Putting the ‘Adaptive’ in Adaptive Monitoring: From Fast Data to Meaningful Ecological Change

Despite repeated calls for ‘adaptive monitoring’, monitoring programs typically rely on fixed protocols that fail to capture the complex and dynamic natural world. New technologies offer this long sought flexibility, yet paradoxically risk our ability to detect trends by generating fragmented, high frequency data untethered to broader monitoring objectives. Here, we introduce ROAM (Routine-Opportunistic Adaptive Monitoring)--a hybrid framework that pairs goal-oriented baseline sampling with the ability to capture critical, transient events and experiment with optimized sampling protocols. We demonstrate ROAM with case studies of spring phenology shifts, biogeochemical pulses, wildlife demography and early warning detection. Needed developments include: 1) infrastructure for real-time communication and rapid sensor deployment, 2) integrated statistical methods for event detection, sampling optimization, and, importantly, merging high-frequency bursts with long-term collection, and 3) equitable technology transfer, training and funding models. This type of monitoring could finally deliver the adaptive, integrated systems both science and policy demand.