The relationship between carbonate chemistry, estuarine metabolism, and spring-neap tidal cycles in a northern temperate estuary

The relationship between carbonate chemistry, estuarine metabolism, and spring-neap tidal cycles in a northern temperate estuary | 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 The relationship between carbonate chemistry, estuarine metabolism, and spring-neap tidal cycles in a northern temperate estuary Catherine Mara Liberti, Jeremy M. Testa, Lawrence M. Mayer, Joseph E. Salisbury, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6813991/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Many estuaries are highly productive areas for shellfish aquaculture while also subject to low alkalinity and low aragonite saturation state (𝛀Ar) from both offshore and freshwater. Due to the influence and interaction of these source water conditions and the biological processes that occur within the estuary, 𝛀Ar can be highly variable. To better understand how 𝛀Ar changes from daily to seasonal time scales within estuaries, we described high frequency changes in aragonite saturation state in the largest oyster growing region in northern New England, the Damariscotta River estuary, Maine, in 2018 using hourly buoy data and discrete samples. 𝛀Ar ranged from 1 to 2.5 between late May and early October with daily ranges frequently exceeding 0.5. 𝛀Ar was predominantly controlled by temperature and salinity at the seasonal scale but driven by ecosystem metabolism on daily - bi-weekly time scales. The prominent feature of this system was the importance of spring-neap tidal cycles, with spring tides increasing turbidity, nitrate, and respiration, and decreasing primary production, dissolved oxygen, and 𝛀Ar. Here, we disentangle the strong interconnection between estuary morphology, tides, ecosystem metabolism, and 𝛀Ar in an important oyster growing area with implications for the timing of seeding, site selection, water quality management, and analyzing future acidification scenarios in estuaries that share similar oceanographic conditions. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Acidification of the marine environment is threatening the success of many marine calcifiers because it makes forming their protective shell more physiologically expensive (Sabine et al., 2004 ; Orr et al., 2005 ; Doney et al., 2009 ; Kroeker et al., 2013 ; Waldbusser et al., 2015 ; Liu et al., 2020 ; Gledhill et al., 2015 ) leads to stunted larval growth and death (Salisbury et al., 2008 ; Waldbusser and Salisbury, 2014 ; Ekstrom et al., 2015 ) and can even increase the vulnerability of future generations of shellfish to acidification through transgenerational effects (Griffith and Gobler, 2017 ). Adding carbon dioxide (CO 2 ) to the ocean is acidifying the water and reducing the calcium carbonate saturation state (𝛀), the thermodynamic stability of the mineral forms of calcium carbonate, which governs how easily an organism can calcify their shell (Doney et al., 2009 ; Waldbusser and Salisbury, 2014 ). 𝛀 has been identified as an important predictor of larval and juvenile shellfish growth and survival, particularly aragonite saturation state (𝛀Ar) < 1.5 which can lead to slow growth, deformed shells, or larval death (Salisbury et al., 2008 ; Ekstrom et al., 2015 ; Waldbusser et al., 2015 ). The processes driving ocean acidification (OA) in open ocean waters are relatively well understood and future change can be estimated with high confidence (Doney et al., 2009 ; Siedlecki et al., 2021 ). The pH of the world's surface ocean has decreased by 0.1 units since the start of the Industrial Revolution and is expected to further decrease by up to 0.2–0.3 units by 2100 (Feely et al., 2004 ; Doney et al., 2009 ; Bopp et al., 2013 ) due to continually increasing atmospheric carbon dioxide concentrations (Keeling and Graven, 2021 ). However, the vast majority of commercially important marine calcifiers live in the coastal regions of the world’s oceans, which can experience higher frequency and more severe changes in 𝛀 due to changes in temperature, salinity, exchange with adjacent waters, and other inputs of CO 2 . Processes such as ecosystem metabolism (Duarte and Krause-Jensen, 2018 ; Pacella et al., 2018 ; Lowe et al., 2019 ; Wallace et al., 2021 ; Pacella et al., 2024 ) that are altered by eutrophication (Duarte et al., 2013 ; Wallace et al., 2014 ; Rheuban et al., 2019 ; Kim et al., 2020 ) can influence 𝛀, as well as freshwater intrusion (Salisbury et al., 2008 ), coastal upwelling (Feely et al., 2008 ; Hauri et al., 2009 ; Hauri et al., 2013 ; Reum et al., 2016 ), long term changes in ambient physical conditions (Salisbury and Jönsson, 2018 ; Stewart et al., 2025 ), and calcification and dissolution (Cai et al., 2017 ; Rheuban et al., 2019 ). In coastal environments, these factors can lead to increased amplitude or more frequent changes in 𝛀 (Harris et al., 2013 ; Waldbusser and Salisbury, 2014 ; Hales et al., 2017 ; Cotovicz et al., 2022 ), where high carbonate variability is likely a natural feature of estuaries. Many early laboratory studies of the impacts of low 𝛀 on marine calcifiers used static exposures over time (Gazeau et al., 2007 ; Kurihara et al., 2007 ; Talmage and Gobler, 2010 ; Hettinger et al., 2012 ; Timmins-Schiffman et al., 2013 ; Waldbusser et al., 2015 ), when in the natural environment, these coastal organisms are likely experiencing high frequency variation in carbonate parameters (Hofmann et al., 2011 ; Hales et al., 2017 ; Kapsenberg et al., 2018 ; Tomasetti et al., 2021 ). More recently, researchers have been incorporating high frequency diel-cycling and multi-stressor components to their study designs (Frieder et al., 2014 ; Gimenez et al., 2018 ; Onitsuka et al., 2018 ; Kapsenberg and Cyronak, 2019 ; Shang et al., 2020 ); however, there are still relatively few data on what conditions marine calcifiers are experiencing in situ in a variety of ocean habitats (Hofmann et al., 2011 ; Shaw et al., 2013 ; Fairchild and Hales, 2021 ; Rosenau et al., 2021 ; Torres et al., 2021 ), over timescales ranging from tidal, diel, fortnightly (spring- neap tidal cycles), seasonal and beyond seasonal, and interannual to decadal. Quantifying and ascribing drivers to this natural variation is critical to improving laboratory studies and ultimately our understanding of how marine calcifiers may respond to future and ongoing ocean and coastal acidification. Aquacultured bivalves are vulnerable to high variability in 𝛀 because they cannot move out of unfavorable conditions. Despite this, bivalve aquaculture has been expanding steadily worldwide and in the United States over the past 20 years (Naylor et al., 2021 ; NOAA Fisheries, 2021 ) due to increased demand (The Hale Group, LTD and Gulf of Maine Research Institute, 2016; Wijsman et al., 2019 ; Naylor et al., 2021 ). Likewise, interest in how acidification is impacting shellfish is also increasing (Doney et al., 2020 ; Siedlecki et al., 2021 ), with the goal of providing projections and vulnerability assessments to support this growing and important industry. Some bivalve aquaculture relies on hatcheries to produce larvae (e.g., oysters), while others rely on wild caught larvae to seed farms (e.g., blue mussels and sea scallops). Regardless of larval production methods, all larvae can be susceptible to detrimental open water conditions with respect to carbonate saturation states. For example, Sanford et al. ( 2014 ) found that Olympia oysters, Ostrea lurida , raised in acidified waters were approximately 30–40% smaller and far more likely to be eaten by oyster drills than oysters raised in ambient conditions, indicating that oysters are not only susceptible to ocean acidification during their larval phase. Here, we analyze a unique high frequency 𝛀 dataset to understand controls on OA in a temperate estuarine aquaculture growing area over a variety of timescales. Our objective was to better constrain our understanding of OA variability to better anticipate and predict long term trends and OA effects on shellfish. The study was carried out in the Damariscotta River estuary, which has the largest oyster growing area in Northern New England, producing more than 10 million oysters annually (Maine Department of Marine Resources, 2021 ). This system is an ideal location to test this question, due to vulnerability to OA as a function of relatively cold and therefore less saturated Gulf of Maine source waters, multiple drivers, and relatively large shellfish industry. Methods The Damariscotta River estuary is a drowned river valley in the midcoast of Maine, USA. The estuary supports the majority (~ 68%, Maine Department of Marine Resources, 2020 ) Maine’s oyster aquaculture industry in the upper reaches, has low human population density in its watershed, and low freshwater flow (1–3 m3 s-1, on average; McAlice, 1977 ). The bathymetry and long, narrow shape of this estuary (Fig. 1 ) creates a basin in the upper third of the estuary which is ​​shallow (average 5.5 m) and has a longer residence time than the rest of the estuary, leading to increased temperature and particle retention (i.e. phytoplankton and other oyster food sources; Mayer et al., 1996 ; Adams et al., 2019 ; Newell et al., 2021 ; Jiang et al., 2022 ). The main source of water to the Damariscotta River estuary is the Gulf of Maine, a highly productive shelf sea in the Northwest Atlantic Ocean. The Gulf of Maine produces over half of the US East Coast’s value in commercial fisheries but comprises only about 10% of its area (National Marine Fisheries Service, 2021 ). Due to the large seasonal temperature and productivity range in the Gulf of Maine, 𝛀 can vary significantly throughout the year (Wang et al., 2017 ; Siedlecki et al., 2021 ) but are the lowest regional saturation states on the US East Coast (Wang et al., 2013 ). A Land-Ocean Biogeochemical Observatory (LOBO) (Jannasch et al., 2008 ) was deployed in the oyster growing area of the Damariscotta River estuary (Fig. 1 ) between May and October of 2018. The LOBO measured hourly temperature (°C), salinity, dissolved oxygen (ml l − 1 ), turbidity (NTU), chlorophyll fluorescence (µg L − 1 ), and fluoresced Dissolved Organic Matter (fDOM, QSDE) via a Sea-Bird Scientific WQMx, hourly nitrate (µM N) via a Satlantic/Sea-Bird Scientific Submersible Ultraviolet Nitrate Analyzer (SUNA), hourly pH (total scale) via a Satlantic/Sea-Bird Scientific SeaFET, and hourly current speed (cm s − 1 ) using a Nortek Aquadopp. All measurements were made at a depth of 1 m below the surface where the vast majority of oysters are cultivated in the region. We measured total alkalinity (TA) in discrete samples taken bi-weekly to monthly from May–September (nine samples) at the LOBO in the growing area. We used a Yellow Springs Incorporated (YSI) 6920 multiparameter sonde to measure salinity, temperature (°C), pH nbs , dissolved oxygen (mg O 2 L − 1 ), and chlorophyll- a (RFU), which was used to estimate chlorophyll concentration at the LOBO when the TA samples were collected. We measured these parameters at every half meter from the surface to the bottom, which confirmed the water column was well mixed. We calibrated the sonde the day prior to sampling using YSI solutions or recommended calibration practices. We collected three discrete TA samples using a Niskin sampler from 1 m below the surface and transferred the sample using clean flexible tubing to 60 ml borosilicate glass bottles. To avoid forming bubbles on the walls of the bottle, we placed the tube at the bottom of the bottle and inverted the bottle such that water coated all sides of the bottle. The bottle was then turned upright, and water was allowed to overflow the bottle for 10 sec, or approximately 250 ml. The bottle was capped with a ground glass stopper and less than 1% headspace was left in the bottles. All samples were stored unpreserved on ice until returned to the laboratory refrigerator before analysis. We analyzed all samples within 24 hours of collection. We analyzed the TA samples using an open cell potentiometric titration, following the methods described in Guide to Best Practices for Ocean CO 2 (Dickson et al., 2007 ). When ready for analysis, the samples were removed from the refrigerator and kept in a 25°C water bath for 10 min prior to analysis. A weighed sample was placed in a jacketed beaker maintained at 25°C and a liquid junction pH electrode was submerged in the sample and gently stirred with a magnetic stir bar. The electrode was calibrated at the beginning of each analysis using commercially available low-ionic strength pH NBS 4, 7, and 10 buffers. Hydrochloric acid (0.1 N) was added to the sample at a rate slower than 0.05 ml sec − 1 until the pH was 2.9. We used the titration data and the package SeaCarb (Gattuso et al., 2020 ) in R (R Core Team, 2019 ) to calculate TA using the Gran method. The average standard deviation for triplicate samples over the season was 15.1 µmol kg − 1 . We calculated hourly estimates of aragonite saturation state with hourly pH, temperature, and salinity data from the LOBO and the biweekly TA data using the CO2SYS program (van Heuven et al., 2011 ). The biweekly TA data was linearly interpolated to hourly values to match the time step of the temperature and salinity. We selected the K1 and K2 dissociation constants derived by Mehrbach et al. ( 1973 ) and refit by Dickson and Millero ( 1987 ), the KSO 4 − dissociation constants by Dickson (1990), and total borate constant of Uppström ( 1974 ). Although we highlight the changes in 𝛀Ar specifically, the trends in calcite saturation state (𝛀Ca), are identical. We recognize that non-carbonate alkalinity may contribute to TA (Hunt et al., 2011 ; Song et al., 2020 ; Hunt et al., 2025 ) and lead to an overestimation of 𝛀. This discussion, however, focuses on trends in 𝛀 over varying timescales, not the absolute value of 𝛀 in the estuary, and assumes that non-carbonate alkalinity does not contribute to TA significantly. We sought to tease apart the different physical and biological processes that were contributing to changes in 𝛀Ar. First, we removed the influence of daily tides on the current speed, turbidity, nitrate, chlorophyll a, and 𝛀Ar using a center weighted 24 h moving average to better understand how each parameter was related to the spring neap tidal cycles and 𝛀Ar. The nitrate was also filtered using a center weighted two week moving average to explore the importance of spring-neap tidal dynamics in delivering nitrate to the estuary. Seasonal biological controls on 𝛀Ar variability To isolate the impact of biological processes on 𝛀Ar, we removed the impacts of changing physical conditions on 𝛀Ar by first examining the processes that impacted both TA and pH. To account for changes in TA due to changes in salinity, we normalized the measured TA to salinity using the method described in Friis et al. ( 2003 ), which regresses the measured salinity with the measured TA, using a linear best fit to find the y - intercept, and assumes the y-intercept represents the value of the freshwater end member TA, using the following equation: \(\:nTA=\:\frac{{TA}_{meas}-\:{TA}_{s=0}}{{S}_{meas}}*{S}_{ref}+\:{TA}_{s=0}\) Eq. 1 where, TA meas was the TA measured in the growing area bi-weekly, TA s=0 was the assumed freshwater end member TA calculated from the regression between measured salinity and measured TA, S meas was the measured salinity, and S ref was the reference salinity of 30.5 psu. We parameterized TA s=0 in this way because this system has very low freshwater flow originating from a variety of sources, which can lead to alteration of the salinity to alkalinity ratio between the river endmember and the study site. Next, we normalized the calculated 𝛀Ar by temperature and salinity, using the normalized TA to remove the effect of changing physical conditions on 𝛀Ar such that we could attribute the remaining variability to biological processes. We linearly interpolated the TA values between sampling dates to match the hourly time step of the temperature and salinity. We normalized the 𝛀Ar by using a constant temperature of 20°C, a constant salinity (30.5 psu) and TA normalized to salinity 30.5 using the method in Friis et al. ( 2003 ). We then used CO2SYS (van Heuven et al., 2011 ) to calculate the normalized 𝛀Ar (n𝛀Ar) using constant temperature, salinity, and normalized TA. We utilized the “temperature in” (actual temperature measured) and the “temperature out” (20°C) feature to standardize for temperature such that the effect of temperature on pH was included in the calculation. The average seasonal temperature and salinity measured in the growing area were 19.8°C and 30.5 psu, respectively. We estimated the impact of seasonal changes in photosynthesis and calcification to characterize the role of biological impacts on TA. We estimated the production of TA from photosynthesis via nitrate use in the estuary by using the net drawdown of nitrate over the season, measured by the SUNA on the LOBO buoy. We used the estimate of TA drawdown by oyster shell growth of the 2018 season from Liberti et al. ( 2022 ) to account for calcification. The biological uptake of nitrate requires the excretion of another negative ion to maintain equilibrium within the cell. In this case, OH − is produced, which contributes to the total alkalinity, thus when nitrate is used by organisms, the total alkalinity in the surrounding water increases (Lejart et al., 2012 ). Oysters reduce the total alkalinity of the surrounding water by removing bi-carbonate when building their shells (Lejart et al., 2012 ; Liberti et al., 2022 ). Estimating light attenuation and primary productivity To better understand how biological processes can impact 𝛀Ar, we first modeled light attenuation using in situ parameters from the buoy, then estimated primary production using the chlorophyll a concentration, light attenuation, and surface PAR. The light attenuation was estimated using a model developed by Ganju et al. ( 2020 ). Their model was built off a method developed by Gallegos et al. ( 2011 ). The model estimates spectral attenuation caused by both suspended and dissolved constituents, including water, CDOM, phytoplankton, and turbidity. We include absorption by: (1) we assumed absorption by water followed the spectral characteristics of pure water; (2) CDOM absorption was estimated from a ratio of measured CDOM absorption to fDOM concentration. Four CDOM samples were collected at the LOBO buoy in July and August of 2018 and the attenuation coefficient was calculated using the equation from Oestreich et al. ( 2016 ). Absorbance measurements were recorded in 0.5 nm increments over the span of 340–440 nm. The average ratio was then used to convert the fDOM measurements (QSDE) to CDOM absorption (1 m −1 ) for use in the model. The spectral slope was also calculated (Oestreich et al., 2016 ) for each of the CDOM samples and the average was used as a parameter in the model; (3) phytoplankton absorption was proportional to chlorophyll a concentration and the absorption peak at 675 nm (initial value for peak absorption was taken as aψ, 675 = 0.0235 m 2 (mg chl a ) − 1 , within the range provided by Bricaud et al. ( 1995 ) was used to normalize the spectral shape; and lastly (4) non-algal absorption was assumed to be proportional to the suspended sediment concentration (turbidity) with a spectral shape (Bowers and Binding, 2006 ) that included a baseline of cx1 = 0.0024 m 2 g −1 , cx2 = 0.04 m 2 g −1 was set as the absorption cross section (Bowers and Binding, 2006 ), and sx = 0.009 nm −1 was used as the spectral slope (Boss et al., 2001 ). The backscattering ratio of water was set at 0.5, while CDOM was considered non-scattering (Mobley and Stramski, 1997 ), and the particulate effective backscattering ratio bbx was initially set at 0.017. The PAR used in the model was converted from PAR measured in air to PAR just below the surface skin using the methods in Mobley and Boss ( 2012 ). For all parameters, only measurements between 10am – 3pm (6 hourly measurements) were used to create a daily average from which the light attenuation coefficient (Kd) was modeled. Kd was compared to the observed light attenuation coefficient (Kd obs ) calculated from two in situ PAR sensors on the LOBO. The PAR in air measurements were collected using a Sea-Bird PAR sensor and converted to PAR just below surface as noted above. The underwater PAR measurements were collected with a Sea-Bird ECO PAR sensor that was mounted on an arm that was 0.43 m below the surface and 1 m horizontally away from the buoy float to minimize shading. Kd obs was calculated using the following equation: \(\:{Kd}_{obs}=\:-\frac{1}{dz}\text{l}\text{n}\left(\frac{{PAR}_{lower}}{{PAR}_{upper}}\right)\) Eq. 2 where dz was the distance between the sensors (in meters), PAR lower was the PAR measured 0.43 meters below the surface, and PAR upper was the PAR calculated from the in-air PAR sensor converted to be just below the surface of the water. Kd obs was also only calculated from the daily averaged PAR values for the hours of 10am – 3pm. Any negative values of Kd obs were removed from the results and the average was compared to the average modeled Kd to verify the model was accurately representing Kd in the growing area. The modeled Kd was used to calculate the 1% light level using the equation: \(\:1\%LL=\:\frac{Log\left(0.01\right)}{-Kd}\) Eq. 3 Finally, the primary production was estimated using the following equation from Cloern ( 1987 ): \(\:PP=B{Z}_{p}{I}_{o}\) Eq. 4 Where PP was primary production in g C m − 2 d − 1 , B was the estimate of biomass from chlorophyll a concentration (g m − 3 ), Z p was the 1% light level (m), and I o was the surface irradiance (here the PAR upper in Eq. 2). Generalized additive model We developed a generalized additive model (GAM) to assess the coupled contribution of each parameter on weekly - monthly changes in n𝛀Ar. GAMs are generalized linear models that can use smoothing functions to model non-linear trends, which are often observed in ecosystems (Zuur et al., 2007 ; Wood, 2017 ). We assessed the coupled effects of DO, chlorophyll, turbidity, and nitrate on n𝛀Ar. To remove diel and tidal advection from the parameters, a 1 week, centered moving average was applied to the hourly data before a daily average was calculated and used in the GAM. The R (R Core Team, 2019 ) package “mgcv” developed by Wood ( 2011 ), with the function “gam” used to develop the model. Results To better understand the interactions between biogeochemical controls on sub-seasonal variability and the strong tidal variability in the Damariscotta River estuary, we examined high-frequency LOBO data over the growing season. The growing area has semi-diurnal tides that feature a spring-neap tidal cycle. The growing area is characterized by one large (new moon) and one small spring tide (full moon), and two neap tides of similar magnitude each month (Fig. 2 a-e). The current speed was strongly tied to tidal amplitude with an 8 cm s − 1 increase in 24 hour averaged current speed corresponding with the large spring tide each month (Fig. 2 a). The turbidity in the growing area was also strongly tied to tidal height and current speed (Fig. 2 b). The turbidity increases approximately 50% during the largest spring tide of the month, from 3.69 NTU on average over 24 hours to 4.93 NTU on average. Nitrate exhibited a small increase of 0.5–1 µM each month 5 days on average after the peak of the large spring tide (Fig. 2 c). Seasonally, nitrate decreased from 4.0 µM over the summer season until early September when it reached its lowest at 0.2 µM, then it began increasing again and reached 3.3 µM by early October. The monthly increases in nitrate become more apparent when compared to the two-week average nitrate where spring neap cycle dynamics have been removed (Fig. 2 c). Chlorophyll- a increases until early August then decreases to early October, except during the neap tides when a small increase occurs, particularly the neap tide immediately after the peak in nitrate (Fig. 2 d). The small increases during the neap tides average 1 µg l − 1 . The 24-hour averaged 𝛀Ar exhibits peaks during the neap tides of the month, increasing from 1.61 on average, to 1.84 on average between mid-June and early October (Fig. 2 e), with a prolonged increase between mid-August and early September, when 24 h averaged 𝛀Ar reached its seasonal maximum of 2.15. Quantifying the changes in 𝛀Ar from physical, chemical, and biological processes The changes in 𝛀Ar are the summation of changes from biological, chemical, and physical processes. Previous work has shown that 𝛀Ar in estuaries is largely driven by biological and physical processes (Pacella et al., 2018 ; Lowe et al., 2019 ; Rheuban et al., 2019 ; Wallace et al., 2021 ), thus, to isolate the impacts of just the biological processes, we removed the impact of changing physical-chemical conditions on 𝛀Ar. We first characterized the change in TA caused by salinity. The measured TA of the growing area ranged from 1,912 µmol kg −1 in May increasing to a high of 2,129 µmol kg −1 in September (Fig. 3 ). The salinity ranged from 27.6 in May and increased to a high of 31.6 in September. The nTA was highest (2,059 µmol kg −1 ) in early July, decreased to a low of 2,000 µmol kg −1 in mid-August, and rose again to 2,043 µmol kg −1 at the end of September. For all triplicate samples of TA, the average standard deviation was ± 15.1 µmol kg −1 . Temperature and salinity effects on 𝛀 Ar The 24 h averaged temperature of the growing area started at 16.5°C in early June, rose to a maximum of 24.3°C in August, and declined to 15.6°C in early October, with two periods of rapid warming and cooling in early July and early September (Fig. 4 a). The temperature of the growing area dropped approximately 2°C during each large spring tide and rose again during the neap and smaller spring tides. The 24 h averaged salinity increased gradually over the season, starting at 29.2 in early June, reaching a maximum of 31.3 in mid-September and ending the season at 30.6 (Fig. 4 b). Salinity decreased in early July from rainfall and again in mid-September. We normalized the 𝛀Ar to constant temperature, salinity, and nTA such that the resulting n𝛀Ar had all impacts from changing physical conditions removed (Fig. 4 c). In June, the temperature in situ was lower than 20°C and the salinity was lower than 30.5, thus the n𝛀Ar was higher than 𝛀Ar. In early and mid-July, the in situ temperature was 1–2°C higher than 20°C, raising n𝛀Ar, but the salinity was slightly less than 30.5, lowering n𝛀Ar, and the resulting n𝛀Ar was nearly the same as 𝛀Ar. After July 1st, the salinity remained above 30.5, resulting in n𝛀Ar lower than 𝛀Ar for the remainder of the growing season. In late July, August, and early September, the temperature was warmer than 20°C also leading to a reduced n𝛀Ar. Overall, the n𝛀Ar was higher than 𝛀Ar in June, the same in early July, and lower than 𝛀Ar the rest of the season. Calcification and nitrate cycling effects on total alkalinity Calcification by oysters can decrease 𝛀Ar through a reduction in TA (Liberti et al., 2022 ) and uptake of nitrate by primary producers can increase 𝛀Ar on seasonal timescales. The effect of oyster calcification on nTA was likely higher than the impact from nitrate usage in the estuary, but both contributed to seasonal variation in TA (Fig. 5 ). The nTA increased 25 µmol kg −1 between late May and mid-July, when it began to decrease, slowly at first and then sharply for an overall decrease of 52 µmol kg −1 by mid-August (Fig. 5 a). The nTA then increased again though early October. The net change from NO 3 uptake and oyster growth as modeled in Liberti et al. ( 2022 ) was negligible until early July when TA was removed from the water from an acceleration of oyster shell growth in the upper Damariscotta River at peak temperatures, negating the impact of nitrate uptake which increased the TA over the season (Fig. 5 b). Light model and primary production The CDOM absorption coefficients ranged from 1.34 to 4.40 m − 1 with a mean of 2.58 m − 1 while the corresponding fDOM concentrations ranged from 4.53 to 5.40 with a mean of 4.87 (QSDE). The CDOM:fDOM ratios ranged from 0.28 to 0.82 with an average of 0.52, this value was used to convert measured fDOM concentrations to CDOM absorbances. The spectral slopes of the CDOM absorbance ranged from 0.01 to 0.03 with an average of 0.02. Kd had an average of 0.76 with a standard deviation of 0.08 while Kd obs had a mean of 0.73 and a standard deviation of 0.51. Of the 115 daily observations of Kd obs , 30 were removed due to negative values. Kd had relatively higher values July through mid-August, and lower values in June and mid-August – early October (Fig. 6 a). The 1% light level depths had an inverse trend as expected. The 1% light level depths increased in mid-August (Fig. 6 b) concurrent with the large reduction in turbidity and chlorophyll (Fig. 2 b,d). This was also observed in the 1% light level calculated from Kd obs (not shown). The primary production (PP), dissolved oxygen, and n𝛀Ar co-varied on weekly timescales (Fig. 7 ) and were related to tidal stage except for late June through mid-July when PP was decoupled from DO and n𝛀Ar trends. Between June and October, the PP, DO, and n𝛀Ar exhibited large drops during the large spring tide. The DO and PP increased after the large spring tide each month, while the n𝛀Ar stayed relatively static in July, and increased in step with DO and PP in August, resulting in the largest peaks in DO and n𝛀Ar and second largest peak in PP during the smaller amplitude tides. Generalized additive modeling To better understand the relationships between n𝛀Ar and the other parameters influenced by biological processes, we used a generalized linear model to assess the multivariate effects (Fig. 3.9). The model considered the partial effects of chlorophyll, turbidity, nitrate, and DO on n𝛀Ar. The combination of DO, chlorophyll, and nitrate provided the best predictions of n𝛀Ar. DO was nearly linearly related with n𝛀Ar, as DO varied from 81 to 104% saturation, the n𝛀Ar increased from 1.47 to 1.91 (Fig. 8 a). Variations in DO explained 63.7% of the variance in n𝛀Ar. Chlorophyll between 2.5 and 4.5 µg l − 1 were associated with n𝛀Ar between 1.63 and 1.68 on average, while chlorophyll below or above that range was associated with lower n𝛀Ar, between 1.55 and 1.63 (Fig. 8 b). Nitrate was negatively correlated with n𝛀Ar, when nitrate was less than 1.5 µM, n𝛀Ar was between 1.68 and 1.85. Changes in nitrate between 1.5 and 2.5 µM had little effect on n𝛀Ar, while nitrates above 2.5 were associated with decreasing n𝛀Ar to a low of 1.56 (Fig. 8 c). Discussion The trends in 𝛀Ar in the Damariscotta River are the result of a complex interplay between biological, chemical, and physical processes. Flow characteristics in long, narrow estuaries in Maine are most sensitive to changes in tidal range (Alahmed et al., 2022 ), such as the changes in tidal range driven by spring - neap tidal cycles. Spring tides in the upper Damariscotta can increase current speeds by 20%, leading to increased turbidity (Fig. 2 b) from the primarily soft bottom consisting of a sand - silt - clay mixture (McAlice, 1977 ). The spring tides can also increase the tidal amplitude by an additional 1 m, allowing 13% more water originating from the Gulf of Maine to enter the upper Damariscotta than during neap tides. Townsend ( 1991 ) showed that nitrate was available in the western GoM when the water column was mixed but as stratification occurs, nutrients trapped in the surface layer are quickly depleted by primary producers. The time series of nitrate (Fig. 2 c) indicates increases of nitrate 4 days post peak spring tide throughout the summer and fall, but each successive increase was smaller than the previous one. This was consistent with dynamics occurring in the GoM and could indicate the source of this nitrate was from the GoM (Townsend, 1991 ). The combination of increased turbidity and increased nitrate during spring tides likely has impacts on the primary production and standing stock of chlorophyll in the growing area. Increased turbidity has been shown to induce light limitation and inhibit primary production, leading to lower levels of chlorophyll (Kirk, 1985 ; Irigoien and Castel, 1997 ; Nunes et al., 2022 ). However, once the turbidity decreases, the increase in light and nitrate allows primary production to increase (Fig. 7 ), potentially increasing chlorophyll concentrations depending on the grazing conditions. Thus spring-neap driven changes in turbidity, light, and nitrate were likely important controllers of primary production and hence 𝛀Ar in this system. Several processes may be contributing to the increase in nitrate during the large spring tide each month. The volume of water entering the growing area increases and may deliver new nitrate from the Gulf of Maine. The offshore western Gulf of Maine waters are depleted of nitrate (< 1 µM) by the beginning of May (Townsend, 1991 ; Petrie and Yeats, 2000 ) due to intense thermal stratification consistent with the Damariscotta nitrate record observed here. However, the water at the mouths of the estuaries in the western Gulf of Maine is cooler than the water offshore, indicating possible tidal mixing with deeper, more nutrient rich waters (Rebuck and Townsend, 2014 ). LOBO data from a buoy placed closer to the mouth of the river (not shown here) showed a similar increase in nitrate a few days prior to when it was observed in the growing area, indicating the nitrate may be “propagating” up the estuary. O’Connell-Milne et al. ( 2020 ) showed the importance of oceanic exchange for sustaining bivalve filter feeders in an estuary, though in their example, phytoplankton were advected into the estuary instead of locally produced with the supply of new nutrients from outside. The overall decrease in nitrate in the growing area during the summer may be due to increasing denitrification stimulated by oyster aquaculture. Other studies have shown that denitrification is enhanced when the sediments below the oyster farm/within the reef received increased organic matter from oyster deposits (Testa et al., 2015 ; Ray et al., 2020 ; Ray and Fulweiler, 2020 ). Gadeken et al. ( 2021 ) found no evidence of increased organic matter loading below the largest oyster farm in the upper Damariscotta River compared to a control site. However, their control site was located 90 m from their under-farm site which in their calculations, could still be within the footprint of the oyster deposits. It’s possible that despite the large tides, the oysters in the upper Damariscotta River Estuary are increasing organic matter loading to the sediments and stimulating denitrification, leading to a reducing the overall nitrate concentration during the oyster growing season (Fig. 2 c). Biological processes affecting 𝛀Ar Previous work on the processes that contribute to 𝛀Ar variability in estuaries have demonstrated the opposing effects of primary production and respiration (Wallace et al., 2014 ; Pacella et al., 2018 ; Rheuban et al., 2019 ; Wallace et al., 2021 ). These processes were likely important in controlling 𝛀 in this system on fortnightly time scales (Fig. 7 ) as demonstrated by the dissolved oxygen record and primary production estimates. Previous work has shown that 𝛀 in estuaries is often related to metabolism on daily time scales (Pacella et al., 2018 ; Rheuban et al., 2019 ; Tomasetti et al., 2023 ), but the role of tidal stage on carbonate chemistry in estuaries has largely been overlooked but may be important in understanding the variability of both oyster food (primary production) and 𝛀 in non-eutrophic systems. The availability of light, driven by turbidity, was likely controlling primary production and respiration on spring-neap tidal cycles in the Damariscotta River estuary. PP, DO, and n𝛀Ar were reduced during the large spring tide of the month, which is consistent with increased turbidity either causing light limitation to photosynthesis and/or increasing respiration in the water column due to resuspension of organic matter. When the tidal amplitude was the smallest during the second neap tide preceding the large spring tide, PP, DO, and n𝛀Ar increased in tandem (except for late June – mid July when PP did not closely match DO and n𝛀Ar; Fig. 7 ). Significant warming occurred in late June – mid July (4°C in 3 weeks), which decreased the solubility of both DO and pCO 2 , leading to increases in the DO saturation and n𝛀Ar depending on how quickly the gases equilibrated. This may explain the mismatch between the PP and DO/ n𝛀Ar during this time period. While n𝛀Ar was temperature corrected, that only accounts for temperature impacts on K sp , and not the impact of temperature changes on the in situ gas solubility. The largest increase in DO, PP, and n𝛀Ar during the growing season (Fig. 7 ) occurred when there was anomalously low turbidity in mid - late August (Fig. 2 b). However, the chlorophyll also decreased during this time frame (Fig. 2 d), potentially indicating intense grazing pressure and/or a reduction in chlorophyll concentrations within cells as light became more available (Eppley and Sloan, 1966). Previous work on bivalve aquaculture has shown their potential to reduce both particulate organic matter (i.e. phytoplankton, for which chlorophyll concentration is a proxy) and non-organic suspended particulate matter (i.e. turbidity) in high density areas (Tenore et al., 1982 ; Souchu et al., 2001 ; Newell, 2004 ). Oyster filtration is strongly positively tied to water temperature (Galtsoff, 1964 ), and peak temperatures (26°C) occurred in August in this estuary. The large reduction in both turbidity and chlorophyll despite high primary production (Fig. 7 ) may indicate high levels of water filtration by the well over 16 million oysters in the growing area. The upper Damariscotta River is a semi enclosed basin with low freshwater flow where residence time and particle retention are high (Adams et al., 2019 ; Newell et al., 2021 ), allowing more time for the oysters to have an impact on suspended particulate matter concentrations. Loosanoff ( 1958 ) found that 10 cm oysters filtered 9.5 liters an hour at 25°C on average. Most oysters in the growing area during August are smaller than 10 cm, thus using a conservative estimate of 5 liters an hour per oyster, the oysters filter 4.4% of the total water volume in the oyster growing area each day, or 57.2% of the oyster growing area over the 13-day residence time (Liberti et al., 2022 ), potentially accounting for the reduction in turbidity from 4 to 2 NTU. Rapid oyster shell growth in August also contributed to the reduction in TA through the uptake of calcium carbonate for shell building (Yang et al., 2021 ; Liberti et al., 2022 ; Tomasetti et al., 2023 ) as illustrated in Fig. 5 . The nTA trended upward slowly over the season until an abrupt drop coinciding with the same period of decreased turbidity and increased PP, DO, and n𝛀Ar Liberti et al. ( 2022 ) showed oyster shell growth in this system led to a drop in TA during this same time frame, although the change in TA potentially explainable by oyster shell growth was 13 µmol kg −1 while the observed drop in nTA was 44 µmol kg −1 . Despite this reduction in TA, 𝛀Ar still increased significantly during this time (Fig. 3 ). The temperature and salinity were at seasonal maximums during August and early September, leading to an additional increase in 𝛀Ar by up to 0.2 units by the first week of September (Fig. 4 ). Ultimately, the highly productive nature of late August conditions in the estuary conspired to create optimal growing conditions for oysters from both a food production and shell formation perspective. Physical-chemical drivers of 𝛀Ar variability Temperature, salinity, and salinity-induced TA changes in the growing area had the greatest effect on 𝛀 on a seasonal scale. However, there were also notable tidal-scale changes in temperature and 𝛀Ar. Temperature impacts 𝛀Ar through its influence on the apparent solubility product (Ksp), which decreases roughly 0.4% °C − 1 (Mucci, 1983 ), which could account for an average change in 𝛀Ar of 0.09 units with the observed 13.8°C change in temperature over the growing season, assuming rapid equilibration. Sudden changes in physical parameters, such as the relatively fast decrease in both temperature and salinity in September of 2018 (Fig. 4 ) can also exacerbate or ameliorate changes in 𝛀 from biological processes on weekly or fortnightly timescales (~ 2°C on average drop in temperature during spring tides). In September 2018, the 𝛀Ar decreases over 0.6 units on average from a combination of apparent high respiration or lack of production for the majority of the month (Fig. 7 ), a large decrease in temperature (5°C), and a rapid decrease in salinity toward the beginning of the month (Fig. 4 ). Despite an increase in DO in early October, the 𝛀Ar exhibits only a slight increase during an overall decrease, consistent with the continually decreasing primary productivity estimates. Decreasing temperatures may be increasing the solubility of CO 2 and DO in the water (Takahashi et al., 1993 ), and storms on 9/21/2018 and 9/26/2018 increased wind speeds to the two highest speeds of the time series, likely increasing air-sea gas exchange of both gasses (Wanninkhof, 2014 ). Oxygen equilibrates with the atmosphere approximately 28 times faster than CO 2 (Cai et al., 1999 ), thus the dissolved oxygen will increase much faster than pCO 2 will decrease given the same conditions, potentially leading to the observed mismatch in the DO and 𝛀Ar trends. Some industries, such as oyster aquaculture, have found ways to help mitigate the risk of low 𝛀Ar during the larval stage when shellfish are the most susceptible; almost all aquacultured oysters in the US are spawned in hatcheries, and some hatcheries control for both water temperature and carbonate chemistry, providing larvae with optimal growth conditions of 𝛀Ar (Barton et al., 2015 ). However, juvenile and adult oysters in open water can be hindered by low 𝛀Ca as it may slow their growth and make them more susceptible to predators such as oyster drills (Sanford et al., 2014 ) or boring sponges. Early in the season, low temperature, salinity, and TA contributed to the lowest 𝛀Ar (~ 1, 𝛀Ca ~ 2) of the season in late May. In the Damariscotta River, oysters are placed in the open water anywhere from when they are 2 mm to 20 mm long in May and June, thus, exposing them to low 𝛀 conditions during their early juvenile period. Increased real-time monitoring of temperature, salinity, turbidity, and chlorophyll would provide important information to growers as to when to set juvenile oysters in open water; if a low 𝛀Ca event can be avoided by changing the set date by several days to avoid a spring tide, the juveniles would have a less stressful transfer and a better chance of optimal growth. Increased monitoring would also aid in farm site selection not only from temperature and food resources, but also from an 𝛀Ca perspective, all of which can lead to better growth and predator resistance (Zhao et al., 2020 ). From an aquaculture business perspective, a slower growing oyster is more costly to the grower since the longer time it takes to reach market size, the less turnover and profit the grower can glean from the farm. Furthermore, other aquacultured shellfish such as mussels and scallops rely on wild spat to seed their farms (Coleman et al., 2021 ). If similar tidal dynamics are important controllers of 𝛀Ar in other systems, their survival may be impacted by what phase of the tide they are spawned in; benefitting if they are spawned immediately following a large spring tide, particularly if the spawn occurs during the early summer when 𝛀Ar is low due to low temperature and salinity. Farmers may choose to target areas with higher 𝛀Ar that have wild spat as those larvae may be in better condition than those in lower 𝛀Ar areas. With continuing climate change, the Gulf of Maine is expected to get warmer (Brickman et al., 2021 ), fresher (Siedlecki et al., 2021 ), and the 𝛀Ar is projected to decrease up to 0.5 units by 2050 (Siedlecki et al., 2021 ). To put that gulf-scale change into perspective, the upper Damariscotta River estuary experiences daily fluctuations of the same magnitude (Fig. 3 ) from tidal advection and ecosystem metabolism daily. Measuring decadal scale decreases in 𝛀 in estuaries is incredibly difficult given the low signal to noise ratio of 𝛀 due to high net ecosystem metabolism (NEM) rapid changes in salinity, and expanded temperature extremes. Understanding the processes that are impacting physical changes in the Gulf of Maine (Salisbury and Jönsson, 2018 ; Stewart et al., 2025 ) are going to be critical to forecasting changes in the ocean-dominated estuaries growing the majoring of Maine’s shellfish. Using models is the best way to account for these processes and determine an accurate forecast for a system, but to parameterize a model well, understanding how each process impacts 𝛀 is critical. As coastal communities grapple with the reality of our changing oceans and ecosystems, as well as the impact they have on the people who rely on them, improved monitoring will be essential to contextualize change and manage our coastal resources. High frequency monitoring (hourly scale or higher) will be necessary to resolve tidal forcing and shifts in ecosystem metabolism, two of the largest drivers of 𝛀 in estuaries. However, high frequency monitoring is expensive and time consuming and thus is necessarily limited in its spatial coverage. Recently, researchers have been leveraging existing community science water quality monitoring groups to collect data on many estuaries along the Gulf of Maine (Gassett et al., 2021 ; Rheuban et al., 2021 ). This approach can identify which estuaries are currently experiencing the lowest 𝛀 and focus higher frequency monitoring efforts. For estuaries which experience low 𝛀 due to increased respiration rates, reducing organic matter and nutrients entering from land may mitigate acidification (Brush et al., 2020 ; Shen et al., 2020 ; Cai et al., 2021 ). We stress the importance of high-quality covariate characterization, particularly temperature, salinity, dissolved oxygen, pH, and turbidity measured hourly, in understanding biological and physical controls on 𝛀 and encourage state and local governments to prioritize remediation, to lower nutrient and organic matter inputs where necessary, and monitoring to be resilient in a changing world. Conclusion The interplay between ecosystem metabolism, delivery of nitrogen, and oyster growth and filtration has a strong influence on the 𝛀 in the largest oyster growing area in northern New England. Internal production of phytoplankton appears to buffer seawater in the upper estuary during the critical period of peak summer temperatures and oyster feeding keeping 𝛀 relatively high compared with incoming seawater even when accounting for the temperature increase. Previous research has not highlighted the potential dependence of shellfish aquaculture on the delivery of nutrients during spring tides, which stimulates primary production, providing a food source for the shellfish, and increasing 𝛀. In this estuarine system with a long residence time, a necessary precondition for oyster culture at the northern end of the species’ range, oyster filtration may alter the surrounding ecosystem as evidenced by the reduced standing stock of chlorophyll, suspended particulate matter, and salinity normalized TA during peak filtration. Disentangling these processes within the ecosystem will be imperative to contextualize change due to increased ocean acidification and changing oceanographic conditions, and to develop strategies to best mitigate negative impacts from those changes. Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The datasets generated during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Funding This work was partially supported by the National Science Foundation award #IIA-1355457 to Maine EPSCoR and National Sea Grant (NA18OAR4170330) at the University of Maine. Brady, D.C., Mayer, L., Liberti, CM. This project was supported by the U.S. Department of Agriculture, Agricultural Research Service by NACA Agreement Number 58–8030–0-004 with the University of Maine's Aquaculture Research Institute. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture (USDA). USDA is an equal opportunity provider and employer. Liberti, C.M., Brady, D.C. This work was partially supported by the Builders Initiative. Liberti, C.M., Brady, D.C. Authors’ contributions CML collected, analyzed, and interpreted the buoy data and discreet sample data as well as drafted the manuscript. JMT, LM, and DCB contributed to the designs of the data analysis, interpretation of the results and substantially revised the manuscript. JS contributed to the designs of the data analysis, interpretation of the results and edited the manuscript. Acknowledgements We thank Kathleen Thornton for her guidance in laboratory methods and Cheyenne Adams for her management of the LOBO buoys during the study time. References Adams CM, Mayer LM, Rawson P, Brady DC, Newell C. 2019. Detrital protein contributes to oyster nutrition and growth in the Damariscotta estuary, Maine, USA. 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Testa","email":"","orcid":"","institution":"University of Maryland Center for Environmental and Estuarine Studies: University of Maryland Center for Environmental Science","correspondingAuthor":false,"prefix":"","firstName":"Jeremy","middleName":"M.","lastName":"Testa","suffix":""},{"id":477616807,"identity":"42e94910-58df-46b5-9c5e-930b2bd8769c","order_by":2,"name":"Lawrence M. Mayer","email":"","orcid":"","institution":"University of Maine System","correspondingAuthor":false,"prefix":"","firstName":"Lawrence","middleName":"M.","lastName":"Mayer","suffix":""},{"id":477616808,"identity":"a7e1c27d-0280-416e-bd3e-a00801455aef","order_by":3,"name":"Joseph E. Salisbury","email":"","orcid":"","institution":"University of New Hampshire","correspondingAuthor":false,"prefix":"","firstName":"Joseph","middleName":"E.","lastName":"Salisbury","suffix":""},{"id":477616809,"identity":"f62d9bf8-56d7-40a5-af82-102a83cf9b61","order_by":4,"name":"Damian C. Brady","email":"","orcid":"","institution":"University of Maine System","correspondingAuthor":false,"prefix":"","firstName":"Damian","middleName":"C.","lastName":"Brady","suffix":""}],"badges":[],"createdAt":"2025-06-03 18:48:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6813991/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6813991/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89986801,"identity":"c84808b8-7edb-48ab-aa85-1f5bd8cbfa4b","added_by":"auto","created_at":"2025-08-27 06:56:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":592515,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area. The Damariscotta River is a drowned river valley in the Midcoast of Maine. The oyster aquaculture area is in the upper third of the river which has a residence time of 13 days on average. The biogeochemical monitoring buoy was in the middle of the growing area for all years. The growing area is 5.5 meters deep on average, except for a deep channel at the southern end\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/19952c7f3e1aa38ec38c52b3.png"},{"id":89985515,"identity":"3a6d0636-7d40-4e90-8f60-0a8d003b00d2","added_by":"auto","created_at":"2025-08-27 06:48:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":268560,"visible":true,"origin":"","legend":"\u003cp\u003ePhysical and biogeochemical parameters measured in the growing area. Predicted tidal height for the growing area (a-e). 24 h averaged current speed (a), turbidity (b), nitrate (c, blue), and chlorophyll \u003cem\u003ea\u003c/em\u003e (d) measured at the LOBO buoy in the middle of the growing area. Two-week averaged nitrate shown in red (c). 24 h averaged 𝛀Ar (e) normalized with temperature, salinity, and TA\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/5b4af80f88c3384def29285f.png"},{"id":89985517,"identity":"f1ef788f-2513-496d-aade-cbc6c2d7c14c","added_by":"auto","created_at":"2025-08-27 06:48:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":62949,"visible":true,"origin":"","legend":"\u003cp\u003eTotal alkalinity and salinity in the Damariscotta River estuary growing area. Measured total alkalinity (red dots) during the 2018 season, with mean shown by dashed red line. Salinity (teal solid line) was measured by the LOBO buoy (hourly) and verified with an external sensor at the time of water collection. The nTA calculated with the Friis et al. (2003) equation is shown in navy blue\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/233f7f70794b94d92072322b.png"},{"id":89985518,"identity":"d9c3347d-28a5-4ac1-b3fd-e4e7c57ba14d","added_by":"auto","created_at":"2025-08-27 06:48:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":109557,"visible":true,"origin":"","legend":"\u003cp\u003eNormalization of aragonite saturation state. Temperature (a), salinity (b), 𝛀Ar and n𝛀Ar (c) of the growing area between June and October 2018\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/1bccefae00b2e1aa6634fbed.png"},{"id":89985527,"identity":"f01db023-225c-4bd1-87ea-f7e103dfb99f","added_by":"auto","created_at":"2025-08-27 06:48:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85227,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of nitrate uptake and calcification on TA. Change in nTA over the season from initial value of 2033 µmol kg\u003csup\u003e-1\u003c/sup\u003e (black line) and the estimated change in TA from NO\u003csub\u003e3\u003c/sub\u003e and oyster calcification estimated by Liberti et al. (2022) (red line) (a). The effect of NO\u003csub\u003e3\u003c/sub\u003e uptake on TA (blue line), oyster calcification estimated by Liberti et al. (2022) (red line), and the sum of the two processes (purple line) (b)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/42318f5ccb6bb0e2dccfbe52.png"},{"id":89985521,"identity":"2539eed1-5b3f-4538-a97f-9505fafc6338","added_by":"auto","created_at":"2025-08-27 06:48:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":104869,"visible":true,"origin":"","legend":"\u003cp\u003eLight attenuation coefficient and 1% light levels. Modeled and observed KD in the growing area for June – October 2018 (a). 1% light level depths calculated from modeled Kd (b)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/4466e060ca956b8807034df7.png"},{"id":89985537,"identity":"e3518bec-2669-49e2-9518-1bd8d9d4879d","added_by":"auto","created_at":"2025-08-27 06:48:14","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":106076,"visible":true,"origin":"","legend":"\u003cp\u003eDissolved oxygen, primary production, and aragonite saturation state timeseries. Average tidal amplitude (gray), dissolved oxygen (pink), primary production (teal), and n𝛀Ar (purple) from early June 2018 - early October 2018. The DO, PP, and n𝛀Ar are filtered with a 1 week, centered moving average\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/6449b2dbd035218bd417d3ba.png"},{"id":89986802,"identity":"24d15269-8428-4a69-93ca-0a580f72650b","added_by":"auto","created_at":"2025-08-27 06:56:13","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":66979,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized additive model for predicting aragonite saturation state. The results of a generalized additive model to assess the partial effects of dissolved oxygen (A), chlorophyll (B), and nitrate (C) on the daily rate of change in 𝛀Ar\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/2b06c138e1e4e3abb7b43dd9.png"},{"id":89990420,"identity":"58c4659f-f0c0-4a14-ad9a-5aca9e658c28","added_by":"auto","created_at":"2025-08-27 07:12:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2712147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6813991/v1/52f8cbdc-4f8f-4ef3-98e8-5255f56c6ee5.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eThe relationship between carbonate chemistry, estuarine metabolism, and spring-neap tidal cycles in a northern temperate estuary\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAcidification of the marine environment is threatening the success of many marine calcifiers because it makes forming their protective shell more physiologically expensive (Sabine et al., \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Orr et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Doney et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kroeker et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Waldbusser et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Gledhill et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) leads to stunted larval growth and death (Salisbury et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Waldbusser and Salisbury, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ekstrom et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and can even increase the vulnerability of future generations of shellfish to acidification through transgenerational effects (Griffith and Gobler, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Adding carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) to the ocean is acidifying the water and reducing the calcium carbonate saturation state (\u0026#120512;), the thermodynamic stability of the mineral forms of calcium carbonate, which governs how easily an organism can calcify their shell (Doney et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Waldbusser and Salisbury, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). \u0026#120512; has been identified as an important predictor of larval and juvenile shellfish growth and survival, particularly aragonite saturation state (\u0026#120512;Ar)\u0026thinsp;\u0026lt;\u0026thinsp;1.5 which can lead to slow growth, deformed shells, or larval death (Salisbury et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Ekstrom et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Waldbusser et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe processes driving ocean acidification (OA) in open ocean waters are relatively well understood and future change can be estimated with high confidence (Doney et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Siedlecki et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The pH of the world's surface ocean has decreased by 0.1 units since the start of the Industrial Revolution and is expected to further decrease by up to 0.2\u0026ndash;0.3 units by 2100 (Feely et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Doney et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Bopp et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) due to continually increasing atmospheric carbon dioxide concentrations (Keeling and Graven, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the vast majority of commercially important marine calcifiers live in the coastal regions of the world\u0026rsquo;s oceans, which can experience higher frequency and more severe changes in \u0026#120512; due to changes in temperature, salinity, exchange with adjacent waters, and other inputs of CO\u003csub\u003e2\u003c/sub\u003e. Processes such as ecosystem metabolism (Duarte and Krause-Jensen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pacella et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lowe et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wallace et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pacella et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) that are altered by eutrophication (Duarte et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wallace et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rheuban et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) can influence \u0026#120512;, as well as freshwater intrusion (Salisbury et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), coastal upwelling (Feely et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Hauri et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Hauri et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Reum et al., \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), long term changes in ambient physical conditions (Salisbury and J\u0026ouml;nsson, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stewart et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and calcification and dissolution (Cai et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rheuban et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In coastal environments, these factors can lead to increased amplitude or more frequent changes in \u0026#120512; (Harris et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Waldbusser and Salisbury, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hales et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cotovicz et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), where high carbonate variability is likely a natural feature of estuaries.\u003c/p\u003e\u003cp\u003eMany early laboratory studies of the impacts of low \u0026#120512; on marine calcifiers used static exposures over time (Gazeau et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kurihara et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Talmage and Gobler, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Hettinger et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Timmins-Schiffman et al., \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Waldbusser et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), when in the natural environment, these coastal organisms are likely experiencing high frequency variation in carbonate parameters (Hofmann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hales et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Kapsenberg et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Tomasetti et al., \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). More recently, researchers have been incorporating high frequency diel-cycling and multi-stressor components to their study designs (Frieder et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Gimenez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Onitsuka et al., \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Kapsenberg and Cyronak, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Shang et al., \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); however, there are still relatively few data on what conditions marine calcifiers are experiencing \u003cem\u003ein situ\u003c/em\u003e in a variety of ocean habitats (Hofmann et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Shaw et al., \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Fairchild and Hales, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rosenau et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Torres et al., \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), over timescales ranging from tidal, diel, fortnightly (spring- neap tidal cycles), seasonal and beyond seasonal, and interannual to decadal. Quantifying and ascribing drivers to this natural variation is critical to improving laboratory studies and ultimately our understanding of how marine calcifiers may respond to future and ongoing ocean and coastal acidification.\u003c/p\u003e\u003cp\u003eAquacultured bivalves are vulnerable to high variability in \u0026#120512; because they cannot move out of unfavorable conditions. Despite this, bivalve aquaculture has been expanding steadily worldwide and in the United States over the past 20 years (Naylor et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; NOAA Fisheries, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) due to increased demand (The Hale Group, LTD and Gulf of Maine Research Institute, 2016; Wijsman et al., \u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Naylor et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Likewise, interest in how acidification is impacting shellfish is also increasing (Doney et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Siedlecki et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), with the goal of providing projections and vulnerability assessments to support this growing and important industry. Some bivalve aquaculture relies on hatcheries to produce larvae (e.g., oysters), while others rely on wild caught larvae to seed farms (e.g., blue mussels and sea scallops). Regardless of larval production methods, all larvae can be susceptible to detrimental open water conditions with respect to carbonate saturation states. For example, Sanford et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that Olympia oysters, \u003cem\u003eOstrea lurida\u003c/em\u003e, raised in acidified waters were approximately 30\u0026ndash;40% smaller and far more likely to be eaten by oyster drills than oysters raised in ambient conditions, indicating that oysters are not only susceptible to ocean acidification during their larval phase.\u003c/p\u003e\u003cp\u003eHere, we analyze a unique high frequency \u0026#120512; dataset to understand controls on OA in a temperate estuarine aquaculture growing area over a variety of timescales. Our objective was to better constrain our understanding of OA variability to better anticipate and predict long term trends and OA effects on shellfish. The study was carried out in the Damariscotta River estuary, which has the largest oyster growing area in Northern New England, producing more than 10\u0026nbsp;million oysters annually (Maine Department of Marine Resources, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This system is an ideal location to test this question, due to vulnerability to OA as a function of relatively cold and therefore less saturated Gulf of Maine source waters, multiple drivers, and relatively large shellfish industry.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThe Damariscotta River estuary is a drowned river valley in the midcoast of Maine, USA. The estuary supports the majority (~\u0026thinsp;68%, Maine Department of Marine Resources, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) Maine\u0026rsquo;s oyster aquaculture industry in the upper reaches, has low human population density in its watershed, and low freshwater flow (1\u0026ndash;3 m3 s-1, on average; McAlice, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). The bathymetry and long, narrow shape of this estuary (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) creates a basin in the upper third of the estuary which is ​​shallow (average 5.5 m) and has a longer residence time than the rest of the estuary, leading to increased temperature and particle retention (i.e. phytoplankton and other oyster food sources; Mayer et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Adams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Newell et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jiang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe main source of water to the Damariscotta River estuary is the Gulf of Maine, a highly productive shelf sea in the Northwest Atlantic Ocean. The Gulf of Maine produces over half of the US East Coast\u0026rsquo;s value in commercial fisheries but comprises only about 10% of its area (National Marine Fisheries Service, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Due to the large seasonal temperature and productivity range in the Gulf of Maine, \u0026#120512; can vary significantly throughout the year (Wang et al., \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Siedlecki et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but are the lowest regional saturation states on the US East Coast (Wang et al., \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eA Land-Ocean Biogeochemical Observatory (LOBO) (Jannasch et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) was deployed in the oyster growing area of the Damariscotta River estuary (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) between May and October of 2018. The LOBO measured hourly temperature (\u0026deg;C), salinity, dissolved oxygen (ml l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), turbidity (NTU), chlorophyll fluorescence (\u0026micro;g L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and fluoresced Dissolved Organic Matter (fDOM, QSDE) via a Sea-Bird Scientific WQMx, hourly nitrate (\u0026micro;M N) via a Satlantic/Sea-Bird Scientific Submersible Ultraviolet Nitrate Analyzer (SUNA), hourly pH (total scale) via a Satlantic/Sea-Bird Scientific SeaFET, and hourly current speed (cm s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) using a Nortek Aquadopp. All measurements were made at a depth of 1 m below the surface where the vast majority of oysters are cultivated in the region.\u003c/p\u003e\u003cp\u003eWe measured total alkalinity (TA) in discrete samples taken bi-weekly to monthly from May\u0026ndash;September (nine samples) at the LOBO in the growing area. We used a Yellow Springs Incorporated (YSI) 6920 multiparameter sonde to measure salinity, temperature (\u0026deg;C), pH\u003csub\u003enbs\u003c/sub\u003e, dissolved oxygen (mg O\u003csub\u003e2\u003c/sub\u003e L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e ), and chlorophyll-\u003cem\u003ea\u003c/em\u003e (RFU), which was used to estimate chlorophyll concentration at the LOBO when the TA samples were collected. We measured these parameters at every half meter from the surface to the bottom, which confirmed the water column was well mixed. We calibrated the sonde the day prior to sampling using YSI solutions or recommended calibration practices. We collected three discrete TA samples using a Niskin sampler from 1 m below the surface and transferred the sample using clean flexible tubing to 60 ml borosilicate glass bottles. To avoid forming bubbles on the walls of the bottle, we placed the tube at the bottom of the bottle and inverted the bottle such that water coated all sides of the bottle. The bottle was then turned upright, and water was allowed to overflow the bottle for 10 sec, or approximately 250 ml. The bottle was capped with a ground glass stopper and less than 1% headspace was left in the bottles. All samples were stored unpreserved on ice until returned to the laboratory refrigerator before analysis. We analyzed all samples within 24 hours of collection.\u003c/p\u003e\u003cp\u003eWe analyzed the TA samples using an open cell potentiometric titration, following the methods described in \u003cem\u003eGuide to Best Practices for Ocean CO\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (Dickson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). When ready for analysis, the samples were removed from the refrigerator and kept in a 25\u0026deg;C water bath for 10 min prior to analysis. A weighed sample was placed in a jacketed beaker maintained at 25\u0026deg;C and a liquid junction pH electrode was submerged in the sample and gently stirred with a magnetic stir bar. The electrode was calibrated at the beginning of each analysis using commercially available low-ionic strength pH NBS 4, 7, and 10 buffers. Hydrochloric acid (0.1 N) was added to the sample at a rate slower than 0.05 ml sec\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e until the pH was 2.9. We used the titration data and the package SeaCarb (Gattuso et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in R (R Core Team, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to calculate TA using the Gran method. The average standard deviation for triplicate samples over the season was 15.1 \u0026micro;mol kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eWe calculated hourly estimates of aragonite saturation state with hourly pH, temperature, and salinity data from the LOBO and the biweekly TA data using the CO2SYS program (van Heuven et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The biweekly TA data was linearly interpolated to hourly values to match the time step of the temperature and salinity. We selected the K1 and K2 dissociation constants derived by Mehrbach et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) and refit by Dickson and Millero (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1987\u003c/span\u003e), the KSO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e dissociation constants by Dickson (1990), and total borate constant of Uppstr\u0026ouml;m (\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e1974\u003c/span\u003e). Although we highlight the changes in \u0026#120512;Ar specifically, the trends in calcite saturation state (\u0026#120512;Ca), are identical. We recognize that non-carbonate alkalinity may contribute to TA (Hunt et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Song et al., \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hunt et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) and lead to an overestimation of \u0026#120512;. This discussion, however, focuses on trends in \u0026#120512; over varying timescales, not the absolute value of \u0026#120512; in the estuary, and assumes that non-carbonate alkalinity does not contribute to TA significantly.\u003c/p\u003e\u003cp\u003eWe sought to tease apart the different physical and biological processes that were contributing to changes in \u0026#120512;Ar. First, we removed the influence of daily tides on the current speed, turbidity, nitrate, chlorophyll a, and \u0026#120512;Ar using a center weighted 24 h moving average to better understand how each parameter was related to the spring neap tidal cycles and \u0026#120512;Ar. The nitrate was also filtered using a center weighted two week moving average to explore the importance of spring-neap tidal dynamics in delivering nitrate to the estuary.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eSeasonal biological controls on \u0026#120512;Ar variability\u003c/h2\u003e\u003cp\u003eTo isolate the impact of biological processes on \u0026#120512;Ar, we removed the impacts of changing physical conditions on \u0026#120512;Ar by first examining the processes that impacted both TA and pH. To account for changes in TA due to changes in salinity, we normalized the measured TA to salinity using the method described in Friis et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), which regresses the measured salinity with the measured TA, using a linear best fit to find the y - intercept, and assumes the y-intercept represents the value of the freshwater end member TA, using the following equation:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:nTA=\\:\\frac{{TA}_{meas}-\\:{TA}_{s=0}}{{S}_{meas}}*{S}_{ref}+\\:{TA}_{s=0}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;1\u003c/p\u003e\u003cp\u003ewhere, TA\u003csub\u003emeas\u003c/sub\u003e was the TA measured in the growing area bi-weekly, TA\u003csub\u003es=0\u003c/sub\u003e was the assumed freshwater end member TA calculated from the regression between measured salinity and measured TA, S\u003csub\u003emeas\u003c/sub\u003e was the measured salinity, and S\u003csub\u003eref\u003c/sub\u003e was the reference salinity of 30.5 psu. We parameterized TA\u003csub\u003es=0\u003c/sub\u003e in this way because this system has very low freshwater flow originating from a variety of sources, which can lead to alteration of the salinity to alkalinity ratio between the river endmember and the study site.\u003c/p\u003e\u003cp\u003eNext, we normalized the calculated \u0026#120512;Ar by temperature and salinity, using the normalized TA to remove the effect of changing physical conditions on \u0026#120512;Ar such that we could attribute the remaining variability to biological processes. We linearly interpolated the TA values between sampling dates to match the hourly time step of the temperature and salinity. We normalized the \u0026#120512;Ar by using a constant temperature of 20\u0026deg;C, a constant salinity (30.5 psu) and TA normalized to salinity 30.5 using the method in Friis et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). We then used CO2SYS (van Heuven et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) to calculate the normalized \u0026#120512;Ar (n\u0026#120512;Ar) using constant temperature, salinity, and normalized TA. We utilized the \u0026ldquo;temperature in\u0026rdquo; (actual temperature measured) and the \u0026ldquo;temperature out\u0026rdquo; (20\u0026deg;C) feature to standardize for temperature such that the effect of temperature on pH was included in the calculation. The average seasonal temperature and salinity measured in the growing area were 19.8\u0026deg;C and 30.5 psu, respectively.\u003c/p\u003e\u003cp\u003eWe estimated the impact of seasonal changes in photosynthesis and calcification to characterize the role of biological impacts on TA. We estimated the production of TA from photosynthesis via nitrate use in the estuary by using the net drawdown of nitrate over the season, measured by the SUNA on the LOBO buoy. We used the estimate of TA drawdown by oyster shell growth of the 2018 season from Liberti et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) to account for calcification. The biological uptake of nitrate requires the excretion of another negative ion to maintain equilibrium within the cell. In this case, OH\u003csup\u003e\u0026minus;\u003c/sup\u003e is produced, which contributes to the total alkalinity, thus when nitrate is used by organisms, the total alkalinity in the surrounding water increases (Lejart et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Oysters reduce the total alkalinity of the surrounding water by removing bi-carbonate when building their shells (Lejart et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liberti et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEstimating light attenuation and primary productivity\u003c/h3\u003e\n\u003cp\u003eTo better understand how biological processes can impact \u0026#120512;Ar, we first modeled light attenuation using \u003cem\u003ein situ\u003c/em\u003e parameters from the buoy, then estimated primary production using the chlorophyll \u003cem\u003ea\u003c/em\u003e concentration, light attenuation, and surface PAR. The light attenuation was estimated using a model developed by Ganju et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Their model was built off a method developed by Gallegos et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The model estimates spectral attenuation caused by both suspended and dissolved constituents, including water, CDOM, phytoplankton, and turbidity. We include absorption by: (1) we assumed absorption by water followed the spectral characteristics of pure water; (2) CDOM absorption was estimated from a ratio of measured CDOM absorption to fDOM concentration. Four CDOM samples were collected at the LOBO buoy in July and August of 2018 and the attenuation coefficient was calculated using the equation from Oestreich et al. (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Absorbance measurements were recorded in 0.5 nm increments over the span of 340\u0026ndash;440 nm. The average ratio was then used to convert the fDOM measurements (QSDE) to CDOM absorption (1 m\u003csup\u003e\u0026minus;1\u003c/sup\u003e) for use in the model. The spectral slope was also calculated (Oestreich et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) for each of the CDOM samples and the average was used as a parameter in the model; (3) phytoplankton absorption was proportional to chlorophyll \u003cem\u003ea\u003c/em\u003e concentration and the absorption peak at 675 nm (initial value for peak absorption was taken as aψ, 675\u0026thinsp;=\u0026thinsp;0.0235 m\u003csup\u003e2\u003c/sup\u003e (mg chl \u003cem\u003ea\u003c/em\u003e)\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, within the range provided by Bricaud et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) was used to normalize the spectral shape; and lastly (4) non-algal absorption was assumed to be proportional to the suspended sediment concentration (turbidity) with a spectral shape (Bowers and Binding, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) that included a baseline of cx1\u0026thinsp;=\u0026thinsp;0.0024 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e\u0026minus;1\u003c/sup\u003e, cx2\u0026thinsp;=\u0026thinsp;0.04 m\u003csup\u003e2\u003c/sup\u003e g\u003csup\u003e\u0026minus;1\u003c/sup\u003e was set as the absorption cross section (Bowers and Binding, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and sx\u0026thinsp;=\u0026thinsp;0.009 nm\u003csup\u003e\u0026minus;1\u003c/sup\u003e was used as the spectral slope (Boss et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The backscattering ratio of water was set at 0.5, while CDOM was considered non-scattering (Mobley and Stramski, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), and the particulate effective backscattering ratio bbx was initially set at 0.017. The PAR used in the model was converted from PAR measured in air to PAR just below the surface skin using the methods in Mobley and Boss (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For all parameters, only measurements between 10am \u0026ndash; 3pm (6 hourly measurements) were used to create a daily average from which the light attenuation coefficient (Kd) was modeled.\u003c/p\u003e\u003cp\u003eKd was compared to the observed light attenuation coefficient (Kd\u003csub\u003eobs\u003c/sub\u003e) calculated from two \u003cem\u003ein situ\u003c/em\u003e PAR sensors on the LOBO. The PAR in air measurements were collected using a Sea-Bird PAR sensor and converted to PAR just below surface as noted above. The underwater PAR measurements were collected with a Sea-Bird ECO PAR sensor that was mounted on an arm that was 0.43 m below the surface and 1 m horizontally away from the buoy float to minimize shading. Kd\u003csub\u003eobs\u003c/sub\u003e was calculated using the following equation:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Kd}_{obs}=\\:-\\frac{1}{dz}\\text{l}\\text{n}\\left(\\frac{{PAR}_{lower}}{{PAR}_{upper}}\\right)\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;2\u003c/p\u003e\u003cp\u003ewhere dz was the distance between the sensors (in meters), PAR\u003csub\u003elower\u003c/sub\u003e was the PAR measured 0.43 meters below the surface, and PAR\u003csub\u003eupper\u003c/sub\u003e was the PAR calculated from the in-air PAR sensor converted to be just below the surface of the water. Kd\u003csub\u003eobs\u003c/sub\u003e was also only calculated from the daily averaged PAR values for the hours of 10am \u0026ndash; 3pm. Any negative values of Kd\u003csub\u003eobs\u003c/sub\u003e were removed from the results and the average was compared to the average modeled Kd to verify the model was accurately representing Kd in the growing area.\u003c/p\u003e\u003cp\u003eThe modeled Kd was used to calculate the 1% light level using the equation:\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:1\\%LL=\\:\\frac{Log\\left(0.01\\right)}{-Kd}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;3\u003c/p\u003e\u003cp\u003eFinally, the primary production was estimated using the following equation from Cloern (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1987\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:PP=B{Z}_{p}{I}_{o}\\)\u003c/span\u003e\u003c/span\u003e Eq.\u0026nbsp;4\u003c/p\u003e\u003cp\u003eWhere PP was primary production in g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e d\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, B was the estimate of biomass from chlorophyll a concentration (g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), Z\u003csub\u003ep\u003c/sub\u003e was the 1% light level (m), and I\u003csub\u003eo\u003c/sub\u003e was the surface irradiance (here the PAR \u003csub\u003eupper\u003c/sub\u003e in Eq.\u0026nbsp;2).\u003c/p\u003e\n\u003ch3\u003eGeneralized additive model\u003c/h3\u003e\n\u003cp\u003eWe developed a generalized additive model (GAM) to assess the coupled contribution of each parameter on weekly - monthly changes in n\u0026#120512;Ar. GAMs are generalized linear models that can use smoothing functions to model non-linear trends, which are often observed in ecosystems (Zuur et al., \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Wood, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We assessed the coupled effects of DO, chlorophyll, turbidity, and nitrate on n\u0026#120512;Ar. To remove diel and tidal advection from the parameters, a 1 week, centered moving average was applied to the hourly data before a daily average was calculated and used in the GAM. The R (R Core Team, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) package \u0026ldquo;mgcv\u0026rdquo; developed by Wood (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), with the function \u0026ldquo;gam\u0026rdquo; used to develop the model.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTo better understand the interactions between biogeochemical controls on sub-seasonal variability and the strong tidal variability in the Damariscotta River estuary, we examined high-frequency LOBO data over the growing season. The growing area has semi-diurnal tides that feature a spring-neap tidal cycle. The growing area is characterized by one large (new moon) and one small spring tide (full moon), and two neap tides of similar magnitude each month (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe current speed was strongly tied to tidal amplitude with an 8 cm s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e increase in 24 hour averaged current speed corresponding with the large spring tide each month (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The turbidity in the growing area was also strongly tied to tidal height and current speed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The turbidity increases approximately 50% during the largest spring tide of the month, from 3.69 NTU on average over 24 hours to 4.93 NTU on average. Nitrate exhibited a small increase of 0.5\u0026ndash;1 \u0026micro;M each month 5 days on average after the peak of the large spring tide (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Seasonally, nitrate decreased from 4.0 \u0026micro;M over the summer season until early September when it reached its lowest at 0.2 \u0026micro;M, then it began increasing again and reached 3.3 \u0026micro;M by early October. The monthly increases in nitrate become more apparent when compared to the two-week average nitrate where spring neap cycle dynamics have been removed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Chlorophyll-\u003cem\u003ea\u003c/em\u003e increases until early August then decreases to early October, except during the neap tides when a small increase occurs, particularly the neap tide immediately after the peak in nitrate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The small increases during the neap tides average 1 \u0026micro;g l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The 24-hour averaged \u0026#120512;Ar exhibits peaks during the neap tides of the month, increasing from 1.61 on average, to 1.84 on average between mid-June and early October (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), with a prolonged increase between mid-August and early September, when 24 h averaged \u0026#120512;Ar reached its seasonal maximum of 2.15.\u003c/p\u003e\n\u003ch3\u003eQuantifying the changes in 𝛀Ar from physical, chemical, and biological processes\u003c/h3\u003e\n\u003cp\u003eThe changes in \u0026#120512;Ar are the summation of changes from biological, chemical, and physical processes. Previous work has shown that \u0026#120512;Ar in estuaries is largely driven by biological and physical processes (Pacella et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lowe et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Rheuban et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wallace et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), thus, to isolate the impacts of just the biological processes, we removed the impact of changing physical-chemical conditions on \u0026#120512;Ar. We first characterized the change in TA caused by salinity. The measured TA of the growing area ranged from 1,912 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in May increasing to a high of 2,129 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in September (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The salinity ranged from 27.6 in May and increased to a high of 31.6 in September. The nTA was highest (2,059 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e) in early July, decreased to a low of 2,000 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e in mid-August, and rose again to 2,043 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e at the end of September. For all triplicate samples of TA, the average standard deviation was \u0026plusmn;\u0026thinsp;15.1 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTemperature and salinity effects on\u003c/b\u003e \u0026#120512;\u003cb\u003eAr\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe 24 h averaged temperature of the growing area started at 16.5\u0026deg;C in early June, rose to a maximum of 24.3\u0026deg;C in August, and declined to 15.6\u0026deg;C in early October, with two periods of rapid warming and cooling in early July and early September (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The temperature of the growing area dropped approximately 2\u0026deg;C during each large spring tide and rose again during the neap and smaller spring tides. The 24 h averaged salinity increased gradually over the season, starting at 29.2 in early June, reaching a maximum of 31.3 in mid-September and ending the season at 30.6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSalinity decreased in early July from rainfall and again in mid-September. We normalized the \u0026#120512;Ar to constant temperature, salinity, and nTA such that the resulting n\u0026#120512;Ar had all impacts from changing physical conditions removed (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). In June, the temperature \u003cem\u003ein situ\u003c/em\u003e was lower than 20\u0026deg;C and the salinity was lower than 30.5, thus the n\u0026#120512;Ar was higher than \u0026#120512;Ar. In early and mid-July, the \u003cem\u003ein situ\u003c/em\u003e temperature was 1\u0026ndash;2\u0026deg;C higher than 20\u0026deg;C, raising n\u0026#120512;Ar, but the salinity was slightly less than 30.5, lowering n\u0026#120512;Ar, and the resulting n\u0026#120512;Ar was nearly the same as \u0026#120512;Ar. After July 1st, the salinity remained above 30.5, resulting in n\u0026#120512;Ar lower than \u0026#120512;Ar for the remainder of the growing season. In late July, August, and early September, the temperature was warmer than 20\u0026deg;C also leading to a reduced n\u0026#120512;Ar. Overall, the n\u0026#120512;Ar was higher than \u0026#120512;Ar in June, the same in early July, and lower than \u0026#120512;Ar the rest of the season.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eCalcification and nitrate cycling effects on total alkalinity\u003c/h2\u003e\u003cp\u003eCalcification by oysters can decrease \u0026#120512;Ar through a reduction in TA (Liberti et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and uptake of nitrate by primary producers can increase \u0026#120512;Ar on seasonal timescales. The effect of oyster calcification on nTA was likely higher than the impact from nitrate usage in the estuary, but both contributed to seasonal variation in TA (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The nTA increased 25 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e between late May and mid-July, when it began to decrease, slowly at first and then sharply for an overall decrease of 52 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e by mid-August (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The nTA then increased again though early October. The net change from NO\u003csub\u003e3\u003c/sub\u003e uptake and oyster growth as modeled in Liberti et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) was negligible until early July when TA was removed from the water from an acceleration of oyster shell growth in the upper Damariscotta River at peak temperatures, negating the impact of nitrate uptake which increased the TA over the season (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eLight model and primary production\u003c/h3\u003e\n\u003cp\u003eThe CDOM absorption coefficients ranged from 1.34 to 4.40 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with a mean of 2.58 m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e while the corresponding fDOM concentrations ranged from 4.53 to 5.40 with a mean of 4.87 (QSDE). The CDOM:fDOM ratios ranged from 0.28 to 0.82 with an average of 0.52, this value was used to convert measured fDOM concentrations to CDOM absorbances. The spectral slopes of the CDOM absorbance ranged from 0.01 to 0.03 with an average of 0.02. Kd had an average of 0.76 with a standard deviation of 0.08 while Kd\u003csub\u003eobs\u003c/sub\u003e had a mean of 0.73 and a standard deviation of 0.51. Of the 115 daily observations of Kd\u003csub\u003eobs\u003c/sub\u003e, 30 were removed due to negative values. Kd had relatively higher values July through mid-August, and lower values in June and mid-August \u0026ndash; early October (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The 1% light level depths had an inverse trend as expected. The 1% light level depths increased in mid-August (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) concurrent with the large reduction in turbidity and chlorophyll (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb,d). This was also observed in the 1% light level calculated from Kd\u003csub\u003eobs\u003c/sub\u003e (not shown).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe primary production (PP), dissolved oxygen, and n\u0026#120512;Ar co-varied on weekly timescales (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) and were related to tidal stage except for late June through mid-July when PP was decoupled from DO and n\u0026#120512;Ar trends. Between June and October, the PP, DO, and n\u0026#120512;Ar exhibited large drops during the large spring tide. The DO and PP increased after the large spring tide each month, while the n\u0026#120512;Ar stayed relatively static in July, and increased in step with DO and PP in August, resulting in the largest peaks in DO and n\u0026#120512;Ar and second largest peak in PP during the smaller amplitude tides.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eGeneralized additive modeling\u003c/h3\u003e\n\u003cp\u003eTo better understand the relationships between n\u0026#120512;Ar and the other parameters influenced by biological processes, we used a generalized linear model to assess the multivariate effects (Fig.\u0026nbsp;3.9). The model considered the partial effects of chlorophyll, turbidity, nitrate, and DO on n\u0026#120512;Ar. The combination of DO, chlorophyll, and nitrate provided the best predictions of n\u0026#120512;Ar. DO was nearly linearly related with n\u0026#120512;Ar, as DO varied from 81 to 104% saturation, the n\u0026#120512;Ar increased from 1.47 to 1.91 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). Variations in DO explained 63.7% of the variance in n\u0026#120512;Ar. Chlorophyll between 2.5 and 4.5 \u0026micro;g l\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were associated with n\u0026#120512;Ar between 1.63 and 1.68 on average, while chlorophyll below or above that range was associated with lower n\u0026#120512;Ar, between 1.55 and 1.63 (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Nitrate was negatively correlated with n\u0026#120512;Ar, when nitrate was less than 1.5 \u0026micro;M, n\u0026#120512;Ar was between 1.68 and 1.85. Changes in nitrate between 1.5 and 2.5 \u0026micro;M had little effect on n\u0026#120512;Ar, while nitrates above 2.5 were associated with decreasing n\u0026#120512;Ar to a low of 1.56 (Fig. \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe trends in \u0026#120512;Ar in the Damariscotta River are the result of a complex interplay between biological, chemical, and physical processes. Flow characteristics in long, narrow estuaries in Maine are most sensitive to changes in tidal range (Alahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), such as the changes in tidal range driven by spring - neap tidal cycles. Spring tides in the upper Damariscotta can increase current speeds by 20%, leading to increased turbidity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) from the primarily soft bottom consisting of a sand - silt - clay mixture (McAlice, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e1977\u003c/span\u003e). The spring tides can also increase the tidal amplitude by an additional 1 m, allowing 13% more water originating from the Gulf of Maine to enter the upper Damariscotta than during neap tides. Townsend (\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) showed that nitrate was available in the western GoM when the water column was mixed but as stratification occurs, nutrients trapped in the surface layer are quickly depleted by primary producers. The time series of nitrate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) indicates increases of nitrate 4 days post peak spring tide throughout the summer and fall, but each successive increase was smaller than the previous one. This was consistent with dynamics occurring in the GoM and could indicate the source of this nitrate was from the GoM (Townsend, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). The combination of increased turbidity and increased nitrate during spring tides likely has impacts on the primary production and standing stock of chlorophyll in the growing area. Increased turbidity has been shown to induce light limitation and inhibit primary production, leading to lower levels of chlorophyll (Kirk, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Irigoien and Castel, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Nunes et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, once the turbidity decreases, the increase in light and nitrate allows primary production to increase (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), potentially increasing chlorophyll concentrations depending on the grazing conditions. Thus spring-neap driven changes in turbidity, light, and nitrate were likely important controllers of primary production and hence \u0026#120512;Ar in this system.\u003c/p\u003e\u003cp\u003eSeveral processes may be contributing to the increase in nitrate during the large spring tide each month. The volume of water entering the growing area increases and may deliver new nitrate from the Gulf of Maine. The offshore western Gulf of Maine waters are depleted of nitrate (\u0026lt;\u0026thinsp;1 \u0026micro;M) by the beginning of May (Townsend, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Petrie and Yeats, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) due to intense thermal stratification consistent with the Damariscotta nitrate record observed here. However, the water at the mouths of the estuaries in the western Gulf of Maine is cooler than the water offshore, indicating possible tidal mixing with deeper, more nutrient rich waters (Rebuck and Townsend, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). LOBO data from a buoy placed closer to the mouth of the river (not shown here) showed a similar increase in nitrate a few days prior to when it was observed in the growing area, indicating the nitrate may be \u0026ldquo;propagating\u0026rdquo; up the estuary. O\u0026rsquo;Connell-Milne et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed the importance of oceanic exchange for sustaining bivalve filter feeders in an estuary, though in their example, phytoplankton were advected into the estuary instead of locally produced with the supply of new nutrients from outside.\u003c/p\u003e\u003cp\u003eThe overall decrease in nitrate in the growing area during the summer may be due to increasing denitrification stimulated by oyster aquaculture. Other studies have shown that denitrification is enhanced when the sediments below the oyster farm/within the reef received increased organic matter from oyster deposits (Testa et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ray et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ray and Fulweiler, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Gadeken et al. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found no evidence of increased organic matter loading below the largest oyster farm in the upper Damariscotta River compared to a control site. However, their control site was located 90 m from their under-farm site which in their calculations, could still be within the footprint of the oyster deposits. It\u0026rsquo;s possible that despite the large tides, the oysters in the upper Damariscotta River Estuary are increasing organic matter loading to the sediments and stimulating denitrification, leading to a reducing the overall nitrate concentration during the oyster growing season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBiological processes affecting\u003c/b\u003e \u0026#120512;Ar\u003c/p\u003e\u003cp\u003ePrevious work on the processes that contribute to \u0026#120512;Ar variability in estuaries have demonstrated the opposing effects of primary production and respiration (Wallace et al., \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pacella et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rheuban et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Wallace et al., \u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These processes were likely important in controlling \u0026#120512; in this system on fortnightly time scales (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) as demonstrated by the dissolved oxygen record and primary production estimates. Previous work has shown that \u0026#120512; in estuaries is often related to metabolism on daily time scales (Pacella et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rheuban et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tomasetti et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), but the role of tidal stage on carbonate chemistry in estuaries has largely been overlooked but may be important in understanding the variability of both oyster food (primary production) and \u0026#120512; in non-eutrophic systems. The availability of light, driven by turbidity, was likely controlling primary production and respiration on spring-neap tidal cycles in the Damariscotta River estuary. PP, DO, and n\u0026#120512;Ar were reduced during the large spring tide of the month, which is consistent with increased turbidity either causing light limitation to photosynthesis and/or increasing respiration in the water column due to resuspension of organic matter. When the tidal amplitude was the smallest during the second neap tide preceding the large spring tide, PP, DO, and n\u0026#120512;Ar increased in tandem (except for late June \u0026ndash; mid July when PP did not closely match DO and n\u0026#120512;Ar; Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Significant warming occurred in late June \u0026ndash; mid July (4\u0026deg;C in 3 weeks), which decreased the solubility of both DO and pCO\u003csub\u003e2\u003c/sub\u003e, leading to increases in the DO saturation and n\u0026#120512;Ar depending on how quickly the gases equilibrated. This may explain the mismatch between the PP and DO/ n\u0026#120512;Ar during this time period. While n\u0026#120512;Ar was temperature corrected, that only accounts for temperature impacts on K\u003csub\u003esp\u003c/sub\u003e, and not the impact of temperature changes on the \u003cem\u003ein situ\u003c/em\u003e gas solubility.\u003c/p\u003e\u003cp\u003eThe largest increase in DO, PP, and n\u0026#120512;Ar during the growing season (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) occurred when there was anomalously low turbidity in mid - late August (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). However, the chlorophyll also decreased during this time frame (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), potentially indicating intense grazing pressure and/or a reduction in chlorophyll concentrations within cells as light became more available (Eppley and Sloan, 1966). Previous work on bivalve aquaculture has shown their potential to reduce both particulate organic matter (i.e. phytoplankton, for which chlorophyll concentration is a proxy) and non-organic suspended particulate matter (i.e. turbidity) in high density areas (Tenore et al., \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e1982\u003c/span\u003e; Souchu et al., \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Newell, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Oyster filtration is strongly positively tied to water temperature (Galtsoff, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), and peak temperatures (26\u0026deg;C) occurred in August in this estuary. The large reduction in both turbidity and chlorophyll despite high primary production (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) may indicate high levels of water filtration by the well over 16\u0026nbsp;million oysters in the growing area. The upper Damariscotta River is a semi enclosed basin with low freshwater flow where residence time and particle retention are high (Adams et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Newell et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), allowing more time for the oysters to have an impact on suspended particulate matter concentrations. Loosanoff (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1958\u003c/span\u003e) found that 10 cm oysters filtered 9.5 liters an hour at 25\u0026deg;C on average. Most oysters in the growing area during August are smaller than 10 cm, thus using a conservative estimate of 5 liters an hour per oyster, the oysters filter 4.4% of the total water volume in the oyster growing area each day, or 57.2% of the oyster growing area over the 13-day residence time (Liberti et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), potentially accounting for the reduction in turbidity from 4 to 2 NTU.\u003c/p\u003e\u003cp\u003eRapid oyster shell growth in August also contributed to the reduction in TA through the uptake of calcium carbonate for shell building (Yang et al., \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liberti et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tomasetti et al., \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The nTA trended upward slowly over the season until an abrupt drop coinciding with the same period of decreased turbidity and increased PP, DO, and n\u0026#120512;Ar Liberti et al. (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) showed oyster shell growth in this system led to a drop in TA during this same time frame, although the change in TA potentially explainable by oyster shell growth was 13 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e while the observed drop in nTA was 44 \u0026micro;mol kg\u003csup\u003e\u0026minus;1\u003c/sup\u003e. Despite this reduction in TA, \u0026#120512;Ar still increased significantly during this time (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The temperature and salinity were at seasonal maximums during August and early September, leading to an additional increase in \u0026#120512;Ar by up to 0.2 units by the first week of September (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Ultimately, the highly productive nature of late August conditions in the estuary conspired to create optimal growing conditions for oysters from both a food production and shell formation perspective.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003ePhysical-chemical drivers of \u0026#120512;Ar variability\u003c/h2\u003e\u003cp\u003eTemperature, salinity, and salinity-induced TA changes in the growing area had the greatest effect on \u0026#120512; on a seasonal scale. However, there were also notable tidal-scale changes in temperature and \u0026#120512;Ar. Temperature impacts \u0026#120512;Ar through its influence on the apparent solubility product (Ksp), which decreases roughly 0.4% \u0026deg;C\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Mucci, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e1983\u003c/span\u003e), which could account for an average change in \u0026#120512;Ar of 0.09 units with the observed 13.8\u0026deg;C change in temperature over the growing season, assuming rapid equilibration. Sudden changes in physical parameters, such as the relatively fast decrease in both temperature and salinity in September of 2018 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) can also exacerbate or ameliorate changes in \u0026#120512; from biological processes on weekly or fortnightly timescales (~\u0026thinsp;2\u0026deg;C on average drop in temperature during spring tides). In September 2018, the \u0026#120512;Ar decreases over 0.6 units on average from a combination of apparent high respiration or lack of production for the majority of the month (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), a large decrease in temperature (5\u0026deg;C), and a rapid decrease in salinity toward the beginning of the month (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Despite an increase in DO in early October, the \u0026#120512;Ar exhibits only a slight increase during an overall decrease, consistent with the continually decreasing primary productivity estimates. Decreasing temperatures may be increasing the solubility of CO\u003csub\u003e2\u003c/sub\u003e and DO in the water (Takahashi et al., \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), and storms on 9/21/2018 and 9/26/2018 increased wind speeds to the two highest speeds of the time series, likely increasing air-sea gas exchange of both gasses (Wanninkhof, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Oxygen equilibrates with the atmosphere approximately 28 times faster than CO\u003csub\u003e2\u003c/sub\u003e (Cai et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), thus the dissolved oxygen will increase much faster than pCO\u003csub\u003e2\u003c/sub\u003e will decrease given the same conditions, potentially leading to the observed mismatch in the DO and \u0026#120512;Ar trends.\u003c/p\u003e\u003cp\u003eSome industries, such as oyster aquaculture, have found ways to help mitigate the risk of low \u0026#120512;Ar during the larval stage when shellfish are the most susceptible; almost all aquacultured oysters in the US are spawned in hatcheries, and some hatcheries control for both water temperature and carbonate chemistry, providing larvae with optimal growth conditions of \u0026#120512;Ar (Barton et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, juvenile and adult oysters in open water can be hindered by low \u0026#120512;Ca as it may slow their growth and make them more susceptible to predators such as oyster drills (Sanford et al., \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) or boring sponges. Early in the season, low temperature, salinity, and TA contributed to the lowest \u0026#120512;Ar (~\u0026thinsp;1, \u0026#120512;Ca\u0026thinsp;~\u0026thinsp;2) of the season in late May. In the Damariscotta River, oysters are placed in the open water anywhere from when they are 2 mm to 20 mm long in May and June, thus, exposing them to low \u0026#120512; conditions during their early juvenile period. Increased real-time monitoring of temperature, salinity, turbidity, and chlorophyll would provide important information to growers as to when to set juvenile oysters in open water; if a low \u0026#120512;Ca event can be avoided by changing the set date by several days to avoid a spring tide, the juveniles would have a less stressful transfer and a better chance of optimal growth. Increased monitoring would also aid in farm site selection not only from temperature and food resources, but also from an \u0026#120512;Ca perspective, all of which can lead to better growth and predator resistance (Zhao et al., \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From an aquaculture business perspective, a slower growing oyster is more costly to the grower since the longer time it takes to reach market size, the less turnover and profit the grower can glean from the farm. Furthermore, other aquacultured shellfish such as mussels and scallops rely on wild spat to seed their farms (Coleman et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). If similar tidal dynamics are important controllers of \u0026#120512;Ar in other systems, their survival may be impacted by what phase of the tide they are spawned in; benefitting if they are spawned immediately following a large spring tide, particularly if the spawn occurs during the early summer when \u0026#120512;Ar is low due to low temperature and salinity. Farmers may choose to target areas with higher \u0026#120512;Ar that have wild spat as those larvae may be in better condition than those in lower \u0026#120512;Ar areas.\u003c/p\u003e\u003cp\u003eWith continuing climate change, the Gulf of Maine is expected to get warmer (Brickman et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), fresher (Siedlecki et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the \u0026#120512;Ar is projected to decrease up to 0.5 units by 2050 (Siedlecki et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To put that gulf-scale change into perspective, the upper Damariscotta River estuary experiences daily fluctuations of the same magnitude (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) from tidal advection and ecosystem metabolism daily. Measuring decadal scale decreases in \u0026#120512; in estuaries is incredibly difficult given the low signal to noise ratio of \u0026#120512; due to high net ecosystem metabolism (NEM) rapid changes in salinity, and expanded temperature extremes. Understanding the processes that are impacting physical changes in the Gulf of Maine (Salisbury and J\u0026ouml;nsson, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Stewart et al., \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) are going to be critical to forecasting changes in the ocean-dominated estuaries growing the majoring of Maine\u0026rsquo;s shellfish. Using models is the best way to account for these processes and determine an accurate forecast for a system, but to parameterize a model well, understanding how each process impacts \u0026#120512; is critical.\u003c/p\u003e\u003cp\u003eAs coastal communities grapple with the reality of our changing oceans and ecosystems, as well as the impact they have on the people who rely on them, improved monitoring will be essential to contextualize change and manage our coastal resources. High frequency monitoring (hourly scale or higher) will be necessary to resolve tidal forcing and shifts in ecosystem metabolism, two of the largest drivers of \u0026#120512; in estuaries. However, high frequency monitoring is expensive and time consuming and thus is necessarily limited in its spatial coverage. Recently, researchers have been leveraging existing community science water quality monitoring groups to collect data on many estuaries along the Gulf of Maine (Gassett et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Rheuban et al., \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This approach can identify which estuaries are currently experiencing the lowest \u0026#120512; and focus higher frequency monitoring efforts. For estuaries which experience low \u0026#120512; due to increased respiration rates, reducing organic matter and nutrients entering from land may mitigate acidification (Brush et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shen et al., \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). We stress the importance of high-quality covariate characterization, particularly temperature, salinity, dissolved oxygen, pH, and turbidity measured hourly, in understanding biological and physical controls on \u0026#120512; and encourage state and local governments to prioritize remediation, to lower nutrient and organic matter inputs where necessary, and monitoring to be resilient in a changing world.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe interplay between ecosystem metabolism, delivery of nitrogen, and oyster growth and filtration has a strong influence on the \u0026#120512; in the largest oyster growing area in northern New England. Internal production of phytoplankton appears to buffer seawater in the upper estuary during the critical period of peak summer temperatures and oyster feeding keeping \u0026#120512; relatively high compared with incoming seawater even when accounting for the temperature increase. Previous research has not highlighted the potential dependence of shellfish aquaculture on the delivery of nutrients during spring tides, which stimulates primary production, providing a food source for the shellfish, and increasing \u0026#120512;. In this estuarine system with a long residence time, a necessary precondition for oyster culture at the northern end of the species\u0026rsquo; range, oyster filtration may alter the surrounding ecosystem as evidenced by the reduced standing stock of chlorophyll, suspended particulate matter, and salinity normalized TA during peak filtration. Disentangling these processes within the ecosystem will be imperative to contextualize change due to increased ocean acidification and changing oceanographic conditions, and to develop strategies to best mitigate negative impacts from those changes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe datasets generated during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe authors declare that they have no competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the National Science Foundation award #IIA-1355457 to Maine EPSCoR and National Sea Grant (NA18OAR4170330) at the University of Maine. Brady, D.C., Mayer, L., Liberti, CM.\u003c/p\u003e\n\u003cp\u003eThis project was supported by the\u0026nbsp;U.S. Department of Agriculture, Agricultural Research Service\u0026nbsp;by NACA Agreement Number 58–8030–0-004 with the\u0026nbsp;University of Maine's Aquaculture Research Institute. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the U.S. Department of Agriculture (USDA). USDA is an equal opportunity provider and employer. Liberti, C.M., Brady, D.C.\u003c/p\u003e\n\u003cp\u003eThis work was partially supported by the Builders Initiative.\u0026nbsp;Liberti, C.M., Brady, D.C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCML collected, analyzed, and interpreted the buoy data and discreet sample data as well as drafted the manuscript. JMT, LM, and DCB contributed to the designs of the data analysis, interpretation of the results and substantially revised the manuscript. JS contributed to the designs of the data analysis, interpretation of the results and edited the manuscript.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Kathleen Thornton for her guidance in laboratory methods and Cheyenne Adams for her management of the LOBO buoys during the study time.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdams CM, Mayer LM, Rawson P, Brady DC, Newell C. 2019. 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Springer.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6813991/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6813991/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany estuaries are highly productive areas for shellfish aquaculture while also subject to low alkalinity and low aragonite saturation state (\u0026#120512;Ar) from both offshore and freshwater. Due to the influence and interaction of these source water conditions and the biological processes that occur within the estuary, \u0026#120512;Ar can be highly variable. To better understand how \u0026#120512;Ar changes from daily to seasonal time scales within estuaries, we described high frequency changes in aragonite saturation state in the largest oyster growing region in northern New England, the Damariscotta River estuary, Maine, in 2018 using hourly buoy data and discrete samples. \u0026#120512;Ar ranged from 1 to 2.5 between late May and early October with daily ranges frequently exceeding 0.5. \u0026#120512;Ar was predominantly controlled by temperature and salinity at the seasonal scale but driven by ecosystem metabolism on daily - bi-weekly time scales. The prominent feature of this system was the importance of spring-neap tidal cycles, with spring tides increasing turbidity, nitrate, and respiration, and decreasing primary production, dissolved oxygen, and \u0026#120512;Ar. Here, we disentangle the strong interconnection between estuary morphology, tides, ecosystem metabolism, and \u0026#120512;Ar in an important oyster growing area with implications for the timing of seeding, site selection, water quality management, and analyzing future acidification scenarios in estuaries that share similar oceanographic conditions.\u003c/p\u003e","manuscriptTitle":"The relationship between carbonate chemistry, estuarine metabolism, and spring-neap tidal cycles in a northern temperate estuary","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 06:48:09","doi":"10.21203/rs.3.rs-6813991/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-06-28T15:18:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-28T04:27:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2025-06-06T19:44:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T04:13:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2025-06-03T14:47:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"269f3fe3-4ac4-498b-bfc6-502815ad386b","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T06:48:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 06:48:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6813991","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6813991","identity":"rs-6813991","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}