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Published on: December 8, 2017
High prevalence does not necessarily equal maintenance species: Avoiding biased claims of disease reservoirs when
Mark Q Wilber1, Joseph DeMarchi1, Nina H Fefferman2,3
1Department of Forestry, Wildlife, and Fisheries, University of Tennessee Institute of Agriculture, Knoxville, TN, USA.
Abstract:
Many pathogens of public health and conservation concern persist in host communities. Identifying candidate maintenance and reservoir species is therefore a central component of disease management. The term maintenance species implies that if all species but the putative maintenance species were removed, then the pathogen would still persist. In the absence of field manipulations, this statement inherently requires a causal or mechanistic model to assess. However, we lack a systematic understanding of (i) how often conclusions are made about maintenance and reservoir species without reference to mechanistic models (ii) what types of biases may be associated with these conclusions and (iii) how explicitly invoking causal or mechanistic modelling can help ameliorate these biases. Filling these knowledge gaps is critical for robust inference about pathogen persistence and spillover in multihost-parasite systems, with clear implications for human and wildlife health. To address these gaps, we performed a literature review on the evidence previous studies have used to make claims regarding maintenance or reservoir species. We then developed multihost-parasite models to explore and demonstrate common biases that could arise when inferring maintenance potential from observational prevalence data. Finally, we developed new theory to show how model-driven inference of maintenance species can minimize and eliminate emergent biases. In our review, we found that 83% of studies used some form of observational prevalence data to draw conclusions on maintenance potential and only 6% of these studies combined observational data with mechanistic modelling. Using our model, we demonstrate how the community, spatial and temporal context of observational data can lead to substantial biases in inferences of maintenance potential. Importantly, our theory identifies that model-driven inference of maintenance species elucidates other streams of observational data that can be leveraged to correct these biases. Model-driven inference is an essential, yet underused, component of multidisciplinary studies that make inference about host reservoir and maintenance species. Better integration of wildlife disease surveillance and mechanistic models is necessary to improve the robustness and reproducibility of our conclusions regarding maintenance and reservoir species.
Insights
Identifying pathogen maintenance species often lacks mechanistic models, leading to biases. Integrating mechanistic models with observational data improves accuracy in disease management and wildlife health surveillance.
Area of Science:
- Ecology
- Epidemiology
- Conservation Biology
Background:
- Pathogen persistence in host communities necessitates identifying maintenance and reservoir species for effective disease management.
- Current methods often infer these roles without mechanistic models, potentially introducing biases.
Purpose of the Study:
- To systematically assess how often maintenance/reservoir species conclusions are drawn without mechanistic models.
- To identify biases associated with these conclusions and demonstrate how mechanistic modeling can mitigate them.
- To improve inference in multihost-parasite systems for public and wildlife health.
Main Methods:
- Conducted a literature review on evidence used for maintenance/reservoir species claims.
- Developed multihost-parasite models to simulate biases from observational prevalence data.
- Formulated theory for model-driven inference to correct observational biases.
Main Results:
- 83% of studies relied on observational prevalence data; only 6% integrated mechanistic modeling.
- Observational data, influenced by community, spatial, and temporal context, can create significant inference biases.
- Model-driven inference highlights how other observational data can correct these biases.
Conclusions:
- Mechanistic modeling is crucial but underutilized for identifying host reservoir and maintenance species.
- Integrating wildlife disease surveillance with mechanistic models enhances the robustness of findings.
- Improved modeling approaches are essential for accurate disease management and understanding pathogen spillover.
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