Patterns of species associations have been commonly used to infer interactions among species. If species positively co-occur they may form predominantly neutral assemblages and such patterns suggest a relatively weak role for compensatory dynamics. The main objective of this study was to test this prediction on temporal samples of bird assemblages (n = 19, 10−57 years) by presence/absence and quantitative null models on assemblage and guild levels. These null model outcomes were further analysed to evaluate the effects of various dataset characteristics on the outcomes of the null models. The analysis of two binary null models in combination with three association indices revealed 20 % with significant aggregations, 61 % with random associations and only 19 % with significant segregations (n = 95 simulations). The results of the quantitative null model simulations detected more none-random associations: 61 % aggregations, 6 % random associations and 33 % segregations (n = 114 simulations). Similarly, quantitative analyses on guild levels showed 58 % aggregations, 20 % segregations and 22 % random associations (n = 450 simulations). Bayesian GLMs detected that the outcomes of the binary and quantitative null models applied to the assemblage analyses were significantly related to census plot size, whereas the outcomes of the quantitative analyses were also related to the mean population densities of species in the data matrices. In guild level analyses, only 9 % of the GLMs showed a significant influence of matrix properties (plot size, matrix size, species richness, and mean species population densities) on the null model outcomes. The results did not show the prevalence of negative associations that would have supported compensatory dynamics. Instead, we assume that a similar response of the majority of species to climate-driven and stochastic factors may be responsible for the revealed predominance of positive associations.