Additional file 3 of Species distribution modeling to predict tsetse fly (Glossina spp.) habitat suitability in Kenya

Additional file 3: Fig. S2. Variable contribution analysis of the 10 most important predictor variables in four modeling algorithms, i.e., generalized linear model (GLM), random forest (RF), support vector machines (SVM), and maximum entropy (MaxEnt) used in predicting potential habitat for Glossina spp. (a – d) and G. pallidipes (e – h) in Kenya. In this case, min = minimum; max = maximum; med = median; LST = land surface temperature; NDVI = normalized difference vegetation index; SSMOIST = surface soil moisture.