The effects of taxonomy, diet, and ecology on the microbiota of riverine macroinvertebrates
Bibliographic record
Abstract
Freshwater macroinvertebrates play key ecological roles in riverine food webs, such as the transfer of nutrients to consumers and decomposition of organic matter. Although local habitat quality drives macroinvertebrate diversity and abundance, little is known about their microbiota. In most animals, the microbiota provides benefits, such as increasing the rate at which nutrients are metabolized, facilitating immune system development, and defending against pathogenic attack. Our objectives were to identify the bacteria within aquatic invertebrates and determine whether their composition varied with taxonomy, habitat, diet, and time of sample collection. In 2016 and 2017, we collected 264 aquatic invertebrates from the mainstem Saint John (Wolastoq) River in New Brunswick, Canada, representing 15 orders. We then amplified the V3-V4 hypervariable region of the 16S rRNA gene within each individual, which revealed nearly 20,000 bacterial operational taxonomic units (OTUs). The microbiota across all aquatic invertebrates were dominated by Proteobacteria (69.25% of the total sequence reads), but they differed significantly in beta diversity, both among host invertebrate taxa (genus-, family-, and order-levels) and temporally. In contrast to previous work, we observed no microbiota differences among functional feeding groups or traditional feeding habits, and neither water velocity nor microhabitat type structured microbiota variability. Our findings suggest that host invertebrate taxonomy was the most important factor in modulating the composition of the microbiota, likely through a combination of vertical and horizontal bacterial transmission, and evolutionary processes. This is one of the most comprehensive studies of freshwater invertebrate microbiota to date, and it underscores the need for future studies of invertebrate microbiota evolution and linkages to environmental bacteria and physico-chemical conditions.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".