Global Patterns and Controls of Nutrient Immobilization on Decomposing Cellulose in Riverine Ecosystems
Bibliographic record
Abstract
Abstract Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low‐nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low‐nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature‐dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature. Collectively, we demonstrated that exogenous nutrient supply and immobilization are critical control points for decomposition of organic matter.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".