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
non-linear interactions among a large collection of variables that rarely
adhere to the assumptions of parametric regression modeling. We adopted a
non-parametric 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 2001–2020 from eight USDA
Forest Service National Grasslands across the range of BTPD in central
North America. We then modeled extinctions due to plague and colony
recovery of BTPD 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
summer/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.