Environmental variables
We used 19 bioclimatic variables with 30s resolution (~ 1km2 near the equator) that gives current climate data (Fick and Hijmans, 2017 available inhttp://worldclim.org/version2) as the environmental variables. Bioclimatic variables are derived from the monthly temperature and rainfall values to make them biologically more meaningful; for example, mean annual temperature, maximum temperature of the warmest month, annual precipitation and precipitation of the wettest quarter etc. (Fick and Hijmans, 2017). In addition, we also used elevation (AsterDEM_Version3_drukref03.im obtained from Watershed Management Division, Ministry of Agriculture and Forest) as an environmental variable. We used bioclimatic variables as environmental variables even for the fish as previous studies have found SDMs built using bioclimatic and hydrological variables did not differ for fish (McGarvey et al. 2018). We also did not reduce bioclimatic variables since our study is exploratory in nature, and collinearity among environmental variables was not an issue in a machine learning environment like Maxent (Elith et al. 2011, Marco Júnior and Nóbrega 2018) though some literature (e.g. Merow, Smith, and Silander Jr., 2013) suggests being cautious when interpreting SDMs resulting from the use of correlated environmental variables.