Host traits and environmental variation shape gut microbiota diversity in wild threespine stickleback
Bibliographic record
Abstract
BACKGROUND: Despite the growing recognition of the importance of gut microbiota in host ecology and evolution, our understanding of the relative contributions of host-associated and environmental factors shaping gut microbiota composition within and across wild populations remains limited. Here, we investigate how host morphology, sex, genetic divergence, and environmental characteristics influence the gut microbiota of threespine stickleback fish populations from 20 lakes on Vancouver Island, Canada. RESULTS: Our findings reveal substantial variation in gut microbiota composition and diversity among populations, with host traits exerting a relatively stronger influence on bacterial alpha diversity than environmental characteristics. Previous studies have suggested a link between stickleback body shape and niche specialization, and our results indicate that aspects of host morphology may be associated with gut microbiota divergence among populations, though whether this is related to trophic ecology remains to be explored. Within and across populations, we only observed a weakly defined core microbiota and limited sharing of amplicon sequence variants (ASVs) among hosts, indicating that gut microbiota composition is individualized. Additionally, we detected sex-dependent differences in microbial diversity, opening avenues for future research into the mechanisms driving this variation. CONCLUSIONS: In sum, our study emphasizes the need to consider both host-associated and environmental factors in shaping gut microbiota dynamics and highlights the complex interplay between host organisms, their associated microbial communities, and the environment in natural settings. Ultimately, these insights add to our understanding of the eco-evolutionary implications of host-microbiota interactions while underscoring the need for further investigation into the underlying mechanisms.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".