Historical datasets can be useful tools to aid in understanding the impacts of global change on natural ecosystems. Resampling of historically sampled sites (“snapshot resampling”) has often been used to detect long-term shifts in ecological populations and communities, because it allows researchers to avoid long-term monitoring costs and investigate a large number of potential trends. But recent simulation-based research has called the reliability of resampling into question, and its utility has not been comprehensively evaluated. Here we combine long-term empirical datasets with novel community-level simulations to explore the accuracy of snapshot resampling of both population- and community-level metrics under a variety of conditions.

We show that snapshot resampling often yields spurious conclusions, but the accuracy of results increases when inter-annual variability in the response variable is low or the magnitude of change through time is high. Snapshot resampling also generally performs better for community-level metrics (e.g. species richness) as opposed to population-level metrics pertaining to a single species (e.g. abundance). Finally, we evaluated strategies to improve the accuracy of snapshot resampling, including sampling multiple years at the end of the study, but these produced mixed results. Ultimately, we found that snapshot resampling should be used with caution, but under certain circumstances, can be a useful for understanding long-term global change impacts.