Species mixtures enhance fine root biomass but inhibit root decay under throughfall manipulation in young natural boreal forests
Bibliographic record
Abstract
Fine roots play crucial roles in terrestrial biogeochemical cycles. Although biodiversity loss and changes in precipitation are two major drivers of global change, our understanding of their effects on fine root biomass (FRB), root functional traits, and fine root decay (FRD) remains incomplete. We manipulated precipitation in young boreal forests dominated by Populus tremuloides , Pinus banksiana , and their relatively even mixtures using 25 % addition, ambient, and 25 % reduction in throughfall during the growing season. We collected root samples using soil core and trunk-traced methods to quantify FRB and root traits, and we simulated fine root decay using an in-situ experiment over 531 days. We found that compared to the average of single-species-dominated stands, species mixtures increased FRB by 41 % under ambient throughfall, by 89 % under throughfall reduction and by 71 % under throughfall addition. Root surface area, fine root volume, and root length density responded to species mixtures similarly to FRB. Meanwhile, species mixtures reduced FRD across all water treatments. There was a positive relationship between the effect of species mixtures on the FRD of absorptive roots and those on the FRB. Our results highlight that species mixtures could modify carbon cycling by enhancing fine root biomass accumulation and reducing its decomposition of young boreal forests under changing precipitation. • Compared to pure stands, the mixtures had a 41 % higher fine-root biomass (FRB). • Mixture effects on FRB were both enhanced under throughfall reduction and addition. • Species mixtures reduced root decay rates across water treatments. • Species mixture effects on FRB and root decay rate were positively correlated.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".