Combining field experiments and individual-based modeling to identify the dynamically relevant organizational scale in a field system

Community ecologists continually strive to build analytical models that rea listically describe long-term dynamics of the systems they study. A key ste p in this process is identifying which details are relevant for predicting dynamics. Currently. this remains a limiting step in development of analyti cal theory because experimental field ecology, which provides the key empir ical insight, and theoretical ecology, which translates empirical knowledge into analytical theory, remain weakly linked. I illustrate how an individu al-based computational model of species interactions is a useful way to bri dge the gulf between empirical research and theory development. I built a c omputational model that reproduced key natural history and biological detai l of an old-field interaction web composed of a predator species, a herbivo re species and two plant groups that had been the subject of extensive prev ious field research. I examined, using simulation experiments, how individu al behavior of herbivores in response to changing resource and predator abu ndance scaled to long-term population-level and community-level dynamics. T he simulation experiments revealed that the long-term community dynamics co uld be highly predictable because of two counterintuitive reasons. First, s easonality was a strong forcing variable on the system that removed the pos sibility of serial dependence in population abundance over time. Second, be cause of seasonality, short-term behavioral responses of herbivores played a much stronger role in shaping community structure than longer-term proces ses such as density responses. So, simply knowing the short-term responses of herbivores at the evolutionary ecological level was sufficient to foreca st the long-term outcome of experimental manipulations. This study shows th at an individual-based model, once it is calibrated to the real-world held system, can provide key insight into the biological detail that analytical models should include to predict long-term dynamics.