Deep Soil Layers of Drought-Exposed Forests Harbor Poorly Known Bacterial and Fungal Communities
Bibliographic record
Abstract
Soil microorganisms such as bacteria and fungi play important roles in the biogeochemical cycling of soil nutrients, because they act as decomposers or are mutualistic or antagonistic symbionts, thereby influencing plant growth and health. In the present study, we investigated the vertical distribution of the soil microbiome to a depth of 2 m in Swiss drought-exposed forests of European beech and oaks on calcareous bedrock. We aimed to disentangle the effects of soil depth, tree (beech, oak), and substrate (soil, roots) on microbial abundance, diversity, and community structure. With increasing soil depth, organic carbon, nitrogen, and clay content decreased significantly. Similarly, fine root biomass, microbial biomass (DNA content, fungal abundance), and microbial alpha-diversity decreased and were consequently significantly related to these physicochemical parameters. In contrast, bacterial abundance tended to increase with soil depth, and the bacteria to fungi ratio increased significantly with greater depth. Tree species was only significantly related to the fungal Shannon index but not to the bacterial Shannon index. Microbial community analyses revealed that bacterial and fungal communities varied significantly across the soil layers, more strongly for bacteria than for fungi. Both communities were also significantly affected by tree species and substrate. In deep soil layers, poorly known bacterial taxa from Nitrospirae, Chloroflexi, Rokubacteria, Gemmatimonadetes , Firmicutes and GAL 15 were overrepresented. Furthermore, archaeal phyla such as Thaumarchaeota and Euryarchaeota were more abundant in subsoils than topsoils. Fungal taxa that were predominantly found in deep soil layers belong to the ectomycorrhizal Boletus luridus and Hydnum vesterholtii . Both taxa are reported for the first time in such deep soil layers. Saprotrophic fungal taxa predominantly recorded in deep soil layers were unknown species of Xylaria . Finally, our results show that the microbial community structure found in fine roots was well represented in the bulk soil. Overall, we recorded poorly known bacterial and archaeal phyla, as well as ectomycorrhizal fungi that were not previously known to colonize deep soil layers. Our study contributes to an integrated perspective on the vertical distribution of the soil microbiome at a fine spatial scale in drought-exposed forests.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".