Expanding Antarctic biogeography: microbial ecology of Antarctic island soils
Bibliographic record
Abstract
The majority of islands surrounding the Antarctic continent are poorly characterized in terms of microbial macroecology due to their remote locations, geographical isolation and access difficulties. The 2016/2017 Antarctic Circumnavigation Expedition (ACE) provided unprecedented access to a number of these islands. In the present study we use metagenomic methods to investigate the microbial ecology of soil samples recovered from 11 circum‐Antarctic islands as part of ACE, and to investigate the functional potential of their soil microbial communities. Comparisons of the prokaryote and lower eukaryote phylogenetic compositions of the soil communities indicated that the various islands harbored spatially distinct microbiomes with limited overlap. In particular, we identified a high prevalence of lichen‐associated fungal taxa in the soils, suggesting that terrestrial lichens may be one of the key drivers of soil microbial ecology on these islands. Differential abundance and redundancy analyses suggested that these soil microbial communities are also strongly shaped by multiple abiotic factors, including soil pH and average annual temperatures. Most importantly, we demonstrate that the islands sampled in this study can be clustered into three distinct large‐scale biogeographical regions in a conservation context, the sub‐, Maritime and Continental Antarctic, which are distinct in both environmental conditions and microbial ecology, but are consistent with the widely‐used regionalization applied to multicellular Antarctic terrestrial organisms. Functional profiling of the island soil metagenomes from these three broad biogeographical regions also suggested a degree of functional differentiation, reflecting their distinct microbial ecologies. Taken together, these results represent the most extensive characterization of the microbial ecology of Antarctic island soils to date.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".