Marine Micropaleontology
Diatoms from the Pearl River estuary, China and their suitability as water salinity
indicators for coastal environments
Yongqiang Zong a,*, Fengling Yu b, Jeremy M. Lloyd b, Guangqing Huang c, Wyss W.-S. Yim a
a
Department of Earth Sciences, The University of Hong Kong, Hong Kong, SAR China
b
Department of Geography, University of Durham, Durham, U.K.
c
Guangzhou Institute of Geography, Guangzhou, China
Abstract
We collected modern diatom samples from 77 sites across the Pearl River estuary to
analyze the relationship between diatom assemblages and the environmental parameters
including water salinity, water depth and particle size of surface sediments. Results show that
marine diatoms appear mostly in the high salinity environment around Hong Kong and the outer
part of the estuary. Brackish water diatoms are found in high abundance in the central part of the
estuary. Both marine and brackish water diatoms are predominantly planktonic taxa. Freshwater
diatoms are recorded in low salinity environment, with planktonic taxa in the deep tidal channel
and benthic species in the shallow deltaic distributaries. Statistical tests indicate a strong
correlation between the diatom assemblages and the water salinity, and thus a diatom-based
salinity transfer function was developed. The relationship between observed and diatompredicted water salinity suggested accurate and precise reconstruction of palaeo-salinity are
possible.
Keywords: diatoms, salinity, transfer function, estuary, Pearl River
* Corresponding author: Tel.: +852 22194815; Fax: +852 25176912. E-mail address:
yqzong@hkucc.hku.hk (Y Zong)
1. Introduction
Variability in freshwater discharge from a river into an estuary is an important factor that
causes changes in the sedimentary regime, salinity and bio-productivity within an estuarine
environment (Chen et al., 2007; Zhang et al., 2008; Zong et al., 2009). Since freshwater
discharge is closely related to precipitation patterns of a given river catchment, the changing
freshwater flux can be used to reflect changes in climate conditions. In the case of Asian
Monsoon, long-term changes in summer precipitation result in fluctuations in freshwater flux
entering an estuary (Cliff and Plumb, 2008). The stronger the summer monsoon was, the greater
amount of freshwater was discharged into an estuary, and vice versa (Zong et al., 2006).
Accurate knowledge of monsoon climate change and more importantly the associated variability
in freshwater flux in the recent past is critical for improving our understanding of what may
happen in the near future to the coastal environment of East Asia and beyond. Furthermore,
water supply is becoming an important issue for human society in the 21st century (Zhang et al.,
2008).
Most of recent studies regarding Holocene Asian monsoon variability are based on loess
sequences for summer monsoon strength (e.g. An et al., 2004), marine deposits for SST (e.g.
Tambuini et al., 2003; Wang et al., 2005), lake sequences for local productivity and vegetation
history (e.g. Mingram et al., 2004; Shen et al., 2006), and stalagmites (e.g. Wang et al., 2008).
However, few attempts have been made to reconstruct the history of monsoonal driven
freshwater flux. A pilot study (Zong et al., 2006) has explored the possibility of using
microfossils (diatom) as an indicator of freshwater flux, and examined the qualitative
relationship between diatom assemblages and water salinity. In order to improve this method, in
this paper we document the distributional patterns of modern diatoms across the Pearl River
estuary based on an enriched data set and analyze the statistical relationship between diatom
assemblages and a number of environmental variables including water salinity. We develop and
test the applicability of the first diatom-based salinity transfer functions to aid reconstruction of
freshwater flux history from estuarine sediments of the Pearl River estuary and beyond.
2. Study area
The Pearl River drains its latitude-orientated drainage catchment which lies between 26º
N and 22º N (Figure 1a), a transitional region between tropic and temperate zones under the
influence of humid summer monsoon from the south and dry winter monsoon from the north.
Three main rivers (the East River, the North River and the West River) drain into the drowned
coastal basin and create two deltaic complexes which are separated by the estuary of about 1740
km2 in area (Figure 1). The estuary is protected from storm waves by a cluster of offshore rocky
islands. Wave energy within the estuary is low, except during the passage of a typhoon. The
main tidal channel runs directly south between Hong Kong and Macau into the South China Sea.
A secondary tidal channel runs southeast, through Hong Kong, and turns south into the sea.
At present the annual average precipitation in the catchment area is between 1600 and
2000 mm/yr, but over 80% of rainfall occurs during spring and summer, indicative of a warm
humid summer and a dry cool winter. Such seasonal differences in precipitation result in
freshwater discharges as low as 2000 m3/s in a dry winter and as high as 46,300 m3/s in a 100year flood event (Huang et al., 2004). On average, the Pearl River discharges 302,000 x 10 6 m3
of water and 83.4 x 106 tons of suspended sediment a year (Zong et al., 2009). However, recent
construction of reservoirs in the catchment area has reduced the sediment load to only 54.0 x 10 6
tons a year since the mid-1990s (Zhang et al., 2008).
Spring tidal range within the estuary ranges from 0.86 m at the mouth area to 1.57 m at
the head of the estuary, but increase to 2.29 m and 3.36 m respectively during highest
astronomical tides (Huang et al., 2004). Despite the small tidal range, the average volume of
flood tides is as high as 73,500 m3/s, which is nearly 13 times the average freshwater discharge.
Due to the easterly offshore currents, the freshwater runoff tends to concentrate on the western
side of the estuary and flows southwestwards when it reaches offshore Macau. Thus, on the
eastern side of the estuary, water salinity is higher than that on the western side. A clear salinity
gradient is observed, with the highest salinity in the waters east of Hong Kong, decreasing
through waters west of Hong Kong, eastern side and then to western side of the estuary. The
lowest salinity is recorded from sites within the deltaic distributaries.
3. Methods
3.1.Modern sediment samples and environmental variables
Surface sediment samples were collected from 77 locations across the estuary (Figure 1b)
for the analysis of particle size distribution and diatom assemblages. At each sampling location, a
grab sampler was used to capture the top 10 cm surface sediment. Water depth and salinity were
measured when sediment samples were taken. The water salinity measurements were compared
with the average values produced by the Guangzhou Institute of Geography (for the estuarine
and deltaic parts) and the Environmental Protection Department of the Hong Kong government
for the Hong Kong waters, who have repeatedly measured water salinity across the estuary
(Figure 1c, d).
The samples for diatom analysis are prepared following standard procedures, by mixing
each sediment sample to average the seasonal variability (e.g. Horton et al., 2007; Zong, 1997;
Zong and Horton, 1999; Zong et al., 2003). Diatom valves and frustules were identified under a
microscope with 1000 magnification to species level according to key references (e.g. van der
Werff and Huls, 1958-1966). The diatom counts were displayed using the TILIA package
(Grimm, 2004), and expressed as percentage of total diatom valves. It is noted that at each
sampling site, the diatom assemblages is likely to be a mixture of locally produced taxa and
transported taxa (e.g. Horton et al., 2007; Zong, 1997; Zong and Horton, 1998). Such a mixture
of allochthonous and autochthonous diatom valves would also occur in sediments that deposited
in the past (Zong, 1998; Sawai, 2001). Thus, we did not attempt to separate the allochthonous
component from the diatom assemblages.
3.2.Statistical analyses
To display the spatial distribution of diatoms across the estuary, we used the kriging
function in ArcGIS to perform spatial interpolation based on the diatom data. The outputs show
relative abundance of key diatom taxa. To detect, describe and classify the distribution of
diatoms across the estuary, we used unconstrained cluster analysis based on unweighted
Euclidean distance (CONISS) within the TILIA package (Grimm, 2004) and detrended
correspondence analysis (DCA) (Hill and Gauch, 1980). These two methods are complementary
(Birks, 1992) and help group similar sediment samples together and set apart dissimilar samples.
The cluster analysis help identify more or less homogeneous groups of sediment samples that
have similar diatom assemblages, but DCA provides further information about the pattern of
variation within and between groups (ter Braak and Smilauer, 1997-2003).
To test the relative strength of relationships between diatoms and environmental variables
(salinity, water depth and sand content), we applied canonical correspondence analysis (CCA)
with Monte Carol test of 199 random permutations (ter Braak and Smilauer, 1997-2003). To
develop a diatom-based water-salinity transfer function, we used unimodal-based regressions
(Birks, 1995; Gasse et al., 1997; Juggins, 1992) known as weighted averaging with partial least
squares (WA-PLS) (ter Braak and Juggins, 1993) using the calibration program C2 (Juggins,
2004). This analysis used only species with an abundance of over 2%. The diatom data set was
not transformed or standardized (Horton et al., 2007).
4. Spatial distribution of modern diatoms
From the surface sediment samples (Figure 1), we have found 73 diatom taxa in total,
with 57 species having an abundance over 2% and with 17 taxa in abundance of 10% or higher.
The frequencies of these 17 diatom species are presented in Figure 2, and the 77 sediment
samples are divided into 9 groups based on the cluster analysis (CONISS). Among these
dominant taxa, there are two groups of freshwater diatoms that appear abundant in the sediment
samples. Freshwater benthic diatoms, including Cymbella affinis, Fallacia subhamulata,
Gomphonema parvulum and Synedra ulna, dominate group B of sediment samples (Figure 2a)
which are from the two distributaries of the delta plain (also see Figure 3a). Freshwater
planktonic diatoms, such as Cyclotella meneghiniana, Cyc. radiosa and Aulacoseira granulata,
however, features strongly in groups A and C (Figure 2a) or the inner part of the estuary and the
main tidal channel reaching Guangzhou (Figure 3b). It is noted that Aulacoseira granulate is also
abundant in the central part of the estuary (group E samples). This species seems to be able to
follow the outflow of freshwater further into the brackish water area. Thus, the spatial
distribution patterns of the freshwater diatoms suggest that they are strongly associated with
water salinity. However, to some extent, water depth is also determining factor in the
distributional patterns of benthic and planktonic taxa. This illustrated by the average water depth
for each zone (Table 1).
The most abundant brackish water diatoms are Coscinodiscus blandus, Cos. divisus,
Cyclotella striata and Cyc. stylorum. These taxa are all planktonic types, and they concentrate in
groups D, E, F and G (Figure 2a), or the central and outer parts of the estuary (Figure 3c, d). The
dominant marine species are also planktonic types, with Chaetoceros radians, Paralia sulcata,
Skeletonema costatum and Thalassionema nitzschioides concentrating in groups F, G, H and I
(Figure 2a). In particular, Chaetoceros radians and Skeletonema costatum appear most abundant
in the area of southern Hong Kong (Figure 3e). Thalassionema nitzschioides is most abundant in
the four sediment samples southeast of Hong Kong, and its abundance decreases towards the
inner estuary (Figure 3f).
The DCA results show clear divisions of modern diatom assemblages. In the DCA bi-plot
(Figure 2b), sediment samples from groups A, B and C with dominant freshwater diatoms are
plotted on the left hand side, with the two brackish water groups (D and E) plotted in the central
part, followed by the two brackish-marine groups (F and G) on the right hand side, and finally
the two marine groups (H and I) on the far right of the diagram. Overall, the spatial distributions
of the diatom samples show a strong correlation with water salinity from the freshwater end to
the marine end of the study area, as shown by axis 1 which is 3.60 in standard unit length and
represents 29.7% of total variance in the diatom data. Axis 2 represents 11.2% of total variance
of the diatom data and distinguishes groups B and D from groups A, C and E, emphasizing the
influence of water depth (Figure 2b). Furthermore, the DCA results confirm the validity of the
divisions of diatom samples which are based on the cluster analysis (CONISS). Figure 4 shows
the range of annual average water salinity for samples of each zone, which again suggest the
diatom zones are strongly related to water salinity.
5. Relationships between modern diatoms and environmental variables
The CCA results are listed in Table 2. The three canonical axes account for 32.4% of the
explained variance in the diatom data. Partial CCAs show that the total explained variance is
composed of 51.9% for salinity, 20.1% for water depth, 13.2% for sand content and 14.8% for
inter-correlation of the three environmental variables. The associated Monte Carol permutation
tests indicate that water salinity gradient accounts for a significant portion of the total variance in
the diatom data (p < 0.005 with 199 random permutations), whilst both water depth and sand
content gradients are not significant (p > 0.030 with 199 permutations for both cases). Therefore,
statistically significant transfer functions quantifying the relationship between modern diatom
assemblages and water salinity can be developed.
The CCA results are also graphically illustrated in species-environment and sampleenvironment bi-plots (Figure 5). On the bi-plots, the position of species or sediment samples
projected on to the environmental arrows approximate their weighted average optima along each
environmental variable. In the bi-plots, salinity aligns closely with CCA axis 1, which has an
eigenvalue of 0.522 that represents 89.1% of the total canonical eigenvalues, whereas the
environmental variables of axis 2 are unclear. All the marine and brackish diatom taxa are
plotted to the left of axis 1, and the freshwater species, on the opposite side (Figure 5a). Similarly,
sediment samples collected from the high salinity environment appear on the left hand side,
whilst sediment samples from distributaries are found on the opposite side (Figure 5b). These
features in the sample-environment and species-environment relationships suggest salinity is the
most important environmental variable. Both water depth and sand content have much lower
contribution to the variance in the diatom data. Furthermore, the contribution from the intercorrelation between environmental variables is also low. However, there is still about two-third
of the variance (67.6%) unexplained. It is unclear what other factors are involved. It is noted that
there is a certain degree of variation along the DCA axis 2 among the sediment samples which
contain dominantly freshwater diatoms (Figure 2b). Thus part of the unexplained variance in the
diatom data may possibly involve nutrient of agricultural and industrial effluence. Nevertheless,
the unexplained percentage is considerably lower than reported biological data (Gasse et al.,
1995; Jones and Juggins, 1995; Zong and Horton, 1999).
6. The diatom-based salinity transfer function
In order to decide on whether or not a unimodal-based technique such as WA-PLS should
be used, detrended canonical correspondence analysis (DCCA) was performed. The DCCA
result indicates that axis 1 is highly correlated with salinity (weighted correlation, r = 0.97) and
represents 85.7% of total variance in the species-environment relationship. The length of
gradient is long (3.14 standard deviations). Based on such a strong relationship between diatoms
and salinity recorded in the Pearl River estuary, a transfer function is developed. Results from
WA-PLS with cross validation indicate that both apparent r2 and prediction r2 are all high in the
five components produced (between 0.94 and 0.98, Table 3). On the one hand, such high
correlation between the recorded diatom assemblages and the observed salinity without outliers
(Figure 6a) suggests a strong predictive power for the transfer function developed. The
prediction r2 reported here is similar to, or a little higher than many comparable studies, e.g.
diatom-based transfer functions for estuarine salinity (e.g. Juggins, 1992), nutrient level (e.g.
Anderson and Vos, 1992), lake water salinity (e.g. Fritz et al., 1991), acidity (e.g. Battarbee et al.,
2005), and sea level (e.g. Zong and Horton, 1999; Zong et al., 2003; Horton et al., 2007). On the
other, cautions must be taken here, because the sediment samples for this study are collected
from sites close to each other across an environmental gradient. This sampling scheme is likely
to produce samples resembling one another more than randomly selected sites, resulting in
positive spatial autocorrelation in the diatom data (e.g. Telford and Birks, 2005). Such
autocorrelation is somewhat unavoidable due to the nature and the purpose of the study. Thus
positive spatial autocorrelation is likely present in our data, and its effects should not be ignored.
Particularly, the predicted r2 (Table 3) may be artificially a little too high.
In terms of the potential errors in prediction, the RMSEP varies between 2.81 and 1.84 ‰,
suggesting that the transfer function is capable of producing predictions for palaeo-salinity
reconstruction with reasonable precisions (Figure 6b). The predicted error range is comparable
with many diatom-based transfer functions (e.g. Battarbee et al., 2005, 2008; Horton et al., 2007;
Zong and Horton, 1999; Zong et al., 2003) in terms of the error range proportional to the range
of the environmental gradient studied. Estuaries are dynamical environments where fluvial and
marine waters are mixing and exchanging. As Figures 3 and 4 show, freshwater diatoms appear
abundantly in the freshwater environments. However, they are also found in low numbers in
brackish water and even marine environments, possibly being brought to these environments by
floodwater. Likewise, low numbers of marine and brackish water diatoms are recorded in the
freshwater environments, possibly being transported by incoming tidal currents. Due to the
nature of the estuarine environment, an average error of c. 2 ‰ is considered as a very good
result. However, as mentioned above, the cross-validation RMSEP estimates may be a little over
optimistic because of the presence of possible autocorrelation in the modern diatom data (Telford
et al., 2004). Nevertheless, the strength of the correlation between the diatom assemblages and
the observed salinity recorded in the Pearl River estuary suggests accurate and precise
reconstructions of palaeo-salinity history are possible using the diatom-based salinity transfer
function developed.
Conclusion
We have examined the modern diatom assemblages from 77 sites across the Pearl River estuary.
Among these sediment samples, we have identified 73 diatom taxa, with 17 species of over 10%
abundance. The majority of marine and brackish water diatoms are planktonic taxa. Marine
diatoms appear mostly in the Hong Kong waters and the outer part of the estuary, whilst brackish
water diatoms concentrate in the central part of the estuary. Both planktonic and benthic
freshwater diatoms appear prominently in samples collected from the low salinity environments.
Freshwater planktonic diatoms are recorded from the deep channel, whilst freshwater benthic
diatoms are found from two deltaic distributaries. Statistical analyses confirm the strong
correlation between diatom assemblages and salinity. The relationship between observed and
diatom-predicted water salinity suggested accurate and precise reconstruction of palaeo-salinity
are possible.
Acknowledgements
This research is supported by a research grant from the National Science Foundation of China
(No. 40771218) to Huang and Zong, two research grants from the Research Grants Council of
the Hong Kong SAR, China (No. HKU7058/06P and HKU7052/08P) to Yim and a
NERC/EPSRC 05-08 (UK) PhD studentship from the Dorothy Hodgkin Postgraduate Award to
Yu. The authors thank the director of the Environmental Protection Department, Hong Kong
SAR for the collection of surface sediment samples and water salinity in the Hong Kong area.
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Captions
Figure 1. The Pearl River estuary and sampling locations.
Figure 2. (a) The diatom diagram shows the frequencies of 17 most abundant diatom taxa. (b)
Results of the Detrended Correspondence Analysis are shown. Percentages of the diatom species
and the associated environmental variables can be sought from the lead author.
Figure 3. Maps show the spatial distribution patterns of dominant diatom taxa across the Pearl
River estuary, based on the results of spatial analysis (kriging) of the modern diatom data.
Figure 4. Ranges of observed water salinity for each group of sediment samples are shown.
Figure 5. Results of the Canonical Correspondence Analysis are shown as bi-plots of species
against environmental variables (a) and samples against environmental variables (b).
Figure 6. Results of the diatom-based salinity transfer function (component 5): (a) The diatompredicted salinity against the observed salinity and (b) the residuals of the regression using
weighted averaging partial least squares (WA-PLS).
Table 1. Summary of the environmental variables recorded for each sampling sites
Group
A (5 samples)
B (9 samples)
C (10 samples)
D (13 samples)
E (7 samples)
F (15 samples)
G (7 samples)
H (7 samples)
I (4 samples)
Water salinity (‰)
3.7 ± 2.0
3.7 ± 3.7
8.0 ± 4.8
17.3 ± 4.1
21.7 ± 3.1
25.6 ± 4.3
30.6 ± 0.9
31.8 ± 1.1
33.4 ± 0.2
Water depth (m)
7.2 ± 2.6
2.6 ± 0.7
3.6 ± 1.2
6.4 ± 3.1
10.3 ± 6.1
10.1 ± 4.2
14.2 ± 11.3
13.7 ± 4.0
27.0 ± 3.2
Sand content (%)
34.0 ± 6.8
32.6 ± 21.5
23.5 ± 11.1
20.3 ± 15.6
20.3 ± 10.6
16.4 ± 2.9
21.3 ±14.6
15.0 ± 6.5
13.7 ± 7.2
Table 2. Summary of CCA results from modern diatom assemblages
Axis
Eigenvalues
Species-environment correlations
Cumulative percentage variance
of species data
of species-environment relationship
Sum of all unconstrained eigenvalues
Sum of all canonical eigenvalues
1
0.522
0.974
2
0.046
0.570
3
0.017
0.496
4
0.300
0.000
28.9
89.2
31.4
97
32.4
100.0
49.0
0.0
Total inertia
1.807
1.807
0.586
Table 3. Apparent and jack-knifed errors of estimation and prediction for the diatom-based water
salinity transfer function
Component
RMSE (‰)
1
2
3
4
5
2.52
1.70
1.43
1.29
1.16
RMSEP
(jack ‰)
2.66
1.96
1.80
1.68
1.63
Apparent
(r2)
0.946
0.975
0.983
0.986
0.989
Prediction
(jack r2)
0.940
0.967
0.973
0.976
0.977
Jack_Ave_
Bias
-0.046
0.039
-0.135
0.043
-0.035
Jack_Max_
Bias
3.281
1.841
1.600
1.611
1.202