标题： 从时间序列推断微生物群落中的资源竞争 显示英文标题

标题： Inferring resource competition in microbial communities from time series

摘要： 微生物群落中资源的竞争是其显著特征。 在许多情况下，从土壤到宿主相关的群落，高度多样的微生物被组织成具有相似资源偏好的代谢群体或功能群。 这些功能群由个体分类群的资源偏好所产生，理解这些偏好对于了解资源在群落中的通量以及系统中多样性结构至关重要。 然而，在群落内推断个体分类群的代谢能力及其与其他分类群的竞争关系仍然具有挑战性且未解决。 在此，我们通过利用群落中丰度的动态测量来填补这一知识空白。 我们表明，简单的相关性通常在预测资源竞争方面具有误导性。 我们表明，考虑时间延迟效应的谱方法，如交叉功率谱密度（CPSD）和相干性，是推断群落中资源竞争结构的更优指标。 我们首先在从具有时间依赖性资源可用性的消费者-资源模型中生成的合成数据上证明了这一事实，其中分类群被组织成具有相似资源偏好的群体或功能群。 通过将谱方法应用于海洋浮游生物的时间序列数据，我们证明这些方法可以检测出具有相似基因组序列的物种之间的相互作用结构。 我们的结果表明，分析多个时间尺度上的时间数据可以揭示群落内部资源竞争的潜在结构。 显示英文摘要

摘要： The competition for resources is a defining feature of microbial communities. In many contexts, from soils to host-associated communities, highly diverse microbes are organized into metabolic groups or guilds with similar resource preferences. The resource preferences of individual taxa that give rise to these guilds are critical for understanding fluxes of resources through the community and the structure of diversity in the system. However, inferring the metabolic capabilities of individual taxa, and their competition with other taxa, within a community is challenging and unresolved. Here we address this gap in knowledge by leveraging dynamic measurements of abundances in communities. We show that simple correlations are often misleading in predicting resource competition. We show that spectral methods such as the cross-power spectral density (CPSD) and coherence that account for time-delayed effects are superior metrics for inferring the structure of resource competition in communities. We first demonstrate this fact on synthetic data generated from consumer-resource models with time-dependent resource availability, where taxa are organized into groups or guilds with similar resource preferences. By applying spectral methods to oceanic plankton time-series data, we demonstrate that these methods detect interaction structures among species with similar genomic sequences. Our results indicate that analyzing temporal data across multiple timescales can reveal the underlying structure of resource competition within communities.