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

TL;DR

This study demonstrates that a unified three-parameter stochastic model can accurately describe abundance fluctuations across microbial, forest, and urban populations, revealing underlying similarities despite scale differences.

Contribution

It introduces a universal framework applying a single model to diverse complex populations, enabling cross-system comparison and improved fluctuation prediction.

Findings

A three-parameter model reproduces empirical abundance distributions.

Different systems occupy similar parameter space when scaled by generations.

A two-parameter distribution effectively characterizes abundance fluctuations.

Abstract

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.