evaluating and modifying forest management methods, understanding the dynamics of forest stands, and achieving biodiversity conservation. We assessed the quantitative characters and diameter distributions of deadwood in mixed and thinned Chinese fir [Cunninghamia lanceolata (Lamb.) Hook.] forests over one rotation. We used the g(r) function and spatial parameters to analyze the spatial structure of deadwood, and used logistic regression and Hegyi’s competition index (HCI) to explore competition and mortality. Our results indicate that: (1) Chinese fir dominated in all groups of deadwood (snags, broken wood, and fallen wood), and the abundance, volume, and mortality rates of deadwood were much lower in the thinning forest compared to the MF. (2) Later coming populations (LCPs) comprised the majority of the small diameter classes in the thinning forest, but only accounted for a small proportion of the MF. (3) Broken wood in the thinning forest was randomly distributed, while the other types of deadwood were clustered at most spatial scales. In contrast, the spatial patterns in the MF were random at most spatial scales. (4) Total deadwood in both stands was in a status of intermediate and was randomly surrounded by its four nearest neighbors. All types of deadwood were highly mixed in the thinning forest and moderately mixed in the MF. Our case study suggests that thinning and mixing result in different stand development processes and thus influence the type, amount, and structure of deadwood. Thinning significantly reduces competition, which is the main driver of tree mortality. Converting pure Chinese fir plantations into mixed stands by thinning should be taken in future. Understanding tree mortality after conversion is essential to select appropriate silvicultural treatments and achieve ultimately sustainable forest management.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".