A Tree Species Effect on Soil That Is Consistent Across the Species’ Range: The Case of Aspen and Soil Carbon in North America
Bibliographic record
Abstract
Trembling aspen covers a large geographic range in North America, and previous studies reported that a better understanding of its singular influence on soil properties and processes is of high relevance for global change questions. Here we investigate the potential impact of a shift in aspen abundance on soil carbon sequestration and soil carbon stability at the continental scale by conducting a systematic literature review using 23 published studies. Our review shows that aspen’s effect on soil carbon is relatively consistent throughout the species range. Aspen stores less C in the forest floor but similar amounts in the mineral soil relative to conifers. However, a robust set of indicators of soil C stability, for example, degree of organo-mineral associations, proportion of readily-available or labile C estimated during long-term soil incubations or using hot-water extraction, pattern of soil C distribution, and temperature sensitivity of soil heterotrophic respiration, reveals that the soil organic carbon (SOC) stock under aspen is more stable, rendering it more protected against environmental changes and soil disturbances. Therefore, our continental-scale analysis highlights that an increase in the abundance of trembling aspen in North American forests may increase the resistance and resilience of soil C stocks against global changes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".