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ARS Home » Plains Area » Fort Collins, Colorado » Center for Agricultural Resources Research » Rangeland Resources & Systems Research » Research » Publications at this Location » Publication #395877

Research Project: Adaptive Grazing Management and Decision Support to Enhance Ecosystem Services in the Western Great Plains

Location: Rangeland Resources & Systems Research

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

Author
item BARILLE, GABRIEL - COLORADO STATE UNIVERSITY
item Augustine, David
item Porensky, Lauren
item DUCHARDT, COURTNEY - OKLAHOMA STATE UNIVERSITY
item SHOEMAKER, KEVIN - UNIVERSITY OF NEVADA
item HARTWAY, CYNTHIA - WILDLIFE CONSERVATION SOCIETY
item Derner, Justin
item HUNTER, ELIZABETH - U.S. GEOLOGICAL SOCIETY
item DAVIDSON, ANA - COLORADO STATE UNIVERSITY

Submitted to: Ecological Applications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/10/2023
Publication Date: 2/27/2023
Citation: Barille, G., Augustine, D.J., Porensky, L.M., Duchardt, C., Shoemaker, K., Hartway, C., Derner, J.D., Hunter, E., Davidson, A. 2023. A big data–model integration approach for predicting epizootics and population recovery in a keystone species. Ecological Applications. Article e2827. https://doi.org/10.1002/eap.2827.
DOI: https://doi.org/10.1002/eap.2827

Interpretive Summary: The black-tailed prairie dog is a colonial, burrowing rodent that occurs in grasslands of the western Great Plains, from Montana to New Mexico. Populations of this species often fluctuate dramatically because of die-offs caused by disease outbreaks (plague) followed by colony re-growth over a series of years. As populations grow and colonies expand, they can negatively affect forage for livestock. When populations crash due to disease, this negatively affects native predators and some grassland birds. Being able to predict where and when disease outbreaks will occur, as well as where and when populations will recover is a major need for both rangeland managers and wildlife conservationists. We used a modelling approach based on machine learning to predict both disease outbreaks in prairie dog colonies, and colony regrowth patterns for 8 different study sites located on National Grasslands in Wyoming, Colorado, Kansas, New Mexico, and Oklahoma. The model was calibrated using and extensive colony mapping dataset collected at these study sites during 2001-2020. Overall, our model showed high predictive capacity for both disease outbreaks and colony growth, and revealed that disease outbreaks are more likely when colonies were highly connected, in closer proximity to previously plague-affected sites, following cooler than average temperatures during the previous summer, and when wetter winter/springs were preceded by a drier summer and fall. The model can be used to support strategic management of prairie dog populations (e.g., plague mitigation, prairie dog control) to reduce population volatility and mitigate impacts to livestock operations.

Technical Abstract: Emerging infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging as disease outbreaks result from complex non-linear interactions among a large collection of variables that rarely adhere to the assumptions of traditional regression modeling. To address this gap, we adopted a flexible, 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 plague epizootics and colony recovery of BTPD in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Epizootics 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. These results underscore the interdependency of exogenous (e.g., climatic variation) and endogenous factors (e.g., host distribution) in wildlife disease dynamics. Furthermore, 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 can reduce conflicts among differing 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 forecasting disease-induced population fluctuations, which has spatially-explicit managerial implications for natural resource management.