Amino acid cycling in the Mississippi River Plume and effects from the passage of Hurricanes Isadore and Lili

Journal of Marine Systems 136 (2014) 10–21

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Amino acid cycling in the Mississippi River Plume and effects from the passage of Hurricanes Isadore and Lili Thomas S. Bianchi a,⁎, Bryan L. Grace b,1, Kevin R. Carman c, Ivan Maulana d a

Department Geological Sciences, University of Florida, Gainesville, FL 32611, USA Department of Earth and Environmental Sciences Tulane University, New Orleans, LA 70118, USA Department of Biology, University of Nevada, Reno, NV 89557, USA d Department of Geology, Texas A&M University, TX 77843, USA b c

a r t i c l e

i n f o

Article history: Received 22 September 2013 Received in revised form 7 February 2014 Accepted 27 March 2014 Available online 5 April 2014 Keywords: Amino acids Hurricane Desorption/sorption Mississippi River Plume Particulate and dissolved organic carbon

a b s t r a c t We present data on the effects of Hurricanes Isadore and Lili on the spatial and temporal variations in concentrations of amino acids, and other bulk dissolved and particulate constituents in surface waters of the Mississippi River Plume (MRP) collected during 3 survey cruises (March 2002, October 2002, and April 2004). Abiotic factors (e.g., particle sorption and sediment resuspension) had the largest contribution in describing DAA and PAA dynamics in the MRP. The range of dissolved organic carbon (DOC) (88.61 to 699.90 μM) and particulate organic carbon (POC) (0.08 to 32.72 μM) values was slightly higher than the range observed for a broader region of the Louisiana shelf, but in general agreed with peak values at the mid-salinity range of the plume. The positive and negative correlations between acidic (e.g., aspartic acid and glutamic acid) and basic (e.g., histidine and arginine) DAA and salinity, respectively, in the MRP, were largely controlled by differential partitioning of amino acids with suspended sediments. Concentrations of β-alanine, γ-aminobutyric acid, and δ-aminovaleric acid were significantly higher during October 2002 compared to spring sampling events, due to resuspension of shelf sediments caused by the recent passage of Hurricane Isadore and the approach of Hurricane Lili, as it entered the Gulf of Mexico during our sampling. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Coastal waters contain a diverse suite of dissolved amino acids (DAAs) and particulate amino acids (PAAs), which make up a significant fraction of the total dissolved organic nitrogen (DON) pool (Capone et al., 2008; Henrichs and Farrington, 1987; Lomstein et al., 1998). Amino acids are particularly abundant in highly productive waters and serve as important intermediates in the marine nitrogen cycle (Capone et al., 2008, and references therein). Past work has shown that a significant fraction of DON in marine waters consists of amide nitrogen (Aluwihare et al., 2005; McCarthy et al., 1997). Photochemical breakdown of proteins and peptides into amides and amino acids may in part be an important mechanism for creating amide nitrogen (Mopper and Kieber, 2002; Wang et al., 2000). In areas such as river plumes, with high suspended loads and high primary productivity, it is expected that the flux of such compounds will be significant in the

⁎ Corresponding author. Tel.: +1 352 392 6138; fax: +1 352 392 9294. E-mail address: tbianchi@ufl.edu (T.S. Bianchi). 1 Present address: CH2M HILL, 3900 N. Causeway Boulevard, Suite 1250, Metairie, LA 70002, USA.

http://dx.doi.org/10.1016/j.jmarsys.2014.03.011 0924-7963/© 2014 Elsevier B.V. All rights reserved.

DON pool (Capone et al., 2008; Mulholland et al., 1998; Pantoja and Lee, 1999). Bacterial nitrogen dynamics in the Mississippi River Plume (MRP) have been shown to be strongly linked to the cycling of dissolved free amino acids (DFAAs), largely derived from “fresh” organic matter (Gardner et al., 1996, 1997). DFAA concentrations can vary over short time scales (Gardner et al., 1996, 1997) and over steep salinity gradients (Grace and Bianchi, 2010) in the MRP. Amino acids represent an important component of the semi-labile fraction of riverine organic matter (Duan and Bianchi, 2007; Ittekkot and Zhang, 1989; Spitzy and Ittekkot, 1991). A greater understanding of the controls on amino acid abundance and composition will thus provide an important insight into the sources and biogeochemical cycling of organic matter in rivers and river plumes (Aufdenkampe et al., 2001; Hedges et al., 1994; Ittekkot and Arian, 1986). Extreme gradients in light, salinity, DOM, and dissolved inorganic nitrogen (DIN) have significant impacts on the temporal and spatial variability of carbon dioxide (CO2) fluxes in the MRP (Cai, 2003; Dagg et al., 2004; Keul et al., 2010; Lohrenz and Cai, 2006). Many productive plume regions of large-river delta-front estuary (LDE) systems, particularly those receiving large inputs of nutrients (as does the Mississippi River), are global sinks for CO2 in regions where light availability is sufficient to support large phytoplankton blooms. Some regions of the

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11

plume have also been shown to be sources of CO2 (Bianchi et al., 2013; Cai, 2003), where light is limited but DOM (e.g., amino acids) is likely available for microbial consumption. In this study, we examine the changes in composition and abundance of dissolved and particulate amino acids across a salinity gradient in the MRP. This work is an extension of prior experimental work that examined the partitioning of amino acids in MRP waters with different ionic strength (Grace and Bianchi, 2010). The objectives of this work were to examine: (1) abiotic and biotic controls on dissolved and particulate amino acid abundance and composition with bulk C and N across a salinity gradient in the MRP; and (2) determine the effects of a large perturbation event, such as a hurricane, on the relationships between amino acids and bulk C and N indices in the MRP.

DOC and TDN measurements were performed on a Shimadzu TOCVCSH/CSN using high-temperature catalytic oxidation (HTCO) and chemiluminescence, respectively (Guo et al., 1994; Sharp et al., 2002). We used certified references of DOC and TDN stored in sealed ampoules provided by Dr. Wenhao Chen, University of Miami; the average DOC value for these standards measured on our instrument was 45 μM ± 3 (n = 27). Sample analyses of both DOC and TDN were made with a precision of approximately ±2%.

2. Materials and methods

2.3. Particulate organic carbon and particulate nitrogen analysis

2.1. Sample collection and processing

POC/PN sample filters were oven-dried (50 °C) for 24 h, acidified with 12 N HCl vapor in a dessicator for 24 h to remove inorganic carbon, and then oven-dried (50 °C) for 1 h. Acidified filters were then packed into solvent-cleaned tin boats for analysis on an EA 1108 FISONS Elemental Analyzer.

Water samples were collected aboard the R/V Pelican during three cruises in the MRP, conducted in March 2002, October 2002, and April 2004. Water column samples were collected in acid-washed glass bottles from a rosette of Niskin bottles and immediately filtered (under low pressure) through combusted GF/F filters (nominal pore size 0.7 μm). Filtered waters and filters, for dissolved amino acid (DAA), particulate organic carbon (POC) and particulate amino acid (PAA) analyses, respectively, were kept in the dark, stored in liquid nitrogen onboard ship, and stored in a −80 °C freezer after returning to the laboratory. Mississippi River discharge during the 12 d sampling event (measured at Tarbert Landing in New Orleans, Louisiana) was on the rising limb of the hydrograph (increasing discharge) during the March 2002 cruise, starting at 13,000 m3 s−1 and ending at 28,000 m3 s− 1. During the October 2002 cruise, discharge was lower, starting at 7000 m3 s− 1 and ending at 8000 m3 s− 1, over a 9-d sampling event. In the April 2004 cruise, discharge fell from 25,000 m3 s−1 to 15,000 m3 s−1 over the 11-d sampling event. It has been shown that the rising and falling limbs on river hydrographs are critical phases that can affect the biogeochemistry of river waters and their associated coastal plume regions (Deksissa et al., 2004). Water samples were collected across a salinity gradient in the MRP during all three cruises (Fig. 1). These stations were located in regions that covered a broad range of particulate and dissolved constituents in the surface waters of the MRP. In October 2002, our sampling in the MRP unexpectedly occurred between Hurricanes Isadore (September 14–27, 2002) and Lili (September 21 to October 4, 2002); the effects of the approach of Lili as it entered the Gulf of Mexico (e.g., increasing wave height) were apparent during our sampling in October 2002. DOC, total dissolved nitrogen (TDN), dissolved amino acids (DAAs), particulate organic carbon (POC), particulate nitrogen (PN), and particulate amino acid (PAA) samples were collected from each surface water sampling location visited during these three research cruises. However, due to longer sample collection and required processing time, nutrient data were only collected from approximately 50% of the sample locations visited. Bacterial abundance and productivity were generally measured three to five times daily as the ship traversed between riverine and marine stations of the MRP to allow for requisite incubation time during processing. Total suspended matter (TSM) was measured as the difference in mass between pre-weighed combusted GFF and after filtering 1 L of surface water. Filtered water collected for dissolved organic carbon (DOC) and TDN analyses was stored in acid-washed combusted 40 mL amber vials, preserved with 100 μL of 2 N HCl, and capped with Teflon-coated septa and stored at − 20 °C. DAA samples were collected in combusted 20-mL scintillation vials, and the filtrate was stored in 50-mL plastic screw top bottles. POC, PN, and PAA were collected by filtering 100 mL of water on combusted 25-mm (0.7-μm nominal pore size) GFFs using a vacuum pump (under low pressure). Chlorophyll a was analyzed on select number of POC samples. All filters

were folded in half, placed in combusted aluminum foil envelopes, and immediately stored frozen at −80 °C. 2.2. Dissolved organic carbon and total dissolved nitrogen analyses

2.4. Chlorophyll a extraction and analysis Chlorophyll a was extracted from POC samples according to the methods of Bianchi et al. (1995), as modified by Chen et al. (2001). Extracts were analyzed using reversed-phase high performance liquid chromatography (RP-HPLC) (Waters 610) coupled with an on-line 996 photodiode array detector (PDA) and fluorescence detector (Shimadzu-RF 535). The absorbance detector was set at 438 nm and the fluorescence detector at an excitation of 440 nm and an emission of 660 nm, according to the methods of Wright et al. (1991), as modified by Bianchi et al. (1995) and Chen et al. (2001). Chlorophyll a standards (obtained from DHI Water and Environment Co., Denmark) were run individually to determine retention times, spectra, and response factors. Identification was performed by comparing retention times and UV spectra of the peaks in each sample chromatogram to those of the standards. Detection limits were ca. 1 nmol L−1 g OC−1. 2.5. Amino acid analysis Amino acids were analyzed by RP-HPLC, using a modification of the pre-column o-phthaldialdehyde (OPA) derivatization technique (Lee et al., 2000; Lindroth and Mopper, 1979). The HPLC system consisted of a Dionex GP50 Gradient Pump coupled with a Dionex RF 2000 Fluorescence Detector (excitation at 330 nm and emission at 418 nm). All samples were auto-injected (40 μL) using a Dionex ASI-100 refrigerated auto-sampler. Separation of amino acids was accomplished using an Alltech Alltima C18 column (5 μm, 250 × 4.6 mm) fitted with an Econosphere guard column, at a flow rate of 1.0 mL min−1. A binary gradient of 0.05 M sodium acetate buffer with 5% tetrahydrofuran added (pH adjusted to 5.5 with acetic acid) (eluant A) and HPLC grade methanol (eluant B) was used, where the gradient began at time zero with 22% B, ramped to 60% over 40 min, and finally to 100% B at 50 min, where it remained isocratic for an additional 10 min. Individual amino acids were identified on the basis of individual standards (Sigma Chemical Company) concentrations and corrected for individual responses from a standard amino acid mix (Pierce Standard-H). Methionine data were not included because methionine is sensitive to the hydrolysis process employed for these experiments. Non-protein amino acids, β-alanine, γ-aminobutyric acid, and δ-aminovaleric acid were added individually to the standard mixture. Detection limits for individual amino acids ranged from 0.5 nM (e.g., valine) to 5 nM (e.g., glycine) with an average of 1.7 nM for all amino acids measured. For DAA analysis, 2 mL of 0.7 μm filtered water was added to screw top test tubes containing 2 mL of 6 N HCl (containing 0.5% phenol),

12

T.S. Bianchi et al. / Journal of Marine Systems 136 (2014) 10–21

Fig. 1. Sample location map from samples collected in March 2002, October 2002, and April 2004. Sample locations identified by a circle when sites were visited multiple times during the March 2002 and October 2002 cruises.

sealed under an N2 atmosphere, and hydrolyzed at 110 °C for 24 h. L-norvaline was added to samples prior to hydrolysis and served as an internal standard and the recovery was always greater than 90%. Hydrolyzed samples were then neutralized with NaOH and transferred to 2 mL crimp-top vials, derivatized, and injected on the HPLC. Coefficients of variation from duplicate DAA samples were usually on the order of b5.0%. For PAA analysis, thawed filters were sealed under N2 in glass, screw top test tubes containing 6 N HCl and 0.2% phenol and hydrolyzed for 20 h at 110 °C. Norvaline was added to samples prior to hydrolysis and served as an internal recovery standard. Hydrolyzed samples were centrifuged at 4000 rpm at 4 °C for 5 min to remove particulates; the supernatant was filtered using a 0.2 μm sterile syringe filter and placed in 2 mL crimp top vials. Samples were processed as described above. Asparagine and glutamine are converted to aspartic acid and glutamic acid, respectively, during the hydrolysis process and are

included in reported measurements of aspartic acid and glutamic acid. Coefficients of variation from triplicate PAA samples were usually b 10%. 2.6. Bacterial productivity and abundance Bacterial abundance was determined by direct counting of DAPI(4,6-diamidino-2-phenylindole) stained cells by employing 2% final concentration glutaraldehyde fixative (Porter and Feig, 1980). Bacteria were concentrated on Irgalin Black-stained polycarbonate filters (0.2-μm pore size; Poretics) and abundance was counted in ten fields of view for each sample counted using epifluorescence microscopy (Olympus BX-50). Bacterial productivity was determined by 3 H-leucine incorporation (Kirchman, 1993). Samples (10 mL) were incubated with [3H]leucine for 30 min. The specific activity of the added leucine was 50 μCi nmol− 1 and the final added concentration was

T.S. Bianchi et al. / Journal of Marine Systems 136 (2014) 10–21

0.5 nM. Incubations were terminated by the addition of formalin (2% final concentration).

13

3. Results

average salinity of 25.9, 25.7, and 26.5 in March 2002, October 2002, and April 2004, respectively. The average water column depths at all stations in March 2002, October 2002, and April 2004 cruises were 37.6, 36.6, and 43.2 m, respectively. The river station had a water depth of ≤6 m, while the deepest shelf stations generally exceeded 100 m during each of the cruises. The average total suspended material (TSM) concentrations during the March 2002, October 2002, and April 2004 cruises were 9.5, 10.4, and 11.9 mg L−1, respectively. The large standard deviation for TSM values reflected the inherent variability of TSM across steep salinity gradients in the MRP and outer slope region. While POC was not as variable as TSM, an average concentration change of greater than 50% was observed over the three cruises (this includes all stations). PN concentrations were approximately 10% to 20% of POC concentrations, but showed variability similar to that of POC. DOC was highest during the March 2002 cruise (mean of 459 μM ± 137), compared to the lower means in October 2002 (168 μM ± 78) and April 2004 (241 μM ± 79). TDN ranged between 11.44% of the DOC concentration in April 2002, and 15.86% in October 2002 (TDN was not measured during the March 2002 cruise). Ammonium was only measured during the April 2002 cruise (mean = 1.28 μM ± 1.13). Average nitrate concentrations were 10 times higher in April 2004 than in March and October 2002. Bulk hydrographic data collected in the MRP were significantly correlated with surface water salinity (Fig. 2A). Salinity in surface waters increased with water depth, which generally corresponded to distance from the river mouth at Southwest Pass to deeper shelf waters. It was assumed that, during periods of relativity mild wind events, mixing of bottom and surface waters at these relatively shallow stations in the MRP (between 4.5 and 10 m for all but the deepest stations investigated during the 3 cruises) was minimal, with the exception of storm events (Corbett et al., 2006), as observed during the October 2002 cruise. TSM showed a strong negative correlation with salinity, as riverine particulate materials became more diluted at higher-salinity deeper shelf stations. POC and PN showed no clear patterns. However, concentrations of DOC and TDN were negatively correlated with salinity during all cruises. Ammonia was only measured on the April 2004 cruise and showed a similar negative correlation to salinity at that time, as did nitrite/nitrate concentrations in the MRP.

3.1. Bulk parameters

3.2. Biomarker and bacterial parameters

Bulk C and N, particulate, and hydrographic parameters are presented in Table 1. Salinity ranged from 0.2 to 36.5 over the 3 cruises, with an

Chlorophyll a, particulate and dissolved amino acids, bacterial biomass, and bacterial productivity data collected during all three cruises

2.7. Statistical analysis Correlation analyses were performed using Microsoft Excel 2007 and Statgraphics (Version 10). Pearson Product Moment correlation matrices, a dimensionless index that ranges from −1.0 to 1.0 inclusive and reflects the extent of a linear relationship between two datasets, were generated to examine the linear predictability among bulk parameters, bacterial productivity and abundance, and pigments and amino acid pools, both within individual cruise datasets and within the cumulative dataset from all 3 cruises. Amino acid composition and organic matter diagenesis were linked using the “degradation index” (DI). The DI was derived using principal component analysis (PCA), from samples ranging from fresh phytoplankton to highly degraded turbidite sediment samples (Dauwe and Middelburg, 1998; Dauwe et al., 1999). The range of organisms and marine sediments used to construct the DI included algae (Brown, 1991), phytoplankton (Cowie and Hedges, 1992), bacteria, zooplankton, trap material and surface sediment (0–1 cm) and deep sediment (1–15 cm) of Saanich Inlet and Dabob Bay, oxidized turbidite, unoxidized turbidite and surface sediments in North Sea (Dauwe and Middelburg, 1998). We used the Dauwe et al. (1999) PCA to directly compare our PAA and DAA using the mol% amino acid data and the following formula: DI ¼ ∑i ½ðvari – avg vari =std vari Þloadingi  where, vari = mole percent (mol%) of amino acid; avg vari = mean amino acid mol%; std vari = standard deviation of amino acid mol%; and loadingi = PCA derived loading of amino acidi. The more negative the DI value, the more degraded the sample, with positive DI values indicative of fresh materials. Although this method is more commonly used for marine systems (primarily sediments), Duan and Bianchi (2007) successfully applied this to DAA and PAA in Mississippi River waters.

Table 1 Hydrographic, nutrient, particulate organic carbon (POC), particulate nitrogen (PN), dissolved organic carbon (DOC), total dissolved nitrogen (TDN), dissolved amino acids (DAAs), particulate amino acids (PAAs), total suspended matter (TSM), bacterial productivity, bacterial biomass, and chlorophyll a from all three cruises in the Gulf of Mexico collected in March 2002, October 2002, and April 2004. Mar 02

Salinity Total depth (m) TSM (mg L−1) POC (μM) PN (μM) DOC (μM) TDN (μM) NH4 (μM) NO3 (μM) Bacterial productivity (g CL−1 h−1) Bacterial biomass (cells L−1) Chlorophyll a (μM) Total DAA (μM) Total PAA (μM)

Oct 02

Apr 04

Minimum

Maximum

Average (n = 34)

Minimum

Maximum

Average (n = 57)

Minimum

Maximum

Average (n = 48)

0.2 4.5 0.5 5.8 0.1 209 – – 1.42 1.42 E−06

34.6 102 49.7 32.7 3.0 699 – – 11.20 2.41E−06

25.9 ± 8.8 37.63 ± 30.9 9.5 ± 14.7 16.8 ± 8.6 1.2 ± 0.9 459 ± 137 – – 3.33 ± 2.93 1.86E−06 ± 2.99E−07

1.2 5.8 1.3 0.1 0.1 88 5.57 – 0.21 2.20E−07

36.5 106 32.4 18.9 2.7 429 95.31 – 35.32 2.66E−06

25.7 ± 7.4 36.5 ± 28.5 10.4 ± 8.1 7.9 ± 4.2 1.0 ± 0.8 168 ± 78 26.70 ± 18.50 – 3.32 ± 7.00 1.13E−06 ± 7.62E−07

2.0 5.8 0.6 2.0 0.1 127 1.07 0.32 0.54 1.04E−06

36.36 101.60 47.80 21.13 7.14 437.31 118.66 4.79 142.40 2.23E−06

26.54 ± 7.72 43.20 ± 24.42 11.97 ± 12.71 8.58 ± 5.87 1.54 ± 1.66 241.62 ± 79.26 27.63 ± 33.64 1.28 ± 1.13 32.12 ± 41.68 1.63E−06 ± 3.21E−07

1.68E+08

2.21E+09 9.28E+08 ± 5.26E+08 2.37E+08 1.07E+09 5.78E+08 ± 2.34E+08

2.77E+08

1.70E+09

9.72E+08 ± 4.67E+08

0.69 ND ND

15.74 1.94 0.56

– 0.02 ND

– 2.00 0.53

– 1.36 ± 0.58 0.25 ± 0.14

4.41 ± 4.48 1.02 ± 0.58 0.18 ± 0.16

0.02 ND ND

4.87 5.42 1.86

1.46 ± 1.80 0.85 ± 0.99 0.41 ± 0.39

Units for each parameter are included in parentheses. Minimum, maximum, and average results are given for each parameter. The number of samples collected during each cruise is provided as “n”. Standard deviations for the results are given in parenthesis next to the average result. Parameters not analyzed during a specific cruise are indicated with a dash (–). ND indicates sample results that are not detected.

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A

Pearson Product Moment Correlation between Salinity and Bulk Parameters

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

Mar-02

Total Depth

TSM

POC

PN

DOC

TDN

Ammonia

Oct-02

Nitrate

Apr-04 All Cruises

B

Pearson Product Moment Correlation between Salinity and Biotic Parameters

1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

Productivity

Biomass

Chlorophyll

Total DAA

Total PAA

Fig. 2. Pearson Product Moment correlations between (A) salinity and bulk parameters (total depth, total suspended material [TSM], particulate organic carbon [POC], dissolved organic carbon [DOC], total dissolved nitrogen [TDN], ammonia, and nitrate) and (B) salinity and biotic parameters (bacterial productivity, bacterial biomass, chlorophyll, total dissolved amino acid [DAA] and total particulate amino acid [PAA]). Correlations were generated both within individual cruises (March 2002, October 2002, and April 2004) and cumulative for all cruises.

3.3. Amino acid composition The relative compositions of DAA and PAA are shown in Fig. 4. The two dominant amino acids in both the DAA and PAA pools were glycine (up to 20 mol%) and alanine (up to 15 mol%) at all stations during all cruises. While the relative amount of dissolved glycine and alanine was similar among sampling events, the relative amount of particulate glycine was higher by almost 1.8 mol% in October 2002, relative to the spring sampling events. Dissolved glutamic acid, which

contains an acidic side-chain, was consistently over 12 mol% during all sampling events; particulate glutamic acid mol% was significantly higher (p b 0.05) in October 2002 compared to March 2002 and April

Total Particulate Amino Acids (µM)

A Mar-02 2.00 Oct-02 1.80 Apr-04 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20 0.00 0.00 10.00

20.00

30.00

40.00

B Total Dissolved Amino Acids (µM)

are shown in Table 1. Bacterial productivity was relatively consistent between the two spring sampling events, but lower during the October 2002 cruise. A similar difference in bacterial biomass was seen in October 2002 results, showing a decrease of 3.72 × 108 cells L− 1 compared to the average of the spring cruises. Chlorophyll a was more than 3 times higher in March 2002 compared to October 2002 (chlorophyll a was not measured in April 2004). The average total DAA concentration was lower in October 2002 than in March 2002 and April 2004. The highest total DAA concentration was observed in October 2002. The average total PAA concentration was almost twice as high in October 2002 (0.41 μM) compared to March 2002 and October 2004. Total DAA and PAA concentrations were highest in the mid-salinity regions of the plume (Fig. 3). The highest concentration of both DAA and PAA occurred during the October 2002 sampling event at salinity 19, but most of the high concentrations were found between salinities 27 and 29. However, the first observed increase in PAA and DAA, across a salinity gradient in the MRP, occurred between 5 and 13, suggesting that these amino acids were likely introduced from the river. These peaks corresponded to sampling locations with a water depth less than 15 m, where turbidity is high, and chlorophyll is low. In October 2002, a spike was observed in DAA and PAA and was likely due to mixing of resuspended sediments and porewaters with surface waters during storm activity from Hurricanes Isadore and Lili.

6.00 5.00 4.00 3.00 2.00 1.00 0.00 0.00

10.00

20.00

30.00

40.00

Salinity Fig. 3. Total particulate (A) and dissolved (B) amino acids (μM) plotted against salinity within each cruise (March 2002, October 2002, and April 2004).

T.S. Bianchi et al. / Journal of Marine Systems 136 (2014) 10–21

A

15

Average Indivdual DAA 25%

Mol%

20% 15% 10% 5% Mar-02 Oct-02

0% ASP GLU SER GLY THR ALA TYR VAL PHE

B

ILE

LEU

HIS ARG

ILE

LEU

HIS ARG

Apr-04

Average Individual PAA 25%

Mol%

20% 15% 10% 5% 0% ASP GLU SER GLY THR ALA TYR VAL PHE

Fig. 4. Average individual mole percent (mol%) contribution of both individually dissolved (A) and particulate (B) amino acids within each cruise (March 2002, October 2002, and April 2004); ASP, aspartic acid; GLU, glutamic acid; SER, serine; GLY, glycine; THR, threonine; ALA, alanine; TYR, tyrosine; VAL, valine; PHE, phenylalanine; ILE, isoleucine; LEU, leucine; HIS, histidine; ARG, arginine. Error bar presents the standard deviation of the amino acid mol% mean.

2004. Particulate aspartic acid was almost 2 mol% higher in October 2002 compared to the two spring sampling events. As observed in the particulate pool, the relative increase of these two acidic amino acids during October 2002 was accompanied by a corresponding decrease in two basic amino acids (histidine and arginine). While dissolved serine was relatively stable between cruises in the dissolved pool (approximately 5 mol%), particulate serine was significantly lower during October 2002 (approximately 5 mol%) compared to March 2002 and April 2004 (approximately 10 mol%). Threonine and valine were similar at all times and in both dissolved and particulate pools (approximately 6 to 9 mol%). Leucine was relatively consistent in the dissolved pool at approximately 5 to 6 mol%, but was equally prevalent in both the dissolved particulate pools. No trends were observed for dissolved tyrosine, isoleucine, histidine, and arginine. Relative amounts of particulate histidine and arginine were significantly lower (p b 0.05) than their respective dissolved pools. The statistical relationship between salinity and individual amino acid mol% concentrations in both the particulate and dissolved pools is shown in Fig. 5. While the majority of individual amino acids did not show a consistent correlation with salinity, correlations were observed between the acidic amino acids (aspartic acid and glutamic acid) and basic amino acids (histidine and arginine). Dissolved acidic amino acids and particulate basic amino acids were positively correlated with salinity. Conversely, acidic amino acids in the particulate pool and basic amino acids in the dissolved pool were negatively correlated with salinity. Particulate glycine, valine, tyrosine (except for October 2002) and phenylalanine were positively correlated with salinity, while alanine and isoleucine were slightly negatively correlated with salinity. Threonine and leucine did not show any significant trends with salinity. The most striking relationship between salinity and individual PAA was with aspartic acid and histidine (Fig. 5). While aspartic acid mol% concentration generally ranged between 6 and 8% in lower-

salinity surface water (between 0 and 22), the mol% concentration decreased significantly with higher salinity (N26); mol% of histidine showed the opposite trend. In general, individual amino acids generally showed no temporal trends between cruises (Fig. 5). However, the particulate non-protein-forming amino acids, β-alanine, γ-aminobuteric acid, and δ-aminovaleric acid, were significantly higher in surface waters during the October 2002 cruise compared to the two spring cruises (p b 0.05). 3.4. Bacterial and primary production Fig. 6 shows bacterial productivity, bacterial biomass, and total chlorophyll relative to salinity. Bacterial productivity was relatively consistent across salinity gradients during the two spring cruises, but was more variable during October 2002, specifically between salinities 26 to 29. Bacterial biomass generally followed the same trend as bacterial productivity during March 2002 and April 2004, but became somewhat decoupled with respect to bacterial productivity during October 2002. Chlorophyll a showed no trend relative to salinity in October 2002 (r2 = 0.06, p b 0.42), but showed a negative relationship with salinity in April 2002 (r2 = 0.63, p b 0.01). POC was strongly correlated to PN during the March 2002 cruise (r2 = 0.83, p b 0.0002, which was expected because POC and PN are both components of organic matter), but intriguingly less correlated during the October 2002 and April 2004 cruises (r2 = 0.20, p b 0.23 and 0.44, p b 0.03 respectively). POC and DOC were somewhat decoupled from each other during the spring cruises (r2 = 0.33, p b 0.08) and poorly correlated during the October 2002 cruise (r2 = 0.12, p b 0.07). DOC was well correlated with total DAA and PAA during the April 2002 cruise (r2 = 0.52, p b 0.0003 and 0.51, 0.0004, respectively), but poorly correlated during the March and October 2002 cruises. PN was correlated with DAA and PAA during

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T.S. Bianchi et al. / Journal of Marine Systems 136 (2014) 10–21

A 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

B 1 0.8 0.6 0.4 0.2 0 -0.2 -0.4 -0.6 -0.8 -1

Pearson Product Moment Correlation between Salinity and DAA mol%

Mar-02 Oct-02

Asp

Glu

His

Arg

Ser

Gly

Thr

Ala

Tyr

Val

Phe

Iso

Leu

Apr-04 All Cruises

Pearson Product Moment Correlation between Salinity and PAA mol%

Asp

Glu

His

Arg

Ser

Gly

Thr

Ala

Tyr

Val

Phe

Iso

Leu

Fig. 5. Pearson Product Moment correlations between salinity and (A) individual dissolved amino acids and (B) individual particulate amino acids generated both within individual cruises (March 2002, October 2002, and April 2004) and cumulative for all cruises; ASP, aspartic acid; GLU, glutamic acid; HIS, histidine; ARG, arginine; SER, serine; GLY, glycine; THR, threonine; ALA, alanine; TYR, tyrosine; VAL, valine; PHE, phenylalanine; ILE, isoleucine; LEU, leucine.

the spring cruises, but was not correlated with both pools of amino acids in October 2002. Generally, bacterial productivity, bacterial biomass, chlorophyll concentration, and total amino acids were all strongly and positively correlated with each other in the MRP. Few distinct seasonal patterns were observed between cruises. As expected, bacterial productivity was moderately correlated with bacterial biomass (r2 = 0.50, p b 0.02 in April 2004; r2 = 0.62, p b 0.007 in October 2002). Also, the correlation coefficient between bacterial productivity and chlorophyll was approximately 50% higher in October 2002 than in March 2002. Chlorophyll a was poorly correlated to bacterial biomass during the March 2002 cruise (r2 = 0.18, p b 0.27), but highly correlated during the October 2002 cruise (r2 = 0.59, p b 0.017). Total DAA and PAA were highly correlated during the March and October 2002 cruises (r2 N 0.90, p b 0.0001), and less correlated during the April 2004 cruise (r2 = 0.70, p b 0.0001). 4. Discussion 4.1. Changes in bulk parameters and amino acids across a salinity gradient Amino acids contributed the most carbon to POC and DOC at midsalinities (i.e., 22 to 33) in the MRP, most likely because this is typically the zone with the highest primary production (Dagg et al., 2004; Green et al., 2006; Lohrenz et al., 1999). However, as mentioned earlier, high concentrations of PAA and DAA across a salinity range of 5 to 13, suggested that a pulse of amino acids was introduced from the river, supporting earlier work by Duan and Bianchi (2007). Moreover, in October 2002, a spike was observed in DAA and PAA and was likely due to mixing of resuspended sediments and porewaters with surface waters during storm activity from Hurricanes Isadore and Lili, see below. The percent of total DOC represented by DAA carbon (%C-DAA) was 0.28%, 0.51%, and 0.56% for March 2002, October 2002, and April 2004, respectively. These results were

only about half of those found by Benner and Kaiser (2011), who observed a higher %C-DAA of total DOC in two samples (1.94 and 2.31%) collected in the Broad River, South Carolina. The general range of %C of amino acids in DOC in rivers is 1 to 3%, but can account for as much as 5 to 10% of the bioavailable DOC (Benner, 2003). Our lower values (e.g., b1%) likely reflected greater input of processed terrestrially-derived DOC (Bianchi et al., 2004; Duan and Bianchi, 2007) from Mississippi River waters and/or resuspended porewaters. In the lower salinity locations we first see the high concentrations of PAA and DAA moving across a salinity gradient in the MRP, which occur in waters typically less than 15 m, where resuspension events have been shown to be important (Corbett et al., 2006). The range of DOC and POC values in this work was slightly higher than the range observed for a broader region of the Louisiana shelf, but in general agreed with peak values at the mid-salinity range of the plume (Wysocki et al., 2006). A high percentage of total POC represented by PAA carbon (%C-PAA) has commonly been used as an indicator of phytoplankton in past studies (Cowie and Hedges, 1994; Ittekkot and Arian, 1986). Although chlorophyll a did not show a consistent positive relationship with DOC, POC, and TDN, total DAA and PAA were always positively correlated with these bulk parameters (albeit not with a high degree of significance). This de-coupling between chlorophyll a and POC and DOC in the MRP was likely due to inputs of riverine POC and DOC (Guo et al., 2009; Wysocki et al., 2006). Thus, there remained some linkages between biomarker and bulk carbon and N parameters, despite the heterogeneity and steep physical gradients in the MRP. The correlation of DAA with POC and not with TDN and PN, was not clear, especially considering the very high DIN concentrations (particularly nitrate) in these plume waters, largely derived from the Mississippi River (Dagg et al., 2004). This may have something to do with perhaps selective loss of C versus N pools from decomposing phytoplankton cells in the plume and/or inputs of terrestrial derived DAA that may not be coupled to phytoplankton growth and N cycling in these plume waters.

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10-7 Bacterial Productivity (g CL-1h-1)

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Salinity Fig. 6. (A) Bacterial productivity (g C−1 L−1 h), (B) bacterial biomass (cells L−1) and (C) chlorophyll (μM) plotted against salinity within each cruise (March 2002, October 2002, and April 2004).

The neutral amino acids, serine, glycine and alanine, had higher mol% abundance of all amino acids at all stations in the MRP, and were likely linked with phytoplankton sources. Serine and glycine have been shown to be present in the siliceous exoskeletons of diatoms (Ingalls et al., 2003), the dominant phytoplankton source in the midsalinity regions of the MRP (Lohrenz et al., 1999). However, there was a significant de-coupling of bulk of parameters and certain amino acids across salinity gradients. For example, while bulk DOC and POC generally decreased with increasing salinity, many individual amino acids (e.g., aspartic acid, glutamine, histidine, arginine, tyrosine, phenylalanine) increased at higher salinities (Fig. 5). Other work has also shown that while POC and DOC generally decreased with increasing salinity in the MRP, concentrations of individual biomarkers (e.g., uronic acids and carbohydrates) increased (Guo et al., 2009). These results and our amino acid data demonstrate that even though bulk DOC and POC decrease with distance, from river sources in the near plume region (Bianchi et al., 2004, 2008), certain individual biomarkers increased with salinity and were most likely due to phytoplankton source inputs in the higher productivity waters in the mid-plume regions.

4.2. Physiochemical versus biological partitioning Abiotic processes, such as simple sorption and desorption on particle surfaces, have been shown to be particularly important in controlling amino acid cycling in particle-rich river plumes (Aufdenkampe et al., 2001; Grace and Bianchi, 2010; Mannino and Harvey, 2000). For example, the positive correlation between DAA acidic amino acids (e.g., aspartic acid and glutamic acid) and salinity, and the negative correlation of DAA basic amino acids (e.g., histidine and arginine) and salinity in the MRP were largely controlled by differential partitioning of amino acids (Fig. 5A). Amino acids can be removed from the dissolved pool by adsorption to negatively charged mineral sources (Hedges and Hare, 1987); one of the dominant minerals in the particle plume of the MRP is the negatively charged montmorillonite (Grace and Bianchi, 2010). Thus, basic amino acids, with their positively charged N side chains, preferentially sorb to negatively charged aluminosilicate clay minerals. The apparent dominance of the basic amino acids (histidine and arginine) in the PAA pool and acidic amino acids (aspartic acid and glutamic acid) in the DAA pool further supports previous

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experimental work on the importance of the partitioning of amino acids across steep salinity gradients in controlling amino acid cycling in the MRP (Fig. 5B). However, some of the glutamic acid observed in the MRP could have been formed from the reaction of ammonium with α-ketoglutaric acid (De Stefano et al., 2000). This may be particularly important since Gardner et al. (1993) found that 50 to 70% of 15Nlabeled DAA taken up by bacteria in surface waters of the MRP was mineralized to ammonium. Unlike basic amino acids, sorption of bulk DOC in the MRP across salinity gradients was not found to be important (Benner and Opsahl, 2001); although this was expected, since bulk DOC is largely anionic and behaves differently from cations, it does illustrate again the differences in the controls on bulk C versus biomarker cycling in coastal regions. Finally, while charge was clearly an important parameter in controlling partitioning of acidic and basic amino acids, the degree of hydrophobicity was also likely a factor in determining which amino acids were in the DAA and PAA pools (Grace and Bianchi, 2010). For example, an aromatic amino acid like phenylalanine has one of the highest partitioning coefficients of all amino acids in MRP

A

plume waters, significantly different from many other amino acids. This is further supported by the positive correlation between particulate phenylalanine and salinity (Fig. 5B). While changing ionic strength in the MRP in part controlled the abundance and distribution of amino acids, linkages between bacterial cycling and PAA and DAA pools were unequivocal (Fig. 7A). Bacterial productivity and bacterial biomass peaked in the mid-salinity region of the MRP, where we also found the highest %C-DAA and %C-PAA, but this was likely linked with higher phytoplankton biomass. Although we found that bacterial production was more pronounced at the midsalinity, this is where we also find peaks of chlorophyll a. Guo et al. (2012) calculated that net community production was highest in the MRP at salinities between 18 and 27. Gardner et al. (1996) used radiolabeled nitrogen compounds (ammonium and DFAA) to investigate the effects of high-molecular-weight DOM (HMW DOM) on nitrogen dynamics in the MRP and found strong linkages with both abiotic and biotic factors that control nutrient cycling (e.g., wind, light, primary production, river flow) in MRP surface waters. So, while amino acids have

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DAA DI

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DAA % PAA % nonnon-protein protein

Fig. 7. (A) Percent amino acid as DOC and (B) percent amino acid as POC plotted against salinity within each cruise (March 2002, October 2002, and April 2004). Also, (C) Pearson Product Moment correlations between salinity and degradation indicators (carbon-normalized particulate amino acids [PAAs], carbon-normalized dissolved amino acid [DAA], degradation index for DAA, degradation index for PAA, %non-protein forming amino acids in DAA and %non-protein forming amino acids in PAA). Correlations were generated both within individual cruises (March 2002, October 2002, and April 2004) and cumulative for all cruises.

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long been known to be important C and N sources for coastal microbial communities (Crawford et al., 1974; Keil and Kirchman, 1991; Middelboe et al., 1995), further work using microbial biomarkers (e.g., muramic acid and D-enantiomer amino acids), would be needed to make better linkages between microbes and amino acids in the MRP.

4.3. Indices of amino acid degradation and the effects of hurricanes There were no significant trends in the relative abundance of non-protein forming amino acids, β-alanine, γ-aminobuteric acid, and δ-aminovaleric acid across salinity gradients in the MRP. Elevated levels of β-alanine, γ-aminobuteric acid, and δ-aminovaleric acid have been shown to be an index of amino acid decay (Cowie and Hedges, 1994; Keil et al., 1998). For example, β-alanine and γ-aminobuteric are produced enzymatically during decay of the precursors aspartic acid and glutamic acid, respectively. Percent non-protein PAA and DAA during the October 2002 sampling event was significantly higher than in March 2002 and April 2004, which we attribute to the resuspension of more highly degraded sedimentary material from the shallow shelf caused by wave action from Hurricane Isadore. Interestingly, while %non-protein PAA varied greatly in October 2002 compared to other cruises, the %non-protein DAA, although generally higher, was considerably more similar to other cruises. This lack of difference in the %non-protein DAA compared to %non-protein PAA was likely due to mixing and dilution effects on porewaters that may have been resuspended during Hurricane Isadore. Finally, bacterial productivity was relatively consistent across salinity gradients during the two spring cruises, but was more variable during October 2002, specifically between salinities 26 to 29. This variability in productivity in bacterial productivity could not be clearly ascribed to variability in amino-acid concentration or other measured parameters. We suggest that the perturbation was associated with unmeasured biotic and/or biotic parameters that were influenced by Hurricanes Isadore and Lili. The effects of hurricanes and tropical storms on the transport and biogeochemical dynamics of sediments on the Louisiana shelf have been largely ignored until recently (Goñi et al., 2006; Meade and Goñi, 2006). Sampere et al. (2008) noted that Hurricane Ivan mobilized sedimentary organic carbon from the shelf significantly offshore to the Mississippi Canyon. β-alanine, γ-aminobuteric acid, and δ-aminovaleric acid are commonly derived from fermentative processes in sediments (Burdige and Martens, 1990), and the higher values in MRP waters suggest that resuspension from nearshore inner shelf sediments/ porewaters likely contributed to the enhanced decay signature in surface waters. Hurricane and tropical storm activities have been shown to provide adequate energy to resuspend mobile-mud sediments from the shallow waters just outside of the river mouth of this region (Corbett et al., 2006). Short-term pulsing events can change the dynamics of amino acids in the shallow plume, but they are likely “reset” relatively quickly (within a few months) to conditions similar to those before the storm due to rapid cycling. In fact, the bio-optical properties of these coastal waters measured on the same cruise in October 2002 (D'Sa et al., 2006, 2007), showed that the backscattering properties, suspended particulate matter (SPM), and non-algal particle absorption as related to linkages with Sea-viewing Wide Field-of-View Sensor (SeaWifs) data showed that there was a significant resuspension in the waters in October 2002 due to the storm. Finally, the average transmittance values of the entire water column of all the stations sampled (Wetlab C-Star, path length of 25 cm) for April 2004, March 2002, and October 2002 (all data not shown) were 56.42 ± 25.60%, 40.00 ± 18.74%, and 49.96 ± 25.73%, respectively. So, the transmittance observed in October was significantly (p b 0.0001, t-test) lower than in April 2004; these months represent a more comparable timeframe for similarity in river discharge, primary production, and local storm events, than in March 2002, when local northern fronts are more frequently moving through the region (Hetland and DiMarco, 2008).

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Percent non-protein amino acid concentrations during the March 2002 and April 2004 cruises were slightly less than those found in the Mississippi River channel during the March 2002 and April 2004 cruises (Duan and Bianchi, 2007). The amino acid signal in the Mississippi River was also found to be more degraded downstream (Duan and Bianchi, 2007), due to bacterial processing as river materials travel from the flood basin through the river canal (Cowie and Hedges, 1994; Ittekkot et al., 1985; Lee and Cronin, 1982). These similarities with riverderived particles further support the conclusion that the resuspension of river-derived sediments from the seabed likely contributed to the differences of %non-protein values for PAA during the hurricanes in October 2002 compared to April 2004 and March 2002. The DI values reported in this study were close to those in Duan and Bianchi (2007) reported in Mississippi River particulates and HMW DOM. Nevertheless, we did not see any trends across a salinity gradient in the MRP using the DI. We found that the DI and %non-protein amino acids also were not useful predictors of diagenesis relative to salinity gradients in surface water of the plume. Fig. 7C shows the strong relationship between carbon-normalized amino acid content in the dissolved pool compared to the particulate pool. The use of carbonnormalized amino acid content appeared to be the most appropriate degradation proxy for the MRP due to the abundance of freshly produced material and relatively high rates of bacterial productivity and abundance. Generally, fresher OM is characterized by high carbonnormalized yields of amino acids. Davis et al. (2009) concluded that carbon-normalized yield of amino acids is the most sensitive indicator of degradation in early stages of diagenesis, while mol% non-protein amino acids were more sensitive in later stages. 5. Conclusions The primary conclusions from this research are: 1. Abiotic factors (e.g., particle sorption and sediment resuspension) had the largest contribution in describing DAA and PAA dynamics in the MRP. These observations in the field further support the same partitioning patterns observed in a previously conducted controlled microcosm experiment with MRP waters (Grace and Bianchi, 2010). 2. While bulk DOC and POC decreased with distance from river sources in the near-plume region and phytoplankton sources in the midplume region, certain individual biomarkers increased with salinity and were most likely due to phytoplankton source inputs in the higher productivity waters in the mid-plume regions. 3. Due to the abundance of freshly produced material and relatively high rates of bacterial productivity and abundance, carbon-normalized amino acid content is a more appropriate degradation proxy when studying surface water coastal margins adjacent to a large river system such as the MRP system. 4. Higher values of β-alanine, γ-aminobuteric acid, and δ-aminovaleric acid in October 2002 suggested that resuspension from nearshore inner shelf sediments/porewaters, due to the passage of Hurricanes Isadore and Lili, likely contributed to the enhanced decay signature in surface waters, optical properties of the waters (D'Sa et al., 2006, 2007), and the low transmissometry values. References Aluwihare, L.I., Repeta, D.J., Pantoja, S., Johnson, C.G., 2005. Two chemically distinct pools of organic nitrogen accumulate in the ocean. Science 308 (5724), 1007–1010. http://dx.doi.org/10.1126/science.1108925. Aufdenkampe, A.K., Hedges, J.I., Richey, J.E., Krusche, A.V., Llerena, C.A., 2001. Sorptive fractionation of dissolved organic nitrogen and amino acids onto fine sediments within the Amazon Basin. Limnol. Oceanogr. 46, 1921–1935. http://dx.doi.org/10. 4319/lo.2001.46.8.1921. Benner, R., 2003. Molecular indicators of the bioavailability of dissolved organic matter. In: Findlay, S.E.G., Sinsabaugh, R.L. (Eds.), Aquatic Ecosystems: Interactivity of Dissolved Organic Matter. Academic, New York, pp. 121–137.

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T.S. Bianchi et al. / Journal of Marine Systems 136 (2014) 10–21

Benner, R., Kaiser, K., 2011. Biological and photochemical transformations of lignin phenols and amino acids in riverine dissolved organic matter. Biogeochemistry 102, 209–222. http://dx.doi.org/10.1007/s10533-010-9435-4. Benner, R., Opsahl, S., 2001. Molecular indicators of the sources and transformations of dissolved organic matter in the Mississippi River plume. Org. Geochem. 32, 597–607. http://dx.doi.org/10.1016/S0146-6380(00)00197-2. Bianchi, T.S., Lambert, C., Biggs, D., 1995. Distribution of chlorophyll a and phaeopigments in the northwestern GOM: a comparison between fluorometric and high-performance HPLC measurements. Bull. Mar. Sci. 56 (1), 25–32. Bianchi, T.S., Filley, T., Dria, K., Hatcher, P., 2004. Temporal variability in sources of dissolved organic carbon in the lower Mississippi River. Geochim. Cosmochim. Acta 68, 959–967. http://dx.doi.org/10.1016/j.gca.2003.07.011. Bianchi, T.S., DiMarco, S.F., Allison, M.A., Chapman, P., Cowan Jr., J.H., Hetland, R.D., Morse, J.W., Rowe, G., 2008. Controlling hypoxia on the U.S. Louisiana shelf: beyond the nutrient-centric view. EOS Trans. AGU 89 (26), 236–237. http://dx.doi.org/10.1029/ 2008EO260005. Bianchi, T.S., Garcia-Tigreros, F., Yvon-Lewis, S., Shields, M., Mills, H., Butman, D., Osburn, C., Raymond, P., Shank, G., DiMarco, S., Walker, N., Reese, B., Mullins-Perry, R., Quigg, A., Aiken, G., Grossman, E., 2013. Enhanced transfer of terrestrially derived carbon to the atmosphere in a flooding event. Geophys. Res. Lett. 40 (1-7). http://dx.doi.org/10. 129/2012GL054145. Brown, M.R., 1991. The amino-acid and sugar composition of 16 species of microalgae used in mariculture. J. Exp. Mar. Biol. Ecol. 145, 79–99. http://dx.doi.org/10.1016/ 0022-0981(91)90007-J. Burdige, D.J., Martens, C.S., 1990. Biogeochemical cycling in an organic-rich marine basin. The sedimentary cycling of dissolved, free amino acids. Geochim. Cosmochim. Acta 54, 3033–3052. http://dx.doi.org/10.1016/0016-7037(90)90120-A. Cai, W.J., 2003. Riverine inorganic carbon flux and rate of biological uptake in the Mississippi River plume. Geophys. Res. Lett. 30, 1032–1036. http://dx.doi.org/10. 1029/2002GL016312. Capone, D.G., Bronk, D.A., Mulholland, M.R., Carpenter, E.J., 2008. In: Capone, D.G., Carpenter, E.J. (Eds.), Nitrogen in the Marine Environment, 2nd ed. Academic Press, San Diego, CA. Chen, N.H., Bianchi, T.S., McKee, B.A., Bland, J.M., 2001. Historical trends of hypoxia on the Louisiana shelf: application of pigments as biomarkers. Org. Geochem. 32, 543–561. http://dx.doi.org/10.1016/S0146-6380(00)00194-7. Corbett, R., McKee, B., Allison, M., 2006. Nature of decadal-scale sediment accumulation on the western shelf of the Mississippi River delta. Cont. Shelf Res. 26 (17–18), 2125–2140. http://dx.doi.org/10.1016/j.csr.2006.07.012. Cowie, G.L., Hedges, J.I., 1992. Improved amino acid quantification in environmental samples: charged-matched recovery standards and reduced analysis time. Mar. Chem. 37, 223–238. http://dx.doi.org/10.1016/0304-4203(92)90079-P. Cowie, G.L., Hedges, J.I., 1994. Biochemical indicators of diagenetic alteration in natural organic matter mixtures. Nature 369, 304–307. http://dx.doi.org/10.1038/369304a0. Crawford, C.C., Hobbie, J.E., Webb, K.L., 1974. The utilization of dissolved free amino acids by estuarine microorganisms. Ecology 55, 551–563. http://dx.doi.org/10.2307/ 1935146. Dagg, M., Benner, R., Lohrenz, S.E., Lawrence, D., 2004. Transformation of dissolved and particulate materials on continental shelves influenced by large rivers: plume processes. Cont. Shelf Res. 24, 833–858. http://dx.doi.org/10.1016/j.csr.2004.02.003. Dauwe, B., Middelburg, J.J., 1998. Amino acids and hexosamines as indicators of organic matter degradation state in North Sea sediments. Limnol. Oceanogr. 43, 782–798. http://dx.doi.org/10.4319/lo.1998.43.5.0782. Dauwe, B., Middelburg, J.J., Herman, P.M.J., Heip, C.H.R., 1999. Linking diagenetic alteration of amino acids and bulk organic matter reactivity. Limnol. Oceanogr. 447, 1809–1814. http://dx.doi.org/10.4319/lo.1999.44.7.1809. Davis, J., Kaiser, K., Benner, R., 2009. Amino acid and amino sugar yields and compositions as indicators of dissolved organic matter diagenesis. Org. Geochem. 40, 343–352. http://dx.doi.org/10.1016/j.orggeochem.2008.12.003. De Stefano, C., Foti, C., Gianguzza, A., Sammartano, S., 2000. The interaction of amino acids with the major constituents of natural waters at different ionic strengths. Mar. Chem. 72, 61–76. http://dx.doi.org/10.1016/S0304-4203(00)00067-0. Deksissa, T., Meirlean, J., Ashton, P.J., Vanrolleghem, P.A., 2004. Simplifying dynamic river water quality modeling: a case study of inorganic nitrogen dynamics in the Crocodile River (South Africa). Water Air Soil Pollut. 155, 303–320. http://dx.doi.org/10.1023/B: WATE.0000026548.20608.a0. D'Sa, E.J., Miller, R.L., Del Castillo, C., 2006. Bio-optical properties and ocean color algorithms for coastal waters influenced by the Mississippi River during a cold front. Appl. Opt. 45 (28), 7410–7428. http://dx.doi.org/10.1364/AO.45.007410. D'Sa, E.J., Miller, R.L., McKee, B.A., 2007. Suspended particulate matter dynamics in coastal waters from ocean color: application to the northern Gulf of Mexico. Geophys. Res. Lett. 34 (23), L23611. http://dx.doi.org/10.1029/2007GL031192. Duan, S., Bianchi, T.S., 2007. Particulate and dissolved amino acids in the lower Mississippi and Pearl Rivers (USA). Mar. Chem. 107, 214–229. http://dx.doi.org/10.1016/j. marchem.2007.07.003. Gardner, W.S., Cotner, J.B., Herche, L.R., 1993. Chromatographic measurement of nitrogen mineralization rates in marine coastal waters with N15. Mar. Ecol. Prog. Ser. 65, 65–73. Gardner, W.S., Benner, R., Amon, R.M.W., Cotner, J.B., Cavaletto, J.F., Johnson, J.R., 1996. Effects of high-molecular weight dissolved organic matter on nitrogen dynamics in the Mississippi River plume. Mar. Ecol. Prog. Ser. 133, 287–297. http://dx.doi.org/10. 3354/meps133287. Gardner, W.S., Cavaletto, J.F., Cotner, J.B., Johnson, J.R., 1997. Effects of natural light on nitrogen cycling rates in the Mississippi River plume. Limnol. Oceanogr. 42, 273–281. http://dx.doi.org/10.4319/lo.1997.42.2.0273. Goñi, M.A., Gordon, E.S., Monacci, N.M., Clinton, R., Gishewhite, R., Allison, M.A., Kineke, G.C., 2006. The effect of Hurricane Lili on the distribution of organic matter in the inner

Louisiana shelf (Gulf of Mexico, USA). Cont. Shelf Res. 26, 2260–2280. http://dx.doi. org/10.1016/j.csr.2006.07.017. Grace, B.L., Bianchi, T.S., 2010. Sorption and desorption dynamics in bulk dissolved organic matter and amino acids on the Mississippi River plume — a microcosm study. Mar. Freshwat. Res. 61, 1067–1081. http://dx.doi.org/10.1071/MF09181. Green, R.E., Bianchi, T.S., Dagg, M.J., Walker, N.D., Breed, G.A., 2006. An organic carbon budget for the Mississippi River turbidity plume and plume contributions to air–sea CO2 fluxes and bottom-water hypoxia. Estuar. Coasts 29, 579–597. http://dx.doi. org/10.1007/BF02784284. Guo, L.D., Coleman, C.H., Santschi, P.H., 1994. The distribution of colloidal and dissolved organic-carbon in the Gulf of Mexico. Mar. Chem. 45, 105–119. http://dx.doi.org/10. 1016/0304-4203(94)90095-7. Guo, L.D., White, D.M., Xu, C., Santschi, P.H., 2009. Chemical and isotopic composition of colloidal organic matter from the Mississippi River plume. Mar. Chem. 114, 63–71. http://dx.doi.org/10.1016/j.marchem.2009.04.002. Guo, X., Cai, W., Huang, W., Wang, Y., Chen, F., Murrell, M.C., Lohrenz, S.E., Jiang, L., Dai, M., Hartmann, J., Lin, Q., Culp, R., 2012. Carbon dynamics and community production in the Mississippi River plume. Limnol. Oceanogr. 57, 1–17. http://dx.doi.org/10.4319/ lo.2012.57.1.0001. Hedges, J.I., Hare, P.E., 1987. Amino acid adsorption by clay minerals in distilled water. Geochim. Cosmochim. Acta 51, 255–259. http://dx.doi.org/10.1016/0016-7037(87) 90237-7. Hedges, J.I., Cowie, G.L., Richey, J.E., Quay, P.D., Benner, R., Strom, M., Forsberg, B.R., 1994. Origins and processing of organic matter in the Amazon River as indicated by carbohydrates and amino acids. Limnol. Oceanogr. 39, 743–761. Henrichs, S.M., Farrington, J.W., 1987. Early diagenesis of amino acids and organic matter in two coastal marine sediments. Geochim. Cosmochim. Acta 51, 1–15. http://dx.doi. org/10.1016/0016-7037(87)90002-0. Hetland, R.D., DiMarco, S.F., 2008. How does the character of oxygen demand control the structure of hypoxia on the Texas–Louisiana continental shelf? J. Mar. Syst. 70, 49–62. Ingalls, A.E., Lee, C., Wakeham, S.G., Hedges, J.I., 2003. The role of biominerals in the sinking flux and preservation of amino acids in the Southern Ocean along 170°W. Deep-Sea Res. II 50, 713–738. http://dx.doi.org/10.1016/S0967-0645(02)00592-1. Ittekkot, V., Arian, R., 1986. Nature of particulate organic matter in the river India, Pakistan. Geochim. Cosmochim. Acta 50 (8), 1643–1653 (doi: 2048/http://dx.doi. org/10.1016/0016-7037(86)90127-4). Ittekkot, V., Zhang, S., 1989. Pattern of particulate nitrogen transport in world rivers. Global Biogeochem. Cycles 3 (4), 383–391. http://dx.doi.org/10.1029/GB003i004p00383. Ittekkot, V., Safiullah, S., Mycke, B., Seifert, R., 1985. Seasonal variability and geochemical significance of organic matter in the River Ganges, Bangladesh. Nature 317, 800–803. http://dx.doi.org/10.1038/317800a0. Keil, R.G., Kirchman, D.L., 1991. Contribution of dissolved free amino acids and ammonium to the nitrogen requirements of heterotrophic bacterioplankton. Mar. Ecol. Prog. Ser. 128, 109–120. Keil, R.G., Tsamakis, E., Diddings, J.C., Hedges, J.I., 1998. Biochemical distributions (amino acids, neutral sugars, and lignin phenols) among size-classes of modern marine sediments from the Washington coast. Geochim. Cosmochim. Acta 62, 1347–1364. http://dx.doi.org/10.1016/S0016-7037(98)00080-5. Keul, N.R., Morse, J.M., Wanninkhof, R., Gledhill, D.K., Bianchi, T.S., 2010. Carbonate chemistry dynamics of surface waters in the Northern Gulf of Mexico. Aquat. Geochem. http://dx.doi.org/10.1007/s10498-010-9091-2. Kirchman, D.L., 1993. Leucine incorporation as a measure of biomass production by heterotrophic bacteria. In: Kemp, P.F., et al. (Eds.), Current Methods in Aquatic Microbial Ecology. Lewis Publishers, New York, pp. 509–512. Lee, C., Cronin, C., 1982. The vertical flux of particulate organic nitrogen in the sea — decomposition of amino-acids in the Peru upwelling area and the equatorial Atlantic. J. Mar. Res. 40, 227–251. Lee, C., Wakeham, S.G., Hedges, J.I., 2000. Composition and flux of particulate amino acids and chloropigments in equatorial Pacific seawater and sediments. Deep-Sea Res. I 47, 535–1568. http://dx.doi.org/10.1016/S0967-0637(99)00116-8. Lindroth, P., Mopper, K., 1979. High performance liquid chromatographic determination of subpicomolar amounts of amino acids by precolumn fluorescence derivatization with o-phthaldialdehyde. Anal. Chem. 51, 1667–1674. Lohrenz, S.E., Cai, W.J., 2006. Satellite ocean color assessment of air–sea fluxes of CO2 in a river-dominated coastal margin. Geophys. Res. Lett. 33, L01601. http://dx.doi.org/10. 1029/2005GL023942. Lohrenz, S.E., Fahnenstiel, G.L., Redalje, D.G., Lang, G.A., Dagg, M.J., Whitledge, T.E., Dortch, Q., 1999. Nutrients, irradiance, and mixing as factors regulating primary production in coastal waters impacted by the Mississippi River plume. Cont. Shelf Res. 19 (9), 1113–1141 (doi: 2048/http://dx.doi.org/10.1016/S0278-4343(99)00012-6). Lomstein, B.A., Jensen, A.G.U., Hansen, J.W., Andreasen, J.B., Hansen, L.S., Berntsen, J., Kunzendorf, H., 1998. Budgets of sediment nitrogen and carbon cycling in the shallow water of Knebel Vig, Denmark. Aquat. Microb. Ecol. 14, 69–80. http://dx.doi.org/10. 3354/ame014069. Mannino, A., Harvey, H.R., 2000. Biochemical composition of particles and dissolved organic matter along an estuarine gradient: sources and implications for DOM reactivity. Limnol. Oceanogr. 45, 755–788. McCarthy, M., Pratum, T., Hedges, J., Benner, R., 1997. Chemical composition of dissolved organic nitrogen in the ocean. Nature 390, 150–153. http://dx.doi.org/10.1038/36535. Meade, R., Goñi, M.A., 2006. A lipid molecular marker assessment of sediments from the Northern Gulf of Mexico before and after the passage of Hurricane Lili. Org. Geochem. 37, 1115–1129. http://dx.doi.org/10.1016/j.orggeochem.2006.04.010. Middelboe, M., Borch, N.H., Kirchman, D.L., 1995. Bacterial utilization of dissolved free amino acids, dissolved combined amino acids and ammonium in the Delaware Bay estuary: effects of carbon and nitrogen limitation. Mar. Ecol. Prog. Ser. 128, 109–120. http://dx.doi.org/10.3354/meps128109.

T.S. Bianchi et al. / Journal of Marine Systems 136 (2014) 10–21 Mopper, K., Kieber, D.J., 2002. Photochemistry and the cycling of carbon, sulfur, nitrogen, and phosphorus. In: Hansell, D.A., Carlson, C.A. (Eds.), Biogeochemistry of Dissolved Organic Matter. Academic, San Diego, Calif, pp. 455–507. Mulholland, M.R., Glibert, P.M., Berg, G.M., Van Heukelem, L., Pantoja, S., Lee, C., 1998. Extracellular amino acid oxidation by microplankton: a cross-ecosystem comparison. Aquat. Microb. Ecol. 15, 141–152. http://dx.doi.org/10.3354/ame015141. Pantoja, S., Lee, C., 1999. Peptide decomposition by extracellular hydrolysis in coastal seawater and salt marsh sediment. Mar. Chem. 63, 272–291. http://dx.doi.org/10.1016/ S0304-4203(98)00067-X. Porter, K.G., Feig, Y.S., 1980. Use of DAPI for identifying and counting aquatic microflora. Limnol. Oceanogr. 25, 943–948. http://dx.doi.org/10.4319/lo.1980.25.5.0943. Sampere, T.P., Bianchi, T.S., Wakeham, S.G., Allison, M.A., 2008. Sources of organic matter in surface sediments of the Louisiana continental margin: effects of major depositional/transport pathways and Hurricane Ivan. Cont. Shelf Res. 28, 2472–2487. http://dx. doi.org/10.1016/j.csr.2008.06.009. Sharp, J.H., Rinker, K.R., Savidge, K.B., Abell, J., Benaim, J.Y., Bronk, D., Burdige, D.J., Cauwet, G., Chen, W.H., Doval, M.D., Hansell, D., Hopkinson, C., Kattner, G., Kaumeyer, N., McGlathery, K.J., Merriam, J., Morley, N., Nagel, K., Ogawa, H., Pollard, C., Pujo-Pay,

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M., Raimbault, P., Sambrotto, R., Seitzinger, S., Spyres, G., Tirendi, F., Walsh, T.W., Wong, C.S., 2002. A preliminary methods comparison for measurement of dissolved organic nitrogen in seawater. Mar. Chem. 78, 171–184. http://dx.doi.org/10.1016/ S0304-4203(02)00020-8. Spitzy, A., Ittekkot, V., 1991. Dissolved and Particulate Organic Matters in Rivers. John Wiley and Sons Ltd., New York. Wang, W., Tarr, M., Bianchi, T.S., Engelhaupt, E., 2000. Ammonium photoproduction from aquatic humic and colloidal matter. Aquat. Geochem. 6, 275–292. http://dx.doi.org/ 10.1023/A:1009679730079. Wright, S.W., Jeffery, S.W., Mantoura, R.F.C., Llewellyn, C.A., Blornland, T.B., Repeta, D., Welschmeyer, N., 1991. Improved HPLC method for the analysis of chlorophyll and carotenoids from marine phytoplankton. Mar. Ecol. Prog. Ser. 77, 183–196. Wysocki, L.A., Bianchi, T.S., Powell, R., Reuss, N., 2006. Spatial variability of phytoplankton in the Mississippi River plume. Estuar. Coast. Shelf Sci. 69, 47–63. http://dx.doi.org/ 10.1016/j.ecss.2006.03.022.