Effects of size, neighbors, and site condition on tree growth in a subtropical evergreen and deciduous broad‐leaved mixed forest, China
Bibliographic record
Abstract
Abstract Successful growth of a tree is the result of combined effects of biotic and abiotic factors. It is important to understand how biotic and abiotic factors affect changes in forest structure and dynamics under environmental fluctuations. In this study, we explored the effects of initial size [diameter at breast height ( DBH )], neighborhood competition, and site condition on tree growth, based on a 3‐year monitoring of tree growth rate in a permanent plot (120 × 80 m) of montane Fagus engleriana–Cyclobalanopsis multiervis mixed forest on Mt. Shennongjia, China. We measured DBH increments every 6 months from October 2011 to October 2014 by field‐made dendrometers and calculated the mean annual growth rate over the 3 years for each individual tree. We also measured and calculated twelve soil properties and five topographic variables for 384 grids of 5 × 5 m. We defined two distance‐dependent neighborhood competition indices with and without considerations of phylogenetic relatedness between trees and tested for significant differences in growth rates among functional groups. On average, trees in this mixed montane forest grew 0.07 cm year −1 in DBH . Deciduous, canopy, and early‐successional species grew faster than evergreen, small‐statured, and late‐successional species, respectively. Growth rates increased with initial DBH , but were not significantly related to neighborhood competition and site condition for overall trees. Phylogenetic relatedness between trees did not influence the neighborhood competition. Different factors were found to influence tree growth rates of different functional groups: Initial DBH was the dominant factor for all tree groups; neighborhood competition within 5 m radius decreased growth rates of evergreen trees; and site condition tended to be more related to growth rates of fast‐growing trees (deciduous, canopy, pioneer, and early‐successional species) than the slow‐growing trees (evergreen, understory, and late‐successional species).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".