Freshwater Microbial Eukaryotic Core Communities, Open-Water and Under-Ice Specialists in Southern Victoria Island Lakes (Ekaluktutiak, NU, Canada)
Bibliographic record
Abstract
Across much of the Arctic, lakes and ponds dominate the landscape. Starting in late September, the lakes are covered in ice, with ice persisting well into June or early July. In summer, the lakes are highly productive, supporting waterfowl and fish populations. However, little is known about the diversity and ecology of microscopic life in the lakes that influence biogeochemical cycles and contribute to ecosystem services. Even less is known about the prevalence of species that are characteristic of the seasons or whether some species persist year-round under both ice cover and summer open-water conditions. To begin to address these knowledge gaps, we sampled 10 morphometrically diverse lakes in the region of Ekaluktutiak (Cambridge Bay), on southern Victoria Island (NU, Canada). We focused on Greiner Lake, the lakes connected to it, isolated ponds, and two nearby larger lakes outside the Greiner watershed. The largest lakes sampled were Tahiryuaq (Ferguson Lake) and the nearby Spawning Lake, which support commercial sea-run Arctic char (Salvelinus alpinus) fisheries. Samples for nucleic acids were collected from the lakes along with limnological metadata. Microbial eukaryotes were identified with high-throughput amplicon sequencing targeting the V4 region of the 18S rRNA gene. Ciliates, dinoflagellates, chrysophytes, and cryptophytes dominated the lake assemblages. A Bray–Curtis dissimilarity matrix separated communities into under-ice and open-water clusters, with additional separation by superficial lake area. In all, 133 operational taxonomic units (OTUs) occurred either in all under-ice or all open-water samples and were considered “core” microbial species or ecotypes. These were further characterized as seasonal indicators. Ten of the OTUs were characteristic of all lakes and all seasons sampled. Eight of these were cryptophytes, suggesting diverse functional capacity within the lineage. The core open-water indicators were mostly chrysophytes, with a few ciliates and uncharacterized Cercozoa, suggesting that summer communities are mixotrophic with contributions by heterotrophic taxa. The core under-ice indicators included a dozen ciliates along with chrysophytes, cryptomonads, and dinoflagellates, indicating a more heterotrophic community augmented by mixotrophic taxa in winter.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".