Algae bloom

Caused by climate change and other human activities, the intensity and frequency of algal blooms in coastal upwelling regions are expected to increase. Understanding and forecasting these blooms is essential to mitigate their impacts on fish populations and marine ecosystems.

Chlorophyll concentration measurements in the water provide an estimate of phytoplankton abundance. Sound scattering layers (SSLs), visible in echosounder backscatter data, indicate aggregations of zooplankton. By monitoring SSL variations, we can detect increases in zooplankton presence, which may signal the onset of an algal bloom. Adaptive thresholds applied to SSL metrics enable automated detection; once an SSL is identified, a yo-yo AUV mission is triggered to survey the upper 40 meters of the water column.

Data collected by the SilCam are processed using PyOPIA (PyOPIA- Python Ocean Particle Image Analysis ), which classifies each particle individually to determine zooplankton type and size. When combined with navigation data, this provides the precise position and depth of each organism.

The resulting particle information (type, size, and concentration/density), together with modeled current data from the NorKyst800 ocean model, is fed into the OpenDrift particle transport model to study the dispersion of zooplankton. This integrated approach enhances our understanding of ecological processes and population dynamics in marine environments.