Drought-induced shifts in cowpea rhizoplane bacterial communities across different vegetative and reproductive stages
Bibliographic record
Abstract
• Genotype, drought, and growth stages shaped cowpea rhizoplane bacterial diversity. • Drought decreased microbial activity but increased the utilization of complex carbon sources. • Genotypic-specific recruitment of bacterial taxa observed under drought. • Soil enzymes and plant physiological traits influence microbial community structure. • Random forest and SHAP values identified key drought-enriched bacterial biomarkers. The increasing prevalence of drought poses significant challenges to global food security, necessitating a deeper understanding of plant-microbiome interactions which help crop production. This study investigated the dynamics of drought stress-induced changes in rhizosphere-associated bacterial communities of two cowpea ( Vigna unguiculata L.) genotypes (EpicSelect4 and UCR369) across four growth stages. Community-level physiological profiling using Biolog EcoPlate analysis revealed that drought reduced rhizosphere microbial metabolic activity (carbon substrate utilization) in both genotypes, but UCR369 maintained higher metabolic capability than EpicSelect4 across growth stages. Further, integration of amplicon metagenomics and physiological data showed that drought significantly altered rhizoplane bacterial communities in cowpea, with distinct genotype-specific responses. There was a decline in Alpha diversity under drought, while community composition shifted based on genotype. Beta diversity results revealed that genotype and drought significantly influenced microbial community structure across growth stages. Proteobacteria dominated the root zone of the EpicSelect4 genotype, while UCR369 showed an increase in Actinobacteria under drought conditions. Redundancy analysis revealed that soil enzyme activities (β-glucosidase and N-acetyl-glucosaminidase) and physiological traits werecorrelated significantly with microbial community shifts. Interpretable machine learning approach identified Actinobacteriota and Cyanobacteria as the key biomarkers enriched under drought, with genera such as Streptomyces and Ensifer potentially contributing to drought tolerance. The Random Forest model coupled with SHapley Additive exPlanations (SHAP) values demonstrated high predictive accuracy for identifying drought-related biomarkers, aligning with DeSeq2 analysis results. These models provided insights into the potential contributions of specific microbial taxa to cowpea drought tolerance, offering a promising avenue for developing microbiome-based strategies to improve crop resilience and sustainability under drought conditions.
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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 | 0.000 | 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) | 0.001 | 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".