Universal population fluctuations in microbial communities, tropical forests, and urban populations

What is it about?

In this study the authors analyze time-series data from three disparate complex populations ranging from the microscopic to the macroscopic— microbial species in the human gut sampled daily for over 300 days, employees in multiple economic sectors in U.S. cities for 17 years, and forest tree species in the Barro Colorado Island in Panama for two decades. They found that the emergent behavior of all these populations, despite the vast differences in scale, is described by a single simple model, resembling the universality observed in many physical systems. For each “species” in the system, its temporal trajectory is described by three parameters: an equilibrium abundance, how long it takes for a trajectory to return to equilibrium after a perturbation, and a strength of stochasticity. Further, the parameter values describing a species in each system became similar when time was measured in generations rather than days or years. In other words, although the fluctuations observed in these systems may appear different, this shows that this difference is primarily due to the different physical timescales associated with each system. The study provides a two parameter prediction for the distributions of species abundance and fluctuations to improve risk estimation and forecasting of rare events.

Featured Image

Photo by Tantan Wiguna on Unsplash

Photo by Tantan Wiguna on Unsplash

Why is it important?

The growth of complex populations, such as microbial communities, forests, and cities, occurs over vastly different spatial and temporal scales. Large fluctuations in all of these populations can have disastrous consequences—they can trigger ecological catastrophes and economic collapse. Understanding these fluctuations is crucial for mitigating these rare events. However, models of these systems are often too detailed to fully parameterize and analyze; further, not all these details affect the statistical behavior of the population. By analyzing data from populations of microbes, trees, and employees, the study uncovers a single model that captures fluctuations in all three populations. It provides a two parameter prediction for the distributions of species abundance and fuctuations to improve risk estimation and forecasting of rare events. This advance will not only aid in developing new prediction methods in each system, but also motivate the cross-pollination of concepts and techniques across these seemingly disparate fields.