TY - JOUR
T1 - Universal abundance fluctuations across microbial communities, tropical forests, and urban populations
AU - George, Ashish B.
AU - O'Dwyer, James Patrick
N1 - We would like to thank Zachary Miller, Alice Doucet Beaupré, and members of the O’Dwyer group for feedback and comments. We acknowledge D. Loudermilk, who licensed the image used in Fig. 1A (CC BY-SA 4.0). We thank the three reviewers for their thoughtful feedback and comments. We acknowledge funding support from Simons Foundation Grant #376199 and McDonnell Foundation Grant #220020439 to J.O. The Barro Colorado Island forest dynamics research project was made possible by NSF grants to S.P. Hubbell: DEB #0640386, DEB #0425651, DEB #0346488, DEB #0129874, DEB #00753102, DEB #9909347, DEB #9615226, DEB #9405933, DEB #9221033, DEB #9100058, DEB #8906869, DEB #8605042, DEB #8206992, DEB #7922197, support from Center for Tropical Forest Science, the Smithsonian Tropical Research Institute, the John D. and Catherine T. MacArthur Foundation, the Mellon Foundation, the Small World Institute Fund, and numerous private individuals, and through the hard work of over 100 people from 10 countries over the past two decades. The Barro Colorado Island plot study is part of the Forest Global Earth Observatory, a global network of large-scale demographic tree plots.
PY - 2023/10/31
Y1 - 2023/10/31
N2 - 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, 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 datasets. 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.
AB - 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, 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 datasets. 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.
KW - urban dynamics
KW - microbial ecology
KW - fluctuations
KW - stochastic population dynamics
KW - science of cities
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U2 - 10.1073/pnas.2215832120
DO - 10.1073/pnas.2215832120
M3 - Article
C2 - 37874854
AN - SCOPUS:85175660656
SN - 1091-6490
VL - 120
JO - Proceedings of the National Academy of Sciences
JF - Proceedings of the National Academy of Sciences
IS - 44
M1 - e2215832120
ER -