Fungal pathogens pose a potential threat to animal and plant health in desertified and pika-burrowed alpine meadows on the Tibetan Plateau
Bibliographic record
Abstract
Intact Tibetan meadows provide significant defense against soil-borne pathogen dispersal. However, dramatic meadow degradation has been observed due to climate change and pika damage, but their impacts on soil-borne pathogens are still unclear. With approximately 40% of the world's population living in Tibetan Plateau and its downstream watersheds, this lack of knowledge should be of great concern. Here, we used Illumina amplicon sequencing to characterize the changes in potential human, domestic animal, plant, and zoonotic bacterial and fungal pathogens in nondegraded, desertified, and pika-burrowed meadows. The relative abundance of bacterial domestic animal pathogens and zoonotic pathogens were significantly increased by desertification. Pika burrowing significantly increased the relative abundance of bacterial human pathogens and zoonotic pathogens. The species richness and relative abundance of fungal pathogens was significantly increased by desertification and pika burrowing. Accordingly, fungal plant and animal pathogens categorized by FUNGuid significantly increased in desertified and pika-burrowed meadows. Soil chemical and plant properties explained 38% and 64% of the bacterial and fungal pathogen community variance, respectively. Therefore, our study indicates for the first time that both alpine meadow desertification and pika burrowing could potentially increase infectious disease risks in the alpine ecosystem, especially for fungal diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".