Are functional traits a good predictor of global change impacts on tree species abundance dynamics in a subtropical forest?
Bibliographic record
Abstract
Significant changes in the composition of tree species have been observed in various forests worldwide. We hypothesised that these changes might result from variable sensitivities of species to global change, and species sensitivities might be quantified, using functional traits. Employing long-term (1978-2010) species abundance data of 48 tree species from a permanent subtropical forest plot, where multiple global change factors have been observed, including soil drying, we examined the relationships between temporal trends in abundance and suits of functional traits. We found that species with high photosynthesis rates, leaf phosphorus and nitrogen concentrations, specific leaf area, hydraulic conductivity, turgor loss point and predawn leaf water potential had increased in abundance, while species with opposite trait patterns had decreased. Our results demonstrate that functional traits underlie tree species abundance dynamics in response to drought stress, thus linking traits to compositional shifts in this subtropical forest under global changes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".