A framework for integrating inferred movement behavior into disease risk models

Abstract

Movement behavior is an important contributor to habitat selection and its incorporation in disease risk models has been somewhat neglected. The habitat preferences of host individuals affect their probability of exposure to pathogens. If preference behavior can be incorporated in ecological niche models (ENMs) when data on pathogen distributions are available, then variation in such behavior may dramatically impact exposure risk. Here we use data from the anthrax endemic system of Etosha National Park, Namibia, to demonstrate how integrating inferred movement behavior alters the construction of disease risk maps. We used a Maximum Entropy (MaxEnt) model that associated soil, bioclimatic, and vegetation variables with the best available pathogen presence data collected at anthrax carcass sites to map areas of most likely Bacillus anthracis (the causative bacterium of anthrax) persistence. We then used a hidden Markov model (HMM) to distinguish foraging and non-foraging behavioral states along the movement tracks of nine zebra (Equus quagga) during the 2009 and 2010 anthrax seasons. The resulting tracks, decomposed on the basis of the inferred behavioral state, formed the basis of step-selection functions (SSFs) that used the MaxEnt output as a potential predictor variable. Our analyses revealed different risks of exposure during different zebra behavioral states, which were obscured when the full movement tracks were analyzed without consideration of the underlying behavioral states of individuals. Pathogen (or vector) distribution models may be misleading with regard to the actual risk faced by host animal populations when specific behavioral states are not explicitly accounted for in selection analyses. To more accurately evaluate exposure risk, especially in the case of environmentally transmitted pathogens, selection functions could be built for each identified behavioral state and then used to assess the comparative exposure risk across relevant states. The scale of data collection and analysis, however, introduces complexities and limitations for consideration when interpreting results.

Correlated host movements can reshape spatio-temporal disease dynamics: modeling the contributions of space use to transmission risk using movement data

“…Individual movement is a critical factor influencing wildlife disease dynamics (Dougherty et al, 2018; Manlove et al, 2022); Movement determines encounters with other individuals of the same species, other species, or pathogens in the environment (Martinez-Garcia et al, 2020; Das et al, 2023). These encounters are necessary for the transmission of infectious diseases, and efforts have sought to identify where they occur, how often, and how they are influenced by environmental and social drivers (Titcomb et al, 2021; Dougherty et al, 2022; Webber et al, 2023). Formally linking social factors, environmental factors, animal movement, contact, and pathogen transmission would improve our ability to predict and prevent outbreaks and represent a significant advancement for management of wildlife diseases.…”

A multistate Langevin diffusion for inferring behavior‐specific habitat selection and utilization distributions

“…The identification of important habitat is critical to wildlife population management and conservation, as well as our understanding of the links between animal behavior and their environment (e.g., Camaclang et al, 2015; Dougherty et al, 2022; Lennox et al, 2019; Rice et al, 2017). This is often accomplished through animal‐borne telemetry studies that attempt to quantify habitat selection and/or utilization distributions (e.g., Hooten et al, 2017; Matthiopoulos et al, 2020).…”

“…Other specific examples include behavior‐dependent habitat selection in foraging and nonforaging movements of zebras (Dougherty et al, 2022), encamped and exploratory movements of African elephants (Roever et al, 2014), sage grouse (Picardi et al, 2022), and black bears (Karelus et al, 2017), resting, foraging, and traveling in wild pigs (Clontz et al, 2021) and African lions (Suraci et al, 2019), within‐patch and between‐patch movements in caribou (Johnson et al, 2002) and California pumas (Zeller et al, 2014), resting and active movements in Canada lynx (Squires et al, 2013), resting, predatory, and traveling movements in coyotes (Wilson et al, 2012), and resting, running, and traveling movements in African wild dogs (Abrahms et al, 2016). Clearly, attempting to infer habitat selection and utilization distributions across individuals exhibiting a range of different behaviors in response to internal and external drivers could result in erroneous inferences and potentially ineffective management decisions (e.g., Abrahms et al, 2016; Dougherty et al, 2022; Picardi et al, 2022; Roever et al, 2014; Wilson et al, 2012). As certain behaviors are often more relevant to specific conservation or management objectives, behavior‐specific inferences could help improve our ability to identify and prioritize important habitats.…”

Correction: A framework for integrating inferred movement behavior into disease risk models

“…Following publication of the original article [1], it was noted that the Additional file citations in the body of the article were incorrect. The citations to the supplementary tables should refer to Additional file 2 instead of Additional file 1 and the citations to the supplementary figures should refer to Additional file 3 instead of Additional file 1.…”

“…In addition, the citation to the Supplementary Materials should be changed to a citation to Additional file 1. The original article [1] has been corrected.…”