Stutz William E, Blaustein Andrew R, Briggs Cheryl J, Hoverman Jason T, Rohr Jason R, Johnson Pieter T J
Department of Ecology and Evolutionary Biology, University of Colorado at Boulder, Boulder, CO 80309-0334.
Integrative Biology, 3029 Cordley Hall, Oregon State University, Corvallis, OR 97331-2914.
Methods Ecol Evol. 2018 Apr;9(4):1109-1120. doi: 10.1111/2041-210X.12938. Epub 2017 Nov 13.
Associations among parasites affect many aspects of host-parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales.Here we illustrate how hierarchical, multiresponse modeling can characterize parasite associations by allowing for hierarchical structuring, offering estimates of uncertainty, and incorporating correlational model structures. After introducing the general approach, we apply this framework to investigate coinfections among four amphibian parasites (the trematodes and spp., the chytrid fungus , and ranaviruses) and among >2000 individual hosts, 90 study sites, and five amphibian host species.Ninety-two percent of sites and 80% of hosts supported two or more pathogen species. Our results revealed strong correlations between parasite pairs that varied by scale (from among hosts to among sites) and classification (microparasite versus macroparasite), but were broadly consistent across taxonomically diverse host species. At the host-scale, infection by the trematode correlated positively with the microparasites, and ranavirus, which were themselves positively associated. However, infection by a second trematode ( spp.) correlated negatively with and ranavirus, both at the host- and site-level scales, highlighting the importance of differential relationships between micro- and macroparasites.Given the extensive number of coinfecting symbiont combinations inherent to natural systems, particularly across multiple host species, multiresponse modeling of cross-sectional field data offers a valuable tool to identify a tractable number of hypothesized interactions for experimental testing while accounting for uncertainty and potential sources of co-exposure. For amphibians specifically, the high frequency of co-occurrence and coinfection among these pathogens - each of which is known to impair host fitness or survival - highlights the urgency of understanding parasite associations for conservation and disease management.
寄生虫之间的关联会影响宿主 - 寄生虫动态的许多方面,但缺乏分析工具限制了对观测数据中寄生虫相关性的研究,这些数据通常嵌套在空间和生物尺度上。在这里,我们说明了分层多响应建模如何通过允许分层结构、提供不确定性估计以及纳入相关模型结构来表征寄生虫关联。在介绍了一般方法之后,我们应用这个框架来研究四种两栖类寄生虫(吸虫和 属物种、壶菌真菌 和蛙病毒)以及2000多个个体宿主、90个研究地点和五种两栖类宿主物种之间的共感染情况。92%的地点和80%的宿主感染了两种或更多的病原体物种。我们的结果揭示了寄生虫对之间的强相关性,这种相关性因尺度(从宿主之间到地点之间)和分类(微寄生虫与大寄生虫)而异,但在分类学上不同的宿主物种中大致一致。在宿主尺度上,吸虫 的感染与微寄生虫 以及蛙病毒呈正相关,而 与蛙病毒本身也呈正相关。然而,第二种吸虫( 属物种)的感染在宿主和地点尺度上均与 和蛙病毒呈负相关,这突出了微寄生虫和大寄生虫之间差异关系的重要性。鉴于自然系统中固有的大量共感染共生体组合,特别是在多个宿主物种之间,横断面野外数据的多响应建模提供了一个有价值的工具,可在考虑不确定性和共同暴露的潜在来源的同时,确定数量可控的假设相互作用以供实验测试。特别是对于两栖动物来说,这些病原体中每一种都已知会损害宿主健康或生存,它们之间共现和共感染的高频率凸显了为保护和疾病管理而了解寄生虫关联的紧迫性。