Global Patterns of Leaf Litter C:N:P Stoichiometry Under Current and Future Climate Scenarios
Bibliographic record
Abstract
Abstract Plant litter carbon (C), nitrogen (N), and phosphorus (P) stoichiometry can indicate ecosystem nutrient use efficiency and limitation. Yet, a comprehensive quantification of plant litter C:N:P ratios at the global scale remains elusive, limiting our understanding of how their variation responds to future climate change. We constructed a database comprising 11,807 records of leaf litter C:N:P ratios, quantifying their global patterns under current and future (2041–2100) climate scenarios using the random forest method. We found that global mean leaf litter C:N, C:P and N:P ratios were 46.5, 669.4 and 16, respectively, while they were dependent on mycorrhizal association, taxonomic division, and/or plant functional type. Leaf litter C:N and N:P ratios showed opposite latitudinal patterns, being larger in high and low latitude regions, respectively, while the C:P ratio remained relatively stable in low latitude regions but increased significantly toward the poles. Our simulations further revealed that increasing climate warming decreased the leaf litter C:N ratio but increased the C:P and N:P ratios in terrestrial plants, despite the fact that their variations were largely dependent on ecosystem type. These findings clearly benefit us to understand the role of leaf litter in regulating the cycling of C and nutrients, responding to ecosystem plant development with climate change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".