light intensity influences on algal pigments, proteins and carbohydrates
TRANSCRIPT
LIGHT INTENSITY INFLUENCES ON ALGAL PIGMENTS, PROTEINS AND CARBOHYDRATES: IMPLICATIONS FOR PIGMENT-BASED
CHEMOTAXONOMY
by
Cidya Grant
A Dissertation Submitted to the Faculty of
The Charles E. Schmidt College of Science
in Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Florida Atlantic University
Boca Raton, FL
December 2011
iii
ACKNOWLEDGEMENTS
Special thanks to my research advisor Dr. J. W. Louda, for his guidance and
support during this dissertation research. To the members of my dissertation committee:
Drs. J. E. Haky, C. Parkanyi and S. Hagerthey, for answering pertinent questions and
steering me on the right path to fulfilling the objectives and goals of this research.
To the FAU-Harbor Branch Oceanographic Institute for NMR sample analyses:
special thanks to Dr. Amy Wright for granting permission for instrument use and to her
post- doctoral associate Dr. P. Winder for her assistance with experiment set-up.
To the West natural products research group at FAU, particularly Dr. L. West, his
post-doctoral associate Dr. P. Gupta and graduate student T. Vansach: thank you for the
technical assistance with LC-MS analyses and NMR interpretation.
To my teaching supervisors and mentors at FAU: Drs. D. Chamely-Wiik and E.
Rezler, thank you for always challenging me to reach the highest academic standards, in
research and teaching. The encouragement and assistance were all greatly appreciated.
Funding for this material is based in part upon work supported by the National
Science Foundation under Grant no. DGE: 0638662. Any opinions, findings and
conclusions or recommendations expressed in this material are those of the author and do
not reflect the views of the National Science Foundation.
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ABSTRACT
Author: Cidya Grant
Title: Light Intensity Influences on Algal Pigments, Proteins and Carbohydrates: Implications for Pigment-Based Chemotaxonomy
Institution: Florida Atlantic University
Dissertation Advisor: Dr. J. W. Louda
Degree: Doctor of Philosophy
Year: 2011
Phytoplankton Chlorophyll a (CHLa), total protein, colloidal carbohydrates,
storage carbohydrates and taxonomic pigment relationships were studied in two
cyanophytes (Microcystis aeruginosa and Synnechococcus elongatus), two chlorophytes
(Dunaliella tertiolecta and Scenedesmus quadricauda), one cryptophyte (Rhodomonas
salina), two diatoms (Cyclotella meneghiniana and Thalassiosira weissflogii) and one
dinophyte (Amphidinium carterae) to assess if algal biomass could be expressed in other
indices than just chlorophyll a alone. Protein and carbohydrates are more useful
currencies for expressing algal biomass, with respect to energy flow amongst trophic
levels. These phytoplankton were grown at low light (LL = 37 µmol photons m-2 s-1),
medium light (ML = 70-75 µmol photons m-2 s-1), and high light (HL= 200 µmol photons
m-2 s-1). Even though pigment per cell increased with increasing light intensity,
v
statistically light had very little effect on the CHL a: taxonomic marker pigment ratios, as
they covaried in the same way. Protein, colloidal carbohydrates and storage
carbohydrates per cell all increased with increasing light intensity, but they did not co-
vary with CHLa. Statistical data showed that light intensity had a more noticeable effect
on protein: CHL a, colloidal carbohydrate: CHLa, storage CHO: CHLa, therefore a
general mathematical expression for these relationships cannot be generated. This study
showed that light intensity does have an influence on these biomass indices, therefore,
seasonal and latitudinal formulas may be required for meaningful algal biomass
estimation. However, more studies are needed if that goal is to be realized.
While studying the effects of light intensity on algal pigment content and
concentration, a new pigment was isolated from a cyanophyte (Scytonema hofmanii)
growing between 300-1800 µmol photons·m-2·s-1 and from samples collected in areas of
the Florida Everglades. This pigment was characterized and structurally determined to
possess indolic and phenolic subunits that are characteristic of scytonemin and its
derivatives. In addition, the pigment has a ketamine functionality which gives it its
unique polarity and spectral properties. Based on the ultra violet/visible absorbance data,
this pigment was postulated to be protecting the chlorophyll a and cytochrome Soret
bands as well as α and β bands of the cytochromes (e.g. cyt-c562) in the photosynthetic
unit.
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LIGHT INTENSITY INFLUENCES ON ALGAL PIGMENTS, PROTEINS AND CARBOHYDRATES: IMPLICATIONS ON PIGMENT-BASED
CHEMOTAXONOMY
LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES ............................................................................................................ X
I. INTRODUCTION ........................................................................................................... 1
The working hypothesis .................................................................................................. 4
BACKGROUND ............................................................................................................ 4
Methods for estimating algal biomass ........................................................................ 4
Converting CHLa to biomass.................................................................................... 10
Select algal metabolites which may serve as biomass indices .................................. 17
Photosynthesis overview ........................................................................................... 23
Novel sunscreen pigment .............................................................................................. 30
Overall goals of this study ............................................................................................ 33
II. MATERIALS AND METHODS ................................................................................. 34
Experimental organisms................................................................................................ 34
Algal culturing .............................................................................................................. 36
Culture conditions ..................................................................................................... 37
Cell counting. ................................................................................................................ 38
Chemical Analyses........................................................................................................ 39
Algal protein extraction ................................................................................................ 39
Algal protein measurement ....................................................................................... 39
Algal colloidal and storage carbohydrate extraction .................................................... 40
Algal colloidal and storage carbohydrate measurement ........................................... 40
Algal total organic carbon (TOC) extraction ................................................................ 41
Colorimetric determination of extracted TOC samples ............................................ 42
vii
Nutrient analyses ........................................................................................................... 42
Pigment Analyses.......................................................................................................... 43
Ultra Violet - Visible (UV/Vis) Analyses of Extracts .............................................. 45
High Performance Liquid Chromatography (HPLC) ................................................... 46
HPLC Data Calculations ........................................................................................... 47
Statistical analyses ........................................................................................................ 49
Isolation and characterization of a new pigment. ............................................................. 50
IR analysis ................................................................................................................. 52
Mass Spectrometry .................................................................................................... 52
NMR analyses ........................................................................................................... 54
Acetylation reactions ................................................................................................ 54
Deuterium exchange reactions .................................................................................. 55
III. RESULTS - STATISTICAL ANALYSES ................................................................. 56
Significance of the algal species used in this study .................................................. 56
Analyses overview .................................................................................................... 59
Synechococcus elongatus .............................................................................................. 60
Microcystis aeruginosa ................................................................................................. 70
Dunaliella tertiolecta .................................................................................................... 78
Scenedesmus quadricauda ............................................................................................ 87
Rhodomonas salina ....................................................................................................... 95
Cyclotella meneghiniana ............................................................................................ 103
Thalassiosira Weissflogii ............................................................................................ 111
Amphidinium carterae ................................................................................................ 119
IV. DISCUSSION ........................................................................................................... 127
Growth patterns ........................................................................................................... 127
Phytoplankton protein as a biomass indicator ............................................................ 128
Phytoplankton colloidal carbohydrate (CHO) as a biomass indicator ........................ 137
Phytoplankton storage carbohydrate (CHO) as a biomass indicator .......................... 139
Marker pigments as indicators of algal biomass ......................................................... 140
Phytoplankton chlorophyll a, protein and carbohydrate relationships to biovolume . 145
viii
V. CONCLUSION: IMPLICATIONS FOR CHEMOTAXONOMY ............................ 147
VI. CHARACTERIZATION OF NOVEL PIGMENT .................................................. 149
The ‘scytoneman’ skeleton ......................................................................................... 149
New pigment – putative structure elucidation ............................................................ 155
Mass interpretation ...................................................................................................... 159
IR analysis ................................................................................................................... 166
Ecological significance of the new pigment ............................................................... 167
VII. APPENDICES ......................................................................................................... 170
I- Pigment calculation and data handling .................................................................... 171
II. Select photoprotectorant and accessory pigments .................................................. 179
III- Spectroradiometric output .................................................................................... 181
IV- Calibration curves and equations ......................................................................... 185
V- Retention times and UV-Vis maximas .................................................................. 187
VI-ANOVA tables ...................................................................................................... 190
VII- Cellular concentration of CHLa and photosynthates .......................................... 264
VIII- Typical chromatograms of species studied ........................................................ 267
IX- Specific growth rate (µ) curves ............................................................................ 271
X – NMR SPECTRA .................................................................................................. 275
XI – Mass Spectra ....................................................................................................... 284
VIII. REFERENCES....................................................................................................... 295
ix
LIST OF TABLES
Table 1: Methods for estimating algal biomass .................................................................. 5
Table 2: Marker pigments having stoichiometric relationships with CHLa in biomass
estimations .......................................................................................................... 12
Table 3: Colloidal and storage carbohydrate composition of the taxonomic groups
studied. ............................................................................................................... 20
Table 4: Gradient program used in FAU OGG laboratory ............................................... 47
Table 5: Gradient program used for LC-MS runs ............................................................. 53
Table 6: Cellular concentration of chlorophyll a and products of photosynthesis ......... 130
Table 7: Protein:CHLa (log10) ratios of the species as influenced by irradiance ........... 134
Table 8: Colloidal CHO/CHLa (log 10) ratios as a function of irradiance ...................... 138
Table 9: Storage CHO/CHLa (log 10) ratios as a function of irradiance ........................ 140
Table 10:Three new pigments isolated form Scytonema sp. ........................................... 152
Table 11: Scytonemin- a comparison of literature and observed values ........................ 153
Table 12: 1H and 13C NMR data for putative structure of pigment ............................... 157
x
LIST OF FIGURES Figure 1: Structure of Chlorophyll a ................................................................................... 2
Figure 2:. Xanthophyll cycling in (a) Chrysophytes and (b) Chlorophytes. .................... 26
Figure 3: Structure and UV/Vis spectra of Scytonemin ................................................... 31
Figure 4: New pigments isolated form Scytonema sp.. ..................................................... 32
Figure 5: Flow Chart of Analytical Scheme. .................................................................... 35
Figure 6: Schematic of inoculation procedure. ................................................................. 36
Figure 7: Synechococcus elongatus Marker pigment/CHLa ........................................... 62
Figure 8: Synechococcus elongatus Protein/CHLa relationships ..................................... 64
Figure 9: Synechococcus elongatus Colloidal CHO/CHLa .............................................. 66
Figure 10: Synechococcus elongatus Storage CHO/CHLa .............................................. 69
Figure 11: Microcystis aeruginosa Markerpigment/CHLa .............................................. 71
Figure 12: Microcystis aeruginosa Protein/CHLa relationships ...................................... 73
Figure 13: Microcystis aeruginosa Colloidal CHO/CHLa ............................................... 75
Figure 14: Microcystis aeruginosa Storage CHO/CHLa.................................................. 77
Figure 15: Dunaliella tertiolecta Marker pigment/CHLa ................................................ 79
Figure 16: Dunaliella tertiolecta Protein/CHLa relationships ......................................... 81
Figure 17: Dunaliella tertiolecta Colloial CHO/CHLa .................................................... 84
Figure 18: Dunaliella tertiolecta Storage CHO/CHLa ..................................................... 86
Figure 19: Scenedesmus quadricauda Marker pigment/CHLa ........................................ 88
Figure 20: Scenedesmus quadricauda Protein/CHLa relationships ................................. 89
Figure 21: Scenedesmus quadricauda Colloidal CHO/CHLa .......................................... 92
Figure 22: Scenedesmus quadricauda Storage CHO/CHLa ............................................. 94
Figure 23: Rhodomonas salina Marker pigment/CHLa ................................................... 96
Figure 24: Rhodomonas salina Protein/CHLa .................................................................. 98
Figure 25: Rhodomonas salina Colloidal CHO/CHLa ................................................... 100
xi
Figure 26: Rhodomonas salina Storage CHO/CHLa ...................................................... 102
Figure 27: Cyclotella meneghiniana Marker pigment/CHLa ......................................... 104
Figure 28: Cyclotella meneghiniana Protein/CHLa relationships ................................. 105
Figure 29: Cyclotella meneghiniana Colloidal CHO/CHLa .......................................... 108
Figure 30: Cyclotella meneghiniana StorageCHO/CHLa .............................................. 110
Figure 31: Thalassiosira weissflogii Marker pigment/CHLa ......................................... 112
Figure 32: Thalassiorira weissflogii : Protein /CHLa relationships ............................... 113
Figure 33: Thalassiosira weissflogii Colloidal CHO/CHLa ........................................... 116
Figure 34: Thalassiosira weissflogii Storage CHO/CHLa ............................................. 118
Figure 35: Amphidinium carterae Marker pigment/CHLa ............................................. 120
Figure 36: Amphidinium carterae Protein/CHLa relationships ...................................... 121
Figure 37: Amphidinium carterae Colloidal CHO/CHLa .............................................. 123
Figure 38: Amphidinium carterae Storage CHO/CHLa ................................................. 125
Figure 39: The scytoneman skeleton ............................................................................. 150
Figure 40: Red Rock aerial - areas where samples, scraped off rocks, contain the visible
light sunscreen pigment. ............................................................................... 151
Figure 41 HPLC of observed scytonemin and new pigment .......................................... 154
Figure 42: UV/VIS absorption spectra of the new pigment ........................................... 154
Figure 43: Scytonemin oxidized and new pigment – overlay ......................................... 155
Figure 44: Molecular structure of new pigment from 1H, HSQC, HMBC ..................... 156
Figure 45: HR ESI-TOF MS of new pigment ................................................................. 160
Figure 46: Initial dissociation of m/z 602 [M+H] + ion .................................................. 161
Figure 47: (+) ESI- MS/MS dissociation. ....................................................................... 162
Figure 48: Fragmentation patterns of new pigment ........................................................ 163
Figure 49: HPLC/UV. .................................................................................................... 164
Figure 50: Mass analysis of pigment after acetylation ................................................... 165
Figure 51: IR spectra of new pigment ............................................................................. 167
Figure 52: New pigment and chlorophyll a – overlay spectra ........................................ 168
Figure 53: Excitation, emission spectral overlay of new pigment .................................. 168
1
I. INTRODUCTION
Phytoplankton constitute approximately 40-60 % of the primary production of
the world’s aquatic environments (Antoine et al., 1996; Falkowski, 1994). During the last
few decades, phytoplankton have been monitored in an increasing number of marine
environments, where they have been and are used as indicators of environmental (Pybus,
1996; Edward’s et al., 2002 Boyce et al., 2010) and climatic (Moline and Prezelin 1996;
Hallegraeff, 2010; Marinov et al., 2010) changes. These changes include early warning
signals of potentially harmful species becoming dominant in a population as well as the
onset of algal blooms. Assessing and monitoring the biomass of different algal
communities is therefore necessary as rapid indicators of ‘true’ biomass are currently not
available. Thus, facile methods are needed for estimating phytoplankton biomass (algal
biological material per unit area and/or volume).
The direct measurement of phytoplankton organic matter is not normally possible
with rapid sampling to data turnaround times and biomass is therefore estimated using
alternate methods. Cell counting, assessment of cellular biovolume by microscopy and
determination of chlorophyll a (CHLa) concentrations are among the most commonly
used methods (Sournia, 1978). The conversion of phytoplankton CHLa to cell number
and/or biovolume has been reasonably done by linear regression (Gieskes et al., 1998;
2
Schulter and Havskum, 1997), simultaneous equations (Andersen et al., 1996; Tester et
al., 1995; Letelier et al., 1993), advanced algorithms (Mackey et al., 1996; Mackey et al.,
1998; Higgins and Mackey, 2000) and Bayesian/MCMC estimation (Van den Meersche
et al., 2008). Thus, routine ‘biomass’ assessment of phytoplankton communities are made
using CHLa (structure shown in Figure 1) as biomass proxy. However, what does that
equal in metabolizable biomass?
N N
N
Mg
N
COOCH3
H
H
H
O
O
H3C H
HH3C
II
IIIIV
O
I
V
Figure 1: Structure of Chlorophyll a
The ecological significance of phytoplankton (algae) lies in the fact that they trap
and convert to organic matter almost all of the energy used in the pelagic ecosystem. This
study examined how changes in light intensity affected the relationships between CHLa
to the following: taxonomically significant pigments, protein, two functional groups of
carbohydrate (colloidal and storage) and organic carbon. These were measured in relation
to cell numbers and biovolume as a way to estimate ‘true’ biomass in the context of the
metabolites involved in food chains and energy flow in aquatic ecosystems. The resultant
3
data and conclusions should have implications and applications in algal biomass
estimations from CHLa, be they by spectrophotometry (Johnsen and Sakshaug, 1993),
fluorescence (Wilhelm et al., 1991), or HPLC (Millie et al., 1993; Wright et al., 1996)
analyses.
Additionally , besides total algal biomass from CHLa estimations, pigment-based
chemotaxonomy using extracted pigments (see chapters in Jeffrey et al., 1997) and even
by advanced spectral algorithms with satellite and airborne telemetry (O’Reilly et al.,
1998; 2001) should then yield not only cell number estimations from taxon-specific
CHLa estimations but biomass estimations including protein, carbohydrate and total
organic carbon values. These results will have application in limnology, oceanography as
well as pure algal cultures. The latter is important when dealing with bulk culture aimed
at biomass conversion as fuel feedstock (Gouveia and Oliveira, 2009). The question here
then becomes, is there a way to relate CHLa to the ‘food’ available in a particular aquatic
ecosystem? Then will an ecological modeler be able to predict if a certain plankton group
could support/provide food for the organisms in the trophic level?
Light has never been adequately factored into the CHLa based phytoplankton
biomass estimations. That is, with different seasons there are different intensities of light
seasonally, latitudinally and other parameters such that overall light conditions are not the
same. In order to get a real understanding for this type of modeling, one will need large
numbers of species, enough light studies, enough temperature studies et cetera, such that
so potential summer formulas, a winter onset formula, an early spring formula, et cetera
can be generated.
4
The working hypothesis for this study would therefore be as follows:
Chlorophyll a – per se is not the ultimate descriptor of phytoplankton biomass. That is,
large variations in ‘true’ biomass, defined here as metabolizable organic matter (proteins,
carbohydrates, lipids) exist between phytoplankton groups (taxa) and within each taxon
by variations in light and/or nutrient availability. The null hypothesis would be:
Chlorophyll a alone perfectly describes phytoplankton biomass.
In the present study, correlations between CHLa and biomass parameters (protein,
carbohydrates, cell number, biovolume) under the influence of light intensity were
investigated in order to ascertain if that they can be used for ‘true’ biomass estimation.
This chapter will introduce methods used to determine algal biomass, methods for
measuring CHLa, mathematical methods of converting CHLa to biomass, marker
pigments used for estimating CHLa content of different taxonomic groups, significance
of other biomass parameters, a brief look at photosynthesis and roles of pigments in
photosynthesis. Additionally the author introduces a second project which involves the
structure elucidation of a potential visible light sunscreen pigment isolated from a
cyanobacteria (Scytonema hoffmanii), grown at high light conditions in the laboratory and
from samples collected in the Florida Everglades.
BACKGROUND Methods for estimating algal biomass:
Since phytoplankton carbon content in natural environments often has
interference from detritus and other organic materials, alternate methods for biomass
‘estimation’ have been developed, as given in Table 1.
5
Microscopy: Microscopic assessment may be used to determine algal cell number
and biovolume (Stevenson et al., 1985). Cells are mounted on microscope slides, which
may be prepared in counting chambers, inverted microscopes or in different types of
media on regular microscopes (Palmer, 1962).
Table 1: Methods for estimating algal biomass – Adapted from Stevenson et al., 1985 Measurement Detail: advantages, disadvantages
Cell density Microscope generally required; good indicators of algal species composition, biovolume and biomass if size and mass of all cells assumed to be the same; variation in cell size lead to biomass error. Flow cytometry may also be used for cell density determinations.
Biovolume Microscope used to accurately assess algal biomass; most time consuming; error due to cell vacuoles have to be accounted for.
C,N,P Several analytical methods exist; may be used for assessing nutrient status of cells; for field samples, biomass of living and non-living matter included.
Dry Mass Involves inexpensive gravimetry; biases arise when inorganic matter and non-algal organic matter are present.
Ash free dry mass (AFDM)
Simple laboratory heating and gravimetric procedures; field samples include living and detrital matter.
Chlorophyll a Ubiquitous to all photosynthetic algae; several analytical methods exits; light and nutrient adaptations may bias biomass estimates.
Microscopy gives detailed information on the composition and diversity of microalgal
assemblages, to the species level, but due to the high level of expertise required, it can be
tedious and costly (Millie et al., 1993).
Algal cell volume measurement via microscopy is a relatively good indicator of
algal biomass if most of the algae of each species are of similar size and the mass of all
cells is assumed to be the same (Smayda, 1978). The method involves measuring cell
dimensions using an ocular micrometer and geometric formulae for calculating cell
volume. Algal biovolume measurements will give very good estimates of algal biomass if
6
the assumption is made that the mass of algal cytoplasm is the same among each taxon.
Biovolume can correct cell density estimates of biomass by accounting for size
differences among species. When the vacuole size is estimated and subtracted from cell
volume to estimate cytoplasm volume, then algal biovolume becomes a relatively
accurate measure of algal biomass, especially when large variations in cell size exists in a
community.
However, species volume measurement is laborious and highly dependent on the
skills of the researchers. Samples usually have to be fixed in a solution or preserved until
microscopic analysis. The nature and concentration of this fixative has been shown to
alter cellular volume (Montagnes et al., 1994) and there is an increased level of
uncertainty due to the small size of the organisms being analyzed. The development and
use of such techniques as epifluorescence microscopy (Daley and Hobbie, 1975), electron
microscopy (Johnson and Sieburth, 1982), flow cytometry (Olson et al., 1985) and
immunofluorescence (Shapiro et al., 1989) have vastly improved the study of
phytoplankton. However, these methods are still very time consuming and do not allow
for the rapid spatial and temporal monitoring of phytoplankton.
Carbon, Nitrogen and Phosphorus: Particulate organic matter (POM) is an
important component in an ecosystem. POM includes particulate organic carbon (POC),
particulate organic nitrogen (PON), and particulate organic phosphorus (POP), among
other detritus. POM provides a primary food source for aquatic food webs. Dissolved
organic matter (DOM) on the other hand, consists of organic matter which is not in cells
per se and passes through a 0.45 μm filter. However a cut off of 0.22 μm is becoming
standard as well. DOM can contribute to the acidity of a water body and can increase
7
light attenuation, thus detrimentally affecting phototrophic organisms in an aquatic
environment (Eby, 2004; Hansell and Carlson, 2001).
POC and PON are generally determined by high temperature dry combustion
using a carbon, hydrogen, nitrogen (CHN) analyzer. POP is often analyzed by wet
chemical oxidation using potassium peroxydopersulfate (Menzel and Corwin, 1967).
With advances in instrumentation, POP, PON and POC determination can now be done
simultaneously from the same filter (Raimbault, 1999). The procedure generally involves
collection of particulate matter sample by filtration; placement of the filters in digestion
flasks; elimination of inorganic carbon by acidification and bubbling; digestion in an
autoclave; automated analysis of C, N, and P species. The resulting data will typically be
biased by other living (e.g. bacteria and zooplankton) or non-living matter.
Dry mass and Ash-free dry mass: The dry mass (DM) or ash free dry mass
(AFDM) can then be determined with the use of glass fiber filters (GF/F), an analytical
balance, drying oven and/or muffle furnace (APHA, 1995). For measuring DM, the
sample is filtered on to a pre-weighed filter then reweighed to get the difference in wet
weight. The sample and filter are then dried overnight at low temperature (60ºC) then
reweighed to get the dry weight by difference (U.S. EPA, 1995a). Samples for AFDM
are filtered and frozen, dried (100 ºC) for twenty four hours, weighed (DM), ashed (450
ºC) for four hours (to remove all organic carbon), rehydrated with water, dried for twenty
four hours and re-weighed (U.S. EPA, 1995a). These methods are relatively inexpensive,
but DM and AFDM estimates of algal biomass may both be biased by inorganic and non-
algal organic matter (detritus, bacteria, fungi, etc.) present in the sample (Steinman and
Lamberti, 1996). Therefore, these methods are poor indicators of algal biomass,
8
especially when inorganic deposition and other organisms constitute a significant portion
of the sampling community. In the case of samples with a significant (5%) proportion of
the DM as carbonate, a decalcification step needs to be included. That is, treatment of the
sample with hydrochloric acid (HCl), done best in the gas phase, washing out salts, re-
drying to constant decalcified DM and then proceeding to AFDW (APHA, 1995).
Chlorophyll a (CHLa) measurement: Chlorophylls are the molecules in
photosynthetic bacteria and plants that capture light energy for carbon fixation and the
splitting of H2A (H2O or H2S). Chlorophyll a is the most widely used estimator of algal
biomass because it is relatively unaffected by non-algal substances. It is assumed to be
and accepted as a fairly accurate measure of algal ‘biomass’ (weight and volume) and can
serve to indicate interactions between nutrient concentration and a number of biological
phenomena in lakes and rivers (Berkman and Canova, 2007). Several methods exist for
measuring chlorophylls, as detailed below.
Spectrophotometry: Development of spectrophotometric analyses of chlorophyll
pigments began in the 1930’s – 1940’s (Weber et al., 1986). A trichromatic technique
was later introduced (Richards and Thompson 1952) for measuring chlorophylls a,b,c
and attempted to remove overlapping absorbance by the other chlorophylls at the
absorption maximum for each chlorophyll. Several modifications have been made to
these equations over the past decades and each claim to produce better estimates of the
chlorophylls (Jeffrey and Humphrey, 1975; UNESCO, 1966). However, when these
equations are compared with the concentration of the ‘alternate’ chlorophylls (-b, -c)
obtained via physical separation techniques (e.g. chromatography), the degree of
correspondence is low (Louda and Mongkhonsri, 2004). The trichromatic chlorophyll a,
9
for instance, is chlorophyll a, minus the interference wavelengths from the other
chlorophylls, but includes all of the degradation products of chlorophyll a, which share
the primary absorbance maxima.
Fluorometry: Chlorophyll molecules fluoresce in the red region of the
electromagnetic spectrum when exposed to blue light. Fluorometry is a highly sensitive
method which has its own multi-chromatic fluorescence equations (Loftus and Carpenter,
1971). Even though fluorometry is more sensitive than spectrophotometry, it is not
typically recommended for routine work (Aminot, 2000). There are no independent
fluorometric chlorophyll attenuation coefficients, and each individual fluorometer must
be calibrated daily against spectrophotometric standards (Standard Methods, APHA,
1991).
High Performance Liquid Chromatography: This is an analytical method that
makes it possible to gain information about the community composition of phytoplankton
(Ansotegui et al., 2001; Gieskes and Kraay, 1998b), as well as correct chlorophyll a data.
The analysis of algal pigments using high performance liquid chromatography (HPLC)
allows the separation, identification and quantitation of taxon- specific, diagnostic
marker- pigments, in addition to the chlorophylls and their breakdown products (Millie et
al., 1993). In contrast to microscopic enumerations, analysis by HPLC is reproducible
and the method allows for rapid examination of phytoplankton composition. When an
autosampler is connected to the HPLC system, more than 40 samples can be analysed per
day. HPLC can therefore provide faster examination of the spatio-temporal dynamics of
phytoplankton populations than has been possible using enumeration of phytoplankton
under the microscope (Schulter et al., 2000).
10
Converting CHLa to biomass:
Pigment- based chemotaxonomy is a viable method for studying the assemblage
of phytoplankton communities (Louda, 2008). The chemotaxonomic analysis of
phytoplankton communities using marker pigments allows the calculation of the relative
abundance of distinct algal taxa or groups (Millie et al., 1993; Jeffrey et al., 1997). A
marker pigment is one that is found only in certain groups (taxa) of algae and has a
distinctive relationship to that group. The major algal groups include: chlorophytes,
prochlorophytes, cyanophytes, cryptophytes, dinophytes and diatoms. There is some need
for caution with pigment-based chemotaxonomy, as some marker pigments are shared
between several algal groups (Rowan, 1989), so the conversion to biomass estimates is
not always straightforward.
Pigment per cell may change for a number of reasons including light intensity
(Grant and Louda, 2010), growth rate and nutritional state (Llewellyn and Gibb 2000).
However, it has been shown that the concentrations of chlorophylls and specific
carotenoids in certain but not all taxa vary in a similar way (Goericke and Montoya,
1998), so ratios between them do not change very drastically. Therefore to estimate the
contribution of each algal group to the total population, most methods use ratios of CHLa
to a marker pigment, or the inverse, for that group (Gieskes et al., 1988, Mackey et al.,
1996, Wright et al., 1996). Examples of these marker pigments include: chlorophyll b for
chlorophytes, echinenone for filamentous cyanophytes, zeaxanthin for coccoidal
cyanophytes, alloxanthin for cryptophytes, peridinin for peridinin containing dinophytes
and fucoxanthin for diatoms and other chrysophytes. Table 2 contains the structures of
these pigments. The implementation of reversed phase high performance liquid
11
chromatography (HPLC: Mantoura and Llewelyn, 1983), combined with real time
spectrophotometric detection (photodiode array – PDA, a.k.a. diode array detector or
DAD), along with improvements in pigment extraction procedures (Hagerthey et al.,
2006) has become central to the development of pigment-based chemotaxonomy. The
basic method uses preliminary estimates for pigment ratios and then refines these values
iteratively using measured chlorophyll a as a criterion, followed by calculation of the
contributions of different groups of microalgae to total chlorophyll a from the optimized
pigment ratios.
Experiments with cultures remain central to the understanding of microalgal
responses to environmental variability and, as such, studies have been carried out with
lab grown cultures to determine more reliable pigment ratios for use in the mathematical
applications for determining algal biomass (Gieskes et al., 1998; Grant and Louda 2010;
references in Jeffrey et al, 1997). The only disadvantage with the mathematical methods,
and currently no solution exists, is that it has to be assumed that the ratios in the
calculations reflect the same physiological state of the population being studied (Mackey
et al., 1996). Thus, in addition to taxonomic structure, the physiological state of the
community also needs to be addressed.
For biomass estimations utilizing ratios of CHLa to a marker pigment, these ratios
are used in linear regression equations (Gieskes et al., 1998), simultaneous equations
(Tester at al., 1995), factor analysis and iterative methods (Mackey at al., 1996). Early
linear regression equations (Gieskes et al., 1988) did not distribute CHLa evenly among
algal groups and did not distinguish those with shared marker pigments, so other methods
12
Table 2: Marker pigments having stoichiometric relationships with CHLa in biomass estimations
Taxa Marker pigment Chlorophytes
Chlorophyll b
Cryptophytes
Alloxanthin
Cyanophytes
Zeaxanthin – coccoidal cyanophytes
Echinenone – filamentous cyanophytes
Dinophytes
Peridinin – peridinin containing dinoflagellates
Diatoms
Fucoxanthin
13
have been applied to determine the abundance of individual phytoplankton groups from
the concentration of marker pigments (Mackey et al., 1996; Van den Meersche et al.,
2008).
All of these methods rely on some knowledge of ratios of CHLa to marker
pigment. However, when these ratios are applied to the mathematical models, the light
intensity, depth of sampling, irradiance at depth, growth condition and nutritional state
are rarely considered. Extensive knowledge of the influence of light intensity, light
quality, and nutritional state on the CHLa to pigment ratios of different phytoplankton
species is therefore needed and certain inroads have been made (Grant and Louda, 2010
and references therein).
In the present study, algal cultures grown in nutrient replete media will be used to
better determine the influence of varying light intensities on the ratios of CHLa to
pigment with the aim of producing more robust and reliable ratios for use in the
mathematical applications and models for estimating algal biomass. Algal species were
selected based on their relevance with ecology and biogeochemical contexts. Working
with lab grown cultures is critical for understanding how microalgae respond to different
environmental variables (MacIntyre and Cullen, 2005).
Three mathematical methods currently being used in pigment-based chemotaxonomy
For the assessment of algal class abundance and community structure, some type
of mathematical relationship is pre-determined, refined and then used to describe real
14
systems. Here, three mathematical approaches: simultaneous linear equations (SLE),
CHEMTAX and Bayesian Compositional Estimator (BCE) will be briefly reviewed. In a
separate study done at FAU in the Environmental Geochemistry Laboratory (see J. L.
Brown thesis Florida Atlantic University, 2010), these three methods were compared to
see which, if any, could accurately enumerate the periphyton (phototrophic group of
algae and cyanobacteria living attached to aquatic vegetation and sediments, often
forming microbial mats) community composition. The methods were applied to artificial
data sets, mixed lab cultures of known composition and Florida Everglades periphyton
samples. All three methods gave somewhat accurate sample compositions for artificial
and mixed lab cultures. SLE and CHEMTAX performed better than BCE.
Simultaneous Linear Equation (SLE): These models apply a series of
straightforward equations to the problem: using one ratio of one biomarker to CHLa for
each algal class in the sample. This method only has one possible answer. If the ratios
and the algal classes used are accurate and complete for the sample, the answer will be
correct. SLE is the model currently in use at the FAU Environmental Biogeochemistry
Laboratory and shows promising results when applied to marine phytoplankton in Florida
Bay (Louda 2008). Equation 1 shows the SLE used in our laboratory and evolved using
data collected from cultures grown in our laboratory as well as samples collected from
Lake Okeechobee and Florida Bay. The refined ratios in the SLE given here reflect the
influence that irradiance has on pigment concentration per cell (Grant and Louda 2010).
∑ CHLa = ((1.1 x ZEA) + (11 x ECHIN)) + (2.5 x CHLb) + (1.2 x FUCO) + (1.5 x PER) Equation 1: Current SLE, used in our laboratory
15
SLE is sometimes called the fixed coefficient method since, unlike numerical
methods (such as CHEMTAX and BCE) which change coefficients within certain
parameters, the SLE coefficients cannot be altered during the calculations. Each
coefficient is the estimated ratio of a biomarker pigment to CHLa, which is considered
typical in a given class. SLE has no provision for shared pigments, so only unique or
nearly unique biomarkers can be used. The amount of each biomarker pigment in the
sample is multiplied by its respective coefficient to determine the estimated amount of
CHLa contributed by each algal class (taxon specific CHLa). Each of the estimated class
contributions may then be divided by the sum of all estimated contributions to arrive at
an algal class composition in percent form. An example of how algal species pigment
calculation is done in our lab is shown in Appendix I.
CHEMTAX: This is a factor analysis program developed in 1996 (Mackey et al.,
1996) and licensed through CSIRO Marine Laboratories. It is written to run inside
MATLAB (The MathWorks, Inc. 2008). CHEMTAX works by evaluating groups of
samples, with pigment data arranged in matrix form. Biomarker ratios to CHLa are also
arranged in a matrix. Using (unknown) algal class composition of the samples as a third
matrix, this forms a linear inverse problem which is solved by matrix factorization, using
a straightforward algorithm to provide the least-squares solution (Mackey et al., 1996). In
contrast to SLE, which takes only sample data and biomarker ratios as input, CHEMTAX
allows input as to how much and which ratios are allowed to vary and how the data are
weighted. Results from CHEMTAX include algal class composition of each sample,
16
revised ratio matrix, residuals (from the least-squares calculations), and breakdowns of
pigments assigned to each algal class within each sample, as well as information
regarding the iterative calculation process.
The Bayesian Compositional Estimator (BCE): This is a chemotaxonomic
program, developed in 2007 by researchers at the Netherlands Institute of Ecology (Van
den Meersche et al., 2008). BCE is implemented as a package (Van den Meersche and
Soetaert 2009) in the open source software R (R Development Core Team 2009). The
BCE program was designed in part to specifically address certain shortcomings in
CHEMTAX (Van den Meersche et al., 2008). Like CHEMTAX, BCE uses a ratio matrix
and an unknown sample composition matrix to compose a linear inverse problem. BCE,
however, uses Bayesian methods to fit a probability distribution to the data and find a
maximum likelihood solution for the problem. BCE first finds a least-squares solution
and uses it as a starting point for a Markov Chain Monte Carlo (MCMC) simulation. The
program provides a number of diagnostic outputs in order to check the performance of
the simulation, and this output must be inspected prior to acceptance of any results. This
output includes the number of runs as well as plots which indicate the extent and
randomness of the sampling of the solution space. Final results of the program include
the algal class composition of each sample, a revised ratio matrix, standard deviations
and covariance matrices for the ratio and class compositions (see Brown, 2010 for further
information).
17
Select algal metabolites which may serve as biomass indices
Chlorophyll a is used as an index of biomass because it is unique to oxygenic
photosynthetic organisms. However, changes in algal physiology are not confined to
CHLa: pigment ratios, but is also reflected in other indices of biomass such as proteins,
carbohydrates and organic carbon. The determination of the protein and carbohydrate
content of microalgae may provide important information for phytoplankton biomass
assessment, which can in turn be used to investigate protein and carbohydrate dependent
physiological processes in cells as well as with studies of nutritional value of
phytoplankton (Clayton et al., 1988).
Algal carbohydrates: Carbohydrates are the major products of photosynthesis
and are represented by polysaccharides and storage structure compounds - including
cellulose, hemicellulose and pectin found in plants (Aspinall, 1983), as well as laminaran
and starch found in some algae and cyanobacteria (Stewart, 1974).
Carbohydrates play important roles in biogeochemical cycles in the water column
and water sediment interface (Hedges et al., 1994), in cellular metabolism and structure
(Granum and Myklestad, 2001; Handa, 1969), and are major storage compounds in
autotrophic organisms. Carbohydrates, particularly polysaccharides, contribute
significantly to the organic matter of diatoms, green algae and cyanobacteria (Fernandez
et al., 1992).
The two major groups of carbohydrates in microalgae are extracellular, loosely
bound colloidal carbohydrates and intracellular storage polysaccharides (glucans and
18
starch). Several groups of microalgae have been shown to secrete copious amounts of
carbohydrates (Geesey, 1982). These secretions, though composed of some small sugar
moieties, are largely polysaccharide in composition and are thought to be involved in the
transfer of nutrients in lower food webs (Decho, 1990). These products of photosynthesis
can be excreted within a few hours of formation and are thought to be light dependent
(Underwood et al., 2004). Studies have shown that the concentration of secreted, loosely
bound carbohydrates in sediments is closely related to the biomass of diatoms
(Underwood et al., 1995). Colloidal carbohydrate fractions have been shown to contain
mucopolysaccharides, extracellular polymeric substances (EPS), transparent exo-
polymers (TEP), and others, each with its own function (Thornton, 2002). However,
these secretions have largely been ignored in studies regarding microalgal production and
trophic energy transfer.
In the water column, EPS is now being observed within phytoplankton bloom
sedimentation and other types of marine snow (Riemann, 1989; Alldredge et al., 1986).
Epipelic diatoms secrete mucopolysaccharides to facilitate movement. These secretions
then represent sources of food for bacteria and invertebrates (Decho 1990, Goto et al.
2001). Mucopolysaccharides have been shown to be the main component of the mucous
matrix of algal colonies (Aldercamp et al., 2006). In addition to function in the mucous
matrix of diatoms, mucopolysaccharides have also been reported to serve as storage
polysaccharides (Lancelot & Mathot 1985). Few studies have been done to investigate
the influence of light on mucopolysaccharide production in phytoplankton. Studies
carried out on Cyanospira capsulate and Synechococcus strains grown under various
light/dark cycles showed that both produced smaller amounts of mucopolysaccharides in
19
comparison to control cultures grown under continuous light (Philips et al., 1989, De
Philippis et al., 1995). Decreased production of mucopolysaccharides equate to shorter
light periods. Therefore it could be concluded that the synthesis and release of these
polysaccharides is light dependent (Philips et al., 1989).
The storage polysaccharide in Phaeophyceae (macroalgae) is a β-1, 3-glucan and
has been classified as laminaran (Meeuse 1962). Laminarins are primarily composed of
D-glucose residues (Peat et al., 1958). Chrysolaminarin is also a β-(1, 3) glucan and is
also a major storage product of Chrysophyceae and diatoms, it is produced in the light
and consumed in the dark (Janse et al., 1996b). The quantity of storage glucans in algal
cells and thus their contribution to algal biomass largely depend on nutrient status, light
intensity and growth condition of the cells. For example, it has been observed in the
diatom Chaeotoceros that cellular glucan content accumulated markedly under nutrient
deficiency (Myklestad 1974). For Phaeocystis, an increase in the ratio of total
carbohydrate to total carbon was observed in nutrient limited batch cultures and at the
end of a spring bloom (Fernandez et al. 1992, Van Rijssel et al., 2000). Generally, algal
carbohydrate content changes in response to light variations over a diel cycle, and when
nutrients and irradiance are sufficient to sustain high photosynthetic rates (higher than
metabolic demands). Thus, the excess photosynthates are stored when photosynthetic
production (P) exceeds respiration (R) utilization (P>R). During this time, glucan
accumulates during the day and in the night it can be respired as an energy supply to
maintain cell metabolism and provide carbon and energy for protein synthesis (Lancelot
& Mathot 1985, Granum et al. 2002). Since environmental factors influence the
accumulation or degradation of storage polysaccharides (Heldt, et al, 1977), knowing
20
how storage carbohydrate levels are controlled will have potential impacts on the
estimation of the world’s primary productivity. Table 3 is a simple summary of the types
of carbohydrates typical of the two functional groups that are being investigated in this
study.
Table 3: Colloidal and storage carbohydrate composition of the taxonomic groups studied. Adapted from Stewart, (1974) Taxonomic Group Colloidal
carbohydrate Storage carbohydrate
Chlorophytes Polysaccharides; simple sugars (Hough et al., 1952; Fogg, 1952)
Starch (Meeuse, 1962)
Cyanophytes Polysaccharides; simple sugars (Lewin, 1956; Moore and Tisher, 1964)
Glucans (Richardson et al., 1968)
Diatoms Polysaccharides (Lewin, 1955)
Chrysolaminarin (Meeuse, 1962)
Dinophytes Polysaccharides (McLaughlin et al,1960)
Starch (Bursa, 1968)
Rhodophytes Polysaccharides (Sieburth, 1969)
Starch (Archibald et al., 1960)
The phenol-sulfuric assay (Dubois et al., 1956) is a commonly used method for
assessing algal carbohydrates (intra-cellular or secreted). The calorimetric method is
sensitive to a wide range of carbohydrates, including sugars, methylated sugars and both
neutral and acidic polysaccharides. This study will only take into consideration the
colloidal fraction in a broad sense along with storage carbohydrate fractions, as too many
extraction techniques for the colloidal fraction components are currently being used to
make reasonable comparisons.
Algal proteins: Proteins are essential, biomolecular components of cells and have
the following roles: regulating metabolic activities, providing structural support and also
21
are the pre-cursors as well as end-products of macromolecular synthesis and catabolism
(Clayton et al., 1988). Therefore, knowledge of the quantity of total protein present is
important for understanding a broad range of biological processes in phytoplankton cells.
Although bulk proteins of algae are not expected to differ much in their overall
proportions of amino acids (Stewart, 1967), some algal cell walls contain appreciable
proportions of proteins, which may prove to be of taxonomic value (Thompson and
Preston, 1967). Several studies have been carried out to investigate different aspects of
protein metabolism in phytoplankton (Dortch et al., 1982; 1984). With respect to
biomass, the proteins in phytoplankton cells are also important to the secondary
consumers that feed on them and the benthic consumers that receive particulate organic
matter derived from phytoplankton residues in sediments. The quantitative information
on the protein content in phytoplankton cells, as well as their relationships to chlorophyll
a, will be important to a variety of studies that are directly and indirectly related to
various aspects of cellular nitrogen metabolism as well as predictors of phytoplankton
dynamics and physiological state. To date, very few studies have reported generalized
relationships between algal protein and chlorophyll a. For example, a weight- to- weight
ratio for protein/CHLa = 8.57:1 has been reported and is often used in the literature
(Meyers and Kratz, 1955). However, that work only focused on one species of blue-green
alga, Anacystis nidulans.
Protein synthesis in algae is believed to be mainly a component of algal night (i.e.
dark) metabolism (Morris et al,. 1974). These workers showed that ratios of labeled pools
of carbohydrate carbon: protein carbon changed during day/night experiments. They
postulated that these changes were due to the flow of carbon from storage polymers
22
through metabolites into protein. Many zooplankton migrate vertically to feed in surface
waters at night, therefore night time protein synthesis in algae will have implications on
zooplankton nutrition (Scott 1980). Environmental factors such as prior light intensity
history, nutrient status and the species composition of the population are determinants of
the algal dark (night) metabolism and growth (Cuhel et al., 1984).
Interestingly, in another study done on Dunaliella tertiolecta, CHLa accumulation
in the photosynthetic apparatus was linked to the synthesis of apoproteins of pigment-
protein complexes and a high ratio of protein to pigment in light harvesting and other
complexes (Mortain-Bertrand et al,. 1990). It is therefore only assumed that in light
saturating and nutrient limiting conditions, when CHLa concentrations decrease, the
concentrations of the associated proteins will also decrease.
A number of methods exist for the extraction and determination of phytoplankton
protein (Bradford, 1976; Lowry et al., 1951). Consideration of the various analytical
methodologies used by different authors makes inter-comparison of micro-algal protein
contents difficult (Berges et al., 1993; Clayton et al., 1988; Hach et al., 1987; Rausch,
1980). For the present study, we have chosen to adapt the warm sodium hydroxide
extraction and micro biuret assay as developed, evaluated and standardized by Rausch
and co-workers (Rausch, 1981).
Algal total organic carbon (TOC): Even though biomass is expressed in terms
of CHLa, organic carbon concentration is normally what is desired (Cullen 1982).
However, organic carbon cannot be measured directly because of interference from
zooplankton and non-living organic matter. Estimations have to be made based on some
multiplying parameter or ratio of Carbon: CHLa (Banse, K., 1977). The carbon: CHLa
23
ratio (θ) is a poorly studied factor in phytoplankton growth and ecology (Geider, 1987).
The ratio has been assumed to be constant in ecological studies, for example,
recommendations of θ = 30 g C·g-1 CHLa for nutrient rich waters and (θ) = 60 g C·g-1
CHLa for nutrient poor waters have been made reported by Strickland (1960). However,
due to phenotypic variation in chemical composition and rates of physiological processes,
a universal ratio cannot be utilized (Geider, 1987). In diatoms, the ratio can vary from 10-
200 g C·g-1 CHLa depending on light level, temperature or nutrient availability (Geider,
1984, Osborne and Geider, 1986). These variations are indicative of the physiological
plasticity of microalgae and the need for additional study.
We therefore intend to use pigment-based chemotaxonomy to gain a better
understanding of the relationships of chlorophyll a with algal functional carbohydrates
and proteins as well as organic carbon under the influence of light and nutrient
conditions. By investigating if correlations exist between CHLa and these components,
then a novel approach may be able to be introduced where it may be possible to describe
biomass in terms of colloidal and storage carbohydrates, proteins and organic carbon
based on CHLa concentration. This will also represent a large step in the direction of the
possible use of chemotaxonomy to relate to algal organic carbon, protein and
carbohydrates in energy flow/food web models.
Photosynthesis overview
Photosynthesis is driven by visible light (400-700 nm), termed photosynthetically
active radiation (PAR). Photosynthesis in eukaryotic algae takes place on inner foldings
of chloroplasts called the thylakoid membranes as well as in the stroma, the cytoplasm of
24
the chloroplasts. The thylakoid membrane is folded upon itself, forming many discs
called grana. The reactions of photosynthesis can be broken in a series of light-dependent
and light independent reactions. The light dependent reactions occur on the thylakoid
membranes, while the light independent reactions occur in the stroma. In prokaryotic
organisms such as cyanobacteria, photosynthesis occurs within the cell membrane (Zak et
al., 2001).
Light harvesting pigment-protein complexes form clusters called antennas on the
thylakoid membranes. Algae contain various pigments, which can be classified into two
major groups: The photosynthetic accessory pigments (PAP) and the photoprotectorant
pigments (PPP). The most abundant chlorophyll is chlorophyll a (CHLa). CHLa
molecules can act as light absorbers in the light harvesting or antenna complexes and as
electron donors and receivers in the reaction centers of the two photosystems where
photosynthesis occurs. Photosynthetic accessory pigments (PAP) absorb energy that
CHLa does not absorb and pass this energy to the other antenna pigments and finally to
the reaction centers for photosynthesis. Accessory pigments include: CHLb ,
Chlorophylls c1/c2/c3 (CHLsc1/c2/c3), Fucoxanthin (FUCO), and Peridinin (PER), among
others (Appendix II). Photoprotectorant pigments mainly protect the plant, cyanobacteria
or algae from photo-oxidative damage. Photoprotectorant pigments are often taxon
specific and include: Myxoxanthophyll (MYXO), Scytonemin (SCYTO), Echinenone
(ECH), Canthaxanthin (CANTHA), Lutein (LUT) and others (Appendix II). Carotenoids
are a highly colored (red, orange and yellow) group of fat-soluble isoprenoid pigments.
Carotenoids comprise some of the photosynthetic accessory pigments and many are
photoprotectorant pigments (PPPs).
25
The xanthophylls: These are a diverse group of oxygenated carotenoids with
various structures and multiple functions (Britton, 1995). The interconversion of
violaxanthin (diepoxide), antheraxanthin (monoepoxide), and zeaxanthin (epoxide free) is
termed the xanthophyll cycle and is found in both higher plants and green algae
(chlorophytes). This cycle is shown in Figure 2. Sapozhnikov et al. (1957) were the first
to describe the xanthophyll cycle. The scheme of reactions that takes place in the light
involves two de-epoxidation steps through which violaxanthin, via the intermediate
antheraxanthin, becomes zeaxanthin, (Hager and Stransky, 1970). The role of the
xanthophyll cycle in plants and algae was first thought to be linked to oxygen evolution
(Sapozhnikov et al., 1957). Hager (1980) later proposed a role in the electron transfer
activity in the photosystems during photosynthesis. Krinsky (1971) was first to suggest a
role as a photodamage protection mechanism. That is, if more energy is harvested by
chlorophyll than what can be used in photosynthesis, then that excess energy has the
potential to cause damage to the intracellular components of photosynthetic organism.
The phenomenon that causes a reduction in photosynthetic efficiency due to exposure of
the photosynthetic apparatus to excess photons is termed photoinhibition (Powles, 1984).
Overall, the xanthophylls can function as accessory light-harvesting (antenna) pigments,
as structural entities within the antenna complex and as molecules required for the
protection of photosynthetic organisms from the potentially damaging effects of light
(Niyogi et al., 1997).
Carotenoid protection against photodamage is of paramount importance.
Demmig- Adams (1990) introduced one mechanism by which carotenoids function to
26
HO
O
OH
HO
OH
(DD)
(DIATO)
-O+O
OH
O
O
HO
OH
O
HO
OH
HO
(VIOLA)
(ZEA)
(ANTH)
-O
-O+O
+O
protect against photodamage. Here, specific xanthophylls are involved in the de-
excitation of singlet chlorophyll that accumulates in the light-harvesting (antenna)
complex. This accumulation occurs under conditions of excessive light. The de-excitation
is measured as nonphotochemical quenching of chlorophyll fluorescence, and is
dependent on a large trans-thylakoid proton gradient that becomes established in
excessive light. This nonphotochemical quenching was determined to correlate with the
synthesis of zeaxanthin and antheraxanthin from violaxanthin via the xanthophyll cycle.
a)
b) Figure 2:. Xanthophyll cycling in (a) Chrysophytes and (b) Chlorophytes.
27
At low light intensities, or if all of the energy harvested is utilized for
photosynthesis, no zeaxanthin is formed. However, as light intensity is increased, and the
amount of light energy harvested starts to exceed that needed for photosynthesis, more
zeaxanthin is formed from violaxanthin. It is believed that zeaxanthin formation in
chlorophytes and higher plants aids the thermal dissipation of the excess energy within
the light harvesting system.
In algal divisions such as Chrysophyta (diatoms and golden-brown algae) and
Dinophyta (dinoflagellates), the xanthophyll cycle described above, is paralleled by a
xanthophyll cycle that alternates diadinoxanthin with diatoxanthin. Diadinoxanthin is
converted to diatoxanthin via a single epoxidation step (Figure 2). The formation of
diatoxanthin, like zeaxanthin, correlates with the nonphotochemical quenching of singlet
chlorophyll described earlier.
Photosynthesis: This very complex process is initiated when an antenna molecule
absorbs a photon (Govindjee and Braun, 1974). Absorption takes place in about a
femtosecond (Kok and Businger, 1956) and causes a transition from an electronic ground
state to an excited state. In a few seconds this excited state would decay by vibrational
relaxation to the first excited singlet state. However, because of proximity to other
antenna molecules, the excited state energy has a high probability of being transferred by
resonance energy to a close neighbor (van Grondelle and Amesz, 1986). Photosynthetic
antenna systems thus act as funnels, which efficiently transfer excitons to the reaction
centers. Photosystem I (PS I) and photosystem II (PS II) comprise the two reaction
centers of photosynthesis.
28
PS II is a dimeric chlorophyll-protein complex that absorbs maximally at 680 nm
and is the site of the light dependent reactions of photosynthesis. When energy in the
form a photon arrives at the PSII reaction center, an electron (e-) in chlorophyll a
becomes excited. The electron can then travel through a series of redox reactions, by
electron carriers, such as pheophytin, cytochromes and plastoquinone. Water is oxidized
and plastoquinone is reduced in PSII. Water oxidation requires two molecules of water
and involves four turnovers of the reaction center (Kok et al., 1970). Each photochemical
reaction creates an oxidant that removes one electron and the net reaction results in the
release of one oxygen molecule. Four protons are deposited into the stroma and four
electrons are transferred to the plastoquinone pool, where they reduce two plastoquinone
molecules (Klein et al., 1993). Reduced plastoquinone debinds itself from the reaction
center and diffuses into the hydrophobic core of the membrane and travels to PSI to start
electron transport there. Tyrosine pulls one of the electrons produced from the oxidation
of water and uses it to replace the one that was lost from the reaction center. An oxidized
plastoquinone finds its way back to the quinine pool and the process is repeated.
Photosystem I is a chlorophyll-protein complex that absorbs maximally at 700
nm. The reactions of PSI can take place with or without light. At PS I, an excited electron
travels through a series of electron transfer components, such as special proteins
(A˚ and A1), three ferrodoxin proteins and then on to ferrodoxin. Ferrodoxin reductase
then facilitates the production of NADPH (nicotinamide adenine dinucleotide phosphate)
from NADP+. Along the electron transport pathway from water to NADP, a fraction of
light energy is used to synthesize ATP (adenosine tri- phosphate) from ADP (adenosine
29
di-phosphate) and inorganic phosphate. With sufficient NADPH and ATP available,
enzymatic reduction of CO2 to the carbohydrate level (Calvin-Benson cycle) becomes
possible. The equations (Figure 5) below represent the general reactions that take place
at the two reaction centers. As shown, the reactions are not stoichiometrically balanced.
H2O + NADP+ + ADP +Pi PS-II O2 + NADPH + ATP
CO2 + NADPH + H + ATP PS-I Glucose + NADP+ +ADP + Pi
Equation 2: Summary of two major reactions in the photosynthesis process.
Briefly, in the Calvin Benson cycle, CO2 combines with a ribulose 1, 5-
bisphosphate (RuBP) molecule to yield two molecules of a three carbon compound called
3-phosphoglycerate (PGA). In the presence of ATP and NADPH, PGA is reduced to 3-
phosphoglyceraldehyde (PGAL). PGAL is a 3-carbon sugar, and more than half of these
molecules are used to regenerate RuBP so the process can continue. The rest of the
PGAL molecules that are not recycled condense to form hexose phosphates. Hexose
phosphates yield sucrose, starch and cellulose. The sugars produced during this carbon
metabolism go on to produce carbon skeletons that are used for other metabolic reactions,
such as the production of amino acids and lipids. The carbohydrates, proteins and lipids
produced from photosynthesis in algal cells serve as an energy source for the consumers
in the next trophic level. Since light is a major factor driving photosynthesis, we believe
that a link can be established between chlorophyll a, a major participant in photosynthesis
and some of the products of photosynthesis.
30
Novel sunscreen pigment isolated from Scytonema hofmanii grown at high light and from samples collected in the Florida Everglades.
Scytonemin is a known ultraviolet radiation screening pigment, produced in
cyanobacterial sheaths (Garcia-Pichel et al., 1992; Dillon and Castenholz, 1999).
Cyanobacteria can produce a variety of secondary metabolites, including notorious
toxins, some of which potential therapeutic agents. They are able to inhabit and thrive in
a variety of hostile environments, from intense radiation to intense dessicating conditions
(Flemming and Castenholz, 2007; Butel-Ponce et al., 2004). Scytonemin is a photostable
dimeric pigment with indolic and phenolic subunits, as characterized by Proteau and
coworkers (1993). This structure is unique in nature and has been termed the
‘scytoneman’ skeleton. Two forms of this pigment exist: a yellow-brown oxidized form
and a red-brown reduced form. Reduced scytonemin is not considered to be biologically
active, but has been suggested to be a transformation product formed in reducing
environments (Garcia-Pichel and Castenholz, 1991). Both structures along with their
UV/Vis spectra are shown in figure 3.
31
Scytonemin reduced form Scytonemin Figure 3: Structure and UV/Vis spectra of Scytonemin (reduced form) and Scytonemin
Three new pigments (Figure 4), related to the scytonemin skeleton, were isolated
and structurally identified in a study aimed at investigating plant succession in the
Mitaraka inselberg in French Guyana (Butel-Ponce et al., 2004). These molecules are
derived from condensation of tryptophanyl- and tyrosyl-derived subunits with a linkage
32
Figure 4: New pigments isolated form Scytonema sp. collected on Mitaraka Inselberg, French Guyana (Butel-Ponce et al., 2004).
Tetramethoxyscytonemin: Purple amorphous solid; UV: 212nm, 562 nm;
m/z [M+H]+ 671
Dimethoxyscytonemin: dark red amorphous solid; UV: 215 nm, 316
nm, 422; m/z [M+H]+ 609
Scytonine: brown amorphous solid; UV: 207 nm, 225 nm, 270 nm; m/z
[M+H]+ 519
33
between the units unique among natural. The compounds have been termed
tetramethoxyscytonemin, dimethoxyscytonemin and scytonine.
A new pigment, believed to be related in structure to the scytonemin was isolated
from lab grown cultures of Scytonemin hoffmanii and from samples collected from areas
of the Florida Everglades. Partial characterization of this pigment was done as a second
project in this study. The structural properties of this pigment, as well as a putative
structure characterization are presented and discussed in Chapter VI.
Overall goals of this study
The preceding pages were written to familiarize the reader with pigment-based
chemotaxonomy, the relevance of algal biomass parameters in pelagic communities and
the need for better assessment of these parameters. The study was therefore conducted to
investigate the possibility of partitioning algal protein and two functional classes of
carbohydrates that are being contributed from the various taxonomic groups in a
population. This partitioning is suggested to be based on their relationship with
chlorophyll a. In the same vein that taxonomic marker pigments are used to partition
chlorophyll a among the different taxonomic groups and then applied to mathematical
formulae for estimating algal class abundance, we want to determine if the same concept
can be extended to the taxonomic estimation of algal proteins and carbohydrates.
The second project represents a fortuitous finding, as a novel sunscreen pigment was
isolated and characterized. Postulations are forwarded regarding the physiological and
ecological significances of this new pigment based on its UV/Vis absorbance spectral
data.
34
II. MATERIALS AND METHODS
The overall experimental design for algal growth, cell counting, harvesting,
pigment analyses, carbohydrate functional group analyses, and protein analyses is shown
in Figure 5 and the batch culture preparation is shown in Figure 6.
Experimental organisms: The following fresh water and marine microalgal
species were purchased from the Carolina Biological Supply Company (Burlington,
N.C.): Cyanobacteria; Synechococcus elongatus (marine), Microcystis aeruginosa
(fresh), Chrysophyta; Thalassiosira weissflogii (marine), Cyclotella meneghiniana
(marine), Chlorophyta; Scenedesmus sp. (fresh), Pyrrophyta, Dinophyceae; Amphidinium
carteri (marine). Additionally, the following species were purchased from the University
of Texas (UTEX) algal culture collection (Austin, TX): Rhodophyta; Rhodomonas salina
(marine), Chlorophyta; Dunaliella tertiolecta (marine).
The sample vials containing each unicellular culture were gently vortexed to achieve
homogeneous distribution of the cells as soon as they arrived at the Florida Atlantic
University Organic Geochemistry laboratory. Approximately 5 mL quantities were taken
from each vial for filtering and initial HPLC analysis, described below. This initial
analysis served to verify that the samples had not reached the senescent/death stage of
growth or were not overtly contaminated with another taxon.
35
8 ecologically significant algal species
Inoculate, test nutrients
Grow at 3 irradiance levels
Monitor growth / cell counts
Harvest: cell counting; collect samples on separate GF/F for pigment, protein, total organic carbon (TOC) analyses;colloidal and storage carbohydrate (CHO) analyses; nutrient tests of media filtrate.
Pigment extract: 90% MADW &
Extractant + Ion pairing
RP HPLC
PDA detector
Concentrations used to determine relationships between CHLa/pigment, protein/CHLa, colloidal CHO/CHLa, : homoscedasticity and ANOVA tests performed
Protein extract: 0.5N NaOH, per Rausch, 1980
Microbiuret assay: extractant +CuSO4/NaOH
Abs@310 nm, compare to standard curve
Carbohydrate extract: culture volume centrifuged
Phenol/H2SO4 assay on supernatant- colloidal CHO fraction
Pellet re-suspended in warm water for storage CHO
Filter, lyophilize, Phenol/H2SO4 assay; compare to standard curves
TOC extract: K2Cr2O7 & conc. H2SO4, per Walkley-Black, 1934
Measure abs @ 610 nm; compare to standard curves
Figure 5:. Flow Chart of Analytical Scheme.
36
unicellular algal stock culture
Batch 1
Batch 2 Batch 3 Batch 4 Batch 5 ……etc.
Figure 6: Schematic of inoculation procedure. Each species grown 5-7 times (inoculation, lag growth, exponential growth phase). Each batch is inoculated from stock culture, to prevent pseudo replication.
Algal culturing: All species were grown in 2 L batches in 4L cylindrical
polycarbonate containers (CAMBRO. Huntington Beach, CA). Autoclaved (122 º C and
37
2 atm.) Zephyrhills ® Natural Spring Water was used for the freshwater cultures and the
media prepared according to Guillard’s (Guillard, 1975) f/2 medium.
The seawater for the marine cultures was collected from coastal water (FAU Gumbo
Limbo Environmental Complex and Nature Center, Boca Raton, Florida) and autoclaved
(122 º C and 2 atm.) after filtering. The addition of nutrients, including vitamins and trace
metals were also based on Guillard’s (Guillard, 1975) f/2 medium. Erdschreiber’s
(Schreiber, 1927) medium was used to prepare Dunaliella tertiolecta and Rhodomonas
salina.
Culture conditions: Light levels are given here as; high (180-200 µmol
photons·m-2·s-1), moderate (70-75 μmol photons·m-2·s-1), low (35-37 μmol photons·m-2·s-
1), and dim (10 μmol photons·m-2·s-1). Light conditions were achieved within three
temperature controlled (25oC) growth chambers: a Revco-Harris growth chamber was
used for the high light experiments, while two Precision low temperature Illuminator 818
growth chambers were used for the remaining light levels. All growth was at 25oC with a
12 Light: 12 Dark diurnal cycle. Temperature control was observed to be + 1.5oC. The
samples in the two Precision growth chambers were illuminated from the front only
(fluorescent tubes vertically attached to the inside door) with two 34W Econo (Philips)
120 cm long fluorescent tubes, covered by a diffuser screen for the medium light
experiments and without a diffuser screen for the low light experiments. Samples for the
high light experiments (Revco-Harris growth chamber) were illuminated from the top and
both sides with sunlight quality (Verilux Instant Sun™), full Spectrum™ (ValuTek) and
“aquarium” quality (Sylvania Gro-Lux™) fluorescent tubes. Three 8W (Westwek 20121)
cool white fluorescent tubes were attached horizontally on the inside door of the
38
Precision growth chamber, and used for illumination in the dim light experiments. Only
one of the species in the study: Synechococcus elongatus, grew in dim light level, and the
dim light experiments are eventually discontinued for the remaining species. Light
intensity (PAR radiation: 400 – 700nm) was measured with a 4π spherical radiometer and
Li-Cor LI-250 Light Meter. Spectroradiometric data for the fluorescent light sources used
in the three main light levels in this study are shown in Appendix III. Transmission of
light through the culture flasks is also given in Appendix III.
Cell counting: Coulter Counter model ZM electronic cell counter was used for
rapid cell counting. The method of counting and sizing used by the Coulter is based on
the detection and measurement of changes in electrical resistance produced by a particle,
suspended in a conductive liquid, traversing a small aperture.
Cell counting was carried out on the same day that the algal samples were to be harvested
and every two to three days during growth to follow and plot logarithmic growth plots.
ISOTON® II diluent (electrolyte solution) was pipetted (20 mL) into the counting vial
(Fisher ‘Accuvette’) and 100μL of suspended algal cells was added. Each vial was placed
in the counter for electronic counting. The three most consistent counts out of six were
averaged and used as the corrected count. The dilution factor was determined, and the
number of cells per milliliter was calculated by multiplying the corrected count by the
dilution factor, as follows: Dilution Factor (DF) = (mL sample + mL electrolyte)/
(manometer setting x mL sample). DF x corrected counts = cells mL-1.
The number of cells per milliliter was determined from cell counting and the
concentration of each analyte (pigments, proteins, carbohydrates, organic carbon) could
then be calculated.
39
Chemical Analyses: All analyses described below were performed on replicate aliquots
collected at the same time.
Algal protein extraction: The procedure was adapted from Rausch (Rausch,
1981) with some modifications, and is as follows: 100 mL aliquots of algal culture were
filtered on to pre-combusted glass fiber filters. The filters were then folded in halves, then
quarters and refrigerated at -80 ˚C until analysis. Analyses were usually done one week
after filtering. For extraction, samples were extracted in 0.5M sodium hydroxide (NaOH),
by grinding the filters in 12 mL test tubes using tissue grinders (glass mortar with Teflon
® pestle e.g. Kontes Dwall). The tubes were next heated at 80 ˚C for 10 minutes to
further extract the proteins. After this step, the tubes were quickly cooled to room
temperature, and then centrifuged (Fisher Scientific, Centrific Model 228) for 5 minutes
at approximately 2800 rpm. The supernatant was then transferred to 10 mL graduated
tubes for subsequent protein analysis. A second extraction was then carried out on the
remaining filter debris (extraction in 0.5 M NaOH at 80 ˚C for 10 minutes, followed by
cooling and centrifugation), and the supernatants were combined in the 10 mL graduated
tubes. A third extraction was carried out (0.5M NaOH at 100 ˚C for 10 minutes) for green
algae and cyanobacteria, as prescribed by Rausch (1981). The combined supernatants
were then made up to a definite volume (6-10 mL) with 0.5M NaOH and used for protein
measurement.
Algal protein measurement: The micro-biuret method for estimating proteins as
adapted from Itzhaki and Gill (1964) and was slightly modified. The procedure used is as
follows: 2 mL of algal protein extract was assayed with 1 mL of 0.21% CuSO4.5H2O in
30% NaOH at 310 nm in a 1 cm quartz cuvette and another 2 mL of algal protein extract
40
was assayed with 1 mL of 30% NaOH at 310 nm in a 1 cm quartz cuvette. The
absorbance of the protein was obtained from the difference between the absorbance of the
sample in 30% NaOH and that following reaction in 0.21% CuSO4.5H2O in 30% NaOH.
All samples were measured against distilled water. Bovine serum albumin was used for
calibration. See Appendix IV for the BSA calibration curve and equation.
Algal colloidal and storage carbohydrate extraction: The method was adapted
from Chiovitti et al. (2004) and developed with some modifications. The adapted
method is as follows: aliquots of approximately 50 mL algal cultures in the logarithmic
stage of growth were collected in 50 mL centrifuge tubes and centrifuged (Dynac
Centrifuge, Becton Dickinson and Co, Parsippany N.J.) at 3300 rpm for 30 minutes. The
supernatant was decanted to leave ~ 0.5-1 mL of wet cells, and 2 mL of the supernatant
was used for colloidal carbohydrate analysis. The remaining wet cells were re-suspended
in 30 mL ultra-pure water (Milli-Q® Ultra-pure water systems, Millipore Corporation)
and heated in a water bath for an hour, stirring every 10 minutes. The solutions were then
sonicated for 5 minutes (Burdloff et al., 2001), followed by pelleting the cells via
centrifugation for 30 minutes. The resulting supernatant containing mostly water soluble
(storage carbohydrates) was then filtered through 0.22 µm membrane filters (Fisher
Scientific). The filtrates were then lyophilized and the dried material used for
carbohydrate analysis.
Algal colloidal and storage carbohydrate measurement: The two extracted
carbohydrate fractions were analyzed using the phenol- sulfuric acid assay (Dubois et al,
1956). The lyophilized samples were dissolved in exactly 2 mL of ultra pure water and
pipetted into 10 mL disposable test tubes. For the colloidal fractions, exactly 2 mL of the
41
initial supernatant was pipetted into 10 mL disposable test tubes. Next, 0.05 mL of 80%
phenol was added to each tube followed by the rapid addition of 5 mL concentrated
H2S04. The tubes were allowed to stand for 10 minutes, after which they were placed for
approximately 20 minutes in a water bath at 25-30˚C with occasional shaking. The
resulting champagne - dark orange solutions were then measured at 485 nm against
distilled water in a Perkin Elmer UV/Vis Lambda 2 spectrometer. Alpha-D (+)-Glucose
was used for preparing calibration curves (see Appendix IV for calibration plots and
equations).
Algal total organic carbon (TOC) extraction: The method used herein was
adapted from Walkley and Black (Walkley, A and Black, I.A., 1934) as updated by Chan
and coworkers (Chan et al,. 1995) and involves the rapid dichromate oxidation of organic
matter according to the following equation:
2Cr2O72- + 3C0 + 16H+ = 4Cr 3+ + 3CO2 + 8H2O
Equation 3: Dichromate oxidation of organic matter
This method is typically used for analyzing soils and sediments. Therefore, modifications
were carried out to allow for the application of the method to algal organic carbon.
Although the method is given here, it should be noted that it had to be abandoned as the
TOC was grossly overestimated. An external source was contacted for automated sample
analyses, but at the time of this writing, no validation has been obtained as whether our
samples could be accurately analyzed using the available analytical protocol for that
instrument. The modified Walkley-Black method is as follows: known volumes (100-
200 mL) of algal cultures were collected on pre-combusted glass fiber filters (Whatman
42
GF/F, 0.7 micron pore size borosilicate glass fiber), folded in half, then quarters, wrapped
in aluminum foil and stored at -80ºC until extraction. For extraction, the glass fiber
filters are retrieved, and opened to reveal the collected algal cells. The cells are pretreated
with a 1 mL solution of 2N H2SO4/5%FeSO4 to remove any inorganic carbon present.
This solution is added in increments until any effervescence present stops. The GF/F
filters are cut into 1/8 pieces and combined in 50 mL Erlenmeyer flasks. Approximately
1-1.5 mL of 1/6 M K2Cr2O7 is added, followed by 5 mL concentrated H2SO4. To
overcome any possibility of incomplete digestion of organic matter, the sample and
extraction solutions are gently heated at 145ºC for 30 minutes (Mebius, 1960). The
temperature has to be strictly controlled as the acid-dichromate solution decomposes at
temperatures above 150 ºC (Charles and Simmons, 1986). Following heating, the flasks
are cooled to room temperature and 5 mL water is added to quench the reaction.
Colorimetric determination of extracted TOC samples: Quantitation of total
organic carbon is performed through the measurement of the color change that results
from the presence of Cr 3+ in solution. The digestate was poured into 12 mL disposable
centrifuge tubes and centrifuged, then filtered through a 0.45 μm pore filter attached to a
3mL syringe. The filtrate is placed in a 1 mL cuvette and the absorbance measured at 610
nm against distilled water in a Perkin Elmer UV/Vis Lambda 2 spectrometer.
Quantitation is performed by determining the concentration from a standard curve.
Potassium Hydrogen Phthalate (KHP) and sucrose are used for validation of the method
and for preparing standard curves. See Appendix IV for plots and equations.
Nutrient analyses: The nitrate and phosphate content of the enriched algal
growth media were determined before inoculation and at harvest to follow and ensure the
43
cultures were grown in nutrient replete conditions (this data will not be shown, as it was
only used to verify nutrient conditions of the batch cultures). The Hach DR 5000
spectrometer (Hach Company World Headquarters, P.O. Box 389. Loveland, CO) was
used for all analyses. All procedures used were per the Hach DR 5000 procedures
manual. The Chromotropic acid method (10020) was used for measuring high range (0.2-
30.0 mgL-1) nitrate; the Molydbovanadate method (8114) was used for high range (HR,
1.0 to 100.0 mgL-1) phosphorus, namely reactive orthophosphate (SRP PO43-); the
PhosVer 3 (ascorbic acid) lower range method (8048) for 0.2 to 2.5 mgL-1 PO43- was
used several times at culture harvest when the HR PO43- tests were not sensitive enough
to detect the small amounts of orthophosphate remaining in the growth media.
Pigment Analyses: All pigment analyses were carried out under dim yellow light
conditions to prevent photo-oxidative alterations, such as pigment isomerization. For
harvesting and for pigment monitoring during growth, culture volumes (25-100 mL),
volume dependent on stage of growth/cell density) were filtered onto glass microfiber
filters (Whatman GF/F, 0.7 micron pore size borosilicate glass fiber). The filters were
removed from the filter funnel, folded in half and blotted between paper towels. The
filters are then folded into quarters, re-blotted and wrapped in aluminum foil and then
immersed in liquid nitrogen for quick freezing. The individual samples were removed
from the liquid nitrogen and stored in a refrigerator at -80ºC until extraction.
For extraction, the filters were unwrapped from the aluminum foil, placed in pre-
chilled glass tissue grinders (Kontes “Duall” 15 mL) and extracted by grinding (Barnant
variable speed mixer Series 20, ~ 350 rpm) with 3 mL of an extraction solvent containing
a procedural internal standard. The extraction solvent used was a mixture of
44
acetone/methanol/dimethylformamide/water, 30:30:30:10. (90% MADW). This mixture
has been shown to give better extraction efficiency and peak resolution than those
previously used (Hagerthey et al., 2006). The internal standard (IS) used was Copper
Mesoporphyrin- IX Dimethyl Ester (CuMESO IX DME). This was dissolved in 90%
MADW and the absorbance readings at 394nm and 715nm baseline were taken. Using
Beer Lambert’s law (A= εcl), and εmM =305 (Fuhrhop and Smith, 1975) at 394 nm, the
concentration of the internal standard added was determined. The extraction solvent was
made up such that the IS absorbance at 394nm would not exceed 1.0 AU. Having the
internal standard in the extracting solvent allowed the monitoring of its recovery through
the extraction as well as the chromatography process. A system response factor was
applied to all the pigments based on the ratio IS added/IS detected, giving correction factors
ranging from 1.2-1.5. During the HPLC analysis, the internal standard eluted as a sharp
peak in the 394 nm integration but did not show in the 440 nm or slightly in the 410 nm
integration where the pigments were detected and quantified. This allowed the internal
standard peak and peak area to be readily identified and quantified without interference
from other pigments. However, CuMESO IX DME did partially co-elute with
canthaxanthin. Canthaxanthin (β, β-carotene- 4,4’- dione) could still be adequately
quantified since only absorption of the internal standard is absent at 440 nm.
The extraction slurry was next sonicated in ice water for 30 seconds (tissue grinders in
bath style sonicator) to further disrupt the cells in the samples. Sporadic sonication was
shown to give good extraction (Hagerthey et al., 2006; Louda and Mongkhonsri 2004).
The extracts were then steeped for 1-2 hours at 4-6 ºC in a refrigerator. Following this,
the extracts in the tissue grinders were then centrifuged (Centrific Cenrifuge Model 228,
45
Fisher Scientific) for 2-3 minutes and the supernatant was decanted into a vial. This was
labeled ‘Raw’ extract. The remaining slurry was then placed in a 2 mL centrifuge filter
(Ultra free-CL PVDM, 0.45 µm diameter) and re-centrifuged to recover remaining
extract. The pooled ‘Raw’ extract was collected in a 3mL syringe and passed through a
0.45 μm pore diameter Cameo Syringe Filter into a second vial. This vial was labeled
‘Filtered’ extract. This procedure typically gave an overall total recovery of 90% (~
2.7/3.0 mL).
Ultra Violet - Visible (UV/Vis) Analyses of Extracts: The UV/Vis absorption
spectra (350-800 nm) of 1.0 mL aliquots of the filtered extracts were recorded on a
Perkin Elmer Lambda - 2 UV/Vis Spectrophotometer. This spectrophotometer was
calibrated for wavelength vs. holium oxide and absorbance vs. potassium chromate in
aqueous potassium hydroxide (Rao, 1967). The visible absorption spectrum of the
extracts gives a rough spectrophotometric estimate of total chlorophyll and carotenoids
using the Beer- Lambert relationship and was used to determine if dilution of the extract
was required (e.g. A430 > ~ 1.2) prior to injection into the HPLC system.
1.0 mL of the filtered extract was then added to a pre-chilled vial containing 0.125 mL of
ion pairing solution (Mantoura and Llewellyn, 1983), giving a total of 1.125 mL. This
vial was labeled ‘Mix’. Next, 100 μL of this mixture (extract = 88.89 μL of the 100 μL)
was injected into the HPLC system. The ion pairing (IP) or ion suppression solution used
in the injectate solution consisted of 15.0 g tetrabutyl ammonium acetate, 77.0 g
ammonium acetate and nano-pure water to equal a final volume of 1L. Incorporation of
ion pairing agent allows the separation of highly polar substances on reversed phase
46
HPLC columns by masking such highly polar groups such as carboxylic acids (Poole and
Poole. 1991).
High Performance Liquid Chromatography (HPLC): The High Performance
Liquid Chromatography system consisted of a Consta Metric 4100 series Quaternary
Solvent Delivery Systems pump (Thermo Separation Products, Riviera Beach, Fl.), a
Rheodyne model 7125 syringe loading sample injector fitted with a 100 μL injection
loop, a 250 mm long Waters Symmetry ® C18 column with an internal diameter of 4.6
mm (4µm spherical particle size, 100 ºA pore size, 335 m2 g-1 surface area) and a Waters
996 Photodiode Array Detector (PDA: 190-800 nm). This system was coupled to a Dell
PC using Millennium 32 software.
Gradient elution was carried out using a mixture of three solvents with the following
solvent ratios (see Table 4): a combination of 60% solvent A (0.5M ammonium acetate in
methanol: water, 85/15) and 40% solvent B (90/10 acetonitrile: water) for the first five
minutes. This combination provides a good ratio of polar and lipophilic solvents. Eluted
during this time are the solvent front of the injectate, the highly polar peridinin
derivatives P-468 and P-457, chlorophyllide-a, chlorophylls-c1/c2. The gradient is then
changed to 100% solvent C (100% ethyl acetate) from 5-10 minutes. The peaks eluted
here were the scytonemins, fucoxanthinol, pyrochlorophyllide- a, and peridinin. At 10
minutes, the gradient changes to 100% solvent B. This gradient changes gradually up to
35 minutes, where a ratio of 35% solvent B and 65% solvent C is reached. The
carotenoids neoxanthin, fucoxanthin, cis-fucoxanthin, violaxanthin, dinoxanthin,
antheraxanthin, astaxanthin, diadinoxanthin, myxoxanthophyll, diatoxanthin, lutein,
canthaxanthin, zeaxanthin, followed by chlorophylls-b, chlorophylls-a, and echinenone
47
eluted during this time. The gradient then changes to 30% solvent B and 70% solvent C
between 35 and 45 minutes. The pheophytins (-b/-a) plus beta-carotene are highly non-
polar and are eluted between 36 and 40 minutes. The gradient then changes to 100%
solvent C for 2 minutes in order to flush any highly lipophilic compounds that may be
left. The gradient then returns to the original 60% solvent A: 40% solvent B at 48
minutes, thus restoring the column to the required conditions for next use. A 100%
solvent D (85/15 methanol: water) is ran through the column for 15 minutes prior to
storage of the column in that solvent.
Solvents:
A = 0.5M Ammonium acetate in MeOH/water, 85:15 B = Acetonitrile/water, 90:10 C = Ethyl acetate, 100% D = Methanol/water, 85:15 (storage solvent) Table 4: Gradient program used in FAU OGG laboratory
HPLC Data Calculations: Pigment analysis was achieved 2 dimensionally based
on retention time and spectral absorbance from the photodiode array (PDA) detector. As
the sample extract partitioned between the stationary and mobile phase of the column, the
pigments were separated based on their solubility in the changing solvent gradients and
their affinity for the stationary phase. The integrated areas of the peaks from the
Time (min) Solvents A/B/C 0 60/40/0 5 60/40/0 10 0/100/0 40 0/30/70 45 0/30/70 46 0/0/100 47 0/100/0 48 60/40/0
48
chromatogram were used in Beer-Lambert calculations to obtain the molar quantities and
weight of each compound.
The UV/Vis spectrum of each separated pigment was recorded with the PDA
detector from 300 – 800 nm. The conjugated C=C structure of the pigments is referred to
as the chromophore or the color bearing group of the compound. It is well known that the
number of conjugated double bonds (N), the conjugated end groups and the solvent
influence the absorption spectra of carotenoids. The position of the λmax of the absorption
spectra is unique for individual carotenoids, with λmax being mainly influenced by the N
value in carotenoids, increasing as the N value increases (Takaichi, 2000). Therefore, the
identity of carotenoids as well as chlorophylls can be determined by a combination of the
HPLC retention times and the absorption spectra, the so-called ‘2-D advantage’.
The retention times and spectral absorbance of the pigments were also compared
to those from the FAU Organic Geochemistry Group’s (FAU-OGG) library of standards.
Known standards are always required for any chromatographic system. Standards are
obtained either as pure compounds or as part of well-known accepted mixtures in
unicellular bacterial or algal cultures (Jeffrey et al., 1997). The members of our lab group
have obtained a large number (>80) of chlorophylls, carotenoids and their derivatives
through partial synthesis or derivatization and several have been purchased from VKI
(Denmark). The performance of these standard compounds (retention time and spectral
absorbance) was used to verify the identity of the algal pigments in this research. See
Appendix V for a detailed table of retention times and UV/Vis PDA spectral data for the
pigments typically encountered in our research group and in this study.
49
The HPLC software automatically integrated peaks on the chromatogram and
reported the areas under the peaks as time - based microvolt second (µV*s) units. This
gave a relation to the quantity of each compound. The µV*s data was previously
standardized versus AU· min-1 data from Waters 990 PDA and versus known
concentrations of pigments. The flow rate used was 1.00 mL/min, giving the integrated
peak area in microvolt second units·mL (µV*s ·mL). Manual integration was used to
separate pigment peaks that overlapped. Very small peaks were also integrated manually.
The µV*s data was next entered in an in-house (Florida Atlantic Organic Geochemistry
Group) generated spreadsheet called “PIGCALC” (pigment calculation). This
spreadsheet contains standardized equations and specific absorption coefficients and is
used to calculate the quantity of each pigment, sums and ratios of pigments and then
converts that information into taxonomic divisions of algae, (see Appendix I for a
simplified pigment calculation example). The ratios of interest from each spreadsheet
were extracted and placed in another spreadsheet where they were pooled according to
the species grown at different light intensities over a particular growth period. The ratios
of interest at the different light levels were extracted and plots were made of
CHLa/biomarker as well as biomass parameter/CHLa and detailed analyses made of the
generated ratios and plots.
Statistical analyses: All statistical analyses were carried out using PASW
statistics software (SPSS Inc.). Data was tested for homoscedasticity (F-test) and
heteroscedastic data were log transformed (Miller and Miller, 2005) before analysis. The
means of the CHLa/marker pigment, protein/CHLa, colloidal CHO/CHLa and storage
CHO/CHLa ratios over the high, medium and low light levels were made using one-way
50
analysis of variance (ANOVA), followed by post hoc analysis (Tukey and Games-
Howell tests). See appendix VI for detailed tables of the outputs of these statistical tests.
Isolation and characterization of a new pigment:
Samples (periphyton) were obtained from those collected as part of the Florida
Everglades Comprehensive Everglades Restoration Plan (CERP). This same pigment had
been seen before in Scytonema hoffmanii cultures grown in the laboratory at 300 -1800
μmol photons·m-2·s-1. Samples were frozen and lyophilized prior to extraction. The
freeze dried samples were ground with a mortar, and then steeped in acetone for two to
three days to allow for complete extraction of all pigments. The extract was collected in a
50 mL syringe and passed through a 0.45 μm pore diameter Cameo Syringe Filter. The
extracts were pooled and concentrated by evaporation. The dried pigment film was then
re-dissolved in 90% acetone (1-2 mL) and reversed phase low pressure HPLC (LP-
HPLC) was used for bulk pigment separation.
The LP-HPLC system consisted of an Autochrom Products Model 500 ternary
gradient HPLC pump with a Model 2360 gradient programmer, a 85 mm long Michel-
Miller (ACE Glass, Vincland, N. J.) column with an internal diameter of 8mm, a 300 mm
long Michel-Miller Chromatographic injection column with an internal diameter of 22
mm, and a micro flow cell in aSpectronic-20 UV/Vis spectrophotometer, set to 430 nm.
This system was coupled to a DELL PC using Peak Simple® Chromatography data
software. Both columns were packed with C18 Silica Premium Rf, end-capped, with a
pore size of 70 Å and particle sizes between 20-45 µm. Gradient elution was initially
carried out using the following three solvent systems: 40% solvent A (90%
51
Acetonitrile/water) and 60% solvent B (5M Ammonium Acetate in Methanol: water
80/20) from 0-5 minutes, 50% solvent B and 50% solvent C (100% Ethyl Acetate) from
5-25 minutes, 30% solvent A and 70 % solvent C from 25-65 minutes, and 100% solvent
C from 65-90 minutes. The column returned to 100% solvent A for the last 5 minutes and
for storage.
The extraction method above allowed for the identification of chlorophylls and
carotenoids from the Scytonema sp. cultures, but failed to isolate the unknown and
scytonemin pigments in good purity from the field samples. The isolation plan was to
collect the unknown pigment in higher (~ mg) amounts. With this in mind, the initial
extraction method was changed to extraction with 100% Methanol (x3) to remove most
of the chlorophylls and some of the carotenoids, and then 100% Acetone (x3) to obtain
most of the scytonemin pigments. The gradient program was changed to an isocratic
elution using 100% solvent A (90% Acetonitrile/water) for 0-90 minutes, followed by
100% solvent C (100% Ethyl Acetate) from 90-120 minutes. The new pigment was
collected at approximately 60 minutes and scytonemin was collected at approximately 75
minutes. All other procedures remained the same.
The filtrate was next evaporated and the dark solid film re-dissolved in 100%
acetone (new pigment and scytonemin fractions) and UV/Vis absorbance reading (190-
800 nm) taken of a 1 mL aliquot of each to determine if the correct fraction had been
collected. For further verification, the 1 mL aliquot of the acetone/pigment solution was
evaporated, and the solid was dissolved in 90% MADW and IS. Ion pairing was added
and 100 µL of this was injected on to the main (Waters 996) HPLC - PDA system used
for separating the pigments of all the other algal species (described previously). The rest
52
of the fractions collected were evaporated to dryness, and purged under Argon gas before
storage at -80 ºC.
IR analysis: The pure, evaporated unknown pigment was dissolved in methylene
chloride; a thin film of this solution was placed on a Sodium Chloride (NaCl) window to
dry. Analysis was carried out on a Thermo Scientific iS5 Fourier Transform IR
Spectrometer, coupled to a PC using OMNIC ® software.
Mass Spectrometry: Four mass spectrometry methods were used to assist in
elucidating the structure of the new pigment. The first and second methods were
conducted at Florida Atlantic University, Boca Raton and the other two were done at the
University of Florida, Gainesville.
Matrix-Assisted Laser Desorption Ionization – Time of Flight Mass
(MALDI-TOF) Spectrometry: The puified new pigment and purified scytonemin
pigment were added to an a-cyano - 4- hydroxycinnamic acid (CHCA) matrix and mass
analysis was carried out on a MALDI-TOF mass spectrometer (Applied Biosystems
Model), coupled to a PC using Data Explorer Software®. The analyte and matrix were
layered on a stage and the stage was bombarded with a laser beam (matrix-assisted laser
desorption), which ionized the analyte, spalled the ions off the stage and into the
electrostatic lenses. The electrostatic lenses guided the ions into the tube of the time of
flight mass analyzer. The ions are initially filtered to have a specific kinetic energy. The
velocities of the ions in the tube then vary inversely with their masses. The lighter ions
move faster and arrive at the detector first.
Liquid Chromatography –Mass Spectroscopy (LC-MS): analyses was carried
out on an Agilent Technologies 1200 series liquid chromatographic system, coupled to a
53
single quadrupole 6120 LC/MS. Mass analysis was done in both positive and negative
mode, with a 100 -1000 mass range. Electrospray ionization was the ionization method
used. Column separation was achieved on a Phenomenex Luna® C18 (2) column, which
was 150 mm long with an internal diameter of 4.60 mm (5µm spherical particle size, 100
Å pore size, 400 m2 g-1 surface area). Gradient elution, with a flow rate of 0.8 mL/min
was achieved with the following two solvent systems, according to table 4 below:
Solvent A = Water: formic acid (1000:1) Solvent B = Acetonitrile: formic acid (1000:1)
Table 5: Gradient program used for LC-MS runs Time (mins) Solvent % A:B 0.00 80/20 2.50 80/20 15.00 10/90 20.00 10/90 20.50 80/20 24.00 80/20 24.01 80/20
Mass analysis at the University of Florida’s mass spectrometry facility: High
resolution mass spectrometry (HR MS) was obtained using an Agilent 6210 time of flight
(TOF) mass spectrometer, using an electrospray ionization (ESI) source. Sodium ions
were incorporated into the ionization source. Thus, the major ion and that of the sodium
adduct was obtained in the spectra. The sample was injected directly into the ionization
source.
HPLC - MSn: (further fragmentation of parent ion) This was conducted using an
Agilent (Palo Alto, CA) 1100 series HPLC system coupled to a Thermo Finnigan (San
Jose, CA) mass spectrometer, with an ESI source. The HPLC system consisted of a
54
G1322A degasser, G1312A binary pump, a Thermo scientific Hypurity C8 column (5µm;
2.1 x 100mm + guard column), an Agilent 1100 G1314A UV/Vis detector (set at 254 nm)
and a Rheodyne 7125 manual injector with a 25µL injection loop. A 25µL Hamilton
1702 gastight syringe was used for sample injection. Gradient elution was carried out
using 2mM ammonium acetate in water (solvent A) and HPLC grade methanol (solvent
B) at 0.25 mL/min according to the following gradient program: A:B (min) = 95:5 (0)
through to 5:95 (45-60).
The ESI- MSn collision - induced dissociation (CID) product spectra were
obtained with 5 u isolation of the precursor ion, using 42.5 percent CID energy (qCID 0.3
and 30 ms). Nitrogen was used for the sheath gas (N2 = 65) and auxiliary gas (N2 = 5).
The heated capillary temperature was set at 250 º C, the spray voltage at 3.3kV and
heated capillary voltage set at +12.5V (positive mode) and -10V (negative mode).
NMR analyses: 1D and 2D NMR spectra were obtained on a JEOL nuclear
magnetic resonance spectrometer with standard pulse sequences operating at 600 MHz.
For the analyses: approximately 3 mg of purified pigment was dissolved in deuterated
dimethyl sulfoxide (DMSO- d6). Delta software was used for analyzing the spectra. The
NMR instrument was located at FAU-Harbor Branch Oceanographic Institute in Fort
Pierce, Florida. Permission for instrument use was obtained from Dr. Amy Wright. Dr.
Wright’s post- doctoral associate, Dr. Priscilla Winder, assisted with these analyses.
Acetylation reactions: The purified pigment sample (~0.5 mg) was first
lyophilized. Acetic anhydride and pyridine were next added and the acetylation reaction
was allowed to progress overnight. Following the reaction, the solvent was evaporated
and the sample was again lyophilized. LC-MS was then used to assess the increased
55
sample mass. These acetylation reactions were repeated several times (using different
pigment sample each time).
Deuterium exchange reactions: Deuterated methanol (~600 µL) was added to
the pigment sample and 1D and 2D NMR analyses was done to investigate if previously
seen OH and possibly NH proton signals had been exchanged with the heavier
deuterated proton.
56
III. RESULTS - STATISTICAL ANALYSES
Eight unicellular algal species were grown in nutrient rich batch cultures under
three light conditions: low light (LL = 37 µmol photons·m-2·s-1), medium light (ML = 70-
75 µmol photons·m-2·s-1), and high light (HL= 200 µmol photons·m-2·s-1). Between 4 and
6 culture batches were grown for each species at each light level.
Significance of the algal species used in this study:
Microcystis is a common unicellular colonial cyanobacteria found in freshwater
environments. The species belong to the phylum Cyanobacteria, order Chroococcales and
family Microcystaceae. The existence of intracellular structures such as gas vesicles
provides cells with buoyancy. Microcystis aeruginosa, which was used in this study,
occurs in large amounts on the surface waters of lakes and reservoirs in spring and
summer months. It is one of the most damaging species, due to its toxicity to aquatic and
terrestrial organisms and is known to occur in many Florida Lakes (Bigham et al., 2009;
Phlips et al., 2002; Ross et al., 2006).
Synechococcus spp. are oxygenic phototrophs that can photolyze either H2O or
H2S. Synechococcus is the main source of primary production in oligotrophic, pelagic
marine, open, warm waters. They have been known to cause harmful but not directly
toxic algal blooms in Florida Bay (Phlips et al., 1999). Harmful here, is under the
definition of Paerl (1997), which includes blooms leading to anoxia, disruption of socio-
57
economic function, environmental change, and the like. The dominance of this species in
the center of the bay may be attributable to it physiochemical characteristics: small size,
buoyancy and tolerance to high light intensity (Phlips et al., 1999).
Dunaliella is a unicellular, ovoid, biflagellate, naked green alga. Twenty eight
species of Dunaliella are presently recognized (Jayappriyan et al., 2010). The cells are
motile and have two equal, long smooth whiplash flagella which belong to the order
Volvacales, family Polyblepharidaceae and the class of Chlorophyceae. It was first
identified by a French scientist Michael Felix Dunal in 1838 and later it was re-
discovered by Teodoresco in 1905. The unique morphological feature of Dunaliella is
that it lacks a cell wall. The cell is enclosed by a thin plasma membrane or periplast,
which permits rapid changes in cell shape and volume in response to osmotic changes. To
survive, these organisms have high concentrations of β-carotene to protect against the
intense light and high concentrations of glycerol to provide protection against osmotic
pressure.
Scenedesmus is a non-motile alga consisting of 2, 4, and 8 elongated cells, often
with long spines on the terminal cell (Smith 1916), belonging to the order
Chlorococcales, family Scenedesmaceae and the class Chlorophyceae. This genus is very
common in eutrophic freshwater ponds and as planktonic forms in rivers and lakes; it is
reported worldwide in all climates and is rarely found in brackish water (Wehr and
Sheath, 2003). Some species are produced in mass culture and used as food because of
their protein and mineral content, or used for other purposes in biochemical industry
(Krauss and Thomas, 1954).
58
Rhodomonas of the phylum Cryptophyta, class Cryptophyceae and order
Pyrenomonadales, are a small group of marine flagellates which contain chloroplasts.
The cells are ovoid and flattened in shape with an anterior groove; two slightly unequal
flagella are present for locomotion. They occur in marine and brackish water (Jeffrey and
Vesk, 1990). This species contain fragile cell membranes and have the name hidden-plant
(crypto-phyte). Cryptophytes can be detected in oceanic populations by the presence of
the marker pigment, alloxanthin (Gieskes and Kraay, 1983). These species are very
fragile and are often lost in fixed samples, thus, specific pigment markers (viz.
alloxanthin) are essential for their identification.
Cyclotella is a small, centric diatom with cells only 3-5 µm in diameter. This alga
belongs to the phylum Bacillariophyta and family Stephanodiscaeae. The valves are short
and drum shaped; the cells have long chitinouos bristles that help decrease settling.
Cyclotella meneghiniana, used in this study, is perhaps the best known species and is
widely used in growth experiments (Mitrovic et al, 2010; Finlay et al., 2002; Tedrow et
al., 2002; Bourne et al., 1992; inter alia). Species belonging to this genus, particularly
Cyclotella choctawatcheeana and Cyclotella striata have been found in Choctawatchee
Bay, Florida (Prasad et al., 1990). Members of the genus are also reported to form a
dominant part of the planktonic assemblage in Florida Bay USA (Prasad and Nienow,
2006).
Thalassiosira species grow primarily in marine waters and belong to the family
Thalassiosiraceae and order Biddulphiales. The fultoportulae, or strutted processes,
secrete β-chitin, which is considered to offer resistance to settling (Johansen and Theriot,
1987). Some species within the genus are found in estuaries, high conductance waters
59
and rivers, polluted ponds, and other aquatic systems that have been impacted by human
activities (Spaulding and Edlund, 2009). Species belonging to this genus are widely used
in the shrimp and shellfish larviculture industry and are considered by several hatcheries
to be the single best algae for larval shrimp (Jensen et al., 2006).
Amphidinium spp are brown tide organisms with species that forms harmful algal
blooms – toxins, physical irritants and noxious events. Most species produce toxins that
affect humans and also fish (ichthyotoxic). Amphidinium belongs to the class
Dinophyceae and family Gymnodiniaceae. Amphidinium carterae, used in this study, has
a dorso-ventrally compressed body with a very small epitheca (Hulburt, 1957). This
species, as well as others in the genus are CFP (ciguatera fish poisoning) producers
(Hallegraeff et al., 1993; Anderson et al., 1987; Yasumoto et al., 1987; inter alia).
Analyses overview
Protein, colloidal carbohydrate (CHO), and storage CHO analyses were
performed in triplicate. Pigments were analyzed during growth and at also harvest.
Relationships between Chlorophyll a (CHLa)-to- taxon-specific marker pigment; protein-
to- CHLa; colloidal CHO-to- CHLa; storage CHO-to- CHLa in relation to light
treatments were analyzed for each species using one-way analysis of variance (ANOVA).
The ratios (relationships) were used as the dependent variable and the light treatments
were used as the independent variable. All statistical analyses were carried out at the 0.05
alpha level, using PASW® statistics version 18 software. The ANOVA F-test is very
robust to mild departures from homogeneous variances (Lentner 1993). However, all
ratios (except CHLa: marker pigment) were log transformed in an attempt to limit
60
departures from homogeneity. Levene’s test for homogeneity of variance was conducted
in combination with the one-way ANOVAs. In addition, post hoc follow-up tests were
also conducted, to assess difference in treatment (group) means if the one-way ANOVAs
were significant. Tukey’s honestly significant difference (HSD) post hoc follow-up test
was used if the homogeneity assumption was not violated and Games- Howell post hoc
follow-up test was used for samples with non-homogeneous sample variances. For all the
species in this study, one-way ANOVA tested the null hypothesis that the group means
are not significantly different, that is, the three light treatments have the same effect on
the ratios being investigated. Pertinent results for each species are presented below. The
software output from the one-way ANOVA analyses and other statistical tests are
tabulated in appendix VI. Cellular concentrations of chlorophyll a, protein, and the two
functional classes of carbohydrates, as well as their relationship to biovolume for each
species, at each light level are tabulated in Appendix VII. Typical chromatograms of all
the species in this study are shown in Appendix VIII. Throughout this section, in text and
in figures, the acronyms LL, ML and HL will be used for low, medium and high light
levels respectively.
Synechococcus elongatus (Cyanophyta; cyanobacteria): The pigments
identified for this species were: polar myxoxanthophyll (MYXOL), myxoxanthophyll
(MYXO), zeaxanthin (ZEA), canthxanthin (CANTH), chlorophyll a allomer (CHLa
allo), chlorophyll a (CHLa), chlorophyll a epimer (CHLa’), echinenone (ECHIN), beta
carotene (BETA). The taxonomically significant pigment identified for coccoidal and
filamentous cyanobacteria are ZEA and ECHIN respectively (Nichols, 1973). These
pigments are typically photoprotectorant pigments which change in relation to light
61
intensity, as a result different ratios were seen at each light treatment. This species was
the only one that grew successfully at the dim (DL, 10 µmol photons·m-2·s-1) light
treatments. The DL treatments had CHLa/ZEA and CHLa/ECHIN ratios from 6.07 to
6.76 and 50.09 to 68.03 respectively. The LL treatments had CHLa/ZEA and
CHLa/ECHIN ratios ranging from 3.85-5.52 and 57.13 to 68.89 respectively. The ML
experiments gave CHLa/ZEA and CHLa/ECHIN ratios between 2.86 and 3.4 and from
28.57 to 37.31 respectively. The HL experiments had CHLa/ZEA and CHLa/ECHIN
ratios ranging from 0.64 to 1.01 and 10.2 to15.61. See plots in Figure 7 (a- b) of these
relationships. These pigment ratios were not log transformed prior to statistical analyses.
Levene’s statistical test for homogeneity of variance for the CHLa/ZEA ratios for
the four light groups was not violated, as F (3, 18) = 2.194 and p = 0.124. The overall
ANOVA F was significant, with F (3, 18) = 237.968 and p < 0.001, indicating that at
least one of the means was significantly different from the others. Tukey’s (HSD) post
hoc follow up tests showed that all of the group means were significantly different from
each other at the 0.05 level. The Levene’s statistical test was significant for the
CHLa/ECHIN ratios for the four light groups, with F (3, 18) = 3.858 and p = 0.027. The
Brown- Forsythe robust test of equality of means and the overall ANOVA F were also
significant. The Games-Howell post hoc follow tests showed that all the means except for
the DL and LL were significantly different from each other.
62
Figure 7: Synechococcus elongatus (a): CHLa/ZEA v Light Intensity; (b): CHLa/ECHIN v Light Intensity
Synechococcus elongatus – protein/CHLa relationships: Protein concentrations were
assessed for the four light levels that batch cultures of this species were grown under,
revealing the following: Concentrations of protein in the dim light (DL, 10 µmol
photonsss·m-2·s-1) experiments ranged from 12.9 to 59.46 pg cell-1; The LL experiments
showed concentration ranges from 25.5 pg cell -1 to 675 pg cell-1, while the ML and HL
experiments had protein concentrations of from 31.5 pg cell -1 to 430 pg cell-1 and 600 pg
cell-1 to 1986 pg cell -1 respectively. The protein/CHLa ratios for this experimental group
6.49
4.73
3.06
0.870
2
4
6
8
0 50 100 150 200 250C
HL
a/Z
EA
Light intensity (μmol photons·m-2·s-1)
(a)
58.06 63.12
34.18
13.62
0
20
40
60
80
0 50 100 150 200 250
CH
La/
ECH
IN
Light Intensity (μmol photons·m-1·s-1)
(b)
63
are as follows: DL experiments resulted in ratios between 3.07 and 43.96, the LL
experiment ratios were from 110.43 to 136.28; ML ratios were between 161.04 and
195.69 and the HL ratios ranged from 371 to 596.10. All ratios were log transformed
prior to statistical analyses to avoid possible violation of homogeneity of variances
assumptions. Regression and dot plots are shown in Figure 8 (a - d).
Levene’s statistic gave an F value that was not significant, with F (3, 16) = 2.544
and p = 0.093. This indicated that the variances in the means were not significant.
However, the overall F ANOVA was significant, with F (3, 16) = 278.265 and p < 0.001,
indicating that at least one of the groups of means is different from the others. Tukey’s
(HSD) post hoc follow-up tests showed the following: The DL group (M= 1.78) was
significantly different from the LL group (M = 2.09), with a mean difference of -0.31 and
a p value < 0.001. The DL group (M = 1.78) was significantly different from the ML
group (M = 2.24), with a mean difference of -0.46 and a p value < 0.001. The DL group
(M = 1.78) was also significantly different from the HL group (M = 2.28), with a mean
difference of -0.50 and a p value < 0.001. The LL group (M = 2.09) was also significantly
different from the ML group (M = 2.24), with a mean difference of -0.16, with a p value
< 0.001. The LL group (M = 2.09) was also significantly different from the HL group (M
= 2.28), with a mean difference of -0.193 and a p value < 0.001. The ML (M = 2.24) was
however, not significantly different from the HL group (M = 2.28), with a mean
difference of -0.03 and a p value of 0.228.
64
Figure 8: Synechococcus elongatus (a): Protein v CHLa; (b): Protein v Light Intensity; (c): CHLa v Light Intensity
y = 7.1073x ‐ 105.54R² = 0.9282
‐500
0
500
1000
1500
2000
0 50 100 150 200 250
Pro
tein
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
y = 0.0344x + 0.0226R² = 0.8692
0
2
4
6
8
10
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
‐500
0
500
1000
1500
2000
2500
‐5 0 5 10 15P
rote
in (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
DL
LL
ML
HL
65
Figure 8 contd.: Synechococcus elongatus; (d): Light treatment effect on protein/CHLa ratios (dot plot)
Synechococcus elongatus – colloidal carbohydrates/CHLa relationships: The
concentration of colloidal carbohydrates for the DL experiment group ranged from 2.22
to 7.48 pg cell-1 and colloidal CHO/CHLa ratios ranged from 4.43 to 5.98. The LL
experiments had colloidal carbohydrate concentrations between 1.82 and 60.3 pg cell-1,
with ratios from 9.54 to 10.87. The ML experiments gave colloidal carbohydrate
concentrations ranging from 1.97 to 24.1 pg cell-1, with colloidal CHO/ CHLa ratios from
10.01 to 11.25. The HL experiments had colloidal carbohydrate concentrations between
43.8 pg cell-1 and 114 pg cell-1 and colloidal CHO/CHLa ratios from 11.90 to 13.79. All
ratios were log transformed prior to statistical analyses. See regression and dot plots in
Figure 9 (a - c).
1.6
2
2.4
0 50 100 150 200 250pr
otei
n/C
HL
a(l
og)
Light intensity (µmol photons·m-2·s-1)
(d)
DL
LL
ML
HL
66
0.8
0.9
1
1.1
1.2
0 50 100 150 200 250
Col
loid
al C
HO
/CH
La
(log
)
Light intensity (µmol photons·m-2·s-1)
(c)
DL
LL
ML
HL
Figure 9: Synechococcus elongatus (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity; (c): Light treatment effect on colloidal CHO/CHLa ratios (dot plot)
‐50
0
50
100
150
‐5 0 5 10 15C
ollo
idal
CH
O (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
DL
LL
ML
HL
y = 0.4469x ‐ 3.7532R² = 0.8897
0
50
100
150
0 50 100 150 200 250
Col
loid
al C
HO
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
67
Levene’s test for homogeneity of variance in the group means was not significant,
with F (3, 16) = 1.805 and p = 0.187. This proved that the assumption of homogeneity of
variance had not been violated. However, the overall ANOVA F was significant, with F
(3, 16) = 12.487 and p < 0.001, which means that at least one of the experimental group
means is significantly different from the others.
Tukey’s (HSD) post hoc follow-up tests was then done to assess which of the
group means was significant. The following results were obtained: the DL group (M =
0.96) was not significantly different from the LL (M = 0.99), with a mean difference of -
0.04 and a p value of 0.503. The DL group (M = 0.96) was also not significantly different
from the ML group (M = 1.02), with a mean difference of -0.06 and a p value of 0.096.
The DL group (M = 0.96) was significantly different from the HL group (M = 1.10), with
a mean difference of -0.14 and a p value < 0.001. The LL group (M = 0.99) was not
significantly different from the ML group (M = 1.02), with a mean difference of -0.02
and a p value of 0.696. The LL group (M = 0.99) was also significantly different from the
HL group (M = 1.10), with a mean difference of -0.11 and a p value < 0.001.
Additionally, the ML group (M = 1.02) was significantly different from the HL group (M
= 1.10) with a mean difference of -0.08 and a p value of 0.008.
Synechococcus elongatus – Storage carbohydrate (CHO)/CHLa relationships: The
storage CHO concentrations and subsequent storage CHO/CHLa ratios were determined
for the four light levels used in the study for this species and are as follows: the DL
experiment group had storage carbohydrate concentrations between 23.6 and 93.5 pg cell
-1 and storage CHO/CHLa ratios from 39.51 to 45.17. The LL experiments showed
storage CHO concentrations from 10.6 to 337 pg cell-1 and storage CHO/CHLa ratios
68
between 56.42 and 62.17. The ML experiments had storage CHO concentrations between
10.4 pg cell-1 and 142 pg cell-1, and storage CHO/CHLa ratios from 76.38 – 106.26.
The HL experiment group had storage CHO concentrations from 161 to 590 pg cell-1 and
storage CHO/CHLa ratios between 105.22 and 178.98. The storage CHO/CHLa ratios
were log transformed prior to statistical analyses in an attempt to prevent violation of the
assumption of homogeneity of variance in the group means. See plots of these
relationships in Figure 10 (a - c).
Levene’s test for homogeneity of variance was not significant, with F (3, 16) =
0.566 and p = 0.646, indicating that the assumption had not been violated. The overall
ANOVA F was significant, with F (3, 16) = 32.465 and p < 0.001, indicating that at least
one of the group means was significantly different from the others. Tukey’s (HSD) post
hoc follow-up test was then carried out to identify which of the group means was
significant. The following was determined: The DL experiment group (M = 1.62) was
significantly different from the LL group (M = 1.77), with a mean difference of – 0.15
and p value < 0.001. The DL group (M = 1.62) was significantly different from the ML
group (M = 1.74), with a mean difference of -0.11 and a p value < 0.001. The DL group
(M = 1.62) was also significantly different from the HL group (M = 1.74), with a mean
difference of -0.12 and p value < 0.001. The LL group (M = 1.77) was not significantly
different from the ML group (M = 1.74), with a mean difference of 0.03 and a p value of
0.136. The LL group (M = 1.77) was not significant from the HL group (M = 1.74), with
a mean difference of 0.03 and a p value of 0.305. The ML group (M = 1.74)
69
1.5
1.7
1.9
0 50 100 150 200 250stor
age
CH
O/C
HL
a (l
og)
Light Intensity (µmol photons·m-2·s-1)
(c)
DL
LL
ML
HL
Figure 10: Synechococcus elongatus (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
‐200
0
200
400
600
800
‐5 0 5 10 15
Sto
rage
CH
O (
pg c
ell-1
)CHLa (pg cell-1)
(a)
DL
LL
ML
HL
y = 1.87x + 9.9563R² = 0.852
0
100
200
300
400
500
0 50 100 150 200 250
Sto
rage
CH
O (
pg c
ell-1
)
Light Intensity (µmol photons·m-2·s-1)
(b)
70
was also not significantly different from the HL group (M = 1.74), with a mean
difference of -0.006 and a p value of 0.973.
Microcystis aeruginosa (Cyanophyta; blue-green algae): the pigments identified for
this species are: polar myxoxanthophyll (MYXOL), myxoxanthophyll (MYXO),
zeaxanthin (ZEA), canthxanthin (CANTH), chlorophyll a allomer (CHLa allo),
chlorophyll a (CHLa), chlorophyll a epimer (CHLa’), echinenone (ECHIN), beta
carotene (BETA). The taxonomically significant pigment identified for coccoidal and
filamentous cyanobacteria are ZEA and ECHIN respectively (Nichols, 1973). These
pigments are typically photoprotectorant pigments which change in relation to light
intensity, as a result different ratios were seen at each light treatment (Grant and Louda
2010). The LL treatments had CHLa/ZEA and CHLa/ECHIN ratios ranging from 25.14
-29.12 and 15.32 – 22.92 respectively. The ML experiments gave CHLa/ZEA and
CHLa/ECHIN ratios between 16.88 – 21.12 and 17.78 – 27.31 respectively. The HL
experiments had CHLa/ZEA and CHLa/ECHIN ratios ranging from 9.21 – 12.34 and
13.26 – 16.23 respectively. These pigment ratios were not log transformed. See plots of
these relationships in Figure 11 (a - b).
The Levene’s statistical test for homogeneity of variance for the means of the
CHLa/ZEA ratios for the three light groups was not violated, as F (3, 13) = 0.477and p =
0.631. The overall ANOVA was significant, with F (3, 13) = 171.783 and p < 0.001,
indicating that at least one of the means was significantly different from the others.
Tukey’s post hoc follow up tests showed that all of the group means were significantly
different from each other at the 0.05 level.
71
Figure 11: Microcystis aeruginosa (a) CHLa/ZEA v Light Intensity; (b): CHLa/ECHIN v Light Intensity
Levene’s statistical test for homogeneity of variances for the CHLa/ECHIN ratios
over the three light levels was not significant since F (2, 13) = 2.672 and p = 0.107,
indicating that the homogeneity of variances assumption had not been violated. The
overall ANOVA F was significant, with F (2, 13) = 10.152 and p = 0.002. Tukey’s (HSD)
post hoc follow up test showed that the LL (M = 18.68) group was not significantly
different from the ML (M = 22.01) group, with a mean difference of -3.33 and a p value
of 0.170. The LL group was also not significantly different from the HL (M = 14.60)
18.6822.01
14.6
0
5
10
15
20
25
0 50 100 150 200 250
CH
La/
EC
HIN
Light intensity (μmol photons.m-2.s-1)
(b)
26.67
19.35
10.67
0
5
10
15
20
25
30
0 50 100 150 200 250C
HL
a/Z
EA
Light intensity (μmol photons.m-2.s-1)
(a)
72
group, with a mean difference of 4.03 and a p value of 0.068. The ML (M = 22.01) group
was however, significantly different from the HL (M = 14.60) group, with a mean
difference of 7.41 and a p value of 0.002.
Microcystis aeruginosa – protein/CHLa relationships: The LL experiments gave protein
concentrations between 3.36 pg cell-1 and 7.06 pg cell-1 and protein/CHLa ratios from
49.28 to 60.61. The ML experiments resulted in protein concentrations from 3.65 pg cell-
1 and 16.00 pg cell-1; with protein/CHLa ratios between 58.94 and 71.60. The HL
experiments gave protein concentration for this species ranging from 16.80 pg cell-1 to
85.90 pg cell-1 and protein/CHLa ratios between 72.11 and 95.89. All ratios were log
transformed prior to statistical analyses. Regression and dot plots are shown in Figure 12
(a - d).
Levene’s statistic to test for homogeneity of variances in the group means for the
different light treatments was not significant as F (2, 14) = 0.005 and p = 0.995,
indicating that the assumption had not been violated. The overall ANOVA F was
significant, with F (2, 14) = 29.249 and p < 0.001, indicating that at least one of the group
means was significantly different from the others. Tukey’s (HSD) post hoc follow- up
tests showed that all the means were different from each other according to the following
73
Figure 12: Microcystis aeruginosa (a): Protein v CHLa; (b): Protein v Light Intensity; (c): CHLa v Light Intensity
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8 1P
rote
in (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.2788x ‐ 7.5557R² = 0.9626
0
10
20
30
40
50
60
0 50 100 150 200 250
Pro
tein
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
y = 0.0031x ‐ 0.0367R² = 0.9512
0
0.2
0.4
0.6
0.8
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
74
1.6
1.8
2.0
0 50 100 150 200 250
Pro
tein
/CH
La
(log
)
Light Intensity (µmol photons·m-2·s-1)
(d)
LL
ML
HL
Figure 12 contd.: Microcystis aeruginosa (d): Light treatment effect on protein/CHLa ratios (dot plot)
results: The LL (M = 1.73) group was significantly different from the ML (M = 1.80)
group, with a mean difference of -0.72 and a p value of 0.023. The LL (M = 1.73) group.
was also significantly different from the HL (M = 1.91) group, with a mean difference of
-0.18 and a p value < 0.001. The ML (M = 1.80) group was also significantly different
from the HL (M= 1.91) group, with a mean difference of -0.11 and a p value of 0.001
Microcystis aeruginosa– colloidal CHO/CHLa relationships: Colloidal carbohydrate
concentration in the LL experiments ranged from 0.132 pg cell-1 to 0.862 pg cell-1, the
ML had concentrations between 0.122 pg cell-1 and 0.849 pg cell-1, while those in the HL
experiments were between 1.19 pg cell-1 and 6.49 pg cell-1. The colloidal CHO/CHLa
ratios were between 5.09 and 6.56 for the LL experiments, between 5.79 and 8.34 for the
ML experiments and between 5.75 and 7.45 for the HL experiments respectively. The
colloidal CHO/CHLa ratios were log transformed before statistical analyses, in an
75
attempt to meet the homogeneity of variances assumption. Regression and dot plots are
shown in Figure 13 (a - c).
Levene’s test for homogeneity of variances, was not violated as F (2, 14) = 0.596
and p = 0.564. The overall ANOVA was significant, with F (2, 14) = 4.750 and p =
0.027, indicating that at least one of the group means was significantly different from the
others. Tukey’s (HSD) post hoc follow-test gave the following results:
Figure 13: Microcystis aeruginosa (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity
0
2
4
6
8
0 0.2 0.4 0.6 0.8 1Col
loid
al C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.0231x ‐ 0.4487R² = 0.9654
0
1
2
3
4
5
0 50 100 150 200 250Col
loid
al C
HO
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
76
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
0 50 100 150 200 250C
ollo
idal
/CH
La
(log
)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
Figure 13 contd.: Microcystis aeruginosa (c): Light treatment effect on colloidal CHO/CHLa ratios (dot plot)
The LL (M = 0.76) group was significantly different from the ML (M = 0.837) group,
with a mean difference of -0.078 and a p value of 0.048. The LL (M = 0.76) was also
significantly different from the HL (M = 0.841) group, with a mean difference of -0.814
and a p value of 0.037. However, the ML (M = 0.837) was not significantly different
from the HL (M = 0.841) group, with a mean difference of -0.0038 and a p value of
0.990.
Microcystis aeruginosa – storage CHO/CHLa biomass relationships: The LL
experiments gave storage carbohydrate concentrations between 2.19 and 10.85 pg cell-1;
The ML experiments gave concentrations between 4.50 pg cell-1 and 15.54 pg cell-1; The
HL experiments gave concentrations between 23.68 pg cell-1 and 95.62 pg cell-1. The
storage CHO/CHLa ratios were from 75.72 to 89.14 for the LL experiments, from 62.56
and 100.13 for the ML experiments and from 96.29 to 115.74 for the HL experiments
77
1.5
1.7
1.9
2.1
0 50 100 150 200 250
Sto
rage
CH
O/C
HL
a (l
og)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
Figure 14 : Microcystis aeruginosa (a) Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
0
50
100
150
0 0.2 0.4 0.6 0.8 1S
tora
ge C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.345x ‐ 8.0582R² = 0.9473
0
20
40
60
80
0 50 100 150 200 250
Sto
rage
CH
O (
pg c
ell -1
)
Light Intensity (µmol photons·m-2·s-1)
(b)
78
respectively. All ratios were log transformed prior to statistical analyses. Plots of some of
these relationships are shown in Figure 14 (a - c) above.
The Levene statistic was not significant as F (2, 14) = 1.999 and p =0.172,
indicating that that the variances in the group means was equal. The overall ANOVA F
was significant, with F (2, 14) = 8.459 and p = 0.004. Tukey’s (HSD) post hoc follow-up
test gave the following results: the LL (M = 1.919) group was not significantly different
from the ML (M= 1.921) group, with a mean difference of -0.0021 and a p value of
0.997. The LL was significantly different from the HL (M = 2.03) group, with a mean
difference of -0.1078 and a p value of 0.010. The ML (1.921) group was also
significantly different from the HL (M = 2.03) group, with a mean difference of -0.1057
and a p value of 0.008.
Dunaliella tertiolecta (Chlorophyta; green algae): The pigment composition for this
species is as follows: chlorophyllide b (CHLideb), chlorophyllide a (CHLide a),
pyrochlorophyllide a (pCHLidea), neoxanthin (NEO), violaxanthin (VIOLA),
antheraxanthin (ANTH), lutein (LUT), chlorophyll b (CHLb), chlorophyll a allomer
(CHLa allo), chlorophyll a (CHLa), chlorophyll a epimer (CHLa’), pheophytin (PHtin
a), alpha carotene (ALPH), beta carotene (BETA). Typical chromatograms of the eight
species are presented in appendix VIII. The most stable class/pigment group specific
marker was identified to be chlorophyll b (Jeffrey and Vesk 1997). The molar ratios of
chlorophyll a/ chlorophyll b (CHLa/CHLb) in the LL experiments ranged from 2.26 to
2.58, the ML ratios ranged from 2.24 to 2.92 and those for the HL experiments were
79
between 1.77 and 2.41. Figure 15 (a - b) shows the plots of these ratios per culture batch
over the light levels studied.
Levene’s test was done to check the assumption that the variances of the three
light levels are equal. The Levene’s test is not significant, F (2, 12) = 0.622, p = 0.553
and the homogeneity of variance assumption is not violated. However, the F ratio
(ANOVA) is significant at the 0.05 level: F (2, 12) = 4.542, p = 0.034.
Figure 15: Dunaliella tertiolecta (a): CHLa/CHLb v Light Intensity; (b): CHLa/CHLb per batch
2.41 2.542.14
0
0.5
1
1.5
2
2.5
3
0 50 100 150 200 250
CH
La/
CH
Lb
Light Intensity (µmol photons·m-2·s-1)
(a)
0
1
2
3
4
0 2 4 6 8
CH
La/
CH
Lb
culture batches
(b)
LL
ML
HL
80
As the means are significantly different, but homogeneity has not been violated, the
Tukey HSD (honestly significant difference) follow-up tests were done to determine
which means differ from the other. The Tukey HSD test revealed that LL group (M =
2.41) is not significantly different from the ML group (M = 2.54), with a mean difference
of -0.13 and a p value of 0.68. Also the LL group (M = 2.41) is not significantly different
from the HL group (M = 2.14), with a mean difference of 0.27 and a p value of 0.19.
However, the ML group (M = 2.41) is significantly different from the HL (M = 2.14),
with a mean difference of 0.39 and a p value of 0.03.
Dunaliella tertiolecta - protein/CHLa relationships: The LL experiments resulted in
protein content between 48.19 pg cell-1 and 62.32 pg cell-1 and protein/CHLa ratios from
69.91 to 73.02. The protein content of the ML experiments was between 33.07 pg cell-1
and 114.78 pg cell-1, with protein/CHLa ratios from 73.67 to 132.62. Additionally the HL
experiments showed protein content between 109.84 pg cell-1and 163.04 pg cell-1 and
protein/CHLa ratios with a minimum of 376.24 and maximum of 482.36. The
protein/CHLa ratios for the light levels were log transformed in order to meet the
homoscedasticity assumption of the data. Regression and dot plots of some pertinent
relationships are shown in Figure 16 (a - d).
Levene’s test was done prior to one-way ANOVA analysis to check the
assumption that the variances of the three light level experiments are equal. The Levene’s
test is significant: F (2, 12) = 14.618, p = 0.001 at the 0.05 alpha level. Thus, the
assumption of homogeneity is not met. A more robust test for equality of means was
carried out and it was also significant.
81
0
100
200
300
0 0.5 1 1.5P
rote
in (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
Figure 16: Dunaliella tertiolecta (a): Protein v CHLa; (b): Protein v Light Intensity; (c): CHLa v Light Intensity
y = 0.6727x + 24.307R² = 0.985
0
50
100
150
200
0 50 100 150 200 250
Pro
tein
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
y = 0.0013x + 0.6224R² = 0.4358
0
0.2
0.4
0.6
0.8
1
1.2
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
82
Figure 16 contd.: Dunaliella tertiolecta (d): Light treatment effect on protein/CHLa ratios (dot plot) That is: Brown-Forsythe F (2, 9.557) = 10.158, p = 0.004.The one-way ANOVA, F ratio
is also significant: F (2, 12) = 34.08, p < 0.001. Since the assumption of homogeneity was
not met, even after log transformation, the Games-Howell post-hoc follow-up test was
done to assess which of the means from the three groups differed from each other. The
results are as follows: the LL group (M = 1.85) is significantly different from the ML
group (M = 2.04), with a mean difference of -0.19 and a p value of 0.006. The LL (M =
1.85) group is also significantly different from the HL group (M = 2.24), with a mean
difference of -0.39 and a p value < 0.001. Additionally the ML group (M = 2.04) is
significantly different from the HL group (M = 2.24), with a mean difference of - 0.20
and a p value < 0.05.
Dunaliella tertiolecta - colloidal CHO/CHLa relationships: colloidal carbohydrate
content in the LL experiments ranged between 6.11 pg cell-1and 7.38 pg cell-1, while the
ML experiments ranged between 1.31 pg cell-1 and 5.09 pg cell-1, and the HL ranged
between 2.32 pg cell-1 and 7.78 pg cell-1. Additionally, the colloidal CHO/CHLa ratios
1.6
2.1
2.6
0 50 100 150 200 250pr
otei
n/C
HL
a(l
og)
Light Intensity (µmol photons·m-2·s-1)
(d)
LL
ML
HL
83
for the LL experiments were between 7.50 and 9.28, the ML experiments between 3.23
and 5.88, while the HL group had ratios between 3.99 and 7.03. The colloidal
CHO/CHLa ratios for the light levels were log transformed in order to meet the
homogeneity assumption for the data. Regression and dot plots are shown in Figure 17 (a
- c).
Results from the Levene’s test show that it is not significant, with F (2, 12) =
3.748 and p = 0.054. Therefore, the assumption that the variances of the three light levels
are equal can be retained. However, one-way ANOVA gives an F ratio that is significant,
as F (2, 12) = 10.158 and p = 0.004. This resulted in a rejection of the null hypothesis that
the sample means from the light levels are equal. Tukey’s (HSD) post hoc follow-up test
showed the following results: The LL group (M = 0.93) is significantly different from the
ML group (M = 0.65), with a mean difference of 0.28 and a p value of 0.003. Also, the
LL group (M = 0.93) is significantly different from the HL group (M = 0.74), with a
mean difference of 0.19 and a p value of 0.033. However, the HL (M = 0.74) group is not
significantly different from the ML (M = 0.65), with a mean difference of 0.09 and a p
value of 0.309.
84
0.4
0.6
0.8
1
1.2
0 50 100 150 200 250
Col
loid
al/C
HL
a (l
og)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
Figure 17: Dunaliella tertiolecta (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity; (c): Light treatment effect on Colloidal CHO/CHLa ratios (dot plot)
0
2
4
6
8
10
0 0.5 1 1.5
Col
loid
al C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = ‐0.0013x + 5.0693R² = 0.0035
0
2
4
6
8
0 50 100 150 200 250Col
loid
al C
HO
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
85
Dunaliella tertiolecta - storage CHO/CHLa relationships: The LL experiments had
storage carbohydrate content between 44.68 and 50.45 pg cell-1, while the storage
CHO/CHLa ratios were between 57.23 and 64.82. The ML experiments had storage
carbohydrate content from 32.10 pg cell-1 to 84.28 pg cell-1 and the storage CHO/CHLa
ratios were between 71.50 and 97.33. The HL experiments had storage carbohydrate
content from a minimum of 79.01 pg cell-1 to a maximum of 116.56 pg cell-1, while the
storage CHO/CHLa ratios ranged from 93.50 to 115.57. Plots of these relationships are
shown in Figure 18 (a - c). The storage CHO/CHLa ratios were log transformed in order
to meet the homogeneity assumption for the data.
Levene’s test indicated that assumption of homogeneity of variance had not been
violated, that is: F (2, 12) = 1.080, p = 0.370. One - way ANOVA results showed that at
least one of the group means was significantly different from the others, indicating
rejection of the null hypothesis: F (2, 12) = 50.279, p < 0.001. The Tukey (HSD) post hoc
analysis was done, and showed that all three group means were significantly different: the
LL group (M = 1.78) is significantly different from the ML group (M = 1.93), with a
mean difference of - 0.16and a p value < 0.001. Also, the LL group (M = 1.78) is
significantly different from the HL group (M = 2.04), with a mean difference of -0.26 and
a p value < 0.001. The ML group (M = 1.93) is significant from the HL group (M =
2.04), with a mean difference of- 0.10 and a p value of 0.003.
86
1.5
1.6
1.7
1.8
1.9
2
2.1
0 50 100 150 200 250
Sto
rage
CH
O/C
HL
a(l
og)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
Figure 18 : Dunaliella tertiolecta (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
0
50
100
150
0 0.5 1 1.5S
tora
ge C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.2981x + 37.284R² = 0.9029
0
20
40
60
80
100
120
0 50 100 150 200 250Sto
rage
CH
O (
pg c
ell-
1)
Light Intensity (µmol photons·m-2·s-1)
(b)
87
Scenedesmus quadricauda (Chlorophyta: green algae): The pigment composition for
this species is as follows: chlorophyllide b (CHLideb), chlorophyllide a (CHLide a),
pyrochlorophyllide a (pCHLidea), neoxanthin (NEO), violaxanthin (VIOLA),
antheraxanthin (ANTH), lutein (LUT), chlorophyll b (CHLb), chlorophyll a allomer
(CHLa allo), chlorophyll a (CHLa), chlorophyll a epimer (CHLa’), pheophytin (PHtin
a), BETA carotene (BETA). Typical chromatograms of the eight species are presented in
appendix VIII. The most stable class/pigment group specific marker was identified to be
chlorophyll b (Jeffrey and Vesk 1997). The molar ratios of chlorophyll a/ chlorophyll b
(CHLa/CHLb) in the LL experiments ranged from 2.52 to 2.88, ML experiment ratios
ranged from 2.07 to 2.92 and those for the HL experiments were between 1.98 and 3.18.
Plots of these ratios per culture batch, over the light levels studied are shown in Figure 19
(a - b).
The Levene’s test was done prior to one-way ANOVA to test the homogeneity
assumption. This test showed that the homogeneity assumption had not been violated: F
(2, 14) = 3.647, p = 0.053. Going further, the one-way ANOVA results showed that null
hypothesis can be retained, as F (2, 14) = 0.355 and p = 0.708. That is, the CHLa: marker
pigment ratios were not significantly different over the three light treatments.
88
0
1
2
3
4
0 2 4 6 8
CH
La/
CH
Lb
culture batches
(b)
LL
ML
HL
Figure 19: Scenedesmus quadricauda (a): CHLa/CHLb v Light Intensity; (b): CHLa/CHLb ratios per batch
Scenedesmus quadricauda - protein/CHLa relationships: The LL experiments resulted in
protein concentrations between 13.48 pg cell -1 and 93.34 pg cell -1 and protein/CHLa
ratios between 488.57 and 854.70. The ML experiments gave protein concentrations
between 50.50 pg cell -1 and 189 pg cell-1, with protein/CHLa ratios from 164.78 to
236.13. The HL experiments had protein concentrations between 489 pg cell -1 and 1239
2.74 2.69 2.57
0
0.5
1
1.5
2
2.5
3
3.5
0 50 100 150 200 250C
HL
a/C
HL
b
Light Intensity (µmol photons·m-2·s-1)
(a)
89
Figure 20: Scenedesmus quadricauda (a): Protein v CHLa; (b): Protein v Light Intensity (c): CHLa v Light Intensity
0
500
1000
1500
0 5 10 15P
rote
in (p
g ce
ll-1
)CHLa (pg cell-1)
(a)
LL
ML
HL
y = 4.2189x ‐ 139.71R² = 0.9854
0
200
400
600
800
0 50 100 150 200 250
Pro
tein
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
y = 0.0272x + 0.765R² = 0.5739
0
2
4
6
8
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
90
1.7
2.2
2.7
3.2
0 50 100 150 200 250P
rote
in/C
HL
a
Light Intensity (µmol photons·m-2·s-1)
(d)
LL
ML
HL
Figure 20 contd.: Scenedesmus quadricauda (d): Light treatment effect on protein/CHLa ratios (dot plot)
pg cell -1 and protein/CHLa ratios from 118.12 and 137.19. Plots of some of these
relationships are shown in Figure 20 (a - d). Protein/CHLa ratios were log transformed
prior to statistical analysis to test the homogeneity of variances assumption.
Levene’s test was significant, with F (2, 15) = 15.945 and p < 0.001, indicating
that the assumption was violated. A robust test of equality of means was done using the
Brown-Forsythe statistic, but the null hypothesis that the variances are equal was still
violated. This indicated that at least one of the group means was significantly different
from the others. That is, the adjusted F (2, 5.449) equaled 81.411 and p < 0.001. One-
way ANOVA also resulted in a rejection of the null hypothesis, as F (2, 15) = 99.450 and
p < 0.001. Since the homogeneity of variances assumption was not met, the Games-
Howell post hoc follow up test was done to assess which of the group means was
significant from the others. The test showed that the LL (M= 2.77) group was
significantly different from the ML (M= 2.26) group with a mean difference of 0.52 and a
91
p value of 0.001. The LL (M=2.27) was also significantly different from the HL
(M=2.10) group with a mean difference of 0.67 and a p value of 0.001. The ML
(M=2.26) group was also significantly different from the HL (M=2.10) group, with a
mean difference of 0.15 and a p value of 0.001.
Scenedesmus quadricauda – colloidal CHO/CHLa relationships: The LL experiments
had colloidal carbohydrate concentration ranging from 2.85 pg cell-1 to 19.65 pg cell-1
and colloidal CHO/CHLa ratios from 51.31 and 179.91. The ML colloidal concentration
range was between 1.88 pg cell-1 and 6.94 pg cell-1 and colloidal CHO/CHLa ratios from
6.12 to 10.06. The HL experiments gave colloidal CHO concentration between 30.06 pg
cell-1 and 74.55 pg cell-1 and colloidal CHO/CHLa ratios from 6.75 to 7.87. Plots of
pertinent relationships are given in Figure 21(a - c). The ratios were log transformed prior
to statistical analyses, to assess the assumption that variances in the group means (three
light levels) were homogeneous.
The Levene’s test was significant, with F (2, 15) = 4.723 and p = 0.026, indicating
rejection of the null hypothesis. A more robust test for equality of means was done and
this was also significant. That is, the Browne- Forsythe analysis yielded F (2, 5.939) =
128.928 and p < 0.001. The overall one-way ANOVA F was also significant, with F (2,
15) = 152.592 and p < 0.001. This indicated that the null hypothesis that the three group
means are equal would be rejected. Since the homogeneity of variances had been
violated, the Games- Howell post hoc follow-up test was done to assess which of the
group means was significantly different from the others. The test showed that the LL (M
92
0.5
1
1.5
2
2.5
0 50 100 150 200 250
Col
loid
al C
HO
/CH
La
(log
)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
Figure 21: Scenedesmus quadricauda (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity; (c): Light treatment effect on colloidal CHO/CHLa ratios (dot plot)
‐20
0
20
40
60
80
100
0 2 4 6 8 10 12C
ollo
idal
CH
O (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.2215x ‐ 3.9731R² = 0.9106
0
10
20
30
40
50
0 50 100 150 200 250Col
loid
al C
HO
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
93
= 1.996) group was significantly different from the ML (M= 0.863), with a mean
difference of 1.13 and a p value < 0.001. The LL (M =1.996) group was significantly
different from the HL (M = 0.868) group, with a mean difference of 1.13 and a p value <
0.001.The ML (M = 0.863) was however not significantly different from the HL (M =
0.868) group, with a mean difference of -0.005 and a p value of 0.992.
Scenedesmus quadricauda – storage CHO/CHLa relationships: The storage carbohydrate
concentrations in LL experiments ranged from 13.62 pg cell-1 to 71.51 pg cell-1 and had
storage CHO/CHLa ratios between 493.89 and 642.97. The ML experiments had storage
carbohydrate concentrations from 11.79 pg cell-1 to 35.40 pg cell-1 and storage
CHO/CHLa ratios ranging from 26.98 to 47.67. The HL experiments resulted in storage
carbohydrate concentration between 69.46 pg cell-1 and 1925.25 pg cell-1 and storage
CHO/CHLa ratios from 16.58 to 20.09. Ratios were log transformed before statistical
analysis was carried out in order to meet the homogeneity of variances assumption. Plots
of some of these relationships are shown in Figure 22 (a – c).
Levene’s test of homogeneity of variances was significant, with F (2, 15) = 5.098
and p = 0.020. This indicated a violation of the homogeneity of means assumption. A
more robust test for equality of means was also significant. That is, the Brown-Forsythe
model gave F (2, 10.107) = 93.945 and p < 0.001. The overall F ANOVA was also
significant, as F (2, 15) = 86.701 and p < 0.001. This indicated that at least one of the
group means was significantly different from the others. Games-Howell post hoc follow-
up test indicated that all of the group means from the three light treatments were
94
Figure 22: Scenedesmus quadricauda (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
0
50
100
150
200
250
‐5 0 5 10 15
Sto
rage
CH
O (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.4183x + 17.118R² = 0.8061
0
20
40
60
80
100
120
0 50 100 150 200 250
Sto
rage
CH
O (
pg c
ell-1
)
Light Intensity (µmol photons·m-2·s-1)
(b)
1
1.5
2
2.5
3
3.5
0 1 2 3 4
Sto
rage
CH
O/C
HL
a
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
95
different. That is, the LL(M = 3.03) group was significantly different from the ML (M =
1.74) group, with a mean difference of 1.29 and a p value < 0.001. The LL (M = 3.03)
was also significantly different from the HL (M = 1.27) group with a mean difference of
1.76 and a p value < 0.001. The ML (M = 1.74) group was significantly different from
the HL (M = 1.27) group with a mean difference of 0.47 and a p value of 0.015.
Rhodomonas salina (Cryptophyta, cryptomonad): The pigments identified from
spectroscopic and chromatographic analyses for this species are: chlorophyllide a
(CHLidea), Chlorophylls c1/c2 (CHLs c1/c2) pyrochlorophyllide a (pCHLidea),
alloxanthin (ALLO), monadoxanthin (MONADO) chlorophyll a allomer (CHLa allo),
chlorophyll a (CHLa), chlorophyll a epimer (CHLa’), and alpha carotene (ALPH). The
taxonomically significant pigment identified for cryptomonads is ALLO (Gieskes and
Kraay, 1983). The molar ratios of chlorophyll a / alloxanthin (CHLa/ALLO) in the LL
experiments ranged from 2.37 to 3.03, ML experiment ratios ranged from 2.26 to 3.03
and those for the HL experiments were between 2.42 and 2.67. Plots of these ratios per
batch culture and light level are shown in Figure 23 (a - b). Typical chromatograms of the
species investigated in this study are shown in appendix VIII. These pigment ratios were
not log transformed.
Levene’s statistical test for homogeneity of variance for the means of the
CHLa/ALLO ratios for the three light groups was not violated, as F (3, 14) = 1.455 and p
= 0.267.
96
Figure 23: Rhodomonas salina (a): CHLa/ALLO v Light Intensity; (b): CHLa/ALLO per batch
The overall ANOVA was not significant, with F (2, 14) = 0.163 and p = 0.851, indicating
that the group means from the three light treatments were the same. Tukey’s post hoc
follow up tests further showed that none of the group means were significantly different
from each other at the 0.05 level.
Rhodomonas salina – protein/CHLa biomass relationships: The LL experiments gave
protein concentrations between 7.05 pg cell-1 and 20.55 pg cell-1; The ML experiments
gave protein concentrations from 24.76 pg cell-1 to 46.62 pg cell-1; The HL experiments
showed this species having protein concentrations between 122.87 pg cell-1 and 195.66
0
1
2
3
4
0 2 4 6 8
CH
La/
AL
LO
batch
(b) LL
ML
HL
2.59 2.61 2.54
0
1
2
3
0 50 100 150 200 250C
HL
a/A
LL
O
Light Intensity (µmol photons·m-2·s-1)
(a)
97
pg cell-1. The protein/CHLa ratios ranged from 265.96 to 299.52 for the LL experiments;
227.50 to 307.50 for the ML experiments and 307.31 to 414.01 for the HL experiments.
The ratios were log transformed prior to statistical analyses. Regression and dot plots of
some of these biomass relationships are shown in Figure 24 (a - d).
Levene’s statistic for homogeneity of variances was not significant, with F (2, 13)
= 1.955 and p = 0.181, suggesting that the null hypothesis that the group mean variances
are equal can be retained. The overall ANOVA F was significant, as F (2, 13) = 17.464
and p = 0.001, indicating that at least one of the group means was significantly different
from the others. Tukey’s (HSD) post hoc follow-up test showed that the LL (M = 2.45)
group was not significantly different from the ML (M = 2.44) group, with a mean
difference of 0.010 and a p value of 0.913. The LL (M= 2.45) group was significantly
different from the HL (M = 2.55) group, with a mean difference of -0.108 and a p value
of 0.001. The ML (M = 2.44) group was also significant from the HL (M = 2.55) group,
with a mean difference of -0.11 and a p value < 0.001.
98
Figure 24: Rhodomonas salina (a) Protein v CHLa; (b): Protein v Light Intensity; (c): CHLa v Light Intensity
0
50
100
150
200
250
0 0.2 0.4 0.6 0.8P
rote
in (p
g ce
ll-1
)
CHLa (pg cell-1)
(a) LL
ML
HL
y = 0.9887x ‐ 31.175R² = 0.9907
0
50
100
150
200
0 50 100 150 200 250
Pro
tein
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
y = 0.0027x ‐ 0.0631R² = 0.9984
0
0.1
0.2
0.3
0.4
0.5
0.6
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
99
2.2
2.4
2.6
2.8
0 50 100 150 200 250
Pro
tein
/CH
La
(log
)
Light Intensity (µmol photons·m-2·s-1)
(d)
LL
ML
HL
Figure 24 contd.: Rhodomonas salina (d): Light treatment effect on protein/CHLa ratios (dot plot)
Rhodomonas salina – colloidal CHO/CHLa relationships: The LL experiments had
colloidal carbohydrate concentrations between 0.78 pg cell-1 and 2.50 pg cell-1, with
colloidal CHO/CHLa ratios from 23.26 to 36.47. The ML experiments had colloidal
carbohydrate concentrations ranging from 2.23 pg cell -1 to 3.97 pg cell-1 and colloidal
CHO/CHLa ratios from 23.51 to 26.59. The HL experiments showed concentration
between 5.71 pg cell-1 and 12.38 pg cell-1, with colloidal CHO/CHLa ratios from 17.52 to
23.53. All ratios were log transformed prior to statistical tests. Regression and dot plots
of some of these relationships are shown in Figure 25 (a - c).
Levene’s test for homogeneity of variances was not significant, with F (2, 13) =
1.815 and p = 0.202, indicating that the null hypothesis that the variances of the means
from the three light experiments are equal can be retained. The overall ANOVA F was
significant, as F (2, 13) = 10.929 and p = 0.002, indicating that at least one of the group
means was different from the others. Tukey’s (HSD) post hoc follow-up tests showed that
100
1.2
1.3
1.4
1.5
1.6
0 50 100 150 200 250Col
loid
al C
HO
/CH
La
(log
)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
Figure 25: Rhodomonas salina (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity; (c): Light treatment effect on colloidal CHO/CHLa ratios (dot plot)
0
5
10
15
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7Col
loid
al C
HO
(pg
cell
-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.0494x ‐ 0.3851R² = 0.9982
0
2
4
6
8
10
12
0 50 100 150 200 250
Col
loid
al C
HO
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
101
the LL (M = 1.46) group was not significantly different from the ML (M = 1.38) group,
with a mean difference of 0.08 and a p value of 0.105. The LL (M = 1.46) was
significantly different from the HL (M = 1.29) group, with a mean difference of 0.16 and
a p value of 0.001. The ML (M = 1.38) group was not significantly different from the HL
(M = 1.29) group with a mean difference of 0.082 and a p value of 0.083.
Rhodomonas salina – storage CHO/CHLa relationships: The LL experiments had storage
carbohydrate concentrations between 4.14 pg cell-1 and 14.40 pg cell-1, with storage
CHO/CHLa ratios from 111.28 to 209.90. The ML experiments resulted in storage
carbohydrate concentrations between 9.61 pg cell-1 and 21.27 pg cell-1 and storage
CHO/CHLa ratios from 82.96 – 140.15. The HL experiments had storage carbohydrate
concentrations between 47.57 pg cell-1 and 89.54 pg cell-1 and storage CHO/CHLa ratios
from 138.40 to 160.28. All ratios were log transformed before statistical analyses. Plots
illustrating some of these relationships are shown in Figure 26 (a - c).
Levene’s statistic to test the homogeneity of variances assumption was
significant, giving F (2, 13) = 4.292 and p = 0.037. This indicated that the null hypothesis
could be retained. The overall ANOVA F was significant, with F (2, 13) = 16.436 and p <
0.001, suggesting that at least one of the group means was significantly different from the
others. Tukey’s (HSD) post hoc follow-up test showed that the LL (M= 2.004) was not
significantly different from the ML (M= 1.956) group, with a mean difference of 0.048
and a p value of 0.663. The LL (M= 2.004) was significantly different from the HL (M =
2.233) group with a mean difference of - 0.229 and a p value of 0.002. The ML (1.956)
102
Figure 26: Rhodomonas salina (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
1.6
1.8
2
2.2
2.4
0 50 100 150 200 250
Sto
rage
CH
O/C
HL
a(l
og)
Light Intensity (µmol photons·m-2·s-1)
c) LL
ML
HL
0
20
40
60
80
100
0 0.2 0.4 0.6 0.8S
tora
ge C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a) LL
ML
HL
y = 0.4073x ‐ 12.027R² = 0.9811
0
20
40
60
80
0 50 100 150 200 250Sto
rage
CH
O (
pg c
ell-1
)
Light Intensity (µmol photons·m-2·s-1)
(b)
103
was also significantly different from the HL (M= 2.233) group, with a mean difference of
- 0.277 and a p value < 0.001.
Cyclotella meneghiniana (Bacillarophyta; diatom): The pigment composition for this
species is as follows: chlorophyllide a (CHLidea), Chlorophylls c1/c2 (CHLs c1/c2)
pyrochlorophyllide a (pCHLidea), fucoxanthinol (FUCOL), fucoxanthin (FUCO), cis-
fucoxanthin (cis-FUCO), diadinoxanthin (Diad), diatoxanthin (Diato), phytylated-type
chlorophyll c, (Phyt chlc), chlorophyll a allomer (CHLa allo), chlorophyll a (CHLa),
chlorophyll a epimer (CHLa’), pheophytin a (pHtin a), beta carotene (BETA). Typical
chromatograms of the eight species are presented in appendix IV. The most stable
class/pigment group specific marker was identified to be FUCO (Stauber and Jeffrey,
1988). The molar ratios of CHLa/FUCO in the LL experiments ranged from a minimum
of 1.02 to a maximum of 1.56, ML experiment ratios ranged from 1.07-1.20 and those for
the HL experiments were between 0.99 and 1.28. Plots of these ratios per culture batch,
over the light levels studied, are illustrated in Figure 27 (a - b).
Levene’s test for equality of variances was not significant, with F (2, 14) = 2.640
and p = 0.0.106. Thus the assumption that the variances in the means are homogeneous
was not violated. The one-way ANOVA overall F ratio was not significant, as F (2, 14)
= 0.469 and p = 0.635. Thus, the null hypothesis that the means are equal was accepted.
104
1.18 1.12 1.11
0
0.5
1
1.5
2
0 50 100 150 200 250C
HL
a/F
UC
OLight Intensity (µmol photons·m-2·s-1)
(a)
Figure 27: Cyclotella meneghiniana (a): CHLa/FUCO v Light Intensity; (b): CHLa/FUCO per batch
Cyclotella meneghiniana – protein/CHLa relationships: the protein content at the LL
experiments for this species ranged from 7.23 pg cell-1 to 91.54 pg cell-1; the ML
experiments had protein content between 9.23 pg cell-1 and 155.76 pg cell-1 and the HL
experiments had a minimum of 14.76 pg cell-1 to a maximum of 91.54 pg cell-1 protein.
The protein/CHLa ratios for the LL experiments were from 90.20 – 105.06, those in the
ML experiments were from 43.85-106.08 and those in the HL experiments ranged from
122.57 -172.74. The protein/CHLa ratios for the three light levels were log transformed
0
0.5
1
1.5
2
0 2 4 6 8
CH
La/
FUC
O
culture batches
(b) LL
ML
HL
105
in an attempt to prevent violation of the homogeneity assumption. See regression and dot
plots in Figure 28 (a - d).
Figure 28: Cyclotella meneghiniana (a): Protein v CHLa; (b): Protein v Light Intensity
0
100
200
300
400
500
0 1 2 3
Pro
tein
(pg
cel
l-1)
CHLa (pg cell -1)
(a)
LL
ML
HL
y = 1.1459x ‐ 22.133R² = 0.9791
0
50
100
150
200
250
0 50 100 150 200 250
Pro
tein
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
106
Figure 28 contd.: Cyclotella meneghiniana (c): CHLa v Light Intensity; (d): Light treatment effect on protein/CHLa ratios (dot plot)
Levene’s test for homogeneity was not significant, with F (2, 15) = 0.049 and p =
0.952. Thus, the homogeneity of variance assumption was not violated. The overall one-
way ANOVA gave an F (2, 15) = 24.596 and p < 0.001 that is significant, indicating that
the null hypothesis was to be rejected. Tukey’s (HSD) post hoc follow test showed that
all the means were significantly different at the 0.05 level. That is, the LL group (M =
1.996) was significantly different form the ML group (M = 2.07), with a mean difference
of - 0.08 and a p value of 0.009. The LL group (M = 1.996) was also significantly
y = 0.0064x + 0.1376R² = 0.9293
0
0.5
1
1.5
2
0 50 100 150 200 250C
HL
a (p
g ce
ll-1
)
Light Intensity (µmol photons·m-2·s-1)
(c)
107
different from the HL group (M = 2.17), with a mean difference of -0.17 and a p value <
0.001. Additionally, the HL group (M = 2.17) was significantly different from the ML
group (M = 2.07), with a mean difference of 0.09 and a p value of 0.003.
Cyclotella meneghiniana – colloidal CHO/CHLa relationships: The colloidal
carbohydrate content at for the LL experiments were from 0.63 pg cell-1 to 2.60 pg cell-1,
while the colloidal CHO /CHLa ratios ranged from 7.09 to 9.50. The ML experiments
had colloidal carbohydrate concentrations from 0.16 pg cell-1 to 16.23 pg cell-1, with
colloidal CHO/CHLa ratios from 10.36 to 16.21. The HL experiments had concentrations
from 11.50 pg cell-1 to 48.86 pg cell-1 and ratios ranging from a minimum of 19.89 to
28.33. Prior to statistical analyses, the colloidal CHO/CHLa ratios for the three light
levels were log transformed in an attempt to prevent violation of the homogeneity
assumption. Regression and dot plots are shown in Figure 29 (a - c).
Levene’s test to check the assumption that the variances from three light levels are
equal was not significant, giving F (2, 15) = 0.520, p = 0.605. This indicated that the
homogeneity assumption was not violated. However, the overall F ANOVA was
significant, as F (2, 15) = 78.199 and p < 0.001. This indicated a rejection of the null
hypothesis, since the means were different. Tukey’s (HSD) post hoc follow up tests
showed that the LL group (M = 0.92) was significantly different from the ML group (M =
1.08), with a mean difference of -0.16 and a p value of 0.002. The LL group (M = 0.92)
was significantly different from the HL (M = 1.38), with a mean difference of -0.47 and p
108
Figure 29: Cyclotella meneghiniana (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity; (c): Light treatment effect on colloidal CHO/CHLa ratios (dot plot)
‐10
0
10
20
30
40
50
60
‐0.5 0 0.5 1 1.5 2 2.5 3
Col
loid
al C
HO
(pg
cel
l -1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.2005x ‐ 6.8628R² = 0.9952
0
10
20
30
40
0 50 100 150 200 250
Col
loid
al C
HO
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
109
< 0.001 The ML group (M = 1.08) was also significantly different from the HL group (M
= 1.38), with a mean difference of -0.30 and a p value < 0.001.
Cyclotella meneghiniana – storage CHO/CHLa relationships: The storage carbohydrate
concentration for the LL experiments ranged from 0.11 pg cell-1 to 6.44 pg cell-1, with
storage CHO/CHLa ratios from 37.99 to 80.32. The ML experiments showed storage
carbohydrate concentrations ranging from 0.18 pg cell-1 to 204.33 pg cell-1 and storage
CHO/CHLa ratios from 52.78 to 109.62. The HL experiments had concentrations from
118.87 pg cell-1 to 575.01 pg cell-1 and ratios from 200.21 to 277.44. The storage
CHO/CHLa ratios were log transformed in an attempt to maintain the homogeneity of
variance assumption, prior to conducting statistical analyses. Regression and dot plots are
shown in Figure 30 (a - c).
Levene’s test was not significant, with F (2, 15) = 3.009 and p = 0.080, indicating
that the assumption of homogeneity of variance was not violated. The overall ANOVA F
was significant, with F (2, 15) = 65.101 and p < 0.001. This indicated that at least one of
the group means was significantly different from the others. Tukey’s (HSD) post hoc
follow up test was done to identify how different the group means were from each other.
The test showed that the LL group (M = 1.75) was significantly different from the ML
(M = 2.14), with a mean difference of - 0.39 and a p value < 0.001. The LL group (M =
1.75) was significantly different from the HL group (M = 2.38), with a mean difference
of -0.63 and a p value < 0.001. The ML group (M = 2.14) was also significantly different
from the HL group (M = 2.38), with a mean difference of - 0.24and a p value of 0.001.
110
1.4
1.6
1.8
2
2.2
2.4
2.6
0 50 100 150 200 250stor
age
CH
O/C
HL
a(l
og)
Light Intensity (µmol photons·m-2·s-1)
(c)LL
ML
HL
Figure 30: Cyclotella meneghiniana (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
0
200
400
600
800
0 0.5 1 1.5 2 2.5 3S
tora
ge C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 2.1031x ‐ 81.291R² = 0.9912
‐100
0
100
200
300
400
0 50 100 150 200 250Sto
rage
CH
O (
pg c
ell-1
)
Light Intensity (µmol photons·m-2·s-1)
(b)
111
Thalassiosira Weissflogii ( Bacillariophyceae; diatom): The pigment composition for
this species was as follows: chlorophyllide a (CHLidea), Chlorophylls c1/c2 (CHLs c1/c2)
pyrochlorophyllide a (pCHLidea), fucoxanthinol (FUCOL), fucoxanthin (FUCO), cis
fucoxanthin (cis-FUCO), diadinoxanthin (Diad), diatoxanthin (Diato), phytolated-type
chlorophyll c, (Phyt chlc), chlorophyll a allomer (CHLa allo), chlorophyll a (CHLa),
chlorophyll a epimer (CHLa’), pheophytin a (PHtin a), beta carotene (BETA). Typical
chromatograms of the eight species are presented in VIII. The most stable class/pigment
group specific marker was identified to be FUCO (Stauber and Jeffrey 1988). The molar
ratios of CHLa/FUCO in the LL experiments ranged from 1.11 to of 1.19, ML
experiment ratios ranged from 1.14 to 1.20 and those for the HL experiments were
between 1.12 and 1.19. See Figure 31(a - b) for plots of these ratios per culture batch over
the light levels studied.
Statistical analyses was carried out determine if the variances in the ratios could
be partitioned within the variances of the samples and between the different sample
groups. That is, test the null hypothesis that the group means from all three light
treatments are equal. All tests were carried out at the 0.05 level. Levene’s test is not
significant, as F (2, 14) = 0.396 and p = 0.680. Thus the assumption of homogeneity of
the group means was not violated. The one-way ANOVA overall F ratio was not
significant, as F (2, 14) = 1.787and p = 0.219. Therefore, the null hypothesis that the
means were equal was accepted.
112
Figure 31: Thalassiosira weissflogii (a): CHLa/FUCO v Light Intensity; (b): CHLa/FUCO per batch
Thalassiosira weissflogii – protein/CHLa relationships: The LL experiments gave protein
concentration in this species between 25.89 pg cell-1 and 115.96 pg cell -1, while the
protein/CHLa ratios ranged from 137.37 to 186.62. The ML experiments had protein
concentration from 18.09 pg cell-1 to 124.46 pg cell -1, with protein/CHLa ratios between
166.40 and 195.76. The HL experiments had protein concentration ranging from 60.50 pg
cell-1 to 489 pg cell-1 and protein/CHLa ratios from 192.03 to 238.06. All ratios
were log transformed prior to statistical analysis. Plots are shown in Figure 32 (a - d).
1.14 1.17 1.15
0
0.5
1
1.5
2
0 50 100 150 200 250C
HL
a/F
UC
O
Light Intensity (µmol photons·m-2·s-1)
(a)
1
1.2
1.4
0 2 4 6 8
CH
La/
FUC
O
Culture batches
(b)
LL
ML
HL
113
Figure 32: Thalassiorira weissflogii (a): Protein v CHLa; (b): Protein v Light Intensity; (c):CHLa v Light Intensity;
‐100
0
100
200
300
400
500
600
0 1 2 3P
rote
in (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.0651x ‐ 3.0734R² = 0.9441
‐2
0
2
4
6
8
10
12
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
y = 0.9343x + 18.087R² = 0.9052
0
50
100
150
200
250
0 50 100 150 200 250
Pro
tein
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
114
Figure 32 contd.: Thalassiorira weissflogii (d): Light treatment effect on protein/CHLa ratios (dot plot)
Levene’s test for homogeneity of variance was not significant, as F (2, 13) =
1.231 and p = 0.324. This indicated that the null hypothesis that variance in the group
means over the three light treatment levels were the same. However, the overall ANOVA
F was significant, with F (2, 13) = 13.124 and p = 0.001. Tukey’s (HSD) post hoc follow-
up test was next done to assess which of the group means was significantly different from
the others and gave the following results: The LL (M= 2.21) group was not significantly
different from the ML (M= 2.25) group with a mean difference of -0.046 and a p value of
0.185. The LL (M= 2.21) was significantly different from the HL (M = 2.33) group, with
a mean difference of -0.118 and a p value of 0.001. The ML (2.25) group was also
significantly different from the HL (M = 2.33) with a mean difference of -0.072 and p
value of 0.022.
Thalassiosira weissflogii – colloidal CHO/CHLa relationships: The LL experiments
resulted in colloidal carbohydrate (CHO) concentrations of 1.15 pg cell-1 to 6.22 pg cell-1
and colloidal CHO/CHLa ratios between 6.31 and 10.56. The ML experiments had
115
colloidal CHO concentrations from 1.23 pg cell-1 to 7.53 pg cell-1 and colloidal
CHO/CHLa ratios between 11.33 and 12.29. The HL experiments gave colloidal CHO
concentrations between 5.37pg cell-1 and 41.77 pg cell-1 and colloidal CHO/CHLa ratios
from 14.26 to 17.68. All ratios were log transformed prior to statistical analyses. Plots of
the ratio relationships are shown in Figure 33 (a - c).
Figure 33 : Thalassiosira weissflogii (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity
0
10
20
30
40
50
0 1 2 3
Col
loid
al C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.0834x ‐ 1.0306R² = 0.9288
0
5
10
15
20
0 50 100 150 200 250
Col
loid
al C
HO
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
116
Figure 34 contd.: Thalassiosira weissflogii (c): Light treatment effect on colloidal CHO/CHLa ratios (dot plot)
Levene’s test was significant, as F (2, 13) = 5.685 and p = 0.017, indicating that
the homogeneity of variance assumption had been violated. A more robust test of equality
of means was done and it was also significant. That is, Brown-Forsythe gave F (2, 5.732)
= 41.465 and p < 0.001. The overall ANOVA F was also significant, with F (2, 13) =
43.920 and p <0.001, indicating that at least one of the group means was significantly
different from the others. The Games-Howell post hoc follow-up test showed that all the
group means were different, according to the following results: The LL (M= 0.892) group
was significantly different from the ML (M=1.07) group, with a mean difference of -
0.1802 and a p value of 0.020. The LL (M= 0.892) group was also significantly different
from the HL (M =1.202) group, with a mean difference of -0.310 and a p value of 0.001.
The ML (M = 1.07) group was significantly different from the HL (M = 1.202) group,
with a mean difference of -0.1301 and a p value < 0.001.
Thalassiosira weissflogii – storage carbohydrate (CHO)/CHLa relationships: The storage
carbohydrate concentrations for the LL experiments were between 12.24 and 90.84 pg
117
cell-1, and the storage CHO/CHLa ratios ranged from 88.19 to 121.55. The ML
experiments had storage CHO concentrations from 17.94 to 107.78 pg cell-1 and storage
CHO/CHLa ratios between 125.93 and 169.52. The HL light experiments had storage
CHO concentration from 73.40 to 238 pg cell-1 and storage CHO/CHLa ratios from
156.04 to 236.27. All ratios relationships were log transformed before statistical analysis
in an attempt to meet the homogeneity of variances assumption. Regression and dot plots
are shown in Figure 34 (a - c).
Levene’s test for homogeneity of variances was not significant, with F (2, 13) = 0.358
and p = 0.706, indicating that the assumption had not been violated. However, the overall
ANOVA F was significant, with F (2, 13) = 25.271 and p < 0.001. Tukey’s (HSD) post
hoc follow-up test was then done to identify which of the group means was significantly
different from the others. The results showed that the LL (M = 2.03) group was
significantly different from the ML (M = 2.16) group, with a mean difference of -0.12
and a p value of 0.014. The LL (M= 2.03) group is also significantly different from the
HL (M = 2.29) group, with a mean difference of- 0.26 and a p value < 0.001. The ML (M
= 2.16) is significantly different from the HL (M = 2.29) group, with a mean difference of
-0.131 and a p value of 0.008.
118
Figure 35: Thalassiosira weissflogii (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity; (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
‐200
0
200
400
600
800
0 1 2 3
Sto
rage
CH
O (
pg c
ell-1
)CHLa (pg cell-1)
(a)
LL
ML
HL
y = 1.0524x ‐ 4.4881R² = 0.9382
0
50
100
150
200
250
0 50 100 150 200 250
Sto
rage
CH
O (
pg c
ell -1
)
Light Intensity (µmol photons·m-2·s-1)
(b)
1.8
2
2.2
2.4
2.6
0 50 100 150 200 250
Sto
rage
CH
O/C
HL
a
Light Intensity (µmol photons·m-2·s-1)
(c)LL
ML
HL
119
Amphidinium carterae (Dinophyta; dinoglagellate): The main pigments identified from
the chromatographic plots and spectral analyses for this species were: two glycosylated
carotenoids (P-457and P-468), Chlorophylls c1/c2 (CHLs c1/c2), Peridinin (PER),
Dinoxanthin (DINO), Diadinoxanthin (DD), Chlorophyll a allomer (CHLa allo),
Chlorophyll a (CHLa), Chlorophyll a epimer (CHLa’), and Beta Carotene (BETA).
Appendix IV shows typical chromatograms of the eight species investigated in this study.
The marker pigment for this species was determined to be PER (Jeffery et al., 1997). The
molar ratios of chlorophyll a/ peridinin (CHLa/PER) in the LL experiments ranged from
0.92 to 1.15, ML ratios ranged from 1.12 to 1.49 and those for the HL experiments were
between 0.80 and 1.48. Plots of these ratios per culture batch, over the light levels
studied, are shown in Figure 35 (a - b).
Levene’s test is not significant, with F (2, 14) = 2.982 and p = 0.083. Thus the
assumption that the variances in the means were homogeneous was not violated. The one-
way ANOVA gave an overall F ratio that was not significant, with F (2, 14) = 3.159 and
p = 0.074. Therefore, the null hypothesis that the means are equal was accepted.
Amphidinium carterae – protein/CHLa relationships: Protein concentrations in the LL
experiments ranged from 45.98 – 91.85 pg cell-1, ML concentrations were between 95.07
and 243.10 pg cell -1, while the HL experiments had had protein concentration ranges
from 124.32 – 216.28 pg cell-1. Protein/CHLa ratios for these light treatments were
between 176.46 and 268.84 for LL, 287.63 and 638.01for ML and 403.18 and 551.27 for
HL. The ratios were log transformed prior to statistical analysis to prevent
120
Figure 36: Amphidinium carterae (a): CHLa/PERI v Light Intensity; (b): CHLa/PERI per batch
violation of the homogeneity of variances assumption. Pertinent regression and dot plots
are shown in Figure 36 (a - d).
1
1.31.1
0
0.5
1
1.5
2
0 50 100 150 200 250C
HL
a/P
ERI
rati
os
Light Intensity (µmol photons·m-2·s-1)
(a)
0
0.5
1
1.5
2
0 2 4 6 8
CH
La/
PER
I
batch
(b)
LL
ML
HL
121
Figure 37: Amphidinium carterae (a): Protein v CHLa; (b): Protein v Light Intensity; (c): CHLa v Light Intensity
0
50
100
150
200
250
300
0 0.2 0.4 0.6P
rote
in (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.5105x + 75.74R² = 0.7285
0
50
100
150
200
0 50 100 150 200 250
Pro
tein
(pg
cel
l-1)
Light Intensity (µmol photons·m-2·s-1)
(b)
y = 6E‐05x + 0.3196R² = 0.106
0
0.1
0.2
0.3
0.4
0 50 100 150 200 250
CH
La
(pg
cell
-1)
Light Intensity (µmol photons·m-2·s-1)
(c)
122
2
2.2
2.4
2.6
2.8
3
0 50 100 150 200pr
otei
n/ch
la(l
og)
Light Intensity (µmol photons·m-2·s-1)
(d)
LL
ML
HL
Figure 36 contd.: Amphidinium carterae (d): Light treatment effect on protein/CHLa ratios (dot plot)
Levene’s statistic was not significant, with F (2, 14) = 0.315 and a p value of
0.735. The overall F ANOVA was significant, with F (2, 14) = 26.215 and p ≤ 0.001,
indicating that at least one of the means was significantly different from the others. Since
the homogeneity of means assumption was not violated, Tukey’s (HSD) post hoc follow-
up test was done to assess the differences in the means. The LL group (M = 2.33) was
significantly different from the ML group (M = 2.68), with a mean difference of -0.34
and a p value < 0.001. The LL group (M = 2.33) was significantly different from the HL
group (M = 2.69), with a mean difference of -0.36 and a p value < 0.001. However, the
ML group (M = 2.68) was not significantly different from the HL group (M = 2.69), with
a mean difference of -0.02 and a p value of 0.938.
Amphidinium carterae – colloidal carbohydrates/CHLa relationships: The concentration
of the colloidal carbohydrate pool in the LL experiments ranged from 12.72 to 93.87 pg
cell -1, the ML had concentration ranges between 11.10 and 31.87 pg cell -1, and the HL
123
experiment showed concentrations between 30.67 and 38.26 pg cell -1. The colloidal
CHO/CHLa ratios were between 29.40 and 60.35 for the LL experiments, the ML ratios
were between 50.99 and 82.49, and the HL treatments showed ratios ranging from 92.35
and 113.92. These ratios were log transformed prior to statistical analysis to meet the
assumption of homogeneity of variances. Regression and dot plots are shown in Figure
37 (a – c).
Figure 38: Amphidinium carterae (a): Colloidal CHO v CHLa; (b): Colloidal CHO v Light Intensity
0
10
20
30
40
50
0 0.2 0.4 0.6 0.8Col
loid
al C
HO
(pg
cel
l-1)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.1239x + 9.6736R² = 0.9999
0
10
20
30
40
0 50 100 150 200 250Col
loid
al C
HO
(pg
cel
l -1)
Light Intensity (µmol photons·m-2·s-1)
(b)
124
Figure 37 contd.: Amphidinium carterae (c): Light treatment effect on protein/CHLa ratios (dot plot)
Levene’s test for homogeneity of variance was not significant, with F (2, 14) =
3.20 and p = 0.072.Thus, the homogeneity of variance assumption had not been violated.
However, the overall ANOVA was significant, with F (2, 14) = 33.295 and a p value <
0.001. Tukey’s (HSD) follow up tests were done to determine which of the means were
significantly different from the others. The following results were obtained: The LL
group (M = 1.62) was significantly different from the ML group (M = 1.79), with a mean
difference of - 0.17 and a p value of 0.010. The LL group (M = 1.62) was significantly
different the HL group (M = 2.02), with a mean difference of - 0.39 and a p value <
0.001. Lastly, the ML (M = 1.79) group was also significantly different from the HL
group (M = 2.02, with a mean difference of - 0.22 and a p value of 0.001.
Amphidinium carterae – Storage carbohydrates/CHLa relationships: The storage
carbohydrate concentrations in the light treatments are as follows: LL concentrations
1.2
1.4
1.6
1.8
2
2.2
0 50 100 150 200coll
oida
l C
HO
/CH
La
(log
)
Light Level (µmol photons·m-2·s-1)
(c)
LL
ML
HL
125
ranged between 49.09 and 76.42 pg cell-1, the ML experiments showed concentrations
between 5.25 and 108.62 pg cell -1, and the HL experiments had concentrations between
93.49 and 195.07 pg cell -1. The storage carbohydrate/CHLa had ratios were between
157.07 and 203.09 for the LL experiments, 196.77 to 256.83 for the ML experiments and
317.89 to 492.64 for the HL experiments. All ratios were log transformed prior to
statistical tests. Relevant plots are given in Figure 38 (a - c).
Figure 39: Amphidinium carterae (a): Storage CHO v CHLa; (b): Storage CHO v Light Intensity
0
50
100
150
200
250
0 0.2 0.4 0.6 0.8
Sto
rage
CH
O (
pg c
ell-1
)
CHLa (pg cell-1)
(a)
LL
ML
HL
y = 0.4993x + 37.105R² = 0.9758
0
50
100
150
200
0 50 100 150 200 250Sto
rage
CH
O (
pg c
ell-
1)
Light Intensity (µmol photons·m-2·s-1)
(b)
126
Figure 38 contd.: Amphidinium carterae (c): Light treatment effect on storage CHO/CHLa ratios (dot plot)
Levene’s test for homogeneity of variance was not significant, with F (2, 14) =
2.815 and p = 0.094, indicating that the variances in the group means were equal. The
overall ANOVA was significant, with F (2, 14) = 41.663 and a p value < 0.001. This
indicated that at least one of the means was significantly different from the others.
Since the homogeneity of variances assumption had not been violated, Tukey’s (HSD)
post hoc follow-up test was done to determine which of the means was significant.
These tests showed that the LL group (M = 2.26) was not significantly different from the
ML group (M = 2.35), with a mean difference of -0.09 and a p value of 0.073. The LL
group (M = 2.26) was however significantly different the HL group (M = 2.59), with a
mean difference of -0.33 and a p value < 0.000. The ML group (M = 2.35) was also
significantly different from the HL group (M = 3.59), with a mean difference of - 0.24
and a p value < 0.001.
2
2.5
3
0 50 100 150 200 250
stor
age
CH
O/C
HL
a(l
og)
Light Intensity (µmol photons·m-2·s-1)
(c)
LL
ML
HL
127
IV. DISCUSSION
Growth patterns The specific growth rate constants (µ) were calculated for the cultures studied
from the slope of the linear portion of the semilog plot of growth versus time (Appendix
IX). Growth rate is a function of photon flux density (PFD) in nutrient sufficient cultures
at constant temperature (Geider, 1987). All species exhibited increasing specific growth
rate constants with increasing light intensity. This indicated that the light intensities used
did not limit or inhibit the growth of the algal cells. Of the two bacillariophytes
investigated, Cyclotella meneghiniana had a higher µ at LL than Thalassiosira
weissflogii, but at ML and HL the growth rate constants for the former species were
lower. Conversely, Dunaliella tertiolecta grew at a faster rate than Scenedesmus
quadricauda at all three light levels.
Falkowski (1980) observed that CHLa cellular concentration varies as a linear
function of irradiance in nutrient replete batch cultures of microalgae. It is expected that
the concentration of CHLa and other light harvesting pigments will be higher at lower
irradiance levels for capturing the limited supply of available photons. The opposite is
expected for higher irradiance levels. That is, the concentrations of CHLa and the
accessory pigments will be lower, while the photoprotecting pigments will be higher. The
photoprotecting pigment concentration increases in order to prevent photoinhibition and
128
photodynamic action. In another paper, Falkowski (1981) also noted that increased CHLa
content appears to be a ubiquitous response by algae to decreased levels of incident light,
and cited previous works (Falkowski et al., 1980; Prezlin et al., 1978; Seneger et al.,
1978). Falkowski (1981) only investigated two chlorophyte species at two irradiance
levels (30 and 600 μmol photon·m-2·s-1), with observations supporting the hypothesis. In
the study conducted by Grant and Louda (2010), chlorophytes, cyanophytes,
bacillariophytes, dinophytes and a chrysophyte were grown at 44.5 μmol photon·m-2·s-1,
108 μmol photon·m-2·s-1, 100-120 μmol photon·m-2·s-1, 300 μmol photon·m-2·s-1, 1600
μmol photon·m-2·s-1and 1800 μmol photon·m-2·s-1 irradiance levels. While the
observations of CHLa concentration in that study largely followed the expected
hypothesis, the concentration of CHLa did not start to decrease until the 300 μmol
photon·m-2·s-1 experiments. This leads us to think that photoinhibition and thus an
increase in the need for photoprotecting pigments by the algal species, occurred at 300
μmol photon·m-2·s-1 and higher light intensities. In this present study, the highest
irradiance level is 200 μmol photon·m-2·s-1, thus, observing increasing CHLa
concentration with irradiance, up to this light level is acceptable.
Phytoplankton protein as a biomass indicator
Protein is typically the major biological component of algae (Brown and Jeffrey
1992). Rapidly growing cells are characterized by high protein and low carbohydrate
content. When cells have reached the stationary phase, more carbon is incorporated into
carbohydrate and/ or lipids (Piorreck and Pohl, 1984). In this study, similar trends were
129
observed, as algal cultures were harvested during logarithmic growth phase or at very
early transition to the stationary phase. Our data for the eight species investigated,
showed that protein concentration per cell increased from the LL to the HL experiments
while also being higher than colloidal carbohydrate concentration per cell for each
particular species. Apart from a few anomalies, the protein concentration per cell was
also higher than the storage carbohydrate concentration per cell at high light for the eight
species investigated. Where storage carbohydrate concentration per cell was shown to be
greater than the protein concentrations per cell, such as with Cyclotella meneghiniana at
HL, it is likely that cells had reached a stationary phase of growth, or a point where there
was not sufficient nutrients to further facilitate production of proteins.
Concentration of proteins, chlorophyll a, colloidal carbohydrates and storage
carbohydrates per cell and biovolume are tabulated in Table 6 below, and also in
Appendix VII. Regression plots of protein versus chlorophyll a (CHLa) in Chapter III
gave positive correlation between CHLa and protein for each of the species studied.
Ratios of protein: CHLa were log 10 transformed in an attempt to satisfy such problems as
skewed data, outliers, unequal variation, as well as for general easier statistical handling.
One-way analysis of variance (ANOVA) showed that light intensity does have an
effect on the protein: CHLa relationships for all of the species investigated, though more
for some than for others. Dunaliella tertiolecta, Cyclotella meneghiniana, Thalassiosira
weissflogii, Microcystis aeruginose all exhibited significantly different effects to each
light level, with lowest log10 ratios in the LL experiments and highest log 10 ratios in the
HL experiments, as shown in the dot plots in the results section and in Table 7.
130
Table 6: Cellular concentration of chlorophyll a and products of photosynthesis, (blank cells = run not performed; VL = 10 µmol photons·m-2·s-1; L = 37 µmol photons·m-2·s-1; ML = 70-75 µmol photons·m-2·s-1; HL = 200 µmol photons·m-2·s-1; 1µm3 = 1x10-9 µL) Genus Lt R 1 R 2 R 3 R 4 R 5 R 6 Biovol* Item/
biovol pg cell-
¹ pg cell-¹
pg cell-¹
pg cell-¹
pg cell-¹
pg cell-¹
cell -¹ (µm³)
fg (item) µm³·cell
A. carterae 432 CHLa L 0.44 0.26 0.31 0.37 0.29 0.1901 M 0.33 0.54 0.24 0.17 0.35 0.21 0.2333 H 0.33 0.27 0.33 0.4 0.35 0.1728 protein L 91.85 45.98 84.02 65.75 73.11 39.6792 M 95.07 243.1 130.4 111.2 169.9 110.6 73.4227 H 179.2 124.3 196.4 216.28 139.59 93.4330 colloidal CHO
L 13.04 93.87 12.72 22.25 14.48 40.5518
M 16.85 31.87 14.08 11.1 21.37 17.64 13.7678 H 36.2 31.28 30.67 38.26 35.87 15.6384 storage CHO L 76.42 49.9 49.09 74.87 55.98 33.0134 M 65.03 108.62 5.25 43.54 78.04 54.91 46.9238 H 108.25 93.49 158.64 195.07 137.59 84.2702 C. meneghiniana
2720
CHLa L 0.17 0.99 0.14 0.07 0.37 2.6928 M 0.62 0.2 1.09 0.15 0.09 0.19 2.9648 H 1.17 0.44 1.32 2.15 2.45 1.16 6.6640 protein L 15.28 91.54 14.76 72.31 32.86 248.9888 M 85.21 24.59 155.76 17.55 9.23 23.15 423.6672 H 155.12 73.34 197.46 290.73 380.01 167.61 1033.6272 colloidal CHO
L 1.61 1.77 1.18 0.63 2.6 4.8144
M 7.34 3.27 0.16 1.63 0.91 1.97 19.9648 H 31.8 11.5 32.95 43.3 48.86 32.82 132.8992 storage CHO L 6.43 0.16 0.11 4.11 0.16 17.4896 M 0.89 0.36 204.34 0.18 0.12 0.21 555.8048 H 235.17 118.87 368.16 496.26 575.01 268.58 1564.0272 T.weissflogii 2813 CHLa L 0.182 0.139 0.727 0.62 0.747 2.1013 M 0.164 0.173 0.636 0.109 0.205 1.7891 H 2.55 30.4 88.2 1.36 58.9 47.1 248.1066 protein L 31.42 25.89 99.8 99.25 115.96 326.1955 M 29.39 31.49 124.46 18.09 35.85 350.1060 H 489.62 60.5 210.01 272.04 133.66 103.31 1377.3011 colloidal CHO
L 1.15 1.47 6.22 4.4 5.34 17.4969
M 1.86 2.13 7.53 1.23 2.5 21.1819 H 41.77 5.37 12.58 19.81 9.2 8.15 117.4990
(Table 6 continued)
131
storage CHO L 21.19 12.24 79.6 65.3 90.84 255.5329 M 20.62 22.84 107.78 17.94 26.79 303.1851 H 602 61.1 176 238 121 73.4 1693.4260 D. tertiolecta 43.42 CHLa L 0.69 0.86 0.78 0.79 0.0373 M 0.52 0.87 0.64 0.45 0.41 n/a 0.0378 H 0.93 1.25 0.7 0.85 0.91 0.87 0.0543 protein L 48.19 62.32 56.94 53.26 2.7059 M 47.64 114.78 84.91 33.07 54.15 n/a 3.6868 H 141.73 226.76 109.84 151.46 170.79 163.04 7.4157 colloidal CHO
L 6.11 6.42 6.72 7.38 0.3204
M 2.39 5.09 3.72 1.69 1.32 n/a 0.2210 H 3.82 7.78 2.81 5.62 5.62 6.13 0.3378 storage CHO L 44.68 50.45 45.69 45.46 2.1905 M 43.5 84.28 53.94 32.1 38.38 3.6594 H 96.83 116.56 79.01 96.69 105.34 99.8 5.0610 S. quadricauda
45
CHLa L 0.028 0.055 0.096 0.109 0.126 n/a 0.0057 M 0.401 0.3 0.689 0.774 1.12 0.875 0.0394 H 10.5 4.19 4.45 6.79 4.41 3.83 0.3056 protein L 13.48 28.68 78.3 93.34 53.33 n/a 0.3321 M 94.7 50.5 114 135 189 144 8.5050 H 1239 575 567 871 551 489 55.7550 colloidal CHO
L 3.37 2.85 9.31 19.65 11.02 n/a 0.8843
M 3.61 1.88 6.94 4.75 6.86 4.98 0.3123 H 74.55 30.77 30.06 52.96 32.63 30.12 3.3548 storage CHO L 13.62 29.85 51.83 70.22 71.51 n/a 3.2180 M 19.12 11.79 31.85 22.79 30.27 23.84 0.0057 H 1929.25 69.46 81.91 1250.72 88.54 73.74 56.2824 S.elongatus 4.2 CHLa DL 0.55 0.94 0.23 0.7 0.0039 L 0.19 2.64 0.76 1.9 5.73 0.0241 M 0.36 0.79 1.77 0.2 0.4 2.39 0.0100 H 9.25 7.88 5.8 3.18 0.1 0.0389 protein DL 35.2 59.46 12.99 41.72 0.1752 L 0.26 291.82 0.96 229.7 674.68 2.8337 M 0.58 137.02 346.61 0.31 0.75 430.16 1.8067 H 1706.69 1539.28 1095.9 599.76 1986.11 8.3417 colloidal CHO
DL 4.79 4.26 0.22 7.48 0.0314
L 1.82 0.23 8.31 0.19 0.6 0.0349 M 3.59 8.61 0.18 1.97 1.54 0.24 0.0362 H 114.26 0.94 0.77 0.44 125.51 0.5271 storage CHO DL 0.24 0.37 0.93 0.32 0.0039 L 0.11 149.67 0.48 112.74 336.79 1.4145 M 0.2 0.4 0.93 0.1 0.22 142.21 0.5973
(Table 6 continued)
132
H 519.69 462.17 313.28 161.2 590.26 2.4791 M. aeruginosa
65
CHLa L 0.147 0.086 0.1 0.059 0.246 0.0160 M 0.0509 0.089 0.107 0.102 0.248 0.177 0.0161 H 0.826 0.896 0.207 0.216 0.533 0.899 0.0584 protein L 7.06 5.22 5.89 3.36 12.1 0.7865 M 3.65 5.25 7.33 5.79 16 11.1 0.7215 H 70.41 85.9 16.9 16.8 41.7 5.5835 colloidal CHO
L 0.862 0.438 0.577 0.39 0.132 0.0560
M 0.389 0.589 0.62 0.849 0.155 0.122 0.0552 H 6.15 6.34 1.19 1.52 3.86 6.49 0.4219 storage CHO L 10.85 6.51 8.6 5.3 2.17 0.7053 M 4.5 8.92 9.16 9.47 15.54 13.4 0.1411 H 95.62 91.06 23.86 23.68 51.28 90.78 6.2153 R. salina 141 CHLa L 0.024 0.044 0.047 0.035 0.069 0.0097 M 0.095 0.097 0.152 0.14 0.151 0.0213 H 0.297 0.503 0.449 0.602 0.493 0.509 0.0849 protein L 7.05 11.61 12.88 9.41 20.55 2.8976 M 24.76 25.87 46.62 32.66 39.93 6.5734 H 122.87 195.66 168.54 185.01 158.27 180.8 27.5881 colloidal CHO
L 0.777 1.015 1.191 0.984 2.503 0.3529
M 2.23 2.57 3.97 3.82 3.67 0.5598 H 5.71 9.14 8.9 12.38 11.6 8.92 1.7456 storage CHO L 4.14 6.08 5.27 5.73 14.4 2.0304 M 9.61 9.43 21.27 13.19 12.5 1.8598 H 47.57 69.62 68.42 89.54 70.79 78.21 12.6251 * Olenina et al., 2006
The positions on the dot plot for Scenedesmus quadricauda are reversed, with the LL
having the highest log10 ratios and the HL having the lowest, possibly due to the fact this
species grew very slowly at LL and the CHLa concentrations were very low. In
Amphidinium carterae, the ML and HL treatments had similar effects on the protein:
CHLa relationships when log10 transformed, as shown in the dot plot in Figure 36d.
This may be due to the fact that CHLa and protein concentrations only varied slightly in
both experiments and therefore the ratios were very similar. In Rhodomonas salina, the
LL and ML experiments had similar CHLa and protein concentrations, as illustrated in
133
the regression plot (Figure 23a), therefore the dot plot showed a similar effect of light on
these variables for these experiments (Figure 23d). In Synechococcus elongatus, the DL
and LL experiments showed protein concentration increasing as CHLa increased.
However, the ML and HL experiments showed relatively steady concentrations of CHLa
and protein as shown in the regression and dot plots in Chapter III (Figure 8a - 8d).
Interspecies variation was observed for the two chlorophytes investigated in this
study. As shown in Table 7, Scenedesmus quadricauda has a higher concentration of
CHLa and protein per cell in the HL experiments when compared to Dunaliella
tertiolecta at the same light intensity. The CHLa concentration per cell in Scenedesmus
quadricauda was more than four times that of Dunaliella tertiolecta and the protein
concentrations in the two species at HL showed approximately a two-fold difference.
Variation in the CHLa and protein concentrations was also observed for the two
cyanophytes used in the study. While both species showed trends of increasing CHLa and
protein with increasing irradiance, the CHLa concentration per cell at HL was
significantly higher in Synechococcus elongatus than in Microcystis aeruginosa - a more
than eight-fold difference in some instances. Three to four-fold differences in the protein
concentrations per cell were also observed between the two species at the HL
experiments. In the two bacillariophytes (diatoms) studied, similar trends were seen with
CHLa and protein per cell increasing with increasing irradiance levels.
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Table 7: Protein: CHLa (log10) ratios of the species as influenced by irradiance Species Low Light Medium Light High Light Scenedesmus quadricauda
2.774 ± 0.1381 2.258 ± 0.0592 2.104 ± 0.0209
Dunaliella tertiolecta 1.849 ± 0.0175 2.039 ± 0.1185 2.238 ± 0.0401
Thalassiosira weissflogii 2.208 ± 0.0500 2.254 ± 0.0257 2.327 ± 0.0370
Cyclotella meneghiniana 1.996 ± 0.0368 2.078 ± 0.0461 2.167 ± 0.0358
Synechococcus elongatus 2.086 ± 0.0340 2.244 ± 0.0342 2.279 ± 0.0067
Microcystis aeruginose 1.732 ± 0.0374 1.804 ± 0.0380 1.911 ± 0.0421
Amphidinium carterae 2.330 ± 0.0852 2.676 ± 0.1176 2.694 ± 0.0648
Rhodomonas salina
2.446 ± 0.0241 2.436 ± 0.0288 2.554 ± 0.0450
Ratios represent means of replicate cultures.
The CHLa concentration per cell was significantly higher in the Thalassiosira weissflogii
batches grown at HL than those of the corresponding Cyclotella meneghiniana batches.
However, apart from what may be an anomaly in one of the batches, the protein
concentration in both species is arguably not significantly different. This similarity may be
due to Cyclotella meneghiniana reaching the stationary phase of growth much earlier than
Thalassiosira weissflogii at the HL experiments; hence protein synthesis in the cells may
have started to slow down instead of turn over. The interspecies variation reported here is
evidence that a universal protein: CHLa ratio cannot be developed for a taxonomic group,
as species in the same taxon respond differently to the same environmental condition.
However, for ecological modeling, if a community structure is known at the species level,
then protein may be able to be adequately estimated from
pigment-based chemotaxonomic CHLa values.
135
Very few studies have attempted to determine protein: CHLa relationships. Myers
and Kratz (1955) studied the cyanobacterium Anacystis nidulans (a.k.a. Synechococcus),
and obtained estimates of cells with highest pigment content to contain 2.8% CHL a and
24% phycocyanin, thus deriving a phycocyanin: CHLa ratio of 8.57:1 (Myers and Kratz,
1955). They conducted their investigations of these relationships at 25 and 39 ºC and at
320 and 960 Watts PAR respectively. They reported that even though there was a three to
four-fold variation in either component, there was only small variation in the
phycocyanin: CHLa ratio. Phycocyanin is a light harvesting pigment, unique to
cyanobacteria, from the phycobiliprotein family. Since this pigment is unique to
cyanobacteria, it can be useful as a potential biomarker. It has been shown that CHL a
does covary in the same way as specific biomarker pigments (Goericke and Montoya,
1998), so the report that the ratios only varied slightly over the light intensities studied is
not surprising. Additionally, the reported phycocyanin: CHLa ratio would have only
allowed the phycobiliprotein component of the cells to be estimated, and would therefore
not allow for any estimation of total protein from CHLa. Thus, since the major objective
of our current investigation is to ultimately estimate algal cellular protein from CHLa
data, our protein: CHLa results cannot be reasonably compared to the results of the work
done by Myers and Kratz.
Muscatine and Marian (1982) reported a protein: CHLa ratio of 28:1 for algae
isolated from the tissues of Mastigias jellyfish that consumed phytoplankton. That study
investigated the dissolved nitrogen flux in symbiotic and non-symbiotic medusa – they
reported that the Mastigias had selective symbiotic relationships with nitrogen
136
assimilating dinoflagellates and would move up and down the water column to satisfy the
nutrient needs of the algal symbionts. The species of the dinoflagellates in the marine
lakes were not mentioned in the study, and apart from stating that the medusa had a high
aversion to light intensity and preferred more shaded habitats, irradiance levels were not
reported. If the ratio reported by Muscatine and Marian (1982) were to be log10
transformed, it would still be lower than what we obtained for the lowest irradiance
treatments for Amphidinium carterae, the only dinoflagellate investigated in our study
(Table 6). We know that protein and CHLa concentrations will vary among species of the
same taxonomic group and since we have no knowledge of the dinoflagellate symbionts
in the study, a true comparison cannot be made between the protein: CHLa ratios
reported by Muscatine and Marian (1982) and ours. However, their results represents
tentative confirmation that algal protein can be predicted from CHLa data, once prior
information is obtained on the species involved and the conditions of the environment
being investigated.
Although the relationship is not perfect, it is clear from our results that positive
correlations exist between algal protein and CHLa as a function of light intensity. As this
is one of the aims of this study, it is now apparent that algal protein content can be at least
estimated from CHLa concentration, once prior studies are done on the environmental
conditions for that population under study. As discussed below, the physiological state of
the algae can also be predicted from the protein and carbohydrate concentrations. Our
work represents preliminary findings if pigment-based chemotaxonomy is to be taken to
this level.
137
Phytoplankton colloidal carbohydrate (CHO) as a biomass indicator
Studies have shown that positive correlations exist between colloidal
carbohydrate concentrations and chlorophyll a biomass of benthic microalgae and
cyanobacteria (Underwood et al, 1995; Fabiano & Danovaro, 1994). The extracellular
polymeric substances (EPS) are a major component of the colloidal carbohydrate fraction
of benthic diatoms (Hoagland et al, 1993) and are considered to be analogous to those
produced by planktonic diatoms (Welker et al., 2002). Studies of cultures under
controlled conditions have shown that factors such as nutrient availability and irradiance
affect the release of these colloidal fractions (Myklestad et al., 1989). Smith and
Underwood (1997) developed a model [(log (conc. Coll carbo. +1) = 1.40+1.02 (log (chla
conc. +1))] for predicting colloidal carbohydrate concentration from chlorophyll a data
based on the assumption that if close positive correlation exists between epipelic diatom
biomass and colloidal carbohydrate concentration, then it should be possible to predict
certain components of the colloidal fraction in diatoms in specific environments from
chlorophyll a concentration. Using the assumptions made about periphyton by Smith and
Underwood (1997) and, based on the results obtained from our culture studies, we feel
that once the environmental conditions are known, then it may also be possible to also
predict planktonic colloidal carbohydrate concentrations as well from chlorophyll a data.
In our study, Thalassiosira weissflogii, Cyclotella meneghiniana and
Amphidinium carterae all exhibited significantly different log10 ratios under the three
light treatments, indicating rejection of that the null hypothesis that irradiance had no
effect on the colloidal CHO: CHLa ratios. The regression plots in Chapter III for these
138
species also indicate close positive correlations between chlorophyll a concentration and
colloidal carbohydrate concentration. Light treatment did have effect on the remaining
species in the study, though not as significant as the diatoms and dinoflagellate studied.
Dunaliella tertiolecta, Scenedesmus quadricauda, Microcystis aeruginose, and
Rhodomonas salina all showed LL experiment log10 ratios significantly different from the
ML and HL experiments, though the latter two gave similar ratios, as shown in Table 8
below.
Chlorophytes are not known to secrete huge amounts of colloidal carbohydrates in
response to environmental changes, as also shown from the regression plots for the two
species investigated herein (Chapter III). Though chlorophyll a concentrations were
changing as irradiance increased, the colloidal carbohydrate concentration changed
almost linearly and CHO/CHLa decreased with increased light.
Table 8: Colloidal CHO/CHLa (log 10) ratios as a function of irradiance Species Low Light Medium Light High Light Scenedesmus quadricauda
1.996 ± 0.0200 0.863 ± 0.1031 0.868 ± 0.0249
Dunaliella tertiolecta
0.932 ± 0.0398 0.651 ± 0.1183 0.744 ± 0.1081
Thalassiosira weissflogii
0.892 ± 0.0872 1.072 ±0.0169 1.202 ± 0.0385
Cyclotella meneghiniana
0.917 ± 0.0475 1.0773 ± 0.0701 1.3819 ± 0.0659
Synechococcus elongatus
0.993 ± 0.0387 1.017 ± 0.0215 1.102 ± 0.0288
Microcystis aeruginose
0.760 ± 0.0438 0.837 ± 0.0581 0.841 ± 0.0409
Amphidinium carterae
1.624 ± 0.1224 1.793 ± 0.0690 2.016 ± 0.0354
Rhodomonas salina
1.457 ± 0.0793 1.377 ± 0.0423 1.295 ± 0.0452
Ratios represent means of replicate cultures.
139
Phytoplankton storage carbohydrate (CHO) as a biomass indicator
As previously mentioned, light availability has been recognized as one of the
main factors regulating phytoplankton community growth (Welker et al., 2002).
Additionally, many phytoplankton species respond to nutrient limitation by producing
energy storage materials, and as a result, storage carbohydrates may accumulate inside
the cells (Myklestad, 1988/1989). In our study, the effect of irradiance on algal biomass
relationships was investigated using nutrient replete culture batches. Statistical tests
showed that irradiance did have an effect on storage carbohydrate to chlorophyll a log10
relationships for all the species investigated, though some more significantly than for
others. Although the cultures were inoculated into nutrient replete media, the media was
not replenished with nutrients, and nutrient test results at inoculation and at harvest
showed that nutrients decreased but was not completely depleted during growth (data not
shown). This decrease in nutrients could have facilitated the accumulation of cellular
storage materials, as is a typical occurrence in many phytoplankton species (Borsheim et
al., 2005; Granum et al., 2002; Myklestad 1988/1989). Amphidinium carterae,
Thalassiosira weissflogii, Cyclotella meneghiniana, Dunaliella tertiolecta all showed
significantly different log10 ratios for the three light treatments, with the LL experiments
having the lowest ratios and the HL having the highest ratios, as shown in Table 9.
Irradiance had no effect on log10 ratios of storage CHO: CHLa for Rhodomonas salina
and Microcystis aeruginosa in the LL and ML experiments, but showed effect when these
ratios were compared to those in the HL experiments, even though there were changes in
the CHO and CHLa concentrations with increasing irradiance, as shown in the regression
plots (Figures26a - 26d and 14a - 14d respectively). For Synechococcus elongatus, even
140
though chlorophyll a concentration increased with irradiance (Figure 8c), storage
carbohydrate increased from the DL to the ML experiments, but remained relatively
stable from the LL to the ML experiments. Therefore, statistically, irradiance had no
effect when the log10 ratios of the LL and ML experiments were compared (Figure 10c).
Table 9: Storage CHO/CHLa (log 10) ratios as a function of irradiance
Species Low Light Medium Light High Light Scenedesmus quadricauda
3.029 ± 0.2273 1.737 ± 0.3062 1.267 ± 0.0278
Dunaliella tertiolecta
1.777 ± 0.0238 1.932 ± 0.0526 2.036 ± 0.1134
Thalassiosira weissflogii
2.032 ± 0.0536 2.157 ± 0.0614 2.288 ± 0.0621
Cyclotella meneghiniana
1.746 ± 0.1358 2.140 ± 0.0815 2.378 ± 0.0506
Synechococcus elongatus
1.768 ± 0.0172 1.735 ± 0.0251 1.741 ± 0.0236
Microcystis aeruginose
1.919 ± 0.0337 1.921 ± 0.0729 2.026 ± 0.0330
Amphidinium carterae
2.263 ± 0.0457 2.353 ± 0.0468 2.588 ± 0.0826
Rhodomonas salina
2.004 ± 0.0861 1.956 ±0.1272 2.233 ± 0.0244
Ratios represent means of replicate cultures.
Marker pigments as indicators of algal biomass
Numerous studies have reported the use of the relationship between algal
taxonomic marker pigments and CHLa (Grant and Louda, 2010; Louda, 2008; Eker-
Develi et al., 2008; Hagerthey et al., 2006; Millie et al., 1992, among others) and their
implications on algal biomass estimation. These relationships are typically incorporated
in the computational methods (see Chapter I) for determining composition and abundance
of phytoplankton populations (Everitt et al., 1990; Gieskes and Kraay, 1983a, 1986b;
141
Gieskes et al., 1988; Goericke and Montoya, 1988; Letelier et al., 1993; Mackey et al.,
1996; Wright et al., 1996; Pinckney et al., 1998; Van den Meersche et al., 2008; Van den
Meersche and Soetaert 2009).
These relationships are typically refined and re-investigated in an attempt to
address the limitations of the mathematical methods, the inability to address variations of
pigment content within various taxa, even at the species level. Thus, ratios and especially
their variability have to be clearly defined in ways that will allow reliable/verifiable
estimates of CHLa contributions for each taxon (Jeffrey et al., 1999; Mackey et al., 1996;
Peeken 1997). There is still a need for data describing the pigment ratios of major species
over a wide range of light and nutrient regimes (Jeffrey et al., 1999). This is especially
critical since past studies (Carreto et al., 2008) have shown a logarithmic decrease in
pigments (e.g. CHL a per cell) and decreasing CHL a: marker pigment ratios with
increasing irradiance.
In the present study (cf. Grant and Louda 2010), we evaluated irradiance, an
easily measured laboratory and field parameter, as a driver for changes in CHLa: marker
pigment ratios. The statistical tests (Chapter III and Appendix VI) showed that the
irradiance levels investigated had little effect on the CHLa: marker pigment ratios for all
the algal species, except the two cyanophytes investigated. This means that, although
pigment (CHLa) per cell increased with irradiance, as shown in Table 6 and in Grant and
Louda (2010), CHLa and certain accessory biomarker pigments co-vary (cf. Goericke
and Montoya 1988). That is, while CHLa concentration per cell is decreasing with
increasing photon flux (300 μmol photon·m-2·s-1 and higher), the corresponding marker
pigment is decreasing. Thus, the ratios only varied slightly over the irradiance levels.
142
The CHLa: CHLb ratio for Dunaliella tertiolecta was approximately 2.4:1 and
that of Scenedesmus tertiolecta was approximately 2.7:1. The CHL a:CHL b ratio in
CHL b-containing organisms ranges from 2:1 to 3:1 (Halldal 1970, Strain et al., 1971,
Meeks 1974), the data presented here and that in Grant and Louda (2010) both confirm
this.
The two diatoms used in this study: Cyclotella meneghiniana and Thalassiosira
weissflogii, exhibited the characteristic pigments of diatoms with FUCO as the marker
pigment for CHLa divisional estimation. Although CHLc is a known accessory pigment
in diatoms (Stauber and Jeffrey, 1988), it was not considered here as a diatom marker
pigment, as it is also present in dinoflagellates, prymnesiophytes, cryptophytes, and
others (Jeffrey & Vesk 1997, Jeffrey & Wright 2006), and would therefore give errors in
taxonomic estimation if a mixed algal sample were being analyzed (see Grant and Louda
2010 for further reasoning). The molar ratios found during this study were about 1.1:1 for
Cyclotella meneghiniana and 1.2:1 for Thalassiosira weissflogii. Chl a: FUCO (molar
converted) ratios of 2.34:1 (Gieskes et al., 1988), 1.21:1 (Wilhelm et al., 1991), and 1.8
to 2.6:1 (Garibotti et al., 2003) have been reported from studies on North Sea, Pacific and
Antarctic waters respectively.
The quantitative marker pigment for ‘peridinin-containing’ algae belonging to the
division Dinophyceae is peridinin (PERI), (Jeffrey et al., 1975; Johansen et al., 1974).
The CHLa: PERI ratio was approximately 1.13:1 for Amphidinium carterae investigated
at the three irradiance levels in this study. This ratio compares very well with that
reported by Grant and Louda (2010) for the same species. They reported ratios for
Amphidinium carterae between 0.8:1 and 1.0:1, at irradiance levels of 30-45 through
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1800 µmol photons·m-2·s-1. Although the ratios presented here are in good agreement
with that of Grant and Louda (2010), they are both lower than the 1.5:1 conversion factor
used by Louda (2008) to estimate the CHL a contributed from dinoflagellates in studies
of Florida Bay, an intense light (~ 750 µmol photons·m-2·s-1) environment. CHLa: PERI
ratios as high as 2.35:1 (Everitt et al., 1990; Ondrusek et al., 1991) and 3.96:1 (Barlow et
al., 1995) have been reported.
Marker pigments for cyanobacteria are typically ZEA and/or ECHIN. Ratios of
CHLa/ZEA = 1.1:1 and CHLa/ECHIN = 11.0:1 have been used for estimating coccoidal
or filamentous cyanobacteria, respectively, in Florida Bay (Louda 2008; Louda et al.,
2000), and Everglades (Hagerthey et al., 2006) studies. Similar ratios have been
previously reported for samples grown without light or nutrient limitations (Barlow et al.,
1995; Wilhelm et al., 1991). Although the CHLa/ZEA ratios for the two cyanophytes
investigated: Microcystis aeruginosa (LL: 26.67 to HL: 10.67) and Synechococcus
elongatus (LL: 6.49 to HL: 0.87), were significantly different (Figures 11a and 7a:
Chapter III), both species showed a marked decrease in the ratios from the low light to
the high light experiment levels. This decrease suggests that ZEA functions to protect the
species from photodamage (cf. Paerl et al., 1983; Bidigare et al., 1989). Ratios based on
CHLa to photoprotective pigments generally decrease with irradiance (Ruivo et al.,
2011). It was previously found that Synechococcus sp. (elongatus?) had CHLa: ZEA
ratios of 2.5:1 or 1.0:1 in the dark brown humic waters of Whitewater Bay or the clearer
waters of Florida Bay proper, respectively (Louda 2008: both greater than 600 µmol
photons ·m-2·s-1). In this study, with lower light levels, Synechococcus elongatus had
CHLa: ZEA ratios between 6.49:1 and 0.87: 1. Even though ZEA acts as a
144
photoprotective pigment (PPP) in these two cyanophytes, it is still required as a marker
pigment for coccoidal cyanobacteria lacking other carotenoids. ZEA does occur in the
chlorophytes as well but in very much reduced concentrations. The decrease in the ratio
CHLa: ZEA is explained wholly or partly by decreases in cellular CHLa contents as
previously reported for another cyanophytes: Anacystis nidulans (Allen 1968) and with
the largest decreases occurring above 300 µmol photons·m-2·s-1 (Utkilen et al., 1983).
Alloxanthin (ALLO) is the recognized marker pigment for phytoplankton
belonging to the cryptophyte algal division (Chapman, 1966). It is the opinion of J. W.
Louda (pers comm. 2011) that it may be difficult to correlate the microscopic and
pigment-based (chemo) taxonomy of cryptophytes with natural communities. He suspects
that the phycoerythrin-containing chloroplasts of ruptured cryptophytes cells may be
mistaken for coccoidal cyanobacteria during microscopic exams, especially with
phycobilin-based epifluorescence methods. Thus, ruptured fragile cryptophytes cells
could decrease the cryptophytes count and, at the same time, increase the coccoidal
cyanobacterial count. However, the cryptophytes estimate based on alloxanthin as the
marker would remain.
The CHLa: ALLO ratios for Rhodomonas salina, the only cryptophyte in this
study, showed relative stability at about 2.6:1. This ratio compares very well with the
1.8:1 – 2.9:1 reported by Hendriksen (Hendriksen et al., 2002) for their study on the
effects of nutrient and light regimes on marine phytoplankton pigments, isolated from
Northern European waters. Their reported ratios are in agreement with ours and are from
a variety of cryptophytes, including Rhodomonas salina, harvested during the exponential
growth phase.
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Phytoplankton chlorophyll a, protein and carbohydrate relationships to biovolume
Phytoplankton are at the base of the pelagic ecosystem, where they foster the flow
of energy through the trophic levels. The understanding and modeling of these
ecosystems are not possible without knowledge of species composition and biomass.
According to Paasche (1960), cell concentration data is inadequate for estimating a mixed
phytoplankton community, and for observations on a community containing a wide range
of size classes, biovolume will give a more accurate picture. Cell volumes can be
calculated from size and shapes using the appropriate geometric formulas. The use of a
standardized species list with fixed size classes and biovolumes is a decisive method for
improving the quality of phytoplankton counting methods (Olenena et al., 2006).
In this study, the biovolume of the species investigated were obtained from a
standardized phytoplankton species biovolume list (Olenena et al., 2006) and used to
make relationships with the obtained chlorophyll a, protein and carbohydrate
measurements. Chlorophyll is believed to have an allometric relationship with
phytoplankton biovolume and has also been observed to be dependent on light intensity,
temperature and phytoplankton composition (Felip and Catalan, 2000). As shown in
Table 5, certain allometric trends were observed in our study, particularly with Cyclotella
meneghiniana and Thalassiosira weissflogii. These two diatoms have a reported high unit
volume per cell and also gave higher unit CHLa per cell volume than the other species.
Where allometric trends were not readily observed, unit CHLa per cell volume was noted
to generally increase with light intensity for the irradiance levels investigated.
A general trend of increasing unit protein per cell volume with increasing light
intensity was observed for all the species investigated in this study. Although allometric
146
relationships were observed between protein and cell volume, the species in this study
with the lowest cell biovolume, Synechococcus elongatus, had a higher unit protein per
cell volume when compared to Microcystis aeruginosa (the other cyanophyte
investigated). The difference in cellular volume between the two species was more than
15 fold. Protein biovolume are also reported in Table 6.
As shown in Table 6, the species in this study that had large reported cell volumes
(the two diatoms) also showed colloidal carbohydrate having an allometric relationship
with biovolume when compared with the other species used. The species in this study
belonging to taxonomic groups known for secreting large amounts of colloidal
carbohydrates in response to environmental variation, particularly light intensity, also
gave higher unit colloidal carbohydrates per cell volume.
Storage carbohydrate per biovolume was noted to show a general increase as light
intensity increased, though the increase was more significant for some species than
others. This was particularly true for Amphidinium carterae, Cyclotella meneghiniana
and Thalassiosira weissflogii. Additionally, these species were reported to have larger
cell volumes (Olenena et al, 2006), thus confirming that an allometric relationship does
exist between storage carbohydrates and biovolume.
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V. CONCLUSION: IMPLICATIONS FOR CHEMOTAXONOMY
This study showed that light intensity has significant effects on protein/CHLa,
colloidal CHO/CHLa and storage CHO/CHLa ratio relationships, but a less significant
effect on the known and established marker pigment/CHLa ratios for the species
investigated. Interspecies variation was also observed for protein/CHLa, colloidal
CHO/CHLa and storage CHO/CHLa, and leads to the general conclusion that extensive
knowledge of the influence of light intensity on these parameters are needed before they
can be applied to the methods used for chemotaxonomic assessments. Further, the
biomass parameter/CHLa ratios to be used in the mathematical applications for
estimating algal biomass should come from the more abundant phytoplankton species
that are native to the communities studied. Universal ratios of biomass parameter/CHLa
cannot be determined. Seasonal ratios would need to be determined if these parameters
are to find use in the mathematical applications. That is, a set of ratios for the high light
intensity summer months and another set of ratios for the low light, late autumn and
winter months.
Algal cells are physiologically plastic. That is, cellular components, particularly
pigments, proteins, colloidal and storage carbohydrates are altered in response to
environmental variables including nutrients, temperature and light. Only the influence of
148
light was investigated in this study. It is known that some of these cellular components,
particularly marker pigments and CHLa change with these environmental variables, but
tend to co-vary in the same way (Goericke and Montoya 1988; Schulter et al., 2000). The
relationships that co-vary and are hence most stable are best for use in the mathematical
applications used for estimating algal biomass in terms of CHLa. However, converting
CHLa to a more useful currency (unit) of biomass for ecological modeling is still
difficult.
In this study attempts were made to determine ratios for the relationships between
chlorophyll CHLa and proteins and CHLa and two functional classes of carbohydrates,
so that these more useful units of algal biomass could be determined from chlorophyll
CHLa. The study showed that robust relationships exist between CHLa and these other
biomass units and prove these relationships to be useful in estimating algal biomass in
more useful currencies. CHLa is easily measured or derived from remote sensing. This
study showed that algal biomass cannot only be confined to one biomass parameter, but
to several – in our case, pigments, proteins and carbohydrates. Fats and total organic
carbon could also be considered. The relations determined here can thus find application
in the current mathematical methods used for estimating algal biomass, providing more
studies are done to assess these relationships in more algal species under different
conditions of light and possibly variant nutrient regimes.
149
VI. CHARACTERIZATION OF NOVEL PIGMENT
The ‘scytoneman’ skeleton
The presence of scytonemin in cyanobacteria has been observed in more than 300
species, where sheaths covered with yellow to brown pigments are described (Edwards et
al., 2000). Scytonemin absorbs strongly in the UVA spectral region (315-400 nm),
however, there is absorbance in the violet and blue region as well as in the UVB (280-
320 nm) and UVC (190-280 nm) regions. Until 2004, scytonemin has been the only
sunscreen pigment identified from the series, with the characteristic indolic and phenolic
subunits – termed the ‘scytoneman’ skeleton (shown in Figure 39). Additionally, some
cyanobacteria contain a red to purple pigment called gloeocapsin, instead of scytonemin
(Garcia-Pichel et al., 1993; Garcia-Pichel and Castenholz, 1993). The structure of
gloeocapsin is still not known. The production of these molecules is believed to be
related to those of other suncreens such as mycosporin-like amino acids in phytoplankton
and fungi (Sinha et al., 1998).
Three new pigments, related to the scytonemin skeleton, were isolated and
structurally identified in a study aimed at investigating plant succession in the Mitaraka
Inselberg in French Guyana (Butel-Ponce et al., 2004). These molecules are believed to
be derived from condensation of tryptophanyl- and tyrosyl-derived subunits with a
150
linkage between the units unique among natural compounds. These new pigments have
been termed tetramethoxyscytonemin, dimethoxyscytonemin and scytonine. They are
related to the scytoneman skeleton and the structures and select spectroscopic
characteristics are presented in Table 10. All three of these, as well as scytonemin exhibit
even molecular masses. By the “nitrogen-rule” they have an even number of N atoms, a
point to be brought out below.
Figure 40: The scytoneman skeleton (oxidized form of scytonemin is shown)
We report here the isolation and putative structural elucidation of a novel
sunscreen pigment, isolated form lab grown cultures of Scytonema hofmanii grown at
high light intensities (300-1800 µmol photons·m-2·s-1; Grant and Louda, 2010) as well as
from samples collected in areas of the Florida Everglades (Figure 40). We believe this
pigment to possess the scytoneman skeleton. It has similar spectroscopic properties to
scytonemin, though with enhanced absorbance maximas in visible region of the
electromagnetic spectrum.
OH
N
O
HO
N
O12
11
15
109
3
3a4
4a
58
8a
8b
1
1'
3'11'
5'
151
Figure 41: Red Rock aerial - areas where samples, scraped off rocks, contain the visible light sunscreen pigment.
At this time we are also postulating that the ecological significance of this
pigment in the photosynthetic unit is for the protection of CHLa and cytochrome soret
bands, as well as the α and β bands of cytochromes (e.g. cyt-c562). This is detailed at the
end of this chapter
The oxidized form of scytonemin was also isolated from the same sources as the
unknown pigment. Spectroscopic results were used to compare the isolated scytonemin
with that reported by Proteau and coworkers (Proteau et al, 1993). The 1H and 13C NMR
data presented in Table 11 shows that our data compares very well with that of Proteau
and coworkers. We are therefore sufficiently satisfied that our extraction and purification
method gave scytonemin in good purity.
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Table 10: Three new pigments isolated form Scytonema sp. collected on Mitaraka Inselberg, French Guyana (Butel-Ponce et al., 2004).
Derivative Name; color m/z [M+H]+ UV/Vis (nm)
OH
NH
O
HO
HN
O
OMe
MeO
OMeMeO
Tetramethoxy scytonemin; Purple, amorphous solid
671
212; 562
OH
NH
O
HO
HN
O
OMe
MeO
Dimethoxy Scytonemin; Dark red, amorphous solid
609
215; 316; 422
OH
NH
HN
O
H3CO
O
H
H3CO
O
Scytonine; Brown, amorphous solid
519
207; 225; 270
153
Table 11: Scytonemin- a comparison of literature values with those that we obtained (* Proteau et al., 1993, ** FAU – OGG lab work, X signal not observed)
no. Scytonemin* 13
C 1H * Scytonemin
**13
C
1H **
1 129.83 x 2 194.17 195.22 3 118.67 118.46 3a 174.30 175.15 4a 163.94 164.48 5 122.08 7.76d (7.7) 122.09 7.51 d (6.0)6 135.14 7.49 ddd (7.7, 7.6,
1.1)135.89 7.54 t (12,
9.0) 7 126.64 7.22 dd (7.6, 7.2) 126.59 7.18 t (6.0)8 129.67 7.89 d (7.2) 129.72 7.64 d (6.0)8a 125.61 x 8b 158.63 x 9 139.42 8.00 s 139.36 7.58 s 10 126.36 125.84 15,11 136.86 9.00 d (8.7) 136.86 8.64 d (6.0)14,12 117.08 7.34 d (8.7) 116.77 6.94 d (6.0)13 163.55 163.46 HO 10.34 bs 10.59 bs
The new highly polar pigment is red-to-mahogany colored in solution and also
exhibits spectroscopic properties, somewhat similar to those reported for scytonemin and
scytonemin derivatives. The pigment is more much polar than any of the pigments that
we usually encounter, eluting from our reversed phase HPLC system before five minutes,
as shown in Figure 41. This red compound gave absorption maximas in both the UV and
Visible regions (237 nm, 366 nm, 437 nm, and 564 nm) of the electromagnetic spectrum,
as given in Figure 42. When compared to the spectra of scytonemin (Figure 43), the
increased intensity of the absorbance from the new pigment in the visible region of the
spectrum suggests the presence of an altered chromophore.
154
Figure 42 HPLC of observed scytonemin and new pigment
Figure 43: UV/VIS absorption spectra of the new pigment
New pigment
Red
uced
scy
tone
min
Oxidised scytonemin Sol
vent
fro
nt
155
Figure 44: Scytonemin oxidized and new pigment – overlay
New pigment – putative structure elucidation: (NMR spectra provided in Appendix X)
The 1H NMR spectrum of this red compound (C39H27N3O4 : ESI-TOF-MS)
indicated a methyl group resonating as a singlet at δ 1.91 and two geminal protons
resonating as doublets at δ3.20 and 3.67. A primary ketimine signal was observed at
δ175.83 and a methylene signal at δ54.76. The HMBC correlations between these proton
and carbon signals were used to build this first part of the molecule and to establish an
attachment to the quaternary carbons at positions 3 and 3a and 4a as shown in Figure 44
and Table 12. At this time, the δ175.83 chemical shift observed for the primary imine is
not known from the literature. However, we validated the proposed structure of this novel
pigment, using mass spectroscopy and other chemical tests.
.
Scytonemin – oxidized
New pigment
156
Figure 45: Molecular structure of new pigment from 1H, HSQC, HMBC (black arrows) and COSY (double headed arrows) NMR spectroscopic analyses in DMSO-d6 solution. Chemical name: 3, 3’-Bis-(4-hydroxy-benzylidine)-3a-(2-imino-propyl)-3a,4-dihydro-3H, 3’H-[1,1’]bi[cyclopenta[b]indolyl]-2,2’-dione
Two protons were observed at δ10.13 and 10.20. These are likely the hydroxyl
protons of the para-substituted phenols. Two carbonyl signals were observed at δ 193.84
and 197.02, quaternary carbons at δ 105.99 and 63.52 , two aromatic quaternary carbons
(possibly bearing the phenol functions), and several other quaternary carbons. A broad
157
Table 12: 1H and 13C NMR data for putative structure of pigment in DMSO-d6 (13C assignment achieved from HSQC and HMBC experiments)
No δ1H (m, JHz) δ13C * 1 129.19 2 193.84 3 105.99 3a 63.52 4 11.91 (broad) 4a 135.12 5 7.49 (d, 7.8, 1H) 126.18 6 6.48 (t, 7.4,7.98 1H) 110.58 7 6.5 (t, 7.98 7.4, 1H) 118.91 8 6.88(d, 8.64, 1H) 128.56 8a 116.21 8b 150.63 9 7.36 (s, 1H) 136.24
10 136.17 11 8.26 (d, 8.26, 1H) 135.83 12 6.86 (d, 8.64, 1H) 116.22 13 160.91 14 6.86 (d, 8.64, 1H) 116.2 15 8.26 (d, 8.26, 1H) 135.83 16 10.20 (s, 1H) 17 3.67 (d, 18.0, 1H) 54.76 17a 3.2 (d, 18.6, 1H) 54.76 18 175.83 19 1.91 (s, 3H) 19.91 20 11.91 (broad) 1' 129.21 2' 197.02 3' 132.44 3'a 173.05 4'a 144.64 5' 7.21(d, 8.22, 1H) 127.23 6' 7.12 (t, 7.68, 7.44, 1H) 121.87 7' 7.24 (t, 7.56, 7.68, 1H) 125.46 8' 7.51 (d, 7.8, 1H) 113.88 8'a 119.12 8'b 150.49 9' 7.34 (s, 1H) 129.54
10' 126.04 11' 7.72 (m, 1H) 131.81 12' 6.95 (d, 1H) 116.9 13' 160.56 14' 6.95 (d, 1H) 116.9 15' 7.72 (m, 1H) 131.87 16' 10.30 (s, 1H)
158
weak signal at δ11.91 was observed in the 1H spectrum, and we are postulating that this
represents the N-H protons at positions 4 and 20. The amine signals of compounds
having the scytoneman skeleton have reported 1H chemical shifts between 11 ppm and
12ppm (Butel-Ponce et al., 2004; Proteau et al., 1993). Signals H-4 and H-20 were not
observed in any of our COSY, NOSY, HMBC and NH HMBC experiments.
A second part of the molecule was built, starting from the H-11/H-15 and H-
12/H-14 signals, as these protons were observed in COSY correlations. HMBC
correlations were observed with H-11/H-15 protons and the quaternary carbon (δ136.17)
at position 10 and the methine carbon at position 9. The H-9 signal had correlations with
the carbonyl and the quaternary carbons at positions 2 and 3 and 10, thus showing the
link between the first and second parts of the molecule. The indole ring was established
by COSY and HMBC correlations of the signals H-5 to H-8 and long range correlations
observed for H-8 to positions 8a and 8b, and H-6 to 4a. The H-17 signals showed long
range correlations with position 4a and 3a, and H-5 showing long range correlation to 3a,
established the link between the indole and parts one and two of the molecule.
The remaining half of the molecule (labeled prime) was built similarly to that of
scytonemin. Starting with positions H-11’/H-15’ and H-12’/H-14’, the para- substituted
phenol as well the vinyl proton at position 9 and the quaternary carbons at positions 3’
and 2’ were established. The indole ring was also established by HMBC and COSY
correlations of the signals H-5’ to H-8’ and long range correlations of H-8’ to positions
8’b and H-1’. The quaternary carbons at positions C-1 and C-1’ provided the connection
between the two halves of the molecule.
159
Only small amounts of this pigment could be isolated in good purity (≤ 3 mg). As
a result, spectral data is limited: For example, clean 13C NMR signals could not be
obtained. All NMR spectra are shown in Appendix X. Mass spectroscopy and several
analytical tests were used to give further verification of the compound’s structure.
Mass interpretation
The proposed structure for this unknown pigment, from mass analyses, has a mass
of 601.2074 Da, calculated for proposed formula C39H27N3O4. The high resolution ESI-
TOF- MS gave m/z 602 [M+H] + and m/z 624 [M+Na] +, as is shown in Figures 45. Our
proposed structure is consistent with this molecular weight and chemical formula. The
odd number molecular weight of this compound would suggest that it has an odd number
of nitrogens, per the nitrogen rule for organic compounds (McLafferty, 1980). Since the
scytoneman skeleton contains two nitrogens, a third nitrogen would have to be present as
part of a substituent on the scytoneman core to support the observed odd number
molecular weight. Thus, our reasoning for the presence of a ketimine functionality. The
imine functionality would also justify the polarity of this pigment, as shown in the
chromatogram (Figure 41).
Initially we had proposed acetate or methyl ketone functionalities, but these
groups would create more discrepancies: A carbon signal of an acetate group would give
chemical shifts between δ160 and δ180, which is consistent with our observed chemical
shift. However, an acetate group would give a molecular weight of 618 Da, which is not
consistent with our confirmed molecular weight. The carbon signal of a methyl ketone
would not give a chemical shift as low as δ 175 and the molecular weight would not be
160
consistent with our confirmed molecular weight. Additionally, acetate and methyl ketone
functional groups would give the compound an even number molecular weight.
Figure 46: HR ESI-TOF MS of new pigment – m/z 602 [M+H]+, m/z 624 [M+Na]+
161
Mass analysis was obtained with LC-MS and MALDI-TOF instrumentation at
Florida Atlantic University. Additional verification of the molecular weight was obtained
from high resolution ESI TOF-MS instrumentation at the University of Florida,
Gainesville. Fragmentation patterns (MSn) were also obtained from LC-MS
instrumentation at the University of Florida. The positive mode ESI- MS/MS of the m/z
602 [M+H] + ion produced prominently m/z 545 via loss of 56 Da. As is shown in Figures
46, this mass is consistent with the loss of the CH2 C (NH) CH3 functionality that
Figure 47: Initial dissociation of m/z 602 [M+H] + ion
we are proposing the structure to have. The m/z 545 is highly aromatic and is thus
difficult to dissociate or interpret any observed dissociation. Figure 47 shows the
fragmentation of this ion. Though resistant to dissociation, this ion produced m/z 528,
517/518 and 489/490 ions. These ions are consistent with those observed from the
atmospheric pressure chemical ionization (APCI) liquid chromatography/MSn of
scytonemin (Squier et al., 2004). According to these authors, the ions at m/z 528
represents the loss of 17 Da and is likely due to elimination of a hydroxyl radical from
OHO
N
OH
O
NH2+
CH3
NH
Molecular Formula = C39H27N3O4
Monoisotopic Mass = 601.200156 Da
OHO
N
OH
O
NH+
m/z 602 [M+H]+m/z 545
162
one of the phenol groups. The m/z 517/518 ions represent a loss of 28 Da, and have been
assigned to expulsion of CO from one of the cyclopentyl carbonyl groups. The ion at m/z
489 corresponds to loss two molecules of CO. Alternately, the CO losses could occur
from the ketone tautomer of the phenol substituents (McLafferty, 1980), as shown in
Figure 48.
Figure 48: (+) ESI- MS/MS dissociation of m/z 602 produced m/z545, which further dissociates to give m/z 528,518, 517 and 489 ions.
SEQ-17095-02 8/16/2011 2:03:56 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(+)ESI
SEQ-17095-02 #1316-1328 RT: 38.32-38.50 AV: 2 NL: 7.07E5T: + c ESI sid=1.00 Full ms [ 125.00-1000.00]
150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
602.2
603.3
546.3
545.3 604.2547.3
264.2 624.1202.7149.1 218.8 413.4284.8186.9 239.2 601.3548.5 660.3428.8391.2300.8 937.4750.2674.4 877.0816.8 958.1332.7 794.7 902.5543.2 707.0464.7 738.3505.0 986.6782.0 866.7340.9
SEQ-17095-02 #1317 RT: 38.34 AV: 1 NL: 1.00E6T: + c sid=1.00 d Full ms2 [email protected] [ 185.00-1215.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
545.4
546.3
547.3 556.3557.4
584.4574.5510.4496.4329.4
SEQ-17095-02 #1317-1329 RT: 38.37-38.55 AV: 2 NL: 2.32E5T: + c sid=1.00 d Full ms3 [email protected] [email protected] [ 140.00-1100.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
517.4
489.4
528.3
490.5516.5
529.3546.3
515.5425.4400.4 501.4 547.3451.4 488.6423.4 530.3344.4 472.8373.4 426.4
163
O
NN
OH
C35H22N2O3Exact mass: 518.16
Mol. Wt.: 518.56
O
N
O
N
OH
C36H20N2O3Exact Mass: 528.16
Mol. Wt.: 528.56
Figure 49: Fragmentation patterns of the highly aromatic portion of the new pigment (consistent with that of scytonemin).
An additional molecular weight of 727 Da was also observed during this analysis. The
HPLC data is provided (Figure 49), but no mass interpretation is given, as we feel that
-OH
-CO
Alternate -
164
this may be an impurity that is associated with our pigment. NMR spectra showed that
the sample was not fully pure. All mass spectra and associated HPLC data are given in
Appendix XI.
SEQ-17095-02 8/16/2011 2:03:56 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(+)ESI
RT: 0.00 - 60.02
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Time (min)
0.055
0.060
0.065
0.070
Inte
nsi
ty
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
39.00602.3
50.80386.7
51.63684.2 54.32
832.149.12237.1 56.33
906.038.50602.2
59.19980.1
48.27257.2
RT: 39.00BP: 602.3
RT: 38.50BP: 602.2
40.90728.1
40.55728.1
59.02728.358.51
728.154.32728.453.49
727.839.84728.1
51.63727.6
46.40727.7
RT: 39.03BP: 0.0
RT: 38.36BP: 0.0
1.870.0
49.960.0
49.070.0
46.950.0
57.250.0
56.270.0
53.930.0
52.240.0
46.180.0
60.000.0
43.590.0
41.910.035.97
0.034.20
0.031.81
0.030.72
0.029.57
0.025.69
0.024.90
0.022.70
0.020.38
0.019.19
0.018.36
0.015.10
0.012.54
0.09.480.0
4.690.0
2.700.0
9.860.0
9.200.0
7.000.0
1.110.0
NL: 5.26E6
Base Peak F: + c ESI sid=1.00 Full ms [ 125.00-1000.00] MS SEQ-17095-02
NL: 5.26E6
m/z= 601.7-602.7 F: + c ESI sid=1.00 Full ms [ 125.00-1000.00] MS SEQ-17095-02
NL: 3.42E5
m/z= 727.6-728.6 F: + c ESI sid=1.00 Full ms [ 125.00-1000.00] MS SEQ-17095-02
NL: 7.08E-2
UV Analog SEQ-17095-02
Figure 50: HPLC/UV. The 601 Da compounds are shaded; The 727 Da compound is likely an impurity associated with the pigment
165
Deuterium exchange reactions were used to confirm the presence of the two
alcohol substituents, as well as the NH protons: The pigment was dissolved in deuterated
methanol (CD3OD) and 1H NMR analysis was done (spectrum shown in Appendix X).
The disappearance of the two phenol protons, due to exchange with the heavier hydrogen,
confirmed their presence. The disappearance of the postulated N-H signals (position 4
and 20 in Figure 44) also confirmed their presence.
Further, acetylation reactions were carried out to confirm the 601 molecular weight as
well as the presence of the NH and OH functional groups. As is shown in Figure 50, the
increased mass of m/z 686 (M+H) + mass units verify that two functional groups on the
compound was acetylated. A second signal at m/z 728 (M+H) + mass units is likely the
same impurity that was observed in the LC/MSn analysis done at the University of
Florida.
Figure 51: mass analysis of pigment after acetylation- m/z 686 [M+H] + confirms acetylation of the two phenol OH groups
166
The pigment was further acetylated, in an attempt to acetylate all of the OH and
NH groups on the compound. HPLC analysis shows that multiple actylations took place.
However, LC-MS of this acetylated product failed to give any further information. One
distinct peak was seen in the LC analysis, but a mass was not obtained for this peak, due
to the fact that if all the NH and OH protons became acetylated, then no H would be
available for ionization in ESI mode.
IR analysis
The IR spectrum of the unknown pigment is shown in Figure 51. The broad signal
at 3411.8 cm-1 likely corresponds to the phenol functionality. Alcohol/phenol compounds
are known to have characteristic broad O-H stretching vibrations between 3200 cm-1 and
3552 cm-1. Signals for N-H functional groups of amines and imines are typically
observed between 3300 cm-1 and 3500 cm-1, thus it is likely that the broad O-H phenol
signal has overlapped these signals. The signals at 2962cm-1 and 2922cm-1 are typical of
C-H stretching vibrations of alkanes. Alkanes give C-H stretching signals between 2850
cm-1 and 3000 cm-1.Therefore, these signals are likely due to the aliphatic portion of the
molecule. However, C-H stretching vibration modes of aromatics also resonate in this
area of the spectrum, so these signals may also reflect vibrations from the aromatic
portion of the molecule as well. Signals for C=O of ketones, C=C of aromatics and
aliphatics, C=N of imines and N-H of primary amines are typically observed at 1665 cm-
1- 1710 cm-1, 1640 cm-1- 1680 cm-1, 1500 cm-1- 1700 cm-1, 1620 cm-1- 1690 cm-1 and,
1580 cm-1- 1650 cm-1 respectively. Since all of these functional groups are present in our
proposed structure, the signals at 1716 cm-1 and 1655 cm-1 are the overlapping vibrations
167
from these groups. The signal at 1451 cm-1 may be due to C-H bending vibrations, which
are typically observed between 1450 cm-1- 1470 cm-1. The C-O stretching vibration
modes are generally observed at 1000 cm-1- 1320 cm-1. Thus, the medium signal at
1376cm-1 is probably from this functional group, as it is present in our structure.
Figure 52: IR spectra of new pigment
Ecological significance of the new pigment
This pigment was isolated from samples of Scytonemin hofmanii grown at light
intensities between 300 and 1800 µmol photons·m-2·s-1(Grant and Louda, 2010). It was
not observed in any of the cultures grown at 100 or 180 µmol photons·m-2·s-1. The
pigment was also isolated from samples collected in areas of the Florida Everglades that
are subject to intense light conditions (> 1500 µmol photons·m-2·s-1). For this reason, and
according to the spectral overlay and absorption emission spectrum shown in Figures 52
168
and 53, we believe that the role of this pigment is to protect the chlorophyll a and
cytochrome soret bands from the excesses of visible light radiation, around 430 - 440 nm.
Figure 53: New pigment and chlorophyll a – overlay spectra
Figure 54: Excitation, emission spectral overlay of new pigment
It likely functions to reduce the amount of excitation energy reaching the chlorophyll a
molecules in PS II. In doing this, electron transport chains and the critical D-1 protein are
not damaged, and photosynthesis can continue without a reduction in efficiency.
Chlorophyll a soret absorption maxima occur at 430 nm, while one of the absorption
maximas for this pigment is at 434 nm. The concentration per cell of the new pigment is
169
typically higher than that of chlorophyll a at the light intensities where it observed (Grant
and Louda, 2010). That is, assuming εmm of the new pigment is of similar magnitude to
that of scytonemin in the violet/UVa region.
Electrons that are generated as a result of excitation of the specialized chlorophyll
a molecules of PSII are carried to PS I via an assembly of membrane proteins known as
cytochrome b563 and c552. These cytochromes have α band absorbance maxima at 563 nm
and 552 nm respectively. Studies have shown that in addition to shuttling electrons to
PSI, the cytochromes also play a protective role during the photoactivation role of PSII
(Schweitzer and Brudvig 1995). That is, they act as a possibly cyclic ‘molecular switch’,
redirecting electron flow within PS II by changing from a high to a low potential form
(Falkowski et al., 1986; Prasil et al., 1996; Mor et al., 1997). This molecular switch
would thus shuttle excess electrons away from photosynthetic electron transport chain. It
would therefore seem critical for these cytochromes to not become activated by light, but
only by resonance energy transfer from the components in the PS II and PS I pathway.
Excitation of the cytochromes by light would only serve to introduce more electrons
(excited states) that could ultimately directly or indirectly (reactive oxygen species
generation) damage the reaction centers of the photosystems. Since our new pigment has
absorbance maxima at 562-564 nm (solvent effect), we speculate at this time that it may
also be protecting the cytochrome α and β absorbance bands as well as the Soret from
light activation. Future work is obviously needed to verify its protective role.
170
VII. APPENDICES
171
I- Pigment calculation and data handling
Data Handling: The entire method, from purchase of the samples to the generation of
ratios, taxonomic division and concentration per cell will be illustrated here with a
Amphidinium carterae sample.
The dinoflagellate Amphidinium carterae was purchased from the Carolina Biological
Supply Company. Growth media was prepared and the sample was grown and analyzed
as per materials and methods section.
Pigment Calculation: The absorbance (µV·s) data was next entered into an in-house
(Florida Atlantic Organic Geochemistry group) generated Excel® spreadsheet called
“PIGCALC”. This spreadsheet contains standardized equations and specific absorption
coefficients and was used to calculate the quantity of each pigment, sums and ratios of
pigments and then converted that information into taxonomic divisions of algae. The
PIGCALC spreadsheet presented here only contains the pigments that are specific to
Amphidinium carterae. Otherwise, a generic PIGCALC, containing all of the pigments
associated with cyanobacteria and eukaryotic microalgae was used.
Pigment calculation was carried out as follows: The sample name and weight or
volume was entered in the appropriate cells. The number of dilutions (from UV/Vis
aliquot) was entered in cell I2. The UV/Vis spectra of the chlorophylls and carotenoids
of this species was below 1, so no dilution was needed. The corrected weight of the
sample is calculated in cell J1 by dividing the original weight by the dilution factor. The
corrected weight is given in column K also (0.051).
172
The Internal Standard UV/Vis value of the day’s extractant solvent (A394 – A750)
was entered at cell G4. The software calculates the IS added in I4 and H36. The Internal
Standard recovered from HPLC was quantified from the peak at 394nm using ε mM = 305,
this is the extinction per millimolar solution per 1cm light path. The extinction coefficient
of the Internal Standard is different from that used for pigments. The pigment extinction
coefficients are E 1% 1cm: 1% values. That is, the absorption of a solution over a 1cm light
path, as given in the literature (see Davies, 1965).
The absorbance (µV·s) value of the Internal Standard peak at 23.60 minutes at the
394nm integration (0.004819617) was entered in cell O36. The molecular weight was
entered in cell C36. The µV·s value, is divided by the molecular weight and the result is
multiplied 0.001 to give the number of moles of Internal Standard (cell F36) found in the
sample (IS found). The molecular weight divided by the number of moles (C36/F36) gives
the weight of Internal Standard found in the sample. The correction factor in J36 was IS
added/IS found (H36/F36). The correction factor formed was 1.57x. All other pigment
corrections are then by a factor of 1.57x.
The absorbance (µV·s) values for each identified peak was entered in column O,
the molecular weights in column C and the extinction coefficients (E 1% 1cm) in column B.
The weight of each pigment (column E) was calculated by dividing the µV·s values by
the extinction coefficient, multiplied by 100. The number of moles of each pigment
(column F) was determined by dividing the corresponding values in column E by those in
column C. The moles of total chlorophyll a were summed and are shown in G7:G18 and
D19 (1.38775 E-10). Total chlorophyll represents the sum of all chlorophyll a derivatives
173
and the degradation products. The number of moles of the pigments from F7: F18 are
then each expressed as a percentage of total chlorophyll a (column H).
The ion pairing solution was added before injection (0.125 mL IP to 1.00 mL
extract). The final volume of the prepared injectate was 1.125 mL, of which 0.100 mL
was injected. Thus, the extract actually injected was 88.89 µL. Correction to original 3.0
mL was 3.0000/0.08889 =33.75. The concentration per mL of the pigments (column J)
was determined by multiplying the weight of each pigment by 33.75 and then dividing
that by the corrected weight of the sample.
Chlorophyll b and chlorophyll c are never included in the total for chlorophyll a,
since they are not found in all algae. In this case, chlorophylls c1/c2 are present in
Amphidinium carterae, and the calculations for this pigment is shown in row 32.
The carotenoid pigments found in Amphidinium carterae, as well as those associated with
cyanobacteria, phytoplankton and plants are shown in column A, lines 41 to 69, with two
spaces for unknown carotenoids also included. In this illustration, the absorbance (µV·s)
data for Peridinin (retention times 15.28 and 16.45) are entered in cell O44. The Peridinin
weight (E44) and number of moles (F44) are calculated as stated above. The number of
moles of all the carotenoids was summed and is shown in G41: G69 (same number). Each
pigment is then expressed as a percentage of total number of moles of carotenoids
(column H, lines 41:69). In this example, Peridinin made up 64.89% of the total
carotenoids. Molar ratios of each pigment to total CHLa (D19) is given in column I
(lines 41:69) and the inverse, total CHLa to each pigment is given in column M (lines
41:69). The molar ratio of peridinin to total CHLa was found to be 0.82 (I44) and the
inverse, total CHLa to PERI was calculated to be 1.22 (M44).
174
Ratios of CHLa to specific carotenoids were then calculated by summing the
moles of chlorophyll a (i.e. CHLa allomer, CHLa and CHLa epimer) and dividing that by
the moles of the carotenoid i.e. (SUM F11: F13) /F44. In this example the chlorophyll
a/Peridinin ratio is 1.18 (B75). These are the ratios of interest, and they are shown in the
section titled ‘Other Ratios’.
For the Divisional Estimate, the only divisional estimator in this example is
Peridinin for the Dinoflagellates. The number of moles of this marker pigment is entered
in cell H85. The chlorophyll a estimate per division marker is calculated in cell H86 by
multiplying H85 by 1.5. This 1.5 represents what we have determined the appropriate
ratio of total chlorophyll to the marker pigment for this species should be. It is based on
ratios derived from extracting algae in our Organic Geochemistry group’s laboratory and
on previously reported ratios, all of which were not related to light studies. The calculated
total CHLa / PERI ratio for this sample is 1.22 (M44), potentially revealing a need for
adjusting the ratios for changes in light intensity. A histogram plot is then made to show
the percentage contribution that each division makes to the sample. In this case, the
sample is obviously 100% dinoflagellate.
The chlorophyll a concentration was calculated as follows: The mass of ‘live’
chlorophyll a was summed in cell C108: ((SUM(E7:E8) + SUM(E12:E14)). This total is
then multiplied by the IS correction factor to give the corrected mass in C109. The
corrected mass is multiplied by 33.75 to give the total mass of live chlorophyll a (C110).
The mass in grams is converted to micrograms in C111. Finally, C111 is divided by the
corrected volume of the sample (H107) to give the concentration of chlorophyll a in
175
μg/mL of sample. The same method was used to calculate the concentration of
chlorophylls c and phaeophytins in the sample.
The percent CHLa estimate from HPLC was calculated as follows: The CHLa
estimate per division (E117) was divided by the sum of live chlorophyll a (C88/(F7+F8)
+SUM (F11:F13))) and multiplied by 100. Summing the number of moles of
pheopigments and dividing by the molar sum of chlorophyll a, then multiplying by 100
calculated the percentage of pheo pigments. Taking the sum of the number of moles of
the derivatives and dividing by the molar sum of CHLa calculated the percentage of
CHLa derivatives.
Pigment per cell was calculated as follows: The number of cells in 1 mL of this
Amphidinium carterae grown at 70-75 μmol photons·m-2·s-1 was determined to be
208638 (0.209 x 106). The concentration per mL of each pigment in the sample was
determined from PIGCALC (column J). This data was exported to another spreadsheet
and pigment concentration was divided by the number of cells counted, to give the
concentration of that pigment per cell.
The protein concentration, colloidal carbohydrate concentration and storage
concentrations were determined as described in chapter two. The concentrations were
exported to spreadsheets where they were compared to chlorophyll a concentration for
that sample, per milliliter and per cell.
176
HPLC Chromatogram (λ = 440 nm) for Amphidinium carterae
Chromatogram peak areas, measured at 440 nm
P468
P45
7
CHLs c1/c2 PERI
DIN
O
DIA
D
DIA
T
CH
La
allo
CHLa
CH
La`
BETA
177
Pigment calculation (PIGCALC)
178
Pigment calculation (PIGCALC), continued
179
II. Select photoprotectorant and accessory pigments
Selected carotenoids: photosynthetic accessory and photoprotectorant pigments
β- Carotene α- Carotene
OH
HO Diatoxanthin Diadinoxanthin
OH
O
OH
AcO Dinoxanthin Lutein
OH
O
OH
HO
Neoxanthin Zeaxanthin
OH
HO
O
Antheraxanthin Violaxanthin
OH
O
HO
OH
HO
OH
HO
180
N
N N
N
M g
C O O C H 3
H
C O O H
O
O
O
Canthaxanthin Echinenone
OHHO
C6H11O4-O
Myxoxanthophyll (Myxo) Myxoxanthophyll (Myxol)
O
OH
HO
O
Fucoxanthinol Astaxanthin
Chlorophyll c1 Chlorophyll c2
N
N N
N
M g
CO O CH 3
H
C O O H
O
O
OH
OHHO
O
OHHO
HO
181
III- Spectroradiometric output Spectroradiometer output for the three main light levels – obtained using HR4000 Spectrometer (Ocean Optics Inc.), coupled to OOI base 32 software using a Dell PC
High Light Experiment – Inside empty growth chamber High Light Experiment – Inside (center) Thalassiosira weisflogii culture
365
405
436
476 635
655
708
811
365
405
436
476
635
655
708
811
182
Medium Light Experiments – Inside empty growth chamber
Medium Light Experiments – Inside (center) Thalassiosira weissflogii culture
436
542
581 604
542
436
581
542 604
183
Low Light Experiments – Inside empty growth chamber
Low Light Experiments – Inside (center) Dunaliella tertiolecta culture
404
487
546 586 612
709
404
487
546
586 612 709
184
Approximately 90% of light is transmitted through the growth containers
UVC-B
UVA PAR Near-IR
185
IV- Calibration curves and equations
Bovine Serum Albumin calibration curve and equation for microbiuret assay
Glucose calibration curve and equation used for phenol sulfuric acid assay
186
Potassium hydrogen phthalate standard curve for Walkley-Black assay of total organic carbon
y = 0.4401xR² = 0.9984
0
0.2
0.4
0.6
0.8
1
1.2
0 0.5 1 1.5 2 2.5
Absorbance
TOC (mg/mL)
TOC standard curve
187
V- Retention times and UV-Vis maximas
Retention times and UV/Vis (PDA) spectral data for Chlorophylls, Chlorophyll derivatives, carotenoids and scytonemin for C-18 column on Waters® 996 HPLC-PDA system.
PIGMENT________________________TIME(min.)____UV/VIS(nm)__________ Solvent Front ~4.5 N/A Scytonemin-like 5.10 372, 440, 562 Bacteriochlorophyllides-d unkn 412, 428, 616, 658 “P468” 5.12 472 “P457” 5.14 460 Scytonemin (Reduced) 7.103 Chlorophyllinde-b unkn 464, 654 Chlorophyllide-a 9.31 426, 582, 616, 660 Chlorin-e6 free acid unkn 414, 514, 554, 606, 660 Chlorophylls-c1/-c2 5.95 446, 582, 628 Scytonemin (oxidized form) 11.11 388 Fucoxanthinol 12.24 452 Cu-chlorophyllin unkn 406,508, (575), 628 Pyro-Chlorophyllide-a* 11.57 426, 582, 616, 660 Peridinin 13.98 474 Pyropheophorbide-b 14.61 438, 530, 600, 656 Vaucheriaxanthin (19-hydroxy-neoxanthin)unkn (422), 440, 476 19’-butanoyloxyfucoxanthin 15.59 446, 470 Siphonoxanthin* unkn 448, (468) Pheophorbide-a 15.95 408, 506, 534, 610, 668 Fucoxanthin 16.26 452 Neoxanthin 16.94 414, 438, 466 Bacteriopheophorbides-d unkn 408, 426, 614, 656 “Polar” MYXO (= aphanizophyll ?) unkn (448), 476, 508 19’-hexanoyloxyfucoxanthin 15.68 446, 468 Pyropheophorbide-a 16.95 412, 510, 540, 608, 666 Violaxanthin 18.60 418, 442, 470 Prasinoxanthin 18.27 454 Pheophorbide-b ME unkn 436, 526, 598, 654 Pheophorbide-b’ ME unkn 436, 526, 598, 654 Myxoxanthophyll (MYXO) 18.76 (448), 476, 508 Astaxanthin unkn 480 Cu-Pheophorbide-a-ME unkn 408, 500,540, (590), 642 Dinoxanthin 19.58 418, 442, 470 cis-Fucoxanthin 20.28 320 , 440, (462) Diadinoxanthin 20.61 (426), 448, 476 Cu-Mesopyropheophorbide-a-ME unkn 418, 544, 592, 636
188
PIGMENT________________________TIME(min_UV/VIS(nm)_________ Bacteriochlorophyll-d(1) 26.11 408, 428, 614, 656 Antheraxanthin* 21.95 446, 473 Cu-Chlorine-e6-TME unkn 406, 500, 634 Pyropheophorbide-b ME unkn 436, 526, 598, 654 Bacteriochlorophyll-d(2) unkn 408, 428, 614, 656 BCHL-c3(7%:4n-Pr, 5Et, 2S)* unkn 434, 630, 666 Pheophorbide-a ME 19.40 410, 508, 538, 608, 666 Cu-Chlorin-p6-TME unkn 406, 500, 538, 640 Phoenicoxanthin unkn 480 Alloxanthin 22.45 (426), 454, 482 BCHL-c5 (71%: 4Et, 5Et, 2R)* unkn 434, 630, 666 Pheophorbide-a’ ME 21.26 410, 508, 538, 608, 666 Diatoxanthin 23.58 (426), 454, 484 BCHL-c4 (17%: 4n-Pr, 5Et, 2R)* unkn 434, 630, 666 BCHL-c1 (5%: 4iBu, 5Et, 2S)* unkn 434, 630, 666 Monadoxanthin unkn (422), 448, 476 Bacteriochlorophyll-d(3) unkn 408, 428, 614, 656 Pyropheophorbide-a ME 21 410, 508, 538, 608, 666 Cu-Purpurin-18-ME unkn 416, 504, 544, 622, 670 Phoenicoxanthin unkn 476 Lutein (,-carotene-3,3’-diol) 24.27 (422), 446, 476 Isozeaxanthin (,-carotene-4,4’-diol) unkn (424), 454, 480 Zeaxanthin (,-carotene-3,3’-diol) 24.61 (424), 454, 480 Bacteriochlorophyll-d(4) unkn 408, 428, 614, 656 4’-hydroxy-echinenone* 26.11 462 (7-?) cis-zeaxanthin* unkn 336, (426), 448, 474 Siphonein* unkn 334, 452, (478) Bacteriochlorophyll-d(5) unkn 408, 428, 614, 656 Bacteriochlorophyll-agg unkn 360, 580, 770 Canthaxanthin (,-carotene-4,4’-dione) 27.45 472 Cu Mesoporphyrin-IX DME (Int.Std.) 27.78 394, 524, 558 Gyryoxanthin Diester 26.61 (422), 448, 470 Bacteriopheophytin-c3* ( 7%) unkn 412, 518, 550, 614, 668 Monodemethylated spirilloxanthin* unkn 468, 494, 530 Rhodovibrin* unkn 458, 484, 518 Bacteriopheophytin-d(1) unkn 424, 520, 612, 652 Bacteriopheophytin-c5* ( 62%) unkn 412, 518, 550, 614, 668 Bacteriopheophytin-c4* ( 27%) unkn 412, 516, 552, 614, 668 Bacteriopheophytin-d(2) unkn 424, 520, 612, 652 Bacteriochlorophyll-ap 29.97 358, 580, 772 Chlorophyll-b 30.78 458, 596, 646 Cyclopyropheophorbide-a-enol unk 360,426, 156, 628, 686 3,4-Didehydrorhodopin* unkn (458), 486, 520
189
PIGMENT________________________TIME(min_UV/VIS(nm)_________ Crocoxanthin unkn (422), 448, 476 Bacteriopheophytin-c1* ( 4%) unkn 412, 518, 550, 614, 668 Rhodopin* unkn 474, (505) Spirilloxanthin unkn 470, 496, 530 Chlorophyll-b’ (epimer) 31.61 458, 596, 646 131-oxydeoxo-Chlorophyll-a (prep:BH4
-.) unkn 416, 514, 562, 606, 654 Chlorophyll-a-allomer (“132-OH-Chl-a”) 32.11 430, 582, 616, 662 Cryptoxanthin unkn (428), 456, 480 Isocryptoxanthin unkn (428), 456, 480 Chlorophyll-a 32.76 430, 582, 616, 662 Echinenone (,-caroten-4-one) unk 462 Chlorophyll-a’ (epimer) 33.61 430, 582, 616, 662 Anhydrorhodovibrin* unkn 460, 482, 518 Pheophytin-b-allomer (“132-OH-PP-b”) unkn 436, 528, 598, 656 Bacteriopheophytin-agg unkn 358, 526, 750 Bacteriopheophytin-ap 34.41 358, 526, 750 Pheophytin-b 35.28 436, 528, 598, 656 Bacteriopheophytin-ap'(epimer) unkn 358, 526, 750 Pheophytin-b’ (epimer) unkn 436, 528, 598, 656 Astaxanthin esters (Panulirus argus) unkn 478 Lycopene unkn 448, 474, 506 Pheophytin-a-allomer (“132-OH-PP-a”) 36.28 410, 502, 536, 610, 666 Pyrobacteriopheophytin-ap unkn 358, 526, 750 Pyropheophytin-b 36.78 436, 528, 598, 656 Pheophytin-a 37.13 410, 502, 536, 610, 666 -Carotene 37.82 440, 465, 495 Pheophytin-a’ (epimer) 37.45 410, 502, 536, 610, 666 -Carotene 39.18 (422), 448, 476 -Carotene (all-trans, all-E) 39.26 (428), 456, 482 cis--Carotene (15-Z, tent.) 39.56 338, (424), 448, 476 Purpurin-18-phytyl Ester* unkn 360, 408, 546, 696 Pyropheophytin-a 40.19 410, 502, 536, 610, 666 Pheophorbide-a-steryl ester(s) 41.27 410, 502, 536, 610, 666 Pyropheophorbide-a-steryl esters* 41.94 410, 502, 536, 610, 666
190
Amphidinium carterae - CHLa/PERI ratios
Descriptives
CHLa/PERI
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.0320 0.11032 0.04934 0.8950 1.1690 0.92 1.15
2.00 6 1.2983 0.14091 0.05753 1.1505 1.4462 1.12 1.49
3.00 6 1.0967 0.25944 0.10591 0.8244 1.3689 0.80 1.48
Total 17 1.1488 0.20964 0.05084 1.0410 1.2566 0.80 1.49
ANOVA
CHLa/PERI
Sum of Squares df Mean Square F Sig.
Between Groups .219 2 0.109 3.159 0.074
Within Groups .484 14 0.035
Total .703 16
Test of Homogeneity of Variances
CHLa/PERI
Levene Statistic df1 df2 Sig.
2.982 2 14 0.083
VI-A
NO
VA
tables
191
Robust Tests of Equality of Means
CHLa/PERI
Statistica df1 df2 Sig.
Brown-Forsythe 3.365 2 9.804 0.077
a. Asymptotically F distributed.
Post Hoc tests
Multiple Comparisons
Dependent Variable: CHLa/peridinin
(I) amphidinium (J) amphidinium Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.26633 0.11265 0.079 -0.5612 0.0285
3.00 -0.06467 0.11265 0.836 -0.3595 0.2302
2.00 dimension3
1.00 0.26633 0.11265 0.079 -0.0285 0.5612
3.00 0.20167 0.10740 0.182 -0.0794 0.4828
3.00 dimension3
1.00 0.06467 0.11265 0.836 -0.2302 0.3595
2.00 -0.20167 0.10740 0.182 -0.4828 0.0794
Games-Howell
dimension2
1.00 dimension3
2.00 -0.26633* 0.07579 0.016 -0.4780 -0.0547
3.00 -0.06467 0.11684 0.848 -0.4088 0.2795
2.00 dimension3
1.00 0.26633* 0.07579 0.016 0.0547 0.4780
3.00 0.20167 0.12053 0.275 -0.1454 0.5488
3.00 dimension3
1.00 0.06467 0.11684 0.848 -0.2795 0.4088
2.00 -0.20167 0.12053 0.275 -0.5488 0.1454
192
Multiple Comparisons
Dependent Variable: CHLa/peridinin
(I) amphidinium (J) amphidinium Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.26633 0.11265 0.079 -0.5612 0.0285
3.00 -0.06467 0.11265 0.836 -0.3595 0.2302
2.00 dimension3
1.00 0.26633 0.11265 0.079 -0.0285 0.5612
3.00 0.20167 0.10740 0.182 -0.0794 0.4828
3.00 dimension3
1.00 0.06467 0.11265 0.836 -0.2302 0.3595
2.00 -0.20167 0.10740 0.182 -0.4828 0.0794
Games-Howell
dimension2
1.00 dimension3
2.00 -0.26633* 0.07579 0.016 -0.4780 -0.0547
3.00 -0.06467 0.11684 0.848 -0.4088 0.2795
2.00 dimension3
1.00 0.26633* 0.07579 0.016 0.0547 0.4780
3.00 0.20167 0.12053 0.275 -0.1454 0.5488
3.00 dimension3
1.00 0.06467 0.11684 0.848 -0.2795 0.4088
2.00 -0.20167 0.12053 0.275 -0.5488 0.1454
*. The mean difference is significant at the 0.05 level.
193
Amphidinium carterae – Protein/CHLa relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.3300 0.08520 0.03810 2.2242 2.4358 2.25 2.43
2.00 6 2.6762 0.11764 0.04802 2.5528 2.7997 2.46 2.80
3.00 6 2.6944 0.06483 0.02647 2.6264 2.7625 2.61 2.77
Total 17 2.5808 0.18805 0.04561 2.4841 2.6775 2.25 2.80
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
0.315 2 14 0.735
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.447 2 0.223 26.215 0.000
Within Groups 0.119 14 0.009
Total 0.566 16
194
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Amphidinium (J) Amphidinium Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.34620* 0.05588 0.000 -0.4925 -0.1999
3.00 -0.36443* 0.05588 0.000 -0.5107 -0.2182
2.00 dimension3
1.00 0.34620* 0.05588 0.000 0.1999 0.4925
3.00 -0.01823 0.05328 0.938 -0.1577 0.1212
3.00 dimension3
1.00 0.36443* 0.05588 0.000 0.2182 0.5107
2.00 0.01823 0.05328 0.938 -0.1212 0.1577
Games-Howell
dimension2
1.00 dimension3
2.00 -0.34620* 0.06130 0.001 -0.5178 -0.1746
3.00 -0.36443* 0.04639 0.000 -0.4992 -0.2296
2.00 dimension3
1.00 0.34620* 0.06130 0.001 0.1746 0.5178
3.00 -0.01823 0.05484 0.941 -0.1759 0.1394
3.00 dimension3
1.00 0.36443* 0.04639 0.000 0.2296 0.4992
2.00 0.01823 0.05484 0.941 -0.1394 0.1759
*. The mean difference is significant at the 0.05 level.
195
Amphidinium carterae – colloidal CHO/CHLa relationships
Descriptives
Colloidal CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.6238 0.12242 0.05475 1.4718 1.7758 1.47 1.78
2.00 6 1.7930 0.06895 0.02815 1.7206 1.8653 1.71 1.92
3.00 6 2.0163 0.03540 0.01445 1.9792 2.0535 1.97 2.06
Total 17 1.8221 0.17993 0.04364 1.7295 1.9146 1.47 2.06
Test of Homogeneity of Variances
Colloidal CHO/CHLa
Levene Statistic df1 df2 Sig.
3.200 2 14 0.072
ANOVA
Colloidal CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.428 2 0.214 33.295 0.000
Within Groups 0.090 14 0.006
Total 0.518 16
196
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Amphidinium (J) Amphidinium Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.16911* 0.04855 0.010 -0.2962 -0.0421
3.00 -0.39249* 0.04855 0.000 -0.5196 -0.2654
2.00 dimension3
1.00 0.16911* 0.04855 0.010 0.0421 0.2962
3.00 -0.22338* 0.04629 0.001 -0.3445 -0.1022
3.00 dimension3
1.00 0.39249* 0.04855 0.000 0.2654 0.5196
2.00 0.22338* 0.04629 0.001 0.1022 0.3445
Games-Howell
dimension2
1.00 dimension3
2.00 -0.16911 0.06156 0.074 -0.3575 0.0193
3.00 -0.39249* 0.05662 0.003 -0.5833 -0.2017
2.00 dimension3
1.00 0.16911 0.06156 0.074 -0.0193 0.3575
3.00 -0.22338* 0.03164 0.000 -0.3152 -0.1316
3.00 dimension3
1.00 0.39249* 0.05662 0.003 0.2017 0.5833
2.00 0.22338* 0.03164 0.000 0.1316 0.3152
*. The mean difference is significant at the 0.05 level.
197
Amphidinium carterae – Storage carbohydrate/ CHLa relationships
Descriptives
Storage/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.2627 0.04569 0.02043 2.2060 2.3195 2.20 2.31
2.00 6 2.3528 0.04679 0.01910 2.3037 2.4019 2.29 2.41
3.00 6 2.5879 0.08262 0.03373 2.5012 2.6746 2.50 2.69
Total 17 2.4093 0.15236 0.03695 2.3310 2.4876 2.20 2.69
Test of Homogeneity of Variances
Storage/CHLa
Levene Statistic df1 df2 Sig.
2.815 2 14 0.094
ANOVA
Storage
Sum of Squares df Mean Square F Sig.
Between Groups 0.318 2 0.159 41.663 0.000
Within Groups 0.053 14 0.004
Total 0.371 16
198
Post Hoc Tests
Multiple Comparisons
Dependent Variable:Storage
(I) Amphidinium (J) Amphidinium Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.09004 0.03741 0.073 -0.1879 0.0079
3.00 -0.32518* 0.03741 0.000 -0.4231 -0.2273
2.00 dimension3
1.00 0.09004 0.03741 0.073 -0.0079 0.1879
3.00 -0.23513* 0.03567 0.000 -0.3285 -0.1418
3.00 dimension3
1.00 0.32518* 0.03741 0.000 0.2273 0.4231
2.00 0.23513* 0.03567 0.000 0.1418 0.3285
Games-Howell
dimension2
1.00 dimension3
2.00 -0.09004* 0.02797 0.027 -0.1686 -0.0115
3.00 -0.32518* 0.03944 0.000 -0.4379 -0.2125
2.00 dimension3
1.00 0.09004* 0.02797 0.027 0.0115 0.1686
3.00 -0.23513* 0.03876 0.001 -0.3462 -0.1241
3.00 dimension3
1.00 0.32518* 0.03944 0.000 0.2125 0.4379
2.00 0.23513* 0.03876 0.001 0.1241 0.3462
*. The mean difference is significant at the 0.05 level.
199
Cyclotella meneghiniana – Chlorophyll a: marker pigment relationship
Descriptives
CHLa/FUCO
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.1820 0.21730 0.09718 0.9122 1.4518 1.02 1.56
2.00 6 1.1200 0.04858 0.01983 1.0690 1.1710 1.07 1.20
3.00 6 1.1083 0.09766 0.03987 1.0058 1.2108 0.99 1.28
Total 17 1.1341 0.12870 0.03121 1.0679 1.2003 0.99 1.56
Test of Homogeneity of Variances
CHLa/FUCO
Levene Statistic df1 df2 Sig.
2.640 2 14 0.106
ANOVA
CHLa/FUCO
Sum of Squares df Mean Square F Sig.
Between Groups 0.017 2 0.008 0.469 0.635
Within Groups 0.248 14 0.018
Total 0.265 16
200
Post Hoc Tests
Multiple Comparisons
Dependent Variable:Chla /FUCO
(I) Cyclotella (J) Cyclotella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.06200 0.08065 0.728 -0.1491 0.2731
3.00 0.07367 0.08065 0.641 -0.1374 0.2848
2.00 dimension3
1.00 -0.06200 0.08065 0.728 -0.2731 0.1491
3.00 0.01167 0.07690 0.987 -0.1896 0.2129
3.00 dimension3
1.00 -0.07367 0.08065 0.641 -0.2848 0.1374
2.00 -0.01167 0.07690 0.987 -0.2129 0.1896
Games-Howell
dimension2
1.00 dimension3
2.00 0.06200 0.09918 0.815 -0.2792 0.4032
3.00 0.07367 0.10504 0.773 -0.2605 0.4078
2.00 dimension3
1.00 -0.06200 0.09918 0.815 -0.4032 0.2792
3.00 0.01167 0.04453 0.963 -0.1180 0.1414
3.00 dimension3
1.00 -0.07367 0.10504 0.773 -0.4078 0.2605
2.00 -0.01167 0.04453 0.963 -0.1414 0.1180
201
Cyclotella meneghiniana – Protein/Chlorophyll a relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.9958 0.03677 0.01644 1.9501 2.0414 1.96 2.04
2.00 7 2.0780 0.04607 0.01741 2.0354 2.1206 1.99 2.14
3.00 6 2.1670 0.03579 0.01461 2.1294 2.2046 2.12 2.21
Total 18 2.0848 0.07861 0.01853 2.0457 2.1239 1.96 2.21
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
0.049 2 15 0.952
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.081 2 0.040 24.596 0.000
Within Groups 0.025 15 0.002
Total 0.105 17
202
Post Hoc Tests Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Cyclotella (J) Cyclotella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.08228* 0.02369 0.009 -0.1438 -0.0208
3.00 -0.17124* 0.02450 0.000 -0.2349 -0.1076
2.00 dimension3
1.00 0.08228* 0.02369 0.009 0.0208 0.1438
3.00 -0.08896* 0.02251 0.003 -0.1474 -0.0305
3.00 dimension3
1.00 0.17124* 0.02450 0.000 0.1076 0.2349
2.00 0.08896* 0.02251 0.003 0.0305 0.1474
Games-Howell
dimension2
1.00 dimension3
2.00 -0.08228* 0.02395 0.016 -0.1482 -0.0164
3.00 -0.17124* 0.02200 0.000 -0.2333 -0.1092
2.00 dimension3
1.00 0.08228* 0.02395 0.016 0.0164 0.1482
3.00 -0.08896* 0.02273 0.006 -0.1504 -0.0275
3.00 dimension3
1.00 0.17124* 0.02200 0.000 0.1092 0.2333
2.00 0.08896* 0.02273 0.006 0.0275 0.1504
*. The mean difference is significant at the 0.05 level.
203
Cyclotella meneghiniana – Colloidal carbohydrate/ Chlorophyll a relationships
Descriptives
Colloidal/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 0.9166 0.04747 0.02123 0.8577 0.9755 0.85 0.98
2.00 7 1.0773 0.07010 0.02649 1.0125 1.1422 1.02 1.21
3.00 6 1.3819 0.06586 0.02689 1.3128 1.4510 1.30 1.45
Total 18 1.1342 0.20114 0.04741 1.0342 1.2342 0.85 1.45
Test of Homogeneity of Variances
Colloidal/CHLa
Levene Statistic df1 df2 Sig.
0.520 2 15 0.605
ANOVA
Colloidal CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.628 2 0.314 78.199 0.000
Within Groups 0.060 15 0.004
Total 0.688 17
204
Post Hoc Tests
Dependent Variable: Colloidal CHO/CHLa
(I) Cyclotella (J) Cyclotella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.16074* 0.03709 0.002 -0.2571 -0.0644
3.00 -0.46532* 0.03836 0.000 -0.5649 -0.3657
2.00 dimension3
1.00 0.16074* 0.03709 0.002 0.0644 0.2571
3.00 -0.30457* 0.03524 0.000 -0.3961 -0.2130
3.00 dimension3
1.00 0.46532* 0.03836 0.000 0.3657 0.5649
2.00 0.30457* 0.03524 0.000 0.2130 0.3961
Games-Howell
dimension2
1.00 dimension3
2.00 -0.16074* 0.03395 0.002 -0.2538 -0.0677
3.00 -0.46532* 0.03426 0.000 -0.5612 -0.3694
2.00 dimension3
1.00 0.16074* 0.03395 0.002 0.0677 0.2538
3.00 -0.30457* 0.03775 0.000 -0.4067 -0.2024
3.00 dimension3
1.00 0.46532* 0.03426 0.000 0.3694 0.5612
2.00 0.30457* 0.03775 0.000 0.2024 0.4067
*. The mean difference is significant at the 0.05 level.
205
Cyclotella meneghiniana – Storage carbohydrate/ Chlorophyll a relationships
Descriptives
Storage/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.7463 0.13582 0.06074 1.5777 1.9150 1.58 1.90
2.00 7 2.1402 0.08153 0.03082 2.0648 2.2156 2.05 2.26
3.00 6 2.3777 0.05055 0.02064 2.3246 2.4307 2.30 2.44
Total 18 2.1100 0.26834 0.06325 1.9765 2.2434 1.58 2.44
Test of Homogeneity of Variances
Storage CHO/CHLa
Levene Statistic df1 df2 Sig.
3.009 2 15 0.080
ANOVA
Storage CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 1.098 2 0.549 65.101 0.000
Within Groups 0.126 15 0.008
Total 1.224 17
206
Post Hoc Tests Multiple Comparisons
Dependent Variable: Storage CHO/CHLa
(I) Cyclotella (J) Cyclotella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.39392* 0.05376 0.000 -0.5336 -0.2543
3.00 -0.63136* 0.05560 0.000 -0.7758 -0.4869
2.00 dimension3
1.00 0.39392* 0.05376 0.000 0.2543 0.5336
3.00 -0.23744* 0.05108 0.001 -0.3701 -0.1048
3.00 dimension3
1.00 0.63136* 0.05560 0.000 0.4869 0.7758
2.00 0.23744* 0.05108 0.001 0.1048 0.3701
Games-Howell
dimension2
1.00 dimension3
2.00 -0.39392* 0.06811 0.003 -0.6023 -0.1855
3.00 -0.63136* 0.06415 0.000 -0.8413 -0.4215
2.00 dimension3
1.00 0.39392* 0.06811 0.003 0.1855 0.6023
3.00 -0.23744* 0.03709 0.000 -0.3389 -0.1360
3.00 dimension3
1.00 0.63136* 0.06415 0.000 0.4215 0.8413
2.00 0.23744* 0.03709 0.000 0.1360 0.3389
*. The mean difference is significant at the 0.05 level.
207
Thalassiosira weissflogii – Chlorophyll a: marker pigment relationship
Descriptives
CHLa/FUCO
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.1420 0.03114 0.01393 1.1033 1.1807 1.11 1.19
2.00 6 1.1717 0.02137 0.00872 1.1492 1.1941 1.14 1.20
3.00 6 1.1517 0.02787 0.01138 1.1224 1.1809 1.12 1.19
Total 17 1.1559 0.02808 0.00681 1.1414 1.1703 1.11 1.20
Test of Homogeneity of Variances
CHLa/FUCO
Levene Statistic df1 df2 Sig.
0.396 2 14 0.680
ANOVA
CHLa/FUCO
Sum of Squares df Mean Square F Sig.
Between Groups 0.003 2 0.001 1.787 0.204
Within Groups 0.010 14 0.001
Total 0.013 16
208
Post Hoc Tests Multiple Comparisons
Dependent Variable: CHLa/FUCO
(I) Thalassiosira (J) Thalassiosira Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.02967 0.01622 0.196 -0.0721 0.0128
3.00 -0.00967 0.01622 0.825 -0.0521 0.0328
2.00 dimension3
1.00 0.02967 0.01622 0.196 -0.0128 0.0721
3.00 0.02000 0.01547 0.422 -0.0205 0.0605
3.00 dimension3
1.00 0.00967 0.01622 0.825 -0.0328 0.0521
2.00 -0.02000 0.01547 0.422 -0.0605 0.0205
Games-Howell
dimension2
1.00 dimension3
2.00 -0.02967 0.01644 0.237 -0.0782 0.0189
3.00 -0.00967 0.01798 0.855 -0.0608 0.0415
2.00 dimension3
1.00 0.02967 0.01644 0.237 -0.0189 0.0782
3.00 0.02000 0.01434 0.382 -0.0197 0.0597
3.00 dimension3
1.00 0.00967 0.01798 0.855 -0.0415 0.0608
2.00 -0.02000 0.01434 0.382 -0.0597 0.0197
209
Thalassiosira weissflogii – protein/ chlorophyll a relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.2081 0.05003 0.02237 2.1460 2.2702 2.14 2.27
2.00 5 2.2540 0.02572 0.01150 2.2221 2.2860 2.22 2.29
3.00 6 2.3265 0.03702 0.01511 2.2876 2.3653 2.28 2.38
Total 16 2.2668 0.06266 0.01567 2.2334 2.3002 2.14 2.38
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
1.231 2 13 0.324
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.039 2 0.020 13.124 0.001
Within Groups 0.020 13 0.002
Total 0.059 15
210
Post Hoc Tests Multiple Comparisons
Dependent Variable: Protein/ CHLa
(I) Thalassiosira (J) Thalassiosira Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.04594 0.02450 0.185 -0.1106 0.0188
3.00 -.011835* 0.02346 0.001 -0.1803 -0.0564
2.00 dimension3
1.00 0.04594 0.02450 0.185 -0.0188 0.1106
3.00 -0.07241* 0.02346 0.022 -0.1343 -0.0105
3.00 dimension3
1.00 0.11835* 0.02346 0.001 0.0564 0.1803
2.00 0.07241* 0.02346 0.022 0.0105 0.1343
Games-Howell
dimension2
1.00 dimension3
2.00 -0.04594 0.02516 0.240 -0.1232 0.0313
3.00 -0.11835* 0.02700 0.007 -0.1971 -0.0396
2.00 dimension3
1.00 0.04594 0.02516 0.240 -0.0313 0.1232
3.00 -0.07241* 0.01899 0.011 -0.1257 -0.0191
3.00 dimension3
1.00 0.11835* 0.02700 0.007 0.0396 0.1971
2.00 0.07241* 0.01899 0.011 0.0191 0.1257
*. The mean difference is significant at the 0.05 level.
211
Thalassiosira weissflogii – colloidal carbohydrate/chlorophyll a relationships
Descriptives
Colloidal CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 0.8918 0.08721 0.03900 0.7835 1.0001 0.80 1.02
2.00 5 1.0720 0.01649 0.00738 1.0515 1.0925 1.05 1.09
3.00 6 1.2020 0.03848 0.01571 1.1617 1.2424 1.15 1.25
Total 16 1.0645 0.14185 0.03546 0.9889 1.1400 0.80 1.25
Test of Homogeneity of Variances
Colloidal CHO/CHLa
Levene Statistic df1 df2 Sig.
5.685 2 13 0.017
ANOVA
Colloidal CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.263 2 0.131 43.920 0.000
Within Groups 0.039 13 0.003
Total .302 15
212
Post Hoc Tests Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Thalassiosira (J) Thalassiosira Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.18020* 0.03460 0.000 -0.2716 -0.0888
3.00 -0.31025* 0.03313 0.000 -0.3977 -0.2228
2.00 dimension3
1.00 0.18020* 0.03460 0.000 0.0888 0.2716
3.00 -0.13005* 0.03313 0.005 -0.2175 -0.0426
3.00 dimension3
1.00 0.31025* 0.03313 0.000 0.2228 0.3977
2.00 0.13005* 0.03313 0.005 0.0426 0.2175
Games-Howell
dimension2
1.00 dimension3
2.00 -0.18020* 0.03969 0.020 -0.3174 -0.0430
3.00 -0.31025* 0.04205 0.001 -0.4444 -0.1761
2.00 dimension3
1.00 0.18020* 0.03969 0.020 0.0430 0.3174
3.00 -0.13005* 0.01735 0.000 -0.1811 -0.0790
3.00 dimension3
1.00 0.31025* 0.04205 0.001 0.1761 0.4444
2.00 0.13005* 0.01735 0.000 0.0790 0.1811
*. The mean difference is significant at the 0.05 level.
213
Thalassiosira weissflogii – Storage Carbohydrate/Chlorophyll a relationships
Descriptives
Storage CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.0324 0.05363 0.02398 1.9658 2.0990 1.95 2.08
2.00 5 2.1568 0.06144 0.02748 2.0805 2.2331 2.10 2.23
3.00 6 2.2875 0.06205 0.02533 2.2224 2.3527 2.19 2.37
Total 16 2.1670 0.12224 0.03056 2.1018 2.2321 1.95 2.37
Test of Homogeneity of Variances
Storage CHO/CHLa
Levene Statistic df1 df2 Sig.
0.358 2 13 0.706
ANOVA
Storage CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.178 2 0.089 25.271 0.000
Within Groups 0.046 13 0.004
Total 0.224 15
214
Post Hoc Tests Multiple Comparisons
Dependent Variable: Storage CHO/CHLa
(I) Thalassiosira (J) Thalassiosira Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.12442* 0.03756 0.014 -0.2236 -0.0252
3.00 -0.25513* 0.03596 0.000 -0.3501 -0.1602
2.00 dimension3
1.00 0.12442* 0.03756 0.014 0.0252 0.2236
3.00 -0.13071* 0.03596 0.008 -0.2257 -0.0358
3.00 dimension3
1.00 0.25513* 0.03596 0.000 0.1602 0.3501
2.00 0.13071* 0.03596 0.008 0.0358 0.2257
Games-Howell
dimension2
1.00 dimension3
2.00 -0.12442* 0.03647 0.023 -0.2290 -0.0198
3.00 -0.25513* 0.03489 0.000 -0.3526 -0.1577
2.00 dimension3
1.00 0.12442* 0.03647 0.023 0.0198 0.2290
3.00 -0.13071* 0.03737 0.018 -0.2358 -0.0257
3.00 dimension3
1.00 0.25513* 0.03489 0.000 0.1577 0.3526
2.00 0.13071* 0.03737 0.018 0.0257 0.2358
*. The mean difference is significant at the 0.05 level.
215
Dunaliella tertiolecta – Chlorophyll a: marker pigment relationships
Descriptives
CHLa/CHLb
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 2.4125 0.13401 0.06700 2.1993 2.6257 2.26 2.58
2.00 5 2.5380 0.25636 0.11465 2.2197 2.8563 2.24 2.92
3.00 6 2.1467 0.23019 0.09397 1.9051 2.3882 1.77 2.41
Total 15 2.3480 0.27019 0.06976 2.1984 2.4976 1.77 2.92
Test of Homogeneity of Variances
CHLa/CHLb
Levene Statistic df1 df2 Sig.
0.622 2 12 0.553
ANOVA
CHLa/CHLb
Sum of Squares df Mean Square F Sig.
Between Groups 0.440 2 0.220 4.542 0.034
Within Groups 0.582 12 0.048
Total 1.022 14
216
Post Hoc Tests
Multiple Comparisons
Dependent Variable: CHLa/CHLb
(I) Dunaliella (J) Dunaliella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.12550 0.14769 0.681 -0.5195 0.2685
3.00 0.26583 0.14212 0.189 -0.1133 0.6450
2.00 dimension3
1.00 0.12550 0.14769 0.681 -0.2685 0.5195
3.00 0.39133* 0.13332 0.031 0.0357 0.7470
3.00 dimension3
1.00 -0.26583 0.14212 0.189 -0.6450 0.1133
2.00 -0.39133* 0.13332 0.031 -0.7470 -0.0357
Games-Howell
dimension2
1.00 dimension3
2.00 -0.12550 0.13279 0.634 -0.5286 0.2776
3.00 0.26583 0.11542 0.113 -0.0644 0.5961
2.00 dimension3
1.00 0.12550 0.13279 0.634 -0.2776 0.5286
3.00 0.39133 0.14824 0.068 -0.0299 0.8126
3.00 dimension3
1.00 -0.26583 0.11542 0.113 -0.5961 0.0644
2.00 -0.39133 0.14824 0.068 -0.8126 0.0299
*. The mean difference is significant at the 0.05 level.
217
Dunaliella tertiolecta – Protein/ Chlorophyll a relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 1.8492 0.01749 0.00875 1.8213 1.8770 1.83 1.86
2.00 5 2.0387 0.11846 0.05298 1.8916 2.1858 1.87 2.12
3.00 6 2.2383 0.04013 0.01638 2.1962 2.2804 2.18 2.27
Total 15 2.0680 0.17625 0.04551 1.9704 2.1656 1.83 2.27
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
14.618 2 12 0.001
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.370 2 0.185 34.083 0.000
Within Groups 0.065 12 0.005
Total 0.435 14
218
Robust Tests of Equality of Means
Protein/CHLa
Statistica df1 df2 Sig.
Brown-Forsythe 35.066 2 5.036 0.001
a. Asymptotically F distributed.
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Dunaliella (J) Dunaliella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.18953* 0.04941 0.006 -0.3213 -0.0577
3.00 -0.38911* 0.04754 0.000 -0.5160 -0.2623
2.00 dimension3
1.00 0.18953* 0.04941 0.006 0.0577 0.3213
3.00 -0.19958* 0.04460 0.002 -0.3186 -0.0806
3.00 dimension3
1.00 0.38911* 0.04754 0.000 0.2623 0.5160
2.00 0.19958* 0.04460 0.002 0.0806 0.3186
Games-Howell
dimension2
1.00 dimension3
2.00 -0.18953* 0.05370 0.048 -0.3764 -0.0026
3.00 -0.38911* 0.01857 0.000 -0.4433 -0.3349
2.00 dimension3
1.00 0.18953* 0.05370 0.048 0.0026 0.3764
3.00 -0.19958* 0.05545 0.037 -0.3832 -0.0159
3.00 dimension3
1.00 0.38911* 0.01857 0.000 0.3349 0.4433
2.00 0.19958* 0.05545 0.037 0.0159 0.3832
219
Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Dunaliella (J) Dunaliella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.18953* 0.04941 0.006 -0.3213 -0.0577
3.00 -0.38911* 0.04754 0.000 -0.5160 -0.2623
2.00 dimension3
1.00 0.18953* 0.04941 0.006 0.0577 0.3213
3.00 -0.19958* 0.04460 0.002 -0.3186 -0.0806
3.00 dimension3
1.00 0.38911* 0.04754 0.000 0.2623 0.5160
2.00 0.19958* 0.04460 0.002 0.0806 0.3186
Games-Howell
dimension2
1.00 dimension3
2.00 -0.18953* 0.05370 0.048 -0.3764 -0.0026
3.00 -0.38911* 0.01857 0.000 -0.4433 -0.3349
2.00 dimension3
1.00 0.18953* 0.05370 0.048 0.0026 0.3764
3.00 -0.19958* 0.05545 0.037 -0.3832 -0.0159
3.00 dimension3
1.00 0.38911* 0.01857 0.000 0.3349 0.4433
2.00 0.19958* 0.05545 0.037 0.0159 0.3832
*. The mean difference is significant at the 0.05 level.
220
Dunaliella tertiolecta – Colloidal carbohydrate/ Chlorophyll a relationships
Descriptives
Colloidal CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 0.9315 0.03977 0.01988 0.8682 0.9948 0.88 0.97
2.00 5 0.6514 0.11837 0.05293 0.5044 0.7984 0.51 0.77
3.00 6 0.7442 0.10814 0.04415 0.6307 0.8577 0.60 0.85
Total 15 0.7632 0.14569 0.03762 0.6825 0.8439 0.51 0.97
Test of Homogeneity of Variances
Colloidal CHO/CHLa
Levene Statistic df1 df2 Sig.
3.748 2 12 0.054
ANOVA
Colloidal CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.178 2 0.089 8.952 0.004
Within Groups 0.119 12 0.010
Total 0.297 14
221
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Dunaliella (J) Dunaliella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.28007* 0.06687 0.003 0.1017 0.4585
3.00 0.18726* 0.06435 0.033 0.0156 0.3589
2.00 dimension3
1.00 -0.28007* 0.06687 0.003 -0.4585 -0.1017
3.00 -0.09282 0.06036 0.309 -0.2539 0.0682
3.00 dimension3
1.00 -0.18726* 0.06435 0.033 -0.3589 -0.0156
2.00 0.09282 0.06036 0.309 -0.0682 0.2539
Games-Howell
dimension2
1.00 dimension3
2.00 0.28007* 0.05655 0.009 0.0970 0.4631
3.00 0.18726* 0.04842 0.016 0.0435 0.3310
2.00 dimension3
1.00 -0.28007* 0.05655 0.009 -0.4631 -0.0970
3.00 -0.09282 0.06893 0.410 -0.2883 0.1027
3.00 dimension3
1.00 -0.18726* 0.04842 0.016 -0.3310 -0.0435
2.00 0.09282 0.06893 0.410 -0.1027 0.2883
*. The mean difference is significant at the 0.05 level.
222
Dunaliella tertiolecta – storage carbohydrate/ Chlorophyll a relationships
Descriptives
Storage CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 1.7770 0.02382 0.01191 1.7390 1.8149 1.76 1.81
2.00 5 1.9321 0.05264 0.02354 1.8667 1.9974 1.85 1.99
3.00 6 2.0357 0.03575 0.01460 1.9982 2.0732 1.97 2.06
Total 15 1.9322 0.11335 0.02927 1.8694 1.9949 1.76 2.06
Test of Homogeneity of Variances
Storage CHO/CHLa
Levene Statistic df1 df2 Sig.
1.080 2 12 0.370
ANOVA
Storage CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.161 2 0.080 50.279 0.000
Within Groups 0.019 12 0.002
Total 0.180 14
223
Post Hoc Tests Multiple Comparisons
Dependent Variable: Storage CHO/CHLa
(I) Dunaliella (J) Dunaliella Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.15513* 0.02682 0.000 -0.2267 -0.0836
3.00 -0.25877* 0.02580 0.000 -0.3276 -0.1899
2.00 dimension3
1.00 0.15513* 0.02682 0.000 0.0836 0.2267
3.00 -0.10364* 0.02421 0.003 -0.1682 -0.0391
3.00 dimension3
1.00 0.25877* 0.02580 0.000 0.1899 0.3276
2.00 0.10364* 0.02421 0.003 0.0391 0.1682
Games-Howell
dimension2
1.00 dimension3
2.00 -0.15513* 0.02638 0.003 -0.2369 -0.0734
3.00 -0.25877* 0.01884 0.000 -0.3126 -0.2049
2.00 dimension3
1.00 0.15513* 0.02638 0.003 0.0734 0.2369
3.00 -0.10364* 0.02770 0.018 -0.1856 -0.0216
3.00 dimension3
1.00 0.25877* 0.01884 0.000 0.2049 0.3126
2.00 0.10364* 0.02770 0.018 0.0216 0.1856
*. The mean difference is significant at the 0.05 level.
224
Scenedesmus quadricauda – Chlorophyll a: marker pigment relationships
Descriptives
Pigment/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 2.7425 0.15924 .07962 2.4891 2.9959 2.52 2.88
2.00 7 2.6929 0.29004 .10963 2.4246 2.9611 2.07 2.92
3.00 6 2.5700 0.46143 .18838 2.0858 3.0542 1.98 3.18
Total 17 2.6612 0.32871 .07972 2.4922 2.8302 1.98 3.18
Test of Homogeneity of Variances
Pigment/CHLa
Levene Statistic df1 df2 Sig.
3.647 2 14 0.053
ANOVA
Pigment/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.083 2 0.042 0.355 0.708
Within Groups 1.645 14 0.118
Total 1.729 16
225
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Pigment/CHLa
(I) Scenedesmus (J) Scenedesmus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.04964 0.21488 0.971 -0.5128 0.6120
3.00 0.17250 0.22129 0.721 -0.4067 0.7517
2.00 dimension3
1.00 -0.04964 0.21488 0.971 -0.6120 0.5128
3.00 0.12286 0.19073 0.799 -0.3763 0.6221
3.00 dimension3
1.00 -0.17250 0.22129 0.721 -0.7517 0.4067
2.00 -0.12286 0.19073 0.799 -0.6221 0.3763
Games-Howell
dimension2
1.00 dimension3
2.00 0.04964 0.13549 0.929 -0.3287 0.4280
3.00 0.17250 0.20451 0.691 -0.4389 0.7839
2.00 dimension3
1.00 -0.04964 0.13549 0.929 -0.4280 0.3287
3.00 0.12286 0.21795 0.842 -0.4971 0.7428
3.00 dimension3
1.00 -0.17250 0.20451 0.691 -0.7839 0.4389
2.00 -0.12286 0.21795 0.842 -0.7428 0.4971
226
Scenedesmus quadricauda – Protein/ Chlorophyll a relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.7741 0.13814 0.06178 2.6026 2.9456 2.63 2.93
2.00 7 2.2583 0.05923 0.02239 2.2035 2.3131 2.22 2.37
3.00 6 2.1043 0.02094 0.00855 2.0823 2.1263 2.07 2.14
Total 18 2.3503 0.28900 0.06812 2.2065 2.4940 2.07 2.93
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
15.945 2 15 0.000
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 1.320 2 0.660 99.450 0.000
Within Groups 0.100 15 0.007
Total 1.420 17
227
Robust Tests of Equality of Means
Protein/CHLa
Statistica df1 df2 Sig.
Brown-Forsythe 81.411 2 5.449 0.000
a. Asymptotically F distributed.
Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Scenedesmus (J) Scenedesmus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.51579* 0.04771 0.000 0.3919 0.6397
3.00 0.66978* 0.04934 0.000 0.5416 0.7979
2.00 dimension3
1.00 -0.51579* 0.04771 0.000 -0.6397 -0.3919
3.00 0.15400* 0.04533 0.010 0.0363 0.2717
3.00 dimension3
1.00 -0.66978* 0.04934 0.000 -0.7979 -0.5416
2.00 -0.15400* 0.04533 0.010 -0.2717 -0.0363
Games-Howell
dimension2
1.00 dimension3
2.00 0.51579* 0.06571 0.001 0.3029 0.7287
3.00 0.66978* 0.06237 0.001 0.4513 0.8883
2.00 dimension3
1.00 -0.51579* 0.06571 0.001 -0.7287 -0.3029
3.00 0.15400* 0.02396 0.001 0.0849 0.2231
3.00 dimension3
1.00 -0.66978* 0.06237 0.001 -0.8883 -0.4513
2.00 -0.15400* 0.02396 0.001 -0.2231 -0.0849
*. The mean difference is significant at the 0.05 level.
228
Scenedesmus quadricauda – Colloidal carbohydrate/ Chlorophyll a relationships
Descriptives
Colloidal/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.9958 0.20010 0.08949 1.7474 2.2443 1.71 2.26
2.00 7 0.8626 0.10308 0.03896 0.7673 0.9580 0.76 1.00
3.00 6 0.8675 0.02494 0.01018 0.8413 0.8937 0.83 0.90
Total 18 1.1790 0.53391 0.12584 0.9135 1.4445 0.76 2.26
Test of Homogeneity of Variances
Colloidal/CHLa
Levene Statistic df1 df2 Sig.
4.723 2 15 0.026
ANOVA
Colloidal/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 4.619 2 2.309 152.592 0.000
Within Groups 0.227 15 0.015
Total 4.846 17
229
Robust Tests of Equality of Means
Colloidal/CHLa
Statistica df1 df2 Sig.
Brown-Forsythe 128.928 2 5.939 0.000
a. Asymptotically F distributed.
Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Scenedesmus (J) Scenedesmus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 1.13321* 0.07204 0.000 0.9461 1.3203
3.00 1.12834* 0.07449 0.000 0.9348 1.3218
2.00 dimension3
1.00 -1.13321* 0.07204 0.000 -1.3203 -0.9461
3.00 -0.00487 0.06844 0.997 -0.1827 0.1729
3.00 dimension3
1.00 -1.12834* 0.07449 0.000 -1.3218 -0.9348
2.00 0.00487 0.06844 0.997 -0.1729 0.1827
Games-Howell
dimension2
1.00 dimension3
2.00 1.13321* 0.09760 0.000 0.8262 1.4402
3.00 1.12834* 0.09006 0.000 0.8111 1.4456
2.00 dimension3
1.00 -1.13321* 0.09760 0.000 -1.4402 -0.8262
3.00 -0.00487 0.04027 0.992 -0.1243 0.1145
3.00 dimension3
1.00 -1.12834* 0.09006 0.000 -1.4456 -0.8111
2.00 0.00487 0.04027 0.992 -0.1145 0.1243
230
Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Scenedesmus (J) Scenedesmus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 1.13321* 0.07204 0.000 0.9461 1.3203
3.00 1.12834* 0.07449 0.000 0.9348 1.3218
2.00 dimension3
1.00 -1.13321* 0.07204 0.000 -1.3203 -0.9461
3.00 -0.00487 0.06844 0.997 -0.1827 0.1729
3.00 dimension3
1.00 -1.12834* 0.07449 0.000 -1.3218 -0.9348
2.00 0.00487 0.06844 0.997 -0.1729 0.1827
Games-Howell
dimension2
1.00 dimension3
2.00 1.13321* 0.09760 0.000 0.8262 1.4402
3.00 1.12834* 0.09006 0.000 0.8111 1.4456
2.00 dimension3
1.00 -1.13321* 0.09760 0.000 -1.4402 -0.8262
3.00 -0.00487 0.04027 0.992 -0.1243 0.1145
3.00 dimension3
1.00 -1.12834* 0.09006 0.000 -1.4456 -0.8111
2.00 0.00487 0.04027 0.992 -0.1145 0.1243
*. The mean difference is significant at the 0.05 level.
231
Scenedesmus quadricauda – Storage carbohydrate/ Chlorophyll a relationships
Descriptives
Storage CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 3.0290 0.22731 0.10165 2.7468 3.3112 2.78 3.27
2.00 7 1.7367 0.30617 0.11572 1.4536 2.0199 1.28 2.22
3.00 6 1.2671 0.02778 0.01134 1.2379 1.2962 1.22 1.30
Total 18 1.9391 0.75572 0.17812 1.5633 2.3150 1.22 3.27
Test of Homogeneity of Variances
Storage CHO/CHL a
Levene Statistic df1 df2 Sig.
5.098 2 15 0.020
ANOVA
Storage CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 8.936 2 4.468 86.701 0.000
Within Groups 0.773 15 0.052
Total 9.709 17
232
Robust Tests of Equality of Means
Storage CHO/CHLa
Statistica df1 df2 Sig.
Brown-Forsythe 93.945 2 10.107 0.000
a. Asymptotically F distributed.
Multiple Comparisons
Dependent Variable: Storage CHO/CHLa
(I) Scenedesmus (J) Scenedesmus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 1.29226* 0.13292 0.000 0.9470 1.6375
3.00 1.76193* 0.13746 0.000 1.4049 2.1190
2.00 dimension3
1.00 -1.29226* 0.13292 0.000 -1.6375 -0.9470
3.00 0.46968* 0.12630 0.005 0.1416 0.7977
3.00 dimension3
1.00 -1.76193* 0.13746 0.000 -2.1190 -1.4049
2.00 -0.46968* 0.12630 0.005 -0.7977 -0.1416
Games-Howell
dimension2
1.00 dimension3
2.00 1.29226* 0.15403 0.000 0.8696 1.7149
3.00 1.76193* 0.10229 0.000 1.4015 2.1224
2.00 dimension3
1.00 -1.29226* 0.15403 0.000 -1.7149 -0.8696
3.00 0.46968* 0.11628 0.015 0.1148 0.8245
3.00 dimension3
1.00 -1.76193* 0.10229 0.000 -2.1224 -1.4015
2.00 -.46968* 0.11628 0.015 -0.8245 -0.1148
*. The mean difference is significant at the 0.05 level.
233
Synechococcus elongatus – Chlorophyll a: marker pigment relationships
Descriptives
CHLa/Zea
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 6.4850 0.29939 0.14969 6.0086 6.9614 6.07 6.76
2.00 7 4.7343 0.53344 0.20162 4.2409 5.2276 3.85 5.52
3.00 5 3.0580 0.20669 0.09243 2.8014 3.3146 2.86 3.40
4.00 6 0.8683 0.13045 0.05326 0.7314 1.0052 0.64 1.01
Total 22 3.6173 2.07898 0.44324 2.6955 4.5390 0.64 6.76
Test of Homogeneity of Variances
CHLa/Zea
Levene Statistic df1 df2 Sig.
2.194 3 18 0.124
ANOVA
CHLa /Zea
Sum of Squares df Mean Square F Sig.
Between Groups 88.533 3 29.511 237.968 0.000
Within Groups 2.232 18 0.124
Total 90.766 21
234
Multiple Comparisons
Dependent Variable: CHLa/Zea
(I) Synechococcus (J) Synechococcus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00
dimension3
2.00 1.75071* 0.22072 0.000 1.1269 2.3745
3.00 3.42700* 0.23623 0.000 2.7593 4.0947
4.00 5.61667* 0.22731 0.000 4.9742 6.2591
2.00
dimension3
1.00 -1.75071* 0.22072 0.000 -2.3745 -1.1269
3.00 1.67629* 0.20620 0.000 1.0935 2.2591
4.00 3.86595* 0.19592 0.000 3.3122 4.4197
3.00
dimension3
1.00 -3.42700* 0.23623 0.000 -4.0947 -2.7593
2.00 -1.67629* 0.20620 0.000 -2.2591 -1.0935
4.00 2.18967* 0.21324 0.000 1.5870 2.7923
4.00
dimension3
1.00 -5.61667* 0.22731 0.000 -6.2591 -4.9742
2.00 -3.86595* 0.19592 0.000 -4.4197 -3.3122
3.00 -2.18967* 0.21324 0.000 -2.7923 -1.5870
Games-Howell
dimension2
1.00
dimension3
2.00 1.75071* 0.25112 0.000 .9664 2.5350
3.00 3.42700* 0.17593 0.000 2.7856 4.0684
4.00 5.61667* 0.15889 0.000 4.9499 6.2834
2.00
dimension3
1.00 -1.75071* 0.25112 0.000 -2.5350 -.9664
3.00 1.67629* 0.22180 0.000 .9708 2.3818
4.00 3.86595* 0.20854 0.000 3.1710 4.5609
3.00 dimension3
1.00 -3.42700* 0.17593 0.000 -4.0684 -2.7856
235
2.00 -1.67629* 0.22180 0.000 -2.3818 -.9708
4.00 2.18967* 0.10668 0.000 1.8295 2.5498
4.00
dimension3
1.00 -5.61667* .15889 0.000 -6.2834 -4.9499
2.00 -3.86595* .20854 0.000 -4.5609 -3.1710
3.00 -2.18967* .10668 0.000 -2.5498 -1.8295
*. The mean difference is significant at the 0.05 level.
Descriptives
CHLa/Echinenone
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 58.0550 8.10271 4.05136 45.1618 70.9482 50.09 68.03
2.00 7 63.1243 4.50956 1.70445 58.9536 67.2949 57.12 68.89
3.00 5 34.1800 3.58971 1.60537 29.7228 38.6372 28.57 37.31
4.00 6 13.6233 2.09319 .85454 11.4267 15.8200 10.20 15.61
Total 22 42.1241 21.47420 4.57831 32.6030 51.6452 10.20 68.89
Test of Homogeneity of Variances
CHLa/Echinenone
Levene Statistic df1 df2 Sig.
3.858 3 18 0.027
236
ANOVA
CHLa/Echinenone
Sum of Squares df Mean Square F Sig.
Between Groups 9291.535 3 3097.178 142.062 0.000
Within Groups 392.430 18 21.802
Total 9683.965 21
Robust Tests of Equality of Means
CHLa/Echinenone
Statistica df1 df2 Sig.
Brown-Forsythe 115.099 3 6.385 0.000
a. Asymptotically F distributed.
Multiple Comparisons
Dependent Variable: CHLa/Echinenone
(I) synechococcus (J) synechococcus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00
dimension3
2.00 -5.06929 2.92659 0.337 -13.3407 3.2021
3.00 23.87500* 3.13221 0.000 15.0225 32.7275
4.00 44.43167* 3.01397 0.000 35.9133 52.9500
2.00
dimension3
1.00 5.06929 2.92659 0.337 -3.2021 13.3407
3.00 28.94429* 2.73402 0.000 21.2172 36.6714
4.00 49.50095* 2.59772 0.000 42.1590 56.8429
237
3.00
dimension3
1.00 -23.87500* 3.13221 0.000 -32.7275 -15.0225
2.00 -28.94429* 2.73402 0.000 -36.6714 -21.2172
4.00 20.55667* 2.82736 0.000 12.5657 28.5476
4.00
dimension3
1.00 -44.43167* 3.01397 0.000 -52.9500 -35.9133
2.00 -49.50095* 2.59772 0.000 -56.8429 -42.1590
3.00 -20.55667* 2.82736 0.000 -28.5476 -12.5657
Games-Howell
dimension2
1.00
dimension3
2.00 -5.06929 4.39530 0.681 -22.7630 12.6245
3.00 23.87500* 4.35783 0.019 6.0068 41.7432
4.00 44.43167* 4.14050 0.004 25.5549 63.3084
2.00
dimension3
1.00 5.06929 4.39530 0.681 -12.6245 22.7630
3.00 28.94429* 2.34145 0.000 21.7543 36.1342
4.00 49.50095* 1.90667 0.000 43.5117 55.4902
3.00
dimension3
1.00 -23.87500* 4.35783 0.019 -41.7432 -6.0068
2.00 -28.94429* 2.34145 0.000 -36.1342 -21.7543
4.00 20.55667* 1.81864 0.000 14.3218 26.7915
4.00
dimension3
1.00 -44.43167* 4.14050 0.004 -63.3084 -25.5549
2.00 -49.50095* 1.90667 0.000 -55.4902 -43.5117
3.00 -20.55667* 1.81864 0.000 -26.7915 -14.3218
*. The mean difference is significant at the 0.05 level.
238
Synechococcus elongatus – Protein/Chlorophyll a relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 1.7805 0.02884 0.01442 1.7346 1.8263 1.74 1.81
2.00 5 2.0862 0.03402 0.01522 2.0440 2.1284 2.04 2.13
3.00 6 2.2443 0.03420 0.01396 2.2084 2.2802 2.21 2.29
4.00 5 2.2791 0.00667 0.00298 2.2708 2.2874 2.28 2.29
Total 20 2.1207 0.19186 0.04290 2.0309 2.2105 1.74 2.29
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
2.544 3 16 0.093
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.686 3 0.229 278.265 0.000
Within Groups 0.013 16 0.001
Total 0.699 19
239
Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Synechococcus (J) Synechococcus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00
dimension3
2.00 -0.30575* 0.01923 0.000 -0.3608 -0.2507
3.00 -0.46388* 0.01851 0.000 -0.5168 -0.4109
4.00 -0.49867* 0.01923 0.000 -0.5537 -0.4436
2.00
dimension3
1.00 0.30575* 0.01923 0.000 0.2507 0.3608
3.00 -0.15813* 0.01736 0.000 -0.2078 -0.1085
4.00 -0.19292* 0.01813 0.000 -0.2448 -0.1410
3.00
dimension3
1.00 0.46388* 0.01851 0.000 0.4109 0.5168
2.00 0.15813* 0.01736 0.000 0.1085 0.2078
4.00 -0.03479 0.01736 0.228 -0.0845 0.0149
4.00
dimension3
1.00 0.49867* 0.01923 0.000 0.4436 0.5537
2.00 0.19292* 0.01813 0.000 0.1410 0.2448
3.00 0.03479 0.01736 0.228 -0.0149 0.0845
Games-Howell
dimension2
1.00
dimension3
2.00 -0.30575* 0.02096 0.000 -0.3753 -0.2362
3.00 -0.46388* 0.02007 0.000 -0.5294 -0.3983
4.00 -0.49867* 0.01473 0.000 -0.5660 -0.4314
2.00
dimension3
1.00 0.30575* 0.02096 0.000 0.2362 0.3753
3.00 -0.15813* 0.02065 0.000 -0.2231 -0.0931
4.00 -0.19292* 0.01551 0.001 -0.2538 -0.1320
3.00 dimension3
1.00 0.46388* 0.02007 0.000 0.3983 0.5294
2.00 0.15813* 0.02065 0.000 0.0931 0.2231
240
4.00 -0.03479 0.01428 0.178 -0.0858 0.0162
4.00
dimension3
1.00 0.49867* 0.01473 0.000 0.4314 0.5660
2.00 0.19292* 0.01551 0.001 0.1320 0.2538
3.00 0.03479 0.01428 0.178 -0.0162 0.0858
*. The mean difference is significant at the 0.05 level.
Synechococcus elongatus – Colloidal carbohydrate/ Chlorophyll a relationships
Descriptives
Colloidal CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 0.9572 0.05893 0.02947 0.8634 1.0510 0.89 1.03
2.00 5 0.9926 0.03869 0.01730 0.9446 1.0407 0.94 1.04
3.00 6 1.0173 0.02147 0.00877 0.9947 1.0398 1.00 1.05
4.00 5 1.1018 0.02877 0.01287 1.0661 1.1375 1.08 1.14
Total 20 1.0202 0.06286 0.01406 0.9908 1.0496 0.89 1.14
Test of Homogeneity of Variances
Colloidal CHO/CHLa
Levene Statistic df1 df2 Sig.
1.805 3 16 0.187
241
ANOVA
Colloidal CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.053 3 0.018 12.847 0.000
Within Groups 0.022 16 0.001
Total 0.075 19
Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Synechococcus (J) Synechococcus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00
dimension3
2.00 -0.03545 0.02489 0.503 -0.1067 0.0358
3.00 -0.06011 0.02395 0.096 -0.1286 0.0084
4.00 -0.14464* 0.02489 0.000 -0.2159 -0.0734
2.00
dimension3
1.00 0.03545 0.02489 0.503 -0.0358 0.1067
3.00 -0.02466 0.02247 0.696 -0.0889 0.0396
4.00 -0.10920* 0.02346 0.001 -0.1763 -0.0421
3.00
dimension3
1.00 0.06011 0.02395 0.096 -0.0084 0.1286
2.00 0.02466 0.02247 0.696 -0.0396 0.0889
4.00 -0.08454* 0.02247 0.008 -0.1488 -0.0203
4.00
dimension3
1.00 0.14464* 0.02489 0.000 0.0734 0.2159
2.00 0.10920* 0.02346 0.001 0.0421 0.1763
3.00 0.08454* 0.02247 0.008 0.0203 0.1488
242
Games-Howell
dimension2
1.00
dimension3
2.00 -0.03545 0.03417 0.738 -0.1617 0.0908
3.00 -0.06011 0.03074 0.349 -0.1938 0.0735
4.00 -0.14464* 0.03215 0.034 -0.2734 -0.0159
2.00
dimension3
1.00 0.03545 0.03417 0.738 -0.0908 0.1617
3.00 -0.02466 0.01940 0.610 -0.0918 0.0425
4.00 -0.10920* 0.02156 0.005 -0.1796 -0.0388
3.00
dimension3
1.00 0.06011 0.03074 0.349 -0.0735 0.1938
2.00 0.02466 0.01940 0.610 -0.0425 0.0918
4.00 -0.08454* 0.01557 0.004 -0.1355 -0.0336
4.00
dimension3
1.00 0.14464* 0.03215 0.034 0.0159 0.2734
2.00 0.10920* 0.02156 0.005 0.0388 0.1796
3.00 0.08454* 0.01557 0.004 0.0336 0.1355
*. The mean difference is significant at the 0.05 level.
243
Synechococcus elongatus – Storage carbohydrate/chlorophyll a relationships
Descriptives
Storage
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 4 1.6214 0.02771 0.01386 1.5773 1.6654 1.60 1.65
2.00 5 1.7681 0.01716 0.00767 1.7468 1.7894 1.75 1.79
3.00 6 1.7351 0.02514 0.01026 1.7087 1.7615 1.70 1.77
4.00 5 1.7411 0.02362 0.01056 1.7118 1.7705 1.71 1.77
Total 20 1.7221 0.05753 0.01286 1.6952 1.7490 1.60 1.79
Test of Homogeneity of Variances
Storage CHO/CHLa
Levene Statistic df1 df2 Sig.
.566 3 16 0.646
ANOVA
Storage CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.054 3 0.018 32.465 0.000
Within Groups 0.009 16 0.001
Total 0.063 19
244
Multiple Comparisons
Dependent Variable:Storage CHO/CHLa
(I) Synechococcus (J) Synechococcus Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00
dimension3
2.00 -0.14675* 0.01580 0.000 -0.1919 -0.1016
3.00 -0.11373* 0.01520 0.000 -0.1572 -0.0702
4.00 -0.11979* 0.01580 0.000 -0.1650 -0.0746
2.00
dimension3
1.00 0.14675* 0.01580 0.000 0.1016 0.1919
3.00 0.03302 0.01426 0.136 -0.0078 0.0738
4.00 0.02696 0.01489 0.305 -0.0156 0.0696
3.00
dimension3
1.00 0.11373* 0.01520 0.000 0.0702 0.1572
2.00 -0.03302 0.01426 0.136 -0.0738 0.0078
4.00 -0.00606 0.01426 0.973 -0.0469 0.0347
4.00
dimension3
1.00 0.11979* 0.01580 0.000 0.0746 0.1650
2.00 -0.02696 0.01489 0.305 -0.0696 0.0156
3.00 0.00606 0.01426 0.973 -0.0347 0.0469
Games-Howell
dimension2
1.00
dimension3
2.00 -0.14675* 0.01584 0.001 -0.2062 -0.0873
3.00 -0.11373* 0.01724 0.002 -0.1731 -0.0543
4.00 -0.11979* 0.01742 0.002 -0.1802 -0.0594
2.00
dimension3
1.00 0.14675* 0.01584 0.001 0.0873 0.2062
3.00 0.03302 0.01281 0.115 -0.0072 0.0733
4.00 0.02696 0.01305 0.249 -0.0158 0.0697
3.00 dimension3
1.00 0.11373* 0.01724 0.002 0.0543 0.1731
245
2.00 -0.03302 0.01281 0.115 -0.0733 0.0072
4.00 -0.00606 0.01473 0.975 -0.0522 0.0401
4.00
dimension3
1.00 0.11979* 0.01742 0.002 0.0594 0.1802
2.00 -0.02696 0.01305 0.249 -0.0697 0.0158
3.00 0.00606 0.01473 0.975 -0.0401 0.0522
*. The mean difference is significant at the 0.05 level.
Microcystis aeruginose – Chlorophyll a: marker pigment relationships
Descriptives
CHLa/Zeaxanthin
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 26.6680 1.57357 0.70372 24.7142 28.6218 25.14 29.12
2.00 5 19.3480 1.62603 0.72718 17.3290 21.3670 16.88 21.12
3.00 6 10.6667 1.11355 0.45460 9.4981 11.8353 9.21 12.34
Total 16 18.3800 6.98471 1.74618 14.6581 22.1019 9.21 29.12
246
Test of Homogeneity of Variances
CHLa/ Zeaxanthin
Levene Statistic df1 df2 Sig.
0.477 2 13 0.631
ANOVA
CHLa/ Zeaxanthin
Sum of Squares df Mean Square F Sig.
Between Groups 705.113 2 352.556 171.783 0.000
Within Groups 26.680 13 2.052
Total 731.793 15
Multiple Comparisons
Dependent Variable: CHLa/Zeaxanthin
(I) Microcystis (J) Microcystis Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 7.32000* 0.90605 0.000 4.9276 9.7124
3.00 16.00133* 0.86748 0.000 13.7108 18.2919
2.00 dimension3
1.00 -7.32000* 0.90605 0.000 -9.7124 -4.9276
3.00 8.68133* 0.86748 0.000 6.3908 10.9719
3.00 dimension3
1.00 -16.00133* 0.86748 0.000 -18.2919 -13.7108
2.00 -8.68133* 0.86748 0.000 -10.9719 -6.3908
247
Games-Howell
dimension2
1.00 dimension3
2.00 7.32000* 1.01194 0.000 4.4278 10.2122
3.00 16.00133* 0.83779 0.000 13.5385 18.4642
2.00 dimension3
1.00 -7.32000* 1.01194 0.000 -10.2122 -4.4278
3.00 8.68133* 0.85759 0.000 6.1463 11.2164
3.00 dimension3
1.00 -16.00133* 0.83779 0.000 -18.4642 -13.5385
2.00 -8.68133* 0.85759 0.000 -11.2164 -6.1463
*. The mean difference is significant at the 0.05 level.
Microcystis aeruginose – Chlorophyll a: marker pigment relationships
Descriptives
CHLa/Echinenone
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1 5 18.6820 2.81530 1.25904 15.1863 22.1777 15.32 22.92
2 5 22.0100 3.85852 1.72558 17.2190 26.8010 17.78 27.31
3 6 14.6017 1.06792 0.43598 13.4810 15.7224 13.26 16.23
Total 16 18.1919 4.06930 1.01732 16.0235 20.3603 13.26 27.31
248
Test of Homogeneity of Variances
CHLa/Echinenone
Levene Statistic df1 df2 Sig.
2.672 2 13 0.107
ANOVA
CHLa/Echinenone
Sum of Squares df Mean Square F Sig.
Between Groups 151.429 2 75.715 10.152 0.002
Within Groups 96.959 13 7.458
Total 248.388 15
Post Hoc Tests
Multiple Comparisons
Dependent Variable:CHLa/ Echinenone
(I) Microcystis (J) Microcystis Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1 dimension3
2 -3.32800 1.72723 0.170 -7.8887 1.2327
3 4.08033 1.65370 0.068 -0.2862 8.4468
2 dimension3
1 3.32800 1.72723 0.170 -1.2327 7.8887
3 7.40833* 1.65370 0.002 3.0418 11.7748
3 dimension3
1 -4.08033 1.65370 0.068 -8.4468 0.2862
2 -7.40833* 1.65370 0.002 -11.7748 -3.0418
249
Games-Howell
dimension2
1 dimension3
2 -3.32800 2.13607 0.321 -9.5527 2.8967
3 4.08033 1.33239 0.062 -0.2677 8.4284
2 dimension3
1 3.32800 2.13607 0.321 -2.8967 9.5527
3 7.40833* 1.77981 0.024 1.3871 13.4296
3 dimension3
1 -4.08033 1.33239 0.062 -8.4284 0.2677
2 -7.40833* 1.77981 0.024 -13.4296 -1.3871
*. The mean difference is significant at the 0.05 level.
Microcystis aeruginose – Protein/Chlorophyll a relationships
Descriptives
Protein/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.7317 0.03736 0.01671 1.6853 1.7781 1.69 1.78
2.00 6 1.8037 0.03795 0.01549 1.7639 1.8435 1.75 1.85
3.00 6 1.9112 0.04213 0.01720 1.8670 1.9554 1.86 1.98
Total 17 1.8205 0.08372 0.02031 1.7774 1.8635 1.69 1.98
250
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
0.005 2 14 0.995
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.090 2 0.045 29.249 0.000
Within Groups 0.022 14 0.002
Total 0.112 16
Post Hoc Tests Multiple Comparisons
Dependent Variable: Protein/CHLa
(I) Microcyctis (J) Microcyctis Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.07198* 0.02382 0.023 -0.1343 -0.0096
3.00 -0.17951* 0.02382 0.000 -0.2418 -0.1172
2.00 dimension3
1.00 0.07198* 0.02382 0.023 0.0096 0.1343
3.00 -0.10753* 0.02271 0.001 -0.1670 -0.0481
3.00 dimension3
1.00 0.17951* 0.02382 0.000 0.1172 0.2418
251
2.00 0.10753* 0.02271 0.001 0.0481 0.1670
Games-Howell
dimension2
1.00 dimension3
2.00 -0.07198* 0.02279 0.029 -0.1360 -0.0080
3.00 -0.17951* 0.02398 0.000 -0.2465 -0.1125
2.00 dimension3
1.00 0.07198* 0.02279 0.029 0.0080 0.1360
3.00 -0.10753* 0.02315 0.002 -0.1711 -0.0440
3.00 dimension3
1.00 0.17951* 0.02398 0.000 0.1125 0.2465
2.00 0.10753* 0.02315 0.002 0.0440 0.1711
*. The mean difference is significant at the 0.05 level.
Microcystis aeruginosa – Colloidal carbohydrate/Chlorophyll a relationships
Descriptives
Colloidal CHO/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 0.7596 0.04382 0.01960 0.7052 0.8140 0.71 0.82
2.00 6 0.8372 0.05806 0.02370 0.7762 0.8981 0.76 0.92
3.00 6 0.8410 0.04089 0.01669 0.7981 0.8839 0.76 0.87
Total 17 0.8157 0.05874 0.01425 0.7855 0.8459 0.71 0.92
252
Test of Homogeneity of Variances
Colloidal CHO/CHLa
Levene Statistic df1 df2 Sig.
0.596 2 14 0.564
ANOVA
Colloidal CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.022 2 0.011 4.750 0.027
Within Groups 0.033 14 0.002
Total 0.055 16
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Colloidal CHO/CHLa
(I) Microcystis (J) Microcystis Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.07753* 0.02935 0.048 -0.1543 -0.0007
3.00 -0.08136* 0.02935 0.037 -0.1582 -0.0045
2.00 dimension3
1.00 0.07753* 0.02935 0.048 0.0007 0.1543
3.00 -0.00383 0.02798 0.990 -0.0771 0.0694
3.00 dimension3
1.00 0.08136* 0.02935 0.037 0.0045 0.1582
2.00 0.00383 0.02798 0.990 -0.0694 0.0771
253
Games-Howell
dimension2
1.00 dimension3
2.00 -0.07753 0.03075 0.076 -0.1635 0.0084
3.00 -0.08136* 0.02574 0.030 -0.1542 -0.0085
2.00 dimension3
1.00 0.07753 0.03075 0.076 -0.0084 0.1635
3.00 -0.00383 0.02899 0.990 -0.0848 0.0771
3.00 dimension3
1.00 0.08136* 0.02574 0.030 0.0085 0.1542
2.00 0.00383 0.02899 0.990 -0.0771 0.0848
*. The mean difference is significant at the 0.05 level.
Microcystis aeruginose – Storage carbohydrate/Chlorophyll a relationships
Descriptives
Storage/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.9186 0.03368 0.01506 1.8768 1.9604 1.88 1.95
2.00 6 1.9207 0.07289 0.02976 1.8442 1.9972 1.80 2.00
3.00 6 2.0264 0.03296 0.01345 1.9918 2.0610 1.98 2.06
Total 17 1.9574 0.07101 0.01722 1.9209 1.9939 1.80 2.06
254
Test of Homogeneity of Variances
Storage CHO/CHLa
Levene Statistic df1 df2 Sig.
1.999 2 14 0.172
ANOVA
Storage CHO/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.044 2 0.022 8.459 0.004
Within Groups 0.037 14 0.003
Total 0.081 16
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Storage/CHLa
(I) Microcystis (J) Microcystis Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.00213 0.03093 0.997 -0.0831 0.0788
3.00 -0.10778* 0.03093 0.010 -0.1887 -0.0268
2.00 dimension3
1.00 0.00213 0.03093 0.997 -0.0788 0.0831
3.00 -0.10565* 0.02949 0.008 -0.1828 -0.0285
3.00 dimension3
1.00 0.10778* 0.03093 0.010 0.0268 0.1887
255
2.00 0.10565* 0.02949 0.008 0.0285 0.1828
Games-Howell
dimension2
1.00 dimension3
2.00 -0.00213 0.03335 0.998 -0.0994 0.0951
3.00 -0.10778* 0.02019 0.001 -0.1647 -0.0509
2.00 dimension3
1.00 0.00213 0.03335 0.998 -0.0951 0.0994
3.00 -0.10565* 0.03266 0.034 -0.2020 -0.0093
3.00 dimension3
1.00 0.10778* 0.02019 0.001 0.0509 0.1647
2.00 0.10565* 0.03266 0.034 0.0093 0.2020
*. The mean difference is significant at the 0.05 level.
Rhodomonas salina – Chlorophyll a: marker pigment relationships
Descriptives
CHLa/Alloxanthin
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.5880 0.25927 0.11595 2.2661 2.9099 2.37 3.03
2.00 6 2.6133 0.27818 0.11357 2.3214 2.9053 2.26 3.03
3.00 6 2.5400 0.10450 0.04266 2.4303 2.6497 2.42 2.67
Total 17 2.5800 0.21316 0.05170 2.4704 2.6896 2.26 3.03
256
Test of Homogeneity of Variances
CHLa/Alloxanthin
Levene Statistic df1 df2 Sig.
1.455 2 14 0.267
ANOVA
CHLa/Alloxanthin
Sum of Squares df Mean Square F Sig.
Between Groups 0.017 2 0.008 0.163 0.851
Within Groups 0.710 14 0.051
Total 0.727 16
Post Hoc Tests Multiple Comparisons
Dependent Variable: CHLa/Alloxanthin
(I) Rhodomonas (J) Rhodomonas Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 -0.02533 0.13640 0.981 -0.3823 0.3317
3.00 0.04800 0.13640 0.934 -0.3090 0.4050
2.00 dimension3
1.00 0.02533 0.13640 0.981 -0.3317 0.3823
257
3.00 0.07333 0.13006 0.841 -0.2671 0.4137
3.00 dimension3
1.00 -0.04800 0.13640 0.934 -0.4050 0.3090
2.00 -0.07333 0.13006 0.841 -0.4137 0.2671
Games-Howell
dimension2
1.00 dimension3
2.00 -0.02533 0.16230 0.987 -0.4800 0.4293
3.00 0.04800 0.12355 0.921 -0.3517 0.4477
2.00 dimension3
1.00 0.02533 0.16230 0.987 -0.4293 0.4800
3.00 0.07333 0.12132 0.823 -0.2925 0.4392
3.00 dimension3
1.00 -0.04800 0.12355 0.921 -0.4477 0.3517
2.00 -0.07333 0.12132 0.823 -0.4392 0.2925
Rhodomonas salina – Protein/Chlorophyll a relationships
Descriptives
Protein
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.4459 0.02405 0.01075 2.4160 2.4757 2.42 2.48
2.00 5 2.4363 0.02884 0.01290 2.4004 2.4721 2.42 2.49
3.00 6 2.5543 0.04969 0.02029 2.5022 2.6065 2.49 2.62
Total 16 2.4835 0.06649 0.01662 2.4481 2.5190 2.42 2.62
258
Test of Homogeneity of Variances
Protein/CHLa
Levene Statistic df1 df2 Sig.
1.955 2 13 0.181
ANOVA
Protein/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.048 2 0.024 17.464 0.000
Within Groups 0.018 13 0.001
Total 0.066 15
Post Hoc Tests Multiple Comparisons
Dependent Variable :Protein/CHLa
(I) Rhodomonas (J) Rhodomonas Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.00962 0.02353 0.913 -0.0525 0.0717
3.00 -0.10844* 0.02252 0.001 -0.1679 -0.0490
2.00 dimension3
1.00 -0.00962 0.02353 0.913 -.0717 0.0525
3.00 -0.11806* 0.02252 0.000 -0.1775 -0.0586
259
3.00 dimension3
1.00 0.10844* 0.02252 0.001 0.0490 0.1679
2.00 0.11806* 0.02252 0.000 0.0586 0.1775
Games-Howell
dimension2
1.00 dimension3
2.00 0.00962 0.01679 0.838 -0.0387 0.0579
3.00 -0.10844* 0.02296 0.004 -0.1750 -0.0418
2.00 dimension3
1.00 -0.00962 0.01679 0.838 -0.0579 .0387
3.00 -0.11806* 0.02404 0.003 -0.1864 -0.0497
3.00 dimension3
1.00 0.10844* 0.02296 0.004 0.0418 0.1750
2.00 0.11806* 0.02404 0.003 0.0497 0.1864
*. The mean difference is significant at the 0.05 level.
Rhodomonas salina – Colloidal carbohydrate/Chlorophyll a relationships
Descriptives
Colloidal/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 1.4569 0.07929 0.03546 1.3584 1.5553 1.37 1.56
2.00 5 1.3765 0.04288 0.01917 1.3233 1.4297 1.32 1.42
3.00 6 1.2948 0.04519 0.01845 1.2474 1.3423 1.24 1.37
Total 16 1.3710 0.08738 0.02185 1.3244 1.4175 1.24 1.56
260
Test of Homogeneity of Variances
Colloidal/CHLa
Levene Statistic df1 df2 Sig.
1.815 2 13 0.202
ANOVA
Colloidal/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups 0.072 2 0.036 10.929 0.002
Within Groups 0.043 13 0.003
Total 0.115 15
Post Hoc Tests
Multiple Comparisons
Dependent Variable: Colloidal/CHLa
(I) Rhodomonas (J) Rhodomonas Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.08036 0.03625 0.105 -0.0154 0.1761
3.00 0.16203* 0.03471 0.001 0.0704 0.2537
2.00 dimension3
1.00 -0.08036 0.03625 0.105 -0.1761 0.0154
261
3.00 0.08167 0.03471 0.083 -0.0100 0.1733
3.00 dimension3
1.00 -0.16203* 0.03471 0.001 -0.2537 -0.0704
2.00 -0.08167 0.03471 0.083 -0.1733 0.0100
Games-Howell
dimension2
1.00 dimension3
2.00 0.08036 0.04031 0.193 -0.0424 0.2031
3.00 0.16203* 0.03997 0.015 0.0400 0.2841
2.00 dimension3
1.00 -0.08036 0.04031 0.193 -0.2031 0.0424
3.00 0.08167* 0.02661 0.033 0.0071 0.1563
3.00 dimension3
1.00 -0.16203* 0.03997 0.015 -0.2841 -0.0400
2.00 -0.08167* 0.02661 0.033 -0.1563 -0.0071
*. The mean difference is significant at the 0.05 level.
Rhodomonas salina – Storage carbohydrate/Chlorophylll a relationship
Descriptives
Storage/CHLa
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
1.00 5 2.0041 0.08606 0.03849 1.8972 2.1109 1.92 2.15
2.00 5 1.9560 0.12717 0.05687 1.7981 2.1139 1.85 2.15
3.00 6 2.2331 0.02436 0.00995 2.2076 2.2587 2.20 2.27
Total 16 2.0749 0.15128 0.03782 1.9943 2.1556 1.85 2.27
262
Test of Homogeneity of Variances
Storage/CHLa
Levene Statistic df1 df2 Sig.
4.292 2 13 0.037
ANOVA
Storage/CHLa
Sum of Squares df Mean Square F Sig.
Between Groups .246 2 0.123 16.436 0.000
Within Groups .097 13 0.007
Total .343 15
Robust Tests of Equality of Means
Storage/CHLa
Statistica df1 df2 Sig.
Brown-Forsythe 14.835 2 7.349 0.003
a. Asymptotically F distributed.
263
Multiple Comparisons
Dependent Variable :Storage/CHLa
(I) Rhodomonas (J) Rhodomonas Mean
Difference (I-J) Std. Error Sig.
95% Confidence Interval
Lower Bound Upper Bound
Tukey HSD
dimension2
1.00 dimension3
2.00 0.04806 0.05471 0.663 -0.0964 0.1925
3.00 -0.22907* 0.05238 0.002 -0.3674 -0.0908
2.00 dimension3
1.00 -0.04806 0.05471 0.663 -0.1925 0.0964
3.00 -0.27713* 0.05238 0.000 -0.4154 -0.1388
3.00 dimension3
1.00 0.22907* 0.05238 0.002 0.0908 0.3674
2.00 0.27713* 0.05238 0.000 0.1388 0.4154
Games-Howell
dimension2
1.00 dimension3
2.00 0.04806 0.06867 0.771 -0.1540 0.2501
3.00 -0.22907* 0.03975 0.007 -0.3633 -0.0949
2.00 dimension3
1.00 -0.04806 0.06867 0.771 -0.2501 0.1540
3.00 -0.27713* 0.05774 0.016 -0.4775 -0.0768
3.00 dimension3
1.00 0.22907* 0.03975 0.007 0.0949 0.3633
2.00 0.27713* 0.05774 0.016 0.0768 0.4775
*. The mean difference is significant at the 0.05 level.
264
VII- Cellular concentration of CHLa and photosynthates Cellular concentration of chlorophyll a and products of photosynthesis, in relation to biovolume
Genus Lt R 1 R 2 R 3 R 4 R 5 R 6 Biovol* Item/ biovol
pg cell-¹
pg cell-¹
pg cell-¹
pg cell-¹
pg cell-¹
pg cell-¹
cell -¹ (µm³)
fg (item) µm³·cell
A. carterae 432 CHLa L 0.44 0.26 0.31 0.37 0.29 0.1901 M 0.33 0.54 0.24 0.17 0.35 0.21 0.2333 H 0.33 0.27 0.33 0.4 0.35 0.1728 protein L 91.85 45.98 84.02 65.75 73.11 39.6792 M 95.07 243.1 130.4 111.2 169.9 110.6 73.4227 H 179.2 124.3 196.4 216.28 139.59 93.4330 colloidal CHO
L 13.04 93.87 12.72 22.25 14.48 40.5518
M 16.85 31.87 14.08 11.1 21.37 17.64 13.7678 H 36.2 31.28 30.67 38.26 35.87 15.6384 storage CHO L 76.42 49.9 49.09 74.87 55.98 33.0134 M 65.03 108.62 5.25 43.54 78.04 54.91 46.9238 H 108.25 93.49 158.64 195.07 137.59 84.2702 C. meneghiniana
2720
CHLa L 0.17 0.99 0.14 0.07 0.37 2.6928 M 0.62 0.2 1.09 0.15 0.09 0.19 2.9648 H 1.17 0.44 1.32 2.15 2.45 1.16 6.6640 protein L 15.28 91.54 14.76 72.31 32.86 248.9888 M 85.21 24.59 155.76 17.55 9.23 23.15 423.6672 H 155.12 73.34 197.46 290.73 380.01 167.61 1033.6272 colloidal CHO
L 1.61 1.77 1.18 0.63 2.6 4.8144
M 7.34 3.27 0.16 1.63 0.91 1.97 19.9648 H 31.8 11.5 32.95 43.3 48.86 32.82 132.8992 storage CHO L 6.43 0.16 0.11 4.11 0.16 17.4896 M 0.89 0.36 204.34 0.18 0.12 0.21 555.8048 H 235.17 118.87 368.16 496.26 575.01 268.58 1564.0272 T.weissflogii 2813 CHLa L 0.182 0.139 0.727 0.62 0.747 2.1013 M 0.164 0.173 0.636 0.109 0.205 1.7891 H 2.55 30.4 88.2 1.36 58.9 47.1 248.1066 protein L 31.42 25.89 99.8 99.25 115.96 326.1955 M 29.39 31.49 124.46 18.09 35.85 350.1060 H 489.62 60.5 210.01 272.04 133.66 103.31 1377.3011 colloidal CHO
L 1.15 1.47 6.22 4.4 5.34 17.4969
M 1.86 2.13 7.53 1.23 2.5 21.1819 H 41.77 5.37 12.58 19.81 9.2 8.15 117.4990 storage CHO L 21.19 12.24 79.6 65.3 90.84 255.5329 M 20.62 22.84 107.78 17.94 26.79 303.1851
265
H 602 61.1 176 238 121 73.4 1693.4260 D. tertiolecta 43.42 CHLa L 0.69 0.86 0.78 0.79 0.0373 M 0.52 0.87 0.64 0.45 0.41 n/a 0.0378 H 0.93 1.25 0.7 0.85 0.91 0.87 0.0543 protein L 48.19 62.32 56.94 53.26 2.7059 M 47.64 114.78 84.91 33.07 54.15 n/a 3.6868 H 141.73 226.76 109.84 151.46 170.79 163.04 7.4157 colloidal CHO
L 6.11 6.42 6.72 7.38 0.3204
M 2.39 5.09 3.72 1.69 1.32 n/a 0.2210 H 3.82 7.78 2.81 5.62 5.62 6.13 0.3378 storage CHO L 44.68 50.45 45.69 45.46 2.1905 M 43.5 84.28 53.94 32.1 38.38 3.6594 H 96.83 116.56 79.01 96.69 105.34 99.8 5.0610 S. quadricauda
45
CHLa L 0.028 0.055 0.096 0.109 0.126 n/a 0.0057 M 0.401 0.3 0.689 0.774 1.12 0.875 0.0394 H 10.5 4.19 4.45 6.79 4.41 3.83 0.3056 protein L 13.48 28.68 78.3 93.34 53.33 n/a 0.3321 M 94.7 50.5 114 135 189 144 8.5050 H 1239 575 567 871 551 489 55.7550 colloidal CHO
L 3.37 2.85 9.31 19.65 11.02 n/a 0.8843
M 3.61 1.88 6.94 4.75 6.86 4.98 0.3123 H 74.55 30.77 30.06 52.96 32.63 30.12 3.3548 storage CHO L 13.62 29.85 51.83 70.22 71.51 n/a 3.2180 M 19.12 11.79 31.85 22.79 30.27 23.84 0.0057 H 1929.25 69.46 81.91 1250.72 88.54 73.74 56.2824 S.elongatus 4.2 CHLa DL 0.55 0.94 0.23 0.7 0.0039 L 0.19 2.64 0.76 1.9 5.73 0.0241 M 0.36 0.79 1.77 0.2 0.4 2.39 0.0100 H 9.25 7.88 5.8 3.18 0.1 0.0389 protein DL 35.2 59.46 12.99 41.72 0.1752 L 0.26 291.82 0.96 229.7 674.68 2.8337 M 0.58 137.02 346.61 0.31 0.75 430.16 1.8067 H 1706.69 1539.28 1095.9 599.76 1986.11 8.3417 colloidal CHO
DL 4.79 4.26 0.22 7.48 0.0314
L 1.82 0.23 8.31 0.19 0.6 0.0349 M 3.59 8.61 0.18 1.97 1.54 0.24 0.0362 H 114.26 0.94 0.77 0.44 125.51 0.5271 storage CHO DL 0.24 0.37 0.93 0.32 0.0039 L 0.11 149.67 0.48 112.74 336.79 1.4145 M 0.2 0.4 0.93 0.1 0.22 142.21 0.5973 H 519.69 462.17 313.28 161.2 590.26 2.4791 M. aeruginosa
65
CHLa L 0.147 0.086 0.1 0.059 0.246 0.0160
266
(R= run; blank cells = run not performed; VL = 10 µmol photons·m-2·s-1; L = 37 µmol photons·m-2·s-1; ML = 70-75 µmol photons·m-2·s-1; HL = 200 µmol photons·m-2·s-1; 1µm3 = 1x10-9 µL).
M 0.0509 0.089 0.107 0.102 0.248 0.177 0.0161 H 0.826 0.896 0.207 0.216 0.533 0.899 0.0584 protein L 7.06 5.22 5.89 3.36 12.1 0.7865 M 3.65 5.25 7.33 5.79 16 11.1 0.7215 H 70.41 85.9 16.9 16.8 41.7 5.5835 colloidal CHO
L 0.862 0.438 0.577 0.39 0.132 0.0560
M 0.389 0.589 0.62 0.849 0.155 0.122 0.0552 H 6.15 6.34 1.19 1.52 3.86 6.49 0.4219 storage CHO L 10.85 6.51 8.6 5.3 2.17 0.7053 M 4.5 8.92 9.16 9.47 15.54 13.4 0.1411 H 95.62 91.06 23.86 23.68 51.28 90.78 6.2153 R. salina 141 CHLa L 0.024 0.044 0.047 0.035 0.069 0.0097 M 0.095 0.097 0.152 0.14 0.151 0.0213 H 0.297 0.503 0.449 0.602 0.493 0.509 0.0849 protein L 7.05 11.61 12.88 9.41 20.55 2.8976 M 24.76 25.87 46.62 32.66 39.93 6.5734 H 122.87 195.66 168.54 185.01 158.27 180.8 27.5881 colloidal CHO
L 0.777 1.015 1.191 0.984 2.503 0.3529
M 2.23 2.57 3.97 3.82 3.67 0.5598 H 5.71 9.14 8.9 12.38 11.6 8.92 1.7456 storage CHO L 4.14 6.08 5.27 5.73 14.4 2.0304 M 9.61 9.43 21.27 13.19 12.5 1.8598 H 47.57 69.62 68.42 89.54 70.79 78.21 12.6251 * Olenina et al., 2006
267
VIII- Typical chromatograms of species studied
Typical chromatograms of the species studied (see Appendix V for pigment abbreviation codes) Scenedesmus quadricauda chromatogram showing characteristic pigments Rhodomonas salina chromatogram showing characteristic pigments
Chl
lide
a
Pyr
o C
hllid
e a
NE
O VIO
LA
AN
TH
LUT
CH
Lb
CHLa
BE
TA
Chl
lide
a
CHLs c1/c2
ALLO
CH
L a
allo
CHL a
AL
PH
268
Microcystis aeruginosa chromatogram showing characteristic pigments
Thalassiosira weissflogii chromatogram showing characteristic pigments
MY
XO
L
MY
XO
ZE
A
CA
NT
H
CHLa
CH
La`
+ E
CH
IN
AL
PHA
Chl
lide
a
CH
Ls
c1/
c 2
Pyr
o C
hllid
e a
FUCO
cis
FUC
O
DIAD
DIA
TO
CH
L a
allo
CH
L a
CH
L a
`+ E
CH
IN
BETA
269
Dunaliella tertiolecta chromatogram showing characteristic pigments
Synechococcus elongatus chromatogram, showing characteristic pigments
Chl
lide
a
NE
O
VIO
AN
TH
LUT
CH
L b
CHLa
CH
La`
AL
PHA
Chl
lide
a
MY
XO
L
ZEA
CHLa C
HL
a+ E
CH
IN
BETA
270
Amphidinium carterae chromatogram showing characteristic pigments
Cyclotella meneghiniana chromatogram showing characteristic pigments
P468
P45
7
CHLs c1/c2 PERI
DIN
O
DIA
D
DIA
T
CH
La
allo
CHLa
CH
La`
BETA
CH
llid
e a
CHLs c1/c2
FU
CO
L
FUCO
19`
Hex
DIA
D
DIA
T
Phyt
ylat
ed -
c
CH
L a
allo
CHL a C
HL
a`
BETA
271
IX- Specific growth rate (µ) curves
Specific growth rate constants (μ) calculated from the slopes of the semilog plots of growth versus time for the 8 species at each light level.
LL (y1: μ = 0.075 day-1); ML (y2: μ = 0.1493 day-1); HL (y3: μ = 0.1764 day-1)
LL (y1: μ = 0.1707 day-1); ML (y2: μ = 0.238 day-1); HL (y3: μ = 0.2408day-1)
y1 = 0.075x + 10.844R² = 0.8327
y2 = 0.1493x + 10.873R² = 0.8931
y3 = 0.1764x + 10.982R² = 0.8608
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 5 10 15 20 25 30
ln(c
ell d
ensi
ty)
Time (day)
Amphidinium carterae- specific growth rates LL
ML
HL
y1 = 0.1707x + 10.606R² = 0.9524
y2 = 0.238x + 11.766R² = 0.9363
y3 = 0.2408x + 11.616R² = 0.9712
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
0 5 10 15 20 25
ln (c
ell d
ensi
ty)
Time (day)
Cyclotella meneghiniana - specific growth rate LL
ML
HL
272
LL (y1: μ = 0.132 day-1); ML (y2: μ = 0.2262 day-1); HL (y3: μ = 0.436 day-1)
LL (y1: μ = 0.1589 day-1); ML (y2: μ = 0.1652day-1); HL (y3: μ = 0.26 day-1)
y1 = 0.132x + 11.007R² = 0.9834
y2 = 0.2262x + 11.611R² = 0.8233
y3 = 0.436x + 11.12R² = 0.9512
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
0 5 10 15 20 25 30
ln (c
ell d
ensi
ty)
Time (day)
Thalassiosira weissflogii - specific growth rate
LL
ML
HL
y1 = 0.1589x + 11.309R² = 0.8913
y2 = 0.1652x + 11.228R² = 0.9337
y3 = 0.26x + 12.025R² = 0.7852
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
0 5 10 15 20 25
ln (c
ell d
ensi
ty)
Time (day)
Dunaliellla tertiolecta - specific growth rate
LL
ML
HL
273
LL (y1: μ = 0.1008 day-1); ML (y2: μ = 0.1671 day-1); HL (y3: μ = 0.1712 day-1)
LL (y1: μ = 0.2074 day-1); ML (y2: μ = 0.228 day-1); HL (y3: μ = 0.2284 day-1)
y1 = 0.1008x + 10.585R² = 0.9692
y2 = 0.1671x + 10.793R² = 0.9293
y3 = 0.1712x + 10.541R² = 0.998
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 5 10 15 20
ln (c
ell d
ensi
ty)
Time (day)
Scenedesmus quadricauda - specific growth rate
LL
ML
HL
y1 = 0.2074x + 12.971R² = 0.9712
y2 = 0.228x + 12.496R² = 0.852
y3 = 0.2284x + 13.158R² = 0.8983
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
0 5 10 15 20
ln (c
ell d
ensi
ty)
Time (day)
Microcystis aeruginose - specific growth rate
LL
ML
HL
274
DL (y1: μ = 0.0485 day-1); LL (y2: μ = 0.171 day-1); ML (y3: μ = 0.2275 day-1); HL (y4: μ = 0.2549 day-1)
LL (y1: μ = 0.0833 day-1); ML (y2: μ = 0.1209 day-1); HL (y3: μ = 0.2431 day-1)
y1 = 0.0485x + 11.865R² = 0.8806
y2 = 0.171x + 10.571R² = 0.9785
y3 = 0.2275x + 10.298R² = 0.9721
y4 = 0.2549x + 10.328R² = 0.9597
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 5 10 15 20 25 30 35
ln (c
ell d
ensi
ty)
Time (day)
Synechococcus elongatus - specific growth rate
VL
LL
ML
HL
y1 = 0.0833x + 10.856R² = 0.6205
y2 = 0.1209x + 11.342R² = 0.7885
y3 = 0.2431x + 10.5R² = 0.9558
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
0 5 10 15 20 25
ln (c
ell d
ensi
ty)
Time (day)
Rhodomonas salina - specific growth rate
LL
ML
HL
275
X – NMR SPECTRA
1H NMR spectra of the new pigment (with integration) 1H NMR spectra of the new pigment (possible N-H and OH signals expanded)
276
1H NMR spectra – new pigment, aromatic region (expanded)
HSQC – unknown pigment 1H NMR spectra of new pigment (aliphatic region expanded)
277
HSQC – new pigment
HMBC – new pigment
278
Selective HMBC – new pigment
COSY - new pigment
279
NOESY – new pigment NH – HMBC – new pigment
280
1H NMR – new pigment (in CD3OD) – Deuterium exchange experiment
281
1H NMR - Scytonemin Oxidized HSQC – Scytonemin oxidized
282
\ HMBC – Scytonemin oxidized Selective HMBC – Scytonemin oxidized
283
COSY – Scytonemin oxidized
284
XI – Mass Spectra MASS SPECTRA (experiments used to characterize the new pigment) HR MS-ESI-TOF: Unknown pigment m/z 602.2074[M+H]+; m/z 624.1899 [M+Na]+
285
HR –MS-ESI: Unknown pigment m/z 1226.3617 [2M+Na] +
286
HR-MS-ESI formula results for the new pigment.
287
LC-MS for the new pigment m/z 602 [M+H] +
288
MALDI –TOF MS for the new pigment m/z 602 [M+H] +
289
LC-MS of acetylated new pigment: m/z 686 [M+H] + (likely from the acetylation of the two OH moieties of the phenols) and 728 [M+H] + (possible contaminant).
290
SEQ-17095-01 8/16/2011 12:27:32 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(-)ESI
RT: 2.23 - 60.16
4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Time (min)
0.055
0.060
0.065
0.070
Inte
nsi
ty
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce MW 601-B
MW 601-A
RT: 38.99BP: 600.5
MW 727 isomers
40.79726.3
RT: 38.99BP: 600.5
RT: 38.50BP: 600.5
40.79726.3
RT: 38.99BP: 0.0
RT: 38.34BP: 0.0
38.910.0
49.670.0
48.810.0
59.370.0
54.780.0
51.930.0
47.330.0
44.710.0
59.070.0
44.020.0
41.860.0
35.430.0
34.310.0
32.070.0
30.610.0
29.680.0
27.050.0
25.890.0
24.020.0
21.830.0
20.610.0
19.390.0
16.490.0
14.150.0
13.340.0
11.370.0
9.700.0
8.470.0
7.090.0
4.240.0
NL: 4.82E6
Base Peak F: - c ESI sid=1.00 Full ms [ 125.00-700.00] MS SEQ-17095-01
NL: 1.22E6
Base Peak F: - c ESI sid=3.00 Full ms [ 690.00-1800.00] MS SEQ-17095-01
NL: 4.82E6
m/z= 600.0-601.0 F: - c ESI sid=1.00 Full ms [ 125.00-700.00] MS SEQ-17095-01
NL: 1.22E6
m/z= 725.8-726.8 F: - c ESI sid=3.00 Full ms [ 690.00-1800.00] MS SEQ-17095-01
NL: 7.27E-2
UV Analog SEQ-17095-01
C8 HPLC/UV/(-)ESI-MSn. There were at least two MW 601 isomers as indicated by the labels on the shaded peaks. There were also at least two MW 727 isomers. (not shaded).
291
SEQ-17095-01 8/16/2011 12:27:32 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(-)ESI
SEQ-17095-01 #1441-1450 RT: 38.30-38.50 AV: 2 NL: 7.35E5T: - c ESI sid=1.00 Full ms [ 125.00-700.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600 620 640 660 680 700
m/z
0
10
20
30
40
50
60
70
80
90
100R
ela
tive
Ab
un
da
nce
600.5
601.5
602.4
603.5 682.1141.0 622.5
SEQ-17095-01 #1441-1450 RT: 38.31-38.52 AV: 2 NL: 7.36E5T: - c sid=1.00 d Full ms2 [email protected] [ 185.00-1215.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
544.5
545.5
601.5
602.4600.5
546.4462.8 572.5453.3286.6
SEQ-17095-01 #1439-1449 RT: 38.21-38.35 AV: 2 NL: 1.94E5T: - c sid=1.00 d Full ms3 [email protected] [email protected] [ 135.00-1095.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
e A
bu
nd
an
ce
544.4
545.4
515.7543.6
499.7516.6
500.7 546.4407.8 487.7423.6 451.9 517.6395.7 541.7
MW 601-A: The MW 601-A produced an m/z 600 [M-H]- ion (top) which was dissociated to form m/z 544 via loss of 56 u (middle). The m/z 544 was relatively resistance to dissociation but did produce some m/z 515/516 and 499/500 product ions (bottom)
292
SEQ-17095-02 8/16/2011 2:03:56 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(+)ESI
RT: 0.00 - 60.02
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60
Time (min)
0.055
0.060
0.065
0.070
Inte
nsi
ty
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
0
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
20
40
60
80
100
Re
lativ
e A
bu
nd
an
ce
39.00602.3
50.80386.7
51.63684.2 54.32
832.149.12237.1 56.33
906.038.50602.2
59.19980.1
48.27257.2
RT: 39.00BP: 602.3
RT: 38.50BP: 602.2
40.90728.1
40.55728.1
59.02728.358.51
728.154.32728.453.49
727.839.84728.1
51.63727.6
46.40727.7
RT: 39.03BP: 0.0
RT: 38.36BP: 0.0
1.870.0
49.960.0
49.070.0
46.950.0
57.250.0
56.270.0
53.930.0
52.240.0
46.180.0
60.000.0
43.590.0
41.910.035.97
0.034.20
0.031.81
0.030.72
0.029.57
0.025.69
0.024.90
0.022.70
0.020.38
0.019.19
0.018.36
0.015.10
0.012.54
0.09.480.0
4.690.0
2.700.0
9.860.0
9.200.0
7.000.0
1.110.0
NL: 5.26E6
Base Peak F: + c ESI sid=1.00 Full ms [ 125.00-1000.00] MS SEQ-17095-02
NL: 5.26E6
m/z= 601.7-602.7 F: + c ESI sid=1.00 Full ms [ 125.00-1000.00] MS SEQ-17095-02
NL: 3.42E5
m/z= 727.6-728.6 F: + c ESI sid=1.00 Full ms [ 125.00-1000.00] MS SEQ-17095-02
NL: 7.08E-2
UV Analog SEQ-17095-02
HPLC/UV/(+)ESI-MSn analysis. The MW 601 compounds are shaded
293
SEQ-17095-02 8/16/2011 2:03:56 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(+)ESI
SEQ-17095-02 #1337-1357 RT: 38.85-39.31 AV: 4 NL: 3.26E6T: + c ESI sid=1.00 Full ms [ 125.00-1000.00]
150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950 1000
m/z
0
10
20
30
40
50
60
70
80
90
100R
ela
tive
Ab
un
da
nce
602.2
603.3
546.3
545.3604.2
547.3
624.3601.3186.9 264.2 548.4159.0 702.6413.4239.1 813.1517.4149.2 218.7 646.4 928.9828.9 892.4660.1 748.9455.2300.8 375.0 994.1473.0 494.6 782.6365.1 857.5324.7 722.3 958.1426.2
SEQ-17095-02 #1336-1354 RT: 38.86-39.16 AV: 3 NL: 5.64E6T: + c sid=1.00 d Full ms2 [email protected] [ 185.00-1215.00]
140 160 180 200 220 240 260 280 300 320 340 360 380 400 420 440 460 480 500 520 540 560 580 600
m/z
0
10
20
30
40
50
60
70
80
90
100
Re
lativ
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bu
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ce
545.6
546.4
556.4
585.3545.0 574.3
SEQ-17095-02 #1336-1354 RT: 38.90-39.19 AV: 3 NL: 1.76E6T: + c sid=1.00 d Full ms3 [email protected] [email protected] [ 140.00-1100.00]
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517.5
518.6528.3
489.8
490.6 529.4
491.3 546.3543.5
547.2515.9425.7 493.6451.7400.8 472.9 530.5397.1 411.7 488.7
(+)ESI-MS produced m/z 602 [M+H]+, m/z 624 [M+Na]+ and m/z 546 [M+H-56 u] fragment ion (top). (+)ESI-MS/MS dissociation of m/z 602 produced m/z 545 and 546 (middle) which were further dissociated t produce m/z 528, 518, 517 and 489
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MW 727: (+)ESI-MS/MS (top and 3rd) and –MS/MS/MS (2nd and bottom) of the MW 727 isomers appeared very similar but (+)ESI-MS/MS/MS mass chromatograms show some differences (not shown)
SEQ-17095-02 8/16/2011 2:03:56 PM CIDYA; 5 uLHypurity C8;0.25;95:5(5)>5:95(45-60)/254 nm/(+)ESI
SEQ-17095-02 # 1397 RT: 40.57 AV: 1 NL: 3.38E5T: + c sid=1.00 d Full ms2 [email protected] [ 230.00-1470.00]
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682.2
710.6700.1636.0455.1 687.0495.5 555.7440.9 465.4 572.1
SEQ-17095-02 # 1396 RT: 40.60 AV: 1 NL: 9.79E4T: + c sid=1.00 d Full ms3 [email protected] [email protected] [ 170.00-1350.00]
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544.2
615.2 654.1516.5 642.3616.3
545.2488.4 515.6 655.2626.2543.4 669.0499.4 577.7 597.0 671.4451.7423.3 551.3 641.2614.6526.4474.3399.2344.2 426.6 514.4454.3409.3 580.2388.0314.4 383.4 562.3
SEQ-17095-02 # 1399 RT: 40.57 AV: 1 NL: 3.38E5T: + c sid=1.00 d Full ms2 [email protected] [ 230.00-1470.00]
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SEQ-17095-02 # 1409 RT: 40.96 AV: 1 NL: 9.31E4T: + c sid=1.00 d Full ms3 [email protected] [email protected] [ 170.00-1350.00]
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643.3615.2
644.2 654.0
642.3
655.1616.2
544.2614.4 626.4515.8 669.0526.0488.5425.2 544.9471.5 536.8397.3 451.4 499.9 641.0625.2596.7407.5 551.1 579.1440.3 675.0371.1 576.9411.5
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