DOI: 10.25349/d9pg77
title: High prevalence does not necessarily equal maintenance species: Avoiding biased claims of disease reservoirs when using surveillance data

description: 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 modeling 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 a 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 modeling. 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.
description: We used a two-step search process to compile a list of studies
 that made claims or statements regarding `maintenance' or
 `reservoir' species. First, we performed a search on Web of Science
 Core Collection using the search string TS=((``maintenance
 species'' OR ``reservoir species'' OR ``reservoir
 population'' OR ``maintenance population'' OR
 ``reservoir* of infection'' OR ``maintenance host'' OR
 ``reservoir host'' OR ``multihost'') AND
 (``disease'' OR ``pathogen'' OR
 ``parasit*'')). The search was initiated on September 4, 2021.
 The search string returned 1,819 papers ranging from 1991 to 202, which we
 reviewed given the criteria described below. However, the terms
 `reservoir*' and 'maintenance*' are used broadly in disease
 ecology and the 1,819 papers returned above represented a sub-sample of
 all papers that make inference regarding reservoir and maintenance
 species. To ensure that our choice of search terms did not lead to biased
 results, we generalized our search to  TS=((``reservoir'' OR
 ``maintenance'' OR ``multihost'') AND
 (``pathogen'' OR ``parasit*'')), which returned 12,512
 results when the search was initiated on Nov. 1, 2021. From these results,
 we randomly chose 700 papers, which based on our previous search were
 predicted to yield approximately 100 studies that fit our review
 criteria.  We compared our results between the two searches to determine
 whether the proportion of papers in each category was consistent.  We
 included studies in our analysis that drew some conclusions on the
 maintenance or reservoir potential of one or more non-human animal
 species.  This included studies where testing for maintenance potential
 was a central question and studies that drew post-hoc conclusions on
 maintenance potential. We excluded literature reviews, meta-analyses, and
 human-focused studies from our analysis. However, we included studies that
 looked at non-human reservoir hosts that could contribute to infection in
 humans (e.g., non-human mammals contributing to schistosomiasis).  In
 total our first search yielded 346 papers for inclusion and the second
 86. For each article that met our criteria we recorded the study type,
 which we divided into four broad categories: field study, laboratory
 study, mathematical modeling, and life history extrapolation. Articles
 could fit into multiple categories. For example, some studies used
 field-collected data to parameterize mathematical models. Therefore, those
 studies would be classified as both a field study and mathematical model.
 We did not count statistical models, such as generalized linear models
 that were fit to prevalence data or correlation analyses, as mathematical
 modeling. We further classified studies by the sub-category "methods
 and measurements". This sub-category included prevalence surveys,
 challenge assays, models, transmission assays, histopathology, genetics,
 and species removals. Methods and measurements varied widely between
 studies and individual studies often used multiple "methods and
 measurements".
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