Tree-based attributes of large trees more effectively regulate aboveground carbon stock than trait-based ones in temperate deciduous forests
Bibliographic record
Abstract
In forests, a few large trees (L-trees) versus small-medium trees (S-trees) are often considered the major reservoir of aboveground carbon stock (AGCS). Here, we hypothesize that tree species' functional strategies regulate AGCS by tree sizes in temperate deciduous forests across local scale environmental gradients. Using data from 99 plots, we modelled the multivariate effects of the tree-based (tree diversity, stand density and multidimensional tree size inequality) versus the trait-based (multi-trait diversity and single-trait dominance) attributes of L-trees versus S-trees, along topographic and soil conditions, to predict AGCS through four L-trees threshold size (i.e., ≥ 50 cm fixed-diameter, top 95th percentile, ≥ top 50% cumulative AGCS descending-ranked ordered, and mean threshold size) approaches. The tree-based and trait-based attributes of L-trees and S-trees shaped species co-occurrence processes but L-trees regulated AGCS more effectively (31.29-93.20%) than S-trees and abiotic factors across four thereshold size approaches and two concepts. Although L-trees threshold size and tree-based attributes mattered for AGCS, the dominant resource-acquisitive strategy of structurally complex L-trees having higher specific leaf area but lower leaf dry matter content and lesser multi-trait dispersion could promote AGCS better than the resource-conservative strategy (low specific leaf area) of S-trees. Capturing tree species' functional strategies, synergies and trade-offs across tree sizes can enhance our understanding of how to achieve nature-based carbon neutrality and lessen climate change. Thus, forest management and restoration initiatives should prioritize high-functioning tree species with dominant productive traits while conserving multi-trait diversified species in temperate deciduous forests.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".