Global Retrieval of Diatom Abundance Based on Phytoplankton Pigments and Satellite Data

Soppa, Mariana Altenburg; Hirata, Takafumi; Silva, Brenner; Dinter, Tilman; Peeken, Ilka; Wiegmann, Sonja; Bracher, Astrid

doi:10.3390/rs61010089

Cited by 55 publications

(53 citation statements)

References 36 publications

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“…Several new approaches for detecting phytoplankton biomass and specific PFTs, including coccolithophores (Balch et al, 1991(Balch et al, , 1996Bracher et al, 2015), Trichodesmium (Subramaniam and Carpenter, 1994;Subramaniam et al, 1999aSubramaniam et al, ,b, 2002Hu et al, 2010), and diatoms (Sathyendranath et al, 2004;Soppa et al, 2014) have been developed. Other algorithms characterize size class distributions (Ciotti et al, 2002;Yoder, 2005, 2010;Kostadinov et al, 2009;Brewin et al, 2010;Devred et al, 2011;Hirata et al, 2011;Organelli et al, 2013;Roy et al, 2013), PFT groups (Alvain et al, 2005;Hardman-Mountford et al, 2008;Bracher et al, 2009;Hirata et al, 2011;Moisan et al, 2011;Sadeghi et al, 2012;Campbell et al, 2013;IOCCG, 2014;Navarro et al, 2014) and select pigment concentrations (Pan et al, 2010), while others have utilized abundance based approaches (Uitz et al, 2006;Hirata et al, 2011;Chase et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

et al. 2017

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Phytoplankton functional diversity plays a key role in structuring the ocean carbon cycle and can be estimated using measurements of phytoplankton functional type (PFT) groupings. Concentrations of 18 phytoplankton pigments were calculated using a linear matrix inversion algorithm, with an average r 2 value of 0.70 for all pigments with p-values below the statistical threshold of 0.05. The inversion algorithm was then used with a chlorophyll-based absorption spectra model and Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) chlorophyll observations to calculate phytoplankton pigment concentrations in an area of the Atlantic Ocean off the United States east coast. Pigment distributions were analyzed to assess the distribution of PFTs. Five unique PFTs were found and delineated into three distinct offshore, transition, and open ocean groups. Group 1 (Diatoms) had highest abundance along the coast. Group 2 (prymnesiophytes, prokaryotes, and green algae) was a year-round stable offshore community that extended at reduced levels into the coast. Group 3 (dinoflagellates) dominated offshore between the Groups 1 and 2. Phytoplankton communities were delineated into coastal and offshore populations, with Group 2 having a dampened seasonal cycle, relative to the coastal populations. Shannon Diversity Indices (H) for the PFTs showed both spatial and temporal variability and had a clear non-linear relationship with chlorophyll. Diversity levels varied seasonally with changes in chlorophyll a levels. Peak PFT H was observed on the shelf where frontal features dominate, with diversity levels declining nearshore and offshore. This region marks an ecotone for phytoplankton in the study domain, and is associated with the coastal-side boundary of dinoflagellate dominance. Highest levels of diversity were observed in the tidally well-mixed regions of the Gulf of Maine and along a band that ran along the shelf region of the study area that was narrowest in the summer periods and broadened during the winter. These peak diversity zones were associated with moderate levels (∼0.8 mg m −3 ) of chlorophyll a. While the sign in the linear trends in chlorophyll between 2002 and 2016 varied depending on the region, the trends in the PFT H values were nearly all negative due to the non-linear relationship between chlorophyll levels and H.

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Section: Introductionmentioning

confidence: 99%

Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

et al. 2017

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“…To date, the majority of existing PG satellite retrieval approaches have relied on HPLC pigment data to derive in situ PG data: for developing and validating algorithms large in situ PT (e.g., Alvain et al, 2005;Hirata et al, 2011;Soppa et al, 2014;Swan et al, 2016) and PSC (e.g., Uitz et al, 2006;Brewin et al, 2010) data sets have been complied, complemented by the global pigment data set compiled under the MAREDAT project (Peloquin et al, 2013) and recent submissions to public data bases: e.g., SEABASS (http://seabass. gsfc.nasa.gov/), BODC (http://www.bodc.ac.uk), LTER Network Data Portal (https://portal.lternet.edu/nis/home.jsp), PANGAEA Data Publisher for Earth & Environmental Science (https:// www.pangaea.de).…”

Section: Approachmentioning

confidence: 99%

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

et al. 2017

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To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit

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“…Identification and quantification of pigments were carried out by comparing their retention times and absorption spectra using the EMPOWER software provided by Waters. Part of the pigment data were reported in Soppa et al (2014) and are publicly available at http://doi.pangaea.de/10.1594/ PANGAEA.848591.…”

Section: Nutrientsmentioning

confidence: 99%

Importance of deep mixing and silicic acid in regulating phytoplankton biomass and community in the iron-limited Antarctic Polar Front region in summer

Cheah

Soppa

Wiegmann

et al. 2017

Deep Sea Research Part II: Topical Studies in Oceanography

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a b s t r a c tPhytoplankton community structure and their physiological response in the vicinity of the Antarctic Polar Front (APF; 44°S to 53°S, centred at 10°E) were investigated as part of the ANT-XXVIII/3 Eddy-Pump cruise conducted in austral summer 2012. Our results show that under iron-limited ð o 0:3 μmol m À 3 Þ conditions, high total chlorophyll-a (TChl-a) concentrations ð 40:6 mg m À 3 Þ can be observed at stations with deep mixed layer ð 4 60 mÞ across the APF. In contrast, light was excessive at stations with shallower mixed layer and phytoplankton were producing higher amounts of photoprotective pigments, diadinoxanthin (DD) and diatoxanthin (DT), at the expense of TChl-a, resulting in higher ratios of (DD þDT)/ TChl-a. North of the APF, significantly lower silicic acid (Si(OH) 4 ) concentrations ð o 2 mmol m À 3 Þ lead to the domination of nanophytoplankton consisting mostly of haptophytes, which produced higher ratios of (DD þDT)/TChl-a under relatively low irradiance conditions. The Si(OH) 4 replete ð 4 5 mmol m À 3 Þ region south of the APF, on the contrary, was dominated by microphytoplankton (diatoms and dinoflagellates) with lower ratios of (DD þDT)/TChl-a, despite having been exposed to higher levels of irradiance. The significant correlation between nanophytoplankton and (DD þDT)/TChl-a indicates that differences in taxon-specific response to light are also influencing TChl-a concentration in the APF during summer. Our results reveal that provided mixing is deep and Si(OH) 4 is replete, TChl-a concentrations higher than 0:6 mg m À 3 are achievable in the iron-limited APF waters during summer.

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Global Retrieval of Diatom Abundance Based on Phytoplankton Pigments and Satellite Data

Cited by 55 publications

References 36 publications

Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

Importance of deep mixing and silicic acid in regulating phytoplankton biomass and community in the iron-limited Antarctic Polar Front region in summer

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