cological interaction networks are a valuable approach to understandingplant–pollinator interactions at the community level. Highly structured daily activity patternsare a feature of the biology of many flower visitors, particularly provisioning female bees,which often visit different floral sources at different times. Such temporal structure impliesthat presence/absence and relative abundance of specific flower–visitor interactions (links) ininteraction networks may be highly sensitive to the daily timing of data collection. Further,relative timing of interactions is central to their possible role in competition or facilitation ofseed set among coflowering plants sharing pollinators. To date, however, no study hasexamined the network impacts of daily temporal variation in visitor activity at a communityscale. Here we use temporally structured sampling to examine the consequences of dailyactivity patterns upon network properties using fully quantified flower–visitor interaction datafor a Kenyan savanna habitat. Interactions were sampled at four sequential three-hour timeintervals between 06:00 and 18:00, across multiple seasonal time points for two sampling sites.In all data sets the richness and relative abundance of links depended critically on when duringthe day visitation was observed. Permutation-based null modeling revealed significanttemporal structure across daily time intervals at three of the four seasonal time points, drivenprimarily by patterns in bee activity. This sensitivity of network structure shows the need toconsider daily time in network sampling design, both to maximize the probability of samplinglinks relevant to plant reproductive success and to facilitate appropriate interpretation ofinterspecific relationships. Our data also suggest that daily structuring at a community levelcould reduce indirect competitive interactions when coflowering plants share pollinators, as iscommonly observed during flowering in highly seasonal habitats.