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Abstract:The effect of photosynthetically active radiation (PAR) and temperature on photosynthesis of the cultivated red alga, Pyropia yezoensisf. narawaensis(Saga-#5 Strain, Bangiales), was determined for microscopic sporophytes and macroscopic gametophytes with pulse-amplitude fluorometry and dissolved oxygen sensors. A clear difference in the temperature response of the maximum quantum yield (Fv/Fm) and oxygenic gross photosynthesis (GP) was revealed between the two life-history stages. The sporophyte Fv/Fmwas not sensitive to temperature, although the model revealed an Fv/Fmmaximum (0.60) at 16.7°C. In contrast, the gametophyte Fv/Fmwas clearly sensitive to temperature and attained a maximum mean value of 0.55 at 14.7°C. The relationship between GP and temperature was also different. The maximum rates of GP of the sporophyte occurred at 30.7°C and was 17.1 μg O2mgchl-a−1min−1(15.4–19.4, 95% Bayesian credible interval); however, maximum rates of GP for the gametophyte were much higher (110 μmol O2mgchl-a−1min−1) and occurred at a lower temperature (14.4°C). The response of oxygenic net photosynthesis to PAR also varied, and the initial slope (α), the saturation PAR (Ek) and the maximum photosynthetic rate (Pmax) of the gametophyte were much higher than the sporophyte. Therefore, we hypothesised that in species with a heteromorphic life history, such as those in the genus Pyropia, physiological responses to environmental gradients would be dissimilar.

Algal blooms dominated by the raphidophyte Chattonellaspp. and diatom Skeletonemaspp. are a regular summer‐time phenomenon in the Ariake Sea (Japan). Given its high‐temporal frequency, the Geostationary Ocean Color Imager (GOCI) affords us the extraordinary ability to investigate short‐temporal scale dynamic of these blooms. Here we present a bloom detection method and classification criteria named Normalized Difference Red Peak Index (NDRI), which relies on a combination of data from the red wavebands of GOCI, with the spectral shape of remote sensing reflectance (Rrs(λ)) around 490 nm [SS(490)] to distinguish bloom pixels from nonbloom pixels. Diurnal changes in the bloom distribution and intensity were successfully captured by the new method with GOCI data. In the next step, an optical discrimination method was developed to differentiate Chattonellaspp. from Skeletonemaspp. blooms. Specifically, the backscattering at 555 nm of bloom waters was retrieved from a bio‐optical algorithm, bbp_index(555), based on the satellite Rrs(λ)of green and red bands. Combined with the NDRI, bloom species were successfully differentiated in the featured distribution of bbp_index(555). Further validations using GOCI images series and local observations indicate that the newly developed algorithm yields robust classification results. Moreover, transitions in Skeletonemaspp. and Chattonellaspp. blooms were captured in a 20 days time series of daily composite GOCI for July 2018. The successful application of the classification approach to GOCI opens up the possibility of understanding the factors influencing daily changes of harmful blooms in Ariake Sea, which at present is limited by the paucity of field observations. Harmful blooms reflect anomalous growth of phytoplankton that are either toxic or can have negative impacts on the environment, seafood production, and other ecosystem services that are important for coastal economies. The Ariake Sea which is known for its high biological productivity in Japan, suffers on account of frequent outbreaks of harmful blooms during summer, a phenomenon that has exacerbated in recent years. Chattonellaspp. and Skeletonemaspp. are the most common bloom forming phytoplankton species. In this study, bloom waters were distinguished from nonbloom waters by the unusual spectral shape of remote sensing reflectance at blue and red wavebands. Chattonellaspp. blooms were discriminated from Skeletonemaspp. blooms using a backscattering‐based index developed from bio‐optical models. More than 80% of Chattonellaspp. blooms were correctly classified using this index. Transitions of blooms from Skeletonemaspp. to Chattonellaspp. were captured by applying this method to daily composite GOCI images. Harmful algal blooms were discriminated using GOCI imagesThe bbp_index(555)algorithm performs well in discriminating phytoplankton bloom typesTransitions of the Skeletonemaspp. and Chattonellaspp. blooms were captured by the daily composite GOCI images Harmful algal blooms were discriminated using GOCI images The bbp_index(555)algorithm performs well in discriminating phytoplankton bloom types Transitions of the Skeletonemaspp. and Chattonellaspp. blooms were captured by the daily composite GOCI images