The Genomic Capabilities of Microbial Communities Track Seasonal Variation in Environmental Conditions of Arctic Lagoons
Bibliographic record
Abstract
In contrast to temperate systems, Arctic lagoons that span the Alaska Beaufort Sea coast face extreme seasonality. Nine months of ice cover up to ∼1.7 m thick is followed by a spring thaw that introduces an enormous pulse of freshwater, nutrients, and organic matter into these lagoons over a relatively brief 2-3 week period. Prokaryotic communities link these subsidies to lagoon food webs through nutrient uptake, heterotrophic production, and other biogeochemical processes, but little is known about how the genomic capabilities of these communities respond to seasonal variability. Replicate water samples from two lagoons and one coastal site near Kaktovik, AK were collected in April (full ice cover), June (ice break up), and August (open water) to represent winter, spring, and summer, respectively. Samples were size fractionated to distinguish free-living and particle-attached microbial communities. Multivariate analysis of metagenomes indicated that seasonal variability in gene abundances was greater than variability between size fractions and sites, and that June differed significantly from the other months. Spring (June) gene abundances reflected the high input of watershed-sourced nutrients and organic matter via spring thaw, featuring indicator genes for denitrification possibly linked to greater organic carbon availability, and genes for processing phytoplankton-derived organic matter associated with spring blooms. Summer featured fewer indicator genes, but had increased abundances of anoxygenic photosynthesis genes, possibly associated with elevated light availability. Winter (April) gene abundances suggested low energy inputs and autotrophic bacterial metabolism, featuring indicator genes for chemoautotrophic carbon fixation, methane metabolism, and nitrification. Winter indicator genes for nitrification belonged to Thaumarchaeota and Nitrosomonadales, suggesting these organisms play an important role in oxidizing ammonium during the under-ice period. This study shows that high latitude estuarine microbial assemblages shift metabolic capabilities as they change phylogenetic composition between these extreme seasons, providing evidence that these communities may be resilient to large hydrological events in a rapidly changing 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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".