Use of the Field Data for Assessment of Hazardous Concentration of Pollutants in Soil and Modelling of Species Sensitivity Distribution

  • V. K. ShitikovEmail author
  • A. E. Ivanova
  • K. A. Kydralieva
  • V. A. Terekhova
Conference paper
Part of the Springer Geography book series (SPRINGERGEOGR)


A rapid assessment of Environmental Quality Criteria and probability of Ecological Risk without special toxicometric experimenting is an actual problem in environmental science. The article presents a statistical prediction methodology of approximated no-effect concentrations (NOEC) and modelling of Species Sensitivity Distribution (SSD) based on the field observation data. We use values of species abundance of tested community, which were located on a set of sites of region under study with wide variation range of polluting substances concentration. Statistical processing includes the following sequence stages: (1) calculation of distances matrix in multidimensional species’ space between each pair sites; (2) nonmetric multidimensional scaling (NMDS) is applied to reduce a 2-dimensional plot matrix of sites and species projections; (3) the analyzed contamination mediafactors are interpreted as an ecological gradient in species compositions and construction of the additional ordination axes; (4) generalized additive models (GAM) are build and 3D smoothing surfaces of spatial distribution of pollutant’s concentration on ordination plot are fitted; (5) using the fitted models predicted values of PV ecological maxima and the upper boundary values of TV confidence intervals of each species for each single compounds are found; (6) obtained data are used for SSDs modelling. The methodology has been supported by results of bioindication for communities of microscopic fungi of soil samples from the former uranium mining province (Kyrgyzstan). Threshold values of six soil contamination indicators that ensure a pre-given admissible probability of environmental risk have been determined.


Bioindication Technogenic soil contamination Fungi communication Nonmetric multidimensional scaling (NMS) Species sensitivity distribution (SSD) Risk assessment 



This research was financed by the International Science and Technology Center (ISTC Project KR-2092) and Russian Science Foundation 14-50-00029 (isolation and identification of microfungi).


  1. 1.
    Brown, V.K.: Southwood TRE Secondary succession: patterns and strategies. In: Gray, A.J., Crawley, M.J., Edwards, P.J. (eds.) Colonization, Succession and Stability. Blackwell, Oxford, pp. 315–338 (1987)Google Scholar
  2. 2.
    Clements, W.H., Rohr, J.R.: Community responses to contaminants: using basic ecological principles to predict ecotoxicological effects. Environ. Toxicol. Chem. 28, 1789–1800 (2009). doi: 10.1897/09-140.1 CrossRefGoogle Scholar
  3. 3.
    Solomon, K.R., Brock, T.C.M., De Zwart, D., et al. (eds.): Extrapolation Practice for Ecotoxicological Effect Characterization of Chemicals. SETAC Press and CRC Press, Boca Raton (2008)Google Scholar
  4. 4.
    Newman, M.C., Ownby, D.R., Mezin, L.C.A., Powell, D.C., Christensen, T.R.L., Lerberg, S.B., Anderson, B.A.: Applying species-sensitivity distributions in ecological risk assessment: assumptions of distribution type and sufficient numbers of species. Environ. Toxicol. Chem. 19, 508–515 (2000). doi: 10.1002/etc.5620190233 Google Scholar
  5. 5.
    Posthuma, L., Suter, I.I., Glenn, W., Traas, T.P.: Species Sensitivity Distributions in Ecotoxicology. CRC Press, Boca Raton (2001)CrossRefGoogle Scholar
  6. 6.
    Gottschalk, F., Nowack, B.: A probabilistic method for species sensitivity distributions taking into account the inherent uncertainty and variability of effects to estimate environmental risk. Integr. Environ. Assess. Manage. 9, 79–86 (2013). doi: 10.1002/ieam.1334 CrossRefGoogle Scholar
  7. 7.
    Smetanová, S., Bláha, L., Liess, M., Schafer, R.B., Beketov, M.A.: Do predictions from species sensitivity distributions match with field data? Environ. Pollut. 189, 126–133 (2013). doi: 10.1016/j.envpol.2014.03.002 CrossRefGoogle Scholar
  8. 8.
    Del Signore, A., Hendriks, A.J., Lenders, H.J.R., Leuven, R.S.E.W., Breure, A.M.: Development and application of the SSD approach in scientific case studies for ecological risk assessment. Environ. Toxicol. Chem. 35, 2149–2161 (2016). doi: 10.1002/etc.3474 CrossRefGoogle Scholar
  9. 9.
    Verdonck, F.A.M., Aldenberg, T., Jaworska, J., Vanrolleghem, P.A.: Limitations of current risk characterization methods in probabilistic environmental risk assessment. Environ. Toxicol. Chem. 22, 2209–2213 (2003). doi: 10.1897/02-435 CrossRefGoogle Scholar
  10. 10.
    Scott-Fordsmand, J., Damgaar, C.: Uncertainty analysis of single-concentration exposure data for risk assessment - introducing the species effect distribution approach. Environ. Toxicol. Chem. 25, 3078–3081 (2006). doi: 10.1897/05-200R.1 CrossRefGoogle Scholar
  11. 11.
    Forbes, V.E., Calow, P.: Species sensitivity distributions revisited: a critical appraisal. Hum. Ecol. Risk Assess. 8, 473–492 (2002). doi: 10.1080/20028091057033 CrossRefGoogle Scholar
  12. 12.
    Suter II, G.W.: Comments on the interpretation of distributions in “Overview of recent developments in ecological risk assessment”. Risk Anal. 18, 3–4 (1998)CrossRefGoogle Scholar
  13. 13.
    Smith, E.P., Cairns, J.J.: Extrapolation methods for setting ecological standards for water quality, statistical and ecological concerns. Ecotoxicology 2, 203–219 (1993)CrossRefGoogle Scholar
  14. 14.
    Cairns, J.J., Niederlehner, B.R.: Problems associated with selecting the most sensitive species for toxicity testing. Hydrobiology 153, 87–94 (1987)CrossRefGoogle Scholar
  15. 15.
    Austin, M.P., Cunningham, R.B., Flemming, P.M.: New approach to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio 55, 11–27 (1984). doi: 10.1007/BF00039976 CrossRefGoogle Scholar
  16. 16.
    Jongman, R.H.G., ter Braak, C.J.F., van Tongeren, O.F.R.: Data Analysis in Community and Landscape Ecology. Pudoc, Wageningen (1987)Google Scholar
  17. 17.
    ter Braak, C.J.F., Looman, C.W.N.: Weighted averaging, logistic regression and the Gaussian response model. Vegetatio 65, 3–11 (1986). doi: 10.1007/BF00032121 CrossRefGoogle Scholar
  18. 18.
    Oksanen, J., Läärä, E., Tolonen, K., Warner, B.: Confidence intervals for the optimum in the Gaussian response function. Ecology 82, 1191–1197 (2001)CrossRefGoogle Scholar
  19. 19.
    Angulo, E.: The Tomlinson pollution load index applied to heavy metal “Mussel-Watah” data: a useful index to assess coastal pollution. Sci. Total Environ. 187, 19–56 (1996)CrossRefGoogle Scholar
  20. 20.
    Gadd, J.M.: Geomycology: biogeochemical transformations of rocks, minerals, metals and radionuclides by fungi, bioweathering and bioremediation. Mycol. Res. 111(1), 3–49 (2007)CrossRefGoogle Scholar
  21. 21.
    Legendre, P., Legendre, L.: Numerical Ecology. Elsevier Science B.V., Amsterdam (2012)Google Scholar
  22. 22.
    Wolda, H.: Similarity indices, sample size and diversity. Oecologia 50, 296 (1981). doi: 10.1007/BF00344966 CrossRefGoogle Scholar
  23. 23.
    Kruskal, J.B.: Multidimensional scaling by optimizing goodness-of-fit to a nonmetric hypothesis. Psychometrika 29, 1–28 (1964). doi: 10.1007/BF02289565 CrossRefGoogle Scholar
  24. 24.
    McCune, B., Grace, J.B.: Analysis of Ecological Communities. MjM Software Design, GlenedenBeach (2002)Google Scholar
  25. 25.
    Wood, S.N.: Generalized Additive Models: An Introduction with R. Chapman, Hall/CRC, Boca Raton (2006)Google Scholar
  26. 26.
    Davison, A.C., Hinkley, D.V.: Bootstrap methods and their application. Cambridge University Press, Cambridge (1997)CrossRefGoogle Scholar
  27. 27.
    Oksanen, J., Blanchet, F.G., Kindt, R., et al.: Vegan: Community Ecology Package. R package version 2.0-2. (2011). Accessed 24 July 2016
  28. 28.
    Hopkin, S.P.: Ecological implications of “95% protection levels” for metals in soil. Oikos 66, 137–141 (1993)CrossRefGoogle Scholar
  29. 29.
    Efroymson, R.E., Will, M.E., Suter II, G.W., Hull, R.N.: Toxicological benchmarks for screening contaminants of potential concern for effects on terrestrial plants. ES/ER/TM-85/R3. Oak Ridge National Laboratory, Oak Ridge, TN, USA (1997)Google Scholar
  30. 30.
    Coudun, C., Gégout, J.-C.: The derivation of species response curves with Gaussian logistic regression is sensitive to sampling intensity and curve characteristics. Ecol. Model. 199, 164–175 (2006). doi: 10.1016/j.ecolmodel.2006.05.024 CrossRefGoogle Scholar
  31. 31.
    Jansen, F., Oksanen, J.: How to model species responses along ecological gradients – Huisman-Olff-Fresco models revisited. J. Veg. Sci. 24, 1108–1117 (2013). doi: 10.1111/vs1250 CrossRefGoogle Scholar
  32. 32.
    Comte, L., Lek, S., de Deckere, E., de Zwar, D., Gevrey, M.: Assessment of stream biological responses under multiple-stress conditions. Environ. Sci. Pollut. Res. 17, 1469–1478 (2010). doi: 10.1007/s11356-010-0333-z CrossRefGoogle Scholar
  33. 33.
    Tropina, O.V., Terekhova, V.A., Semenova, T.A.: The variability of the structure of micromycete complexes as the result of soil heterogeneity. Mikologiya I Fitopatologiya 37(6), 74–79 (2003)Google Scholar
  34. 34.
    Terekhova, V.A.: The importance of mycological studies for soil quality control. Eurasian Soil Sci. 40(5), 643–648 (2007). CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • V. K. Shitikov
    • 1
    Email author
  • A. E. Ivanova
    • 2
  • K. A. Kydralieva
    • 3
  • V. A. Terekhova
    • 2
    • 4
  1. 1.Institute of Ecology of the Volga Basin, Russian Academy of SciencesTogliattiRussia
  2. 2.Lomonosov Moscow State UniversityMoscowRussia
  3. 3.Moscow Aviation InstituteMoscowRussia
  4. 4.Severtsov Institute of Ecology and Evolution, Russian Academy of SciencesMoscowRussia

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