Use of the Field Data for Assessment of Hazardous Concentration of Pollutants in Soil and Modelling of Species Sensitivity Distribution
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.
KeywordsBioindication 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.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
- 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.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
- 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
- 21.Legendre, P., Legendre, L.: Numerical Ecology. Elsevier Science B.V., Amsterdam (2012)Google Scholar
- 24.McCune, B., Grace, J.B.: Analysis of Ecological Communities. MjM Software Design, GlenedenBeach (2002)Google Scholar
- 25.Wood, S.N.: Generalized Additive Models: An Introduction with R. Chapman, Hall/CRC, Boca Raton (2006)Google Scholar
- 27.Oksanen, J., Blanchet, F.G., Kindt, R., et al.: Vegan: Community Ecology Package. R package version 2.0-2. (2011). https://cran.r-project.org/web/packages/vegan. Accessed 24 July 2016
- 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
- 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