Effect of leaf habit and soil type on nutrient resorption and conservation in woody species of a dry tropical environment
Bibliographic record
Abstract
We examined 90 dry tropical tree species growing on contrasting soil types (relatively infertile Ultisol and more fertile Inceptisol) for leaf traits such as leaf habit, specific leaf mass (SLM, leaf dry mass per leaf area), leaf chemistry (nutrient concentrations and C/N ratios), and nutrient resorption. Across the species, SLM ranged from 4.06 to 15.74 mg·cm 2 in mature leaves and from 2.60 to 15.12 mg·cm 2 in senesced leaves. Mature leaf N and P concentrations varied from 0.86% to 4.11% and 0.13% to 0.21%, respectively. Senesced leaf N concentrations varied from 0.49% to 1.90% and P from 0.04% to 0.47%. Resorption efficiencies varied from 26% to 83% (mean = 58.32% ± 1.20%) for N and from 16% to 80% (mean = 49.57% ± 1.48%) for P indicating that the woody species of dry tropical environments resorbed different nutrients in substantial amounts to support new growth. Deciduous species had greater resorbed nutrient pools and resorption efficiencies than evergreen species. Compared with the nutrient-rich environment, species from the nutrient-poor environment had a lower resorbed P pool and lower resorption efficiencies for N and P, but had similar N and P concentrations in mature leaves. Resorption efficiencies for C, N, and P were generally correlated, suggesting that the resorbed C pool acted as a vehicle for mobilizing nutrients, especially N. Species with a low or high C/N ratio in senesced leaf and a low or high N resorption efficiency occurred in both nutrient-poor and nutrient-rich environments, as well as among deciduous and evergreen leaf habits, indicating individualistic adaptations to optimize the efficiency of nutrient resource use and conservation of the dry tropical woody vegetation.Key words: leaf chemistry, leaf traits, resorption efficiency, resorbed nutrient pools, substrate-quality stability.
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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".