Article | Published:

Microbial residence time is a controlling parameter of the taxonomic composition and functional profile of microbial communities

The ISME Journal (2019) | Download Citation


A remaining challenge within microbial ecology is to understand the determinants of richness and diversity observed in environmental microbial communities. In a range of systems, including activated sludge bioreactors, the microbial residence time (MRT) has been previously shown to shape the microbial community composition. However, the physiological and ecological mechanisms driving this influence have remained unclear. Here, this relationship is explored by analyzing an activated sludge system fed with municipal wastewater. Using a model designed in this study based on Monod-growth kinetics, longer MRTs were shown to increase the range of growth parameters that enable persistence, resulting in increased richness and diversity in the modeled community. In laboratory experiments, six sequencing batch reactors treating domestic wastewater were operated in parallel at MRTs between 1 and 15 days. The communities were characterized using both 16S ribosomal RNA and non-target messenger RNA sequencing (metatranscriptomic analysis), and model-predicted monotonic increases in richness were confirmed in both profiles. Accordingly, taxonomic Shannon diversity also increased with MRT. In contrast, the diversity in enzyme class annotations resulting from the metatranscriptomic analysis displayed a non-monotonic trend over the MRT gradient. Disproportionately high abundances of transcripts encoding for rarer enzymes occur at longer MRTs and lead to the disconnect between taxonomic and functional diversity profiles.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


  1. 1.

    Curtis TP, Sloan WT, Scannell JW. Estimating prokaryotic diversity and its limits. Proc Natl Acad Sci USA. 2002;99:10494–9.

  2. 2.

    Locey KJ, Lennon JT. Scaling laws predict global microbial diversity. Proc Natl Acad Sci USA. 2016;113:5970–5.

  3. 3.

    Antwis RE, Griffiths SM, Harrison XA, Aranega-Bou P, Arce A, Bettridge AS, et al. Fifty important research questions in microbial ecology. FEMS Microbiol Ecol. 2017;93:1–10.

  4. 4.

    Vuono DC, Benecke J, Henkel J, Navidi WC, Cath TY, Munakata-Marr J, et al. Disturbance and temporal partitioning of the activated sludge metacommunity. ISME J. 2015;9:425–35.

  5. 5.

    Meerburg FA, Vlaeminck SE, Roume H, Seuntjens D, Pieper DH, Jauregui R, et al. High-rate activated sludge communities have a distinctly different structure compared to low-rate sludge communities, and are less sensitive towards environmental and operational variables. Water Res. 2016;100:137–45.

  6. 6.

    Bagchi S, Tellez BG, Rao HA, Lamendella R, Saikaly PE. Diversity and dynamics of dominant and rare bacterial taxa in replicate sequencing batch reactors operated under different solids retention time. Appl Microbiol Biotechnol. 2015;99:2361–70.

  7. 7.

    Akarsubasi AT, Eyice O, Miskin I, Head IM, Curtis TP. Effect of sludge age on the bacterial diversity of bench scale sequencing batch reactors. Environ Sci Technol. 2009;43:2950–6.

  8. 8.

    Fang H, Chen Y, Huang L, He G. Analysis of biofilm bacterial communities under different shear stresses using size-fractionated sediment. Sci Rep. 2017;7:1299.

  9. 9.

    Roager HM, Hansen LB, Bahl MI, Frandsen HL, Carvalho V, Gøbel RJ, et al. Colonic transit time is related to bacterial metabolism and mucosal turnover in the gut. Nat Microbiol. 2016;1:16093.

  10. 10.

    Kreuzinger N, Clara M, Strenn B, Kroiss H. Relevance of the sludge retention time (SRT) as design criteria for wastewater treatment plants for the removal of endocrine disruptors and pharmaceuticals from wastewater. Water Sci Technol. 2004;50:149–56.

  11. 11.

    Falås P, Andersen HR, Ledin A, la Cour Jansen J. Impact of solid retention time and nitrification capacity on the ability of activated sludge to remove pharmaceuticals. Environ Technol. 2012;33:865–72.

  12. 12.

    Johnson DR, Lee TK, Park J, Fenner K, Helbling DE. The functional and taxonomic richness of wastewater treatment plant microbial communities are associated with each other and with ambient nitrogen and carbon availability. Environ Microbiol. 2015;17:4851–60.

  13. 13.

    Pholchan MK, Baptista JDC, Davenport RJ, Sloan WT, Curtis TP. Microbial community assembly, theory and rare functions. Front Microbiol. 2013;4:68.

  14. 14.

    Louca S, Parfrey LW, Doebeli M. Decoupling function and taxonomy in the global ocean microbiome. Science. 2016;353:1272–7.

  15. 15.

    Louca S, Jacques SM, Pires AP, Leal JS, Srivastava DS, Parfrey LW, et al. High taxonomic variability despite stable functional structure across microbial communities. Nat Ecol Evol. 2017;1:0015.

  16. 16.

    Frossard A, Gerull L, Mutz M, Gessner MO. Disconnect of microbial structure and function: enzyme activities and bacterial communities in nascent stream corridors. ISME J. 2012;6:680–91.

  17. 17.

    Purahong W, Schloter M, Pecyna MJ, Kapturska D, Däumlich V, Mital S, et al. Uncoupling of microbial community structure and function in decomposing litter across beech forest ecosystems in CentralEurope. Sci Rep. 2014;4:7014.

  18. 18.

    Kivlin SN, Hawkes CV. Temporal and spatial variation of soil bacteria richness, composition, and function in a neotropical rainforest. PLoS ONE. 2016;11:e0159131.

  19. 19.

    Burke C, Steinberg P, Rusch D, Kjelleberg S, Thomas T. Bacterial community assembly based on functional genes rather than species. Proc Natl Acad Sci USA. 2011;108:14288–93.

  20. 20.

    Boon E, Meehan CJ, Whidden C, Wong DHJ, Langille MG, Beiko RG. Interactions in the microbiome: communities of organisms and communities of genes. FEMS Microbiol Rev. 2014;38:90–118.

  21. 21.

    Daims H, Taylor MW, Wagner M. Wastewater treatment: a model system for microbial ecology. Trends Biotechnol. 2006;24:483–9.

  22. 22.

    Downing AL, Hopwood AP. Some observations on the kinetics of nitrifying activated-sludge plants. Schweiz Z Hydrol. 1964;26:271–88.

  23. 23.

    Lawrence AW, McCarty PL. Unified basis for biological treatment design and operation. J Sanit Eng Div. 1970;96:757–78.

  24. 24.

    Henze M, Gujer W, Mino T, Van Loosdrecht MCM. Activated sludge models ASM1, ASM2, ASM2d and ASM3. IWA publishing; London, UK, 2000.

  25. 25.

    Degnan PH, Ochman H. Illumina-based analysis of microbial community diversity. ISME J. 2012;6:183–94.

  26. 26.

    Sinclair L, Osman OA, Bertilsson S, Eiler A. Microbial community composition and diversity via 16S rRNA gene amplicons: evaluating the illumina platform. PLoS ONE. 2015;10:e0116955.

  27. 27.

    Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C, Debelius J, et al. Best practices for analysing microbiomes. Nat Rev Microbiol. 2018;16:410–22.

  28. 28.

    van Bodegom P. Microbial maintenance: a critical review on its quantification. Microb Ecol. 2007;53:513–23.

  29. 29.

    Achermann S, Falas P, Joss A, Mansfeldt C, Men Y, Vogler B, et al. Trends in micropollutant biotransformation along a solids retention time gradient. Environ Sci Technol. 2018;52:11601–11.

  30. 30.

    Guo F, Ju F, Cai L, Zhang T. Taxonomic precision of different hypervariable regions of 16S rRNA gene and annotation methods for functional bacterial groups in biological wastewater treatment. PLoS ONE. 2013;8:e76185.

  31. 31.

    Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.

  32. 32.

    Schmieder R, Edwards R. Quality control and preprocessing of metagenomic datasets. Bioinformatics. 2011;27:863–4.

  33. 33.

    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.

  34. 34.

    Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. 2011;17:10.

  35. 35.

    McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.

  36. 36.

    Hill MO. Diversity and evenness: a unifying notation and its consequences. Ecology. 1973;54:427–32.

  37. 37.

    Chao A, Chiu CH, Jost L. Phylogenetic diversity measures based on Hill numbers. Philos Trans R Soc B—Biol Sci. 2010;365:3599–609.

  38. 38.

    Chao A, Gotelli NJ, Hsieh TC, Sander EL, Ma KH, Colwell RK, et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol Monogr. 2014;84:45–67.

  39. 39.

    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.

  40. 40.

    Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.

  41. 41.

    Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.

  42. 42.

    Monod J. The growth of bacterial cultures. Annu Rev Microbiol. 1949;3:371–94.

  43. 43.

    Liu G, Wang J. Modeling effects of DO and SRT on activated sludge decay and production. Water Res. 2015;80:169–78.

  44. 44.

    Saikaly PE, Oerther DB. Bacterial competition in activated sludge: theoretical analysis of varying solids retention times on diversity. Microb Ecol. 2004;48:274–84.

  45. 45.

    Hsu SB, Hubbell S, Waltman P. A mathematical theory for single-nutrient competition in continuous cultures of micro-organisms. SIAM J Appl Math. 1977;32:366–83.

  46. 46.

    Saikaly PE, Oerther DB. Diversity of dominant bacterial taxa in activated sludge promotes functional resistance following toxic shock loading. Microb Ecol. 2011;61:557–67.

  47. 47.

    Habermacher J, Benetti AD, Derlon N, Morgenroth E. The effect of different aeration conditions in activated sludge–side-stream system on sludge production, sludge degradation rates, active biomass and extracellular polymeric substances. Water Res. 2015;85:46–56.

  48. 48.

    Martínez-García CG, Fall C, Olguín MT. Activated sludge mass reduction and biodegradability of the endogenous residues by digestion under different aerobic to anaerobic conditions: comparison and modeling. Bioresour Technol. 2016;203:32–41.

  49. 49.

    Friedrich M, Takács I. A new interpretation of endogenous respiration profiles for the evaluation of the endogenous decay rate of heterotrophic biomass in activated sludge. Water Res. 2013;47:5639–46.

  50. 50.

    Friedrich M, Jimenez J, Pruden A, Miller JH, Metch J, Takács I. Rethinking growth and decay kinetics in activated sludge–towards a new adaptive kinetics approach. Water Sci Technol. 2017;75:501–6.

  51. 51.

    Soetaert KER, Petzoldt T, Setzer RW. (2010). Solving differential equations in R: package deSolve. J Stat Softw. 2010;33:1–25.

  52. 52.

    Duan L, Moreno-Andrade I, Huang CL, Xia S, Hermanowicz SW. Effects of short solids retention time on microbial community in a membrane bioreactor. Bioresour Technol. 2009;100:3489–96.

  53. 53.

    Saikaly PE, Stroot PG, Oerther DB. Use of 16S rRNA gene terminal restriction fragment analysis to assess the impact of solids retention time on the bacterial diversity of activated sludge. Appl Environ Microbiol. 2005;71:5814–22.

  54. 54.

    Gonzalez-Martinez A, Rodriguez-Sanchez A, Lotti T, Garcia-Ruiz MJ, Osorio F, Gonzalez-Lopez J, et al. Comparison of bacterial communities of conventional and A-stage activated sludge systems. Sci Rep. 2016;6:18786.

  55. 55.

    Zhang T, Shao MF, Ye L. 454 Pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants. ISME J. 2011;6:1137–47.

  56. 56.

    Saunders AM, Albertsen M, Vollertsen J, Nielsen PH. The activated sludge ecosystem contains a core community of abundant organisms. ISME J. 2016;10:11–20.

  57. 57.

    Pollice A, Tandoi V, Lestingi C. Influence of aeration and sludge retention time on ammonium oxidation to nitrite and nitrate. Water Res. 2002;36:2541–6.

  58. 58.

    Jiang L. Negative selection effects suppress relationships between bacterial diversity and ecosystem functioning. Ecology. 2007;88:1075–85.

  59. 59.

    Prosser JI, Head IM, Stein LY. The family Nitrosomonadaceae. In: Rosenberg E, DeLong EF, Lory S, Stackebrandt E, Thompso F (eds.). The Prokaryotes, Berlin, Heidelberg: Springer; 2014. p. 901–18.

  60. 60.

    Vuono DC, Regnery J, Li D, Jones ZL, Holloway RW, Drewes JE. rRNA gene expression of abundant and rare activated-sludge microorganisms and growth rate induced micropollutant removal. Environ Sci Technol. 2016;50:6299–309.

  61. 61.

    Grime JP, Pierce S. The evolutionary strategies that shape ecosystems. John Wiley & Sons; West Sussex, UK, 2012.

  62. 62.

    Ho A, Di Lonardo DP, Bodelier PL. Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol. 2017;93:1–14.

  63. 63.

    Andrews JH, Harris RF. r-and K-selection and microbial ecology. In: Marshall KC (ed.). Advances in microbial ecology, Boston, MA: Springer; 1986. p. 99–147.

  64. 64.

    Větrovský T, Baldrian P. The variability of the 16S rRNA gene in bacterial genomes and its consequences for bacterial community analyses. PLoS ONE. 2013;8:e57923.

  65. 65.

    Chandran K, Smets BF. Estimating biomass yield coefficients for autotrophic ammonia and nitrite oxidation from batch respirograms. Water Res. 2001;35:3153–6.

  66. 66.

    Friedrich M, Takács I, Tränckner J. Physiological adaptation of growth kinetics in activated sludge. Water Res. 2015;85:22–30.

  67. 67.

    Vuono DC, Munakata‐Marr J, Spear JR, Drewes JE. Disturbance opens recruitment sites for bacterial colonization in activated sludge. Environ Microbiol. 2016;18:87–99.

  68. 68.

    Blazewicz SJ, Barnard RL, Daly RA, Firestone MK. Evaluating rRNA as an indicator of microbial activity in environmental communities: limitations and uses. ISME J. 2013;7:2061–8.

  69. 69.

    Yu K, Zhang T. Metagenomic and metatranscriptomic analysis of microbial community structure and gene expression of activated sludge. PLoS ONE. 2012;7:e38183.

  70. 70.

    Prosser JI. Dispersing misconceptions and identifying opportunities for the use of omics’ in soil microbial ecology. Nat Rev Microbiol. 2015;13:439–46.

  71. 71.

    Bar-Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Tawfik DS, et al. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry. 2011;50:4402–10.

  72. 72.

    de Sousa Abreu R, Penalva LO, Marcotte EM, Vogel C. Global signatures of protein and mRNA expression levels. Mol Biosyst. 2009;5:1512–26.

  73. 73.

    Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32.

  74. 74.

    Daims H, Lücker S, Wagner M. A new perspective on microbes formerly known as nitrite-oxidizing bacteria. Trends Microbiol. 2016;24:699–712.

  75. 75.

    Salvado H. Effect of mean cellular retention time on ciliated protozoan populations in urban wastewater treatment plants based on a proposed model. Water Res. 1994;28:1315–21.

Download references


Data produced and analyzed in this paper were generated in collaboration with the Genetic Diversity Centre (GDC), ETH Zurich, Switzerland and the Genomics Facility at the University of Basel, Switzerland. We thank the operators and the staff of the WWTP ARA Niederglatt for providing activated sludge. We acknowledge financial support from the European Research Council under the European Union’s Seventh Framework Program (ERC grant agreement no. 614768, PROduCTS). We also thank Dr. Paola Meynet for assistance in the preparation of the 16S control libraries.

Author information

Author notes

  1. These authors contributed equally: Cresten Mansfeldt, Stefan Achermann


  1. Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Überlandstrasse 133, 8600, Dübendorf, Switzerland

    • Cresten Mansfeldt
    • , Stefan Achermann
    •  & Kathrin Fenner
  2. Institute of Biogeochemistry and Pollutant Dynamics, ETH Zürich, 8092, Zürich, Switzerland

    • Stefan Achermann
    •  & Kathrin Fenner
  3. Department of Civil and Environmental Engineering, University of Illinois, 205N. Mathews Ave., Urbana, IL, 61801, USA

    • Yujie Men
  4. Department of Environmental Systems Science, Genetic Diversity Centre, ETH Zürich, Universitätstrasse 16, 8006, Zürich, Switzerland

    • Jean-Claude Walser
  5. Department of Process Engineering, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Überlandstrasse 133, 8600, Dübendorf, Switzerland

    • Kris Villez
    •  & Adriano Joss
  6. Department of Environmental Microbiology, Swiss Federal Institute of Aquatic Science and Technology, Eawag, Überlandstrasse 133, 8600, Dübendorf, Switzerland

    • David R. Johnson
  7. Department of Chemistry, University of Zürich, Winterthurerstrasse 190, 8057, Zürich, Switzerland

    • Kathrin Fenner


  1. Search for Cresten Mansfeldt in:

  2. Search for Stefan Achermann in:

  3. Search for Yujie Men in:

  4. Search for Jean-Claude Walser in:

  5. Search for Kris Villez in:

  6. Search for Adriano Joss in:

  7. Search for David R. Johnson in:

  8. Search for Kathrin Fenner in:

Conflict of interest

The authors declare that they have no conflict of interest.

Corresponding author

Correspondence to Cresten Mansfeldt.

Supplementary information

About this article

Publication history