MaxEnt distribution modeling for predicting Oreochromis niloticus invasion into the Ganga river system, India and conservation concern of native fish biodiversity

In order to assess the distribution pattern and understand the prevailing factors for predicting further expansion of an exotic fish Oreochromis niloticus, this study was undertaken in the Ganga river flowing through the state of Uttar Pradesh using MaxEnt model. The authors report the distribution pattern of O. niloticus and prevailing causative factors mounting the expansion of O. niloticus in the Ganges based on MaxEnt modeling technique. The presence only occurrence data-set for this invasive species was prepared from the field data and also from data collated from the authenticated publications of different fisheries researchers. The data-set was analyzed with environmental and topographical variables typically incorporating seasonal and temporal variability using MaxEnt, a maximum entropy algorithm which showed that the area under curve was much closer to 1 ( 0.999). The model predicted elevation as the most influential predictor variable with permutation importance of 69.2% followed by slope_steepness (10.1%), Tmax_1 (7.3%) and Srad_5 (6.8%). The findings from the results suggest that invasive O. niloticus tend to spread in rivers where elevation is lower as well as slope_steepness of the river is higher and thus indicated that invasion might be higher in the downstream of the river. The model suggests that topography and its derived variable are the most significant predictors for distribution of invasive O. niloticus. The results of this study also confirm that the water qualities of the Ganga river are suitable for O. niloticus and if the model is supplemented with water quality variables data, the influential predictor variable in water quality can be well investigated with permutation importance.