Niche-processes induced differences in plant growth, carbon balance, stress resistance, and regeneration affect community assembly over succession
Bibliographic record
Abstract
The relationship between plant traits and species relative abundance along environmental gradients can provide important insights on the determinants of community structure. Here we bring extensive data on six key traits (specific leaf area (SLA), seed mass, seed germination rate, height, leaf proline content and photosynthesis rate) to test trait-abundance relationships in a successional chronosequence of subalpine meadow plant communities. Our results show that in late-successional meadows, abundant species had higher values for seed mass, seed germination rate, and SLA, but had lower values for height, photosynthesis rate, and leaf proline content than rarer species. The opposite patterns of trait-abundance relationships were observed for early-successional meadows. Observations of strong trait convergence and divergence in these successional communities lend greater support for niche processes compared to neutral community assembly. We conclude that species' niches that determine plant growth (plant height and photosynthesis rate), carbon balance (SLA, photosynthesis rate), regeneration (seed mass and seed germination rate), and abiotic stress resistance (leaf proline content) under different environmental conditions have strong influence on species relative abundance in these sub-alpine meadow communities during succession.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".