Biomarker & productivity

1. 1
Course Presentation : ORGANIC GEOCHEMISTRY
Presented by

2. 14. Marine Biomarkers: Productivity

3. FOCUS OF MY PERSENTATION
3
• Finding – Where, Who, When
• Application
• Comparison
• Advantages & Limitation
• Prospect

4. FINDING – WHERE, WHO, WHEN

5. FINDING – WHERE, WHO, WHEN
• Pearson et al, (2001): Relative estimation of TOM and MOM role in Carbon sinks
and global climates changes
• Hedges et al, (1997): Marine sedimentary organic matter determination by
biomarker
• Smith et al , (2010, 2012): Estimating TOM contribution and multiple proxies
approaches (New Zealand south island)
• Xing et al, (2011): Holocene carbon cycle reconstructing of TOM & MOM(Yellow
sea).

6. • Takahashi . T, et al.(2009) : CO2 Flux and fixation domination by phytoplankton and
Export production.
• Brzezinski Ma et al(2002, 2004) : Glacial CO2 cycles, Si(OH)4 increased during
glacial times from cocolithophores to diatoms production(southern ocean).
• Matsumoto. K (2008) : ATM CO2 shifts in phytoplankton community despite a
reduction in ventilation of glacial water associated Si(OH)4 flux(Southern Ocean)
• Bradtmiiller. Li et al (2006) : Diatoms production instead of in its relative contribution
to total production compared with cocolithophores.(Sothern ocean).

7. APPLICATION
• Molecular Biomarker is potential of paleoceanographic traces to reconstruct
past changes in phytoplankton composition.
• Biological(phytoplankton: Diatom and Cocolithophores, foraminifera)
Mediated Co2 sequestration and deglacial rise in atmospheric Co2.
• Multiproxy record surface ocean productivity, dust inputs and thermocline
based on biomarker.

8. • Long-chain alkenones, di-tri-unsaturated C37 alkenones interpret the
abundance of haptophyte algae, such as coccolithopores in ocean water.
• Marine and terrestrial biomarker proxies can be interpret the spatial and
temporal variation and delivery mechanisms of organic matters.

9. COMPARISON
• Marine Biomarkers(long-chain alkenones) and productivity(cocotihophorid and
diatom).
• Comparing marine productivity, dust inputs & circulation proxies of δ 18 O
planktonic foraminifera, Terrestrial biomarker C26 alcohol & Th232 fluxes, C37
alkenones as proxy for cocolithophore production, Brassicasterol as proxy of
diatom production and δ13C for thermocline-dwelling foraminifera(fig.1 & 2)

10. Fig. 1. Comparison between marine
productivity and Biomarker

11. Fig. 2. Climate records Comparison
between marine productivity and
Biomarker

12. • Terrestrial & marine biomarker Proxies based on the measurements is applied to
quantitatively and comparatively estimate the sources and variations of sedimentary organic
matter.
• Comparison of spatial distribution of T&M biomarker of TOC/TN content, corrected
TOC/TON, TOC δ13C, TOC normalized brGDGT content and TOC normalized crearchaeol
content in surface sediment (Fig. 3)
• Binary model based proxies, the spatial Distribution of TOM, BIT and TMBR in surface
sediments

13. Fig. 3. Spatial Distribution & Corrected of TOC/
TN and TOC δ13C, n-alkanes, brGDGTs,
Crenarchaeol.

14. Binary model based
proxies, the spatial
Distribution of TOM, BIT
and TMBR in surface
sediments.

15. ADVANTAGES & LIMITATION
•Advantages :
• Marine Biomarker based can predicating and control atmospheric CO2 during the
past and into future.
• Multi proxy records of surface ocean productivity, dust inputs and thermocline
conditions.
• Phytoplankton composition changes were responsible for atmospheric CO2 changes
observed during the last transition from glacial to interglacial conditions.

16. • Biomarker based analysed the past productivity of cocolithophorid algae and
diatoms abundances.
• Phytoplankton composition changes can predicted by SALH, could have lowered
atmospheric CO2 concentration, although the dust-simulated increases in total
productivity.
• Multiple proxies employing C/N. δ13C, BIT and TMBR for surface sediments used to
distinguish and quantitatively and comparatively estimates the source and spatial
variation in sediments of organic matter

17. • Limitation
• Marine biomarker do not show the expected shift between cocolithophores and
diatoms predicted by the SALH during the last glacial periods that could account
for lowering of atmospheric CO2 concentrations through reduced carbonate pump.
• Did not determined changes in carbonate pump, a requirement of SAHL.
• Did not take into account that decrease in diatom as a consequences of the
increased local iron input during glacial times

18. • Biomarker records indicate that Fe induced Si excess was not large enough to
promote the phytoplanktonic shift during glacial time perhaps diatom growth
somehow limited by other ecological factors.
• TOC/TON ratio displayed no spatial patterns, not a reliable indicator of
TOM input.
•

19. PROSPECT
• Marine Biomarker based (biologically)control atmospheric CO2 is vital for
predicting their influence during the past and into the future.
• Phytoplankton productivity and composition associated based can be interpret
the equatorial upwelling intensity & influence Si-rich concentration.
• Phytoplankton compositional changes will responsible for the atmospheric
CO2 Changes can be predicted the glacial/ interglacial conditions.

20. • Increases in diatom and cocolithophora productivity occurred during
deglaciation, resulting reduction of the carbonated pump, insufficient to
counter the return to the atmosphere large amount of CO2 delivered by
the ocean and enhanced the ventilation of deep water.

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