The relative importance of climate, stand variables and liana abundance for carbon storage in tropical forests
Bibliographic record
Abstract
Abstract Aim To develop an integrative framework to evaluate variation in aboveground carbon storage ( AGC ). A model that can be applied to understand and predict how global‐change drivers influence tropical carbon sinks. Location Old‐growth tropical forests world‐wide. Methods Using structural equation modelling ( SEM ), we propose an a priori model to evaluate the direct and indirect effects of climate, stand variables (basal area, tree diameter and wood density at plot level) and liana abundance on AGC . Our model indicated that stand variables increased AGC while liana abundance decreased AGC indirectly via negative effects on stand variables. We used a multigroup SEM to test the generality of our framework using a standardized dataset of 145 plots (0.1 ha) in dry, moist and wet tropical forests. Results Our model explained over 85% variation in AGC and showed a positive and consistent relationship between stand variables and AGC across forests types. The effects of climate on AGC were indirect rather than direct, with negative effects of temperature in all forests. Liana abundance reduced tree diameter and basal area in moist forests, but did not affect AGC in wet or dry forests. Main conclusions Our results suggest that climate affects AGC indirectly, via its direct influence on stand variables and liana abundance. The effects of lianas on AGC result from reductions in stand variables and are as important as climate for moist forests, which harbour the greatest tropical carbon pools. Our model was consistent across forest types. This highlights the usefulness of an integrative framework to improve predictions of the effects of drivers of global change on tropical carbon sinks.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".