Forest Type and Climate Outweigh Soil Bank in Shaping Dynamic Changes in Macrofungal Diversity in the Ancient Tree Park of Northeast China
Bibliographic record
Abstract
The community structure of macrofungi is influenced by multiple complex factors, including climate, soil, vegetation, and human activities, making it challenging to discern their individual contributions. To investigate the dynamic changes in macrofungal diversity in an Ancient Tree Park located in Northeast China and explore the factors influencing this change, we collected 1007 macrofungi specimens from different habitats within the park and identified 210 distinct fungal species using morphological characteristics and ITS sequencing. The species were classified into 2 phyla, 6 classes, 18 orders, 55 families, and 94 genera. We found macrofungal compositions among different forest types, with the mixed forest displaying the highest richness and diversity. Climatic factors, particularly rainfall and temperature, positively influenced macrofungal species richness and abundance. Additionally, by analyzing the soil fungal community structure and comparing aboveground macrofungi with soil fungi in this small-scale survey, we found that the soil fungal bank is not the main factor leading to changes in the macrofungal community structure, as compared to the influence of climate factors and forest types. Our findings provide valuable insights into the dynamic nature of macrofungal diversity in the Ancient Tree Park, highlighting the influence of climate and forest type.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".