Global changes alter plant multi‐element stoichiometric coupling
Bibliographic record
Abstract
Plant stoichiometric coupling among all elements is fundamental to maintaining growth-related ecosystem functions. However, our understanding of nutrient balance in response to global changes remains greatly limited to plant carbon : nitrogen : phosphorus (C : N : P) coupling. Here we evaluated nine element stoichiometric variations with one meta-analysis of 112 global change experiments conducted across global terrestrial ecosystems and one synthesis over 1900 species observations along natural environment gradients across China. We found that experimentally increased soil N and P respectively enhanced plant N : potassium (K), N : calcium (Ca) and N : magnesium (Mg), and P : K, P : Ca and P : Mg, and natural increases in soil N and P resulted in qualitatively similar responses. The ratios of N and P to base cations decreased both under experimental warming and with naturally increasing temperature. With decreasing precipitation, these ratios increased in experiments but decreased under natural environments. Based on these results, we propose a new stoichiometric framework in which all plant element contents and their coupling are not only affected by soil nutrient availability, but also by plant nutrient demand to maintain diverse functions under climate change. This study offers new insights into understanding plant stoichiometric variations across a full set of mineral elements under global changes.
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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 | 0.002 | 0.001 |
| 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 | 0.000 | 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".