Modelling occurrence of invasive water hyacinth (Eichhornia crassipes) in wetlands.
Eichhornia crassipes is one of the most ubiquitous invasive aquatic species in the world that negatively impact on native fauna and flora. The information on the occurrence of such invasive plant is crucial for the river/wetland management. The aim of the present study is to apply classification tree model (testing with the highest level of pruning confidence factor) integrated with an optimizer technique (greedy stepwise search algorithm) to predict the occurrence of this exotic species based on water quality and physical-habitat parameters. The ten sites (in the Anzali wetland, located in northern Iran) were monthly measured where the exotic species was present in 50% of sampling sites and it was absent in the remaining of the sites. In total, 120 samples of E. crassipes (60 presence and 60 absence instances) were monthly measured together with 12 environmental variables during 1-year study period (2017-2018). For the model, two-third of datasets (80 instances) was employed for the training and the remainder for the validation set (40 instances). Before model optimizing (with the pruning confidence factor, PCF = 0.01), six variables were predicted by the model in three folds confirming that the non-occurrence of the exotic species might be associated with increasing flow velocity, depth of ecosystem, water turbidity, bicarbonate and dissolved oxygen concentration, while the occurrence of the exotic species (in terms of the abundance) might show an increase with increasing the concentration of nutrients such as phosphate. After model optimizing (with PCF = 0.01), the model selected five variables in three folds (flow velocity, depth, phosphate, bicarbonate, and nitrate) so that except nitrate, other selected variables were in common before and after variable selection. CT integrated with GS model thus proved to have a high potential when applied for decision-making in the context of wetland management.