Global variability of phytoplankton functional types from space: assessment via the particle size distribution

Abstract. A new method of retrieving the parameters of a power-law particle size distribution (PSD) from ocean color remote sensing data was used to assess the global distribution and dynamics of phytoplankton functional types (PFT's). The method retrieves the power-law slope, ξ, and the abundance at a reference diameter, N0, based upon the shape and magnitude of the particulate backscattering coefficient spectrum. Relating the PSD to PFT's on global scales assumes that the open ocean particulate assemblage is biogenic. The retrieved PSD's can be integrated to define three size-based PFT's by the percent volume concentration contribution of three phytoplankton size classes – picoplankton (0.5–2 μm in equivalent spherical diameter), nanoplankton (2–20 μm) and microplankton (20–50 μm). Validation with in-situ HPLC diagnostic pigments resulted in better match-ups for the pico- and micro-phytoplankton size classes as compared to nanoplankton. Global decadal averages derived from SeaWiFS monthly data reveal PFT and particle abundance spatial patterns that are consistent with current understanding. Oligotrophic gyres are characterized by lower particle abundance and higher contribution by picoplankton-sized particles than transitional or eutrophic regions. Seasonal succession patterns for size-based PFT's reveal good correspondence between increasing chlorophyll concentration and percent contribution by microplankton, as well as increasing particle abundance. Long-term trends in particle abundances are generally well correlated with the MEI index indicating increased oligotrophy (i.e. lower particle abundance and increased contribution of picoplankton-sized particles) during the warm phase of an El Nino event. This work demonstrates the utility and future potential of assessing phytoplankton functional types using remote characterization of the particle size distribution.

[1]  S. Maritorena,et al.  Merged satellite ocean color data products using a bio-optical model: Characteristics, benefits and issues , 2010 .

[2]  H. Claustre,et al.  Vertical distribution of phytoplankton communities in open ocean: An assessment based on surface chlorophyll , 2006 .

[3]  K. Wolter,et al.  Measuring the strength of ENSO events: How does 1997/98 rank? , 1998 .

[4]  D. Diverrès,et al.  A seasonal tropical sink for atmospheric CO2 in the Atlantic ocean : the role of the Amazon river discharge , 2000 .

[5]  C. Junge,et al.  Air chemistry and radioactivity , 1963 .

[6]  H. Sverdrup,et al.  On Conditions for the Vernal Blooming of Phytoplankton , 1953 .

[7]  Farooq Azam,et al.  Major role of bacteria in biogeochemical fluxes in the ocean's interior , 1988, Nature.

[8]  S. Maritorena,et al.  Bio-optical modeling of primary production on regional scales: the Bermuda BioOptics project , 2001 .

[9]  Victor Smetacek,et al.  Pelagic ecosystem structure: Heterotrophic compartments of the plankton and their relationship to plankton size fractions 1 , 1978 .

[10]  Antoine Sciandra,et al.  Introduction to the special section bio-optical and biogeochemical conditions in the South East Pacific in late 2004: the BIOSOPE program , 2008 .

[11]  H. Freeland,et al.  Variability in upper-ocean water properties in the NE Pacific Ocean , 1999 .

[12]  Ajit Subramaniam,et al.  Influence of the Amazon River on the surface optical properties of the western tropical North Atlantic Ocean , 2004 .

[13]  D. A. Siegel,et al.  The North Atlantic Spring Phytoplankton Bloom and Sverdrup's Critical Depth Hypothesis , 2002, Science.

[14]  J. Randerson,et al.  Primary production of the biosphere: integrating terrestrial and oceanic components , 1998, Science.

[15]  Dariusz Stramski,et al.  Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean , 2006 .

[16]  P. J. Werdell,et al.  A multi-sensor approach for the on-orbit validation of ocean color satellite data products , 2006 .

[17]  Michael J. Behrenfeld,et al.  Significant contribution of large particles to optical backscattering in the open ocean , 2009 .

[18]  E. Carpenter,et al.  Amazon River enhances diazotrophy and carbon sequestration in the tropical North Atlantic Ocean , 2008, Proceedings of the National Academy of Sciences.

[19]  Lee Karp-Boss,et al.  INHERENT OPTICAL PROPERTIES OF NON-SPHERICAL MARINE-LIKE PARTICLES — FROM THEORY TO OBSERVATION , 2007 .

[20]  F. D’Ortenzio,et al.  Climate-Driven Basin-Scale Decadal Oscillations of Oceanic Phytoplankton , 2009, Science.

[21]  H. Claustre,et al.  Relationships between the surface concentration of particulate organic carbon and optical properties in the eastern South Pacific and eastern Atlantic Oceans , 2007 .

[22]  M. Kahru,et al.  Ocean Color Chlorophyll Algorithms for SEAWIFS , 1998 .

[23]  Stéphane Maritorena,et al.  Optimization of a semianalytical ocean color model for global-scale applications. , 2002, Applied optics.

[24]  Trevor Platt,et al.  Remote sensing of phytoplankton functional types , 2008 .

[25]  I. N. McCave.  Vertical flux of particles in the ocean , 1975 .

[26]  David A. Siegel,et al.  Retrieval of the particle size distribution from satellite ocean color observations , 2009 .

[27]  C. McClain.  A decade of satellite ocean color observations. , 2009, Annual review of marine science.

[28]  Dariusz Stramski,et al.  The role of seawater constituents in light backscattering in the ocean , 2004 .

[29]  Nicolas Gruber,et al.  Ocean deoxygenation in a warming world. , 2010, Annual review of marine science.

[30]  D. Siegel,et al.  Inherent optical property inversion of ocean color spectra and its biogeochemical interpretation: 1. Time series from the Sargasso Sea , 1997 .

[31]  R. Evans,et al.  Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database , 2001 .

[32]  Paul J. Harrison,et al.  Station Papa Time Series: Insights into Ecosystem Dynamics , 2002 .

[33]  P. C. Reid,et al.  Identifying four phytoplankton functional types from space: An ecological approach , 2008 .

[34]  C. McClain,et al.  Mixing and phytoplankton bloom in the wake of the Marquesas Islands , 1999 .

[35]  Watson W. Gregg,et al.  Ocean primary production and climate: Global decadal changes , 2003 .

[36]  P. Falkowski,et al.  Phytoplankton and Their Role in Primary, New, and Export Production , 2003 .

[37]  C. Moulin,et al.  Seasonal distribution and succession of dominant phytoplankton groups in the global ocean: A satellite view , 2008 .

[38]  P. Raimbault,et al.  Nutrient limitation of primary productivity in the Southeast Pacific (BIOSOPE cruise) , 2007 .

[39]  S. Maritorena,et al.  Ocean color observations and modeling for an optically complex site: Santa Barbara Channel, California, USA , 2007 .

[40]  Nicholas R. Bates,et al.  Pelagic functional group modeling: Progress, challenges and prospects , 2006 .

[41]  Charles R. McClain,et al.  Subtropical Gyre Variability Observed by Ocean Color Satellites , 2004 .

[42]  P. Falkowski,et al.  Biogeochemical Controls and Feedbacks on Ocean Primary Production , 1998, Science.

[43]  B. Peterson,et al.  Particulate organic matter flux and planktonic new production in the deep ocean , 1979, Nature.

[44]  M. Behrenfeld,et al.  Evolved physiological responses of phytoplankton to their integrated growth environment , 2008, Philosophical Transactions of the Royal Society B: Biological Sciences.

[45]  L. Stemmann,et al.  A vertical model of particle size distributions and fluxes in the midwater column that includes biological and physical processes—Part I: model formulation , 2004 .

[46]  Sean G. Herring,et al.  Shape of the particulate beam attenuation spectrum and its inversion to obtain the shape of the particulate size distribution. , 2001, Applied optics.

[47]  David M. Karl,et al.  Seasonal and interannual variability in primary production and particle flux at Station ALOHA , 1996 .

[48]  C. McClain,et al.  Recent trends in global ocean chlorophyll , 2005 .

[49]  François-Marie Bréon,et al.  Remote sensing of phytoplankton groups in case 1 waters from global SeaWiFS imagery , 2005 .

[50]  Andrew J. Watson,et al.  Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models , 2005 .

[51]  R. Arnone,et al.  Deriving inherent optical properties from water color: a multiband quasi-analytical algorithm for optically deep waters. , 2002, Applied optics.

[52]  Deborah K. Steinberg,et al.  Revisiting Carbon Flux Through the Ocean's Twilight Zone , 2006, Science.

[53]  H. Bader,et al.  The hyperbolic distribution of particle sizes , 1970 .

[54]  A. Longhurst.  Ecological Geography of the Sea , 1998 .

[55]  Karen S. Baker,et al.  Optical classification of natural waters 1 , 1978 .

[56]  P. Boyd,et al.  Phytoplankton dynamics in the NE subarctic Pacific , 1999 .

[57]  Melanie Abecassis,et al.  Ocean's least productive waters are expanding , 2008 .

[58]  Hervé Claustre,et al.  Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter , 2001 .

[59]  David A. Siegel,et al.  Carbon‐based ocean productivity and phytoplankton physiology from space , 2005 .

[60]  D. Stramski,et al.  Estimation of the inherent optical properties of natural waters from the irradiance attenuation coefficient and reflectance in the presence of Raman scattering. , 2000, Applied optics.

[61]  David A. Siegel,et al.  Climate-driven trends in contemporary ocean productivity , 2006, Nature.

[62]  Scott C. Doney,et al.  Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity , 2010 .

[63]  J. Strickland,et al.  Sinking rates of marine phytoplankton measured with a fluorometer , 1967 .

[64]  Dariusz Stramski,et al.  Phytoplankton class‐specific primary production in the world's oceans: Seasonal and interannual variability from satellite observations , 2010 .

[65]  H. Ducklow,et al.  Plankton succession and carbon cycling at 47°N 20°W during the JGOFS North Atlantic Bloom Experiment , 1993 .

[66]  A. Longhurst.  TOWARD AN ECOLOGICAL GEOGRAPHY OF THE SEA , 2007 .

[67]  C. Moulin,et al.  Seasonal distribution and succession of dominant phytoplankton groups in the global ocean : a satellite view - art. no. GB3001 , 2008 .

[68]  Connie M. Borror,et al.  Probability and Statistics for Engineers and Scientists, 6th Ed. , 2000 .

[69]  Paul G. Falkowski,et al.  A consumer's guide to phytoplankton primary productivity models , 1997 .

[70]  Kirk Knobelspiesse,et al.  Unique data repository facilitates ocean color satellite validation , 2003 .

[71]  M. Behrenfeld,et al.  Independence and interdependencies among global ocean color properties: Reassessing the bio‐optical assumption , 2005 .