Ecological landscapes guide the assembly of optimal microbial communities

Abstract

Assembling optimal microbial communities is key for various applications in biofuel production, agriculture, and human health. Finding the optimal community is challenging because the number of possible communities grows exponentially with the number of species, and so an exhaustive search cannot be performed even for a dozen species. A heuristic search that improves community function by adding or removing one species at a time is more practical, but it is unknown whether this strategy can discover an optimal or nearly optimal community. Using consumer-resource models with and without cross-feeding, we investigate how the efficacy of search depends on the distribution of resources, niche overlap, cross-feeding, and other aspects of community ecology. We show that search efficacy is determined by the ruggedness of the appropriately-defined ecological landscape. We identify specific ruggedness measures that are both predictive of search performance and robust to noise and low sampling density. The feasibility of our approach is demonstrated using experimental data from a soil microbial community. Overall, our results establish the conditions necessary for the success of the heuristic search and provide concrete design principles for building high-performing microbial consortia.

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

“…Large fluctuations in these populations are associated with catastrophic events such as disease, economic or ecological collapse [19][20][21][22] ; hence understanding these fluctuations is crucial for risk assessment, quantitative biological methods, and forecasting 19,[23][24][25][26][27] . Yet, many models of these populations study the equilibrium and steady-state properties such as the average population abundance 1,2,22,[28][29][30][31] . This restriction is in part because 1) analyzing dynamical properties of complex models is harder than analyzing their steady-state behavior and 2) temporal data required to fit and validate complex models has been lacking.…”

Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes

“…We justify this assumption by noting, as in [15], that this simple behavior is what has been frequently observed in small experimental communities. Furthermore, this behavior is predicted by many mechanistic models [15]. Still, even with this assumption, if there are any interactions among species at all, it is reasonable to assume that the abundance of a species at steady state will in general depend on which other species are present, and may therefore be quite different in different seed communities.…”

“…Thus, for each species, we have 2 16 abundance data points. Since each species is present in only half the combinations, the effective total data for each species is 2 15 . Further, we considered the relative abundance of a species in each combination.…”

“…These maps have been referred to as ecological landscapes. [15,20,26,27] Here, we focus on the notion of ecological landscape, where the mapping of interest is between the initial composition of the community, and the final, steady-state abundance of each species. We thus consider as many ecological landscapes as there are species.…”

“…Putting these two issues together makes a brute-force approach to cataloging potential microbiomes impossible: with as few as ten species from which to build an initial community, 2 10 , or around 1000 experiments would be needed to survey the full range of possible outcomes arising as the dynamics of communities play out. With 100 potential initial species, that number becomes 10 30 [15].…”