Obtaining temporal biodiversity trends in the light of rapid global change is crucial to estimating future impacts – yet the lack of temporally replicated monitoring data limits our ability. Here, we identify imprints of temporal change in static data and utilize them to predict temporal biodiversity trends without requiring a time-series. We used data from temporally replicated breeding bird atlases from four regions worldwide and measured the change in each bird species by log ratio of occupancy change (measure of loss or gain) and by temporal Jaccard index (measure of turnover of sites). We calculated predictors from the configuration of each species’ spatial distribution, traits, diversity metrics, and from study region characteristics and used machine-learning to link these to the temporal change. Static predictors failed to predict the log ratio of occupancy change, but they did predict the magnitude of site turnover. Variables that characterize the spatial configuration of the species range were sufficient to predict the change, while all others contributed only marginally. Here, we show that static data, especially the details of the spatial configuration of species ranges, provide signals of the processes of temporal change. This holds promise for estimating biodiversity change in situations without temporal data.