Associations Between Gut Microbiota Diversity and a Host Fitness Proxy in a Naturalistic Experiment Using Threespine Stickleback Fish
Bibliographic record
Abstract
The vertebrate gut microbiota is a critical determinant of organismal function, yet whether and how gut microbial communities affect host fitness under natural conditions remains largely unclear. We characterised associations between a fitness proxy-individual growth rate-and bacterial gut microbiota diversity and composition in threespine stickleback fish introduced to large semi-natural ponds. We detected a 63% higher richness of bacterial taxa (α-diversity) in the guts of high-fitness fish compared to low-fitness fish, which might be driven by stronger bacterial dispersal among high-fitness fish according to the fit of a neutral community model. Further, microbial communities of high-fitness fish were more similar to one another (i.e., exhibited lower β-diversity) than those of low-fitness fish. The lower β-diversity found to be associated with higher host fitness is consistent with the Anna Karenina principle-that there are fewer ways to have a functional microbiota than a dysfunctional microbiota. Our study links differences in α- and β-diversity to a fitness-related trait in a vertebrate species reared under naturalistic conditions and our findings provide a basis for functional tests of the fitness consequences of host-microbiota interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".