The invasive grass Agropyron cristatum doubles belowground productivity but not soil carbon
Bibliographic record
Abstract
Root dynamics are among the largest knowledge gaps in determining how terrestrial carbon (C) cycles will respond to environmental change. Increases in productivity accompanying plant invasions and introductions could increase ecosystem C storage, but belowground changes are unknown, even though roots may account for 50-90% of production in temperate ecosystems. We examined whether the introduction of a widespread invasive grass with relatively high shoot production also increased belowground productivity and soil C storage, using a multiyear rhizotron study in 50-year-old stands dominated either by the invasive C3 grass Agropyron cristatum or by largely C4 native grasses. Relative to native vegetation, stands dominated by the invader had doubled root productivity. Soil carbon isotope values showed that the invader had made detectable contributions to soil C. Soil C content, however, was not significantly different between invader-dominated stands (0.42 mg C/g soil) and native vegetation (0.45 mg C/g soil). The discrepancy between enhanced production and lack of soil C changes was attributable to differences in root traits between invader-dominated stands and native vegetation. Relative to native vegetation, roots beneath the invader had 59% more young white tissue, with 80% higher mortality and 19% lower C:N ratios (all P < 0.05). Such patterns have previously been reported for aboveground tissues of invaders, and we show that they are also found belowground. If these root traits occur in other invasive species, then the global phenomenon of increased productivity following biological invasion may not increase soil C storage.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".