title: A preliminary and qualitative study of resource ratio theory to nitrifying lab-scale bioreactors.
abstract: The incorporation of microbial diversity in design would ideally require predictive theory that would relate operational parameters to the numbers and distribution of taxa. Resource ratio-theory (RRT) might be one such theory. Based on Monod kinetics, it explains diversity in function of resource-ratio and richness. However, to be usable in biological engineered system, the growth parameters of all the bacteria under consideration and the resource supply and diffusion parameters for all the relevant nutrients should be determined. This is challenging, but plausible, at least for low diversity groups with simple resource requirements like the ammonia oxidizing bacteria (AOB). One of the major successes of RRT was its ability to explain the 'paradox of enrichment' which states that diversity first increases and then decreases with resource richness. Here, we demonstrate that this pattern can be seen in lab-scale-activated sludge reactors and parallel simulations that incorporate the principles of RRT in a floc-based system. High and low ammonia and oxygen were supplied to continuous flow bioreactors with resource conditions correlating with the composition and diversity of resident AOB communities based on AOB 16S rDNA clone libraries. Neither the experimental work nor the simulations are definitive proof for the application of RRT in this context. However, it is sufficient evidence that such approach might work and justify a more rigorous investigation.
authors: School of Civil Engineering and Geosciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK; Dipartimento di Scienze Agrarie, Alimentari ed Ambientali, Università di Foggia, via Napoli 25, Foggia, 71122, Italy.
authors: Micol
authors: M
authors: Bellucci
authors: Irina D
authors: ID
authors: Ofiţeru
authors: Luciano
authors: L
authors: Beneduce
authors: David W
authors: DW
authors: Graham
authors: Ian M
authors: IM
authors: Head
authors: Thomas P
authors: TP
authors: Curtis
mesh_terms: D000641:Ammonia / Q000378:metabolism; D001419:Bacteria / Q000145:classification* / Q000254:growth & development* / Q000378:metabolism*; D044822:Biodiversity; D019149:Bioreactors / Q000382:microbiology*; D016000:Cluster Analysis; D003198:Computer Simulation; D003470:Culture Media / Q000737:chemistry; D004275:DNA, Ribosomal / Q000737:chemistry / Q000235:genetics; D008954:Models, Biological; D008969:Molecular Sequence Data; D058465:Nitrification; D010100:Oxygen / Q000378:metabolism; D010802:Phylogeny; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D017422:Sequence Analysis, DNA; D012722:Sewage / Q000382:microbiology
chemical_list: D003470:Culture Media; D004275:DNA, Ribosomal; D012336:RNA, Ribosomal, 16S; D012722:Sewage; D000641:Ammonia; D010100:Oxygen