Functional correlations between specific leaf area and specific root length along a regional environmental gradient in Inner Mongolia grasslands
Bibliographic record
Abstract
Summary Among above‐ and below‐ground traits, specific leaf area (SLA, cm2 g−1) and specific root length (SRL, m g−1) are the two key traits reflecting species resource acquisition strategies. However, patterns of variation in SLA and SRL have rarely been examined simultaneously across evolutionary history and environmental gradients, and the SLA–SRL relationship is still controversial on several grounds. We examined the inter‐ and intraspecific variations in SLA and SRL of different root branching orders and the SLA–SRL relationship across 55 species and 21 plant communities of four vegetation types along a 2000‐km transect in the Inner Mongolia grassland. With increasing root branching order, the interspecific variation in SRL increased, but the intraspecific variation in SRL decreased considerably, and the form of SLA–SRL relationship shifted from positive to negative. This indicates that acquisition of soil resources (e.g. water and nutrients) is a fundamental strategy for plant investment to root length. When inter‐ and intraspecific variations in SLA and SRLs were partitioned into alpha (within‐community) and beta (among‐community) components, the alpha component exhibited substantially greater inter‐ and intraspecific variations than the beta component. Across the transect, the evolutionarily late diverged species in phylogenetic tree evolved towards low SLA and SRL‐1 (SRL for first‐order roots) and tended to distribute in resource‐poor conditions along the environmental gradient. The early diverged species, in contrast, had high SLA and SRL‐1 and mostly distributed in resource‐rich conditions. Our findings suggest that patterns of inter‐ and intraspecific variations in SLA and SRL of different root branching orders and the form of SLA–SRL relationship could be well explained by within‐ and among‐community filtering processes and species divergence time. Coordination and trade‐offs between leaves and roots do not mutually exclude but operate simultaneously at different scales and among different root branching orders in arid and semi‐arid grasslands.
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 | 0.001 | 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".