C., 2008. Identifying four phytoplankton functional types from space: an eco- logical approach. Limnol. Oceanogr. 53 (2), 605-613.RAITSOS D E,LAVENDER S J,MARAVELIAS C D. I-dentifying four phytoplankton functio
Remote sensing of phytoplankton groups in case 1 waters from global SeaWifs imagery. Deep Sea Res. Part I 52, 1989–2004 (2005). Article Google Scholar Sathyendranath, S. et al. Phytoplankton functional types from space. Reports of the International Ocean-Colour Coordinating Group, No. 15 ...
Using this approach, they were able to discriminate four major phytoplankton functional types based on probability of occurrence (diatoms, dinoflagellates, coccolithophores, and silicoflagellates) with an accuracy of >70%. Palacz et al. (2013) used another approach that relies on an artificial ...
Global variability of phytoplankton functional types from space: assessment via the particle size distribution Biogeosciences, 7 (2010), pp. 3239-3257, 10.5194/bg-7-3239-2010 View in ScopusGoogle Scholar Kramer and Siegel, 2019 S.J. Kramer, D.A. Siegel How can phytoplankton pigments be best ...
2008. Identifying four phytoplankton functional types from space: an ecological approach. Limnology and Oceanography, 53(2): 605–613. Article Google Scholar Roesler C S. 1998. Theoretical and experimental approaches to improve the accuracy of particulate absorption coefficients derived from the ...
Maritorena, “Global variability of phytoplankton functional types from space: Assessment via the particle size distribution,” Biogeosciences 7, 3239–3257 (2010). Article Google Scholar C. T. Kremer, M. K. Thomas, and E. Litchman, “Temperature- and size-scaling of phytoplankton population ...
Functional richness is then defined as the number of phytoplankton types (biogeochemical functional types and size classes) that coexist at a particular location and timestep. The functional Shannon index is defined as: $${{{\mathrm{Shannon}}}=-\mathop{\sum }\limits_{s=1}^{s}{p}_{i}{...
Functional gene annotation Taxonomic annotation of the functional gene was determined by Kraken with default parameters (Table S9). The signal peptides were predicted by SignalP (v5.0) and further finer subcellular localization of proteins with signal peptides was predicted using PSORTb with default ...
During the last decade, the analysis of the ocean color satellite imagery has allowed determining the dominant phytoplankton groups in surface waters through the development of bio-optical models aimed at identifying the main phytoplankton functional types (PFTs) or size classes from space. One of th...
Fig. 2: Comparison of thermal dependencies among phytoplankton functional types. aAbsolute change in performance for each PFT (Coccolithophores = CO, cyanobacteria = CY, diatoms = DT, dinoflagellates = DF), determined by analyzing the rate of change from 20% of the maximum gr...