Elevation-related climate trends dominate fungal co-occurrence patterns on Mt. Norikura, Japan
Bibliographic record
Abstract
Abstract Although many studies have explored patterns of fungal community diversity and composition along various environmental gradients, the trends of co-occurrence networks across similar gradients remain elusive. Here, we constructed co-occurrence networks for fungal community along a 2300 m elevation gradient on Mt Norikura, Japan, hypothesizing a progressive decline in network connectivity with elevation due to reduced niche differentiation caused by declining temperature and ecosystem productivity. Results agreed broadly with predictions, with an overall decline in network connectivity with elevation for all fungi and the high abundance phyla. However, trends were not uniform with elevation, most decline in connectivity occurred between 700 m and 1500 m elevation, remaining relatively stable above this. Temperature and precipitation dominated variation in network properties, with lower mean annual temperature (MAT) and higher mean annual precipitation (MAP) at higher elevations giving less network connectivity, largely through indirect effects on soil properties. Among keystone taxa that played crucial roles in network structure, the variation in abundance along the elevation gradient was also controlled by climate and also pH. Our findings point to a major role of climate gradients in mid-latitude mountain areas in controlling network connectivity. Given the importance of the orographic precipitation effect, microbial community trends seen along elevation gradients might not be mirrored by those seen along latitudinal temperature gradients. Importance Although many studies have explored patterns of fungal community diversity and composition along various environmental gradients, it is unclear how the topological structure of co-occurrence networks shifts across environmental gradients. In this study, we found that the connectivity of the fungal community decreased with increasing elevation, and that climate was the dominant factor regulating co-occurrence patterns, apparently acting indirectly through soil characteristics. Assemblages of keystone taxa playing crucial roles in network structure varied along the elevation gradient and were also largely controlled by climate. Our results provide insight into the shift of soil fungal community co-occurrence structure along elevational gradients, and possible driving mechanisms behind this. Graphic abstract
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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 | 0.001 |
| 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".