Uncovering the complex relationship between plant diversity, soil properties, and mycorrhizal inoculum potential in threatened Miombo woodlands
Bibliographic record
Abstract
Abstract Arbuscular mycorrhizal fungi (AMF) are key members of soil microbial communities and play a key role in regulating ecosystem processes. However, the mutual interdependence of plants and AMF in threatened Miombo woodlands has not yet been fully elucidated. In this study, we investigated the relationship between plant ecological indicators and soil mycorrhizal inoculum potential (MIP) in the Miombo forest fallow of Haut‐Katanga, Democratic Republic of Congo, and identified plant species that positively influence soil MIP. We conducted a floristic inventory on 32 plots and collected soil cores for physicochemical and AMF characterization. Trap cultures using Crotalaria juncea seedlings were performed to determine the soil MIP. We also tested the AMF colonization status of randomly collected living roots of mature woody and dominant herbaceous species to identify explanatory variables for MIP. Our results showed that MIP was twice as high in silty clay soils (63.13%) than in clay soils (30%). Furthermore, MIP increased accordingly with the relative abundance of a group of woody and herbaceous species (e.g., Albizia adianthifolia , Baphia bequaertii , and Setaria pumila ) and decreased with others (e.g., Combretum collinum , Harungana madagascariensis and Hyparrhenia diplandra ). Linear regressions showed that MIP increased significantly with the specific richness of woody species identified as indicators and with the amount of annual herbs. Woody legumes with high root colonization by AMF appeared to be refuge plants, and primary AMF dispersal vectors, increasing soil MIP. This study provides baseline data that can be used to formulate ecological restoration strategies, including soil and vegetation protection.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".