Resource allocation in multi-UAV systems is one of the fundamental challenges in intelligent mission planning due to the limited processing power, memory capacity, and communication bandwidth of unmanned aerial vehicles. Inefficient allocation of these resources can lead to data redundancy, increased computational cost, and reduced spatial accuracy. To address this challenge, this study introduces a biologically inspired CORES algorithm, which integrates dynamic quadtree data structures with coexistence modeling of living organisms to achieve adaptive and balanced resource utilization. In the simulation experiments, synthetic environmental datasets representing terrestrial, aquatic, and vegetated areas were used. A total of 150 independent runs were performed under varying combinations of field-of-view, decision threshold, and memory budget parameters. Quantitative results show that CORES improves the average data quality index by up to 18% and the spatial overlap index by up to 23% compared with baseline methods. Qualitative observations also indicate more uniform data distribution and better spatial coverage. Overall, CORES demonstrates strong adaptability and efficiency for dynamic resource allocation in UAV-based systems.