Modeling the chemical and toxic water status of the Scheldt basin (Belgium), using aquatic invertebrate assemblages and an advanced modeling method.

Self-Organizing Maps have been used on monitoring sites in several Scheldt sub-basins to identify the main aquatic invertebrate assemblages and relate them to the physico-chemical and toxic water status. 12 physico-chemical variables and 2 estimates of toxic risk were available for a dataset made up of a total of 489 records. Two of the five defining clusters reflecting a relatively clean environment were composed by very well diversified functional feeding groups and sensitive taxa. The cleanest assemblage was mainly linked to the sites from the Nete sub-basin. The three other clusters were inversely described with a dominance of oligochaetes and deposit feeders as well as a bad water quality. Such an analysis can be used to support ecological status assessment of rivers and thus might be useful for decision-makers in the evaluation of chemical and toxic water status, as required by the EU Water Framework Directive.

Assessment of stream biological responses under multiple-stress conditions.

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 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.

Patterning ecological risk of pesticide contamination at the river basin scale.

Ecological risk assessment was conducted to determine the risk posed by pesticide mixtures to the Adour-Garonne river basin (south-western France). The objectives of this study were to assess the general state of this basin with regard to pesticide contamination using a risk assessment procedure and to detect patterns in toxic mixture assemblages through a self-organizing map (SOM) methodology in order to identify the locations at risk. Exposure assessment, risk assessment with species sensitivity distribution, and mixture toxicity rules were used to compute six relative risk predictors for different toxic modes of action: the multi-substance potentially affected fraction of species depending on the toxic mode of action of compounds found in the mixture (msPAF CA(TMoA) values). Those predictors computed for the 131 sampling sites assessed in this study were then patterned through the SOM learning process. Four clusters of sampling sites exhibiting similar toxic assemblages were identified. In the first cluster, which comprised 83% of the sampling sites, the risk caused by pesticide mixture toward aquatic species was weak (mean msPAF value for those sites<0.0036%), while in another cluster the risk was significant (mean msPAF<1.09%). GIS mapping allowed an interesting spatial pattern of the distribution of sampling sites for each cluster to be highlighted with a significant and highly localized risk in the French department called "Lot et Garonne". The combined use of the SOM methodology, mixture toxicity modelling and a clear geo-referenced representation of results not only revealed the general state of the Adour-Garonne basin with regard to contamination by pesticides but also enabled to analyze the spatial pattern of toxic mixture assemblage in order to prioritize the locations at risk and to detect the group of compounds causing the greatest risk at the basin scale.

Diagnosis of ecosystem impairment in a multiple-stress context--how to formulate effective river basin management plans.

The Water Framework Directive (WFD) of the European Union requires member states to attain a good ecological status for all water bodies by the year 2015. This implies that the bioecological protection endpoint itself is upfront, next to abiotic chemical quality standards, as tools to protect those endpoints. Within the requirements of the Directive, ecological status and abiotic conditions will be monitored extensively. Based on the analysis of the monitoring data, authorities are required to derive Programs of Measures (PoMs) for impacted sites. Optimization of these programs requires diagnosis, to provide site-specific or catchment-specific information on the causes of observed deviations from a good ecological status. This article shows one pilot analysis of monitoring data (Scheldt River, Belgium) compiled in the scope of the EU MODELKEY project. Ecological, ecotoxicological, and statistical models are combined to quantify local ecological impact magnitudes and to identify site-specific factors that are associated with those impacts. Results show significant ecological effects in terms of taxa loss at study sites, which are highly variable among sites, with variable combinations of environmental factors associated with those effects. The results of the diagnostic approach are discussed, which appear to be complementary to the assessment of chemical status required by the Directive. Both types of assessment are useful to assist in the derivation of optimized PoMs. In addition, it could be concluded that the acute toxic pressure parameter relates to reduced taxon abundance for more than half of the studied taxa and that this parameter relates to the fraction of taxa lost under field conditions. Finally, various lessons for the execution of monitoring programs are derived because the Scheldt (bio)monitoring data set has its weaknesses, although it can be seen as typical for current monitoring programs.

Prediction of global distribution of insect pest species in relation to climate by using an ecological informatics method.

The aim of this work was to predict the worldwide distribution of two pest species-Ceratitis capitata (Wiedemann), the Mediterranean fruit fly, and Lymantria dispar (L.), the gypsy moth-based on climatic factors. The distribution patterns of insect pests have most often been investigated using classical statistical models or ecoclimatic assessment models such as CLIMEX. In this study, we used an artificial neural network, the multilayer perceptron, trained using the backpropagation algorithm, to model the distribution of each species. The data matrix used to model the distribution of each species was divided into three data sets to (1) develop and train the model, (2) validate the model and prevent over-fitting, and (3) test each model on novel data. The percentage of correct predictions of the global distribution of each species was high for Mediterranean fruit fly for the three data sets giving 95.8, 81.5, and 80.6% correct predictions, respectively, and 96.8, 84.3, and 81.5 for the gypsy moth. Kappa statistics used to test the level of significance of the results were highly significant (in all cases P < 0.0001). A sensitivity analysis applied to each model based on the calculation of the derivatives of each of a large number of input variables showed that the variables that contributed most to explaining the distribution of C. capitata were annual average temperature and annual potential evapotranspiration. For L. dispar, the average minimum temperature and minimum daylength range were the main explanatory variables. The ANN models and methods developed in this study offer powerful additional predictive approaches in invasive species research.

Application of artificial neural network models to analyse the relationships between Gammarus pulex L. (Crustacea, Amphipoda) and river characteristics.

This study aimed at analysing the relationship between river characteristics and abundance of Gammarus pulex. To this end, four methods which can identify the relative contribution and/or the contribution profile of the input variables in neural networks describing the habitat preferences of this species were compared: (i) the "PaD" ("Partial Derivatives") method consists of a calculation of the partial derivatives of the output in relation to the input variables; (ii) the "Weights" method is a computation using the connection weights of the backpropagation Artificial Neural Networks; (iii) the "Perturb" method analyses the effect of a perturbation of the input variables on the output variable; (iv) the "Profile" method is a successive variation of one input variable while the others are kept constant at a fixed set of values. The dataset consisted of 179 samples, collected over a three-year period in the Zwalm river basin in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Gammarus pulex were used in this study. The different contribution methods gave similar results concerning the order of importance of the input variables. Moreover, the stability of the methods was confirmed by gradually removing variables. Only in a limited number of cases a shift in the relative importance of the remaining input variables could be observed. Nevertheless, differences in sensitivity and stability of the methods were detected, probably as a result of the different calculation procedures. In this respect, the "PaD" method made a more severe discrimination between minor and major contributing environmental variables in comparison to the "Weights", "Profile" and "Perturb" methods. From an ecological point of view, the input variables "Ammonium" and to a smaller extent "COD", were selected by these methods as dominant river characteristics for the prediction of the abundance of Gammarus pulex in this study area.