Tue, Aug 03, 2021:On Demand
Background/Question/Methods
Many traditional disease models whose study provides the basis for theory and management typically focus on a specialist pathogen and a single host. In the age of emerging infectious diseases we must update our theory and techniques to meet the challenges posed by non-traditional, novel pathogens, such as fungal and generalist pathogens. Here, we demonstrate a framework for predicting focal host disease outcomes using non-invasive sampling techniques which focus on 1) environmental variables and 2) detecting the pathogen in a wide variety of biotic and abiotic hosts and reservoirs. We use the amphibian-killing fungal pathogen, Batrachochytrium dendrobatidis (Bd), as our model organism and develop a Bayesian hierarchical model which predicts population-level disease states in four species of amphibians. Using data collected from nearly two years of bi-monthly sampling events at 20 ponds to parameterize and ground-truth the model, we demonstrate how this framework may be applied to other generalist, emergent pathogens.
Results/Conclusions Over the course of our field work, we identified Bd outbreaks at a subset of our sites, creating a natural experiment to test our model against. Our findings demonstrate that prediction of focal host disease patterns is challenging without directly sampling the host, but can be done effectively under this framework. Seasonal shifts from wet to dry and pond hydroperiod, which affect the presence and abundance of certain hosts and reservoirs, introduce modeling challenges and highlight the importance of exploring seasonality as a driver of disease dynamics. These non-invasive sampling methods, when paired with the modeling framework, can increase the accessibility of estimating focal host disease outcomes especially when the focal host is a sensitive or rare species. These community-focused methods can provide further insight into the true ecological niche of the generalist pathogen and reveal novel management strategies for protection and conservation of the focal host.
Results/Conclusions Over the course of our field work, we identified Bd outbreaks at a subset of our sites, creating a natural experiment to test our model against. Our findings demonstrate that prediction of focal host disease patterns is challenging without directly sampling the host, but can be done effectively under this framework. Seasonal shifts from wet to dry and pond hydroperiod, which affect the presence and abundance of certain hosts and reservoirs, introduce modeling challenges and highlight the importance of exploring seasonality as a driver of disease dynamics. These non-invasive sampling methods, when paired with the modeling framework, can increase the accessibility of estimating focal host disease outcomes especially when the focal host is a sensitive or rare species. These community-focused methods can provide further insight into the true ecological niche of the generalist pathogen and reveal novel management strategies for protection and conservation of the focal host.