Changes in nutrient concentrations of leaves and roots in response to global change factors
Bibliographic record
Abstract
Abstract Global change impacts on biogeochemical cycles have been widely studied, but our understanding of whether the responses of plant elemental composition to global change drivers differ between above‐ and belowground plant organs remains incomplete. We conducted a meta‐analysis of 201 reports including 1,687 observations of studies that have analyzed simultaneously N and P concentrations changes in leaves and roots in the same plants in response to drought, elevated [CO2], and N and P fertilization around the world, and contrasted the results within those obtained with a general database (838 reports and 14,772 observations) that analyzed the changes in N and P concentrations in leaves and/or roots of plants submitted to the commented global change drivers. At global level, elevated [CO2] decreased N concentrations in leaves and roots and decreased N:P ratio in roots but no in leaves, but was not related to P concentration changes. However, the response differed among vegetation types. In temperate forests, elevated [CO2] was related with lower N concentrations in leaves but not in roots, whereas in crops, the contrary patterns were observed. Elevated [CO2] decreased N concentrations in leaves and roots in tundra plants, whereas not clear relationships were observed in temperate grasslands. However, when elevated [CO2] and N fertilization coincided, leaves had lower N concentrations, whereas root had higher N concentrations suggesting that more nutrients will be allocated to roots to improve uptake of the soil resources not directly provided by the global change drivers. N fertilization and drought increased foliar and root N concentrations while the effects on P concentrations were less clear. The changes in N and P allocation to leaves and root, especially those occurring in opposite direction between them have the capacity to differentially affect above‐ and belowground ecosystem functions, such as litter mineralization and above‐ and belowground food webs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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".