Sensitivity analysis is used to qualitatively or quantitatively apportion the variation of model output to different source of variation. It is a very useful tool in model parameterization and calibration, and has important ecological significance by identifying the governing factors for a certain ecological process simulated. There are two schools of sensitivity analysis, local sensitivity analysis and global sensitivity analysis. The former examines the local response of the output(s) by varying input parameters one at a time, holding other parameters to a central value; and the latter examines the global response (averaged over the variation of all the parameters) of model output(s) by exploring a finite (or even an infinite) region. Since it is very easy to conduct local sensitivity analysis, it is very popular in ecological models. However, local sensitivity analysis is not computationally effective, because it can only get the sensitivity of a single parameter at a time. It can not take into consideration the effect of interaction of different parameters. Additionally, the value of other parameters will affect the sensitivity of the parameter specified. In view of this, global sensitivity analysis is increasingly preferred to local sensitivity in recent years. However, for most of the ecological modeling study published in Chinese, only local sensitivity analysis is conducted. To provide a toolbox of alternative sensitivity analysis algorithms for ecological model development in future study, we reviewed the main methods of both local sensitivity analysis and global sensitivity analysis, including one at a time method, multivariate regression, Morris' method, Sobol's method, Fourier Amplitude Sensitivity Analysis, and Extended Fourier Amplitude Sensitivity Analysis. Based on the state-of-the-art research on sensitivity analysis, the sensitivity of the interaction between two or more than two model parameters, the sensitivity of common model parameters in a set of models, and the sensitivity analysis in spatially explicit landscape model simulation were identified as the key areas and difficulties of future study on sensitivity analysis in ecological modeling.