Biofilms are complex polymicrobial communities which are often associated with human infections such as the oral disease periodontitis. Studying these complex communities under controlled conditions requires biofilm model systems that mimic the natural environment as close as possible. This study established a multispecies periodontal model in the drip flow biofilm reactor in order to mimic the continuous flow of nutrients at the air-liquid interface in the oral cavity. The design is engineered to enable real-time characterization. A community of five bacteria, -GFPmut3*, -GFPmut3*, -pVMCherry, , and -SNAP26 is visualized using two distinct fluorescent proteins and the SNAP-tag. The biofilm in the reactor develops into a heterogeneous, spatially uniform, dense, and metabolically active biofilm with relative cell abundances similar to those in a healthy individual. Metabolic activity, structural features, and bacterial composition of the biofilm remain stable from 3 to 6 days. As a proof of concept for our periodontal model, the 3 days developed biofilm is exposed to a prebiotic treatment with L-arginine. Multifaceted effects of L-arginine on the oral biofilm were validated by this model setup. L-arginine showed to inhibit growth and incorporation of the pathogenic species and to reduce biofilm thickness and volume. Additionally, L-arginine is metabolized by -GFPmut3* and -pVMCherry, producing high levels of ornithine and ammonium in the biofilm. In conclusion, our drip flow reactor setup is promising in studying spatiotemporal behavior of a multispecies periodontal community.ImportancePeriodontitis is a multifactorial chronic inflammatory disease in the oral cavity associated with the accumulation of microorganisms in a biofilm. Not the presence of the biofilm as such, but changes in the microbiota (i.e., dysbiosis) drive the development of periodontitis, resulting in the destruction of tooth-supporting tissues. In this respect, novel treatment approaches focus on maintaining the health-associated homeostasis of the resident oral microbiota. To get insight in dynamic biofilm responses, our research presents the establishment of a periodontal biofilm model including , , , , and . The added value of the model setup is the combination of simulating continuously changing natural mouth conditions with spatiotemporal biofilm profiling using non-destructive characterization tools. These applications are limited for periodontal biofilm research and would contribute in understanding treatment mechanisms, short- or long-term exposure effects, the adaptation potential of the biofilm and thus treatment strategies.