A positive association between gut microbiota diversity and vertebrate host performance in a field experiment
Bibliographic record
Abstract
Abstract The vertebrate gut microbiota is a critical determinant of organismal function, yet it remains unclear if and how gut microbial communities affect host fitness under natural conditions. Here, we investigate associations between growth rate (a fitness proxy) and gut microbiota diversity and composition in a field experiment with threespine stickleback fish ( Gasterosteus aculeatus ). We detected on average 63% more bacterial taxa in the guts of high-fitness fish compared to low-fitness fish (i.e., higher α-diversity), suggesting that higher diversity promotes host growth. The microbial communities of high-fitness fish had higher similarity (i.e., lower β-diversity) than low-fitness fish, supporting the Anna Karenina principle— that there are fewer ways to have a functional microbiota than a dysfunctional microbiota. Our findings provide a basis for functional tests of the fitness consequences of host-microbiota interactions. Significance statement The vertebrate gut microbiota is important for many aspects of their hosts’ biology—such as nutrient metabolism and defense against pathogens—that could ultimately affect host fitness. However, studies investigating the effects of gut microbiota composition on vertebrate host fitness under natural conditions remain exceedingly rare. We tested for associations between gut microbiota diversity and growth rate (a fitness proxy) in threespine stickleback fish reared in large outdoor ponds. We found evidence that a more diverse gut microbiota was predictive of higher growth rate and therefore increased host fitness. Notably, high-fitness fish had higher gut microbiota similarity to one another than did low-fitness fish, providing experimental evidence for the Anna Karenina principle—that there are fewer ways to have a functional microbiota than a dysfunctional microbiota—as it relates to host fitness.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".