Optical Detection of Harmful Algal Blooms in the Belgian Coastal Zone: A Cautionary Tale of Chlorophyll c3

Abstract

Phaeocystis globosa is a nuisance haptophyte species that forms annual blooms in the southern North Sea and other coastal waters. At high biomass concentration, these are considered harmful algal blooms due to their deleterious impact on the local ecosystems and economy, and are considered an indicator for eutrophication. In the last two decades, methods have been developed for the optical detection and quantification of these blooms, with potential applications for autonomous in situ or remote observations. However, recent experimental evidence suggests that the interpretation of the optical signal and its exclusive association with P. globosa may not be accurate. In the North Sea, blooms of P. globosa are synchronous with those of the diatom Pseudo-nitzschia delicatissima, another harmful bloom-forming species with similar pigmentation and optical signature. Here we combine new and published measurements of pigmentation composition and inherent optical properties from pure cultures of several algal and cyanobacterial groups, together with environmental spectroscopy data, to identify the pigments generating the optical signals captured by two established algorithms. We further evaluate the association of those pigments and optical signals with P. globosa. We found that the interpretation of the pigment(s) generating the optical signals were incorrect and that previous methods are not specific to P. globosa, even in the context of the phytoplankton assemblage of the southern North Sea. Additionally, we found that the optical and pigment signatures of Phaeocystis species are part of a broad pigmentation trend across unrelated taxonomic groups related to chlorophyll c3 presence, with important consequences for the interpretation of pigment and optical data. We then develop and evaluate an algorithm to detect this pigmentation pattern with minimal influence of co-occurring species and elaborate general recommendations for the future development of algorithms.