Reimagining adaptive sampling in the age of technology and crowd sourced science

Nature is inherently variable—its patterns shift across landscapes and through seasons, often in ways that challenge our ability to keep up. Yet traditional biodiversity monitoring has remained comparatively rigid, with protocols that stay fixed for long periods even as ecological and social conditions evolve. The value of routine, regular monitoring is clear in the sense that it is the best way to truly estimate a statistically robust biological trend. However, routing monitoring over long periods depends on continuously funding large monitoring projects that span social trends and political cycles. 

On the other hand, more opportunistic data is being fueled by technology and flowing in from all directions in the form of camera traps, acoustic sensors, eDNA, satellite imagery, drones, and citizens science. These more ‘rapid’ data serve a complementary role and eventually could also produce long-term patterns. However, so far, most of these efforts are missing the longer, broader perspective and the carefully crafted goals and targets required of traditional ‘adaptive monitoring’. 

We must reimagine adaptive monitoring for these new technologies. We first introduce a framework ‘Routine-Opportunistic Adaptive Monitoring’ (ROAM) that is a hybrid framework that combines responsive, short-term sampling within a design that still supports long-term trend detection. We outline general examples from phenology, stream disruption, disease surveillance, wildlife observation and animal movement, and a more detailed example using citizen science. Using results from the first summer of Blitz the Gap (blitzthegap.org) in Canada, we demonstrate the power of incentivized sampling to strategically fill in missing data gaps in a way that answers the goals of monitoring networks and, more specifically, the needs of reporting trends and indicators in a coordinated way across entire countries. 

We find that small, strategic adjustments to sampling and engagement can make monitoring efforts more intelligent, scalable and policy-relevant. We illustrate an example using Key Biodiversity Areas (KBAs), which are designated in part when key species are found in an area. By guiding citizen science to these species, more observations of those species were added and new KBAs can be established. Overall, if done well, adaptive monitoring can pave the way for adaptive action.