Divergent responses of biomass accumulation and its allocation to the altered precipitation regimes among different degraded grasslands in China
Bibliographic record
Abstract
Abstract Purpose Climate models predict shifts in precipitation patterns characterized by increased precipitation amount and decreased frequency for semi-arid grasslands in northeast China. However, under these novel climatic conditions, potential differences in plant biomass and its allocation among different degraded grasslands remain unclear.Methods We conducted a mesocosm experiment to test the effects of higher precipitation amount (increased by 50% from the long-term mean) and lower frequency (decreased by 50%) on plant biomass and allocation in the lightly degraded (LDG), moderately degraded (MDG), and severely degraded grasslands (SDG).Results Lower precipitation frequency promoted belowground biomass (BGB), while reducing aboveground biomass (AGB) allocation through enhancing soil water variability. Higher precipitation amount enhanced AGB in LDG and MDG, but not in SDG due to less soil inorganic nitrogen. Lower precipitation frequency weakened the positive effects of higher precipitation amount on biomass. Under altered precipitation, adjustment of AGB vs. BGB allocation was the primary biomass allocation strategy in LDG and SDG. However, to maintain water acquirement, plants in MDG preferred to adjust root vertical distribution, and allocated more roots to the deep soil layer where had a relatively stable water source. This strategy was driven by the changes in plant community composition of the dominant species in MDG.Conclusions The findings of this research emphasized the importance of considering the degradation level of grasslands when predicting the responses of the ecosystem functions to the projected changes in precipitation regime. These findings are critical for making feasible decisions for the sustainable management of degraded grasslands.
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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 | 0.001 | 0.001 |
| 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".