Abstract: Generalized additive models (GAM) is applicable to analyze nonlinear and nonmonotonic relationships between the response variable and explanatory variables. Increased attention has been paid to GAM in ecology study recently. In this study, longterm monitoring data from Lake Taihu were used to analyze the effect of environmental factors on Microcystis biomass. Data screening, link function confirmation, model selection and assessment were carried out by R. The results indicated that water temperature, total phosphorus, chemical oxygen demand and electrical conductivity were the main environmental factors that affected Microcystis biomass in Lake Taihu. The use of selfcoding functions can be effective in reducing code numbers. In addition, R+RStudio might be the optimal programming environment.

Key words: tillering dynamics, yield components, leaf nitrogen concentration, fish activity, diet analysis