An intercomparison of HPLC phytoplankton pigment methods using in situ samples: application to remote sensing and database activities

Claustre, Hervé; Hooker, Stanford B.; Heukelem, Laurie Van; Berthon, Jean-François; Barlow, Ray; Ras, Joséphine; Sessions, H.; Targa, Cristina; Thomas, Crystal; Linde, Dirk van der; Marty, Jean-Claude

doi:10.1016/j.marchem.2003.09.002

Cited by 112 publications

(78 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Total phytoplankton biomass and the contribution of the different algal groups were achieved by HPLC analysis of marker pigments. It was demonstrated that the accuracy of Chla and chemotaxonomic carotenoid quantification by HPLC is less than 10 and 25%, respectively (Claustre et al, 2004). Data were subsequently processed with CHEMTAX (for CHEmical TAXonomy) (Mackey et al, 1996), as described in Descy et al (2005) and Sarmento et al (2006).…”

Section: Phytoplankton Biomassmentioning

confidence: 99%

Primary production in a tropical large lake: The role of phytoplankton composition

Darchambeau

Sarmento

Descy

2014

Science of The Total Environment

View full text Add to dashboard Cite

• We provide a 7-year dataset of primary production in a tropical great lake.• Specific photosynthetic rate was determined by community composition.• Annual primary production varied between 143 and 278 mg C m − 2 y − 1 .• Pelagic production was highly sensitive to climate variability. Phytoplankton biomass and primary production in tropical large lakes vary at different time scales, from seasons to centuries. We provide a dataset made of 7 consecutive years of phytoplankton biomass and production in Lake Kivu (Eastern Africa). From 2002 to 2008, bi-weekly samplings were performed in a pelagic site in order to quantify phytoplankton composition and biomass, using marker pigments determined by HPLC. Primary production rates were estimated by 96 in situ 14 C incubations. A principal component analysis showed that the main environmental gradient was linked to a seasonal variation of the phytoplankton assemblage, with a clear separation between diatoms during the dry season and cyanobacteria during the rainy season. A rather wide range of the maximum specific photosynthetic rate (P Bm ) was found, ranging between 1.15 and 7.21 g carbon g −1 chlorophyll a h −1 , and was best predicted by a regression model using phytoplankton composition as an explanatory variable. The irradiance at the onset of light saturation (I k ) ranged between 91 and 752 μE m −2 s −1 and was linearly correlated with the mean irradiance in the mixed layer. The inter-annual variability of phytoplankton biomass and production was high, ranging from 53 to 100 mg chlorophyll a m −2 (annual mean) and from 143 to 278 g carbon m −2 y −1 , respectively. The degree of seasonal mixing determined annual production, demonstrating the sensitivity of tropical lakes to climate variability. A review of primary production of other African great lakes allows situating Lake Kivu productivity in the same range as that of lakes Tanganyika and Malawi, even if mean phytoplankton biomass was higher in Lake Kivu. a b s t r a c t a r t i c l e i n f o

show abstract

Section: Phytoplankton Biomassmentioning

confidence: 99%

Primary production in a tropical large lake: The role of phytoplankton composition

Darchambeau

Sarmento

Descy

2014

Science of The Total Environment

View full text Add to dashboard Cite

show abstract

“…Different phytoplankton groups contain diverse sets of light-absorbing pigments in addition to Chl (25,27,28). Light absorption is also influenced by the manner in which pigments are arranged or "packaged" within the phytoplankton (29).…”

Section: Absorption Propertiesmentioning

confidence: 99%

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

Dierssen

2010

Proc. Natl. Acad. Sci. U.S.A.

168

107

View full text Add to dashboard Cite

Phytoplankton biomass and productivity have been continuously monitored from ocean color satellites for over a decade. Yet, the most widely used empirical approach for estimating chlorophyll a (Chl) from satellites can be in error by a factor of 5 or more. Such variability is due to differences in absorption and backscattering properties of phytoplankton and related concentrations of colored-dissolved organic matter (CDOM) and minerals. The empirical algorithms have built-in assumptions that follow the basic precept of biological oceanography -namely, oligotrophic regions with low phytoplankton biomass are populated with small phytoplankton, whereas more productive regions contain larger bloom-forming phytoplankton. With a changing world ocean, phytoplankton composition may shift in response to altered environmental forcing, and CDOM and mineral concentrations may become uncoupled from phytoplankton stocks, creating further uncertainty and error in the empirical approaches. Hence, caution is warranted when using empirically derived Chl to infer climaterelated changes in ocean biology. The Southern Ocean is already experiencing climatic shifts and shows substantial errors in satellitederived Chl for different phytoplankton assemblages. Accurate global assessments of phytoplankton will require improved technology and modeling, enhanced field observations, and ongoing validation of our "eyes in space." biological oceanography | phytoplankton | primary productivity | satellite oceanography | ocean optics

show abstract

“…Conversely, the objective of P equal to 0.5 would require bias values of approximately 2%. Considering uncertainties on chl-a in situ data (Claustre et al 2004), results of algorithms evaluations (Brewin et al 2015), chl-a satellite products validation analyses (Gregg and Casey 2004;Mélin, Zibordi, and Berthon 2007) or inter-comparison Mélin 2010, and Figure 2(a)), uncertainties associated with the satellite-derived remote-sensing reflectance (Mélin and Franz 2014), and limitations inherent to the calibration of radiometers in space (Zibordi et al 2015), it is unlikely that, in the current state of technology and algorithm development, these levels of biases will be achieved by merely applying a fully consistent processing chain for the various missions (even though this is highly desirable). This study suggests that bias correction methods should become an integral part of the strategy to create ocean colour multi-mission CDRs.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Impact of inter-mission differences and drifts on chlorophyll-atrend estimates

Mélin

2016

International Journal of Remote Sensing

View full text Add to dashboard Cite

The chlorophyll-a (chl-a) concentration is an Essential Climate Variable, and the study of its variability at global scale requires a succession of satellite ocean colour missions to cover a period suitable for climate research. In the context of a multi-mission data record, inter-mission differences can introduce artefacts affecting trend evaluations, and the impact of the bias between the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) chl-a products is shown to be significant in a substantial part of the ocean. The assessment of trends can also be directly impacted by a drift in the chl-a time series resulting from sensor functions. These issues are addressed by a sensitivity analysis that compares slopes of linear regression obtained for varying levels of intermission bias and drift with respect to a 15-year reference series built with SeaWiFS and MODIS data. The relationship, constructed for a representative set of ocean provinces, between bias and the level of significance associated with the comparison of slopes shows that a bias on the order of ±5-6% generally induces a slope that is significantly different from the reference case, while a threshold on bias values not exceeding 2% largely alleviates this effect. Moreover, the study suggests that a drift larger than 2% per decade on the chl-a series can result in misleading conclusions from a trend analysis. All results have a clear regional dependence that needs to be taken into account in bias-correction and merging efforts. Low chl-a regions, such as the oligotrophic subtropical gyres, appear particularly sensitive to perturbations and require still higher levels of consistency and stability.ARTICLE HISTORY

show abstract

An intercomparison of HPLC phytoplankton pigment methods using in situ samples: application to remote sensing and database activities

Cited by 112 publications

References 25 publications

Primary production in a tropical large lake: The role of phytoplankton composition

Primary production in a tropical large lake: The role of phytoplankton composition

Perspectives on empirical approaches for ocean color remote sensing of chlorophyll in a changing climate

Impact of inter-mission differences and drifts on chlorophyll-atrend estimates

Contact Info

Product

Resources

About