Arbuscular Mycorrhizal Fungi Alleviate Salt Stress Damage by Coordinating Nitrogen Utilization in Leaves of Different Species
Bibliographic record
Abstract
With the intensification of coastal erosion, damage to coastal shelterbelts has gradually increased. Arbuscular mycorrhizal fungi (AMF) can improve the salinity tolerance and productivity of plants in saline–alkali soils using various strategies including nutrient uptake, osmotic regulation, soil shaping, etc. Thus, the application of AMF to alleviate the impacts of salinization for these shelterbelts has become a research hotspot. For this study, we investigated the effects of inoculation with different AMF strains on the growth and nitrogen (N) utilization of Gleditsia sinensis Lam. and Zelkova serrata (Thunb.) Makino leaves under different salt concentrations. As the salt concentration increased, the growth rates and leaf areas of the autoclaved AMF inoculant (CK) treatment exhibited a decreasing trend for both G. sinensis and Z. serrata, while Funneliformis mosseae (FM) and Corymbiglomus tortuosum (CT) treatments weakened this trend. Between them, on average, FM increased the G. sinensis height growth rate by 396.9%, ground diameter growth rate by 99.0%, and Z. serrata leaf area by 29.1%. At a salt concentration of 150 mM, the chlorophyll content and nitrate reductase activities of leaves under the FM treatment for both tree species were significantly higher than for CK, with an average increase in chlorophyll content of 106.1% and nitrate reductase activities by 74.6%. Moreover, the AMF inoculation significantly reduced the leaf N content and photosynthetic N-use efficiency of G. sinensis in contrast to Z. serrata. Further, in contrast to G. sinensis, the photosynthetic N-use efficiency was significantly positively correlated with the growth rate and leaf area of Z. serrata. Meanwhile, the nitrate reductase activity contributed most to the growth rate and leaf area of Z. serrata. Our results suggest that the issues with coastal shelterbelts might be effectively alleviated through appropriate AMF–plant combinations, which is of great significance for the optimization of forestry production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".