, Volume 21, Issue 2, pp 336–352 | Cite as

Sensitivity assessment of freshwater macroinvertebrates to pesticides using biological traits

  • A. Ippolito
  • R. Todeschini
  • M. Vighi


Assessing the sensitivity of different species to chemicals is one of the key points in predicting the effects of toxic compounds in the environment. Trait-based predicting methods have proved to be extremely efficient for assessing the sensitivity of macroinvertebrates toward compounds with non specific toxicity (narcotics). Nevertheless, predicting the sensitivity of organisms toward compounds with specific toxicity is much more complex, since it depends on the mode of action of the chemical. The aim of this work was to predict the sensitivity of several freshwater macroinvertebrates toward three classes of plant protection products: organophosphates, carbamates and pyrethroids. Two databases were built: one with sensitivity data (retrieved, evaluated and selected from the U.S. Environmental Protection Agency ECOTOX database) and the other with biological traits. Aside from the “traditional” traits usually considered in ecological analysis (i.e. body size, respiration technique, feeding habits, etc.), multivariate analysis was used to relate the sensitivity of organisms to some other characteristics which may be involved in the process of intoxication. Results confirmed that, besides traditional biological traits, related to uptake capability (e.g. body size and body shape) some traits more related to particular metabolic characteristics or patterns have a good predictive capacity on the sensitivity to these kinds of toxic substances. For example, behavioral complexity, assumed as an indicator of nervous system complexity, proved to be an important predictor of sensitivity towards these compounds. These results confirm the need for more complex traits to predict effects of highly specific substances. One key point for achieving a complete mechanistic understanding of the process is the choice of traits, whose role in the discrimination of sensitivity should be clearly interpretable, and not only statistically significant.


Sensitivity prediction Pesticides Traits Freshwater macroinvertebrates Multivariate analysis Chemometrics 



The PhD grant of Alessio Ippolito is covered by DOW Agrosciences. The authors thank Sylvain Dolédec for trait data on some organisms and Astrid Schmidt-Kloiber for providing access to the web database

Supplementary material

10646_2011_795_MOESM1_ESM.pdf (284 kb)
Online Resource 1 (OR_1.pdf): used trait database and references used for the implementation of the trait database; (PDF 284 kb)
10646_2011_795_MOESM2_ESM.pdf (28 kb)
Online Resource 2 (OR_2.pdf): resume of the scores attributed for the assessment of behavioural complexity; (PDF 29 kb)
10646_2011_795_MOESM3_ESM.pdf (37 kb)
Online Resource 3 (OR_3.pdf): complete reports of the selected regression models (variables used, evaluation of predictive capacity, fitting and boostrap validation). (PDF 37 kb)


  1. Baird DJ, Van Den Brink PJ (2007) Using biological traits to predict species sensitivity to toxic substances. Ecotox Environ Safe 67:296–301CrossRefGoogle Scholar
  2. Baird DJ, Rubach MN, Van Den Brink PJ (2008) Trait-based ecological risk assessment (TERA): the new frontier? Integr Environ Assess Manag 4:2–3CrossRefGoogle Scholar
  3. 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
  4. Brock TC, Alix A, Brown CD et al (2009) Linking aquatic exposure and effects: risk assessment of pesticides, 1st edn. CRC Press\SETAC Press, Boca Raton\PensacolaGoogle Scholar
  5. Buchwalter D, Jenkins J, Curtis L (2002) Respiratory strategy is a major determinant of [3H] water and [14C] chlorpyrifos uptake in aquatic insects. Can J Fish Aquat Sci 59:1315–1322CrossRefGoogle Scholar
  6. Chevenet F, Doledec S, Chessel D (1994) A fuzzy coding approach for the analysis of long-term ecological data. Freshw Biol 31:295–309CrossRefGoogle Scholar
  7. De Lange HJ, Sala S, Vighi M, Faber JH (2010) Ecological vulnerability in risk assessment—a review and perspectives. Sci Total Environ 408:3871–3879CrossRefGoogle Scholar
  8. Deamer D, Evans J (2006) Numerical analysis of biocomplexity. In: Seckbach J (ed) Life as we know it. Springer Verlag, Dordrecht, pp 199–212CrossRefGoogle Scholar
  9. Dimitrov SD, Mekenyan OG, Schultz TW (2000) Interspecies modeling of narcotics toxicity to aquatic animals. B Environ Contam Tox 65:399–406CrossRefGoogle Scholar
  10. Dolédec S, Statzner B, Bournard M (1999) Species traits for future biomonitoring across ecoregions: patterns along a human-impacted river. Freshw Biol 42:737–758CrossRefGoogle Scholar
  11. EC (2011) Common Implementation Strategy for the Water Framework Directive (2000/60/EC). Guidance document. Technical guidance for deriving environmental quality standards. Office for official publications of the European communities, 2011, LuxembourgGoogle Scholar
  12. Hansch C (1969) A quantitative approach to biochemical structure–activity relationships. Acc Chem Res 2:232–239CrossRefGoogle Scholar
  13. Heneghan PA, Biggs J, Jepson PC, Kedwards T, Maund SJ, Sherratt TN, Shillabeer N, Stickland TR, Williams P (1999) Pond-FX: ecotoxicology from pH to population recovery (online database), 1st edn. Oregon State University: Department of Entomology. Available from Internet: Accessed 18 Mar 2010
  14. Hoekstra JA, Vaal MA, Notenboom J, Slooff W (1994) Variation in the sensitivity of aquatic species to toxicants. B Environ Contam Tox 53:98–105CrossRefGoogle Scholar
  15. Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 2nd edn. The MIT Press, CambridgeGoogle Scholar
  16. Ippolito A, Sala S, Faber JH, Vighi M (2010) Ecological vulnerability analysis: a river basin case study. Sci Total Environ 408:3880–3890CrossRefGoogle Scholar
  17. Kefford B, Palmer C, Jooste S, Warne M, Nugegoda D (2005) What is meant by ‘95% of Species’? an argument for the inclusion of rapid tolerance testing. Hum Ecol Risk Assess 11:1025–1046CrossRefGoogle Scholar
  18. Koch C, Laurent G (1999) Complexity and the nervous system. Science 284:96–98CrossRefGoogle Scholar
  19. Laverack MS (1988) The numbers of neurons in Decapod crustacea. J Crustac Biol 8:1–11CrossRefGoogle Scholar
  20. Leach AR (2001) Molecular modelling: principles and applications, 2nd edn. Pearson Education, HarlowGoogle Scholar
  21. Leardi R, Boggia R, Terrile M (1992) Genetic algorithms as a strategy for feature selection. J Chemomet 6:267–281CrossRefGoogle Scholar
  22. Liess M, Von Der Ohe PC (2005) Analyzing effects of pesticides on invertebrate communities in streams. Environ Toxicol Chem 24:954–965CrossRefGoogle Scholar
  23. Mayer FLJ, Ellersieck MR (1986) Manual of acute toxicity: interpretation and data base for 410 chemicals and 66 species of freshwater animals. Fish and Wildlife Service, Resource Publication 160, Washington, DCGoogle Scholar
  24. Poff NLR (1997) Landscape filters and species traits: towards mechanistic understanding and prediction in stream ecology. J N Am Benthol Soc 16:391–409CrossRefGoogle Scholar
  25. Posthuma L, Suter GW, Trass PT (2001) Species sensitivity distributions in ecotoxicology. Lewis Publishers, Boca RatonCrossRefGoogle Scholar
  26. Rubach MN, Baird DJ, Van Den Brink PJ (2010) A new method for ranking mode-specific sensitivity of freshwater arthropods to insecticides and its relationship to biological traits. Environ Toxicol Chem 29:476–487CrossRefGoogle Scholar
  27. Sala S, Migliorati S, Monti G, Vighi M (2011) SSD-based rating system for the classification of pesticide risk on biodiversity. Aquat Toxicol (submitted)Google Scholar
  28. Schmidt-Kloiber A, Hering D (eds) (2009) The taxa and autecology database for freshwater organisms (version 4.0), Accessed 9 Sep 2010
  29. Southwood TRE (1977) Habitat, the templet for ecological strategies? J Anim Ecol 46:336–365CrossRefGoogle Scholar
  30. Statzner B, Bis B, Dolédec S, Usseglio-Polatera P (2001) Perspectives for biomonitoring at large spatial scales: a unified measure for the functional composition of invertebrate communities in European running waters. Basic App Ecol 2:73–85CrossRefGoogle Scholar
  31. Tachet H, Richoux P, Bournaud M, Usseglio-Polatera P (2002) Invertébrés d’eau douce: systematique, biologie, ecologie. CNRS Edition, ParisGoogle Scholar
  32. Todeschini R, Vighi M, Provenzali R, Finizio A, Gramatica P (1996) Modeling and prediction by using WHIM descriptors in QSAR studies: toxicity of heterogeneous chemicals on daphnia magna. Chemosphere 32:1527–1545CrossRefGoogle Scholar
  33. Todeschini R, Consonni V, Mauri A, Pavan M (2003) MobyDigs: software for regression and classification models by genetic algorithms. Data Handl Sci Technol 23:141–167CrossRefGoogle Scholar
  34. Tomlin CDS (2003) The pesticide manual: a world compendium. thirteenth. BCPC (British Crop Protection Council), AltonGoogle Scholar
  35. Townsend CR, Hildrew AG (1994) Species traits in relation to a habitat templet for river systems. Freshw Biol 31:265–275CrossRefGoogle Scholar
  36. Tremolada P, Finizio A, Villa S, Gaggi C, Vighi M (2004) Quantitative inter-specific chemical activity relationships of pesticides in the aquatic environment. Aquat Toxicol 67:87–103CrossRefGoogle Scholar
  37. Turner BL, Kasperson RE, Matson PA, McCarthy JJ, Corell RW, Christensen L, Eckley N, Kasperson JX, Luers A, Martello ML, Polsky C, Pulsipher A, Schiller A (2003) A framework for vulnerability analysis in sustainability science. Proc Natl Acad Sci USA 100:8074–8079CrossRefGoogle Scholar
  38. Usseglio-Polatera P (1994) Theoretical habitat templets, species traits, and species richness: aquatic insects in the upper Rhône river and its floodplain. Freshw Biol 31:417–437CrossRefGoogle Scholar
  39. Van Leeuwen CJ, Van Der Zandt PTJ, Aldenberg T, Verhaar HJM, Hermens JLM (1992) Application of QSARs, extrapolation and equilibrium partitioning in aquatic effects assessment. I. narcotic industrial pollutants. Environ Toxicol Chem 11:267–282CrossRefGoogle Scholar
  40. Van Straalen NM (1993) Biodiversity of ecotoxicological responses in animals. Neth J Zool 44:112–129CrossRefGoogle Scholar
  41. Verro R, Finizio A, Otto S, Vighi M (2009) Predicting pesticide environmental risk in intensive agricultural areas. I: risk of individual chemicals in surface waters. Environ Sci Technol 43:522–529CrossRefGoogle Scholar
  42. Vieira NKM, Poff NLR, Carlisle DM, Moulton II SR, Koski ML, Kondratieff BC (2006) A database of lotic invertebrate traits for North America. U.S. Geological Survey Data Series 187. Accessed 25 Mar 2010

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  1. 1.Department of Environmental SciencesUniversity of Milano BicoccaMilanItaly
  2. 2.Milano Chemometrics and QSAR Research Group, Department of Environmental SciencesUniversity of Milano-BicoccaMilanItaly

Personalised recommendations