Limnol. Oceanogr. 65, 2020, 515–528© 2019 Association for the Sciences of Limnology and Oceanography

doi: 10.1002/lno.11320

Impact of human disturbance on the biogeochemical silicon cyclein a coastal sea revealed by silicon isotopes

Zhouling Zhang ,1,2* Xiaole Sun,3 Minhan Dai ,1 Zhimian Cao,1 Guillaume Fontorbe,2 Daniel J. Conley21State Key Laboratory of Marine Environmental Science, Xiamen University, Xiamen, China2Department of Geology, Lund University, Lund, Sweden3Baltic Sea Center, Stockholm University, Stockholm, Sweden

AbstractBiogeochemical silicon (Si) cycling in coastal systems is highly influenced by anthropogenic perturbations in

recent decades. Here, we present a systematic study on the distribution of stable Si isotopes of dissolved silicate(δ30SiDSi) in a highly eutrophic coastal system, the Baltic Sea. Besides the well-known processes, diatom produc-tion and dissolution regulating δ30SiDSi values in the water column, we combined field data with a box model toexamine the role of human disturbances on Si cycling in the Baltic Sea. Results reveal that (1) damming led toincreased δ30SiDSi values in water but had little impacts on their vertical distribution; (2) decrease in saltwaterinflow due to enhanced thermal stratification had negligible impacts on the δ30SiDSi distribution. An atypicalvertical distribution of δ30SiDSi with higher values in deep water (1.57–1.95‰) relative to those in surface water(1.24–1.68‰) was observed in the central basin. Model results suggest the role of enhanced biogenic silica (BSi)deposition and subsequently regenerated dissolved silicate (DSi) flux from sediments. Specifically, eutrophica-tion enhances diatom production, resulting in elevated exports of highly fractionated BSi to deep water and sed-iments. In situ sedimentary geochemical processes, such as authigenic clay formation, further fractionate Siisotopes and increase pore-water δ30SiDSi values, which then leads to pore-water DSi flux carrying higher δ30SiDSi

compositions into deep water. Our findings provide new quantitative information on how the isotope-based Sicycle responds to human perturbations in coastal seas and shed lights on shifts of Si export to open ocean.

Coastal areas cover only 10% of the world’s ocean, but areresponsible for a third of the global primary production(Cloern et al. 2014). Diatom, a phytoplanktonic microalgaethat requires dissolved silicate (DSi) to build their siliceousfrustules (further referred to as biogenic silica [BSi]), accountsfor approximately half of primary production and up to 40%of carbon export in coastal areas (Field et al. 1998). Therefore,understanding of the biogeochemical Si cycle is of particularimportance. More specifically, availability of DSi in coastalareas and processes controlling its dynamics are prominent forsustainability of marine primary production and nutrientexport to the open ocean.

However, during the recent decades, the Si cycles in coastalseas have undergone drastic changes driven by climate changeand increasing anthropogenic pressures, such as eutrophica-tion, and change of riverine loads due to land use changes(Tréguer and De La Rocha 2013). Since the 1980s, substantialDSi concentration decreases in coastal seas have been observed

worldwide (cf. Ragueneau et al. 2006a and references therein)and DSi-limited primary production has become a commonfeature in coastal areas (e.g., Turner et al. 1998). This decreasehas been attributed to a decline in DSi river loading due to riverregulation (i.e., reservoirs, dams, etc.) as well as eutrophicationin river basins that sequesters DSi in riverine networks(e.g., Humborg et al. 2000). Meanwhile, widespread eutrophica-tion in coastal seas caused by additions of N and P has led toan increase in diatom biomass and subsequent BSi sedimenta-tion, which further reduces DSi concentrations and ultimatelyresults in a decline in the water column DSi reservoir(e.g., Conley et al. 1993). It has been suggested that the coastalsea is likely to further progress toward Si-limited phytoplank-tonic growth (Tréguer and De La Rocha 2013). In contrast, ben-thic fluxes from sediments are estimated to be an importantinput of DSi to deep water particularly in eutrophic coastal sys-tems from the recycling of BSi deposited in the sediment, forexample, the Black Sea (Friedl et al. 1998) and the Gulf of Fin-land (Tallberg et al. 2017). Tallberg et al. (2017) assessed thatmore than half of the sedimentation flux of DSi is released backinto the water column in the Gulf of Finland. Enhanced dia-tom production and export draws down the DSi in the surface

*Correspondence: zlzhang@stu.xmu.edu.cn

Additional Supporting Information may be found in the online version ofthis article.

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water, and leads to higher sedimentation and the subsequentbenthic release which brings DSi back into the ecosystem. Thiscascade of processes indicates significant internal recycling of Siwithin the system. The exchange of water from the open oceanalso influences the distribution of DSi in coastal areas, rangingfrom as low as 5% to almost 100% of the total DSi supply(Ragueneau et al. 2006b and references therein), which is alsodemonstrated to be sensitive to recent global warming (Hordoiret al. 2017). Further changes in the Si cycle related to anthropo-genic perturbations are expected to increase in coastal systems(Laruelle et al. 2009) and thus detailed investigations of Sidynamics will allow for exploring ways of mitigation in suchcoastal areas. Determining the impacts of human pressures onSi cycling, especially in high-latitude systems where effects ofglobal warming are amplified, is major challenge to the man-agement of this anthropogenically impacted ecosystem (Reuschet al. 2018).

Stable Si isotopes are a powerful tool for identifying Sisources and tracking Si processes over various temporal andspatial scales (Tréguer and De La Rocha 2013). Si isotope frac-tionation occurs during formation of siliceous diatom frustulesthrough preferential uptake of lighter Si isotopes over heavierones (De La Rocha et al. 1997) and possibly during dissolution(Demarest et al. 2009). A recent study also observed Si isotopefractionation in the sediment during formation of authigenicclay minerals (Ehlert et al. 2016). Variations in Si isotopevalues of both DSi (δ30SiDSi) and BSi (δ30SiBSi) in the ocean canbe used to reflect either biological activity or mixing betweendifferent water masses carrying distinct isotope ratios (e.g., deSouza et al. 2012). In the past three decades, both field andmodeling studies on Si isotopic distributions have increased inopen oceans, which display a typical pattern of δ30SiDSi verti-cal distribution with higher values in the surface layer due tobiological uptake compared to deep water (Sutton et al. 2018).Only a few field studies have been conducted in coastal areas(Cao et al. 2012, 2015; Ehlert et al. 2012; Singh et al. 2015;Zhang et al. 2015; Grasse et al. 2016; Cassarino et al. 2017)mainly focusing on controlling mechanisms of Si isotopic dis-tributions. However, influence of human disturbances on stableSi isotope distribution in coastal ecosystems has been under-studied, leading to large uncertainty in the present-day Si iso-tope budget.

The Baltic Sea is a semi-enclosed coastal sea, which is proneto anthropogenic perturbations and can serve as a representa-tive of systems disturbed by a variety of multistressors (Reuschet al. 2018). A significant decline in DSi concentration wasobserved throughout the Baltic Sea between 1970 and 1990(e.g., Papush and Danielsson 2006), due to silicon retention inriver basins and autochthonous diatom production in the BalticSea (Conley et al. 2008). Internal recycling, including benthicprocesses and exchange through the sediment–water interface,significantly influence nutrient dynamics in the Baltic Sea(Conley et al. 2009a). In addition, it has been suggested that

the overturning circulation in the Baltic Sea will decrease fromthe end of the 20th century toward the end of the 21st century,due to increasing stratification and decreasing saltwater inflowfrom the North Sea (Hordoir et al. 2017).

In this study, we combine a systematic investigation of Siisotope distributions in the Baltic Sea with a two-box isotopemodel to (1) reveal the major factors and processes controllingSi dynamics (both concentrations and isotope values) and(2) assess the influence of various anthropogenic and naturalperturbations on Si isotope distributions in the water columnof coastal areas.

Materials and methodsStudy area

The Baltic Sea (Fig. 1) is the world’s second largest brackishsea with an area of 3.93 × 105 km2 and an average depth of55 m. It is a semi-enclosed, inland and microtidal sea surroundedby nine highly industrialized countries. The Baltic Sea is geo-graphically divided into different basins connected by narrowsills and channels. This study covers the following areas: theTransition Zone (Skagerrak, Kattegat, Sound) connecting theBaltic Sea to the North Sea and the Atlantic Ocean, the SouthernBaltic Sea (Arkona Basin, Bornholm Basin, Gdansk Deep) cover-ing the part south of 56�N in the Baltic Sea, and the GotlandBasin (Eastern Gotland Basin, Western Gotland Basin), which isthe largest central basin of the Baltic Sea.

The Baltic Sea is fed with both freshwater from rivers(430 km3 yr−1) and salt water from the North Sea via the Transi-tion Zone (Matthäus 1995). The freshwater supply from riverscauses large salinity gradients between the northern sub-basinsand the entrance area, and between the well-mixed brackish sur-face layer and the saltier bottom layer. This results in an estuarine-like circulation which is characterized by outflowing brackishwater at the surface and inflowing saltier, denser water in the deeplayer (Neumann et al. 2017). The Gotland Basin is permanentlystratified between the less saline surface water and the more salinedeeper waterbodies. This results in poor natural ventilation and along water residence time of around 30 yr (Matthäus 1995) rela-tive to other coastal systems actively connected with the openocean, such as North Sea and Irish Sea (Prandle 1984), making theBaltic Sea the world’s largest anthropogenic hypoxic zone due toeutrophication (Conley et al. 2009b).

Ventilation of the deepest waters in the Baltic Sea wasthought to be mainly driven by the gale-forced barotropic salt-water inflows (Meier et al. 2006), which can carry large amountsof oxygenated salt water (on average 193 km3; Matthäus andFranck 1992) to the bottom of the central basins and are referredto as Major Baltic Inflows (MBIs). MBIs occurred with a periodic-ity of once a year before 1983 (Matthäus 2006) and sporadicallythereafter with a periodicity of about once a decade (1993, 2003,and 2014; Neumann et al. 2017). They usually progressed north-ward through the Eastern Gotland Basin, encircle Gotland Islandcounterclockwise, and finally moved southward through the

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Western Gotland Basin (Fig. 1), oxygenating the central basin ofthe Baltic Sea. However, a recent study (Neumann et al. 2017)suggests that oxygen concentration of the MBI seems to be ofless importance for oxygenation effect on the central BalticSea due to strong dilution effect. In contrast, minor inter-mittent inflows driven by the baroclinic pressure gradientthat occur throughout the year are shown to have greaterimpacts on the Baltic environmental conditions than previ-ously thought (Mohrholz et al. 2006).

Cruise in March 2016—sampling and analysesSampling was conducted onboard the R/V Aranda during a

spring cruise (15th–22nd March 2016) organized by SwedishMeteorological and Hydrological Institute (SMHI) as a part ofthe Swedish Marine Monitoring Program, along a transect fol-lowing the general pathway of the incoming salt water fromthe North Sea (Fig. 1; Supporting Information Table S1). Sea-water samples were collected for analyses of oxygen,chlorophyll a (Chl a), nutrients (DSi, dissolved inorganicnitrogen [DIN; NO−

3 +NO−2 +NH+

4 ], and dissolved inorganicphosphate [DIP]), and stable Si isotopes of DSi (δ30SiDSi).

Nutrients, oxygen, and Chl a concentrations were analyzedimmediately onboard following the Baltic Marine EnvironmentProtection Commission (HELCOM) COMBINE Manual (Ulfsboet al. 2011). Nutrient concentrations were determined by seg-mented flow analysis using classic spectrophotometric methods.Uncertainties of DSi, DIN, and DIP concentration measurementwere 8%, 7%, and 12% for low concentrations (0.1–5.0 μmol L−1,0.10–1.50 μmol L−1, and 0.02–0.20 μmol L−1, respectively) and

3%, 4%, and 4% for high concentrations (5.0–200.0 μmol L−1,1.5–50.0 μmol L−1, and 0.20–10.00 μmol L−1, respectively). Oxy-gen concentrations were determined by the potentiometric titra-tion method, with an uncertainty of 6%. Chl a concentrationswere measured by ethanol extraction and fluorometric detectionwith uncertainties of 20% for low concentrations (0.2–1.0 μg L−1)and 12% for high concentrations (1.0–100.0 μg L−1). High-resolution temperature, salinity, and oxygen data were obtainedfrom a conductivity-temperature-depth (CTD) recorder, and oxy-gen concentrations were later calibrated with bottle dataobtained by the potentiometric titration method. In addition,nutrient concentrations and phytoplankton species identificationat the same sampling locations in January and February 2016from SMHI Shark Database (https://sharkweb.smhi.se/) were usedfor comparison, which were based on the SMHI monthly moni-toring cruises within the same program as our sampling cam-paign in March.

Samples for δ30SiDSi measurements were purified using atwo-step brucite-coprecipitation method (Reynolds et al. 2006),followed by an ion-exchange chromatography procedure(Georg et al. 2006). δ30SiDSi measurements were then carriedout on a Nu Plasma II multicollector inductively coupledplasma mass spectrometer (MC-ICP-MS; Nu Instruments, UK)at Vegacenter, Swedish Museum of Natural History, Stockholm.Si isotopic composition is reported as δ30Si in ‰ deviationrelative to the international standard NBS28 (δ30Si =[(30Si/28Si)sample/(

30Si/28Si)NBS28 − 1] × 1000). The uncertainty offield δ30Si data provided in this contribution (SupportingInformation Table S1) is given as 2 standard deviations (2SD) of

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Fig. 1. Bathymetric map of the Baltic Sea showing the sampling stations in the Transition Zone (Skagerrak [Sk], Kattegat [Ka], Sound [Sd]), the SouthernBaltic Sea (Arkona Basin [AB], Bornholm Basin [BB], Gdansk Deep), and the Gotland Basin (Eastern Gotland Basin [EGB], Western Gotland Basin [WGB]).Note that the Gotland Deep (Sta. BY15) is located in the central area of the EGB. The red arrow indicates the transect following the general pathway ofthe incoming salt water from the North Sea. The inset shows a zoom-out view of the sampling area.

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the average δ30Si value from either the bracketing measure-ments of the same sample solution during a single day(2SDbracketing �0.01–0.25‰) or duplicate sample measurementson different days (2SDduplicates �0.09–0.17‰) for 12 out of thetotal 80 samples. Long-term reproducibility is 0.09‰ (2SDexternal),based on the repeated measurements of the seawater standardALOHA1000m (1.21 � 0.09‰; n = 19). When duplicates are notavailable, we assume that the long-term reproducibility is represen-tative of the uncertainties of the δ30SiDSi data unless 2SDbracketing islarger than 2SDexternal. Additionally, secondary standards—Diatomite and Big Batch—weremeasured during the entire sessionand yielded values in good agreement with reported values(Reynolds et al. 2007). Linear regressionof δ29Si vs. δ30Si for all sam-ples (δ29Si = 0.5063 × δ30Si; R2 = 0.92) falls on themass-dependentfractionation lines, indicating polyatomic interferences-free deter-mination of δ30Si (Supporting Information Fig. S1). More detailedinformationon sampling and analyses of δ30SiDSi and instrumenta-tion can be found in Supporting Information Text S1andTable S2.

Two-box modelModel description

A two-box model, including two distinct periods: thepreindustrial period (before 1950s) and the industrial period(mid-1960s to 2016), was used to examine impacts of humandisturbances on the Si dynamics in the Baltic Sea. A conceptualrepresentation of the model describing the Si flows between dif-ferent compartments of the Baltic Sea can be found in Fig. 2.All equations used in the model can be found in SupportingInformation Text S1. The boundary was set between the ArkonaBasin and the Bornholm Basin. The model was divided into asurface box above the halocline (volume, Vs, km

3) and a deepbox below the halocline (Vd, km3). The model simulatedchanges in salinity, DSi concentration, and δ30SiDSi in the sur-face box (Ss, DSis, and δ30SiDSi_s, respectively) and the deep box(Sd, DSid, and δ30SiDSi_d, respectively), and δ30Si of diatoms insediment (δ30SiBSi) and the pore waters (δ30SiDSi_pw) over time.

As shown in Fig. 2, DSi is supplied to the surface box viariverine inputs (water flux, Wriver, km

3 yr−1; salinity, Sriver; DSiconcentration, DSiriver, μmol L−1; δ30Si value, δ30SiDSi_river, ‰)and upwelling from the deep box (Wup, Sd, DSid, δ30SiDSi_d),delivered to the deep box via inflow from the Arkona Basin(Winflow, Sinflow, DSiinflow, δ30SiDSi_inflow) and downwellingfrom the surface box (Wdown, Ss, DSis, δ30Sis), and removedfrom the system via outflow to the Arkona Basin from the sur-face box (Woutflow). In the surface box, a portion of DSi wastransformed into BSi by diatoms (flux of BSi gross production,F(BSiproduce)) with the remaining portion (f ) unutilized.BSiproduce is partially exported to the deep box (F(BSiexport)) asa function of the production to export ratio (RPE). Part of thisBSiexport is dissolved in the deep box and at the sediment–waterinterface (F(BSidiss), δ30SiBSi) while the rest is deposited in sedi-ments. Deposited BSi in sediments either is buried as BSi(F(BSiburial)) as a function of the opal preservation efficiency(Eburial), or dissolves back into sediment pore water (F(BSidissolve in

sediment)). A fraction of the dissolving BSi (frw) reprecipitates inthe form of authigenic clay minerals (F(Sirw)), and the rest is ulti-mately released back to the deep box through pore-water DSi fluxacross the sediment–water interface (F(DSipw), δ30SiDSi_pw).

Si isotopes are fractionated in the surface box during diatomproduction (30εuptake), which 30εuptake value is reported to be−1.10 � 0.40‰ (De La Rocha et al. 1997) and ranges from−0.54‰ to −2.09‰ for polar and subpolar marine diatoms(Sutton et al. 2013). The formation of authigenic clay minerals(30εrw) in the sediment has only been estimated to be −2.00‰in the Peruvian upwelling region (Ehlert et al. 2016). Si isotopic

Surface (S

s, DSi

s, δ30Si

DSi_s)

Deep(S

d, DSi

d, δ30Si

DSi_d)

Halocline

Sediment(δ30Si

DSi_pw, δ30Si

BSi)

Woutflow

Wup

Wdown

Wriver

(Sriver

, DSiriver

, δ30SiDSi_river

)

F(BSiproduce

)

F(BSidiss

)

F(BSiBurial

)

F(DSipw

)

F(BSiexport

)

F(Sirw

)

Winflow

(Sinflow

, DSiinflow

, δ30Siinflow

)

Fig. 2. Conceptual representation of the two-box model describing theflows of Si between the surface and the deep boxes. Black arrows and textsindicate water fluxes (km3 yr−1) and their salinity, DSi concentration(μmol L−1), and δ30SiDSi value (‰). Wriver, Woutflow, Winflow, Wup, andWdown: volume of the river input, outflow, inflow from the Arkona Basin,upwelling, and downwelling, respectively; Sriver, DSiriver, and δ30SiDSi_river:salinity, DSi concentration, and δ30SiDSi value of the river input, respectively;Sinflow, DSiinflow, and δ30SiDSi_inflow: salinity, DSi concentration, and δ30SiDSivalue of the inflow from Arkona Basin, respectively. Orange arrows andtexts indicate Si fluxes involved in biogeochemical processes(× 106 mol yr−1). F(BSiproduce), flux of BSi gross production; F(BSiexport), fluxof BSi exported toward the deep box; F(BSidiss), flux of BSi dissolved in thedeep box and at the sediment–water interface; F(BSiburial), flux of BSi buriedin sediments; F(Sirw), flux of the dissolving BSi in sediments that repre-cipitates in the form of authigenic clay minerals; and F(DSipw), pore-waterDSi flux across the sediment–water interface. Blue texts indicate the modelvariables of salinity, DSi concentration, and δ30Si value in different compart-ments. Ss, DSis, and δ30SiDSi_s: salinity, DSi concentration, and δ30SiDSi valueof the surface box, respectively; Sd, DSid, and δ30SiDSi_d: salinity, DSi con-centration, and δ30SiDSi value of the deep box, respectively. δ30SiDSi_pw andδ30SiBSi: δ30Si value of the pore waters and sediment diatoms, respectively.

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fractionation during the dissolution of BSi (30εdiss) is alsoassessed in the literature with an isotope fractionation of−0.55 � 0.05‰ (Demarest et al. 2009); however, other studiesreported no Si isotope fractionation (e.g., Sun et al. 2014; Wetzelet al. 2014; Gao et al. 2016). We tested the influence of this puta-tive fractionation on the distribution of Si isotopes and saw neg-ligible impact (Supporting Information Fig. S4, XI). Thereby, theSi isotope fractionation factor during BSi dissolution has notbeen taken into account in our model calculations.

Note that we include the authigenic clay mineral formationprocess in the model since our unpublished data show meanδ30Si values of pore waters (δ30SiDSi_pw, 1.67 � 0.11‰, n = 52)were higher than that of diatoms (δ30SiBSi, 1.21 � 0.17‰,n = 23) in five short sediment cores (10–17 cm long) from theEastern Gotland Basin sampled in August/September 2013,July/August 2015, and March 2016, respectively (Sommeret al. 2017), suggesting the potential occurrence of thisprocess.

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Fig. 3. Distributions of temperature (a), salinity (b), and oxygen (c) along the transect shown in Fig. 1. High-resolution temperature, salinity, and oxy-gen data were obtained from a CTD recorder. Plots created with ODV (R. Schlitzer, Ocean Data View, 2009, available at http://odv.awi.de) using bathym-etry data from high-resolution “GEBCO_2013_6×6min_Global” database.

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Model initializationThe initial conditions represented those of the Ancylus

Lake stage when the system was filled with freshwater fromthe continent (Yu 2003) and isolated from the North Sea.Conley et al. (2008) estimated the pristine yields of DSi foreach geological subregion of the Baltic catchment, providingthe pristine value of DSiriver of 80.0 μmol L−1. δ30SiDSi_river wasset at 0.80‰ for the pristine condition (De La Rocha and Bickle2005). Therefore, the initial conditions of the model were set at80.0 μmol L−1 of DSi with a δ30SiDSi value of 0.80‰ in bothsurface and deep boxes, equal to the values of the pristine riverinput. The water inflow from the Arkona Basin was set at

8.0 μmol L−1 of DSiinflow with a δ30SiDSi_inflow of 1.80‰,corresponding to the mean values in the Arkona Basin obtainedfrom field data in this study. In the surface box, the unutilizedDSi fraction (f ) was set at 0.65 and the ratio of production toexport (RPE) was set at 2, which was adopted from the value inthe Bay of Brest considered as representative of global averagefor coastal zones (Tréguer and De La Rocha 2013). Based on thecalculation in “Si isotope fractionation during diatom growthin the mixed layer” section, 30εuptake was −1.03‰ using thesteady-state model. Ehlert et al. (2016) found that approxi-mately 24% of the dissolving BSi is reprecipitated in the sedi-ments in the form of authigenic clay minerals and estimated

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(b) δ30SiDSi (‰)

(c) Chl a (μg L–1)

Fig. 4. Distributions of DSi (a), δ30SiDSi (b), and Chl a (c) along the transect shown in Fig. 1. Sampling positions/depths are marked by black dots. Depthprofiles of δ30SiDSi values are superimposed on panel (a) with white dots and lines. Plots created with ODV (R. Schlitzer, Ocean Data View, 2009, availableat http://odv.awi.de) using bathymetry data from high-resolution “GEBCO_2013_6×6min_Global” database.

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the fractionation factor of −2.00‰ between the precipitatesand the pore waters using a reactive-transport model. We thusset 30εrw to −2.00‰ and frw to 0.24 in the model. Conley et al.(2008) suggested the overall estimated F(BSiburial) was about324 ktons Si yr−1 in the relatively unperturbed Baltic Sea, thusEburial was calculated to be 0.07 accordingly. We adapted waterfluxes from Papush et al. (2009) which used a 10-box model tocalculate water exchanges between the major basins in the

Baltic Sea and with the Arkona Basin, assuming unchangedhydrological conditions for the whole period. All the initialconditions are summarized in SupportingInformation Table S3.

Perturbations during the industrial periodDuring the industrial period, the model was run for 51 yr

(mid-1960s to 2016) with various anthropogenic and naturalperturbations, including damming, eutrophication, and stratifi-cation. These perturbations are fed in the model as a linearchange of different parameters over the industrial period insteadof an instantaneous pulse. Corresponding parameter changesdue to different perturbations will be discussed further below.

ResultsField study in March 2016Hydrographic settings

In March 2016, sea surface temperature decreased from theTransition Zone to the Gotland Basin (Fig. 3a), due to earlierspring warming in the shallow Transition Zone. The variationsof salinity from the Transition Zone to the Gotland Basin wereparticularly pronounced (Fig. 3b), decreasing quickly from32.0 to 9.0 in the Transition Zone, and varying within a smallrange between 9.0 and 6.5 afterward, in the surface waters.The halocline was well established and the mixed layerdepth—as defined by Levitus (1983)—increased from �5–10 min the Transition Zone to �50–60 m in the Gotland Basin.Surface waters in the Transition Zone and the Southern BalticSea resulted from a mixture of the brackish, cold GotlandBasin surface water and the saline, warm Skagerrak surfacewater. In addition, deep waters in the Bornholm Basin werelargely influenced by the intrusion of North Sea surface waterwith high salinities.

Variables Pristine valuesSensitivity tests of parameters in industrial period

Ss

Sd

δ30SiBSi

δ30SiDSi_pw

DSis

DSid

δ30SiDSi_s

δ30SiDSi_d

Δδ30SiDSi_ds

4.7

7.1

0.77‰

1.25‰

28.8 μmol L−1

83.4 μmol L−1

1.44‰

1.10‰

-0.34‰

DSiriver ↓ δ30SiDSi_river ↑ f ↓ RPE ↓ F(DSipw) ↑ Eburial ↑ frw ↑ DSiinflow ↑ δ30SiDSi_inflow ↑ W inflow ↓× 0.67

1.0

1.0

0.9

0.9

0.8

0.8

1.0

1.0

1.1

0.0

+ 0.36

1.0

1.0

1.0

1.0

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1.2

1.2

1.2

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0.3

= 0.1

1.0

1.0

1.5

1.3

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1.4

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× 0.5

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1.0

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1.1

1.1

0.8

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Fig. 6. Heat map chart of sensitivity tests of all parameters. The values are the magnification of model outputs running with certain change in eachparameter compare to the preindustrial values (preindustrial equilibrium level without perturbations). Colors highlighting the values depict the impacts ofparameter changes on different variables (red, increase; white, no impact; and blue, decrease). Please see the description of all parameters and variablesin “Two-box model” section and Fig. 2 caption. Δδ30SiDSi_ds represents the difference of δ30SiDSi values between deep and surface box(i.e., δ30SiDSi_d − δ30SiDSi_s). Detailed results of the sensitivity tests can be found in Supporting Information Fig. S4.

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Gotland Basin Southern Baltic Sea

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Fig. 5. δ30SiDSi vs. 1/DSi for all samples, showing three major watermasses with distinct signatures of DSi concentration and isotopes inMarch 2016 in the Baltic Sea. Hollow circles and solid triangles indicatesamples from depth < 60 m and depth > 60 m, respectively. Orange,blue, and green symbols indicate samples from the Gotland Basin, theSouthern Baltic Sea, and the Transition Zone, respectively.

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Dissolved oxygen was saturated in the surface waters, whiledecreasing from �6.0 mL L−1 in the Transition Zone to completedepletion in the Gotland Basin in the deep waters (Fig. 3c). OneMBI in 2014 (MBI 2014) and two following moderate inflows in2015 and 2016 (represent asMBI 2015 andMBI 2016 in this study)entered theBaltic Sea before our sampling andpropagated as densebottom currents influencing the deep water (Mohrholz 2018).Information on these MBIs can be found in SupportingInformation Table S4. As a result of these oxygen-rich MBIs, theSouthern Baltic Sea and Eastern Gotland Basin were well venti-lated, although hypoxia was still observed at depths below70–80 m in the Gotland Deep (Fig. 3c). Different water masses areidentified via their distinct temperature, salinity, and oxygen con-centration in a T-S diagram (Supporting Information Fig. S2).

Distribution of DSi and δ30SiDSi

DSi concentrations ranged from 0.1 to 62.8 μmol L−1 in thewater column, increasing both vertically from surface to bot-tom and horizontally from the Transition Zone to the GotlandBasin (Fig. 4a). In the mixed layer, DSi was nearly depleted inthe Transition Zone and significantly enriched in the GotlandBasin with concentrations up to 18.1 μmol L−1 (Fig. 4a).

δ30SiDSi values ranged from 1.24 to 2.29‰ in the water col-umn (Fig. 4b), falling in the range observed in other coastal sys-tems (Cao et al. 2012, 2015; Ehlert et al. 2012; Singh et al. 2015;Zhang et al. 2015; Grasse et al. 2016; Cassarino et al. 2017). In themixed layer, δ30SiDSi values decreased from the Transition Zoneto the Gotland Basin showing large variation (Fig. 4b). While thehighest δ30SiDSi value of 2.29 ‰ was observed in the mixed layerof the Kattegat, the lowest δ30SiDSi value of 1.24‰ was observedin that of the Western Gotland Basin. Note that due to DSi deple-tion, no δ30SiDSi data were obtained in the mixed layer of theSkagerrak. Vertical distributions of δ30SiDSi in the Transition Zoneand the Southern Baltic Sea mirrored those of DSi concentrations(Fig. 4b), showing a general decrease with increasing depth asobserved in other marine systems (e.g., Cao et al. 2012). In con-trast, water column profiles of δ30SiDSi in the Gotland Basin dis-played an atypical pattern with lower values in the surface waters(1.24–1.68‰) compared to the deep waters (1.57–1.95‰), whichis for the first time observed in marine systems (Fig. 4b).

We can identify three major water masses with distinct sig-natures of DSi concentrations and isotopes (Fig. 5): (1) thehighly fractionated surface water in the Transition Zone withthe highest δ30SiDSi values up to 2.29‰; (2) the surface waterin the Gotland Basin with the lowest δ30SiDSi values as low as1.24‰; and (3) the deep water in the Gotland Basin withhighest DSi concentrations up to 62.8 μmol L−1 and higherδ30SiDSi values (�1.80‰) than in the overlying surface waters.

Two-box model resultsPreindustrial period

From the initial conditions—representing the Ancylus Lakestage—the system reached equilibrium in less than 200 yr(Supporting Information Fig. S3). At equilibrium, surface

salinity (Ss) and deep salinity (Sd) were in good agreement withmodern values (Ss = 4.7 and Sd = 7.1 from the model; Ss �5 andSd �8 from Papush et al. 2009). Concentrations of DSi in thesurface and deep layer equilibrated at 28.8 μmol L−1 and83.4 μmol L−1, respectively, which are similar to previous obser-vation of DSi concentrations in the Baltic Sea in the 1950s(�30.0 μmol L−1 and �80.0 μmol L−1, respectively; Conleyet al. 2008). The δ30SiDSi values show a typical vertical distribu-tion with higher values in the surface layer (1.44 ‰) than inthe deep layer (1.10 ‰). Unfortunately, no δ30Si data precedingthe industrial period exist for validation of the modeled δ30Sivalues.

Sensitivity tests of all parameters in the industrial periodField data from this study are used to constrain a “best-fit”

of parameter changes in the industrial period (SupportingInformation Table S5), which is defined as the set of parameterchanges that best reproduce the data—that is, the set ofparameter changes that reproduce salinity, DSi, and δ30SiDSi insurface and deep box and δ30Si of diatoms and pore waters insediments closest to the measured values in this study. Sensi-tivity tests of different parameters in the industrial period revealdominant parameters controlling salinity, DSi concentration,and δ30SiDSi value in the Baltic Sea (Fig. 6). Salinity is only alteredby the saltwater inflow flux (Winflow). DSi concentrations aremost sensitive to the unutilized DSi fraction (f) and the ratio ofproduction to export (RPE) in the surface. The difference ofδ30SiDSi values between surface and deep layers (Δδ30SiDSi_ds,i.e., δ30SiDSi_d − δ30SiDSi_s) diminishes when the DSi utilizationratio increases (i.e., f decreases) and pore-water DSi flux (F(DSipw))increases. In particular, increases of surface DSi utilization ratioreduces Δδ30SiDSi_ds to the greatest extent, hence has the largestpotential to derive higher δ30SiDSi values in the deep layer thanin the surface. Detailed results of the sensitivity tests are givenin Supporting Information Fig. S4. Mechanisms behind thesechanges will be discussed further below.

DiscussionThe vertical distribution of δ30SiDSi in the Gotland Basin

exhibits an atypical pattern. Contrary to the majority of marinesystems, where vertical distribution of δ30SiDSi displays highervalues in surface water compared to deep water due to the bio-logical uptake of DSi in the euphotic zone, the Gotland Basinharbors higher δ30SiDSi values in the deep water relative to thesurface. This pattern of increasing δ30SiDSi with water depth isthe first observation of its type in the global ocean. The tradi-tional narrative might not be appropriate to describe the Sicycle in the Gotland Basin. In order to further investigate thisatypical vertical profile, a two-box model was employed to sim-ulate the Si dynamics in the Baltic Sea. Below we start by dis-cussing the mechanisms controlling the surface δ30SiDSi

distribution and resolving the 30εuptake value in the mixed layer,which is then used in the two-box model. This is followed by adiscussion on the impacts of different perturbations on Si

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dynamics during the industrial period and resolving the mecha-nisms controlling the atypical vertical distribution of δ30SiDSi inthe Gotland Basin using the two-box model.

Si isotope fractionation during diatom growth in themixed layer

In order to investigate the mechanisms behind the largespatial variations of DSi concentrations (0.1–18.1 μmol L−1)and δ30SiDSi values (2.29–1.24‰) observed from the Transi-tion Zone to the Gotland Basin in the mixed layer, temporalvariations of nutrient concentrations (DIN, DIP, and DSi;Fig. 7) combined with Chl a concentrations (Fig. 4c) and phy-toplankton community compositions (Skjevik 2016a,b) in themixed layer were used to assess the blooming conditions inMarch 2016. In the Transition Zone, nutrient concentrationschanged only marginally from January to February but a large

proportion was consumed in March. This was associated withhigh Chl a concentrations and diatom-dominated phyto-plankton community in March (Skjevik 2016b). In the South-ern Baltic Sea, moderately high Chl a concentrations, low DSiconcentrations (nondepletion), and the presence of variousspecies of diatoms (Skjevik 2016b) indicated the beginning ofthe spring bloom in March. In contrast, in the Gotland Basin,high DSi concentrations, low Chl a concentrations, and lowphytoplankton cell concentrations from January to March(Johansen 2016; Skjevik 2016a,b) implied that the diatombloom had not yet started. Therefore, it is likely that the varia-tions of DSi concentrations and δ30SiDSi values in the mixedlayer from the Transition Zone to the Gotland are reflection ofdifferences in timing of the spring bloom, which is consistentwith previous studies (Wasmund et al. 2013).

However, biological activity is not the sole mechanismbehind this spatial gradient. Physical mixing is another pro-cess that may alter Si distribution. To test this, an overallphysical mixing scheme was assumed in the mixed layerbetween the Gotland Basin and the Atlantic Ocean (Fig. 8).The Gotland Basin surface water was considered as a conser-vative endmember in our sampling season, due to the lowbiological activity and absent diatom growth. A twoendmember mixing model was used to separate biologicalactivity from physical mixing. Salinity, DSi concentrations,and δ30SiDSi values following conservative mixing (denotedwith “con” subscript) are:

Salinitycon = Salinitye1 × Fe1 + Salinitye2 × Fe2 ð1ÞDSicon =DSie1 × Fe1 +DSie2 × Fe2 ð2Þ

δ30SiDSi_con =δ30SiDSi_e1 ×DSie1 × Fe1 + δ30SiDSi_e2 ×DSie2 × Fe2

DSie1 × Fe1 +DSie2 × Fe2

ð3Þ

where “Fe1” and “Fe2” are the fractions of the two endmemberwater masses e1 and e2, the Gotland Basin surface water andthe Atlantic Ocean surface water, respectively. DSicon concen-trations and δ30SiDSi_con values are then used as the initial con-dition prior to any chemical reactions or biological uptake ofDSi. Salinity, DSi concentration, and δ30SiDSi values for theGotland Basin endmember are the averages in the mixed layerfrom Sta. BY10, BY20, BY32, and BY38 (7.1 � 0.2 μmol L−1,15.7 � 1.3 μmol L−1, and 1.39 � 0.05‰, respectively). As noδ30SiDSi data for the North Sea endmember are available in theliterature, we chose an Atlantic Ocean endmember for salin-ity, DSi concentration, and δ30SiDSi values (35.6 μmol L−1,0.9 μmol L−1, and 2.89 � 0.18‰; Sta. 69/14, 40 m; de Souza et al.2012). Sensitivity test of the seawater endmember can be foundin Supporting Information Text S2.

Both the DSi concentrations and the δ30SiDSi values showdeviations from the physical mixing line (Fig. 8), indicatingthat spatial variations of DSi concentrations and δ30SiDSi

values were caused by a combination of physical mixing and

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Fig. 7. Mixed layer DSi (a), DIN (b), and DIP (c) concentrations at thesame sampling locations along the transect shown in Fig. 1 in January2016 (red circles), February 2016 (blue triangles), and March 2016 (greensquares). The vertical error bars indicate standard deviations of mean valuesin the mixed layer. Data in January and February are from SMHI Shark Data-base (https://sharkweb.smhi.se/) based on the SMHI monthly monitoringcruises within the same program as our sampling campaign in March.

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biological processes in the mixed layer. DSi concentrationsshow a removal of DSi while δ30SiDSi values reveal enrich-ment in 30Si—consistent with biological uptake of DSi—especially in the Transition Zone. Si isotope fractionationduring DSi consumption can be described using either aRayleigh or a steady-state model (cf. Cao et al. 2012 and refer-ences therein). The Rayleigh model describes a closed systemwithout further input of the substrate (DSi) from externalsources, and is illustrated by the following equations:

δ30SiDSi_obs = δ30SiDSi_ini + 30εuptake × ln fMarch ð4Þ

fMarch =DSiobsDSiini

ð5Þ

where the subscripts “obs” and “ini” denote observed and ini-tial values, respectively, with the latter equal to δ30SiDSi_con

and DSicon in Eqs. 2, 3. The term fMarch indicates the fractionof remaining DSi in March relative to the initial concentra-tion. The Si isotope fractionation factor 30εuptake is thus esti-mated as:

30εuptake =δ30SiDSi_obs−δ30SiDSi_ini

ln f=δ30SiDSi_obs−δ30SiDSi_ini

lnDSiobsDSiini

ð6Þ

In contrast, the steady-state model describes an open sys-tem with a continuous supply of substrate (DSi) from externalsources:

δ30SiDSi_obs = δ30SiDSi_ini + 30εuptake × 1− fMarch

� � ð7Þ

In this case, 30εuptake is calculated as:

30εuptake =δ30SiDSi_obs−δ30SiDSi_ini

1− fMarch=δ30SiDSi_obs−δ30SiDSi_ini

1− DSiobsDSiini

ð8Þ

Using Eqs. 1–3, we obtain the expected initial DSi andδ30SiDSi values. Based on Eq. 5, we estimate that the DSi draw-down (1 − fMarch) in the surface water ranges from 87% in theTransition Zone to 16% in the Bornholm Basin during our sam-pling period. 30εuptake is estimated to be −0.76 � 0.57‰ by theRayleigh model (Eq. 6) and −1.03 � 0.54‰ by the steady-statemodel (Eq. 8). These values represent the mean values andstandard deviations of calculated 30εuptake values for all sta-tions. 30εuptake values from both models agree well with the lit-erature ones ranging from −0.54‰ to −2.09‰ with an averagevalue of −1.10‰ during diatom production (e.g., De La Rochaet al. 1997; Milligan et al. 2004; Sutton et al. 2013; Meyerinket al. 2017). The 30εuptake value from the Rayleigh model falls atthe lower range, which might be caused by a decrease due to dis-solution (e.g., Grasse et al. 2016) or attributed to the semi-opensystem. In addition, the mean δ30SiDSi values showed both agood logarithmic relationship (R2 = 0.74) and a good linear rela-tionship (R2 = 0.81) with mean DSi concentrations in the surfacemixed layer (Supporting Information Fig. S5), which suggestthat the system might lie in between a closed system (Rayleighmodel) and an open system (steady-state model).

Mechanisms controlling the atypical vertical distributionof δ30SiDSi in the Gotland Basin

Equilibrium reached in preindustrial period from the box-model projection features a classical vertical distribution ofδ30SiDSi with higher values in the surface layer relative to thedeep layer. This implies that the observed atypical vertical distri-bution of δ30SiDSi in the Gotland Basin with lower values in thesurface waters may be caused by one or several perturbationsduring the industrial period such as damming, eutrophication,or changes in stratification. Model outputs from different pertur-bations based on the “best-fit” parameter changes during the

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Fig. 8. Mixed layer DSi concentrations (a) and δ30SiDSi values (b) alongthe salinity gradient in the Gotland Basin (orange circle), the SouthernBaltic Sea (blue circle), and the Transition Zone (green circle). Verticalerror bars indicate standard deviations of mean values in the mixed layer.Solid lines predict the conservative mixing for DSi and δ30SiDSi betweenthe Gotland Basin brackish water endmember (orange triangle) and Atlan-tic Ocean seawater endmember (red triangle), which were derived frommean values of field measurements in this study and from a previousstudy (de Souza et al. 2012), respectively. Dashed lines above and belowthe corresponding solid line indicate errors deduced from the uncertaintyin estimating the DSi and δ30SiDSi endmember values.

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industrial period reveal mechanisms responsible for this pattern(Table 1).

First, damming has been shown to drastically alter the DSiconcentration and δ30SiDSi value of rivers. Damming increasesthe diatom productivity upstream of the dam and decreasesDSi concentrations downstream, as it creates a lake-like system(Humborg et al. 2000). Furthermore, the Si isotope fraction-ation during diatom production results in an increase in theδ30SiDSi value downstream. Hughes et al. (2012) observed sig-nificant increase in δ30SiDSi_river value and decrease in DSiriverconcentration downstream of the Masinga Dam on the TanaRiver, Kenya. In our simulation, a decrease in riverine DSi con-centration (DSiriver) and an increase in δ30SiDSi_river value leadto a decrease in DSi concentrations and an increase in δ30SiDSi

values both in the surface and deep layers in the Baltic Sea(Table 1; Supporting Information Fig. S6). However, in thissetup, only the δ30SiDSi values are affected whereas the differ-ence of δ30SiDSi values between the surface and deep layers(Δδ30SiDSi_ds) is hardly impacted. Therefore, the influence ofdamming alone on the Si cycle in the Baltic Sea is not suffi-cient to explain the atypical vertical distribution of δ30SiDSi.

Second, eutrophication in the Baltic Sea greatly impacts theSi cycle. Here, eutrophication is considered on the basis ofabundant delivery of nutrients (mostly N and P) from the con-tinent (Andersen et al. 2017). An increase of nutrients stimu-lates the autochthonous growth of diatoms (Conley et al. 1993;Conley et al. 2008), leading to a higher degree of DSi utilization(i.e., “f” decreases) and enhanced diatom burial efficiency(i.e., Eburial increases; Schelske et al. 1983). It subsequentlyincreases pore-water DSi flux (i.e., F(DSipw) increases; Gehlenet al. 1995), as one can expect sediments to receive higheramounts of BSi due to the close pelagic-benthic coupling in theBaltic Sea (Griffiths et al. 2017).

Sensitivity analysis has shown that both enhanced utiliza-tion of DSi and increased pore-water DSi flux lead to smallerdifference of δ30SiDSi values between the surface and deep layers(Fig. 6; Supporting Information Fig. S4 III, V). Increase in DSiutilization in the surface layer results in a slight decrease inδ30SiDSi value in the surface layer. While this might seem coun-terintuitive if only the increase in the degree of DSi utilizationis taken into account, one must remember that yearly δ30SiDSi

values as implemented in our model represent a weighted aver-age of all inputs and outputs of Si from different compart-ments. Hence, as the surface DSi pool decreases due toincreasing DSi utilization by diatoms, the relative contributionof fresh DSi entering the surface layer from rivers and upwellingincreases. Since δ30SiDSi values of riverine inflow and upwellingare low compared to that of the surface waters after biologicaluptake, the resulting weighted average value of δ30SiDSi

becomes slightly lower. On the other hand, increased DSi utili-zation leads to the deposition of highly fractionated BSi(i.e., high δ30SiBSi value) which dissolves into the deep layer,contributing to raising δ30SiDSi in the deep layer. As a result, thedifference between δ30SiDSi values in the surface and in thedeep layers decreases. Increased pore-water DSi flux leads tohigher δ30SiDSi values in both surface and deep layers. In sedi-ments, part of BSi dissolves into pore water and contributes tothe formation of clay minerals that leaves heavy Si isotopes inpore waters (Ehlert et al. 2016). The pore waters carrying higherδ30SiDSi values in the top-most sediments then diffuse into thebottom water, which in turn increase δ30SiDSi values in deepwater and, to a much lower extent, in surface water.

The combined effect of eutrophication with increasing Si utili-zation, burial efficiency, and pore-water DSi flux leads to notice-able decrease of δ30SiDSi value in the surface and significantincrease of δ30SiDSi value in the deep layer, producing atypical

Table 1. Model outputs representing present conditions with different perturbations based on the “best-fit” parameter changes dur-ing the industrial period. Preindustrial equilibrium values and measured values in this study are also listed for comparison. Δδ30SiDSi_dsrepresents the difference of δ30SiDSi values between deep and surface layers (δ30SiDSi_d − δ30SiDSi_s).

Variables Unit Preindustry* Damming† Eutrophication‡Damming andeutrophication§ All perturbations|| Measured values

Ss 4.7 4.7 4.7 4.7 3.8 —

Sd 7.1 7.1 7.1 7.1 5.6 —

δ30SiBSi ‰ 0.77 0.90 1.18 1.38 1.37 1.21 � 0.17

δ30SiDSi_pw ‰ 1.25 1.37 1.66 1.84 1.84 1.67 � 0.11

DSis μmol L−1 28.8 22.4 12.0 8.4 8.5 15.6 � 3.8

DSid μmol L−1 83.4 67.1 61.4 43.9 51.9 51.4 � 8.3

δ30SiDSi_s ‰ 1.44 1.62 1.29 1.48 1.48 1.48 � 0.20

δ30SiDSi_d ‰ 1.10 1.22 1.56 1.76 1.74 1.73 � 0.10

Δδ30SiDSi_ds ‰ −0.34 −0.39 0.27 0.28 0.26 0.25

*Preindustrial equilibrium level without perturbations.†“Damming” includes the changes of DSiriver (× 0.67) and δ30SiDSi_river (0.36).‡“Eutrophication” includes the changes of f (= 0.1), F(DSipw) (× 2.5), and Eburial (× 2).§“Damming and eutrophication” includes all changes in “damming” and “eutrophication”.||“All perturbations” includes all changes in “damming and eutrophication” and the change of Winflow (× 0.5).

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vertical distribution of δ30SiDSi with higher δ30SiDSi value in thedeep layer relative to that in the surface (Table 1 and SupportingInformation Fig. S7). Furthermore, the combined effects of dam-ming and eutrophication sustained for ca 50 yr are able to pro-duce DSi concentrations and δ30SiDSi values in good agreementwith observed values from our field study (Table 1 andSupporting Information Fig. S8). Additionally, the output of F(BSiburial) was about 518 ktons Si yr−1 at present, which is in goodagreement with the model estimation by Conley et al. (2008)suggesting ca 620 ktons Si yr−1 for the present-day yield.

Last, global warming is thought to lead to increased thermalstratification (i.e., increased depth of the mixed layer) in theBaltic Sea, which results in a decrease in saltwater inflow influx(Winflow) corresponding to the overturning circulation (Hordoiret al. 2017). The impact of a decrease in saltwater inflow on thevertical distribution of δ30SiDSi is negligible compared to thoseof damming and eutrophication (Table 1 and Supporting Infor-mation Fig. S9). Another aspect of saltwater inflow to be consid-ered is the MBIs and their impact on Si isotope distributions.Nowadays, MBIs occur with a decadal frequency and can beassimilated as a pulse of saltwater entering the Baltic Sea. In ourmodel, MBIs can produce the atypical vertical distribution ofδ30SiDSi; however, this requires a MBI with a volume one-orderof magnitude higher than the yearly inflow (Supporting Infor-mation Fig. S10). This is very unlikely because the volume ofMBI 2014 was estimated to be 198 km3 of saline waters into theBaltic Sea, which was about half of the yearly inflow (Mohrholz2018). Furthermore, a recent study suggested that during anMBI event, only 10%, at most, of the Kattegat water can betransported to the central Gotland (Neumann et al. 2017).

From all the above, we infer that while damming increasesδ30SiDSi values in the system, it has little impact on the verticaldistribution of δ30SiDSi; instead, eutrophication-induced stronginternal recycling of Si is the main driver for the atypical verti-cal distribution of δ30SiDSi we observed. It has been shown inother coastal systems that benthic fluxes of DSi contribute topreventing DSi limitation in the overlying water column(Raimonet et al. 2013; Chen et al. 2019). In eutrophic coastalsystems, changes in the efficiency of internal Si recyclingunder anthropogenic perturbations may help to mitigate thepotential of DSi limitation and secure the prevalence of sili-ceous phytoplankton within the ecosystem. This suggests thatstrong pelagic-benthic coupling can play a critical role incoastal ecosystems, especially in a semi-enclosed eutrophicsystem where deep ventilation of the basins diminishes.

ConclusionsIn March 2016, the δ30SiDSi distribution in the mixed layer

of the Baltic Sea was primarily controlled by physical mixingand different stages in the spring diatom growth, with δ30SiDSi

values decreasing gradually from the Transition Zone (� 2.29‰)to the Gotland Basin (�1.24‰). Based on our box model, the Sicycle in the Baltic Sea was perturbed by various anthropogenic

and natural forcings, including damming, eutrophication, and,to a lesser extent, saltwater inflow. The most notable feature ofthe δ30SiDSi distribution in the Baltic Sea was deep water withhigher δ30SiDSi values (1.57–1.95‰) compared to those in thesurface water (1.24–1.68‰) in the Gotland Basin, which wascaused by eutrophication-induced strong internal Si recycling.This study reveals large impact of anthropogenic perturbationson the Si dynamics in the Baltic Sea and implies the importanceof pelagic-benthic coupling for marine ecosystem functioning ina changing world, which in turn alters the nutrient export toopen oceans.

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AcknowledgmentsWe would like to thank the SMHI crew for help with the auxiliary data

collection, Carla Nantke for her assistance in sample collection, and the crewof R/V Aranda for the cruise assistance. We thank the staff at Vegacenter forproviding us with instrument time on the MC-ICP-MS, and Melanie Schmittfor the help during the Si isotopes measurements. We would like to thankPatricia Grasse and one anonymous reviewer for their constructive commentson a previous version of the manuscript. This is contribution #018 from theVegacenter. This study was partly supported by a Swedish Research Councilgrant to D. J. Conley. Z. Zhang was supported by the China ScholarshipCouncil through a joint PhD program scholarship (201606310162), theNational Key Scientific Research Project sponsored by the Ministry of Scienceand Technology of the People’s Republic of China (grants 2015CB954001),and the Xiamen University Graduate School.

Conflict of InterestNone declared.

Submitted 15 December 2018

Revised 20 May 2019

Accepted 10 August 2019

Associate editor: James Moffett

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