Local Habitat Filtering Shapes Microbial Community Structure in Four Closely Spaced Lakes in the High Arctic
Bibliographic record
Abstract
Arctic lakes are experiencing increasingly shorter periods of ice cover due to accelerated warming at northern high latitudes. Given the control of ice cover thickness and duration over many limnological processes, these changes will have pervasive effects. However, due to their remote and extreme locations even first-order data on lake ecology is lacking for many ecosystems. The aim of this study was to characterize and compare the microbial communities of four closely spaced lakes in Stuckberry Valley (northern Ellesmere Island, Canadian Arctic Archipelago), in the coastal margin zone of the Last Ice Area, that differed in their physicochemical, morphological and catchment characteristics. We performed high-throughput amplicon sequencing of the V4 16S rRNA gene to provide inter- and intra-lake comparisons. Two deep (>25 m) and mostly oxygenated lakes showed highly similar community assemblages that were distinct from those of two shallower lakes (<10 m) with anoxic bottom waters. Proteobacteria, Verrucomicrobia, and Planctomycetes were the major phyla present in the four water bodies. One deep lake contained elevated proportions of Cyanobacteria and Thaumarchaeota that distinguished it from the others, while the shallow lakes had abundant communities of predatory bacteria, as well as microbes in their bottom waters that contribute to sulfur and methane cycles. Despite their proximity, our data suggest that local habitat filtering is the primary determinant of microbial diversity in these systems. This study provides the first detailed examination of the microbial assemblages of the Stuckberry lakes system, resulting in new insights into the microbial ecology of the High Arctic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".