| Abstract Detail
Ecology Haydt, Natalie [1], Neuman-Lee, Lori [1], Jeff, Shaver [2], Randolph, Jay [3], Bellis, Emily [4]. Predicting the Distribution of Soil Microbes Associated with an Emerging Fungal Disease. Soil microbiomes are shaped by many factors including the diversity of associated plant and vertebrate communities and in turn, soil microbiomes shape the microbial communities hosted by co-occurring plants and vertebrates. For example, snakes are in constant dermal contact with soil, picking up skin-associated microbes transferred from the soil as they move through diverse environments. Environmental microbial communities can facilitate or suppress the establishment and subsequent transfer of microbial pathogens, such as the causative agent of snake fungal disease (SFD), Ophidiomyces ophiodiicola (Oo). Ophidiomyces ophiodiicola is a soil-dwelling microbe that has emerged as a threat to North American snake populations over the past two decades. Currently, however, there are few datasets describing the landscape-scale distribution and environmental associations of Oo. Furthermore, there are no published records of Oo detection for a handful of states, including Arkansas. To fill these gaps, we developed a joint species distribution model to predict distributions of microbial genera known to facilitate Oo globally and projected these models to Arkansas, where there is a paucity of both soil microbiome data and Oo detection data for snake populations. In the absence of fine-scale occurrence data for Oo, we trained our model using global soil microbiome datasets to identify abiotic environments that might be particularly likely to harbor Oo-associated microbial communities and thus pose higher SFD risk. Using environmental predictors characterizing habitat type (including vegetation type, soil, and climate) and habitat disturbance, we extracted predictors for over 2,500 unique soil sampling sites. We then predicted the occurrence probability of Oo-associated microbial genera to 5km grid cells throughout Arkansas. Preliminary presence and absence data for Oo detection in Arkansas for soil samples collected throughout Arkansas in spring and summer of 2022 and soil samples collected from Arkansas tallgrass prairie in 2019 and 2020 were used to test model predictions. The final model serves as a management tool to predict habitats in which snakes are more likely to come into contact with microbial communities positively associated with occurrence of O. ophiodiicola. Predictions from our model can inform areas chosen for further study of SFD in Arkansas and may indicate that identification of pathogen-prone soils is possible in the absence of fine-scale occurrence data for species distribution model training.
1 - Arkansas State University, Department of Biological Sciences, Jonesboro, AR, 72467, USA
2 - University of Arkansas Fort Smith, Department of Biology, Fort Smith, AR, 72904, USA
3 - Ben Geren Golf Course, Fort Smith, AR, 72903, USA
4 - Arkansas State University, Department of Computer Science, Jonesboro, AR, 72467, USA
Keywords:
microbes
snake fungal disease
Species Distribution Modeling.
Presentation Type: Poster
Number: PEC012
Abstract ID:656
Candidate for Awards:None |
