Department of Forestry, Wildlife, and Fisheries, University of Tennessee Institute of Agriculture, Knoxville, TN, USA.
Department Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA.
J Anim Ecol. 2022 Sep;91(9):1740-1754. doi: 10.1111/1365-2656.13774. Epub 2022 Jul 30.
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.
许多对公共卫生和保护有重要意义的病原体仍然存在于宿主群落中。因此，确定候选维持和储存物种是疾病管理的核心组成部分。维持物种一词意味着，如果除了假定的维持物种之外所有物种都被移除，那么病原体仍将持续存在。在没有野外操作的情况下，这一说法本质上需要一个因果或机制模型来评估。然而，我们缺乏系统的认识：（i）在没有参考机制模型的情况下，关于维持和储存物种的结论是如何做出的；（ii）这些结论可能与哪些类型的偏差有关；（iii）明确调用因果或机制模型如何帮助减轻这些偏差。填补这些知识空白对于在多宿主-寄生虫系统中对病原体持久性和溢出进行稳健推断至关重要，这对人类和野生动物健康具有明确的意义。为了填补这些空白，我们对前人研究中用于确定维持或储存物种的证据进行了文献综述。然后，我们开发了多宿主-寄生虫模型，以探索和展示从观察性流行率数据推断维持潜力时可能出现的常见偏差。最后，我们提出了新的理论，展示了如何通过基于模型的推断来最小化和消除新兴偏差。在我们的综述中，我们发现 83%的研究使用某种形式的观察性流行率数据来得出维持潜力的结论，而只有 6%的研究将观察性数据与机制模型相结合。使用我们的模型，我们展示了观察性数据的群落、空间和时间背景如何导致对维持潜力的推断产生重大偏差。重要的是，我们的理论确定了基于模型的维持物种推断阐明了其他可以利用来纠正这些偏差的观察数据流。基于模型的推断是进行关于宿主储库和维持物种推断的多学科研究中必不可少但未被充分利用的组成部分。更好地整合野生动物疾病监测和机制模型是提高我们关于维持和储存物种结论的稳健性和可重复性的必要条件。
