MEDDELANDEN
från
STOCKHOLM UNIVERSITETS INSTITUTION
för
GEOLOGISKA VETENSKAPER
No.351
Isotope-based reconstruction of
the biogeochemical Si cycle
Implications for climate change and human perturbation
Xiaole Sun
Stockholm 2012
Department of Geological Sciences
Stockholm University
SE-106 91 Stockholm
Sweden
A dissertation for the Degree of Doctor of Philosophy in Natural Science
Xiaole Sun
Department of Geological Sciences
Stockholm University
SE-10691 Stockholm
Sweden
Abstract
The global silicon (Si) cycle is of fundamental importance for the global carbon cycle. Diatom growth
in the oceans is a major sequestration pathway for carbon on a global scale (often referred to as the
biological pump). Patterns of diatoms preserved in marine sediment records can reveal both natural
and anthropogenic driven environmental change, which can be used to understand silicon dynamics
and climate change. Si isotopes have been shown to have great potential in order to understand the Si
cycle by revealing both past and present patterns of dissolved Si (DSi) utilization, primarily when
diatoms form their siliceous frustules (noted as biogenic silica, BSi). However, studies using Si
isotopes are still scarce and only a few studies exist where stable Si isotopes are used to investigate
the biogeochemical Si cycle in aquatic systems. Therefore, this thesis focuses on developing analytical
methods for studying BSi and DSi and also provides tools to understand the observed Si isotope
distribution, which may help to understand impacts of climate change and human perturbations on
marine ecosystems. The Baltic Sea, one of the biggest estuarine systems in the world, was chosen as
the study site. BSi samples from a sediment core in Bothnian Bay, the most northern tip of the Baltic
Sea, and diatom samples from the Oder River, draining into the southern Baltic Sea were measured
and reported in Paper II and III, after establishing a method for Si isotope measurements (Paper I). Si
isotope fractionation during diatom production and dissolution was also investigated in a laboratorycontrolled experiment (Paper IV) to validate the observations from the field. The major result is that
Si isotope signatures in BSi can be used as an historical archive for diatom growth and also related to
changes in climate variables. There is isotopic evidence that the Si cycle has been significantly altered
in the Baltic Sea catchment by human activities.
Keywords: diatoms, biogenic silica (BSi), dissolved Si (DSi), Si isotope fractionation, the Baltic Sea
© Sun, Xiaole. Stockholm 2012
ISBN: 978-91-7447-559-3
Cover: top left: picture of Paper I; top right: clean diatoms (by Xiaole Sun); bottom: the Baltic Sea
(by Xiaole Sun); middle right: bathymetry of the Baltic Sea (by Xiaole Sun, data source: HELCOM datasets)
Printed by US-AB SU, Stockholm 2012
Isotope-based reconstruction of the biogeochemical Si cycle
Implications for climate change and human perturbation
Xiaole Sun
Department of Geological Sciences, Stockholm University, SE-10691, Stockholm, Sweden
This doctoral thesis consists of a summary and four papers, referred to by the Roman numerals.
Below is the list of the four papers.
Paper I:
Sun, X., Andersson, P., Land, M., Humborg, C., Mörth, C.-M., 2010. Stable silicon isotope analysis on
nanomole quantities using MC-ICP-MS with a hexapole gas-collision cell. Journal of Analytical Atomic
Spectrometry 25, 156-162.
Paper II:
Sun, X., Andersson, P., Humborg, C., Gustafsson, B., Conley, D.J., Crill, P., Mörth, C.-M., 2011. Climate
dependent diatom production is preserved in biogenic Si isotope signatures. Biogeosciences 8, 34913499.
Paper III:
Sun, X., Andersson, P., Humborg, C., Pastuszak, M., Mörth, C.-M., 2012. Silicon isotope enrichment in
diatoms during nutrient-limited blooms in a eutrophied river system. Submitted to Journal of Chemical
exploration.
Paper IV:
Sun, X., Olofsson, M., Andersson, P., Legrand, C., Humborg, C., Mörth, C.-M., 2012. Effect of diatom
growth and dissolution on silicon isotope fractionation in an estuarine system. Submitted to
Geochimica et Cosmochimica Acta.
Paper I is Reproduced by permission of The Royal Society of Chemistry, with the link to the paper via
http://pubs.rsc.org/en/content/articlelanding/2010/ja/b911113a
All the work in this thesis has been carried out by the author with the exception for Si isotope
measurements before 2005 (Land, M.) in Paper I, sampling of the sediment core and measurement of
BSi content (Conley, D.) and calculation of mixed layer depth (Gustafsson, B.) in Paper II, sampling of
diatoms and concentration measurements (Pastuszak, M.) in Paper III, diatom cultivation (Olofsson,
M.) in Paper IV. All of the co-authors contributed to the four papers by important scientific discussion
and text revision.
Xiaole Sun
Stockholm, August 2012
To my grandpa, Yao, I know you would have been proud
of what I have achieved today……
Isotope-based reconstruction of the biogeochemical Si cycle
1. Introduction
The aquatic ecosystems on Earth have been
tremendously influenced by human activities
(Carpenter et al., 1992). Ecosystem response depends
mainly on changes in the hydrological cycle and
variations in environmental cycles of carbon and
other nutrients like N, P and Si. Perturbations of these
cycles, natural or anthropogenic, have been given
considerable interests in the past decades (Chahine,
1992; Falkowski et al., 2000; Gruber and Galloway,
2008; Laruelle et al., 2009). However, until now, most
of these studies have focused on carbon budgets due
to carbon ubiquity in the biosphere and nitrogen and
phosphorus because of eutrophication as a result of
human activities. Silicon (Si) is to a lesser extend
investigated in the study of nutrient cycling in
ecosystems.
The importance of studying the biogeochemical Si
cycle arises from its close relation to the global carbon
cycle (Berner, 2004). About three quarters of the
primary production in coastal and nutrient rich areas
of the world oceans is carried out by diatoms, a
phytoplankton group that essentially dissolved Si (DSi)
to form their unique siliceous frustule, noted as
biogenic silica (BSi). In low nutrient areas diatoms
still contribute to about 30% of the marine primary
production (Nelson et al., 1995). Diatoms transport
carbon from atmosphere to deep oceans by
sedimentation, a process also known as the
“biological pump” (Buesseler, 1998; Goldman, 1988;
Nelson et al., 1995). Moreover, diatoms also form the
foundation of the shortest desirable food chains,
(Cushing, 1989; Ryther, 1969). Diatoms are sensitive
to environmental change because of their direct
response to many physical, chemical, and biological
changes. When DSi concentrations becomes low, it
could severely limit diatom biomass build up, leading
to ecosystem shifts from production of diatom to nonsiliceous algae (Officer and Ryther, 1980), possibly
leading to harmful algae blooms and decreased Si
export to the open ocean (Dugdale et al., 1995). After
diatoms die, preservation of BSi in sediments allows
for investigating diatom production over time (Conley
and Schelske, 2002).
Silicon is the second most abundant element in the
continental crust after oxygen, 28.8 wt% (Wedepohl,
1995), and weathering of silicate minerals are the
main source for DSi and silicate containing particles in
aquatic systems. From weathering sources to the
ocean, DSi transported by rivers serves diatoms as an
indispensable nutrient in many biogeochemical
processes (Ragueneau et al., 2006; Ragueneau et al.,
2000). This makes BSi and DSi potential proxies for
investigating environmental change.
Biogenic silica in sediments have been successfully
used for reconstructing past environmental changes
including climatic variations, for example relating
diatoms to temperature (Rosén et al. 2000, Blass et al.
2007, McKay et al. 2008) and to pH and changes in
nutrient concentrations (Andren et al., 1999, Braak
and Dame 1989, Bennion et al. 1996, Weckström
2006, Olli et al. 2008). However, these studies are
associated with large uncertainty because of the
difficulty to discriminate between biological and
physical processes as driving forces for the observed
variations in BSi records. Here this thesis will show
that Si isotope signatures in DSi and BSi can be of
good complementary use for constraining those
uncertainties.
Si isotope fractionation by diatoms
Silicon has three stable isotopes 28Si, 29Si, 30Si with
natural abundances of 92.22%, 4.69% and 3.09%,
respectively (De Laeter et al., 2003) and one naturally
radioactive isotope, 32Si with a half-life of 178±10
years (Nijampurkar et al., 1998).
It has been shown that diatoms preferentially take up
28Si to produce BSi, resulting in an isotopic
fractionation (ɛ30/28-value) of approximately -1.1‰
(De La Rocha et al., 1997); Later, the ɛ-value was
shown to be -1.08‰ in freshwater (Alleman et al.,
2005; Hughes et al., 2011) whereas as a range of -0.6‰
to -2.2‰ for in situ measurements in oceanic water
was indicated (Cardinal et al., 2005; Cardinal et al.,
2007; De La Rocha et al., 1997; Varela et al., 2004).
The documented δ30Si values in BSi of diatoms ranges
from -0.3‰ to +2.6‰ (Alleman et al., 2005; Cardinal
et al., 2007; De La Rocha et al., 1998; Varela et al.,
2004), which is slightly higher values compared to BSi
observed in phytoliths produced in terrestrial
systems by vascular plants, ranging from -1.7‰ to
+2.5‰ (Douthitt, 1982; Ziegler et al., 2005). During
the formation of BSi, DSi (residual) is enriched in
heavier Si isotopes. In river waters, the measured
δ30Si values has been shown to range from +0.4‰ to
+3.4‰ (De La Rocha et al., 2000; Ding et al., 2004;
Ding et al., 2011; Engström et al., 2010; Georg et al.,
2007; Hughes et al., 2011). DSi in a soil solution
shows δ30Si values ranging from -0.8‰ to +1.7‰
(Ziegler et al., 2005) and a similar range of -0.2‰ to
+1.3‰ is found in groundwater (Georg et al., 2009;
Opfergelt et al., 2011). In seawater, the δ30Si values in
DSi range from +0.5‰ to +3.2‰ (Beucher et al.,
2008; Cardinal et al., 2005; De La Rocha et al., 2000;
Reynolds et al., 2006; Varela et al., 2004). Demarest et
al. (2009) showed a Si isotope fractionation of -0.55‰
in enriching residual BSi with heavier Si isotopes
observed during BSi dissolution of diatoms sampled
1
Xiaole Sun
from the Southern Ocean if > 20% of BSi was
dissolved.
Quantitative relationship between the Si isotopic
compositions of BSi and DSi
Because of the discrimination against heavier Si
isotopes during diatom production, δ30Si values of DSi
and BSi during diatom production follows a Rayleigh
distillation behaviour (Fig. 1), which describes the
partitioning of Si isotopes in a closed system between
two Si reservoirs: DSi and BSi. Comparing a closed
and open system, as the fraction of DSi remaining in
water (f) decreases i.e. diatom production, the δ³ᵒSi
values of DSi and the produced BSi increases. At the
same time, δ³ᵒSi values of accumulated BSi in both
systems also increase as DSi concentrations decreases,
but only up to the initial δ³ᵒSi value of DSi. This means
that the accumulated BSi can never reach a δ30Si value
higher compared to the initial DSi. δ30Si values of
instantly produced BSi in a closed system differs
always by -1.1‰ (ɛ-value) compared to remaining
DSi, if assuming a fractionation factor (α) of 0.9989
(De La Rocha et al., 1997). In contrast for an open
system, the difference between δ30Si values of the
whole pools of DSi and BSi is always 1.1‰.
2. Thesis objectives
So far Si isotope data in the literature are still scarce,
and only a few studies have attempted to use stable Si
isotopes as tracers for understanding the
biogeochemical Si cycle and its variations in aquatic
systems. Therefore, the aim of this thesis is to provide
Method
development
Methods
•Paper Ι to develop an
analytical technique
for precisely and
accurately determine
the stable Si isotopes
using MC-ICP-MS.
Method
application
accumulated BSi (closed)
instant BSi (closed)
accmulated BSi (open)
Remaining DSi (open)
δ ³ᵒSi (‰)
The range of observed Si isotope values imply that
tracing enriched or depleted BSi caused by diatominferred environmental changes and evolvement with
time in geological records is possible.
remaining DSi (closed)
1.0
0.8
0.6
0.4
0.0
f, fraction of remaining DSi
Fig. 1 Expected Si isotope variations in a closed and an
open system.
data for Si isotope distribution in important Si
reservoirs and to show how these data can be used to
reconstruct diatom DSi utilization as a function of
climate and environmental change, sometimes also
superimposed by anthropogenic disturbance.
Four papers each with their specific objectives are
formulated as shown in Fig. 2. The work started with
a method development for Si isotope measurements
(Paper I), and then the methods were applied to two
case studies investigating diatom-regulated Si
dynamics (Paper II and III). Afterwards the data
derived from the case studies were used to validate
results from a lab-controlled experiment for
investigating Si isotope fractionation patterns during
diatom production and dissolution (Paper IV).
Data
•Paper II to investigate the
variations in time of diatom
production vs. climate
change.
•Paper III to examine the
enrichment of heavier Si
isotopes following seasonal
diatom production
patterns.
Lab-controlled
experiment
•Paper IV to investigate
the Si isotope
fractionation factor
during diatom
production and
dissolution.
Fig. 2 Flow chart of the four papers in the thesis and their specific objectives
2
0.2
Isotope-based reconstruction of the biogeochemical Si cycle
3. Site description
The Baltic Sea is located in Northern Europe, from
53°N to 66°N latitude and from 20°E to 26°E
longitude and is usually separated into seven basins:
Bothnian Bay, Bothnian Sea, Gulf of Finland, Baltic
Proper, Gulf of Riga, Arkona and Kattegat (Fig. 3).
The Baltic Sea is a semi-closed brackish water body,
but also one of the many aquatic ecosystems in the
world that show long-term decreasing DSi
concentrations in the water column, especially from
1970 to 1990 (Conley et al., 1993; Sandén et al., 1991).
Due to the decrease in DSi concentration the
biogeochemical cycle of Si in the Baltic Sea has
changed within the 20th century. One of the reasons is
reduction of DSi in riverine transport due to retention
of Si in dams (Conley, 2002; Humborg et al., 2000).
Changes in water flow paths due to river regulations
leading to less weathering, is also a possible
explanation for reduction in Si concentrations
(Humborg et al., 2002; Humborg et al., 2004). Another
reason is eutrophication (Conley et al., 1993; Schelske
et al., 1983), which stimulate diatom production and
simultaneously increases the accumulation of BSi in
sediments of the Baltic Sea (Conley et al., 2008).
The diatom spring bloom in the Baltic Sea is
sometimes terminated by the depletion of DSi
(Smetacek, 1985), especially in the Gulf of Riga DSi
limitation may occur during certain periods of the
year (Olli et al., 2008). Studies of different aspects of
the biogeochemical Si cycle in the Baltic Sea including
riverine Si fluxes, estuarine Si fluxes, and concluded
that the decreasing DSi concentrations is an on going
problem. This decrease might ultimately lead to Si
limitation in the entire Baltic Sea. However, recent
reports show the decreasing DSi concentrations are
levelling out instead of declining further (Papush and
Danielsson, 2006).
In all, the Baltic Sea is a good site for studying the
interactions between diatoms and Si under human
perturbation. In this thesis, two specific sites have
been chosen for more detailed studies: one is the
northern part of the Baltic Sea, Bothnian Bay and the
other is the Oder River draining into the southern
Baltic Sea (Fig. 3).
Bothnian Bay
Bothnian Bay (235 499 km2) is the northern portion
of the Baltic Sea (Fig. 3). The area has been subjected
to recurring glaciations. The last permanent ice
melted from the area about 9300 years ago. The
average depth of the bay is 43 m and maximum depth
is 147 m. The catchment area is sparsely populated
and consists mainly of forest, wetland and only a
small amount of agriculture land (~1%). The total
water volume of the bay is about 1500 km3, which is 7%
of the total water volume of the Baltic Sea. The total
water discharge into the Bothnian Bay is 97 km3 yr-1
(Humborg et al., 2008) and 60% of the water is
derived from rivers regulated by dams. River
regulation with the purpose of generating electricity
started in the beginning of the 20th century and
continued until ca. 1970, with most of the dam
constructions between 1940 and 1970, e.g. Kemijoki
and Luleå River (Humborg et al., 2006). A
consequence is decreased fluxes of weathering
related elements such as base cations and Si
(Humborg et al., 2002; Humborg et al., 2000), due to
changes in water pathways through the catchments
and particle trapping of BSi behind dams (Humborg et
al., 2006). This has led to a decreased DSi load in the
northern Baltic Sea (Humborg et al., 2007).
Temperature variations in the water column are >
15 °C annually. Formation of a winter ice cover in
Bothnian Bay starts from October to the end of
November. The entire bay is generally covered by ice,
even during the mildest winters, and the ice cover
often remains for more than 120 days per year.
During the years 1961-1990, the summer (ice-free
period) average maximum surface temperature in the
open sea is around 16 ᵒC (Dannenberger et al., 1997).
A surface thermocline in Bothnian Bay develops about
a month earlier than in the other sub-basins of the
Baltic Sea and lasts for about three months. Mean DSi
concentrations in Bothnian Bay observed between
1970-2001 are 31 µM in winters and decrease to an
average concentration of 23 µM in summers
(Danielsson et al., 2008). Residence time for DSi is
approximate 3.3 years (Papush et al., 2009).
The Oder River
The Oder River is one of the largest rivers in the
catchment basin of the Baltic Sea. For the first 112 km
from its source, it passes through the Czech Republic,
but its middle reach (a distance of 186 km)
constitutes the boundary between Poland and
Germany before reaching the Baltic Sea via the
Szczecin lagoon (Fig. 3). The total length of the Oder
River is 854 km, of which 738 km lies in Poland. The
total watershed area has been calculated to 119 000
km2, of which about 90% is in Polish territory. The
annual discharge of the Oder River is 17 km3
(Radziejewska and Schernewski, 2008). 62% of the
river basin is in agricultural use, of which 54% is
arable land, 8% grassland and 32% of the area is
covered by forest.
3
Xiaole Sun
Fig. 3 Map of the Baltic Sea area (left) and two maps of study sites, Bothnian Bay (upper right) and the Oder River (lower right, data from source of
USGS and HELCOM). BB=Bothnian Bay, BS=Bothnian Sea, GF=Gulf of Finland, GR=Gulf of Riga, BP=Baltic Proper, AR=Arkona, KA=Kattegat.
4
Isotope-based reconstruction of the biogeochemical Si cycle
Increased anthropogenic influence has accelerated
eutrophication in the Oder River over the past 50
years. Intensive algal blooms, low water transparency,
oxygen depletion in some parts, and fish mortality
have become common. The present state of the Oder
Lagoon is described as eutrophic, hypertrophic or
polytrophic, depending on the trophic system
(Radziejewska and Schernewski, 2008).
4. Materials and methods
Silicon isotope analysis using MC-ICP-MS (Multi
Collectors Inductively Coupled Plasma Mass
Spectrometry) requires a relatively free-of-matrix
solution for obtaining precise and accurate results,
therefore, a series of sample preparation steps are
adopted in this thesis, and these methods can be
possibly applied to various types of Si samples (Fig. 4).
Diatom extraction
The method for sediment diatom extraction in Paper
II is a procedure modified from Morley et al. (2004).
Organic and inorganic carbon was removed by mixing
samples with 15% H2O2 and 10% HCl. After washing
the samples (centrifuging between each wash) each
sample was sieved through a 10 µm sieve cloth and an
80 µm steel mesh to recover the 10-80 µm fraction
(which contains most of the diatoms) and to remove
silt and clay. The sieved samples were collected in
centrifuge tubes containing sodium polytungstate
(SPT). The density of the SPT was adjusted to be
between 1.8 g cm-3 and 2.3 g cm-3 to separate diatoms
from the remaining residuals. This was followed by
drying diatom samples at 40 ˚C.
Diatom alkaline digestion
Clean diatom samples derived from sediments in
Paper II and from filtrated river water in Paper III
were fused by using solid sodium hydroxide (NaOH)
in Ag crucibles at 730°C in a muffle furnace and
thereafter washed with MQ-e water and collected in a
0.12 M HCl solution (Georg et al., 2006b).
Another wet digestion method was used in Paper IV,
which was adopted from Ragueneau et al. (2005). A
0.25 mol L-1 NaOH solution was added into tubes
which contained freeze-dried diatoms collected on
polyethersulfone filters. HCl was used to stop the
digestion by neutralising the pH. After centrifugation,
the supernatant was diluted for further treatment.
precipitation technique adopted from Reynolds et al.
(2006). A 1 mol L-1 NaOH solution was added into
seawater samples and this was repeated twice. After
each addition, samples were shaken and left for 1
hour for precipitating Mg(OH)2 with Si co-precipitated,
and thereafter samples were centrifuged to collect the
precipitates. The precipitates were dissolved in 4 mol
L-1 HCl. The Si concentration of the removed
supernatant was checked to make sure that the Si
removal was complete.
Chromatographic purification
The final solutions derived from the methods contain
a cationic matrix. Therefore a chromatographic
separation method is required to purify the Si
solution prior to Si isotope analysis. The used
purification process was modified from Georg et al.
(2006b). Cation exchange resin AG 50W-X12 (100200mesh) in H+ form was filled in chromatography
columns. The samples were loaded on the column, in
form of H4SiO4, which passed through the resin
whereas the resin retains the cations.
Silicon isotope analyses
Silicon isotope analyses are described in detail in
Paper I using an IsoProbe MC-ICP-MS equipped with a
hexapole collision cell at the Laboratory for Isotope
Geology, Swedish Museum of Natural History. Due to
instrument limitation of only having low-resolution
mode (m/Δm ~ 450), measurement of δ³ᵒSi values is
not possible with the current set up. The instrumental
mass discrimination caused by a number of processes
in the plasma and in the transmission through the ion
optical system (Becker, 2007) was corrected by using
the standard-sample-standard bracketing technique.
The sensitivity for 28Si is ~12 V/ 36 µmol L-1 Si (1 mg
L-1), with an uptake rate of ~70 µl min-1 via a Cetac®
Aridus desolvating nebulizer. The Si isotope ratio of
each sample is calculated and is given in δ29Si values
(‰) relative to the accepted reference material for
silicon isotopes, NBS 28 according to Eq.1. All of the
δ30Si values in this thesis are calculated from the
relationship δ30Si = δ29Si×1.96, assuming massdependent fractionation (Reynolds et al., 2007).
  29 Si 

  28 

  Si sample

29
 Si   29
 1 1000
 Si 
  28 

  Si standard



Eq.1
DSi extraction from seawater
Dissolved Si in the Baltic Sea water samples in Paper
IV was extracted by a magnesium two-step co5
Xiaole Sun
Diatom extraction from sediments
15% H2O2
10% HCl
Seive 10-80 µm
SPT
3
1.8 – 2.3 g/cm
Clean diatoms after drying
Diatom alkaline digestion
Solid NaOH
Clean diatoms
-1
0.25 mol L NaOH
Chromatographic
purification
DSi extraction from seawater
-1
1 mol L
NaOH (twice)
Precipitates
dissolved in HCl
Store in
0.12 M HCl
Store in
0.12 M HCl
MC-ICP-MS for
Si isotope analysis
Fig. 4 Sample preparation steps prior to Si isotope analysis using MC-ICP-MS.
6
Isotope-based reconstruction of the biogeochemical Si cycle
5. Summary of results
The results of the four papers are briefly described
below.
Paper I: Stable silicon isotope analysis on
nanomole quantities using MC-ICP-MS with a
hexapole gas-collision cell
Paper I reports a method developed for precisely and
accurately measuring the δ29Si value (2S.D., 0.2‰)
using MC-ICP-MS with a total Si consumption of less
than 14 nmol (390 ng Si). Data was collected during a
four-year period (2005-2009) and the average δ29Si
value of IRMM-018 relative to NBS-28 was measured
to -0.95‰ (n=23, 2S.D. 0.16‰), with a 95%
confidence interval of ±0.028‰. The mean δ29Si value
of the Big-Batch standard was found to be -5.50‰
(n=6, 2S.D. 0.26‰). This demonstrates that the used
methods give long-term reproducibility with respect
to both precision and accuracy. However, it was also
shown that the presence of cations, such as Mg, Na, Al
etc., can cause severe matrix effects on the Si isotope
measurements.
Paper II: Climate dependent diatom production is
preserved in biogenic silica isotope signatures
Paper II is the first case study using the method
developed in Paper I. The obtained Si isotope values
in diatoms are used to reconstruct diatom production
in Bothnian Bay. The data are based on down-core
analysis of Si isotope distribution in BSi covering the
period 1820 to 2000. The sediment core is dated
using 210Pb. Results show a linear 210Pb decay relation
with depth in the sediment core (r2 = 0.97). This
linearity suggests minimal bioturbation, indicating an
average sedimentation rate in this Bothnian Bay core
of approximate 2.1mm yr−1. The δ30Si values of BSi
ranges between -0.18‰ and +0.58‰. By assuming
Rayleigh distillation, diatom production can be
calculated as fraction of remaining DSi (f values)
inferred from measured Si isotope values. This
reveals that the sediment core record can be divided
into two periods, an unperturbed period from 1820 to
1950 and another period affected by human activities
from 1950 to 2000 (Fig. 5). The shift in Si isotope
values toward higher values after 1950 is most likely
caused by large scale damming of rivers.
The production is shown to be correlated with air and
water temperature, which in turn were correlated
with the mixed layer depth (ML). The deeper ML
depth observed in colder years resulted in lower
diatom production. These finding corroborated by
pelagic investigations in the 1990’s, have clearly
shown that the ML depth controls diatom production
in the Baltic Sea (Wasmund et al., 1998). Especially
after cold winters and deep water mixing, diatom
production was low and DSi concentrations were not
depleted in the water column after the spring bloom.
18
0.8
16
0.6
14
0.4
12
0.2
0
1800
Observed remaining DSi
Calculated f (δ³ᵒSi = 1.1‰)
Calculated f (δ³ᵒSi = 1.4‰)
Annual average summer temp.
5 per. Mov. Avg. (Annual average summer temp.)
1850
1900
1950
Year
Average summer Temperature (°C)
Fraction of remaining DSi in water (f)
1
10
2000
Fig. 5 Fraction of the remaining DSi (f values) in the Bothnian Bay water column and f values calculated from observations
during the summers 1980 to 2000, plotted together with average summer air temperature through years. The inferior and
superior error bars on f values are the first quartile and third quartile for each f value derived from the Monte Carlo
simulations (Paper II).
7
Xiaole Sun
Paper III: Silicon isotope enrichment in diatoms
during nutrient-limited blooms in a eutrophied
river system
Previous studies imply that knowledge about Si
isotope fractionation is required for a better
understanding of the biogeochemical cycle of Si, in
estuarine and coastal areas where primary
production stands for some 30-50% of the global
marine production. Paper IV reports systematical
measurements of variations in Si isotope values of DSi
and BSi following diatom growth and dissolution of
BSi to investigate Si isotope fractionation patterns
during these processes. Two species of diatoms from
the Baltic Sea, were selected, Thalassiosira baltica
(TBTV1) and Skeletonema marinoi (SMTV1). The Chl a
(chlorophyll a) concentrations of the two species
increased exponentially, while DSi concentrations
decreased during the initial growth period, reaching a
level of 200 µg L-1 for TBTV1 and 530 µg L-1 for
SMTV1. The DSi concentrations during growth days
decreased progressively and the experiment was
terminated when DSi concentrations were below 0.4
µmol L-1. The Si isotope values measured in the two
species varied by more than 0.7‰ for δ 29Si and 1.3‰
for δ 30Si, ranging from +0.6‰ up to +1.3‰ for δ 29Si,
and from +1.2‰ up to +2.6‰ for δ 30Si. The
measured δ29Si values in seawater samples increased
from +1.35‰ to +2.58‰, δ30Si values from +2.64‰
to +5.07‰, calculated by multiplying δ29Si values
with 1.96 (Reynolds et al., 2007). The two species
fractionate the Si isotopes during their growth
identically (Fig. 6); 29α fractionation factors were
0.9993 ± 0.0004 (2σ, TBTV1) and 0.9992 ± 0.0004 (2σ,
SMTV1). The average value for 29α for all diatom
sample measurements was 0.99925 ± 0.0003 (2σ).
The calculated average value for 30α was 0.9984 ±
0.0004 (2σ).
The Oder River is a eutrophied river entering the
southern Baltic Sea. The river concentrations in DSi,
DIN and DIP display similar variations, i.e. decreasing
throughout the spring and summer and increasing in
the autumn. In contrast to the dissolved nutrients, BSi
shows increasing concentration, which indicates a
period of rapid diatom growth in spring and summer,
followed by decreasing concentration in late summer
and autumn. The analysis of Si:N:P ratios indicates
that the diatom production in the Oder River is
limited by different nutrients throughout the studied
period.
The δ³ᵒSi values increased from a minimum value of
+0.75‰ in the spring to a maximum of +3.05‰ at
the beginning of August, which are some of the
highest values ever recorded in freshwater diatoms.
The δ³ᵒSi values started to decrease in late August,
and in September the δ³ᵒSi values reached +1.09‰,
similar to observed values in April. We suggest that
dissolution of BSi becomes more important later in
the summer when phosphate limits the diatom
production. The Rayleigh model used here to predict
δ30Si values of DSi suggests that the initial value is
close to +2‰ (before the start of the diatom bloom).
This means that there is also a biologic control of the
Si entering the river and this is probably caused by Si
isotope fractionation during uptake of Si in for
example phytoliths.
Paper IV: Effect of diatom growth and dissolution
on silicon isotope fractionation in an estuarine
system
8
δ³°Si of SMTV1
δ³°Si of DSi
DSi (Rayleigh)
BSi (Rayleigh)
6
δ³ᵒSi, ‰
6
δ³ᵒSi, ‰
8
δ³°Si of TBTV1
δ³°Si of DSi
DSi (Rayleigh)
BSi (Rayleigh)
4
4
2
2
0
0
1
0.5
0
f, fraction of remaining DSi in water
1
0.8
0.6
0.4
0.2
0
f, fraction of remaining DSi in water
Fig. 6 Plot of Si isotope compositions of diatoms and DSi following diatom growth as a function of f values (fraction of
remaining DSi in water). Lines are expected change for Rayleigh fractionation behaviour in a closed system. Error bars of
Si isotope values are 2σ (Paper IV).
8
Isotope-based reconstruction of the biogeochemical Si cycle
During the dissolution experiment, the Chl a
concentrations of SMTV1 gradually decreased to 267
µg L-1, while the DSi concentration increased steadily
for 20 days to 50 µmol L-1, and was constant (50-60
µmol L-1) for another 20 days, indicating dissolution
of diatom frustules. During the first 25 days in the
dark, the siliceous frustules were linearly dissolved
(r2=0.9) and 75% of the Si was released back to the
medium. Thereafter, during the last 10 days, 90% of
the initial BSi was dissolved and released back to the
medium. The Si isotope values of SMTV1 and DSi
seemed constant during dissolution. Calculation gives
the Si isotope fractionation factor during dissolution
for 29Si and 30Si as 0.9999 ± 0.0002 and 0.99974 ±
0.00022 (2σ). This is in contrast to findings from an
open ocean species, where Si isotope fractionation
during dissolution was observed (Demarest et al.
2009).
6. Discussion
Reconstruction of DSi utilization by δ³ᵒSi values
To understand the biogeochemical Si cycle and its link
with climate, Si isotope signatures of BSi have been
used to trace diatom production by reconstructing DSi
utilization in oceans e.g. Reynolds et al. (2006),
Beucher et al. (2008), De la Rocha et al. (1998), van
den Boorn et al. (2010) and in lakes, e.g. StreetPerrott et al. (2008), Opfergelt et al. (2011). However,
to date, no studies have reconstructed DSi utilization
by diatoms in estuaries or in coastal areas. Estuaries,
with gradients in water turnover times, salinity,
nutrients and primary production, are also
considered to be of significance for the global
biogeochemical nutrient and carbon cycles (Fry, 2002;
Voss et al., 2000). Paper II attempts to reconstruct DSi
utilization during the past 200 years using a
sedimentary record of δ³ᵒSi values in preserved BSi in
Bothnian Bay.
Important to all of these applications is the
uncertainty in isotope fractionation during Si uptake
by diatoms used in the Rayleigh model (Fig. 1), no
matter which aquatic ecosystems diatoms live in. A
few studies have investigated the variability in the Si
isotope fractionation factor during diatom growth
among different species (De La Rocha et al., 1997), in
the oceans (Street-Perrott et al., 2008; Varela et al.,
2004) and in lakes (Alleman et al., 2005). A study by
Demarest et al. (2009) also looked into the Si isotope
fractionation during diatom dissolution and showed
that a Si isotope fractionation factor of -0.55‰ during
dissolution may complicate this Si isotope-based
reconstruction of DSi utilization. Again, there are no
such studies in estuaries. Paper IV examines Si
isotope fractionation patterns during BSi production
and dissolution using diatoms isolated from the Baltic
Sea. The two species of diatoms examined exhibit an
identical Si isotope fractionation factor during growth.
This is in agreement with the Si isotope fractionation
factors in the oceans and in freshwater. However, no
Si isotope fractionation during dissolution was
observed, even after 90% of the diatoms were
dissolved. This may be attributed to the smaller size
of the diatoms living in estuarine systems with lower
salinity compared to the open ocean. This suggests
that information about the original environmental
conditions in estuarine and even coastal systems is
directly retained. However, in terms of the complexity
of the mechanism behind those biological/physical/
chemical processes, these kinds of studies are
important but are still scarce.
Our knowledge of the current diatom production in
water and the historical diatom production derived
from sedimentary records is largely a result of
applying the Rayleigh model. The Si isotope
fractionation factor is the most important parameter
in the model. Higher Si isotope fractionation factor
can cause overestimates of DSi utilization; likewise
lower Si isotope fractionation factor can result in
underestimates of DSi utilization. The problem to
estimate DSi utilization can lead to erroneous
conclusions about effects from for example climate
change. The variability in Si isotope fractionation
should be studied in detail in order to constrain the
uncertainty in Si isotope data.
Si isotope-inferred climate variations
One important reason to trace diatom dynamics is the
sensitivity of diatoms to changes in environmental
conditions, for example temperature. Historical
records of air and water temperature are required to
be able to understand the causes and mechanisms
behind current climate change and impact from
human activities. Especially high-latitude systems are
predicted to experience the greatest impact from
climate fluctuations (Carpenter et al., 1992; Douglas
et al., 1994; Smol et al., 2005). Annual average surface
water temperature in the Baltic Sea has increased by
1.4 ᵒC during the last 100 years, which is higher
compared to 63 other studied large marine
ecosystems (Mackenzie and Schiedek, 2007). The
widely used method for reconstructing temperature is
by analyzing oxygen isotope compositions of diatoms,
because of a dependence of oxygen isotope
fractionation between BSi and water (Swann and
Leng, 2009). Variation in BSi content in sediments is
also used to reconstruct air temperature (Blass et al.,
2007; McKay et al., 2008).
9
Xiaole Sun
A major result from this thesis is to provide a possible
way of connecting δ³ᵒSi values and temperature. This
is done by sedimentary δ³ᵒSi BSi values that can be
used as an archive for estimating the fraction of DSi
utilization (Paper II). This fraction is a function of
water turbulence and light condition, which in turn is
a function of temperature. The correlation implies
that higher temperature causes higher diatom
production due to a shallower mixed layer depth, i.e.
more diatoms can float to water surface to obtain
enough light. Lower temperature leads to lower
diatom production due to a deeper mixed layer depth
without enough light penetration.
Temperature is predicted to increase in the future
(IPCC, 2007), which in general stimulates diatom
production. A model developed by Omstedt et al.
(2009) indicates that the Baltic Sea acts as a source
for CO2 before 1950, but after 1950, the increased
primary production enhances the uptake of CO2 due
to eutrophication, which makes the Baltic Sea work as
both a sink and source of CO2. This suggests that
temperature-induced increased diatom production in
the future may continue the enhancement of CO2
uptake and possibly shift the Baltic Sea to be a sink for
CO2.
Although the sediment records could reveal the past
climate and models could help the understanding of
the ecosystem’s future, the effect of climate change on
ecosystems are difficult to predict. Climate-induced
increased water flux also increases nutrient transport
to the Baltic Sea, which in turn stimulates diatom
growth. It is clear that increased biological processes
will have a major influence on the conditions for biota
in aquatic systems. This will also affect species
composition, distributions, and interactions in ways
that are only partly understood at the present time.
Si isotope-inferred human perturbation
Recently, the biogeochemical Si cycle has also been
perturbed by human activities. Eutrophication and
hydrological alterations have been shown to cause
DSi depletion and diatom growth in the Baltic Sea
(Conley et al., 2008; Conley et al., 1993; Humborg et
al., 2006; Humborg et al., 2008). This thesis shows
that both eutrophication and hydrological alterations
have most likely resulted in increasing δ³ᵒSi values in
the Baltic Sea.
The heavily eutrophied Oder River studied in Paper
III is subject to nutrient-limited diatom growth.
Depletion of DSi during the excessive diatom bloom
leads to very high δ³ᵒSi values in produced BSi,
indicating that the Oder River is a strong source for Si
with high δ³ᵒSi values, which can probably be traced
in the Baltic Sea.
10
Paper II suggests that there is a shift in δ³ᵒSi values
from +1.1‰ measured in a pristine Kalix river
(Engström et al., 2010) to +1.4‰ (assumed in Paper
II by fitting data into field measurements). This is
most likely caused by river damming. Increased BSi
behind dams decreases DSi concentrations, leading to
increasing δ³ᵒSi values in both BSi and DSi in river
water.
By examining Si isotope fractionation during diatom
production and dissolution (Paper IV), different Si
isotope patterns in the Baltic Sea basins can be
suggested. Si-limited diatom production is observed
in Gulf of Riga (Olli et al., 2008), which predicts δ³ᵒSi
values for BSi of +0.85‰ resulting in the δ³ᵒSi value
of residual DSi of +3.64‰. Paper IV also shows that
the entire Baltic Sea may face higher δ³ᵒSi values in
DSi although a Si-limited diatom production has not
been observed in other basins yet. This forecast is
supported by Conley et al (2008), where it is
suggested that DSi will be a limiting nutrient in the
Baltic Sea in the near future.
Enhanced diatom production can possibly induce
oxygen depletion in the bottom water by exporting
organic matter into bottom water, accelerating
development of hypoxia in the Baltic Sea. This can
lead to changes in biogeochemical cycles and habitat
loss for fish and other biota (Conley et al., 2011;
Conley and Johnstone, 1995) since sufficient oxygen is
essential to support healthy biological communities
(Vaquer-Sunyer and Duarte, 2009). Kabel et al. (2012)
concluded that increasing temperature can
substantially deteriorate bottom water conditions in
the Baltic Sea by stimulating cyanobacteria blooms. Si
isotope values of BSi can be used to estimate the
order of magnitude of diatom blooms from a given
year by analysing sediment samples. By comparing
the results with cyanobacteria blooms, the findings
may help us to understand what is most important for
the hypoxia in the Baltic Sea: the diatom spring bloom
or the summer cyanobacteria bloom. However, to
what degree the hypoxic areas influences nutrient
biogeochemical cycles are not yet fully understood.
More studies with multiple approaches and isotopebased techniques are therefore required.
Implications for control of δ³ᵒSi values of Si inputs
to the oceans
About 85% of the DSi delivered to the oceans is
carried by rivers, which constitute the major Si source
for diatom production in the ocean. Rivers may
exhibit large variability in concentration and fluxes of
DSi and Si isotope signatures. This variability can
drive a shift in Si isotopic compositions of BSi in
surface waters of the ocean by changing the relative
degree of DSi utilization (and therefore the Rayleigh
Isotope-based reconstruction of the biogeochemical Si cycle
model). This will finally change the Si isotope values
of BSi preserved in sediments and thus influence the
sedimentary records. Therefore, river inputs with
variable Si flux and δ³ᵒSi values carry important
information for the interpretations of the oceanic Si
cycles.
Many processes can cause the variability of δ³ᵒSi
values of DSi and BSi in rivers over time, such as river
damming (Paper II) and uptake of Si by diatoms
(Paper III), phytoliths (Ding et al., 2011) and
terrestrial plants (Ding et al., 2008). Another process
linked to climate is weathering, which also generates
varying δ³ᵒSi values, which are also linked climate
(Georg et al., 2006a). An important process is
retention of DSi and BSi in estuaries (Conley and
Malone, 1992; Treguer et al., 1995). Estuaries can be
effective traps for DSi and BSi and prevent Si
transport to the ocean. However, these processes will
be difficult to distinguish from each other since Si
isotope fractionation is always small, only up to a few
permil. Analysis of δ³ᵒSi values combined with
analysis of other indicators, such as Ge:Si and Al:Si
ratios may provide additional information about
processes controlling isotope composition of Si inputs
to the oceans. Investigation of Si isotope signature
distribution and behavior in rivers and estuaries will
help to reveal internal and external Si source control
in estuarine systems over time and also have
implications for understanding δ³ᵒSi records in the
ocean.
7. Future perspectives
More studies on historical records of climate and
environmental changes are needed to understand the
past to improve our possibility to predict the future
variations. Factors controlling uptake, storage and
recycling of Si in and estuarine and coastal systems
must be studied in detail. This is important in order to
quantitatively assess the response of ecosystems to
changes in Si inputs and climate and what impacts
these changes will affect the Si cycle in the ocean.
These factors will also help to constrain Si budgets for
systems with different geological settings in both
perturbed and pristine systems. It is also essential
that future-monitoring programs includes DSi and BSi
measurements and that the monitoring should be
extended to samples taken before and after building
of dams, as well as river mouths. This will ensure
datasets and time series for evaluation and modeling.
Modeling the Si cycle both on global and regional
scale is also required for constraining the
uncertainties associated with technical, practical and
economic limitations, and for revealing how the
resilience of diatom-based ecosystems responding to
possible ecosystem changes. The future priorities
should be pointed out as well.
The Si isotope fractionation factor should be carefully
studied before interpreting Si isotope data in different
systems and Si isotope compositions in rivers also
need more studies to better understand land-sea
fluxes of Si.
Acknowledgements
Time flies! Now it is time to finish my doctoral thesis
and have the chance to thank the people who have
helped and supported me in all kinds of ways in the
past four years.
First of all, I would like to express my sincere
gratitude to my supervisors, Carl-Magnus Mörth,
Christoph Humborg and Per Andersson. Thank you,
Magnus, for offering me the opportunity to walk on
this great journey in Stockholm, and for your heartfelt
encouragements and supports along the way. I will
never forget how many good ideas and inspirations
you offered me. Thank you, Christoph, for your
resourceful ideas and constructive feedbacks along
my work. Thank you, Per, not only for your very
helpful guidance and invaluable discussions, but also
for great Sushi parties we had together every year. It
is fantastic to work with you all!
I am very grateful for my co-authors, Bo Gustafsson,
Patrick Crill, Daniel Conley, Marianna Pastuszak,
Martin Olofsson, Catherine Legrand and Magnus Land
for scientific contributions and insightful comments
to greatly improve the manuscripts.
Many thanks to Klara Hajnal and Heike Siegmund for
so kindly helping me with lab work and sample
measurements; Many thanks also to Hans Schöberg,
Torsten Persson and Karin Wallner for assisting me
with sample preparation and providing technical
knowledge for MC-ICP-MS.
I want to express my gratitude to the Geochemistry
group for insightful discussions during lunch
seminars. I would like to particularly thank Volker
Brüchert for offering me opportunities to be a
teaching assistant several times from which I have
learnt a lot.
Special thanks to all of my cute former and present
fellow PhD students. We were having a lot of fun
together.
I am also grateful for the help from Eve Arnold,
Monica Rosenblom and Elisabeth Däcker with all of
administrative work.
11
Xiaole Sun
Many thanks to all the former and present colleagues
at Department of Geological Sciences, Stockholm
University and Laboratory for Isotope Geology,
Swedish Museum of Natural History for creating a
warm working environment and making me so
pleasant during my stay in Stockholm.
Funding from the Swedish Research Council (VR.
2007-4731) is greatly acknowledged.
With love and gratitude, I would like to thank all my
family and friends in Sweden and in China. The
greatest thank you is deserved by my Mummy, my
Daddy and my Deguo, for your endless love and
always believing in me. Without your love and
encouragements, this journey would have been much
more difficult.
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15
Xiaole Sun
16
Paper I
Journal of Analytical Atomic Spectrometry
www.rsc.org/jaas
Volume 25 | Number 2 | February 2010 | Pages 93–216
ISSN 0267-9477
PAPER
Sun et al.
Stable silicon isotope analysis on
nanomole quantities using MC-ICP-MS
with a hexapole gas-collision cell
COMMUNICATION
Günther et al.
Development of direct atmospheric
sampling for laser ablation-inductively
coupled plasma-mass spectrometry
ASU REVIEW
Atomic spectrometry update.
Environmental analysis
PAPER
www.rsc.org/jaas | Journal of Analytical Atomic Spectrometry
Stable silicon isotope analysis on nanomole quantities using MC-ICP-MS
with a hexapole gas-collision cell
Xiaole Sun,*a Per Andersson,b Magnus Land,c Christoph Humborgd and Carl-Magnus M€orthe
Received 8th June 2009, Accepted 13th November 2009
First published as an Advance Article on the web 25th November 2009
DOI: 10.1039/b911113a
We demonstrate in this study that a single focusing multiple collector inductively coupled plasma mass
spectrometer (MC-ICP-MS) equipped with a hexapole gas-collision cell (GV-instrument Isoprobe) can
precisely determine the d29Si (2S.D., 0.2&) using a total Si consumption of less than 14 nmole (390 ng
Si). Testing and evaluation of background, rinse time, and major matrix effects have been performed in
a systematic way to establish a procedure to measure d29Si in small quantities. Chemical purification
prior to analysis is required to remove potential interferences. For data collected during a four-year
period, the average d29Si value of IRMM-018 relative to NBS-28 was found to be 0.95& (n ¼ 23,
2S.D. 0.16&) with a 95% confidence interval (0.95 0.028&). The mean d29Si value of the Big-Batch
standard was found to be 5.50& (n ¼ 6, 2S.D. 0.26&). Although determination of the d30Si
measurements is not possible, with our current instrument we demonstrate that this system provides
a fast and long-term reliable method for the analysis of d29Si in purified samples with low Si
concentration (18 mM Si).
Introduction
Silicon is one of the most abundant elements in the continental
crust, 28.8 wt%,1 and constitutes the backbone of silicate
minerals, which are continuously altered by weathering
processes. Mass-balance studies of the oceanic Si cycle2 have
demonstrated that the gross input is primarily derived from the
continents (84%) as dissolved silicate (DSi). The largest part by
far is supplied by river transport, but eolian transport (7.5%) is
also important. Submarine weathering of basalt is an additional
source primarily at sea floor hydrothermal areas. As an essential
element, silicon fertilizes the seas by stimulating the production
of diatoms, which are a key phytoplankton group responsible for
fuelling of the pelagic and benthic food webs and dominating the
biological carbon sequestration in the oceans.3–5 Weathering of
continental silicates is also suggested to be a long-term regulator
of atmospheric CO2 by sequestration of CO2 carbonates during
weathering and consecutive storage in the ocean and the deep sea
sediments.6 The silicon cycle and its variation through time are
crucial for the understanding of marine productivity and carbon
sequestration and stable silicon isotopes constitute a tool that
can be used to study these processes.
Variations in the stable silicon isotope composition (28Si, 29Si,
30
Si with abundances of 92.22%, 4.69% and 3.09%, respectively)7
a
Department of Geology and Geochemistry, Stockholm University, and
Laboratory for Isotope Geology, Swedish Museum of Natural History,
Stockholm, Sweden. E-mail: [email protected]
b
Laboratory for Isotope Geology, Swedish Museum of Natural History,
SE-10405 Stockholm, Sweden. E-mail: [email protected]
c
WSP Environmental, SE-12188 Stockholm, Sweden. E-mail: magnus.
[email protected]
d
Department of Applied Environmental Science, Stockholm University, SE10691 Stockholm, Sweden. E-mail: [email protected]
e
Department of Geology and Geochemistry, Stockholm University, SE10691 Stockholm, Sweden. E-mail: [email protected]
156 | J. Anal. At. Spectrom., 2010, 25, 156–162
have been documented in geological materials since the early
1950s. It has been shown that minerals formed at different
temperatures were isotopically lighter (i.e. enriched in 28Si) as
a function of decreasing crystallization temperature.8 Based on
theoretical calculations, Grant9 predicted a positive correlation
between 30Si enrichment and increasing silicon content in rocks.
Another study showed that the enrichment of 30Si in coexisting
minerals in igneous rocks increases in the mineral order of biotite, quartz, feldspar, and that granitic rocks were enriched the
most in 30Si compared to meteorites and mafic rocks.10 However,
like most of the other elements above 20 amu, only small isotopic
variations of Si are found in nature (<10&). Therefore, the
analytical methods for determining Si isotope ratios need to have
sufficient accuracy and reproducibility to determine silicon
isotope ratio variations.
Currently, studies on variations in stable Si isotope ratios are
focused on low-temperature processes such as biogenic opal
formation,11–14 clay formation15 and chemical weathering
processes in groundwater aquifers.16,17 Opal formation in the
ocean is dominated by diatom assimilation of dissolved silicate
from the surrounding aqueous phase to build up their silicified
cell wall, a process which occurs in micromole Si concentration
environments. The opal formed has an isotope value of about
1& lower than the source isotope value of DSi,18 i.e., the heavier
Si isotopes are enriched in surface waters as diatoms preferentially sequester the 28Si to form opal. When diatom growth
become Si-limited (DSi < 5 mmol/l) as is often the case in
coastal19,20 and open ocean marine systems,5 the isotope fractionation becomes even higher. Thus, heavier Si isotopes in
surface waters might indicate a shift in the ecosystem, which has
occurred in many coastal waters from siliceous (diatoms) to nonsiliceous phytoplankton species.21–23 Therefore, methods
addressing stable isotope fractionation during opal formation
should be operational on micromole concentrations.
This journal is ª The Royal Society of Chemistry 2010
Until recently, Si isotope data in the literature have been
limited, largely due to analytical difficulties. The traditional
method of analysing Si isotope ratios is by fluorination of silicon
and introducing the resulting SiF4 gas into a gas-source isotope
ratio mass spectrometer (IRMS).10,11,24 A new preparation
method using the acid decomposition of Cs2SiF6 followed by the
analysis of IRMS25 improved the precision compared with the
previous IRMS method. However, this technique is fairly
complicated and requires a potentially hazardous preparation
process.
The development of multiple-collector inductively coupled
plasma mass spectrometry (MC-ICP-MS) has opened up new
possibilities for the determination of stable silicon isotopes with
a precision comparable with that of IRMS.26–30 The major
advantages with ICP source mass spectrometry is that the sample
preparation is simplified and less hazardous, and it requires
shorter analytical time and smaller sample size compared to gassource IRMS.
Stable Si isotope measurements using MC-ICP-MS has mainly
utilised double-focusing mass spectrometers with high mass
resolution, i.e. m/Dm > 1000, to overcome the problem with
polyatomic interferences derived from the ICP source. Instrumentation used for high-resolution analysis includes the
NuPlasma HR, the NuPlasma 1700 from NU instruments and
the Neptune from ThermoFisher.26,30,31
In this study, we have established a technique to analyse stable
silicon isotopes using a single-focusing low-resolution (m/Dm 450) MC-ICP-MS, the IsoProbe from GV instruments. This
instrument is equipped with a hexapole collision cell, which
works as an energy filter. The technique permits higher sensitivity
and less consumption of Si compared to the double-focusing
high-resolution MC-ICP-MS.26,31,32 Below, we present detailed
investigations of matrix effects, polyatomic interferences as well
as instrument memory and background. Moreover, we document the long-term performance over four years using international standard materials.
Experimental
Si isotope standards
It is important that the standard materials used are well documented so that results from different sample measurements can
be normalized and properly compared. Therefore, several standard materials with different isotope compositions have been
introduced to compile data on silicon isotopes. Previously, the
standard to which samples were compared with was the Caltech
Rose Quartz33 followed by the RM8564-NBS28-Silica Sand
(National Institute of Standards and Technology); the latter is
labelled in the following as NBS-28. NBS-28 is nowadays widely
used as an universally accepted zero reference material for silicon
isotopes, and has a similar Si isotope composition to that of
Caltech Rose Quartz.34 Other standard materials such as the BigBatch and diatomite with an isotopic composition different than
NBS-28 have been produced and used as secondary reference
materials.16,17,30–32 An additional reference material, IRMM-018,
has shown a larger than expected variation in isotopic compositions, which might be due to heterogeneity in this material.31,32,35
This journal is ª The Royal Society of Chemistry 2010
Sample preparation
Solid materials obtained as part of the inter-laboratory
comparison of Si isotope reference materials published by Reynolds et al.30 have been prepared and used throughout this study.
This included the standard material NBS-28 and IRMM-018,
and the isotopically highly enriched (both in 29Si and 30Si)
material called Big-Batch (prepared at UCSB, University of
California Santa Barbara, and initially originated from
a commercial Na-metasilicate).
The solid samples of standards were prepared by fusion with
LiBO2 and digested in HCl. About 42.5 mg SiO2 was mixed with
150 mg LiBO2 in a graphite crucible and fused at 1000 C for 30
minutes. After cooling, the formed glass pearl was digested in
20 ml 2.5 M HCl on a shaking table, resulting in a solution
containing 35.4 mM Si. This solution was immediately diluted 50
times with 0.075 M HCl, yielding a stock solution with
a concentration of 0.7 mM Si in 0.12 M HCl, which was stored in
acid-cleaned high-density polyethylene bottles. It is important to
keep the Si concentration in samples below about 2 mM Si to
avoid polymerization during storage.36 After four-year storage,
the Si concentration in the stock solution was still 0.7 mM Si,
indicating that polymerization did not influence the concentration during this period. Water used for dilution was obtained
from a Millipore Milli-Q water purification system. The stock
solution was diluted to 18 mM Si in 0.12 M HCl for Si isotope
measurements.
The Big-Batch standard material contains about 1.5 mg/g
Mo30,37 as a result from the preparation process using a molybdate co-precipitation technique, which may cause potential
matrix effects. Screening of our stock solution by ICP-OES
showed that there was about 4 mM Mo. Furthermore about
2.6 mM Li and 2.6 mM B was present in the Si solution.
Instrumentation
All the isotope measurements were carried out in the Laboratory
of Isotope Geology at the Swedish Museum of Natural History
using an IsoProbe MC-ICP-MS. The operating conditions of the
instrument are summarized in Table 1. The ions generated in the
plasma are introduced into the hexapole collision cell, where they
collide/react with a collision gas. This reduces the energy spread
from about 15 V to less than 1 V, and makes the ions suitable,
after acceleration, for direct entry into the mass spectrometer.
The collision cell also reduces the presence of ions interfering
with the measured Si isotopes by collisions between the ions and
the collision gas as described below. The low energy spread of the
ions allows the refocused beam width to be less than the width of
the collector slits, leading to the flat-topped peaks required for
accurate isotope ratio measurements. A large variety of gases and
gas mixtures can be used in the collision cell and for isotope ratio
measurement noble gases such as Ar are frequently used. For
optimal transmission of light isotopes, Helium is selected as
a collision gas.38
A typical torch material is quartz, but for the Si isotope
measurements we used a torch in which the outer tube of the
torch is made of syalon, an aluminium–silicon–oxynitride alloy,
which has a high wear resistance, good durability and low
contamination potential. The inner part of the torch is made of
J. Anal. At. Spectrom., 2010, 25, 156–162 | 157
Table 1 Instrument settings
Multi-collector settings
Cup
Mass
Axial
High 3
High 6
28
Si
Si
30
Si
29
a
Denoted values of these parameters are kept constant during one entire
measurement, but it may vary between different measurements in this
given range.
ICP parameters
High tensiona
Cooling Ar flowa
Intermediate Ar flowa
Collision He flowa
Neb 1 gas flowa
Hexapole RF amplitudea
Outer tube of torch
Inner tube of torch
Cone
Values
Units
60006020
14
0.951.05
89.9
5060
3050
Syalon
Alumina
Nickel
V
l/min
l/min
l/min
ml/min
%
—
—
—
Cetac Aridus settings
Spray Chamber
Desolvator
Sweep gas flowa
N2 gas flow
95
160
3.603.80
0
C
C
ml/min
ml/min
pure aluminium, which not only has extremely high-temperature
stability and wear resistance, but also helps to provide protective
atmospheres at high temperature to eliminate contamination or
impurity.
The Si concentrations both in the standard and unknown
solution were kept at about 18 mM. The solution was introduced
into the plasma using a Cetac Aridus desolvating nebulizer with
a sample uptake rate of approximate 50–60 mL/min. A 28Si signal
of 810 V was usually obtained, but the 28Si signal could occasionally decrease to 7 V during measurements. The formation
of gaseous SiCl4 can be neglected in water-rich solution.
Interferences
The Si isotope ratio measurement using MC-ICP-MS is usually
affected by mass bias, isobaric interferences, and matrix effects.
Potential isobaric interferences include 14N2 and 12C16O on the
28
Si peak, 14N15N and 12C16O1H on the 29Si peak and 14N16O on the
30
Si peak. The mass resolution on our Isoprobe is too low (m/Dm
450) to fully resolve the polyatomic interferences, but they
could be reduced by using the hexapole collision cell. The three Si
isotopes are influenced by the interfering ions, but compared to
the atomic ions the polyatomic ions are slightly heavier and they
mainly affect the peaks on the high-mass side (Fig. 1). By
adjusting the measurement position to the low-mass side, a well
defined flat-top peak can found for 28Si and 29Si (Fig. 1).
However, due to a relatively large isobaric interference from
14 16
N O on the smallest peak, 30Si, no flat area can be defined and
we are thus not able to measure this isotope (Fig. 1).
158 | J. Anal. At. Spectrom., 2010, 25, 156–162
Fig. 1 Mass scans with the signals in volts for the 28Si, 29Si and 30Si peaks
and possible isobaric interferences from polyatomic ions using 18 mM Si
solutions. The isobaric interferences are given and marked by the shade
area as well as their contribution to the signal in volts. The dashed vertical
lines on low and high mass side show the positions for test measurements
of Si isotope ratios, and the area between them is the measurement
position.
Keeping the measurement position stable is important and this
was tested by determination of the 29Si/28Si ratio at two positions,
one on the low mass side and the other on the high mass side, of
the flat area for 28Si and 29Si. The 29Si/28Si ratio was measured and
it was found that the ratios determined on both sides of the
measurement position (marked in Fig. 1) differ from the ratio
determined at the measurement position. This demonstrates that
it is necessary to reduce the isobars to a minimum and carefully
adjust the position of the peak before data collection.
By using the hexapole as a reaction cell we can reduce the
isobaric interferences to obtain sufficient peak flatness to
measure the 29Si/28Si ratio. This is similar to what Moureau
et al.39 have demonstrated when injecting N2O in a reaction cell
to remove the isobaric interference 92Zr for 92Mo efficiently and
thus make it possible to accurately measure Mo isotope ratios
also by using an Isoprobe.
Measurement procedure
An individual measurement of a standard/sample consisted of 5
blocks and 12 cycles per block, with the integration time of 10
seconds for each cycle. Before each measurement the inlet system
was rinsed for 180 seconds with 0.12 M HCl. After the rinsing,
there was an uptake period of 180 seconds before data acquisition commenced. In total it took about 13 minutes to measure
one standard/sample. With an uptake flow of 5060 mL/min, the
This journal is ª The Royal Society of Chemistry 2010
amount of Si consumed during the measurement was approximately 14 nmol (390 ng).
In order to obtain the relative Si isotope variation, we used the
standard-sample-standard bracketing technique. This method is
frequently applied for determination of isotope ratio using MCICP-MS but requires identical matrix conditions in the sample
and standard solutions.39 Each sample was measured using the
standard-sample-standard bracketing technique including nine
brackets, i.e., nine individual measurements. Before and after
each measurement, a blank solution was introduced for 210
seconds and measured with an integration time of 60 seconds. It
took approximately 160 minutes to acquire each reported isotope
ratio, which was the average value calculated from nine brackets
with two times the standard deviation (2S.D.) or at 95% confidence interval.
Instrument drift
When using the Isoprobe for isotope ratio determination, a drift
in the ratio during the measurement period is commonly
observed. This drift can be both in the upward or downward
direction, i.e., increasing or decreasing ratios with time. Typically
a systematic variation towards a single direction was observed
(Fig. 2). Fig. 2a shows the machine upward drift in an individual
measurement, which was the case for most of the analyses, and
Fig. 2b illustrates the downward drift. The drift in the 29Si/28Si
ratio appeared not only in individual measurements, but was also
observed during the entire analysis using the standard-samplestandard bracketing technique (Fig. 3). The systematic drift in
the ion beam signal is independent of the integration time. This
type of drift in the isotope ratio is frequently observed also for
other heavier elements such as S and Pb, but can mostly be
Fig. 3 (a) examples of upward and (b) downward drift in two
measurements of 29Si/28Si for IRMM-018 vs. NBS-28 and Big-Batch vs.
NBS-28. (a) Nine points with the NBS-28 measured 5 times and (b),
IRMM-018/Big-Batch as unknown solutions was measured 4 times. Both
graphs show the linear trend.
effectively corrected using the standard-sample-standard bracketing technique.
Mass discrimination or mass bias is observed in all mass
spectrometric techniques and can generally be described as the
time dependent deviation between observed and ‘‘true value’’. In
ICP-MS the mass bias is the result of a number of processes in the
plasma and in the transmission through the ion optical system.40
Generally the mass bias decreases with increasing mass and is
most pronounced for the light isotopes.41 The applied standardsample-standard bracketing technique can be used to compensate for mass bias, matrix interferences and backgrounds.28
However, this requires that the signals obtained from both the
standard and the samples are equally high and that the blank
solutions have the same matrix as the analytical solution.
The reported delta values in per mil units were calculated
according to Equation 1,
0 29 1
Si
B 28 Si
C
sample
B
C
d 29 Si ¼ B 29 1C,1000
(Equation 1)
@
A
Si
28 Si
standard
Matrix effects
Fig. 2 (a) example of upward and (b) downward drift in the measured
29
Si/28Si ratios from two individual measurements including 60 cycles. The
dotted lines show the 95% confidence interval and the solid line shows the
linear trend.
This journal is ª The Royal Society of Chemistry 2010
Matrix effects are caused by the presence of additional ions, such
as Na+, Mg2+, Al3+, Ca2+, K+, in the Si solution. These ions can
affect the stability of the ion beam signal and lead to inaccurate
results. Presence of Na+ in the analytical solution is known to
suppress the ion beam of the analyte, i.e. Si. In this study, we
made a detailed study of the effect of increasing concentration of
Mg2+ in the analytical solution on the d29Si value. Aluminium,
J. Anal. At. Spectrom., 2010, 25, 156–162 | 159
the most abundant metal in the earth crust, might also be
a potential problem causing matrix effects. To examine these
possible interferences on the d29Si value, a series of measurements
with varying concentrations of Mg and Al were performed by
adding these elements to the IRMM-018 standard solution. The
experiments were performed in 2005 and repeated using different
machine settings during 2008/09.
The presence of Mg shown as an increased Mg/Si ratio (Fig. 4)
seems to increase the spread in the d29Si value even though the
average value of each measurement was similar. At high Mg/Si
ratio, >1, the d29Si seems to decrease towards lower values and
the precision also seems to decrease. This might be due to
changing conditions in the plasma when the ion strength is
increasing.
In contrast to Mg, the presence of Al causes a substantial effect
on the d29Si value and larger errors (Fig. 5). At Al/Si weight ratios
as low as 0.1, the d29Si is lowered by >0.7&. In addition to these
detailed investigations of the effects of Mg and Al, we have
observed that Si intensity can be greatly reduced in solutions with
high concentrations of Na+ and Ca2+, which might also cause
poor measurement accuracy. However, this effect on the d29Si has
not been quantitatively determined.
The formation of 26MgH2+, 27AlH+ and 27AlH2 are potential
problems in that they cause isobaric interferences. However, the
formation of metal hydrides is likely to be small and can most
Fig. 4 Testing the influence of varying ions in the matrix on the d29Si by
using increasing Mg/Si weight ratios. Data were collected both in 2005
and 2008/09, respectively. The used Si concentration was 71 mM in 2005
and 18 mM in 2008/09.
likely be omitted. In addition, van den Boorn et al.42 reported
that anionic species, such as SO42, could also cause a substantial
matrix effect on the Si isotope measurement.
The Li and B (67.5 mM Li and 65 mM B) in the 18 mM Si
solution of the fused standards materials were not removed.
However, we cannot exclude the possibility for a matrix effect
from those elements on the Si isotope ratio. This was avoided by
addition of Li and B to all solutions to keep the Li and B
concentrations on the same level and thus affect the plasma in
a similar way. We adjusted the amount of Li and B in the samples
and measured the elemental concentrations by ICP-OES before
Si isotope analysis to assure that the matrix of Li and B were
properly adjusted.
Clearly, the presence of alkali metal and alkaline earth metals
as well as Al ions in the analytical solution could severely affect
the precision and accuracy of the Si isotope ratio measurements.
Also other species including both cations and anions could be
a potential problem for high precision Si isotope measurements.
Therefore it is crucial to remove interferences and isobars by
chemical purification prior to the measurements. Alternatively,
matrix effects from elements like Li and B, which are hard to
quantitatively remove by column chemistry, could be eliminated
by determining and adjusting the concentrations in samples.
Background and rinse time test
Since the variations in d29Si values between samples are likely to
be small, even a tiny amount of Si from a previous standard/
sample remaining in the nebuliser and the analytical system
could potentially affect the measured ratio. The system was
rinsed using 0.12 M HCl between each individual measurement
but the effectiveness of this cleaning needed to be tested thoroughly. The rinse time was investigated by measuring IRMM018 relative to NBS-28 and the results show that a rinse time of at
least 150 seconds between samples is necessary to remove the
previous samples. A rinse time of 180 seconds was applied to
ensure that the entire system was clean. We efficiently eliminated
the instrument memory effects and could reach an error of d29Si
less than 0.2& (2S.D.).
Instrumental background is mainly derived from the analysed
solutions, but the build-up of impurities on the cones as well as
on the transfer optics over time also contributes to the background. We investigated the background contribution by using
a 180 s rinse time and conducting blank determinations at the
beginning and end of each measurement. The observed background contributed up to approximately 2.5% on the 28Si and 29Si
intensities. The background was found to be similar before and
after the analysis. Part of this background comes from build-up
of impurities in the inlet area of the instrument. By careful
cleaning it could be reduced to about 0.6%. However, during
a session of measurements the background was generally found
to be stable and could be corrected using the standard-samplestandard bracketing technique.
Results and discussion
29
Fig. 5 Testing the influence of varying ions in the matrix on the d Si by
using increasing Al/Si weight ratios. Data were collected both in 2005 and
2008/09, respectively. The used Si concentration was 71 mM in 2005, and
18 mM in 2008/09.
160 | J. Anal. At. Spectrom., 2010, 25, 156–162
Long-term reproducibility
To determine our ability to precisely measure and reproduce the
d29Si value using a MC-ICP-MS equipped with a collision cell, we
This journal is ª The Royal Society of Chemistry 2010
Fig. 6 The d29Si measured in IRMM-018 relative to NBS-28. Section 1
was performed in 2005 and Section 2 was measured in 2008/09. The same
solutions and the same machine were used in both sections although the
settings of the machine varied between these two periods. Each point
represents the average value of nine brackets in one measurement.
Fig. 7 The d29Si measured in the Big-Batch relative to NBS-28. The two
sections are the results measured in 2005 and 2008/09. Each point
represents the average value of nine brackets in one measurement.
have analysed the IRMM-018 and the Big Batch standard. The
data have been obtained in periods during 2005 and from
October 2008 to August 2009 (Fig. 6 and 7). Data from
measurements of IRMM-018 relative to NBS-28 are reported in
Fig. 6, comprising the first measurement period in 2005 with the
integration time of 5 and 7 seconds and the second in 2008/2009
with the integration time of 10 seconds. The data for IRMM-018
comprising the entire period has been examined statistically
using a two sample t-test assuming equal variance and normally
distributed errors to test whether the measured results in both
periods were similar. No statistically distinct difference between
the two periods could be found (p # 0.0014, n ¼ 23).
Table 2 Summary of the results in this study and comparison with other
published data
Standard
d29Si (&)
2S.D.
n
IRMM-18
IRMM-18a
Big-Batch
Big-Batcha
Big-Batchb
Big-Batchc
0.95
0.85
5.51
5.35
5.39
5.39
0.16
0.14
0.25
0.3
0.3
0.17
21
740
6
198
28
15
a
Inter-laboratory calibrated Si isotope values.30 b Measurements from
van den Boorn et al.37 c Measurements from Chmeleff et al.31
This journal is ª The Royal Society of Chemistry 2010
The results for both the IRMM-018 and the Big-Batch are
summarised in Table 2 along with literature data. The average
d29Si value for IRMM-018 during the entire four-year period was
found to be 0.95& (2S.D. 0.16&) with a 95% confidence
interval of 0.028&. The results are in agreement with the value
of 0.85& (2S.D., 0.14&) obtained in the inter-calibration study
by Reynolds et al.30 The precision (2S.D.) for a reported d29Si
value is typically between 0.1 and 0.2&, although smaller and
larger errors (0.061 to 0.27&) occurred occasionally, most
probably caused by machine random errors during measurements.
Similar to IRMM-018, we have also analysed the Big-Batch
standard and our mean d29Si value was found to be 5.50&
(2S.D., 0.26&, Fig. 7). This can be compared to previously
published values of 5.39& (2S.D., 0.17&),31 5.39& (2S.D.,
0.3&)37 and 5.35& (2S.D., 0.3&) reported by Reynolds et al.30
in the inter-calibration study (Table 2).
The amount of Si consumed during one measurement was
approximate 400 ng per sample. This is comparable with other
methods using double-focusing high-resolution MC-ICP-MS
consuming between 200 and 500 ng Si,26,30,31 but lower than the 3
mg Si typically used by a double-focusing low-resolution MCICP-MS.28 The sensitivity for 28Si was found to be 18 V/36 mM
Si (1 mg/L), which is >3 times higher than 5 0.5 V/36 mM
observed by Engstr€
om et al.27 and slightly higher than 13 V/
36 mM reported by Georg et al.26 using a high-resolution MCICP-MS. Clearly, our method yields high signals using small
amounts of Si which can produce d29Si values with a precision
similar to other methods.
Conclusion
This study demonstrates that by using a single focusing lowresolution MC-ICP-MS equipped with a hexapole gas-collision
cell, we can precisely and accurately measure the d29Si (2S.D.,
0.2&) with a total Si consumption of less than 14 nmol (390 ng
Si). Analysis of the same standard material performed during
2005 and 2008/09 gave a similar precision and accuracy. Given
that the instrument’s hardware and settings have been partly
changed during the last four years, the reproducibility in d29Si is
found to be stable. For precise measurement of d29Si values it is
necessary to use a Si solution with low concentrations of interference ions to avoid matrix effects. Thus, chemical purification
to remove these interferences of the Si solution prior to analysis is
required. Alternatively, matrix effects from elements like Li and
B, which are not easy to remove by using ion-exchange, can be
eliminated by determining and adjusting the concentrations in
samples and standards to the same level.
Acknowledgements
We would like to thank Hans Sch€
oberg and Torsten Persson in
the Laboratory for Isotope Geology at the Swedish Museum of
Natural History for assisting with sample preparation and
providing technical support. We are also grateful for the funding
from the Swedish Research Council (VR-2007-4763). We
appreciate insightful comments and constructive suggestions
from two anonymous reviewers, who greatly improved this
manuscript.
J. Anal. At. Spectrom., 2010, 25, 156–162 | 161
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This journal is ª The Royal Society of Chemistry 2010
Paper II
Biogeosciences, 8, 3491–3499, 2011
www.biogeosciences.net/8/3491/2011/
doi:10.5194/bg-8-3491-2011
© Author(s) 2011. CC Attribution 3.0 License.
Biogeosciences
Climate dependent diatom production is preserved in biogenic Si
isotope signatures
X. Sun1,2 , P. Andersson2 , C. Humborg3,4 , B. Gustafsson4 , D. J. Conley5 , P. Crill1 , and C.-M. Mörth1,4
1 Department
of Geological Sciences, Stockholm University, 10691, Stockholm, Sweden
for Isotope Geology, Swedish Museum of Natural History, 10405, Stockholm, Sweden
3 Department of Applied Environmental Science, Stockholm University, 10691, Stockholm, Sweden
4 Baltic Nest Institute, Stockholm Resilience Centre, Stockholm University, 10691 Stockholm, Sweden
5 Department of Earth and Ecosystem Sciences, Lund University, 22362, Lund, Sweden
2 Laboratory
Received: 28 January 2011 – Published in Biogeosciences Discuss.: 12 April 2011
Revised: 16 September 2011 – Accepted: 9 November 2011 – Published: 29 November 2011
Abstract. The aim of this study was to reconstruct diatom
production in the subarctic northern tip of the Baltic Sea,
Bothnian Bay, based on down-core analysis of Si isotopes
in biogenic silica (BSi). Dating of the sediment showed that
the samples covered the period 1820 to 2000. The sediment
core record can be divided into two periods, an unperturbed
period from 1820 to 1950 and a second period affected by human activities (from 1950 to 2000). This has been observed
elsewhere in the Baltic Sea. The shift in the sediment core
record after 1950 is likely caused by large scale damming
of rivers. Diatom production was inferred from the Si isotope composition which ranged between δ 30 Si −0.18 ‰ and
+0.58 ‰ in BSi, and assuming fractionation patterns due to
the Raleigh distillation, the production was shown to be correlated with air and water temperature, which in turn were
correlated with the mixed layer(ML) depth. The sedimentary
record showed that the deeper ML depth observed in colder
years resulted in less production of diatoms. Pelagic investigations in the 1990’s have clearly shown that diatom production in the Baltic Sea is controlled by the ML depth. Especially after cold winters and deep water mixing, diatom production was limited and dissolved silicate (DSi) concentrations were not depleted in the water column after the spring
bloom. Our method corroborates these findings and offers a
new method to estimate diatom production over much longer
periods of time in diatom dominated aquatic systems, i.e. a
large part of the world’s ocean and coastal seas.
Correspondence to: X. Sun
([email protected])
1
Introduction
Dissolved silicate (DSi) is an essential nutrient for diatoms,
which play an important role in regulating the uptake and fate
of C and N in the world oceans (Smetacek, 1998). Diatom
dynamics is an effective measure of environmental change
due to its sensitivity to a variety of physical and ecological
conditions. In high-latitude ecosystems of the subarctic and
arctic region, climate fluctuations on short growing seasons
for diatoms may lead to major biological influences (Ding et
al., 2011). After diatom death and cell lysis, the siliceous cell
walls of diatoms can be well preserved in sediments. This allows for the reconstruction of diatom production by analysis
of the preservation of biogenic silica (BSi) over time (Conley and Schelske, 2002). BSi has been used previously to
track climate-related changes in aquatic production over millennial time scales (Blass et al., 2007; Rosén et al., 2000).
Recently, the lacustrine BSi flux was used to reconstruct air
temperature with decadal resolution back to 1580 CE in the
Swiss Alps (Blass et al., 2007) and also to quantitatively infer summer temperature for the past 2 kyr in South-Central
Alaska (McKay et al., 2002).
The discovery of Si isotopic fractionation of about −1.1 ‰
during formation of diatom cell walls (De La Rocha et al.,
1997) has provided a proxy for investigation of diatomrelated processes. Many other studies have shown that
isotopic fractionation is independent of temperature, interspecies effect and the partial pressure of CO2 (De La Rocha
et al., 1997; Milligan et al., 2004; Varela et al., 2004). Hence,
variations in the δ 30 Si values of diatoms are therefore related to DSi utilization efficiency in water during diatom
Published by Copernicus Publications on behalf of the European Geosciences Union.
3492
X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
production and can provide information on the role of the biological pump (the transport of C into deep water body and
the regulation of atmospheric concentrations of CO2 ) in paleoclimatic and paleoceanographic studies (Brzezinski et al.,
2002; De La Rocha et al., 1998; De la Rocha, 2006; Beucher
et al., 2007; van den Boorn et al., 2010), but also ongoing
environmental changes (Reynolds et al., 2006; Brzezinski et
al., 2001; De La Rocha et al., 2000; Cardinal et al., 2005;
Wille et al., 2010). For example, DSi utilization efficiency
in the Baltic Sea is also controlled by environmental conditions such as the supply of macronutrients (N, P, Si) and
light which are functions of the physical mixing regime of
the upper water column (Wasmund et al., 1998). The physical mixing regime is a function of local wind and temperature conditions and overall climate. The Si isotope signature
of sedimentary BSi could therefore be a potential tracer for
climate variation.
In this study we report an application of Si isotope analyses of diatoms to reconstruct diatom production variability
from 1820 to 2000 in the subarctic Bothnian Bay. The studied period can be divided into an unperturbed period from
1820 to 1950 and a perturbed period from 1950 to 2000.
2
Study site
Bothnian Bay (235 499 km2 ), located between the latitude
63.5◦ N and 66◦ N, is the northern portion of the Baltic Sea
(Fig. 1). The area has been subjected to recurring ice ages.
The last permanent ice melted from the area about 9300 yr
ago. The average depth of the bay is 43 m and its maximum
is 147 m. The catchment area is sparsely populated and consists mainly of forest, wetland, other natural areas and only
a small amount (∼1 %) of agriculture land. The total water volume of the Bay is about 1500 km3 which is 7 % of
the total water volume of the Baltic Sea. Total water discharge into Bothnian Bay is 97 km3 yr−1 (Humborg et al.,
2008) and, 60 % of the water is derived from rivers regulated
by dams. River regulation started at the beginning of the 20th
century and continued until ca. 1960, with most of the dam
construction between 1940 and 1960, e.g. on the Kemijoki
and Luleå River (Humborg et al., 2006). A consequence of
which is a decrease in fluxes of weathering related elements
such as base cations and Si (Humborg et al., 2002, 2000) due
to changes in water pathways through the catchments and
particle trapping of BSi behind dams (Humborg et al., 2006).
This has ultimately led to a decreased DSi load in the Northern Baltic Sea (Humborg et al., 2007).
Temperature variations in the water column are >15 ◦ C
annually. Formation of a winter ice cover in Bothnian Bay
starts between the beginning of October and the end of
November. The entire bay is generally covered by ice even
during the mildest winters and the ice cover is often maintained for more than 120 days per year. During the years
1961–1990, the summer (ice-free period) average maximum
Biogeosciences, 8, 3491–3499, 2011
surface temperature in the open sea is above 16 ◦ C (Haapala and Alenius, 1994). A surface thermocline in Bothnian Bay develops about a month earlier than in the other
sub-basins of the Baltic Sea and lasts for about three months.
Mean DSi concentrations in Bothnian Bay observed between
1970–2001 are 31 µM in winters and decrease to an average
concentration of 23 µM in summers (Danielsson et al., 2008).
Residence time for DSi is approximate 3.3 yr (Papush et al.,
2009).
3
Materials and methods
3.1
Sediment core
A short sediment core (∼38 cm) taken at a depth of
112 m in Bothnian Bay, Station 263870 (64◦ 33.581 N and
21◦ 54.769 E, Fig. 1) was sliced into 1 cm sections and
freeze-dried. This core was dated and sediment accumulation rates were determined by analysing 210 Pb (46.51 keV),
214 Pb (351.99 keV) and 137 Cs (661.63 keV) on an EG&G
ORTEC® co-axial low energy photo spectrometer (LEPS)
with a high-purity germanium crystal. Each sediment section
was measured for 2 to 3 days after having been left standing for 2 weeks to achieve radionuclide re-equilibrium of decay products. Thereafter, the externally calibrated standard
(pitchblende, Stackebo, Sweden) was added to 5 of the already measured samples at different depths to determine the
relatively efficiency of the gamma detector system. The sedimentation rate was calculated by Eq. (1):
210
Pbx =210 Pb0 · e−λt
(1)
where 210 Pbx is the activity of 210 Pb per mass weight of sample at depth x, 210 Pb0 is the activity of 210 Pb at the surface
(x = 0), λ is the decay constant of 210 Pb (0.0311 yr−1 ), and t
is the age of the sediment sample. The BSi content in sediments was determined using the method described by Conley
and Schelske (2002).
3.2
Diatom extraction
The method used for extraction of diatoms from the sediment was modified from Morley et al. (2004). Organic carbon was removed from each sediment slice by repeated mixing with 20 ml 15 % H2 O2 followed by incubation for 12 h
and then heating to 90 ◦ C until no bubbles were evident after subsequent addition of H2 O2 . Addition of 10 ml 10 %
HCl and allowing the reaction to proceed for several hours
removed inorganic carbon. There was relatively little inorganic carbon, <0.1 %, in most of the samples. The samples
were washed 3 times at each step using MilliQ-e water and
centrifuged between each wash. Thereafter each sample was
sieved through a 10 µm sieve cloth and an 80 µm steel mesh
to recover the 10–80 µm fraction and to remove silt and clay.
The 10–80 µm fraction contained most of the diatoms. Purity
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X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
3493
Fig. 1. Map of the study area. The regulated rivers are marked with solid lines. Unregulated rivers are indicated with dashed lines. The
sampling site is marked with a star.
was evaluated with an optical microscope and diatoms were
found to constitute about 95 % of the phytoplankton. The
sieved samples were collected in centrifuge tubes containing sodium polytungstate (SPT, 3Na2 WO4 9WO3 ·H2 O). The
density of the SPT was adjusted to be between 1.8 g cm−3
and 2.3 g cm−3 in order to separate diatoms from the remaining clay and mineral materials. Centrifugation was at
4200 rpm for 5 min and repeated until clean diatoms were
retrieved. Subsequently, all diatom samples from each sediment slice were carefully rinsed with MilliQ-e water and
sieved at 5 µm to remove all traces of SPT. Finally, the diatom samples were dried at 40 ◦ C for 24 h.
3.3
Diatom fusion and chromatographic purification
About 5 mg of each extracted diatom sample were mixed
with ca. 180 mg solid NaOH and fused in Ag crucibles at
730 ◦ C for 10 min in a muffle furnace. This was followed by
washing with MilliQ-e water into a 0.12 M HCl solution in
order to neutralize the NaOH (Georg et al., 2006). The final
stock solution was diluted to 200 ml to yield a Si concentration of ∼10 mg l−1 and stored in pre-cleaned HDPE bottles
to minimize Si polymerization.
Interfering elements remaining after diatom dissolution
were removed prior to Si isotope analyses by using cation exchange columns. A protonated 1 ml BioRad cation exchange
resin AG 50W-X12 (100–200 mesh) was placed in 2 ml BioRad columns. The resin rinsing and diatom sample load processes are reported in Table 1, and the recovery shown in
Fig. 2.
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3.4
Si isotope analysis
The δ 29 Si value of each sediment slice was measured using
a single focus multiple-collector inductively coupled plasma
mass spectrometer (MC-ICP-MS) equipped with a hexapole
gas-collision cell. Analytical precision for replicate samples
was better than 0.2 ‰ (2σ ) with a total Si consumption of
less than 14 nmol (390 ng) and the internal reproducibility
was tested by measuring IRMM-18 and Big Batch (Sun et
al., 2010). The 30 Si could not be measured directly due
to the instrumental limitation of using low mass resolution
(m/1m−1 ∼450) which does not fully resolve the isobars.
However, the δ 30 Si values were calculated from the mass ratio relationship δ 30 Si = δ 29 Si × 1.96 which assumes massdependent fractionation (Reynolds et al., 2007). δ x Si ‰ was
expressed as relative to the standard material NBS 28 (Eq.2):

x Si
 28 Si sample

δ Si =  x − 1 · 1000
x
Si
28 Si
(2)
NBS 28
where x = 29,30.
4
4.1
Results and discussion
Silicon recovery of cation-exchange columns
The quality of the chemical purification is of vital importance to obtain good precision and accuracy of the Si isotope analysis. Figure 2 shows the recovery of Si when using
the cation-exchange column. Predominant Si species after
NaOH fusion and HCl dissolution exhibit no affinity for the
resin and thus pass directly through the resin. More than
Biogeosciences, 8, 3491–3499, 2011
3494
X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
Table 1. The purification process used in this study for dissolved
diatom samples from Georg et al. (2006).
BioRad cation-exchange resin AG 50W-X12
(100–200 mesh) in H+ form, 1 ml resin bed
Step
Pre-cleaning
Pre-cleaning
Pre-cleaning
Conditioning
Sample load
Elution
Solution
4 M HCl
8 M HCl
4 M HCl
MQ-e water
Acidified diatom samples
MQ-e water
Volume (ml)
3
3
3
6
2
4
Fig. 3. Downcore variations in 210 Pb. (A) The depth profile of
total 210 Pb activity in the sediment core. The error bars represent one standard deviation. (B) Correlation between depth and
ln (210 Pb0 −210 Pbx ), i.e. sample 210 Pb activity normalized to the
surface value.
Fig. 2. Recovery of Si using a cation-exchange resin (AG 50W-X12,
100–200 mesh) in a 1 ml resin bed.
60 % of the loaded Si is recovered from the initial elution of
the 2 ml sample aliquot through the resin. The remaining Si
in the resin is eluted using 4 ml MilliQ-e water. No breakthrough of ambient cations is observed if only MilliQ-e water is added. The cations start to elute when adding 4 M HCl
(Fig. 2). Si is quantitatively recovered using this chromatographic purification with recovery efficiencies greater than
99 %.
4.2
210 Pb
dating
The sediment core is dated using 210 Pb as shown in Fig. 3.
Figure 3a exhibits a gradually decreasing trend with increasing depth. Fig. 3b shows the linear 210 Pb decay relation with
depths in the sediment core (r 2 = 0.97). The linearity suggests minimal bioturbation and the slope indicates an average
sedimentation rate in this Bothnian Bay core of approximate
2.1 mm yr−1 which is consistent with previous measurements
from this area (Grasshoff et al., 1983).
4.3
BSi vs. Si isotopes in BSi
The depth profiles of BSi contents and δ 30 Si values are plotted in Fig. 4 with the corresponding time scale obtained
from the 210 Pb dating. The BSi content of the sediment
Biogeosciences, 8, 3491–3499, 2011
core (Fig. 4a) is approximately 3.5 % below 10 cm depth
which is material deposited before the 1950s. It increases
to a maximum of 7.8 % in the surface sediments. This is
due to increased diatom production, probably caused by anthropogenic nutrient emissions, especially N and P, to the
Baltic Sea (Conley et al., 2008). Four periods of decreases
in BSi contents were observed around 1825, 1850, 1880 and
1912. These dates are extrapolated from the sedimentation
rate from the 210 Pb dating. The range of δ 30 Si values are
displayed in Fig. 4b and vary between −0.18 ‰ and 0.58 ‰.
Before the 1950s the average δ 30 Si is about 0.16 ‰ with a
small negative peak of −0.18 ‰ at 17 cm, i.e. 1910s, followed by a gradual increase to ∼0.5 ‰ toward the surface.
In general, increased BSi contents result in higher δ 30 Si values.
It should be noted that the sedimentary BSi is not simply a measure of the entire Bothnian Bay primary production, but is a balance between diatom production and dissolution. The nearly linear sedimentation rate (Fig. 3) suggests
that the variation in the sedimentation rate does not drive the
variability of the BSi contents. During diatom production,
there is Si isotopic fractionation of −1.1 ‰ (De La Rocha et
al., 1997). However, dissolution of diatoms also exhibits Si
isotopic fractionation of −0.55 ‰ if the dissolution is more
than 20 % of the total amount of BSi (Demarest et al., 2009).
This means that the δ 30 Si values of the preserved BSi can
potentially increase, i.e. 28 Si is preferentially released during diatom dissolution. Although no diatom dissolution rate
estimates exist for Bothnian Bay, the excellent preservation
of diatoms and the low mineralization rate in the bottom water combined with a shallow water depth of 112 m and rapid
sedimentation rates imply that dissolution of BSi is probably
low. This means that the potential shift of Si isotope values
in BSi caused by dissolution may be ignored in this study.
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X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
3495
Fig. 5. Fraction of the remaining DSi (f ) in the water column of
Bothnian Bay using Eq.( 3) and f -values calculated from observations during the summers 1980 to 2000, plotted together with average summer air temperature through years. The inferior and superior error bars on f -values are the first quartile and third quartile for
each f -value derived from Monte Carlo simulations, respectively.
Fig. 4. Sediment profiles. (A) BSi content (%); (B) δ 30 Si values in
BSi. The bars in 4 B represent the 95 % confidence interval.
4.4
Quantitatively reconstructing diatom production
The amount of DSi remaining in the water column is controlled by diatom production which occurs between the
middle of May and September in Bothnian Bay. Due to the
relatively long residence time of DSi compared to a short diatom growing season, Bothnian Bay can be assumed to be
a closed system, i.e. a steady state with respect to the physical input of nutrients and its biological removal, to which
Rayleigh distillation equations (Eq. 3) can be applied (Hoefs,
2009). The fraction of remaining DSi (f ), is a factor between
0 and 1. A number close to 1 indicates that very little DSi is
taken up by BSi in the water. The f -values can be calculated
using δ 30 Si values according to Eq. (3):
1−f α
δ 30 SiBSi + 1000
= 30
1−f
δ Siriver input + 1000
(3)
where α = 0.9989 and denotes the Si isotope fractionation
factor during diatom production (De La Rocha et al., 1997);
δ 30 SiBSi and δ 30 Siriver input represent δ 30 Si of BSi and of the
river input to Bothnian Bay, respectively. δ 30 SiBSi is the values shown in Fig. 4 and the δ 30 Siriver input value is taken to
be +1.1 ‰ which is derived from measurements of an unregulated boreal river, the Kalix River (Opfergelt et al., 2011).
This represents unperturbed river inflow.
Each point in Fig. 5 represents the calculated 5 yr average
f value using Eq. (3) (each 1 cm section of sediments corresponds to approximately five years). The error of the calculation due to the uncertainty of the measured δ 30 SiBSi values was examined by Monte Carlo analysis. 10 000 random
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δ 30 SiBSi values were generated assuming a normal distribution around a mean value and the standard deviation of that
mean. Here the average of measured δ 30 SiBSi values and its
standard deviation is used to produce 10 000 f -values. The
first quartile and third quartile for each f value were calculated and are shown in Fig. 5 as error bars, which means that
50 % of the f -values fall in this range. It should also be noted
that the calculated variations in f -values are independent of
the α-value, i.e. the absolute values of f will change with the
α-value, but the variation will be unchanged.
The air temperature data displayed in Fig. 5 are the observed average temperatures between May and September of
each year from 1824 to 2000 with a 5 yr moving average.
By plotting air temperature and the calculated fraction of the
remaining DSi (f ) from Si isotope measurements a clear pattern emerges where cold periods are associated with high f values, i.e. low diatom production, and warm periods show
relatively lower f -values, i.e. high diatom production.
The temperature history and correlation with the isotopically derived f -values stand out particularly for several
peaks. For example, the last period of cooling associated
with the Little Ice Age corresponds to the late 19th century (Bradley and Jonest, 1993). Another cold period in the
early 19th century is possibly caused by the Dalton Minimum, a period with low solar activity (Büntgen et al., 2006).
The largest amount of remaining DSi, f = 0.98, is observed
around 1910 which corresponds to a period with very cold
summers. More recently, another period with high f -values
is observed around 1983, which also was a period with cold
summers.
Wasmund et al. (1998) showed that diatom blooms in
the southern Baltic Sea were triggered by the reduction in
depth of the mixed layer (ML) improving light conditions
Biogeosciences, 8, 3491–3499, 2011
3496
X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
Fig. 6. Exponential regression fit of the fraction of remaining DSi
(f ) vs. the 5 yr moving average of air temperature for each depth
interval.
Fig. 7. Comparison of air temperature and water temperature between 1948 and 1963. (A) Plot of daily air temperature vs. daily
water temperature. (B) Plot of average summer air temperature vs.
average summer water temperature.
for phytoplankton growth. A possible explanation for the
Si isotope-based diatom-temperature relationship observed
in Bothnian Bay is therefore that temperature-induced variations of the ML depth control diatom production. This would
imply that air temperature regulates the water temperature
which is correlated with the ML depth. In fact, Fig. 7a
shows daily air temperature plotted vs. daily water temperature between 1948 and 1963 giving a correlation coefficient
of R 2 = 0.78. The correlation is improved if average summer
temperatures are used, R 2 = 0.93 (Fig. 7b). To further support our hypothesis that diatom growth and air temperature
are linked via the depth of the ML, an estimate of the correlation is calculated between stratification strength and surface
water temperature for each month from May to August (Appendix A, Fig. 8). Generally, the depth of the ML represented
by the pycnocline depth is negatively correlated with summer
surface temperature. The steepest slope appears in June and
July (Fig. 8b and 8c), indicating a strong correlation between
Biogeosciences, 8, 3491–3499, 2011
the pycnocline depth and the surface temperature. Figure 8a
shows a gentle slope possibly due to the ice cover in May
causing low diatom production. In August, the slope is less
steep than those in June and July. This indicates that summer stratification isolates the growing diatoms from the deep
nutrient reservoir, leading to phosphorus-limited diatom production (Humborg et al., 2003). Therefore, diatom production in June and July represents classical spring growth conditions and contributes most to the diatoms found preserved
in sediments. In summary, diatom production in Bothnian
Bay is controlled by depth of the ML, which is negatively
correlated with the air temperature.
Figure 5 shows that the fraction of the remaining DSi (f values) can be divided into two periods, before and after
1950. Before 1950, Bothnian Bay is likely to have been
less disturbed due to anthropogenic eutrophication and river
regulations (Humborg et al., 2006) and may be used here
to infer the general relationship between diatom production
and air temperature in Bothnian Bay. Earlier than 1950, the
δ 30 Siriver input value used for the calculations of the f -value
is +1.1 ‰ derived from measurements in the unperturbed
Kalix River (Engström et al., 2010). Although we cannot
state with absolute certainty that the Kalix River value can
be extended to represent all the unperturbed rivers draining
into Bothnian Bay during the studied period it is unlikely
that there is a significant difference in δ 30 Si values among
Nordic unperturbed rivers. This assumption can be made because the catchments have similar soil covers derived from
the last glacial retreat. Soils in the area are mainly tills of local origin reflecting the Fennoscandian bedrock. The mineral
composition of the bedrock in the entire region is dominated
by granite and gneiss and is homogenous. The soils show little disturbance due to human activities. Homogenous Si isotopic compositions have been observed in eight of the most
common species of vegetation in the boreal forest (Engström
et al., 2008).
However, if we assume that the data before the 1950s represent a relatively undisturbed situation both on land and in
the sea, a positive exponential relationship (Eq. (3), r 2 =
0.61) can be derived by the comparison of the fraction of
remaining DSi (f ) with the 5 yr moving average of air temperature (T ) from 1824 to 1955 (Fig. 6).
f = 5.1e−0.15T
(4)
Equation (4) is valid as long as temperature controls the ML
depth and no nutrient limitation occurs. We also have to
keep in mind that our calculated f -values are dependent on
the isotopic fractionation factor (α in Eq. 3), which might
vary between different areas. Although the exact α-value
for Bothnian Bay is lacking, the validation using the data
between 1980 and 2000 indicates that an α-value of 0.9989
is representative of the Si isotopic fractionation factor. For
other areas, calculations with Eq. (4) might change. In addition, this method is only valid as long as Rayleigh distillation
can be applied, i.e. the assumption of a closed system, in our
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X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
5
3497
Conclusions
Three conclusions can be drawn from this study in the subarctic area of the Baltic Sea, Bothnian Bay as follows:
1. stable Si isotope signature in sedimentary BSi can be
used to study variations in time of diatom production;
2. air and water temperatures can be used as a proxy for
the mixed layer depth (which controls diatom production) and as shown also correlates well with diatom production;
3. large scale anthropogenic activities such as changing the
hydrological regimes in rivers by damming are likely to
be imprinted on the sedimentary Si isotopic record.
Appendix A
Fig. 8. Plot of the estimated pycnocline depth vs. water surface
temperature for May (A), June (B), July (C), August (D). The correlation squared is also indicated in each panel.
Estimations of the relation between stratification
strength and surface temperature
A1
case, much longer DSi residence time than diatom growth
periods in Bothnian Bay.
After ca. 1950, the f -values exhibit a decrease (Fig. 5).
This is not consistent with calculated f -values using field
observations from 1980 and onwards. To get a good fit between observed and calculated data the δ 30 Siriver input value
used for calculations in Eq.(3) has to be shifted to +1.4 ‰.
The shift coincides in time with a period of large scale hydroelectric and flood control projects in the major rivers draining into Bothnian Bay (Humborg et al., 2002, 2006), leaving
only a few rivers unregulated today. There is no change in
bedrock and vegetation in the catchment which suggests that
the isotope composition of source Si is constant. The shift
in the fractionation factor is thus most likely to occur during the transport of DSi into the Bothnian Bay. Damming
of rivers increases diatom production in the reservoirs behind the dams (Humborg et al., 2006) and enriches the remaining DSi in the river water with the heavier isotopes, ultimately leading to increased δ 30 Siriver input values. To test
this hypothesis, additional analysis of samples from major
rivers draining into Bothnian Bay and behind dams as well
as diatoms should be made. However, assuming that the
δ 30 Siriver input value of +1.4 ‰ is correct, the remaining fraction of DSi (f ) can be calculated for the summers between
1980 and 2000 and can be compared with f -values calculated with Eq.(4) using the observed air temperatures for
the same period. A paired t-test shows that there is no significant difference between the observed and calculated f values (p ≤0.003).
www.biogeosciences.net/8/3491/2011/
Estimation of stratification strength from
observations
A convenient way of analysing continuous stratification is to
use the integrated properties profile potential energy and integrated buoyancy, i.e. steric height. This is analogous to the
applications in various systems of the world (e.g. Gustafsson,
1999; Björk et al., 2001; Andersson and Stigebrandt, 2005).
The profile potential energy is defined as:
Z
g 0
(A1)
P=
(ρ0 − ρ)zdz
ρ0 D
g
B=
ρ0
Z
0
(ρ0 − ρ)dz
(A2)
D
where g is the acceleration of gravity, ρ0 is the density at
depth D and ρ is the density profile.
The two layer equivalents for these are
P=
g(ρ0 − ρ1 ) 2
h
2ρ0
(A3)
B=
g(ρ0 − ρ1 )
h
ρ0
(A4)
where ρ1 is a surface layer density and h the depth of the
surface layer. From Eqs. (A3–A4) we can derive:
h=
2P
B
g(ρ0 − ρ1 ) B 2
=
ρ0
2P
(A5)
(A6)
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X. Sun et al.: Climate dependent diatom production is preserved in biogenic Si isotope signatures
Thus, by integrating Eqs. (A1–2) from measured profiles,
we can estimate an equivalent homogeneous surface layer using Eqs (A5–6). In the present application, the equivalent
depth h gives us a rough estimate on the depth of the surface
layer.
A2
Application to Bothnian Bay
606 profiles were extracted from the BED data base. A criterion for selection was that the vertical resolution was at least
5 m in the upper 20 m and at least 10 m down to the reference depth D, which was chosen to 50 m. Each profile was
interpolated to 1 m resolution using a cubic spline, with normal conditions at the surface and at a maximal depth of the
analysis. h was calculated for each profile using eq.s (A1–2
and 5).
Acknowledgements. We thank Hans Schöberg and Karin Wallner
from Laboratory for Isotope Geology, Swedish Museum of Natural
History for assisting with sample preparation. Funding from the
Swedish Research Council (VR. 2007-4763) is acknowledged. We
are also grateful to the constructive comments from anonymous
reviewers and George Swann.
Edited by: I. Semiletov
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Biogeosciences, 8, 3491–3499, 2011
Paper III
Silicon isotope enrichment in diatoms during nutrient-limited blooms
in a eutrophied river system
Xiaole Suna1, Per Anderssonb, Christoph Humborgc, Marianna Pastuszakd, Carl-Magnus Mörtha
a
Department of Geological Sciences, Stockholm University, 10691, Stockholm, Sweden
Laboratory for Isotope Geology, Swedish Museum of Natural History, 10405, Stockholm, Sweden
c
Department of Applied Environmental Science, Stockholm University, 10691, Stockholm, Sweden
d
Department of Fisheries Oceanography and Marine Ecology, Sea Fisheries Institute, 81332, Gdynia, Poland
b
Abstract
We examined the Si isotope fractionation in diatoms by following a massive nutrient
limited diatom bloom from a eutrophied natural system. We hypothesized that the Si
isotope fractionation should be larger in comparison to observations in less nutrient rich
environments. The Oder River, which is a eutrophied river draining the western half of
Poland and entering the southern Baltic Sea, shows that a diatom bloom may cause extreme
Si isotope fractionation. The rapid nutrient depletion and fast biogenic silica (BSi) increase
observed during the spring bloom suggests a Rayleigh behavior for a closed system for
dissolved Si (DSi) and BSi in the river at certain time scales. An enrichment factor (ε) of up
to -1.6‰ is found based on observations between April and June, 2004. A very high δ30Si
value of up to +3.05‰ is measured in diatoms. This is about 2 times higher than previously
recorded δ30Si in freshwater diatoms. The Rayleigh model used to predict the δ30Si values
of DSi suggests that the initial value before the start of the diatom bloom is close to +2‰.
This indicates that there is a biological control of the Si isotope compositions entering the
river, probably caused by Si isotope fractionation during uptake of Si in phytoliths. Clearly,
eutrophied rivers with enhanced diatom blooms deliver 30Si-enriched DSi and BSi to the
coastal ocean, which can be used to trace the biogeochemistry of DSi/BSi in estuaries.
Keywords: biogenic silica, Si isotopes, a eutrophied river system, nutrient limitation
1
Corresponding author: [email protected]
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
1. Introduction
The total algal growth in an aquatic ecosystem is
primarily regulated by the availability of N and P.
Availability of Si relative to N and P, i.e. Si:N and
Si:P ratios, can influence the composition of the
coastal phytoplankton community. A decreasing Si:N
ratio may exacerbate eutrophication by reducing the
potential for diatom growth, in favor of noxious
flagellates (Officer and Ryther 1980; Conley et al.
1993; Humborg et al. 1997). Non-diatom species are
known to be less available to higher trophic levels
and some non-diatom based food webs are
economically undesirable. Therefore, the proportion
of diatoms in the phytoplankton community is of
primary importance globally for many fisheries
(Struyf et al. 2009).
Silicon isotopes have been recognized as an
important proxy, not only for paleoclimatic and
paleoceanographic studies (e.g. De La Rocha et al.
1998; Brzezinski et al. 2002; Van Den Boorn et al.
2010), but also for tracing present environmental and
ecological changes (e.g. Cardinal et al. 2005;
Engström et al. 2010; Wille et al. 2010). The
principle behind these studies is that diatoms
remaining DSi (closed)
accumulated BSi (closed)
instant BSi (closed)
accmulated BSi (open)
Remaining DSi (open)
δ30Si (‰)
Dissolved Si (DSi) is an essential nutrient in aquatic
systems for the growth of siliceous organisms such as
diatoms, radiolarians, and sponges, which play an
important role in carbon sequestration (Goldman,
1988; Nelson et al. 1995). These organisms use Si to
produce structural components made of opal, i.e.
biogenic silica (BSi). The interactions between Si
and aquatic ecosystems have been of wide interest
since Schelske and Stoermer (1971) introduced the
hypothesis that DSi depletion caused by excessive
growth of diatoms was due to increased fluxes of
phosphorus in Lake Michigan, USA. The same
phenomenon has also been described for the other
Great Lakes in North America (Schelske et al. 1983;
Conley et al. 1993). Exhaustion of DSi has also been
reported in Chesapeake Bay (Fisher et al. 1988;
Conley and Malone 1992) and in the Mississippi
River plume, where the DSi concentration decreased
to < 0.93 µmol L-1 (Nelson and Dortch 1996).
Around 90% depletion of DSi has been reported in
High Nutrient Low Chlorophyll (HNLC) coastal
ocean water around Peru (Dugdale et al. 1995), while
approximately 77% utilization of DSi was recorded
in the Southern Ocean (Brzezinski et al. 2001;
Leynaert et al. 2001). This indicates that some
coastal areas are approaching Si limitation and
therefore understanding the interaction between
siliceous organisms and DSi is important.
1.0
0.8
0.6
0.4
0.2
0.0
f, fraction of remaining DSi
Fig. 1 Si isotope variations under closed- and opensystem Rayleigh conditions for diatom production.
During diatom production (fraction of remaining DSi
drops), the δ30Si of DSi and instant BSi increases
correspondingly. At the same time, the δ30Si of the
accumulated BSi in both systems also increases as DSi
concentration decreases, but only up to the initial δ30Si of
DSi, i.e. dashed line.
preferentially take up 28Si during cell wall formation,
resulting in a Si isotope fractionation factor of
approximate -1.1‰ (De La Rocha et al. 1997). This
selective utilization of DSi leads to lower Si isotope
values in diatom opal shells, leaving behind nonutilized DSi enriched in heavier Si isotopes with time
(higher Si isotope values). Thus, measuring Si
isotopes in DSi in aquatic systems and knowing the
range of isotope fractionation that diatom blooms
produce will allow calculating the magnitude of
diatom blooms. The magnitude of Si isotope
fractionation is apparently neither affected by CO2
levels (Milligan et al. 2004), nor temperature in three
species of diatoms during their growth (De La Rocha
et al., 1997), and it is similar for diatoms both in
freshwater and in marine systems (Alleman et al.
2005). Therefore, the change in Si isotopic
composition generally depends on the availability of
DSi, i.e. input and output of Si to the system, and
whether the system is closed or open. The Si isotope
composition in a closed and open system can be
calculated by assuming a Rayleigh distillation
behavior (Fig. 1). In this way, Si isotopes determined
in DSi may be used to estimate both the magnitude of
diatom blooms and other related fluxes, e.g. the
downward flux of carbon.
-1-
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
The Si isotope fractionation caused by diatom
production is also considered to be responsible for an
enrichment of the heavier Si isotopes in river waters
in comparison to primary minerals in igneous rocks
in river catchments (De La Rocha et al. 2000; Ding et
al. 2004; Georg et al. 2006a). Silicon taken up by
vegetation (vascular plants) in the watershed is
another possible explanation for the positive shift to
higher δ29Si and δ30Si values in river water (Opfergelt
et al. 2006). Furthermore, the production rate of
diatoms is rather high (Conley and Malone 1992),
which suggests that effects on the DSi concentration
and Si isotope values may occur on very short time
scales. A recent study with monthly data on Si
isotopes in DSi and BSi in the Congo River has
shown seasonal variations explained by diatom
production, but the seasonality effect from diatom
growth in a tropical river is limited (Hughes et al.
2011).
In the Baltic Sea, especially in the Gulf of Riga and
the Gulf of Finland, DSi-limited diatom production
has been reported (Conley et al. 2008; Danielsson et
al. 2008). In rivers such as the Oder River, draining
into the southwestern Baltic Sea, there is evidence
for DSi-limited diatom production. The BSi fraction
has in some cases been reported to comprise 99.8%
of the total Si pool during spring and summer, but in
winter, the fraction of BSi diminishes to only 0.8%
(Humborg et al. 2006; Pastuszak et al. 2008). Thus,
BSi from the Oder River can be considered as an
important source for Si in the Baltic Sea (Humborg et
al. 2006). Since this river system is eutrophic and has
high diatom production with frequently occurring Si
limitation, it is a potentially good site to evaluate
seasonal changes in Si isotopes in river water, and
may even show the maximum possible range of Si
fractionation in a natural aquatic system.
ml of water was filtered using polycarbonate 0.6
micron  47 mm filters and Sartorius polycarbonate
holders for vacuum sterile filtration. Filters with the
samples were dried at room temperature and stored in
microbiologically sterilized  47 mm Millipore®
petri dishes. BSi analyses from a portion of the filter
samples were performed according to the method by
DeMaster (1981) (Pastuszak et al. 2008), while the
remaining fraction was subjected to diatom analyses
performed at Stockholm University. The monthly
discharge in the Oder River during 2004 was
obtained from IMWM (2004) (Fig. 3).
Krajnik Dolny
Fig.2 Map of the lower Oder River indicating the sampling
site in Krajnik Dolny.
2.1 Nutrient and water flow data sources
The biweekly water sampling for nutrients (nitrogen,
phosphorus, and silicon) was performed at the
lowermost Oder River monitoring station (Krajnik
Dolny) (Fig. 2) by the Sea Fisheries Institute (SFI)
Branch in Świnoujście (Poland) during the period
January 2003 - June 2005 within the EU SIBER
project. Nutrient analyses were done by the SFI
according to standard methods as recommended by
Grasshoff (1983) and UNESCO (1983). The nutrient
data have been published in Pastuszak et al. (2008)
and Pastuszak and Witek (2009a,b). The biweekly
measurements of BSi at the same sampling site were
performed from April 2003 to June 2005. The BSi
samples were prepared in duplicates; 200 ml to 400
water discharge (m3/s)
2. Materials and methods
800
600
400
200
0
Sampling date
Fig.3 Water discharge in the Oder river during 2004
(source: IMWM, 2004)
2.2 Sample preparation
-2-
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
Diatom samples from April to September 2004 were
selected in this study due to availability of samples
and BSi contents although DSi samples were not
available since the DSi concentrations were very low
and therefore could not be measured. Each diatom
sample deposited on the filter was mixed with 4 mL
dichloromethane in a polypropylene centrifugation
tube. The filter dissolved rapidly and left diatoms and
insoluble organic and mineral materials residue.
After centrifugation, the top clear solution was
removed and new dichloromethane was added. This
was repeated several times to ensure that the entire
polycarbonate filter was dissolved and removed.
After this procedure the samples were dried at 40 °C
to evaporate all of the dichloromethane.
In order to remove the organic carbon the dried
diatom samples were treated with 15% H2O2 and
heated until all visible bubbles disappeared. This was
followed by treatment with SPT heavy liquid
(3Na2WO49WO3·H2O) to separate the diatom
fraction from the remaining inorganic material. The
SPT density was adjusted to a range between 1.9 g
cm-3 and 2.3 g cm-3 (Morley et al. 2004). Thereafter,
all diatom samples were carefully rinsed with
MilliQ-e water to remove all the SPT, and dried at 40
˚C for 24 h. The samples were examined under an
optical microscope to ensure a pure diatom fraction.
The dried diatoms were fused with NaOH and
dissolved in 0.12 mol L-1 HCl. Finally, a cationexchange column was used to remove the matrix of
the diatom solutions. The methods used in this step
was adopted and modified from the preparation
technique by Georg et al. (2006b).
2.3 Si isotope analyses
The Si isotope values of the diatom samples were
determined by MC-ICP-MS (Multi Collector
Inductively Coupled Plasma Mass Spectrometer)
equipped with a hexapole gas-collision cell, capable
of precisely and accurately measuring the δ29Si value
with a total Si consumption of less than 14 nmol (390
ng). The standard-sample bracketing technique was
employed and each sample was measured at least 5
times. The internal reproducibility was examined by
measuring IRMM-18 and Big Batch, was better than
0.2‰ (2). The detailed instrument settings and
measurement procedure is documented in Sun et al.
2010. The isotope composition is expressed as δ29Si
per mil (‰) relative to NBS 28 (Eq.1).
  29 Si 

  28 

Si

sample

 29 Si   29
 1 1000
 Si 
  28 

  Si  NBS 28



Because of the instrumental limitation of measuring
in low mass resolution (m/Δm ~ 450), the δ30Si was
not measured in this study. Therefore, the δ30Si was
calculated using the formula δ30Si = δ29Si×1.96 i.e.
assuming a mass-dependent fractionation of Si
isotopes (Reynolds et al., 2007). This relationship has
been demonstrated by others and it has been shown
that long-term reproducibility are within an error of
less than 0.15‰ for δ29Si and 0.24‰ for δ30Si
(Engström et al. 2006; Reynolds et al. 2007;
Chmeleff et al. 2008).
3. Results and discussion
3.1 Possible range of Si isotope values in diatoms
The average concentrations of DSi, BSi, DIN
(dissolved inorganic nitrogen) and DIP (dissolved
inorganic phosphate), calculated for the period April
to September 2004, are 37.2 µmol L-1, 58.5 µmol L-1,
47.9 µmol L-1, and 0.5 µmol L-1, respectively in the
river water (Table 1). The concentrations of DSi,
DIN and DIP display similar variations, i.e.
decreasing throughout the spring and summer and
increasing in the autumn (Fig. 4). In contrast to the
nutrients, BSi shows increasing concentration, which
indicates a period of rapid diatom growth in spring
and summer, and decreasing concentration in late
summer and autumn (Fig. 4). The Oder River shows
peak discharge during snowmelt and early spring
(Fig. 3) and during peak flow the fast flowing water
carries more suspended matter (soil particles)
compared to the slowly flowing water during the
other seasons, leading to the increased water turbidity
and reduced light condition, which is a crucial
parameter in diatom development. Despite abundant
nutrients at this time of the year, diatoms show low
production. After the fast transition from the
snowmelt to late spring, soil particles settle out of the
water column.
There are a number of shortcomings in using N:P:Si
ratios to evaluate the limiting role of nutrients in
phytoplankton growth. These ratios might be
inaccurate when reaching very low concentrations
close to the detection limit, and calculated ratios may
be affected by large errors. Nonetheless, nutrient
ratios have still been widely used (Howarth 1988;
Fisher et al. 1992). It is shown that diatoms in
freshwater having sufficient nutrients produce
biomass with a C:N:Si:P ratio of 106:16:84:1
(Conley et al. 1989; Goldman 1988) and that
variations in the stoichiometry of dissolved inorganic
nutrients can provide evidence for which nutrient is
most likely to limit the biomass production.
Eq.1
-3-
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
Table 1 Temporal nutrient and BSi concentrations, as well as Si isotopic compositions of BSi expressed by δxSi in the
Oder River. The uncertainty for δxSi is reported by 95% confidence interval (C.I.).
Sampling
DSi
BSi
DIN
DIP
δ29Si
δ30Si
95%
DIN:DIP
DSi:DIN DSi:DIP
a
a
a
a
date 2004
(µM)
(µM)
(µM)
(µM)
(‰)
(‰)b
C.I.
5 April
158.3
21.3
186.4
0.9
200.4
0.8
170.2
0.38
0.75
0.24
21 April
78.8
33.5
168.0
0.7
258.4
0.5
121.2
0.58
1.13
0.19
10 May
12.7
68.5
111.4
0.9
131.1
0.1
15.0
0.76
1.49
0.24
24 May
7.2
78.7
74.2
0.3
239.4
0.1
23.3
0.61
1.20
0.22
07 June
0.2
81.3
14.4
0.4
36.1
0.0
0.4
1.12
2.12
0.18
21 June
1.7
94.6
8.4
0.4
22.6
0.2
4.7
1.25
2.35
0.14
08 July
2.0
100.2
1.0
0.1
6.9
4.0
27.5
23 July
5.8
89.7
0.2
0.1
2.9
34.2
98.7
6 August
23.0
76.7
0.5
0.5
1.0
48.5
49.9
1.56
3.05
0.21
24 August
32.9
36.2
1.5
0.4
3.7
21.6
80.0
1.29
2.53
0.21
07 Sept.
46.3
14.4
0.6
0.2
2.8
73.4
202.2
0.60
1.18
0.24
21Sept.
73.9
10.4
7.8
0.6
12.1
9.5
114.9
0.56
1.09
0.13
Average
37.2
58.5
47.9
0.5
76.4
16.1
75.7
0.87
1.71
0.092
a
Measured by the SFI, Gdynia, Poland and published by Pastuszak et al. (2008) and Pastuszak and Witek (2009)
b
Calculated by δ30Si = δ29Si×1.96 (Reynolds et al., 2007)
- sample not available
Fig. 4 DIN, DIP, DSi and BSi concentrations in the Oder
River during April to September 2004. The inset shows a
magnified scale for the June and July DIN, DIP and DSi
data (source: Pastuszak et al., 2008; Pastuszak and Witek,
2009).
The DIP concentration decreased to about 0.5 µmol
L-1 during spring blooms (Fig. 4) and thus seems to
be the primary limiting nutrient for the diatom
growth. The DSi:DIP ratio reached the highest value,
with a maximum of about 170 on 5 April 2004
(Table 1). In June there is a sharp decrease in the DSi
concentrations from 1.7 to 0.2 µmol L-1 (Pastuszak et
al. 2008), which is below the level of approximately
2 M reported as a threshold concentration for
diatom growth (Egge and Aksnes 1992). The
DSi:DIP ratio decreased to very low values, 0.4 to
4.7, and thus the limiting nutrient shifted from DIP to
DSi. The δ30Si values in diatoms increased from
+0.75‰ in April to values more than +2.46‰ in June.
The period from July to September was characterized
by a sharp decrease in the DIN concentrations
(DIN=NO3-N+NO2-N+NH4-N), from 7.8 to 0.2 µmol
L-1 (Pastuszak and Witek 2009a; Pastuszak and
Witek 2009b) simultaneously with the diatom
biomass peak of 100.2 µmol L-1 in July, which is
followed by a decrease due to the exhausted nutrient
supply. The very low DIN concentration during
summer was a result of the low water flux, which
contributes to lower N emissions to the river basin.
Nitrogen mainly originates from diffuse sources,
with agricultural activity playing a key role
(Pastuszak et al. 2011). The DSi:DIN ratio reaches
48.5 whereas the DIN:DIP decreased to as low as 1.0
in August 2004 and the limiting nutrient was
thereafter shifted to nitrogen (Table 1).
The BSi shows positive δ29Si values throughout the
sampling period with an average of +0.87‰, and a
calculated δ30Si value of +1.7‰ (Fig. 5). This is
consistent with previously reported δ30Si values in
natural diatoms ranging from -0.3 to +2.6‰ (De La
Rocha et al., 1998; Varela et al., 2004). However, the
Si isotope compositions in the diatoms show large
variations during spring and summer. The δ30Si
values increase from a minimum value of +0.75‰ in
the spring to a maximum in excess of +3‰ at the
beginning of August, which is higher than previously
recorded values in freshwater diatoms, although there
is a small drop in May (Fig. 5). The δ30Si values start
to decrease in late August, and in September the δ30Si
values reach around +1.09‰ similar to that observed
in April, which is the starting value of the spring
bloom. These changing ratios during the growth
-4-
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
δ³°Si in BSi
BSi concentration (µM)
DSi concentration (µM)
200
3
150
2
100
1
50
0
DSi, BSi concentration (µM)
δ 30Si (‰)
4
(R2=0.66), which is a transformation of Eq.2 (Fig. 6).
In addition, a curve of the δ30Si in the remaining DSi
fraction is constructed using the Rayleigh equation
for the depletion of remaining DSi (Eq.4):
 30 SiBSi  1000
1 f 
 30
1 f
 Siriver input  1000
 30 SiBSi 
0
Eq.2
1  f 0.9984
 (2.0  1000)  1000
1 f
Eq.3
Apr. Apr. May May June June July July Aug. Aug. Sep. Sep.
05 21 10 24 07 21 08 23 06 24 07 21
 30 SiDSi  ( 30 Siriver input  1000)  f ( 1)  1000
Sampling date
season indicate that the Oder River is facing an
alternating nutrient limitation of the diatom
production, which is simultaneously leading to the
very high δ30Si values in the diatoms.
3.2 Si isotope fractionation during spring blooms
The relationship between the variations of the δ30Si
values and DSi and BSi concentrations between
April and June, 2004 (Fig. 5) suggests that the
diatom production dominates, as demonstrated by the
rapid and sharp decrease in the DSi concentration. It
is checked under a light microscope that BSi mainly
originates from the diatom bloom and not from
phytoliths or weathering of silicate minerals
delivered from the terrestrial environment. Fig. 6
shows that δ30Si values between April and June are in
agreement with the curve of accumulated BSi in a
closed system. Therefore, we hypothesize that the
river system is a closed Si reservoir at the timescale
of diatom blooms, i.e. the production rate of diatoms
is considerably faster than replenishment of new DSi
to the system, especially during the DIP and DSi
dramatically decreasing period. In contrast to the
observed concentrations, a completely open system
would show very small changes in the DSi
concentrations.
The Rayleigh model for a closed system is
introduced here to constrain the isotope variations.
Fig. 6 shows that the measured data from April to
June, 2004 are plotted together with the Rayleigh
model for the accumulated BSi (Eq. 2) (Hoefs 2009).
The reason why this is not extended to September
will be discussed later. Apparently, the measured
data do not fit for the Rayleigh model for an open
system. The fractionation factor (α30/28) of 0.9984 as
well as initial δ30Si of riverine input is derived from
the model for a closed system expressed by Eq.3
where α is the Si isotope fractionation factor during
diatom production; δ30SiBSi, δ30SiDSi and δ30Siriver input
represent δ30Si in BSi, remaining DSi and the river
input, respectively.
The Si isotope fractionation factor (α30/28) during
diatom production in the Oder River reached 0.9984
corresponding to an enrichment factor εDSi-BSi of 1.6‰. The error of the calculation due to the
uncertainty of the measured δ30SiBSi values was
examined by Monte Carlo analysis. 5000 random
δ30SiBSi values were generated assuming a normal
distribution around a mean value and the standard
deviation of that mean. Each of measured δ30SiBSi
values and its standard deviation is used to produce
5000 εDSi-BSi values, giving an average εDSi-BSi of -1.6
± 0.04‰ (95% confidence interval). However, the
fractionation factor is still lower than -1.1‰ reported
for diatom culturing laboratory experiments (De La
Rocha et al. 1997) and -1.1‰ ± 0.4‰ found in Lake
Tanganyika (Alleman et al. 2005).
measured
DSi (closed)
DSi (open)
BSi (open)
accumulated BSi (closed)
instant BSi (closed)
8
6
δ 30Si (‰)
Fig. 5 The Si isotopic composition of BSi reported as
δ30Si, and DSi and BSi concentrations in the Oder River
during April to September 2004. The δ30Si error bars
represent the 95% confidence interval. DSi and BSi values
from Pastuszak et al., 2008.
Eq.4
4
August
June
2
April
0
1.0
0.8
0.6
May
September
0.4
0.2
0.0
f, fraction of remaining DSi
Fig. 6 The δ30Si plotted vs. the fraction of remaining of
DSi. Si isotope change under closed- and open-system
Rayleigh model for diatom production with α= 0.9984.
Open symbols represent measured data from April to
September 2004.
-5-
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
Compared to marine diatoms, the εDSi-BSi of -1.6‰ is
also lower than the values observed in the Southern
Ocean of -0.6‰ (Demarest et al. 2009), but within
the range of -1.1‰ to -1.9‰ found in the Antarctic
circumpolar current (Varela et al. 2004). The rapid
depletion of DSi leads to a variation in the δ30Si
values in diatoms in excess of +2.30‰ (Fig.5), which
is around 1.5-2 times higher compared to previously
reported values in diatoms from freshwater systems.
These findings indicate that diatoms are able to
fractionate Si isotopes to very high values in a
freshwater system limited by nutrient supply. As
shown in Fig.6, if the DSi concentration decreases to
a very low level and with a simultaneous increase in
the BSi concentration, the Si isotope values in the
remaining DSi could in the Oder River be extremely
higher than any current recorded values. The
regression curve in Fig. 6 indicates that the BSi
accumulation in the Oder River follows a Rayleigh
behavior for a closed system, which means that Eq.2
could be used to reconstruct diatom production
represented by f values if α30/28 and the Si isotope
values in BSi and DSi are known. It should be noted
that clay formation can also lead to Si isotope
fractionation, and it remains unclear as to what
degree this process is controlling the δ30Si values in
the Oder River. This question calls for future studies,
with a high-resolution time series of water and
sediment samples.
The regression curve (Eq.3) also suggests that the
δ30Si value in DSi of river water (δ30Siriver input) prior
to the spring bloom is around +2‰, i.e. the isotope
starting value for diatom-driven Si isotope
fractionation. This value is in agreement with the
range of BSi in phytoliths documented to be in the
range between -1.4 and +2.8‰ (Douthitt 1982) and
also in the range of DSi in soil solution, -0.8 to
+1.7‰. One possible reason for this relatively high
value of +2‰ could be that the DSi contribution to
the Oder River preferentially comes from the upper
soil layers, where biogeochemical cycling of Si
results in more enriched, positive δ30Si values, than
in the lower soil layers indicating a biological control
of δ30Si values export to this aquatic system (Derry et
al. 2005; Ding et al. 2008). This might be a
fundamental process in a variety of geological
settings, supporting Si uptake by vegetation as
important for Si cycling.
In August 2004, the Si isotope composition in BSi
increased to a peak value of +3.05‰, while the DSi
concentration increased with decreasing BSi
concentration. This does not fit Rayleigh distillation
behavior in a closed system (Fig. 6). The high δ30Si
value is most likely affected by other processes. One
reason could be that the diatom dissolution rate is
enhanced, whereas the diatom production rate is low
due to the nutrient limitations, especially DIN. This
dissolution can lead to preferentially releasing 28Si,
which can exhibit an enrichment factor of -0.55‰,
enriching the heavier Si isotope in the remaining BSi,
if dissolution is higher than 20% of the total BSi
(Demarest et al., 2009). This can potentially shift the
δ30Si values in the remaining BSi towards even
higher values. The true mechanism of dissolutioninduced influence on high δ30Si value is not clear. It
is also possible that increased input of a source with a
high δ30Si value is coming from upstream eutrophied
areas in the watershed area, mostly consisting of
agricultural lands and forest (Humborg et al. 2006;
Humborg et al. 2008). This would lead to not only
enhanced primary production in the river, but also
increased uptake of DSi by terrestrial and aquatic
vegetation (Ding et al. 2005). This does not seem to
be a likely reason because of rather low river flow
and very limited diatom production. It is also
possible that Si is incorporated into secondary
minerals, e.g. kaolinites, and since 28Si is
preferentially bonded to secondary mineral phases
this can result in an increasing δ30Si value of the
dissolved phase (De La Rocha et al., 2000; Ziegler et
al., 2005). However, this process is kinetically very
slow and therefore not very likely. Another possible
reason is that the samples from August is sampled
near the surface of water column, so it is actually a
mixture of instant BSi and accumulated BSi, as
shown in Fig. 6 those data points stand in the middle
of instant and accumulated curves. DSi distribution
in river is not homogeneous, the sampled diatoms is
from a part of water with little DSi, and water
samples from another part of river with more DSi.
This means in Fig. 6, the data points in August are
not associated with real f values, i.e. not real Si
utilization during production of this sampled diatom.
Since eutrophied rivers, such as the Oder River, with
enhanced diatom production and dissolution are
important sources for DSi delivered to the coastal
ocean, the enrichment of 30Si in DSi makes it
possible to trace the fate and transport of the DSi and
BSi in the Baltic Sea and other similar environments.
4. Conclusion
This rapid nutrient depletion and fast diatom
production during the spring bloom allows a closed
system approximation for the DSi in the river. A
Rayleigh distillation model is used to calculate a Si
isotope fractionation factor of -1.6‰ which is higher
compared to other freshwater systems, -1.1‰ and is
-6-
Silicon isotope enrichment in diatoms during nutrient-limited blooms in a eutrophied river system
possibly the upper range of Si fractionation in natural
aquatic systems. The δ30Si values for the river
diatoms are also constructed and similar to the
observed data showing a seasonal pattern. The
calculated input of the δ30Si value before the onset of
the diatom bloom is about +2‰. This value coincides
with reported data for phytoliths and therefore
suggests that there is a biologic control of the Si
isotopic composition of the input to the river. A high
δ30Si value up to 3.05‰ in diatoms is observed in
August with simultaneous rapid and strong depletion
of the major nutrients (N, P and Si). Since eutrophied
rivers, such as the Oder River, involving many
complex processes, the signatures of Si isotopes are
helpful to unravel diatom-related processes in the
Baltic Sea and other similar environments.
Acknowledgements
We would like to thank Hans Schöberg from the
Laboratory for Isotope Geology at the Swedish
Museum of Natural History for assisting with sample
preparation. Thanks also to Eve Arnold for English
checking. We are grateful for the funding from the
Swedish Research Council (VR-2007-4763).
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-9-
Paper IV
Effect of diatom growth and dissolution on silicon isotope fractionation
in an estuarine system
Xiaole Suna1, Martin Olofssonb, Per Anderssonc,
Catherine Legrandb, Christoph Humborgd,e, Carl-Magnus Mörtha,e
a
Department of Geological Sciences, Stockholm University, SE-10691, Stockholm, Sweden
Centre for Ecology and Evolution in Microbial model Systems (EEMiS), School of Natural Sciences, Linnæus
University, SE-39182 Kalmar, Sweden.
c
Laboratory for Isotope Geology, Swedish Museum of Natural History, SE-10405, Stockholm, Sweden
d
Department of Applied Environmental Science, Stockholm University, SE-10691, Stockholm, Sweden
e
Baltic Nest Institute, Stockholm Resilience Centre, Stockholm University, SE-10691 Stockholm, Sweden
b
Abstract
Si isotopes provide a powerful tool to reveal past and present patterns in diatom production. Most studies
have focused on Si fractionation factors during diatom growth in open ocean systems and have found
lower Si isotope values in diatom shells (biogenic silica). Recent findings indicate that even the
fractionation of Si isotopes during the physicochemical dissolution of diatom shells in the opposite
direction produces higher δ30Si values in the remaining biogenic silica (BSi), allowing for the
interpretation of diatom production patterns over geological time scales. However, estuarine and coastal
primary production represents approximately 30-50% of global marine production, and there are hardly
any studies on Si isotope fractionation during either diatom growth or dissolution. In this study, Si isotope
fractionation during diatom growth and the dissolution of the frustule were measured. Two species of
diatoms from the Baltic Sea, one of the largest estuarine systems in the world, were selected for this study.
The results show that both species of diatoms during growth yields an identical Si isotope fractionation
factor of 0.99925 for 29Si and 0.9984 for 30Si. In contrast to findings from open ocean species, no Si
isotope fractionation during dissolution was observed even after 90% of the diatoms dissolved. Whether
there is isotope fractionation during dissolution or not will have profound implications for studies using Si
isotopes to interpret the Si cycle in marine and estuarine systems. We propose that the small size of the
diatoms living in estuarine systems with low salinity may explain the non-existence of Si isotope
fractionation during dissolution. Therefore, we suggest that Si isotopes are an instrumental variable
holding information about original environmental conditions of estuarine and even coastal systems.
Finally, we tested the Si isotope fractionation patterns gained from the lab experiments on a sediment core,
corroborating the observed dissolved Si (DSi) uptake rates in the above water column during diatom
growth.
1
Corresponding author [email protected]
1
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
1 Introduction
Land-ocean fluxes from rivers contribute 80% of the
dissolved Si (DSi) load to marine systems (Treguer et al.,
1995); therefore, estuaries are a crucial link between land
and marine systems. One of the most important
characteristics of estuaries is that those systems are
highly dynamic, resulting in gradients in water turnover
times, salinity, nutrients and primary production.
Meanwhile, eutrophication, a major worldwide problem
caused by human activities, is concentrated around
estuaries and shallow coastal waters (Conley et al., 1993;
Fisher et al., 1988; Schelske and Stoermer, 1971). The
high input of nutrients derived from land can result in
high biological activity in estuaries, leading to the
removal of DSi from water and a decrease in the DSi flux
to open oceans. Hence, estuarine systems in general can
be regarded as a sink for soluble silica during low water
turnover and high primary productivity (Treguer et al.,
1995). Due to the complexity of these systems, their
biogeochemical Si cycle is difficult to study and to model
conceptually. To better assess DSi land-ocean fluxes, it is
essential to identify and quantify various biological
processes in estuaries controlling DSi; indeed, the
production, retention and dissolution of diatoms are the
most important regulators for DSi in these processes. The
uncertainty in production, retention and dissolution is
mainly attributed to our poor understanding of the
distribution and biogeochemical fate of BSi and DSi and
also heavily influenced by eutrophication (Conley et al.,
2008; Conley et al., 1993) and river damming (Humborg
et al., 2000; Humborg et al., 1997; Humborg et al., 2006).
Thus, considerable work is required to assess the
estuarine cycling of Si, in particular the role of siliceous
algae, such as diatoms, which are a dominating algae
species and play an essential role in the biogeochemical
cycles of Si (Buesseler, 1998; Nelson et al., 1995).
The recognition that diatoms preferentially take up 28Si,
resulting in an isotope fractionation (δ-value) of
approximately -1.1‰ (De La Rocha et al., 1997), has
afforded the possibility to estimate past and current
diatom production in various systems because diatom
production quantitatively corresponds to δ30Si values of
BSi. Currently, an increasing number of studies on
diatoms and Si isotopes have appeared regarding open
ocean and terrestrial systems. In oceans, δ30Si values of
DSi range from +0.5‰ to +3.2‰, corresponding to δ30Si
values of BSi in diatoms ranging from -0.3‰ to +2.6‰
(Beucher et al., 2008; Cardinal et al., 2005; De La Rocha
et al., 2000; Reynolds et al., 2006; Varela et al., 2004);
these values are slightly higher than the BSi of phytoliths
produced in terrestrial systems, which range from -1.7‰
to +2.5‰ (Douthitt, 1982; Ziegler et al., 2005). In river
waters, the measured δ30Si values range from +0.4‰ to
+3.4‰ (De La Rocha et al., 2000; Ding et al., 2004; Ding
et al., 2011; Engström et al., 2010; Georg et al., 2007;
Hughes et al., 2011). DSi in a soil solution also shows
δ30Si values ranging from -0.8‰ to +1.7‰ (Ziegler et al.,
2005), and a similar range of values is found in
groundwater, -0.2‰ to +1.3‰ (Georg et al., 2009;
Opfergelt et al., 2011). Based on all these measured δ30Si
values, the Si isotope fractionation factor is indicated to
be -1.08‰ for freshwater (Alleman et al., 2005; Hughes
et al., 2011) and to be in the range of -0.6‰ to -2.2‰ for
in situ measurements of oceanic waters (Cardinal et al.,
2005; Cardinal et al., 2007; De La Rocha et al., 1997;
Varela et al., 2004). However, none of these studies has
been performed for estuarine and coastal areas, which are
responsible for 30-50% of global marine production.
Using Si isotopes to estimate the role of diatoms in
biogeochemical fluxes along the aquatic continuum from
land to the open ocean still requires further study,
especially in estuarine and coastal systems, and there
might be typical ranges of Si isotopes and fractionation
factors in the various realms. When interpreting Si
isotope signatures in diatoms and DSi, much of our
knowledge still comes from assumptions about Si isotope
fractionation factors (Reynolds et al., 2006; Sun et al.,
2011). Moreover, a recent study by Demarest et al. (2009)
shows a Si isotope fractionation value during dissolution
of -0.55‰, enriching residual BSi with heavier Si
isotopes if > 20% BSi is lost during dissolution. This
implies that if Si isotope fractionation is not taken into
account, diatom production may be significantly
overestimated, i.e. higher dissolution-induced δ30Si
values of BSi are not equivalent to actual production in
water.
Therefore, to study Si isotope fractionation factors and to
evaluate the potential of using Si isotopes as
environmental traces from land to oceans, two estuarine
species of diatoms from the Baltic Sea, one of the largest
estuarine systems in the world, were selected for this
study. We systematically analysed the dynamics of Si
isotope values during diatom growth and dissolution in a
laboratory-controlled system. The resulting fractionation
factors were tested by a sedimentary record of BSi of the
most northern basin of the Baltic Sea, Bothnian Bay.
2 Materials and methods
2.1 Diatom cultures and experimental setup
Two species of diatoms, the solitary Thalassiosira
baltica (TBTV1) and the chain-forming Skeletonema
marinoi (SMTV1), were isolated from the Gulf of
Finland by Dr. Anke Kremp (Finnish Environment
Institute-SYKE, Helsinki, Finland), and cultured at the
School of Natural Sciences, Linnæus University, Sweden.
The stock cultures were grown in f/2 medium (Guillard,
1975) prepared from 1 µm filtered and autoclaved Baltic
seawater (salinity 7 psu). Diatoms were grown at 8-10 °C,
under a photon flux of 200-250 µmol photons m-2 s-1 and
a 16/8h light/dark cycle. To measure Si isotope values
-1-
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
during diatom growth, cells were collected in the
exponential phase by filtering the stock culture through
5-10 µm nylon nets. Cells were rinsed four times and
resuspended with silicate-free f/2 medium (f/2-Si) to
minimise the input of DSi from the stock cultures. Cells
were inoculated in duplicate into 5 L PET bottles in f/2Si Baltic seawater medium at chlorophyll a (Chl a) at
concentrations of 10 µg Chl a L-1 for TBTV1 and 7 µg
Chl a L-1 for SMTV1. The initial Baltic seawater used for
the inoculation of cultures contained approximately 20
µmol L-1 DSi. To ensure a reliable diatom biomass and
remaining DSi for Si isotopic analyses, additional DSi
was added to the culture medium to reach final DSi
concentrations above 60 µmol L-1. The diatom cultures
were gently bubbled with air to mix the cells and to keep
them in suspension until DSi depletion, and incubated
under the same conditions as the stock culture. TBTV1
and SMTV1 cultures were monitored daily for 5 and 7
days for cell counts, Chl a, DSi concentrations and Si
isotopic analyses.
After DSi was depleted in the two cultures, the growth
experiment was terminated. To measure Si isotope values
during the dissolution of the diatom frustule, a fraction of
the SMTV1 culture (4 L) was distributed into two 2 L
polycarbonate bottles. The bottles were kept in the dark
at 22-24 °C for 44 days. The same set of samples used
during diatom growth was initially taken daily, but less
frequently after the fifth day until 90% of the diatom
frustules were dissolved. In addition, the O2
concentration was measured regularly during dissolution
to ensure that no anoxia occurred.
Duplicate aliquots (2 ml) from each replicate were fixed
with acidic Lugol’s solution, and cells were counted in an
inverted light microscope (Olympus BX50) using a
Palmer-Maloney counting chamber. Samples (2 x 5-15
ml) were filtered (Pall AE/E filters) and extracted with
96 % ethanol, overnight at room temperature for
chlorophyll a fluorometric analysis (Jespersen and
Christoffersen, 1987). To collect diatom cells, 150-200
ml of water samples was filtered daily (alternatively
twice a day if DSi decreased rapidly) through 0.2 µm
polyethersulphone membrane filters. One hundred
millilitres of the filtrate was immediately analysed for
DSi concentration according to Valderrama (1995). The
remaining filtrates (50 ml) were kept at 4 °C, and diatom
cells collected onto the filters were freeze-dried prior to
preparations for Si isotope analysis.
2.2 Sample preparation prior to Si isotope analysis
The method used for BSi digestion was modified from
Ragueneau et al. (2005). Polyethersulphone filters
containing 2-7 mg of freeze-dried diatoms were placed
into 15 ml polypropylene (PP) tubes. A 5 ml solution of
0.25 mol L-1 NaOH was added and the samples were
heated in a water bath for 1 hour at 90 °C. After cooling,
HCl was used to stop the digestion by neutralising pH.
Centrifugation was performed at 4200 rpm for 5 minutes.
The supernatant was transferred to another PP tube and
diluted to 10 ml solution for further treatment.
Dissolved Si in water samples was extracted by a
magnesium 2-step co-precipitation technique adopted
from Reynolds et al. (2006). Two hundred microlitres of
1 mol L-1 NaOH was added to 10 ml of the water samples.
Samples were shaken and left for 1 hour, and thereafter
centrifuged at 4200 rpm for 5 minutes. Another 100 µl of
1 mol L-1 NaOH was dropped into the separated
supernatant to precipitate more Mg(OH)2. After samples
were shaken and left for another hour, they were
centrifuged and the supernatant was removed. The
precipitates were then dissolved in 4 mol L-1 HCl. The
removed supernatant was analysed to make sure that the
Si removal was complete.
The purification of Si from the dissolved diatom solution
and water samples was performed using a BioRad
AG50W-X12 cation-exchange resin. Five hundred
microliters of the samples in 0.2 mol L-1 HCl was loaded
on the column with a 1 ml resin bed and eluted with 4 ml
Mille-Q water. Si having undergone no charges flowed
directly through the resin with Mille-Q water. Detailed
information regarding this step can be found in Sun et al.
(2011).
2.3 Si isotope analysis
The Si isotope compositions of both DSi and BSi were
analysed in the Laboratory for Isotope Geology at the
Swedish Museum of Natural History, Stockholm,
Sweden, using an IsoProbe MC-ICP-MS with a hexapole
collision cell. Generally, this instrument is capable of
precisely and accurately measuring δ29Si (2 S.D., better
than 0.2‰), although measuring δ30Si is not possible due
to the instrument’s low resolution (m/Δm~450). The
instrumental mass discrimination is corrected by
applying the standard-sample bracketing technique. The
sensitivity for 28Si is ~12 V/ 36 µmol L-1 Si (1 mg L-1),
with an uptake rate of ~70 µl min-1 via a Cetac® Aridus
desolvating nebuliser. The detailed measurement
procedure and the long-term reproducibility shown by
IRMM-18 and NBS 28 have been documented in Sun et
al. (2010).
The Si isotope ratio of each sample is calculated and
expressed in δ29Si values (‰) relative to the standard,
NBS 28, and according to Eq.1, δ30Si values are
calculated from the relationship δ30Si = δ29Si×1.96, which
assumes mass-dependent fractionation (Reynolds et al.,
2007).
  29 Si 

  28 

Si
sample


29
 Si   29
 1 1000
 Si 
  28 

  Si standard



2.4 Data reduction
-2-
(Eq.1)
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
ln(

 29 Sisubstrate  1000
)
 29 Si0  1000
ln f
1
(Eq.4)
Alternatively by rearranging Eq. 3, α can be solved as
shown in Eq. 5:
ln[1 

 29 Si product  1000
*(1  f )]
 29 Si0  1000
(Eq.5)
ln f
3 Results
3
400
2
200
1
Chl a (TBTV1)
δ³°Si of TBTV1
A-2
0
6
80
4
60
40
DSi conc.
20
2
δ³°Si of DSi
(TBTV1)
0
0
2
4
Days
6
8
4
B-1
3
400
2
200
1
Chl a (SMTV1)
δ³°Si of SMTV1
0
100
δ30Si, ‰
DSi concentration
(µmol L-1)
0
100
600
δ30Si, ‰
Chl a (µg L-1)
Table 1 and Fig. 1 and 2 summarise the BSi and DSi
concentrations and the Si isotope values of BSi and DSi
during the culture experiment. For TBTV1 and SMTV1
the Chl a concentrations increased exponentially, while
600
4
A-1
0
6
B-2
80
4
60
40
DSi conc.
20
2
δ³°Si of DSi
(SMTV1)
0
0
0
10
δ30Si, ‰
where δ29Sisubstrate, δ29Siproduce and δ29Si0 are Si isotope
values of the substrates, products and initial substrate,
respectively; α is the Si isotopic fractionation factor, and
f is the fraction of remaining substrates in water. By
rearranging Eq. 2, α can be solved as shown in Eq. 4:
2
4
Days
6
δ30Si, ‰
(Eq.3)
Fig.1 also shows that the two species of diatoms have
similar Si isotopic compositions and that the δ30Si values
increased during diatom growth both in cells and in the
remaining DSi. During diatom growth, the Si isotope
values in the two species varied by more than 0.7‰ for
δ29Si and 1.3‰ for δ 30Si, ranging from +0.6‰ up to +1.3‰
for δ29Si and from +1.2‰ up to +2.6‰ for δ30Si,
respectively. The two species fractionate Si isotopes
during their growth, producing almost identical 29α
fractionation factors of 0.9992 ± 0.0003 and 0.9993 ±
0.00038 (2σ), respectively (calculated by Eq. 5). A twosample t-test, assuming equal variance, shows that there
is no significant difference between TBTV1 and SMTV1
(p ≤ 0.061). The average value for 29α over all diatom
sample measurements was 0.99925 ± 0.00034 (2σ),
corresponding to a difference in Si isotopic compositions
between DSi and diatoms up to +0.75‰ for δ29Si. The
measured δ29Si values in seawater samples increased
from 1.35‰ to 2.58‰; δ30Si values are calculated by
multiplying δ29Si by 1.96 (Reynolds et al., 2007), giving
values ranging from +2.64‰ to +5.07‰. The calculated
average value of 30α is 0.9984 ± 0.0004 (2σ),
corresponding to a difference of +1.6‰ for δ30Si, i.e. the
seawater is continually enriched with the heavier Si
isotopes.
DSi concentration
(µmol L-1)
 29 Si product  1000 1  f 

 29 Si0  1000
1 f
the DSi concentrations decreased during the initial the
DSi concentrations decreased during the initial growth
period (Fig.1). During exponential growth, the maximum
growth rate was 0.42 day-1 for TBTV1 and 1.08 day-1 for
SMTV1. Chl a levels reached 200 µg L-1 for TBTV1 and
530 µg L-1 for SMTV1 (Figure 1A-1 and 1B-1). The DSi
concentrations decreased progressively during growth
days and the experiment was terminated when the DSi
concentrations fell below 0.4 µmol L-1 (Fig. 1A-2 and
1B-2).
Chl a (µg L-1)
A closed system usually means that a substrate is
continuously removed to form a product, but neither
undergoes any exchange with the outside system. During
the diatom experiment, we set up a closed system in 5 L
containers without replenishing DSi and BSi. To interpret
the Si isotope values of such a system, the Rayleigh
distillation equations are used. The relationship between
Si isotope values in products and substrates can be
expressed by the following equations:
 29 Sisubstrate  1000
(Eq.2)
 f ( 1)
 29 Si0  1000
0
8
Fig.1 Plot of Si isotope values following diatom growth curves as a function of time. A-1 and A-2 are plots of TBTV1 growth; B1 and B-2 are plots of SMTV1 growth. Error bars of Si isotope values are 2σ.
-3-
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
Table 1 Summary of Si concentrations and Si isotope values of DSi and BSi during SMTV1 and TBTV1 production and dissolution, as well as the fractionation factor (α)
calculated by Eq. 4 and 5. The 2σ of the calculated α is shown below the average values.
Chl a
(µg/L)
SMTV1 production
0
6.8
2
25.7
3
73.3
3.5
145.3
4
305.0
5
527.7
Average
2σ
Days
29
α by Eq.
5
30
α by Eq.
5
DSi conc.
(µM)
f
δ 29SiBSi
2σ
δ 30SiBSi
δ 29Si DSi
2SD
δ30SiDSi
64.8
57.8
42.1
34.3
0.4
0.4
1.00
0.89
0.65
0.53
0.01
0.01
+0.63
+0.69
+0.80
+0.86
+1.32
+1.29
0.25
0.15
0.14
0.24
0.09
0.10
+1.23
+1.36
+1.57
+1.70
+2.58
+2.59
+1.35
+1.52
+1.56
+1.81
N/A
N/A
0.15
0.13
0.13
0.10
N/A
N/A
+2.64
+2.98
+3.05
+3.55
N/A
N/A
N/A
0.99930
0.99932
0.99932
0.99942
0.99900
0.9993
0.00034
N/A
0.99864
0.99866
0.99868
0.99832
0.99801
0.99847
0.0004
0.8
0.5
1.3
8.8
20.9
36.0
47.6
48.5
58.6
1.00
0.96
0.99
0.91
0.91
0.85
0.81
0.54
0.49
+1.29
+1.30
+1.30
+1.29
+1.34
+1.27
+1.32
+1.37
+1.36
0.18
0.22
0.10
0.09
0.19
0.12
0.07
0.14
0.18
+2.54
+2.55
+2.54
+2.53
+2.63
+2.48
+2.58
+2.69
+2.66
N/A
N/A
N/A
N/A
+1.10
+1.14
+1.23
+1.35
+1.32
N/A
N/A
N/A
N/A
0.13
0.25
0.15
0.14
0.10
N/A
N/A
N/A
N/A
+2.36
+2.32
+2.40
+2.56
+2.46
N/A
N/A
N/A
N/A
0.99977
0.99977
0.99986
1.00000
0.99996
0.99990
0.00020
N/A
N/A
N/A
N/A
0.99949
0.99965
0.99973
1.00000
0.99992
0.99974
0.0004
81.8
75.2
67.2
58.7
54.3
46.8
42.3
26.4
19.6
2.9
1.00
0.92
0.82
0.72
0.66
0.57
0.52
0.32
0.24
0.04
+0.61
+0.64
+0.67
+0.65
+0.70
+0.84
+0.74
+0.79
+1.01
+1.24
0.07
0.19
0.11
0.14
0.13
0.13
0.17
0.19
0.12
0.24
+1.20
+1.25
+1.31
+1.27
+1.36
+1.65
+1.46
+1.54
+1.99
+2.43
+1.35
+1.43
+1.45
+1.55
+1.74
+1.66
+1.80
+2.58
+2.46
N/A
0.15
0.11
0.16
0.14
0.09
0.13
0.07
0.13
0.13
N/A
+2.64
+2.81
+2.84
+3.03
+3.40
+3.25
+3.52
+5.07
+4.83
N/A
N/A
0.99926
0.99925
0.99918
0.99920
0.99932
0.99914
0.99896
0.99925
0.99920
0.9992
0.0002
N/A
0.99855
0.99853
0.99838
0.99842
0.99867
0.99833
0.99797
0.99855
0.99826
0.99842
0.0004
SMTV1 dissolution
7
550.1
9
527.6
12
542.5
17
499.3
22
501.9
28
467.4
32
448.1
43
298.3
49
270.6
Average
2σ
TBTV1 production
0
11.9
1
15.3
2
26.2
3
42.2
3.5
43.1
4
65.0
4.5
69.2
5
109.1
5.5
118.4
6
133.1
Average
2σ
-4-
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
A
4
isotopes a powerful tracer for past diatom blooms in
estuarine systems.
3
400
2
200
4.1 Mechanism of Si isotope fractionation
δ30Si, ‰
Chl a (µg L-1)
600
The calculated Si isotope fractionation factors during the
Baltic Sea diatom’s growth, 29α and 30α, were calculated
to be 0.99925 ± 0.00034 (2σ) and 0.9984 ± 0.00035 (2σ),
respectively, which are in the same range as values
previously reported for other cultured marine diatoms:
Thalassiosira weissflogii and Skeletonema costatum (De
La Rocha et al., 1997). Our values are also consistent
with the value of 0.99944 for 29α found in Lake
Tanganyika (Alleman et al., 2005) and 0.9981-0.9989 for
30
α reported in Antarctic surface water (Varela et al.,
2004).
1
Chl a (SMTV1)
δ³°Si of SMTV1
0
100
0
6
4
50
δ30Si, ‰
DSi concentration
(µmol L-1)
B
2
DSi conc.
δ³°Si of DSi (SMTV1)
0
8
18
28
Days
38
0
48
Fig. 2 Plot of Si isotope values following SMTV1 dissolution
curve as a function of time. A and B are the variation of Chl a
and the DSi concentration, respectively. Error bars of Si
isotope values are 2σ.
During the dissolution experiment, the Chl a
concentrations of SMTV1 gradually decreased to 267 µg
L-1 after the growth period (Fig. 2A), while the DSi
concentration increased steadily for 20 days to 50 µmol
L-1, and remained constant (50-60 µmol L-1) for another
20 days (Fig. 2B), indicating the dissolution of diatom
frustules. During the first 25 days in the dark, the
siliceous frustules were linearly dissolved (r2=0.9) and 75%
of the silicate was released back to the medium.
Thereafter, during the last 10 days, the dissolution
released 90% BSi back in to the medium. The Si isotope
values of SMTV1 and DSi appeared to remain constant
during dissolution. Calculation using Eq. 5 gives the Si
isotope fractionation factor during dissolution for 29Si
and 30Si as 0.9999 ±0.0002 and 0.99974 ± 0.0004 (2σ).
4 Discussion
Our findings suggest that i) diatoms from estuarine
systems show identical Si isotope fractionation factors of
0.99925 for 29Si and 0.9984 for 30Si, which are very
similar to those of oceanic species; ii) the fractionation
factors during diatom production closely follow the
theoretical values calculated by the Rayleigh distillation
approach; iii) when applying these fractionation factors
to a sediment record, we can reproduce the observed
water column data on the fraction of remaining DSi
during the growth period; iv) in contrast to previous
studies on diatom dissolution, the decrease in BSi in the
lab experiments is not associated with Si isotope
fractionation (at least up to 90% dissolution), making Si
The exact mechanism of Si isotope fractionation during
diatom growth is difficult to explain. A plot of δ30Si
values as a function of f values (Fig. 3) shows a very
good agreement between the experimental results and the
Rayleigh model expressed by Eq. 4 and 5. This behaviour
is typical for a kinetic isotope effect in a closed system,
i.e., the Si isotope values vary with f values such that the
remaining DSi is enriched in the heavier Si isotopes. The
fact that diatoms are an effective ‘trap’ for keeping Si in
the water column without exchange with the remaining
DSi does not indicate an isotope equilibrium effect in this
study.
BSi dissolution commences when dissociated hydroxyls
from water weaken and eventually break the adjacent SiO bond (Dove and Crerar, 1990). The open linkage binds
with hydroxyl to form Si-OH. This process is repeated
until all Si-O bonds are broken, and the surface then
assumes the form Si(OH)4. This silica-water interaction
tends to be slow and is influenced by many different
factors, but it is commonly believed that temperature, pH,
the presence of solution species, particle size, etc., can
have a great impact on the dissolution of BSi (Fournier et
al., 1982; Iler, 1979; Loucaides et al., 2008). The
experiments conducted in our study and those in the
study by Demarest et al. (2009) were laboratory
controlled, and the variations in temperature are similar;
thus, they are unlikely to be the reason for the difference
in the observed Si isotope fractionation between these
two studies. A possible reason could be that cations
present in the solution are attracted to the negatively
charged surface sites of BSi by electrostatic forces. In
Baltic Sea water with much lower salinity (7 PSU
measured in this experiment) than that in the Southern
Ocean, the surface of BSi is less ‘saturated’ than BSi in
ocean water. This allows hydroxyls to easily approach
the xSi-O bonds on the surface and break them without
biasing Si isotopes. Another possible explanation is that
the effective surface area increases with a decrease in
particle size (e.g., Knauss and Wolery, 1988). Because
-5-
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
8
8
B
A
δ³°Si of TBTV1
δ³°Si of DSi
DSi (Rayleigh)
BSi (Rayleigh)
δ30Si, ‰
δ 30Si, ‰
6
4
δ³°Si of SMTV1
δ³°Si of DSi
DSi (Rayleigh)
BSi (Rayleigh)
6
4
2
2
0
0
1
0.8
1
0.6
0.4
0.2
0
f, fraction of remaining DSi in water
0.8
0.6
0.4
0.2
0
f, fraction of remaining DSi in water
Fig. 3 Plot of Si isotope values of diatoms and DSi following diatom growth as a function of f values (fraction of remaining DSi in
water). Lines are expectations for Rayleigh fractionation in a closed system. Error bars of Si isotope values are 2σ.
the diatom size in low-salinity water with a shallower
mixed layer depth is normally smaller than that of
diatoms growing in ocean water (Litchman et al., 2009),
BSi dissolution in the Baltic Sea could be faster and more
effective, which would make the different Si isotopes
subject to simultaneous dissolution.
Although we try to explain the lack of isotope
fractionation during BSi dissolution, we cannot fully rule
out possible Si isotope fractionation during BSi
dissolution. Perhaps this fractionation is too small to be
detected by the MC-ICP-MS employed in this study. One
limitation is that only δ29Si can be measured; the mass
difference between 28Si and 29Si is harder to detect than
the difference between 28Si and 30Si considering the
current analytical error. However, this is unlikely in this
case because the deviation between the calculated δ30Si
values of remaining BSi after 90% dissolution is still
smaller than the documented analytical errors for δ30Si,
indicating an undetectable Si isotope fractionation
between the δ30Si values of remaining BSi.
4.2 Applications of the Si isotope fractionation factor
for estuarine and even coastal studies. In our study, we
tested this notion by applying our measured Si isotope
fractionation factor to our measured δ30Si values
preserved in BSi derived from Bothnian Bay, the most
northern part of the Baltic Sea.
The Baltic Sea, located in Northern Europe, has a surface
area of 377 000 km2 and a water volume of 21 000 km3.
As one of the largest estuarine system in the world, it
consists of several basins, from north to south: Bothnian
Bay, Bothnian Sea, Gulf of Finland, Baltic Proper, Gulf
of Riga, Danish Sounds and Kattegat, and finally
connects to the North Sea. The shallow sills between the
North Sea and the Baltic Sea restrict the water exchange
leading to a long water residence time of approximately
20 years (Papush et al., 2009). The residence time of DSi
is 11.2 years for the whole Baltic Sea (Wulff and
Stigebrandt, 1989), which makes the Baltic Sea resemble
a closed system comparable to the experimental set up of
our lab experiments.
There is a biological control on Si isotope signatures in
the Baltic Sea water due to the large proportion of
diatoms in phytoplankton (Klais et al., 2011). Given an
29
α of 0.99925 and the calculated 30α of 0.9984 for
diatom production, it is possible to address the potential
range of variations in the Si isotope values in the Baltic
Sea. Fig. 3 shows the behaviour of diatom production
following the Rayleigh model and illustrates the possible
Si isotope values in remaining DSi and diatoms
determined by the experiments performed in this study.
Using a δ30Si value of +1.4‰ in rivers draining into
Bothnian Bay (Sun et al., 2011) as δ30Si0, an 30α of 0.9984
and approximately 30% depletion of DSi (Danielsson et
al., 2008), i.e., f = 0.7, +2.0‰ of δ30Si of remaining DSi
is calculated by Eq. 2. Likewise, +0.15‰ of δ30Siproduct in
accumulated BSi is calculated by Eq.3. Monte Carlo
analysis is adopted to estimate the uncertainty of this
It is well known and has been shown that Si isotopes
have the potential to reveal both past and present patterns
of DSi utilisation mainly by diatoms, which can
contribute to both modern and paleoceanographic studies.
However, Deramest et al. (2009) suggest that Si isotope
fractionation during diatom dissolution may complicate
this Si potential of revealing DSi utilisations and cause
discrepancies between the measured δ30Si of BSi and the
real oceanic conditions inhabited by diatoms. In contrast,
our observation of unfractionated diatom dissolution
implies that diatoms can still possibly record Si isotope
information generated under original environmental
conditions. We therefore suggest that δ30Si isotopes of
diatoms smaller than oceanic species and alternatively
growing in low-salinity can be reliably used as a proxy
-6-
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
Table 2 Potential Si isotope values in the major Baltic Sea basins calculated by measured α of 0.9984 ± 0.00035 (2σ)
Average depletion
f
δ30Si of accumulated
δ30Si of remaining
Basins
δ30Si of input DSi
1
of DSi (%)
values
diatom (‰) ± 2σ
DSi (‰) ±2σ
Bothnian Bay
30%
0.70
+1.4‰2
+0.15 ±0.30
+1.95 ±0.13
Bothnian Sea
40%
0.60
+1.4‰ (assumed)
+0.21 ±0.28
+2.20 ±0.18
Gulf of Finland
62%
0.38
+1.6‰ (assumed)
+0.70 ±0.21
+3.10 ±0.35
Gulf of Riga
73%
0.27
+1.6‰ (assumed)
+0.85 ±0.17
+3.64 ±0.40
Baltic Proper
50%
0.50
+1.6‰ (measured)
+0.53 ±0.25
+2.67 ±0.25
Kattegat
67%
0.33
+1.6‰ (assumed)
+0.75 ±0.20
+3.32 ±0.38
1
2
Calculated by interannual variations in mean DSi concentrations between 1970 and 2000 (Danielsson et al. 2008)
Sun et al. (2011)
calculation derived from the measured Si isotope Riga, indicating ongoing DSi depletion (Olli et al., 2008)
fractionation factor. Five thousand 30α are randomly mainly caused by eutrophication in its catchments. This
generated assuming a normal distribution based on the is also a known problem occurring throughout the entire
average 30α of 0.9984 and its 2σ of 0.00035, giving an Baltic Sea, especially in the southern part over the past
average δ30Si value of accumulated BSi, +0.15‰ ± 0.30‰ several decades. However, our knowledge about DSi and
(2σ). This is comparable with our previously reported BSi in the Baltic Sea and in its catchments prior to the
δ30Si value of +0.40‰ ± 0.20‰ (2σ) in sedimentary BSi 1960s is very limited due to the lack of historical
(Sun et al. 2011) without statistically significant observations. Therefore, Si isotope values in sedimentary
differences between these values. It also implies that BSi could be a complementary tool for reconstructing
30
there is no measurable dissolution-induced Si isotope past Si dynamics by transforming the observed δ Si
fractionation, i.e., BSi preserved in sediments still values of sedimentary BSi to DSi utilisation in water.
preserves information concerning the initial water 5 Conclusion
conditions.
This study shows that diatom production yields an
We further suggest possible Si isotope patterns in the identical non-species-dependent Si isotope fractionation
various basins of the Baltic Sea using the measured Si factor of 0.99925 for 29Si and 0.9984 for 30Si in an
isotope fractionation factor to be tested in future studies. estuarine system. In contrast to the results of another
The Baltic Sea shows a range of DSi uptake rates study, the Si isotope fractionation during dissolution of
expressed as the relative amount of DSi depleted (f
diatom frustules is not observed, possibly due to the
values) after the growth period that vary between years lower salinity of the surrounding water and the smaller
(Wasmund et al. 1997) but also shows a typical range
size of the diatoms. This indicates that δ30Si signatures
between the various marine basins. Table 2 shows an preserved in BSi retain direct information about original
estimate of Si isotope values of diatoms and DSi in
environmental conditions. These findings may contribute
basins of the Baltic Sea based on measurements and other to a better understanding of the biogeochemical cycle of
estimates, not taking into account dissolution effects. The Si in estuarine and coastal environments and hold
f values of different basins are derived from average DSi implications for marine studies using Si isotopes as a
concentrations measured before and after diatom blooms
proxy to estimate the historical development and
between 1970 and 2000 (Danielsson et al., 2008). The magnitude of recent and past diatom blooms.
δ30Si0 value of Baltic Proper before diatom blooms was
measured to be +1.6‰ in this study, resulting in δ30Si
values of +0.53‰ ± 0.25‰ (2σ) and +2.67‰ ± 0.25‰
Acknowledgements
(2σ) for δ30Si in accumulated diatoms and remaining DSi,
respectively. The values 2σ is derived from 5000 values We are grateful to Mireia Bertos Fortis for assistance in
of δ30Si calculated by randomly generating 5000 30α as the laboratory. Funding for diatom cultivation was
mentioned above. In particular, the δ30Si values of DSi in provided by the Swedish Research Council Formas
the Gulf of Finland and Gulf of Riga possibly increase to through the programme ECOCHANGE “Ecosystem
approximately +3.1‰ ± 0.35‰ (2σ) and +3.64‰ ± 0.40‰ dynamics in the Baltic Sea in a changing climate
(2σ), respectively. Generally, the Baltic Sea seems to perspective”. This is a contribution to the LnU Centre for
exhibit increasing δ30Si values, both in diatoms and in Ecology and Evolution using Microbial Models Systems
DSi from north to south caused by increased DSi (EEMiS). We are also grateful for the funding from the
consumption during diatom production over the past Swedish Research Council for sample preparation and Si
several decades. The highest expected Si isotope values isotope analysis (VR-2007-4763).
of +0.85‰ ± 0.17‰ of BSi are obtained for the Gulf of
-7-
Effect of diatom growth and dissolution on silicon isotope fractionation in an estuarine system
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