scholarly journals Integrating microbial community data with ecological theory

2011 ◽  
Author(s):  
Steven Allison
Author(s):  
Reza Barati Rashvanlou ◽  
Mahdi Farzadkia ◽  
Abbas Ali Moserzadeh ◽  
Asghar Riazati ◽  
Chiang Wei ◽  
...  

Introduction: One of biological wastewater treatment methods that utilizes to both digesting waste activated sludge and methane production is anaerobic digestion (AD). It is believed to be most effective solution in terms of energy crisis and environmental pollution issues. Materials and Methods: In this study the sludge was digested anaerobically sampled from a full-scale WWTP, located at south of Tehran, Iran for evaluation. To study the microbial community within the sludge the MiSeq Sequencing method utilized. Based on our field data (data not shown) and microbial community data, a schematic diagram of probable leading pathways was made in the studied digester. Results: At first, the community variety in the bulk sludge and richness were enhanced followed by loading increasing. Meanwhile, the loading change enhanced the community richness and variety of the sludge. By comparing the rank-abundance distributions, a shallow gradient would show high evenness since the abundances of diverse species are alike. The results showed all the communities were extremely diverse and 15 phyla were distinguished in the sludge sample. The dominant phyla of the community were Bacteroidetes and Firmicutes and quantity of the two phyla were 21% and 11%, respectively. Anaerobaculum, Acinetobacter, Syntrophomonas, and Coprothermobacter were the chief genera for the microbial communities and the sum of four genera were 7%, 3%, 3%, and 2%, respectively. Conclusion: It was shown that syntrophic acetate oxidizing bacterias (SAOBs) metabolized acetate through hydrogen trophic methanogenesis in the digester. Generally, the findings may be useful to help the wastewater operators to utilize an effective method that able to treat waste sludge plus methane production, simultaneously.


2019 ◽  
Vol 133 ◽  
pp. 64-71 ◽  
Author(s):  
Wenfang Cai ◽  
Keaton Larson Lesnik ◽  
Matthew J. Wade ◽  
Elizabeth S. Heidrich ◽  
Yunhai Wang ◽  
...  

mSystems ◽  
2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Karoline Faust

ABSTRACT The investigation of microbial community dynamics is hampered by low resolution, a lack of control, and a small number of replicates. These deficiencies can be tackled with defined communities grown under well-controlled conditions in high-throughput automated cultivation devices. Besides delivering high-quality microbial community data, automated cultivation will also ease measurement of the basic parameters needed to parameterize mathematical models of microbial communities. Better experimental data will allow revisiting classical ecological questions, such as the impact of community structure on dynamics. In addition, such data will allow validation and comparison of community models and benchmarking of microbial data analysis software. In summary, high-throughput automated cultivation will lead to a deeper understanding of microbial community dynamics through better models and software.


PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0173183 ◽  
Author(s):  
Mark Hanemaaijer ◽  
Brett G. Olivier ◽  
Wilfred F. M. Röling ◽  
Frank J. Bruggeman ◽  
Bas Teusink

2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Deni Ribicic ◽  
Kelly Marie McFarlin ◽  
Roman Netzer ◽  
Odd Gunnar Brakstad ◽  
Anika Winkler ◽  
...  

2018 ◽  
Author(s):  
Jacob R. Price ◽  
Stephen Woloszynek ◽  
Gail Rosen ◽  
Christopher M. Sales

Abstracttheseus is a collection of functions within the R programming framework [1] to assist microbiologists and molecular biologists in the interpretation of microbial community composition data.


2019 ◽  
Author(s):  
David W. Armitage ◽  
Stuart E. Jones

ABSTRACTMicrobial community data are commonly subjected to computational tools such as correlation networks, null models, and dynamic models, with the goal of identifying the ecological processes structuring microbial communities. Researchers applying these methods assume that the signs and magnitudes of species interactions and vital rates can be reliably parsed from observational data on species’ (relative) abundances. However, we contend that this assumption is violated when sample units contain any underlying spatial structure. Here, we show how three phenomena — Simpson’s paradox, context-dependence, and nonlinear averaging — can lead to erroneous conclusions about population parameters and species interactions when samples contain heterogeneous mixtures of populations or communities. At the root of this issue is the fundamental mismatch between the spatial scales of species interactions (micrometres) and those of typical microbial community samples (millimetres to centimetres). These issues can be overcome by measuring and accounting for spatial heterogeneity at very small scales, which will lead to more reliable inference of the ecological mechanisms structuring natural microbial communities.


2015 ◽  
Vol 11 (1) ◽  
pp. e1004008 ◽  
Author(s):  
Andreas Wilke ◽  
Jared Bischof ◽  
Travis Harrison ◽  
Tom Brettin ◽  
Mark D'Souza ◽  
...  

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Jessica L Metcalf ◽  
Laura Wegener Parfrey ◽  
Antonio Gonzalez ◽  
Christian L Lauber ◽  
Dan Knights ◽  
...  

Establishing the time since death is critical in every death investigation, yet existing techniques are susceptible to a range of errors and biases. For example, forensic entomology is widely used to assess the postmortem interval (PMI), but errors can range from days to months. Microbes may provide a novel method for estimating PMI that avoids many of these limitations. Here we show that postmortem microbial community changes are dramatic, measurable, and repeatable in a mouse model system, allowing PMI to be estimated within approximately 3 days over 48 days. Our results provide a detailed understanding of bacterial and microbial eukaryotic ecology within a decomposing corpse system and suggest that microbial community data can be developed into a forensic tool for estimating PMI.


2018 ◽  
Vol 14 (6) ◽  
pp. e1006143 ◽  
Author(s):  
Koichi Higashi ◽  
Shinya Suzuki ◽  
Shin Kurosawa ◽  
Hiroshi Mori ◽  
Ken Kurokawa

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