Permafrost thaw stimulates primary producers but has a moderate effect on primary consumers in subarctic ponds
Bibliographic record
Abstract
Abstract Frozen tundra soils hold one of the Earth's largest pools of organic carbon. Climate warming and the associated permafrost thaw release a large fraction of this carbon into circumpolar lakes, inducing extreme browning that fuels the heterotrophic microbial food web. How this permafrost carbon affects organisms higher in the food chain remains unknown. Using dissolved organic matter properties, total phosphorus, chlorophyll a , fatty acids, and stable isotopes, we investigated the influence of thawing permafrost on primary producers and primary consumers of the planktonic food web. We sampled four subarctic thaw ponds that were affected by permafrost carbon and another four ponds that were not. Our results highlight the stimulating influence of eroding and degrading ice‐rich permafrost on nutrients and planktonic algae. Relative to the non‐thaw ponds, the permafrost thaw‐influenced freshwaters had higher total phosphorus concentrations (14.8 vs. 70.4 µg/L, respectively). This in turn led to a higher chlorophyll a (2.7 vs. 45.2 µg/L) and seston omega‐3 fatty acid concentrations (7.3 vs. 53.5 µg/L) despite significantly reduced light for primary production. Differences between the thaw and non‐thaw ponds were less marked at the primary consumer level. Daphnia pulex , which dominated the crustacean zooplankton community, did not respond to the higher omega‐3 availability in the thaw ponds but rather assimilated the high‐quality fatty acids equally in all ponds, possibly because their metabolic needs were already saturated. However, some lower quality terrestrial carbon compounds from permafrost ended up in the D. pulex body mass, resulting in a median allochthony of 18% based on fatty acid mixing model; non‐thaw ponds had median allochthony mixing model estimates of 8%. The high availability of algal resources seemed to prevent extensive zooplankton allochthony in subarctic thaw ponds.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".