Stability in subtropical forests: The role of tree species diversity, stand structure, environmental and socio‐economic conditions
Bibliographic record
Abstract
Abstract Aim Tree species diversity can increase the stability of ecosystem productivity by increasing mean productivity and/or reducing the standard deviation in productivity. However, stand structure, environmental and socio‐economic conditions influence plant diversity and might strongly influence the relationships between diversity and stability in natural forest communities. The relative importance of these factors for community stability remains poorly understood in complex (species‐rich) subtropical forests. Location Subtropical area of southern China. Time period 1999–2014. Major taxa studied Forest trees. Methods We conducted bivariate analyses to examine the mechanisms (overyielding and species asynchrony) underlying the effects of diversity on stability. Multiple regression models were then used to determine the relative importance of tree species diversity, stand structure, socio‐economic factors and environmental conditions on stability. Structural equation modelling was used to disentangle how these variables directly and/or indirectly affect forest stability. Results Tree species richness exerted a positive effect on stability through overyielding and species asynchrony, and this effect was stronger in mountainous forests than in hilly forests. Species richness positively affected the mean productivity, whereas species asynchrony negatively affected the variability in productivity, hence increasing forest stability. Structural diversity also had a positive effect, whereas population density had a negative effect on stability. Precipitation variability and slope mainly had indirect influences on stability through their effects on tree species richness. Main conclusions Overall, tree species diversity governed stability; however, stand structure, socio‐economic conditions and environmental conditions also played an important role in shaping stability in these forests. Our work highlights the importance of regulating stand structure and socio‐economic factors in forest management and biodiversity conservation, to maintain and enhance their stability to provide ecosystem services in the face of unprecedented anthropogenic activities and global climate change.
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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 | 0.002 |
| 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".