Data from: Explaining productivity variation in response to nitrogen addition and warming requires intraspecific variability
Bibliographic record
Abstract
1. Recent studies have shown that intraspecific trait variability is an important source of total trait variation. However, the contribution of intraspecific variability to ecosystem functions in the face of global change remains unknown. 2. We measured plant height, leaf area, specific leaf area, leaf dry matter content and leaf thickness of 26 species at individual or leaf level and aboveground net primary productivity in 48 subplots subjected to 8 years of nitrogen addition and warming in a Tibetan alpine meadow. We split community weighted mean (CWM) and Rao’s quadratic entropy (Rao) of individuals’ traits within a given community into “fixed value” (only caused by interspecific difference of traits) and intraspecific variability (only caused by intraspecific difference), respectively, using a variance partitioning method. 3. We found that productivity showed a humped back response to nitrogen addition: it was highest at intermediate levels of nitrogen fertilization. The response trend was mediated by the changes of plant functional structure. Productivity was positive with fixed Rao of plant height and intraspecific variability of leaf area, i.e. community having higher fixed variability of plant height and individuals producing bigger leaf area can increase productivity via niche complementary and dominance effect. Warming reduced productivity directly and marginally decreased individuals’ leaf area which suppresses productivity indirectly. 4. Our research suggests the non-negligible role of plant intraspecific trait variability in maintaining ecosystem functions, especially in the face of global change.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.040 |
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".