Soil Salinity and pH Drive Soil Bacterial Community Composition and Diversity Along a Lateritic Slope in the Avon River Critical Zone Observatory, Western Australia
Bibliographic record
Abstract
Soils are crucial in regulating ecosystem processes, such as nutrient cycling, and supporting plant growth. To a large extent, these functions are carried out by highly diverse and dynamic soil microbiomes that are in turn governed by numerous environmental factors including weathering profile and vegetation. In this study, we investigate geophysical and vegetation effects on the microbial communities of iron-rich lateritic soils in the highly weathered landscapes of Western Australia (WA). The study site was a lateritic hillslope in southwestern Australia, where gradual erosion of the duricrust has resulted in the exposure of the different weathering zones. High-throughput amplicon sequencing of the 16S rRNA gene was used to investigate soil bacterial community diversity, composition and functioning. We predicted that shifts in the microbial community would reflect variations in certain edaphic properties associated with the different layers of the lateritic profile and vegetation cover. Our results supported this hypothesis, with electrical conductivity, pH and clay content having the strongest correlation with beta diversity, and many of the differentially abundant taxa belonging to the phyla Actinobacteria and Proteobacteria. Soil water repellence, which is associated with eucalyptus vegetation, also affected beta diversity. This enhanced understanding of the natural system could help to improve future crop management in WA since the physicochemical properties of the agricultural soils in this region are inherited from laterites via the weathering and pedogenesis processes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".