Leaf Nutrient Resorption Efficiency Aligns with the Leaf but Not Root Economic Spectrum in a Tropical Mangrove Forest
Bibliographic record
Abstract
Leaf nutrient resorption efficiency (NuRE) is critical for plant nutrient conservation, yet its relationship with leaf and root economic traits remains poorly understood in mangroves. We quantified nitrogen (N) and phosphorus (P) resorption across ten mangrove species (five trees and five shrubs) in Hainan, China, and related NuRE to key leaf (leaf mass per area, LMA; leaf dry mass content, LDMC; and green leaf nitrogen and phosphorus contents, Ngr and Pgr, respectively) and root (specific root length, SRL; root tissue density, RTD; root diameter, RD; and root nitrogen content, Nroot) traits. We found that species with a lower leaf structural investment (LMA = 103–173 g m−2, LDMC = 19–27%) presented a 6–45% greater N and P resorption efficiency than those with a higher structural investment (LMA = 213–219 g m−2, LDMC = 26–31%). Contrary to global meta-analyses, higher green leaf N and P contents also predicted a greater NuRE, implying enhanced internal recycling under chronic nutrient limitation. Root traits (SRL, RTD, RD, and Nroot) had no significant influence on NuRE, indicating decoupled above- versus belowground strategies. Trees and shrubs diverged in size but converged in NuRE–leaf trait relationships. These findings refine plant economics theory and guide restoration by prioritizing species with acquisitive, high-NuRE foliage for nutrient-poor coasts.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".