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

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

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 taxonomic markers see Wright & Jeffrey (2005).

#### **Pigments in relation to taxa**

110 Remote Sensing of Biomass – Principles and Applications

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

**Chlorophylls Carote nes Xanth ophylls**

 - c a r

Chl orophyc e a e \_ \_ • \_ \_ \_ \_ \_ P ra si nophyc e a e \_ \_ • \_ • \_ \_ \_

Eust igm at ophyt a \_ \_ \_ •

A l lo

\_

\_ \_ \_ \_

19 \_-B ut

\_ \_ \_ \_ \_

D i adi no

Dino

Fuco

19 \_-Hex

Lut

Neo

P e r P ras V i ola Zea

et al 2008).

*Prokaryota* 

*Eukaryota*

*Synechococcus* 

*Prochlorococcus* 

Jeffrey & Vesk 1997)

C hl-*a* C hl*b*

Rhodophyt a \_ \_

Crypt ophyta \_ \_ \_ \_

D V C hl-*a* D V C hl*b*

C hl*c*1 C hl*c*2 C hl*c*3

Eulgenophyt a \_ \_ \_ \_ •

Dinophyta \_ \_ • \_ • \_

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

Ba c il la ri ophyt a \_ \_ \_ • \_ \_

P rym ne siophyt a \_ \_ \_ \_ • • \_ \_ \_ \_ Chrys ophyc e a e \_ \_ \_ • \_ \_ \_ Raphidophyceae \_ \_ \_ \_ \_ \_

 c ar

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).
