Research Article

Environmental Science and Pollution Research

, Volume 17, Issue 8, pp 1469-1478

First online:

Assessment of stream biological responses under multiple-stress conditions

  • Lise ComteAffiliated withLaboratoire « Evolution et Diversité Biologique », Université de Toulouse III
  • , Sovan LekAffiliated withLaboratoire « Evolution et Diversité Biologique », Université de Toulouse III
  • , Eric de DeckereAffiliated withInstitute of Environment and Sustainable Development/Department of Biology, University of Antwerp
  • , Dick de ZwartAffiliated withLaboratory for Ecological Risk Assessment (LER), National Institute for Public Health and the Environment (RIVM)
  • , Muriel GevreyAffiliated withLaboratoire « Evolution et Diversité Biologique », CNRS Email author 

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Background, aim and scope

Due to the numerous anthropogenic stress factors that affect aquatic ecosystems, a better understanding of the adverse consequences on the biological community of combined pressures is needed to attain the objectives of the European Water Framework Directive. In this study we propose an innovative approach to assess the biological impact of toxicants under field conditions on a large spatial scale.

Materials and methods

Artificial Neural Network (ANN) analyses, focusing on impacts at the community level, were carried out to identify the relative importance of environmental and toxic stress factors on the patterns observed in the aquatic invertebrate fauna from the Scheldt basin (Belgium).

Results and discussion

Our results show that the use of the backpropagation algorithm of the ANN is a promising method to highlight the relationship between environmental pollution and biological responses. This method allows the effects of chemical exposure to be distinguished from the effects caused by other stressors in running waters. Moreover, the use of an overall estimate for toxic pressure in predictive models enables the links between toxicants and community alterations in the field to be clarified. The ANN correctly predicts 74% of samples with an area under the curve of 0.89 and a Cohen’s κ coefficient of 0.64. Organic load, oxygen availability, water temperature and the nitrate concentration appeared important factors in predicting aquatic invertebrate assemblages. On the other hand, toxic pressure did not seem relevant for these assemblages, suggesting that the water quality characteristics were therefore more important than exposure to toxicants in the water phase for the aquatic invertebrate communities in the study area. However, we suggest that the high organic load encountered in the Scheldt basin may lead to an underestimation of the impact of toxicity.


Toxic impact Aquatic invertebrate assemblages Artificial neural network msPAF Scheldt basin