Lake size shapes the relationship between body mass and gut microbiota in threespine stickleback ( <i>Gasterosteus aculeatus</i> )
Bibliographic record
Abstract
Abstract Host-microbe interactions are shaped by both host and environmental factors. However, little is known about how host-microbe interactions vary across populations within a species. Here, we characterized the gut microbiota of 191 wild threespine stickleback fish ( Gasterosteus aculeatus ) from six populations from Alaskan lakes spanning a gradient of surface area. We tested how environmental context (lake size and ecotype) and host traits (sex, body mass, gravidity, Schistocephalus solidus ( S. solidus ) infection, and fibrosis) influence stickleback gut microbial composition using 16S rRNA gene sequencing. We found that the lake surface area strongly predicted fish gut microbial alpha diversity. Fish from intermediate-sized lakes harbored significantly more diverse microbiota than those from small and large lakes, independent of ecotype. Body mass was associated with gut microbial diversity. Model-predicted marginal effects from the mass and lake surface area interaction analysis showed that the association between fish mass and microbial alpha diversity was strongly negative in the smallest lakes, weakest in intermediate-sized lakes, and strongly positive in the largest lakes. In addition, sex and S. solidus infection were significantly associated with gut microbiota alpha and beta diversity, whereas fibrosis and gravidity showed minimal effects. Differential abundance analysis revealed lake size-dependent associations between body mass and individual taxa. Together, these results demonstrate that both habitat context and host variation interactively shape stickleback gut microbial communities in the wild. Integrating lake-level and individual-level analyses reveals how ecological setting modulates host-microbe associations, offering insights into the role of the gut microbiota in host adaptation and population divergence.
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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".