Global Diversity of Desert Hypolithic Cyanobacteria
Bibliographic record
Abstract
Global patterns in diversity were estimated for cyanobacteria-dominated hypolithic communities that colonise ventral surfaces of quartz stones and are common in desert environments. A total of 64 hypolithic communities were recovered from deserts on every continent plus a tropical moisture sufficient location. Community diversity was estimated using a combined t-RFLP fingerprinting and high throughput sequencing approach. The t-RFLP analysis revealed desert communities were different from the single non-desert location. A striking pattern also emerged where Antarctic desert communities were clearly distinct from all other deserts. Some overlap in community similarity occurred for hot, cold and tundra deserts. A further observation was that the producer-consumer ratio displayed a significant negative correlation with growing season, such that shorter growing seasons supported communities with greater abundance of producers, and this pattern was independent of macroclimate. High-throughput sequencing of 16S rRNA and nifH genes from four representative samples validated the t-RFLP study and revealed patterns of taxonomic and putative diazotrophic diversity for desert communities from the Taklimakan Desert, Tibetan Plateau, Canadian Arctic and Antarctic. All communities were dominated by cyanobacteria and among these 21 taxa were potentially endemic to any given desert location. Some others occurred in all but the most extreme hot and polar deserts suggesting they were relatively less well adapted to environmental stress. The t-RFLP and sequencing data revealed the two most abundant cyanobacterial taxa were Phormidium in Antarctic and Tibetan deserts and Chroococcidiopsis in hot and cold deserts. The Arctic tundra displayed a more heterogenous cyanobacterial assemblage and this was attributed to the maritime-influenced sampling location. The most abundant heterotrophic taxa were ubiquitous among samples and belonged to the Acidobacteria, Actinobacteria, Bacteroidetes and Proteobacteria. Sequencing using nitrogenase gene-specific primers revealed all putative diazotrophs were Proteobacteria of the orders Burkholderiales, Rhizobiales and Rhodospirillales. We envisage cyanobacterial carbon input to the system is accompanied by nitrogen fixation largely from non-cyanobacterial taxa. Overall the results indicate desert hypoliths worldwide are dominated by cyanobacteria and that growing season is a useful predictor of their abundance. Differences in cyanobacterial taxa encountered may reflect their adaptation to different moisture availability regimes in polar and non-polar deserts.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".