Unveiling the influence of seawater intrusion and vegetation type on coastal arbuscular mycorrhizal fungal communities in China
Bibliographic record
Abstract
Abstract Continuously rising sea levels pose severe threats to the diversity and stability of coastal ecosystems. However, the responses of endospheric and rhizospheric arbuscular mycorrhizal (AM) fungal communities to accelerated seawater intrusion and changing vegetation types unfortunately remains unknown. Such insights will assist in revealing the structure of AM fungi across different tidal zones, enabling the development of effective measures to conserve biodiversity. Here, we investigated changes in endospheric and rhizospheric AM fungal communities associated with four types of coastal vegetation in Dafeng City, Jiangsu Province, China, in response to sea level variations by analyzing their characteristics and connections to selected environmental factors. It was found that the AM fungal communities of coastal vegetation differed significantly in their compositions, diversity, and stability (the resilience or resistance of a community). Despite the responses of endospheric and rhizospheric AM fungi to soil environmental factors, the soil electrical conductivity, cation exchange content, and inorganic carbon were identified as core elements that influenced the characteristics of AM fungal communities between different coastal vegetation species. Lastly, the major endospheric and rhizospheric species of AM fungi were important predictors that explained much of the variation in the soil environment. Consequently, the dynamics of AM fungal communities were correlated with the plant–soil system under different conditions. Apart from the coastal vegetation species, the characteristics of AM fungal communities can also be driven by high salinity and the inorganic carbon content induced by the intrusion of seawater.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".