Cultivable bacterial diversity along the altitudinal zonation and vegetation range of tropical Eastern Himalaya
Bibliographic record
Abstract
The Northeastern part of India sprawls over an area of 262 379km2 in the Eastern Himalayan range. This constitutes a biodiversity hotspot with high levels of biodiversity and endemism; unfortunately, is also a poorly known area, especially on its microbial diversity. In this study, we assessed cultivable soil bacterial diversity and distribution from lowlands to highlands (34 to 3 990m.a.s.l.). Soil physico-chemical parameters and forest types across the different altitudes were characterized and correlated with bacterial distribution and diversity. Microbes from the soil samples were grown in Nutrient, Muller Hinton and Luria-Bertani agar plates and were initially characterized using biochemical methods. Parameters like dehydrogenase and urease activities, temperature, moisture content, pH, carbon content, bulk density of the sampled soil were measured for each site. Representative isolates were also subjected to 16S rDNA sequence analysis. A total of 155 cultivable bacterial isolates were characterized which were analyzed for richness, evenness and diversity indices. The tropical and sub-tropical forests supported higher bacterial diversity compared to temperate pine, temperate conifer, and sub-alpine rhododendron forests. The 16S rRNA phylogenetic analysis revealed that Firmicutes was the most common group followed by Proteobacreria and Bacteroidetes. Species belonging to the genera Bacillus and Pseudomonas were the most abundant. Bacterial CFU showed positive but insignificant correlation with soil parameters like pH (r=0.208), soil temperature (r=0.303), ambient temperature (r=0.443), soil carbon content (r=0.525), soil bulk density (r=0.268), soil urease (r=0.549) and soil dehydrogenase (r=0.492). Altitude (r=-0.561) and soil moisture content (r=-0.051) showed negative correlation. Altitudinal gradient along with the vegetation and soil physico-chemical parameters were found to influence bacterial diversity and distribution. This study points out that this is a biome with a vast reservoir of bacteria which decrease with increasing altitudes, and highlights the microbiological importance of the poorly studied Eastern Himalayan range, justifying efforts to explore the prevalence of novel species in the biome.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".