CHEMTAX - a program for estimating class abundances from chemical markers:application to HPLC measurements of phytoplankton

Mackey,; Dj, Mackey; Hw, Higgins; Sw, Wright

doi:10.3354/meps144265

Cited by 1,208 publications

(953 citation statements)

References 19 publications

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“…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). CHEMTAX is a computer program that allows allocating Chla among different algal groups defined by a suite of markers.…”

Section: Phytoplankton Biomassmentioning

confidence: 99%

“…CHEMTAX is a computer program that allows allocating Chla among different algal groups defined by a suite of markers. From an initial ratio matrix (or input matrix) usually derived from pure cultures of phytoplankton, the program uses an iterative process to find the optimal pigment:Chla ratios and generates the fraction of the total Chla pool belonging to each pigment-determined group (Mackey et al, 1996). A supplementary 50 mL sample was preserved immediately after collection with Lugol's solution and neutral formaldehyde (2-4% final concentration) for microscopic identification of main taxa, according to Sarmento et al (2007).…”

Section: Phytoplankton Biomassmentioning

confidence: 99%

“…Phytoplankton pigments, determined by high performance liquid chromatography (HPLC) analysis, have been used widely for assessing biomass at the class level, with many applications in marine, estuarine, and freshwater environments (e.g., review in Sarmento and Descy, 2008). The assessment of algal abundance from pigment concentrations uses different techniques, involving ratios of marker pigments to chlorophyll a (Chla) (Mackey et al, 1996). As it is based on a fast, automatic and reliable analytical technique, the pigment approach has been largely used in oceanographic studies and monitoring programs (Jeffrey et al, 1997), as well as in large lake studies (Descy et al, 2005;Fietz and Nicklish, 2004;Fietz et al, 2005;Sarmento et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

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

Darchambeau

Sarmento

Descy

2014

Science of The Total Environment

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• 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

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Section: Phytoplankton Biomassmentioning

confidence: 99%

Section: Phytoplankton Biomassmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Darchambeau

Sarmento

Descy

2014

Science of The Total Environment

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“…Contributions of individual phytoplankton groups to total chlorophyll a were then calculated using the software CHEMTAX (Mackey et al, 1996).…”

Section: Sample Analysismentioning

confidence: 99%

Ocean Acidification-Induced Restructuring of the Plankton Food Web Can Influence the Degradation of Sinking Particles

et al. 2018

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Ocean acidification (OA) is expected to alter plankton community structure in the future ocean. This, in turn, could change the composition of sinking organic matter and the efficiency of the biological carbon pump. So far, most OA experiments involving entire plankton communities have been conducted in meso-to eutrophic environments. However, recent studies suggest that OA effects may be more pronounced during prolonged periods of nutrient limitation. In this study, we investigated how OA-induced changes in low-nutrient adapted plankton communities of the subtropical North Atlantic Ocean may affect particulate organic matter (POM) standing stocks, POM fluxes, and POM stoichiometry. More specifically, we compared the elemental composition of POM suspended in the water column to the corresponding sinking material collected in sediment traps. Three weeks into the experiment, we simulated a natural upwelling event by adding nutrient-rich deep-water to all mesocosms, which induced a diatom-dominated phytoplankton bloom. Our results show that POM was more efficiently retained in the water column in the highest CO 2 treatment levels (>800 µatm pCO 2 ) subsequent to this bloom. We further observed significantly lower C:N and C:P ratios in post-bloom sedimented POM in the highest CO 2 treatments, suggesting that degradation processes were less pronounced. This trend is most likely explained by differences in micro-and mesozooplankton abundance during the bloom and postbloom phase. Overall, this study shows that OA can indirectly alter POM fluxes and stoichiometry in subtropical environments through changes in plankton community structure.

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“…Separation and analysis of chlorophyll-a (chl-a) and groupspecific marker pigments was carried out by a HPLC system from JASCO (Japan). Contribution of diatoms, dinophytes, cryptophytes, prymnesiophytes, and prasinophytes to total chl-a was estimated using the CHEMTAX software (Mackey et al, 1996) with initial pigment ratios derived from Schlüter et al (2000). See SUP.…”

Section: Discrete Measurements Of Phytoplankton Pigments and Total Sumentioning

confidence: 99%

Analysis of phytoplankton distribution and community structure in the German Bight with respect to the different size classes

Wollschläger

Wiltshire

Petersen

et al. 2015

Journal of Sea Research

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Investigation of phytoplankton biodiversity, ecology, and biogeography is crucial for understanding marine ecosystems. Research is often carried out on the basis of microscopic observations, but due to the limitations of this approach regarding detection and identification of picophytoplankton (0.2-2 μm) and nanophytoplankton (2-20 μm), these investigations are mainly focused on the microphytoplankton (20-200 μm). In the last decades, various methods based on optical and molecular biological approaches have evolved which enable a more rapid and convenient analysis of phytoplankton samples and a more detailed assessment of small phytoplankton. In this study, a selection of these methods (in situ fluorescence, flow cytometry, genetic fingerprinting, and DNA microarray) was placed in complement to light microscopy and HPLC-based pigment analysis to investigate both biomass distribution and community structure of phytoplankton. As far as possible, the size classes were analyzed separately. Investigations were carried out on six cruises in the German Bight in 2010 and 2011 to analyze both spatial and seasonal variability. Microphytoplankton was identified as the major contributor to biomass in all seasons, followed by the nanophytoplankton. Generally, biomass distribution was patchy, but the overall contribution of small phytoplankton was higher in offshore areas and also in areas exhibiting higher turbidity. Regarding temporal development of the community, differences between the small phytoplankton community and the microphytoplankton were found. The latter exhibited a seasonal pattern regarding number of taxa present, alphaand beta-diversity, and community structure, while for the nano-and especially the picophytoplankton, a general shift in the community between both years was observable without seasonality. Although the reason for this shift remains unclear, the results imply a different response of large and small phytoplankton to environmental influences.

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CHEMTAX - a program for estimating class abundances from chemical markers:application to HPLC measurements of phytoplankton

Cited by 1,208 publications

References 19 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

Ocean Acidification-Induced Restructuring of the Plankton Food Web Can Influence the Degradation of Sinking Particles

Analysis of phytoplankton distribution and community structure in the German Bight with respect to the different size classes

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