Identifying four phytoplankton functional types from space: An ecological approach

Deriving maps of phytoplankton taxa based on remote sensing data using bio‐optical properties of phytoplankton alone is challenging. A more holistic approach was developed using artificial neural networks, incorporating ecological and geographical knowledge together with ocean color, bio‐optical characteristics, and remotely sensed physical parameters. Results show that the combined remote sensing approach could discriminate four major phytoplankton functional types (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of more than 70%. Models indicate that the most important information for phytoplankton functional type discrimination is spatio‐temporal information and sea surface temperature. This approach can supply data for large‐scale maps of predicted phytoplankton functional types, and an example is shown.

[1]  P. C. Reid,et al.  Coccolithophore bloom size variation in response to the regional environment of the subarctic North Atlantic , 2006 .

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

[3]  David W. Sims,et al.  Using continuous plankton recorder data , 2006 .

[4]  Thomas R. Anderson,et al.  Plankton functional type modelling : running before we can walk? , 2005 .

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

[6]  H. Bouman,et al.  Physical forcing and phytoplankton distributions , 2005 .

[7]  P. C. Reid,et al.  Extending the SeaWiFS chlorophyll data set back 50 years in the northeast Atlantic , 2005 .

[8]  Kozo Takahashi,et al.  Silicoflagellate fluxes and environmental variations in the northwestern North Pacific during December 1997–May 2000 , 2005 .

[9]  Nicholas H. Putnam,et al.  The Genome of the Diatom Thalassiosira Pseudonana: Ecology, Evolution, and Metabolism , 2004, Science.

[10]  M. Edwards,et al.  Impact of climate change on marine pelagic phenology and trophic mismatch , 2004, Nature.

[11]  T. Cokacar,et al.  Satellite-detected early summer coccolithophore blooms and their interannual variability in the Black Sea , 2004 .

[12]  Paul G. Falkowski,et al.  The Evolution of Modern Eukaryotic Phytoplankton , 2004, Science.

[13]  T. Platt,et al.  Discrimination of diatoms from other phytoplankton using ocean-colour data , 2004 .

[14]  J. Young,et al.  Coccolithophores : from molecular processes to global impact , 2004 .

[15]  T. Tyrrell,et al.  Emiliania huxleyi: bloom observations and the conditions that induce them , 2004 .

[16]  M. Edwards,et al.  Phytoplankton biomass from continuous plankton recorder data: an assessment of the phytoplankton colour index , 2003 .

[17]  Costas Papaconstantinou,et al.  Predicting demersal fish species distributions in the Mediterranean Sea using artificial neural networks , 2003 .

[18]  P. C. Reid,et al.  Achievements of the Continuous Plankton Recorder Survey and a vision for its future - Preface. , 2003 .

[19]  David Archer,et al.  Association of sinking organic matter with various types of mineral ballast in the deep sea: Implications for the rain ratio , 2002 .

[20]  D. Harbour,et al.  Copepod hatching success in marine ecosystems with high diatom concentrations , 2002, Nature.

[21]  U. Båmstedt,et al.  Zooplankton growth, diet and reproductive success compared in simultaneous diatom- and flagellate-microzooplankton-dominated plankton blooms , 2001 .

[22]  E. Carpenter,et al.  Detecting Trichodesmium blooms in SeaWiFS imagery , 2001 .

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

[24]  C. Brown.  Remote sensing of coccolithophore blooms in the Western South atlantic ocean , 1997 .

[25]  T. Tyrrell,et al.  Importance of light for the formation of algal blooms by Emiliania huxleyi , 1996 .

[26]  V. Smetácek,et al.  Organism life cycles, predation, and the structure of marine pelagic ecosystems , 1996 .

[27]  Ron Kohavi,et al.  The Power of Decision Tables , 1995, ECML.

[28]  S. Nixon.  Coastal marine eutrophication: A definition, social causes, and future concerns , 1995 .

[29]  M. S. Finch,et al.  A biogeochemical study of the coccolithophore, Emiliania huxleyi, in the North Atlantic , 1993 .

[30]  F. Jochem,et al.  Naked Dictyocha speculum — a new type of phytoplankton bloom in the Western Baltic , 1989 .

[31]  D. F. Specht,et al.  Probabilistic neural networks for classification, mapping, or associative memory , 1988, IEEE 1988 International Conference on Neural Networks.

[32]  S. Pond,et al.  Introductory Dynamical Oceanography , 1983 .

[33]  Stephen Pond,et al.  Introductory dynamic oceanography , 1978 .

[34]  F. A. Richards,et al.  The influence of organisms on the composition of sea-water , 1963 .