ABSTRACT
Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low‐altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low‐altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions.Alternate abstract:Plain Language SummaryDiseases of marine organisms are increasing in many regions worldwide, therefore, efficient time‐series monitoring is critical for understanding the dynamics of disease and examining its progression in time to implement management interventions. In the first study of its kind, we use high‐resolution Unoccupied Aerial Vehicle (UAV) imagery collected to detect disease at 12 sites across the North‐East Pacific coast of North America spanning 18 degrees of latitude. The low altitude UAV visible‐bands imagery achieved 1.5 cm spatial resolution, and analysis was performed at the seagrass leaf scale based on object‐oriented image analysis. Our findings suggest that drone mapping of coastal plants may substantially increase the scale of disease risk assessments in nearshore habitats and further our understanding of seagrass meadow spatial‐temporal dynamics. These can be scaled up by searching for environmental signals of the pathogen, for example, with surveillance of wastewater for signs of Covid in human populations. This application could easily apply to other areas to construct a high‐resolution monitoring network for seagrass conservation.