Patterns and Mechanisms of Nutrient Resorption in Plants
Bibliographic record
Abstract
Nutrient resorption (NR) plays a key role in the nutrient conservation of plants. However, a fundamental understanding of the mechanisms that control NR remains limited. In this review, we examine how intrinsic controls (e.g., genetic variability and plant development) and extrinsic environmental controls (e.g., climate and soil fertility) influence NR. We also examined conceptual NR advances, mass loss correction, measurement in non-leaf plant tissues for whole-plant nutrient budget accounting, and the use of stoichiometric ratios in place of individual elements. Nutrient resorption from senescing leaves is greater than that from stems/culms or roots. Nutrients resorbed from stems and roots in woody plants are lower than in non-woody plants. Deciduous plants are more efficient in resorbing leaf nutrients prior to senescence than are evergreen plants. Furthermore, reproductive efforts tend to increase NR. Along a latitudinal gradient of terrestrial biomes, nitrogen resorption efficiency decreases and phosphorus resorption efficiency increases with increasing temperature and precipitation; however, latitudinal patterns reflect the influences of several coupling factors such as genetic variation, climate, soil, and disturbance history. Nutrient fertilization experiments have demonstrated that increased soil fertility reduces NR. The inquiries into the impacts of ongoing climate change on NR are still at a nascent stage. Future NR studies are needed to better understand the independent effects of a wide range of genetic variation, plant development, and environment, and possibly the different responses of plants to environmental change; particularly elevated atmospheric CO2 concentrations and global warming.
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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 | 0.000 |
| 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 | 0.001 |
| 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".