Abstract
Despite the recognition of the importance of seasonal forcing in nature, remarkably few studies have theoretically explored periodically forced community dynamics. Here we employ a novel approach called “successional state dynamics” (SSD) to model a seasonally forced predator–prey system. We first generated analytical predictions of the effects of altered seasonality on species persistence and the timing of community state transitions. We then parameterized the model using a zooplankton–phytoplankton system and tested quantitative predictions using controlled experiments. In the majority of cases, timing of zooplankton and algal population peaks matched model predictions. Decreases in growing‐period length delayed algal blooms, consequently delaying peaks in zooplankton abundance. Predictions of increased probability of predator extinction at low growing‐period lengths were also upheld experimentally. Our results highlight the utility of the SSD modeling approach as a framework for predicting the effects of altered seasonality on the structure and dynamics of multitrophic communities.