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

Atmospheric CO2 flux and planktonic food web relationships in temperate marsh systems: Insights from in situ water measurements | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Atmospheric CO2 flux and planktonic food web relationships in temperate marsh systems: Insights from in situ water measurements Xaus Lucila, Moncelon Raphaël, Mayen Jérémy, Bergeon Lauriane, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4768272/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2025 Read the published version in International Microbiology → Version 1 posted 7 You are reading this latest preprint version Abstract While research has extensively investigated the dynamics of CO 2 water partial pressure (pCO 2 ) and planktonic food webs (PFWs) separately, there has been limited exploration of their potential interconnections, especially in marsh typologies. This study’s objectives were to (1) investigated if pCO 2 and atmospheric CO 2 flux can be elucidated by PFW topologies, and (2) ascertain if these potential relationships are consistent across two distinct “Blue Carbon” ecosystems. Abiotic and biotic variables were measured in two contrasting wetlands at the Atlantic French coast: a saltwater (SM, L’Houmeau) and a freshwater marsh (FM, Tasdon). SM acted as a weak carbon source, with pCO 2 between 542 and 842 ppmv. Conversely, FM exhibited strong atmospheric CO 2 source or sink characteristics, varying with seasons and stations, with pCO 2 between 3201 and 114 ppmv. Five PFW topologies were linked to varying pCO 2 across the two ecosystems: three stable topologies ('biological winter', 'microbial', 'multivorous' PFW) exhibited consistently high pCO 2 values (FM: 971, 1136, 3020 ppmv; SM: 'biological winter' not observed, 842, 832 ppmv), while two transient topologies ('weak multivorous' and 'weak herbivorous') displayed lower and more variable pCO 2 values (FM: from 127 to 1402 ppmv; SM: from 638 to 749 ppmv). Seasonality emerged as an influencing factor for both pCO 2 dynamics and PFW. However, PFW in FM did not demonstrate a seasonal equilibrium state, potentially hindering a clearer understanding of the relationship between pCO 2 and PFW. This is the first documented association between PFW topologies and pCO 2 dynamics in “Blue Carbon” marsh environments. Blue Carbon wetlands planktonic food web pCO2 air-water CO2 variations Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction For decades, atmospheric CO 2 emissions have surged due to human activities like heavy reliance on fossil fuels, deforestation, and agriculture (Canadell et al. 2021 ). Consequently, extensive research has been dedicated to this issue. Accurately assessing anthropogenic CO 2 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, CO 2 partial pressure (pCO 2 ) 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 CO 2 fluxes involves assessing the disparity between surface water and atmospheric pCO 2 levels (Mayen et al. 2023 ; Polsenaere et al. 2023; Takahashi et al. 1997 ). These fluxes undergo changes influenced by both the water-air pCO 2 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., CaCO 3 dissolution/precipitation, primary production, and respiration) influence water pCO 2 (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 CO 2 flux and temperature appears to be influenced by factors other than temperature. This indicates a significant biological control on water pCO 2 , along with the effects of horizontal advection and water-sediment exchanges in these shallow land-sea interface ecosystems (Mayen et al. 2023 ). 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 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 pCO 2 (Mayen et al. 2023 ), Eddy Covariance (EC) measurements of atmospheric CO 2 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 CO 2 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: ‘herbivore’, ‘multivorous, ‘microbial food web’, ‘microbial loop’, and ‘biological winter’, each presenting different degrees of PFW nuances (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 pCO 2 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 include Berg et al. ( 2019 ), Mayen et al. ( 2024 ) and Polsenaere et al. ( 2013 ), which examined the links between atmospheric CO 2 flux, water pCO 2 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 CO 2 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 marsh (Adamczyk and Shurin 2015 ; Masclaux et al. 2014 ). Consequently, much about the relationship between PFWs and water CO 2 in marshes remains unknown. The aim of this study is to (1) investigate whether variations in pCO 2 and atmospheric CO 2 flux can be explained by PFWs, and (2) determine if the potential relationships between PFWs and water CO 2 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 pCO 2 measurements and atmospheric CO 2 flux estimations, leading to the first known relationships between PFW topology and water pCO 2 in temperate marshes. 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 m 3 . 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 m 3 , 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. 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 (Table 1 ). 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 water input (TA), one with direct river discharges (TB), and one with oceanic influence (TC) (Table 1 , Fig. 2 -D). Table 1 Samples and measurement methods for the saltwater (SM, L’Houmeau) and freshwater (FM, Tasdon) marshes. Abiotic parameters: Temperature, Salinity, Turbidity, O 2 %, Wind gust, pCO 2 (water CO 2 partial pressure), CO 2 flux, Nutrients (NO 3 − , NO 2 − , NH 4 + , PO 4 3− , Si) and DOC (dissolved organic carbon). Biotic parameters; Chl a (Chlorophyll-a), Meso (mesozooplankton), Micro (microzooplankton), Proto (heterotrophic protozoan), HTTP (heterotrophic prokaryotes). Sampling periods are mentioned at the bottom of the table Temperature (°C) Salinity Turbidity (NTU) O 2 % Wind gust (m − 1 s − 1 ) pCO 2 (ppmv) CO 2 flux (mmol m − 2 h − 1 ) SM YSI sensor (continuous) + VWR multimeter (discrete) Infoclimat.fr C-Sense probe, 24h, measured every minute Estimation (see Polsenaere et al. (2023)) FM YSI sensor (continuous) Infoclimat.fr C-Sense probe, 24h, 3 days, measured every minute Estimation (see Polsenaere et al. (2023)) Nutrients (µmol L − 1 ) DOC (mg L − 1 ) Triplicated Triplicated SM SEAL AA3 autoanalyzer Standard NF EN 1484 FM SEAL AA3 autoanalyzer - Chl a (µg L − 1 ) Meso (ind m − 3 ) Micro (ind m − 3 ) Proto (ind L − 1 ) HTTP (ind mL − 1 ) Triplicated Triplicated Triplicated Triplicated SM Filters: 20 µm, later 3 µm and 0.7 µm Filters: 200 µm - - Flow cytometry analysis FM Filters: 20 µm, later 3 µm and 0.7 µm Filters: 200 µm Filters: 63 µm Flowcam analysis Flow cytometry analysis Chl a Meso Micro Proto HTTP Carbon biomass (µgC.L − 1 ) SM 50 a 1.44 (ind L − 1 ) b - - *14 (fgC cell − 1 ) e FM 50 a 0.768 to 1.44 (ind L − 1 ) b 0.028 (ind L − 1 ) b 2318 (cil) (pgC cell − 1 ) c , 225 (din) (pgC cell − 1 ) d *14 (fgC cell − 1 ) e Conversion factors used: a (Tilzer and Dubinsky 1987); b (Dumont, Van de Velde, and Dumont 1975 ); Ciliates (cil): c (Putt and Stoecker 1989 ); Dinoflagelates (din): d (Fournier et al. 2012 ); e (Gundersen et al. 2002 ). Abiotic and water CO 2 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. Nutrients, DOC and biotic 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. 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 (%) (Table 1 ). Additionally, discrete measurements (once a month) of water temperature, salinity, and dissolved oxygen were taken with a VWR multimeter. An autonomous pCO 2 underwater probe (C-Sense™ pCO 2 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 pCO 2 and water Photosynthetic Active Radiation (PAR), respectively, continuously (per minute) over a 24-hour period (Table 1 ) (Mayen et al. 2023 ). Water-air CO 2 fluxes were estimated following the methodology described in Mayen et al. ( 2023 ) and Polsenaere et al. (2023). The CO 2 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 (NpCO 2 ) and temperature (TpCO 2 ) effects on diurnal pCO 2 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 Eqs. (1) and (2). TpCO 2 is related to the physical effects of temperature on water pCO 2 (physical pump, Fig. 1 ), while NpCO 2 corresponds to pCO 2 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 × exp [0.0423 × (T obs − T mean )] (1) NpCO 2 = pCO 2obs × exp [0.0423 × (T mean − T obs )] (2) where T obs and pCO 2obs represent mean of the observed temperature and pCO 2 values, respectively, measured every minute by the probes. T mean and pCO 2mean refer to the seasonal (annual mean) or diurnal (mean per 24-hour cycle) average values. The CO 2 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 = α × k × ΔpCO 2 (3) where FCO 2 (mmol m − 2 h − 1 ) represents the estimated air–water CO 2 fluxes, where α (mol kg − 1 atm − 1 ) is the CO 2 solubility coefficient in saltwater, k (cm h − 1 ) denotes the transfer velocity of CO 2 gas, and ΔpCO 2 (ppmv) is the difference between water and air pCO 2 means. For further details on CO 2 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. The methodology outlined by Aminot and Kerouel (2007) and Aminot and Kérouel ( 2004 ) was followed to determine nutrient concentrations (nitrate (NO 3 − ), nitrite (NO 2 − ), ammonium (NH 4 + ), phosphate (PO 4 3− ) 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 (Table 1 ). 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 (Table 1 ). Chlorophyll-a (Chl a ) 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 (Table 1 ). 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 (Table 1 ). 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 (Table 1 ). For more detailed information regarding identification and measurement methodologies, please see Bergeon et al. ( 2023 ) and Moncelon ( 2022 ). 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 Chl a 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 CO 2 fluxes, pCO 2 , O 2 %, and wind speed between day and night. A Food Web (FW) type analysis was carried out using hierarchical agglomerative clustering (HAC), to analyze the biological parameters (HTTP, Chl a , and metazoans by size class), PP, and DOC 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), factoexctra (Kassambara and Mundt 2017 ) cluster (Maechler 2018 ), ade4 (Thioulouse et al. 1997 ), and agricolae (De Mendiburu 2020 ). 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. DOC values were generally low but showed a slight increasing from 0.5 ± 0.0 mg L − 1 in March to 3.6 ± 0.1 mg L − 1 in August. Nutrient concentrations did not display a clear trend. NO 3 − and NH 4 + reached their maximum concentrations in April (22.1 ± 1.4 µmol L − 1 and 5.3 ± 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). NO 2 − 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 ). PO 4 3− concentrations increased steadily from 0.1 ± 0.0 µmol L − 1 in March to 1.42 ± 0.0 µmol L − 1 in August. Si varied from 33.7 ± 0.4 µmol L − 1 in March to 45.5 ± 0.4 µmol L − 1 in June. 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. 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. 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. Turbidity was highest in summer at 178.2 ± 5.3 NTU (TA) and lowest in spring at 15.4 ± 5.6 NTU (TC). Nutrient concentrations did not follow a consistent pattern throughout the sampling period. Both NO 3 − and NO 2 − reached their peak concentrations in autumn, with NO 3 − at 503.5 ± 10.6 µmol L − 1 (TB) and NO 2 − at 9. 5 ± 1.2 µmol L − 1 (TC). PO 4 3− 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 . 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). Finally, NH 4 + levels varied from 0.4 ± 0.0 µmol L − 1 in winter (TB) to 22.3 ± 1.3 µmol L − 1 in spring (TA). 3.2. Variations in water pCO 2 /O 2 , wind speed, and water-air CO 2 fluxes During the study period at the SM, water pCO 2 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, CO 2 fluxes were consistently positive, indicating a source for the atmosphere. Notably, both pCO 2 and NpCO 2 exhibited similar trends, while TpCO 2 deviated from this pattern (Fig. 3 -A). Moreover, a seasonal pattern emerged, showing an inverse correlation between pCO 2 and O 2 %, with May recording the highest pCO 2 levels (842 ± 81 ppmv) and the lowest O 2 % (68.7 ± 1.7%) (Fig. 4 -A, a, b). Conversely, CO 2 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 CO 2 fluxes, pCO 2 , O 2 %, and wind speeds (July and August only) (Kruskal-Wallis test, p-value < 0.05). Additionally, April stood out with significantly different CO 2 flux, pCO 2 , and wind gust values compared to other months (Dunn’s post hoc test, p-value < 0.05). Seasonal variations in water pCO 2 were pronounced across all three stations at the FM, with TA exhibiting the most remarkable shift. During summer, TA displayed oversaturated pCO 2 levels (2595 ± 198 ppmv), contrasting with the remaining seasons where waters were undersaturated (252 ± 108 ppmv) (Fig. 3 -B, a). Similar to observations at the SM, NpCO 2 values closely mirrored measured pCO 2 , while TpCO 2 exhibited an inverse pattern. The most substantial discrepancy in CO 2 flux occurred during summer, with TB recording the highest value (5.5 ± 1.7 mmol m − 2 h − 1 ) and TC the lowest (-3.7 ± 2.4 mmol m − 2 h − 1 ) (Fig. 4 -B, c). Additionally, during summer, O 2 % values at TA were slightly lower compared to the other two stations (41.8 ± 4.1%) (Fig. 4 -B, b). At all three sites, significant differences were observed in pCO 2 , O 2 %, CO 2 flux, and wind gust, with either positive (TA - TB, TA - TC) or negative (TB - TC) variations (Dunn’s post hoc test, p-value < 0.05). Moreover, all four parameters showed significant differences between day and night (Kruskal-Wallis test, p-value 0.05). 3.3. Temporal dynamics in biotic parameters and planktonic food web analysis In June, the SM exhibited its peak Chl a biomass alongside Meso and HTTP abundances (Chl a : 10.37 µg L − 1 , Meso: 1149.30 ind m − 3 , HTTP: 7.48e 05 cells mL − 1 ) (Fig. 4 -A, e, f, g). Conversely, April marked the nadir for Chl a biomass and Meso abundance (Chl a = 0.12 µg L − 1 , Meso = 225.35 ind m − 3 ), while HTTP hit its lowest count in July (3.92e 04 cells mL − 1 ). Monthly analysis revealed significant disparities in these biotic parameters (Kruskal-Wallis tests, p-value < 0.05). Notably, while Chl a nano and pico fractions exhibited similar patterns without significant differences, Chl a 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 Chl a micro fraction consistently ranked lowest in PP production from March to August (Fig. 4 -A, h). 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, Chl a 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 Chl a micro was always significantly lower than the nano fractions at each station and season (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 Chl a , Meso, HTTP and DOC, alongside low nutrients concentrations (NO 3 − , NO 2 − , PO 4 3− , and NH 4 + ) (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 Chl a PP and Meso presence. Lastly, FW3 appeared as a ‘weak multivorous’ in August, characterized by high Chl a biomass across all size fractions, relatively lower heterotrophic biomasses (Meso and HTTP), and elevated nutrient concentrations. In the FM, a large variability in 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 Chl a 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). . Figure 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 pCO 2 values is shown (A and B, b) 3.4. Relationships between water pCO 2 and planktonic food webs At the SM, high daily mean water pCO 2 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 pCO 2 values (averaging 638 ppmv over 24 hours). The relationship between pCO 2 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 pCO 2 values (ranging between 689 and 749 ppmv on average over 24 hours), while the 'microbial food web' manifested when pCO 2 values peaked (averaging 842 ppmv over 24 hours) (Fig. 5-A). Kendall correlation tests failed to reveal significant correlations between water pCO 2 and biotic parameters (Chl a , Meso, HTTP) (p-value > 0.05). Conversely, negative correlations emerged between CO 2 fluxes and Meso and HTTP (p-value < 0.05; Kendall’s tau = -0.46 and − 0.22, respectively). Chl a exhibited negative correlation with O 2 % (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 O 2 % (p-value < 0.05, Kendall’s tau = -0.55). Lastly, pCO 2 showed positive correlation with DOC concentrations (p-value < 0.05; Kendall’s tau = 0.41). In the FM, comparing water pCO 2 values across stations revealed distinct patterns. The highest mean pCO 2 value (3020 ppmv) recorded at TA coincided with a 'multivorous' FW, while at TB, the highest mean pCO 2 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 pCO 2 values were linked to the 'microbial food web', with the second-largest pCO 2 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. At TA, both Meso and Micro exhibited positive relationships with pCO 2 (p-value > 0.05; Kendall’s tau = 0.33 for both) and negative correlations with O 2 (p-value < 0.05; Kendall’s tau = -0.33 for both). HTTP was negatively correlated with pCO 2 , CO 2 fluxes, and O 2 (p-value < 0.05; Kendall’s tau = 0.60, 0.30, and 0.30, respectively). At TB, inverse correlations were observed between CO 2 fluxes and both Chl a and Meso (p-value < 0.05; Kendall’s tau = -0.45 and − 0.33, respectively). Additionally, Micro exhibited negative relationships with both pCO 2 and O 2 (p-value < 0.05; Kendall’s tau = -0.33 for both), while HTTP showed direct correlations with CO 2 flux and pCO 2 (p-value < 0.05; Kendall’s tau = 0.30 and 0.60, respectively). Proto displayed a positive correlation with CO 2 flux but a negative correlation with O 2 (p-value < 0.05; Kendall’s tau = 0.27 and − 0.58). Lastly, at TC, Chl a and Proto were negatively correlated with O 2 (p-value < 0.05; Kendall’s tau = -0.55 and − 0.79, respectively), while Meso and Micro exhibited inverse correlations with CO 2 fluxes and pCO 2 (p-value < 0.05; Kendall’s tau = -0.33 for both). Similarly, HTTP showed negative relationships with O 2 , CO 2 fluxes, and pCO 2 (p-value < 0.05; Kendall’s tau = -0.60, -0.60, -0.30, respectively). The PCA results facilitated the creation of a FW 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, Chl a , CO 2 flux, NO 3 − , and wind speed, while PC2 was primarily explained by PO 4 and turbidity. A conspicuous association emerged between elevated levels of Chl a , Meso, and HTTP with high pCO 2 and PP. Moreover, all biotic factors, alongside pCO 2 values, were related to FW1, representative of June (Fig. 3 -A and 6 ). Simultaneously, this seasonal gradient indicated a decline in O 2 % saturation and CO 2 flux for that particular month. The variability within FW2 revealed a negative association with turbidity and PO 4 in March, but a positive relationship with CO 2 flux and O 2 % in April, with pCO 2 in May, and with turbidity and PO 4 in July. Lastly, FW3 was also associated with both turbidity and PO 4 values (Fig. 7 ). 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 CO 2 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 CO 2 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 CO 2 sink to a light CO 2 source. In the present study, at the FM, lower CO 2 emissions were measured post-restoration Mayen ( 2024 ), with some periods even exhibiting CO 2 sink behavior, depending on the station and season. For instance, at station TC, with the input of saltwater during the summer of 2021, CO 2 flux was recorded at -3.70 ± 2.37 mmol m − 2 h − 1 . In contrast, the SM remained a CO 2 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 CO 2 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 O 2 % values, which were inversely related to pCO 2 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 CO 2 oversaturation with pCO 2 variations between 600 and 900 ppmv. In our study, both temperature (TpCO 2 ) and non-temperature (NtpCO 2 ) effects predominantly influenced water pCO 2 at both the SM and the FM, though NpCO 2 appeared to have a greater impact on the measured pCO 2 levels (Fig. 3 -A, B). For instance, at the SM, the ΔTpCO 2 was smaller than ΔNpCO 2 (465 ppmv versus 682 ppmv, respectively) throughout the entire study period. A similar pattern was observed at the FM between spring and autumn 2021. ΔTpCO 2 and ΔNpCO 2 were: at TA 2831 ppmv (ranging from 287 to 3118 ppmv) and 3446 ppmv (ranging from 216 to 3662 ppmv) respectively; at TB ΔTpCO 2 was 1338 ppmv (ranging from 119 to 1457 ppmv) and ΔNpCO 2 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 NpCO 2 on pCO 2 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 pCO 2 dynamics in salt marshes (salt ponds) near the Fier d’Ars (Île de Ré, France). In this study, many factors could have influenced the observed changes in CO 2 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 Chl a phytoplankton biomass and shifts in FW topology. Biotic parameters were also crucial in controlling pCO 2 at the SM, as indicated by O 2 % values inversely related to pCO 2 values from March to August 2021, likely reflecting low phytoplankton production and higher respiration rates. Conversely, the FM shifted from being strong water CO 2 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 pCO 2 at studied marshes Although no significant correlation between water pCO 2 and biotic factors was found at the SM, relationships between pCO 2 and PFW were clearly established during our study. Three different FW topologies were identified, each with nuances: a ‘multivorous’ FW in June (FW1) and a ‘microbial food web’ in May (FW2) exhibited mean high pCO 2 values (832 ppmv and 842 ppmv, respectively), while a ‘weak multivorous’ FW was associated with a lower mean pCO 2 value (638 ppmv) during August (FW3). Additionally, a ‘weak herbivorous’ FW occurred in March, April and July (FW2), with variable mean pCO 2 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 pCO 2 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 pCO 2 . Conversely, the transitory FWs (‘weak herbivorous’ and ‘weak multivorous’) were associated with medium or low mean pCO 2 values (Fig. 6 ). These findings suggest that pCO 2 tends to accumulate more during stable FW occurrences than during transient ones. At the FM, the absence of clear seasonality in FW 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 pCO 2 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 pCO 2 values (127 and 299 ppmv, respectively). At station TA, extreme pCO 2 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 Chl a 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 Chl a biomasses measured during summer. Conversely, no clear relationship was found between the ‘weak multivorous’ FWs and pCO 2 values, likely due to the lack of biological equilibrium. 4.3. Conclusions This comparative analysis of two distinct marsh FW topologies allowed us to discern both similarities and differences between sites regarding carbon and FW relationships. Despite their typological disparities, both the SM and the FM functioned as CO 2 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 pCO 2 values (Fig. 8 ). These included three stable types ('biological winter', 'microbial food web', 'multivorous' food webs) with high pCO 2 values at both sites, as well as two transient types ('weak multivorous' and 'weak herbivorous') with lower and more variable pCO 2 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. 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 pCO 2 . Declarations Statement and Declaration The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the ANR Project PAMPAS (2019-2024, Evolution de l’identité PAtrimoniale des Marais des Pertuis charentais en réponse à l’Aléa de Submersion marine), LEFE 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). Competing interests The authors declare that they have no known competing or financial interests or personal relationships that could have appeared to influence the work reported in this paper. Contribution The study was conceptualized by C.D. and P.Po. . J.M., R.M. and L.B. contributed with formal analysis. F-X.R., P.Pi., C.E. and B.D. contributed to the methodology and data sampling and curation. 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Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2025 Read the published version in International Microbiology → Version 1 posted Editorial decision: Revision requested 13 Jan, 2025 Reviews received at journal 08 Sep, 2024 Reviewers agreed at journal 28 Aug, 2024 Reviewers invited by journal 01 Aug, 2024 Editor assigned by journal 01 Aug, 2024 Submission checks completed at journal 30 Jul, 2024 First submitted to journal 19 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4768272","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":341254068,"identity":"65df7b2a-c5a5-471d-8fb4-6f68baf8edd9","order_by":0,"name":"Xaus 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14:32:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4768272/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4768272/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10123-025-00650-x","type":"published","date":"2025-03-04T15:57:09+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63372837,"identity":"a0f4453d-bba2-4a9f-94b0-2675e46bafba","added_by":"auto","created_at":"2024-08-27 12:14:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":312159,"visible":true,"origin":"","legend":"\u003cp\u003eConceptualized scheme of planktonic community CO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e exchanges during daytime and nighttime periods in the water column of a coastal environment (water height differences: 50 cm). DOC: dissolved organic carbon, Nutrients: NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, SI, HTTP: heterotrophic prokaryotes, Zoo: micro and mesozooplankton, Phyto: pico, nano, and microphytoplankton. The equations depict gas (CO\u003csub\u003e2\u003c/sub\u003e/O\u003csub\u003e2\u003c/sub\u003e) exchanges and gradients at the water-air interface, along with biological processes involving the carbonate system (dissolved CO\u003csub\u003e2\u003c/sub\u003e CO\u003csub\u003e2(d)\u003c/sub\u003e, bicarbonate ions HCO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, carbonate ions CO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e2-\u003c/sup\u003e and calcium carbonate CaCO\u003csub\u003e3\u003c/sub\u003e) through photosynthesis/respiration and CaCO\u003csub\u003e3\u003c/sub\u003e dissolution/precipitation. Illustration by L. Xaus\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/11e4ba5fa8967624ee549e22.png"},{"id":63371708,"identity":"9b425df5-c193-43a3-a02c-3c2db2df6176","added_by":"auto","created_at":"2024-08-27 11:58:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":455133,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Location of the La Rochelle metropolitan area on the westAtlantic French coast. (B) Location of L’Houmeau (SM) and Tasdon (FM) within the La Rochelle metropolitan area. (C) Experimental site of the L’Houmeau saltwater marsh (SM). The red arrow in the satellite image (geoportal) indicatesthe location of the studied basin (46°12'17.4\"N 1°11'41.3\"W). Lightblue lines represent the hydraulic network and water pathwaysbetween the marsh and sea waters. Thedark blue areas denote a small fraction of the Marine Natural Park, and thegreen 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) (D, a). Light blue lines represent the hydraulic network and water pathwaysbetween the marsh and seawaters. Darkblue areas indicate a small fraction of the Marine Natural Park, and green areas represent a ZNIEFF type I. StationsTA, TB and TC (46°8'56.4'' N, 1°7'26.4'' W; 46° 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)\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/556117a997c573448815cf22.png"},{"id":63371712,"identity":"cd313210-ec4c-4766-87b2-aee987481a45","added_by":"auto","created_at":"2024-08-27 11:58:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":358353,"visible":true,"origin":"","legend":"\u003cp\u003eVariations in water pCO\u003csub\u003e2 \u003c/sub\u003e(water CO\u003csub\u003e2\u003c/sub\u003e partial pressure; orange line), TpCO\u003csub\u003e2\u003c/sub\u003e (temperature effects on pCO\u003csub\u003e2\u003c/sub\u003e variations; dotted blue line), and NpCO\u003csub\u003e2\u003c/sub\u003e (non-temperature effects on pCO\u003csub\u003e2\u003c/sub\u003e 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 pCO\u003csub\u003e2\u003c/sub\u003e value is represented by the green line\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/532365c8296f1ba69b10f0c7.png"},{"id":63371711,"identity":"f07d4ddc-2b93-4a93-95b7-c1e488c3b8d4","added_by":"auto","created_at":"2024-08-27 11:58:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":530831,"visible":true,"origin":"","legend":"\u003cp\u003e(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 CO\u003csub\u003e2\u003c/sub\u003e partial pressure (ppmv), (b) O\u003csub\u003e2\u003c/sub\u003e % saturation, (c) estimated water-air CO\u003csub\u003e2\u003c/sub\u003e flux (mmol m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e), (d) wind speed (m s\u003csup\u003e-1\u003c/sup\u003e), (e) Meso abundance (individuals m\u003csup\u003e-3\u003c/sup\u003e); (f) Chl\u003cem\u003ea\u003c/em\u003e biomass (µg L\u003csup\u003e-1\u003c/sup\u003e ± sd) by size class (micro: microphytoplankton (\u0026gt;20 μm), nano: nanophytoplankton (3-20 μm) and pico: picophytoplankton (\u0026lt; 3 μm)), and (g) HTTP abundance (cells mL\u003csup\u003e1\u003c/sup\u003e). (A, h) Chl\u003cem\u003ea\u003c/em\u003e PP by fraction (mg C m\u003csup\u003e-3\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e) (micro, nano, pico) (mean ± sd) and (B, h) Heterotrophic protozoan (cell mL\u003csup\u003e-1\u003c/sup\u003e ± sd). Atmospheric pCO\u003csub\u003e2\u003c/sub\u003e value is represented by the green horizontal line (417 ppmv; A, a and B, a)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/4bea5b734f63e0d013cf9c14.png"},{"id":63372286,"identity":"a9a6105c-0da5-4e5c-af69-05e271c6dd49","added_by":"auto","created_at":"2024-08-27 12:06:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":135151,"visible":true,"origin":"","legend":"\u003cp\u003eClustering 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 pCO\u003csub\u003e2\u003c/sub\u003e values is shown (A and B, b)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/f127d18be5dcdeee044f1644.png"},{"id":63371714,"identity":"fef041b5-3a08-41ae-86b9-989bee7c7367","added_by":"auto","created_at":"2024-08-27 11:58:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":102831,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eBox plots \u003c/em\u003edisplaying the mean per food web type for Chl\u003cem\u003ea\u003c/em\u003e (Chlorophyll-\u003cem\u003ea\u003c/em\u003e) by size class, Meso (Mesozooplankton), HTTP (Heterotrophic prokaryote), and PP (Chl\u003cem\u003ea\u003c/em\u003e primary production) by fraction 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\u003csup\u003e-1\u003c/sup\u003e) but scales were different. In addition, \u003cem\u003ebox plots\u003c/em\u003e with water CO\u003csub\u003e2\u003c/sub\u003e partial pressure (pCO\u003csub\u003e2\u003c/sub\u003e, ppmv)\u003csub\u003e,\u003c/sub\u003e CO\u003csub\u003e2\u003c/sub\u003e flux (mmol m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e), and O\u003csub\u003e2 \u003c/sub\u003esaturation % (%) mean per food web were added\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/dd067b5b6710cc516782d70b.png"},{"id":63371710,"identity":"8f663bbd-dac3-4031-b952-0e0444d114a9","added_by":"auto","created_at":"2024-08-27 11:58:22","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":116938,"visible":true,"origin":"","legend":"\u003cp\u003ePrincipal 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), O\u003csub\u003e2\u003c/sub\u003e% (%), water pCO\u003csub\u003e2\u003c/sub\u003e (ppmv), water-air CO\u003csub\u003e2\u003c/sub\u003e fluxes (fCO\u003csub\u003e2\u003c/sub\u003e, mmol m\u003csup\u003e-2\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e), wind gust (wind, m s\u003csup\u003e-1\u003c/sup\u003e), DOC (mg L\u003csup\u003e-1\u003c/sup\u003e), NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3-\u003c/sup\u003e, Si (µmol L\u003csup\u003e-1\u003c/sup\u003e)) and biotic parameters: Chl\u003cem\u003ea\u003c/em\u003e biomass (Chlorophyll-\u003cem\u003ea\u003c/em\u003e, µg L\u003csup\u003e-1\u003c/sup\u003e), PP (Chl\u003cem\u003ea\u003c/em\u003e primary production, mg C m\u003csup\u003e-3\u003c/sup\u003e h\u003csup\u003e-1\u003c/sup\u003e), Meso (mesozooplankton, individuals m\u003csup\u003e-3\u003c/sup\u003e) and HTTP (Heterotrophic prokaryote, cells mL\u003csup\u003e1\u003c/sup\u003e). 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)\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/62af4d8d54f02e8f48efbe52.png"},{"id":63372288,"identity":"e42226af-4b70-4e63-9d02-f0ad5ce5762d","added_by":"auto","created_at":"2024-08-27 12:06:22","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":112930,"visible":true,"origin":"","legend":"\u003cp\u003eRelationships (arrows) between food webs and water pCO\u003csub\u003e2 \u003c/sub\u003ein studied coastal marshes (Tasdon FM and L’Houmeau SM)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/3f887682fbef542f434d51b0.png"},{"id":78181549,"identity":"c9e8e300-ce70-451d-8e01-783b45770858","added_by":"auto","created_at":"2025-03-10 17:47:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3420528,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4768272/v1/6f597aea-e02a-4e3a-8821-c1880ef0f3a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atmospheric CO2 flux and planktonic food web relationships in temperate marsh systems: Insights from in situ water measurements","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eFor decades, atmospheric CO\u003csub\u003e2\u003c/sub\u003e emissions have surged due to human activities like heavy reliance on fossil fuels, deforestation, and agriculture (Canadell et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, extensive research has been dedicated to this issue. Accurately assessing anthropogenic CO\u003csub\u003e2\u003c/sub\u003e 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. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver short time spans, CO\u003csub\u003e2\u003c/sub\u003e partial pressure (pCO\u003csub\u003e2\u003c/sub\u003e) in the atmosphere remains relatively stable (Takahashi et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, in surface waters, it can fluctuate dramatically spatially and temporally, varying by more than four orders of magnitude (Sobek, Tranvik, and Cole \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Takahashi et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Calculating atmospheric CO\u003csub\u003e2\u003c/sub\u003e fluxes involves assessing the disparity between surface water and atmospheric pCO\u003csub\u003e2\u003c/sub\u003e levels (Mayen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Polsenaere et al. 2023; Takahashi et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). These fluxes undergo changes influenced by both the water-air pCO\u003csub\u003e2\u003c/sub\u003e 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., CaCO\u003csub\u003e3\u003c/sub\u003e dissolution/precipitation, primary production, and respiration) influence water pCO\u003csub\u003e2\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Moreau et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Nonetheless, Dai et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) found that in other coastal systems, such as marshes, the strong relationship between oceanic CO\u003csub\u003e2\u003c/sub\u003e flux and temperature appears to be influenced by factors other than temperature. This indicates a significant biological control on water pCO\u003csub\u003e2\u003c/sub\u003e, along with the effects of horizontal advection and water-sediment exchanges in these shallow land-sea interface ecosystems (Mayen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this context, coastal vegetated systems such as salt marshes, seagrass meadows, and mangroves are recognized for their main role in \u0026ldquo;Blue Carbon\u0026rdquo; sequestration and storage (Chmura et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; C. M. Duarte, Middelburg, and Caraco \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Greiner et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Macreadie, Nielsen, et al. 2017; Macreadie, Serrano, et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mcleod et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with an average sequestration rate of over 200\u0026thinsp;\u0026plusmn;\u0026thinsp;24 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Mcleod et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Specially, the La Rochelle metropolitan area in France (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A, B) contains nearly 25,580 hectares of wetlands, accounting for almost 45% of its surface area (Afonso \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These wetlands have garnered attention from researchers studying their carbon dynamics through \u003cem\u003ein situ\u003c/em\u003e measurements of water pCO\u003csub\u003e2\u003c/sub\u003e (Mayen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Eddy Covariance (EC) measurements of atmospheric CO\u003csub\u003e2\u003c/sub\u003e exchanges (\u0026minus;\u0026thinsp;483 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e saltwater marsh storage capacity (Mayen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)), and sediment analysis (up to \u0026minus;\u0026thinsp;345 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in high carbon sequestration rates (Amann et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)). 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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B, D). Additionally, these marshes may be managed by humans for activities such as shellfish farming and agriculture, as seen in the L\u0026rsquo;Houmeau saltwater marsh used in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-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. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePrimary 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Shiomoto (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) found that in the Okhotsk Sea, small-sized nano- (2 to 20 \u0026micro;m) and pico- (0.2 to 2 \u0026micro;m) phytoplankton could contribute up to 70% of primary production. On the Atlantic coast of France (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A), Moncelon et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) proposed that microphytobenthos, along with pico- and nanophytoplankton, may significantly contribute to the total primary production in freshwater marshes. Del Giorgio and Williams (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) 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 CO\u003csub\u003e2\u003c/sub\u003e emissions associated with them.\u003c/p\u003e \u003cp\u003ePFWs have been extensively described in marine and coastal areas (Legendre and Rassoulzadegan 1995; Legendre and Rivkin \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). On the west coast of France (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A), the functional role of the \u0026lsquo;microbial food web\u0026rsquo; 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. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Recent studies focusing on marshes (Bergeon et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Masclaux et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tortajada \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) have defined five main PFWs: \u0026lsquo;herbivore\u0026rsquo;, \u0026lsquo;multivorous, \u0026lsquo;microbial food web\u0026rsquo;, \u0026lsquo;microbial loop\u0026rsquo;, and \u0026lsquo;biological winter\u0026rsquo;, each presenting different degrees of PFW nuances (Tortajada \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, studying PFWs has proven invaluable for understanding ecosystem functioning (Beaugrand \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Masclaux et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Vincent, Luczak, and Sautour 2002). As previously mentioned, water pCO\u003csub\u003e2\u003c/sub\u003e variations in costal ecosystems are greatly influenced by biological activities (Mayen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Moreau et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Only a few studies have focused on the relationships between food webs and carbon exchange. Notable examples include Berg et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Mayen et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Polsenaere et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which examined the links between atmospheric CO\u003csub\u003e2\u003c/sub\u003e flux, water pCO\u003csub\u003e2\u003c/sub\u003e 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. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Nyman \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Verhoeven et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEven though, both PFW topologies and the CO\u003csub\u003e2\u003c/sub\u003e cycle have been broadly studied in coastal zones, few studies have examined their association (Legendre and Rivkin \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Moreau et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Niquil et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), particularly in diverse marsh (Adamczyk and Shurin \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Masclaux et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Consequently, much about the relationship between PFWs and water CO\u003csub\u003e2\u003c/sub\u003e in marshes remains unknown.\u003c/p\u003e \u003cp\u003eThe aim of this study is to (1) investigate whether variations in pCO\u003csub\u003e2\u003c/sub\u003e and atmospheric CO\u003csub\u003e2\u003c/sub\u003e flux can be explained by PFWs, and (2) determine if the potential relationships between PFWs and water CO\u003csub\u003e2\u003c/sub\u003e exchanges are consistent across two contrasting \u0026ldquo;Blue Carbon\u0026rdquo; ecosystems. To address these objectives, monthly samplings were conducted in 2021 in a saltwater marsh (L\u0026rsquo;Houmeau marsh; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C), and seasonal samplings in a restored freshwater marsh (Tasdon; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-D). Abiotic and biotic variables were monitored alongside simultaneous water pCO\u003csub\u003e2\u003c/sub\u003e measurements and atmospheric CO\u003csub\u003e2\u003c/sub\u003e flux estimations, leading to the first known relationships between PFW topology and water pCO\u003csub\u003e2\u003c/sub\u003e in temperate marshes.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Overview of the studied marsh sites\u003c/h2\u003e \u003cp\u003eThe L\u0026rsquo;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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-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 m\u003csup\u003e3\u003c/sup\u003e. A mechanical valve system manages the inflow and outflow of saltwater from the sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C, a, b). For this study, the water volume fluctuated between 90 and 144 m\u003csup\u003e3\u003c/sup\u003e, 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 (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and are briefly presented in sections \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e. and 2.3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Tasdon freshwater marsh (FM) is a shallow wetland spanning 123 hectares, located within the urban area of La Rochelle (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-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.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-D). Biotic and abiotic data monitored at this site are briefly presented in sections \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e2.2\u003c/span\u003e. and 2.3, and described in detail by Bergeon et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Mayen (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, the research is associated with several projects: PAMPAS (2019\u0026ndash;2024, Evolution de l\u0026rsquo;identit\u0026eacute; PAtrimoniale des Marais des Pertuis charentais en r\u0026eacute;ponse \u0026agrave; l\u0026rsquo;Al\u0026eacute;a de Submersion marine), Dycidemaim (LEFE 2021\u0026ndash;2022, Dynamique du carbone aux interfaces d\u0026rsquo;\u0026eacute;change terrestre-aquatique-atmosph\u0026eacute;rique des marais temp\u0026eacute;r\u0026eacute;s), and LRTZC (2019\u0026ndash;2027, La Rochelle territoire z\u0026eacute;ro carbone).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Abiotic parameter samplings\u003c/h2\u003e \u003cp\u003eAt the SM, measurement samplings were conducted monthly between March and August 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Over 90% of the water column was refreshed monthly by the mechanical valve system (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C, b), with an adjustment period of 2\u0026ndash;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 water input (TA), one with direct river discharges (TB), and one with oceanic influence (TC) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-D).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSamples and measurement methods for the saltwater (SM, L\u0026rsquo;Houmeau) and freshwater (FM, Tasdon) marshes. Abiotic parameters: Temperature, Salinity, Turbidity, O\u003csub\u003e2\u003c/sub\u003e%, Wind gust, pCO\u003csub\u003e2\u003c/sub\u003e (water CO\u003csub\u003e2\u003c/sub\u003e partial pressure), CO\u003csub\u003e2\u003c/sub\u003e flux, Nutrients (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, Si) and DOC (dissolved organic carbon). Biotic parameters; Chl\u003cem\u003ea\u003c/em\u003e (Chlorophyll-a), Meso (mesozooplankton), Micro (microzooplankton), Proto (heterotrophic protozoan), HTTP (heterotrophic prokaryotes). Sampling periods are mentioned at the bottom of the table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"36\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c27\" colnum=\"27\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c28\" colnum=\"28\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c29\" colnum=\"29\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c30\" colnum=\"30\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c31\" colnum=\"31\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c32\" colnum=\"32\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c33\" colnum=\"33\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c34\" colnum=\"34\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c35\" colnum=\"35\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c36\" colnum=\"36\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eSalinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003eTurbidity (NTU)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e \u003cp\u003eO\u003csub\u003e2\u003c/sub\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c23\" namest=\"c17\"\u003e \u003cp\u003eWind gust (m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c31\" namest=\"c24\"\u003e \u003cp\u003epCO\u003csub\u003e2\u003c/sub\u003e (ppmv)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e flux (mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c36\" namest=\"c36\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c15\" namest=\"c3\"\u003e \u003cp\u003eYSI sensor (continuous)\u0026thinsp;+\u0026thinsp;VWR multimeter (discrete)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c24\" namest=\"c16\"\u003e \u003cp\u003eInfoclimat.fr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c31\" namest=\"c25\"\u003e \u003cp\u003eC-Sense probe, 24h, measured every minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e \u003cp\u003eEstimation (see Polsenaere et al. (2023))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c36\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c15\" namest=\"c3\"\u003e \u003cp\u003eYSI sensor (continuous)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c24\" namest=\"c16\"\u003e \u003cp\u003eInfoclimat.fr\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c31\" namest=\"c25\"\u003e \u003cp\u003eC-Sense probe, 24h, 3 days, measured every minute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c35\" namest=\"c32\"\u003e \u003cp\u003eEstimation (see Polsenaere et al. (2023))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c36\" namest=\"c36\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNutrients (\u0026micro;mol L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c16\" namest=\"c10\"\u003e \u003cp\u003e\u003cb\u003eDOC (mg L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"11\" nameend=\"c27\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c36\" namest=\"c28\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"14\" nameend=\"c16\" namest=\"c3\"\u003e \u003cp\u003eTriplicated Triplicated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c28\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c36\" namest=\"c29\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eSEAL AA3 autoanalyzer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c16\" namest=\"c10\"\u003e \u003cp\u003eStandard NF EN 1484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"20\" nameend=\"c36\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c9\" namest=\"c3\"\u003e \u003cp\u003eSEAL AA3 autoanalyzer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c16\" namest=\"c10\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"20\" nameend=\"c36\" namest=\"c17\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eChl\u003c/b\u003e\u003cb\u003ea\u003c/b\u003e \u003cb\u003e(\u0026micro;g L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e \u003cp\u003e\u003cb\u003eMeso (ind m\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c25\" namest=\"c17\"\u003e \u003cp\u003e\u003cb\u003eMicro (ind m\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;3\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c29\" namest=\"c26\"\u003e \u003cp\u003e\u003cb\u003eProto (ind L\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c34\" namest=\"c30\"\u003e \u003cp\u003e\u003cb\u003eHTTP (ind mL\u003c/b\u003e\u003csup\u003e\u003cb\u003e\u0026minus;\u0026thinsp;1\u003c/b\u003e\u003c/sup\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eTriplicated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c17\" namest=\"c12\"\u003e \u003cp\u003eTriplicated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c22\" namest=\"c18\"\u003e \u003cp\u003eTriplicated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c30\" namest=\"c23\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c34\" namest=\"c31\"\u003e \u003cp\u003eTriplicated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eFilters: 20 \u0026micro;m, later 3 \u0026micro;m and 0.7 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e \u003cp\u003eFilters: 200 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c25\" namest=\"c17\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c29\" namest=\"c26\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c34\" namest=\"c30\"\u003e \u003cp\u003eFlow cytometry analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c11\" namest=\"c3\"\u003e \u003cp\u003eFilters: 20 \u0026micro;m, later 3 \u0026micro;m and 0.7 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c16\" namest=\"c12\"\u003e \u003cp\u003eFilters: 200 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c25\" namest=\"c17\"\u003e \u003cp\u003eFilters: 63 \u0026micro;m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c29\" namest=\"c26\"\u003e \u003cp\u003eFlowcam analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c34\" namest=\"c30\"\u003e \u003cp\u003eFlow cytometry analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eChl\u003c/b\u003e\u003cb\u003ea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c12\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eMeso\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c19\" namest=\"c13\"\u003e \u003cp\u003e\u003cb\u003eMicro\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"13\" nameend=\"c32\" namest=\"c20\"\u003e \u003cp\u003e\u003cb\u003eProto\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c34\" namest=\"c33\"\u003e \u003cp\u003e\u003cb\u003eHTTP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"33\" nameend=\"c34\" namest=\"c2\"\u003e \u003cp\u003eCarbon biomass (\u0026micro;gC.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c13\" namest=\"c6\"\u003e \u003cp\u003e1.44 (ind L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c20\" namest=\"c14\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c32\" namest=\"c21\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c34\" namest=\"c33\"\u003e \u003cp\u003e*14 (fgC cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c13\" namest=\"c6\"\u003e \u003cp\u003e0.768 to 1.44 (ind L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c20\" namest=\"c14\"\u003e \u003cp\u003e0.028 (ind L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c32\" namest=\"c21\"\u003e \u003cp\u003e2318 (cil) (pgC cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003ec\u003c/sup\u003e, 225 (din) (pgC cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c34\" namest=\"c33\"\u003e \u003cp\u003e*14 (fgC cell\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c36\" namest=\"c35\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eConversion factors used: \u003csup\u003ea\u003c/sup\u003e(Tilzer and Dubinsky 1987); \u003csup\u003eb\u003c/sup\u003e(Dumont, Van de Velde, and Dumont \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1975\u003c/span\u003e); Ciliates (cil): \u003csup\u003ec\u003c/sup\u003e (Putt and Stoecker \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1989\u003c/span\u003e); Dinoflagelates (din): \u003csup\u003ed\u003c/sup\u003e(Fournier et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e); \u003csup\u003ee\u003c/sup\u003e(Gundersen et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAbiotic and water CO\u003csub\u003e2\u003c/sub\u003e measurement dates: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFM\u003c/span\u003e: Spring: April 13th to 15th, Summer: August 16th to 18th, Autumn: December 13th to 15th, 2021, Winter: March 1st to 3rd, 2022; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSM\u003c/span\u003e: March 17th, April 14th, May 18th, June 14th, July 15th, August 9th, 2021.\u003c/p\u003e \u003cp\u003eNutrients, DOC and biotic sampling dates: \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFM\u003c/span\u003e: Spring: April 15th, Summer: August 25th, Autumn: November 16th, 2021, Winter: March 9th, 2022; \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSM\u003c/span\u003e: March 18th, April 14th, May 19th, June 13th, July 15th, August 9th, 2021.\u003c/p\u003e \u003cp\u003eFor 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 \u0026plusmn;\u0026thinsp;0.1\u0026deg;C for temperature, \u0026plusmn;\u0026thinsp;0.5 \u0026micro;S cm⁻\u0026sup1; for salinity/conductivity, \u0026plusmn;\u0026thinsp;0.3 NTU for turbidity, \u0026plusmn;\u0026thinsp;3.1 \u0026micro;mol L⁻\u0026sup1; for dissolved oxygen concentration, and \u0026plusmn;\u0026thinsp;1% for oxygen saturation percentage (%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, discrete measurements (once a month) of water temperature, salinity, and dissolved oxygen were taken with a VWR multimeter.\u003c/p\u003e \u003cp\u003eAn autonomous pCO\u003csub\u003e2\u003c/sub\u003e underwater probe (C-Sense\u0026trade; pCO\u003csub\u003e2\u003c/sub\u003e 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 pCO\u003csub\u003e2\u003c/sub\u003e and water Photosynthetic Active Radiation (PAR), respectively, continuously (per minute) over a 24-hour period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Mayen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Water-air CO\u003csub\u003e2\u003c/sub\u003e fluxes were estimated following the methodology described in Mayen et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Polsenaere et al. (2023). The CO\u003csub\u003e2\u003c/sub\u003e transfer coefficients, normalized to a Schmidt number of 600 and obtained from Raymond and Cole (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), were converted to the gas transfer velocity at the \u003cem\u003ein situ\u003c/em\u003e temperature following (J\u0026auml;hne, Heinz, and Dietrich \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). The non-temperature (NpCO\u003csub\u003e2\u003c/sub\u003e) and temperature (TpCO\u003csub\u003e2\u003c/sub\u003e) effects on diurnal pCO\u003csub\u003e2\u003c/sub\u003e variations were calculated as described by Takahashi et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and applied to the marsh ecosystems as done in Mayen et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and shown in Eqs.\u0026nbsp;(1) and (2). TpCO\u003csub\u003e2\u003c/sub\u003e is related to the physical effects of temperature on water pCO\u003csub\u003e2\u003c/sub\u003e (physical pump, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), while NpCO\u003csub\u003e2\u003c/sub\u003e corresponds to pCO\u003csub\u003e2\u003c/sub\u003e 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. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTpCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;pCO\u003csub\u003e2mean\u003c/sub\u003e \u0026times; \u003cem\u003eexp\u003c/em\u003e[0.0423 \u0026times; (T\u003csub\u003eobs\u003c/sub\u003e \u0026minus; T\u003csub\u003emean\u003c/sub\u003e)] (1)\u003c/p\u003e \u003cp\u003eNpCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;pCO\u003csub\u003e2obs\u003c/sub\u003e \u0026times; \u003cem\u003eexp\u003c/em\u003e[0.0423 \u0026times; (T\u003csub\u003emean\u003c/sub\u003e \u0026minus; T\u003csub\u003eobs\u003c/sub\u003e)] (2)\u003c/p\u003e \u003cp\u003ewhere T\u003csub\u003eobs\u003c/sub\u003e and pCO\u003csub\u003e2obs\u003c/sub\u003e represent mean of the observed temperature and pCO\u003csub\u003e2\u003c/sub\u003e values, respectively, measured every minute by the probes. T\u003csub\u003emean\u003c/sub\u003e and pCO\u003csub\u003e2mean\u003c/sub\u003e refer to the seasonal (annual mean) or diurnal (mean per 24-hour cycle) average values.\u003c/p\u003e \u003cp\u003eThe CO\u003csub\u003e2\u003c/sub\u003e flux was calculated following the methodology outlined by Polsenaere et al. (2023) and Ribas-Ribas, G\u0026oacute;mez-Parra, and Forja (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), as represented in Eq.\u0026nbsp;(3):\u003c/p\u003e \u003cp\u003eFCO\u003csub\u003e2\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;α\u0026thinsp;\u0026times;\u0026thinsp;k\u0026thinsp;\u0026times;\u0026thinsp;ΔpCO\u003csub\u003e2\u003c/sub\u003e (3)\u003c/p\u003e \u003cp\u003ewhere FCO\u003csub\u003e2\u003c/sub\u003e (mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) represents the estimated air\u0026ndash;water CO\u003csub\u003e2\u003c/sub\u003e fluxes, where α (mol kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e atm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is the CO\u003csub\u003e2\u003c/sub\u003e solubility coefficient in saltwater, k (cm h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) denotes the transfer velocity of CO\u003csub\u003e2\u003c/sub\u003e gas, and ΔpCO\u003csub\u003e2\u003c/sub\u003e (ppmv) is the difference between water and air pCO\u003csub\u003e2\u003c/sub\u003e means. For further details on CO\u003csub\u003e2\u003c/sub\u003e flux estimation and C-Sense probe calibration, please refer to Mayen et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the other references cited above.\u003c/p\u003e \u003cp\u003eAt both sites, surface water samples were collected in triplicates to measure concentrations of inorganic nutrients and dissolved organic carbon. The methodology outlined by Aminot and Kerouel (2007) and Aminot and K\u0026eacute;rouel (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) was followed to determine nutrient concentrations (nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), nitrite (NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e), ammonium (NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e), phosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e) and silicate (Si)) in filtered water (0.7 \u0026micro;m GF/F glass fiber membrane, Whatman) using a SEAL AA3 autoanalyzer. The detection limit was 0.02 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Aminot and Kerouel 2007). Dissolved organic carbon (DOC, mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) concentrations were only measured at the SM and were determined by the QUALYSE laboratory following standard NF EN 1484 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sampling of biotic parameters\u003c/h2\u003e \u003cp\u003eAt 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 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Chlorophyll-a (Chl\u003cem\u003ea\u003c/em\u003e) 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 \u0026micro;m (micro), 3 \u0026micro;m (nano), and 0.7 \u0026micro;m (pico) filters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring each sampling period, metazoan mesozooplankton (Meso) abundance was assessed using a 200 \u0026micro;m mesh size net (WP2 plankton net), and its abundance was measured in individuals per cubic meter (ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e). Metazoan microzooplankton (Micro) abundance was determined at the FM by filtering 6 L of water through a 63 \u0026micro;m mesh size net (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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 \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Primary production (PP) by size class (pico, nano, and micro, in mg C m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) was exclusively measured at the SM using Nielsen (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1951\u003c/span\u003e) radioactivity protocol (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For more detailed information regarding identification and measurement methodologies, please see Bergeon et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Moncelon (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical 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\u0026thinsp;\u0026lt;\u0026thinsp;0.05), non-parametric tests were employed for exploratory analysis. Specifically, the rstatix package (Kassambara \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and ggbreak package (Xu et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) were utilized. Differences in Chl\u003cem\u003ea\u003c/em\u003e 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\u0026rsquo;s test (package: dunn.test (Dinno and Dinno \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e)) when necessary, following identification of significant differences (if Kruskal-Wallis test presented a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The same methodology was applied to examine differences in CO\u003csub\u003e2\u003c/sub\u003e fluxes, pCO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e%, and wind speed between day and night.\u003c/p\u003e \u003cp\u003eA Food Web (FW) type analysis was carried out using hierarchical agglomerative clustering (HAC), to analyze the biological parameters (HTTP, Chl\u003cem\u003ea\u003c/em\u003e, and metazoans by size class), PP, and DOC at the SM. All parameters were converted to carbon biomass (\u0026micro;gC L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) to standardize and compare these different metrics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). 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. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The analysis was performed using the following R packages: FactoMineR (L\u0026ecirc;, Josse, and Husson 2008), factoexctra (Kassambara and Mundt \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) cluster (Maechler \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), ade4 (Thioulouse et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and agricolae (De Mendiburu \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo summarize and understand the relationships between PFWs, abiotic factors, and water carbon variables, a Principal Component Analysis (PCA) was performed (package: vegan (Dixon \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)). 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. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Temporal fluctuations in abiotic parameters\u003c/h2\u003e \u003cp\u003eAt the SM, average salinity and temperature values increased from 26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 and 11.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5\u0026deg;C in March to 34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 and 24.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u0026deg;C in July, respectively. DOC values were generally low but showed a slight increasing from 0.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in March to 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 mg L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in August. Nutrient concentrations did not display a clear trend. NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e reached their maximum concentrations in April (22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 5.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively) and their minimum concentrations in June (0.0 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively). NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e concentrations also hit a low in June (0.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.1 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), but peaked in July (2.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.4 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e concentrations increased steadily from 0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in March to 1.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in August. Si varied from 33.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in March to 45.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in June. Turbidity showed no seasonal pattern, with the highest value of 34.4\u0026thinsp;\u0026plusmn;\u0026thinsp;72.8 NTU recorded in August and the lowest value of 9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6 NTU recorded in March.\u003c/p\u003e \u003cp\u003eAt 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\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2 (TB) for salinity and 23.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u0026deg;C (TC) for temperature. In contrast, the lowest salinity and temperature values were observed during winter at 0.3\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 (TA) and during autumn at 6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u0026deg;C (TA), respectively. Turbidity was highest in summer at 178.2\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 NTU (TA) and lowest in spring at 15.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.6 NTU (TC). Nutrient concentrations did not follow a consistent pattern throughout the sampling period. Both NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e reached their peak concentrations in autumn, with NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e at 503.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.6 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (TB) and NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e at 9. 5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (TC). PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e concentrations ranged from 0.02 to 0.9 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, except for site TC in winter, which saw a spike to 4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.0 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. Si showed its lowest value in winter at 15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and its highest in summer at 429.1\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (TA). Finally, NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e levels varied from 0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in winter (TB) to 22.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3 \u0026micro;mol L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in spring (TA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Variations in water pCO\u003csub\u003e2\u003c/sub\u003e/O\u003csub\u003e2\u003c/sub\u003e, wind speed, and water-air CO\u003csub\u003e2\u003c/sub\u003e fluxes\u003c/h2\u003e \u003cp\u003eDuring the study period at the SM, water pCO\u003csub\u003e2\u003c/sub\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A). Throughout this period, CO\u003csub\u003e2\u003c/sub\u003e fluxes were consistently positive, indicating a source for the atmosphere. Notably, both pCO\u003csub\u003e2\u003c/sub\u003e and NpCO\u003csub\u003e2\u003c/sub\u003e exhibited similar trends, while TpCO\u003csub\u003e2\u003c/sub\u003e deviated from this pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A). Moreover, a seasonal pattern emerged, showing an inverse correlation between pCO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e%, with May recording the highest pCO\u003csub\u003e2\u003c/sub\u003e levels (842\u0026thinsp;\u0026plusmn;\u0026thinsp;81 ppmv) and the lowest O\u003csub\u003e2\u003c/sub\u003e% (68.7\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A, a, b). Conversely, CO\u003csub\u003e2\u003c/sub\u003e fluxes and wind speeds exhibited a synchronous trend, peaking in April (7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 11.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.4 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and reaching their lowest in August (0.1\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, 3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.0 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A, c, d). Furthermore, significant day and night variations were observed for CO\u003csub\u003e2\u003c/sub\u003e fluxes, pCO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e%, and wind speeds (July and August only) (Kruskal-Wallis test, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Additionally, April stood out with significantly different CO\u003csub\u003e2\u003c/sub\u003e flux, pCO\u003csub\u003e2\u003c/sub\u003e, and wind gust values compared to other months (Dunn\u0026rsquo;s post hoc test, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSeasonal variations in water pCO\u003csub\u003e2\u003c/sub\u003e were pronounced across all three stations at the FM, with TA exhibiting the most remarkable shift. During summer, TA displayed oversaturated pCO\u003csub\u003e2\u003c/sub\u003e levels (2595\u0026thinsp;\u0026plusmn;\u0026thinsp;198 ppmv), contrasting with the remaining seasons where waters were undersaturated (252\u0026thinsp;\u0026plusmn;\u0026thinsp;108 ppmv) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B, a). Similar to observations at the SM, NpCO\u003csub\u003e2\u003c/sub\u003e values closely mirrored measured pCO\u003csub\u003e2\u003c/sub\u003e, while TpCO\u003csub\u003e2\u003c/sub\u003e exhibited an inverse pattern. The most substantial discrepancy in CO\u003csub\u003e2\u003c/sub\u003e flux occurred during summer, with TB recording the highest value (5.5\u0026thinsp;\u0026plusmn;\u0026thinsp;1.7 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and TC the lowest (-3.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B, c). Additionally, during summer, O\u003csub\u003e2\u003c/sub\u003e% values at TA were slightly lower compared to the other two stations (41.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B, b). At all three sites, significant differences were observed in pCO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e%, CO\u003csub\u003e2\u003c/sub\u003e flux, and wind gust, with either positive (TA - TB, TA - TC) or negative (TB - TC) variations (Dunn\u0026rsquo;s post hoc test, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Moreover, all four parameters showed significant differences between day and night (Kruskal-Wallis test, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except for CO\u003csub\u003e2\u003c/sub\u003e fluxes during spring, summer and winter at TA, and for all seasons at TC; and pCO\u003csub\u003e2\u003c/sub\u003e values in winter at station TB (Kruskal-Wallis test, p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Temporal dynamics in biotic parameters and planktonic food web analysis\u003c/h2\u003e \u003cp\u003eIn June, the SM exhibited its peak Chl\u003cem\u003ea\u003c/em\u003e biomass alongside Meso and HTTP abundances (Chl\u003cem\u003ea\u003c/em\u003e: 10.37 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Meso: 1149.30 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e, HTTP: 7.48e\u003csup\u003e05\u003c/sup\u003e cells mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A, e, f, g). Conversely, April marked the nadir for Chl\u003cem\u003ea\u003c/em\u003e biomass and Meso abundance (Chl\u003cem\u003ea\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12 \u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, Meso\u0026thinsp;=\u0026thinsp;225.35 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), while HTTP hit its lowest count in July (3.92e\u003csup\u003e04\u003c/sup\u003e cells mL\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Monthly analysis revealed significant disparities in these biotic parameters (Kruskal-Wallis tests, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Notably, while Chl\u003cem\u003ea\u003c/em\u003e nano and pico fractions exhibited similar patterns without significant differences, Chl\u003cem\u003ea\u003c/em\u003e micro biomass was significantly smaller than nano and pico fractions (Dunn\u0026rsquo;s post hoc test, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Although the highest PP rate occurred in June, no significant difference was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-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\u0026thinsp;\u0026plusmn;\u0026thinsp;31.25 mg C m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), whereas Chl\u003cem\u003ea\u003c/em\u003e micro fraction consistently ranked lowest in PP production from March to August (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A, h).\u003c/p\u003e \u003cp\u003eAt 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\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and Micro: 0.86 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) compared to TB (max Meso: 10.37 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and Micro: 0.90 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) and TC (max Meso: 6.05 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e and Micro: 0.18 ind m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B, e). However, Chl\u003cem\u003ea\u003c/em\u003e biomass did not follow the same trend, peaking in autumn at TB (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B, e, f). Both HTTP and Proto registered their lowest values at station TB (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B, g, h). While Chl\u003cem\u003ea\u003c/em\u003e micro was always significantly lower than the nano fractions at each station and season (Dunn\u0026rsquo;s post hoc test, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), exceptions were noted for TA in winter and TC in spring, summer and winter 2021 (Dunn\u0026rsquo;s post hoc test, p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Additionally, Meso, Micro, HTTP, and Proto abundances showed significant differences between stations and seasons (Dunn\u0026rsquo;s post hoc tests, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eAt the SM, HAC analysis revealed three distinct PFW topologies, labeled as FW1, FW2 and FW3 (Fig.\u0026nbsp;5-A, a). FW1, identified in June, emerged as a \u0026lsquo;multivorous\u0026rsquo; FW, characterized by elevated carbon biomasses across all three fractions of Chl\u003cem\u003ea\u003c/em\u003e, Meso, HTTP and DOC, alongside low nutrients concentrations (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, NO\u003csub\u003e2\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e, and NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Within FW2, a temporal FW succession unveiled three distinct FWs, notably a \u0026lsquo;weak herbivore\u0026rsquo; in March, April, and July, attributed to important nutrient levels, low Meso biomass, and relatively high microphytoplankton production (Fig.\u0026nbsp;5-A, b and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). May revealed a \u0026lsquo;microbial food web\u0026rsquo;, possibly due to accumulating DOC resulting from Chl\u003cem\u003ea\u003c/em\u003e PP and Meso presence. Lastly, FW3 appeared as a \u0026lsquo;weak multivorous\u0026rsquo; in August, characterized by high Chl\u003cem\u003ea\u003c/em\u003e biomass across all size fractions, relatively lower heterotrophic biomasses (Meso and HTTP), and elevated nutrient concentrations.\u003c/p\u003e \u003cp\u003eIn the FM, a large variability in 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.\u0026nbsp;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 Chl\u003cem\u003ea\u003c/em\u003e values and limited predator and HTTP presence alongside fluctuating nutrient concentrations, and a 'weak herbivorous' FW during autumn and winter (Fig.\u0026nbsp;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.\u0026nbsp;5-B, a).\u003c/p\u003e \u003cp\u003e .\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;5\u003c/b\u003e Clustering dendrograms for the HAC (hierarchical agglomerative clustering) applied to the biological matrix at (A, a) the L\u0026rsquo;Houmeau saltwater marsh (SM), with different food webs (FW1, FW2, FW3) defined by the cutting method \u0026ldquo;Ward.D1\u0026rdquo; (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 \u0026ldquo;Ward.D2\u0026rdquo; (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 pCO\u003csub\u003e2\u003c/sub\u003e values is shown (A and B, b)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Relationships between water pCO\u003csub\u003e2\u003c/sub\u003e and planktonic food webs\u003c/h2\u003e \u003cp\u003eAt the SM, high daily mean water pCO\u003csub\u003e2\u003c/sub\u003e values (832 ppmv) were associated with the 'multivorous' FW type (Fig.\u0026nbsp;5-A, b and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Conversely, the 'weak multivorous' FW was related to the lowest pCO\u003csub\u003e2\u003c/sub\u003e values (averaging 638 ppmv over 24 hours). The relationship between pCO\u003csub\u003e2\u003c/sub\u003e and FW2 ('weak herbivore' and 'microbial food web') appeared less clear due to the high variability within this FW type (Fig.\u0026nbsp;5-A, b and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). 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 pCO\u003csub\u003e2\u003c/sub\u003e values (ranging between 689 and 749 ppmv on average over 24 hours), while the 'microbial food web' manifested when pCO\u003csub\u003e2\u003c/sub\u003e values peaked (averaging 842 ppmv over 24 hours) (Fig.\u0026nbsp;5-A). Kendall correlation tests failed to reveal significant correlations between water pCO\u003csub\u003e2\u003c/sub\u003e and biotic parameters (Chl\u003cem\u003ea\u003c/em\u003e, Meso, HTTP) (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Conversely, negative correlations emerged between CO\u003csub\u003e2\u003c/sub\u003e fluxes and Meso and HTTP (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.46 and \u0026minus;\u0026thinsp;0.22, respectively). Chl\u003cem\u003ea\u003c/em\u003e exhibited negative correlation with O\u003csub\u003e2\u003c/sub\u003e% (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.26) and positive correlation with PP (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau\u0026thinsp;=\u0026thinsp;0.48). An inverse correlation was observed between Meso and O\u003csub\u003e2\u003c/sub\u003e% (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Kendall\u0026rsquo;s tau = -0.55). Lastly, pCO\u003csub\u003e2\u003c/sub\u003e showed positive correlation with DOC concentrations (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau\u0026thinsp;=\u0026thinsp;0.41).\u003c/p\u003e \u003cp\u003eIn the FM, comparing water pCO\u003csub\u003e2\u003c/sub\u003e values across stations revealed distinct patterns. The highest mean pCO\u003csub\u003e2\u003c/sub\u003e value (3020 ppmv) recorded at TA coincided with a 'multivorous' FW, while at TB, the highest mean pCO\u003csub\u003e2\u003c/sub\u003e values (1402 ppmv) were associated with a 'weak multivorous' FW, followed by a 'biological winter' (971 ppmv) (Fig.\u0026nbsp;5-B, b). At TC, both the highest and lowest pCO\u003csub\u003e2\u003c/sub\u003e values were linked to the 'microbial food web', with the second-largest pCO\u003csub\u003e2\u003c/sub\u003e value occurring alongside a 'biological winter' FW (Fig.\u0026nbsp;5-B). Kendall correlations for biotic and abiotic parameters did not reveal a consistent pattern within stations. At TA, both Meso and Micro exhibited positive relationships with pCO\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026gt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau\u0026thinsp;=\u0026thinsp;0.33 for both) and negative correlations with O\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.33 for both). HTTP was negatively correlated with pCO\u003csub\u003e2\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e fluxes, and O\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau\u0026thinsp;=\u0026thinsp;0.60, 0.30, and 0.30, respectively). At TB, inverse correlations were observed between CO\u003csub\u003e2\u003c/sub\u003e fluxes and both Chl\u003cem\u003ea\u003c/em\u003e and Meso (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.45 and \u0026minus;\u0026thinsp;0.33, respectively). Additionally, Micro exhibited negative relationships with both pCO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.33 for both), while HTTP showed direct correlations with CO\u003csub\u003e2\u003c/sub\u003e flux and pCO\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau\u0026thinsp;=\u0026thinsp;0.30 and 0.60, respectively). Proto displayed a positive correlation with CO\u003csub\u003e2\u003c/sub\u003e flux but a negative correlation with O\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau\u0026thinsp;=\u0026thinsp;0.27 and \u0026minus;\u0026thinsp;0.58). Lastly, at TC, Chl\u003cem\u003ea\u003c/em\u003e and Proto were negatively correlated with O\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.55 and \u0026minus;\u0026thinsp;0.79, respectively), while Meso and Micro exhibited inverse correlations with CO\u003csub\u003e2\u003c/sub\u003e fluxes and pCO\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.33 for both). Similarly, HTTP showed negative relationships with O\u003csub\u003e2\u003c/sub\u003e, CO\u003csub\u003e2\u003c/sub\u003e fluxes, and pCO\u003csub\u003e2\u003c/sub\u003e (p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Kendall\u0026rsquo;s tau = -0.60, -0.60, -0.30, respectively).\u003c/p\u003e \u003cp\u003eThe PCA results facilitated the creation of a FW discrimination graphic at the monthly scale, utilizing both biotic and abiotic parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e). 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, Chl\u003cem\u003ea\u003c/em\u003e, CO\u003csub\u003e2\u003c/sub\u003e flux, NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e, and wind speed, while PC2 was primarily explained by PO\u003csub\u003e4\u003c/sub\u003e and turbidity. A conspicuous association emerged between elevated levels of Chl\u003cem\u003ea\u003c/em\u003e, Meso, and HTTP with high pCO\u003csub\u003e2\u003c/sub\u003e and PP. Moreover, all biotic factors, alongside pCO\u003csub\u003e2\u003c/sub\u003e values, were related to FW1, representative of June (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Simultaneously, this seasonal gradient indicated a decline in O\u003csub\u003e2\u003c/sub\u003e% saturation and CO\u003csub\u003e2\u003c/sub\u003e flux for that particular month. The variability within FW2 revealed a negative association with turbidity and PO\u003csub\u003e4\u003c/sub\u003e in March, but a positive relationship with CO\u003csub\u003e2\u003c/sub\u003e flux and O\u003csub\u003e2\u003c/sub\u003e% in April, with pCO\u003csub\u003e2\u003c/sub\u003e in May, and with turbidity and PO\u003csub\u003e4\u003c/sub\u003e in July. Lastly, FW3 was also associated with both turbidity and PO\u003csub\u003e4\u003c/sub\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Marsh typologies as carbon sinks around the globe\u003c/h2\u003e \u003cp\u003e\u0026ldquo;Blue Carbon\u0026rdquo; 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 \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Carlos M. Duarte et al. 2013; Mcleod et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Monnoyer-Smith \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Both saltwater and freshwater marshes can act as important atmospheric CO\u003csub\u003e2\u003c/sub\u003e sinks (Guo et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kostyrka \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mayen et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sch\u0026auml;fer et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or sources (Kayranli et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), depending on spatial (water bodies, habitats, biological/sedimentary stocks, management) and temporal (diurnal, tidal, seasonal, (inter-annual) scales. Furthermore, studies by Artigas et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Miller and Fujii (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), Sch\u0026auml;fer et al. (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Tuittila et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) highlighted that wetland restoration can transform marshes, deltas, or peatlands from atmospheric CO\u003csub\u003e2\u003c/sub\u003e sources to sinks. Conversely, Jimenez et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) observed that anthropogenic disruption (e.g., human-driven hydrologic changes) caused a freshwater marsh to shift from a strong CO\u003csub\u003e2\u003c/sub\u003e sink to a light CO\u003csub\u003e2\u003c/sub\u003e source.\u003c/p\u003e \u003cp\u003eIn the present study, at the FM, lower CO\u003csub\u003e2\u003c/sub\u003e emissions were measured post-restoration Mayen (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), with some periods even exhibiting CO\u003csub\u003e2\u003c/sub\u003e sink behavior, depending on the station and season. For instance, at station TC, with the input of saltwater during the summer of 2021, CO\u003csub\u003e2\u003c/sub\u003e flux was recorded at -3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;2.37 mmol m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. In contrast, the SM remained a CO\u003csub\u003e2\u003c/sub\u003e source throughout the study period (from March to August; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-A, a, c). This finding contradicts previous research indicating that saltwater environments typically act as CO\u003csub\u003e2\u003c/sub\u003e sinks (Mayen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), as well as \u003cem\u003ein situ\u003c/em\u003e 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 \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Mayen et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thorhaug et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). 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 O\u003csub\u003e2\u003c/sub\u003e% values, which were inversely related to pCO\u003csub\u003e2\u003c/sub\u003e values, likely indicating low phytoplankton production and higher respiration rates.\u003c/p\u003e \u003cp\u003eAs mentioned earlier, from March until August 2021, the SM remained a weak atmospheric carbon source characterized by periods of water CO\u003csub\u003e2\u003c/sub\u003e oversaturation with pCO\u003csub\u003e2\u003c/sub\u003e variations between 600 and 900 ppmv. In our study, both temperature (TpCO\u003csub\u003e2\u003c/sub\u003e) and non-temperature (NtpCO\u003csub\u003e2\u003c/sub\u003e) effects predominantly influenced water pCO\u003csub\u003e2\u003c/sub\u003e at both the SM and the FM, though NpCO\u003csub\u003e2\u003c/sub\u003e appeared to have a greater impact on the measured pCO\u003csub\u003e2\u003c/sub\u003e levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-A, B). For instance, at the SM, the ΔTpCO\u003csub\u003e2\u003c/sub\u003e was smaller than ΔNpCO\u003csub\u003e2\u003c/sub\u003e (465 ppmv versus 682 ppmv, respectively) throughout the entire study period. A similar pattern was observed at the FM between spring and autumn 2021. ΔTpCO\u003csub\u003e2\u003c/sub\u003e and ΔNpCO\u003csub\u003e2\u003c/sub\u003e were: at TA 2831 ppmv (ranging from 287 to 3118 ppmv) and 3446 ppmv (ranging from 216 to 3662 ppmv) respectively; at TB ΔTpCO\u003csub\u003e2\u003c/sub\u003e was 1338 ppmv (ranging from 119 to 1457 ppmv) and ΔNpCO\u003csub\u003e2\u003c/sub\u003e 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 NpCO\u003csub\u003e2\u003c/sub\u003e on pCO\u003csub\u003e2\u003c/sub\u003e 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. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who showed that horizontal advection processes (upstream and downstream) significantly influence on water pCO\u003csub\u003e2\u003c/sub\u003e dynamics in salt marshes (salt ponds) near the Fier d\u0026rsquo;Ars (\u0026Icirc;le de R\u0026eacute;, France).\u003c/p\u003e \u003cp\u003eIn this study, many factors could have influenced the observed changes in CO\u003csub\u003e2\u003c/sub\u003e behavior. These include temperature and particularly non-temperature effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e-B), replanted vegetation (63,000 aquatic plants), and nutrient concentration along with salinity variations. These factors induced important changes, such as increases in Chl\u003cem\u003ea\u003c/em\u003e phytoplankton biomass and shifts in FW topology. Biotic parameters were also crucial in controlling pCO\u003csub\u003e2\u003c/sub\u003e at the SM, as indicated by O\u003csub\u003e2\u003c/sub\u003e% values inversely related to pCO\u003csub\u003e2\u003c/sub\u003e values from March to August 2021, likely reflecting low phytoplankton production and higher respiration rates. Conversely, the FM shifted from being strong water CO\u003csub\u003e2\u003c/sub\u003e source to exhibiting a balanced behavior as both a weak source and a sink, depending on the seasons and stations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e-B, a, c).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Food web topologies and their relationship with water pCO\u003csub\u003e2\u003c/sub\u003e at studied marshes\u003c/h2\u003e \u003cp\u003eAlthough no significant correlation between water pCO\u003csub\u003e2\u003c/sub\u003e and biotic factors was found at the SM, relationships between pCO\u003csub\u003e2\u003c/sub\u003e and PFW were clearly established during our study. Three different FW topologies were identified, each with nuances: a \u0026lsquo;multivorous\u0026rsquo; FW in June (FW1) and a \u0026lsquo;microbial food web\u0026rsquo; in May (FW2) exhibited mean high pCO\u003csub\u003e2\u003c/sub\u003e values (832 ppmv and 842 ppmv, respectively), while a \u0026lsquo;weak multivorous\u0026rsquo; FW was associated with a lower mean pCO\u003csub\u003e2\u003c/sub\u003e value (638 ppmv) during August (FW3). Additionally, a \u0026lsquo;weak herbivorous\u0026rsquo; FW occurred in March, April and July (FW2), with variable mean pCO\u003csub\u003e2\u003c/sub\u003e values ranging between 689 and 749 ppmv. These FW topologies have been previously described by Legendre and Rassoulzadegan (1995), Masclaux et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Tortajada (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Legendre and Rassoulzadegan (1995) noted that some PFWs, such as \u0026lsquo;multivorous\u0026rsquo; and \u0026lsquo;microbial food web\u0026rsquo; FWs, were more stable over time compared to others, like the \u0026lsquo;herbivorous\u0026rsquo; FW. At the SM, the two stable FWs (\u0026lsquo;multivorous\u0026rsquo; and \u0026lsquo;microbial food web\u0026rsquo;) were associated with high mean pCO\u003csub\u003e2\u003c/sub\u003e 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 \u0026lsquo;microbial food web\u0026rsquo;. Prairie, Bird, and Cole (2002) and Lapierre et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) have shown that DOC increases can directly raise water pCO\u003csub\u003e2\u003c/sub\u003e. Conversely, the transitory FWs (\u0026lsquo;weak herbivorous\u0026rsquo; and \u0026lsquo;weak multivorous\u0026rsquo;) were associated with medium or low mean pCO\u003csub\u003e2\u003c/sub\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e). These findings suggest that pCO\u003csub\u003e2\u003c/sub\u003e tends to accumulate more during stable FW occurrences than during transient ones.\u003c/p\u003e \u003cp\u003eAt the FM, the absence of clear seasonality in FW types observed throughout 2021 could be attributed to the recent restoration process initiated in 2019, which may have disrupted the marsh\u0026rsquo;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 \u0026lsquo;biological winter\u0026rsquo; FW identified in spring 2021 at station TB (FW3.b) and in winter at station TC (FW5.b) were both associated with elevated pCO\u003csub\u003e2\u003c/sub\u003e values (971 and 959 ppmv, respectively). In contrast, the \u0026lsquo;weak herbivorous\u0026rsquo; FW observed from autumn to winter at TB (FW4.b) was linked to the lowest pCO\u003csub\u003e2\u003c/sub\u003e values (127 and 299 ppmv, respectively). At station TA, extreme pCO\u003csub\u003e2\u003c/sub\u003e values (298 and 3020 ppmv in autumn and summer, respectively) were attributed to the \u0026lsquo;multivorous\u0026rsquo; FW (FW2.b). This association could be explained by lower Chl\u003cem\u003ea\u003c/em\u003e nano and pico biomasses along with higher HTTP and Meso biomasses measured in summer compared to autumn. A similar pattern was observed for the \u0026lsquo;microbial food web\u0026rsquo; identified at TC (FW6.b), which was associated with very high Chl\u003cem\u003ea\u003c/em\u003e biomasses measured during summer. Conversely, no clear relationship was found between the \u0026lsquo;weak multivorous\u0026rsquo; FWs and pCO\u003csub\u003e2\u003c/sub\u003e values, likely due to the lack of biological equilibrium.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Conclusions\u003c/h2\u003e \u003cp\u003eThis comparative analysis of two distinct marsh FW topologies allowed us to discern both similarities and differences between sites regarding carbon and FW relationships. Despite their typological disparities, both the SM and the FM functioned as CO\u003csub\u003e2\u003c/sub\u003e 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 pCO\u003csub\u003e2\u003c/sub\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). These included three stable types ('biological winter', 'microbial food web', 'multivorous' food webs) with high pCO\u003csub\u003e2\u003c/sub\u003e values at both sites, as well as two transient types ('weak multivorous' and 'weak herbivorous') with lower and more variable pCO\u003csub\u003e2\u003c/sub\u003e values (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e). While four of these food webs had been previously described in literature (Legendre and Rassoulzadegan 1995; Masclaux et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Tortajada \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), 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.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs 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 pCO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eStatement and Declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work was supported by the ANR Project PAMPAS (2019-2024, Evolution de l\u0026rsquo;identit\u0026eacute; PAtrimoniale des Marais des Pertuis charentais en r\u0026eacute;ponse \u0026agrave; l\u0026rsquo;Al\u0026eacute;a de Submersion marine), LEFE Dycidemaim (LEFE 2021-2022, Dynamique du carbone aux interfaces d\u0026rsquo;\u0026eacute;change terrestre-aquatique-atmosph\u0026eacute;rique des marais temp\u0026eacute;r\u0026eacute;s) and LRTZC (2019-2027, La Rochelle territoire z\u0026eacute;ro carbone).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting\u003c/strong\u003e \u003cstrong\u003einterests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing or financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conceptualized by C.D. and P.Po. . \u0026nbsp;J.M., R.M. and L.B. contributed with formal analysis. F-X.R., P.Pi., C.E. and B.D. contributed to the methodology and data sampling and curation. M.V. helped with founding and editing. L.X. has done the data analysis, visualization, and writing of the original draft. Reviewing and editing the manuscript was done by L.X., C.D.and P.Po.. \u0026nbsp;L.X., R.M., J.M., L.B., B.D., P.Pi., C.E., M.V., F-X.R., F.A., M.T., C.D., P.Po. contributed to later versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest. This study does not involve human participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be made available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdamczyk, Emily M., and Jonathan B. 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Frontiers in Genetics, 12, 2122.\u0026rdquo;\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYentsch, Charles S, and David W Menzel. 1963. \u0026ldquo;A Method for the Determination of Phytoplankton Chlorophyll and Phaeophytin by Fluorescence.\u0026rdquo; Deep Sea Research and Oceanographic Abstracts 10(3): 221\u0026ndash;31. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0011-7471(63)90358-9\u003c/span\u003e\u003cspan address=\"10.1016/0011-7471(63)90358-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"intm","sideBox":"Learn more about [International Microbiology](https://www.springer.com/journal/10123)","snPcode":"10123","submissionUrl":"https://submission.nature.com/new-submission/10123/3","title":"International Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Blue Carbon, wetlands, planktonic food web, pCO2, air-water CO2 variations","lastPublishedDoi":"10.21203/rs.3.rs-4768272/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4768272/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWhile research has extensively investigated the dynamics of CO\u003csub\u003e2\u003c/sub\u003e water partial pressure (pCO\u003csub\u003e2\u003c/sub\u003e) and planktonic food webs (PFWs) separately, there has been limited exploration of their potential interconnections, especially in marsh typologies. This study\u0026rsquo;s objectives were to (1) investigated if pCO\u003csub\u003e2\u003c/sub\u003e and atmospheric CO\u003csub\u003e2\u003c/sub\u003e flux can be elucidated by PFW topologies, and (2) ascertain if these potential relationships are consistent across two distinct \u0026ldquo;Blue Carbon\u0026rdquo; ecosystems. Abiotic and biotic variables were measured in two contrasting wetlands at the Atlantic French coast: a saltwater (SM, L\u0026rsquo;Houmeau) and a freshwater marsh (FM, Tasdon). SM acted as a weak carbon source, with pCO\u003csub\u003e2\u003c/sub\u003e between 542 and 842 ppmv. Conversely, FM exhibited strong atmospheric CO\u003csub\u003e2\u003c/sub\u003e source or sink characteristics, varying with seasons and stations, with pCO\u003csub\u003e2\u003c/sub\u003e between 3201 and 114 ppmv. Five PFW topologies were linked to varying pCO\u003csub\u003e2\u003c/sub\u003e across the two ecosystems: three stable topologies ('biological winter', 'microbial', 'multivorous' PFW) exhibited consistently high pCO\u003csub\u003e2\u003c/sub\u003e values (FM: 971, 1136, 3020 ppmv; SM: 'biological winter' not observed, 842, 832 ppmv), while two transient topologies ('weak multivorous' and 'weak herbivorous') displayed lower and more variable pCO\u003csub\u003e2\u003c/sub\u003e values (FM: from 127 to 1402 ppmv; SM: from 638 to 749 ppmv). Seasonality emerged as an influencing factor for both pCO\u003csub\u003e2\u003c/sub\u003e dynamics and PFW. However, PFW in FM did not demonstrate a seasonal equilibrium state, potentially hindering a clearer understanding of the relationship between pCO\u003csub\u003e2\u003c/sub\u003e and PFW. This is the first documented association between PFW topologies and pCO\u003csub\u003e2\u003c/sub\u003e dynamics in \u0026ldquo;Blue Carbon\u0026rdquo; marsh environments.\u003c/p\u003e","manuscriptTitle":"Atmospheric CO2 flux and planktonic food web relationships in temperate marsh systems: Insights from in situ water measurements","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-27 11:58:17","doi":"10.21203/rs.3.rs-4768272/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-13T13:24:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-08T07:11:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199854247508406635491762527644634912536","date":"2024-08-29T01:11:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-01T15:10:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-01T15:04:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-30T18:21:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Microbiology","date":"2024-07-19T14:30:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"intm","sideBox":"Learn more about [International Microbiology](https://www.springer.com/journal/10123)","snPcode":"10123","submissionUrl":"https://submission.nature.com/new-submission/10123/3","title":"International Microbiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f30822da-053e-43b1-9d7b-1edcdfddd22a","owner":[],"postedDate":"August 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-03-10T17:09:43+00:00","versionOfRecord":{"articleIdentity":"rs-4768272","link":"https://doi.org/10.1007/s10123-025-00650-x","journal":{"identity":"international-microbiology","isVorOnly":false,"title":"International Microbiology"},"publishedOn":"2025-03-04 15:57:09","publishedOnDateReadable":"March 4th, 2025"},"versionCreatedAt":"2024-08-27 11:58:17","video":"","vorDoi":"10.1007/s10123-025-00650-x","vorDoiUrl":"https://doi.org/10.1007/s10123-025-00650-x","workflowStages":[]},"version":"v1","identity":"rs-4768272","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4768272","identity":"rs-4768272","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}