A Polyphasic Approach for Assessing Eco-System Connectivity Demonstrates that Perturbation Remodels Network Architecture in Soil Microcosms

  • G. P. Stamou
  • N. Monokrousos
  • D. Gwynn-Jones
  • D. E. Whitworth
  • E. M. PapatheodorouEmail author
Soil Microbiology


Network analysis was used to show changes in network attributes by analyzing the relations among the main soil microbial groups in a potted tomato soil inoculated with arbuscular mycorrhizal fungus, treated with low doses of Mentha spicata essential oil, or both, and then exposed to tenfold higher oil addition (stress pulse). Pretreatments were chosen since they can induce changes in the composition of the microbial community. Cellular phospholipid fatty acids (PLFAs) and the activity of six soil enzymes, mainly involved in the N-cycle were measured. Networks were constructed based on correlated changes in PLFA abundances. The values of all parameters were significantly different from those of random networks indicating modular architecture. Networks ranked from the lowest to highest modularity: control, non-pretreated and stressed, inoculated and stressed, oil treated and stressed, inoculated and treated with oil and stressed. The high values of network density and 1st/2nd eigenvalue ratio are related to arylamidase activity while N-acetyl-glucosaminidase, acid phosphomoesterase, and asparaginase activities related to high values of the clustering coefficient index. We concluded that modularity may be an efficient indicator of changes in the network of interactions among the members of the soil microbial community and the modular structure of the network may be related to the activity of specific enzymes. Communities that were stressed without a pretreatment were relatively resistant but prone to sudden transition towards instability, while oil or inoculation pretreatments gave networks which could be considered adaptable and susceptible to gradual change.


Network analysis Modularity Asparaginase Glutaminase Arylamidase Clustering coefficient 



This study was funded by the Research Committee of the Aristotle University of Thessaloniki (Project No. 89434).

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


  1. 1.
    Proulx R, Parrott L (2008) Measures of structural complexity in digital images for monitoring the ecological signature of an old-growth forest ecosystem. Ecol. Indic. 8:270–284. CrossRefGoogle Scholar
  2. 2.
    Parrott L (2010) Measuring ecological complexity. Ecol. Indic. 10:1069–1076. CrossRefGoogle Scholar
  3. 3.
    Kapagianni PD, Boutsis G, Argyropoulou MD, Papatheodorou EM, Stamou GP (2010) The network of interactions among soil quality variables and nematodes: short-term responses to disturbances induced by chemical and organic disinfection. Appl. Soil Ecol. 44:67–74. CrossRefGoogle Scholar
  4. 4.
    Stamou GP, Papatheodorou EM (2016) Studying the complexity of the secondary succession process in the soil of restored open mine lignite areas; the role of chemical template. Appl. Soil Ecol. 103:56–60. CrossRefGoogle Scholar
  5. 5.
    Simard S (2009) Mycorrhizal networks and complex systems: contributions of soil ecology science to managing climate change effects in forested ecosystems. Can. J. Soil Sci. 89:369–382. CrossRefGoogle Scholar
  6. 6.
    Fitter AH, Gilligan CA, Hollingworth K, Kleczkowski A, Twyman RM, Pitchford JW, the members of the NERC Soil Biodiversity Programm (2005) Biodiversity and ecosystem function in soil. Funct. Ecol. 19:369–377. CrossRefGoogle Scholar
  7. 7.
    Gibbons SM, Scholz M, Hutchison AL, Dinner AR, Gilbert JA, Coleman ML (2016) Disturbance regimes predictably alter diversity in an ecologically complex bacterial system. mBio 7(6):e01372–e01316. CrossRefGoogle Scholar
  8. 8.
    Rykiel EJ (1985) Towards a definition of ecological disturbance. Aust. J. Ecol. 10:361–365CrossRefGoogle Scholar
  9. 9.
    Tobor-Kaplon MA, Bloem J, Romkens PFAM, De Ruiter PC (2005) Functional stability of microbial communities in contaminated soils. Oikos 111:119–129. CrossRefGoogle Scholar
  10. 10.
    Norris TB, Wraith JM, Castenholz RW, McDermott TR (2002) Soil microbial community structure across a thermal gradient following a geothermal heating event. Appl. Environ. Microbiol. 68:6300–6309. CrossRefGoogle Scholar
  11. 11.
    Azarbad H, Cornelis van Gestel CAM, Niklinska M, Laskowski R, Röling WFM, van Straalen NM (2016) Resilience of soil microbial communities to metals and additional stressors: DNA-based approaches for assessing “stress-on-stress” responses. Int. J. Mol. Sci. 17:933. CrossRefGoogle Scholar
  12. 12.
    Philippot L, Cregut M, Cheneby D, Bressan M, Dequiet S, Martin-Laurent F, Ranjard L, Lemanceau P (2008) Effect of primary mild stresses on resilience and resistance of the nitrate reducer community to a subsequent severe stress. FEMS Microbiol. Lett. 285:51–57. CrossRefGoogle Scholar
  13. 13.
    Rillig MC, Rolff J, Tietjen B, Wehner J, Andrade-Linares DR (2015) Community priming—effects of sequential stressors on microbial assemblages. FEMS Microbiol. Ecol.
  14. 14.
    Nannipieri P, Ascher J, Ceccherini MT, Landi L, Pietramellara G, Renella G (2003) Microbial diversity and soil functions. Eur J Soil Sci 54:655–670CrossRefGoogle Scholar
  15. 15.
    Wagg C, Franz Bendera S, Widmerc F, van der Heijdena MGA (2014) Soil biodiversity and soil community composition determine ecosystem multifunctionality. PNAS 111:5266–5270. CrossRefGoogle Scholar
  16. 16.
    Graham EB, Knelman JE, Schindlbacher A, Siciliano S, Breulmann M, Yannarell A, Beman JM, Abell G, Philippot L, Prosser J, Foulquier A, Yuste JC, Glanville HC, Jones DL, Angel R, Salminen J, Newton RJ, Bürgmann H, Ingram LJ, Hamer U, Siljanen HMP, Peltoniemi K, Potthast K, Bañeras L, Hartmann M, Banerjee S, Yu R-Q, Nogaro G, Richter A, Koranda M, Castle SC, Goberna M, Song B, Chatterjee A, Nunes OC, Lopes AR, Cao Y, Kaisermann A, Hallin S, Strickland MS, Garcia-Pausas J, Barba J, Kang H, Isobe K, Papaspyrou S, Pastorelli R, Lagomarsino A, Lindström ES, Basiliko N, Nemergut DR (2016) Microbes as engines of ecosystem function: when does community structure enhance predictions of ecosystem processes? Front. Microbiol. 7:214. Google Scholar
  17. 17.
    Prosser JI (2012) Ecosystem processes and interactions in a morass of diversity. FEMS Microbiol. Ecol. 81:1–13. CrossRefGoogle Scholar
  18. 18.
    Shade A, Peter H, Allison SD, Baho DL, Berga M, Bürgmann H, Huber DH, Langenheder S, Lennon JT, Martiny JBH, Matulich KL, Schmidt TL, Handelsman J (2012) Fundamentals of microbial community resistance and resilience. Front. Microbio 3:417. CrossRefGoogle Scholar
  19. 19.
    Sinha S (2005) Complexity vs. stability in small-world networks. Physica A 346:147–153CrossRefGoogle Scholar
  20. 20.
    Proulx SR, Promislow DEL, Phillips PC (2005) Network thinking in ecology and evolution. TREE 20:345–353. Google Scholar
  21. 21.
    Alon U (2003) Biological networks: the tinkerer as an engineer. Science 301:1866–1867. CrossRefGoogle Scholar
  22. 22.
    Zhou J, Deng Y, Luo F, He Z, Tu Q, Zhi X (2010) Functional molecular ecological networks. mBio 1(4):e00169–e00110. Google Scholar
  23. 23.
    Stamou GP, Konstadinou S, Monokrousos N, Mastrogianni A, Orfanoudakis M, Hassiotis C, Menkissoglu-Spiroudi U, Vokou D, Papatheodorou EM (2017) The effects of arbuscular mycorrhizal fungi and essential oil on soil microbial community and N-related enzymes during the fungal early colonization phase. AIMS Microbiol 3:915–936. CrossRefGoogle Scholar
  24. 24.
    Kadoglidou K, Lagopodi A, Karamanoli K, Vokou D, Bardas GA, Menexes G, Constantinidou H-IA (2011) Inhibitory and stimulatory effects of essential oils and individual monoterpenoids on growth and sporulation of four soil-borne fungal isolates of Aspergillus terreus, Fusarium oxysporum, Penicillium expansum, and Verticillium dahliae. Eur. J. Plant Pathol. 130:297–309. CrossRefGoogle Scholar
  25. 25.
    Vokou D (2007) Allelochemicals, allelopathy and essential oils: a field in search of definitions and structure. Allelopathy J 19:119–135Google Scholar
  26. 26.
    Watt M, Kirkegaard JA, Passioura JB (2006) Rhizosphere biology and crop productivity—a review. Soil Res 44:299–317. CrossRefGoogle Scholar
  27. 27.
    Vokou D, Liotiri S (1999) Stimulation of soil microbial activity by essential oils. Chemoecology 9:41–45CrossRefGoogle Scholar
  28. 28.
    Allison SD, Jastrow JD (2006) Activities of extracellular enzymes in physically isolated fractions of restored grassland soils. Soil Biol. Biochem. 38:3245–3256. CrossRefGoogle Scholar
  29. 29.
    Sinsabaugh RL, Reynolds H, Long TM (2000) Rapid assay for amidohydrolase (urease) activity in environmental samples. Soil Biol. Biochem. 32:2095–2097. CrossRefGoogle Scholar
  30. 30.
    Tabatabai M (1994) Soil enzymes. In: Weaver R, Angles J, Bottomley P (eds) Methods of soil analysis. Part 2. Microbiological and biochemical properties, Madison, Soil Sci Soc Am, pp 775–833Google Scholar
  31. 31.
    Acosta-Martínez V, Tabatabai MA (2000) Arylamidase activity of soils. Soil Sci. Soc. Am. J. 64:215–221. CrossRefGoogle Scholar
  32. 32.
    Papadopoulou ES, Karpouzas DG, Menkissoglu-Spiroudi U (2011) Extraction parameters significantly influence the quantity and the profile of PLFAs extracted from soils. Microb. Ecol. 62:704–714. CrossRefGoogle Scholar
  33. 33.
    McKinley VL, Peacock AD, White DC (2005) Microbial community PLFA and PHB responses to ecosystem restoration in tallgrass prairie soils. Soil Biol. Biochem. 37:1946–1958. CrossRefGoogle Scholar
  34. 34.
    Myers RT, Zak DR, White DC, Peacock A (2001) Landscape-level patterns of microbial community composition and substrate use in upland forest ecosystems. Soil Sci. Soc. Am. J. 65:359–367CrossRefGoogle Scholar
  35. 35.
    Zak DR, Ringelberg DB, Pregitze KS, Randlett DL, White DC, Curtis PS (1996) Soil microbial communities beneath Populus grandidentata crown under elevated atmospheric CO2. Ecol. Appl. 6:257–262. CrossRefGoogle Scholar
  36. 36.
    Rillig MC, Mummey DL, Ramsey PW, Klironomos JN, Gannon JE (2006) Phylogeny of arbuscular mycorrhizal fungi predicts community composition of symbiosis-associated bacteria. FEMS Microbiol. Ecol. 57:389–395. CrossRefGoogle Scholar
  37. 37.
    Frostegård A, Tunlid A, Bååth E (1993) Phospholipid fatty acid composition, biomass, and activity of microbial communities from two soil types experimentally exposed to different heavy metals. Appl. Environ. Microbiol. 59:3605–3617Google Scholar
  38. 38.
    White D, Stair J, Ringelberg D (1996) Quantitative comparisons of in situ microbial biodiversity by signature biomarker analysis. J. Ind. Microbiol. Biotechnol. 17:185–196. CrossRefGoogle Scholar
  39. 39.
    Smith GA, Nickels JS, Kerger BD, Davis JD, Collins SP, Wilson JT, McNabb JF, White DC (1986) Quantitative characterization of microbial biomass and community structure in subsurface material: a prokaryotic consortium responsive to organic contamination. Can. J. Microbiol. 32:104–111CrossRefGoogle Scholar
  40. 40.
    Butts CT (2008) A relational event framework for social action. Sociol. Methodol. 38:155–200. CrossRefGoogle Scholar
  41. 41.
    Borgatti SP (2002) NetDraw: graph visualization software. Analytic Technologies: Cambridge, MAGoogle Scholar
  42. 42.
    Borgatti SP (2002) NetDraw software for network visualization. Lexington, Analytic TechnologiesGoogle Scholar
  43. 43.
    Huisman M, Van Duijn MJ (2005) Software for social network analysis. In: Carrington PJ, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, New York, pp 270–316CrossRefGoogle Scholar
  44. 44.
    O’Malley AJ, Marsden PV (2008) The analysis of social networks. Health Serv Outcomes Res Methodol 8:222–269. CrossRefGoogle Scholar
  45. 45.
    Borrett SR, Moody J, Edelmann A (2014) The rise of network ecology: maps of the topic diversity and scientific collaboration. Ecol. Model. 293:111–127. CrossRefGoogle Scholar
  46. 46.
    Rampelotto PH, Barboza ADM, Pereira AB, Triplett EWR, Schaefer CEGR, de Oliveira Camargo FA, WurdigRoesch LF (2014) Distribution and interaction patterns of bacterial communities in an Ornithogenic soil of Seymour Island, Antarctica. Microb. Ecol. 69:684–694. CrossRefGoogle Scholar
  47. 47.
    Telesford QK, Simpson SL, Jonathan H, Burdette JH, Satoru Hayasaka S, Paul J, Laurienti PJ (2011) The brain as a complex system: using network science as a tool for understanding the brain. Bain Connect 1:297–307. Google Scholar
  48. 48.
    Humphries MD, Gurney K (2008) Network ‘small-world-ness’: a quantitative method for determining canonical network equivalence. PLoS One 3(4):e0002051. CrossRefGoogle Scholar
  49. 49.
    Tobor-Kaplon MA, Bloem J, de Ruiter PC (2006a) Functional stability of microbial communities from long term strongly stressed soils to additional disturbance. Environ. Toxicol. Chem. 25:1993–1999. CrossRefGoogle Scholar
  50. 50.
    Tobor-Kaplon MA, Bloem J, Romkens PFAM, De Ruiter PC (2006b) Functional stability of microbial communities in contaminated soils near a zinc smelter (Budel, the Netherlands). Ecotoxicology 15:187–197. CrossRefGoogle Scholar
  51. 51.
    Kurvers RHJM, Krause J, Croft DP, Wilson ADM, Wolf M (2014) The evolutionary and ecological consequences of animal social networks: emerging issues. TREE 29:326–335. Google Scholar
  52. 52.
    Hanneman RA, Riddle M (2005a) Introduction to social network methods: centrality and power. Accessed 15 July 2018
  53. 53.
    Hanneman RA, Riddle M (2005b) Introduction to social network methods. Univ. California, RiversideGoogle Scholar
  54. 54.
    Olesen JM, Bascompte J, Dupont YL, Jordano P (2007) The modularity of pollination networks. Proc. Natl. Acad. Sci. U. S. A. 104:19891–19896. CrossRefGoogle Scholar
  55. 55.
    Schizas D, Katrana E, Stamou G (2013) Introducing network analysis into science education: methodological research examining secondary school students’ understanding of ‘decomposition. Int J Environ Sci Educ 8:175–198Google Scholar
  56. 56.
    Wey T, Blumstein DI, Shen W, Jordan F (2008) Social network analysis on animal behaviour: a promising tool for the study of sociality. Anim. Behav. 75:333–344. CrossRefGoogle Scholar
  57. 57.
    Scott J (2000) Social network analysis: a handbook2nd edn. Sage, Newbury ParkGoogle Scholar
  58. 58.
    Scheffer M, Carpenter SR, Lenton TM, Bascompte J, Brock W, Dakos V, van de Koppel J, van de Leemput IA, Levin SA, van Nes EH, Pascual M, Vandermeer J (2012) Anticipating critical transitions. Science 338:344–348. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • G. P. Stamou
    • 1
  • N. Monokrousos
    • 2
  • D. Gwynn-Jones
    • 3
  • D. E. Whitworth
    • 3
  • E. M. Papatheodorou
    • 1
    • 4
    Email author
  1. 1.International Hellenic UniversityThessalonikiGreece
  2. 2.Department of Soil Science of AthensInstitute of Soil and Water Resources, Hellenic Agricultural Organization-DEMETERAthensGreece
  3. 3.Institute of Biological Environmental and Rural SciencesAberystwyth UniversityCeredigionUK
  4. 4.Department of Ecology, School of BiologyAristotle University of ThessalonikiThessalonikiGreece

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