Assessing the responses of <i>Sphagnum</i> micro-eukaryotes to climate changes using high throughput sequencing
Bibliographic record
Abstract
Current projections suggest that climate warming will be accompanied by more frequent and severe drought events. Peatlands store ca. one third of the world’s soil organic carbon. Warming and drought may cause peatlands to become carbon sources through stimulation of microbial activity increasing ecosystem respiration, with positive feedback effect on global warming. Micro-eukaryotes play a key role in the carbon cycle through food web interactions and therefore, alterations in their community structure and diversity may affect ecosystem functioning and could reflect these changes. We assessed the diversity and community composition of Sphagnum-associated eukaryotic microorganisms inhabiting peatlands and their response to experimental drought and warming using high throughput sequencing of environmental DNA. Under drier conditions, micro-eukaryotic diversity decreased, the relative abundance of autotrophs increased and that of osmotrophs (including Fungi and Peronosporomycetes) decreased. Furthermore, we identified climate change indicators that could be used as early indicators of change in peatland microbial communities and ecosystem functioning. The changes we observed indicate a shift towards a more “terrestrial” community in response to drought, in line with observed changes in the functioning of the ecosystem.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".