Laboratoire Evolution et Diversité Biologique, Université de Toulouse III, UMR 5174, CNRS-Univ. Paul Sabatier 118, route de Narbonne, 31062, Toulouse cedex 4, France.
Environ Sci Pollut Res Int. 2010 Sep;17(8):1469-78. doi: 10.1007/s11356-010-0333-z. Epub 2010 Apr 25.
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.
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).
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 kappa 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.
背景、目的和范围：由于影响水生生态系统的人为压力因素众多，因此需要更好地了解联合压力对生物群落的不利影响，以实现欧洲水框架指令的目标。在这项研究中，我们提出了一种创新的方法，以便在大的空间尺度上评估野外条件下有毒物质对生物的影响。
人工神经网络（ANN）分析侧重于群落层面的影响，用于确定环境和有毒压力因素对来自斯凯尔特盆地（比利时）的水生无脊椎动物区系观察到的模式的相对重要性。
我们的结果表明，使用 ANN 的反向传播算法是一种很有前途的方法，可以突出环境污染与生物反应之间的关系。该方法可以将化学暴露的影响与流水环境中其他胁迫因素的影响区分开来。此外，在预测模型中使用对有毒压力的总体估计，可以阐明有毒物质与野外群落变化之间的联系。ANN 正确预测了 74%的样本，曲线下面积为 0.89，科恩氏 kappa 系数为 0.64。有机负荷、氧气可用性、水温和硝酸盐浓度似乎是预测水生无脊椎动物组合的重要因素。另一方面，有毒压力似乎对这些组合并不重要，这表明在研究区域，水质特征比水相中有毒物质的暴露对水生无脊椎动物群落更为重要。然而，我们认为斯凯尔特盆地遇到的高有机负荷可能导致对毒性影响的低估。
