Is leaf dry matter content a better predictor of soil fertility than specific leaf area?
Bibliographic record
Abstract
BACKGROUND AND AIMS: Specific leaf area (SLA), a key element of the 'worldwide leaf economics spectrum', is the preferred 'soft' plant trait for assessing soil fertility. SLA is a function of leaf dry matter content (LDMC) and leaf thickness (LT). The first, LDMC, defines leaf construction costs and can be used instead of SLA. However, LT identifies shade at its lowest extreme and succulence at its highest, and is not related to soil fertility. Why then is SLA more frequently used as a predictor of soil fertility than LDMC? METHODS: SLA, LDMC and LT were measured and leaf density (LD) estimated for almost 2000 species, and the capacity of LD to predict LDMC was examined, as was the relative contribution of LDMC and LT to the expression of SLA. Subsequently, the relationships between SLA, LDMC and LT with respect to soil fertility and shade were described. KEY RESULTS: Although LD is strongly related to LDMC, and LDMC and LT each contribute equally to the expression of SLA, the exact relationships differ between ecological groupings. LDMC predicts leaf nitrogen content and soil fertility but, because LT primarily varies with light intensity, SLA increases in response to both increased shade and increased fertility. CONCLUSIONS: Gradients of soil fertility are frequently also gradients of biomass accumulation with reduced irradiance lower in the canopy. Therefore, SLA, which includes both fertility and shade components, may often discriminate better between communities or treatments than LDMC. However, LDMC should always be the preferred trait for assessing gradients of soil fertility uncoupled from shade. Nevertheless, because leaves multitask, individual leaf traits do not necessarily exhibit exact functional equivalence between species. In consequence, rather than using a single stand-alone predictor, multivariate analyses using several leaf traits is recommended.
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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 | 0.001 |
| 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) | 0.002 | 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".