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Reptiles and amphibians (herptiles) are some of the most endangered and threatened species on the planet and numerous conservation strategies are being implemented with the goal of ensuring species recovery. Little is known, however, about the gut microbiome of wild herptiles and how it relates to the health of these populations. Here, we report results from the gut microbiome characterization of both a broad survey of herptiles, and the correlation between the fungus Basidiobolus, and the bacterial community supported by a deeper, more intensive sampling of Plethodon glutinosus, known as slimy salamanders. We demonstrate that bacterial communities sampled from frogs, lizards, and salamanders are structured by the host taxonomy and that Basidiobolus is a common and natural component of these wild gut microbiomes. Intensive sampling of multiple hosts across the ecoregions of Tennessee revealed that geography and host:geography interactions are strong predictors of distinct Basidiobolus operational taxonomic units present within a given host. Co-occurrence analyses of Basidiobolus and bacterial community diversity support a correlation and interaction between Basidiobolus and bacteria, suggesting that Basidiobolus may play a role in structuring the bacterial community. We further the hypothesis that this interaction is advanced by unique specialized metabolism originating from horizontal gene transfer from bacteria to Basidiobolus and demonstrate that Basidiobolus is capable of producing a diversity of specialized metabolites including small cyclic peptides.IMPORTANCEThis work significantly advances our understanding of biodiversity and microbial interactions in herptile microbiomes, the role that fungi play as a structural and functional members of herptile gut microbiomes, and the chemical functions that structure microbiome phenotypes. We also provide an important observational system of how the gut microbiome represents a unique environment that selects for novel metabolic functions through horizontal gene transfer between fungi and bacteria. Such studies are needed to better understand the complexity of gut microbiomes in nature and will inform conservation strategies for threatened species of herpetofauna.

Emerging infectious diseases are increasingly recognized as a significant threat to global biodiversity conservation. Elucidating the relationship between pathogens and the host microbiome could lead to novel approaches for mitigating disease impacts. Pathogens can alter the host microbiome by inducing dysbiosis, an ecological state characterized by a reduction in bacterial alpha diversity, an increase in pathobionts, or a shift in beta diversity. We used the snake fungal disease (SFD; ophidiomycosis), system to examine how an emerging pathogen may induce dysbiosis across two experimental scales. We used quantitative polymerase chain reaction, bacterial amplicon sequencing, and a deep learning neural network to characterize the skin microbiome of free-ranging snakes across a broad phylogenetic and spatial extent. Habitat suitability models were used to find variables associated with fungal presence on the landscape. We also conducted a laboratory study of northern watersnakes to examine temporal changes in the skin microbiome following inoculation with Ophidiomyces ophidiicola. Patterns characteristic of dysbiosis were found at both scales, as were nonlinear changes in alpha and alterations in beta diversity, although structural-level and dispersion changes differed between field and laboratory contexts. The neural network was far more accurate (99.8% positive predictive value [PPV]) in predicting disease state than other analytic techniques (36.4% PPV). The genus Pseudomonas was characteristic of disease-negative microbiomes, whereas, positive snakes were characterized by the pathobionts Chryseobacterium, Paracoccus, and Sphingobacterium. Geographic regions suitable for O. ophidiicola had high pathogen loads (>0.66 maximum sensitivity + specificity). We found that pathogen-induced dysbiosis of the microbiome followed predictable trends, that disease state could be classified with neural network analyses, and that habitat suitability models predicted habitat for the SFD pathogen., (© 2024 The Author(s). Conservation Biology published by Wiley Periodicals LLC on behalf of Society for Conservation Biology.)