**4. Algorithm development of ocean color phytoplankton functional types**

PFT algorithms have been developed to map the distribution of numerically- and ecologically-important organisms that demonstrate an anomalous relationship to a satellitederived reflectance product, such as chlorophyll-a. Generally, algorithm development has capitalized on the backscattering or absorption characteristics of a particular phytoplankton group, or utilized sophisticated modeling efforts. In earlier years, a particular species/group was targeted, whereas more recently mathematically sophisticated algorithms have produced multiple products relating to phytoplankton community structure. Both approaches provide valuable data products, which enhance our understanding of the spatial and temporal variability in ecological structure and function. We present a generalized description of present algorithms for *in situ* data and satellitederived data that describe phytoplankton taxonomic composition.

Ocean Color Remote Sensing of Phytoplankton Functional Types 111

Because conventional light microscopy is labor-intensive and biased towards the larger phytoplankton, biological oceanographers have sought alternative approaches to derive information on community structure of the entire phytoplankton population. The most common method to achieve this is by measuring the phytoplankton pigment composition. Algal pigments have been routinely used as chemotaxonomic markers in studies of phytoplankton ecology and biogeochemistry. Chlorophyll-a, in its monovinyl and divinyl forms, is found within all photosynthetic microalgae and cyanobacteria, and is used as a universal proxy of autotrophic biomass. However, there also exist other chlorophylls and carotenoids that are routinely measured by High Performance Liquid Chromatography

It should be noted that most algal pigments are found in more than one class, while some are not necessarily present in every member of the same class. Given the complexity of the distribution of pigments between and within various phytoplankton taxa, a number of statistical and mathematical methods have been developed to partition the bulk pigment biomass into the various phytoplankton groups. In addition to using pigments as classspecific markers, pigments indices of phytoplankton classes have been developed based on certain diagnostic pigments that tend to dominate in a particular size class. In this section we briefly describe these approaches. For a more detailed review of the use of pigments as

The increased use of HPLC pigment analyses in ecological, remote sensing and biogeochemical studies of marine systems has created an impetus to try to extract from pigment data not only the bulk chlorophyll-a biomass of the entire population, but also the distribution of phytoplankton groups from the class to the genus level. The quantitative use of pigment markers to assess the relative contribution of taxa to chlorophyll-a biomass is still a relatively new and developing area of research. The task of estimating the contribution of various taxa to the pigment signature of natural samples is complex, given that many pigments are found in more than one algal class. The distribution of phytoplankton pigments across the various phytoplankton classes is illustrated in Table 1. There are three basic approaches used to extract information on algal taxonomic composition based on pigments: multiple linear regression (Gieskes & Kraay 1983, 1986), inverse methods (Bidigare & Ondrusek 1996, Everitt et al. 1990, Letelier et al. 1993, Vidussi et al 2001) and matrix factorization analysis (Mackey et al. 1996, Wright & Jeffrey 2006).

The principal motivation behind the use of multiple linear regression studies of phytoplankton pigments was to establish a relationship between diagnostic pigments & the ubiquitous light harvesting pigment, chlorophyll-*a*, a universal proxy of phytoplankton biomass. One of the aims of the approach was to account for the fraction of phytoplankton cell biomass that may not be detected by conventional microscopy counts. Using multiple linear regression of diagnostic pigments against chlorophyll-a concentration, Gieskes & Kraay (1983) revealed that cryptophytes accounted for about half of the chlorophyll-a standing stock during a spring bloom study of the central North Sea, although these cells were not detected by microscope counts. The evidence was compelling, given that cryptophytes have a unique 'pigment

**5. Pigments as a diagnostic tool for identifying phytoplankton functional** 

(HPLC) and may serve as class-specific markers.

taxonomic markers see Wright & Jeffrey (2005).

**Pigments in relation to taxa** 

**Multiple linear regression analysis** 

**types** 

Fig. 6. Phytoplankton absorption spectra for a range of Chla (24.6, 18.9, 13.0, 1.91, 0.68, 0.21 mg m−3) and taxonomic size classes (pico, nano and micro) with decreasing slope from high to low aph (λ) and Chla; inset spectra of pico and nanoplankton at expanded range (Hirata et al 2008).


Table 2. Distribution of pigments across the divisions and classes of the algae that are conventionally measured by high performance liquid chromatography. ● = major pigment (>10%); •=minor pigment (≤10%) of the total chorophylls or carotenoids. (Modified from Jeffrey & Vesk 1997)
