Introduction
Although global estimates of marine primary production tend to converge on a number around 40–50 GTyr-1, the
accuracy and precision on regional scales of the estimation protocols remain relatively poor, partly as a result of an
incomplete understanding of how the photosynthetic performance of marine phytoplankton varies in the global ocean (Carr
et al., 2006; Lee et al., 2015). Photosynthesis–irradiance (P-E) parameters derived from carbon uptake
experiments conducted over a controlled range of available-light levels provide a means of comparing the photosynthetic
characteristics of marine phytoplankton across different natural populations and cultured isolates (Platt and Jassby,
1976; Prézelin et al., 1989; MacIntyre et al., 2002). The P-E experiment exposes algal cells to
a range of light intensities from near-zero to those levels typically available at the sea surface (Lewis and Smith, 1983;
Babin et al., 1994). The photosynthetic rates are then normalised to the concentration of chlorophyll a (a useful and
practical index of phytoplankton biomass relevant for photosynthesis) found within the sample. This normalisation serves
two purposes: first, dividing by pigment biomass reduces the variability of photosynthesis rates due to differences in
biomass alone, facilitating the comparison of photosynthetic performance across trophic gradients, and second,
chlorophyll-normalised photophysiological parameters may be applied in the estimation of primary production over large
scales by using satellite-derived maps of chlorophyll concentration (Longhurst et al., 1995; Antoine and Morel,
1996). A schematic diagram showing the biomass-normalised data generated from these experiments plotted against the light
intensity at which each bottle was incubated is shown in Fig. 1 to illustrate how the ensemble of data, when fitted to
a suitable non-linear equation, forms a P-E curve. The curve may be represented by a variety of
mathematical forms (Jassby and Platt, 1976; Platt et al., 1980). In cases where photoinhibition is negligible, all
equations suitable for describing the P-E curve can be represented using just two parameters: the
initial slope, αB, which represents the photosynthetic efficiency under light levels close to zero, and the
asymptote of the curve, PmB, which is the photosynthetic rate at light saturation (Jassby and Platt, 1976; Platt
et al., 1980; Sakshaug et al., 1997).
Numbers corresponding to biogeochemical province and domain as described by Longhurst (2007) included in the MAPPS database.
Provincenumber
Longhurst domain
Longhurst province
1
Polar
Boreal Polar Province
2
Polar
Atlantic Arctic Province
3
Polar
Atlantic Subarctic Province
4
Westerlies
North Atlantic Drift Province
5
Westerlies
Gulf Stream Province
6
Westerlies
North Atlantic Subtropical Gyre Province (West)
7
Trades
North Atlantic Tropical Gyre Province
8
Trades
Western Tropical Atlantic Province
9
Trades
Eastern Tropical Atlantic Province
10
Trades
South Atlantic Gyre Province
11
Coastal
North East Atlantic Shelves Province
12
Coastal
Canary Coastal Province
15
Coastal
North West Atlantic Shelves Province
17
Trades
Caribbean Province
18
Westerlies
North Atlantic Subtropical Gyre Province (East)
20
Coastal
Brazil Current Coastal Province
21
Coastal
South West Atlantic Shelves Province
22
Coastal
Benguela Current Coastal Province
30
Trades
Indian Monsoon Gyres Province
33
Coastal
Red Sea, Persian Gulf Province
34
Coastal
North West Arabian Upwelling Province
37
Coastal
Australia–Indonesia Coastal Province
50
Polar
North Pacific Epicontinental Province
51
Westerlies
Pacific Subarctic Gyres Province (East)
53
Westerlies
Kuroshio Current Province
58
Westerlies
Tasman Sea Province
60
Trades
N. Pacific Tropical Gyre Province
63
Trades
W. Pacific Warm Pool Province
64
Trades
Archipelagic Deep Basins Province
68
Coastal
Chile–Peru Current Coastal Province
69
Coastal
China Sea Coastal Province
80
Westerlies
S. Subtropical Convergence Province
81
Westerlies
Subantarctic Province
82
Polar
Antarctic Province
83
Polar
Austral Polar Province
Summary of contributions to the MAPPS database.
Dataset provider
Regions
Dates
N
Non-linear equation(s) fitted to experimental data
Database
Relevant publication(s)
Trevor Platt,Plymouth Marine Laboratory(tplatt@dal.ca)
Arctic, Arabian Sea, Azores, Caribbean Sea, Celtic Sea, Georges Bank, Grand Banks, Humboldt Current System, Hudson Bay, Labrador Sea, Mid-Atlantic Ridge, New England Seamounts, Sargasso Sea,Scotian Shelf, Vancouver Island
1977–2003
2146
Photoinhibition function (Platt et al., 1980)
BIOCHEM(www.meds-sdmm.dfo-mpo.gc.ca)
Bouman et al. (2005); Harrison and Platt (1986); Kyewalyanga et al. (1998); Platt et al. (1980); Platt et al. (1982); Platt et al. (1993); Sathyendranath et al. (1999)
Francisco Rey,Institute of Marine Research(pancho@IMR.no)
Barents Sea
1980–1989
223
Photoinhibition function (Platt et al., 1980)
Rey (1991)
Pierre Pepin,Fisheries and Oceans Canada, Northwest Atlantic Fisheries Centre(pierre.pepin@dfo-mpo.gc.ca)
Grand Banks
2004–2012
524
Photoinhibition function (Platt et al., 1980)
Unpublished
Heather Bouman,University of Oxford(heather.bouman@earth.ox.ac.uk)
Subtropical Atlantic,Greenland Sea, Norwegian Sea
1996,2010,2013
195
Photoinhibition function (Platt et al., 1980)
Bouman et al. (2000a); Jackson (2013); Bouman (unpublished)
Michael Hiscock,National Center for Environmental Research US Environmental Protection Agency (hiscock.michael@epa.gov)
Southern Ocean – Pacific sector
1997–1998
172
Photoinhibition function (Platt et al., 1980)
Hiscock (2004); Hiscock et al. (2003)
Vivian Lutz, Instituto Nacional de Investigación y Desarrollo Pesquero(vlutz@inidep.edu.ar)
Argentine Sea
2005–2006
69
Photoinhibition function (Platt et al., 1980)
Dogliotti et al. (2014); Lutz et al. (2010); Segura et al. (2013)
Gavin Tilstone,Plymouth Marine Laboratory(ghti@pml.ac.uk)
Benguela upwelling system, eastern tropical Atlantic, North Atlantic Subtropical Gyre, Canary coastal system, North Atlantic Drift Province
1998
129
Photoinhibition function (Platt et al., 1980)
BODC (www.bodc.ac.uk)
Tilstone et al. (2003)
Bangqin Huang,State Key Laboratory of Marine Environmental Science/Key Laboratory of Coastal and Wetland Ecosystems Ministry of Education, Xiamen University(bqhuang@xmu.edu.cn)
South China Sea
2010–2012
130
Photoinhibition function (Platt et al., 1980)
Xie et al. (2015)
Anna Hickman, National Oceanography Centre Southampton(a.hickman@noc.soton.ac.uk)
North Atlantic Subtropical Gyre, North Atlantic Drift Province, Canary coastal system, South Atlantic Subtropical Gyre, western tropical Atlantic
2004
31
Hyperbolic tangent function (Jassby and Platt, 1976).
BODC (www.bodc.ac.uk)
Hickman (2007); Lawrenz et al. (2013)
Kristinn Gudmundsson,Marine Research Institute, Iceland(kristinn@hafro.is)
Iceland and Faroes
1981–2007
559
Photoinhibition function (Platt et al., 1980) and hyperbolic tangent function (Jassby and Platt, 1976).
Gudmundsson (1998); Pálsson et al. (2012); Zhai et al. (2012)
Francisco G. Figueiras, Instituto de Investigaciones Marinas (CSIC)Eduardo Cabello 6, 36208 Vigo, Spain(paco@iim.csic.es)
Antarctic Peninsula
1995
51
Exponential without photoinhibitionWebb et al. (1974)
JGOFS International Collection Volume 1: Discrete Datasets (1989–2000) DVD
Lorenzo et al. (2002)
Martina Doblin,University of Technology, Sydney(martina.doblin@uts.edu.au)
Southern Ocean, Antarctic Peninsula, Tasman Sea
1990–2013
1482
Photoinhibition function (Platt et al., 1980)
AADC (https://data.aad.gov.au/metadata)MARLIN(http://www.marine.csiro.au/marq/edd_search.search_choice?tFre=primary+production&ch1=freetext&cSub=%3E%3E)CSIRO Marine National Facility (http://www.marine.csiro.au/nationalfacility/voyages/datasets.htm)PANGAEA(http://doi.pangaea.de/10.1594/PANGAEA.103773, http://doi.pangaea.de/10.1594/PANGAEA.843554,
Mackey et al. (1995); Griffiths et al. (1999); Hanson et al. (2005a); Hanson et al. (2005b); Westwood et al. (2011)
List of environmental variables and P-E parameters in the MAPPS database and their corresponding units.
Header
Description
Units
LAT
Latitude of sampling station
Decimal degrees
LON
Longitude of sampling station
Decimal degrees
DEPTH
Depth at which sample was collected
m
YEAR
Year of sample collection
MONTH
Month of sample collection
DAY
Day of sample collection
TCHL
Chlorophyll concentration measured using either high-performance liquid chromatography or the fluorometric method.
mgChlam-3
ALPHA
αB, the initial slope of the photosynthesis–irradiance curve normalised to phytoplankton chlorophyll concentration.
mgC(mgChla)-1h-1 (µmolquantam-2s-1)-1
PMB
PmB, the rate of photosynthesis at saturating irradiance, normalised to phytoplankton chlorophyll concentration.
mgC(mgChla)-1h-1
EK
Ek, irradiance at which the onset of saturation occurs, calculated as the ratio of PmB to αB.
µmolquantam-2s-1
PROVNUM
The corresponding biogeochemical province defined by Longhurst (please refer to Table 2).
Data
Chlorophyll a concentrations and photosynthesis–irradiance (P-E) parameters collected from four
oceanic domains and 35 biogeochemical provinces (Longhurst, 2007, Table 1) were compiled from individual investigators and
online data repositories (Table 2). P-E data were obtained by 14C and 13C
(Argentine Sea) uptake experiments, with incubation times varying from 1.5 to 4 h. Chlorophyll concentrations used to
normalise the carbon fixation rates were measured using either high-performance liquid chromatography (HPLC) or the
standard fluorometric method (Mantoura et al., 1997). An intercomparison between HPLC and fluorometrically determined
chlorophyll a concentrations revealed that pheopigment-correcting acidification methods such as Holm-Hansen et al. (1965) show a good overall correlation
(r2 = 0.85). However, the study noted that the presence of the accessory pigment chlorophyll b could lead to an
underestimation of chlorophyll a concentration by 2–19 % (Martoura et al., 1997). This potential source of bias in
fluorometrically determined chlorophyll a concentration would result in an overestimation of the chlorophyll a
normalised photosynthetic parameters of up to 19 % where relative chlorophyll b concentrations are high (e.g. the deep
chlorophyll maxima of the subtropical gyres). Further details on the experimental methodology for individual field
campaigns are provided in the original publications (see Table 2). The environmental variables and photosynthetic
parameters included the MAPPS database and their corresponding units are listed in Table 3.
Table 2 includes information on which functional form was fitted to the P-E data for each of the
data sets used in this study. In cases where photoinhibition was absent (photosynthetic rates stayed independent of
irradiance in the light-saturated range), or where the fit was applied to data unaffected by photoinhibition,
a two-parameter curve fit was used, of the form
PB(E)=PmBtanhαBEPmB,
where PB(E) is the chlorophyll-normalised photosynthetic rate (mgC(mgChla)-1h-1)
and E is the available light, which in this study is expressed in µmolquantam-2s-1. The light
saturation parameter, Ek, is defined by the following
relationship,
Ek=PmBαB,
and is illustrated in Fig. 1 by the drawing a line from the intersection of the initial slope with the plateau of the
curve onto the abscissa and has dimensions of irradiance.
In most cases, however, data were fit to the three-parameter function of Platt et al. (1980), which also describes the
decrease in photosynthetic rate with irradiances much higher than saturating light levels, as follows:
PB(E)=PsB1-exp-αBEPsBexp-βBEPsB,
where βB is the photoinhibition parameter describing the decrease in photosynthetic rate at high irradiance and
PsB is the hypothetical maximum photosynthetic rate in the absence of photoinhibition. Hence when βB=0,
PsB=PmB. When photoinhibition was present, values of PmB were derived using the following equation:
PmB=PsBαBαB+βBβBαB+βBβα.
Quality control for the MAPPS P-E database
Experimental conditions
The P-E experiments were performed in incubators that maintained samples under in situ temperature
conditions using either temperature-controlled water baths or the ship's underway water supply. Samples where incubation
temperatures differed from in situ temperatures by more than 2 ∘C were removed from the database. It is
well known that the light spectrum has a significant effect on the magnitude of light-limited photosynthesis
(αB) and the derived light saturation parameter (Ek) (Kyewalyanga et al., 1997; Schofield et al., 1991). We
have included in the database quality flags indicating whether a correction factor for the spectrum of the lamp was
applied to obtain a readily intercomparable broad-band (white light) value (e.g. Kyewalyanga et al., 1997; Xie
et al., 2015). This broad-band αB combined with information on the shape of the phytoplankton absorption
spectrum has been shown to provide an accurate estimate of the photosynthetic action spectrum αB(λ). The
correction factor X can be used to convert the measured αB from the incubation experiment using a given
artificial light source to an estimate of αB if the sample were subject to a spectrally neutral light
environment: it is the ratio of the unweighted mean absorption coefficient of phytoplankton (a‾p) to the
mean absorption coefficient weighted by the shape of the emission spectrum of the lamp source
(a‾L),
X=a‾pa‾T,
where a‾p is determined by
a‾p=∫400700ap(λ)dλ∫400700dλ,
and a‾T is computed as
a‾T=∫400700ap(λ)ET(λ)dλ∫400700ET(λ)dλ.
Spectral shapes of the tungsten-halogen lamp used in the incubations
conducted by the Bedford Institute from 1984 to 2003 (red line) and in vivo
absorption spectra of marine phytoplankton collected in the North Atlantic
and subpolar waters (Labrador Sea). Phytoplankton and lamp spectra were
normalised to their mean value to define the shape.
Information on the light sources and filters used for photosynthesis–irradiance experiments for each of the data set providers. Also noted is whether the spectral correction of Kyewalyanga et al. (1997) shown in equation was applied to values of αB.
Dataset provider
Lamp source details
Spectral correction
Platt
GTE Sylvania PAR 150
N
Combination of GTE Sylvania PAR 150 (irradiances <200 W) and New Haline OHS tungsten halogen (irradiances 200–1000 W)
N
2000 W tungsten-halogen lamp (New Haline OHS 2000) with a maximum intensity of 1000 Wm-2 (PAR)
N
Gilway Technical Lamp L 7391 tungsten halogen. Spectral correction applied to samples collected from 1994 onwards.
Y
Rey
Low-light incubator (LL, 0 to 390 mmolm-2s-1) was equipped with daylight-type fluorescent tubes (OSRAM 191 Daylight 5000 de Luxe). High-light incubator (HL, 0 to 1700 µmolm-2s-1) was equipped with a halogen-metal lamp (OSRAM Power Star, 400 W).
N
Pepin
ENH-type tungsten-halogen quartz projection lamps directed through a heat filter (solution of copper sulfate 20 gL-1) to remove the infrared emission (no additional corrections made).
N
Bouman
Gilway Technical Lamp L 7391 tungsten halogen, Spectral correction applied.
Y
Lee 1/4 CT blue filter was placed in front of incubator window to diminish the spectral dependency of the light source (2 × 50 W Sylvania 2315 tungsten halogen). Spectral correction applied.
Y
Hiscock
250 W tungsten-halogen slide projector lamp (Gray Co. #ENH), spectrally modified using a heat mirror, a broad-band cool mirror (Optical Coating Laboratory, Inc.), and blue stage-lighting screens (Cinemills Corp. #M144).
Y
Lutz
70 W Westinghouse halogen lamp. Spectral correction applied.
Y
Tilstone
AMT 6 and 20: tungsten-halogen lamps spectrally corrected. AMT 23: both tungsten-halogen and LED lamps used and spectrally corrected.
Y
Huang
150 W metal halide lamps with an ultraviolet filter. Spectral correction applied.
Y
Hickman
Tungsten halogen lamps behind blue light filters (no additional corrections made).
N
Figueiras
50 W (12 V) tungsten halogen lamp. Spectral correction applied.
Y
Gudmundsson
Fluorescent tubes (Philips TLF 20 W/33). No additional corrections made.
N
Doblin
Cool daylight fluorescent tubes (Philips TLD 36 W/54). A mix of grey and blue filters (Rosco 3402, 50 % neutral density filter, i.e. grey strips, and Rosco 3204 half blue) were used to attenuate the light intensity in the incubator.
N
The incubators in this study used a range of light sources, including tungsten halogen, halogen, metal halide, and
fluorescent lamps. Tungsten halogen lamps are the most commonly used light source in P-E experiments
because they provide intensities sufficiently high to resemble irradiances at the sea surface
(∼ 2000 µmolquantam-2s-1). One limitation of using tungsten lamps is that they have
a spectrum heavily weighted towards the red and infrared (see Fig. 2), unless the light first passes through a filter that
removes the red emission. Table 4 describes the various lamps and filters used in the P-E incubators used
in this study.
(a) Density plots of the initial slope (αB) obtained using a tungsten halogen lamp (cyan) and corrected values using the shape of the phytoplankton absorption spectrum (red) as described in Kyewalyanga et al., 1997. (b) Density plots of data collected in the North Atlantic using different lamp types (red – GTE Sylvania PAR 150; green – combination of GTE Sylvania PAR 150 (irradiances < 200 W) and New Haline OHS tungsten halogen (irradiances 200–1000 W); cyan – New Haline OHS 2000 tungsten halogen; purple – Gilway Technical Lamp L 7391 tungsten halogen). Both plots are using data collected within the top 20 m of the water column.
To estimate the impact of a tungsten halogen lamp compared with a white light source on the magnitude of the αB
and consequently Ek, which is derived from estimates of αB (Eq. 2), we used a data set from the North
Atlantic that spanned several decades. From 1994, P-E data have been corrected for the spectrum of the
lamp source following the method of Kyewalyanga et al. (1997), whereas prior to 1994, no correction was made due to a lack
of information on the excitation spectrum of the lamp and the absorptive properties of the phytoplankton communities. By
comparing data from similar regions and seasons as lamp sources have changed, we are able to assess how the light source
may cause variability in the photosynthetic parameter αB. In the post-1994 data set, with corresponding lamp and
absorption spectra, the correction factor X varied from 1.30 to 2.06 (mean = 1.70 with a standard deviation of 0.15). This variation
in X (Fig. 3a) is sufficient to account for difference in magnitudes of αB obtained using incubators with
different light sources across the North Atlantic cruise data set (Fig. 3b). Note that potential errors in the computation
of primary production due to changes in αB caused by spectral differences in light sources will be most acute
deeper in the water column, where the influence of the magnitude of αB on primary production is greatest (Ulloa
et al., 1997; Bouman et al., 2000a) and thus errors for integrated water-column primary production will be modest since
productivity rates are highest at the surface and decrease in an exponential manner once E(z)≪Ek (Ulloa
et al., 1997; Bouman et al., 2000a).
Global distribution of MAPPS P-E data set that passed quality control (5711 samples). The blocked colours represent the four primary biomes as described in Longhurst (2007): Polar (blue), Westerlies (yellow), Trades (orange) and Coastal (green).
The number of P-E experiments in the MAPPS database for the four Longhurst oceanic domains by (a) year and (b) month and hemisphere (north and south).
Theoretical maxima
The photophysiological constraints of marine phytoplankton are well known and provide a useful check on the quality of
the carbon-uptake experiments. The theoretical maximum quantum yield of carbon fixation ϕmT is
0.125 molC(molquanta)-1 (Platt and Jassy, 1976; Sakshaug et al., 1997). The realised maximum quantum
yield of photosynthesis (ϕm) is calculated by dividing αB by a‾∗, the
chlorophyll-specific absorption coefficient of phytoplankton averaged over the visible spectrum (Platt and Jassby,
1976),
and multiplying by a factor of 0.0231, which converts milligrams to moles of C, micromoles to moles of
photons, and hours to seconds. Values of ϕm were calculated using either simultaneous measurements
of a‾∗, or estimates derived from a global relationship between chlorophyll concentrations
and a‾∗ (Bouman, unpublished data) and samples with ϕm well above
the theoretical maximum (> 0.15 molC(molquanta)-1) were discarded from the database. We also set
a lower limit for the light saturation parameter PmB of 0.2 mgC(mgChla)-1h-1 and
the initial slope αB of
0.002 mgC(mgChla)-1h-1(µmolquantam-2s-1)-1. Data from experiments on
sea-ice algae with Chl a concentrations exceeding 50 mgChlam-3 were also removed. Using these
criteria, 278 experiments were excluded from the global database.
Results
Spatio-temporal patterns of the MAPPS P-E database
In this study we adopt the Longhurst's (2007) geographical classification system of domains and provinces to partition the
global data set according to the prevailing physical conditions that shape the structure and function of phytoplankton
communities over large (basin) scales. The rationale behind using Longhurst's approach to estimate primary productivity is
that physical forcing dictates the supply of nutrients and the average irradiance within the surface mixed layer and these
factors directly impact the physiological capacity of algal cells. The four domains (also referred to as Longhurst biomes)
are found in each ocean basin and are subject to distinct mechanisms of physical forcing: in the polar domain the density
structure of the surface layer is strongly influenced by sea-ice melt; in the westerlies domain the mixed layer dynamics
are governed by a local balance of heat-driven stratification and wind-driven turbulent mixing by winds; and in the trades
domain, the depth of the mixed layer is governed by geostrophic responses to seasonal changes in the strength and location
of the trade winds, while in the coastal domain, terrestrial influx of freshwater and interaction of local winds with
topography play a critical role in governing ecosystem properties. The next level of partition is biogeochemical
provinces, which embrace a wider set of environmental factors that govern regional ocean circulation and stratification
that in turn influence ecological structure. Although it would be preferable that both domain and provincial boundaries
were dynamic to accommodate seasonal, annual and decadal changes in ocean circulation (Devred et al., 2007), to exploit
the entire MAPPS P-E parameter data set, which contains a large number samples that were collected prior to
the launch of ocean-colour satellites, we used the fixed rectilinear boundaries of Longhurst (2007) with the understanding
that some of the within-province variability may be the result of the estimated and actual provincial boundaries being
spatially offset. Thus, for each data point, a Longhurst province was assigned based on geographic location and is denoted
in the database by the province number as shown in Table 1.
Histograms illustrating the sample distribution across concentrations of chlorophyll a and range of P-E parameter values for the four Longhurst oceanic domains.
Roughly half of the quality-controlled samples were collected from within the upper 20 m of the water column,
accounting for approximately 54 % of the data set. Most of the data fall within the Atlantic Basin (Fig. 4), with
a large region of the Pacific Basin being grossly undersampled in both space and time. The latitudinal coverage of the
database is relatively sparse in the tropics and the mid-latitudes of the Southern Hemisphere. In this study seasons were
divided into 3-month intervals in order for data to be used with monthly climatologies and satellite composite
data. Thus, in the case of the Northern Hemisphere, “spring” covers the months of March to May, “summer” covers June to August, “autumn” covers September to November and “winter” covers December to February. The seasonal distribution of data shows the majority of samples were
collected during the spring (40 %), summer (34 %) and autumn (22 %), and only 3 % of the samples collected
during the winter period (Table 2, Fig. 5). Across all seasons, the data set covers a range of trophic conditions, with
chlorophyll a concentrations representative of highly oligotrophic conditions (0.02 mgm-3) to spring bloom
conditions (39.8 mgm-3) (Fig. 6). The dynamic range of the photosynthetic parameters was similar to that
reported in other global studies, with values PmB ranging from 0.21 and
25.91 mgC(mgChla)-1h-1 with an average value of 3.11 and a standard deviation of 2.28, and values
of αB ranging from 0.002 to
0.373 mgC(mgChla)-1h-1(µmolquantam-2s-1)-1 with an average value of
0.043 and a standard deviation of 0.034.
Seasonal mean values and standard deviation for the photosynthetic
parameters PmB and αB for each of the 35 Longhurst
provinces. The blocked data represent the four primary biomes of the upper
ocean: Polar (first quarter), Westerlies (second quarter), Trades (third
quarter) and Coastal (fourth quarter).
PROV
Spring
Summer
Autumn
Winter
PmB
αB
PmB
αB
PmB
αB
PmB
αB
N
Mean
SD
Mean
SD
N
Mean
SD
Mean
SD
N
Mean
SD
Mean
SD
N
Mean
SD
Mean
SD
Polar
BPLR
194
1.86
0.91
0.037
0.032
385
1.93
1.29
0.030
0.031
50
2.10
1.12
0.029
0.022
13
1.56
0.76
0.019
0.007
ARCT
421
2.46
1.52
0.047
0.031
203
2.38
1.27
0.042
0.028
37
2.53
0.84
0.056
0.032
18
2.30
0.73
0.023
0.008
SARC
150
2.67
1.18
0.046
0.023
41
3.08
2.38
0.045
0.022
2
1.40
0.46
0.088
0.045
0
ANTA
79
3.55
1.69
0.101
0.047
145
3.50
1.70
0.051
0.023
78
3.50
1.70
0.051
0.023
22
4.18
2.21
0.096
0.026
APLR
119
1.84
1.22
0.044
0.023
222
1.79
1.05
0.042
0.019
12
1.79
1.05
0.042
0.019
0
BERS
0
0
3
3.43
2.03
0.059
0.043
0
Westerlies
NADR
40
3.49
1.13
0.033
0.010
5
4.41
4.64
0.029
0.025
48
2.10
1.06
0.028
0.018
0
GFST
55
4.41
2.40
0.031
0.013
12
3.57
2.30
0.016
0.007
32
3.41
1.85
0.056
0.039
0
NASW
85
5.92
3.30
0.026
0.014
91
1.72
1.34
0.025
0.022
35
3.67
2.15
0.066
0.072
0
NASE
15
2.63
1.42
0.030
0.006
6
4.02
1.88
0.031
0.013
37
3.17
2.65
0.057
0.042
0
PSAE
0
0
2
3.41
1.05
0.034
0.010
0
TASM
0
7
2.89
1.59
0.057
0.022
16
7.18
2.20
0.070
0.017
0
SSTC
50
6.81
4.58
0.076
0.033
116
3.10
2.19
0.035
0.025
6
4.24
0.37
0.035
0.008
6
6.36
2.94
0.066
0.008
SANT
162
4.67
2.35
0.085
0.040
165
3.34
2.19
0.066
0.041
107
3.88
1.55
0.054
0.018
14
4.69
1.94
0.075
0.024
KURO
6
9.25
4.25
0.027
0.009
0
1
10.50
0.023
0
Trades
NATR
6
3.33
2.29
0.037
0.017
66
2.54
1.50
0.057
0.048
20
2.97
2.69
0.041
0.035
0
WTRA
0
8
5.08
4.53
0.064
0.015
17
1.34
0.63
0.022
0.020
ETRA
11
3.00
0.96
0.027
0.011
0
0
0
SATL
48
1.29
0.65
0.027
0.024
0
2
3.58
2.47
0.028
0.016
0
CARB
20
3.71
2.16
0.011
0.005
0
15
2.15
1.75
0.026
0.020
0
MONS
0
0
62
4.05
2.04
0.024
0.007
4
5.66
1.60
0.026
0.005
WARM
0
64
2.23
1.67
0.039
0.027
167
2.44
1.65
0.032
0.019
0
ARCH
70
3.62
3.41
0.034
0.016
0
30
5.86
3.72
0.025
0.009
18
3.89
3.09
0.034
0.030
NPTG
0
0
4
2.62
1.99
0.054
0.074
0
Coastal
CHIL
7
2.50
0.93
0.029
0.004
0
0
0
NECS
29
3.20
1.07
0.023
0.010
0
1
2.22
0.062
0
CNRY
0
0
23
4.62
1.40
0.065
0.017
0
AUSW
44
3.04
2.02
0.038
0.015
15
2.61
1.93
0042
0.026
16
2.12
1.67
0.032
0.017
0
BRAZ
9
2.37
1.86
0.041
0.035
5
3.63
2.05
0.014
0.005
0
1
0.87
0.019
REDS
0
4
4.13
2.07
0.022
0.006
0
0
ARAB
0
132
4.07
1.67
0.021
0.008
79
5.61
1.85
0.027
0.007
0
FKLD
25
2.96
2.85
0.040
0.035
1
1.71
0.010
19
1.85
1.00
0.017
0.006
9
1.06
0.43
0.012
0.003
BENG
0
0
20
3.82
2.13
0.027
0.011
0
CHIN
24
7.50
3.68
0.029
0.012
37
6.87
5.18
0.023
0.016
17
8.92
4.45
0.033
0.015
0
NWCS
543
2.78
1.93
0.039
0.033
259
3.34
1.90
0.025
0.030
405
3.86
1.94
0.042
0.034
42
2.79
1.47
0.042
0.034
The P-E parameters exhibited both spatial (between provinces) and temporal (between seasons)
differences. In general, values of the assimilation number increased with decreasing latitude (Table 5) and tended to be
higher during the summer months in temperate marine systems. However, the seasonal and latitudinal bias in data coverage
has important implications for variability in parameter values in the data set because of the environmental conditions
known to influence phytoplankton photophysiology. High-latitude samples will be associated with lower temperatures, which
may limit their maximum photosynthetic rate for carbon fixation (Smith Jr. and Donaldson et al., 2015), and this is
reflected in the generally low values of PmB in the boreal (BPLR) and austral (APLR) polar provinces. Geographical
variation in surface irradiance may also explain the lower values of PmB in high latitudes compared with low
latitudes. The combination of lower surface irradiances and deep convective mixing in high latitudes results in markedly
lower light levels within the mixed layer, which may result in photoacclimation to lower light levels, by modulating
pigment content per cell and hence the carbon-to-chlorophyll ratio (Cullen et al., 1982; Sathyendranath
et al., 2009). However, it is important to note that some of the polar samples were collected in regions highly influenced
by sea-ice melt, which may lead to the formation of a fresh, shallow and highly stable mixed layer, and consequently higher
average light level than would be the case for deeper mixing.
The paucity of winter data reduces the number of samples with cells acclimated to low growth irradiances. The number of
observations is also low within the tropical and subtropical oceans, which are characterised by warm and hence
strongly stratified mixed layers. Phytoplankton cells in these regions tend to have higher upper bounds of PmB,
due to the combined effect of warmer sea-surface temperatures and the acclimation to high-light conditions. Such
spatio-temporal patterns in the P-E parameters are likely driven by changes in oceanographic conditions
(temperature, stratification, macro- and micronutrient availability) (Geider et al., 1996) as well as in community
structure and other biotic processes that may consume cellular energy at the expense of carbon fixation (Puxty et al.,
2016).
Diagram illustrating the seasonal differences of the
P-E parameters between adjacent provinces by pairwise
comparisons using Bonferroni adjusted t tests. Colours of blocks denote
significance at the 5 % level. Note the orientation of the four blocks
representing the seasonal difference remains as shown in the legend for both
vertical and horizontal comparisons.
To examine whether spatial variation in the photosynthetic parameters for a given season was captured in the boundaries of
the Longhurst provinces, we conducted the Bonferroni adjusted pairwise t tests to analyse differences between adjacent
provinces for each season (Fig. 7). For both photosynthetic parameters differences across the boundaries were detected in
polar regions and in seasons and provinces where the number of observations tended to be high. The static nature of the
province boundaries and the uneven spatial distribution of the data may explain in part the small number of differences in
the P-E parameters between adjacent provinces.
The assimilation number (PmB) plotted against the initial slope (αB). Symbol colours represent the value of Ek in units of µmolquantam-2s-1.
Relationship between the maximum photosynthetic rate and the initial slope
Strong correlations have been reported between the two P-E parameters PmB and αB, which
have been explained on both ecological and photophysiological grounds (Platt and Jassby, 1976; Côté and Platt,
1983; Behrenfeld et al., 2004). The MAPPS data set shows that the data fall largely within the bounds of Ek values
between 20 and 300 µmolquantam-2s-1 (Fig. 8). In general, high-latitude samples (> 65∘)
tend to have lower PmB values for a given value of αB (Ek values averaging
57.7 µmolquantam-2s-1, with 10.0 % of the data falling above
100 µmolquantam-2s-1) when compared with low-latitude samples between 40∘ N and
40∘ S (Ek values averaging 152.4 µmolquantam-2s-1, with 57.1 % of the values
falling above 100 µmolquantam-2s-1).
Density plot and box plots showing the variation in the photoadaptation parameter (Ek) with latitude. Middle horizontal line of boxplot represents the median value and lower and upper boundaries correspond to the first and third quartiles and the length of the whiskers is 1.5 times the inter-quantile range of the boundary. Heat map indicates probability density estimates. Filled circles denote outliers.
Density plot and box plots showing variation in the photoadaptation parameter (Ek) with depth for research cruises focussed on the oligotrophic gyres (DCM, AMT6, AMT15, AMT20 and AMT22). Middle horizontal line of boxplot represents the median value and lower and upper boundaries correspond to the first and third quartiles and the length of the whiskers is 1.5 times the inter-quantile range of the boundary. Heat map indicates probability density estimates. Filled circles denote outliers.
When Ek is plotted as a function of latitude (Fig. 9) for open-ocean samples within the top 25 m of the
water column, a clear pattern emerges, with higher latitude samples being characterised by lower values, whereas data from
the mid- to low latitudes had, on average, higher values, although considerable scatter was observed over the entire range
of temperatures. To illustrate the depth-dependent change in Ek due to vertical changes in irradiance, data from
cruises that predominantly sampled stratified, oligotrophic regions (DCM and AMT cruises) are plotted against the sample
depth (Fig. 10). The strong depth dependence of the photoacclimation parameter is consistent with other open-ocean studies
(Babin et al., 1996). The latitudinal and depth dependence of Ek was also reported in a study which used a subset
(N = 1862) of the MAPPS database from the North Atlantic spanning the tropics to the Arctic: 55 % of the
variance in Ek could be explained using depth, latitude, temperature, nitrate and surface noon irradiance as
predictive variables (Platt and Sathyendranath, 1995).
Discussion
Predicting the photosynthetic efficiency of phytoplankton cells remains one of the major challenges in determining marine
primary production using remote sensing data (Carr et al., 2006). The MAPPS database of P-E parameters
allows us to assess the global variability in phytoplankton photophysiological parameters and could be used to validate
models that aim to provide a mechanistic understanding of changes in the photosynthetic parameters. Here, we attempt to
explain the spatial patterns in the data set drawing on our current understanding of the key environmental factors
governing variability in both PmB and αB.
A positive correlation between PmB and αB has been attributed to a variety of physiological and
ecological factors, including changes in the allocation of ATP and NADPH to carbon fixation (Behrenfeld et al., 2004), as
well as changes in phytoplankton community structure (Côté and Platt, 1983). To disentangle the ecological from
the physiological sources of variability is not straightforward, unless additional information on the taxonomic
composition and photoacclimatory status of natural phytoplankton samples is available. Moreover, culture studies have
invoked viral infection as another potential source of variability that is poorly understood in natural marine systems
(Puxty et al., 2016).
Both taxon-specific and size-specific differences in PmB and αB have been reported in both culture and
field studies (Bouman et al., 2005; Côté and Platt, 1983, 1984; Huot et al., 2013; Xie et al., 2015). As new
remote-sensing algorithms are now starting to yield information on the size and taxonomic structure of phytoplankton, it
would be useful to derive additional information on the P-E response of key phytoplankton taxa and size
classes, especially those implicated as playing key roles in ocean biogeochemical cycles (LeQuéré et al., 2005;
Nair et al., 2008; Bracher et al., 2017). Although detailed information on the taxonomic and size structure of ship-based
experiments was lacking for several of the samples included in this data set, more recent studies include some measure of
phytoplankton community structure, whether it be from use of pigment markers, size fraction of pigment and/or
productivity, or cell counts. As information on the global distribution of key phytoplankton groups is becoming available
from global studies of phytoplankton pigment markers and flow cytometric counts (Buitenhuis et al., 2012; Peloquin
et al., 2013; Swan et al., 2015), links between phytoplankton biogeography and large-scale pattern in photophysiology as
revealed through the P-E parameters may be explored. Although there is a question as to what the standard
indices of community structure should be that can help account for community-based variation in the photophysiological
parameters across oceanographic data sets (Bracher et al., 2017), it is likely that information on gross community
structure alone will not account for a large fraction of the variability in P-E parameters, especially
across regions or seasons with different environmental forcing (Bouman et al., 2005; Smith Jr. and Donaldson, 2015) or
resident ecotypes (Geider and Osborne, 1991). Establishing relationships between taxonomic composition and phytoplankton
photophysiology will require simultaneous measurements of community structure alongside photosynthesis–irradiance
experiments.
The high range of photosynthetic parameters recorded at lower latitudes is largely caused by depth-dependent changes due
to photoacclimation and photoadaptation (Babin et al., 1996; Bouman et al., 2000b; Huot et al., 2007) in highly stratified
waters. Strong vertical gradients in nutrient supply and growth irradiance lead to a vertical layering of ecological
niches, resulting in strong vertical gradients in species composition, and in the case of marine picocyanobacteria,
high-light and low-light ecotypes are observed (Johnson et al., 2006; Zwirglmaier et al., 2007). Although depth-dependent
variability in the photosynthetic parameters can be examined in the MAPPS data set, in particular Ek (Fig. 10), it has
been argued that optical depth may be a more useful predictor of changes in the P-E parameters resulting
from vertical changes in the photoacclimatory status of phytoplankton cells (Babin et al., 1996; Bouman et al., 2000b). In
highly stratified and stable seas such as the oligotrophic gyres this may be the case, yet in more dynamic ocean
conditions such as the Beaufort Sea, optical depth has been shown to have no more predictive skill, and sometimes less,
than using depth alone (Huot et al., 2013). It is important to note that diel changes in the P-E
parameters were not taken into account in this meta-analysis due to a lack of information on the time of sample collection
in a significant number of observations, which can be a significant source of variability (MacCaull and Platt, 1977;
Prézelin and Sweeney, 1977; Prézelin et al., 1986; Harding et al., 1983; Cullen et al., 1992; Bruyant
et al., 2005). However, as noted in the study of Babin and co-authors (1996) such diel and day-to-day variability in the
P-E parameters is likely to be far smaller when compared with differences across biogeochemical provinces
subject to markedly different environmental forcing.
Clear latitudinal differences in the range of Ek values are revealed in the MAPPS data set (Fig. 9), which suggests
that Ek may be controlled by environmental factors that vary strongly with latitude, such as temperature and the
availability of light. Figure 9 shows that samples collected from high-latitude environments, such as the Labrador Sea,
the (sub)Arctic and the Southern Ocean have markedly lower Ek values, reflecting the physical constraints of low
temperatures and, in some cases, low light levels (Harrison and Platt, 1986). The physical dynamics of the upper ocean and their impact on temperature and light
conditions have been shown to play a dominant role in governing the photosynthetic performance of polar and temperate
marine phytoplankton (Harrison and Platt, 1986; Bouman et al., 2005). Although large-scale shifts in the
photophysiological status of the phytoplankton are observed in the data set, spatial differences between adjacent provinces
were only observed at higher latitudes and seasons where the number of experimental observations tended to be higher
(Fig. 7). It is clear from this analysis that more effort must be focused on obtaining information on photophysiology in
oceanic regimes that are highly undersampled both in space and time.