Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class

Brewin, Robert J. W.; Sathyendranath, Shubha; Jackson, T. J.; Barlow, Ray; Brotas, Vanda; Airs, Ruth L.; Lamont, Tarron

doi:10.1016/j.rse.2015.07.004

Cited by 102 publications

(185 citation statements)

References 100 publications

(172 reference statements)

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“…Ocean color algorithms to assess phytoplankton diversity make use of information originating from phytoplankton abundance, cell size, bio-optical properties (such as pigment composition, absorption, and backscattering characteristics) to differentiate PG (Table 2, Figure 1 left). The abundance based approaches of Uitz et al (2006), Brewin et al (2010), Brewin et al (2015), and Hirata et al (2011) use satellite chl-a as input to derive PSC or PT based on empirical relationships linking in situ marker pigments to chl-a which are determined using high precision liquid chromatography (HPLC). Abundance-based approaches use satellite chl-a as input and by that exploit the largest signal in water leaving radiance to extract variability due to PG out of chl-a.…”

Section: State Of the Artmentioning

confidence: 99%

“…Phytoplankton composition product References ABUNDANCE Size classes Uitz et al, 2006;Brewin et al, 2010Brewin et al, , 2015 Size classes and multiple taxa Hirata et al, 2011 SPECTRAL REFLECTANCE Multiple taxa Alvain et al, 2005Alvain et al, , 2008Li et al, 2013;Ben Mustapha et al, 2014 Single taxon Coccolithophores Brown and Yoder, 1994;Moore et al, 2012Trichodesmium Subramaniam et al, 2002Westberry et al, 2005 ABSORPTION Size index Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012 Size classes Devred et al, 2006Devred et al, , 2011Hirata et al, 2008;Fujiwara et al, 2011;Roy et al, 2013 Multiple taxa Bracher et al, 2009;Sadeghi et al, 2012a;Werdell et al, 2014 BACK-SCATTERING Size classes Kostadinov et al, 2009Kostadinov et al, , 2016Fujiwara et al, 2011 ECOLOGICAL Taxonomic groups Palacz et al, 2013 Frontiers in Marine Science | www.frontiersin.orgFIGURE 1 | Illustration of phytoplankton diversity as found in nature impacted by environmental conditions, and how it can be derived from observations and modeling. Through in situ measurements (which represent the most real conditions), phytoplankton are grouped according to cellular traits that influence their optical properties such as pigments, size, morphology, and fluorescence, all also responding to photophysiology, which are named optical features of phytoplankton groups (PG).…”

Section: Approachmentioning

confidence: 99%

“…While the approach of Raitsos et al (2008) was regionally developed for the North-Atlantic, the approach by Palacz et al (2013) is not purely based on remote sensing data but also requires a coupling to a dynamic plankton model. Products obtained from the PG algorithms (Table 2) are typically dominance (Brown and Yoder, 1994;Alvain et al, 2005;Moore et al, 2012;Ben Mustapha et al, 2014), presence or absence of a certain PT (Westberry et al, 2005;Werdell et al, 2014), fraction or concentration of chl-a of the three PSC (Devred et al, 2006(Devred et al, , 2011Uitz et al, 2006;Hirata et al, 2008Hirata et al, , 2011Kostadinov et al, 2009Kostadinov et al, , 2016Brewin et al, 2010Brewin et al, , 2015Fujiwara et al, 2011;Li et al, 2013;Roy et al, 2013) or a size factor characterizing the contribution of pico-(or micro-) phytoplankton to the phytoplankton community (Ciotti and Bricaud, 2006;Mouw and Yoder, 2010;Bricaud et al, 2012). Currently, only the products OC-PFT and PhytoDOAS (Bracher et al, 2009;Sadeghi et al, 2012a) enable the simultaneous determination of chl-a for several PT.…”

Section: Approachmentioning

confidence: 99%

“…It also inhibits the observation of the patchy distribution of phytoplankton communities. To derive certain PT beyond sizeand/or pigment-based discrimination of phytoplankton requires developing empirical methods that rely on covariation: Via the exploitation of additional data (light, temperature, nutrients, ...), retrievals and optical modeling for specific regions could be further constrained (and optimized), as for example in the study by Brewin et al (2015) where information on ambient light field extracted from satellite information was combined with an abundance based PSC approach.…”

Section: Gap 4: Lack Of Regional Capability Of Pg Algorithmsmentioning

confidence: 99%

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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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Section: State Of the Artmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Approachmentioning

confidence: 99%

Section: Gap 4: Lack Of Regional Capability Of Pg Algorithmsmentioning

confidence: 99%

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

et al. 2017

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“…Step 3 ′ Because of the variability of the in-situ CPR data (in time and space), this calibration validation procedure was performed several times (number of separate procedures = 1,200) for different randomly-defined subsets of the calibration and validation to optimize the representativeness of the validation subset and strengthen the results (following a concept applied in ocean-color e.g., Craig et al, 2012;Bracher et al, 2015a;Brewin et al, 2015). This number of separate repetitions (n = 1,200) ensured that each randomly-selected matchup measurement was included at least once in the validation subset (Figure 2, Step 3).…”

Section: Repetition Of the Calibration Procedurementioning

confidence: 99%

Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

et al. 2017

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Over the past years, a large number of new approaches in the domain of ocean-color have been developed, leading to a variety of innovative descriptors for phytoplankton communities. One of these methods, named PHYSAT, currently allows for the qualitative detection of five main phytoplankton groups from ocean-color measurements. Even though PHYSAT products are widely used in various applications and projects, the approach is limited by the fact it identifies only dominant phytoplankton groups. This current limitation is due to the use of biomarker pigment ratios for establishing empirical relationships between in-situ information and specific ocean-color radiance anomalies in open ocean waters. However, theoretical explanations of PHYSAT suggests that it could be possible to detect more than dominance cases but move more toward phytoplanktonic assemblage detection. Thus, to evaluate the potential of PHYSAT for the detection of phytoplankton assemblages, we took advantage of the Continuous Plankton Recorder (CPR) survey, collected in both the English Channel and the North Sea. The available CPR dataset contains information on diatom abundance in two large areas of the North Sea for the period 1998-2010. Using this unique dataset, recurrent diatom assemblages were retrieved based on classification of CPR samples. Six diatom assemblages were identified in-situ, each having indicators taxa or species. Once this first step was completed, the in-situ analysis was used to empirically associate the diatom assemblages with specific PHYSAT spectral anomalies. This step was facilitated by the use of previous classifications of regional radiance anomalies in terms of shape and amplitude, coupled with phenological tools. Through a matchup exercise, three CPR assemblages were associated with specific radiance anomalies. The maps of detection of these specific radiances anomalies are in close agreement with current in-situ ecological knowledge.

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Phytoplankton light absorption and the package effect in relation to photosynthetic and photoprotective pigments in the northern tip of Antarctic Peninsula

et al. 2017

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This study investigates the variability in the spectral absorption of phytoplankton in Antarctic waters. A large in situ data set comprising phytoplankton pigments and hyperspectral absorption was measured in the northern tip of Antarctic Peninsula during 2013 and 2014 summers at several depths. A proxy of package effect was estimated from the phytoplankton absorption spectra, independently of chlorophyll a. Variations in the concentration of photosynthetic and photoprotective pigments were discernible by changes in this metric but not in the chlorophyll a specific absorption coefficient of phytoplankton. The fucoxanthin to chlorophyll a ratio correlated positively to package effect due to an increase in cell size of phytoplankton (diatoms) and increasing fucoxanthin content per cell to maximize light harvesting in depth. The package effect was found to covary inversely with photoprotective pigments relative to chlorophyll a, partially due to their contribution to enhance absorption in the blue part of the spectrum. Using a cluster analysis (k‐means algorithm) on the phytoplankton absorption spectra, we illustrate the capacity to identify a regular increase in the degree of package effect. This approach can be useful to classify the phytoplankton assemblages in Antarctic waters according to different degrees of pigment packaging, each one related to a specific pigment composition. Our results demonstrate the potential for this classification at different temporal and spatial scales from ocean color satellite data. This should improve our understanding of deviations in global bio‐optical algorithms when applied to the Southern Ocean.

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Influence of light in the mixed-layer on the parameters of a three-component model of phytoplankton size class

Cited by 102 publications

References 100 publications

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

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

Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea

Phytoplankton light absorption and the package effect in relation to photosynthetic and photoprotective pigments in the northern tip of Antarctic Peninsula

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