Reconstructing community dynamics from limited observations.

backgroundEcosystems 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. Being able to quantify and predict these dynamics is key to our understanding of how microbial communities vary over time and respond to perturbations.

resultsMechanistic models of microbial community dynamics often fail to characterise observed fluctuations in naturally occurring microbiomes and inform us about key dynamical properties such as stability and resilience. An alternative approach is to characterise the dynamical landscape using non-parametric models. 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 are able to predict stable and tipping regions along a unidimensional stability landscape while simultaneously addressing the associated uncertainty. In particular, we estimate a recently proposed probabilistic metric for resilience in multistable systems: the expected "exit time" out of the current stable state under stochastic fluctuations. We validate our approach on simulated data and highlight in particular that our model is able to distinguish bistability from bimodality, which are often conflated in classical potential analyses. We further demonstrate our approach by re-analysing ecological time series data of lake cyanobacteria abundance, for which we recover similar results as a previous study using three orders of magnitude fewer data points. Finally, we use our model to re-evaluate the stability of previously proposed "tipping elements" in the human gut microbiota.

conclusionsWe introduce a probabilistic non-parametric approach to characterise stationary community dynamics from short time series, which is potentially applicable to a broad range of systems in microbial ecology and beyond. We use this model to clarify the distinction between bistable and bimodal dynamics and to contribute to contemporary debates on the stability and resilience of ecological communities, in particular the human gut microbiota. Video Abstract.