ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING
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Transcript of ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM OPTICAL REMOTE SENSING
ASSESSING BIODIVERSITY OF PHYTOPLANKTON COMMUNITIES FROM
OPTICAL REMOTE SENSINGJulia Uitz, Dariusz Stramski, and Rick A. ReynoldsScripps Institution of OceanographyUniversity of California San Diego
NASA Biodiversity Team Meeting – May 2010 – Washington DC
WHY STUDYING PHYTOPLANKTON DIVERSITY?
•Phytoplankton diversity influences many important biogeochemical processes▫Photosynthetic efficiency▫Fate of carbon fixed via photosynthesis▫Marine biological pump of carbon
•Key questions to be addressed▫Understanding of marine biogeochemical
cycles and modeling capabilities▫Distribution and variability on scales relevant
to environment and climate changes
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PROJECT OBJECTIVES AND STRATEGY
Phytoplankton diversity in the
world’s open oceans from optical remote
sensing
1. Exploit current Chla-based approach
2. Explore the potential of
hyperspectral approach
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OCEAN-COLOR BASED DISCRIMINATION OF DIFFERENT
PHYTOPLANKTON GROUPS•Satellite measurements of ocean color
▫Surface Chla concentration▫Quasi-global spatial scale▫Daily to decade
•New generation of algorithms for discriminating different phytoplankton groups from ocean color▫Dominance (Alvain et al. 2005)▫Surface Chla (Devred et al. 2006; Hirata et al.
2008)▫Vertical profile of Chla (Uitz et al. 2006)
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Conversion to CAbsorbed light energy
OCEAN COLOR-BASED PRIMARY PRODUCTION MODEL
P(t,z) = Chla(z,t) a*(z,t) PAR(z,t) Φc(z,t)
▫ P: Primary production (g C m-3 d-1)▫ PAR: Irradiance available for photosynthesis (mol quanta m-2 s-1)▫ Chla: Concentration of chlorophyll a (mg m-3)▫ a*: Chla-specific absorption coefficient of phytoplankton [m2 (mg
Chla)-1]▫ Φc: Quantum yield of carbon fixation [mol C (mol quanta)-1]
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PRIMARY PRODUCTION AT THE PHYTOPLANTKON GROUP LEVEL
Group-specific profiles of Chla(Uitz et al. JGR 2006)
Group-specific photophysiology(Uitz et al. L&O 2008)
Group-specific primary
production(Uitz et al. GBC in
press)
Ppg(t,z) = Chlapg(z,t) apg*(z,t) PAR(z,t) Φc,pg(z,t)
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Pmicro Pnano Ppico
3. Computation of group-specific primary production rates
(Uitz et al. GBC in press)
2. Bio-optical model of Morel (1991) + photophysiological properties of
Uitz et al. (2008)φmicro φnano φpico
Chlamicro Chlanano
1. Computation of Chla vertical profiles from surface Chla (Uitz et al.
2006)Chlapico
METHODOLOGY
Ppg(t,z) = Chlapg(z,t) apg*(z,t) PAR(z,t) Φc,pg(z,t)
(mg m-3)10-year time series of SeaWiFS
surface Chl (1997-2007)
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GLOBAL ANNUAL PRIMARY PRODUCTION
Total primary production• Pmicro + Pnano + Ppico = 46 Gt C y-1
• Consistent with previous estimates (37-56 Gt C y-1; e.g. Antoine et al. 1996; Behrenfeld & Falkowski 1997; Westberry et al. 2008)Group-specific primary production
• Pmicro (diatoms) 15 Gt C y-1 (32% of total)• Pnano (prymnesiophytes) 20 Gt C y-1 (44%)• Ppico (cyanobacteria) 11 Gt C y-1 (24%)
Carbon export towards deep oceans• 10-16 Gt C y-1 (20-40%; review by Tréguer et al. 2003)
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CLIMATOLOGY OF MICROPHYTOPLANKTON PRODUCTION
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• Boreal winter/Austral summer(Dec-Jan-Feb)
• Boreal summer/Austral winter(Jun-Jul-Aug)
• Temp/subpolar latitudes in summer: high contribution (e.g. Atl Nord >50%)• Near-coastal upwelling systems: 70% (1 g C m-2 d-1)• South Pacific Subtropical Gyre: Minimum contribution (0.02 g C m-2 d-1)
CLIMATOLOGY OF PICOPHYTOPLANKTON PRODUCTION
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• Boreal winter/Austral summer(Dec-Jan-Feb)
• Boreal summer/Austral winter(Jun-Jul-Aug)
• Maximum contribution in oligotrophic subtropical gyres (40-45%)• Contribution reduced to ~15% at high latitudes
CLIMATOLOGY OF NANOPHYTOPLANKTON PRODUCTION
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• Boreal winter/Austral summer(Dec-Jan-Feb)
• Boreal summer/Austral winter(Jun-Jul-Aug)
• Substantial contribution on global scale: 0.07-1 g C m-2 d-1 (30-60%)
• Can be found in extremely diverse environmental conditions (subtropical gyres vs. winter subantarctic waters Biodiversity? (see Liu et al. PNAS 2009)
CONCLUSIONS AND PERSPECTIVES• First climatology of phytoplankton group-specific
primary production on global scale over seasonal to interannual scales▫ Significant contribution to our ability to understand
and quantify marine carbon cycle with implications for carbon export
▫ Key elements required to calibrate/validate new biogeochemical models (e.g. Le Quéré et al. 2005)
▫ Benchmark for monitoring responses of marine pelagic ecosystems to climate change
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• Chla-based approaches▫ Describe general trends across various trophic regimes▫ But do not necessarily account for specific local
conditions• New complementary approaches need to be
developed• Explore the potential of hyperspectral optical
measurement for discriminating different phytoplankton groups▫ Hyperspectral optical measurements have matured into
powerful technologies in the field of remote sensing▫ Yet remain largely unexplored for open ocean
applications
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CONCLUSIONS AND PERSPECTIVES
HYPERSPECTRAL OPTICAL APPROACH
-Input dataset-Pigment composition
Cluster analysis-Reference
classification-
-Input dataset-Optical
measurements (ocean reflectance
and absorption spectra)
Derivative analysis
Cluster analysis
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Evaluation of performance
(Torecilla et al. in prep.)
• “Pilot” study• Small set of stations
from Eastern Atlantic open ocean▫ HPLC pigments▫ Optical data
• Encouraging results▫ Best classification with
hyperspectral derivative spectra
• 2nd cruise in the Atlantic Ocean almost completed!
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HYPERSPECTRAL OPTICAL APPROACH
THANK YOU FOR YOUR ATTENTION
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