Reconstructing ecological community dynamics from limited observations

TL;DR

This paper introduces a Bayesian inference method that combines multiple short ecological time series to predict community stability, tipping points, and resilience, overcoming data scarcity limitations.

Contribution

It develops a novel approach using Gaussian process priors to decompose dynamics and quantify stability from limited observational data.

Findings

Successfully validated with simulated data

Revealed new insights into gut microbiota stability

Challenged classical assumptions about tipping points

Abstract

Ecosystems tend to fluctuate around stable equilibria in response to internal dynamics and environmental factors. Occasionally, they enter an unstable tipping region and collapse into an alternative stable state. Our understanding of how ecological communities vary over time and respond to perturbations depends on our ability to quantify and predict these dynamics. However, the scarcity of long, dense time series data poses a severe bottleneck for characterising community dynamics using existing methods. We overcome this limitation by combining information across multiple short time series using Bayesian inference. By decomposing dynamics into deterministic and stochastic components using Gaussian process priors, we predict stable and tipping regions along the community landscape and quantify resilience while addressing uncertainty. After validation with simulated and real ecological time series, we use the model to question common assumptions underlying classical potential analysis and re-evaluate the stability of previously proposed "tipping elements" in the human gut microbiota.