Declining fungal diversity in Arctic freshwaters along a permafrost thaw gradient
Bibliographic record
Abstract
Climate change-driven permafrost thaw has a strong influence on pan-Arctic regions, via, for example, the formation of thermokarst ponds. These ponds are hotspots of microbial carbon cycling and greenhouse gas production, and efforts have been put on disentangling the role of bacteria and archaea in recycling the increasing amounts of carbon arriving to the ponds from degrading watersheds. However, despite the well-established role of fungi in carbon cycling in the terrestrial environments, the interactions between permafrost thaw and fungal communities in Arctic freshwaters have remained unknown. We integrated data from 60 ponds in Arctic hydro-ecosystems, representing a gradient of permafrost integrity and spanning over five regions, namely Alaska, Greenland, Canada, Sweden, and Western Siberia. The results revealed that differences in pH and organic matter quality and availability were linked to distinct fungal community compositions and that a large fraction of the community represented unknown fungal phyla. Results display a 16%-19% decrease in fungal diversity, assessed by beta diversity, across ponds in landscapes with more degraded permafrost. At the same time, sites with similar carbon quality shared more species, aligning a shift in species composition with the quality and availability of terrestrial dissolved organic matter. We demonstrate that the degradation of permafrost has a strong negative impact on aquatic fungal diversity, likely via interactions with the carbon pool released from ancient deposits. This is expected to have implications for carbon cycling and climate feedback loops in the rapidly warming Arctic.
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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 | 0.002 | 0.001 |
| 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".