Variability in snake skin microbial assemblages across spatial scales and disease states

Abstract

Understanding how biological patterns translate into functional processes across different scales is a central question in ecology. Within a spatial context, extent is used to describe the overall geographic area of a study, whereas grain describes the overall unit of observation. This study aimed to characterize the snake skin microbiota (grain) and to determine host-microbial assemblage-pathogen effects across spatial extents within the Southern United States. The causative agent of snake fungal disease, Ophidiomyces ophiodiicola, is a fungal pathogen threatening snake populations. We hypothesized that the skin microbial assemblage of snakes differs from its surrounding environment, by host species, spatial scale, season, and in the presence of O. ophiodiicola. We collected snake skin swabs, soil samples, and water samples across six states in the Southern United States (macroscale extent), four Tennessee ecoregions (mesoscale extent), and at multiple sites within each Tennessee ecoregion (microscale extent). These samples were subjected to DNA extraction and quantitative PCR to determine the presence/absence of O. ophiodiicola. High-throughput sequencing was also utilized to characterize the microbial communities. We concluded that the snake skin microbial assemblage was partially distinct from environmental microbial communities. Snake host species was strongly predictive of the skin microbiota at macro-, meso-, and microscale spatial extents; however, the effect was variable across geographic space and season. Lastly, the presence of the fungal pathogen O. ophiodiicola is predictive of skin microbial assemblages across macro- and meso-spatial extents, and particular bacterial taxa associate with O. ophiodiicola pathogen load. Our results highlight the importance of scale regarding wildlife host-pathogen-microbial assemblage interactions.

Fig. 1

Host species, geography, season, and pathogen presence are predictive of the microbiome across macro- to microspatial scales. Snake sampling sites and presence of O. ophiodiicola are marked with colored circles across the map. Statistically significant explanatory variables from adonis models are listed with the corresponding coefficient of determination (R²)

Fig. 2

a Beta diversity patterns of the snake skin microbial assemblage compared with environmental microbiota from both soil and water, visualized using a non-metric multidimensional scaling ordination. b Venn diagram showing overlap of OTUs between skin (red), soil (green), and water (blue) samples. c Venn diagram showing results from indicator analysis selecting for OTUs that describe variation in the snake skin microbial assemblage

Fig. 3

Beta diversity patterns of the snake skin microbial assemblage compared by ecomode and visualized using a non-metric multidimensional scaling ordination. a Macroscale—Southern United States. b Mesoscale—Tennessee ecoregions. c Microscale—Southwestern Appalachians ecoregion. d Microscale—Interior plateau ecoregion. e Microscale—Blue ridge mountains ecoregion

Fig. 4

Results depicting constrained analysis of proximities to model the continuous explanatory variable, O. ophiodiicola pathogen load (qPCR results), on Bray–Curtis dissimilarity values from skin microbial assemblages of O. ophiodiicola-positive/negative snakes. Taxonomic affiliation of OTU (e.g., Otu00006) labels is found in Table 1. Colored circles show the microbial assemblage of O. ophiodiicola-positive/negative individuals. Colored ovals show OTUs that correlate with pathogen load across macro-, meso-, and microscale spatial extents. The blue vector labeled “pathogen load” shows the direction of the relationship between higher fungal load, snake skin microbial assemblages, and indicator OTUs