Ecologists recognize that many natural systems experience temporally varying environmental conditions that can buffet populations, enhance background mortality rates, and drive population trajectories far from steady-state conditions. The overriding influence of temporal heterogeneity on community dynamics is most readily apparent in temperate and high latitude systems in which seasonal variation imposes periods of active somatic and population growth followed by periods of depressed metabolic activity, dormancy and increased mortality. Despite the recognition of the importance of temporal variability and seasonal forcing in nature, remarkably few studies have theoretically explored how periodic mortality events alter community dynamics within spatially homogeneous systems. Here we use a recently outlined methodology for theoretically examining the effects of seasonal forcing on community dynamics; called the “slow fluctuation approximation” (SFA), the framework treats seasonal succession of a community as a path taken through different community equilibria during the active growing season which is periodically reset to a near empty state during a phase of intensified mortality (the winter season). The feasibility of transitions between equilibria is determined by the capacity of species at low abundance to invade the resident community (moving the community along the successional path to a new community state). The SFA framework permits quantification of the timing of state transitions as well as the effects of changes in the length of the growing season on biotic responses. We employed the SFA approach to model the dynamics of a periodically forced food chain composed of a single top predator and a single prey. We parameterized our model using a planktonic system composed of a rotifer, Brachionus calyciflorus, and its algal-prey, Chlamydomonas reinhardtii, and generated quantitative predictions of the effects of five different growing period lengths on the timing of state transitions of the community. We then tested these predictions using controlled laboratory experiments.

Results/Conclusions

In the majority of cases, timing of peaks in zooplankton and algal biomass matched model predictions. Decreases in the relative length of the growing period delayed algal blooms, consequently delaying peaks in zooplankton abundance. Congruence between model predictions and data were strongest at shorter period lengths with algal peaks matching model predictions at all but the highest level and zooplankton matching predictions at all but the two highest levels. Our results highlight the utility of the SFA modeling approach as a framework for predicting the effects of altered seasonality on the structure and dynamics of multitrophic communities.