Environmental influences on microbial communities of lake whitefish, cisco, and Arctic char on and surrounding King William Island, Nunavut
Bibliographic record
Abstract
Partnered with the Nunavut community of Gjoa Haven on King William Island, a large-scale Genome Canada project, the Towards a Sustainable Fishery for Nunavummiut (TSFN) project endeavoured to integrate Inuit traditional knowledge and practices with genomic and microbial analyses to assess the sustainability and health of the Coregonus species complex (CSC) and Arctic char (Salvelinus alpinus) fisheries. Encompassed within the goals of the project, fish health was assessed based on microbial diversity and condition factor (K) of the sampled fish. In this region, sampled CSC, which included lake whitefish (C. clupeaformis) as well as two cisco species (C. autumnalis and C. sardinella) and Arctic char displayed anadromy, transitioning annually from the ocean to freshwater lakes and rivers. Inuit fishers collected samples from the ocean, rivers, and lakes in different seasons, and microbiomes from these salmonids were characterized with respect to changing seasonal habitats. Skin- and intestine-associated microbiomes were characterized through amplification of 16S rRNA gene fragments. Overall, lake whitefish, ciscoes, and Arctic char skin-associated microbiota grouped separately in ordination based on salinity while cisco and Arctic char grouped based on seasonal habitat. Higher Shannon diversity in autumn and spring freshwater habitats suggested a transitional state in the autumn riverine and spring lacustrine environments. Core microbiomes, representing taxa found in at least 50% of samples, were also identified within seasonal habitats. Comparison of skin- and intestine-associated core microbiota showed differences in composition across seasonal habitats. Condition factor (K) remained consistent across seasonal habitats for cisco, was higher for lake whitefish in the lacustrine environment, and progressively decreased for char from ocean, to riverine, to overwintering habitat. There was some evidence of dysbiosis in the microbiota of lake whitefish, which may be associated with stress as these fish are at the northern limits of their range. In contrast, cisco and Arctic char appeared to have more stable communities, possibly displaying resilience towards anadromy within the high Arctic. Overall, these findings may inform sustainable fishery practices in regard to how microbiomes respond to stress and what factors may put these fish at risk of pathogen colonization throughout their migrations.
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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 | 0.001 | 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) | 0.001 | 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".