Fungal community composition varies spatially in a commercial potato field in response to soil properties and topographic features
Bibliographic record
Abstract
The factors influencing the spatial distribution of fungal communities are commonly examined over large spatial scales but not at smaller scales. Given this, the extent to which soil properties and topographic features contribute to the diversity and distribution of fungal communities in an agricultural field needs to be further explored. We investigated the spatial distribution of soil fungal community composition from an ∼1100 m long transect with 83 sampling points in a commercial potato field with a rolling landform. The relative abundance of Ascomycota, Basidiomycota, and Mortierellomycota showed medium to strong spatial dependence with an autocorrelation range varying from ∼43 to 92 m, similar to the autocorrelation range of soil properties and topographic features. Most of the variability in fungal and saprotrophic community composition was explained by soil properties (15% and 11%, respectively) and spatial distance (16% and 15%, respectively) while topographic features contributed 8% and 5% of variability to total fungi and saprotrophic community composition, respectively. The fungal and saprotrophic community compositions were correlated with soil organic carbon, pH, and slope curvature, however, richness and Pielou’s evenness of the fungal communities and fungal biomass were not correlated with soil properties or topographic features. The results suggest that the spatial variation in fungal and saprotrophic community composition in response to soil properties and topographic features in this agricultural landscape was due to differences in assemblages of fungal amplicon sequence variants (ASVs) but not in differences in the number of fungal ASVs or fungal biomass measured using phospholipids fatty acids.
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 | 0.000 | 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 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".