Insights into dryland biocrust microbiome: geography, soil depth and crust type affect biocrust microbial communities and networks in Mojave Desert, USA
Bibliographic record
Abstract
Biocrusts are the living skin of drylands, comprising diverse microbial communities that are essential to desert ecosystems. Despite there being extensive knowledge on biocrust ecosystem functions and lichen and moss biodiversity, little is known about factors structuring diversity among their microbial communities. We used amplicon-based metabarcode sequencing to survey microbial communities from biocrust surface and subsurface soils at four sites located within the Mojave Desert. Five biocrust types were examined: Light-algal/Cyanobacteria, Cyanolichen, Green-algal lichen, Smooth-moss and Rough-moss crust types. Microbial diversity in biocrusts was structured by several characteristics: (i) central versus southern Mojave sites displayed different community signatures, (ii) indicator taxa of plant-associated fungi (plant pathogens and wood saprotrophs) were identified at each site, (iii) surface and subsurface microbial communities were distinct and (iv) crust types had distinct indicator taxa. Network analysis ranked bacteria-bacteria interactions as the most connected of all within-domain and cross-domain interaction networks in biocrust surface samples. Actinobacteria, Proteobacteria, Cyanobacteria and Ascomycota functioned as hubs among all phyla. The bacteria Pseudonocardia sp. (Pseudonocardiales, Actinobacteria) and fungus Alternaria sp. (Pleosporales, Ascomycota) were the most connected had the highest node degree. Our findings provide crucial insights for dryland microbial community ecology, conservation and sustainable management.
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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 | 0.000 | 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".