Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts’ biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co-occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a “known” model, where virus–host relationships are based only on data reported in Mexico, and a “potential” model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the “known” model shows only weak dependence on any of the filters, the “potential” model highlights the importance of all three filter types—phylogeny, functional traits, and co-occurrence—in the assemblage of viral communities. The differences between the “known” and “potential” models highlight the utility of modeling at different “scales” so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.

中文翻译：

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基于利基理论的蝙蝠病毒群落组装过程建模

了解共生体群落的组装过程，包括病毒组和微生物组，对于改进对共生体生物地理学和疾病生态学的预测非常重要。在这里，我们使用系统发育、功能和地理过滤器来预测共生群落之间的相似性，并将墨西哥蝙蝠病毒群落中的组装过程用作测试案例。我们构建了广义线性模型来预测病毒群落相似性，由 Jaccard 指数衡量，作为宿主系统发育、宿主功能和空间共现差异的函数，使用 Akaike 信息标准评估模型。构建了两个模型类：“已知”模型，其中病毒-宿主关系仅基于墨西哥报告的数据，以及“潜在”模型，其中使用所有美洲的病毒报告，但随后仅适用于分布在墨西哥的蝙蝠物种。尽管“已知”模型仅显示对任何过滤器的弱依赖，但“潜在”模型突出了所有三种过滤器类型——系统发育、功能特征和共现——在病毒群落的组合中的重要性。“已知”和“潜在”模型之间的差异突出了在不同“尺度”建模的实用性，以便将一个尺度的已知信息与另一个尺度进行比较和对比，例如，与蝙蝠相关的病毒信息非常稀缺. 和共现——在病毒社区的组合中。“已知”和“潜在”模型之间的差异突出了在不同“尺度”建模的效用，以便将一个尺度的已知信息与另一个尺度进行比较和对比，例如，与蝙蝠相关的病毒信息要少得多. 和共现——在病毒社区的组合中。“已知”和“潜在”模型之间的差异突出了在不同“尺度”建模的实用性，以便将一个尺度的已知信息与另一个尺度进行比较和对比，例如，与蝙蝠相关的病毒信息非常稀缺.