Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales

Abstract

Managing and engineering microbial communities relies on the ability to predict their composition. While progress has been made on predicting compositions on short, ecological timescales, there is still little work aimed at predicting compositions on evolutionary timescales. Therefore, it is still unknown for how long communities typically remain stable after reaching ecological equilibrium, and how repeatable and predictable are changes when they occur. Here, we address this knowledge gap by tracking the composition of 87 two- and three-species bacterial communities, with 3–18 replicates each, for ~400 generations. We find that community composition typically changed during evolution, but that the composition of replicate communities remained similar. Furthermore, these changes were predictable in a bottom-up approach—changes in the composition of trios were consistent with those that occurred in pairs during coevolution. Our results demonstrate that simple assembly rules can hold even on evolutionary timescales, suggesting it may be possible to forecast the evolution of microbial communities.

Species interactions constrain adaptation and preserve ecological stability in an experimental microbial community

“…Microbial communities are bound by a range of competitive and cooperative interactions [ 1 ], and a growing number of experimental studies show that changes in species composition can drastically alter the course of evolution for those species that remain [ 2 – 13 ]. Incorporating multiple species into experimental, multigenerational cultures has revealed a range of outcomes, including accelerated molecular and fitness evolution [ 14 , 15 ], decelerated fitness evolution [ 14 ], more efficient use of resources [ 5 , 16 – 18 ], and community stabilisation [ 7 , 8 , 17 ].…”

Interactions between culturable bacteria are highly non-additive

“…In particular, we were interested in finding a model that describes the effects of pairs, based on the data from single species effects. Based on previous studies' success in predicting community structure from pairwise interactions (Friedman, Higgins, and Gore 2017;Meroz et al 2021;Guo and Boedicker 2016;Os et al 2018), we posited that predicting how effects combine based solely on the effects of the single species should also be feasible. To do so, we considered three models: an additive effect model, a mean effect model, and a strongest effect model.…”

“…generalized Lotka-Voltera), don't properly capture microbial community interactions, partially due to the nature of these interactions (chemically mediated as opposed to predator-prey) (Momeni, Xie, and Shou 2017). However other studies have shown that both structures of, and interactions within, larger communities can be accurately predicted from pairwise interactions alone, using variations of said models (Friedman, Higgins, and Gore 2017;Meroz et al 2021;Guo and Boedicker 2016;Os et al 2018). This being the case, how microbial interactions combine into the joint effect of multiple species on a single species of interest is still poorly understood.…”

Untangling the complexity of priority effects in multispecies communities

“…Focusing now on how species composition changes after the introduction of a new species (i.e. knowing the edges in assembly graphs), we suggest that the experimental procedure can follow the standard procedures in assessing the effects of species introductions (Friedman et al, 2017; Grainger et al, 2019a; Meroz et al, 2021; Spaak & De Laender, 2020). In brief, one would introduce an invader species to the resident community at low abundance (relative to the abundance of the resident species) and assess whether species composition changes.…”