Microbial communities display alternative stable states in a fluctuating environment

Abstract

The effect of environmental fluctuations is a major question in ecology. While it is widely accepted that fluctuations and other types of disturbances can increase biodiversity, there are fewer examples of other types of outcomes in a fluctuating environment. Here we explore this question with laboratory microcosms, using cocultures of two bacterial species, P. putida and P. veronii. At low dilution rates we observe competitive exclusion of P. veronii, whereas at high dilution rates we observe competitive exclusion of P. putida. When the dilution rate alternates between high and low, we do not observe coexistence between the species, but rather alternative stable states, in which only one species survives and initial species' fractions determine the identity of the surviving species. The Lotka-Volterra model with a fluctuating mortality rate predicts that this outcome is independent of the timing of the fluctuations, and that the time-averaged mortality would also lead to alternative stable states, a prediction that we confirm experimentally. Other pairs of species can coexist in a fluctuating environment, and again consistent with the model we observe coexistence in the time-averaged dilution rate. We find a similar time-averaging result holds in a three-species community, highlighting that simple linear models can in some cases provide powerful insight into how communities will respond to environmental fluctuations.

Untangling the complexity of priority effects in multispecies communities

“…We have also found that the explanatory power of these four topological features increases when species attempt to invade multiple times (Figure 5C). While the number of alternative stable states has received most of the attention (Schröder et al, 2005;Serván & Allesina, 2020;Amor et al, 2020;Abreu et al, 2020), we show that the other three sources, especially in small communities, can contribute more to community predictability (Figure 5D). The trained neural network has predicted the observed predictability of two empirical assembly dynamics (Drake, 1991;Warren et al, 2003) well (Figure 6).…”

Network-level containment of single-species bioengineering

“…Additionally, one can move beyond the single-species picture and consider instead the multi-species dissemination of mobile DNA (see [45] and references cited). Finally, these in silico experiments could be implemented in microbial microcosms [46, 47] as an intermediate step towards the application of terraformation strategies to real case studies [29].…”

Quantifying the impact of ecological memory on the dynamics of interacting communities

“…Ecological communities are constantly subject to perturbations arising from external factors, as well as from internal processes and interactions between community members. Environmental fluctuations through time have a fundamental influence on ecological communities: they may promote species coexistence, increase community diversity [45,46], contribute to the properties of stable states [37,47], and in some cases, facilitate abrupt regime shifts [47]. Our analysis of memory in perturbed communities is closely linked to recent studies analysing the response of experimental microbial communities to antibiotic pulse perturbation [48,49], or the impact of periodic perturbations on the evolution of antimicrobial August 17, 2021 11/28 resistance [34].…”