1 An evaluation of the application of CHEMTAX to Antarctic coastal pigment data Wendy A ...

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4 An evaluation of the application of CHEMTAX to Antarctic coastal pigment data
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Wendy A. Kozlowskia*, Douglas Deutschmanb, Irene Garibottic, Charles Treesd, and Maria Vernete
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9 a Integrative Oceanography Division, Scripps Institution of Oceanography, Mail Code 0218, La Jolla,
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CA 92093-0218, USA [email protected]
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12 b Department of Biology, San Diego State University, 5500 Campanile Drive, San Diego, CA 92182,
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USA [email protected]
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15 c Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales, C.C. 330, 5500 Mendoza,
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Argentina [email protected]
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18 d NATO Undersea Research Center, La Spezia, Italy [email protected]
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e Integrative Oceanography Division, Scripps Institution of Oceanography, Mail Code 0218, La Jolla,
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21 CA 92093-0218, USA [email protected]
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24 * Corresponding author. Phone/Fax: [phone]
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4 ABSTRACT
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7 The CHEMTAX (CHEMical TAXonomy) program is a chemotaxonomic tool used to determine
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9 phytoplankton composition based on photosynthetic pigment ratios. Although not as detailed as
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11 microscopy, it has three important advantages: it allows for non-unique pigment markers in
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13 phytoplankton groups, it is not dependent on sample preservation and includes all cell sizes, in
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15 particular the <5 micrometer cells, and once pigment data is obtained, it allows for large number of
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17 samples to be processed relatively quickly. In this study we present an evaluation of the application of
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CHEMTAX to Antarctic coastal pigments collected in a 12-year time series along the western
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Antarctic Peninsula (wAP). Overall analytical error is <20% for all pigments involved in the analysis.
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CHEMTAX was stable within a range of input pigment ratios and no changes were detected in output
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pigment ratios from samples collected at different depths within the euphotic zone. Diatoms are the
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27 main components of phytoplankton in this area, with blooms (chlorophyll a concentrations >5 µg L-1)
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dominated primarily by either diatoms or cryptomonads. Mixed flagellates can also be abundant and
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haptophytes and prasinophytes are frequently present in low concentrations. Comparison with
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microscopy shows CHEMTAX to give superior results in identifying haptophytes with favorable results
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for other groups. An onshore-offshore gradient in pigments can be observed along the wAP shelf
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during January, with each taxonomic group showing a distinct distribution; this gradient varies inter-
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annually. Phytoplankton groups estimated with CHEMTAX showed, on average, cryptomonads and
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mixed flagellates to be at their most abundant at the surface (0-10m) in the coastal regions (within 80
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km of the shore). Diatoms and cryptomonads can be abundant along the shelf region (80-150 km
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offshore), and diatoms, haptophytes and prasinophytes are at their most abundant in sub-surface
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waters (30-100 m) offshore (150-240 km from the coast). In years of high biomass, diatoms are
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dominant everywhere, sometimes mixed with cryptomonads (1996) or flagellates (2006). These
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52 results show CHEMTAX to be a reliable and stable tool for providing estimations of phytoplankton
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54 taxa in wAP waters.
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58 Keywords: Phytoplankton; Antarctica; Pigments; CHEMTAX; Microscopy
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4 1. INTRODUCTION
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7 Common methods for estimation of phytoplankton abundance and composition include
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9 microscopy (e.g., Gomi et al., 2007), flow cytometry (e.g., Smith et al., 2007; Sosik and Olson, 2007),
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11 genetic analysis (e.g., Medlin et al., 2006; Countway and Caron, 2006) and several versions of
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13 methods that can be categorized as chemotaxonomic, ie., groupings based on presence of chemical
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15 markers. The latter category includes the use of single or multiple pigment markers and multiple
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17 linear regression analysis to determine contribution by various groups to the total chlorophyll a (chl_a)
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pool (Gieskes et al., 1988; Peeken, 1997), the application of inverse methods to develop a least-
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squares solution to a matrix algorithm (Tarantola et al., 1987; Letelier et al., 1993), and an iterative
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method of matrix factorization for determination of algal class abundance (CHEMTAX, Mackey et al.,
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1996).
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Composition and distribution of phytoplankton communities can be documented using high
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performance liquid chromatography (HPLC) analysis of photosynthetic pigments (Jeffrey, 1980).
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Chemotaxonomic methods, based on the presence of characteristic pigments, mostly carotenoids, in
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algal phyla, are an extension of this approach (Gieskes and Kraay, 1986). The CHEMTAX method
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has been widely used to determine phytoplankton composition, including in the Southern Ocean (e.g.,
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Schlüter et al., 2000). Along with benefits such as having the ability to be reprocessed if needed,
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CHEMTAX has provided a means of estimating phytoplankton composition in data sets that might not
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have otherwise been analyzed for more traditional means of phytoplankton enumeration. CHEMTAX
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results have correlated strongly with those from microscopy, and in some instances have revealed
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presence of groups not detected with traditional enumeration methods (e.g., cryptophytes, Wright et
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al., 1996; Havskum et al., 2004). Primary concerns regarding use of chemotaxonomic methods center
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around non-unique pigment markers (Schlüter and Møhlenberg, 2003; Zapata et al., 2004) and
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potential fluctuations of the pigment ratios both at species and at cellular level under various
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physiological stressors (Jeffrey, 1981; Goericke and Montoya, 1998; DiTullio et al., 2007). However,
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with appropriate precautions, awareness of impacts and implications of physiological stressors, and
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4 some knowledge of potential populations within a sample region (Irigoien et al., 2004), CHEMTAX is
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6 a viable method for determination of phytoplankton composition (Mackey et al., 1998).
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Though many studies have examined variability of Antarctic phytoplankton structure and
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dynamics, our understanding of the large-scale geographic and long-term temporal variability remains
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weak. Previous studies examining phytoplankton composition and distribution have primarily focused
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on short term, limited scale sampling. The Palmer Long Term Ecological Research (Pal LTER) project
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has as its focus the marine ecosystem of the western Antarctic Peninsula (wAP). Pal LTER has been
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collecting data in this region since 1991, providing a temporally extensive data set for better
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understanding of the ecology of the area. HPLC analysis was chosen at the project’s onset as a
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combined approach to taxonomy and optical studies related to phytoplankton under the premise that
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pigment analysis provides high consistency between users and laboratories and low variability/high
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reproducibility (Hooker et al., 2005), requires less time and labor than microscopy, and is a lower cost
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alternative to genetic studies and flow cytometry.
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The specific goal of this study was to establish and apply to the Pal LTER HPLC dataset a
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robust means of determining phytoplankton composition. In this paper, we report results of
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phytoplankton community distribution and concentration in the wAP from an analysis of 12 years of
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pigment data using CHEMTAX. Methods used to process the validity of using CHEMTAX are
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reviewed and evaluated. Results for three years are compared to microscopic classification and
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impacts of variation in light regimes on CHEMTAX output are discussed. Variability and frequently
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observed patterns in the resulting phytoplankton distributions are presented.
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2. Methods
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2.1 Study Area and Sampling Regime
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Data was collected as part of the Pal LTER project along the western Antarctic Peninsula,
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from 63.6°S to 68.2°S and from 64°W to 73°W, bordering the eastern boundary of the Bellingshausen
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Sea to the South (Figure 1). The region is characterized by a glacially sculpted coastline containing a
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series of islands, bays and passages (Anderson, 1999), and is often divided into three sub-regions,
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4 coastal, shelf and slope, based on bathymetry and associated biological and physical dynamics
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6 (Martinson et al., 2008).
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9 FIGURE 1
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12 Pal LTER large-scale sampling stations are laid out on a grid system (Waters and Smith,
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14 1992), with transects (“grid lines”) 100 km apart running roughly south-east (onshore) to north-west
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16 (offshore) across the Antarctic continental shelf, approximately perpendicular to the coast. “Grid
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18 stations” are spaced 20 km apart along those lines (Figure 1b). Stations are identified using a
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20 LLL.SSS nomenclature, where LLL is grid line position, in kilometers, relative to the southernmost
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22 position on the grid and SSS is grid station, in kilometers, relative to the average coast (e.g., station
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24 400.120 is located 400 km north along the peninsula from the southernmost line, and 120 km
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26 offshore). Sampling used in this analysis was limited to five cardinal lines, 200 to 600, which cover the
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region from Southern end of Anvers Island to Marguerite Bay, and included all cardinal grid stations
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as well as other off-grid stations (e.g., near Palmer Station on Anvers Island, stations north and inland
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of Renaud Island and stations in the vicinity of southern Adelaide Island in the mouth of Marguerite
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Bay) occupied during yearly sampling efforts.
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37 Water sampling was done as part of the Pal LTER annual austral summer cruises from 1995
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39 to 2007. Stations were occupied in most years between the first week in January and the first week of
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41 February (Table 1). Sampling was carried out from the R/V Polar Duke in 1995 to 1997 and from the
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43 ASRV Laurence M. Gould from 1998 to 2007. Not all stations were sampled on all cruises due to ice,
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45 weather or scheduling issues. HPLC data from January 2002 is not available.
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48 TABLE 1
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51 2.2 Sampling and Processing Methods
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54 At all stations sampled, water was collected in 10 L Go-Flo (1995-1997) or 12 L Niskin (1998-
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56 2007) bottles, at the surface and at depths corresponding to 50 (± 6), 25 (± 3), 10 (± 2), 5 (± 1) and 1
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58 (± 1) percent of surface photosynthetically available radiation (PAR, 400-700 nm). Light depths were
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60 determined immediately prior to the water collection using a Biospherical Instruments QSP 200L4S
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4 sensor from 1995-2000 and 2003 and the PAR channel from a Biospherical Instruments Profiling
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6 Reflectance Radiometer during the remaining study years (600 Series 2001-2002, 800 Series 2004-
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8 2007). To ensure adequate light availability for instrumentation, water sampling was limited to
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10 daytime hours when the ambient PAR irradiance (4π) was greater than 100 µE m-2*s-1. This typically
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bound water sampling hours to between 0730 GMT and 0100 GMT (next day). For purposes of this
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study, the euphotic zone depth (Z ) is defined as the 1% irradiance depth.
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17 2.3 HPLC Methods
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20 From each depth sampled, 0.5-2 L of seawater was filtered, at < 12 psi vacuum, under dim
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other than 2000, time between filtration and analysis was typically less than one month and no more
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than three months. Samples from January 2000 were transported (in liquid nitrogen) to Scripps
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Institution of Oceanography and analyzed within thirteen months of collection.
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35 Filters were extracted in 90% HPLC grade acetone and either (a) manually crushed with a
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clean Teflon pestle, stored at -80°C for 24 hours, and pre-filtered through another GF/F before
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injection (1995-2001) or (b) ultrasonicated while held in a -20°C bench-top cooler for 10 seconds,
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stored at -80°C for 24 hours and pre-filtered through a 1 µm syringe tip glass fiber filter (2003-2005)
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or 0.45 µm Whatman nylon Puradisk filter (2006-2007) before injection. Testing confirmed that the
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changes in pre-injection filter type affected only pre-column and column longevity, not pigment
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separation or retention times.
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For cruises between 1995 and 1999, pigments were separated using a Hitachi system
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(D-6500 in 1995; D-7000 from 1996 to 1999) with L-4250 fixed wavelength (440 nm) and L-4500
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diode array (DAD, scanning 350-650 nm) detectors. HPLC grade mobile phase eluents followed the
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method of Wright et al. (1991):
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4 Solvent A: 80:20 methanol : 0.5 M aqueous ammonium acetate (pH 7.2)
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13 eluting, highly polar chlorophyll c3 (Table 2). Ultimately, the chlorophyll c3 data was not used in the
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15 analysis due to this peak spreading and the associated difficulties of calibration. Solvents were
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19 Resolve C18, 300 mmx3.9 µm, 5 µm column. Hitachi’s ConcertChrome software package was used
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24 TABLE 2
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27 January 2000 cruise data was analyzed on a Waters 600 controlled system with a Thermo
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31 (VWD) for peak quantification and a Waters 470 Scanning fluorescence detector for peak
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33 identification when applicable (excitation at 440 nm, emission at 665 nm). Data was collected using
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35 the Waters Millennium 32 software package, and peaks were quantified at 440 nm on the VWD.
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38 For cruises between 2001 and 2007, samples were separated using an Agilent Technologies
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40 (Hewlett-Packard) 1100 Series HPLC system, equipped with G1314A variable wavelength (fixed at
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42 440 nm), G1315A diode array (scanning 330-800 nm) and G1321A fluorescence (440 nm excitation,
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44 650 nm emission) detectors. Solvents were degassed using a vacuum degasser and column
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46 temperature was maintained at 25°C with a G1316A column thermostat. Agilent Technologies
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48 ChemStation for LC 3D software was used for system control and data collection and peaks were
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53 From 2000 to 2007 (both Waters and Agilent Technologies systems), HPLC grade mobile
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55 phase eluents followed the method of Zapata et al. (2000):
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6 the mid-chromatogram range xanthophylls (Table 2). Separations with this method were performed
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8 on Waters Symmetry C8, 150 mm x 4.6 µm, 3.5 µm columns, and samples were injected as either (a)
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10 a 2:1 sample:water mixture in 2000 or (b) a 5:4 sample:water mixture from 2001-2007.
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13 Table 3 lists the pigments quantified and used in this study their average retention times, and
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15 extinction coefficients used for determining concentrations of standards. In 1995 and 1996, chl_a,
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extinction coefficients; all other standards were isolated and quantified by Moss Landing Marine
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Laboratory previous to the cruises and were injected as external standards to determine signal
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response and retention times. From 1997-2007 commercially produced plant pigment standards were
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used for system calibration (Sigma Chemical for chl_ a and chl_b and alpha- and beta-carotene until
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2000; DHI (formerly VKI), Denmark (http://www.c14.dhi.dk/PhytoplanktonPigmentStandards.htm) for
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all others). Four to six point response curves were built using standard peak areas measured at 440
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nm, and daily injections of chl_a, as well as random single-point injections of all other pigments, were
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made to confirm system stability and monitor column degradation throughout the duration of sample
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processing. Sample peaks were identified based on retention times and confirmed spectrally using
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the diode array and fluorescence detectors, and pigment concentration was calculated by regression
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in Excel.
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TABLE 3
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2.3.1 HPLC Limits of Detection
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Table 4 lists for each detection system, the mass ranges of the pigments used in the
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CHEMTAX analysis and the uncertainty of the minimum detector response, for each pigment,
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calculated from the regressions as intercept (b) divided by slope (m). Sample injection volumes were
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constrained to these limits to ensure linear response of the detectors.
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TABLE 4
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60 2.4 Microscopy
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prasinophytes, dinoflagellates and unidentified small phytoflagellates.
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15 2.5 CHEMTAX
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18 Phytoplankton composition was determined using CHEMTAX (Mackey et al., 1996), a
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group in the community. For this study, the Microsoft Excel based CHEMTAX version 1.95, a beta
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version received directly from the CHEMTAX authors, was used. The results are output in terms of
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(percentage) of the total chl_a in a sample.
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2.5.1 Phytoplankton Groups
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For this study, the microscopy data sets (Garibotti et al., 2005a) provided the anticipated
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groupings, and were allocated as described in the microscopy section, with the “dinoflagellate” and
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“unidentified phytoflagellate” groups combined to form the derived “mixed flagellates” group. The
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chosen groups likely do not include all taxa present in the ecosystem of this area, but other studies
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completed in the region confirm the consistent dominance of diatoms, cryptophytes, haptophytes,
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flagellates and prasinophytes (Varela et al., 2002).
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2.5.2 Initial Pigment Ratios and Uncertainty Calculation
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55 To accurately allocate chl_a to phytoplankton groups, CHEMTAX requires an input matrix
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van den Enden, 2000; Wright et al., 1996), the eastern Atlantic region (South of South Africa; Wulff
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and Wänberg, 2004), the Southern Pacific sector (South East of New Zealand; DiTullio et al., 2003)
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and in the Gerlache and Bransfield Straits along the Antarctic Peninsula (Rodriguez et al., 2002;
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hereafter referred to as RVZ02). Given the geographic proximity of the RVZ02 data set to the current
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study, and the similarity in included groups to those found in the Pal LTER microscopy data sets, the
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start matrix from RVZ02 was used as the working base matrix for this study (Table 5a).
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Because CHEMTAX is known to be sensitive to the seed values in the initial ratio matrix, two
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additional ratio convergence methods were run. First, following the recommendation of Latasa (2007),
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a series of ten CHEMTAX runs were made on the 2007 data, with the first run using the Table 5b
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start ratio. Each subsequent run applied the output ratio of the previous run as its start ratio, and the
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output calculated from the tenth run was considered “final”. The second method employed was to
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complete 60 repeat runs of the software for each year. Each run had as its initial matrix the pigment
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ratios listed in Table 5b, but to each non-zero number a random amount up to 25% of the ratio value
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was added. During optimization, CHEMTAX determines the pigment content unexplained by the
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solution for each run (residual or root mean square error (RMSE)).Of the 60-run batch, the run with
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43 the lowest RMSE was considered “final”, and those with the lowest 10% of the RMSEs were used to
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45 calculate the coefficient of variation for the taxonomic estimates (see also Wright et al., 2009 for
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49 To compare the three convergence methods (with the 2007 data), the resulting composition
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51 data, by phytoplankton group, was analyzed using ANOVA. The null hypothesis was that there was
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53 no difference between the three data sets, and significance was set at =0.05. Before statistical
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55 analysis, the data were transformed using a square root function in order to assure normality.
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58 TABLE 5
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