Dynamics of Gut Bacteria Across Different Zooplankton Genera in the Baltic Sea
Bibliographic record
Abstract
In aquatic ecosystems, zooplankton-associated bacteria potentially have a great impact on the structure of ecosystems and trophic networks by providing various metabolic pathways and altering the ecological niche of host species. To understand the composition and drivers of zooplankton gut microbiota, we investigated the associated microbial communities of four zooplankton genera from different seasons in the Baltic Sea using the 16S rRNA gene. Among the 143 ASVs (amplified sequence variants) observed belonging to heterotrophic bacteria, 28 ASVs were shared across all zooplankton hosts over the season, and these shared core ASVs represented more than 25% and up to 60% of relative abundance in zooplankton hosts but were present at low relative abundance in the filtered water. Zooplankton host identity had stronger effects on bacterial composition than seasonal variation, with the composition of gut bacterial communities showing host-specific clustering patterns. Although bacterial compositions and dominating core bacteria were different between zooplankton hosts, higher gut bacteria diversity and more bacteria contributing to the temporal variation were found in Temora and Pseudocalanus, compared to Acartia and Synchaeta. Diet diatom and filamentous cyanobacteria negatively correlated with gut bacteria diversity, but the difference in diet composition did not explain the dissimilarity of gut bacteria composition, suggesting a general effect of diet on the inner conditions in the zooplankton gut. Synchaeta maintained high stability of gut bacterial communities with unexpectedly low bacteria-bacteria interactions as compared to the copepods, indicating host-specific regulation traits. Our results suggest that the patterns of gut bacteria dynamics are host-specific and the variability of gut bacteria is not only related to host taxonomy but also related to host behavior and life history traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".