LAUSR

Abstract The frequency and intensity of harmful algal blooms (HABs) are likely to increase with climate change and increases in nutrients due to anthropogenic activities. To manage the environment and health threats to surrounding organisms, it is essential to improve our capability to provide early warning of harmful species. Using Noctiluca scintillans as example, a dinoflagellate that often blooms in Asia and whose presence is rapidly expanding worldwide, we demonstrate a potential way to predict N. scintillans blooms by deploying an in-situ plankton imaging system: PlanktonScope. PlanktonScope is a shadowgraph imaging system and capable of imaging organisms 40 µm – 5 cm. This system was deployed in Shenzhen Bay, China, recording images at 0.5 Hz in April 2016. Using the automated image processing procedure, we found that PlanktonScope recorded nearly a full cycle of a N. scintillans bloom. A simple resource-limited logistic population growth model was then applied to develop a predictive framework for the outburst of N. scintillans. Environmental changes could explain the observed dynamics of N. scintillans; however, it was difficult to quantify the relationship between the dynamics of N. scintillans and environmental variables. Our study demonstrated that a camera system like PlanktonScope could be useful in developing an early warning system for N. scintillans blooms, which could be applicable to other HAB species for proactive management.