Controls of Initial Wood Decomposition on and in Forest Soils Using Standard Material
Bibliographic record
Abstract
Forest ecosystems sequester approximately half of the world’s organic carbon (C), most of it in the soil. The amount of soil C stored depends on the input and decomposition rate of soil organic matter (OM), which is controlled by the abundance and composition of the microbial and invertebrate communities, soil physico-chemical properties, and (micro)-climatic conditions. Although many studies have assessed how these site-specific climatic and soil properties affect the decomposition of fresh OM, differences in the type and quality of the OM substrate used, make it difficult to compare and extrapolate results across larger scales. Here, we used standard wood stakes made from aspen (Populus tremuloidesMichx.) and loblolly pine (Pinus taedaL.) to explore how climate and abiotic soil properties affect wood decomposition across 44 unharvested forest stands located across the northern hemisphere. Stakes were placed in three locations: (i) on top of the surface organic horizons (surface), (ii) at the interface between the surface organic horizons and mineral soil (interface), and (iii) into the mineral soil (mineral). Decomposition rates of both wood species was greatest for mineral stakes and lowest for stakes placed on the surface organic horizons, but aspen stakes decomposed faster than pine stakes. Our models explained 44 and 36% of the total variation in decomposition for aspen surface and interface stakes, but only 0.1% (surface), 12% (interface), 7% (mineral) for pine, and 7% for mineral aspen stakes. Generally, air temperature was positively, precipitation negatively related to wood stake decomposition. Climatic variables were stronger predictors of decomposition than soil properties (surface C:nitrogen ratio, mineral C concentration, and pH), regardless of stake location or wood species. However, climate-only models failed in explaining wood decomposition, pointing toward the importance of including local-site properties when predicting wood decomposition. The difficulties we had in explaining the variability in wood decomposition, especially for pine and mineral soil stakes, highlight the need to continue assessing drivers of decomposition across large global scales to better understand and estimate surface and belowground C cycling, and understand the drivers and mechanisms that affect C pools, CO2emissions, and nutrient cycles.
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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) | 0.001 | 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".