The composition of animal communities changes over time in response to natural processes (disease dynamics, plant community succession) and anthropogenic disturbances (habitat fragmentation, climate change). Detection and analysis of community change is important for regional and site-specific management and for conservation planning. However, formal time series of animal community composition are rare.We describe a distribution- free method for compiling time series from separate studies to test for changes in community composition. The method, based on rank-permutation, is robust to many problems associated with data from separate studies, including unequal sampling effort, variable- length intervals between sampling, and different sampling protocols. We apply the technique to a time series constructed from five surveys of land bird community composition spanning 83 years of forest succession in northern lower Michigan, USA. We found increases in neotropical migrants, area-sensitive birds, and woodland birds. Despite high species turnover, the overall taxonomic composition of the land bird community did not show significant changes. Although more powerful tests can be applied when data are collected under consistent protocols, our approach is a useful alternative when such data are lacking. In the example provided, our method produced coherent results that are consistent with other published studies from the region.