Monitoring Programmes, Multiple Stress Analysis and Decision Support for River Basin Management

  • Peter C. von der OheEmail author
  • Dick de Zwart
  • Elena Semenzin
  • Sabine E. Apitz
  • Stefania Gottardo
  • Bob Harris
  • Michaela Hein
  • Antonio Marcomini
  • Leo Posthuma
  • Ralf B. Schäfer
  • Helmut Segner
  • Werner Brack
Part of the The Handbook of Environmental Chemistry book series (HEC, volume 29)


The identification of plausible causes for water body status deterioration will be much easier if it can build on available, reliable, extensive and comprehensive biogeochemical monitoring data (preferably aggregated in a database). A plausible identification of such causes is a prerequisite for well-informed decisions on which mitigation or remediation measures to take. In this chapter, first a rationale for an extended monitoring programme is provided; it is then compared to the one required by the Water Framework Directive (WFD). This proposal includes a list of relevant parameters that are needed for an integrated, a priori status assessment. Secondly, a few sophisticated statistical tools are described that subsequently allow for the estiation of the magnitude of impairment as well as the likely relative importance of different stressors in a multiple stressed environment. The advantages and restrictions of these rather complicated analytical methods are discussed. Finally, the use of Decision Support Systems (DSS) is advocated with regard to the specific WFD implementation requirements.


Decision support systems Integrated assessment Investigative monitoring Multiple stress Weight-of-evidence 


  1. 1.
    Kolkwitz R, Marsson M (1909) Ökologie der tierischen Saprobien. Beiträge zur Lehre von der biologischen Gewässerbeurteilung. Internationale Revue der gesamten Hydrobiologie und Hydrographie 2:126–152CrossRefGoogle Scholar
  2. 2.
    von der Ohe PC, Apitz SE, Arbačiauskas K, Beketov MA, Borchardt D, de Zwart D, Goedkoop W, Hein M, Hellsten S, Hering D, Kefford BJ, Panov VE, Schäfer RB, Segner H, van Gils J, Vegter JJ, Wetzel MA, Brack W (2014) Status and causal pathway assessments supporting river basin management. In: Brils J, Brack W, Müller-Grabherr D, Négrel P, Vermaat JE (eds) Risk-informed management of European river basins. Springer, HeidelbergGoogle Scholar
  3. 3.
    LAWA (1976) Die Gewässergütekarte der Bundesrepublik Deutschland. Länderarbeitsgemeinschaft Wasser, Mainz, GermanyGoogle Scholar
  4. 4.
    LAWA (1995) Gewässeratlas der Bundesrepublik Deutschland-Biologische Gewässergütekarte. Länderarbeitsgemeinschaft Wasser, Mainz, GermanyGoogle Scholar
  5. 5.
    LAWA (2002) Gewässergüteatlas der Bundesrepublik Deutschland – Gewässerstruktur in der Bundesrepublik Deutschland. Länderarbeitsgemeinschaft Wasser, Mainz, GermanyGoogle Scholar
  6. 6.
    Brils J, Barceló D, Blum W, Brack W, Harris B, Müller-Grabherr D, Négrel P, Ragnarsdottir V, Salomons W, Slob A, Track T, Vegter J, Vermaat JE (2014) Introduction: the need for risk-informed river basin management. In: Brils J, Brack W, Müller-Grabherr D, Négrel P, Vermaat JE (eds) Risk-informed management of European river basins. Springer, HeidelbergGoogle Scholar
  7. 7.
    CEC (2007) Commission staffworking document. Accompanying document to the communication forum from the commission to the European Parliament and the council: “Towards sustainable water management in the European Union.” First stage in the implementation of the Water Framework Directive 2000/60/ECGoogle Scholar
  8. 8.
    Apitz SE (2012) Conceptualizing the role of sediment in sustaining ecosystem services: sediment-ecosystem regional assessment (SEcoRA). Sci Total Environ 415:9–30CrossRefGoogle Scholar
  9. 9.
    Apitz SE (2008) Managing ecosystems: the importance of integration. Integr Environ Assess Manag 4:273Google Scholar
  10. 10.
    White SM, Apitz SE (2008) Conceptual and strategic frameworks for sediment management at the river basin scale. In: Owens PN (ed) Sustainable management of sediment resources: sediment management at the river basin scale, vol 4. Elsevier, Amsterdam, pp 31–53Google Scholar
  11. 11.
    Apitz SE, Carlon C, Oen A, White SM (2007) Strategic frameworks for managing sediment risk at the basin and site-specific scale. In: Heise S (ed) Sediment risk management and communication. Elsevier, Amsterdam, NL, pp 77–106CrossRefGoogle Scholar
  12. 12.
    Babut M, Oen A, Hollert H, Apitz SE, Heise S, White SM (2007) Prioritisation at catchment scale, risk ranking at local scale: suggested approaches. In: Heise S (ed) Sediment risk management and communication. Elsevier, Amsterdam, NL, pp 107–152CrossRefGoogle Scholar
  13. 13.
    Magar VS, Wenning RJ, Menzie C, Apitz SE (2006) Parsing ecological impacts in watersheds. J Environ Eng-Asce 132:1–3CrossRefGoogle Scholar
  14. 14.
    Furse M, Hering D, Moog O, Verdonschot P, Johnson RK, Brabec K, Gritzalis K, Buffagni A, Pinto P, Friberg N, Murray-Bligh J, Kokes J, Alber R, Usseglio-Polatera P, Haase P, Sweeting R, Bis B, Szoszkiewicz K, Soszka H, Springe G, Sporka F, Krno I (2006) The STAR project: context, objectives and approaches. Hydrobiologia 566:3–29CrossRefGoogle Scholar
  15. 15.
    Schäfer RB, Caquet T, Siimes K, Mueller R, Lagadic L, Liess M (2007) Effects of pesticides on community structure and ecosystem functions in agricultural streams of three biogeographical regions in Europe. Sci Total Environ 382:272–285CrossRefGoogle Scholar
  16. 16.
    Birk S, Hering D (2006) Direct comparison of assessment methods using benthic macroinvertebrates: a contribution to the EU Water Framework Directive intercalibration exercise. Hydrobiologia 566:401–415CrossRefGoogle Scholar
  17. 17.
    Metcalf-Schmith JL (1994) Biological water quality assessment of rivers: use of macroinverterbrate communities. In: Calow P, Petts GE (eds) The rivers handbook, vol 2. Blackwell Scientific Publications, London, pp 144–177CrossRefGoogle Scholar
  18. 18.
    von der Ohe PC, de Deckere E, Prüß A, Munoz I, Wolfram G, Villagrasa M, Ginebreda A, Hein M, Brack W (2009) Towards an integrated assessment of the ecological and chemical status of European river basins. Integr Environ Assess Manag 5:50–61CrossRefGoogle Scholar
  19. 19.
    De Zwart D, Posthuma L, Gevrey M, von der Ohe PC, de Deckere E (2009) Diagnosis of ecosystem impairment in a multiple stress context – how to formulate effective river basin management plans. Integr Environ Assess Manag 5:38–49CrossRefGoogle Scholar
  20. 20.
    Chapman PM (1990) The sediment quality triad approach to determining pollution-induced degradation. Sci Total Environ 97(98):815–825CrossRefGoogle Scholar
  21. 21.
    Chapman PM, Hollert H (2006) Should the sediment quality triad become a tetrad, a pentad, or possibly even a hexad? J Soils Sediments 6:4–8CrossRefGoogle Scholar
  22. 22.
    De Deckere E, De Cooman W, Florus M, Devroede-Vanderlinden MP (eds) (2000) A manual for the assessment of sediments in Flanders with the Triad approach. Ministry of the Flemish Community, Brussels, BelgiumGoogle Scholar
  23. 23.
    CEC (2000) Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy. Commission of the European Communities. Official Journal of the European Communities, p 77Google Scholar
  24. 24.
    von der Ohe PC, Prüß A, Schäfer RB, Liess M, de Deckere E, Brack W (2007) Water quality indices across Europe – a comparison of the good ecological status of five river basins. J Environ Monit 9:970–978CrossRefGoogle Scholar
  25. 25.
    Schäfer RB, von der Ohe PC, Rasmussen J, Kefford BJ, Beketov M, Schulz R, Liess M (2012) Thresholds for the effects of pesticides on invertebrate communities and leaf breakdown in stream ecosystems. Environ Sci Technol 46(9):5134–5142Google Scholar
  26. 26.
    Slobodnik J, Mrafkova L, Carere M, Ferrara F, Pennelli B, Schüürmann G, von der Ohe PC, (2012) Identification of river basin specific pollutants and derivation of environmental quality standards: a case study in the Slovak Republic Trac-Trends Anal Chem 41:133–154Google Scholar
  27. 27.
    CIS WG 2 (2004) A Ecological Status (ECOSTAT), Guidance on the intercalibration process, agreed version of WG 2. A Ecological Status meeting held 7–8 October 2004 in Ispra, Version 4.1. Joint Research Centre, Ispra, ItalyGoogle Scholar
  28. 28.
    CEC (2012) Proposal for a directive of the European parliament and of the council amending directives 2000/60/EC and 2008/105/EC as regards priority substances in the field of water policyGoogle Scholar
  29. 29.
    CEC (2008) Directive 2008/105/EC of the European parliament and of the council of 16 December 2008 on environmental quality standards in the field of water policy, amending and subsequently repealing Council Directives 82/176/EEC, 83/513/EEC, 84/156/EEC, 84/491/EEC, 86/280/EEC and amending Directive 2000/60/EC of the European Parliament and of the Council. L 348/84Google Scholar
  30. 30.
    CEC (2011) Technical guidance for deriving environmental quality standards, vol Guidance Document No. 27, p 204Google Scholar
  31. 31.
    Apitz SE (2008) Is risk-based, sustainable sediment management consistent with European policy? J Soils Sediments 8:461–466CrossRefGoogle Scholar
  32. 32.
    Brack W, Apitz SE, Borchardt D, Brils J, Cardoso AC, Foekema EM, van Gils J, Jansen S, Harris B, Hein M, Heise S, Hellsten S, de Maagd PGJ, Müller D, Panov VE, Posthuma L, Quevauviller P, Verdonschot PFM, von der Ohe PC (2009) Toward a holistic and risk-based management of European river basins. Integr Environ Assess Manag 5:5–10CrossRefGoogle Scholar
  33. 33.
    Parsons M, Thoms M, Norris R (2002) Australian river assessment system: AusRivAS physical assessment protocol. Monitoring river health initiative, Technical report no: 22. Cooperative Research Centre for Freshwater Ecology, University of Canberra, Environment Australia, Canberra, Australia. ISSN 1447-1280, ISBN 0 642 54888 9Google Scholar
  34. 34.
    von der Ohe PC, Goedkoop W (2013) Distinguishing the effects of habitat degradation and pesticide stress on benthic invertebrates using stressor-specific metrics. Sci Total Environ 444C:480–490Google Scholar
  35. 35.
    Landis WG, Markiewicz AJ, Matthews RA, Matthews GB (2000) A test of the community conditioning hypothesis: persistence of effects in model ecological structures dosed with the jet fuel JP-8. Environ Toxicol Chem 19:327–336CrossRefGoogle Scholar
  36. 36.
    Kapustka LA, Landis WG (2010) Environmental risk assessment and management from a landscape perspective. Wiley, Hoboken, NJCrossRefGoogle Scholar
  37. 37.
    Brack W, Bakker J, de Deckere E, Deerenberg C, van Gils J, Hein M, Jurajda P, Kooijman SALM, Lamoree MH, Lek S, L¢pez de Alda MJ, Marcomini A, Muñoz I, Rattei S, Segner H, Thomas K, von der Ohe PC, Westrich B, de Zwart D, Schmitt-Jansen M (2005) MODELKEY Models for assessing and forecasting the impact of environmental key pollutants on freshwater and marine ecosystems and biodiversity. Environ Sci Pollut Res Int 12:252–256CrossRefGoogle Scholar
  38. 38.
    USEPA (1992) Framework for ecological risk assessment. EPA/630/R-92/001. United States Environmental Protection Agency, Risk Assessment Forum, Washington, DCGoogle Scholar
  39. 39.
    USEPA (1998) Guidelines for ecological risk assessment. EPA/630/R-95/002F. United States Environmental Protection Agency, Risk Assessment Forum, Washington, DCGoogle Scholar
  40. 40.
    Négrel P, Merly C, Gourcy L, Cerdan O, Petelet-Giraud E, Kralik M, Klaver G, van Wirdum G, Vegter J (2014) Soil – sediment – river connections: catchment processes delivering pressures to river catchments. In: Brils J, Brack W, Müller-Grabherr D, Négrel P, Vermaat JE (eds) Risk-informed management of European river basins. Springer, HeidelbergGoogle Scholar
  41. 41.
    Heugens EHW (2003) Predicting effects of multiple stressors on aquatic biota. University of Amsterdam, AmsterdamGoogle Scholar
  42. 42.
    Vieira LR, Guilhermino L (2012) Multiple stress effects on marine planktonic organisms: influence of temperature on the toxicity of polycyclic aromatic hydrocarbons to Tetraselmis chuii. J Sea Res 72:94–98CrossRefGoogle Scholar
  43. 43.
    De Zwart D, Dyer SD, Posthuma L, Hawkins CP (2006) Predictive models attribute effects on fish assemblages to toxicity and habitat alteration. Ecol Appl 16:1295–1310CrossRefGoogle Scholar
  44. 44.
    USEPA (2000) Stressor identification guidance document. EPA-822-B-00-025. United States Environmental Protection Agency, Office of Research and Development, Washington, DCGoogle Scholar
  45. 45.
    Menzie CA, MacDonnell MM, Mumtaz M (2007) A phased approach for assessing combined effects from multiple stressors. Environ Health Perspect 115:807–816CrossRefGoogle Scholar
  46. 46.
    Forbes VE, Calow P (2002) Analysing weight-of-evidence in retrospective ecological risk assessment when quantitative data are limited. Hum Ecol Risk Assess 8:1625–1639CrossRefGoogle Scholar
  47. 47.
    Suter IGW, Norton SB, Cormier SM (2002) A methodology for inferring the causes of observed impairments in aquatic ecosystems. Environ Toxicol Chem 21:1101–1111CrossRefGoogle Scholar
  48. 48.
    Chapman PM (1986) Sediment quality criteria from the sediment quality triad – an example. Environ Toxicol Chem 5:957–964CrossRefGoogle Scholar
  49. 49.
    Culp JM, Lowell RB, Cash KJ (2000) Integrating in situ community experiments with field studies to generate weight-of-evidence risk assessments for large rivers. Environ Toxicol Chem 19:1167–1173CrossRefGoogle Scholar
  50. 50.
    Lowell RB, Culp JM, Dube MG (2000) A weight-of evidence approach for northern river risk assessment: integrating the effects of multiple stressors. Environ Toxicol Chem 19:1182–1190CrossRefGoogle Scholar
  51. 51.
    Wright JF, Moss D, Armitage PD, Furse MT (1984) A preliminary classification of running-water sites in Great Britain based on macro-invertebrate species and the prediction of community type using environmental data. Freshw Biol 14:221–256CrossRefGoogle Scholar
  52. 52.
    Moss D, Furse MT, Wright JF, Armitage PD (1987) The prediction of the macro-invertebrate fauna of unpolluted running-water sites in Great Britain using environmental data. Freshw Biol 17:41–52CrossRefGoogle Scholar
  53. 53.
    Barbour MT, Yoder CO (2000) The multimetric approach to bioassessment, as used in the United States of America. Assessing the biological quality of freshwaters RIVPACS and other techniques. Freshwater Biological Association, Ambelside, Cumbria, pp 281–292Google Scholar
  54. 54.
    Hawkins CP, Carlisle DM (2001) Use of predictive models for assessing the biological integrity of wetlands and other aquatic habitats. In: Rader R, Batzer DP, Wissinger SA (eds) Bioassessment and management of North American freshwater wetlands. Wiley, New York, NY, pp 59–83Google Scholar
  55. 55.
    Hering D, Buffagni A, Moog O, Sandin L, Sommerhäuser M, Stubauer I, Feld C, Johnson RK, Pinto P, Skoulikidis N, Verdonschot P, Zahrádková S (2003) The development of a system to assess the ecological quality of streams based on macroinvertebrates: design of the sampling programme within the AQEM project. Int Rev Hydrobiol 88:345–361CrossRefGoogle Scholar
  56. 56.
    Stoddard JL, Larsen P, Hawkins CP, Johnson RK (2006) Setting expectations for the ecological condition of running waters: the concept of reference condition. Ecol Appl 16(4):1267–1276CrossRefGoogle Scholar
  57. 57.
    Brauman KA, van der Meulen S, Brils J (2014) Ecosystem services and river basin management. In: Brils J, Brack W, Müller-Grabherr D, Négrel P, Vermaat JE (eds) Risk-informed management of European river basins. Springer, HeidelbergGoogle Scholar
  58. 58.
    Downes BJ, Barmuta LA, Fairweather PG, Faith DP, Keogh MJ, Lake PS, Mapstone BD, Quinn GP (2002) Monitoring ecological impacts. Concepts and practices in flowing waters. Cambridge University Press, New YorkCrossRefGoogle Scholar
  59. 59.
    EC (2003) Rivers and lakes – typology, reference conditions and classification systems. Produced by Working Group 2.3 – REFCOND. Office for Official Publications of the European Communities, LuxembourgGoogle Scholar
  60. 60.
    United States Congress (1972) Federal Water Pollution Control Amendments of 1972, vol P.L. 92-500Google Scholar
  61. 61.
    Karr JR (1981) Assessment of biotic integrity using fish communities. Fisheries 6:21–27CrossRefGoogle Scholar
  62. 62.
    Ohio EPA (1987) Biological criteria for the protection of aquatic life, vol I-III. Ohio Environmental Protection Agency, ColumbusGoogle Scholar
  63. 63.
    DeShon JD (1995) Development and application of the invertebrate community index (ICI). In: Davis WS, Simon T (eds) Biological assessment and criteria: tools for risk-based planning and decision making. Lewis Publishers, Boca Raton, FL, pp 217–243Google Scholar
  64. 64.
    Hawkes HA (1997) Origin and development of the biological monitoring working party (BMWP) score system. Water Res 32:964–968CrossRefGoogle Scholar
  65. 65.
    Smith AJ, Bode RW, Kleppel GS (2007) A nutrient biotic index (NBI) for use with benthic macroinvertebrate communities. Ecol Indic 7:371–386CrossRefGoogle Scholar
  66. 66.
    Liess M, von der Ohe PC (2005) Analyzing effects of pesticides on invertebrate communities in streams. Environ Toxicol Chem 24:954–965CrossRefGoogle Scholar
  67. 67.
    Beketov MA, Liess M (2008) An indicator for effects of organic toxicants on lotic invertebrate communities: independence of confounding environmental factors over an extensive river continuum. Environ Pollut 156:980–987CrossRefGoogle Scholar
  68. 68.
    Karr JR, Fausch KD, Angermeier PL, Yant PR, Schlosser IJ (1986) Assessing biological integrity in running waters: a method and its rationale. Illinois Natural History Survey, IllinoisGoogle Scholar
  69. 69.
    De Zwart D, Posthuma L (2005) Complex mixture toxicity for single and multiple species: proposed methodologies. Environ Toxicol Chem 24:2665–2676CrossRefGoogle Scholar
  70. 70.
    Braunbeck T, Segner H (1992) Pre-exposure temperature acclimation and diet as modifying factors for the tolerance of golden ide (Leuciscus idus melanotus) to short-term exposure to 4-chloroaniline. Ecotoxicol Environ Saf 24:72–94CrossRefGoogle Scholar
  71. 71.
    Winemiller KO, Rose KA (1992) Patterns of life-history diversification in North American fishes: implications for population regulation. Can J Fish Aquat Sci 49:2196–2218CrossRefGoogle Scholar
  72. 72.
    Kooijman SALM (1998) Process-oriented descriptions of toxic effects. In: Schüürmann G, Markert B (eds) Ecotoxicology – ecological fundamentals, chemical exposure and biological effects. Wiley/Spektrum Akademischer Verlag, New York/Heidelberg, pp 483–520Google Scholar
  73. 73.
    Gleason TR, Nacci DE (2001) Risks of endocrine-disrupting compounds to wildlife: extrapolating from effects on individuals to population response. Hum Ecol Risk Assess 7:1027–1042CrossRefGoogle Scholar
  74. 74.
    Segner H, Mothersill C, Mosse I, Seymour C (2007) Ecotoxicology—how to assess the impact of toxicants in a multifactorial environment? Multiple stressors: a challenge for the future NATO Advanced Workshop Environmental Security. Springer, Heidelberg, pp 39–56Google Scholar
  75. 75.
    Ter Braak CJF (1995) Ordination. In: Jongman RHG, Ter Braak CJF, Van Tongeren OFR (eds) Data analysis in community and landscape ecology. Cambridge University Press, Cambridge, pp 91–173CrossRefGoogle Scholar
  76. 76.
    Bro-Rasmussen F, Løkke H (1984) Ecoepidemiology – a casuistic discipline describing ecological disturbances and damages in relation to their specific causes; exemplified by chlorinated phenols and chlorophenoxy acids. Regul Toxicol Pharmacol 4:391–399CrossRefGoogle Scholar
  77. 77.
    Kapo KE, Burton GA (2006) A GIS-based weight-of-evidence approach for diagnosing aquatic ecosystem impairment. Environ Toxicol Chem 25:2237–2249CrossRefGoogle Scholar
  78. 78.
    Smith EP, Lipkovich I, Ye K (2002) Weight of evidence (WOE): quantitative estimation of probability of impairment for individual and multiple lines of evidence. Hum Ecol Risk Assess 8:1585–1596CrossRefGoogle Scholar
  79. 79.
    Bonham-Carter GF (1994) Geographic information systems for geoscientists – modeling in GIS. Elsevier Science, New YorkGoogle Scholar
  80. 80.
    Suter GW, Barnthouse LW, Bartell SM, Mill T, Mackay D, Patterson S (1993) Ecological risk assessment. Lewis Publishers, Boca RatonGoogle Scholar
  81. 81.
    Burton GA, Chapman P, Smith E (2002) Weight of evidence approaches for assessing ecosystem impairment. Hum Ecol Risk Assess 8:1657–1674CrossRefGoogle Scholar
  82. 82.
    Kapo KE, Burton GA Jr, De Zwart D, Posthuma L, Dyer SD (2008) Quantitative lines of evidence for screening-level diagnostic assessment of regional fish community impacts: a comparison of spatial database evaluation methods. Environ Sci Technol 42:9412–9418CrossRefGoogle Scholar
  83. 83.
    Hellawell JM (1986) Biological indicators of freshwater pollution and environmental management. Elsevier Applied Science Publishers, LondonCrossRefGoogle Scholar
  84. 84.
    Metzeling L, Perriss S, Robinson D (2006) Can the detection of salinity and habitat simplification gradients using rapid bioassessment of benthic invertebrates be improved through finer taxonomic resolution or alternative indices? Hydrobiologia 572:235–252CrossRefGoogle Scholar
  85. 85.
    Czerniawska-Kusza I (2004) Use of artificial substrates for sampling benthic macroinvertebrates in the assessment of water quality of large lowland rivers. Pol J Environ Stud 13:579–584Google Scholar
  86. 86.
    Zurada JM (1992) Introduction to artificial neural systems. West Publishing Company, New YorkGoogle Scholar
  87. 87.
    Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Mag 4:4–22CrossRefGoogle Scholar
  88. 88.
    Dedecker AP, Goethals PLM, D’Heygere T, Gevrey M, Lek S, De Pauw N (2005) Application of artificial neural network models to analyse the relationships between Gammarus pulex L. (Crustacea, Amphipoda) and river characteristics. Environ Monit Assess 111:223–241CrossRefGoogle Scholar
  89. 89.
    Gevrey M, Dimopoulos I, Lek S (2003) Review of methods to study the contribution of variables in artificial neural network models. Ecol Model 160:249–264CrossRefGoogle Scholar
  90. 90.
    Chon T-S, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neural network. Ecol Model 90:69–78CrossRefGoogle Scholar
  91. 91.
    Levine ER, Kimes DS, Sigillito VG (1996) Classifying soil structure using neural networks. Ecol Model 92:101–108CrossRefGoogle Scholar
  92. 92.
    Park YS, Cereghino R, Compin A, Lek S (2003) Applications of artificial neural networks for patterning and predicting aquatic insect species richness in running waters. Ecol Model 130:265–280CrossRefGoogle Scholar
  93. 93.
    Park YS, Tison J, Lek S, Giraudel JL, Coste M, Delmas F (2006) Application of a self-organizing map to select representative species in multivariate analysis: a case study determining diatom distribution patterns across France. Ecol Inform 1:247–257CrossRefGoogle Scholar
  94. 94.
    Lek S, Delacoste M, Baran P, Dimopoulos I, Lauga J, Aulagnier S (1996) Application of neural networks to modelling nonlinear relationships in ecology. Ecol Model 90:39–52CrossRefGoogle Scholar
  95. 95.
    Park YS, Grenouillet G, Esperance B, Lek S (2006) Stream fish assemblages and basin land cover in a river network. Sci Total Environ 365:140–153CrossRefGoogle Scholar
  96. 96.
    Recknagel F, French M, Harkonen P, Yabunaka K-I (1997) Artificial neural network approach for modelling and prediction of algal blooms. Ecol Model 96:11–28CrossRefGoogle Scholar
  97. 97.
    Gevrey M, Worner SP (2006) Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method. J Econ Entomol 99:979–986CrossRefGoogle Scholar
  98. 98.
    Gevrey M, Comte L, De Zwart D, De Deckere E, Lek S (2010) Modeling the chemical and toxic water status of the Scheldt basin (Belgium), using aquatic invertebrate assemblages and an advanced modeling method. Environ Pollut 158:3209–3218CrossRefGoogle Scholar
  99. 99.
    Shim JP, Warkentin M, Courtney JF, Power DJ, Sharda R, Carlsson C (2002) Past present and future of decision support technology. Decis Support Syst 33:111–126CrossRefGoogle Scholar
  100. 100.
    Semenzin E, Suter GW (2009) Decision support systems (DSSs) for inland and coastal waters management – gaps and challenges. In: Marcomini A, Suter GW, Critto A (eds) Decision support systems for risk based management of contaminated sites. Springer, New YorkGoogle Scholar
  101. 101.
    Agostini P, Critto A, Semenzin E, Marcomini A (2009) Decision support systems for contaminated land management. In: Marcomini A, Suter GW, Critto A (eds) Decision support systems for risk based management of contaminated sites. Springer, New YorkGoogle Scholar
  102. 102.
    Gottardo S, Semenzin E, Zabeo A, Marcomini A (2009) MODELKEY: decision support system for the assessment and evaluation of impacts on aquatic ecosystems. In: Marcomini A, Suter GW, Critto A (eds) Decision support systems for risk based management of contaminated sites. Springer, New YorkGoogle Scholar
  103. 103.
    EC (2005) Common implementation strategy for the water framework directive (2000/60/EC). Guidance Document n.813. Overall approach to the classification of ecological status and ecological potential. European Commission, Working Group ECOSTAT 2.A on Ecological Status, LuxembourgGoogle Scholar
  104. 104.
    Solimini AG, Cardoso AC, Heiskanen AS (2006) Indicators and methods for the ecological status assessment under the water framework directive. Linkages between chemical and biological quality of surface waters. Institute for Environment and Sustainability, Ispra, ItalyGoogle Scholar
  105. 105.
    Cardoso AC, Solimini AG, Premazzi G (2005) Report on Harmonisation of freshwater biological methods European Commission. Institute of Environment and SustainabilityGoogle Scholar
  106. 106.
    Moss B, Stephen D, Alvarez C, Becares E, Van De Bund WSC, Van Donk E, De Eyto E, Feldmann T, Fern C, Aaez A, Fern M, Aez A, Franken RJM, Garciia-Criado F, Gross EM, Oom MG, Hansson LA, Irvine K, Aarvalt AJ, Jensen JP, Jeppesen E, Kairesalo T, Oow RK, Krause T, Uunnap HK, Laas A, Lill E, Lorens B, Luup H, Miracle MR, Nooges P, Nooges T, Nykänen M, Ott I, Peczula W, Peeters ETHM, Phillips G, Romo S, Russell V, Ooe JS, Scheffer M, Siewertsen K, Smal H, Tesch C, Timm H, Tuvikene L, Tonno I, Virro T, Vicente E, Wilson D (2003) The determination of ecological status in shallow lakes: a tested system (ECOFRAME) for implementation of the European Water Framework Directive. Aquat Conserv 13:507–549CrossRefGoogle Scholar
  107. 107.
    Burton GA, Batley GE, Chapman PM, Forbes VE, Smith EP, Reynoldson T, Schlekat CE, Den Besten PJ, Bailer AJ, Green AS, Dweyer RL (2002) A weight-of-evidence framework for assessment sediment (or other) contamination: improving certainty in the decision making process. Hum Ecol Risk Assess 8:1675–1696CrossRefGoogle Scholar
  108. 108.
    Suter GW (2003) Definitive risk characterization by weighing the evidence. In: Suter GW (ed) Ecological risk assessment, 2nd edn. CRC, Boca RatonGoogle Scholar
  109. 109.
    Von Altrock C (1995) Fuzzy logic and neuro-fuzzy applications explained. Prentice Hall PTR, Upper Saddle RiverGoogle Scholar
  110. 110.
    Gottardo S, Semenzin E, Giove S, Zabeo A, Critto A, de Zwart D, Ginebreda A, von der Ohe PC, Marcomini A (2011) Integrated risk assessment for WFD ecological status classification applied to Llobregat river basin (Spain). Part II – evaluation process applied to five environmental lines of evidence. Sci Total Environ 409:4681–4692CrossRefGoogle Scholar
  111. 111.
    Kiker G, Bridges T, Varghese AS, Seager TP, Linkov I (2005) Application of multi-criteria decision analysis in environmental management. Integr Environ Assess Manag 1:49–58CrossRefGoogle Scholar
  112. 112.
    De Zwart D, Posthuma L, Gevrey M, Lek S (2008) Intercomparison of developed diagnostic models for the Scheldt catchment surface water data. RIVM, Bilthoven, The NetherlandsGoogle Scholar
  113. 113.
    Coors A, de Meester L (2008) Synergistic, antagonistic, and additive effects of multiple stressors: predation threat, parasitism and pesticide exposure in Daphnia magna. J Appl Ecol 45:1820–1828CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Peter C. von der Ohe
    • 1
    Email author
  • Dick de Zwart
    • 2
  • Elena Semenzin
    • 3
    • 4
  • Sabine E. Apitz
    • 5
  • Stefania Gottardo
    • 3
    • 4
  • Bob Harris
    • 6
  • Michaela Hein
    • 7
  • Antonio Marcomini
    • 3
    • 4
  • Leo Posthuma
    • 2
  • Ralf B. Schäfer
    • 8
  • Helmut Segner
    • 9
  • Werner Brack
    • 1
  1. 1.Department of Effect-Directed AnalysisHelmholtz-Centre for Environmental Research—UFZLeipzigGermany
  2. 2.Centre for Sustainability, Environment and Health (DMG)National Institute of Public Health and the Environment (RIVM)BilthovenThe Netherlands
  3. 3.Venice Research Consortium (CVR)Venezia MargheraItaly
  4. 4.Department of Environmental Sciences, Informatics and StatisticsCa’ Foscari University of VeniceVeniceItaly
  5. 5.SEA Environmental Decisions, LtdHertfordshireUK
  6. 6.Catchment Science Centre, The Kroto Research InstituteThe University of SheffieldSheffieldUK
  7. 7.Department of Bioanalytical EcotoxicologyHelmholtz-Centre for Environmental Research—UFZLeipzigGermany
  8. 8.Institute for Environmental SciencesUniversity Koblenz-LandauLandauGermany
  9. 9.Centre for Fish and Wildlife HealthUniversity of BernBernSwitzerland

Personalised recommendations