A novel method for estimating pathogen presence, prevalence, load, and dynamics at multiple scales
FORT staff with the North American Bat Monitoring Program (NABat) co-authored a publication in Scientific Reports on a new modeling framework for pathogen monitoring that can help practitioners identify and manage wildlife populations during the early infectious stages of disease spread.
When pathogens invade a new wildlife population, measures of pathogen abundance may be so low that monitoring methods inconsistently detect pathogen presence. Scientists often exclude such detections from predictions of disease prevalence and spread. However, monitoring frameworks that can include and account for inconsistent detections could be more accurate in predicting pathogen arrival, early pathogen load, and early pathogen prevalence, allowing conservation practitioners to better manage pathogen spread.
In this study, researchers developed a probabilistic modeling framework for analyzing environmental DNA (eDNA) surveys of Pd (Pseudogymnoascus destructans; the fungal vector of white nose-syndrome) and used it to understand the spread of this pathogen among bat hibernacula — caves, mines, tunnels or other places where bats overwinter — from 2012–2017.
The modeling framework accommodates early and imperfect detections to provide invasion histories for each monitored hibernacula with uncertainty. It is applicable to other monitoring programs focused on the spread of disease or invasive species.