Decoupled abiotic and biotic drivers of aboveground and topsoil organic carbon stocks in temperate forests
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Forests are major components of global carbon (C) cycling, and hence, it is crucial to explore the drivers of forest functions related to C sequestration. Here, using the multiple linear regression models (MLMs) and structural equation models (SEMs), we evaluated how abiotic (i.e., soil nutrients and topography) and biotic [i.e., functional trait diversity (FTD) and functional trait identity (FTI)] factors regulate aboveground C (AGC) and topsoil (0–30 cm) organic C (SOC) stocks across 104 plots in temperate forests of Northern Iran. The optimal MLMs showed that the community‐weighted mean (CWM) of wood density and functional divergence increased, but functional evenness decreased AGC stock, where FTI values contributed much (i.e., 74.40%) to the explained variance in AGC stock as compared to FTD indices (12.86%) and abiotic factors (12.74%). On contrary, SOC stock was mainly promoted by soil‐available phosphorus, where abiotic factors contributed much (92.62%) to the explained variance as compared to FTD indices (6.73%) and FTI values (0.65%). The final best‐fitted SEMs showed that AGC stock was strongly controlled ( β = 0.64) by FTI values (i.e., a latent variable of CWM of wood density and plant maximum height), whereas SOC stock was strongly controlled ( β = 0.74) by abiotic factors (i.e., a latent variable of soil‐available phosphorus and total nitrogen). We argue that suitable functional strategies in combination with soil nutrients should be taken into priority during the forestland management and policy plans for the improvement of C stocks in above‐ and belowground compartments of forest ecosystems.
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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 it