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arXiv 2209.07628q-bio.PEcond-mat.stat-mechphysics.soc-ph
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微生物群落、热带森林与城市人口中普遍存在的丰度波动

Universal abundance fluctuations across microbial communities, tropical forests, and urban populations

Ashish B. George, James O'Dwyer

AI总结:

研究跨尺度复杂群体缺乏统一分析的问题,首次用共享框架分析人类肠道微生物组、热带森林和城市就业数据,提出三参数随机群体动态模型重现其丰度波动,并推导出双参数函数形式以改进风险估计和预测。

摘要:

微生物群落、森林和城市等复杂群体的增长发生在截然不同的空间和时间尺度上。尽管不同领域的研究已经开发了详细的、特定于系统的模型来理解每个单独的系统,但仍缺乏对不同复杂群体的统一分析;这样的分析可以加深我们对每个系统的理解,并促进跨领域工具和见解的交叉融合。在这里,我们首次使用一个共享框架来分析人类肠道微生物组、热带森林和城市就业的时间序列数据。我们证明,一个单一的三参数随机群体动态模型能够重现所有三个数据集中群体丰度及其波动的经验分布。表征物种的三个参数衡量其平均丰度、确定性稳定性和随机性。我们的分析表明,尽管在尺度上存在巨大差异,但当时间以世代为单位测量时,所有三个系统都占据相似的参数空间区域。换句话说,尽管在这些系统中观察到的波动可能看起来不同,但这种差异主要是由于每个系统所关联的物理时间尺度不同。此外,我们展示了时间丰度波动的分布仅由两个参数描述,并推导出用于丰度波动的双参数函数形式,以改进风险估计和预测。

英文摘要:

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Although research in different fields has developed detailed, system-specific models to understand each individual system, a unified analysis of different complex populations is lacking; such an analysis could deepen our understanding of each system and facilitate cross-pollination of tools and insights across fields. Here, for the first time we use a shared framework to analyze time-series data of the human gut microbiome, tropical forest, and urban employment. We demonstrate that a single, three-parameter model of stochastic population dynamics can reproduce the empirical distributions of population abundances and fluctuations in all three data sets. The three parameters characterizing a species measure its mean abundance, deterministic stability, and stochasticity. Our analysis reveals that, despite the vast differences in scale, all three systems occupy a similar region of parameter space when time is measured in generations. In other words, although the fluctuations observed in these systems may appear different, this difference is primarily due to the different physical timescales associated with each system. Further, we show that the distribution of temporal abundance fluctuations is described by just two parameters and derive a two-parameter functional form for abundance fluctuations to improve risk estimation and forecasting.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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