| Citation: | Tong Li, Jihui Zhang, Dongling Li, Chengxu Zhou, Chenxi Liu, Hao Xu, Bing Song, Longbin Sha. Diatoms as indicators of environmental change in coastal areas: a case study in Lianjiang coast of East China Sea[J]. Acta Oceanologica Sinica, 2024, 43(8): 47-57. doi: 10.1007/s13131-024-2292-0
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Coastal estuarine areas are among the most dynamic and complex environments on Earth (Rovira et al., 2012). They function as transition zones among marine, river, and terrestrial environments and exhibit large spatiotemporal environmental gradients (Azhikodan and Yokoyama, 2015; Nwe et al., 2021). Coastal sediments have long been recognised as reliable records of marine environmental conditions (López-Belzunce et al., 2020; Triantaphyllou et al., 2009), sea-level changes (Wang et al., 2013; Yu et al., 2023a), freshwater inputs (Espinosa et al., 2022; Fayó et al., 2018), and extreme weather events (Benito et al., 2015; Nakanishi et al., 2022). Variations in freshwater input from rivers and extreme events such as storm surges can strongly disturb the water environment in coastal areas. These disturbances can be partially recorded in the physical and chemical indicators of sediments as well as in microfossil assemblages. For example, Fayó et al. (2018) used the alluvial sediments of the delta of the Colorado River to identify the historic changes in floodplain hydrology. Nakanishi et al. (2022) used diatoms and chemical analyses to reveal the history of extreme wave events in the coastal wetlands of central Hidaka. The Lianjiang coast is located on the western side of the East China Sea and is a typical coastal area with thick sediment accumulations that record river flow dynamics, ocean currents, and extreme events (Huh and Su, 1999). Thus, this area is critical for reconstructing the evolution of the coastal environment and climate.
Coastal and estuarine areas are highly productive regions, with phytoplankton being the primary producer in the food web (Nwe et al., 2021; Saifullah et al., 2019). Diatoms, one of the most important phytoplankton groups, can survive under conditions of high turbidity (Lionard et al., 2008; Mendes et al., 2009). Among the biological proxies preserved in sediments, diatoms are valuable indicators for tracing environmental changes on various timescales (Espinosa et al., 2022). They can tolerate and adapt to a wide range of environmental conditions and respond rapidly to physical, chemical, and biological variations in aquatic ecosystems; moreover, their siliceous frustules are well-preserved in sediments and have a well-defined taxonomy (Chen et al., 2020a; Espinosa et al., 2022; Gregersen et al., 2023). After death, planktonic and benthic diatoms sink to the sediment surface and mix with in situ and displaced species (Chen et al., 2019). This process reflects the local environmental conditions and provides insights into the dynamics of runoff inputs and ocean currents. Therefore, identifying the ecological variables that regulate the seasonal and interannual succession of diatom communities is valuable for monitoring and evaluating regional environmental changes (Mendes et al., 2009).
Statistical inference models based on modern species–environment relationships effectively utilise ecological data derived from biological assemblages, enabling the quantitative estimation of critical environmental parameters (Hassan et al., 2009). Thus, transfer functions are reliable for generating high-resolution quantitative estimates of paleoenvironmental conditions (Hassan et al., 2009). Over the past decades, numerous diatom-based transfer functions have been developed to infer a wide range of environmental variables in lake and marine systems. Fruitful results have been obtained in the study of the relationship between diatoms and environmental variables in freshwater ecosystems, such as temperature (Wang et al., 2014; Szczerba et al., 2023), water depth (Chen et al., 2020b), and eutrophication (Yang et al., 2008; Chen et al., 2022; Wang et al., 2012). Diatom-based environmental transfer function datasets have been established in different regions, such as Europe (Bennion et al., 2001), Asia (Yu et al., 2023b), and South America (Gomes et al., 2014). The study of marine diatom transfer functions has also made significant progress in the reconstruction of the sea level (Zong and Horton, 1999), sea surface temperature (De Sève, 1999; Li et al., 2017), and sea ice extent (Sha et al., 2015). Although the marine system includes estuarine and coastal parts, the environmental characteristics of the land-sea interface area are more distinctive than those of marine and lakes, involving a wider range of environmental factors, which make the development of transfer functions more challenging (Hassan et al., 2009). Some research has been conducted in estuaries and coastal areas. For example, Horton et al. (2006) developed the first diatom-based transfer function for the east coast of North America and used it to reconstruct sea level changes. Hassan et al. (2009) built a two-component Weighted Average (WA) Partial Least Squares (PLS) calibration model to infer salinity in three estuaries along the northeastern coast of Argentina. However, owing to the significant differences in environmental characteristics and explanatory factors among different estuarine and coastal regions, research on diatom transfer functions and database establishment remains incomplete in the East China Sea.
The objectives of this study were to (1) identify the major environmental variables affecting diatom assemblages in the Lianjiang coastal area of the East China Sea, (2) establish estuarine and coastal diatom-based transfer functions, and (3) test the reliability of the transfer functions by reconstructing past environmental events from a sediment core. Our results provide new ecological information on the relationships between diatoms and environmental parameters and their spatiotemporal distribution characteristics, which may contribute to a better understanding of estuarine and coastal ecosystems affected by rivers.
The study area is located on the eastern coast of Lianjiang County, Fujian Province, Southeast China (26.15°–26.30°N, 119.60°–119.80°E) (Fig. 1). This area has a typical mid-subtropical maritime monsoon climate with warm, humid weather and abundant rainfall (Lin et al., 2020; Peng et al., 2021). The average annual rainfall is 1 551 mm, with the wet season (March to September) receiving approximately 80% of the precipitation and the dry season (October to February) receiving the remaining approximately 20%. Typhoons are the primary weather hazards in this region. Lianjiang County experiences an average of 5.5–5.7 tropical cyclones per year (Peng et al., 2021).
Both the Aojiang River and Minjiang River entered the study area from the west (Fig. 1b). The Aojiang River is a medium-sized river, 137 km long, with a watershed area of 2 655 km2, and an annual freshwater discharge of 2.728 × 109 m3 (Lei et al., 2021; Zhang, 2014). The Minjiang River is the largest river in Fujian Province and flows into the southern part of the study area. It is 577 km long with a drainage area of 60 992 km2 and an average annual freshwater discharge of 6.20 × 1010 m3 (Fan et al., 2021). Both the Aojiang River and Minjiang River have strongly tidal estuaries and are characterized by a regular semi-daily tidal range of 0.74 m to 4.5 m, with an average of 3.8 m (Peng et al., 2021).
The study area is controlled by the Zhe-Min Coastal Current; it supplies abundant nutrients to the coastal bays of Fujian in winter, together with the nutrient input maximum from the Aojiang River and Minjiang River, which have a large impact on the local aquatic environment (Xu et al., 2020). Lianjiang County was the second most important county in China for fish and shellfish products over the last three decades (Lei et al., 2021; Yang et al., 2022). The main fish species cultured were Larimichthys crocea, Pagrosomus major, Sciaenops ocellatus, and Lateolabrax japonicus. The main cultured shellfish are Ruditapes philippinarum on sandy beaches and Sinonovacula constricta on clay beaches, which are seeded in March and harvested in September (Lei et al., 2021; Zhou et al., 2022).
A total of 52 surface sediment samples were collected from the Lianjiang coast in October 2020, January 2021, and April 2021. The sampling locations are shown in Fig. 1b. The environmental parameters of each seawater sample, including sea surface temperature (SST), sea surface salinity (SSS), pH, redox potential, electrical conductivity, dissolved oxygen, total dissolved solids, and turbidity were measured using an in situ multiparameter water quality instrument (HORIBA U52G, Japan) at the time of sample collection (Table S1). The depths of the 52 sampling sites ranged from 1.5 m to 17 m, with an average depth of 8 m.
Sediment core HK3 was collected in October 2021 from the tidal flats of the study area (26.25°N, 119.66°E) using a mudflat sampler. The core measured 100 cm in length. Twenty samples were extracted at 5 cm intervals from the core HK3.
Diatom analysis was conducted on 15–16 mg of dried sediment per sample following the preparation methods described by Håkansson (1984). Diatoms were counted and identified using a Motic BA410E microscope at
Diatom fluxes were calculated using the following equation (Battarbee et al., 2001):
| $$ A=\frac{N\times S}{n\times a\times m} ,$$ | (1) |
where A is the diatom concentration (valves/g), N is the number of diatoms counted, S is the area of the Petri dish, n is the number of fields of vision counted, a is the area of one field of vision, and m is the dry weight of the sample (g).
The Shannon-Weaver diversity index (SW index) was used to reflect the biodiversity of diatoms in the samples, and the formula is as follows (Shannon and Weaver, 1949):
| $$ H'=-\sum _{i\;=\;1}^{S}\left(\frac{{N}_{i}}{N}\right){{\mathrm{log}}}_{2}\left(\frac{{N}_{i}}{N}\right), $$ | (2) |
where H' is the SW index, S is the number of diatom species identified, Ni is the number of the ith diatom species, and N is the total number of diatoms identified.
Grain size determination was performed on 72 samples (52 surface samples and 20 core samples) using a Beckman Coulter laser diffraction particle size analyser (LS13320, USA) with a measurement range of 0.04–2 000 µm. Samples were firstly thoroughly mixed and dried at 40℃ for 24 h and then a subsample of about 0.2 g was taken from each sample. This was followed by the addition of 5 mL HCl (10%) to eliminate carbonates, 5 mL H2O2 (10%) to remove organic matter, and 5 mL (NaPO3)6 to promote dispersion before testing (Jiang et al., 2020).
The chronological framework of core HK3 was established by 210Pb dating. After drying at low temperatures, the samples were disaggregated using a mortar and pestle to produce a uniform grain size. The activity of 210Pbex at each level was measured using a high-purity Ge Gamma Spectrometer (GWL-120-15, USA) at the Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences. To provide the best possible new insights into the sedimentary processes of the Lianjiang coast sediment accumulation from the decay-corrected 210Pbex profiles, cores were processed using the clay-normalisation procedure. Initial 210Pbex was recalculated by normalising to average clay based on the percent of the clay-sized sediments (<4 μm) at each sample (Sun et al., 2017, 2020).
The sediment accumulation rates were calculated using the constant initial concentration (CIC) model (Sanchez-Cabeza and Ruiz-Fernández, 2012):
| $$[ {}^{210}{{\mathrm{Pb}}}_{{(m)}}]=[{}^{210}{\mathrm{Pb}}_{(0)}] {\mathrm{e}}^{-{\text{λ}}{{t}}}, $$ | (3) |
where [210Pb(m)] is the specific activity of [210Pbex] (Bq/kg) at depth m, [210Pb(0)] is the surface sediment specific activity (Bq/kg), and λ is the decay constant of [210Pb] (
Diatom assemblages and their relationships with environmental variables were examined using multivariate statistical analysis to determine the principal variables and detect similarities among the diatom samples (Chen et al., 2016).
Principal component analysis (PCA) has the advantage of weight determination because it classifies the original data into several comprehensive variables with characterisation significance using correlation coefficients to accurately reflect the core information for the evaluation indicators (Abdi and Williams, 2010).
Canonical correspondence analysis (CCA) extracts the best synthetic gradients from data on biological communities and environmental variables and intuitively shows the characteristics of the relationship between these variables and biological taxa (Klami et al., 2013). To determine the distribution of the surface sedimentary diatom species, a detrended correspondence analysis (DCA) was required to determine the gradient length of the ordination axes before selecting the appropriate model (Chen et al., 2016). To identify the primary environmental factors influencing the distribution of surface sediment diatoms in the study area, sites with fewer than 200 diatoms were eliminated.
PCA and CCA were conducted using the CANOCO (version 5) software (Ter Braak and Smilauer, 2012). The transfer function was calculated using the C2 software developed by Juggins (2007). The derivation ability of the model was evaluated according to the root-mean-square error of prediction (RMSEPJack) and squared correlation (R2Jack).
The modern SSS data used in this study were obtained from the World Meteorological Organization climate explorer open dataset (
A total of 92 diatom species belonging to 36 genera were identified in surface sediment samples from the Lianjiang coastal area. The dominant species are: Actinocyclus kuetzingii, Actinocyclus octonarius, Amphora coffeaeformis, Cyclotella striata, Paralia sulcata, Planothidium delicatulum, Pleurosigma angulatum, Surirella armoricana, Thalassionema nitzschioides, Thalassiosira leptopus, and Tryblioptychus cocconeisformis (Fig. S1). Among them, Planothidium delicatulum and Aulacoseira granulata are freshwater diatoms (Hartley et al., 1996; Hustedt, 1985); Actinocyclus octonarius, Cyclotella striata, S. armoricana, and Paralia sulcata are commonly found in brackish water in estuarine areas (Jiang et al., 2004; Prelle et al., 2019; Ran and Jiang, 2005). Some marine diatom species, such as Thalassionema nitzschioides, exhibit strong tolerance to SST and SSS (Jousé et al., 1971).
Surface diatom concentrations and SW index values are shown in Fig. 2. In October 2020, diatom concentrations were relatively high in the estuary and offshore areas, with the highest value at site W3 in the offshore area (1.65 × 106 valves/g), followed by site E4 (1.52 × 106 valves/g), which also had the highest SW index value. The nearshore areas had the lowest diatom concentrations, with site Y4 showing an almost complete absence of diatoms and the lowest SW index.
In January 2021, the diatom concentration peaked in the offshore areas, showing significant variation compared to the other zones. The highest values occurred at site W1 (2.64 × 106 valves/g) in the offshore area and the lowest concentrations occurred at the nearshore sites. The area with a low SW index expanded substantially in January compared to October, forming a roughly north-south distribution that roughly divides the study area into three parts. The SW index was relatively high in the estuarine areas, northern part of the central area, and offshore areas and relatively low in the nearshore areas, southern part of the central area, and Minjiang River estuary area.
Generally, low diatom concentrations were recorded in April 2021. The SW index exhibited relatively minor variations across the entire study area, with slightly lower values in the estuarine and nearshore regions than in the central and offshore areas.
PCA of all environmental variables, including SST, SSS, pH, redox potential, electrical conductivity, dissolved oxygen, total dissolved solids, turbidity, and sedimentary mean grain size, was performed to reduce the dimensionality of the dataset and determine the major environmental gradients (Abdi and Williams, 2010). The eigenvalues of the first two principal components (PC1 and PC2) were all greater than 1 and the cumulative proportion of these components was 72.4%, therefore, they were used to explain the main environmental variables.
The factor loading matrix expressed the loading (or degree of influence) of each variable on each principal component. Varimax rotation was applied to the factor-loading matrix and the coefficients of the principal components are shown in Fig. 3. PC1 was strongly related to SSS (52.2%) and PC2 was strongly related to SST (50.6%). Therefore, we concluded that SSS and SST have the greatest influence on the aquatic environment in the study area.
The gradient length of the first axis in the DCA was 2.68 SD, indicating that the diatom data had a nonlinear unimodal distribution (Ter Braak et al., 1988). This implied that the optimal or highest concentration of diatoms occurred within a specific range of habitat gradients. Therefore, the unimodal ordination technique of CCA was used to investigate the relationships between the environmental variables and diatom species from the surface samples. The Variance Inflation Factor (VIF) for each environmental variable was used to determine whether it independently affected the distribution of diatom combinations. The VIF results showed that the SSS, electrical conductivity, and total dissolved solids did not pass the test (VIF > 20). The strong correlation among these three factors indicates a large overlap in the provision of information on the aquatic environment (Blaine McCleskey et al., 2023). After excluding electrical conductivity and total dissolved solids, the remaining environmental variables (SST, SSS, pH, redox potential, dissolved oxygen, turbidity, and sedimentary mean grain size) passed the VIF and Monte Carlo permutations tests (999 unrestricted permutations, p < 0.05).
The CCA results showed that the pseudo-canonical correlations of CCA1 and CCA2 were 0.879 and 0.757, respectively, with the first two axes accounting for 74.67% of the total variances. This indicates that most of the constrained variance can be explained by the two axes.
The contributions of each factor to the CCA axis are shown in Fig. 4. The primary environmental factor influencing CCA1 was pH, which accounted for 79.8% of the total variance. The sedimentary mean grain size contributed 69.2% to CCA1, making it the second most influential factor. SSS had the highest contribution rate (75.4%) to CCA2. Consequently, pH and SSS were regarded as the most significant environmental factors affecting the distribution of diatom assemblages in the surface sediments of the study area, with the sedimentary mean grain size being the second most important factor.
The distribution of diatom species can be separated into three spatial zones based on the CCA findings of the diatom–environmental factors (Fig. 4). Zone Ⅰ is dominated by diatoms such as Planothidium delicatulum, Achnanthes suchlandtii, Amphora coffeaeformis, Achnanthes laterostrata, and Navicula spectabilis. These species were positively correlated with sedimentary mean grain size and turbidity and negatively correlated with pH and SSS. This indicates that they tend to accumulate in areas with coarse-grained sediments, high turbidity, low alkalinity, and high SSS. The nearshore and southern parts of the intertidal zone were the main areas of coarse-grained sediments. Freshwater diatoms transported by the Aojiang and Minjiang rivers enter this area and because of directional sorting, only a small portion of the smaller freshwater diatoms are retained in this area. The relationship between freshwater diatoms and coarse-grained sediments reflects the direct environmental effects on the deposition of marine microorganisms. Zone Ⅱ was characterised by Aulacoseira granulata, Gomphonema parvulum, Cymbella affinis, and Fragilaria capucina. Compared to ZoneⅠ, these species were negatively correlated with SSS but were uncorrelated with the sedimentary mean grain size. Zone Ⅲ was characterised by marine diatom species that occur within the study region, including Actinocyclus octonarius, Cyclotella striata, Diploneis bombus, Nitzschia sociabilis, Paralia sulcata, Surirella armoricana, Thalassionema nitzschioides, Thalassiosira eccentrica, and Tryblioptychus cocconeisformis. They are concentrated at the centre of Fig. 4 in the positive direction of the SSS.
The calculated 210Pbex activity varied from (54.3 ± 9.4) Bq/kg to (161.9 ± 11.45) Bq/kg in core HK3 (Fig. 5). Based on the characteristics of the calculated 210Pbex activity, a CIC model was used to calculate the sedimentation rates of core HK3, which were determined to be 2.56 cm/a. These results are supported by Li et al. (2009) and Liu et al. (2009). The 210Pb chronology indicated dates of AD 1884 at a depth of 95–96 cm and AD 2021 at a depth of 0–1 cm.
The CCA results showed that pH and SSS were the primary environmental factors influencing diatom distribution, whereas the PCA results indicated that SSS was the most influential factor in the study area. Similarly, the relative explanatory power of SSS as a predictor of diatom assemblage composition can be estimated by calculating the ratio of the eigenvalue of the first constrained axis (λ1) with SSS as a single explanatory variable with the first unconstrained axis (λ2). The ratio λ1/λ2 is 2.468 (>1.0), indicating that SSS is the main determinant of diatom distribution in the training set (Ter Braak and Colin Prentice, 1988) (Table 1). Previous studies have also indicated that SSS is one of the most important factors controlling diatom distribution in estuarine environments (Hassan et al., 2007, 2009; Nwe et al., 2021; Sarker et al., 2020). Therefore, a diatom-based SSS transfer function was developed to reconstruct the past changes in coastal environments.
| Variable | λ1 | λ2 | λ1/λ2 |
| SSS | 0.072 | 0.029 | 2.468 |
| SST | 0.025 | 0.085 | 0.292 |
| pH | 0.092 | 0.111 | 0.825 |
| ORP | 0.054 | 0.063 | 0.857 |
| Tur | 0.031 | 0.082 | 0.377 |
| DO | 0.038 | 0.091 | 0.415 |
| MD | 0.053 | 0.114 | 0.469 |
| Note: SSS: sea surface salinity; SST: sea surface temperature; ORP: redox potential; Tur: turbidity; DO: dissolved oxygen; MD: sediment mean grain size. | |||
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Four numerical reconstruction methods were used to define the optimal diatom-based SSS transfer function (Table S3). The PLS method with 3 and 5 components resulted in a high R2Jack (0.32 and 0.36), along with lower RMSEPJack (1.34 and 1.31) and maximum biasJack (4.56 and 3.91) (Table S3). The WA-PLS method with 4 and 5 components also resulted in a high R2Jack (0.36 and 0.37), as well as low RMSEPJack (1.29 and 1.29) and maximum biasJack (3.02 and 3.11) (Table S3). Additionally, a plot of reconstructed SSS versus observed SSS showed a strong linear correlation, with randomly scattered residuals (Fig. 6). Hence, these four numerical reconstruction methods can be employed to obtain diatom-based SSS in the Lianjiang coastal area.
The dominant species in core HK3 were similar to those observed in surface sediment samples, including Achnanthes suchlandtii, Actinocyclus octonarius, Amphora coffeaeformis, Aulacoseira granulata, Cocconeis scutellum, Cyclotella striata, Cymbella affinis, Gomphonema parvulum, Nitzschia sociabilis, Planothidium delicatulum, Parakia sulcata, and Thalassionema nitzschioides. This suggests a continuity of environmental change from the past to the present in this area, which enables us to reconstruct paleoenvironmental conditions using the transfer function.
To determine the best model and test the reliability of the diatom-based SSS transfer function, the diatom data from core HK3 were used to quantitatively reconstruct SSS changes using the four models screened above and the reconstructed SSS were compared with modern SSS data. The results showed that the PLS method (Figs 7c and d) was superior to the WA-PLA method (Figs 7a and b) in terms of reconstruction, with a high correlation coefficient and more significant p-values than modern SSS data. In contrast, the PLS model with three components (Fig. 7c) had a higher correlation coefficient (0.517) and more significant p-value (0.001). Thus, the reconstructed SSS in core HK3 using the PLS model with three components was found to best match the actual SSS variations in the study area and was the most effective model for SSS reconstruction in this area.
The reconstructed SSS shows three low-SSS events, occurring in AD 2019, 2013, and 1999 (Fig. 8), coinciding with remarkably low diatom concentrations and SW index, accompanied by an increase in the relative abundance of freshwater diatoms (Figs 4 and 8; Zones Ⅰ and Ⅱ) and a decrease in the relative abundance of marine diatoms (Figs 4 and 8; Zone Ⅲ). Abrupt low SSS and diatom concentrations, as well as abrupt increases in freshwater diatoms to the marine environment, are probably related to typhoons and floods (Yang et al., 2023). In 2018, Super Typhoon Maria made direct landfall in Lianjiang County, with extraordinarily heavy rainfall of more than 200 mm (Liu et al., 2022). In 2013, Super Typhoon Soulik made landfall along the Huangqi Peninsula coast in Lianjiang County, bringing heavy rainfall to Fujian, and the Minjiang River caused excessive flooding. Additionally, the low SSS in 1999 corresponded to a catastrophic flood event in 1998, when a major flood occurred in the Minjiang River, with the maximum inflow at the Minjiang Estuary Power Station reaching 37 000 m3/s, exceeding the historical maximum. These rapid, high-amplitude increases in freshwater inflows were likely to significantly reduce the SSS, and affect the abundance and structure of the phytoplankton communities in estuarine areas (Qiu et al., 2019; Yang et al., 2023).
In addition, three low-SSS events in AD 2019, 2013, and 1999 corresponded to coarser sedimentary grain sizes in core HK3 (Fig. 8). A study of the sedimentary processes revealed that the sediment discharged by rivers increases under the influence of typhoons and is then deposited in the estuary, accompanied by an expansion of the area of coarse-grained sediments (Yang et al., 2023). Rainfall triggered by a typhoon can cause a dramatic increase in river runoff to the sea, which is several tens of times higher than normal runoff (Zhao et al., 2008), whereas fine-grained suspended sediments remain in suspension and previously deposited fine-grained sediments within the coastal zone are resuspended and redistributed (Zang et al., 2018). This results in the coarsening of the sedimentary grain size and an increase in sand content (Lou et al., 2016).
The analysis of the correlation between the reconstructed SSS and the dominant diatom species showed a strong negative correlation (−0.858, p < 0.01) between salinity and the relative concentration of Planothidium delicatulum. Planothidium delicatulum is a freshwater diatom species commonly found in coastal areas with low salinity (Hartley et al., 1996). A study on the distribution of diatoms in the surface sediments of Qinzhou Bay and Zhenzhu Bay in Guangxi revealed that Planothidium delicatulum is mainly concentrated near the estuaries of the bays and is almost absent in the open sea, which can effectively indicate low sea surface salinity (Huang, 2017; Huang and Huang, 2016). Planothidium delicatulum is mainly found in the Aojiang and Minjiang river estuaries and rarely occurs offshore, also indicating its sensitivity to freshwater runoff. In addition, the relative concentrations of Surirella armoricana and Cyclotella striata showed a positive correlation with the reconstructed SSS, with a correlation of 0.6 (p < 0.01) and 0.51 (p < 0.05). Surirella armoricana and Cyclotella striata are marine benthic species that prefer brackish environments (Shang et al., 2023). Because the change in water salinity in estuarine coastal areas over a short period is closely related to the dilution effect of runoff injection, the variations in Planothidium delicatulum, Surirella armoricana, and Cyclotella striata are likely to be important indicators of river action intensity in estuarine coastal areas, indicating extreme events such as typhoons, rainstorms, and floods.
To quantify the relationship between coastal sediment diatoms and environmental variables in the Lianjiang River estuarine coastal areas and construct diatom-environment transfer functions, 52 surface sediment diatom samples and nine types of environmental factors were collected. The dominant diatom species in this region were Actinocylus octonarius, Amphora coffeaeformis, Actinocyclus kuetzingii, Cyclotella striata, Paralia sulcata, Pleurosigma angulatum, Planothidium delicatulum, Surirella armoricana. The diatom concentrations and SW index were relatively high in the estuary and offshore areas and lower in the nearshore areas.
The PCA results showed that the SSS and SST had the highest contribution rates to the PC1 and PC2 axes. CCA results indicated that pH and SSS were the most important environmental factors affecting the distribution of diatom assemblages in the surface sediments of the study area. The inferred SSS, based on PLS with 3 components model, best matched the actual SSS variation in the study area. This model was used to quantitatively reconstruct the SSS changes from 1984 to 2021 and the results were in good agreement with the measured SSS. In particular, the low SSS events in 1999, 2013, and 2019 were consistent with an increase in the relative concentration of freshwater diatoms and the coarsening of sediment grain size records, corresponding to two super-typhoon events and an extreme flood event in Lianjiang County. This transfer function is potentially useful for reconstructing past SSS in estuarine coastal regions with applications in future paleoceanographic studies.
Acknowledgements: We thank the editor and two anonymous reviewers for valuable comments to improve the manuscript.|
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LiTong_Supplementary data.pdf
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Figures(8) / Tables(1)
Supported by:
Beijing Renhe Information Technology Co. Ltd
Tong Li, Jihui Zhang, Dongling Li, Chengxu Zhou, Chenxi Liu, Hao Xu, Bing Song, Longbin Sha. Diatoms as indicators of environmental change in coastal areas: a case study in Lianjiang coast of East China Sea[J]. Acta Oceanologica Sinica, 2024, 43(8): 47-57. doi: 10.1007/s13131-024-2292-0
| Variable | λ1 | λ2 | λ1/λ2 |
| SSS | 0.072 | 0.029 | 2.468 |
| SST | 0.025 | 0.085 | 0.292 |
| pH | 0.092 | 0.111 | 0.825 |
| ORP | 0.054 | 0.063 | 0.857 |
| Tur | 0.031 | 0.082 | 0.377 |
| DO | 0.038 | 0.091 | 0.415 |
| MD | 0.053 | 0.114 | 0.469 |
| Note: SSS: sea surface salinity; SST: sea surface temperature; ORP: redox potential; Tur: turbidity; DO: dissolved oxygen; MD: sediment mean grain size. | |||
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