# Atmospheric CO2 flux and planktonic food web relationships in temperate marsh systems: insights from in situ water measurements

Lucila Xaus, Raphael Moncelon, Jérémy Mayen, Lauriane Bergeon, Bénédicte

Dubillot, Philippe Pineau, Claire Emery, Marie Vagner, Francois-Xavier

Robin, Frédéric Azémar, et al.

► To cite this version:

Lucila Xaus, Raphael Moncelon, Jérémy Mayen, Lauriane Bergeon, Bénédicte Dubillot, et al. "Atmospheric CO2 flux and planktonic food web relationships in temperate marsh systems: insights from in situ water measurements." International Microbiology, In press, 10.1007/s10123-025-00650-x. hal-04977167

HAL Id: hal-04977167

https://hal.science/hal-04977167v1

Submitted on 5 Mar 2025

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<figure><img src="image_1.png" /></figure>
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7 *Corresponding author: Lucila.Xaus@univ-lr.fr, ORCID: 0009-0002-4095-2797

8 ¹ LIENSs, UMR7266 CNRS- La Rochelle Université, 17000, La Rochelle, France

9 ² Ifremer, LITTORAL, F-17390 La Tremblade, France

10 ³ CNRS, Univ Brest, Ifremer, IRD, LEMAR, 29280 Plouzané, France

11 ⁴ UNIMA, Union des Marais de la Charente-Maritime, France

12 ⁵Centre de Recherche sur la Biodiversité et l'Environnement /UMR 5300 CNRS-Université de Toulouse-INP-IRD

13 FR. Toulouse, France

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15 Abstract

16 While research has extensively investigated the dynamics of CO₂ water partial pressure (pCO₂) and planktonic food webs (PFWs) separately, there has been limited exploration of their potential interconnections, especially in marsh 17 typologies. This study's objectives were to (1) investigated if pCO₂ and atmospheric CO₂ flux can be elucidated by 18 PFW topologies, and (2) ascertain if these potential relationships are consistent across two distinct "Blue Carbon" 19 ecosystems. Abiotic and biotic variables were measured in two contrasting wetlands at the Atlantic French coast: a 20 saltwater (SM, L'Houmeau) and a freshwater marsh (FM, Tasdon). SM acted as a weak carbon source, with pCO₂ 21 between 542 and 842 ppmv. Conversely, FM exhibited strong atmospheric CO₂ source or sink characteristics, 22 varying with seasons and stations, with pCO₂ between 3201 and 114 ppmv. Five PFW topologies were linked to 23 varying pCO₂ across the two ecosystems: three stable topologies ('biological winter', 'microbial', 'multivorous' PFW) 24 exhibited consistently high pCO₂ values (FM: 971, 1136, 3020 ppmv; SM: 'biological winter' not observed, 842, 832 25 ppmv), while two transient topologies ('weak multivorous' and 'weak herbivorous') displayed lower and more 26 variable pCO₂ values (FM: from 127 to 1402 ppmv; SM: from 638 to 749 ppmv). Seasonality emerged as an 27
influencing factor for both pCO₂ dynamics and PFW. However, PFW in FM did not demonstrate a seasonal equilibrium state, potentially hindering a clearer understanding of the relationship between pCO₂ and PFW. This is the first documented association between PFW topologies and pCO₂ dynamics in “Blue Carbon” marsh environments.

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**Key words:** Blue Carbon, wetlands, planktonic food web, pCO₂, air-water CO₂ variations

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## 1. Introduction

For decades, atmospheric CO₂ emissions have surged due to human activities such as heavy reliance on fossil fuels, deforestation, and agriculture (Canadell et al. 2021). Consequently, extensive research has been dedicated to this issue. Accurately assessing anthropogenic CO₂ emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate is critical for deciphering regional and global carbon cycles, predicting future climate changes, and formulating effective climate policies (Friedlingstein et al. 2022).

Over short time spans, CO₂ partial pressure (pCO₂) in the atmosphere remains relatively stable (Takahashi et al. 2002). However, in surface waters, it can fluctuate dramatically spatially and temporally, varying by more than four orders of magnitude (Sobek, Tranvik, and Cole 2005; Takahashi et al. 2002). Calculating atmospheric CO₂ fluxes involves assessing the disparity between surface water and atmospheric pCO₂ levels (Mayen et al. 2023; Polsenaere et al. 2023; Takahashi et al. 1997). These fluxes undergo changes influenced by both the water-air pCO₂ gradient and the nature of the water mass (freshwater vs. seawater). Various physical factors (e.g., temperature, winds, surface water mixing) and biological processes (e.g., CaCO₃ dissolution/precipitation, primary production, and respiration) influence water pCO₂ (Fig. 1) (Moreau et al. 2013). Nonetheless, Dai et al. (2009) found that in other coastal systems, such as marshes, the strong relationship between oceanic CO₂ flux and temperature appears to be influenced by factors other than temperature. This indicates a significant biological control on water pCO₂, along with the effects of horizontal advection and water-sediment exchanges in these shallow land-sea interface ecosystems (Mayen et al. 2023).
<figure><img src="image_2.png" /><figcaption>**Fig. 1** Conceptualized scheme of planktonic community CO₂ and O₂ exchanges during daytime and nighttime periods in the water column of a coastal environment (water height differences: 50 cm). DOC: dissolved organic carbon, Nutrients: NO₃⁻, NO₂⁻, PO₄³⁻, NH₄⁺, SI, HTTP: heterotrophic prokaryotes, Zoo: micro and mesozooplankton, Phyto: pico, nano, and microphytoplankton. The equations depict gas (CO₂/O₂) exchanges and gradients at the water-air interface, along with biological processes involving the carbonate system (dissolved CO₂, CO₂(d), bicarbonate ions HCO₃⁻, carbonate ions CO₃²⁻ and calcium carbonate CaCO₃) through photosynthesis/respiration and CaCO₃ dissolution/precipitation. Illustration by L. Xaus</figcaption></figure>

In this context, coastal vegetated systems such as salt marshes, seagrass meadows, and mangroves are recognized for their main role in "Blue Carbon" sequestration and storage (Chmura et al. 2003; C. M. Duarte, Middelburg, and

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Caraco 2005; Greiner et al. 2013; Macreadie, Nielsen, et al. 2017; Macreadie, Serrano, et al. 2017; Mcleod et al. 2011), with an average sequestration rate of over 200 ± 24 g C m⁻² yr⁻¹ (Mcleod et al. 2011). Specially, the La Rochelle metropolitan area in France (Fig. 2-A, B) contains nearly 25,580 hectares of wetlands, accounting for almost 45% of its surface area (Afonso 2023). These wetlands have garnered attention from researchers studying their carbon dynamics through *in situ* measurements of water pCO₂ (Mayen et al. 2023), Eddy Covariance (EC) measurements of atmospheric CO₂ exchanges (−483 g C m⁻² yr⁻¹ saltwater marsh storage capacity (Mayen et al. 2024)), and sediment analysis (up to −345 g C m⁻² yr⁻¹ in high carbon sequestration rates (Amann et al. 2023)). Coastal marshes can vary in structure depending on their location; they may be connected to inland rivers, include a dam, or be linked to the sea, such as the studied freshwater marsh (Tasdon) (Fig. 2-B, D). Additionally, these marshes may be managed by humans for activities such as shellfish farming and agriculture, as seen in the L'Houmeau saltwater marsh used in this study (Fig. 2-B, C)). Furthermore, the physical characteristics and biota of marshes are site-specific, influenced by tidal regimes, exposure to wind and waves, and sediment supply (Fagherazzi et al. 2013).

Primary production can increase or decrease depending on various factors, including the phytoplankton cell size (Sieburth, Smetacek, and Lenz 1978), the biomass of autotrophic organisms, specific photosynthetic activity, and several abiotic factors such as temperature, light availability, and nutrient concentrations (Fig. 1). Shiomoto (1997) found that in the Okhotsk Sea, small-sized nano- (2 to 20 µm) and pico- (0.2 to 2 µm) phytoplankton could contribute up to 70% of primary production. On the Atlantic coast of France (Fig. 2-A), Moncelon et al. (2022) proposed that microphytobenthos, along with pico- and nanophytoplankton, may significantly contribute to the total primary production in freshwater marshes. Del Giorgio and Williams (2005) suggested that in general coastal ecosystems, mesozooplankton consume between 12 and 35% of primary production daily, while microzooplankton graze between 60 and 75%. Additionally, heterotrophic prokaryotes respiration often exceeds primary production in aquatic ecosystems (Del Giorgio, Cole, and Cimbleris 1997). These findings underscore the importance of studying planktonic food web (PFW) topologies to better understand their ecological and biological behavior in wetlands, as well as the CO₂ emissions associated with them.

PFWs have been extensively described in marine and coastal areas (Legendre and Rassoulzadegan 1995; Legendre and Rivkin 2005). On the west coast of France (Fig. 2-A), the functional role of the ‘microbial food web’ in marshes
has been detailed by Dupuy et al. (1999, 2011), along with the relationship between PFW dynamics and phytoplankton blooms on the continental shelf (Marquis et al. 2007). Recent studies focusing on marshes (Bergeon et al. 2023; Masclaux et al. 2014; Tortajada 2011) have defined five main PFWs, each presenting different degrees of PFW nuances: 'herbivore' (i.e. high biomass of microphytoplankton and mesozooplankton), 'multivorous' (i.e. high biomasses of all trophic compartments and dissolved organic carbon), 'microbial food web'(i.e. high nano- and pico- phytoplankton, microzooplankton and protozoan biomass, as well as high bacterial biomass and dissolved organic carbon concentration), 'microbial loop'(i.e. high picophytoplankton and protozoan biomass, as well as high bacterial biomass and dissolved organic carbon concentration), and 'biological winter' (i.e. low biomasses of all trophic compartments and high nutrients concentrations) (Tortajada 2011). Thus, when referring to a specific planktonic food web topology, the names were used as they appear in previous bibliography (Bergeon, 2023; Dupuy et al., 2011; Legendre and Rassoulzadegan, 1995; Masclaux et al., 2014; Tortajada, 2011). Furthermore, studying PFWs has proven invaluable for understanding ecosystem functioning (Beaugrand 2005; Masclaux et al. 2014; Vincent, Luczak, and Sautour 2002). As previously mentioned, water pCO₂ variations in costal ecosystems are greatly influenced by biological activities (Mayen et al. 2024; Moreau et al. 2013). Only a few studies have focused on the relationships between food webs and carbon exchange. Notable examples included Berg et al. (2019), Mayen et al. (2024) and Polsenaere et al. (2013), which examined the links between atmospheric CO₂ flux, water pCO₂ dynamics, and the metabolism of benthic seagrass and marsh plants. Additionally, planktonic communities in coastal marshes seem to play a purifying role by retaining suspended matter, nutrients, and pollutants in the water column, helping to prevent eutrophication (Azim et al. 2005; Nyman 2011; Verhoeven et al. 2006).

Even though, both PFW topologies and the CO₂ cycle have been broadly studied in coastal zones, few studies have examined their association (Legendre and Rivkin 2005; Moreau et al. 2013; Niquil et al. 2006), particularly in diverse marshes (Adamczyk and Shurin 2015; Masclaux et al. 2014). Consequently, much about the relationship between PFWs and water CO₂ in marshes remains unknown.

The aim of this study is to (1) investigate whether variations in pCO₂ and atmospheric CO₂ flux can be explained by PFWs, and (2) determine if the potential relationships between PFWs and water CO₂ exchanges are consistent across two contrasting “Blue Carbon” ecosystems. To address these objectives, monthly samplings were conducted in 2021 in a saltwater marsh (L'Houmeau marsh; Fig. 2-C), and seasonal samplings in a restored freshwater marsh (Tasdon;
Fig. 2-D). Abiotic and biotic variables were monitored alongside simultaneous water pCO₂ measurements and atmospheric CO₂ flux estimations, leading to the first known relationships between PFW topology and water pCO₂ in temperate marshes.

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## 2. Materials and Methods

### 2.1. Overview of the studied marsh sites

The L'Houmeau saltwater marsh (SM) is a salt pond (basin) located behind a dike and originally used for oyster farming on the north side of La Rochelle city (Atlantic coast, France) (Fig. 2-B, C). The studied basin, which is 10 meters wide and 1.5 meters high at the top, naturally fills when the tide range exceeds 60 cm, reaching a maximum volume of 270 m³. A mechanical valve system manages the inflow and outflow of saltwater from the sea (Fig. 2-C, a, b). For this study, the water volume fluctuated between 90 and 144 m³, corresponding to a basin fill level between 0.50 and 0.80 meters, respectively. Both biotic and abiotic data monitored at this site are described in Moncelon (2022) and are briefly presented in sections 2.2. and 2.3.

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<figure><img src="image_3.png" /><figcaption>**Fig. 2** (A) Location of the La Rochelle metropolitan area on the west Atlantic French coast. (B) Location of L'Houmeau (SM, dark red) and Tasdon (FM, orange) within the La Rochelle metropolitan area. (C) Experimental site of the L'Houmeau saltwater marsh (SM). The red arrow in the satellite image (geoportal) indicates the location of the studied basin (46°12'17.4"N 1°11'41.3"W). Light blue lines represent the hydraulic network and water pathways between the marsh and sea waters. The dark blue areas denote a small fraction of the Marine Natural Park, and the green areas indicate the Natura 2000 site. The studied basin is shown in (C, a) with autonomously deployed water probes. (C, b) Represents the hydraulic system. (Moncelon, 2022; photo: Polseneare Pierre). (D) Satellite image from the geoportal of Tasdon's freshwater marsh (FM). Light blue lines represent the hydraulic network and water pathways between the marsh and seawaters. Dark blue areas denote a small fraction of the Marine Natural Park, and green areas indicate the ZNIEFF type I. Stations TA, TB and TC (46°8'56.4" N, 1°7'26.4" W; 46°</figcaption></figure>

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9'3.6" N, 1°8'9.6" W; 46° 8'49.2" N, 1°8'13.2" W respectively) are marked by red points. (D, a) Images from stations

TA (photo: Pierre Polsenaere)

The Tasdon freshwater marsh (FM) is a shallow wetland spanning 123 hectares, located within the urban area of La Rochelle (Fig. 2-B, D, a). From 2019 to 2021, a restoration process was undertaken to reconnect the marsh with the coastal ocean. This restoration included replanting 63,000 aquatic plants and adding sediment to reshape the sediment stock at three stations. This peri-urban marsh is influenced by different water inputs from both the Atlantic Ocean through the Pertuis Sounds and river discharges (Fig. 2-D). Biotic and abiotic data monitored at this site are briefly presented in sections 2.2. and 2.3, and described in detail by Bergeon et al. (2023) and Mayen (2024). Additionally, the research is associated with several projects: PAMPAS (2019-2024, Evolution de l'identité PAtrimoniale des Marais des Pertuis charentais en réponse à l'Aléa de Submersion marine), Dycidemaim (LEFE 2021-2022, Dynamique du carbone aux interfaces d'échange terrestre-aquatique-atmosphérique des marais tempérés), and LRTZC (2019-2027, La Rochelle territoire zéro carbone).

## 2.2. Abiotic parameter samplings

At the SM, measurement samplings were conducted monthly between March and August 2021 (Abiotic and water CO₂ measurement dates: FM: Spring: April 13th to 15th, Summer: August 16th to 18th, Autumn: December 13th to 15th, 2021, Winter: March 1st to 3rd, 2022; SM: March 17th, April 14th, May 18th, June 14th, July 15th, August 9th, 2021). Over 90% of the water column was refreshed monthly by the mechanical valve system (Fig. 2-C, b), with an adjustment period of 2-3 days before the start of samplings. The remaining 10% above the sediment helped minimize disturbance of the sediment-water interface. At the FM, seasonal samplings were carried out in the years of 2021 and 2022, following the marsh restoration, at three different stations: one with no direct river or oceanic input (TA), one with direct river discharges (TB), and one with oceanic influence (TC) (Fig. 2-D).
For both the SM and the FM, several parameters were measured continuously (one measurement every 15 minutes) in subsurface waters (at 0.50 meters below the surface) using an EXO2 multiparameter probe (YSI) with a precision of ±0.1°C for temperature, ±0.5 µS cm⁻¹ for salinity/conductivity, ±0.3 NTU for turbidity, ±3.1 µmol L⁻¹ for dissolved oxygen concentration, and ±1% for oxygen saturation percentage (%). Additionally, discrete measurements (once a month) of water temperature, salinity, and dissolved oxygen were taken with a VWR multimeter.

An autonomous pCO₂ underwater probe (C-Sense™ pCO₂ sensor, PME/Turner Designs) with a range of 0-2000 ppmv and a precision of 3% of the range, along with a miniPAR logger (PME), were utilized to measure water pCO₂ and water Photosynthetic Active Radiation (PAR), respectively, continuously (per minute) over a 24-hour period (Mayen et al. 2023). Water-air CO₂ fluxes were estimated following the methodology described in Mayen et al. (2023) and Polsenaere et al. (2023). The CO₂ transfer coefficients, normalized to a Schmidt number of 600 and obtained from Raymond and Cole (2001), were converted to the gas transfer velocity at the *in situ* temperature following (Jähne, Heinz, and Dietrich 1987). The non-temperature (NpCO₂) and temperature (TpCO₂) effects on diurnal pCO₂ variations were calculated as described by Takahashi et al. (2002) and applied to the marsh ecosystems as done in Mayen et al. (2023) and shown in Eq. (1) and (2). TpCO₂ is related to the physical effects of temperature on water pCO₂ (physical pump, Fig. 1), while NpCO₂ corresponds to pCO₂ variations related to other effects, such as biological processes, tidal advection, and water-sediment exchanges, which are particularly important in nearshore costal systems (Mayen et al. 2023).

$$ TpCO_2 = pCO_{2mean} \times \exp[0.0423 \times (T_{obs} - T_{mean})] \quad (1) $$

$$ NpCO_2 = pCO_{2obs} \times \exp[0.0423 \times (T_{mean} - T_{obs})] \quad (2) $$

where Tobs and pCO₂obs represent mean of the observed temperature and pCO₂ values, respectively, measured every minute by the probes. Tmean and pCO₂mean refer to the seasonal (annual mean) or diurnal (mean per 24-hour cycle) average values.

The CO₂ flux was calculated following the methodology outlined by Polsenaere et al. (2023) and Ribas-Ribas, Gómez-Parra, and Forja (2011), as represented in Eq. (3):

$$ FCO_2 = \alpha \times k \times \Delta pCO_2 \quad (3) $$
where FCO₂ (mmol m⁻² h⁻¹) represents the estimated air-water CO₂ fluxes, where α (mol kg⁻¹ atm⁻¹) is the CO₂ solubility coefficient in saltwater, k (cm h⁻¹) denotes the transfer velocity of CO₂ gas, and ΔpCO₂ (ppmv) is the difference between water and air pCO₂ means. For further details on CO₂ flux estimation and C-Sense probe calibration, please refer to Mayen et al. (2023) and the other references cited above.

At both sites, surface water samples were collected in triplicates to measure concentrations of inorganic nutrients and dissolved organic carbon (Nutrients and dissolved organic carbon sampling dates: FM: Spring: April 15th, Summer: August 25th, Autumn: November 16th, 2021, Winter: March 9th, 2022; SM: March 18th, April 14th, May 19th, June 13th, July 15th, August 9th, 2021). The methodology outlined by Aminot and Kerouel (2007) and Aminot and Kérouel (2004) was followed to determine nutrient concentrations (nitrate (NO₃⁻), nitrite (NO₂⁻), ammonium (NH₄⁺), phosphate (PO₄³⁻) and silicate (Si)) in filtered water (0.7 µm GF/F glass fiber membrane, Whatman) using a SEAL AA3 autoanalyzer. The detection limit was 0.02 µmol L⁻¹ (Aminot and Kerouel 2007). Dissolved organic carbon (DOC, mg L⁻¹) concentrations were only measured at the SM and were determined by the QUALYSE laboratory following standard NF EN 1484.

## 2.3. Sampling of biotic parameters

At both the SM and the FM, biotic parameters were systematically monitored in triplicates during the day, on a monthly basis (between March and August 2021) at SM and seasonally throughout 2021 and 2022 at FM (Sampling dates: FM: Spring: April 15th, Summer: August 25th, Autumn: November 16th, 2021, Winter: March 9th, 2022; SM: March 18th, April 14th, May 19th, June 13th, July 15th, August 9th, 2021). Chlorophyll-a (Chla) biomass in different phytoplankton size classes was quantified by collecting surface water samples and following the method outlined by Yentsch and Menzel (1963). This involved sequential filtration through 20 µm (micro), 3 µm (nano), and 0.7 µm (pico) filters.
During each sampling period, metazoan mesozooplankton (Meso) abundance was assessed using a 200 µm mesh size net (WP2 plankton net), and its abundance was measured in individuals per cubic meter (ind m⁻³). Metazoan microzooplankton (Micro) abundance was determined at the FM by filtering 6 L of water through a 63 µm mesh size net. Abundance of heterotrophic prokaryotes (HTTP) was measured by flow cytometry of a 1.5 mL water sample according to Marie et al. (1999), while heterotrophic protozoan abundance (Proto) was measured using Flowcam (Buskey and Hyatt 2006). Primary production (PP) by size class (pico, nano, and micro, in mg C m⁻³ h⁻¹) was exclusively measured at the SM using Nielsen (1951) radioactivity protocol. For more detailed information regarding identification and measurement methodologies, please see Bergeon et al. (2023) and Moncelon (2022).

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## 2.4. Statistical analysis

Statistical analysis was conducted using R software (version 4.2.3). Given that the data did not adhere to a normal distribution (Shapiro-Wilk, p-value < 0.05), non-parametric tests were employed for exploratory analysis. Specifically, the rstatix package (Kassambara 2019), and ggbreak package (Xu et al. 2021) were utilized. Differences in Chla biomass, PP by size class, HTTP, Meso abundance (as well as Micro and Proto in the FM), nutrients, and DOC concentrations (in the SM) were assessed within months (SM) and stations (FM) using a one-way Kruskal-Wallis test for non-parametric analysis. Post hoc analysis was performed using Dunn's test (package: dunn.test (Dinno and Dinno 2017)) when necessary, following identification of significant differences (if Kruskal-Wallis test presented a p-value < 0.05). The same methodology was applied to examine differences in CO₂ fluxes, pCO₂, O₂%, and wind speed between day and night.

A Food Web (FW) type analysis was carried out using hierarchical agglomerative clustering (HAC), by analyzing the biological parameters (HTTP, Chla, and metazoans by size class), PP, and DOC (this last parameter was only present at the SM). All parameters were converted to carbon biomass (µgC L⁻¹) to standardize and compare these different metrics (Table 1). For this analysis, Euclidean distance was used to measure the distances between groups, followed by to the Ward method (D1 or D2) as described in Masclaux et al. (2014). The analysis was performed using the following R packages: FactoMineR (Lê, Josse, and Husson 2008), factoextra (Kassambara and Mundt 2017) cluster (Maechler 2018), ade4 (Thioulouse et al. 1997), and agricolae (De Mendiburu 2020). Planktonic food web (PFW) characterization (here the HAC) is held through a functional approach of ecology (autotroph,
heterotroph, prokaryote, eukaryote and size) and not through a biodiversity approach. Thus, FW can be compared in different sites even though they might have different species. On the other hand, when referring to a specific PFW topology, the names were used as they appear in previous bibliography (Bergeon, 2023; Dupuy et al., 2011; Legendre and Rassoulzadegan, 1995; Masclaux et al., 2014; Tortajada, 2011).

**Table 1** Carbon biomass conversion factors to standardize and compare different biotic metrics used for the saltwater (SM, L'Houmeau) and freshwater (FM, Tasdon) marshes. Biotic parameters; Chla (Chlorophyll-a), Meso (mesozooplankton), Micro (microzooplankton), Proto (heterotrophic protozoan), HTTP (heterotrophic prokaryotes)

<table><thead><tr><th></th><th>Chla</th><th>Meso</th><th>Micro</th><th>Proto</th><th>HTTP</th></tr></thead><tbody><tr><td></td><td colspan="5">Carbon biomass (µgC.L<sup>-1</sup>)</td></tr><tr><td>SM</td><td>50<sup>a</sup></td><td>1.44 (ind L<sup>-1</sup>)<sup>b</sup></td><td>-</td><td>-</td><td>14 (fgC cell<sup>-1</sup>)<sup>e</sup></td></tr><tr><td>FM</td><td>50<sup>a</sup></td><td>0.768 to 1.44 (ind L<sup>-1</sup>)<sup>b</sup></td><td>0.028 (ind L<sup>-1</sup>)<sup>b</sup></td><td>2318 (cil) (pgC cell<sup>-1</sup>)<sup>c</sup>, 225 (din) (pgC cell<sup>-1</sup>)<sup>d</sup></td><td>14 (fgC cell<sup>-1</sup>)<sup>e</sup></td></tr></tbody></table>

: <sup>a</sup>(Tilzer and Dubinsky 1987); <sup>b</sup>(Dumont, Van de Velde, and Dumont 1975); Ciliates (cil): <sup>c</sup> (Putt and Stoecker 1989); Dinoflagelates (din): <sup>d</sup>(Fournier et al. 2012); <sup>e</sup>(Gundersen et al. 2002).

To summarize and understand the relationships between PFWs, abiotic factors, and water carbon variables, a Principal Component Analysis (PCA) was performed (package: vegan (Dixon 2003)). This analysis was conducted only for the SM due to insufficient data for each station at the FM. Additionally, a Kendall Tau test was executed to examine the relationships between each parameter, as it is a robust and reliable estimator for small and non-normal samples (Xu et al. 2021).

**3. Results**

**3.1. Temporal fluctuations in abiotic parameters**

At the SM, average salinity and temperature values increased from 26.4 ± 0.1 and 11.5 ± 0.5 °C in March to 34.3 ± 0.1 and 24.3 ± 1.2 °C in July, respectively (Table S1). DOC values were generally low but showed a slight increasing from 0.5 ± 0.0 mg L<sup>-1</sup> in March to 3.6 ± 0.1 mg L<sup>-1</sup> in August (Table S1). Nutrient concentrations did not display a clear trend. NO<sub>3</sub><sup>-</sup> and NH<sub>4</sub><sup>+</sup> reached their maximum concentrations in April (22.1 ± 1.4 µmol L<sup>-1</sup> and 5.3 ± 0.3 µmol L<sup>-1</sup>)
1.9 µmol L⁻¹, respectively) and their minimum concentrations in June (0.0 µmol L⁻¹ and 0.1 ± 0.1 µmol L⁻¹, respectively) (Table S1). NO₂⁻ concentrations also hit a low in June (0.2 ± 0.1 µmol L⁻¹), but peaked in July (2.3 ± 1.4 µmol L⁻¹) (Table S1). PO₄³⁻ concentrations increased steadily from 0.1 ± 0.0 µmol L⁻¹ in March to 1.42 ± 0.0 µmol L⁻¹ in August (Table S1). Si varied from 33.7 ± 0.4 µmol L⁻¹ in March to 45.5 ± 0.4 µmol L⁻¹ in June (Table S1). Turbidity showed no seasonal pattern, with the highest value of 34.4 ± 72.8 NTU recorded in August and the lowest value of 9.4 ± 2.6 NTU recorded in March (Table S1).

At the FM, both salinity and temperature exhibited clear seasonal patterns. The highest values, which were notably high for a freshwater marsh, were recorded during summer at 8.0 ± 0.2 (TB) for salinity and 23.4 ± 2.0 °C (TC) for temperature (Table S2). In contrast, the lowest salinity and temperature values were observed during winter at 0.3 ± 0.0 (TA) and during autumn at 6.9 ± 0.8 °C (TA), respectively (Table S2). Turbidity was highest in summer at 178.2 ± 5.3 NTU (TA) and lowest in spring at 15.4 ± 5.6 NTU (TC) (Table S2). Nutrient concentrations did not follow a consistent pattern throughout the sampling period. Both NO₃⁻ and NO₂⁻ reached their peak concentrations in autumn, with NO₃⁻ at 503.5 ± 10.6 µmol L⁻¹ (TB) and NO₂⁻ at 9.5 ± 1.2 µmol L⁻¹ (TC) (Table S2). PO₄³⁻ concentrations ranged from 0.02 to 0.9 µmol L⁻¹, except for site TC in winter, which saw a spike to 4.2 ± 4.0 µmol L⁻¹ (Table S2). Si showed its lowest value in winter at 15.0 ± 5.5 µmol L⁻¹ and its highest in summer at 429.1 ± 19.3 µmol L⁻¹ (TA) (Table S2). Finally, NH₄⁺ levels varied from 0.4 ± 0.0 µmol L⁻¹ in winter (TB) to 22.3 ± 1.3 µmol L⁻¹ in spring (TA) (Table S2).

## 3.2. Variations in water pCO₂/O₂, wind speed, and water-air CO₂ fluxes

During the study period at the SM, water pCO₂ values consistently remained slightly oversaturated in comparison with the atmospheric equilibrium levels (417 ppmv), ranging between 541 ppmv during nighttime and 842 ppmv during the day (Fig. 3-A). Throughout this period, CO₂ fluxes were consistently positive, indicating a source for the atmosphere. Notably, both pCO₂ and NpCO₂ exhibited similar trends, while TpCO₂ deviated from this pattern (Fig. 3-A). Moreover, a seasonal pattern emerged, showing an inverse correlation between pCO₂ and O₂%, with May recording the highest pCO₂ levels (842 ± 81 ppmv) and the lowest O₂% (68.7 ± 1.7%) (Fig. 4-A, a, b). Conversely, CO₂ fluxes and wind speeds exhibited a synchronous trend, peaking in April (7.3 ± 2.9 mmol m⁻² h⁻¹, 11.4 ± 0.4 m s⁻¹) and reaching their lowest in August (0.1 ± 0.0 mmol m⁻² h⁻¹, 3.6 ± 1.0 m s⁻¹) (Fig. 4-A, c, d). Furthermore, significant day and night variations were observed for CO₂ fluxes, pCO₂, O₂%, and wind speeds (July and August
313 only) (Kruskal-Wallis test, p-value < 0.05). Additionally, April stood out with significantly different CO₂ flux,

314 pCO₂, and wind gust values compared to other months (Dunn's post hoc test, p-value < 0.05).

315

<figure><img src="image_4.png" /></figure>

317 **Fig. 3** Variations in water pCO₂ (water CO₂ partial pressure; orange line), TpCO₂ (temperature effects on pCO₂ variations; dotted blue line), and NpCO₂ (non-temperature effects on pCO₂ variations; dark red line) measured over a 24-hour period (0, 12, 24 hours) at each marsh site and season. (A) Monthly variations in the L'Houmeau saltwater marsh (SM). Missing data at the SM correspond to faulty equipment. (B) Stations in the Tasdon freshwater marsh (FM) (from left to right: TA, TB, and TC) by season. Atmospheric pCO₂ value is represented by the green line

322
323 Seasonal variations in water pCO₂ were pronounced across all three stations at the FM, with TA exhibiting the most
324 remarkable shift. During summer, TA displayed oversaturated pCO₂ levels (2595 ± 198 ppmv), contrasting with the
325 remaining seasons where waters were undersaturated (252 ± 108 ppmv) (Fig. 3-B, a). Similar to observations at the
326 SM, NpCO₂ values closely mirrored measured pCO₂, while TpCO₂ exhibited an inverse pattern. The most
327 substantial discrepancy in CO₂ flux occurred during summer, with TB recording the highest value (5.5 ± 1.7 mmol
328 m⁻² h⁻¹) and TC the lowest (-3.7 ± 2.4 mmol m⁻² h⁻¹) (Fig. 4-B, c). Additionally, during summer, O₂ % values at TA
329 were slightly lower compared to the other two stations (41.8 ± 4.1 %) (Fig. 4-B, b). At all three sites, significant 330 differences were observed in pCO₂, O₂%, CO₂ flux, and wind gust, with either positive (TA - TB, TA - TC) or 331 negative (TB - TC) variations (Dunn's post hoc test, p-value < 0.05). Moreover, all four parameters showed 332 significant differences between day and night (Kruskal-Wallis test, p-value < 0.05), except for CO₂ fluxes during 333 spring, summer and winter at TA, and for all seasons at TC; and pCO₂ values in winter at station TB (Kruskal- 334 Wallis test, p-value > 0.05). 335

A.

a.

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

b.

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

c.
CO₂ FLUX
(mmol m⁻² h⁻¹)

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

d.

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

B.

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

sp: Spring;

su: Summer;

au: Autumn;

wi: Winter

Mar: March;

Apr: April;

May: May;

June: June;

July: July;

Aug: August
**Fig. 4** (A) Biotic variables measured at the L'Houmeau saltwater marsh (SM) (monthly measurements between March and August 2021); and (B) the Tasdon freshwater marsh (FM) (seasonal measurements at stations A, B, and C). (a) Water CO₂ partial pressure (ppmv), (b) O₂ % saturation, (c) estimated water-air CO₂ flux (mmol m⁻² h⁻¹), (d) wind speed (m s⁻¹), (e) Meso abundance (individuals m⁻³); (f) Chla biomass (µg L⁻¹ ± sd) by size class (micro: microphytoplankton (>20 µm), nano: nanophytoplankton (3-20 µm) and pico: picophytoplankton (< 3 µm)), and (g) HTTP abundance (cells mL⁻¹). (A, h) Chla PP by fraction (mg C m⁻³ h⁻¹) (micro, nano, pico) (mean ± sd) and (B, h) Heterotrophic protozoan (cell mL⁻¹ ± sd). Atmospheric pCO₂ value is represented by the green horizontal line (417 ppmv; A, a and B, a)

345

### 3.3. Temporal dynamics in biotic parameters and planktonic food web analysis

In June, the SM exhibited its peak Chla biomass alongside Meso and HTTP abundances (Chla: 10.37 µg L⁻¹, Meso: 1149.30 ind m⁻³, HTTP: 7.48e⁰⁵ cells mL⁻¹) (Fig. 4-A, e, f, g). Conversely, April marked the nadir for Chla biomass and Meso abundance (Chla = 0.12 µg L⁻¹, Meso = 225.35 ind m⁻³), while HTTP hit its lowest count in July (3.92e⁰⁴ cells mL⁻¹). Monthly analysis revealed significant disparities in these biotic parameters (Kruskal-Wallis tests, p-value < 0.05). Notably, while Chla nano and pico fractions exhibited similar patterns without significant differences, Chla micro biomass was significantly smaller than nano and pico fractions (Dunn's post hoc test, p-value < 0.05). Although the highest PP rate occurred in June, no significant difference was observed (Fig. 4-A, h). Across the study period, smaller phytoplankton forms emerged as the most productive, exemplified by the nano fraction's peak PP value in June (77.45 ± 31.25 mg C m⁻³ h⁻¹), whereas Chla micro fraction consistently ranked lowest in PP production from March to August (Fig. 4-A, h).

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At the FM, there were notable differences in seasonal variations of biotic parameters across stations. Particularly, TA exhibited higher Meso and Micro abundances (max Meso: 330.68 ind m⁻³ and Micro: 0.86 ind m⁻³) compared to TB (max Meso: 10.37 ind m⁻³ and Micro: 0.90 ind m⁻³) and TC (max Meso: 6.05 ind m⁻³ and Micro: 0.18 ind m⁻³) (Fig. 4-B, e). However, Chla biomass did not follow the same trend, peaking in autumn at TB (Fig. 4-B, e, f). Both HTTP and Proto registered their lowest values at station TB (Fig. 4-B, g, h). While Chla micro was always significantly lower than the nano fractions at each station and season (Dunn's post hoc test, p-value < 0.05), exceptions were noted for TA in winter and TC in spring, summer and winter 2021 (Dunn's post hoc test, p-value >
0.05). Additionally, Meso, Micro, HTTP, and Proto abundances showed significant differences between stations and seasons (Dunn's post hoc tests, p-value < 0.05).

At the SM, HAC analysis revealed three distinct PFW topologies, labeled as FW1, FW2 and FW3 (Fig. 5-A, a).
FW1, identified in June, emerged as a 'multivorous' FW, characterized by elevated carbon biomasses across all
three fractions of Chla, Meso, HTTP and DOC, alongside low nutrients concentrations (NO₃⁻, NO₂⁻, PO₄³⁻, and
NH₄⁺) (Fig. 6). Within FW2, a temporal FW succession unveiled three distinct FWs, notably a 'weak herbivore' in
March, April, and July, attributed to important nutrient levels, low Meso biomass, and relatively high
microphytoplankton production (Fig. 5-A, b and 6). May revealed a 'microbial food web', possibly due to
accumulating DOC resulting from Chla PP and Meso presence. Lastly, FW3 appeared as a 'weak multivorous' in
August, characterized by high Chla biomass across all size fractions, relatively lower heterotrophic biomasses
(Meso and HTTP), and elevated nutrient concentrations.

In the FM, a large variability in planktonic food webs (PFWs) was observed among stations, prompting separate
HAC analyses for each. This approach uncovered distinct PFW types for each station, revealing nuanced variations
within some (Fig. 5-B, a). Station TA exhibited two distinct 'multivorous' FWs: FW1.b, transitioning from a 'weak
multivorous' state in spring to a 'multivorous' state in summer and autumn, characterized by fluctuating biomasses
across all biotic variables alongside substantial nutrient concentrations, and FW2.b, categorized as 'multivorous
(with low nutrients)' in winter, marked by higher biological biomasses but lower nutrient levels. At TB, FW3.b
emerged as a 'biological winter' during spring, comprising predominantly predator biomasses alongside some
nutrients. FW4.b displayed two distinct topologies: a 'weak multivorous' FW in summer, featuring elevated Chla
values and limited predator and HTTP presence alongside fluctuating nutrient concentrations, and a 'weak
herbivorous' FW during autumn and winter (Fig. 5-B, a). Lastly, TC was divided into FW5.b, manifesting as a 'weak
multivorous' FW in spring and winter alongside a 'biological winter', and FW6.b, characterized by a clear 'microbial
FW' during summer and autumn (Fig. 5-B, a).
A.

a.

<figure><img src="image_21.png" /></figure>

b.

<table><thead><tr><th>Year</th><th>March</th><th>April</th><th>May</th><th>June</th><th>July</th><th>August</th></tr></thead><tbody><tr><td>2021</td><td>689 ppmv</td><td>749 ppmv</td><td>842 ppmv</td><td>832 ppmv</td><td>725 ppmv</td><td>638 ppmv</td></tr></tbody></table>

<figure><img src="image_22.png" /></figure>

Weak Herbi

<figure><img src="image_23.png" /></figure>

Weak Multivorous

<figure><img src="image_24.png" /></figure>

Microbial Food Web

<figure><img src="image_25.png" /></figure>

Multivorous

B.

a.

<figure><img src="image_26.png" /></figure>

TA

FW2.b

HAC_LH_2.ch
hdust ("ward.D2")

b.

<table><thead><tr><th>Year</th><th>Sites</th><th>Spring</th><th>Summer</th><th>Autumn</th><th>Winter</th></tr></thead><tbody><tr><td>2021</td><td>TA</td><td>407 ppmv</td><td>3020 ppmv</td><td>298 ppmv</td><td>606 ppmv</td></tr><tr><td>2021</td><td>TB</td><td>971 ppmv</td><td>1402 ppmv</td><td>127 ppmv</td><td>299 ppmv</td></tr><tr><td>2021</td><td>TC</td><td>446 ppmv</td><td>304 ppmv</td><td>1136 ppmv</td><td>959 ppmv</td></tr></tbody></table>

<figure><img src="image_27.png" /></figure>

Bio Wint

<figure><img src="image_28.png" /></figure>

Multi (- Nutri)

<figure><img src="image_29.png" /></figure>

Weak Herbi

<figure><img src="image_30.png" /></figure>

Weak Multi

<figure><img src="image_31.png" /></figure>

Microbial FW

<figure><img src="image_32.png" /></figure>

Multivorous

TB

<figure><img src="image_33.png" /></figure>

FW3.b

HAC_LH_2.ch
hdust ("ward.D2")

FW4.b

<figure><img src="image_34.png" /></figure>

FW5.b

HAC_LH_2.ch
hdust ("ward.D2")

TC

388

**Fig. 5** Clustering dendrograms for the HAC (hierarchical agglomerative clustering) applied to the biological matrix at (A, a) the L'Houmeau saltwater marsh (SM), with different food webs (FW1, FW2, FW3) defined by the cutting method "Ward.D1" (red line). Each number represents a replicate (1 to 3: March (FW2), 4 to 6: April (FW2), 7 to 9: May (FW2), 10 to 12: June (FW1), 13 to 15: July (FW2), 16 to 18: August (FW3); and (B, a) the Tasdon freshwater marsh (FM) stations (TA, TB, TC). There are two different food webs per station defined by the cutting method "Ward.D2" (red line). Each number represents a replicate (1 to 3: spring, 4 to 6 summer, 7 to 9: autumn, 10 to 12: winter), and colors indicate food web topology (FW1.b, FW2.b, FW3.b, FW4.b, FW5.b, FW6.b). Lastly, the association of food webs with either (A, b) monthly or (B, b) seasonal pCO₂ values is shown (A and B, b)

389

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397
<figure><img src="image_35.png" /><figcaption>**Fig. 6** *Box plots* displaying the mean per food web type for *Chla* (Chlorophyll-*a*) by size class (micro-, nano-, pico-), Meso (Mesozooplankton), HTTP (Heterotrophic prokaryote), and PP (Chla primary production) by fraction (micro-, nano-, pico-) used for the Hierarchical Agglomeration Clustering analysis at the SM (L'Houmeau saltwater marsh). *Box plot* labeled with the same letters are not significantly different (ANOVA followed by Fisher's LSD). All biotic variables have the same unit (µg C L⁻¹) but scales were different. In addition, three *box plots* with water CO₂ partial pressure (pCO₂, ppmv), CO₂ flux (mmol m⁻² h⁻¹), and O₂ saturation % (O₂, %) mean per food web were added to facilitate comparison (at the bottom of the figure)</figcaption></figure>

**3.4. Relationships between water pCO₂ and planktonic food webs**

At the SM, high daily mean water pCO₂ values (832 ppmv) were associated with the 'multivorous' FW type (Fig. 5-A, b and 6). Conversely, the 'weak multivorous' FW was related to the lowest pCO₂ values (averaging 638 ppmv
over 24 hours). The relationship between pCO₂ and FW2 ('weak herbivore' and 'microbial food web') appeared less clear due to the high variability within this FW type (Fig. 5-A, b and 6). However, upon closer examination of FW2 nuances, associations could be discerned within each FW type individually. For instance, the 'weak herbivorous' FW type was mainly associated with lower pCO₂ values (ranging between 689 and 749 ppmv on average over 24 hours), while the 'microbial food web' manifested when pCO₂ values peaked (averaging 842 ppmv over 24 hours) (Fig. 5-A). Kendall correlation tests failed to reveal significant correlations between water pCO₂ and biotic parameters (Chla, Meso, HTTP) (p-value > 0.05). Conversely, negative correlations emerged between CO₂ fluxes and Meso and HTTP (p-value < 0.05; Kendall's tau = -0.46 and -0.22, respectively). Chla exhibited negative correlation with O₂ (%) (p-value < 0.05; Kendall's tau = -0.26) and positive correlation with PP (p-value < 0.05; Kendall's tau = 0.48). An inverse correlation was observed between Meso and O₂ (%) (p-value < 0.05, Kendall's tau = -0.55). Lastly, pCO₂ showed positive correlation with DOC concentrations (p-value < 0.05; Kendall's tau = 0.41) and significant correlations with salinity, wind, temperature and turbidity (p-value < 0.05).

In the FM, comparing water pCO₂ values across stations revealed distinct patterns. The highest mean pCO₂ value (3020 ppmv) recorded at TA coincided with a 'multivorous' FW, while at TB, the highest mean pCO₂ values (1402 ppmv) were associated with a 'weak multivorous' FW, followed by a 'biological winter' (971 ppmv) (Fig. 5-B, b). At TC, both the highest and lowest pCO₂ values were linked to the 'microbial food web', with the second-largest pCO₂ value occurring alongside a 'biological winter' FW (Fig. 5-B). Kendall correlations for biotic and abiotic parameters did not reveal a consistent pattern within stations. However, pCO₂ presented significant correlations with salinity, wind, temperature and turbidity (p-value < 0.05) at all three stations. At TA, both Meso and Micro exhibited positive relationships with pCO₂ (p-value > 0.05; Kendall's tau = 0.33 for both) and negative correlations with O₂ (p-value < 0.05; Kendall's tau = -0.33 for both). HTTP was negatively correlated with pCO₂, CO₂ fluxes, and O₂ (p-value < 0.05; Kendall's tau = 0.60, 0.30, and 0.30, respectively). At TB, inverse correlations were observed between CO₂ fluxes and both Chla and Meso (p-value < 0.05; Kendall's tau = -0.45 and -0.33, respectively). Additionally, Micro exhibited negative relationships with both pCO₂ and O₂ (p-value < 0.05; Kendall's tau = -0.33 for both), while HTTP showed direct correlations with CO₂ flux and pCO₂ (p-value < 0.05; Kendall's tau = 0.30 and 0.60, respectively). Proto displayed a positive correlation with CO₂ flux but a negative correlation with O₂ (p-value < 0.05; Kendall's tau = 0.27 and -0.58). Lastly, at TC, Chla and Proto were negatively correlated with O₂ (p-value < 0.05; Kendall's tau = -0.55 and -0.79, respectively), while Meso and Micro exhibited inverse correlations with CO₂
fluxes and pCO₂ (p-value < 0.05; Kendall's tau = -0.33 for both). Similarly, HTTP showed negative relationships
with O₂, CO₂ fluxes, and pCO₂ (p-value < 0.05; Kendall's tau = -0.60, -0.60, -0.30, respectively).

The PCA results facilitated the creation of a PFW discrimination graphic at the monthly scale, utilizing both biotic
and abiotic parameters (Fig. 7). The first two principal components (PC1 and PC2) explained 66.9 % of the data
variability, unveiling a seasonal gradient predominantly along the first component, with summer positioned at the
left (reflecting maximal temperatures) and winter at the right. Principal components of PC1 included HTTP, Meso,
Chla, CO₂ flux, NO₃⁻, and wind speed, while PC2 was primarily explained by PO₄ and turbidity. A conspicuous
association emerged between elevated levels of Chla, Meso, and HTTP with high pCO₂ and PP. Moreover, all biotic
factors, alongside pCO₂ values, were related to FW1, representative of June (Fig. 3-A and 6). Simultaneously, this
seasonal gradient indicated a decline in O₂ % saturation and CO₂ flux for that particular month. The variability
within FW2 revealed a negative association with turbidity and PO₄ in March, but a positive relationship with CO₂
flux and O₂ % in April, with pCO₂ in May, and with turbidity and PO₄ in July. Lastly, FW3 was also associated with
both turbidity and PO₄ values (Fig. 7).

<figure><img src="image_36.png" /></figure>

Source: factoextra

<caption>**Fig. 7** Principal Component Analysis at the SM (L'Houmeau saltwater marsh) between March 2021 and August 2021 of abiotic parameters: temperature (temp, °C), salinity (Sal), turbidity (Turb, NTU), O₂% (%), water pCO₂</caption>
(ppmv), water-air CO₂ fluxes (fCO₂, mmol m⁻² h⁻¹), wind gust (wind, m s⁻¹), DOC (mg L⁻¹), NO₃⁻, NO₂⁻, NH₄⁺, PO₄³⁻, Si (µmol L⁻¹)) and biotic parameters: Chla biomass (Chlorophyll-a, µg L⁻¹), PP (Chla primary production, mg C m⁻² h⁻¹), Meso (mesozooplankton, individuals m⁻³) and HTTP (Heterotrophic prokaryote, cells mL⁻¹). Food web (FW) types are represented by different colors, and months by group of numbers (1 to 9: March, 10 to 18: April, 19 to 27: May, 28 to 36: June, 37 to 45: July, 46 to 54: August)

459

## 4. Discussion

### 4.1. Marsh typologies as carbon sinks around the globe

"Blue Carbon" ecosystems are not only important from an ecological point of view, but also crucial for the economy and society due to their role as regulatory systems. Their importance stems from their ability to mitigate flooding risks, improve water quality, enhance biodiversity, and store large amounts of carbon in their soils and biomass (C. M. Duarte, Middelburg, and Caraco 2005; Carlos M. Duarte et al. 2013; Mcleod et al. 2011; Monnoyer-Smith 2019). Both saltwater and freshwater marshes can act as important atmospheric CO₂ sinks (Guo et al. 2010; Kostyrka 2021; Mayen et al. 2024; Schäfer et al. 2014) or sources (Kayranli et al. 2010), depending on spatial (water bodies, habitats, biological/sedimentary stocks, management) and temporal (diurnal, tidal, seasonal, (inter-annual) scales. Furthermore, studies by Artigas et al. (2015), Miller and Fujii (2011), Schäfer et al. (2014) and Tuittila et al. (1999) highlighted that wetland restoration can transform marshes, deltas, or peatlands from atmospheric CO₂ sources to sinks. Conversely, Jimenez et al. (2012) observed that anthropogenic disruption (e.g., human-driven hydrologic changes) caused a freshwater marsh to shift from a strong CO₂ sink to a light CO₂ source.

In the present study, at the FM, lower CO₂ emissions were measured post-restoration Mayen (2024), with some periods even exhibiting CO₂ sink behavior, depending on the station and season. For instance, at station TC, with the input of saltwater during the summer of 2021, CO₂ flux was recorded at -3.70 ± 2.37 mmol m⁻² h⁻¹. In contrast, the SM remained a CO₂ source throughout the study period (from March to August; Fig. 3-A and 4-A, a, c). This finding contradicts previous research indicating that saltwater environments typically act as CO₂ sinks (Mayen et al. 2023), as well as *in situ* measurements from wetlands along a land-sea continuum in the La Rochelle metropolitan area (Polsenaere et al., unpublished results). This discrepancy may be due to the closed structure of the SM, which differs from the more commonly studied open saltwater marshes (Alongi 2020; Mayen et al. 2023; Thorhaug et al.
2019). Another possible explanation is that the low vegetation density and reduced photosynthetic activity in the SM result in higher respiration rates remained than primary production. This is supported by the O₂ % values, which were inversely related to pCO₂ values, likely indicating low phytoplankton production and higher respiration rates.

As mentioned earlier, from March until August 2021, the SM remained a weak atmospheric carbon source characterized by periods of water CO₂ oversaturation with pCO₂ variations between 600 and 900 ppmv. In our study, both temperature (TpCO₂) and non-temperature (NtpCO₂) effects predominantly influenced water pCO₂ at both the SM and the FM, though NpCO₂ appeared to have a greater impact on the measured pCO₂ levels (Fig. 3-A, B). For instance, at the SM, the ΔTpCO₂ was smaller than ΔNpCO₂ (465 ppmv versus 682 ppmv, respectively) throughout the entire study period. A similar pattern was observed at the FM between spring and autumn 2021.

ΔTpCO₂ and ΔNpCO₂ were: at TA 2831 ppmv (ranging from 287 to 3118 ppmv) and 3446 ppmv (ranging from 216 to 3662 ppmv) respectively; at TB ΔTpCO₂ was 1338 ppmv (ranging from 119 to 1457 ppmv) and ΔNpCO₂ 3467 ppmv (ranging from 100 to 3567 ppmv); and at TC, they were 987 ppmv (ranging from 251 to 1238 ppmv) and 1680 ppmv (ranging from 100 to 1780 ppmv) respectively. The effects of NpCO₂ on pCO₂ can be linked to environmental factors such as salinity and DOC, indicating advection processes, and biotic factors, including photosynthesis and microbial respiration processes, that occurred at the SM. This result is comparable to Mayen et al. (2023), who showed that horizontal advection processes (upstream and downstream) significantly influence on water pCO₂ dynamics in salt marshes (salt ponds) near the Fier d'Ars (Île de Ré, France).

In this study, many factors may have influenced the observed changes in CO₂ behavior. These include temperature and particularly non-temperature effects (Fig. 3-B), replanted vegetation (63,000 aquatic plants), and nutrient concentration along with salinity variations. These factors induced important changes, such as increases in Chla phytoplankton biomass and shifts in food web topology. Biotic parameters were also crucial in controlling pCO₂ at the SM, as indicated by O₂ % values inversely related to pCO₂ values from March to August 2021, likely reflecting low phytoplankton production and higher respiration rates. Conversely, the FM shifted from being strong water CO₂ source to exhibiting a balanced behavior as both a weak source and a sink, depending on the seasons and stations (Fig. 4-B, a, c).

## 4.2. Food web topologies and their relationship with water pCO₂ at studied marshes
Although no significant correlation between water pCO₂ and biotic factors was found at the SM, relationships between pCO₂ and planktonic food web (PFW) were clearly established during our study. Three different food web (FW) topologies were identified, each with nuances: a ‘multivorous’ FW in June (FW1) and a ‘microbial food web’ in May (FW2) exhibited mean high pCO₂ values (832 ppmv and 842 ppmv, respectively), while a ‘weak multivorous’ FW was associated with a lower mean pCO₂ value (638 ppmv) during August (FW3). Additionally, a ‘weak herbivorous’ FW occurred in March, April and July (FW2), with variable mean pCO₂ values ranging between 689 and 749 ppmv. These FW topologies have been previously described by Legendre and Rassoulzadegan (1995), Masclaux et al. (2014) and Tortajada (2011). Legendre and Rassoulzadegan (1995) noted that some PFWs, such as ‘multivorous’ and ‘microbial food web’ FWs, were more stable over time compared to others, like the ‘herbivorous’ FW. At the SM, the two stable FWs (‘multivorous’ and ‘microbial food web’) were associated with high mean pCO₂ values (832 and 842 ppmv, respectively). This could be attributed to the high abundance of Meso and HTTP and the weak PP for the ‘multivorous’ FW, or to the increased concentration of DOC in May for the ‘microbial food web’. Prairie, Bird, and Cole (2002) and Lapierre et al. (2013) have shown that DOC increases can directly raise water pCO₂. Conversely, the transitory FWs (‘weak herbivorous’ and ‘weak multivorous’) were associated with medium or low mean pCO₂ values (Fig. 6). These findings suggest that pCO₂ tends to accumulate more during stable FW occurrences than during transient ones.

At the FM, the absence of clear seasonality in PFW types observed throughout 2021 could be attributed to the recent restoration process initiated in 2019, which may have disrupted the marsh's return to an equilibrium state by 2021. Therefore, further monitoring of both carbon dynamics and FW topologies is necessary to clarify this absence of seasonality. Nevertheless, specific FW occurrences were notable during the study period. The ‘biological winter’ FW identified in spring 2021 at station TB (FW3.b) and in winter at station TC (FW5.b) were both associated with elevated pCO₂ values (971 and 959 ppmv, respectively). In contrast, the ‘weak herbivorous’ FW observed from autumn to winter at TB (FW4.b) was linked to the lowest pCO₂ values (127 and 299 ppmv, respectively). At station TA, extreme pCO₂ values (298 and 3020 ppmv in autumn and summer, respectively) were attributed to the ‘multivorous’ FW (FW2.b). This association could be explained by lower Chla nano and pico biomasses along with higher HTTP and Meso biomasses measured in summer compared to autumn. A similar pattern was observed for the ‘microbial food web’ identified at TC (FW6.b), which was associated with very high Chla biomasses measured
during summer. Conversely, no clear relationship was found between the 'weak multivorous' FWs and pCO₂ values, likely due to the lack of biological equilibrium.

This comparative analysis of two distinct marsh food web topologies allowed us to discern both similarities and differences between sites regarding carbon and planktonic food web relationships. Despite their typological disparities, both the SM and the FM functioned as CO₂ sources, with the FM exhibiting a weaker source tendency and occasionally acting as a carbon sink. Despite the divergent marsh characteristics (including contrasting salinity values, nutrient concentrations, and water regulation/management), our original approach clearly highlighted five food web topologies and their associated pCO₂ values (Fig. 8). These included three stable types ('biological winter', 'microbial food web', 'multivorous' food webs) with high pCO₂ values at both sites, as well as two transient types ('weak multivorous' and 'weak herbivorous') with lower and more variable pCO₂ values (Fig. 8). While four of these food webs had been previously described in literature (Legendre and Rassoulzadegan 1995; Masclaux et al. 2014; Tortajada 2011), two known PFW types, namely 'herbivorous' and 'microbial loop', were not observed in our study. Additionally, the 'biological winter' FW was not identified at the SM.

**Fig. 8** Relationships (arrows) between food webs and water pCO₂ in studied coastal marshes (Tasdon FM and L'Houmeau SM)
As the first registered study investigating the link between plankton FWs and water carbon in marshes, there is certainly room for improvement. One possible upgrade would be to adjust the sampling frequency, either by conducting monthly or seasonal sampling, and/or extending the duration of the study (over several years). Additionally, incorporating measurements of respiration rate could provide valuable insights into carbon dynamics within the ecosystem. Further research is encouraged to enhance our understanding of the relationship between PFW and water pCO₂.

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777

778

779
780 Declaration

781 Statement and Declaration

782 The authors declare that they have no known competing financial interests or personal relationships that could have
783 appeared to influence the work reported in this paper.

784

785 Funding

786 This work was supported by the ANR Project PAMPAS (2019-2024, Evolution de l'identité PAtrimoniale des
787 Marais des Pertuis charentais en réponse à l'Aléa de Submersion marine), LEFE Dycidemaim (LEFE 2021-2022,
788 Dynamique du carbone aux interfaces d'échange terrestre-aquatique-atmosphérique des marais tempérés) and La
789 Rochelle Territoire Zero Carbone (LRTZC : 2019-2027) through the Plan d'Investissement d'Avenir (PIA) through
790 the Territoires d'Innovation de Grande Envergure (TIGA), managed by the Caisse des Dépôts, as well as from the
791 Région Nouvelle-Aquitaine (grant agreement 25875420).

792

793 Competing interests

794 The authors declare that they have no known competing or financial interests or personal relationships that could
795 have appeared to influence the work reported in this paper.

796

797 Contribution

798 The study was conceptualized by C.D. and P.Po. J.M., R.M. and L.B. contributed with formal analysis. F-X.R.,
799 P.Pi., C.E. and B.D. contributed to the methodology and data sampling and curation. M.V. helped with founding and
800 editing. L.X. has done the data analysis, visualization, and writing of the original draft. Reviewing and editing the
801 manuscript was done by L.X., C.D. and P.Po. L.X., R.M., J.M., L.B., B.D., P.Pi., C.E., M.V., F-X.R., F.A., M.T.,
802 C.D., P.Po. contributed to later versions of the manuscript. All authors read and approved the final manuscript.

803
804 **Ethics declaration**

805 The authors declare that they have no conflict of interest. This study does not involve human participants.

806

807 **Data Availability Statement**

808 Data will be made available on request.

809