Abstract 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.

中文翻译：

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用于预测尼罗罗非鱼入侵印度恒河系统的 MaxEnt 分布模型和本地鱼类生物多样性的保护问题

摘要 为了评估分布模式并了解预测外来鱼类尼罗罗非鱼进一步扩张的主要因素，本研究使用MaxEnt模型在流经北方邦的恒河中进行。作者基于 MaxEnt 建模技术报告了尼罗罗非鱼的分布格局和导致尼罗罗非鱼在恒河中扩张的主要因素。该入侵物种的唯一出现数据集是根据现场数据以及从不同渔业研究人员的经过认证的出版物中整理的数据准备的。数据集使用环境和地形变量进行分析，通常使用 MaxEnt 结合季节性和时间变化，最大熵算法表明曲线下面积更接近 1 ( 0.999)。该模型预测海拔是最有影响的预测变量，排列重要性为 69.2%，其次是 slope_steepness (10.1%)、Tmax_1 (7.3%) 和 Srad_5 (6.8%)。结果表明，入侵的尼罗罗非鱼倾向于在海拔较低以及河流坡度较高的河流中传播，因此表明入侵可能在河流下游较高。该模型表明，地形及其衍生变量是侵入性尼罗罗非鱼分布的最重要预测因子。本研究结果也证实恒河水质适合尼罗罗非鱼，如果模型补充水质变量数据，