scholarly journals Interactions in self-assembled microbial communities saturate with diversity

2018 ◽  
Author(s):  
Xiaoqian Yu ◽  
Martin F. Polz ◽  
Eric J. Alm

AbstractHow the diversity of organisms competing for or sharing resources influences community production is an important question in ecology but has rarely been explored in natural microbial communities. These generally contain large numbers of species making it difficult to disentangle how the effects of different interactions scale with diversity. Here, we show that changing diversity affects measures of community function in relatively simple communities but that increasing richness beyond a threshold has little detectable effect. We generated self-assembled communities with a wide range of diversity by growth of cells from serially diluted seawater on brown algal leachate. We subsequently isolated the most abundant taxa from these communities via dilution-to-extinction in order to compare productivity functions of the entire community to those of individual taxa. To parse the effect of different types of organismal interactions, we developed relative total function (RTF) as an index for positive or negative effects of diversity on community function. Our analysis identified three overall regimes with increasing diversity. At low richness (<12 taxa), potential positive and negative effects of interactions are both weak, while at moderate richness (12-20 taxa), community resource uptake increases but the carbon use efficiency decreases. Finally, beyond 20 taxa, there was no net change in community function indicating a saturation of potential interactions. These data suggest that although more diverse communities had overall greater access to resources, individual taxa within these communities had lower resource availability and reduced carbon use efficiency, indicating that competition due to niche overlap increases with diversity but that these interactions saturate at a specific threshold.

2022 ◽  
Author(s):  
Gayathri Sambamoorthy ◽  
Karthik Raman

Microbes thrive in communities, embedded in a complex web of interactions. These interactions, particularly metabolic interactions, play a crucial role in maintaining the community structure and function. As the organisms thrive and evolve, a variety of evolutionary processes alter the interactions among the organisms in the community, although the community function remains intact. In this work, we simulate the evolution of two-member microbial communities in silico to study how evolutionary forces can shape the interactions between organisms. We employ genomescale metabolic models of organisms from the human gut, which exhibit a range of interaction patterns, from mutualism to parasitism. We observe that the evolution of microbial interactions varies depending upon the starting interaction and also on the metabolic capabilities of the organisms in the community. We find that evolutionary constraints play a significant role in shaping the dependencies of organisms in the community. Evolution of microbial communities yields fitness benefits in only a small fraction of the communities, and is also dependent on the interaction type of the wild-type communities. The metabolites cross-fed in the wild-type communities appear in only less than 50% of the evolved communities. A wide range of new metabolites are cross-fed as the communities evolve. Further, the dynamics of microbial interactions are not specific to the interaction of the wild-type community but vary depending on the organisms present in the community. Our approach of evolving microbial communities in silico provides an exciting glimpse of the dynamics of microbial interactions and offers several avenues for future investigations.


2013 ◽  
Vol 16 (7) ◽  
pp. 930-939 ◽  
Author(s):  
Robert L. Sinsabaugh ◽  
Stefano Manzoni ◽  
Daryl L. Moorhead ◽  
Andreas Richter

2015 ◽  
Vol 89 ◽  
pp. 35-43 ◽  
Author(s):  
Paul Dijkstra ◽  
Elena Salpas ◽  
Dawson Fairbanks ◽  
Erin B. Miller ◽  
Shannon B. Hagerty ◽  
...  

2018 ◽  
Vol 11 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Alireza Araghi ◽  
Jan Adamowski ◽  
Christopher J. Martinez

Abstract Reference evapotranspiration (ETo) is one of the most important factors in the hydrologic cycle and water balance studies. In this study, the performance of three simple and three wavelet hybrid models were compared to estimate ETo in three different climates in Iran, based on different combinations of input variables. It was found that the wavelet-artificial neural network was the best model, and multiple linear regression (MLR) was the worst model in most cases, although the performance of the models was related to the climate and the input variables used for modeling. Overall, it was found that all models had good accuracy in terms of estimating daily ETo. Also, it was found in this study that large numbers of decomposition levels via the wavelet transform had noticeable negative effects on the performance of the wavelet-based models, especially for the wavelet-adaptive network-based fuzzy inference system and wavelet-MLR, but in contrast, the type of db wavelet function did not have a detectable effect on the performance of the wavelet-based models.


2021 ◽  
Author(s):  
Ashish B. George ◽  
Kirill S. Korolev

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.


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