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This contribution identifies species-habitat associations in a temperate forest in north-eastern China, based on the assumption that habitats are spatially autocorrelated and species are spatially aggregated due to limited seed dispersal. The empirical observations were obtained in a large permanent experimental area covering 660 x 320 m. The experimental area was subdivided into four habitat types using multivariate regression tree (MRT) analysis. According to an indicator species analysis, 38 of the 47 studied species were found to be significant indicators of the MRT habitat types. The relationships between species richness and topographic variables were found to be scale-dependent, while the great majority of the species shows distinct habitat-dependence. There are 188 potential species-habitat associations, and 114 of these were significantly positive or negative based on habitat randomization. We identified 139 significant associations using a species randomization. A habitat is not a closed system it may be both, either a sink or a source. Therefore, additional to the randomization, the Poisson Cluster Model (PCM) was applied. PCM considers the spatial autocorrelation of species and habitats, and thus appears to be more realistic than the traditional randomization processes. It identified only 37 associations that were significant. In conclusion, the deviation from the random process, i.e. the high degree of species spatial mingling may be explained by persistent immigration across habitats.