A big data–model integration approach for predicting epizootics and population recovery in a keystone species

Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally > 0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host-pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data-model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making. Thunder Basin Research Initiative; National Institute of Food and Agriculture [2020-67019-31153] Published version We are grateful to Paul Stapp, Eddie Childers, Buck Cully, Jeff Abeglen, Kristin Philbrook, Janice Naylor, Erin Considine, Andy Chappell, Jennifer Avising, Ruben Mares, Gwyn McKee, Peter McDonald, Travis Livieri, Lynne Deibel, Jessica Alexander, Daniel Kinka, Kristy Bly, Nicole Rosmarino, Tara Stephens, and numerous other biologists and field technicians for their data collection and sharing of prairie dog colony boundaries and other associated data across the Great Plains region. We thank David Eads, David Pellatz, and two anonymous reviewers for providing comments that helped to greatly improve the manuscript. This work is part of the Thunder Basin Research Initiative. Funding was provided by the National Institute of Food and Agriculture (Award no.: 2020-67019-31153). Public domain – authored by a U.S. government employee