Spartina alterniflora alters ecosystem DMS and CH4 emissions and their relationship along interacting tidal and vegetation gradients within a coastal salt marsh in Eastern China

Accepted Manuscript Spartina alterniflora alters ecosystem DMS and CH4 emissions and their relationship along interacting tidal and vegetation gradients within a coastal salt marsh in Eastern China Jinxin Wang, Jinshu Wang PII:

S1352-2310(17)30554-X

DOI:

10.1016/j.atmosenv.2017.08.041

Reference:

AEA 15511

To appear in:

Atmospheric Environment

Received Date: 4 January 2017 Revised Date:

13 August 2017

Accepted Date: 16 August 2017

Please cite this article as: Wang, J., Wang, J., Spartina alterniflora alters ecosystem DMS and CH4 emissions and their relationship along interacting tidal and vegetation gradients within a coastal salt marsh in Eastern China, Atmospheric Environment (2017), doi: 10.1016/j.atmosenv.2017.08.041. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

180 120 60 0 800

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CH4 flux（mg m h )

The mudflat S. alterniflora S. salsa A. littoralis

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CH4 flux（mg m h )

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Altered relationship between DMS and CH4

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S. alterniflora invasion

Increased emission

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Graphical abstract

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Wang Jinxin, Wang Jinshu*

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College of Geography, Geomatics, and Planning, Jiangsu Normal University, Xuzhou, 221116, China

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*, Corresponding author

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Correspondence: College of Geography and Geomatics, Jiangsu Normal University, 101 Shanghai Road, Xuzhou

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221116, P. R. China. Telephone: 13585396905; Email:[email protected]

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Spartina alterniflora alters ecosystem DMS and CH4 emissions and their relationship along interacting tidal and vegetation gradients within a coastal salt marsh in Eastern China

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Running head: DMS and CH4 fluxes of salt marsh

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Abstract: Invasive Spartina alterniflora accumulates organic carbon rapidly and can utilize a wide range of

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potential precursors for dimethyl sulfide (DMS) production, as well as a wide variety of methanogenic substrates.

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Therefore, we predicted that S. alterniflora invasion would alter the relationships between DMS and methane

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(CH4) fluxes along the interacting gradients of tidal influence and vegetation, as well as the ecosystem-atmosphere

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exchange of DMS and CH4. In this study, we used static flux chambers to measure DMS and CH4 fluxes in August

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(growing season) and December (non-growing season) of 2013, along creek and vegetation transects in an Eastern

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Chinese coastal salt marsh. S. alterniflora invasion dramatically increased DMS and CH4 emission rates by

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3.8–513.0 and 2.0–127.1 times the emission rates within non-vegetated regions and regions populated with native

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species, respectively, and significantly altered the spatial distribution of DMS and CH4 emissions. We also

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observed a substantial amount of variation in the DMS and CH4 fluxes along the elevation gradient in the salt

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marsh studied. A significant relationship between DMS and CH4 fluxes was observed, with the CH4 flux passively

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related to the DMS flux. The correlation between CH4 and DMS emissions along the vegetation transects was

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more significant than along the tidal creek. In the S. alterniflora salt marsh, the relationship between DMS and

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CH4 fluxes was more significant than within any other salt marsh. Additionally, CH4 emissions within the S.

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alterniflora salt marsh were more sensitive to the variation in DMS emissions than within any other vegetation

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zone. The spatial variability in the relationship observed between DMS and CH4 fluxes appears to be at least partly

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due to the alteration of substrates involved in DMS and CH4 by S. alterniflora invasion. In the S. alterniflora salt

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marsh, methanogenesis was more likely to be derived from non-competitive substrates than competitive substrates,

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but within the creek and other vegetation zones, methanogenesis was inhibited by sulfate reduction. This suggests

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that methanogenesis and sulfate reduction were spatially isolated within the coastal salt marsh. Therefore, we

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conclude that the invasive S. alterniflora altered the ecosystem-atmosphere exchange of DMS and CH4 and the

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responses of the relationship between these two gases to interacting gradients of tidal inundation and vegetation

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within an Eastern Chinese coastal salt marsh.

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Keywords: Invasive species; DMS and CH4; Biogenic sulfur; Gas exchange; Methanogenesis; Tide and

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vegetation; Coastal salt marsh.

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1.INTRODUCTION

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Dimethyl sulfide (DMS) and methane (CH4) are important atmospheric trace compounds, which are tightly

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correlated to the coupling of sulfur and carbon cycling and are important to environmental processes. They also

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play significant roles in global sulfur and carbon cycling. Sources of DMS and CH4 include marine environments

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(Finster et al. 1990, Visscher and van Gemerden 1991, Finster et al. 1992, de Angelis and Lee 1994, Florez-Leiva

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et al. 2010, Florez-Leiva et al. 2013), coastal salt marshes (Oremland and Zehr 1986, Kiene and Visscher 1987,

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Purvaja and Ramesh 2001, Lyimo et al. 2002, Tong et al. 2013, Yuan et al. 2014), estuaries (Kiene et al. 1986),

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hypersaline environments (Kiene et al. 1986, King 1988, Kelley et al. 2015), upwelling ecosystems (Florez-Leiva

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et al. 2013), and anoxic freshwater sediments (Lomans et al. 1999). DMS is the most abundant of the reduced

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sulfur compounds in the atmosphere (Lee et al. 2004), and accounts for approximately 95% of the marine and

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33% of the coastal wetland flux of sulfur gases that are emitted into the atmosphere (Andreae 1990).

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Degradation of DMS leads to CH4 formation (Finster et al. 1990, Finster et al. 1992, Florez-Leiva et al. 2013). CH4

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is the second most important radiative forcing greenhouse gas, and has a global warming potential 25 times

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that of CO2 (Ramaswamy et al. 2001).

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Coastal marshes are usually abundant in potential precursors for DMS (Kiene and Capone 1988), methanogenic

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substrates (Finster et al. 1992, Tallant and Krzycki 1997, Zindler et al. 2012), and bacteria groups that have the

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ability to produce CH4 through metabolism of dimethylsulfoniopropionate (DMSP) and associated degradation

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products (Kiene et al. 1986, Kiene 1988, Oremland et al. 1989, van der Maarel and Hansen 1997). Coastal marshes

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have consistently been confirmed as significant sources of DMS and CH4 (Kiene and Visscher 1987, Kiene 1988,

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De Souza and Yoch 1996, DeLaune et al. 2002, Yuan et al. 2014). Previous studies have found methanogenesis to

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occur when CO2, methyl compounds, or acetate are available as substrates (Oremland and Polcin 1982, King et al.

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1983, Oremland et al. 1991, Whiticar 1999, Parkes et al. 2012, Penger et al. 2012, Costa and Leigh 2014, Toyoda et

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al. 2014, Li et al. 2015b, Lin et al. 2015, Maltby et al. 2015, Musenze et al. 2015, Sorokin et al. 2015, Sun et al.

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2015). DMS can also be a non-competitive substrate for methanogenesis (Zinder and Brock 1978, Jørgensen 1982,

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Kiene et al. 1986, Kiene and Visscher 1987, Fu and Metcalf 2015), which suggests that production and emission of

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DMS and CH4 are likely to be positively correlated. However, production of DMS and CH4 are affected by the

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content and composition of available precursors (Yuan et al. 2014), and the abundance and species of bacteria

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groups present (Tong et al. 2014). For instance, Yuan et al (2014) and Jemaneh Zeleke et al. (2013) found S.

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alterniflora invasions, and accompanied changes in the prevalence of methanogen species, altered levels of

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substances utilized by methanogens. River discharges and coastal upwelling transport substances and terminal

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electron acceptors that influence recycling of carbon and sulfur (Salgado et al. 2014, Webster et al. 2015). Thus, it

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can be speculated that the relationship between DMS and CH4 production and emission should vary along the

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interacting gradients in tide inundation, vegetation composition, and physico-chemical characteristics within

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coastal marshes.

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In this study we investigated the flux of DMS and CH4 within one of the largest salt marshes in China, located in

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the Yancheng Natural Reserve. Within the marsh, water level, salinity, soil physical and chemical properties, and

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vegetation composition vary along the vegetation gradient from sea to land. Further variation within the marsh

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occurs in response to the large sulfate and organic matter loads entering the marsh through the Huaihe River Basin,

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which has historically been, and currently remains, heavily polluted. Therefore, we expected to find large DMS

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and CH4 emissions from this marsh, and the relationship between DMS and CH4 emissions to vary along the

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vegetation gradient of the marsh. The objectives of this study were: (1) to examine the seasonal and spatial patterns

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in DMS and CH4 fluxes along the interacting gradients in tide inundation and vegetation composition within the

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coastal salt marsh, (2) to examine the relative importance of vegetation and tide to DMS and CH4 fluxes, and (3) to

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determine how the invasive S. alterniflora has altered marsh DMS and CH4 emissions and the relationship

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between marsh DMS and CH4 emissions.

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2 METERIALS AND METHODS

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2.1 Experimental site description

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The salt marsh studied is located in the core zone of Yancheng Natural Reserve, Jiangsu Province, China (33°36′N,

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120°36′E; Fig.1) and is inundated by tidal water twice a day. The marsh is currently accreting with the mean high

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water level advancing seaward at a rate of about 200 m per year and the sediment accreting vertically at a rate of

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2.5-10 m per year, on average. Along the elevation gradient, vegetation extends about 10 km from the lowest

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mudflat to a Phragmites australis community located near a reclamation dike (Fig.1). Plant zonation is

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well-defined along the elevation gradient of the study marsh, with monospecific communities dominated by P.

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australis, Imperata cylindrica, Aeluropus littoralis, Suaeda salsa, and S. alterniflora distributed from the inland to

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the marine extent of the marsh (Table 1). The most inland extent of the marsh is inundated only by the highest of

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tides and the furthest marine extent of the marsh is a bare tidal mudflat that is devoid of higher plants (Wang et al.

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2006).

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2.2 Sampling and analyses

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Gas measurements within the marsh were conducted in the morning (flood tide), noon (ebb tide) and afternoon

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(slack tide) of a neap tide (from the 10th to 14th of the month) in August (growing season) and December

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(non-growing season). Three transects were laid out along a salinity and elevation gradient from a common origin

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in the mudflat, and through three different vegetative communities, the most inland of which was an A. littoralis

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marsh. The transects ran through a creek (creek; 0), along the edge of a creek (near creek; 1), and parallel to, but far

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from the creek (far creek; 2), respectively. Each transect was sampled at sampling locations, including the mudflat

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and three vegetative communities. The mudflat (A) was 0.87 m elevation; the S. alterniflora marsh (B) was at 0.95

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m elevation; the S. salsa salt marsh (C) was at 1.79 m elevation; and the A. littoralis marsh (D) was at 2.18 m

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elevation. Accordingly, each transect and its associated stands was identified as a number (0, 1, or 2) and letter (A,

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B, C, or D), respectively, with the 0.87 elevation mudflat stand (A) common to all transects. The resultant transects

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identifiers were A-B0-C0-D0 for the creek, A-B1-C1-D1 for the near creek, and A-B2-C2-D2 for the far creek.

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2.2.1 Collection and analysis of soil samples

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Soil samples were collected in August and December of 2013 from all four sample stands within each of the three

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transects for determination of total nitrogen, organic matter, and total dissolved salt content. For every 10 cm layer

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from the soil surface to 30 cm in depth, triplicate soil samples were extracted from each stand on each sampling

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date. The protocols for these measurements followed the standard methods for observation and analysis described

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in the Chinese Ecosystem Research Network-Description of soil profiles (Liu et al. 1996).

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2.2.2 Collection and analysis of gas samples

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Gas fluxes were measured using opaque static flux chambers that covered a 50cm×50cm surface area. The

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chamber was a stainless steel collar (50cm×50cm×91cm) that was buried 2-3cm into the soil. Each chamber was

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equipped with a small electric fan to circulate the air prior to each sampling, and a vent tube to allow for pressure

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equilibration during sampling. The large chamber size was to reduce possible edge effects and to allow for

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enclosure of entire plants (Wang et al. 2007). When the height of plants exceeded the collar height, a lid

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(50cm×50cm×63cm) was placed on top of the collar. Along the creek, with a width of 5.8m and a height of 2.1 m,

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gas samples were collected by a static chamber with a removable sampling platform across the creek. The movable

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sampling platform consisted of a pair of bamboo ladders with a length of 10m and width of 1.5m.

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Each sample consisted of a control (intact) and a treatment (aboveground biomass removed) measurement. For the

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intact measurements, the chambers were placed in the soil, covering all visible higher plants. At 15 or 20-minute

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intervals, three 900 ml air samples were drawn from the flux chambers into a previously evacuated 1-liter fused

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silica gas sample canister covered with a stainless steel membrane, to prevent adsorption and infiltration of trace

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gases. After control sampling, all aboveground plant biomass was carefully harvested with long scissors, and

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triplicate treatment air samples were collected in the identical manner as control samples. Fresh plant biomass was

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weighed and taken to the laboratory for desiccation, followed by dry mass determination. Air samples were also

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taken to the laboratory for DMS and CH4 measurements that were performed as soon as possible. For each sample,

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ambient chamber and soil temperature and light intensity were recorded.

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DMS concentration was determined by GC/MS (Agilent 7890A/5975C, USA) using a Cryo-Concentration System

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(Entech 7100, USA) at the State Environmental Protection Key Laboratory of Odor Pollution Control in China,

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according to protocols described in “Air and Waste Gas Monitoring Analysis Method” (Fourth Edition) edited by

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SEPA, China (SEPA 2003). A more detailed description of the chromatography protocol is provided by Wang et al.

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(2009). CH4 concentration was determined using a gas chromatographer (Agilent 7890, Santa Clara, CA, USA)

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equipped with a flame ionization detector at the Soil and Environment Analysis Center of the Institute of Soil

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Science of Chinese Academy Of Sciences. Gas standards were acquired from the National Research Center for

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Certified Reference Materials, Beijing, China.

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2.3 Data analysis

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DMS and CH4 fluxes were calculated according to equation 1):

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F=

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where F = DMS flux (µg S ·m-2·h-1) or CH4 flux (mg CH4 ·m-2·h-1); H = sampling chamber height (m); V0 = the

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molecular volume of the measured gas in the standard state (22.41l mol-1); P0 = pressure of standard state (1013.25

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H 273.16 P dc ⋅ ⋅ ⋅ V0 273.16 + T P0 dt

eqn. 1

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hpa); P = the air pressure during sample collection, local air pressure at that time can be obtained by real-time

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inquiry system of the Central Meteorological Station; T = the ambient air temperature during sample collection

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(oC ); dc/dt = the rate of increase in gas concentration within the chamber over time, which was determined by

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applying a linear least squares fit of the measured mole fractions to the time of sampling.

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ANOVA tests were employed to test for significant temporal and spatial variation in gas fluxes, including effects of

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season and stands. ANOVA tests were also used to compare control and treatment fluxes in the study marsh.

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Additionally, the correlation analysis was performed to determine relationships between gas flux and

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environmental conditions using linear regression model attached to the Microsoft Office Excel 2010.

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3 RESULTS

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3.1 DMS and CH4 flux patterns

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Emissions of DMS from the salt marshes occurred in August and December, but varied with creek proximity

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and across vegetation gradients, with the greatest DMS flux occurring in the near creek (Fig.2 and Fig.3). The

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spatial variation in DMS flux was significant (p<0.01) across the vegetation gradient, but was not significant

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within the creek transect (p＞0.5, Table 2). The seasonal variation in DMS flux was significant for the far

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creek transect (p<0.001), but was not significant for the near creek or creek transect (p＞0.5, Table 2). The

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DMS flux within the creek and near creek significantly differed (p<0.05) in August, but not in December (p

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＞0.5, Table 2). The creek and far creek fluxes did not significantly differ (p＞0.5) in August, but did

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significantly differ (p<0.001) in December (Table 2). Fluxes of the near and far creek transects never

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significantly differed (p＞0.5, Table 2).The DMS flux in December was larger than in August (Fig.2 and

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Fig.3). The DMS flux along the gradient covered by the far creek transect in August (with December in

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parentheses) was 2.1±1.3 ug S m-2 h-1 (0.0±0.0 ug S m-2 h-1) at the mudflat, 64.5±33.1 ug S m-2 h-1

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(297.0±24.8 ug S m-2 h-1) at the S. alterniflora marsh, 1.0±0.4 ug S m-2 h-1 (12.5±0.6 ug S m-2 h-1) at the S.

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salsa marsh, and 1.5±0.9 ug S m-2 h-1 (6.0±6.0 ug S m-2 h-1) at the A. littoralis marsh (Fig.2 and Fig.3). The

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August (and December) flux at the S. alterniflora marsh was the greatest, being 43.0 (49.5), 67.9 (23.8), and

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31.5 (0) times higher than the flux at the A. littoralis marsh, S. salsa marsh, and mudflat, respectively (Fig.2

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and Fig.3). Along the gradient covered by the near creek transect, the DMS flux in August (and December)

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was 2.1±1.3 ug S m-2 h-1 (0.0±0.0 ug S m-2 h-1) at the mudflat, 148.5±73.2 ug S m-2 h-1 (513.0±158.7 ug S m-2

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h-1) at the S. alterniflora marsh, 1.7±0.7 ug S m-2 h-1 (1.0±1.0 ug S m-2 h-1) at the S. salsa marsh, and 0.6±0.2

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ug S m-2 h-1 (2.0±2.0 ug S m-2 h-1) at the A. littoralis marsh (Fig.2 and Fig.3). The largest flux was at the S.

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alterniflora marsh, which was 247.5 (256.5), 90.0 (513.0), and 72.4 (0) times higher than that of the A.

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littoralis marsh, the S. salsa marsh, and the mudflat in August (and December), respectively. The DMS flux at

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the S. alterniflora marsh on the near creek transect was 2.3 (1.7) times larger than that the flux for the same

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marsh along the far creek transect in August (and December) (Fig.2 and Fig.3). All stands (including the

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mudflat) along the creek were minor net sources of DMS during both December and August, suggesting that

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coastal saltwater and tidal flow made relatively small contributions to coastal atmospheric DMS (Fig.2 and

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Fig.3).

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The S. alterniflora marsh was a CH4 source during both August and December along the gradient traced by

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the creek transect and the vegetation gradient (Fig.4 and Fig.5). There was a significant (p<0.01) spatial

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variation in CH4 flux along the vegetation gradient and creek transect. The spatial variation in CH4 flux

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within the vegetation gradient (p<0.001) was significantly higher than the variation within the creek transect

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(p<0.05, Table 2). The seasonal variation in CH4 flux was significant for stands along the near creek transect

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(p<0.05), but was not significant for the far creek or the creek transects (p＞0.5, Table 2). In August, creek

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and near creek transect CH4 flux did not significantly differ (p＞0.5), but in December they did significantly

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differ (p<0.01, Table 2). Creek and far creek transect CH4 flux did not significantly differ (p＞0.5) in August

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and December. The CH4 flux of the near and far creek transects significantly differed in December (p<0.01),

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but not in August (p＞0.5; Table 2). Along the far creek transect gradient, the CH4 flux of was -0.03±0.02 mg

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m-2 h-1 (0.05±0.06 mg m-2 h-1) at the mudflat, 1.32±0.27 mg m-2 h-1 (1.54±0.27 mg m-2 h-1) at the S.

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alterniflora marsh, 0.02±0.07 mg m-2 h-1 (0.01±0.03 mg m-2 h-1) at the S. salsa marsh, and 0.05±0.01 mg m-2

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h-1 (0.02±0.02 mg m-2 h-1) at the A. littoralis marsh in August (and December) (Fig.4 and Fig.5). The highest

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CH4 flux was at the S. alterniflora marsh, which was 26.4 (86.4), 61.5 (216.0), and 41.0 (28.8) times higher

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than the A. littoralis marsh, S. salsa marsh, and the mudflat flux, respectively, in August (and December)

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(Fig.4 and Fig.5). Along the near creek transect gradient, the CH4 flux was -0.03±0.02 mg m-2 h-1(0.05±0.06

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mg m-2 h-1) at the mudflat, 1.18±0.24 mg m-2 h-1(0.27±0.17 mg m-2 h-1) at the S. alterniflora marsh,

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-0.17±0.09 mg m-2 h-1(0.05±0.08 mg m-2 h-1) at the S. salsa marsh, and 0.28±0.02 mg m-2 h-1 (0.10±0.02mg

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m-2 h-1) at the A. littoralis marsh in August (and December) (Fig.4 and Fig.5). The CH4 flux of the S.

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alterniflora marsh was 4.2 (2.8), 7.0 (5.8), and 36.7 (5.0) times higher than at the A. littoralis marsh, the S.

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salsa marsh, and the mudflat, respectively in August (and December). The CH4 flux at the S. alterniflora

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marsh on the far creek transect was 1.1 (5.8) times larger than the flux at the same marsh on the near creek

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transect in August (and December) (Fig.4 and Fig.5). The mudflat was a minor source for CH4, but other

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stands along the tidal creek transect were more significant sources for CH4, which could be attributed to

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lateral transportation from ecosystems on both sides of the tidal channel and transportation up the transect

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gradient through tidal flow (Fig.4 and Fig.5).

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3.2 Differences in DMS and CH4 fluxes before and after removal of aboveground higher plant biomass

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Differences in DMS and CH4 fluxes before and after removal of aboveground higher plant biomass differed

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between the near and far creek transects, implying complex influences of aboveground biomass on DMS and CH4

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fluxes, which may with tidal impacts.

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DMS emission rates for control stands were higher than emissions for treatment stands (p<0.001, Table 2, Fig.2)

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within the far creek transect in August and December, which could be attributed to contributions of DMS from

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aboveground higher plant biomass. However, the effect of higher plants on DMS flux within the far creek transect

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in December was more significant than the effect within the far creek transect in August and the near creek transect

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in August and December (p＞0.5, Table 2).

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The effects of higher plants on CH4 flux within the near creek transect in August were more significant than the

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effects within the near creek transect in December and the far creek in August and December. Removal of

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aboveground higher plant biomass significantly increased CH4 emission (p<0.001, Table 2, Fig.4, and Fig.5).

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3.3 Relationship between DMS and CH4 fluxes

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Regression analysis for all stands and the mudflat showed a significant relationship between the DMS and CH4

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fluxes, with DMS flux passively related to CH4 flux (Fig. 6). The correlation between of CH4 and DMS fluxes

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within the vegetation transects were more significant than within the creek, and within the S. alterniflora salt

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marsh. The relationship between DMS and CH4 fluxes was more significant than within any other salt marsh

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(Fig. 6). The emission of CH4 was more sensitive to the variation of DMS compared to other vegetation zones

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(Fig.6).

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3.4 Effect of environmental variables on DMS and CH4 emissions

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Regression analyses showed significant relationships between DMS flux and initial gas concentration, but the

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relationship observed in the creek transect and vegetation gradient differed. DMS flux was positively related to the

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initial gas concentration within the vegetation gradient, but negatively related to the initial gas concentration

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within the creek transect. The difference in the relationship between the DMS flux and its initial gas concentration

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suggests that the gas exchange processes and control mechanisms differ among media (i.e., water bodies and

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vegetation; Table 3). CH4 flux was negatively related to initial gas concentration within all three transects (Table 3).

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DMS and CH4 fluxes were weakly related to environmental factors, such as light intensity and surface temperature,

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in the creek and near creek transects. Whereas, in the far creek transect, DMS and CH4 fluxes were significantly

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positively correlated with environmental factors, such as light intensity and surface temperature. The weak

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relationship in the creek and near creek transects may be due to tidal influences on surface temperature and light

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intensity (Table 3). DMS and CH4 fluxes were positively correlated with environmental factors, such as organic

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matter, dissolved salt, and TN, within the vegetation gradients (Table 3).

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4 DISCUSSION

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In this study, we observed substantial variation in DMS and CH4 fluxes along different salinity gradients in

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differing creek transects and vegetation gradients (Fig.2, Fig.3, Fig.4 and Fig.5). DMS emission rates with and

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without aboveground biomass were far greater within the S. alterniflora marsh than in any of the other stands

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investigated (Fig.2 and Fig.3). The S. alterniflora marsh was a major DMS source within the present study area,

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which is consistent with the findings of previous studies (Cooper et al. 1987, Ansede et al. 1999). CH4 emission

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rates were similar for the S. salsa marsh on the creek transect and the S. alterniflora stand on the far creek transect,

247

which were both greater than the CH4 emission rates of the S. alterniflora stand on the creek transect (Fig.4 and

248

Fig.5). This finding may be due to transport of methanogenic substrates between the transects and along the tidal

249

creek. In general, DMS flux was passively related to CH4 flux, that is, areas with high DMS emission rates tended

250

to also have high CH4 emission rates (Fig. 6). This relationship was consistent across all sample locations.

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4.1 The biotic and abiotic changes caused by S. alterniflora invasion

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The external inputs of sulfate from the Huaihe River, seawater, and atmospheric acid deposition, have greatly

253

increased the sulfate load of soil in the study area. High soil sulfur promotes the invasion of exotic S. alterniflora,

254

which likely utilizes high sulfur levels to compete with indigenous species, such as S. salsa and A. littoralis (Xia et

255

al. 2014). Morris et al. (1996) found sulfide treatments to have marginally significant effects on DMSP

256

concentrations within leaf tissues, but no significant effects on root DMSP of S. alterniflora. Therefore, the exotic

257

S. alterniflora has the potential to cause a series of changes to biotic and abiotic processes.

258

First, habitats invaded with S. alterniflora may experience high accumulations of biomass and soil organic carbon

259

(SOC). Previous studies have found invasive S. alterniflora to have a higher net primary productivity than native

260

plants (Liao et al. 2008). Other studies, undertaken in the same study area as the present one, have found S.

261

alterniflora to grow more aboveground (Wang et al. 2006, Yin et al. 2015, Yuan et al. 2015, Zhou et al. 2015), with

262

more root biomass (Zhang et al. 2010b), and to greater heights (Yin et al. 2015, Yuan et al. 2015, Zhou et al. 2015)

263

than native plants. These differences between S. alterniflora and native plant growth contribute to significant

264

increases in SOC accumulation in S. alterniflora, relative to native plants (Zhou et al. 2015, Yuan et al. 2015),

265

which is primarily attributed to a greater input of carbon from litter and roots and a reduced output of carbon

266

through soil decomposition (Yuan et al. 2015).

267

Second, S. alterniflora may increase the DMSP content, which is the major precursor of DMS in salt environments,

268

within plant biomass. Previous studies have observed synthesis of high concentrations of DMSP from methionine

269

by certain species of micro- and macro-algae and halophytic plants, for regulation of their internal osmotic

270

environments (Yoch 2002, Husband and Kiene 2007). The pathway of DMSP production in S.alterniflora is as

271

follows: methionine→S-methylme-thionine→DMSP-amine→DMSP-aldehyde→DMSP (Kocsis et al. 1998).

272

Kocsis and Hanson (2000) presented evidence suggesting that the enzyme S-methyl-Met decarboxylase (SDC) is

273

specific to DMSP synthesis in S. alterniflora.

274

Third, S. alterniflora may cause increases in the content of non-competitive substances involved in CH4 formation.

275

In a study performed in the same area as the present study, Yuan et al. (2016) found trimethylamine levels within

276

the S.alterniflora marsh to be 2.34–18.4 times higher than other marshes. Trimethylamine is a non-competitive

277

substrate utilized by methanogens.

278

Finally, S. alterniflora invasion may induce changes in the composition and structure of the intertidal microbial

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community. On the one hand, substrate and nutrient availability may be altered in S. alterniflora marshes through

280

changes to root and litter inputs, increases in DMSP content, and elevations in non-competitive trimethylamine

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content, resulting in changes to the composition and structure of the marsh microbial community. Previous studies

282

have shown shifts in microbial species compositions towards species adapted to take advantage of altered root and

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litter inputs, with successful invasions of exotic species (Coleman et al. 2000, Kourtev et al. 2002). The increase in

284

DMSP content within the S. alterniflora marsh may facilitate bacteria that possess the enzyme DMSP lyase, which

285

degrades DMSP to DMS and acrylate, by using DMSP as a carbon and energy source (Kiene 1990, Ansede et al.

286

2001). Other research done in the same area as the present study found invasion of S. alterniflora resulted in

287

increased concentrations of non-competitive trimethylamine, causing a shift in the methanogen community from

288

acetotrophic to facultative methanogens, and an associated increase in abundance of methyl donors (Jemaneh

289

Zeleke et al. 2013, Yuan et al. 2016). S. alterniflora invasion may also have altered the composition and structure

290

of the microbial community by introducing symbiotic bacteria into the rhizosphere. These bacteria may interact

291

with root-associated microbial groups. Previous studies have shown endophytic and rhizosphere bacteria

292

community structures to differ, with S. alterniflora invasion. Additionally, endophytes are more sensitive to S.

293

alterniflora invasion, compared to rhizosphere bacteria, which has the potential to affect carbon and sulfur cycles

294

(Hong et al. 2015).

295

4.2 Effect of S. alterniflora invasion on DMS emission

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Steudler and Peterson (1984, 1985) found very large DMS fluxes to emanate from a salt marsh colonized by S.

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alterniflora. The greatest DMS emissions were observed at the S. alterniflora site, where the flux was 328 ug S

298

m-2h-1; DMS flux at the tidal creek was only 5% of the flux at the S. alterniflora site, with a flux of only 18 ug S m-2

299

h-1. The sea-to-air fluxes of DMS within the Chinese marginal sea, which ranged from 0.85µmol m2 d-1 to 10.6

300

µmol m2 d-1(1.1 ug S m-2 h-1 to 14.1 ug S m-2 h-1), were much lower than our observed flux at the S. alterniflora

301

site (Table 4). Our results were consistent with Steudler and Peterson’s study. In our study, the DMS fluxes of the

302

creek, the mudflat, and the middle and upper marsh dominated by S. salsa and A. littoralis were only about 3% of

303

the flux at the S. alterniflora site (Fig. 2, Fig. 3). These fluxes were similar to those observed in the creeks they

304

studied (Steudler and Peterson 1984, Steudler and Peterson 1985) and the Chinese marginal sea (Table 4), with

305

average flux being 3.4 ug S m-2 h-1(ranging from 0.01 ug S m-2 h-1 to11.5 ug S m-2 h-1) (Fig. 2, Fig. 3). The average

306

DMS flux at the S. alterniflora marsh was 37.5-fold higher than in the creek, the mudflat, and the native plant

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marshes dominated by S. salsa and A. littoralis, where the average flux was 113.5 ug S m-2 h-1(ranging from 3.8 ug

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S m-2 h-1 to 513.0 ug S m-2 h-1). The DMS flux in December was higher than in August (Fig. 2, Fig. 3). Therefore, S.

309

alterniflora invasion effectively stimulated DMS formation within the coastal salt marsh and the S. alterniflora

310

marsh was an important source of DMS in the coastal atmosphere. Several specializations of S. alterniflora might

311

be responsible for promoting DMS emission with S. alterniflora invasion.

312

First, DMSP may be transported throughout the mudflat with tidal water inundation. However, the production of

313

DMS was significantly dependent on the vegetation type. Studies have found high DMSP content in China's

314

offshore and inshore seawater (Yang et al. 2006, Yang et al. 2011, Li et al. 2015a, Yang et al. 2015, Shen et al.

315

2016) Presumably, the tidal water would carry the DMSP into creeks and mudflats, so almost all of the measuring

316

points should have DMS production potential. Consequently, DMS emission between the vegetated and

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non-vegetated zone should differ greatly, but in fact, it does not. Kulkarni et al. (2005) concluded that DMSP-rich

318

phytoplankton taxa may enter creeks from coastal waters during flood tides, and low DMSP-containing

319

re-suspended benthic microalgae are transported from the salt marsh into the creeks during ebb tide. Our

320

measurements found no significant difference in DMS emissions between the creek, mudflat, and native

321

vegetation zones (Table 2, Fig.2 and Fig. 3), and a substantial variation in the DMS flux was observed between the

322

seawater and the native and invaded vegetation zones. DMS emissions at native vegetation zones were far lower

323

than at the S. alterniflora zone (Fig.2 and Fig. 3). Although DMS emissions at the S. alterniflora site were slightly

324

higher than at all other sites along the creek transect, DMS emissions did not significantly differ among the sites

325

making up the creek transect (Table 2, Fig.2, and Fig. 3), potentially due to lateral transport of DMS from the S.

326

alterniflora zone. Along the vegetation transects, the emission-promoting effects of S. alterniflora significantly

327

increased, and were most strongly significant within the near creek transect (Table 2, Fig.2, and Fig. 3), suggesting

328

that the emission-promoting effects are a result of the combined action of vegetation and tidal current.

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Second, DMS emission is related more to S. alterniflora biomass than to soil parameters along the vegetation

330

transects, especially within the far-creek transect (Table 3). This finding is similar to results reported by (De Mello

331

et al. 1987). DMSP is an osmoprotectant accumulated by S. alterniflora and other salt-tolerant plants (Kocsis and

332

Hanson 2000). Among the Spartina species, only S. alterniflora produces high concentrations of DMSP (Otte and

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Morris 1994), because S. alterniflora is able to accumulate DMSP in its tissues (Dacey et al. 1987). DMSP

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biosynthesis capabilities are confined to several algal classes and a few families of higher plants. DMSP should not

335

be considered an osmoticum in the strict sense of being responsible for osmotic balance, but rather as a constitutive

336

compatible solute (Stefels 2000). The enzymatic cleavage of DMSP may then serve as regulatory mechanism to

337

keep DMSP at an equilibrium concentration (Stefels 2000). The formation of DMS and the accumulation of its

338

precursors occur primarily through the physiological processes of higher plants. The correlation analysis

339

performed in this study showed DMS emission rates to be more closely related to plant biomass than soil

340

parameters within the far-creek transect (Table 3), where the effect of tidal water from the creek is relatively weak.

341

However, within the near creek transect, the DMS emission rate was more closely related to soil parameters than

342

the plant biomass (Table 3); this result may be due to impacts of tidal water inundation. The effects of both green

343

and withered aboveground biomass removal on DMS flux showed aboveground S. alterniflora biomass to be a

344

major source of DMS in this study marsh. Moreover, DMS emissions from the withered above-ground biomass

345

were larger than from the green biomass (Fig.2 and Fig. 3). This finding is consistent with findings reported by

346

previous studies. For example, senescing (yellow) leaves of S. alterniflora emit more DMS than healthy (green)

347

leaves (Husband and Kiene 2007) and it has been shown that emission rate increases with leaf decay (Dacey et al.

348

1987). The difference between emissions of green and withered S. alterniflora biomass may be due to higher

349

DMSP lyase activity in senescing tissue, leading to increased liberation of DMS (Husband and Kiene 2007). Bacic

350

et al. (1998) also reported that DMSP lyase-producing fungi associated with DMSP production may degrade

351

DMSP and increase DMS emissions during leaf senescence and decay. Fungi are capable of using organic material

352

found in living S. alterniflora shoots (Newell 1996). This study cannot exclude the possibility of DMS emission

353

originating from the rhizosphere, algae, or fungi that attach to the plant and soil surface. Net emissions from

354

aboveground biomass may also include contributions from epiphytic algae and fungi (Fig.2c and Fig. 3c), because

355

algae and fungi that associate with S. alterniflora possess the ability to produce DMS. However, DMS emissions

356

after aboveground biomass removal may also include soil algae and fungi contributions (Fig.2b and Fig. 3b). We

357

found DMS emissions at the S. alterniflora marsh within the near-creek transect to be higher than within the

358

far-creek transect both before and after aboveground biomass removal (Fig.2 and Fig. 3), likely due to large

359

quantities of algae associated with tidal water from the creek.

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Third, DMS emissions may be associated with the salinity gradient, but it is not clear whether the salinity gradient

361

or plant parameters have a stronger influence on DMS emissions. In this study, the variation in DMS emissions

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correlated with the salinity gradient, but also, and more closely, with plant habitat (Fig.2, Fig. 3 and Table 3).

363

DeLaune et al. (2002) quantified average DMS emissions along a salinity gradient in Louisiana Gulf Coast salt

364

marsh with sample sites in a S. alterniflora saltmarsh (10–12 ppt salinity), S. patens brackishmarsh (5–8 ppt

365

salinity), and S. lancifolia freshwater marsh (0 ppt salinity). They found DMS emissions of 57.3 ug S m-2 h-1

366

(ranging from 1.44 ug S m-2 h-1 to 144.03 ug S m-2 h-1 ), 0.84 ug S m-2 h-1 (ranging from 0.51 ug S m-2 h-1 to 1.87 ug

367

S m-2 h-1), and 0.27 ug S m-2 h-1 (ranging from 0.03 ug S m-2 h-1 to 0.79 ug S m-2 h-1), in the saltmarsh, brackish

368

marsh, and freshwater marsh, respectively, suggesting that DMS emissions were strongly associated with plant

369

habitats and salinity gradients. However, we feel that the variation in DMS emissions observed by DeLaune et al.

370

should be attributed to habitat differences, and not strictly the salinity gradient. Investigations of S. alterniflora

371

collected along salinity and sulfide gradients within a South Carolina river showed that DMSP concentrations are

372

not correlated with either the salinity or sulfide concentrations of soil porewater (Otte and Morris 1994). S.

373

alterniflora DMSP concentrations likely did not correlate with salinity or sulfide concentrations because of the

374

relatively slow adaptation of the plant’s intracellular concentrations with salinity shifts (Stefels 2000).

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Furthermore, the formation of DMS and accumulation of its precursors is directly related to plant parameters,

376

whereas DMS responses to salinity shifts are delayed.

377

4.3 Effect of S. alterniflora invasion on CH4 emission

378

Previous studies have shown dramatic stimulation of CH4 emission within Chinese coastal marshes invaded with S.

379

alterniflora (Zhang et al. 2013, Yuan et al. 2014, Yin et al. 2015, Yuan et al. 2015, Yuan et al. 2016). High CH4

380

emission with S. alterniflora invasion is likely due to the large plant biomass and stem density of S. alterniflora

381

relative to native plants (Cheng et al. 2007, Zhang et al. 2010a). Our results also showed significant increases in

382

CH4 emissions with S. alterniflora invasion, as well as an influence by S. alterniflora on the spatial distribution

383

patterns of CH4 emissions. We recorded the largest CH4 emission rates at the S. alterniflora marsh, where

384

emissions were 1.8–513.0 times the emissions of marshes populated with non-invasive plants (Fig.4 and Fig. 5).

385

Interesting, we found no significant effect of season within the far creek transect, but a significant seasonal effect

386

within the near creek transect. We also found treatment (i.e., after removal of aboveground biomass) CH4 emission

387

rates to be higher than control (i.e., with aboveground biomass intact) emission rates. These results were

388

inconsistent with previous studies, suggesting that, despite a positive correlation between CH4 emission flux and

389

aboveground higher plant biomass (Table 3), the stimulatory effect of S. alterniflora on CH4 emissions cannot be

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purely attributable to the higher plant biomass and root density of S. alterniflora, relative to native plant species.

391

There are a number of potential explanations for these inconsistencies.

392

First, seasonal variations of CH4 emissions are not only related to plant biomass and activity, but may also be

393

influenced by seasonal variation in biological processes involved in the production of CH4, such as availability of

394

substrates and composition and structure of microbial functional groups. The relatively high CH4 emissions within

395

the far creek transect in December was likely due to CH4 production by the rhizosphere and decomposition of

396

aboveground biomass (Fig 4 and Fig 5). Decay of aboveground biomass or litter may be an important source of

397

atmospheric CH4 in December. Husband and Kiene (2007) suggested that elevated DMSP lyase activity of

398

decomposers in senescing tissue can lead to increased liberation of DMS. Furthermore, Bentley and Chasteen

399

(2004) suggested that demethiolation of DMSP and other materials leads to emission of methanethiol (MT).

400

Previous studies have concluded that stimulation of CH4 production through addition of MT or DMS occurs

401

because methanogenic bacteria are able to grow using only MT and DMS as energy sources (Kiene et al. 1986,

402

Finster et al. 1992). MT and DMS can be fermented and dissimilated according to the equation

403

4CH3SH+3H2O→3CH4+HCO3-+4HS-+5H+ and (CH3)2S+1.5H2O→0.5HCO3-+1.5CH4+HS-+1.5H+ (Finster et al.

404

1992). Potentially, increases in CH4 emissions are due to increases in non-competitive substrates, which stem from

405

DMSP cleavage from dead aboveground S. alterniflora biomass and litter, which may increase the abundance of

406

non-competitive substrates. Correlation analysis showed a positive correlation between CH4 and DMS flux (Fig.6).

407

Reed et al. (1984) suggested that S. alterniflora can synthesize praline, glycine betaine and DMSP. Wang and Lee

408

(1994) observed S. alterniflora to release methylated amines, which can act as CH4 precursors during senesce, into

409

salt marsh soils, and the amine concentrations varied seasonally with concentrations peaking in the fall, when S.

410

alterniflora began to senesce. Yuan et al. (2016) found a significant relationship between CH4 production potential

411

and trimethylamine levels. Trimethylamine is the key substrate for methanogensis within S. alterniflora.

412

Trimethylamine concentration within S. alterniflora is approximately 8-fold higher in the fall than in the summer

413

(Yuan et al. 2016). Therefore, the seasonal variation in substrates such as DMS, MT, and trimethylamine may have

414

driven the seasonal variation of CH4 emission observed in this study.

415

Second, the inconsistencies in CH4 emission patterns among studies may be due differences in the experimental

416

design and sampling methods. Many previously published studies performed measurements at randomly chosen

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sampling sites or brackish marsh mesocosms, without considering the effects of tidal creeks (Cheng et al. 2007,

418

Zhang et al. 2010a, Yin et al. 2015). However, our experiment accounted for creek influences by sampling three

419

transects of increasing distance (the creek, near creek, and far creek transects) from the tidal creek. Our variance

420

analysis showed aboveground higher plant biomass to have a significant effect on differences in CH4 emissions

421

between the creek transects and the vegetation gradients (Table 2). However, the effects of aboveground higher

422

plant biomass on the differences in CH4 emission between the two vegetation gradients (near creek, and far creek)

423

varied with season, with a significant effect of aboveground higher plant biomass during the growing season, but

424

not during the non-growing season. Additionally, control (i.e., aboveground biomass intact) CH4 emissions did not

425

significantly differ between the creek transects and the vegetation gradients (Table 2). Our results suggest that the

426

stimulatory effect of S. alterniflora on CH4 emission did not arise from living biomass, but also from dead biomass,

427

litter, and the rhizosphere. Furthermore, lateral transport of CH4 through tidal waters travelling up the creek

428

affected the CH4 emission distribution within different transects, and CH4 was transported up the creek through

429

tidal waters (Fig. 4 and Fig.5).

430

Third, the trend of increased CH4 emissions with removal of aboveground plant biomass observed in our in-situ

431

aboveground biomass removal experiment differed from the findings of another similar experiment (Yin et al.

432

2015) and a mesocosm aboveground biomass removal experiment (Cheng et al. 2007). The elevated CH4

433

emissions with removal of aboveground biomass may indicate a greater contribution to CH4 emissions from the

434

rhizosphere community and litter, than the aboveground biomass. It is possible that removal of aboveground parts

435

facilitated transport of CH4 from the ground and litter to the atmosphere, but the conveying mechanism remains

436

unclear, and this finding requires replication under different circumstances. One potential mechanism of CH4

437

transport facilitation with removal of aboveground biomass is that residual aerenchymas remaining in the ground

438

after removal of aboveground biomass retained the ability to transmit CH4 from the ground and litter to the

439

atmosphere. For our experiment, treatment flux measurements were performed within 12h of aboveground

440

biomass removal. Previous studies performed CH4 measurements from 24h to one week after removal of

441

aboveground biomass (Cheng et al. 2007, Yin et al. 2015). Although, long periods between aboveground biomass

442

removal and emission measurements are effective in reducing errors due to disturbance of the sampling area, the

443

ability of the remaining aerenchyma to transmit CH4 to the atmosphere most likely deteriorates over time.

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Finally, lower CH4 emissions within the near creek during the non-growing season relative to during the growing

445

season may be due to inhibition of sulfate, considering the CH4 release rate was similar to that of the mudflat, and

446

influences of tidal water on CH4 emissions were greatest where sulfate levels were high. A previous study found

447

sulfate reducing bacteria outcompetes methanogenic bacteria for competitive substrates (such as acetate and

448

H2/CO2), because the sulfate reducing bacteria has a higher affinity for competitive substrates (Kristjansson and

449

Schönheit 1983). In a coastal salt marsh, Yuan et al. (2016) found S. alterniflora invasion to elevate the

450

concentration of non-competitive substrates (i.e., methylated amines, especially trimethylamine), and to shift the

451

methanogen community from acetotrophic to facultative methanogens. This means that different methanogenic

452

pathways (including hydrotrophic, acetotrophic, and methyltrophic) may coexist in some specific environments,

453

and which pathways play dominant roles in bacterial CH4 formation depends on the active or labile organic carbon

454

content (Ding 2002). Presumably, methane production and sulfate reduction spatially isolate along environmental

455

gradients due to spatial variation in methanogenic bacteria and methanogenesis substrates.

456

4.4 Effects of S. alterniflora invasion on the relationship between DMS and CH4 fluxes

457

We observed a significant positive relationship between CH4 and DMS fluxes (Fig.6), and the variation in CH4 and

458

DMS fluxes among plant habitats was consistent, regardless of transect or measurements type (i.e., control or

459

treatment; Fig.2, Fig.3, Fig.4 and Fig.5). There are a number of potential explanations for the consistent

460

relationship between CH4 and DMS fluxes.

461

First, CH4 and DMS have relatively consistent spatial distributions. Previous studies have described CH4

462

production through methyltrophic pathways that utilize DMSP and methylated amines (Wang and Lee 1994,

463

Jemaneh Zeleke et al. 2013, Yuan et al. 2016), which are also important precursors to DMS (Kiene 1990, Ansede et

464

al. 2001). S. alterniflora invasion has previously been shown to significantly increase DMSP and methylated

465

amine supply for methanogenesis and DMS formation (Kocsis and Hanson 2000, Yuan et al. 2016). Therefore, S.

466

alterniflora invasion may alter methanogenic pathways and the spatial distribution of substrates necessary for

467

methanogenesis and DMS formation. We speculated that the consistent spatial distribution of CH4 and DMS

468

production is caused by a consistent spatial distribution of the substrates necessary for CH4 and DMS production.

469

Second, DMS may act as a substrate for methanogens. Previous studies have shown methanogenic bacteria to

470

utilize DMS as sole source of energy, resulting in stimulation of CH4 production with DMS supplementation

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(Kiene et al. 1986, Finster et al. 1992).

472

CONCLUSION

473

This study showed the invasive S. alterniflora to dramatically increase CH4 and DMS emission rates to 3.8–513.0

474

and 2.0–127.1 times the emission rates of non-vegetated regions and habitats populated with native plant species,

475

and significantly alter the spatial distribution of CH4 and DMS emissions. A substantial variation in CH4 and DMS

476

fluxes was observed along the elevation gradient of the salt marsh studied. We observed a significant relationship

477

between DMS and CH4 fluxes, with the DMS flux being passively related to the CH4 flux. The correlation between

478

CH4 and DMS fluxes was more significant within the vegetation transects than within the creek. Additionally, the

479

correlation between CH4 and DMS fluxes was more significant within the S. alterniflora salt marsh than within

480

any of the native species salt marshes. Also, within the S. alterniflora salt marsh, CH4 emissions were more

481

sensitive to variation in DMS emissions than in any other vegetation zone. The spatial variation in the relationship

482

between DMS and CH4 fluxes may be partly explained by alterations to DMS and CH4 substrates with S.

483

alterniflora invasion. For example, in the S. alterniflora salt marsh, methanogenesis is more likely to be derived

484

from noncompetitive substrates than competitive substrates; however, within the creek and other vegetative zones,

485

methanogenesis was likely inhibited by sulfate reduction. The interactions between methanogenesis and sulfate

486

reduction suggest that the two processes are spatially isolated from each other in our study area. Therefore, we

487

suggest that S. alterniflora invasion altered the ecosystem-atmosphere exchange of DMS and CH4, and the

488

relationship between these two gases, along the interacting gradients of tidal influence and vegetation, within an

489

Eastern Chinese coastal salt marsh.

490

ACKNOWLEDGEMENTS

491

We thank Chao Li, Wei Zhang and Meng Wang for their field assistance. This study was financially supported by

492

the National Science Foundation of China (No. 41271122 and 31100361). We would also like to thank Christine

493

Verhille at the University of British Columbia for her assistance with English language and grammatical editing of

494

the manuscript.

495

REFERENCES

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Andreae, M. O. 1990. Ocean-atmosphere interactions in the global biogeochemical sulfur cycle. Marine

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Fig.1 Location of the experimental sites and transects in a coastal salt marsh in Eastern China. Each transect and its

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associated stands was identified as a number (0, 1, or 2) and letter (A, B, C, or D), respectively, with the mudflat

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stand (A) common to all transects. The transects ran through a creek (0), along the edge of a creek (1), and parallel

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to, but far from the creek (2), respectively, and each transect was sampled at four stands: a 0.87 m elevation

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mudflat (A); a 0.95 m elevation S. alterniflora marsh (B), a 1.79 m elevation S. salsa salt marsh (C), and a 2.18 m

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elevation A. littoralis marsh (D). The resultant transect identifiers were A-B0-C0-D0 for the creek, A-B1-C1-D1 for

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near creek edge, and A-B2-C2-D2 for the transect running parallel to, but far from the creek.

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Table 1 The plant biological feature of S. alterniflora, S. salsa and A. littoralis communities in a coastal salt

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marsh in Eastern China.

Community

Sites

Height (cm)

Aboveground biomass (g m-2)

S. alterniflora

B

146.3±2.1

875.4±43.2

172.2±6.2

S. salsa

C

48.2±1.4

383.5±27.4

835.5±5.4

A. littoralis

D

23.2±1.3

780.3±5.2

9567.4±34.4

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Density(individuals/m2)

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Fig.2 DMS flux in August, including the measured (a) control (aboveground plant biomass intact), (b) the

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treatment (aboveground-biomass removed), and (c) the control-treatment (aboveground-biomass contribution).

5

The mudflat

4

S. alterniflora

200

S. salsa

3

A. littoralis

150

2 1 0

50

b

-2 -1

100

120

80

c

EP

-2 -1

100

TE D

50

0

DMS flux（ug S m h )

SC

100

0 150 DMS flux（ug S m h )

a

M AN U

-2 -1

DMS flux（ug S m h )

250

RI PT

724 725

60 40

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20 0

-20

The creek

Near creek Transects

26

Far creek

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Table 2 Significant levels for seasonal and spatial effects on DMS and CH4 fluxes in a salt marsh.

Different sources

and near creek

creek

and

far

creek

Far

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and

near creek

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The creek

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Far creek

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Near creek

Stands(n=18) August and December (n=18) Stands(n=24) August Intact and treatment(n=24) Stands(n=24) December Intact and treatment(n=24) Stands(n=24) Intact August and December (n=24) Stands (n=24) Treatment August and December (n=24) Stands (n=24) Aboveground-biomass contribution August and December (n=24) Stands(n=24) August Intact and treatment(n=24) Stands(n=24) December Intact and treatment(n=24) Stands(n=24) Intact August and December (n=24) Stands (n=24) Treatment August and December (n=24) Stands (n=24) Aboveground-biomass contribution August and December (n=24) Intact(n=18) August Treatment(n=18) Aboveground-biomass contribution(n=18) Intact(n=18) December Treatment(n=18) Aboveground-biomass contribution(n=18) Intact(n=18) August Treatment(n=18) Aboveground-biomass contribution(n=18) Intact(n=18) December Treatment(n=18) Aboveground-biomass contribution(n=18) Intact(n=24) August Treatment(n=24) Aboveground-biomass contribution(n=24) Intact(n=24) December Treatment(n=24) Aboveground-biomass contribution(n=24)

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The creek

Significance DMS CH4 ns * ns ns ** *** ns *** * ns ns ns ** *** ns ** ns *** ns *** ns *** ns *** * *** ns ns *** *** *** ns *** *** *** ns ** *** * ns *** ns *** ns * ns * * ns *** ns ** ns ** ns *** ns ns * ns ns ** *** ns * ns *** * ns ns * *** ns *** ns *** * ns ns ns

*p＜0.05, **p＜0.01, ***p＜0.001, ns p＞0.5

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Fig.3 DMS flux in December, including the measured (a) control (aboveground plant biomass intact), (b) the treatment (aboveground-biomass removed), and (c) the control-treatment (aboveground-biomass contribution).

15

The mudflat

a

S. alterniflora S. salsa A. littoralis

600

10

400

5

200

0

b

250 200

M AN U

-2 -1

DMS flux（ug S m h )

0 300

SC

-2 -1

DMS flux（ug S m h )

800

150 100 50 0

300 200

TE D

400

c

EP

-2 -1

DMS flux（ug S m h )

500

RI PT

728 729 730

100 0

731 732 733 734

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The creek

Near creek Transects

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Far creek

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Fig. 4 CH4 flux in August, including the measured (a) control (aboveground plant biomass intact), (b) the treatment (aboveground-biomass removed), and (c) the control-treatment (aboveground-biomass contribution).

a

4 3

RI PT

-2 -1

CH4 flux（mg m h )

5

2 1

b

4

M AN U

-2 -1

CH4 flux（mg m h )

-1 5

SC

0

3 2 1 0

0 -1 -2

c

TE D

1

EP

-2 -1 CH4 flux（mg m h )

-1 2

-3

The mudflat S. alterniflora S. salsa A. littoralis

-4

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735 736 737

The creek

Near creek Transects

29

Far creek

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Fig.5 CH4 flux in December, including the measured (a) control (aboveground plant biomass intact), (b) the treatment (aboveground-biomass removed), and (c) the control-treatment (aboveground-biomass contribution).

-2 -1

CH4 flux（mg m h )

5

a

4

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738 739 740

3 2

b

4

M AN U

-2 -1

CH4 flux（mg m h )

0 5

SC

1

3 2 1

0 -1 -2

c

TE D

1

EP

-2 -1 CH4 flux（mg m h )

0 2

-3

The mudflat S. alterniflora S. salsa A. littoralis

741 742 743 744 745 746 747 748 749 750 751 752

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-4

The creek

Near creek Transects

30

Far creek

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All sites

Higher DMS flux Higher CH4 flux Lower DMS flux Lower CH4 flux

200

3 2 1 0 -1

2

5

S. alterniflora

0

1

50

S. salsa

EP

0

10

TE D

CH4 Flux（mg m-2h-1) CH4 Flux（mg m-2h-1)

0

600

15

20

100

150

-1

0

CH4 Flux（mg m-2h-1)

400

Mudflat

SC

CH4 Flux（mg m-2h-1)

0

5 4 3 2 1 0 -1

mudflat S. alterniflora S. salsa A. littoralis

RI PT

5 4 3 2 1 0 -1

M AN U

CH4 Flux（mg m-2h-1)

Fig.6 The relationship between DMS and CH4 fluxes within an Eastern Chinese coastal salt marsh for All sites: y =0.12x -0.08, R2 = 0.65, p＜0.001, n = 79; the mudflat: y=7.18x-2.42, R2 =0.48, p＜0.001, n=24; the S. alterniflora marsh: y=5.07x +9.64, R2 = 0.75, p＜0.001, n=24; the S. salsa marsh: y =13.07x-3.39, R2 =0.48, p＜ 0.01,n=17; and the A. littoralis marsh: y =13.07x-3.39, R2 =0.48,p＞0.05, N=14.

2

4

6

8

10

A. littoralis

2

AC C

753 754 755 756 757

1 0

-1

0

5

10

15 -2

-1

DMS Flux (ug S m h )

31

20

ACCEPTED MANUSCRIPT

Table 3 Relationship between gases fluxes and environmental factors. DMS

Environmental factors

a

August and December (n=24) Initial gases concentration

-208.89

August (n=12)

-167.87

December (n=12) August and December (n=24) Tidal creek

August (n=12)

Light intensity

December (n=12)

August (n=12) December (n=12)

Organic matter

August (n=24)

Total dissolved salt

EP

August (n=12)

17.21

0.01 ns

0.32

7.43

-23.42.

0.40ns

0.10

ns

2.12

1.31

0.17 ns

**

-55.40

121.93

0.47**

-548.62

1207.80

0.63***

0.90 -0.07 -6.01 -0.06 6.29

0.18

-34.35

**

0.39

7.15

0.19 *

-77.68

165.21

0.60***

-0.34

*

-0.00

9.29

0.02 ns

*

-0.01

30.66

0.35 *

0.05

ns

0.00

-2.05

0.19 ns

0.00

ns

-0.04

10.53

0.45**

**

-0.06

22.58

0.83***

ns

-0.00

2.96

0.13 ns

0.08

0.35

0.23

August (n=12)

-0.03

0.39*

58.32

August (n=12) December (n=12)

32

-180.64

0.51**

0.14

0.65

-3.94

0.35*

***

14.34

-83.06

0.71***

**

29.45

-175.10

0.57***

***

224.30

-71.34

0.56***

***

-120.64

288.99

0.56***

0.67

0.46

0.50

0.03 ns

-110.09

265.15

0.81***

0.34

**

-416.24

873.69

0.52**

***

0.02

-12.55

0.57***

**

0.02

-9.76

0.62***

***

0.02

-16.77

0.62*

***

-0.07

14.31

0.83***

**

-0.06

13.45

0.48*

***

9029.6 0.00 0.00

-1.22 -0.14 -94.50 -8.00 -5.59

0.37 0.52

0.52 0.81

0.88

0.93

0.04

-5.24

0.96

-0.04

6.63

0.58*

0.13

-1.06

0.59***

1.50

-12.67

0.83***

***

3.47

-109.29

0.51**

**

10.04

-65.30

0.52*

***

3.31

-16.43

0.68***

***

3.76

-21.17

0.79***

***

2.17

-7.82

0.34*

***

4.31

-24.46

0.37***

0.16*

6.06

-37.04

0.53***

*

2.31

-12.13

0.22 ns

***

61.16

-18.40

0.75***

***

62.36

-20.52

0.85***

***

79.73

-21.56

0.54***

-21.02 -83.89 -1.67 -3.17 -13.03

0.59

-2.65

0.51

-1.93

85.51

Linear regression(y=ax+b)was applied, *p＜0.05,**p＜0.01,***p＜0.001

-19.24

5.95

ns

-4.56

15.48

December (n=12)

-45.95

0.11

3798.9

13.05

August (n=12)

-19.86

0.09 ns

0.26

3.68

August and December (n=24)

-6.91

-0.51

-3.76

4.13

August and December (n=24)

-5.43

0.29

ns

6219.6

0.60

December (n=12)

0.73

0.04

0.49

August (n=12)

-6.69

0.02

13.98

August and December (n=24)

6.52

12.87

0.69

December (n=12)

30.70

-0.01

0.02

August and December (n=24)

AC C

-0.11

ns

0.04

December (n=12)

759

0.27

0.08

August and December (n=24)

TN

0.20 ns

8.27

December h(n=12)

Total dissolved salt

7.42

3.34

August (n=12)

Organic matter

0.01

ns

1.04

August and December (n=24)

Soil surface temperature

0.14

0.19

December (n=12)

Far Creek

0.25ns

0.02

August (n=12)

Aboveground biomass

24.89

-0.00

August and December (n=24)

Light intensity

-0.01

ns

-0.02

August (n=24)

Initial gases concentration

0.34

0.00

TE D

TN

August (n=24)

0.24*

0.74

0.00

August and December (n=24) Soil surface temperature

117.45

0.19 ns

203.28

August (n=12)

-41.70

28.44

December (n=12)

August and December (n=24) Near Aboveground biomass creek

0.68*

-0.01

27452.00

December (n=12)

486.26

ns

August (n=12)

August (n=12)

-225.91

ns

0.74

M AN U

Light intensity

0.26*

0.13 ns

208.37

August and December (n=24)

356.22

0.49

0.09

August and December (n=24) Initial gases concentration

-158.78

*

0.36

-0.00

0.19

December (n=12)

R2

0.00

0.01

August (n=12)

1.01

b

*

1.09

-0.00

August and December (n=24) Water surface temperature

0.95

a

-13.83

-0.00

December (n=12)

CH4 R2

b

RI PT

Transect

SC

758

-19.77 -3.44 -5.20 -20.21

0.61

0.68 0.44

0.49 0.60

0.21

0.29 0.57

0.66 0.48

ACCEPTED MANUSCRIPT

Table 4 Sea-to-air DMS fluxes from different marine ecosystems.

Date(mm/yy) DMSP(nM)

DMS flux (µmol m-2 d-1)

References

Bohai Sea

03/1993

-

0.85(0.04-3.12)

Hu et al ( 2003)

Yellow Sea

09/1994

-

7.94(0.11-18.88)

Hu et al (2003)

Bohai Sea and northern Yellow Sea

11/2011

17.76(7.56-42.28) b 2.82(0.88-12.88) a

4.21(0.05-27.4 )

Yang et al (2015)

North Yellow Sea

08/2006

-

6.87(0.19–36.38)

Yang et al (2009)

North Yellow Sea

01/2007

4.52(1.76-7.70) 7.21(4.29-11.30) a

5.12(0.60-15.70,by W92), 2.72(0.10-8.39, by LM86), 4.28(0.68-13.80,by N2000)

Yellow Sea

03/2005

7.98(4.89-13.50)a

3.14(0.01–5.68)

Yellow Sea

04/2006

-

6.41(0.04–20.76)

East China Sea and Yellow Sea)

06-07/2006

28.25(13.98-44.93)

7.45(0.12–39.09)

04/1994

-

3.4(0.15–10.7)

Yang et al (2000)

08/1994

-

3.84(Kuroshio region), 5.56(continental shelf))

Uzuka et al (1996)

10/1993

-

10.6(0.7-13)

Yang et al (1996)

11-12/1993

-

7.6(0.19-20.6)

Yang et al (1999)

2.12(0.24–15.07)

Shen et al (2016)

East China Sea

South China Sea

07-08/2000

42.6(4.33-100.62)

AC C

EP

TE D

a particulate DMSP(DMSPp), b dissolved DMSP(DMSPd).

M AN U

b

761

RI PT

Study area

SC

760

33

a

Li et al (2015a)

Yang et al (2006)

Zhang et al (2008) Yang et al ( 2011)

ACCEPTED MANUSCRIPT Highlights Invasion of S. alterniflora dramatically increased CH4 and DMS emission rate. Invasion of S. alterniflora altered the relationship between DMS and CH4. Invasion lead to the emission of CH4 more sensitive to the variation of DMS.

AC C

EP

TE D

M AN U

SC

RI PT

Invasion lead to spatial separation between promoting and suppressing CH4 emission.