Variations in bacterial communities during foliar litter decomposition in the winter and growing seasons in an alpine forest of the eastern Tibetan Plateau
Bibliographic record
Abstract
Bacterial communities are the primary engineers during litter decomposition and related material cycling, and they can be strongly controlled by seasonal changes in temperature and other environmental factors. However, limited information is available on changes in the bacterial community from winter to the growing season as litter decomposition proceeds in cold climates. Here, we investigated the abundance and structure of bacterial communities using real-time quantitative PCR and denaturing gradient gel electrophoresis (DGGE) during a 2-year field study of the decomposition of litter of 4 species in the winter and growing seasons of an alpine forest of the eastern Tibetan Plateau. The abundance of the bacterial 16S rRNA gene was relatively high during decomposition of cypress and birch litter in the first winter, but for the other litters 16S rRNA abundance during both winters was significantly lower than during the following growing season. A large number of bands were observed on the DGGE gels, and their intensities and number from the winter samples were lower than those from the growing season during the 2-year decomposition experiment. Eighty-nine sequences from the bands of bacteria that had been cut from the DGGE gels were affiliated with 10 distinct classes of bacteria and an unknown group. A redundancy analysis indicated that the moisture, mass loss, and elemental content (e.g., C, N, and P) of the litter significantly affected the bacterial communities. Collectively, the results suggest that uneven seasonal changes in climate regulate bacterial communities and other decomposers, thus affecting their contribution to litter decomposition processes in the alpine forest.
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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 | 0.001 | 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) | 0.000 | 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".