Ecological data are often complex. The explanatory and the response variables may be categorical variables or numerical variables. The ecological relationships that need to be defined are often nonlinear and involve high-order interactions between explanatory variables. Missing values for both response and predictor variables are very common, and outliers almost always exist. Random forest (RF), a novel machine learning technique, is ideally suited for the analysis of complex ecological data. RF predictors are a ensemble-learning approach based on regression or classification trees. Instead of building one classification tree (classifier), the RF algorithm builds multiple classifiers using randomly selected subsets of the observations and random subsets of the predictor variables. The predictions from the ensemble of trees are then averaged in the case of regression trees, or tallied using a voting system for classification trees. RF is efficient to support flexible modelling strategies. RF is capable of detecting and making use of more complex relationships among the variables. RF is unexcelled in accuracy among current algorithms and does not overfit. It also generates an internal unbiased estimate of the generalization error as the forest building progresses. Potential applications of RF to ecology include: classification and regression analysis, survival analysis, variable importance estimate and data proximities. Proximities can be used for clustering, detecting outliers, multi-dimensional scaling, and unsupervised classification. RF can interpolate missing value and maintain high accuracy even when a large proportion of the data are missing. RF can handle thousands of input variables without variable exclusion. It runs efficiently on large data bases. RF can also handle a spectrum of response types, including categorical, numeric, ratings, and survival data. Another advantage of the RF is that it requires only two user-defined parameters (The number of trees and the number of randomly selected predictive variables used to split the nodes) to be defined. These two parameters should be optimized in order to improve predictive accuracy. In recent years, RF has been widely used by ecologists to model complex ecological relationships because they are easy to implement and easy to interpret. To understand and use the RF, further information about how they are computed is useful. Here, we summarized the basic principle of RF and showed how RF handle complex data by modelling the geographical distribution of Yunan Pine (Pinus yunnanensis) in China. RF is a robust and widely used technique in the field of species distribution modelling (SDM), since it meets the basic needs of SDM: simulating species distribution and identifying the main drivers of species distribution. In this work, RF showed a high predictive performance in simulating the distribution of Yunan Pine, which was consistent with the multi-dimensional scaling plot that showed it was possible to separate the presences from the absences. We also estimated the relative importance of predictor variables and produced the partial dependence plots for selected predictor variables for random forest predictions of the presences of Yunan Pine. The main aim of the article is to familiarize the reader with the general concepts, terminology and basic principle behind RF. We believe RF will get more applications and development in ecology.