Amazon fish bacterial communities show structural convergence along widespread hydrochemical gradients
Bibliographic record
Abstract
Abstract The world's richest freshwater fish community thrives in gradients of contrasting environments in Amazonia, ranging from ion‐poor acidic black waters, to ion‐rich circumneutral white waters. These hydrochemical gradients structure Amazonian fish assemblages via ecological speciation events. Fish bacterial communities contain an important genetic heritage essential for their hosts' survival and are also involved in adaptive divergence via niche adaptation processes, but the extent to which they evolve in response to hydrochemical gradients in Amazonia is unknown. Here we investigated bacterial communities (gut and skin mucus) of two ecologically and phylogenetically divergent host species (Mesonauta festivus and Serrasalmus rhombeus) distributed throughout these hydrochemical gradients. The goal was to characterize intra‐ and interspecific Amazonian fish microbiome variations across multiple scales. Using a 16S metabarcoding approach, we investigated the microbiota of 43 wild M. festivus, 32 S. rhombeus and seven water samples, collected at seven sampling sites encompassing both water colours. Taxonomical structures of bacterial communities from both host species were significantly correlated to the environmental continua of magnesium, sodium, dissolved organic carbon, calcium, dissolved O2, pH, potassium, hardness and chloride. Analysis of discriminating features in community structures across multiple scales demonstrated intra‐ and interspecific structural parallelisms in the response to the hydrochemical gradients. Together, these parallelisms suggest the action of selection on bacterial community structures along Amazonian hydrochemical gradients. Functional approaches along with reciprocal transplant experiments will provide further insights on the potential contribution of Amazonian fish microbiomes in host adaptation and ecological speciation events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".