Post‐epizootic microbiome associations across communities of neotropical amphibians
Bibliographic record
Abstract
Microbiome-pathogen interactions are increasingly recognized as an important element of host immunity. While these host-level interactions will have consequences for community disease dynamics, the factors which influence host microbiomes at larger scales are poorly understood. We here describe landscape-scale pathogen-microbiome associations within the context of post-epizootic amphibian chytridiomycosis, a disease caused by the panzootic chytrid fungus Batrachochytrium dendrobatidis. We undertook a survey of Neotropical amphibians across altitudinal gradients in Ecuador ~30 years following the observed amphibian declines and collected skin swab-samples which were metabarcoded using both fungal (ITS-2) and bacterial (r16S) amplicons. The data revealed marked variation in patterns of both B. dendrobatidis infection and microbiome structure that are associated with host life history. Stream breeding amphibians were most likely to be infected with B. dendrobatidis. This increased probability of infection was further associated with increased abundance and diversity of non-Batrachochytrium chytrid fungi in the skin and environmental microbiome. We also show that increased alpha diversity and the relative abundance of fungi are lower in the skin microbiome of adult stream amphibians compared to adult pond-breeding amphibians, an association not seen for bacteria. Finally, stream tadpoles exhibit lower proportions of predicted protective microbial taxa than pond tadpoles, suggesting reduced biotic resistance. Our analyses show that host breeding ecology strongly shapes pathogen-microbiome associations at a landscape scale, a trait that may influence resilience in the face of emerging infectious diseases.
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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 | 0.000 |
| 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".