| Literature DB >> 25759686 |
Ellard R Hunting1, Martina G Vijver1, Harm G van der Geest2, Christian Mulder3, Michiel H S Kraak2, Anton M Breure4, Wim Admiraal2.
Abstract
Decomposition of organic matter is an important ecosystem process governed in part by bacteria. The process of decomposition is expected to benefit from interspecific bacterial interactions such as resource partitioning and facilitation. However, the relative importance of resource niche breadth (metabolic diversity) and resource niche overlap (functional redundancy) on decomposition and the temporal stability of ecosystem processes received little scientific attention. Therefore, this study aims to evaluate the effect of an increase in bacterial community resemblance on both decomposition and the stability of bacterial metabolism in aquatic sediments. To this end, we performed laboratory microcosm experiments in which we examined the influence of bacterial consortia differing in number and composition of species on bacterial activity (Electron Transport System Activity, ETSA), dissolved organic carbon production and wavelet transformed measurements of redox potential (Eh). Single substrate affinities of the individual bacterial species were determined in order to calculate the metabolic diversity of the microbial community. Results presented here indicate that bacterial activity and organic matter decomposition increase with widening of the resource niche breadth, and that metabolic stability increases with increasing overlap in bacterial resource niches, hinting that resource niche overlap can promote the stability of bacterial community metabolism.Entities:
Keywords: decomposition; functional redundancy; niche complementarity; niche overlap; redox potential; wavelet transform
Year: 2015 PMID: 25759686 PMCID: PMC4338809 DOI: 10.3389/fmicb.2015.00105
Source DB: PubMed Journal: Front Microbiol ISSN: 1664-302X Impact factor: 5.640
Design of bacterial consortia assembly, consisting of 7 species richness levels (2–12 species), represented by 3 treatments (.
| 2 | 1,2 | 6,7 | 11,12 |
| 3 | 1,2,3 | 6,7,8 | 10,11,12 |
| 4 | 1,2,3,4 | 5,6,7,8 | 9,10,11,12 |
| 5 | 1,2,3,4,5 | 5,6,7,8,9 | 8,9,10,11,12 |
| 7 | 1,2,3,4,5,6,7 | 4,5,6,7,8,9,10 | 7,8,9,10,11,12 |
| 11 | 1–11 | ||
| 12 | 1–12 | ||
1, Azospirillum brasilense; 2, Bacillus subtilis; 3, Paenibacillus polymyxa; 4, Pseudomonas putida; 5, Sphingomonas paucimobilis; 6, Micrococcus luteus; 7, Streptomyces antibiotic; 8, Pseudomonas stutzeri; 9, Flavobacterium sp.; 10, Aeromonas salmonida; 11, Paracoccus pantotrophus; and 12, Aminobacter aminovarans.
Figure 1Correlations of diversity measures and functional parameters. (A) Increase in community metabolic diversity (CMD) with increasing number of bacterial species as determined with single substrate affinities (Biolog GN® plates) of individual bacterial species. Consortia compositions (A–C, following Table 1) are indicated separately and line represents a 3rd order polynomial fit. Correlation of parameters with taxonomic diversity are provided for (B) Electron Transport System Activity (ETSA), (C) Dissolved Organic Carbon accumulation and (D) stability of redox values expressed as the inverse of wavelet variances (1/). Correlations of the same parameters with community metabolic diversity are provided in (E–G). Provided is the summary of correlation statistics of measured parameters depending on species richness (SR) and community metabolic diversity (CMD) based on Pearson correlations (r). Statistical significance indicated as • (p < 0.05), and •• (p < 0.01).
Figure 2Wavelet variances, . Wavelet variances are plotted per bacterial species richness level, in which each treatment (species composition, A–C, Table 1) is presented separately.