Salinity indicators in sediment through the fluvial-to-marine transition (Fraser River, Canada)

Salinity indicators in sediment through the fluvial-to-marine transition (Fraser River, Canada) | 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 Article Salinity indicators in sediment through the fluvial-to-marine transition (Fraser River, Canada) Shahin E. Dashtgard, Aihua Wang, Vera Pospelova, Pei-Ling Wang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1694129/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Many sediment attributes have been proposed as proxies for determining salinity conditions under which sediment is deposited, and six attributes (Sr/Ba-HAc, Sr/Ba-NH 4 Ac, δ 13 C org , C/N, and the relative abundances and concentrations of dinoflagellate cysts) are compared here. In this paper, sediment attributes from the Fraser River Delta, Canada and surrounding coastal areas are compared by depositional position along the fluvial-to-marine transition, by sea-surface salinity, and by sedimentological characteristics. Along the fluvial-to-marine transition, most attributes exhibit distinct trends between parts of the river that experience sustained marine water (saltwater) influence over seasonal and tidal timeframes, and parts that experience only freshwater or periodic saltwater influence. No attributes are reliable indicators of depositional position where saltwater incursion is short lived or where water is fresh. Where marine influence is sustained, Sr/Ba-HAc and Sr/Ba-NH 4 Ac are the most reliable positional indicators along the fluvial-to-marine transition. When compared strictly to salinity, Sr/Ba-HAc, Sr/Ba-NH 4 Ac, and δ 13 C org all correlate predictably except in delta front and prodelta settings. Our data show that all six sediment attributes are heavily impacted by river-derived sedimentation, and it is not appropriate to compare values from strongly river-influenced settings (e.g., deltas) with those from weakly river-influenced settings (e.g., bays and estuaries). Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The salinity of water under which sediment is deposited significantly influences the character of the sediment deposited therein 2 – 5 . For example, mud beds generally extend laterally over longer distances in brackish, shallow-water settings (e.g., estuaries and delas) than in adjacent freshwater and saltwater settings 2 , 6 suggesting that paleosalinity can be used to constrain mudstone-bed lengths in the sedimentary record. This, in turn, directly impacts the recovery of fluids from those rocks and sediments. A wide range of sediment attributes, including both biological and geochemical attributes have been proposed as proxies for estimating paleosalinity in sediments and sedimentary rocks, with data used to develop and support these relations derived mainly from studies of modern depositional systems. Biological proxies include, but are not limited to ichnology 7 – 9 , calcareous microfossils 10 , and organic-walled dinoflagellate cyst abundances 11 – 14 and morphologies 15 . Geochemical proxies include, but are not limited to, δ 13 C org and/or C/N 11 , 16 , B/Ga 17 , 18 , and Sr/Ba 19 , 20 . Quantitative comparisons of biological and geochemical proxies in estuaries and deltas are rare 17 , so the accuracy and utility of different proxies under differing depositional and preservational conditions is poorly constrained. In this study, we compare six sediment attributes, including Sr/Ba (derived using two different extraction methods), δ 13 C org , C/N, and the concentrations and relative abundances of dinoflagellate cysts (referred to herein as dinocysts) to 1) depositional position along the fluvial-to-marine transition (FMT) of the Fraser River and Delta, Canada, 2) sea-surface (upper water column) salinity, and 3) physical sedimentological characteristics (i.e., mean grain size, mud content, and clay content). The FMT is defined herein as extending from the seaward limit of the prodelta to the landward limit of the tidal backwater in the Fraser River. We then comment on the utility of each sediment attribute as a proxy for estimating salinity and predicting depositional position along the FMT, and the potential application of these attributes for estimating paleosalinity in the sedimentary record. Sr/Ba is used as a salinity indicator because terrestrial sediment is typically enriched in Ba and poor in Sr when compared to marine sediment and vice versa 20 . In theory, Sr/Ba values 1.0 are typical of marine sediment 20 , 21 ; however, the concentration of Sr in most terrigenous clastic sediments (or rocks) is 100–300 mg kg − 1 and the concentration of Ba is 300–750 mg kg − 1 such that Sr/Ba in both marine and terrestrial terrigenous , bulk clastic sediments (rocks) is typically < 1.0 17,20,22 . Recently, Wang, et al. 20 argued against using bulk-rock/sediment Sr/Ba for discriminating between marine and terrestrial sedimentary environments. Instead, they proposed two new Sr/Ba ratios derived through selective extraction of Ba and Sr with both 10% acetic acid (Sr/Ba-HAc) and 1 M of ammonium acetate (Sr/Ba-NH 4 Ac). These selective extraction techniques yielded a strong linear correlation between Sr/Ba-HAc and Sr/Ba-NH 4 Ac and salinity in laboratory-controlled experiments, and a reasonable correlation with salinity in the Yangtze River Delta, China. Stable carbon isotopes (δ 13 C org ) and C/N of organic carbon have also been used to establish salinity gradients because of differences in δ 13 C org and C/N of terrestrial organic carbon sources and their marine counterparts 1 , 11 , 16 , 23 , 24 . Within the channelized extent of the FMT, mixing of terrestrial- and marine-sourced carbon occurs as a result of the backwater effect, reversed current flow during flood tides, and potentially due to flow separation in the freshwater tidal reach 11 . Palynological indicators are also commonly employed to resolve sea-surface/upper water column salinity and rely partly on the same mechanisms that produce δ 13 C org trends. Key palynological indicators of salinity conditions include the concentration of dinocysts and their abundance relative to pollen and spores 11 , 12 , 24 , 25 . In this study, samples were acquired along the FMT of the Fraser River, Canada and from surrounding coastal areas in the Strait of Georgia (Fig. 1 ). The Fraser River is a high-gradient system that transports an average 17 x 10 9 kg yr − 1 of sediment to the Strait of Georgia, of which ~ 36% is sand 26 , 27 . Tides are mesotidal and river discharge ranges from 1 000 to 15 200 m 3 s − 1 (mean: 2 710 m 3 s − 1 ). Tidal incursion up the Fraser River is approximately 30 km under low flow conditions ( 8 000 m 3 s − 1 ). The tidal backwater limit (limit of tidal modulation of river flow) is situated at approximately 102 km inland 28 , 29 . Results This study includes both new and published data comprising 111 unique samples and 14 repeat analyses (n = 125; Supplementary Data File A). Of the 111 samples, 98 are from the FMT of Fraser River and an additional 13 are from nearby coastal areas (Fig. 1 B). The mean high salinity value for each sample and its depositional position relative to Sand Heads (49.102901° N, 123.300347° W; Fig. 1 B) are determined. Depositional position is the site of sediment deposition along the FMT of the Fraser River and is measured in river km 30 ; positive values indicate a sample was collected upstream of Sand Heads (i.e., within channels or on the tidal flats). The positions of samples from the tidal flats and Strait of Georgia seafloor are measured perpendicular to the dike (relative to RK 8.4, Fig. 1 B). Mean high salinity of surface water is either 1) the average of measured salinity values taken at maximum tidal incursion and during low river flow, or 2) an estimate based on previous studies 31 – 38 and/or measurements taken at different stages of tidal cycles or river flow. Consequently, mean high salinity values recorded for samples are representative and not absolute. Samples are grouped into Salinity Groups (SG #) based on the mean high salinity under which those sediment were deposited (Fig. 2 ). Mean grain size, and the sand (62.5–2 000 µm), silt (3.91–62.5 µm), clay (< 3.91 µm), and mud (< 62.5 µm) content of samples is also measured and used as a basis for comparison (Supplementary Data File A). Depositional Position Indicators in the Fluvial-to-Marine Transition Qualitative interpretations of graphs reveal that all six sediment attributes show some relation to depositional position within the FMT (Fig. 2 A–D). In nearly all cases there is a significant break in values between samples in the river (RK 110.6 to RK > 11; referred to herein as River; Fig. 1 B) and those derived from various parts of the lower river and delta (RK ≤ 11 to RK -14.2; referred to herein as Delta). Trends and analyses are presently separately for River and Delta samples in the FMT. As well, in the Delta distinctions are made between “shallow” samples (from the upper 1 m of the sediment pile) and ones taken at depth in cores. Sr/Ba-HAc is 0.45 ± 0.05 (mean ± standard deviation; n = 20) and Sr/Ba-NH 4 Ac is 0.35 ± 0.05 (n = 20; Fig. 2 A) through the River region of the FMT. Both ratios increase linearly through the Delta region of the FMT, and Sr/Ba-HAc (y = -0.125x + 2.14, R 2 = 0.52) increases at a rate 3 times greater than Sr/Ba-NH 4 Ac (y = -0.042x + 1.0, R 2 = 0.45; Table 1 ). If only surface and near surface samples are included, the correlation improves between depositional position and Sr/Ba-HAc (R 2 = 0.75) and Sr/Ba-NH 4 Ac (R 2 = 0.58). δ 13 C org values increase seaward through the FMT (Fig. 2 B). The relation between depositional position and δ 13 C org is weak through the River (R 2 = 0.28; Table 1 ) and negligible in the Delta (R 2 = 0.17; Table 1 ). The relation between δ 13 C org and depositional position in the Delta remains negligible if only surface and near surface samples are included (R 2 = 0.22). C/N shows virtually no difference in values between the landward end of the FMT (River; 13.3 ± 1.8 (n = 6)) and the seaward end (Delta; 11.3 ± 2.4 (n = 26); Fig. 2 B). Dinocyst relative abundances show very low (< 1%) and very slightly increasing numbers of cysts seaward through the River (Fig. 2 C) with relative abundances of 0.47% ± 0.48% (n = 22). Dinocyst relative abundances increase rapidly seaward through the Delta (Fig. 2 D), although the correlation to river km is weak (R 2 = 0.36) and does not improve if only surface and near surface samples are considered (R 2 = 0.36; Table 1 ). Dinocyst absolute abundances also show very low and very slightly increasing numbers of cysts seaward through the River (Fig. 2 C) with 81 ± 109 dinocysts g − 1 (n = 22). Dinocyst absolute abundances increase rapidly seaward through the Delta (Fig. 2 D), but correlate weakly to river km (R 2 = 0.44). The correlation to river km improves markedly if only surface and near surface samples are considered (y = -35.56x + 338.8, R 2 = 0.61; Table 1 ). Salinity Indicators Sr/Ba-HAc, Sr/Ba-NH 4 Ac, and δ 13 C org exhibit visually discernable trends from SG1 (0 psu) to SG5 (≤ 25 psu; Fig. 2 E–H), but these trends are less clear when samples from SG6 are included. Consequently, trends between salinity and the six sediment attributes are evaluated separately for SG1–5 and for SG1–6 (Table 1 ). The correlation between Sr/Ba-HAc and mean high salinity is weak (R 2 = 0.25) for SG1–6 and increases markedly for SG1–5 (y = 0.285x − 0.31, R 2 = 0.73; Fig. 2 E). The correlation between mean high salinity and Sr/Ba-NH 4 Ac is negligible to no correlation (R 2 = 0.1) for SG1–6 and moderate (y = 0.245x − 0.35, R 2 = 0.67) for SG1–5. In SG6, both Sr/Ba ratios show a rapid decrease with depth in the sediment reaching apparent baseline values of 0.68 (Sr/Ba-NH 4 Ac) and 1.34 (Sr/Ba-HAc) by 4.5 m depth (Fig. 3 A–B). Note that 4 of 5 samples below 4.5 m are from the same cored interval that was recovered from 134 m water depth (2011004PGC129, Fig. 1 ; Supplementary Data File A). δ 13 C org values correlates moderately well to salinity for SG1–5 (y = 0.16x − 26.44, R 2 = 0.64; Fig. 2 F), and weakly for SG1–6 (R 2 = 0.3). In SG6, δ 13 C org values decrease with depth in the sediment reaching an apparent baseline of approximately − 26‰ at 6 m (Fig. 3 C); all samples below 6 m are derived from the same cored interval (2011004PGC129, Fig. 1 ). C/N shows no relation to salinity for SG1–6 and averages 11.5 ± 2.8. There is also no statistically significant relation between C/N and salinity for SG1–5 (Fig. 2 F), and C/N values show no significant correlation to depth in the sediment (Fig. 3 D). Dinocyst relative abundance correlates weakly (R 2 = 0.3) to salinity for SG1–6 and is constant at 0.67% ± 0.62% (n = 41) through SG1–5 (Fig. 2 G). Of note, nearly all of the samples in SG1–5 (39 of 41) derive from the River segment of the FMT. Dinocyst absolute abundances show a weak (to negligible) correlation (R 2 = 0.25) to salinity through SG1–6 and a more even distribution of 73 ± 99 dinocysts g − 1 through SG1–5 (n = 41; Fig. 2 H; Table 1 ). Attributes Versus Sediment Characteristics There is no obvious correlation between either Sr/Ba ratio and bulk grain size (Fig. 4 A). Similarly, neither δ 13 C org nor C/N correlates to bulk grain size (Fig. 4 B). However, in the outer Delta region of the FMT (delta front and prodelta) and in many Coastal sites (SG6), four of six attributes show a significantly poorer correlation to salinity than in other salinity groups (Fig. 2 ) and assessing the cause of this requires correlation of attributes to both depth in the sediment (discussed previously; Fig. 3 ) and to sediment characteristics (Fig. 4 ; Table 2 ). Sr/Ba-HAc correlates weakly to mud percent (R 2 = 0.46; Fig. 4 C), and this correlation increases markedly when Sr/Ba-HAc is compared to clay percent (y = 0.092x + 0.84, R 2 = 0.65; Fig. 4 F; Table 2 ). Sr/Ba-NH 4 Ac shows a moderate correlation to mud percent (y = 0.013x + 0.20, R 2 = 0.51; Fig. 4 D) and a weak correlation to clay percent (R 2 = 0.46; Fig. 4 G; Table 2 ). δ 13 C org correlates weakly to mud percent (R 2 = 0.37; Fig. 4 E) and moderately to clay percent (y = 0.076x − 25.85, R 2 = 0.57; Fig. 4 H; Table 2 ). Discussion The comparison of sediment attributes both along the FMT of the Fraser River Delta and as a function of sea-surface/upper water column salinity reveal several interesting trends. Trends along the FMT are differentiated from those related to salinity because sedimentation is significantly higher and salinity is significantly more variable along the FMT than in surrounding coastal areas. Sediment Attributes as Indicators of Depositional Position in the FMT The comparison of sediment attributes to depositional position in the FMT reveals a significant increase in both Sr/Ba ratios and δ 13 C org values at approximately River km 11 (Figs. 1 and 2 ). Dinocyst relative and absolute abundances also appear to increase from this point seaward. River km 11 correlates closely to the position of sustained brackish water in the Fraser River 28 , 37 , 38 , and so the response of the various sediment attributes seaward of RK11 appears to record the physical and/or chemical influence of sustained salinity on sedimentation. This hypothesis is supported by trends in the River (RK110.6 to 11), where 4 of 6 attributes (excluding δ 13 C org and C/N) show little to no change in values regardless of depositional position. While this is expected where saltwater does not extend (landward of RK30), it is surprising in the region of saltwater incursion (RK30 to 11) and suggests that brackish water has a limited impact on sedimentation unless it is sustained. Further study is needed to confirm this. Seaward of RK11, in the Delta region, Sr/Ba-HAc and Sr/Ba-NH 4 Ac both show good correlation to depositional position, particularly if only surface and near-surface samples are considered (Table 1 ). The response of Sr/Ba-HAc is three times greater than that of Sr/Ba-NH 4 Ac, suggesting that this is the preferred ratio for predicting depositional position in settings with sustained saltwater. The reason why Sr/Ba-HAc is larger than Sr/Ba-NH 4 Ac in saltwater environments is because both exchangeable and carbonate-bound Sr and Ba are extracted by acetic acid, while exchangeable Sr and exchangeable and barite-bound Ba are extracted by ammonium acetate. Because the Sr extracted by acetic acid is higher than that extracted by ammonium acetate, and the Ba extracted by ammonium acetate is higher than that extracted by acetic acid, the response of Sr/Ba-HAc exceeds that of Sr/Ba-NH 4 Ac in the same sediment. Dinocyst relative and absolute abundances also show responses to depositional position through the Delta region of the FMT, although the correlation between these two variables is generally weak (Table 1 ). The exception to this are dinocyst absolute abundances when only surface and near-surface samples are considered; this relation is moderate. The moderate correlation of dinocyst absolute abundances to depositional position in surface and near surface samples is a direct comparison of sediment deposited under similar conditions, and probably records the increased incorporation of dinocysts into marine sediment with time. The general increase in δ 13 C org through the FMT of the Fraser River records the transition from terrestrially-sourced organic matter (C 3 plants and soil: -26 to -28‰) landward of RK 11 to increasingly marine-sourced organic matter (-21 to -23‰) 1 seaward of RK11 (Fig. 2 ). Interestingly, the seaward increase in δ 13 C org is not constant, and there appears to be a significant increase in δ 13 C org at ~ RK60. The cause of this increase is not immediately apparent but could reflect an increase in C 4 plant material (grasses) incorporated in sediment on the margins of the Fraser River and derived from the surrounding upper delta plain and farmland (Fig. 2 ). C/N exhibits no discernable trends, although this may reflect the paucity of data between ~ RK90 and RK10. Sediment Attributes as Indicators of Salinity The correlation of the six sediment attributes to mean high salinity shows more complicated trends than to depositional position (Fig. 2 ), and this reflects 1) the inclusion of samples derived from both the FMT and Coastal areas, and 2) the impacts of both depositional processes and sedimentological properties on attributes (Figs. 3 and 4 ). First, through SG 1–5, Sr/Ba-HAc, Sr/Ba-NH 4 Ac, and δ 13 C org track changes in salinity reasonably well (Table 1 ) suggesting that all three attributes record physical and/or chemical influences of saltwater in sediment. Dinocyst relative and absolute abundances show no change from SG 1–5, but this probably reflects the paucity of data in SG3–5. Note that the two “outlier” values in SG4 and 5 (Fig. 2 G–H) are from Coastal areas (Fig. 1 B) and are near each other; they do not show any discernable relation to samples from the FMT and other Coastal samples. The decrease in Sr/Ba with depth, and mainly in a single cored interval from 134 m water depth (Fig. 3 A–B), is ascribed to the low clay content in the sediment below 4.5 m (Figs. 3 F and 4 F–G) and to possibly low carbonate content (lower Ca-HAc and inorganic carbon, Supplementary Data File A). The lower carbonate content in samples indicates reduced calcium carbonate shell material, which results in reduced adsorption of Sr and reduced isomorphic Sr extracted by HAc; this is manifested as reduced Sr/Ba 20 . The cause of reduced clay content and lower carbonate content with depth probably records variability in the amount of terrestrial organic material incorporated in rapidly buried fine-grained material 40 , 41 . Indeed, the Fraser Delta front and prodelta experience a wide range of gravity-driven flows that transport sediment from shallow water to deep 42 – 46 and these flows commonly introduce terrestrial organic matter directly from the Fraser River or transport sediment from the tidal flats into the delta front and/or prodelta. In addition, periodic mass-wasting events transport terrestrial organic material offshore 47 . Terrestrial organic material may also be allocthonous and transported to the delta front from other coastal regions of western North America via deep-water renewal events 48 , 49 . Interestingly, δ 13 C org correlates reasonably well to clay percent (Table 2 ) suggesting a positive linkage. In the Fraser Delta, clay is deposited dominantly offshore and in deep water, while silt is the more common mud type deposited in the river, tidal flats, and delta front 50 . The deposition of clay in deep-water marine settings should also be where marine-sourced organic material is most prevalent, and the correlation between clay percent and 13 C enrichment (Fig. 4 H; Table 2 ) probably reflects this. Implications for Paleosalinity Of the various techniques compared herein, Sr/Ba-NH 4 Ac and especially Sr/Ba-HAc show the best response to increasing salinity and appear to correlate well when saline water is present and sustained in a depositional setting. However, our data also indicate that these ratios are impacted by a wide range of depositional processes, and low values should not be interpreted as indicating no or low-salinity conditions without considering alternate causes for their reduction. For example, neither ratio increases in the Fraser River where saline water incurs but is not sustained (RK30–11), and values are low in the delta front and prodelta when shallow-water sediment is transported into deeper water. δ 13 C org is highly variable through the FMT and does not appear to correlate to depositional position (Table 1 ); instead δ 13 C org values reflect the dominance of terrestrial organic matter in deltaic settings. δ 13 C org appears to be a reasonable indicator of salinity conditions, but again is impacted heavily by depositional processes, especially in deltas (Fig. 2 F). The same is true for the relative and absolute abundances of dinocysts, which are also heavily impacted by river-derived sedimentation 11 . Consequently, our data suggest that it is not advisable to compare values from river-influenced settings (e.g., FMT, deltas) with those from non-river influenced settings (shorefaces, bays, and open marine). As well, interpreting salinity trends using sediment proxies is most effective if values are derived along a depositional profile and can be compared relatively. Methods Sample Analyses Grain size was measured for 98 samples using a Malvern Mastersizer 2000. Between 0.7 and 2 g of sediment was extracted from each sample and then treated with 30% H 2 O 2 for 36 hours to remove organic material. The supernatant was then pipetted off and the samples were mixed with 30 mL of 0.5% Sodium Hexametaphosphate solution and left for 24 hours. Samples were then stirred using a magnetic mixer for 5 minutes and were placed in a sonic bath for 1 minute. Following the sonic bath samples were emptied into the Malvern Mastersizer and analyzed for grain size. Sr/Ba was measured for 61 samples (+ 14 repeat analyses) including 38 from along the FMT. Sr/Ba was determined through selective extraction using both 10% acetic acid (Sr/Ba-HAc) and 1 M of ammonium acetate (Sr/Ba-NH 4 Ac) and following the methodology outlined in Wang, et al. 20 , 51 . First, the sample was dried and crushed till it passed through a 0.149 mm (100) mesh. Two, 0.1 g sub-samples were extracted from the crushed and dried sample and were placed in two 15 mL plastic centrifuge tubes. Ten mL of 10% acetic acid was added to one centrifuge tube and 1M ammonium acetate to the other. The mixtures were stirred at room temperature (20–30 ℃) for 2 hours, and then left to stand for 24 hours (mixtures can also be centrifuged for 20 minutes at 4500 rpm to separate the solid and liquid). The supernatant was then used to analyze Sr and Ba using an ICP-AES. Alternatively, the supernatant can be diluted to one-tenth of its original concentration for analysis by ICP-MS. δ 13 C org values were determined for 90 samples, including 48 samples from Czarnecki, et al. 11 , and C/N (% total organic carbon / % total nitrogen) was determined for 42 of these samples. For the 42 new samples, samples were first cleaned used deionized water and then dried in an oven at 60°C for 48 hours. Clean and dried samples were pulverized and sieved through a 0.63 mm mesh, and 1 g of each sample was extracted and treated with 5 mL of 2 N hydrochloric acid (HCl) for approximately 16 hours at room temperature to remove inorganic carbon 1 . A 40 mg sub-sample was extracted from each de-carbonated sample and was placed in a tin capsule in preparation for measuring total organic carbon and nitrogen contents. Elemental analyses were done using an elemental analyzer (vario MICRO cube elementar) in the Marine Geochemistry lab in the Institute of Oceanography, National Taiwan University, Taiwan. The standard used in elemental analysis is soil standard no. 502 − 062 Leco Reference Materials (%C = 0.924, %N = 0.093). All measurements were performed in duplicate and the relative error by multiple analyses of reference material was < %. The stable carbon isotope composition was measured using an elemental analyzer (Thermo Scientific Flash EA) connected with an isotope ratio mass spectrometer (Thermo Scientific Delta V Advantage). Carbon isotopic composition is presented as δ 13 C in the standard δ-notation in per mil (‰) with respect to Vienna Pee Dee Belemnite (VPDB). The measurements were calibrated with the standard reference material IAEA-CH-3 (δ 13 C = − 24.72 ± 0.05‰) and the analytical reproducibility for both δ 13 C is better than 0.2‰. Palynomorph analyses include 45 from Czarnecki, et al. 11 and 12 new samples (n = 57). The 12 new samples were processed at the Paleoenvironmental Laboratory, University of Minnesota, USA. Dinoflagellate cysts, pollen grains and spores were extracted using a standard dinocyst extraction method 52 . Approximately 3 cm 3 of sediment were subsampled and oven-dried at ~ 40°C and then weighed. Two calibrated tablets of Lycopodium clavatum spores (batch 140119321) were added to each sample to estimate palynomorph concentrations 53 . Samples were treated with room-temperature hydrochloric acid (10%) to dissolve carbonates, rinsed twice with reverse osmosis (RO) water, sieved through a 120 µm mesh and retained on a 15 µm nylon Nitex mesh to remove coarse and fine particles. Siliceous material was dissolved by using room-temperature hydrofluoric acid (48%) for up to two weeks. Samples were subsequently treated with hydrochloric acid (10%) to remove precipitated fluorosilicates, rinsed with RO water several times, gently sonicated for < 60 s, and sieved again through a 15 µm mesh sieve. After each step, samples were centrifuged at 3600 rpm for six minutes. Two drops of the residue were mounted between a slide and coverslip in glycerine jelly and marine palynomorphs were counted at 600× magnification. Dinocyst and other palynomorphs were identified and counted using a Nikon Eclipse 80i transmitting light microscope. Cyst identification was made based on of published descriptions 54 . Palynomorphs are grouped into tree pollen, herbs and shrubs pollen, spores, and dinocysts. Dinocysts are subdivided into autotrophs and heterotrophs, and the relative abundances and concentrations of all dinocysts are calculated (Supplementary Data File A). Statistical Methods Sediment attribute data are divided into two discrete populations based on visual inspection of graphs. A break appears in nearly all datasets between values in the river (RK 110.6 to > 11; referred to herein as River) and those from the most seaward extend of the river, the tidal flats, delta front and prodelta (RK ≤ 11 to -14.2; referred to herein as Delta). Means and standard deviations (and the slope equation for δ 13 C org ) of samples along the River use all data between RK110.6 and > 11; however, slope equations for Delta samples are calculated from RK15 and seaward to ensure trends in the Delta population are continuous from the River population (i.e., the River population effectively acts as the y-intercept for the Delta population). The correlation coefficient (r), coefficient of determination (R 2 ), and p-value for each equation is calculated for the River and Delta populations and for all attributes using the Analysis ToolPak in Microsoft® Excel and using linear regression only. Coefficients of determination are labelled as strong (R 2 ≥ 0.75), moderate (0.5 ≤ R 2 < 0.75), weak (0.25 ≤ R 2 < 0.5), or negligible (0.1 ≤ R 2 < 0.25) for the purpose of comparison. For linear regression equations with an R 2 < 0.1 (i.e., slope equation explains < 10% of the data), values are reported as mean ± standard deviation only. Statistically insignificant slope equations (p ≥ 0.05), equations where there is a ≥ 5% chance that there is no relationship between the two variables, are not reported herein. Outliers are identified in all datasets and are excluded from quantitative assessments (linear regression and mean ± standard deviation; Tables 1 and 2 ). This is done to resolve underlying trends in relatively low-n datasets, and to avoid skewing trends considerably based on one or two datapoints. Note that the slope equations listed in Table 1 are only valid when compared together (i.e., relatively) as river km data is unique to the Fraser River and mean high salinity data is representative because of significant salinity changes associated with tidal fluctuations, precipitation, and river discharge. Declarations DATA AVAILABILITY All data generated or analysed during this study are included in this published article [and its supplementary information files]. ACKNOWLEDGEMENTS The authors thank Phillip Hill and Randy Enkin of the Geological Survey of Canada for providing samples from cored intervals from the Strait of Georgia. We thank two anonymous reviewers for their comments on an earlier version of this manuscript. This research was made possible through an NSERC Discovery Grant to S. Dashtgard (grant RGPIN-2019-04528) and a National Natural Science Foundation of China grant to A. Wang (grant 41572096). References Dashtgard, S. E., Löwemark, L., Wang, P.-L., Setiaji, R. 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Palynology and geochemistry of channel-margin sediments across the tidal-fluvial transition, lower Fraser River, British Columbia, Canada. Journal of Marine and Petroleum Geology 51 , 152–166 (2014). Pospelova, V., de Vernal, A. & Pedersen, T. F. Distribution of dinoflagellate cysts in surface sediments from the northeastern Pacific Ocean (43–25°N) in relation to sea-surface temperature, salinity, productivity and coastal upwelling. Marine Micropaleontology 68 , 21–48, doi: 10.1016/j.marmicro.2008.01.008 (2008). Dale, B. in Palynology: principles and applications (eds J. Jansonius & D.C. McGregor) 1249–1275 (AASP Foundation, 1996). Pospelova, V., Chmura, G. L. & Walker, H. A. Environmental factors influencing the spatial distribution of dinoflagellate cyst assemblages in shallow lagoons of southern New England. Review of Palaeobotany and Palynology 128 , 7–34 (2004). Mertens, K. N. et al. Quantitative estimation of Holocene surface salinity variation in the Black Sea using dinoflagellate cyst process length. Quaternary Science Reviews 39 , 45–59, doi: 10.1016/j.quascirev.2012.01.026 (2012). Chmura, G. L. & Aharon, P. Stable carbon isotope signatures of sedimentary carbon in coastal wetlands as indicators of salinity regime. Journal of Coastal Research 11 , 125–135 (1995). Wei, W. & Algeo, T. J. Elemental proxies for paleosalinity analysis of ancient shales and mudrocks. Geochimica et Cosmochimica Acta 287 , 341–366, doi: 10.1016/j.gca.2019.06.034 (2020). Zhang, X., Lin, C., Zahid, M. A., Jia, X. & Zhang, T. Paleosalinity and water body type of Eocene Pinghu Formation, Xihu Depression, East China Sea Basin. Journal of Petroleum Science and Engineering 158 , 469–478, doi: 10.1016/j.petrol.2017.08.074 (2017). Coffey, M. et al. The behaviour of dissolved Barium in estuaries. Estuarine, Coastal and Shelf Science 45 , 113–121 (1997). Wang, A. H., Wang, Z. H., Liu, J. K., Xu, N. C. & Li, H. L. The Sr/Ba ratio response to salinity in clastic sediments of the Yangtze River Delta. Chemical Geology 559 , doi: 10.1016/j.chemgeo.2020.119923 (2021). Zwolsman, J. J. G. & van Eck, G. T. M. Geochemistry of major elements and trace metals in suspended matter of the Scheldt estuary, southwest Netherlands. Marine Chemistry 66 , 91–111 (1999). Ross, D. J. K. & Bustin, R. M. Investigating the use of sedimentary geochemical proxies for paleoenvironment interpretation of thermally mature organic-rich strata: Examples from the Devonian–Mississippian shales, Western Canadian Sedimentary Basin. Chemical Geology 260 , 1–19, doi: 10.1016/j.chemgeo.2008.10.027 (2009). Thornton, S. F. & McManus, J. Application of organic carbon and nitrogen stable isotope and C/N ratios as source indicators of organic matter provenance in estuarine systems: evidence from the Tay Estuary, Scotland. Estuarine, Coastal and Shelf Science 38 , 219–233 (1994). Pospelova, V., Chmura, G. L., Boothman, W. S. & Latimer, J. S. Dinoflagellate cyst records and human disturbance in two neighboring estuaries, New Bedford Harbor and Apponagansett Bay, Massachusetts (USA). The Science of the Total Environment 298 , 81–102 (2002). Pospelova, V., Chmura, G. L., Boothman, W. S. & Latimer, J. S. Spatial distribution of modern dinoflagellate cycts in polluted estuarine sediments from Buzzards Bay (Massachusetts, USA) embayments. Marine Ecology Progress Series 292 , 23–40 (2005). McLean, D. G., Church, M. A. & Tassone, B. Sediment transport along the lower Fraser River. 1. Measurements and hydraulic computations. Water Resources Research 35 , 2533–2548 (1999). Church, M. Bed material transport and the morphology of alluvial river channels. Annual Review of Earth and Planetary Sciences 34 , 325–354 (2006). Dashtgard, S. E. et al. Sedimentation across the tidal-fluvial transition in the lower Fraser River, Canada. The Sedimentary Record 10 , 4–9 (2012). La Croix, A. D. & Dashtgard, S. E. A Synthesis of Depositional Trends In Intertidal and Upper Subtidal Sediments Across the Tidal–Fluvial Transition In the Fraser River, Canada. Journal of Sedimentary Research 85 , 683–698, doi: 10.2110/jsr.2015.47 (2015). Venditti, J. G. & Church, M. Morphology and controls on the position of a gravel-sand transition: Fraser River, British Columbia. Journal of Geophysical Research: Earth Surface 119 , 1651–1681, doi: 10.1002/2014jf003079 (2014). Ages, A. & Woolard, A. The tides in the Fraser Estuary. 100 (Environment Canada, Institute of Ocean Sciences, Ottawa, 1976). Chapman, P. M. & Brinkhurst, R. O. Seasonal changes in interstitial salinities and seasonal movements of subtidal benthic invertebrates in the Fraser River estuary, B.C. Estuarine, Coastal and Shelf Science 12 , 49–66 (1981). Ages, A. The salinity intrusion in the Fraser River: salinity, temperature and current observations 1976, 1977. 193 (1979). Hughes, C. C. & Ages, A. B. Salinity and temperature measurements in the lower Fraser River, 1966–1968, 1970–1973. 295 (Department of Fisheries and Oceans, Sidney, BC, 1975). Kostaschuk, R. A. Flow and sediment dynamics in migrating salinity intrusions: Fraser River Estuary, Caanda. Estuaries 25 , 197–203 (2002). Kostaschuk, R. A. & Atwood, L. A. River discharge and tidal controls on salt-wedge position and implications for channel shoaling: Fraser River, British Columbia. Canadian Journal of Civil Engineering 17 , 452–459 (1990). Sisulak, C. F. & Dashtgard, S. E. Seasonal controls on the development and character of inclined heterolithic stratification in a tide-influenced, fluvially dominated channel: Fraser River, Canada. Journal of Sedimentary Research 82 , 244–257 (2012). Johnson, S. M. & Dashtgard, S. E. Inclined heterolithic stratification in a mixed tidal-fluvial channel: Differentiating tidal versus fluvial controls on sedimentation. Sedimentary Geology 301 , 41–53 (2014). Sisulak, C. F. Seasonal controls on the development and character of inclined heterolithic stratification in a tide-influenced, fluvially dominated channel, Fraser River, Canada M.Sc. thesis, Simon Fraser University, (2011). Ayranci, K. & Dashtgard, S. E. Asymmetrical deltas below wave base: Insights from the Fraser River Delta, Canada. Sedimentology 63 , 761–779 (2016). Johannessen, S. C., Macdonald, R. W. & Paton, D. W. A sediment and organic carbon budget for the greater Strait of Georgia. Estuarine, Coastal and Shelf Science 56 , 845–860 (2003). Ayranci, K., Lintern, D. G., Hill, P. R. & Dashtgard, S. E. Tide-supported gravity flows on the upper delta front, Fraser River delta, Canada. Marine Geology 326–328 , 166–170 (2012). Hill, P. R. & Lintern, D. G. Turbidity currents on the open slope of the Fraser Delta. Marine Geology 445 , 106738, doi: 10.1016/j.margeo.2022.106738 (2022). Hill, P. R., Lintern, D. G. & Pontén, A. Sedimentary processes at the mouth of a tidally-influenced delta: New insights from submarine observatory measurements, Fraser Delta, Canada. Sedimentology 68 , 2649–2670, doi: 10.1111/sed.12868 (2021). Hill, P. R. et al. Sedimentary processes and sediment dispersal in the southern Strait of Georgia, BC, Canada. Marine Environmental Research 66 , S39-S48 (2008). Lintern, D. G., Hill, P. R., Stacey, C. & Talling, P. Powerful unconfined turbidity current captured by cabled observatory on the Fraser River delta slope, British Columbia, Canada. Sedimentology 63 , 1041–1064, doi: 10.1111/sed.12262 (2016). McKenna, G. T., Luternauer, J. L. & Kostaschuk, R. A. Large-scale mass-wasting events on the Fraser River delta front near Sand Heads, British Columbia. Canadian Geotechnical Journal 29 , 151–156 (1992). Ayranci, K. & Dashtgard, S. E. Deep-Water Renewal Events; Insights into Deep Water Sediment Transport Mechanisms. Sci Rep 10 , 6139, doi: 10.1038/s41598-020-63123-3 (2020). Davenne, E. & Masson, D. Water Properties in the Straits of Georgia and Juan de Fuca (British Columbia, Canada). 41 (Institute of Ocean Sciences, Sidney, BC, 2001). Barrie, J. V., Hill, P. R., Conway, K. W., Iwanowska, K. & Picard, K. Environmental Marine Geoscience 4: Georgia Basin: Seabed features and marine geohazards. Geoscience Canada 32 , 145–156 (2005). Wang, A. H., Liu, J. K., Zhang, F. & Li, H. L. Selective extraction of sedimentogenic strontium and barium in terrigenous clastic sediments [P]. USA patent US10151018 B2 (2018). Pospelova, V., Esenkulova, S., Johannessen, S. C., O’Brien, M. C. & Macdonald, R. W. Organic-walled dinoflagellate cyst production, composition and flux from 1996 to 1998 in the central Strait of Georgia (BC, Canada). Marine Micropaleontology 75 , 17–37 (2010). Mertens, K. N., Price, A. M. & Pospelova, V. Determining the absolute abundance of dinoflagellate cysts in recent marine sediments II: Further tests of the Lycopodium marker-grain method. Review of Palaeobotany and Palynology 184 , 74–81 (2012). Zonneveld, K. A. F. & Pospelova, V. A determination key for modern dinoflagellate cysts. Palynology 39 , 387–409, doi: https://doi.org/10.1080/01916122.2014.990115 (2015). Additional Declarations No competing interests reported. Supplementary Files SupplementaryDataFileA.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 25 Jul, 2022 Reviews received at journal 20 Jul, 2022 Reviewers agreed at journal 06 Jul, 2022 Reviewers invited by journal 06 Jul, 2022 Editor assigned by journal 05 Jul, 2022 Editor invited by journal 02 Jun, 2022 Submission checks completed at journal 02 Jun, 2022 First submitted to journal 25 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1694129","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":110659342,"identity":"fb9a54f3-94b4-4f81-a9f7-6aa45a9fc409","order_by":0,"name":"Shahin E. Dashtgard","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBAC+YYEBmYQg5+B8QFxWgwOgLQkMDBINjAbEKmFAarF4ADRWtiTH38u/FErb3z+MJsEQ40dYS3yPc/MpGckHDfcdiMZqOVYMhHW3EgwY+ZJOMa47Qb/MQnGBmZitKR//gzUYr+5H+gwxoZ6YrTkGEjzJNQkbmBIBmk5TFiHwZk3ZdI8aQeSZ9xIZrZIOHacsBb59vTNn3ls6mz7+w8z3vhQU02EwyAA6p4EojUwMNSRoHYUjIJRMApGHAAA06w5Tms99PQAAAAASUVORK5CYII=","orcid":"","institution":"Simon Fraser University","correspondingAuthor":true,"prefix":"","firstName":"Shahin","middleName":"E.","lastName":"Dashtgard","suffix":""},{"id":110659343,"identity":"d06376d7-ccde-42b8-82f2-fc1c992ead61","order_by":1,"name":"Aihua Wang","email":"","orcid":"","institution":"China Geological Survey","correspondingAuthor":false,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Wang","suffix":""},{"id":110659344,"identity":"12bce465-42c7-4be2-afb2-61f1d4817098","order_by":2,"name":"Vera Pospelova","email":"","orcid":"","institution":"University of Minnesota","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Pospelova","suffix":""},{"id":110659345,"identity":"798d6fcb-724a-4a85-a680-1da0899d07c5","order_by":3,"name":"Pei-Ling Wang","email":"","orcid":"","institution":"National Taiwan University","correspondingAuthor":false,"prefix":"","firstName":"Pei-Ling","middleName":"","lastName":"Wang","suffix":""},{"id":110659346,"identity":"d565395b-3812-4382-a311-669bfa171cda","order_by":4,"name":"Andrew La Croix","email":"","orcid":"","institution":"University of Waikato","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"La","lastName":"Croix","suffix":""},{"id":110659347,"identity":"48339845-6f63-401a-afa2-5138849a246d","order_by":5,"name":"Korhan Ayranci","email":"","orcid":"","institution":"King Fahd University of Petroleum and Minerals","correspondingAuthor":false,"prefix":"","firstName":"Korhan","middleName":"","lastName":"Ayranci","suffix":""}],"badges":[],"createdAt":"2022-05-25 21:29:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1694129/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1694129/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22508584,"identity":"7a4b38e5-70aa-4fd6-be49-6ed214a41661","added_by":"auto","created_at":"2022-06-10 15:44:27","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3702959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Position of the Fraser River Delta in Canada and British Columbia. \u003cstrong\u003eB) \u003c/strong\u003eBlue dots mark the 111 unique sample positions. The blue polygon encompasses samples from the fluvial-to-marine transition and include samples from the River (dashed blue line) and Delta (solid blue line). The division between River and Delta samples is at river km (RK) 11 (see text for discussion). The green polygons encompass samples from Coastal areas, and the dashed yellow lines marks the approximate seaward limit of the tidal flats. The solid yellow line marks the approximate position of the dikes along the margin of the Fraser Delta that is used as a baseline for assigning river km values to samples across the tidal flats and in the Strait of Georgia (i.e., Delta). The position of Sand Heads (RK 0), the dyke upstream of Sand Heads (RK 8.4), maximum saltwater incursion (MSWI; ~RK 30) \u003csup\u003e37-40\u003c/sup\u003e, and tidal backwater limit (TBWL; RK 102) are shown (image Source: Landsat 8; USGS and NASA).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1694129/v1/0553d40e1cbb35b3673918af.jpeg"},{"id":22507566,"identity":"86e269a9-474d-4b3b-8294-57ae6bd8a58a","added_by":"auto","created_at":"2022-06-10 15:34:27","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2029760,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs of Sr/Ba, δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e, C/N, and dinocyst relative abundances and concentrations along the FMT (\u003cstrong\u003eA–D\u003c/strong\u003e) and as a function of salinity (\u003cstrong\u003eE–H\u003c/strong\u003e). Data are available in Supplementary Data File A. δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e and C/N values for different sources of organic matter in the legend are derived from Dashtgard, et al. \u003csup\u003e1\u003c/sup\u003e. The linear regression equations and R\u003csup\u003e2\u003c/sup\u003e values are shown in Table 1 (excluding equations for “shallow” samples). The two outliers in\u003cstrong\u003e (E)\u003c/strong\u003e are Coastal samples that are near each other and represent the same depositional environment (MB-21-S3 and X-U1-1, Supplementary Data File A). The two outliers in (\u003cstrong\u003eG)\u003c/strong\u003e and (\u003cstrong\u003eH)\u003c/strong\u003e are also Coastal samples but are geographically distinct (MB-21S2 and PoM21-S2, Supplementary Data File A). These outliers are included to illustrate the distribution of data but are excluded in linear regression calculations (see Methods).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1694129/v1/90107bd95d074cfe3b71ae53.jpeg"},{"id":22508394,"identity":"f7f62fe8-e933-441f-a086-e763e7a26c72","added_by":"auto","created_at":"2022-06-10 15:39:27","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1257275,"visible":true,"origin":"","legend":"\u003cp\u003eGraphs of changes in (\u003cstrong\u003eA–B\u003c/strong\u003e) Sr/Ba, (\u003cstrong\u003eC\u003c/strong\u003e) δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e, (\u003cstrong\u003eD\u003c/strong\u003e) C/N, (\u003cstrong\u003eE\u003c/strong\u003e) mud content, and (\u003cstrong\u003eF\u003c/strong\u003e) clay content with depth in the sediment and by Salinity Group. The coloured polygons are included to illustrate the distribution of data only.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1694129/v1/97fe02f0f6ed6be1b5dd326f.jpeg"},{"id":22508393,"identity":"abc4786a-4405-47b6-a462-8b5f5937e49b","added_by":"auto","created_at":"2022-06-10 15:39:27","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1290380,"visible":true,"origin":"","legend":"\u003cp\u003eA\u003cstrong\u003e)\u003c/strong\u003e Sr/Ba versus bulk grain size. \u003cstrong\u003eB) \u003c/strong\u003eδ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e and C/N versus bulk grain size. Graphs of changes in (\u003cstrong\u003eC\u003c/strong\u003e) Sr/Ba-HAc, (\u003cstrong\u003eD\u003c/strong\u003e) Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, and (\u003cstrong\u003eE\u003c/strong\u003e) δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e versus percent mud content for samples in Salinity Group 6 only. Graphs of changes in (\u003cstrong\u003eF\u003c/strong\u003e) Sr/Ba-HAc, (\u003cstrong\u003eG\u003c/strong\u003e) Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, and (\u003cstrong\u003eH\u003c/strong\u003e) δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e versus percent clay content for samples in Salinity Group 6 only. Linear regression equations and R\u003csup\u003e2\u003c/sup\u003e values are summarized in Table 2. The outlier in (\u003cstrong\u003eC–D\u003c/strong\u003e) and (\u003cstrong\u003eF–G\u003c/strong\u003e) is from the same sample taken from a Coastal area (MB21-S4, Supplementary Data File A), and the two outliers in (\u003cstrong\u003eE\u003c/strong\u003e) and (\u003cstrong\u003eH\u003c/strong\u003e) include the same Coastal sample and one derived from a cored interval. These outliers are not included in the slope equations and correlation coefficients presented in Table 2 and discussed in the text. The coloured polygons are included to illustrate the distribution of data only.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1694129/v1/e4081784cfe1e0c6f8f22471.jpeg"},{"id":22508585,"identity":"9cc35e52-2c75-45ec-bb95-51cbd3681508","added_by":"auto","created_at":"2022-06-10 15:44:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":512015,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1694129/v1/c8063fae-3a39-47bf-ba51-8057f6471f61.pdf"},{"id":22507563,"identity":"da08eae2-9ebe-4b52-b667-6001ea34bed1","added_by":"auto","created_at":"2022-06-10 15:34:27","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":111323,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDataFileA.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-1694129/v1/7516fb3a7e30b8e1a37ab316.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Salinity indicators in sediment through the fluvial-to-marine transition (Fraser River, Canada)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe salinity of water under which sediment is deposited significantly influences the character of the sediment deposited therein \u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. For example, mud beds generally extend laterally over longer distances in brackish, shallow-water settings (e.g., estuaries and delas) than in adjacent freshwater and saltwater settings \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e suggesting that paleosalinity can be used to constrain mudstone-bed lengths in the sedimentary record. This, in turn, directly impacts the recovery of fluids from those rocks and sediments.\u003c/p\u003e \u003cp\u003eA wide range of sediment attributes, including both biological and geochemical attributes have been proposed as proxies for estimating paleosalinity in sediments and sedimentary rocks, with data used to develop and support these relations derived mainly from studies of modern depositional systems. Biological proxies include, but are not limited to ichnology \u003csup\u003e\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, calcareous microfossils \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and organic-walled dinoflagellate cyst abundances \u003csup\u003e\u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and morphologies \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Geochemical proxies include, but are not limited to, δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e and/or C/N \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, B/Ga \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and Sr/Ba \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. Quantitative comparisons of biological and geochemical proxies in estuaries and deltas are rare \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, so the accuracy and utility of different proxies under differing depositional and preservational conditions is poorly constrained. In this study, we compare six sediment attributes, including Sr/Ba (derived using two different extraction methods), δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e, C/N, and the concentrations and relative abundances of dinoflagellate cysts (referred to herein as dinocysts) to 1) depositional position along the fluvial-to-marine transition (FMT) of the Fraser River and Delta, Canada, 2) sea-surface (upper water column) salinity, and 3) physical sedimentological characteristics (i.e., mean grain size, mud content, and clay content). The FMT is defined herein as extending from the seaward limit of the prodelta to the landward limit of the tidal backwater in the Fraser River. We then comment on the utility of each sediment attribute as a proxy for estimating salinity and predicting depositional position along the FMT, and the potential application of these attributes for estimating paleosalinity in the sedimentary record.\u003c/p\u003e \u003cp\u003eSr/Ba is used as a salinity indicator because terrestrial sediment is typically enriched in Ba and poor in Sr when compared to marine sediment and \u003cem\u003evice versa\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In theory, Sr/Ba values\u0026thinsp;\u0026lt;\u0026thinsp;1.0 are typical of terrestrial sediment and values\u0026thinsp;\u0026gt;\u0026thinsp;1.0 are typical of marine sediment \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e; however, the concentration of Sr in most terrigenous clastic sediments (or rocks) is 100\u0026ndash;300 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and the concentration of Ba is 300\u0026ndash;750 mg kg\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e such that Sr/Ba in both marine and terrestrial \u003cem\u003eterrigenous\u003c/em\u003e, bulk clastic sediments (rocks) is typically\u0026thinsp;\u0026lt;\u0026thinsp;1.0 \u003csup\u003e17,20,22\u003c/sup\u003e. Recently, Wang, et al. \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e argued against using bulk-rock/sediment Sr/Ba for discriminating between marine and terrestrial sedimentary environments. Instead, they proposed two new Sr/Ba ratios derived through selective extraction of Ba and Sr with both 10% acetic acid (Sr/Ba-HAc) and 1 M of ammonium acetate (Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc). These selective extraction techniques yielded a strong linear correlation between Sr/Ba-HAc and Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc and salinity in laboratory-controlled experiments, and a reasonable correlation with salinity in the Yangtze River Delta, China.\u003c/p\u003e \u003cp\u003eStable carbon isotopes (δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e) and C/N of organic carbon have also been used to establish salinity gradients because of differences in δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e and C/N of terrestrial organic carbon sources and their marine counterparts \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Within the channelized extent of the FMT, mixing of terrestrial- and marine-sourced carbon occurs as a result of the backwater effect, reversed current flow during flood tides, and potentially due to flow separation in the freshwater tidal reach \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePalynological indicators are also commonly employed to resolve sea-surface/upper water column salinity and rely partly on the same mechanisms that produce δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e trends. Key palynological indicators of salinity conditions include the concentration of dinocysts and their abundance relative to pollen and spores \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn this study, samples were acquired along the FMT of the Fraser River, Canada and from surrounding coastal areas in the Strait of Georgia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Fraser River is a high-gradient system that transports an average 17 x 10\u003csup\u003e9\u003c/sup\u003e kg yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e of sediment to the Strait of Georgia, of which\u0026thinsp;~\u0026thinsp;36% is sand \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Tides are mesotidal and river discharge ranges from 1 000 to 15 200 m\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (mean: 2 710 m\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Tidal incursion up the Fraser River is approximately 30 km under low flow conditions (\u0026lt;\u0026thinsp;2 000 m\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) and is pushed completely out of the river under high flow conditions (freshet; \u0026gt; 8 000 m\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). The tidal backwater limit (limit of tidal modulation of river flow) is situated at approximately 102 km inland \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThis study includes both new and published data comprising 111 unique samples and 14 repeat analyses (n\u0026thinsp;=\u0026thinsp;125; Supplementary Data File A). Of the 111 samples, 98 are from the FMT of Fraser River and an additional 13 are from nearby coastal areas (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). The mean high salinity value for each sample and its depositional position relative to Sand Heads (49.102901\u0026deg; N, 123.300347\u0026deg; W; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB) are determined. Depositional position is the site of sediment deposition along the FMT of the Fraser River and is measured in river km \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e; positive values indicate a sample was collected upstream of Sand Heads (i.e., within channels or on the tidal flats). The positions of samples from the tidal flats and Strait of Georgia seafloor are measured perpendicular to the dike (relative to RK 8.4, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB). Mean high salinity of surface water is either 1) the average of measured salinity values taken at maximum tidal incursion and during low river flow, or 2) an estimate based on previous studies \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e and/or measurements taken at different stages of tidal cycles or river flow. Consequently, mean high salinity values recorded for samples are representative and not absolute. Samples are grouped into Salinity Groups (SG #) based on the mean high salinity under which those sediment were deposited (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Mean grain size, and the sand (62.5\u0026ndash;2 000 \u0026micro;m), silt (3.91\u0026ndash;62.5 \u0026micro;m), clay (\u0026lt;\u0026thinsp;3.91 \u0026micro;m), and mud (\u0026lt;\u0026thinsp;62.5 \u0026micro;m) content of samples is also measured and used as a basis for comparison (Supplementary Data File A).\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003eDepositional Position Indicators in the Fluvial-to-Marine Transition\u003c/h2\u003e\n \u003cp\u003eQualitative interpretations of graphs reveal that all six sediment attributes show some relation to depositional position within the FMT (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;D). In nearly all cases there is a significant break in values between samples in the river (RK 110.6 to RK\u0026thinsp;\u0026gt;\u0026thinsp;11; referred to herein as River; Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB) and those derived from various parts of the lower river and delta (RK\u0026thinsp;\u0026le;\u0026thinsp;11 to RK -14.2; referred to herein as Delta). Trends and analyses are presently separately for River and Delta samples in the FMT. As well, in the Delta distinctions are made between \u0026ldquo;shallow\u0026rdquo; samples (from the upper 1 m of the sediment pile) and ones taken at depth in cores.\u003c/p\u003e\n \u003cp\u003eSr/Ba-HAc is 0.45 \u0026plusmn; 0.05 (mean \u0026plusmn; standard deviation; n\u0026thinsp;=\u0026thinsp;20) and Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc is 0.35 \u0026plusmn; 0.05 (n\u0026thinsp;=\u0026thinsp;20; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA) through the River region of the FMT. Both ratios increase linearly through the Delta region of the FMT, and Sr/Ba-HAc (y = -0.125x\u0026thinsp;+\u0026thinsp;2.14, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.52) increases at a rate 3 times greater than Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc (y = -0.042x\u0026thinsp;+\u0026thinsp;1.0, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.45; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). If only surface and near surface samples are included, the correlation improves between depositional position and Sr/Ba-HAc (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.75) and Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.58).\u003c/p\u003e\n \u003cp\u003e\u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e values increase seaward through the FMT (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). The relation between depositional position and \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e is weak through the River (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.28; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and negligible in the Delta (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.17; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The relation between \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e and depositional position in the Delta remains negligible if only surface and near surface samples are included (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.22). C/N shows virtually no difference in values between the landward end of the FMT (River; 13.3 \u0026plusmn; 1.8 (n\u0026thinsp;=\u0026thinsp;6)) and the seaward end (Delta; 11.3 \u0026plusmn; 2.4 (n\u0026thinsp;=\u0026thinsp;26); Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e\n \u003cp\u003eDinocyst relative abundances show very low (\u0026lt;\u0026thinsp;1%) and very slightly increasing numbers of cysts seaward through the River (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) with relative abundances of 0.47% \u0026plusmn; 0.48% (n\u0026thinsp;=\u0026thinsp;22). Dinocyst relative abundances increase rapidly seaward through the Delta (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), although the correlation to river km is weak (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.36) and does not improve if only surface and near surface samples are considered (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.36; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Dinocyst absolute abundances also show very low and very slightly increasing numbers of cysts seaward through the River (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) with 81 \u0026plusmn; 109 dinocysts g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (n\u0026thinsp;=\u0026thinsp;22). Dinocyst absolute abundances increase rapidly seaward through the Delta (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD), but correlate weakly to river km (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.44). The correlation to river km improves markedly if only surface and near surface samples are considered (y = -35.56x\u0026thinsp;+\u0026thinsp;338.8, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.61; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003eSalinity Indicators\u003c/h2\u003e\n \u003cp\u003eSr/Ba-HAc, Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, and \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e exhibit visually discernable trends from SG1 (0 psu) to SG5 (\u0026le;\u0026thinsp;25 psu; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE\u0026ndash;H), but these trends are less clear when samples from SG6 are included. Consequently, trends between salinity and the six sediment attributes are evaluated separately for SG1\u0026ndash;5 and for SG1\u0026ndash;6 (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe correlation between Sr/Ba-HAc and mean high salinity is weak (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25) for SG1\u0026ndash;6 and increases markedly for SG1\u0026ndash;5 (y\u0026thinsp;=\u0026thinsp;0.285x \u0026minus;\u0026thinsp;0.31, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.73; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eE). The correlation between mean high salinity and Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc is negligible to no correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.1) for SG1\u0026ndash;6 and moderate (y\u0026thinsp;=\u0026thinsp;0.245x \u0026minus;\u0026thinsp;0.35, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.67) for SG1\u0026ndash;5. In SG6, both Sr/Ba ratios show a rapid decrease with depth in the sediment reaching apparent baseline values of 0.68 (Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc) and 1.34 (Sr/Ba-HAc) by 4.5 m depth (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B). Note that 4 of 5 samples below 4.5 m are from the same cored interval that was recovered from 134 m water depth (2011004PGC129, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e; Supplementary Data File A).\u003c/p\u003e\n \u003cp\u003e\u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e values correlates moderately well to salinity for SG1\u0026ndash;5 (y\u0026thinsp;=\u0026thinsp;0.16x \u0026minus;\u0026thinsp;26.44, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.64; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF), and weakly for SG1\u0026ndash;6 (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.3). In SG6, \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e values decrease with depth in the sediment reaching an apparent baseline of approximately \u0026minus;\u0026thinsp;26\u0026permil; at 6 m (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC); all samples below 6 m are derived from the same cored interval (2011004PGC129, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). C/N shows no relation to salinity for SG1\u0026ndash;6 and averages 11.5 \u0026plusmn; 2.8. There is also no statistically significant relation between C/N and salinity for SG1\u0026ndash;5 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF), and C/N values show no significant correlation to depth in the sediment (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD).\u003c/p\u003e\n \u003cp\u003eDinocyst relative abundance correlates weakly (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.3) to salinity for SG1\u0026ndash;6 and is constant at 0.67% \u0026plusmn; 0.62% (n\u0026thinsp;=\u0026thinsp;41) through SG1\u0026ndash;5 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG). Of note, nearly all of the samples in SG1\u0026ndash;5 (39 of 41) derive from the River segment of the FMT. Dinocyst absolute abundances show a weak (to negligible) correlation (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.25) to salinity through SG1\u0026ndash;6 and a more even distribution of 73 \u0026plusmn; 99 dinocysts g\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e through SG1\u0026ndash;5 (n\u0026thinsp;=\u0026thinsp;41; Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eH; Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec5\"\u003e\n \u003ch2\u003eAttributes Versus Sediment Characteristics\u003c/h2\u003e\n \u003cp\u003eThere is no obvious correlation between either Sr/Ba ratio and bulk grain size (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). Similarly, neither \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e nor C/N correlates to bulk grain size (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). However, in the outer Delta region of the FMT (delta front and prodelta) and in many Coastal sites (SG6), four of six attributes show a significantly poorer correlation to salinity than in other salinity groups (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) and assessing the cause of this requires correlation of attributes to both depth in the sediment (discussed previously; Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and to sediment characteristics (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eSr/Ba-HAc correlates weakly to mud percent (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.46; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC), and this correlation increases markedly when Sr/Ba-HAc is compared to clay percent (y\u0026thinsp;=\u0026thinsp;0.092x\u0026thinsp;+\u0026thinsp;0.84, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.65; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc shows a moderate correlation to mud percent (y\u0026thinsp;=\u0026thinsp;0.013x\u0026thinsp;+\u0026thinsp;0.20, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.51; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD) and a weak correlation to clay percent (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.46; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eG; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e correlates weakly to mud percent (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.37; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE) and moderately to clay percent (y\u0026thinsp;=\u0026thinsp;0.076x \u0026minus;\u0026thinsp;25.85, R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.57; Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eH; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe comparison of sediment attributes both along the FMT of the Fraser River Delta and as a function of sea-surface/upper water column salinity reveal several interesting trends. Trends along the FMT are differentiated from those related to salinity because sedimentation is significantly higher and salinity is significantly more variable along the FMT than in surrounding coastal areas.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\n \u003ch2\u003eSediment Attributes as Indicators of Depositional Position in the FMT\u003c/h2\u003e\n \u003cp\u003eThe comparison of sediment attributes to depositional position in the FMT reveals a significant increase in both Sr/Ba ratios and \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e values at approximately River km 11 (Figs. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Dinocyst relative and absolute abundances also appear to increase from this point seaward. River km 11 correlates closely to the position of sustained brackish water in the Fraser River \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and so the response of the various sediment attributes seaward of RK11 appears to record the physical and/or chemical influence of \u003cem\u003esustained\u003c/em\u003e salinity on sedimentation. This hypothesis is supported by trends in the River (RK110.6 to 11), where 4 of 6 attributes (excluding \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e and C/N) show little to no change in values regardless of depositional position. While this is expected where saltwater does not extend (landward of RK30), it is surprising in the region of saltwater incursion (RK30 to 11) and suggests that brackish water has a limited impact on sedimentation unless it is sustained. Further study is needed to confirm this.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable border=\"1\" id=\"Tab2\"\u003e\u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eSeaward of RK11, in the Delta region, Sr/Ba-HAc and Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc both show good correlation to depositional position, particularly if only surface and near-surface samples are considered (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The response of Sr/Ba-HAc is three times greater than that of Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, suggesting that this is the preferred ratio for predicting depositional position in settings with sustained saltwater. The reason why Sr/Ba-HAc is larger than Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc in saltwater environments is because both exchangeable and carbonate-bound Sr and Ba are extracted by acetic acid, while exchangeable Sr and exchangeable and barite-bound Ba are extracted by ammonium acetate. Because the Sr extracted by acetic acid is higher than that extracted by ammonium acetate, and the Ba extracted by ammonium acetate is higher than that extracted by acetic acid, the response of Sr/Ba-HAc exceeds that of Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc in the same sediment.\u003c/p\u003e\n \u003cp\u003eDinocyst relative and absolute abundances also show responses to depositional position through the Delta region of the FMT, although the correlation between these two variables is generally weak (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The exception to this are dinocyst absolute abundances when only surface and near-surface samples are considered; this relation is moderate. The moderate correlation of dinocyst absolute abundances to depositional position in surface and near surface samples is a direct comparison of sediment deposited under similar conditions, and probably records the increased incorporation of dinocysts into marine sediment with time.\u003c/p\u003e\n \u003cp\u003eThe general increase in \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e through the FMT of the Fraser River records the transition from terrestrially-sourced organic matter (C\u003csub\u003e3\u003c/sub\u003e plants and soil: -26 to -28\u0026permil;) landward of RK 11 to increasingly marine-sourced organic matter (-21 to -23\u0026permil;) \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e seaward of RK11 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, the seaward increase in \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e is not constant, and there appears to be a significant increase in \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e at ~\u0026thinsp;RK60. The cause of this increase is not immediately apparent but could reflect an increase in C\u003csub\u003e4\u003c/sub\u003e plant material (grasses) incorporated in sediment on the margins of the Fraser River and derived from the surrounding upper delta plain and farmland (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). C/N exhibits no discernable trends, although this may reflect the paucity of data between ~\u0026thinsp;RK90 and RK10.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec8\"\u003e\n \u003ch2\u003eSediment Attributes as Indicators of Salinity\u003c/h2\u003e\n \u003cp\u003eThe correlation of the six sediment attributes to mean high salinity shows more complicated trends than to depositional position (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), and this reflects 1) the inclusion of samples derived from both the FMT and Coastal areas, and 2) the impacts of both depositional processes and sedimentological properties on attributes (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). First, through SG 1\u0026ndash;5, Sr/Ba-HAc, Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, and \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e track changes in salinity reasonably well (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) suggesting that all three attributes record physical and/or chemical influences of saltwater in sediment. Dinocyst relative and absolute abundances show no change from SG 1\u0026ndash;5, but this probably reflects the paucity of data in SG3\u0026ndash;5. Note that the two \u0026ldquo;outlier\u0026rdquo; values in SG4 and 5 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eG\u0026ndash;H) are from Coastal areas (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eB) and are near each other; they do not show any discernable relation to samples from the FMT and other Coastal samples.\u003c/p\u003e\n \u003cp\u003eThe decrease in Sr/Ba with depth, and mainly in a single cored interval from 134 m water depth (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;B), is ascribed to the low clay content in the sediment below 4.5 m (Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eF and \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eF\u0026ndash;G) and to possibly low carbonate content (lower Ca-HAc and inorganic carbon, Supplementary Data File A). The lower carbonate content in samples indicates reduced calcium carbonate shell material, which results in reduced adsorption of Sr and reduced isomorphic Sr extracted by HAc; this is manifested as reduced Sr/Ba \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The cause of reduced clay content and lower carbonate content with depth probably records variability in the amount of terrestrial organic material incorporated in rapidly buried fine-grained material \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Indeed, the Fraser Delta front and prodelta experience a wide range of gravity-driven flows that transport sediment from shallow water to deep \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and these flows commonly introduce terrestrial organic matter directly from the Fraser River or transport sediment from the tidal flats into the delta front and/or prodelta. In addition, periodic mass-wasting events transport terrestrial organic material offshore \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Terrestrial organic material may also be allocthonous and transported to the delta front from other coastal regions of western North America via deep-water renewal events \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eInterestingly, \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e correlates reasonably well to clay percent (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) suggesting a positive linkage. In the Fraser Delta, clay is deposited dominantly offshore and in deep water, while silt is the more common mud type deposited in the river, tidal flats, and delta front \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The deposition of clay in deep-water marine settings should also be where marine-sourced organic material is most prevalent, and the correlation between clay percent and \u003csup\u003e13\u003c/sup\u003eC enrichment (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eH; Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) probably reflects this.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec9\"\u003e\n \u003ch2\u003eImplications for Paleosalinity\u003c/h2\u003e\n \u003cp\u003eOf the various techniques compared herein, Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc and especially Sr/Ba-HAc show the best response to increasing salinity and appear to correlate well when saline water is present and sustained in a depositional setting. However, our data also indicate that these ratios are impacted by a wide range of depositional processes, and low values should not be interpreted as indicating no or low-salinity conditions without considering alternate causes for their reduction. For example, neither ratio increases in the Fraser River where saline water incurs but is not sustained (RK30\u0026ndash;11), and values are low in the delta front and prodelta when shallow-water sediment is transported into deeper water.\u003c/p\u003e\n \u003cp\u003e\u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e is highly variable through the FMT and does not appear to correlate to depositional position (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e); instead \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e values reflect the dominance of terrestrial organic matter in deltaic settings. \u0026delta;\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e appears to be a reasonable indicator of salinity conditions, but again is impacted heavily by depositional processes, especially in deltas (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eF). The same is true for the relative and absolute abundances of dinocysts, which are also heavily impacted by river-derived sedimentation \u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Consequently, our data suggest that it is not advisable to compare values from river-influenced settings (e.g., FMT, deltas) with those from non-river influenced settings (shorefaces, bays, and open marine). As well, interpreting salinity trends using sediment proxies is most effective if values are derived along a depositional profile and can be compared relatively.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSample Analyses\u003c/h2\u003e \u003cp\u003eGrain size was measured for 98 samples using a Malvern Mastersizer 2000. Between 0.7 and 2 g of sediment was extracted from each sample and then treated with 30% H\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e2\u003c/sub\u003e for 36 hours to remove organic material. The supernatant was then pipetted off and the samples were mixed with 30 mL of 0.5% Sodium Hexametaphosphate solution and left for 24 hours. Samples were then stirred using a magnetic mixer for 5 minutes and were placed in a sonic bath for 1 minute. Following the sonic bath samples were emptied into the Malvern Mastersizer and analyzed for grain size.\u003c/p\u003e \u003cp\u003eSr/Ba was measured for 61 samples (+\u0026thinsp;14 repeat analyses) including 38 from along the FMT. Sr/Ba was determined through selective extraction using both 10% acetic acid (Sr/Ba-HAc) and 1 M of ammonium acetate (Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc) and following the methodology outlined in Wang, et al. \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. First, the sample was dried and crushed till it passed through a 0.149 mm (100) mesh. Two, 0.1 g sub-samples were extracted from the crushed and dried sample and were placed in two 15 mL plastic centrifuge tubes. Ten mL of 10% acetic acid was added to one centrifuge tube and 1M ammonium acetate to the other. The mixtures were stirred at room temperature (20\u0026ndash;30 ℃) for 2 hours, and then left to stand for 24 hours (mixtures can also be centrifuged for 20 minutes at 4500 rpm to separate the solid and liquid). The supernatant was then used to analyze Sr and Ba using an ICP-AES. Alternatively, the supernatant can be diluted to one-tenth of its original concentration for analysis by ICP-MS.\u003c/p\u003e \u003cp\u003eδ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e values were determined for 90 samples, including 48 samples from Czarnecki, et al. \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and C/N (% total organic carbon / % total nitrogen) was determined for 42 of these samples. For the 42 new samples, samples were first cleaned used deionized water and then dried in an oven at 60\u0026deg;C for 48 hours. Clean and dried samples were pulverized and sieved through a 0.63 mm mesh, and 1 g of each sample was extracted and treated with 5 mL of 2 N hydrochloric acid (HCl) for approximately 16 hours at room temperature to remove inorganic carbon \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A 40 mg sub-sample was extracted from each de-carbonated sample and was placed in a tin capsule in preparation for measuring total organic carbon and nitrogen contents. Elemental analyses were done using an elemental analyzer (vario MICRO cube elementar) in the Marine Geochemistry lab in the Institute of Oceanography, National Taiwan University, Taiwan. The standard used in elemental analysis is soil standard no. 502\u0026thinsp;\u0026minus;\u0026thinsp;062 Leco Reference Materials (%C\u0026thinsp;=\u0026thinsp;0.924, %N\u0026thinsp;=\u0026thinsp;0.093). All measurements were performed in duplicate and the relative error by multiple analyses of reference material was \u0026lt; %. The stable carbon isotope composition was measured using an elemental analyzer (Thermo Scientific Flash EA) connected with an isotope ratio mass spectrometer (Thermo Scientific Delta V Advantage). Carbon isotopic composition is presented as δ\u003csup\u003e13\u003c/sup\u003eC in the standard δ-notation in per mil (\u0026permil;) with respect to Vienna Pee Dee Belemnite (VPDB). The measurements were calibrated with the standard reference material IAEA-CH-3 (δ\u003csup\u003e13\u003c/sup\u003eC = \u0026minus;\u0026thinsp;24.72\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u0026permil;) and the analytical reproducibility for both δ\u003csup\u003e13\u003c/sup\u003eC is better than 0.2\u0026permil;.\u003c/p\u003e \u003cp\u003ePalynomorph analyses include 45 from Czarnecki, et al. \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and 12 new samples (n\u0026thinsp;=\u0026thinsp;57). The 12 new samples were processed at the Paleoenvironmental Laboratory, University of Minnesota, USA. Dinoflagellate cysts, pollen grains and spores were extracted using a standard dinocyst extraction method \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Approximately 3 cm\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e of sediment were subsampled and oven-dried at ~\u0026thinsp;40\u0026deg;C and then weighed. Two calibrated tablets of \u003cem\u003eLycopodium clavatum\u003c/em\u003e spores (batch 140119321) were added to each sample to estimate palynomorph concentrations \u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Samples were treated with room-temperature hydrochloric acid (10%) to dissolve carbonates, rinsed twice with reverse osmosis (RO) water, sieved through a 120 \u0026micro;m mesh and retained on a 15 \u0026micro;m nylon Nitex mesh to remove coarse and fine particles. Siliceous material was dissolved by using room-temperature hydrofluoric acid (48%) for up to two weeks. Samples were subsequently treated with hydrochloric acid (10%) to remove precipitated fluorosilicates, rinsed with RO water several times, gently sonicated for \u0026lt;\u0026thinsp;60 s, and sieved again through a 15 \u0026micro;m mesh sieve. After each step, samples were centrifuged at 3600 rpm for six minutes. Two drops of the residue were mounted between a slide and coverslip in glycerine jelly and marine palynomorphs were counted at 600\u0026times; magnification. Dinocyst and other palynomorphs were identified and counted using a Nikon Eclipse 80i transmitting light microscope. Cyst identification was made based on of published descriptions \u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Palynomorphs are grouped into tree pollen, herbs and shrubs pollen, spores, and dinocysts. Dinocysts are subdivided into autotrophs and heterotrophs, and the relative abundances and concentrations of all dinocysts are calculated (Supplementary Data File A).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Methods\u003c/h2\u003e \u003cp\u003eSediment attribute data are divided into two discrete populations based on visual inspection of graphs. A break appears in nearly all datasets between values in the river (RK 110.6 to \u0026gt;\u0026thinsp;11; referred to herein as River) and those from the most seaward extend of the river, the tidal flats, delta front and prodelta (RK\u0026thinsp;\u0026le;\u0026thinsp;11 to -14.2; referred to herein as Delta). Means and standard deviations (and the slope equation for δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e) of samples along the River use all data between RK110.6 and \u0026gt;\u0026thinsp;11; however, slope equations for Delta samples are calculated from RK15 and seaward to ensure trends in the Delta population are continuous from the River population (i.e., the River population effectively acts as the y-intercept for the Delta population).\u003c/p\u003e \u003cp\u003eThe correlation coefficient (r), coefficient of determination (R\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e), and p-value for each equation is calculated for the River and Delta populations and for all attributes using the Analysis ToolPak in Microsoft\u0026reg; Excel and using linear regression only. Coefficients of determination are labelled as strong (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.75), moderate (0.5\u0026thinsp;\u0026le;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.75), weak (0.25\u0026thinsp;\u0026le;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.5), or negligible (0.1\u0026thinsp;\u0026le;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.25) for the purpose of comparison. For linear regression equations with an R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1 (i.e., slope equation explains\u0026thinsp;\u0026lt;\u0026thinsp;10% of the data), values are reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation only. Statistically insignificant slope equations (p\u0026thinsp;\u0026ge;\u0026thinsp;0.05), equations where there is a\u0026thinsp;\u0026ge;\u0026thinsp;5% chance that there is no relationship between the two variables, are not reported herein.\u003c/p\u003e \u003cp\u003eOutliers are identified in all datasets and are excluded from quantitative assessments (linear regression and mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This is done to resolve underlying trends in relatively low-n datasets, and to avoid skewing trends considerably based on one or two datapoints. Note that the slope equations listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are only valid when compared together (i.e., relatively) as river km data is unique to the Fraser River and mean high salinity data is representative because of significant salinity changes associated with tidal fluctuations, precipitation, and river discharge.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Phillip Hill and Randy Enkin of the Geological Survey of Canada for providing samples from cored intervals from the Strait of Georgia. We thank two anonymous reviewers for their comments on an earlier version of this manuscript. This research was made possible through an NSERC Discovery Grant to S. Dashtgard (grant RGPIN-2019-04528) and a\u0026nbsp;National Natural Science Foundation of\u0026nbsp;China grant to A. Wang (grant 41572096).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDashtgard, S. E., L\u0026ouml;wemark, L., Wang, P.-L., Setiaji, R. A. \u0026amp; Vaucher, R. Geochemical evidence of tropical cyclone controls on shallow-marine sedimentation (Pliocene, Taiwan). Geology \u003cb\u003e49\u003c/b\u003e, 566\u0026ndash;570, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1130/g48586.1\u003c/span\u003e\u003cspan address=\"10.1130/g48586.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurton, D. \u0026amp; Wood, L. J. 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F. \u0026amp; Pospelova, V. A determination key for modern dinoflagellate cysts. Palynology \u003cb\u003e39\u003c/b\u003e, 387\u0026ndash;409, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/01916122.2014.990115\u003c/span\u003e\u003cspan address=\"10.1080/01916122.2014.990115\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-1694129/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1694129/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany sediment attributes have been proposed as proxies for determining salinity conditions under which sediment is deposited, and six attributes (Sr/Ba-HAc, Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e, C/N, and the relative abundances and concentrations of dinoflagellate cysts) are compared here. In this paper, sediment attributes from the Fraser River Delta, Canada and surrounding coastal areas are compared by depositional position along the fluvial-to-marine transition, by sea-surface salinity, and by sedimentological characteristics. Along the fluvial-to-marine transition, most attributes exhibit distinct trends between parts of the river that experience \u003cem\u003esustained\u003c/em\u003e marine water (saltwater) influence over seasonal and tidal timeframes, and parts that experience only freshwater or periodic saltwater influence. No attributes are reliable indicators of depositional position where saltwater incursion is short lived or where water is fresh. Where marine influence is sustained, Sr/Ba-HAc and Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc are the most reliable positional indicators along the fluvial-to-marine transition. When compared strictly to salinity, Sr/Ba-HAc, Sr/Ba-NH\u003csub\u003e4\u003c/sub\u003eAc, and δ\u003csup\u003e13\u003c/sup\u003eC\u003csub\u003eorg\u003c/sub\u003e all correlate predictably except in delta front and prodelta settings. Our data show that all six sediment attributes are heavily impacted by river-derived sedimentation, and it is not appropriate to compare values from strongly river-influenced settings (e.g., deltas) with those from weakly river-influenced settings (e.g., bays and estuaries).\u003c/p\u003e","manuscriptTitle":"Salinity indicators in sediment through the fluvial-to-marine transition (Fraser River, Canada)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-10 15:34:25","doi":"10.21203/rs.3.rs-1694129/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-07-25T05:41:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-07-20T20:53:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"dca268f7-8016-46b5-a17e-7118f67d9797","date":"2022-07-06T22:23:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-07-06T21:31:56+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-07-05T12:57:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-06-02T11:59:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-06-02T11:56:07+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-05-25T21:16:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e733d4b9-57b9-42c4-985a-ff5e0cbd3e54","owner":[],"postedDate":"June 10th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2022-08-12T04:59:15+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-10 15:34:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1694129","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1694129","identity":"rs-1694129","version":["v1"]},"buildId":"J0_U0BvcaRcwD8yVFaRlm","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}