JPR Advance Access published November 29, 2007
Submitted: 11 Sept 2007; Date Accepted in Principle: 29 Oct 2007; Date Accepted for
Publication: 26 Nov 2008
Functional links between bioenergetics and bio-optical traits of phytoplankton
taxonomic groups; an overarching hypothesis with applications for ocean colour
remote sensing.
JIM AIKEN1,2, NICK J. HARDMAN-MOUNTFORD1,2, RAY BARLOW3, JAMES
FISHWICK1,2, TAKAFUMI HIRATA1,2 AND TIM SMYTH1.
1
PLYMOUTH MARINE LABORATORY, PROSPECT PLACE, PLYMOUTH, PL1 3DH, UK, 2CENTRE FOR
OBSERVATION OF AIR SEA INTERACTIONS AND FLUXES, PROSPECT PLACE, PLYMOUTH, PL1 3DH,
UK AND MARINE AND 3COASTAL MANAGEMENT, ROGGE BAY 8012, CAPE TOWN, SOUTH AFRICA.
e-mail for communicating author: [email protected]
Communicating editor: K. J. Flynn
© The Author 2007. Published by Oxford University Press. All rights reserved.
For Permissions, please e-mail: [email protected]
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ABSTRACT
We review the concept of phytoplankton functional types (PFTs) in marine ecosystems as a
means of advancing bio-mechanistic models that can be coupled to the global carbon cycle and
the Earth’s climate system. Conventional classification of phytoplankton by size may seem
arbitrary, but there appears clear links between size and environmental characteristics
(availability of essential nutrients and light), that regulate photosynthesis, phytoplankton
selection and succession. Taking a minimalist approach, small phytoplankton (picoplankton)
survive in permanently stratified systems with low nutrients, high surface light and low light in
deep clines, whilst large phytoplankton (microplankton) thrive in high nutrient, turbulent, high
light, near surface systems. Nutrient-light environmental conditions are characteristic properties
of globally, latitudinal-dispersed biogeochemical provinces. These contrasting nutrient-light
regimes define the extreme ends of the bio-energetic scale of photosynthesis and set the end
points of the primary range of phytoplankton functional processes. To determine PFTs from
remotely sensed ocean colour data, there must be a specific bio-optical trait that can be
associated with the phytoplankton species or taxa. We investigate the connection of the bioenergetic scale to phytoplankton types and their bio-optical traits, which is the first, but crucial
step for classifying PFTs on the basis of functional processes, from which refinements and
further partitioning can be developed.
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INTRODUCTION
Understanding the functional role of phytoplankton in aquatic ecosystems is crucial to
quantifying and understanding the Earth System and its control of climate. Quantifying the Earth
system needs representation of marine ecosystems that are realistic, evolving from current simple
system descriptions to more complex bio-mechanistic models (Flynn, 2001), tempered by the
reality that not all the hundreds of plankton species can be included explicitly. The use of
functional types to represent ecosystem functioning is a practical and logical strategy: the
European Regional Seas Ecosystem Model (ERSEM, Blackford et al., 2004) has bacteria, 4
phytoplankton, heterotrophs, and zooplankton; the Dynamic Green Ocean Model (DGOM,
LeQuerre et al., 2005) has bacteria, 6 phytoplankton and 3 zooplankton. Recently there have
been a number of empirical approaches (Ciotti et al., 2002; Sathyendranath et al., 2004; Alvain
et al., 2005; Aiken et al., 2007) to derive the dominant phytoplankton functional types (PFTs),
mostly size classes (see Table I) or representative species, from satellite observations of ocean
colour. Currently there is no consensus, systematic definition of PFTs.
In the marine environment the organisation of plankton, species diversity and seasonal
succession are aspects of ecology that differ regionally and latitudinal. The environmental
differences between ecosystems at different latitudes (light, nutrient availability, temperature,
salinity, turbulence, stratification) and their temporal expressions, force the phytoplankton
diversity and seasonal succession. Margalef’s Mandala (Margalef, 1978; Margalef et al., 1979)
and Reynolds’ Intaglio (Reynolds, 1987) are constructs based on ecological traits, designed to
describe seasonal succession and community structure of marine phytoplankton, with a focus on
dinoflagellate and harmful algal blooms; review Smayda and Reynolds (Smayda and Reynolds,
2001). Both schemes emphasise environmental factors such as turbulence, stratification and topdown grazing-control by foraging zooplankton, in the emergence of the dominant phytoplankton
species.
Functional classification of the phytoplankton
Conventionally phytoplankton types have been classified by size: picoplankton (<2 μm),
comprising pico-prokaryotes (cyanobacteria and other autotrophic bacteria) and pico-eukaryotes
(pico-flagellates); nanoplankton (mostly 2-20 μm) eukaryotic flagellates, (prymnesiophytes,
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chrysophytes, cryptophytes and chlorophytes); microplankton (20-200 μm) diatoms and
dinoflagellates. These size ranges are not robust: microplankton range from 2 μm to 2 mm
overlapping the nanoplankton range and there are pico-flagellates < 2 μm. Hitherto, biological
oceanography has shown an alignment of phytoplankton size classes with the environmental
niches (biogeochemical provinces) that they thrive in, and their photosynthetic activity.
Microplankton bloom in high nutrient environments (upwelling zones and spring blooms), have
high C-biomass (Cph), Chla, high fraction of Chla in pigment (Chla/TP) and high photosynthetic
activity. Picoplankton are dominant in low nutrient zones (oligotrophic gyres), have low Cph,
Chla, Chla/TP and low photosynthetic activity. Nanoplankton grow in regions with some
inorganic nutrients and re-cycled nutrients (organic and inorganic) and have moderate, Cph, Chla,
Chla/TP and photosynthetic activity. Data from the Atlantic Meridional Transect (AMT, Aiken
et al., 2000; Robinson et al., 2006) support these observations: phytoplankton distributions
(Zubkov et al., 1998; 2000; Tarran et al., 2006; Heywood et al., 2006); pigments (Gibb et al.,
2000; Barlow et al., 2002, Barlow et al., 2004; Poulton et al., 2006; Aiken et al., in review),
productivity (Maranon and Holligan, 1999; Maranon et al., 2000; Maranon, 2005; Poulton et al.,
2006), photosynthetic activity (Fishwick et al., 2006; Suggett et al., 2006); notably scaling of
photosynthesis and cell size (Maranon et al., 2007). Observations in diverse ecosystems have
linked bio-optical properties (pigment composition and absorption) to phytoplankton
productivity ‘P’ and photosynthetic activity: Arabian Sea (Marra et al., 2000); N. Atlantic
(Suggett et al., 2001; Suggett et al., 2003; Moore et al., 2005; Suggett et al., 2006); English
Channel (Aiken et al., 2004); Benguela (Fishwick et al., 2006); global (Marra et al., 2007).
Photosynthetic quantum efficiency (PQE) derived from Fv/Fm measured by fast repetition rate
fluorometry (FRRF, Kolber and Falkowski, 1993; Kolber et al., 1998; Suggett et al., 2004;
Rottgers, 2007) or the quantum efficiency for C-fixation φ, are closely related to bioenergetic
(BE) status. Bioenergetics is the transformation of light energy (photosynthesis) through
intermediate stages to the synthesis of plants (Govindgee 1975), regulated by macro and micro
nutrient quality and availability, or by photon flux when light is limiting. Productivity ‘P’ (mols
C m-3 d-1) while dependent on BE status, is driven by light energy, EPAR:
P = const PQE σPSII EPAR Chla (Suggett et al., 2001; Suggett et al., 2004; Smyth et al., 2004);
or comparably, P = φ aph EPAR (Marra et al., 2000).
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Neither expression includes nutrient concentrations explicitly, inferring nutrient regulation of
PQE, σPSII or φ, probably through the synthesis of protein-pigment complexes in the light
harvesting complex (LHC) and photosystems (PSI, PSII). From this concept, PQE (or φ)
emerges as the primary functional parameter for the bioenergetic classification of phytoplankton,
with characteristic properties and discrete ranges that define BE status.
The BE-BOT Hypothesis
We hypothesise that the bioenergetics of photosynthesis, coupled to environmental
properties (nutrients, light fluxes, etc), is the definitive phytoplankton functional process that
determines phytoplankton taxa, size classes and ecosystem trophic status, and that BE status is
quantitatively linked to phytoplankton bio-optical traits (BOT) that are specific properties of
phytoplankton size and taxa. Bio-optical variables with significant BOT are: Chla concentration;
accessory pigments (Chlb, Chlc, carotenoids, phycobillins); pigment ratios (TChla/AP, TChla/TP,
PPC/TC); phytoplankton absorption at 443 nm, aph443 (Chla peak absorption); the spectral slope
of aph, 443-510 nm (or 490 nm). Specific BOT are conferred by the unique absorption spectrum of
Chla (blue, 400-460 nm in vitro, broader in vivo) that is distinct from carotenoid (PSC+PPC)
absorption spectra (blue-green, 400-550 nm, peak ~490 nm). The quantitative relationship
between Chla and accessory pigments changes systematically with BE status and size classes. Fig
1 shows the process diagram linking the bio-energetic and bio-optical traits.
A corollary of this hypothesis is that ocean inherent optical properties (IOPs) determined in
situ or derived from ocean colour are primarily a function of phytoplankton photosynthetic
activity, through the instantaneous absorption of solar radiation (akin to action spectrum) and
secondarily a function of the steady state biomass, (approximated by Chlorophyll-a determined in
vitro from phytoplankton absorption or pigment analyses). Pigments, pigment-protein complexes,
PSI, PSII and LHC are synthesised much slower, over 12-24 h and are cumulative from
photosynthetic activity over the previous few days. The BE-BOT hypothesis quantifies the link
between bioenergetic status and ecosystem environmental-biogeochemical properties, bio-optical
traits and phytoplankton types. The links are circular; it is a mandala.
The objectives of this paper are to attempt a verification of the BE-BOT hypothesis by:
1. A review of the environmental characteristics of marine ecosystems (biomes) that affect the
regulation of photosynthesis and plankton communities, and the bio-optical expressions of
biological, physiological, ecological functional processes.
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2. Examination of historical research on phytoplankton taxa, pigments and colour properties.
3. Re-analysis of data from the iron enrichment experiments, a seasonal study in the western
English Channel, processes cruises in the Atlantic Ocean and the NOMAD data set.
4. Application of the BE-BOT relationships to the analysis of SeaWiFS data, to determine the
distributions of phytoplankton size classes and Chla concentration in the major ocean basins.
REVIEW
Trophic classification of oceanic environments.
There are specific environmental characteristics of biomes that affect the regulation of plankton
communities and population-specific ecological traits. Taking a conceptually minimalist
approach, we identify 3 distinct eco-static biomes (that are dynamic yet functionally consistent,
intra- and inter-annually) and 2 oscillatory (high seasonality) biomes. Biomes are defined as
regions with distinctive environmental, biogeochemical and ecological properties, hosting
phytoplankton assemblages that may vary seasonally. The 3 eco-static biomes are:
1. The permanently stratified, stable (low-seasonality) oligotrophic gyres with very low surface
inorganic nutrients and poor phytoplankton growth, low biomass, Cph, Chla and Chla fraction,
Chla/TP. They are dominated by prochlorophytes, cyanobacteria, pico-eukaryotes and
heterotrophic bacteria. Trophic status is oligotrophic. Examples are oceanic sub-tropical gyres.
2. Episodically-driven, turbulent upwelling systems, high in up-welled inorganic nutrients (NO3-,
SiO44-, PO42- {ion charges !!}) where diatoms prosper (dinoflagellates, if SiO4 exhausted); highly
productive with high Chla, Cph. Trophic status is eutrophic. Examples are Benguela and N.W
African upwelling. Some have seasonal variability and others, though episodic, are persistent
throughout the year.
3. Stratified systems with seasonal variance, marginal areas to 1 & 2 and tropical regions with
complex currents, re-cycled nutrients but some inorganic, allowing flagellate groups to prosper
(microplankton need inorganic nutrients); moderately productive, with moderate Chla, and Cph.
Trophic status is mesotrophic.
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The 2 oscillatory biomes are:
4. The strongly seasonal temperate oceans and shelf seas, that are turbulently mixed in winter (to
100-600m) by convectional overturn and wind-mixing, replenishing inorganic nutrients (but low
light); they stratify in the spring, diatoms and dinoflagellates bloom with flagellates prospering in
stratified summer conditions (re-cycled and some inorganic nutrients). In summer if inorganic
nutrients are exhausted, the surface can be virtually oligotrophic, but episodic summer blooms of
dinoflagellates or flagellates recur, from meteorological forced nutrient pulses. Nutrients are resupplied with autumnal overturn, resulting in autumn blooms. This biome is oscillatory; trophic
status eutrophic or mesotrophic, occasionally oligotrophic.
5. The strongly seasonal polar and sub-polar oceans and shelf seas, that are totally mixed in the
winter by convectional overturn and wind mixing (to >100 m, high nutrients, low light) and
stratify in the summer with diatom and dinoflagellate blooms. Often inorganic nutrients are not
exhausted in summer, though depletion and re-cycled nutrients may allow flagellate groups to
prosper. Trophic status is eutrophic-mesotrophic in summer and oligotrophic in winter.
All these biomes can be detected readily from satellite observations of ocean colour or SST (see
Fig. 2 from Hardman-Mountford et al, in press). There are not a myriad of intermediate states
though there may be diversity of biogeochemical provinces, a few niche systems e.g. recent
study NW Atlantic, (Devred et al., 2007) and there may be exceptions to these generalities.
Environmental control of photosynthesis and pigment synthesis.
The underlying functional processes are the biosynthesis of phytoplankton and the associated
photochemical pathways that are permitted by the bioenergetics of the environment (Porra et al.,
1997). Nitrogen (and P, though rarely yield limiting) is the currency of bioenergetics, though
phytoplankton utilise and cycle other elements: e.g. diatoms use Si; coccolithophores and others
use Ca; many species use S; Fe is used in PS1 and PS2 and is an essential micro-nutrient for all
phytoplankton at variable quotas. Free {you mean photo-pigments?} photo-pigments have no
role in photosynthesis, indeed they may be toxic to cells (Jeffrey and Vesk, 1997). Chlorophylla synthesis has high energetic requirements and is synthesised only when there are sufficient
energy resources to maintain it; Chla formation from ALA (5-aminolevulinic acid) involves
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more than 25 reaction sequences (Porra et al., 1997), and is de-synthesised rapidly when
nutrients restrict growth; e.g. as Fe was used in IronEx II (Behrenfeld et al., 1996). The
chlorophylls (and phycobilins) are the only pigments that use N (Chla is C55H72N4O5Mg, Jeffery
et al., 1997). The system is fuelled by N, an essential element for Chla, a component of
photosystems (PSI, PSII) and a major pigment in the LHC. Phytoplankton types with high Chla
are prevalent in high N environments.
Bio-optical expressions of biological, physiological, ecological functional processes
The chlorophylls (-a, -b, -c), carotenoids and phycobilins (low abundance in the surface ocean),
are the major phytoplankton pigments that colour marine plants and ocean waters observed by
satellite colour sensors. The chlorophylls and carotenoids are structurally, chemically and
optically quite different (their chromophores are different). The chlorophylls have cyclic
tetrapyrrole structures and all have a strong ‘Soret’ absorption band (400-460 nm) and secondary
absorption bands from 640-670 nm. Most major marine phytoplankton have ‘taxa-specific’
carotenoids that are perceived to give specific colour to different taxonomic groups (see Table I
for abbreviations), for example: Fuc for diatoms; Per for dinoflagellates (some have Fuc as the
major pigment); Hex for Prymnesiophytes (some Hex in dinoflagellates); Allo for cryptophytes;
But for chrysophytes (some But in dinoflagellates); Lut and Chlb for Chlorophytes (and
prochlorophytes); Zea for prokaryotes (cyanobacteria). Phycobilins are major pigments in
synechococcus spp. with trace amounts in prochlorococcus spp. (and some in cryptophyta). The
taxa-specific aspects of carotenoids are not at all definitive and there are many ambiguities.
The more significant problem is that most carotenoids have very similar absorption spectra
(peak absorption 450-480 nm) both in vitro (Jeffrey et al., 1997) and in vivo (Bidigare et al.,
1990); in vivo spectra may be shifted by 10-20 nm to longer wavelengths. The xanthophylls
(carotenoids e.g. Fuc, Zea and Lut) are carbohydrates formed by the hydroxylation of β,β-carotene
or β,ε-carotene (Young and Britton, 1993). Neither carotenes nor xanthophylls contain N. All
carotenoids have alternating single and double bonds in the centre of the molecule, responsible for
light absorption, termed the chromophore (Bjornland, 1997). Carotenoids with different molecular
structure but identical chromophores will have similar spectral characteristics. Structural
similarity likely reflects biochemical connections, though there are few data on carotenoid
biosynthesis. Thus it is unlikely that taxa-specific pigment properties can provide an unambiguous
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bio-optical trait for any PFT, except in unusual cases; e.g. extremely high pigment concentrations.
Chlorophyll-a has a unique, blue absorption spectrum that is different from all the carotenoids, and
which gives it a distinctive influence on phytoplankton absorption spectra. This is the most
significant phytoplankton bio-optical trait. Allied to the BE-BOT hypothesis, populations with
high Chla (microplankton) will have a high blue absorption spectra, high green reflectance, and
populations with less Chla will have less blue absorption and green reflectance.
METHOD
Phytoplankton pigments were determined by HPLC analysis, mostly using methods reported by
Barlow et al. (Barlow et al,1997 and Barlow et al, 2004), and largely consistent with the
SCOR/UNESCO recommendations (Jeffrey et al., 1997). We used DPA (Vidussi et al., 2001,
Uitz et al., 2006) to determine the composition of the dominant phytoplankton taxonomic groups
or functional types (PFTs) from diagnostic pigment (DP) indices (defined in Table I). Three
dominant size classes were determined: microplankton, comprising diatoms (DP = Fuc) and
dinoflagellates (DP = Per); nano-flagellates comprising chrysophytes (DP = But),
prymnesiophytes (DP = Hex), cryptophytes (DP = Allo) and chlorophytes (DP = Viol, Chlb);
picoplankton, comprising prokaryotes (DP = Zea), Synechococcus sp. and Prochlorococcus sp.
and pico-eukaryotes (p-E;). There are anomalies and ambiguities with the DPA (Aiken et al., in
review) but it is a useful approximation. The use of pigment ratios (TChla/TP, TChla/AP) has
not been widespread, but the correlations with PQE (Fv/Fm) in the Fe-enrichment experiments
(IronEx II, SOIREE) and in natural ecosystems are significant (Aiken et al., 2004, Moore et al.,
2005; Fishwick et al., 2006).
The fast repetition rate fluorometer (FRRF, Chelsea Instruments, FastTracka; Kolber and
Falkowski, 1993; Kolber et al., 1998; Aiken et al., 2004) gives data of PQE ( derived from
Fv/Fm) and the cross-section for PSII (σPSII), using measurements at night (unquenched values).
Values of PQE and σPSII from the Fe-enrichment experiments (Behrenfeld et al., 1996; Gall et al.,
2001; Rottgers, 2007) were ~0.25 pre-experiment or out-of-patch, rising rapidly to >0.45 after
Fe additions, correlated with slower rises of pigment ratios. These are consistent with data from
natural ecosystems (Aiken et al., 2004; Smyth et al., 2004; Fishwick et al., 2006; Suggett et al.,
2006; Moore et al., 2005) mostly in mesotrophic and eutrophic waters. Oligotrophic waters and
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prokaryote phytoplankton present particular problems for the determination of Fv/Fm and σPSII in
situ by FRRF. Signal to noise ratios are near or below 1 when Chla concentrations <0.2 mg m-3.
The excitation source (blue LEDs ~470 nm) does not target the LHC of cyanobacteria that have
phycobilins absorbing at >550 nm and prokaryotic and eukaryotic algae have different structures
of PSI and PSII that give different active fluorescence responses (discussed by: Johnsen et al.,
1997; Lutz et al., 2001; Suggett et al., 2004; Johnsen and Sakshaug, in press).
RESULTS
Re-assessment of historical research.
Margalef (Margalef, 1967) linked the organisation of plankton and species diversity with plant
pigments (Chlorophyll-a in relation to carotenoids), optical properties (e.g. D430/D665, where
‘D’ is the optical density at wavelengths 430 and 665 nm of phytoplankton extracted in 90%
acetone, an approximate inverse of TChla/TP ratio) and productivity measurements by
radioactive tracers (usually 14C). The ratio D430/D665 was high in populations with low
chlorophyll content and low in cells rich in chlorophyll. Margalef reported that Chla was more
quickly synthesised and decomposed than other pigments (Yentsch and Scagel, 1958; Ballester,
1966) and responded more rapidly than other pigments to external changes in the opportunity for
growth. Diversity of pigments comes mainly from the strong dominance of chlorophyll a over
other photosynthetic pigments. The optical ratio D430/D665 varied over a wide range 2 to 8
(TChla/TP ~0.5 to 0.125) with high values (low TChla/TP) associated with dead plankton, old
populations, nutrients consumed (c.f. Yentsch, 1962). The optical ratio was higher in
dinoflagellates than diatoms and lowest in volvocales. Species that were more yellow (low
TChla/TP) came later in succession than species that were more green (higher TChla/TP).
Pigment composition was a good indicator of the state of populations, since it covered
taxonomic composition and the physiological state of the plankton. Optical ratio increased along
a succession and in going from coast to offshore and it was particularly low (high TChla/TP) in
estuaries and plankton blooms. Ryther and Yentsch, (1957) showed links between the optical
and biological properties of marine plankton (reviewed by Yentsch, 1962).
Laboratory studies (Schluter et al., 1997; Holmboe et al., 1999) showed that the fraction of
carotenoids (equivalent to low TChla/TP) was greater for nutrient starved cultures. Jeffrey and
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Hallegraff (1980) reported that the D480/D665 (approximate carotenoid/Chla ratio) of 2.5-3.0
indicated living cells and higher ratios indicated older cells. Heath et al (1990) reported
C/Chla ratio, D480/D665 and C/N ratio all co-varied, with low values of D480/D665,
corresponding to high Chla/carotenoid, high Chla/C and high N/C, implying healthy
(photosynthesising) cells. Experimental work on nitrogen nutrition and starvation of the marine
prymnesiophyte Isochrysis galbana (Davidson et al., 1992; Flynn et al., 1993) showed similar
responses. In growth situations, net Chla synthesis was closely linked to the absorption of
extracellular N, while carotenoids synthesis continued until C fixation ceased photosynthetic
activity varied with N:C ratio. Net Chla synthesis ceased within 36 h of the start of Ndeprivation but synthesis of some carotenoids continued, with proportions of echinenone
increasing and fucoxanthin decreasing.
Iron enrichment experiments in high nitrate, low chlorophyll (HNLC), Fe depleted regions.
In the second iron enrichment experiment (IronEx II) in the HNLC zone of the central Pacific
(Coale et al., 1996; Behrenfeld et al., 1996) the addition of iron produced a rapid increase of
TChla (0.1 to 1.8 mg.m-3 in 6 days), TP, and Cph, while PQE (Fv/Fm, measured by FRRF at night)
increased rapidly, from 0.25 to 0.48 within the first 24 h. The increase of TChla/TP was delayed
(~1 d) from the rise of Fv/Fm, and pigments decreased slower after the Fe was dissipated (Fig. 3
a) providing an insight into the dynamics of the artificially created ‘bloom’. Fv/Fm was
significantly correlated with TChla/TP (Fig. 3 b) and TChla/Cph (r2 = 0.38) and TChla/TP was
correlated with TChla/Cph (r2 = 0.53; see Table II). Floristic shifts coincided, with the changes in
TChla/TP and Fv/Fm. Prymnesiophytes and dinoflagellates showed modest increases (x1.6 in 3 d
and x 1.4 in 6 d) while diatoms increased by factors of x 30 (cell counts) x 100 (Carbon) and ca
75 (Chla) reaching maximum values after 7 days (Landry et al., 2000). The switch to diatom
dominance occurred at TChla of ~1.1 mg.m-3. Prokaryotes (cyanobacteria, prochlorophytes) and
pico-eukaryotes showed little change in abundance in the patch compared to the control zone.
During SOIREE (Boyd, et al., 2000; 2001; Gall et al., 2001) in the HNLC zone of the Southern
Ocean (temperature 0.5 ºC), the addition of Fe increased TChla, TP and Fv/Fm but slowly; Fv/Fm
increased from 0.22 to 0.42 in 40 h and to 0.48 by day 13 (Fig. 3 c), and it was correlated with
TChla/TP (Fig 3 d) and TChla/Cph (r2 = 0.72; see Table II). TChla/TP and TChla/Cph were
correlated (r2 = 0.69). Diatoms increased greatly (Gall et al., 2001) and were dominant at TChla
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>0.62 mg.m-3. For both IronEx II and SOIREE, the values of the main variables and parameters
outside the fertilised patches, remained nearly unchanged. The values of Fv/Fm (ca 0.25, 0.22)
were among the lowest that have been measured in natural ecosystems (see Rottgers, 2007).
In EisenEx (Southern Ocean) the relationship of TChla vs TChla/TP was ‘typically’ log-linear (r2
= 0.44; see Table II). The population was 70% flagellates (Prymnesiophytes), 30% diatoms, preenrichment (Chla 0.4 mg.m-3) changing to 80% diatoms, 20% flagellates at the peak of the
‘bloom’ (Chla 3.0 mg.m-3), switching to diatom dominance at Chla >1.25 mg.m-3.
In SOFeX (Southern Ocean) the TChla vs TChla/TP) including the out-of-patch data, was again
typically log-linear (r2 = 0.75; see Table II). There was little change of phytoplankton
composition, from 65% diatoms outside the patch to 80% diatoms inside patch.
Western English Channel, L4 time series.
Aiken et al. (Aiken et al., 2004) measured the relationships between phytoplankton PQE
(derived from Fv/Fm, measured by FRRF), pigments (by HPLC), absorption spectra, species
composition (converted to phytoplankton carbon, Cph) over a seasonal cycle in 2001, at an
offshore site in the western English Channel. Surface layer Chla and total pigment (TP)
concentrations were highly correlated for the whole year, yet Chla/TP was not constant with a
distinct seasonal pattern, low in winter and higher in spring, summer and autumn blooms.
Chla/TP was linearly correlated with Fv/Fm throughout most of the year, though more
significantly within seasonal periods (Fig. 4a; see Table II). Fv/Fm and Chla/TP were
significantly correlated with the optical absorption ratios, a674/a443 (Fig. 4b) and a674/a490
indicating optical signatures for both parameters. Chla and TP were both linearly correlated to
Cph. Chla/TP and Fv/Fm were significantly correlated with Chla/Cph ratio (inverse of C/Chla) for
the spring and summer periods only (Fig. 4c, 4d), due to uncertainty of Cph determinations in
winter. The seasonal cycle of PQE provided a bench-mark against which the photosyntheticallydriven seasonal changes of biological properties were explained, in terms of incident solar
radiation and nutrient availability. It was concluded that phytoplankton synthesise Chla
preferentially to other pigments or carbon compounds in conditions beneficial to growth,
independent of species composition, succession or photoadaptation-photoacclimation; i.e.
Chla/TP, Chla/AP and Chla/Cph are greater when plants are growing actively, consistent with
historical research and Fe-enrichment experiments.
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Benguela ecosystem
Measurements of the pigments, bio-optical properties and photosynthetic parameters in the
Benguela ecosystem (Fishwick et al., 2006) were used to distinguish flagellate dominated stations
from microplankton dominated stations. TChla was highly correlated with TP and AP, but there
were different relationships for flagellate (<2 mg.m-3) and microplankton dominated data. TChla
was log-linearly correlated with TChla/TP, TChla/AP, the optical ratio a676/a440 and Fv/Fm and
inversely with σPSII. TChla/TP (or TChla/AP), Fv/Fm and a676/a440 were significantly intercorrelated (see Fig. 5a, b, c; and Table II). New analyses (Fig. 5c) showed that the absolute value
of aph443 was significantly correlated with Fv/Fm and the pigment ratios. The discrete ranges of
pigment concentrations (TChla, TP or AP), pigment ratios (TChla/TP or TChla/AP), optical ratios
or photosynthetic (bio-energetic) parameters (Fv/Fm and σPSII) for the dominant PFTs were used to
partition MERIS data for the Benguela into different phytoplankton types (Aiken et al., 2007).
Atlantic Ocean, FISHES cruise and AMT
Analyses of data from the FISHES cruise in the temperate N. Atlantic (57-66 N, 0-26 W), Moore
et al., (2005), found typical log/linear relationships of TChla vs TChla/TP and the correlation of
TChla/TP vs Fv/Fm (see Table II) as observed previously. Flagellate dominated samples (mostly
prymnisiophytes) partitioned from the diatom dominated stations at Chla >1.0 mg m-3. In the
FISHES cruise, σPSII was inversely related to TChla (log/linearly) and TChla/TP (linearly).
The Atlantic Meridional Transect (AMT) programme has showed that TChla/AP (or TChla/TP)
increased with TChla concentration (Gibb et al., 2000, Barlow et al., 2002, 2004). Aiken et al.,
(in review) quality assured and analysed the pigment data from 16 AMT cruises using DPA
(Uitz et al., 2006). Picoplankton (prokaryotes and pico-eukaryotes) dominated the equatorial
and sub-tropical provinces (35°N to 35°S) with TChla always < 0.25 mg m-3 (typically <0.16 mg
m-3). Nanoplankton and microplankton were abundant only where cruises entered upwelling
zones (e.g. AMT-6 in the Benguela, Barlow et al., 2002, 2004) or temperate zones > 45°N and
35°S. For the prokaryote-dominated provinces, both within cruises (4 provinces combined) and
within provinces, significant correlations between TChla and TChla/AP were observed.
13
NOMAD data set
Hirata et al (in review) developed a model relating the phytoplankton absorption at 443 (aph443)
and the spectral slope of absorption ‘S’ in the range 443 to 510 nm, using data from NASANOMAD (Werdell and Bailey, 2006) that combined phytoplankton HPLC pigment and IOP
(absorption) data. Pigment data were used to derive phytoplankton size classes by DPA. Fig 6a
and 6b show the relationship between Chla and S and aph443 with the data marked for the
dominant size class, derived by DPA. The data were partitioned in both Chla space (pico <0.25;
nano 0.25 to <1.3; micro >1.3 mg m-3) and aph443 space (pico <0.024; nano 0.024 to <0.060;
micro >0.060 m-1). While the thresholds for picoplankton were quite sharp, those for
nano/micro plankton were blurred. For each size class there were significant linear relationships
between the slope ‘S’ (m-1 nm-1) and log[Chla] or log [aph443] :
Spico = 0.00016 log[Chla] + 0.00029 (r2=0.47)
Snano = 0.00045 log[Chla] + 0.00042 (r2=0.29)
Smicro = 0.00120 log[Chla] + 0.00030 (r2=0.39)
Spico = 0.00027 log[aph443] + 0.00064 (r2=0.89)
Snano = 0.00070 log[aph443] + 0.00140 (r2=0.88)
Smicro = 0.00220 log[aph443] + 0.00310 (r2=0.93)
The regression analyses show that the aph443 relationships are much more significant than the
Chla relationship. These relationships can be used to derive global maps of phytoplankton
groups (micro, nano and picoplankton) from SeaWiFS, using aph443 thresholds at < 0.024 and >
0.060 m-1 (see Fig. 7). The determination of aph443 from SeaWiFS used the ocean colour
inversion model of Smyth et al. (Smyth et al., 2006) which has been released through
International Ocean Colour Coordinating Group ($http://www.ioccg.org/groups/software.html$).
Microplankton were dominant in coastal upwelling zones (e.g. Benguela) and temperate
seasonally stratified regions (e.g. North Atlantic spring and summer). Note also the occurrences
of microplankton and nanoplankton in the Southern Ocean in the austral spring and summer.
Nanoplankton were abundant at mid-latitudes after the spring bloom and in equatorial regions.
Picoplankton were dominant in tropical and sub-tropical ocean gyres. These distributions are
consistent with many published data and with the basin-scale observations from the Atlantic
Meridional Transect (AMT) (e.g. Gibb et al., 2000; Zubkov et al., 1998, 2000; Heywood et al.,
2006; Tarran et al., 2006; Aiken et al., in review).
14
Table III shows the occurrences by month (Jan to Dec 2005) of pico, nano and microplankton in the
global ocean, and partitioned into the major ocean basins: Indian, N and S Atlantic, N and S Pacific.
Globally microplankton ranged from 1.3 to 3.5% (annual mean 2.2%). Nanoplankton occurrences
ranged from 15.3 to 18.7% (16.4%). Picoplankton were most abundant ranging from 79.1 to 82.7%
(mean 81.4%). The N. and S. Atlantic and the N. and S. Pacific oceans showed seasonal cycles with
both micro and nanoplankton increasing in spring and summer in each hemisphere, and picoplankton
decreasing. The Indian Ocean had the largest fraction of picoplankton (annual mean 86.7%) and
least microplankton (0.5%), with evidence of seasonal increases of nano and microplankton,
associated with the SW and NE monsoons. Table IIIa shows the mean occurrences of each size class
converted to mean Chla using mean factors (mg m-3) calculated from NOMAD: pico (<0.25) = 0.13;
nano (0.25 to 1.3) = 0.72; micro (>1.3) = 3.31 mg m-3. These factors are comparable to values
derived from AMT data (Aiken et al., in review).
DISCUSSION
We have examined historical data, which showed that chlorophyll-a concentration varied with
environmental conditions (availability of essential nutrients and light). Quantitative measurements
of Chla and Chla to carotenoids ratios from optical ratios (D430/D665) showed that the Chla/TC
fraction increased with Chla concentration (Margalef, 1967; Ryther and Yentsch, 1957).
Laboratory studies (Heath et al., 1990) showed that the TC/Chla ratio (D480/D665, comparable to
in vivo a490/a675) co-varied with C/Chla and C/N ratios, with low values of D480/D665
corresponding to high Chla/TC, high Chla/C and high N/C and consistent with the BE-BOT
hypothesis. The iron enrichment experiments were also informative, showing that Chla increased
after Fe addition, concurrent with increases of TChla/TP, TChla/AP and the photosynthetic
parameter, Fv/Fm, which was significantly correlated with TChla/TP. Analyses of data from the
Atlantic Ocean and western English Channel again showed that as Chla increased, there were
concurrent increases of TChla/TP, TChla/AP and the optical aph676/aph443 or aph 443. Again
Fv/Fm, the pigment ratios and optical ratios were all inter-correlated, consistent with the BE-BOT
hypothesis.
Striking evidence for the BE-BOT hypothesis comes from the analyses of NOMAD, the
NASA data set of global bio-optical measurements. The analyses showed that both TChla and
15
aph443 were correlated with ‘S’ the slope of the phytoplankton absorption spectrum between 443
and 510 nm and relationships that partitioned according to the phytoplankton size classes: Chla
space (pico <0.25; nano 0.25 to <1.3; micro >1.3 mg m-3) and aph443 space (pico <0.024; nano
0.024 to <0.060; micro >0.060 m-1). These results provide the empirical basis for the corollary to
the BE-BOT hypothesis, relating IOPs from ocean colour, to the bioenergetics of photosynthesis
and the partitioning with phytoplankton size classes. These empirical relationships were used to
interpret monthly SeaWiFS ocean colour data, for the distributions of the major size classes and
the associated annual Chla distributions, globally and in the major ocean basins.
The BE-BOT hypothesis and the corollary on ocean colour interpretation are controversial,
but conceptually logical in temporal terms: the absorption of light by phytoplankton is very fast
(~10-15 s) precedes the process of photosynthesis (fast, 10-6 to 10-3 s) whilst the synthesis of C-N
compounds, including Chla, LHC are much slower (~ 1 day). We speculate that phytoplankton
absorption in vivo and the ocean colour spectra are akin to ‘action spectra’ which is closely
approximated by, but subtly different from absorption spectra determined in vitro or reconstructed from pigment measurements (see also Lutz et al., 2001).
In environments with plentiful nutrients and high light, photoautotrophs can synthesise
and maintain Chla readily, providing Chla to both photosystems PS1 and PSII, and the LHC.
With abundant Chla, there is less need to synthesise other pigments for light harvesting and little
need to extend the spectral width of the LHC from the blue (highest energy photons) into the
green region (mid energy photons). Fuc is the major pigment in diatoms that broadens the
spectral light harvesting range of diatoms to 550 nm. Measurements of PQE (Fv/Fm) by FRRF,
gives high values (0.55 to 0.65) for microplankton, and low σPS2, presumably because the LHC
is relatively small.
Why do diatoms have Fuc (C42H58O6) and dinoflagellates have Per (C37H50O7)? Fuc and Per are
chemically, structurally and optically quite similar (similar chromophores and absorption/
fluorescence spectra) and both have high efficiency (~100%) energy transfer to PS1 and PS2.
Fuc can replace Per as the major accessory pigment in dinoflagellates. Both Fuc and Per are
derivatives of C40-carotenoids, requiring extra stages of biosynthesis, but they may be less stable
(having high synthesis and maintenance). This is consistent with a bioenergetic model.
Flagellate groups succeed in less bioenergetic environments, where the abundance of
inorganic N is lower and the ability to synthesise and maintain high concentrations of Chla is
16
correspondingly less. They improve their photosynthetic activity by extending their LHC with
carotenoids, notably Hex for Prymnesiophytes, But for Chrysophytes; all flagellates have Fuc,
the pre-cursor for Hex and But, formed by the esterisation of Fuc. We speculate that these
derivatives are more stable, synthesised and accumulated when photosynthesis is active, but are
less energetically demanding for maintenance and retained when photosynthesis is sub-optimal.
Flagellate groups have smaller values of Fv/Fm (>0.35 and <0.5) with a larger value of σPS2.
Picoplankton (Synechococcus spp., Prochlorococcus spp., pico-eukaryotes and other
bacteria) subsist in the ‘nearly totally N-depleted’ oligotrophic gyres, making it difficult to
synthesise Chla; Chla/TP, Chla/Cph are all low, as is Fv/Fm. There are doubts about Fv/Fm and
σPS2 measured by FRRF for Synechococcus spp., which have phycobilins (absorption 560-600
nm) extending the LHC and for Prochlorococcus spp., which have DVChlb (peak absorption
470 nm) and trace phycobilins that may change the LHC. The FRRF stimulates fluorescence of
PSII using blue LEDs (~470 nm) that fail to target the LHC spectra of picoplankton, leading to
erroneous measurements of Fv/Fm and σPS2 for prokaryotic phytoplankton.
Phaeocystis spp. are an exception. They are flagellates that in single cell assemblages (in
sub-surface Chla maxima) rarely reach high concentrations (<2 mg.m-3), but form massive
blooms (in colonies) in eutrophic conditions; i.e. abundant nutrients especially nitrate. Is the
colonial form helpful in assimilating N? In high N situations Phaeocystis spp. assimilate N
readily and synthesise high concentrations of chlorophyll. In colonial form Phaeocystis spp.
have Fuc as the dominant accessory pigment and little Hex, functioning more like microplankton
(diatoms).
The change in the Chla/TP ratio or more strikingly the change in the Chla/AP ratio, are the
biggest factors that alter the shape of ocean colour spectra (the relative change of blue to green
absorption). With more Chla, there is more absorption at blue wavelengths (400-460, centred at
443 nm) relative to green absorption (carotenoids, 400-550 nm centred at 490 nm). This is
expected from the observed changes in pigment ratios in natural ecosystems and more directly,
from the changes in the optical ratios that are measured concurrently in situ (or in vitro); these
are all highly correlated. The striking differences in optical properties for each of the 3 major
PFT groups associated with the 3 basic biomes, derives from the bioenergetic functional
processes for different PFTs and provides the bio-optical traits that can be used for the
interpretations of PFTs from ocean colour data measured from space.
17
ACKNOWLEDGEMENTS
This paper is a tribute to Prof Patrick Holligan, who has inspired us all as a teacher and
researcher. We thank colleagues who have contributed pigment and other data to this study:
Mark Gall (SOIREE), Bob Bidigare (SOFeX), Ilka Peeken (EisenEx), Mark Moore (FISHES).
This work was undertaken within the Centre for observation of Air-Sea Interactions and Fluxes
(CASIX), funded by the Natural Environment Research Council (NERC) jointly with Plymouth
Marine Laboratory and other NERC centres and part funded by MARQUEST project. This
publication comprises AMT publication XXXX and CASIX contribution number XXXX.
SeaWiFS data were provided by the SeaWiFS project Goddard Space Flight Centre and
ORBIMAGE and used in accordance with the SeaWiFS Research Data Use Terms and
Agreements. We thank two anonymous referees for there valuable comments on the structure of
the paper and the editor, Kevin Flynn for scientific input that has improved the final paper.
18
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Atlantic transect from 50N to 50S. Deep-Sea Res. I, 45, 1339-1355.
Zubkov, M.V., Sleigh, M.A. Burkill, P.H., et al., (2000) Picoplankton community structure on the
Atlantic Meridional Transect: a comparison between seasons. Prog. Oceanog. 45, 369-386.
25
Table I. Summary information and abbreviations for phytoplankton pigments, pigment ratios,
phytoplankton taxa, species and photosynthetic parameters.
Symbol
HPLC
Chla
Description
High-Performance Liquid Chromatography
Chlorophyll a (plus allomers and epimers)
Chlb
Chlorophyll b
Chlc
Chlorophylls c1 + c2 + c3
Chlidea
DVChla
TChla
Allo
But
Caro
Diad
Diato
Fuc
Lut
Hex
Per
Viol
Zea
PSC
PPC
TC
AP
TP or Tpig
DP; DPA
Chlorophyllide a
Divinyl chlorophyll a
Total chlorophyll a
Alloxanthin
19’-Butanoyloxyfucoxanthin
Carotenes, ββ-Carotene + βε -Carotene
Diadinoxanthin
Diatoxanthin
Fucoxanthin
Lutein
19’-Hexanoyloxyfucoxanthin
Peridinin
Violaxanthin
Zeaxanthin
Photosynthetic carotenoids
Photoprotective carotenoids
Total carotenoids
Accessory pigments
Total pigments; TPig in Aiken et al., 2004
Diagnostic Pigments; DP Analyses, Uitz et al.,
2006
Total chlorophyll a to total pigment ratio
Total chlorophyll a to accessory pigments
Phytoplankton functional groups (e.g. Diatoms,
Dinoflagellates, Flagellates or Prokaryotes).
Diatom fraction; size 20-200μm, up to 2mm
Dinoflagellate fraction; size range 20-200 μm
Microplankton fraction = diat + dino
Nano-flagellate fraction; size range 2-20 μm
Prokaryote fraction; size <2 μm
Picoplankton fraction = prok + p-E
Pico-eukaryote fraction; size <2 μm
Synechococcus spp. (cyanobacteria)
Prochlorococcus spp. (cyanobacteria)
Maximum photosynthetic quantum efficiency,
derived from Fv/Fm measured by FRRF
Effective absorption cross section of PS2
Quantum efficiency of C fixation, (maximum)
Absorption by phytoplankton (wavelength)
Phytoplankton C from microscopic counts
TChla/TP
TChla/AP
PFT
diat
dino
micro
nano
prok
pico
p-E
Syn
Pro
PQE (Fv/Fm)
σPS2
φ, φm
aph(λ)
Cph
26
Comment/Formula/Units
Photosynthetic (PS) pigment, constituent of
light harvesting complex (LHC) &
photosystems, PSI and PSII.
PS pigment in chlorophytes, prasinophytes
and prochlorophytes (DVChlb)
PS pigment in Diatoms, dinoflagellates,
prymnesiophytes, chrysophytes
Alteration product of Chla
Chla in prochlorophytes
Chla + DVChla + Chlidea
PPC in cryptophytes
PSC in chrysophytes & prymnisiophytes
PPCs
Diatoms, prymnesiophytes, chrysophytes
Diatoms, prymnesiophytes, chrysophytes
Diatoms, prymnisiophytes & dinoflagellates
Chlorophytes
Prymnesiophytes some dinoflagellates
Dinoflagellates
Chlorophytes
PPC mainly in cyanobacteria
Per, But, Fuc, Hex
Viol, Diad, Allo, Diato, Zea, Caro, Lut
PSC + PPC
TC + Chlb + Chlc1 + Chlc2 + Chlc3
TChla + AP
1.41Fuc + 1.41Per + 1.27 Hex + 0.35But +
0.6Allo + 1.01Chlb + 0.86Zea
TChla/TP
TChla/AP
Usually merged into size classes: micro;
nano; pico (or prok).
1.41Fuc/DP
1.41Per/DP
(0.6Allo + 1.27Hex + 0.35But + Chlb)/DP
0.86Zea/DP
Contains phycobilins mainly phycoerytherin
DVChla & DVChlb, trace phycobilins
Unit-less, range typically 0.2-0.65.
x10-20m2photon-1
molsC (mols photons)-1
m-1
Conversion factors differ by taxa.
Table II. Summary statistics of regression analyses for sub-sets of data from: IronEx II; SOIREE; EisenEx;
SOFeX; L4 (WEC); Benguela; FISHES .
Dataset
IronEx II
Ramp-up
Down
SOIREE
EisenEx
SOFeX
L4
Benguela
FISHES
Ind.
Dep.
Type
Slope
Intercept
r2
N
TChla
TChla
Fv/Fm
TChla
Fv/Fm
TChla
TChla
TChla
Fv/Fm
Fv/Fm
TChla/TP
Fv/Fm
TChla
Fv/Fm
Fv/Fm
TChla/TP
Fv/Fm
TChla
Fv/Fm
TChla/TP
TChla/TP
TChla/TP
TChla/TP
TChla/TP
TChla/TP
TChla/TP
TChla/TP
TChla/TP
a676/a443
TChla/Cph
TChla/Cph
TChla/TP
TChla/TP
a676/a440
aph443
a676/a440
TChla/TP
TChla/TP
Log
Log
Linear
Log
Linear
Log
Log
Log
Linear
Linear
Linear
Linear
Log
Linear
Linear
Linear
log
Log
Linear
0.025
0.093
0.329
0.030
0.329
0.043
0.037
0.051
0.989
2.35
0.196
0.313
0.036
0.516
1.170
1.995
0.197
0.050
0.769
0.481
0.403
0.297
0.322
0.171
0.467
0.473
0.546
0.086
-0.61
-0.095
-0.132
0.493
0.280
-0.175
-0.688
0.273
0.550
0.180
0.689
0.962
0.636
0.357
0.579
0.458
0.749
0.685
0.721
0.397
0.525
0.425
0.710
0.793
0.805
0.703
0.572
0.500
0.630
5
5
10
10
10
19
19
41
23
24
17
26
32
21
11
32
21
19
18
27
Chl a
Threshold
>1.2
>0.63
>1.25
Na
Na
>2.0
>1.0
Table IIIa. Monthly occurrences of micro, nano and pico size classes (by percentage) in the Global Ocean, Indian Ocean, N. and S.
Atlantic, and N and S. Pacific Oceans. Table IIIb. Mean annual values of each size class (by percentage) and mean Chlorophyll (by
percentage).
Tab IIIa
Global Ocean
Indian Ocean
N. Atlantic
S. Atlantic
N. Pacific
S. Pacific
Month micro nano prok micro nano prok micro nano prok micro nano prok Micro nano prok micro nano prok
1
1.8 16.3 81.9
0.7 14.8 84.4
1.4
9.1 89.5
4.3 29.7 66.0
1.2 11.3 87.5
2.0 16.9 81.1
2
1.9 17.0 81.2
0.6 13.5 85.8
1.8 13.9 84.3
3.9 30.9 65.2
1.5 15.5 83.0
2.0 15.3 82.7
3
1.3 16.2 82.6
0.4
9.7 89.9
2.5 22.6 74.9
2.2 28.7 69.2
1.9 18.8 79.3
0.6 11.9 87.6
4
1.6 15.7 82.7
0.3
6.7 92.9
4.4 28.9 66.7
0.9 24.0 75.0
3.3 21.4 75.3
0.4
9.5 90.1
5
2.6 17.3 80.1
0.4
6.5 93.0
6.0 33.8 60.1
1.0 18.5 80.4
5.2 25.1 69.6
0.3
9.9 89.8
6
3.5 15.8 80.8
0.4
8.1 91.5
7.5 29.9 62.5
1.5 16.0 82.4
6.5 22.2 71.2
0.3
8.3 91.4
7
3.3 15.3 81.4
0.5
9.7 89.8
6.8 26.0 67.2
1.6 15.7 82.7
6.1 20.8 73.1
0.3
9.0 90.7
8
3.0 15.3 81.7
0.6 11.8 87.6
5.1 21.1 73.9
1.2 14.8 84.0
5.7 20.0 74.3
0.3 10.8 88.9
9
2.4 16.3 81.4
0.7 14.2 85.0
3.9 18.6 77.5
1.1 23.1 75.8
4.9 19.8 75.3
0.3 11.7 88.0
10
1.9 15.9 82.1
0.6 17.7 81.8
3.1 14.3 82.6
2.0 28.9 69.1
3.7 16.8 79.5
0.4 10.6 89.1
11
1.5 17.0 81.5
0.5 18.8 80.7
1.9
9.6 88.5
2.8 38.6 58.6
2.0 12.4 85.6
1.0 13.2 85.8
12
2.1 18.7 79.1
0.7 21.9 77.4
1.6
8.2 90.3
5.5 36.7 57.8
1.7 12.2 86.0
1.9 17.3 80.7
Tab IIIb
Mean
Chla
2.2
24.9
16.4 81.4
39.6 35.5
0.5
8.1
12.8 86.7
41.3 50.6
28
3.8
34.5
19.7 76.5
38.5 27.0
2.3
21.8
25.5 72.2
51.7 26.5
3.7
34.3
18.0 78.3
36.8 28.9
0.8
12.0
12.0 87.2
38.1 49.9
Fig. 1. Flow diagram for the BE-BOT hypothesis, linking bioenergetic status, trophic status
(environment) and bio-optical traits.
Fig. 2. Classification of biomes from a hierarchical cluster analysis of global mean
chlorophyll (SeaWiFS 1998-2004 average): eutrophic cluster shown in red; mesotrophic
cluster shown in yellow and green (sub-clusters); oligotrophic cluster shown in cyan, blue
and magenta (sub-clusters). From Hardman-Mountford et al. (Hardman-Mountford et al.,
in press) COLOUR
Fig. 3. Summary, unpublished data from the iron enrichment experiments: A) IronEx II, TChla
vs TChla/TP showing ramp-up, days 0-6, ramp-down, days 6-13; B) IronEx II, Fv/Fm vs
TChla/TP; C) SOIREE, time course of TChla/TP and Fv/Fm; D) SOIREE, Fv/Fm vs TChla/TP.
Fig. 4. Sub-set of data from L4 time series data in the western English Channel: A) Fv/Fm vs
TChla/TP; B) Fv/Fm vs a676/a443; C) TChla/TP vs TChla/Cph; D) Fv/Fm vs TChla/Cph.
Fig. 5. Sub-set of data and new data from Benguela: A) Fv/Fm vs TChla/TP vs; B) Fv/Fm vs
a676/a440; C) TChla/TP vs a676/a440; D) Fv/Fm vs aph443.
Fig 6. NOMAD data analyses: top) Chla vs ‘S’ spectral slope of aph(443-510), with nano data
displaced by +0.0009 and pico data displaced by -0.0005; bottom) aph443 vs ‘S’, with nano data
displaced by +0.0004 and pico data displaced by -0.0004. The numbers in the boxes are the
numbers of each size class detected in each size regime. COLOUR
Fig 7. Monthly SeaWiFS data for 2005 interpreted for phytoplankton, size classes: pico (blue);
nano (green); micro (red). COLOUR
29
Fig.1.
Flow diagram summarising the BE-BOT
hypothesis
Bio-Energetic (BE) Status
• Light availability
• Nutrient availability
Specifically N; no other limiting nutrients
(e.g. P, Fe)
High BE (EUTROPHIC)
• Turbulent
• High light and high N supply
• Large cells (micro)
• High Chla
• High Chla/TPig
Medium BE (MESOTROPHIC)
• Stratified
• Intermediate N supply or intermediate
light to surface layer
• Medium cells (nano)
• Intermediate Chla
• Intermediate TChla/TPig
Low BE (OLIGOTROPHIC)
• Stratified, deep surface layer
• Low N supply or low light to surface
layer
• Small cells (pico)
• Low Chla
• Low Chla/TPig
High BOT indicator values
• High blue light abs
• High aph(443)-aph(510)
Medium BOT indicator values
• Medium blue light abs
• Med aph(443)-aph(510)
Low BOT indicator values
• Low blue light abs
• Low aph(443)-aph(510)
Bio-Optical Trait (BOT) indicators
•Blue light abs [aph(443)]
•Blue-green slope [aph(443)-aph(510)]
Fig 2
Fig 3.
2
A
1
0.45
TC hla/TP
4
5
IronEx II
0
0.45
7
9
y = 0.093Ln(x) + 0.403
R2 = 0.962
0.35
IronEx II
B
V
6
8
y = 0.025Ln(x) + 0.481
R2 = 0.689
0.4
0.5
C hla/TP
0.5
0.4
0.35
y = 0.329x + 0.297
R2 = 0.636, n=10, p=0.006
13
0.3
0.3
0
1
TChla
1.5
2
0.2
0.35
SOIREE
C
0.4
TC hla/TP
F v /F m ,TC hla/T P
0.5
0.5
0.3
D
0.4
Fv/Fm
0.5
0.6
SOIREE
0.3
0.25
0.3
y = 0.329x + 0.171
R2 = 0.579, n=10, p=0.011
Chla/TP
Fv/Fm
0.2
0
5
Day
10
0.2
15
0.2
0.3
Fv/Fm
0.4
0.5
Fig 4.
0.70
1
A
M W S B+S Ssur
0.9
0.65
0.8
0.7
a674/a443
0.60
TChla/TP
B
0.55
0.50
0.6
0.5
0.4
0.3
0.2
0.45
0.1
0.40
0.35
0.4
0.45
0.5
0
0.35
0.55
0.4
0.45
Fv/Fm
0.55
Fv/Fm
0.06
0.04
0.5
C
D
0.05
0.03
TChla/Cph
TChla/Cph
0.04
0.03
0.02
0.02
0.01
0.01
0
0.5
0.55
0.6
TChla/TP
0.65
0
0.44
0.46
0.48
0.5
Fv/Fm
0.52
0.54
Fig.5
0.7
0.60
A
0.50
0.6
a676/a440
TChla/TP
0.6
B
0.5
D ia to m s
D ia to m s
0.30
D in o s
0.5
0.40
D in o s
F la g
0.4
0.20
0.40
0.50
Fv/Fm
0.60
0.70
0.25
C
0.5
0.20
0.4
0.15
aph 443
a676/a440
0.6
0.30
F la g
0.20
0.35
0.3
D ia to m s
D in o s
0.2
0.1
0.40
M ix e d
0.50
0.55
TChla/TP
0.60
0.45
0.50
Fv/Fm
0.55
0.60
D
0.10
0.05
F la g
0.45
0.40
0.65
0.00
0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60
Fv/Fm
Fig. 6
−4
20
x 10
Micro
Nano
15
Chla=0.25
Pico
S [1/m/nm]
Mixed
10
Pico 75
Nano 21
Micro 1
5
Pico 3
Nano 73
Micro 48
Pico 0
Nano 16
Micro 39
0
Chla=1.30
−5
−1
10
0
3
Chla [mg/m ]
1
10
10
−4
20
x 10
Micro
aph=0.024 ± 0.002
Nano
15
Pico
S
[1/m/nm]
Mixed
10
Pico 4
Nano 63
Micro 43
Pico 76
Nano 35
Micro 13
5
Pico 0
Nano 17
Micro 34
0
0.060 < aph < 0.075
−5
−2
10
−1
aph(443) [1/m]
10
Fig. 7