Using multi-target clustering trees as a tool to predict biological water quality indices based on benthic macroinvertebrates and environmental parameters in the Chaguana watershed (Ecuador)

Macroinvertebrates were sampled in the Chaguana river basin in SW Ecuador in the wet season (March) and the dry season (September) of 2005 and 2006. To assess the robustness of several biological indicators, correlations were calculated between both years and between the wet and the dry season. In addition, it was tested if the indices gave significantly different results for sites with a bad, poor, moderate and good ecological water quality. Composition measures performed poorly in most cases, however, abundance, diversity and richness measures often performed better and tolerance measures, the so-called biotic indices, performed very well, even indices developed for temperate regions. By using pruned multitarget clustering trees, it was possible to predict several well-performing ecological water quality indices simultaneously on the basis of the occurring key macroinvertebrate taxa or, alternatively, on the basis of key environmental variables. In contrast to unpruned trees, which resulted in complex trees that were difficult to interpret and performed inferiorly, pruning resulted in transparent trees. Water quality indices scored high when Hydropsychidae were present and even higher when in addition also Megapodagrionidae were present. When no Hydropsychidae nor Libellulidae were present, the indices reached the lowest scores. However, this model based on key taxa occurrences did not perform well during validation. Water quality indices scored higher with increasing dissolved oxygen concentrations and a strong current velocity. The latter model based on environmental variables also performed well during validation. In the presented study, the ecological water quality could thus be accurately predicted solely on the basis of dissolved oxygen concentration and current velocity. It can therefore be concluded that multitarget clustering trees can be easily used as a practical tool for cost-effective decision support by water quality managers.