Optimal control in individual-based models: implications from aggregated methods.

Use of individual-based models (IBMs) has been expanding in both theoretical and applied ecology. IBMs include details at the level of individuals that may lead to different conclusions from aggregated modeling methods. There has been essentially no guidance available on how to most effectively manage populations when the underlying dynamics are best modeled through IBMs. Using a simple resource-consumer IBM, we investigate whether optimal control theory applied to an aggregated model (AM) can effectively control a harmful species modeled by an IBM or whether interactions between individuals, their spatial distribution, and/or landscape heterogeneities limit the effectiveness of a control derived for the AM. If optimal policies derived from an AM are determined to be generally effective in managing a population modeled with considerably greater complexity, this provides evidence that optimal management strategies may be relatively insensitive to the details of individual behavior and the associated effects on population response. We investigate these issues and find that if there is weak spatial heterogeneity in the resource, the optimal control derived from the AM can be used effectively to control the harmful species in the IBM. The approach is more limited in the case of very strong spatial heterogeneity in the resource. This suggests investigation of a mixture of simplified models in conjunction with detailed simulation models when individual differences affect population processes.