Predicting Habitat Suitability of Mahseer Fish (Tor spp.) in Tropical River Systems Using MaxEnt and Google Earth Engine: A Geospatial Modeling Approach

Rivers are vital freshwater habitats that face threats of degradation and climate change. Mahseer fish, a key species, is in decline. This study predicted Mahseer fish habitats in Central Java using the Google Earth Engine and the MaxEnt machine learning algorithm. Environmental predictors, including NDVI, elevation, slope, river order, temperature, and rainfall, were extracted from Sentinel, SRTM, MODIS, and CHIRPS data. The model identified river order as the most influential variable (73%), followed by elevation (18%) and rainfall (8%), with an AUC score of 0.7, indicating fair accuracy. Suitable habitats were located in upstream river orders (1–3), typically at higher elevations. These findings provide spatial guidance for conservation planning, such as identifying critical habitats, prioritizing upstream areas, and establishing seasonal fishing ban. This approach supports biodiversity protection and aligns with the Sustainable Development Goals by offering a scalable tool for freshwater ecosystem management. Using MaxEnt with GEE shows promise for rapid, and cost-effective species distribution modeling in data-limited tropical regions.