Insights into dryland biocrust microbiome: geography, soil depth and crust type affect biocrust microbial communities and networks in Mojave Desert, USA
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 it