Composition of the North American wood frog ( <i>Rana sylvatica</i> ) skin microbiome and seasonal variation in community structure
Bibliographic record
Abstract
Abstract While a number of amphibian microbiomes have been characterized, it is unclear how microbial communities might vary in response to seasonal changes in the environment and the behaviors which many amphibians exhibit. Given recent studies demonstrating the importance of the skin microbiome in frog innate immune defenses against pathogens, investigating how changes in the environment impact the microbial species present, and thus their potential contribution to skin host defense, will provide a better understanding of conditions that may alter host susceptibility to pathogens in their environment. We sampled the skin microbiome of North American wood frogs ( Rana sylvatica ) from two breeding ponds in the spring, along with the microbial community present in their vernal breeding pools, and frogs from the nearby forest floor in the summer and fall to determine whether the microbial composition differs by sex, vernal pond site, or temporally across season (spring, summer, fall). Taxon abundance data reveals a profile of bacterial phyla similar to those previously described on anuran skin, with Proteobacteria, Bacteroidetes, and Actinobacteria dominating the wood frog skin microbiome. Our results indicate that sex had no significant effect on skin microbiota diversity, however, this may be due to our limited female sample size. Vernal pool site had a small but significant effect on skin microbiota, but skin-associated communities were more similar to each other than to the communities observed in the frogs’ respective pond water. Across seasons, diversity analyses suggest there are significant differences between the skin microbiome of frogs from spring and summer/fall groups while the average α-diversity per frog remained consistent. Bacterial genera known to have antifungal properties such as Pseudomonas spp. and Rhizobium spp. were prevalent, and several were considered core microbiota during at least one season. These results illustrate seasonal variation in wood frog skin microbiome structure and highlight the importance of considering temporal trends in an amphibian microbiome, particularly for species whose life history requires recurrent shifts in habitat and behavior.
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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) | 0.002 | 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".