Stoichiometric controls on carbon, nitrogen, and phosphorus dynamics in decomposing litter
Bibliographic record
Abstract
The mineralization of nitrogen and phosphorus from plant residues provides an important input of inorganic nutrients to the soil, which can be taken up by plants. The dynamics of nutrient mineralization or immobilization during decomposition are controlled by different biological and physical factors. Decomposers sequester carbon and nutrients from organic substrates and exchange inorganic nutrients with the environment to maintain their stoichiometric balance. Additionally, physical losses of organic compounds from leaching and other processes may alter the nutrient content of litter. In this work, we extend a stoichiometric model of litter nitrogen mineralization to include (1) phosphorus mineralization, (2) physical losses of organic nutrients, and (3) chemical heterogeneity of litter substrates. The enhanced model provides analytical mineralization curves for nitrogen and phosphorus as well as critical litter carbon : nutrient ratios (the carbon : nutrient ratios below which net nutrient release occurs) as a function of the elemental composition of the decomposers, their carbon‐use efficiency, and the rate of physical loss of organic compounds. The model is used to infer the critical litter carbon : nutrient ratios from observed nitrogen and phosphorus dynamics in about 2600 litterbag samplings from 21 decomposition data sets spanning artic to tropical ecosystems. At the beginning of decomposition, nitrogen and phosphorus tend to be immobilized in boreal and temperate climates (i.e., both C:N and C:P critical ratios are lower than the initial ratios), while in tropical areas nitrogen is generally released and phosphorus may be either immobilized or released, regardless of the typically low phosphorus concentrations. The critical carbon : nutrient ratios we observed were found to increase with initial litter carbon : nutrient ratios, indicating that decomposers adapt to low‐nutrient conditions by reducing their carbon‐use efficiency. This stoichiometric control on nutrient dynamics appears ubiquitous across climatic regions and ecosystems, although other biological and physical processes also play important roles in litter decomposition. In tropical humid conditions, we found high critical C:P ratios likely due to high leaching and low decomposer phosphorus concentrations. In general, the compound effects of stoichiometric constraints and physical losses explain most of the variability in critical carbon : nutrient ratios and dynamics of nutrient immobilization and release at the global scale.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".