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Abstract Batrachochytrium salamandrivorans ( Bsal ) is a fungal pathogen of amphibians that is emerging in Europe and could be introduced to North America through international trade or other pathways. To evaluate the risk of Bsal invasion to amphibian biodiversity, we performed dose-response experiments on 35 North American species from 10 families, including larvae from five species. We discovered that Bsal caused infection in 74% and mortality in 35% of species tested. Both salamanders and frogs became infected and developed Bsal chytridiomycosis. Based on our host susceptibility results, environmental suitability conditions for Bsal , and geographic ranges of salamanders in the United States, predicted biodiversity loss is expected to be greatest in the Appalachian Region and along the West Coast. Indices of infection and disease susceptibility suggest that North American amphibian species span a spectrum of vulnerability to Bsal chytridiomycosis and most amphibian communities will include an assemblage of resistant, carrier, and amplification species. Predicted salamander losses could exceed 80 species in the United States and 140 species in North America. 

Abstract 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 withOphidiomyces 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 genusPseudomonaswas characteristic of disease‐negative microbiomes, whereas, positive snakes were characterized by the pathobiontsChryseobacterium,Paracoccus, andSphingobacterium. Geographic regions suitable forO. ophidiicolahad 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.