Abstract
The relationships between host-pathogen population dynamics in wildlife are poorly understood. An impediment to progress in understanding these relationships is imperfect detection of diagnostic tests used to detect pathogens. If ignored, imperfect detection precludes accurate assessment of pathogen presence and prevalence, foundational parameters for deciphering host-pathogen dynamics and disease etiology. Respiratory disease in bighorn sheep (Ovis canadensis) is a significant impediment to their conservation and restoration, and effective management requires a better understanding of the structure of the pathogen communities. Our primary objective was to develop an easy-to-use and accessible web-based Shiny application that estimates the probability (with associated uncertainty) that a respiratory pathogen is present in a herd and its prevalence given imperfect detection. Our application combines the best-available information on the probabilities of detection for various respiratory pathogen diagnostic protocols with a hierarchical Bayesian model of pathogen prevalence. We demonstrated this application using four examples of diagnostic tests from three herds of bighorn sheep in Montana. For instance, one population with no detections of Mycoplasma ovipneumoniae (PCR assay) still had an 6% probability of the pathogen being present in the herd. Similarly, the apparent prevalence (0.32) of M. ovipneumoniae in another herd was a substantial underestimate of estimated true prevalence (0.46: 95% CI = [0.25, 0.71]). The negative bias of naïve prevalence increased as the probability of detection of testing protocols worsened such that the apparent prevalence of Mannheimia haemolytica (culture assay) in a herd (0.24) was less than one third that of estimated true prevalence (0.78: 95% CI = [0.43, 0.99]). We found a small difference in the estimates of the probability that Mannheimia spp. (culture assay) was present in one herd between the binomial sampling approach (0.24) and the hypergeometric approach (0.22). Ignoring the implications of imperfect detection and sampling variation for assessing pathogen communities in bighorn sheep can result in spurious inference on pathogen presence and prevalence, and potentially poorly informed management decisions. Our Shiny application makes the rigorous assessment of pathogen presence, prevalence and uncertainty straightforward, and we suggest it should be incorporated into a new paradigm of disease monitoring.
Highlights
Understanding the ecology of infectious wildlife diseases is critical for the informed management of animal populations
The interface to the application has three main components: 1) a tab that contains information on the Pathogens and protocols available, 2) a tab that contains the inputs required and outputs generated for Estimating prevalence in the herd, and 3) a tab that allows a general exploration of the consequences of Imperfect detection (Fig 2)
Proper pathogen community assessments require information on the probabilities of detection associated with each testing protocol coupled to a Bayesian framework in order to properly account for uncertainty
Summary
Understanding the ecology of infectious wildlife diseases is critical for the informed management of animal populations. At the core of these methods is the simple principle that the failure to detect an individual does not mean it is not present These principles have a long history in the study of human and livestock diseases, where efforts to account for uncertain and imperfect detection (or, test sensitivity) have profound implications for understanding disease processes and pathogen prevalences [15,16,17,18]. To borrow from this wealth of work, it is clear that an assessment of pathogen communities in wildlife populations that does not explicitly account for the sampling process is fundamentally incomplete [19,20,21]
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