EPSL-09812; No of Pages 9
ARTICLE IN PRESS
Earth and Planetary Science Letters xxx (2009) xxx–xxx
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Earth and Planetary Science Letters
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / e p s l
The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton
and sediment samples
Jörg Bollmann a,⁎, Jens O. Herrle b, M.Y. Cortés c, Samuel R. Fielding d
a
Department of Geology, Earth Sciences Centre, University of Toronto, 22 Russell Street, Toronto, Ontario, Canada M5S 3B1
Institut für Geowissenschaften, Facheinheit Paläontologie, Goethe-Universität Frankfurt am Main, Altenhöferallee 1, 60438 Frankfurt am Main, Germany
Departamento de Geología Marina, Universidad Autónoma de Baja California Sur, 9 UABCS, Carretera al Sur Km. 5.5, A.P. 19-B, C.P. 23080, La Paz, México
d
Department of Earth and Ocean Sciences, University of Liverpool, 4 Brownlow Street, Liverpool, L69 3GP, United Kingdom
b
c
a r t i c l e
i n f o
Article history:
Received 22 January 2008
Received in revised form 14 April 2009
Accepted 2 May 2009
Available online xxxx
Editor: M.L. Delaney
Keywords:
coccolithophores
calcareous nannoplankton
salinity
morphometry
Emiliania huxleyi
a b s t r a c t
We analysed the morphology of Emiliania huxleyi from globally distributed plankton samples and
demonstrate that the size of E. huxleyi placoliths is highly correlated to in-situ sea surface water salinity.
We used multiple linear regression analysis to link morphological parameters of E. huxleyi to in-situ salinity
and in-situ temperature. The best multiple regression model yielded an R2 = 0.84 with a standard residual
error of 0.65 for in-situ salinity over a gradient from 32.6 to 38.8. No significant correlation existed with
temperature. Our results suggest that the morphology of E. huxleyi placoliths of recent and ancient sediments
may provide a robust method to reconstruct sea surface salinities. One caveat is that the plankton-derived
multiple regression model for in-situ salinities is different from that reported from previous work on
Holocene sediments. This discrepancy is most likely caused by taphonomic processes or from the
biogeographically biased data sets which were used (open ocean versus near shore). The cause of the
tight relation between salinity and morphological response is not well understood but may be related to cell
turgor regulation that affects the size of the cell and thus the size of a single coccolith.
© 2009 Elsevier B.V. All rights reserved.
1. Introduction
Variations in salinity and temperature result in density differences
of ocean water which is the single most important parameter that
defines water masses, ocean stratification and deep water thermohaline circulation. All of these parameters are major factors affecting the
climate system and therefore, the precise knowledge of past ocean
salinity and temperature is of utmost importance to identify
mechanisms that drive the climate system. In contrast to temperature,
salinity cannot be easily reconstructed from geological archives with
the same accuracy and reliability as temperature because several
assumptions need to be made that lead to a significant error of the
salinity estimates (Rohling and Bigg, 1998; Rohling, 2000). This
explains why no widely accepted paleo-salinity proxy is currently
available (Henderson, 2002; Bollmann and Herrle, 2007).
Recent approaches to develop a reliable salinity proxy utilize single
celled marine algae, coccolithophores. Culture experiments indicate a
significant relationship between the hydrogen isotopic composition of
long chain alkenones synthesized by the species Gephyrocapsa
oceanica and Emiliania huxleyi and salinity (Schouten et al., 2006).
Although the growth rate of G. oceanica and E. huxleyi also affects the
fractionation factor, the hydrogen isotopic composition of alkenones
appears to have great potential for the reconstruction of past salinities,
⁎ Corresponding author. Tel.: +1 416 978 3022; fax: +1 416 978 3938.
E-mail address: [email protected] (J. Bollmann).
especially if it is used in a multi proxy approach (van der Meer et al.,
2007; van der Meer et al., 2008).
Also the morphological variation of the world's most abundant
coccolithophore species, E. huxleyi, in core-top samples is related to
sea surface salinity (Bollmann and Herrle, 2007). Bollmann and Herrle
(2007) established a salinity transfer function based on the morphological variation of E. huxleyi and used it to reconstruct the ocean
salinity during the LGM. Although the results are in good agreement
with published values for the LGM, this promising technique needs to
be further constrained by plankton analysis and controlled laboratory
culture experiments. Towards this goal, we have analysed the
morphology of E. huxleyi from globally distributed plankton samples.
2. Materials and methods
A total of 28 plankton samples were collected from the Atlantic,
Pacific and Southern oceans (Fig. 1, Table 1) covering an in-situ salinity
gradient from 32.6 to 38.8 and an in-situ temperature gradient from
1.93° to 28.31 °C.
2.1. Sample preparation
Two to four litres of sea water from 5 to 24 m water depth were
filtered onto membrane filters for the analysis of “living E. huxleyi”. A
small piece of filter membrane was mounted on an aluminium stub
and sputtered with 15 nm of gold for subsequent morphometric
0012-821X/$ – see front matter © 2009 Elsevier B.V. All rights reserved.
doi:10.1016/j.epsl.2009.05.003
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
ARTICLE IN PRESS
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Fig. 1. Location of 28 plankton samples used in this study and 26 sediment core-top samples published by Bollmann and Herrle (2007).
analyses of E. huxleyi placoliths by a PHILIPS XL30 scanning electron
microscope (SEM) (for details see Bollmann et al. (2002)).
2.2. Morphometry of E. huxleyi
Slow scan images (line time of about 20 ms) of approximately 50
placoliths of E. huxleyi per sample were digitized with a resolution of
1024 × 768 × 8 pixels at a magnification of 16,000× to 20,000×.
Placoliths were measured using the image analysis software, analySIS
3.0. All morphometric analyses were performed following the method
described by Bollmann and Herrle (2007). Morphological measurements were obtained from the distal shield of flat oriented E. huxleyi
placoliths isolated on the filter membrane and on coccospheres,
respectively. The measurements include the length (DL) and width
(DW) of the distal shield, and the length (CAL) and width (CAW) of
the central area (Fig. 2). As an approximation of the area of the central
area (CAA) the length and width of the central area were multiplied
(for details see Table 1 and the electronic supplement).
2.3. Environmental data and statistics
All salinities and temperatures reported here represent in-situ
measurements. Productivity and nutrient data are not available for all
samples.
Multiple linear regression analyses were applied to relate the
morphological parameters of E. huxleyi placoliths to environmental
Table 1
Sample information.
Latitude
Longitude
Sampling depth
(m)
Temperature
(°C)
Salinity
N
DL
(μm)
Stdev
(μm)
DW
(μm)
Stdev
(μm)
CAL
(μm)
Stdev
(μm)
CAW
(μm)
Stdev
(μm)
CAA
(μm2)
Stdev
(μm2)
35° 58.01'S
47° 02.00'S
20° 55.34'S
12° 16.52'S
08° 49.55'S
07° 17.45'N
14° 45.40'N
25° 55.45'N
29° 18.28'N
35° 45.18'N
38° 39.98'N
46° 26.09'N
48° 18.80'S
52° 38.30'S
57° 23.00'S
61° 20.90'S
00° 01.00'N
05° 00.00'S
45° 04.00'N
50° 00.00'N
02° 27.00'N
41° 00.30'S
48° 43.70'N
42° 00.02'S
34° 31.35'N
34° 46.44'N
36° 00.00'N
10° 06.22'N
34° 47.40'W
50° 15.00'W
24° 59.35'W
24° 59.72'W
25° 00.00'W
27° 41.78'W
30° 40.8'W
35° 14.3'W
36° 41.34'W
22° 51.1'W
19° 57.63'W
17° 16.58'W
144° 32.10'E
142° 23.20'E
139° 50.80'E
139° 50.90'E
154° 55.00'W
155° 01.00'W
154° 57.00'W
155° 05.00'W
154° 57.00'W
142° 31.00'E
19° 47.80'W
22° 07.16'W
31° 46.58'E
19° 11.46'E
28° 56.00'W
64° 59.94'E
20
15
20
17
13
15
24
17
17
20
24
20
10
10
10
10
10
10
10
10
10
10
5
5
20
20
25
20
19.59
11.43
25.00
26.81
27.86
27.21
23.90
22.67
20.67
19.05
16.20
14.13
9.44
5.49
4.28
1.93
26.60
26.50
15.30
12.00
26.80
15.70
13.50
15.90
19.80
21.60
23.70
27.48
35.57
34.09
37.23
36.99
36.35
35.60
36.35
37.34
36.91
36.46
36.13
35.81
34.31
33.85
33.90
33.94
35.06
35.19
32.79
32.61
34.97
35.37
35.51
35.00
38.80
38.61
36.47
36.08
50
45
50
50
55
58
50
55
56
55
54
47
52
50
55
45
51
52
52
54
53
54
45
50
56
54
50
52
3.22
3.04
3.20
3.19
3.12
3.16
3.18
3.30
3.36
3.31
3.14
3.24
3.28
2.73
2.80
2.93
2.97
3.09
2.84
2.94
2.96
3.15
3.48
2.95
3.57
3.56
3.23
3.15
0.32
0.32
0.21
0.24
0.30
0.26
0.32
0.25
0.24
0.29
0.35
0.32
0.40
0.32
0.35
0.30
0.31
0.38
0.34
0.37
0.34
0.34
0.30
0.36
0.35
0.36
0.28
0.33
2.64
2.55
2.63
2.63
2.57
2.61
2.62
2.70
2.78
2.75
2.65
2.73
2.79
2.29
2.40
2.50
2.47
2.59
2.42
2.53
2.51
2.69
2.96
2.52
2.94
2.93
2.67
2.66
0.26
0.31
0.20
0.24
0.26
0.21
0.28
0.23
0.22
0.24
0.31
0.30
0.36
0.27
0.33
0.27
0.30
0.37
0.30
0.31
0.33
0.30
0.29
0.34
0.31
0.32
0.26
0.32
1.56
1.23
1.62
1.66
1.59
1.58
1.58
1.70
1.67
1.55
1.35
1.36
1.32
1.05
1.07
1.19
1.23
1.21
1.18
1.14
1.21
1.34
1.42
1.33
1.74
1.74
1.55
1.48
0.24
0.16
0.19
0.17
0.20
0.16
0.20
0.17
0.18
0.19
0.25
0.24
0.20
0.14
0.16
0.17
0.16
0.17
0.14
0.16
0.17
0.20
0.14
0.19
0.21
0.22
0.19
0.18
1.01
0.78
1.09
1.10
1.03
1.02
1.02
1.14
1.10
0.99
0.84
0.82
0.84
0.60
0.63
0.73
0.76
0.77
0.75
0.78
0.75
0.86
0.90
0.86
1.14
1.15
0.99
0.95
0.191
0.181
0.142
0.147
0.162
0.105
0.144
0.145
0.154
0.146
0.193
0.222
0.165
0.107
0.111
0.122
0.131
0.14
0.12
0.117
0.124
0.134
0.142
0.146
0.156
0.162
0.155
0.168
1.58
0.96
1.77
1.83
1.63
1.61
1.61
1.94
1.83
1.53
1.13
1.12
1.11
0.63
0.67
0.87
0.94
0.93
0.89
0.89
0.91
1.15
1.28
1.14
1.97
1.99
1.54
1.41
0.53
0.33
0.43
0.41
0.45
0.32
0.43
0.43
0.44
0.39
0.49
0.51
0.35
0.19
0.20
0.25
0.27
0.29
0.25
0.26
0.28
0.33
0.31
0.36
0.47
0.49
0.41
0.41
Sample name; Location, Latitude; Longitude, Sampling depth, in-situ temperature; in-situ salinity; N = Number of specimens measured; DL = Length of the distal shield;
DW = Width of the distal shield; CAL = Length of the central area; CAW = Width of the central area; CAA = Area of the central area.
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
ARTICLE IN PRESS
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3
to predict unknown values although the R2 of this model is lower than
the R2 of model 9 and 10, respectively (Fig. 4; Tables 2, 3). The multiple
regression model of subset 4 yielded an R2 = 0.84 with a standard
residual error of 0.67 for in-situ salinity (Fig. 4C). The most significant
explaining variables of the model are the length of the distal shield
(DL), the width of the central area (CAW), and the area of the central
area (CAA) (Table 2). Multiple linear regression analysis of temperature and morphological parameters revealed no robust model.
Furthermore, salinity and temperature are not strongly correlated in
our data set (Fig. 5). These results indicate that the salinity variability
in our data set can be described accurately by the morphological
variability of E. huxleyi placoliths.
4. Discussion
Fig. 2. Measurements determined from digitized scanning electron microscope (SEM)
images of a single E. huxleyi placolith: DL = Length of the distal shield; DW = Width of
the distal shield; CAL = Length of the central area; CAW = Width of the central area.
parameters (for details see Kleinbaum et al. (1988) and Bollmann and
Herrle (2007)). The validity and robustness of the multiple regression
models were tested using a cross-validation strategy. Ten subsets with
20 out of 28 samples were randomly generated and multiple
regression models were calculated for each subset (see electronic
supplement). Subsequently each model was applied to the remaining
eight samples of each subset to test for the prognosis error (for details
see Sobol (1991)). All statistical analyses including the random subsampling were performed using the statistics package S-Plus version
2000.
3. Results
The morphology of around 1400 placoliths of the coccolithophore
species E. huxleyi was analyzed from 28 globally distributed plankton
samples that cover an in-situ salinity gradient from 32.6 to 38.8 and an
in-situ temperature gradient from 1.93° to 28.31 °C. From the
morphometric measurements several parameters such as the length
and width of the distal shield (DL, DW) and the area of the central area
(CAA) were calculated (Figs. 2, 3). Multiple linear regression analysis
was applied to this data set, linking the measured morphological
parameters of E. huxleyi to in-situ salinity and in-situ temperature.
The “best” multiple regression model yielded an R2 = 0.87 with a
standard residual error of 0.59 for in-situ salinity (Fig. 4A). In order to
determine the most robust multiple regression model for the
prediction of unknown values, a cross-validation strategy was applied
(Sobol, 1991). Ten subsets with 20 samples each were randomly
generated from the data set by holding out eight of the 28 samples.
Subsequently, multiple regression models were calculated for each
subset and applied to the remaining eight samples of the corresponding subset. All subsets revealed models with a high R2 ranging from
0.78 to 92 and standard residual errors between 0.43 and 0.68. The
application of each model to corresponding hold out samples revealed
a significant shrinkage of R2 down to 0.35 in some models indicating
that these models are not suitable to predict unknown values.
However, three models revealed a constant or increased R2 with standard errors ranging 0.51 to 0.7 (subset 4, 9, 10 in Table 3, Fig. 4C–E).
These models were applied to the complete data set of 28 samples to
analyse the distribution of the residuals. The models obtained from set
9 and 10 both produce a skewed distribution of their residuals along
the salinity range from 32.6 to 38.8 indicating that the error of unknown values is biased towards the ends of the distribution. However,
the model of subset 4 shows evenly distributed residuals and
therefore, we consider this model the most valid and robust model
Our results indicate that the morphological variability of E. huxleyi
placoliths is significantly related to salinity variability and support the
results of Bollmann and Herrle (2007). Their regression model
showed a significant relationship between morphology of E. huxleyi
placoliths and sea surface salinity based on Holocene sediment coretop samples. However, their stepwise regression analysis of the
sediment data set revealed that width and not length (as was found in
this study) of the distal shield was the significant explaining variable.
The analysis of the p-values for length and width in our data set
revealed that the differences between both parameters are small and
the resulting differences in the regression model appear to be
negligible (Fig. 4B; Table 2).
However, the detailed comparison between the plankton and
sediment derived regression models revealed some additional
differences. Using the regression model derived from plankton data
and applying it to the sediment data published by Bollmann and
Herrle (2007) reveals salinity values that are in general too low above
35 and too high below 35 resulting in a smaller reconstructed salinity
gradient (Fig. 6). Morphological variations of E. huxleyi, such as the
length of placoliths, is larger in plankton samples than in the sediment
sample set over a similar salinity gradient (Fig. 7). This results in
different weights attached to the predictor variables such as length for
the two regression models. In general there are five factors which
potentially explain the difference between plankton and sediment
data.
4.1. Taphonomic effects
A typical Holocene core-top sample represents environmental
conditions, such as salinity, integrated over 1000 to 5000 years due to
sediment mixing by biological activity and by current advection at the
ocean floor. It is likely that salinity varied for the last 1000 to 5000 yrs
and thus the core tops reflect an average value over this time period
while plankton data represent only a snap-shot in time.
Furthermore, different morphotypes of E. huxleyi may be differentially preserved in sediments (e.g., smaller morphotypes may be less
affected by carbonate dissolution than large morphotypes). This
would explain the smaller morphological gradient of the sediment
data set. However, so far no data exist concerning preferentially
preservation or resistance to carbonate dissolution of different
morphotypes of E. huxleyi.
4.2. Seasonality
Environmental conditions such as salinity and productivity vary
over a seasonal cycle and thus salinity values reconstructed from core
top sediments (Bollmann and Herrle, 2007) might represent the
salinity during the high productivity season to a larger extent. Thus,
the difference between sediment and plankton samples could simply
be explained by seasonal variation.
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
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Fig. 3. Morphological parameters of E. huxleyi versus in-situ salinity. Error bar = 95% confidence interval for the mean.
4.3. Ecological preferences of different morphotypes
The hypothesis that seasonality affects the core-top based transfer
function assumes that E. huxleyi is a globally genetically homogenous
species, but there is evidence of considerable variability in its habit
that probably reflects genetic differences (Young and Westbroek,
1991; Paasche, 2001). The latter assumption is also supported by the
results of culture experiments reported by Green et al. (1998). Thus,
the morphological measurements of 50 single placoliths as determined in this study and by Bollmann and Herrle (2007), do not
necessarily represent measurements from genetically identical speci-
mens that respond in the same way to salinity changes. Therefore, the
abundance of different strains of E. huxleyi varying over a seasonal
cycle might explain differences between plankton and sediment data.
4.4. Biogeography and ecological preferences of different morphotypes
One obvious difference between the plankton and the sediment
data sets are the locations of the samples. In order to avoid a biased
sample set due to deep-sea carbonate dissolution (taphonomic effect:
see Section 1) the majority of sediment samples were taken from
shallow water coastal regions (Bollmann and Herrle, 2007) including
Fig. 4. Multiple linear regression models for in-situ salinity using three morphological parameters: A) Length of the distal shield (DL), width of the central area (CAW) and the area of
the central area (CAA). B) Width of the distal shield (DL), width of the central area (CAW) and the area of the central area (CAA). C–H) Length of the distal shield (DL), width of the
central area (CAW) and the area of the central area (CAA). Dashed line = 95% confidence interval of the prediction. Solid line = regression and 95% confidence interval of the
regression.
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
ARTICLE IN PRESS
J. Bollmann et al. / Earth and Planetary Science Letters xxx (2009) xxx–xxx
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
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Fig. 4 (continued).
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
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Table 2
Regression models.
Table 3
Cross-validation results of all data sets used.
Multiple regression models
Sample set
R2
Std. err.
Coefficients
All data
Set 1
Set 2
Set 3
Set 4
Set 5
Set 6
Set 7
Set 8
Set 9
Set 10
0.87
0.88
0.89
0.9
0.84
0.87
0.78
0.88
0.92
0.86
0.86
0.59
0.55
0.54
0.58
0.65
0.62
0.68
0.68
0.43
0.51
0.58
Value
Std. Error
t value
A) All data
(Intercept)
34.5793
3.6114
9.5750
DL
2.2512
0.9076
2.4805
CAW
−22.5363
7.1107
− 3.1694
CAA
10.9354
2.7183
4.0229
Residual standard error: 0.5949 on 24 degrees of freedom
Multiple R-squared: 0.8665
F-statistic: 51.93 on 3 and 24 degrees of freedom, the p-value is 1.208e−010
B) All data
(Intercept)
35.8887
3.4562
10.3839
DW
2.2605
1.0057
2.2477
CAW
−24.0347
7.3338
− 3.2772
CAA
11.8332
2.7737
4.2663
Residual standard error: 0.606 on 24 degrees of freedom
Multiple R-squared: 0.8614
F-statistic: 49.74 on 3 and 24 degrees of freedom, the p-value is 1.883e−010
C) Subset 4
(Intercept)
32.9713
5.0224
6.5649
DL
3.1466
1.4718
2.1379
CAW
−23.9491
9.6232
−2.4887
CAA
11.0193
3.5803
3.0777
Residual standard error: 0.6562 on 16 degrees of freedom
Multiple R-squared: 0.8378
F-statistic: 27.56 on 3 and 16 degrees of freedom, the p-value is 1.477e−006
D) Subset 10
(Intercept)
36.2385
3.7490
9.6661
DL
1.1044
1.1023
1.0019
CAW
− 18.9468
7.5906
− 2.4961
CAA
9.9814
2.8887
3.4553
Residual standard error: 0.5851 on 16 degrees of freedom
Multiple R-squared: 0.8586
F-statistic: 32.39 on 3 and 16 degrees of freedom, the p-value is 4.988e−007
E) Subset 9
(Intercept)
33.8637
4.0292
8.4046
DL
1.3688
0.9088
1.5062
CAW
−14.2651
8.5153
− 1.6752
DAA
8.0241
3.2369
2.4789
Residual standard error: 0.5538 on 16 degrees of freedom
Multiple R-squared: 0.8645
F-statistic: 34.02 on 3 and 16 degrees of freedom, the p-value is 3.562e−007
Pr(N|t|)
0.0000
0.0205
0.0041
0.0005
0.0000
0.0341
0.0032
0.0003
0.0000
0.0483
0.0242
0.0072
R2 out
0.82
0.77
0.35
0.88
0.52
0.83
0.81
0.67
0.93
0.87
Std. err.
0.7
0.7
0.64
0.54
0.57
0.51
0.52
0.75
0.48
0.7
R2 tot
Std. err.
0.87
0.59
0.86
0.57
0.86
0.86
0.58
0.58
All data = all 28 samples; Set 1–10: randomly generated subsets with 20 samples each;
R2 = coefficient of determination; Std. err. = standard residual error; R2 out =
coefficient of determination of the 8 hold-out samples using the corresponding multiple
regression model; Std. err. out = standard error of the 8 hold-out samples using the
corresponding multiple regression; R2 tot = coefficient of determination using all 28
samples.
Strain-specific responses of E. huxleyi to salinity have been reported
from culture experiments (Green et al., 1998), and coastal and open
ocean populations/morphotypes have also been reported for G.
oceanica (Brand, 1982; Bollmann, 1997), a close relative of E. huxleyi.
4.5. Physiology of E. huxleyi
0.0000
0.3313
0.0239
0.0033
0.0000
0.1515
0.1133
0.0247
A) Model using: Length of the distal shield (DL), width of the central area (CAW) and
the area of the central area (CAA). B) Model using: Width of the distal shield (DL), width
of the central area (CAW) and the area of the central area (CAA). C) Model for subset 4.)
D) Model for subset 10. E) Model for subset 9.
Residual Standard Error = the estimated standard error of the residuals.
Multiple R-squared = represents the proportion of variance in the observations
explained by the model.
F-statistic = F-statistic to test the null hypothesis, which assumes that the true coefficients
are all 0. If the critical value of F at confidence level of 0.01 (99.5%) is larger than 5.65, this is
the minimum F value for the rejection of the null hypothesis in a model with 3 variables
and 22 degrees of freedom, the Null hypothesis can be rejected; p value = probability value
for the test. Note that the null hypothesis can be rejected for all models presented and that
the data suggest that all models are significant.
Std. Error = Standard error of the coefficients; t value = t-statistics testing whether the
coefficient is significantly different from zero; Pr(N|t|) = p-values for each such t-test.
1 Q = First Quartile; 3 Q = Third Quartile.
samples close to islands or sea mounts that are influenced by island
mass effects ((Hasegawa et al., 2003) (Fig. 1)). In contrast, most
plankton samples were analysed from open ocean regions, primarily
because of the good availability of plankton samples (e.g., from the
unique Atlantic Meridional Transect (AMT) cruises). The difference in
morphology between these two biogeographically biased data sets
may point to the existence of different E. huxleyi populations/
morphotypes adapted either to open ocean or coastal conditions
that exhibit a morphologically different response to salinity change.
In addition, to the points listed above, there might be an unknown
physiological process that explains the response of the morphology of
E. huxleyi to salinity. We speculate that this process is related to
regulation requirements of the cell turgor as a response to varying
salinity. Turgor pressure regulation is a basic and well known process
in plants that leads to swelling and shrinking of cells if there is a
difference in ion concentration (e.g., salinity) between the cytoplasma
and the surrounding media (e.g., sea water). Swelling and shrinking of
cells according to salinity changes have been observed in many plants
(see Kirst (1989) for an overview). But how does the cell size affect the
size of coccoliths?
The formation of coccoliths takes place within the cell of E. huxleyi
in the so called coccolith vesicle (van der Wal et al., 1983) and it starts
with the formation of the protococcolith ring around the central area
on an organic template (Westbroek et al., 1989). This organic template
is directly attached to the nucleus and might vary in size if the size of
the nucleus and the coccolith vesicle is varying. Therefore, swelling
and shrinking of the cell or parts of the cell such as the nucleus or the
coccolith vesicle, due to osmotic adjustment might directly affect the
Fig. 5. Sample distribution with reference to in-situ salinity and temperature.
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
ARTICLE IN PRESS
8
J. Bollmann et al. / Earth and Planetary Science Letters xxx (2009) xxx–xxx
for all applied proxy data, such as the past environmental conditions
being comparable to those covered by the calibration data set (for
details see Imbrie and Kipp (1971); Mix et al., (2000)).
5. Conclusions
Fig. 6. Estimates of mean sea surface salinities of sediment samples using the multiple
regression model of sub set 4. Error bars = 95% probability level.
size of the protococcolith ring and thus coccolith size. If the salinity
outside the of cell increases, water diffuses out of the cell, the ion
concentration inside the cell increases, and in order to adjust the cell
turgor, the cell passive shrinks. If the salinity outside the cell
decreases, water diffuses into the cell and the cell would swell.
However, our data show a positive correlation of coccolith size and
salinity. This points to an active regulation of the cell turgor by E.
huxleyi by controlling the ion flux through the cell membrane or due
to the production of organic compounds such as dimethylsulfoniopropionate (DMSP) (Kirst, 1989).
The hypothesis of cell turgor regulation by E. huxleyi might also
explain the difference between the open ocean data set and the
coastal data set. Salinity variations in coastal areas including the coasts
of islands are much higher than in the open ocean, and swelling and
shrinking of the cell could exceed the structural limits of E. huxleyi
cells. E. huxleyi may have adapted to different environments by
developing an active cell turgor regulation. This may explain the
observation that morphological responses in coastal areas are small
because structural damage due to shrinking and swelling has to be
prevented. In contrast, the salinity variations in open oceans are much
smaller and therefore, there is no need to develop a strong control
mechanism to prevent structural damage. This is might be explanatory as to why the morphological response to salinity changes is
stronger in open ocean areas (plankton data) than in costal areas
(sediment data).
The development of an active turgor regulation could also explain
part of the evolutionary history of E. huxleyi. By developing an active
regulation of the cell turgor, E. huxleyi could have had an evolutionary
advantage during the Late Pleistocene glacial–interglacial cycles when
relatively fast salinity changes occurred. The fact that E. huxleyi
flourishes in brackish and high salinity waters such as the Black and
Red Sea with a salinity of b18 and N39, respectively, also points to an
active regulation of the cell turgor. The development of such a
mechanism by E. huxleyi may account for its global dominance and
distribution in today's oceans.
The data currently available for E. huxleyi only allow us to speculate
about the causes of their morphology response to salinity changes and
the observed differences between the plankton and sediment data
sets. Differential calibrations have also been reported from single
strains of E. huxleyi and G. oceanica in culture, in-situ water analysis,
and core top sediment analysis for alkenones (as a sea surface
temperature proxy), potentially limiting the application of sediment
derived transfer functions (for details see Volkman (2000)). However,
as long as non-analogous conditions can be excluded in the past,
statistical transfer functions such as introduced by Imbrie and Kipp,
(1971) can still be applied and some crucial conditions need to be met
We have demonstrated that the morphology of E. huxleyi, the
world's most abundant coccolithophore species, varies systematically
in plankton samples over a large salinity gradient. Our analysis
confirms that the morphology of E. huxleyi responds in plankton
samples in a similar way to that reported from culture experiments
(Green et al., 1998) and surface sediments (Bollmann and Herrle,
2007). However, the derived multiple linear regression model for insitu salinities is different from that reported from Holocene sediment
core-top samples with annual mean sea surface salinities, most
probably due to biogeographically biased data sets. The data currently
available for E. huxleyi only allow us to speculate about the reasons for
the difference between the two data sets which attribute to
taphonomic effects, seasonality, biogeography and ecological preferences of different morphotypes, and physiological factors. Further
analysis of open ocean sediment samples, near-shore plankton
samples, and monoclonal culture experiments under varying salinities
are needed.
Acknowledgements
We thank Claire Findlay, Patrick Holligan, Ralf Schiebel, Toby
Tyrrell and Cornelius Veltkamp. The manuscript benefited from the
Fig. 7. Length of E. huxleyi placolith versus salinity. A represents plankton data and B
Holocene core-top sediment data published by Bollmann and Herrle (2007). Error
bar = 95% confidence interval for the mean.
Please cite this article as: Bollmann, J., et al., The effect of sea water salinity on the morphology of Emiliania huxleyi in plankton and sediment
samples, Earth Planet. Sci. Lett. (2009), doi:10.1016/j.epsl.2009.05.003
ARTICLE IN PRESS
J. Bollmann et al. / Earth and Planetary Science Letters xxx (2009) xxx–xxx
comments of five anonymous reviewers. Financial support came from
JOH and JB's Natural Sciences and Engineering Research Council
Canada Discovery Grant and JOH's support from Canada Research
Chairs.
Appendix A. Supplementary data
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.epsl.2009.05.003.
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