Elevated atmospheric CO<sub>2</sub> and species mixture alter N acquisition of trees in stand microcosms
Bibliographic record
Abstract
The potential for elevated atmospheric CO2 to increase forest growth depends on how it affects plant acquisition of soil nitrogen (N) in realistic competitive settings. We grew seedling microcosms in large (0.6-m2) boxes of forest soil placed outdoors in CO2-controlled open-top chambers. Loblolly pine (Pinus taeda L.) and sweetgum (Liquidambar styraciflua L.) were grown as single-species stands (monocultures) and as 50:50 pine:sweetgum mixtures, with a factorial combination of CO2 (ambient, twice ambient) and soil water (dry, moist) for two growing seasons. We added N, enriched with 15N, 2 months after planting and used N and 15N content of microcosm components to evaluate treatment effects. Under ambient CO2, species mixture decreased biomass and N accumulation of pine compared with pine in monoculture. Elevated CO2 partially to fully ameliorated this negative effect of species mixture for pine by increasing its biomass and N accumulation irrespective of competitive setting. Sweetgum biomass and N accumulation were improved in mixed culture (compared with monoculture) under moist conditions. However, only sweetgum biomass (not N) responded positively to increasing CO2. Our study suggests that increasing atmospheric CO2 concentration may provide a competitive advantage to pine growing in mixture with sweetgum in low fertility forest soils.
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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 | 0.000 |
| 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".