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|Document Type: ||Article|
|Title: ||Engineering difference: Matrix design determines community composition in
wastewater treatment systems|
|Authors: ||Harris, J. A.|
Baptista, J. D. C.
Curtis, T. P.
Nelson, A. K.
Tyrrel, Sean F.
|Issue Date: ||2012|
|Citation: ||J.A. Harris, J.D.C. Baptista, T.P. Curtis, A.K. Nelson, M. Pawlett, K. Ritz, S.F. Tyrrel, Engineering difference: Matrix design determines community composition in
wastewater treatment systems, Ecological Engineering, Volume 40, March 2012, Pages 183–188.|
|Abstract: ||There is a growing view that the application of ecological theory has the
potential to facilitate a transition from a descriptive to a predictive
framework in wastewater engineering. In this study we tested the hypotheses
that: (i) it is possible to engineer consistent differences between microbial
communities in wastewater treatment modules; (ii) there is a positive
relationship between structural complexity and genetic diversity; (iii) such
interactions are modulated by the availability of energy. We developed four
treatment modules of increasingly complex support material (matrix) design, and
pumped a synthetic wastewater through them for 16 weeks. We then disassembled
the modules and assessed the phylogenetic (general eubacteria and ammonium
diversity of the communities present on the support materials. We found that
different genotypic and phenotypic community structures were reliably generated
by the engineering of their physical environment in terms of structural
complexity (as determined by particle size distribution and therefore pore size
distribution). Furthermore, there was a notably consistent response of the
phenotypic structure to such circumstances, and also to the presence of organic
matter. However, we found no significant relationships between genetic diversity
and structural complexity either for eubacterial or ammonia-oxidiser microbial
groups. This work demonstrates that is it possible to engineer modules of
differing microbial community composition by varying their physical complexity.
This is an essential first step in testing relationships between system
diversity and treatment resilience at a process level.oxidisers, by DGGE
profiling) and phenotypic (by PLFA profiling)|
|Appears in Collections:||Staff publications - School of Applied Sciences|
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