Klára Kubošová

Selection of indicative taxa for river habitats: a case study on benthic macroinvertebrates using indicator species analysis and the random forest methods

The aim of the study was to evaluate the exclusivity and/or preference of macroinvertebrate taxa for river habitats. Indicator species analysis and random forests methods were applied to the data set of macroinvertebrate samples taken from 58 sampling points. Samples were classified according to habitat types defined by the position in a river channel and local hydraulic characteristics. 86 macroinvertebrate taxa were included in the analyses. High indicative values for habitats (importance value ≥50 and/or indicator value ≥40) were identified for 26 taxa. The results of both methods can be considered similar. Merged habitats of channel margin (margin of main channel and side arms) were mainly defined by “negative” indicator taxa (correct classification of given samples was caused by non-occurrence and low abundances of certain taxa in this habitat). In general, there was only a small group of taxa preferring these habitats. Taxa were not fully habitat specific because they mostly occurred in two or three habitat types. This could be the result of autecological plasticity of individual taxa and the connectivity among habitats. According to the experience from this case study, it can be concluded that both random forests and IndVal methods are suitable for the detection of indicative species, and random forests method has some additional advantages.

Application of an unsteady state environmental distribution model to a decadal time series of PAH concentrations in Central Europe

To explain observed decadal trends in concentrations of polycyclic aromatic hydrocarbons (PAHs) in soil at the Kosetice observatory, Czech Republic, an environmental distribution model for persistent organic pollutants (POPs) based on the fugacity approach was developed. Weekly air concentrations were used as input data for the unsteady state model and concentrations in soil were calculated. In general, agreement between measured and predicted soil concentrations of PAHs was observed. Temporal trends in PAH concentrations in Kosetice can be related to changes in residential heating. Predicted soil concentrations of volatile PAHs namely acenaphthylene, fluorene and phenanthrene are in better correspondence with observed data than concentrations of less volatile PAHs i.e. dibenzo(ah)anthracene, benzo(a)pyrene and benzo(ghi)perylene. These discrepancies between model results and field data are probably a result of a simplified description of degradation and aging processes in soil. The results from our dynamic multicompartmental model confirmed our hypothesis about unsteady state conditions between the air and soil, and suggested that a commonly used simple steady state model should be only applied as a predictive tool in a small region when local sources and sinks are well described.