Stoichiometric models of microbial metabolic limitation in soil systems
Bibliographic record
Abstract
Abstract Aim Ecoenzymatic stoichiometry provides a promising avenue for deciphering resource constraints on soil microbial metabolism but is hampered by limitations in current modelling techniques. Innovation Herein we developed new models for quantifying microbial metabolic limitations based on the stoichiometric and metabolic theories of ecology, using an extensive database (n = 2,667) that revealed relationships far from the widely recognized mean ratio of 1:1:1 for carbon : nitrogen : phosphorus (C : N : P) acquiring enzyme activities. We estimated the balance points of P and N acquisition (x0, y0) in the absence of resource constraints to redefine the boundary between P versus N limitation. We then calculated two alternative boundary conditions defining P versus N limitation by scaling the classic threshold element ratio (TER), generating two new models (TEREEA and TERL). In addition, a new enzyme vector (V‐T) model was devised by correcting traditional vector calculations based on observed enzyme activities against these balance points. Main conclusions These three new models more consistently predicted microbial metabolic limitations than the traditional TER and vector models. They also predicted that microbial metabolism in high‐latitude grasslands and low‐latitude forests were predominantly limited by soil N and P, respectively, and that increases in soil organic C with ecosystem development could intensify these limitations. In contrast, fertilizers alleviated these limitations in agricultural ecosystems, suggesting that widespread anthropogenic effects could potentially alter microbial resource limitations even in natural ecosystems. In addition, C limitation to microbial metabolism identified by the new V‐T model showed a consistent negative correlation with microbial C use efficiency among ecosystems, confirming that resource constraints regulate microbial resource allocation. These new models provide more precise predictions of microbial metabolic limitations across a wide range of ecosystems and thus may be useful tools for the study of microbial macroecology.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".