Bacterioplankton play a central role in carbon cycling, yet their relative contributions to carbon production and removal can be difficult to constrain. As part of the Export Processes in the Ocean from RemoTe Sensing (EXPORTS) program, this study identifies potential influences of bacterioplankton community and dissolved organic matter (DOM) composition on carbon cycling at Ocean Station Papa in August 2018. Surface (5–35 m) bacterioplankton production rates and stocks spanned a 2- to 3-fold range over the 3-week cruise and correlated positively with the DOM degradation state, estimated using the mole proportion of total dissolved amino acids. When the DOM was more degraded, 16S rRNA gene amplicon data revealed a less diverse bacterioplankton community with a significant contribution from members of the Flavobacteriaceae family. Over the course of 7–10 d, as the DOM quality improved (became less degraded) and bacterioplankton productivity increased, the responding bacterioplankton community became more diverse, with increased relative contributions from members of the SAR86, SAR11 and AEGEAN-169 clades. The cruise mean for mixed layer, depth-integrated bacterioplankton carbon demand (gross bacterioplankton production) was 5.2 mmol C m−2 d−1, representing 60% of net primary production, where the difference between net primary production and bacterioplankton carbon demand was less than sinking flux at 50 m. The concentrations of dissolved organic carbon (cruise average of 58.5 µM C) did not exhibit a systematic change over the cruise period. Therefore, we hypothesize that carbon supplied from gross carbon production, values that were 2- to 3-fold greater than net primary production, provided the carbon necessary to account for the sinking flux and bacterioplankton carbon demand that were in excess of net primary production. These findings highlight the central contributions of bacterioplankton to carbon cycling at Ocean Station Papa, a site of high carbon recycling.

The oceans play a critical role in sequestering recently produced carbon, transferring carbon to depth via biological (e.g., vertical zooplankton migration) and physical (e.g., mixing down of carbon-rich surface waters) pathways, yet the mechanisms predicting the efficiency of this transfer across varying ecosystems is poorly understood (Siegel et al., 2016). To address this need, one of the primary goals of the EXPORTS program was to develop a predictive understanding of the export and fate of net primary production (NPP) or photosynthetically fixed carbon in the marine food webs of two contrasting ecosystems. The sites selected by the EXPORTS program included a highly regenerative, low-sinking-flux system at the Subarctic North Pacific’s Ocean Station Papa (OSP; Figure 1) and a physically dynamic spring phytoplankton bloom, high-sinking-flux system in the North Atlantic over the Porcupine Abyssal Plain (Siegel et al., 2016). OSP, the focus of the present study, is an ideal location to study carbon recycling within the surface ocean and, in particular, to quantify the contribution of bacterioplankton to this process.

Figure 1.

Sampling locations during EXPORTS cruise RR1813 in 2018. All locations are shown as dots; the subset highlighted as white indicates where samples were collected for 16S rRNA gene amplicon data. The sea surface height anomalies (meters) are represented by both color and contour lines, identifying the general flow (black arrow) of the surface water mass around a large anticyclonic mesoscale eddy feature. Satellite altimetry-based data for sea surface height were averaged over 5 d surrounding August 22, 2018, downloaded from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) website (https://www.aviso.altimetry.fr). For reference, the long-term monitoring site Ocean Station Papa (OSP) located at 50°N and 145°W is marked with a yellow star.

Figure 1.

Sampling locations during EXPORTS cruise RR1813 in 2018. All locations are shown as dots; the subset highlighted as white indicates where samples were collected for 16S rRNA gene amplicon data. The sea surface height anomalies (meters) are represented by both color and contour lines, identifying the general flow (black arrow) of the surface water mass around a large anticyclonic mesoscale eddy feature. Satellite altimetry-based data for sea surface height were averaged over 5 d surrounding August 22, 2018, downloaded from the Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) website (https://www.aviso.altimetry.fr). For reference, the long-term monitoring site Ocean Station Papa (OSP) located at 50°N and 145°W is marked with a yellow star.

Close modal

Heterotrophic bacteria and archaea, referred to here as bacterioplankton, are key to the recycling and retention of carbon within the surface ocean at OSP (Kirchman et al., 1993; Boyd et al., 1995; Tortell et al., 1996; Sherry et al., 1999), contributing to its relatively low sinking export efficiency compared with other nutrient-rich and physically dynamic open ocean environments (Buesseler and Boyd, 2009; Siegel et al., 2016; Buesseler et al., 2020). Primary production at OSP is dominated by small-sized (e.g., <5 µm) phytoplankton cells (Welschmeyer et al., 1993; Boyd and Harrison, 1999) that can be consumed by either migratory zooplankton or microzooplankton grazers (e.g., McNair et al., 2021), resulting in a vertical flux out of the surface ocean via sinking pellets or zooplankton migration to depth. Alternatively, dissolved organic carbon (DOC) could be released from zooplankton (e.g., Maas et al., 2021) or phytoplankton and consumed by bacterioplankton (Sherry et al., 1999).

Dissolved organic matter (DOM) production in the surface of the open ocean is ultimately constrained by the magnitude of photosynthetic carbon fixation. The rates of photosynthetically fixed carbon in the surface ocean are routinely estimated by 14C bicarbonate additions into seawater, which is converted to 14CO2, fixed into organic carbon and retained on a filter (typically 0.2 to 0.7 µm pore size). The rates of 14CO2 uptake are typically referred to as NPP when incubated over 12 to 24 h or gross primary production (GPP) when incubated over 2 to 6 h (Marra, 2009). However, because the 14CO2 uptake technique requires the collection of particulates caught on a filter, this estimate does not include the fraction of fixed carbon released by phytoplankton as DOC. Measures of the dissolved filtrate produced from 14CO2 uptake incubations, often referred to as “extracellular release,” have indicated that DOC released by phytoplankton can range from <5% to 80% of NPP, the partitioning of which is not yet predictable from an independent variable across a range of ecosystems (Baines and Pace, 1991; Nagata, 2000; Carlson, 2002; Morán et al., 2002; Teira et al., 2015).

While the release of DOC from healthy phytoplankton is one mechanism contributing to the DOC pool, there are numerous food web processes that also contribute. These other processes include micro- and macrozooplankton grazer-mediated release and excretion, viral lysis of bacteria and phytoplankton, enzymatic solubilization of detrital particles, and direct release of DOC from living bacterioplankton (Carlson and Hansell, 2015; Moran et al., 2022). Indeed, a recent modeling effort showed that 60% of the labile DOC utilized by bacteria was supplied from processes other than extracellular release from phytoplankton (Moran et al., 2022). In an ideal setting, each of the food web processes contributing to DOC production would be measured directly and simultaneously in the field. However, not only are many of these food web processes difficult to measure directly, but comparisons can be complicated further by the temporal separation of processes, which can differ on time scales of minutes to hours after fixation (e.g., DOC release from healthy phytoplankton) to days after carbon fixation (e.g., viral lysis or particle solubilization; Carlson and Hansell, 2015). Furthermore, the ranges and relative DOM contributions of any given food web process vary depending on food web structure, environmental conditions and ecosystem trophic state (Nagata, 2000).

An alternative approach to estimating the overall magnitude of bioavailable DOM production is to measure the daily heterotrophic carbon demand by bacterioplankton. Measures of net bacterioplankton production (BP), combined with estimates of bacterial growth efficiency (BGE) or bacterial respiration (BR), can be used to estimate bacterial carbon demand (BCD) according to the following:

BCD=BP+BR or BPBGE;BGE=BP(BP+BR)
1

Thus, BCD can be used as a proxy for the flux of DOM derived from all processes (Carlson and Hansell, 2015). Historically, BP (or more accurately as BCD) has been compared directly with rates of photosynthetic carbon fixation to estimate the amount of carbon that enters the microbial food web and fluxes through bacteria in a given system (Ducklow, 1992; Kirchman et al., 1993; Carlson and Ducklow, 1996; Ducklow, 1999, 2000). Given that BCD has exceeded rates of photosynthetic carbon fixation for a range of systems (e.g., Ross Sea, BATS, Equatorial Pacific; Carlson et al., 2007), comparisons of BCD to all forms of new carbon entering the food web (dissolved + particulate) should be considered.

The growth and production of bacterioplankton at OSP have been observed to fluctuate largely with NPP (Sherry et al., 1999), which is similar to findings elsewhere, suggesting that bacterioplankton are limited by the flux of newly fixed organic matter (e.g., Ducklow, 1999). However, BCD can exceed NPP at OSP, likely due to relatively low BGEs (Sherry et al., 1999). Periods when BCD exceeds NPP, measured on a given day, suggest that bacterioplankton are either meeting their metabolic demand by consuming a DOC source other than that measured from NPP, or that there is a fraction of photosynthetically fixed carbon that is released as DOC and not accounted for in the traditional measures of NPP described above (Carlson et al., 2007; Alonso-Sáez et al., 2008).

Bacterioplankton contribute significantly to carbon cycling at OSP (e.g., Sherry et al., 1999), yet an evaluation of the dynamics of the bacterioplankton community structure has only recently been presented, despite the potential for community composition analyses to provide insight into organic substrate utilization (Moran et al., 2016). In one OSP-based study, the bacterioplankton community composition exhibited seasonal differences and shifts during a marine heat wave that may have impacted carbon export efficiency (Traving et al., 2021). A second study, using DOM remineralization bioassays during the EXPORTS OSP field campaign, suggested that a more bioavailable organic matter favored the growth of specific bacterioplankton taxa, which included a range in members from the Bacteroidetes, Alphaproteobacteria and Gammaproteobacteria classes (Stephens et al., 2020). However, the natural variation of the bacterioplankton community in the context of a longer Lagrangian study (approximately a month) and its contribution to carbon cycle dynamics at OSP has not yet been reported.

DOM quality has been shown to affect the response and growth rates of the microbial assemblage at various ocean sites (Cottrell and Kirchman, 2000; Carlson et al., 2004; Nelson and Carlson, 2012; Shen and Benner, 2019; Liu and Liu, 2020). Based on samples collected at OSP throughout the year, DOC can accumulate in the late summer by up to 15–20 µM C over winter values in the surface mixed layer (ML; Bif and Hansell, 2019), suggesting that there could be a shift in DOM bioavailability to bacterioplankton thereby allowing DOM to accumulate. Additionally, an early study at OSP comparing bacterioplankton responses to DOM amendments of varying quality found that dissolved amino acids resulted in the most rapid bacterioplankton growth (Kirchman et al., 1993). However, there are currently no studies that compare the natural variability of the composition of DOM at OSP over time scales of weeks.

The specific aims of this study were to: (1) quantify the gross flux of organic carbon through bacterioplankton (i.e., BCD); (2) characterize the taxonomic composition of bacterioplankton responsible for carbon utilization and cycling at OSP; (3) quantify vertical distribution and temporal variability of bulk DOC and dissolved organic nitrogen (DON); and (4) characterize how the total dissolved amino acid (TDAA) component of the DOM pool could reveal changes in DOM quality relevant to bacterioplankton utilization. This study builds on previously published work from the EXPORTS OSP campaign (Stephens et al., 2020) by investigating bacterioplankton growth response in an ecosystem characterized by enhanced carbon recycling and identifying potential constraints on the bacterioplankton processing of DOM within the context of the broader goals of the EXPORTS program.

2.1. Study region

The two-ship operation of the EXPORTS Pacific campaign are detailed in Siegel et al. (2021): one of the ships performed a process study and the other performed a survey of locations within a larger grid. Samples in the current process-based study were collected aboard the R/V Roger Revelle cruise RR1813, which tracked a Lagrangian float deployed within a coherent mesoscale feature (Figure 1) near OSP (50.1°N, 144.9°W) between August 15 and September 7, 2018. Samples were collected daily (n = 24 d) for the analysis of nutrients, DOC, total dissolved nitrogen (TDN) concentration, bacterioplankton abundance, net bacterioplankton production, and total dissolved amino acid concentration. Based on deployment and retrieval of drifting assets, the cruise activities were divided into three consecutive 8-d periods called epochs (Siegel et al., 2021).

2.2. Environmental data

EXPORTS hydrographic data are available at the NASA Ocean Biology Distributed Active Archive Center (OB.DAAC) via the SeaWiFS Bio-optical Archive and Storage System (SeaBASS; Werdell et al., 2003). Discrete samples were collected over 12 depths within the surface 500 m from daily profiles (between local 1300 and 1800 hours) using a Sea-Bird Scientific SBE-911+ CTD outfitted with a Wet Labs ECO-AFL fluorometer and 12-L Niskin bottles (n = 24) in a typical rosette mount. Discrete samples for chlorophyll-a (Chl-a; Yentsch and Menzel, 1963), nutrients (NO3+NO2, PO4−3, H4SiO4; Strickland and Parsons, 1972) and particulate organic carbon and nitrogen (POC and PON, respectively; Sharp, 1974) were collected and processed by the EXPORTS Hydroteam and are available from the SeaBASS-supported EXPORTS data repository (Siegel et al., 2019). A complete description of nutrient, particulate organic matter (POM), Chl-a and other analyses can be found under investigator “Norman Nelson” at: https://seabass.gsfc.nasa.gov/experiment/EXPORTS.

The surface ML was defined as the first depth with a potential temperature decrease of 0.2°C from 5 m (de Boyer Montégut et al., 2004). Surface ML data are presented as mean concentrations or rates after performing a trapezoidal integration over 2 to 3 discrete depths within the ML then normalizing by the depth of integration (unless otherwise noted). Daily estimations of the ML depth are shown as a bold dashed line in Figure 2a.

Figure 2.

Contour plots of Chl-a, POC, DOC, bacterioplankton biomass, production and specific growth rates. Contour plots over depths from 0 to 120 m based on near-daily depth profiles (black dots) and interpolated using DIVA gridding in Ocean Data View for concentrations of (a) Chl-a, (b) particulate organic carbon (POC), (c) dissolved organic carbon (DOC) and (d) bacterioplankton biomass (BB), and for (e) net bacterioplankton production (BP) and (f) bacterioplankton specific growth rates. The white lines within each plot represent potential density lines at 0.5 kg m−3 spacing, the broad dashed line in (a) represents the mixed layer depth, and dates reflect the local time zone at Ocean Station Papa in 2018. Places with missing black dots either were not sampled or the sample had a measurement or analytical error.

Figure 2.

Contour plots of Chl-a, POC, DOC, bacterioplankton biomass, production and specific growth rates. Contour plots over depths from 0 to 120 m based on near-daily depth profiles (black dots) and interpolated using DIVA gridding in Ocean Data View for concentrations of (a) Chl-a, (b) particulate organic carbon (POC), (c) dissolved organic carbon (DOC) and (d) bacterioplankton biomass (BB), and for (e) net bacterioplankton production (BP) and (f) bacterioplankton specific growth rates. The white lines within each plot represent potential density lines at 0.5 kg m−3 spacing, the broad dashed line in (a) represents the mixed layer depth, and dates reflect the local time zone at Ocean Station Papa in 2018. Places with missing black dots either were not sampled or the sample had a measurement or analytical error.

Close modal

2.2.1. Dissolved organic carbon and nitrogen

Samples for DOC and TDN were gravity-filtered directly from the Niskin bottles through an in-line 47-mm polycarbonate filter cartridge containing a pre-combusted (450°C, 4 h) GF/F (nominal 0.7-µm pore size, Whatman) filter into pre-combusted (450°C, 4 h) 40-mL borosilicate glass vials and acidified with 4 N HCl to pH of approximately 3. DOC and total TDN concentrations were measured via the high temperature combustion method using modified Shimadzu TOC-V or TOC-L analyzers following Halewood et al. (2022). The variability of the TOC analyzer for surface samples was within ±0.75 µM C and within ±0.50 µM N on average for this ocean system. DON was calculated as the difference between TDN and NO3+NO2.

2.2.2. Total dissolved amino acids

Total dissolved amino acid (TDAA) analysis of 18 unique amino acids were measured from the acidified GF/F filtered seawater collected for DOC and TDN samples. TDAA sample preparation and analytical procedures follow those presented previously in Stephens et al. (2020) and as modified from Kaiser and Benner (2006) and Liu et al. (2020). TDAA was converted to carbon units based on the concentrations of each amino acid multiplied by the number carbons within the associated amino acid (TDAA C). TDAA mole percentages were used to calculate the degradation index (DI) score (Dauwe et al., 1999; Kaiser and Benner, 2009; Liu et al., 2020) based on the following equation:

DI= variAVGvariSTDvari*fac.coefi
2

where vari refers to the non-standardized mole percentage of amino acid I, and AVGvari, STDvari and fac.coefi are the mean, standard deviation, and principal component factor coefficients for samples collected from a range of degradation states as described in Davis et al. (2009) and Kaiser and Benner (2009), and as presented previously for cruise RR1813 in Stephens et al. (2020). Systematic variation in the mole percentage of protein-based amino acids can be used to predict the degradation state of DOM, where a more degraded DOM is represented by lower DI values (Dauwe et al., 1999). Thus, an increase in DI is used to infer a shift toward “fresher,” less diagenetically altered DOM compounds (Cowie and Hedges, 1994; Davis et al., 2009).

2.2.3. Bacterioplankton abundance and cell size

We use the term bacterioplankton throughout this manuscript to represent both heterotrophic bacteria and archaea, as many of the methods presented do not differentiate their contributions. Bacterioplankton abundances were determined via 4′,6-diamidino-2-phenylindole dihydrochloride (DAPI, Sigma-Aldrich; Porter and Feig, 1980) staining and epifluorescence microscopy (60× objective, Olympus, and Revolve microscope, Discover Echo Inc.). Samples were digitally imaged, analyzed and sized via ImageJ analysis software after Stephens et al. (2020). The ImageJ script can be found under investigator “Carlson” at: https://seabass.gsfc.nasa.gov/experiment/EXPORTS. Cell sizes were calibrated using standard fluorescent beads pre-mounted on a microscope slide (Invitrogen Tetraspeck Fluorescent Microspheres #T14792; 0.1, 0.2, 0.5, 1.0 and 4.0 µm in diameter). Mean cell biovolumes are presented to provide a context of fluctuations in cell size with time. In order to provide a carbon context for the bacterioplankton abundances, bacterioplankton biomass (BB) was estimated by multiplying cell abundance and estimated cell carbon based on mean cell biovolume using the following published relationship on samples collected from cruise RR1813 and as presented in Stephens et al. (2020):

Cell carbon (fg C cell1)=91.71*(cell biovolume in µm3)0.686
3

2.2.4. Net bacterioplankton production

3H-leucine (L-[4,5- 3H(N)]) was added to 1.7 mL of sample at a final concentration of 20 nM (specific activity 60.0 Ci mmol−1; Perkin Elmer, Boston, MA), where incorporation rates were used as a proxy for BP based on a modified version of the microcentrifuge method (Smith and Azam, 1992) as described in Baetge et al. (2021). 3H-leucine incorporation rates were converted to BP using a cell conversion factor previously determined for OSP as 0.11*1018 cells mol leucine−1 (Kirchman, 1992), then converted to carbon using the cell biovolume relationship referred to in Equation 3. BCD was estimated here by dividing BP by BGE as shown in Equation 1. BGE values were determined from five DOM remineralization bioassays conducted throughout cruise RR1813 and presented in detail in Stephens et al. (2020). Accordingly, a BGE value of 27% was applied to samples collected between August 15 and August 22 and to samples collected between August 31 and September 7; a BGE value of 38% was applied to samples collected between August 23 and August 30.

2.2.5. Net primary production and gross carbon production

NPP was determined on 15 separate days of the RR1813 cruise using 14CO2 uptake incubations (24-h, dawn-to-dawn) following trace metal clean techniques as described in Fox et al. (2020). Estimates of gross carbon production (GCP) were determined for mixed layer waters on 10 of the cruise days using short-term (2-h) 14CO2 uptake incubations (Halsey et al., 2010). Primary production data can be found under investigator “Mike Behrenfeld” at: https://seabass.gsfc.nasa.gov/experiment/EXPORTS. Seawater for NPP and GCP was inoculated with 14C-labelled sodium bicarbonate before being incubated at a range of light levels in a temperature-controlled photosynthetron (Lewis and Smith, 1983; Halsey et al., 2010). Following incubation, samples were filtered (0.2-µm polycarbonate), acidified and degassed for 24 h, and radioactivity was measured on a Packard Tri-Carb scintillation counter. Photosynthesis-irradiance parameters Pmax, the maximum rate of photosynthesis (mg C h−1); α, the light-limited slope (mg C h−1 [µmol m−2 s−1]−1); and Ek, the light saturation parameter for photosynthesis (µmol quanta m−2 s−1) were derived using the exponential model of Webb et al. (1974). The photosynthesis-irradiance parameters were used with measurements of surface irradiance to calculate daily integrated GCP for the ML following the methods described in Tilstone et al. (2005).

GCP estimates from 2-h 14CO2 uptake incubations tend to approach true GCP rates but are often lower due to light or micronutrient limitation (Halsey et al., 2013; Halsey and Jones, 2015) and cannot encompass released DOC given the reliance on filters. To provide constraints on the 14CO2 uptake-based estimates of GCP, gliders equipped with optodes were also used to estimate GCP (GCPg) during the cruise period on waters near cruise RR1813. Mixed layer GCPg was estimated by fitting an idealized photosynthesis versus irradiance curve to observed diel cycles in dissolved oxygen measured by an Aanderaa 4831 oxygen optode from a UW APL Seaglider underwater glider (Nicholson et al., 2015; Barone et al., 2019). The diel approach is based on quantifying the daytime increase in O2 due to photosynthesis and nighttime decrease due to respiration, which is assumed constant over each 24-h period. Mixed layer inventories received small adjustments to account for air-sea O2 flux estimated by a parametrization that includes bubble-mediated exchange (Liang et al., 2013). A p-test (p < 0.05) was used to evaluate days where a statistically robust fit was achieved, which resulted in 10 d of resolvable GCPg estimates during the 24-d cruise period. O2 production rates were converted to carbon units using the commonly applied 1.4:1 O2: C conversion (Laws, 1991).

Neither NPP, GCP nor GCPg estimates definitively includes extracellular DOC released by phytoplankton, given that the 14CO2 uptake-based NPP and GCP incubations only represent the biomass retained on filters and GCPg is estimated by differences in oxygen production and removal. Direct comparisons of BCD to NPP and GCP (and GCPg) presented in this study are used to constrain the potential supply of recent photosynthetically produced carbon that may ultimately enter the dissolved pool via the myriad processes highlighted in the introduction. However, we hypothesize that the GCPg method could include the process of extracellular DOC released by phytoplankton.

2.2.6. 16S rRNA gene amplicon sequencing

Water column profile samples for 16S rRNA gene amplicon sequencing were collected on August 15, 17, 20, 22, 29 and September 7 (white dots in Figure 1). Using positive pressure, 1 L of whole seawater was displaced from polycarbonate bottles (Biotainer, ThermoFisher) either through 0.2-µm pore-size polyethersulfone (Sterivex-GP, Millipore) or 0.2-µm pore-size polytetrafluorethylene (Omnipore, Millipore) filter cartridges and stored at −80°C (Liu et al., 2020). Samples were extracted using phenol-isoamyl alcohol-chloroform (Giovannoni et al., 1996). The extracted samples were then barcoded using a single PCR amplification step with custom V4 515F-Y (5′-GTGYCAGCMGCCGCGGTAA-3′) and 806RB (5′-GGACTACNVGGGTWTCTAAT-3′) primers with custom adapters (Apprill et al., 2015; Parada et al., 2016; Wear et al., 2018), following methods described in Stephens et al. (2020). Amplified and gel-purified libraries were sequenced by Illumina MiSeq at the University of California Davis Genome Center.

Sequencing reads were trimmed and assigned to taxonomies based on a DADA2 pipeline (Callahan et al., 2016) carried out in an R software environment (version 3.6.2, and for the subsequent processing steps detailed below) using matches to the SILVA SSU/LSU 132 database (accessed in December 2019). After chimeric and plastid (e.g., chloroplasts and mitochondrial) sequences were removed, samples had read depths ranging from 12,712 to 35,804 reads per sample (average ± standard deviation of 22,621 ± 5,931, n = 63) and resulted in 1,459 unique amplicon sequence variants (ASVs) across the water column (0 to 500-m depths).

In order to visualize community-based differences in ordination space while also accounting for phylogenetic differences, a phylogenetic tree was generated that could then be used with a weighted UniFrac distancing analysis (Lozupone and Knight, 2005). ClustalW (v2.1) was first used to align sequences, generating an alignment with 2,047 positions and containing 1,459 taxa. A phylogenetic tree was then created with the alignment output using the RaxML (v8.2.10) program (Stamatakis, 2014) running 100 bootstraps on a nucleotide GTRGAMMA model of rate heterogeneity. The GTRGAMMA model was determined to be the most appropriate by a comparison of maximum likelihood scores among the different tested models and was based on results from seeded random starts for each bootstrap (Stamatakis, 2014). To visualize community differences across depth and time, the phylogenetic tree associated with non-rarefied relative abundances was used to first create a “phyloseq” (v1.32.0) object (McMurdie and Holmes, 2013). The phyloseq object was then used to generate a canonical analysis of principal ordination analysis (Anderson and Willis, 2003) based on weighted UniFrac distances (Lozupone and Knight, 2005).

The bioinformatic functions employed here were used to evaluate the intersection of bacterioplankton community composition, its productivity, biomass and DOM datasets (similar in approach to Reiji et al., 2020). The “envfit” (v2.4-2) function within the “vegan” package was used to identify environmental variables (as presented in the cross-correlation analysis below) that were significantly (p < 0.05) correlated with 16S rRNA community composition (Oksanen et al., 2015) with depth and over time in the surface ML. To identify specific ASVs that were associated with shifting environmental conditions in the ML, indicator species associated with significantly different (SIMPROF p < 0.05) groups of samples were identified using the “indicspecies” (v1.7.9) function (de Cáceres and Legendre, 2009).

Alpha diversity Shannon-Wiener index (H) values and associated error for the 16S rRNA amplicons were estimated based on ASV abundances using PAST software (v4.03). The H-value trends did not differ significantly between rarified and non-rarified sample sets, suggesting little effects of variable sampling effort on alpha diversity indices (Lande, 1996); thus, diversity values are presented on the non-rarified datasets. The significance of H between two samples was tested in PAST software based on Hutcheson’s t-test (Hutcheson, 1970). The DivNet package (v 0.2.1) in R was also used to test for significant differences in Shannon indices across depths, while accounting for unobserved species (Willis, 2019).

2.3. Statistical analyses

All statistical tests were considered significant at the p < 0.05 level. The Shapiro-Wilk test (Shapiro and Wilk, 1965) determined if data were normally distributed, indicating whether to use nonparametric-based statistics to evaluate for significance within or between datasets. A normally distributed dataset mean is expressed with ± standard deviation. For non-normal distributions, the deviation around the median value is reported as the median absolute deviation. For comparisons between normal distributions, a paired t-test was used, and for non-normal distributions a Mann-Whitney test (Mann and Whitney, 1947) was used to determine statistically significant differences.

A cross-correlation analysis of environmental variables was conducted using temporal offsets to assess whether applying time lags improved correlation coefficients. Only the most significant correlations are shown in the main text. Given that the chance of finding a significant relationship increases with the number of pairwise comparisons, a Holm-Bonferroni correction was applied to reported p-values (Holm, 1979) for the cross-correlation analysis and in several other comparisons requiring the comparison of multiple correlation coefficients.

3.1. Temporal trends in the mixed layer and sub-mixed layer

During cruise RR1813, hydrographic profiles to 500 m were conducted daily while following a 100-m tethered Lagrangian float over 24 d. Cruise mean profiles of NPP, Chl-a, BP, BB, DOC, DON, POC, PON, DOC:DON, POC:PON, TDAA C and the TDAA DI score showed minimal variability below the 0.1% PAR depth (approximately 110 m) relative to surface waters (Figure S1). Thus, we have focused our analyses on temporal trends within the upper euphotic zone (<100 m).

Chl-a, POC, DOC, BB, BP and specific growth rate (µ; BP/BB) decreased to half of surface ML (approximately 35 m) values by 75 m and exhibited little day-to-day variability between 75 m and 120 m (Figure 2). Samples collected between August 21 and August 25 were marked by decreases in Chl-a, POC, BB and BP within the surface 50 m. Concurrent fluctuations in BB and BP resulted in significantly higher specific growth rates (t-test, p = 0.03) between August 21 and August 22 at 40 m and between August 27 and August 30 at 5–20 m when compared with cruise means (Figure 2). Bulk DOC concentrations attenuated with depth in all profiles, decreasing from approximately 58.0 µM to 50.8 µM by the 0.1% PAR depth and to 43.0 µM by 500 m (Figure S1). The day-to-day variability in bulk DOC concentrations was as high as 4.9 µM C in the mixed layer throughout the station occupation, though daily variation in DOC was 1.0 µM on average for any depth (Figure 2).

Between August 22 and August 30, there were significant increases (one-tail, p < 0.01) in ML depth-weighted mean Chl-a, NPP, BP and BB and significant decreases in NO3+NO2 (Figure 3). A 2-fold increase in ML mean Chl-a (0.16 to 0.34 µg L−1) coincided with decreasing NO3+NO2 concentrations from 9.5 to 8.2 µM (Figure 3a). Over the same time period, NPP, BP and BB increased from 0.20 to 0.36 µM C d−1, from 0.02 to 0.07 µM C d−1 and from 0.50 to 0.98 µM C, respectively (Figure 3b and c). The increase in BP correlated significantly and positively with bacterioplankton biovolume (r = 0.82, p < 0.001), increasing from 0.02 to 0.06 µm3 cell−1 (Figures 3b and c and S2).

Figure 3.

Biological and biogeochemical variability in the surface mixed layer (<35 m). Mixed layer (ML) depth-weighted averages for (a) chlorophyll-a (Chl-a) and NO3+NO2 concentrations, (b) rates of net primary production (NPP) and net bacterioplankton production (BP), (c) bacterioplankton cell biovolume (bac. biovolume) and bacterioplankton biomass (BB), (d) dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) concentrations, (e) particulate organic carbon (POC) and particulate organic nitrogen (PON) concentrations, and (f) total dissolved amino acid carbon (TDAA C) concentrations and degradation (deg.) index. Dates reflect local time of sampling in 2018 (afternoon collections for all but NPP), and error bars represent propagated instrumental error over 2–3 samples collected within the ML (where available). Asterisks along the x-axis of each plot denote collection dates for 16S rRNA gene amplicon data.

Figure 3.

Biological and biogeochemical variability in the surface mixed layer (<35 m). Mixed layer (ML) depth-weighted averages for (a) chlorophyll-a (Chl-a) and NO3+NO2 concentrations, (b) rates of net primary production (NPP) and net bacterioplankton production (BP), (c) bacterioplankton cell biovolume (bac. biovolume) and bacterioplankton biomass (BB), (d) dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) concentrations, (e) particulate organic carbon (POC) and particulate organic nitrogen (PON) concentrations, and (f) total dissolved amino acid carbon (TDAA C) concentrations and degradation (deg.) index. Dates reflect local time of sampling in 2018 (afternoon collections for all but NPP), and error bars represent propagated instrumental error over 2–3 samples collected within the ML (where available). Asterisks along the x-axis of each plot denote collection dates for 16S rRNA gene amplicon data.

Close modal

Similar to the temporal enhancement observed in Chl-a, NPP, BP, and BB concentrations within the ML for the period of August 22 to August 30, POC and PON concentrations also increased from 3.2 to 5.4 µM C and from 0.6 to 0.9 µM N, respectively (Figure 3e). Depth-weighted DOC and DON concentrations ranged from 56.9 to 60.0 (averaging 58.5 ± 0.9, n = 24) µM C and 3.1 to 4.8 (averaging 3.8 ± 0.5, n = 24) µM N, respectively, with no resolvable systematic temporal pattern for DOC in the surface ML (Figure 3d). However, DON concentrations within the surface ML were significantly greater (one-tail t-test, p = 0.033) between August 26 and September 4 compared to the average DON before and after this time period. During the approximate 6-d increase in Chl-a (August 25 to August 30) the decrease in NO3+NO2 by 0.7 µM coincided with increases in PON by 0.3 µM and DON by about 0.5 µM (Figure 3d), suggesting that the NO3+NO2 drawdown led to an increase in both PON and DON. ML POC:PON molar ratios fluctuated between 5.4 and 6.3, and bulk DOC:DON molar ratios fluctuated between 12.4 and 19.2 (as derived from Figure 3d and e).

Despite a lack of systematic temporal variability in DOC concentrations in the ML over the cruise, there were systematic trends in the quality of DOM based on the TDAA-derived DI score (Figure 3f). Between August 22 and September 1, the mean DI increased from 1.5 to 2.7 (Figure 3f) within the surface ML, indicating that the DOM became “fresher” and coincided with the increases in Chl-a, NPP, POC, BP and BB.

3.2. Covariation in Chl-a, NPP, POC, BP and TDAA

Throughout cruise RR1813, NPP, Chl-a, POC, BP, and TDAA-based DI covaried within the ML (Figure 4). Pearson linear correlation coefficients and Bonferroni-corrected p-values were compared among a set of time lags to identify the most significant paired correlations (Table S1). For instance, there were significant positive correlations between NPP and Chl-a with no lag, POC with a 1-d lag, BP with a 2-d lag and TDAA-based DI with a 3-d lag (Figure 4). An increase in NPP and POC was thus followed by an increase in BP; however, there was a delayed shift in the accumulated DOM reservoir, as it only became less diagenetically altered (i.e., more labile) after the peak in BP.

Figure 4.

Linear correlations of variables with net primary production within the surface mixed layer. Correlations of (a) chlorophyll-a (Chl-a), (b) particulate organic carbon (POC), (c) bacterioplankton production (BP) and (d) the total dissolved amino acid degradation (TDAA deg.) index with net primary production (NPP). Bold lines represent the model II linear fit line; thinner dotted lines represent the ±95% confidence intervals. The correlation coefficients are based on Pearson correlations, and the p-values have been corrected using the Holm-Bonferroni correction (Holm, 1979). Y-axis labels include whether the variable was lagged relative to NPP.

Figure 4.

Linear correlations of variables with net primary production within the surface mixed layer. Correlations of (a) chlorophyll-a (Chl-a), (b) particulate organic carbon (POC), (c) bacterioplankton production (BP) and (d) the total dissolved amino acid degradation (TDAA deg.) index with net primary production (NPP). Bold lines represent the model II linear fit line; thinner dotted lines represent the ±95% confidence intervals. The correlation coefficients are based on Pearson correlations, and the p-values have been corrected using the Holm-Bonferroni correction (Holm, 1979). Y-axis labels include whether the variable was lagged relative to NPP.

Close modal

3.3. Bacterioplankton carbon demand relative to gross carbon and net primary production

When integrated over the ML, BCD rates determined from field measurements of BP and from BGEs derived using bioassays conducted on cruise RR1813 (presented in Stephens et al., 2020) ranged from 2.5 to 8.8 mmol C m−2 d−1 (Figure 5a). ML BCD correlated significantly with the rate of DOC removal over approximately 6 d (0.10 to 0.28 µM C d−1) as measured using the bioassay experiments (Figure S3), suggesting that the bottle conditions and derived BGEs captured in the bioassays were representative of in situ conditions. ML-integrated 14CO2 uptake-based estimates of NPP and GCP rates averaged 9.1 ± 2.2 and 18.6 ± 10.8 mmol C m−2 d−1, respectively (Figure 5a). The median GCP:NPP ratio was 2.1 ± 1.4 for the cruise, increasing significantly (t-test, p < 0.01) from an average of 1.7 between August 17 and August 28 to 5.5 on August 31 and 3.7 on September 1. We used spatially co-located glider estimates of GCP for the ML (10.1 to 43.7 mmol C m−2 d−1) to further constrain photosynthetic carbon fixation rates. These GCPg values encompassed a similar range as estimates of GCP (9.8 to 44.7 mmol C m−2 d−1), but GCPg was 8–28 mmol C m−2 d−1 higher than GCP from August 18 to August 28 and the two GCP methods were aligned from August 29 to September 5.

Figure 5.

Temporal trends for NPP, GCP, GCPg and BCD integrated over the surface mixed layer. (a) Mixed layer integrated rates (volumetric rates multiplied by mixed layer depth) of net primary production (NPP), gross carbon production determined from 2-h 14CO2 uptake incubations (GCP), gross carbon production determined from daily changes in glider values of O2 (GCPg) and bacterioplankton carbon demand (BCD) versus time, and (b) ratios of BCD to NPP, BCD to GCP and BCD to GCPg. BCD was adjusted by 2 d as determined in the lagged correlation analysis. Note the break in the y-axis in (a) changes from a minor tick value spacing of 1.75 to 5 mmol C m−2 d−1.

Figure 5.

Temporal trends for NPP, GCP, GCPg and BCD integrated over the surface mixed layer. (a) Mixed layer integrated rates (volumetric rates multiplied by mixed layer depth) of net primary production (NPP), gross carbon production determined from 2-h 14CO2 uptake incubations (GCP), gross carbon production determined from daily changes in glider values of O2 (GCPg) and bacterioplankton carbon demand (BCD) versus time, and (b) ratios of BCD to NPP, BCD to GCP and BCD to GCPg. BCD was adjusted by 2 d as determined in the lagged correlation analysis. Note the break in the y-axis in (a) changes from a minor tick value spacing of 1.75 to 5 mmol C m−2 d−1.

Close modal

On average, a 2-d-lagged mixed layer BCD (Table S1) represented 60% ± 19% of NPP, 31% ± 13% of GCP, and 23% ± 17% of GCPg, respectively (Figure 5b). Despite the large range in BCD:NPP (33% to 87%), dividing the cruise mean BCD by cruise mean NPP (59%) was nearly identical to mean BCD:NPP ratios (60%; Figure 5b), suggesting that there was adequate sample coverage for the comparison of BCD to NPP over the 24-d cruise.

3.4. Bacterioplankton community structure dynamics

In addition to systematic changes between BB, BP and the TDAA-based DI, the 16S rRNA gene amplicon-based estimates of taxa richness (269 unique ASVs for the surface ML) and diversity (Shannon-Wiener H) correlated positively with a 2-d lag in TDAA-based DI (Figure 6). Correlations presented in Figure 6 suggest that, over the duration of the cruise, bacterioplankton communities with a lower alpha diversity were associated with a more degraded DOM. Lineage-specific variability in the bacterioplankton was also observed with depth (Figure 7). Changes to bacterioplankton community structure were largely attributed to a domain-level shift from Bacteria to Archaea that began between 65 m and 80 m (Figures 7 and S4a) and extended into the mesopelagic zone.

Figure 6.

Standardized bacterioplankton taxa abundance and diversity correlated with TDAA-based degradation index. Scatterplots of (a) the number of bacterioplankton taxa and (b) Shannon-Wiener H value versus the total dissolved amino acid-based degradation (TDAA deg.) index. The number of taxa and H values were standardized (observation-mean/standard deviation) by depth to account for variability with depth. Samples from different depths are shown as circles (5 m), squares (20 m) and triangles (35 m). The correlation coefficients are based on Pearson correlations, and the p-values have been corrected using the Holm-Bonferroni correction (Holm, 1979). The direction of dissolved organic matter (DOM) degradation is indicated below the x-axis.

Figure 6.

Standardized bacterioplankton taxa abundance and diversity correlated with TDAA-based degradation index. Scatterplots of (a) the number of bacterioplankton taxa and (b) Shannon-Wiener H value versus the total dissolved amino acid-based degradation (TDAA deg.) index. The number of taxa and H values were standardized (observation-mean/standard deviation) by depth to account for variability with depth. Samples from different depths are shown as circles (5 m), squares (20 m) and triangles (35 m). The correlation coefficients are based on Pearson correlations, and the p-values have been corrected using the Holm-Bonferroni correction (Holm, 1979). The direction of dissolved organic matter (DOM) degradation is indicated below the x-axis.

Close modal
Figure 7.

Relative abundances of 16S rRNA gene amplicon sequence variant data over depth and time. Relative abundances for amplicon sequence variants were aggregated at the family level where available. Dates are month/day in 2018. Samples collected on August 17 from depths of 110 to 500 m were lost (white area).

Figure 7.

Relative abundances of 16S rRNA gene amplicon sequence variant data over depth and time. Relative abundances for amplicon sequence variants were aggregated at the family level where available. Dates are month/day in 2018. Samples collected on August 17 from depths of 110 to 500 m were lost (white area).

Close modal

Within the ML, the bacterioplankton community was dominated by members of the phyla Proteobacteria (primarily Alpha- and Gammaproteobacteria) and Bacteroidetes, representing >80% of the total relative abundances (Figure 7). Temporal fluctuations in the contributions of these broad groups were prevalent. For instance, a less diverse community on August 17 and August 20 was associated with elevated relative abundances of members of the Bacteroidetes Flavobacteriaceae NS4, NS5, NS2b and Formosa genera, and members of the Flavobacteriales Crocinitomicaceae (combined abundances were 45% compared with 16% on August 22 and August 29) and Rhodobacterales Rhodobacteraceae (11% versus 3%) families. A more diverse community observed on August 22 and August 29 was associated with increased relative abundances of the Alphaproteobacteria SAR11 Clade I, II, and IV families (combined genera abundance of 34% versus 8% on August 17 and August 20) and the Gammaproteobacteria SAR86 (15% versus 4%) order.

An analysis of indicator species among ML 16S rRNA gene communities found 74 taxa (Table S2), some of which were significantly (p < 0.05) associated with environmental variables and exhibited temporal variations (Figure S4b). Among the indicator taxa, 14 unique taxa correlated significantly (Holm-Bonferroni-corrected p < 0.05) with a 2-d-lagged TDAA-based DI (Figure 8). The unique amplicon sequence variants highlighted in Figure 8 comprised 15% ± 9% of the ML bacterioplankton community. The relative abundances of members of the Flavobacteriaceae family and Verrucomicrobiales order were associated with more degraded DOM, while the relative abundances of members of the Gammaproteobacteria SAR86 order, members of SAR11 Clades I, II, and IV families, as well as one member of the Rhodospirillales AEGEAN-169 family, were associated with fresher DOM (Figure 8).

Figure 8.

Heatmap of mixed layer indicator taxa correlated with DOM degradation state. The x-axis of the heatmap refers to both collection depth and date in 2018. Only shown are those amplicon sequence variants (ASVs) with significant correlations with the total dissolved amino acid-based degradation index. Taxa names are shaded differently to reflect whether taxa were positively or negatively correlated with the dissolved organic matter (DOM) degradation state. The dot plot to the right corresponds to the range of ASV relative abundances associated with the identified taxa for each collection depth and date. The colors in the dot plot reflect the z-score values shown in the heatmap.

Figure 8.

Heatmap of mixed layer indicator taxa correlated with DOM degradation state. The x-axis of the heatmap refers to both collection depth and date in 2018. Only shown are those amplicon sequence variants (ASVs) with significant correlations with the total dissolved amino acid-based degradation index. Taxa names are shaded differently to reflect whether taxa were positively or negatively correlated with the dissolved organic matter (DOM) degradation state. The dot plot to the right corresponds to the range of ASV relative abundances associated with the identified taxa for each collection depth and date. The colors in the dot plot reflect the z-score values shown in the heatmap.

Close modal

The subarctic Pacific OSP is a high organic matter recycling, low carbon export system compared with more productive bloom systems such as the North Atlantic (Boyd and Harrison, 1999; Buesseler and Boyd, 2009). Prior studies at OSP reported that bacterioplankton productivity and biomass track phytoplankton production (Kirchman, 1992; Sherry et al., 1999) and that BGEs can increase from 10% in spring to >30% in late summer (Sherry et al., 1999; Stephens et al., 2020). The findings presented here suggest that daily shifts in bacterioplankton biomass and growth at OSP were associated with changes in the quantity and quality of carbon stocks in August 2018.

4.1. OSP context and bacterioplankton C conversions

A bacterioplankton carbon conversion factor empirically derived from EXPORTS cruise RR1813 (Stephens et al., 2020) and a 3H-leucine-based conversion factor previously derived for OSP (Kirchman, 1992) were used to estimate BB and BP in this study. The mean estimates of BB and BP in the surface ML for August 2018 were at the lower range of previously reported values from the spring and summer of 1987 and 1988 (Kirchman et al., 1993) and those sampled during 6 cruises (twice per spring, summer and winter) between 1995 and 1997 (Sherry et al., 1999). The relatively low average BB and BP during cruise RR1813 were associated with relatively low NPP and POC concentrations (Boyd and Harrison, 1999; Harrison et al., 2004; Siegel et al., 2021), which were conditions targeted for the EXPORTS North Pacific field campaign (Siegel et al., 2016). However, the RR1813 August 29 increase in BP to 0.081 µM C d−1 and the peak in BB to 1.3 µM C on September 1 (Figures 2 and 3) are similar to some of the highest previously measured values at OSP (Kirchman, 1992; Sherry et al., 1999). Thus, superimposed on the low productivity background, the 2018 EXPORTS cruise captured a surprisingly large range of ecosystem conditions.

4.2. Concurrent changes to DOM composition and bacterioplankton biomass and production

Throughout most oceanic regions the variability in the magnitude of BP and BB is well recognized to correspond with patterns in phytoplankton productivity, indicating that the flux of newly fixed organic matter controls the overall magnitude of BP (Ducklow, 1999). The tightly coupled BP and NPP data presented here (Figures 35) are consistent with Sherry et al. (1999), suggesting that bacterioplankton are controlled by bottom-up supply of organic resources. Furthermore, this study provides evidence that DOM quality, based on TDAA degradation indicators, influenced changes in bacterioplankton growth rates and biomass (Figures 3 and 4). The supply of relatively fresher, less altered DOM at OSP during RR1813 may be associated with phytoplankton growth, as indicated by correlations between BP, NPP, Chl-a and POC (Figure 4).

DOM TDAA-based DI values measured from experiments conducted during cruise RR1813 correlated positively with DOM remineralization rates (Stephens et al., 2020), supporting the hypothesis that DOM removal by bacterioplankton was influenced by DOM quality. Those experiments also showed that the DOM became more degraded over the course of days to weeks to months, based again on decreases in the TDAA-based DI. These data agree with earlier substrate amendment studies at OSP, where bacteria preferred and grew most consistently in response to dissolved amino acids (Kirchman et al., 1989; Kirchman, 1990). The connection between bacterioplankton responses and amino acids in the field-based study presented here (Figures 3 and 4) confirms a direct bacterioplankton growth connection to environmental factors.

Differences in the TDAA-based DI scores have been used previously to infer marine DOM lability, ranging from −2.0 (more recalcitrant) in the subeuphotic zone to approximately 2.0 (more bioavailable) in the surface ocean (Davis et al., 2009; Kaiser and Benner, 2009). We found similar DI score trends with depth during the RR1813 cruise (Figure S1), ranging from −0.2 at 500 m to 2.7 in surface waters (Figure 3), where the surface values likely reflect freshly derived DOM based on a comparison with plankton-derived DOM and POM (Davis et al., 2009). The temporal shift in DI scores ranged from 1.0 to 2.7 during cruise RR1813, and DI began to increase on August 30, which was after the BP increase.

The lagged response in DI scores could be explained one of two ways: either (1) the DI is affected by a tight coupling between BP and amino acid uptake, thus capturing intermediate stages of DOM degradation (longer than days to weeks) more effectively and possibly missing early rapid DOM changes (Davis et al., 2009); or (2) the TDAA measured here include both dissolved free and hydrolysable amino acids from proteins, peptides and other amino acid-containing polymers. While bottom-up controls on the rapid turnover of dissolved amino acids could support the instantaneous response in BP (hypothesis 1), there may have been a time lag between bacterioplankton synthesis of protein and peptide hydrolytic enzymes and uptake of the resulting amino acids, ultimately reflected as a delayed response in the TDAA-based DI. Hypothesis 2 is supported by sustained high BP several days after its peak coupled with the TDAA DI response one day following the initial BP peak (Figure 3b). An increase in TDAA C on August 27 prior to the increase in BP (Figure 3) also suggests that a net accumulation of TDAA C could have in part supported the initial increase in BP. However, while the broad shifts in the TDAA-based DI of DOM lability support bottom-up influences on BP, the exact sources of DOM (e.g., zooplankton or phytoplankton release) contributing to shifts in DOM lability remain unclear from the available data presented here.

4.3. Bacterioplankton carbon demand versus net and gross carbon production

Historically, BP:NPP ratios have been determined in order to constrain the relative contribution of bacterioplankton to carbon cycling (Ducklow, 1999), with BGEs in many of the early studies assumed to be 50% on average to estimate BCD (e.g., Kirchman et al., 1995). However, open ocean BGEs are now recognized to vary from 5% to 50% across marine ecosystem or trophic states (e.g., del Giorgio and Cole, 1998; Carlson, 2002). Thus, site-specific BGE and BCD estimates are necessary to fully constrain the total bioavailable DOM flux required to support metabolic demands of the existing heterotrophic bacterioplankton populations of a system. Seasonal trends in BCD were estimated previously at OSP using a combination of BP and BR (Sherry et al., 1999). This approach resulted in some of the highest BCD rates in spring at OSP when BGEs were approximately 10%, leading to BCD exceeding that of NPP (BCD:NPP of about 125%). As an alternative to estimating rates of respiration, BCD can also be determined by dividing BP by BGEs (Equation 1) determined from bioassays.

BGEs were estimated during cruise RR1813 using dark DOM remineralization bioassays (Stephens et al., 2020), ranging between 25% and 45%. BGEs in August 2018 were within a similar range to those observed at OSP in prior summers (e.g., approximately 40% in summer versus 10% in spring; Sherry et al., 1999). Using the nearest experimentally estimated BGE to water-column-estimated BP values, our cruise mean ML-integrated BCD was 5.2 ± 1.6 mmol C m−2 d−1 (n = 24; Figure 5a), which was nearly identical to the summer BCD estimates for OSP presented in Sherry et al. (1999). A mean NPP of 9.1 ± 2.2 mmol C m−2 d−1 (n = 15) was relatively low compared with prior studies (Boyd et al., 1995; Boyd and Harrison, 1999). The RR1813 cruise mean BCD:NPP was 60% ± 19% (n = 15; Figure 5b), which was greater than the approximate 25% value presented for prior summers (Sherry et al., 1999), likely due to a comparatively reduced NPP.

When the cruise RR1813 mean BCD rate is subtracted from the mean NPP rate, the result is an excess of 3.8 mmol C m−2 d−1. However, the cruise mean vertical sinking carbon flux at 50 m was 5.5 mmol C m−2 d−1 as measured by 234Th (Buesseler et al., 2020; Estapa et al., 2021; Roca-Martí et al., 2021) and so was greater than the calculated excess NPP of 3.8 mmol C m−2 d−1. A vertical sinking flux that exceeds the NPP after accounting for BCD can be explained by one or more of the following mechanisms: (1) a significant fraction of BP was passed to higher trophic levels via the microbial loop, repackaged by micro- and mesozooplankton and sank, thus contributing to vertical sinking flux; (2) a significant fraction of instantaneous BCD was met by consuming DOM that had accumulated before we occupied the study site, thus reflecting a source of DOM not encapsulated by the NPP measurements; (3) measured vertical sinking flux included sources of exported carbon that are temporally and spatially distinct from the local study site and time period; and/or (4) a significant fraction of photosynthetic carbon fixation was released extracellularly as DOM, providing additional substrate to bacterioplankton not accounted for in measures of particulate NPP (Moran et al., 2022).

In consideration of these potential mechanisms explaining the mismatch between excess NPP and carbon flux at 50 m, the BGEs are too low for meaningful contributions of bacterioplankton to higher trophic levels as hypothesized in mechanism (1), and the use of accumulated DOC described in mechanism (2) is unlikely given that ML DOC concentrations did not exhibit any systematic temporal variability throughout the RR1813 cruise (Figure 3). To further test mechanism (2), we determined the slope of DOC versus time in Figure 3 to be −0.03 µM C d−1, which is lower than the instrumental resolution of 0.7 µM C. Changes of 2–4 µM DOC should have been observed when monitored over 24 d if accumulated DOC was the primary source fueling BCD. We cannot assess mechanism (3) with the available data, but sinking particle flux studies conducted during the 2018 EXPORTS field campaign suggest that sinking particles were not reflective of a spatially distinct C source from surface measured rates and stocks (Estapa et al., 2021).

With data in hand, we can evaluate whether mechanism (4) might apply to OSP for cruise RR1813. Previous studies have shown that dissolved photosynthate released by phytoplankton cells can represent 5% to 80% of NPP (Carlson, 2002; Wetz and Wheeler, 2003; Halsey et al., 2010; Halsey and Jones, 2015; Teira et al., 2015) but is a form of DOC production that was not captured in the particulate NPP values measured during RR1813. Part of the released photosynthate might be reflected in the measures of GCP or GCPg, where GCP is typically 2.7-fold to 3.3-fold higher than NPP (Marra, 2002; Halsey et al., 2013). However, neither the potential for GCP estimates to reflect DOC extracellular release nor the influences on the GCP:NPP ratio are well constrained (Juranek and Quay, 2013; Halsey and Jones, 2015). We cannot predict how much of GCP would have been released as DOM without having measured the dissolved fraction produced during the 14CO2-based incubations. During cruise RR1813, mean GCP:NPP ratios ranged from 1.0 to 5.5 (mean of 2.1 ± 1.4; n = 10) within the surface ML, similar to previously reported ratios (Halsey and Jones, 2015). The mean and median differences between GCP and NPP were 8.9 mmol C m−2 d−1 and 4.7 mmol C m−2 d−1, respectively, indicating that additional sources of recently fixed organic matter in excess of NPP estimates were produced that became available to support heterotrophic bacterioplankton production.

Estimates of GCP measured using 2-h 14CO2-uptake incubations are likely underestimates of true GCP when the phytoplankton growth rates are light-limited and/or nutrient-limited (Halsey and Jones, 2015). The lower GCP estimates early in the cruise might be explained by resource-limited phytoplankton growth rates that responded favorably to high NO3+NO2 beginning August 25 (Figure 3). Glider-based measures of GCP made during the same time period at OSP provide an alternative independent approach to estimating GCP not subject to bottle incubation influences, but GCPg is also subject to assumptions, namely an empirically determined photosynthetic quotient (assumed here to be 1.4 O2 to 1 CO2 after Laws, 1991). Nevertheless, both estimates of GCP and GCPg are sufficient to meet the demands of particulate NPP and BCD with enough left over to account for the measured vertical flux.

Higher GCP during the latter half of the cruise also corresponded with a shift toward more labile DOM, elevated BP, larger-sized bacterioplankton cells (Figure 3) and a more diverse bacterioplankton community composition, all of which point toward excess release of labile DOM that fueled bacterioplankton productivity. An increase in cell biovolume concurrent with a shift toward greater SAR11 relative abundance (Figure 7) is unexpected given their generally small size (Giovannoni, 2017), though some studies suggest SAR11 to be equal in size to other marine bacterioplankton (Malmstrom et al., 2004; Malmstrom et al., 2005). Several other taxa (e.g., SAR86 and Rhodospirillales) also increased in abundance, suggesting that an increase in cell biovolume was associated with the supply of greater organic resources (Lever et al., 2015). Given estimates that phytoplankton growth was well balanced by microzooplankton grazing during cruise RR1813 (McNair et al., 2021), some combination of sloppy feeding, egested byproducts or recently produced small volatile molecules (Davie-Martin et al., 2020) may have provided additional organic resources to fuel the increase in BCD in the surface ML later in the cruise RR1813 (Figure 5). In any case, the comparison of temporal shifts in GCP and GCPg with bacterioplankton production and DOM quality provides support for the hypothesis that BCD was responding to shifts in the overall productivity of the system.

4.4. Identification of key taxa from bacterioplankton community composition

Bacterioplankton community structure can vary over time scales of hours to days in response to biological and physicochemical processes (Fuhrman et al., 2015). Under conditions of enhanced organic and inorganic nutrient availability, copiotrophic bacterioplankton, like members of the Gammaproteobacteria and Bacteroidetes classes, can shift rapidly from rare to significant contributors within the total bacterioplankton community (Lauro et al., 2009; Vergin et al., 2013). However, interpreting the succession of microbial taxa in an environmental context can be confounded by inter-domain interactions and competing loss and growth processes among the taxa (Needham and Fuhrman, 2016). At the same time, simultaneous shifts in bacterioplankton community structure, when associated with relevant environmental parameters, can provide insight into potential influences on bacterioplankton while identifying key taxa within the bacterioplankton community (Reji et al., 2020).

4.4.1. Bacterioplankton community composition associations with degraded DOM

During cruise RR1813, there were clear transitions between broad taxonomical groups. Changes to the bacterioplankton community structure corresponded with trends of increasing phytoplankton productivity, BP and DOM quality (Figures 35). For instance, bacterioplankton alpha diversity decreased as DOM became more degraded in composition (Figure 6). Additionally, several ASVs exhibited significant negative correlations with the TDAA-based DI (Figure 8), including members of the Flavobacteriaceae (NS4 and Formosa genera) and Rubritaleaceae (Roseibacillus genus) families. Some Flavobacteriaceae members of the Bacteroidetes phylum are capable of breaking down a wide range of high molecular weight biomolecules such as chitin, proteins and cellulose (see review by Kirchman, 2002). ASVs associated with the Roseibacillus genus have been favored in dissolved peptide amendments (Liu et al., 2017), had elevated relative abundances during post-bloom conditions (Choi et al., 2018) and had particle-associated growth responses likely benefiting from organic material liberated by other bacterioplankton taxa (Orsi et al., 2016). Despite prior relationships between specific taxa and particles, we did not observe any significant relationships between suspended POC and ASVs in the present study. However, similar temporal trends have been reported previously in which members of the Flavobacteriaceae family (and of the weakly correlated Rhodobacteriaceae family) in particular become relatively enriched during periods of increased primary productivity and release of labile DOM (Fandino et al., 2001; Pinhassi et al., 2004; Grossart et al., 2005; Buchan et al., 2014).

Reduced alpha diversity (Figure 6) and significant correlations (p < 0.05) primarily between members of the Flavobacteriaceae family and a shift toward more degraded DOM based on patterns in the TDAA DI (Figure 8) together seem counterintuitive considering that this group is often associated with enhanced organic matter production (Kirchman, 2002; Bowman, 2006). However, experiments conducted during cruise RR1813 found that log2-fold changes by members of the Flavobacteriaceae were most pronounced when DOM removal rates were elevated (Stephens et al., 2020), suggesting that this group was associated with the active removal of DOM and transformation of the remnant pool to a more diagenetically altered state. From this perspective, we hypothesize that elevated Flavobacteriaceae abundances at OSP coincide with degraded DOM, not because they are utilizing degraded DOM, but instead are contributing to the degradation of DOM.

4.4.2. Bacterioplankton community composition associations with less degraded organic matter

At the beginning of and later during cruise RR1813, more diverse bacterioplankton communities were associated with less degraded DOM (Figures 6 and 7) and elevated BP, favoring a diverse set of indicator species (Figure 8). The cross-cruise trends in taxonomic shifts also persisted when normalizing relative abundances to bacterioplankton cell counts (i.e., relative abundance multiplied by bacterioplankton cell abundances). For instance, an increase in cell count-normalized SAR11 on August 29 (2.3 × 108 cells L−1 on August 29 versus 0.5 × 108 cells L−1 on August 20) was associated with a cell count-normalized decrease in Flavobacteriaceae (0.8 × 108 cells L−1 on August 29 versus 2.3 × 108 cells L−1 on August 20), suggesting that there was an increase in SAR11 cells at that time.

While the association of increased relative abundances of SAR11, SAR86 and AEGEAN-169 with less degraded DOM and elevated BP may be unexpected, the observed trends could be reflecting the recently proposed “three-player model” of bacterioplankton communities involving sharing, scavenging and selfish groups (Reintjes et al., 2019a; Arnosti et al., 2020). This model suggests that scavenging groups like SAR11 (Giovannoni, 2017) and the related Alphaproteobacter AEGEAN-169 (Alonso-Sáez et al., 2007; Reintjes et al., 2019b), which could include SAR86, benefit from enhanced extracellular enzymatic activity by “sharing organisms” like certain members of the Gammaproteobacteria phylum. The increase in relative abundances of SAR11 may also reflect environmental conditions that favor taxa with elevated abundances of binding protein (Norris et al., 2021), assuming that a shift toward more labile amino acids was coincident with an increase in SAR11 (e.g., Figure 8) and thus reflected conditions favoring substrate-binding protein complexes. While Alphaproteobacteria groups like SAR11 may not typically respond under conditions of elevated phytoplankton production, their scavenging and protein-binding growth strategies could have benefited from more bioavailable DOM substrate (glycine and alanine amino acids, in particular, which were observed to increase) when BP and Chl-a were high in the RR1813 cruise dataset (Figures 7 and 8). Recently SAR11 and SAR86 were also observed to increase in relative abundances during warmer (by 1°C–4°C) years at OSP (Traving et al., 2021), suggesting that these taxa can be favored under certain conditions at OSP associated with time periods of elevated DOM accumulation. However, surface temperatures fluctuated only by about 0.5°C during the RR1813 cruise (Figure S5; Siegel et al., 2021), which likely did not have an appreciable effect on the shifts in bacterioplankton community composition as found in Traving et al. (2021).

Rapid shifts among the microbial food web at OSP during the 2018 EXPORTS RR1813 cruise led to a tight coupling between increases in Chl-a, POM, bacterioplankton biomass and productivity and changes in DOM composition. A lagged correlation analysis based on the most significant relationships suggested that after increases in NPP, Chl-a and POM concentrations, there were increases in bacterioplankton biomass, net bacterioplankton production and associated specific growth rates and a shift toward more labile DOM. All of these observations are consistent with bottom-up control of bacterioplankton in the open ocean system at OSP.

As the DOM composition became more labile and bacterioplankton became more productive, the community composition shifted from a less diverse community with high abundances of Flavobacteriaceae to a more diverse community with high abundances of SAR86, SAR11 and AEGEAN-169. Results from another study conducted during the 2018 EXPORTS campaign had found BGEs to be elevated (from 27% to 38%; Stephens et al., 2020), which may reflect a diverse community that is able to utilize any bioavailable organic substrates quickly, consistent with effective sharing and scavenging strategies employed by those taxa (SAR86, SAR11 and AEGEAN-169). DOC concentrations during the cruise period did not vary systematically, suggesting that the observed fluctuations in DOM composition, bacterioplankton community and BP were dependent on fluctuations in NPP, Chl-a and POM concentrations and, further, that there was a tight coupling between release of photosynthetically produced DOM and bacterioplankton utilization.

The cruise mean BCD:NPP ratio for this study was 60%, and the portion of particulate NPP that could have escaped microbial remineralization (mean of 3.8 mmol m−2 d−1) was less than the sinking vertical flux (mean 5.5 mmol m−2 d−1) from 50 m (Buesseler et al., 2020; Estapa et al., 2021; Roca-Martí et al., 2021). Thus, additional organic matter production, not accounted for by particulate NPP, was required to support BCD and the sinking export flux at OSP during the study period. The two independent assessments of GCP considered here were, on average, double that of NPP, potentially providing the flux of labile DOM necessary to account for both BCD and vertical particle flux. Comparing processes associated with organic carbon production and consumption from this 24-d Lagrangian study was useful to identify potential sources fueling BCD (e.g., phytoplankton extracellular release) and account for sinking POC export. These combined analyses of independent methods provide reasonably balanced estimates of organic matter production, consumption and export at OSP. Bacterioplankton comprise a significant fraction of the C budget at OSP, and likely in many other ocean regions, and thus should be considered carefully when assessing food web processes, particularly in terms of contributions to the transfer efficiency of a relatively low carbon flux as observed at this study site.

The shipboard data generated for this study can be found in the SeaWiFS Bio-optical and Storage System (SeaBASS). All EXPORTS data can be found at: http://dx.doi.org/10.5067/SeaBASS/EXPORTS/DATA001 or https://seabass.gsfc.nasa.gov/experiment/EXPORTS. Data specific to the bacterioplankton are stored within the Carlson SeaBASS archive and can be found at: https://seabass.gsfc.nasa.gov/search/archive/UCSB/carlson/. 14CO2-NPP and 14CO2-GCP data can be found at: https://seabass.gsfc.nasa.gov/archive/OSU/behrenfeld/EXPORTS/EXPORTSNP/archive. Raw sequence reads are available through NCBI BioProject PRJNA785357. Glider-based raw data used to derive GCP can be found at: https://seabass.gsfc.nasa.gov/investigator/Nicholson,%20David.

The supplemental files for this article can be found as follows:

Figure S1. Mean profiles of biological and biogeochemical variables collected over the 24-d Lagrangian study.

Figure S2. Relationship between ML mean bacterioplankton cell biovolume and 3H-leucine incorporation rates.

Figure S3. Comparison of 5-m bacterioplankton carbon demand and DOC remineralization rates.

Figure S4. Canonical Analysis of Principal (CAP) analysis of 16S rRNA samples.

Figure S5. Surface flowthrough seawater properties from EXPORTS cruise RR1813.

Table S1. Lagged correlation matrix of ecological and biogeochemical variables.

Table S2. Indicator 16S rRNA gene amplicon sequence variants for mixed layer samples (5–35 m).

We would like to thank Keri Opalk for providing high quality DOC analysis. We would also like to thank other members of the Carlson lab for their logistical and feedback in data interpretations, including Nicholas Baetge, Ellie Halewood and Anna James. We thank Jacqui Comstock, Dennis Hansell and Lihini Aluwihare for early discussions on data presented in this manuscript. Members of the EXPORTS program who also provided useful discussion of data presented here include Heather McNair, Susanne Menden-Deuer, Scott Gifford, Jason Graff, Alyson Santoro and Mark Brzezinski. We would like to thank the EXPORTS Project leader Dave Siegel, NASA scientists Inia Soto Ramos and Ivona Cetinic, as well as the captain and crew of RR1813 for enabling the continuous collection of samples presented here. Finally, we would like to thank the domain editor-in-chief, the associate editor and reviewers for providing high quality and careful reviews, which significantly improved the manuscript.

This project was supported by the National Aeronautics and Space Administration under awards 80NSSC18K0437 to CAC and 80NSS17K0568 to KHH and 80NSSC17K0663 to DPN. The overall EXPORTS project is a large-scale NASA-led field campaign within the Earth Science Project Office.

The authors declare that they have no conflicts of interests.

Contributed to conception and design: BMS, CAC.

Contributed to acquisition of data: BMS, JF, DPN, ST, CAC.

Contributed to analysis and interpretation of data: BMS, CAC.

Drafted and/or revised the article: BMS, JF, SL, KHH, DPN, ST, CAC.

Approved the submitted version for publication: BMS, JF, SL, KHH, DPN, ST, CAC.

Alonso-Sáez
,
L
,
Balagué
,
V
,
,
EL
,
Sánchez
,
O
,
González
,
JM
,
Pinhassi
,
J
,
Massana
,
R
,
Pernthaler
,
J
,
Pedrós-Alió
,
C
,
Gasol
,
JM.
2007
.
Seasonality in bacterial diversity in north-west Mediterranean coastal waters: Assessment through clone libraries, fingerprinting and FISH
.
FEMS Microbiology Ecology
60
(
1
):
98
112
. DOI: http://dx.doi.org/10.1111/j.1574-6941.2006.00276.x.
Alonso-Sáez
,
L
,
Vázquez-Domínguez
,
E
,
Cardelús
,
C
,
Pinhassi
,
J
,
Sala
,
MM
,
Lekunberri
,
I
,
Balagué
,
V
,
Vila-Costa
,
M
,
Unrein
,
F
,
Massana
,
R
,
Simó
,
R
,
Gasol
,
JM
.
2008
.
Factors controlling the year-round variability in carbon flux through bacteria in a coastal marine system
.
Ecosystems
11
(
3
):
397
409
. DOI: http://dx.doi.org/10.1007/s10021-008-9129-0.
Anderson
,
MJ
,
Willis
,
TJ.
2003
.
Canonical analysis of principal coordinates: A useful method of constrained ordination for ecology
.
Ecology
84
(
2
):
511
525
. DOI: http://dx.doi.org/10.1890/0012-9658(2003)084[0511:CAOPCA]2.0.CO;2.
Apprill
,
A
,
Mcnally
,
S
,
Parsons
,
R
,
Weber
,
L.
2015
.
Minor revision to V4 region SSU rRNA 806 R gene primer greatly increases detection of SAR11 bacterioplankton
.
Aquatic Microbial Ecology
75
(
2
):
129
137
. DOI: http://dx.doi.org/10.3354/ame01753.
Arnosti
,
C
,
Wietz
,
M
,
Brinkhoff
,
T
,
Hehemann
,
JH
,
Probandt
,
D
,
Zeugner
,
L
,
Amann
,
R.
2020
.
The biogeochemistry of marine polysaccharides: Sources, inventories, and bacterial drivers of the carbohydrate cycle
.
Annual Review of Marine Science
13
(
1
):
81
108
. DOI: http://dx.doi.org/10.1146/annurev-marine-032020-012810.
Baetge
,
N
,
Behrenfeld
,
MJ
,
Fox
,
J
,
Halsey
,
KH
,
Mojica
,
KDA
,
Novoa
,
A
,
Stephens
,
BM
,
Carlson
,
CA.
2021
.
The seasonal flux and fate of dissolved organic carbon through bacterioplankton in the Western North Atlantic
.
Frontiers in Microbiology
12
:
669883
. DOI: http://dx.doi.org/10.3389/fmicb.2021.669883.
Baines
,
SB
,
Pace
,
ML.
1991
.
The production of dissolved organic matter by phytoplankton and its importance to bacteria: Patterns across marine and freshwater systems
.
Limnology and Oceanography
36
(
6
):
1078
1090
. DOI: http://dx.doi.org/10.4319/lo.1991.36.6.1078.
Barone
,
B
,
Nicholson
,
D
,
Ferrón
,
S
,
Firing
,
E
,
Karl
,
D.
2019
.
The estimation of gross oxygen production and community respiration from autonomous time-series measurements in the oligotrophic ocean
.
Limnology and Oceanography: Methods
17
(
12
):
650
664
. DOI: http://dx.doi.org/10.1002/lom3.10340.
Bif
,
MB
,
Hansell
,
DA.
2019
.
Seasonality of dissolved organic carbon in the upper Northeast Pacific Ocean
.
Global Biogeochemical Cycles
33
(
5
):
526
539
. DOI: http://dx.doi.org/10.1029/2018GB006152.
Bowman
,
JP.
2006
.
The Marine Clade of the Family Flavobacteriaceae: The Genera Aequorivita, Arenibacter, Cellulophaga, Croceibacter, Formosa, Gelidibacter, Gillisia, Maribacter, Mesonia, Muricauda, Polaribacter, Psychroflexus, Psychroserpens, Robiginitalea, Salegentibacter
, in
Dworkin
,
M
,
Falkow
,
S
,
Rosenberg
,
E
,
Schleifer
,
K
,
Stackebrandt
,
E
eds.,
The Prokaryotes
.
New York, NY
:
Springer
:
677
694
. DOI: http://dx.doi.org/10.1007/0-387-30747-8_26.
Boyd
,
P
,
Harrison
,
PJ.
1999
.
Phytoplankton dynamics in the NE subarctic Pacific
.
Deep-Sea Research Part II: Topical Studies in Oceanography
46
(
11–12
):
2405
2432
. DOI: http://dx.doi.org/10.1016/S0967-0645(99)00069-7.
Boyd
,
PW
,
Whitney
,
FA
,
Harrison
,
PJ
,
Wong
,
CS.
1995
.
The NE subarctic Pacific in winter: II. Biological rate processes
.
Marine Ecology Progress Series
128
(
1–3
):
25
34
. DOI: http://dx.doi.org/10.3354/meps128025.
Buchan
,
A
,
LeCleir
,
GR
,
Gulvik
,
CA
,
Gonzalez
,
JM.
2014
.
Master recyclers: Features and functions of bacteria associated with phytoplankton blooms
.
Nature Reviews Microbiology
12
(
10
):
686
698
. DOI: http://dx.doi.org/10.1038/nrmicro3326.
Buesseler
,
KO
,
Benitez-Nelson
,
CR
,
Roca-Martí
,
M
,
Wyatt
,
AM
,
Resplandy
,
L
,
Clevenger
,
SJ
,
Drysdale
,
JA
,
Estapa
,
ML
,
Pike
,
S
,
Umhau
,
BP.
2020
.
High-resolution spatial and temporal measurements of particulate organic carbon flux using thorium-234 in the northeast Pacific Ocean during the EXport Processes in the Ocean from RemoTe Sensing field campaign
.
Elementa: Science of the Anthropocene
8
(
1
). DOI: http://dx.doi.org/10.1525/elementa.030.
Buesseler
,
KO
,
Boyd
,
PW.
2009
.
Shedding light on processes that control particle export and flux attenuation in the twilight zone of the open ocean
.
Limnology and Oceanography
54
(
4
):
1210
1232
. DOI: http://dx.doi.org/10.4319/lo.2009.54.4.1210.
de Cáceres
,
M
,
Legendre
,
P
.
2009
.
Associations between species and groups of sites: Indices and statistical inference
.
Ecology
90
(
12
):
3566
3574
. DOI: http://dx.doi.org/10.1890/08-1823.1.
Callahan
,
BJ
,
McMurdie
,
PJ
,
Rosen
,
MJ
,
Han
,
AW
,
Johnson
,
AJA
,
Holmes
,
SP.
2016
.
DADA2: High-resolution sample inference from Illumina amplicon data
.
Nature Methods
13
(
7
):
581
583
. DOI: http://dx.doi.org/10.1038/nmeth.3869.
Carlson
,
CA.
2002
.
Production and removal processes
, in
Hansell
,
DA
,
Carlson
,
CA
eds.,
Biogeochemistry of marine dissolved organic matter
.
Amsterdam, the Netherlands
:
Elsevier Inc
.:
139
151
. DOI: http://dx.doi.org/10.1016/B978-012323841-2/50006-3.
Carlson
,
CA
,
Ducklow
,
HW.
1996
.
Growth of bacterioplankton and consumption of dissolved organic carbon in the Sargasso Sea
.
Aquatic Microbial Ecology
10
(
1
):
69
85
. DOI: http://dx.doi.org/10.3354/ame010069.
Carlson
,
CA
,
del Giorgio
,
PA
,
Herndl
,
GJ.
2007
.
Microbes and the dissipation of energy and respiration: From cells to ecosystems
.
Oceanography
20
(
SPL.ISS. 2
):
89
100
. DOI: http://dx.doi.org/10.5670/oceanog.2007.52.
Carlson
,
CA
,
Giovannoni
,
SJ
,
Hansell
,
DA
,
Goldberg
,
SJ
,
Parsons
,
R
,
Vergin
,
K.
2004
.
Interactions among dissolved organic carbon, microbial processes, and community structure in the mesopelagic zone of the northwestern Sargasso Sea
.
Limnology and Oceanography
49
(
4
):
1073
1083
. DOI: http://dx.doi.org/10.4319/lo.2004.49.4.1073.
Carlson
,
CA
,
Hansell
,
DA.
2015
.
DOM sources, sinks, reactivity, and budgets
, in
Hansell
,
DA
,
Carlson
,
CA
eds.,
Biogeochemistry of marine dissolved organic matter: Second edition
.
Amsterdam, the Netherands
:
Elsevier Inc
.:
65
126
. DOI: http://dx.doi.org/10.1016/B978-0-12-405940-5.00003-0.
Choi
,
DH
,
An
,
SM
,
Yang
,
EC
,
Lee
,
H
,
Shim
,
JS
,
Jeong
,
JY
,
Noh
,
JH.
2018
.
Daily variation in the prokaryotic community during a spring bloom in shelf waters of the East China sea
.
FEMS Microbiology Ecology
94
(
9
). DOI: http://dx.doi.org/10.1093/femsec/fiy134.
Cottrell
,
MT
,
Kirchman
,
DL.
2000
.
Natural assemblages of marine proteobacteria and members of the Cytophaga-flavobacter cluster consuming low- and high-molecular-weight dissolved organic matter
.
Applied and Environmental Microbiology
66
(
4
):
1692
1697
. DOI: http://dx.doi.org/10.1128/AEM.66.4.1692-1697.2000.
Cowie
,
GL
,
Hedges
,
JI.
1994
.
Biochemical indicators of diagenetic alteration in natural organic matter mixtures
.
Nature
369
(
6478
):
304
307
. DOI: http://dx.doi.org/10.1038/369304a0.
Dauwe
,
B
,
Middelburg
,
JJ
,
Herman
,
PMJ
,
Heip
,
CHR.
1999
.
Linking diagenetic alteration of amino acids and bulk organic matter reactivity
.
Limnology and Oceanography
44
(
7
):
1809
1814
. DOI: http://dx.doi.org/10.4319/lo.1999.44.7.1809.
Davie-Martin
,
CL
,
Giovannoni
,
SJ
,
Behrenfeld
,
MJ
,
Penta
,
WB
,
Halsey
,
KH.
2020
.
Seasonal and spatial variability in the biogenic production and consumption of volatile organic compounds (VOCs) by marine plankton in the North Atlantic Ocean
.
Frontiers in Marine Science
7
:
1144
. DOI: http://dx.doi.org/10.3389/fmars.2020.611870.
Davis
,
J
,
Kaiser
,
K
,
Benner
,
R.
2009
.
Amino acid and amino sugar yields and compositions as indicators of dissolved organic matter diagenesis
.
Organic Geochemistry
40
(
3
):
343
352
. DOI: http://dx.doi.org/10.1016/j.orggeochem.2008.12.003.
de Boyer Montégut
,
C
,
Madec
,
G
,
Fischer
,
AS
,
Lazar
,
A
,
Iudicone
,
D.
2004
.
Mixed layer depth over the global ocean: An examination of profile data and a profile-based climatology
.
Journal of Geophysical Research C: Oceans
109
(
12
):
1
20
. DOI: http://dx.doi.org/10.1029/2004JC002378.
Ducklow
,
HW.
1992
.
Factors regulating bottom-up control of bacteria biomass in open ocean plankton communities
.
Microbial Ecology of Pelagic Environments
37
:
207
217
.
Ducklow
,
HW.
1999
.
The bacterial component of the oceanic euphotic zone
.
FEMS Microbiology Ecology
30
(
1
):
1
10
. DOI: http://dx.doi.org/10.1016/S0168-6496(99)00031-8.
Ducklow
,
HW.
2000
. Bacterial production and biomass in the oceans, in
Kirchman
,
DL
ed.,
Microbial ecology of the oceans
.
New York, NY
:
Liss/Wiley
:
85
119
.
Estapa
,
M
,
Buesseler
,
K
,
Durkin
,
CA
,
Omand
,
M
,
Benitez-Nelson
,
CR
,
Roca-Martí
,
M
,
Breves
,
E
,
Kelly
,
RP
,
Pike
,
S.
2021
.
Biogenic sinking particle fluxes and sediment trap collection efficiency at Ocean Station Papa
.
Elementa: Science of the Anthropocene
9
(
1
). DOI: http://dx.doi.org/10.1525/elementa.2020.00122.
Fandino
,
LB
,
Riemann
,
L
,
Steward
,
GF
,
Long
,
RA
,
Azam
,
F.
2001
.
Variations in bacterial community structure during a dinoflagellate bloom analyzed by DGGE and 16 S rDNA sequencing
.
Aquatic Microbial Ecology
23
(
2
):
119
130
. DOI: http://dx.doi.org/10.3354/ame023119.
Fox
,
J
,
Behrenfeld
,
MJ
,
Haëntjens
,
N
,
Chase
,
A
,
Kramer
,
SJ
,
Boss
,
E
,
Karp-Boss
,
L
,
Fisher
,
NL
,
Penta
,
WB
,
Westberry
,
TK
,
Halsey
,
KH.
2020
.
Phytoplankton growth and productivity in the Western North Atlantic: Observations of regional variability from the NAAMES field campaigns
.
Frontiers in Marine Science
7
:
24
. DOI: http://dx.doi.org/10.3389/fmars.2020.00024.
Fuhrman
,
JA
,
Cram
,
JA
,
Needham
,
DM.
2015
.
Marine microbial community dynamics and their ecological interpretation
.
Nature Reviews Microbiology
. DOI: http://dx.doi.org/10.1038/nrmicro3417.
del Giorgio
,
PA
,
Cole
,
JJ.
1998
.
Bacterial growth efficiency in natural aquatic systems
.
Annual Review of Ecology and Systematics
29
(
1
):
503
541
. DOI: http://dx.doi.org/10.1146/annurev.ecolsys.29.1.503.
Giovannoni
,
SJ.
2017
.
SAR11 bacteria: The most abundant plankton in the oceans
.
Annual Review of Marine Science
9
(
1
):
231
255
. DOI: http://dx.doi.org/10.1146/annurev-marine-010814-015934.
Giovannoni
,
SJ
,
Rappé
,
MS
,
Vergin
,
KL
,
Adair
,
NL
.
1996
.
16 S rRNA genes reveal stratified open ocean bacterioplankton populations related to the green non-sulfur bacteria
.
Proceedings of the National Academy of Sciences of the United States of America
93
(
15
):
7979
7984
. DOI: http://dx.doi.org/10.1073/pnas.93.15.7979.
Grossart
,
HP
,
Levold
,
F
,
Allgaier
,
M
,
Simon
,
M
,
Brinkhoff
,
T.
2005
.
Marine diatom species harbour distinct bacterial communities
.
Environmental Microbiology
7
(
6
):
860
873
. DOI: http://dx.doi.org/10.1111/j.1462-2920.2005.00759.x.
Halewood
,
E
,
Opalk
,
K
,
Custals
,
L
,
Carey
,
M
,
Hansell
,
D
,
Carlson
,
CA
.
2022
.
GO-SHIP Repeat Hydrography: Determination of dissolved organic carbon (DOC) and total dissolved nitrogen (TDN) in seawater using high temperature combustion analysis
.
Frontiers in Marine Science
. DOI: http://dx.doi.org/10.25607/OBP-1745.
Halsey
,
KH
,
Jones
,
BM
.
2015
.
Phytoplankton strategies for photosynthetic energy allocation
.
Annual Review of Marine Science
7
(
1
):
265
297
. DOI: http://dx.doi.org/10.1146/annurev-marine-010814-015813.
Halsey
,
KH
,
Milligan
,
AJ
,
Behrenfeld
,
MJ.
2010
.
Physiological optimization underlies growth rate-independent chlorophyll-specific gross and net primary production
.
Photosynthesis Research
103
(
2
):
125
137
. DOI: http://dx.doi.org/10.1007/s11120-009-9526-z.
Halsey
,
KH
,
O’Malley
,
RT
,
Graff
,
JR
,
Milligan
,
AJ
,
Behrenfeld
,
MJ.
2013
.
A common partitioning strategy for photosynthetic products in evolutionarily distinct phytoplankton species
.
New Phytologist
198
(
4
):
1030
1038
. DOI: http://dx.doi.org/10.1111/nph.12209.
Harrison
,
PJ
,
Whitney
,
FA
,
Tsuda
,
A
,
Saito
,
H
,
Tadokoro
,
K.
2004
.
Nutrient and plankton dynamics in the NE and NW gyres of the subarctic Pacific Ocean
.
Journal of Oceanography
60
(
1
):
93
117
. DOI: http://dx.doi.org/10.1023/B:JOCE.0000038321.57391.2a.
Holm
,
S.
1979
.
A simple sequentially rejective multiple test procedure
.
Scandinavian Journal of Statistics
6
:
65
70
.
Available at
https://cir.nii.ac.jp/crid/1571980074031773312.bib?lang=en.
Accessed May 22, 2022
.
Hutcheson
,
K
.
1970
.
A test for comparing diversities based on the shannon formula
.
Journal of Theoretical Biology
. DOI: http://dx.doi.org/10.1016/0022-5193(70)90124-4.
Juranek
,
LW
,
Quay
,
PD.
2013
.
Using triple isotopes of dissolved oxygen to evaluate global marine productivity
.
Annual Review of Marine Science
5
(
1
):
503
524
. DOI: http://dx.doi.org/10.1146/annurev-marine-121211-172430.
Kaiser
,
K
,
Benner
,
R.
2006
.
Hydrolysis-induced racemization of amino acids
.
Limnology and Oceanography: Methods
4
(
8
):
293
293
. DOI: http://dx.doi.org/10.4319/lom.2006.4.293.
Kaiser
,
K
,
Benner
,
R.
2009
.
Biochemical composition and size distribution of organic matter at the Pacific and Atlantic time-series stations
.
Marine Chemistry
113
(
1–2
):
63
77
. DOI: http://dx.doi.org/10.1016/j.marchem.2008.12.004.
Kirchman
,
DL.
1990
.
Limitation of bacterial growth by dissolved organic matter in the subarctic Pacific
.
Marine Ecology Progress Series
. DOI: http://dx.doi.org/10.3354/meps062047.
Kirchman
,
DL.
1992
.
Incorporation of thymidine and leucine in the subarctic Pacific: Application to estimating bacterial production
.
Marine Ecology Progress Series
. DOI: http://dx.doi.org/10.3354/meps082301.
Kirchman
,
DL.
2002
.
The ecology of Cytophaga-Flavobacteria in aquatic environments
.
FEMS Microbiology Ecology
39
(
2
):
91
100
. DOI: http://dx.doi.org/10.1111/j.1574-6941.2002.tb00910.x.
Kirchman
,
DL
,
Keil
,
RG
,
Simon
,
M
,
Welschmeyer
,
NA.
1993
.
Biomass and production of heterotrophic bacterioplankton in the oceanic subarctic Pacific
.
Deep-Sea Research Part A: Oceanographic Research Papers
40
(
5
):
967
988
.
Kirchman
,
DL
,
Keil
,
RG
,
Wheeler
,
PA.
1989
.
The effect of amino acids on ammonium utilization and regeneration by heterotrophic bacteria in the subarctic Pacific
.
Deep Sea Research Part A: Oceanographic Research Papers
36
(
11
):
1763
1776
. DOI: http://dx.doi.org/10.1016/0198-0149(89)90071-X.
Kirchman
,
DL
,
Rich
,
JH
,
Barber
,
RT.
1995
.
Biomass and biomass production of heterotrophic bacteria along 140°W in the equatorial Pacific: Effect of temperature on the microbial loop
.
Deep Sea Research Part II: Topical Studies in Oceanography
42
(
2–3
):
603
619
. DOI: http://dx.doi.org/10.1016/0967-0645(95)00021-H.
Lande
,
R.
1996
.
Statistics and partitioning of species diversity, and similarity among multiple communities
.
Oikos
76
(
1
):
5
. DOI: http://dx.doi.org/10.2307/3545743.
Lauro
,
FM
,
McDougald
,
D
,
Thomas
,
T
,
Williams
,
TJ
,
Egan
,
S
,
Rice
,
S
,
DeMaere
,
MZ
,
Ting
,
L
,
Ertan
,
H
,
Johnson
,
J
,
Ferriera
,
S
,
Lapidus
,
A
,
Anderson
,
I
,
Kyrpides
,
N
,
Munk
,
AC
,
Detter
,
C
,
Han
,
CS
,
Brown
,
MV
,
Robb
,
FT
,
Kjelleberg
,
S
,
Cavicchioli
,
R.
2009
.
The genomic basis of trophic strategy in marine bacteria
.
Proceedings of the National Academy of Sciences of the United States of America
106
(
37
):
15527
15533
. DOI: http://dx.doi.org/10.1073/pnas.0903507106.
Laws
,
EA.
1991
.
Photosynthetic quotients, new production and net community production in the open ocean
.
Deep Sea Research Part A: Oceanographic Research Papers
38
(
1
):
143
167
. DOI: http://dx.doi.org/10.1016/0198-0149(91)90059-O.
Lever
,
MA
,
Rogers
,
KL
,
Lloyd
,
KG
,
Overmann
,
J
,
Schink
,
B
,
Thauer
,
RK
,
Hoehler
,
TM
,
Jørgensen
,
BB.
2015
.
Life under extreme energy limitation: A synthesis of laboratory- and field-based investigations
.
FEMS Microbiology Reviews
. DOI: http://dx.doi.org/10.1093/femsre/fuv020.
Lewis
,
M
,
Smith
,
J.
1983
.
A small volume, short-incubation-time method for measurement of photosynthesis as a function of incident irradiance
.
Marine Ecology Progress Series
. DOI: http://dx.doi.org/10.3354/meps013099.
Liang
,
JH
,
Deutsch
,
C
,
McWilliams
,
JC
,
Baschek
,
B
,
Sullivan
,
PP
,
Chiba
,
D.
2013
.
Parameterizing bubble-mediated air-sea gas exchange and its effect on ocean ventilation
.
Global Biogeochemical Cycles
27
(
3
):
894
905
. DOI: http://dx.doi.org/10.1002/gbc.20080.
Liu
,
S
,
Liu
,
Z.
2020
.
Distinct capabilities of different Gammaproteobacterial strains on utilizing small peptides in seawater
.
Scientific Reports
10
(
1
):
1
11
. DOI: http://dx.doi.org/10.1038/s41598-019-57189-x.
Liu
,
S
,
Parsons
,
R
,
Opalk
,
K
,
Baetge
,
N
,
Giovannoni
,
S
,
Bolaños
,
LM
,
Kujawinski
,
EB
,
Longnecker
,
K
,
Lu
,
YH
,
Halewood
,
E
,
Carlson
,
CA.
2020
.
Different carboxyl-rich alicyclic molecules proxy compounds select distinct bacterioplankton for oxidation of dissolved organic matter in the mesopelagic Sargasso Sea
.
Limnology and Oceanography
. DOI: http://dx.doi.org/10.1002/lno.11405.
Liu
,
S
,
Wawrik
,
B
,
Liu
,
Z.
2017
.
Different bacterial communities involved in peptide decomposition between normoxic and hypoxic coastal waters
.
Frontiers in Microbiology
8
(
MAR
):
353
. DOI: http://dx.doi.org/10.3389/fmicb.2017.00353.
Lozupone
,
C
,
Knight
,
R.
2005
.
UniFrac: A new phylogenetic method for comparing microbial communities
.
Applied and Environmental Microbiology
71
(
12
):
8228
8235
. DOI: http://dx.doi.org/10.1128/AEM.71.12.8228-8235.2005.
Maas
,
AE
,
Miccoli
,
A
,
Stamieszkin
,
K
,
Carlson
,
CA
,
Steinberg
,
DK.
2021
.
Allometry and the calculation of zooplankton metabolism in the subarctic Northeast Pacific Ocean
.
Journal of Plankton Research
43
(
3
):
413
427
. DOI: http://dx.doi.org/10.1093/plankt/fbab026.
Malmstrom
,
RR
,
Cottrell
,
MT
,
Elifantz
,
H
,
Kirchman
,
DL.
2005
.
Biomass production and assimilation of dissolved organic matter by SAR11 bacteria in the Northwest Atlantic Ocean
.
Applied and Environmental Microbiology
71
(
6
):
2979
2986
. DOI: http://dx.doi.org/10.1128/AEM.71.6.2979-2986.2005.
Malmstrom
,
RR
,
Kiene
,
RP
,
Cottrell
,
MT
,
Kirchman
,
DL.
2004
.
Contribution of SAR11 bacteria to dissolved dimethylsulfoniopropionate and amino acid uptake in the North Atlantic Ocean
.
Applied and Environmental Microbiology
70
(
7
):
4129
4135
. DOI: http://dx.doi.org/10.1128/AEM.70.7.4129-4135.2004.
Mann
,
HB
,
Whitney
,
DR.
1947
.
On a test of whether one of two random variables is stochastically larger than the other
.
The Annals of Mathematical Statistics
18
(
1
):
50
60
. DOI: http://dx.doi.org/10.1214/aoms/1177730491.
Marra
,
J.
2002
.
Approaches to the measurement of plankton production
, in
Williams
,
PJB
,
Thomas
,
DN
,
Reynolds
,
CS
eds.,
Phytoplankton productivity
. DOI: http://dx.doi.org/10.1002/9780470995204.ch4.
Marra
,
J.
2009
.
Net and gross productivity: Weighing in with 14C
.
Aquatic Microbial Ecology
56
(
2–3
):
123
131
. DOI: http://dx.doi.org/10.3354/ame01306.
McMurdie
,
PJ
,
Holmes
,
S.
2013
.
Phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data
.
PLoS One
8
(
4
):
e61217
. DOI: http://dx.doi.org/10.1371/journal.pone.0061217.
McNair
,
HM
,
Morison
,
F
,
Graff
,
JR
,
Rynearson
,
TA
,
Menden-Deuer
,
S.
2021
.
Microzooplankton grazing constrains pathways of carbon export in the subarctic North Pacific
.
Limnology and Oceanography
66
(
7
):
2697
2711
. DOI: http://dx.doi.org/10.1002/lno.11783.
Moran
,
MA
,
Ferrer-González
,
FX
,
Fu
,
H
,
Nowinski
,
B
,
Olofsson
,
M
,
Powers
,
MA
,
Schreier
,
JE
,
Schroer
,
WF
,
Smith
,
CB
,
Uchimiya
,
M.
2022
.
The Ocean’s labile DOC supply chain
.
Limnology and Oceanography
. DOI: http://dx.doi.org/10.1002/lno.12053.
Moran
,
MA
,
Kujawinski
,
EB
,
Stubbins
,
A
,
Fatland
,
R
,
Aluwihare
,
LI
,
Buchan
,
A
,
Crump
,
BC
,
Dorrestein
,
PC
,
Dyhrman
,
ST
,
Hess
,
NJ
,
Howe
,
B
,
Longnecker
,
K
,
Medeiros
,
P
,
Niggemann
,
J
,
Obernosterer
,
I
,
Repeta
,
DJ
,
Waldbauer
,
JR
.
2016
.
Deciphering ocean carbon in a changing world
.
Proceedings of the National Academy of Sciences
113
(
12
):
3143
3151
. DOI: https://doi.org/10.1073/pnas.1514645113.
Morán
,
XAG
,
Estrada
,
M
,
Gasol
,
JM
,
Pedrós-Alió
,
C.
2002
.
Dissolved primary production and the strength of phytoplankton-bacterioplankton coupling in contrasting marine regions
.
Microbial Ecology
44
(
3
):
217
223
. DOI: http://dx.doi.org/10.1007/s00248-002-1026-z.
Nagata
,
T
.
2000
.
Production of dissolved organic matter
, in
Kirchman
,
DL
ed.,
Microbial ecology of the oceans
.
New York, NY
:
Liss/Wiley
:
121
152
.
Needham
,
DM
,
Fuhrman
,
JA.
2016
.
Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom
.
Nature Microbiology
1
:
16005
. DOI: http://dx.doi.org/10.1038/nmicrobiol.2016.5.
Nelson
,
CE
,
Carlson
,
CA.
2012
.
Tracking differential incorporation of dissolved organic carbon types among diverse lineages of Sargasso Sea bacterioplankton
.
Environmental Microbiology
14
(
6
):
1500
1516
. DOI: http://dx.doi.org/10.1111/j.1462-2920.2012.02738.x.
Nicholson
,
DP
,
Wilson
,
ST
,
Doney
,
SC
,
Karl
,
DM.
2015
.
Quantifying subtropical North Pacific gyre mixed layer primary productivity from Seaglider observations of diel oxygen cycles
.
Geophysical Research Letters
42
(
10
):
4032
4039
. DOI: http://dx.doi.org/10.1002/2015GL063065.
Norris
,
N
,
Levine
,
NM
,
Fernandez
,
VI
,
Stocker
,
R.
2021
.
Mechanistic model of nutrient uptake explains dichotomy between marine oligotrophic and copiotrophic bacteria
.
PLoS Computational Biology
17
(
5
):
e1009023
. DOI: http://dx.doi.org/10.1371/journal.pcbi.1009023.
Oksanen
,
J
,
Blanchet
,
G
,
Kindt
,
R
,
Legendre
,
P
,
Minchin
,
PR
,
Hara
,
RBO
,
Simpson
,
GL
,
Solymos
,
P
,
Stevens
,
HM
.
2015
.
Vegan: Community Ecology Package. R package vers 22-1
:
263
. DOI: http://dx.doi.org/10.4135/9781412971874.n145.
Orsi
,
WD
,
Smith
,
JM
,
Liu
,
S
,
Liu
,
Z
,
Sakamoto
,
CM
,
Wilken
,
S
,
Poirier
,
C
,
Richards
,
TA
,
Keeling
,
PJ
,
Worden
,
AZ
,
Santoro
,
AE.
2016
.
Diverse, uncultivated bacteria and archaea underlying the cycling of dissolved protein in the ocean
.
ISME Journal
10
(
9
):
2158
2173
. DOI: http://dx.doi.org/10.1038/ismej.2016.20.
Parada
,
AE
,
Needham
,
DM
,
Fuhrman
,
JA.
2016
.
Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples
.
Environmental Microbiology
18
(
5
):
1403
1414
. DOI: http://dx.doi.org/10.1111/1462-2920.13023.
Pinhassi
,
J
,
Sala
,
MM
,
Havskum
,
H
,
Peters
,
F
,
Guadayol
,
Ò
,
Malits
,
A
,
Marrasé
,
C.
2004
.
Changes in bacterioplankton composition under different phytoplankton regimens
.
Applied and Environmental Microbiology
70
(
11
):
6753
6766
. DOI: http://dx.doi.org/10.1128/AEM.70.11.6753-6766.2004.
Porter
,
KG
,
Feig
,
YS.
1980
.
The use of DAPI for identifying and counting aquatic microflora
.
Limnology and Oceanography
25
(
5
):
943
948
. DOI: http://dx.doi.org/10.4319/lo.1980.25.5.0943.
Reintjes
,
G
,
Arnosti
,
C
,
Fuchs
,
B
,
Amann
,
R.
2019
.
Selfish, sharing and scavenging bacteria in the Atlantic Ocean: A biogeographical study of bacterial substrate utilisation
.
ISME Journal
13
(
5
):
1119
1132
. DOI: http://dx.doi.org/10.1038/s41396-018-0326-3.
Reintjes
,
G
,
Tegetmeyer
,
HE
,
Bürgisser
,
M
,
Orlić
,
S
,
Tews
,
I
,
Zubkov
,
M
,
Voß
,
D
,
Zielinski
,
O
,
Quast
,
C
,
Glöckner
,
FO
,
Amann
,
R
,
Ferdelman
,
TG
,
Fuchs
,
BM.
2019
.
On-site analysis of bacterial communities of the ultraoligotrophic South Pacific Gyre
.
Applied and Environmental Microbiology
85
(
14
). DOI: http://dx.doi.org/10.1128/AEM.00184-19.
Reji
,
L
,
Tolar
,
BB
,
Chavez
,
FP
,
Francis
,
CA.
2020
.
Depth-differentiation and seasonality of planktonic microbial assemblages in the Monterey Bay upwelling system
.
Frontiers in Microbiology
11
(
1075
):
1
14
. DOI: http://dx.doi.org/10.3389/fmicb.2020.01075.
Roca-Martí
,
M
,
Benitez-Nelson
,
CR
,
Umhau
,
BP
,
Wyatt
,
AM
,
Clevenger
,
SJ
,
Pike
,
S
,
Horner
,
TJ
,
Estapa
,
ML
,
Resplandy
,
L
,
Buesseler
,
KO.
2021
.
Concentrations, ratios, and sinking fluxes of major bioelements at Ocean Station Papa
.
Elementa: Science of the Anthropocene
9
(
1
). DOI: http://dx.doi.org/10.1525/elementa.2020.00166.
Shapiro
,
S
,
Wilk
,
M.
1965
.
An analysis of variance test for normality (complete samples)
.
Biometrika
52
(
3–4
):
591
611
. DOI: http://dx.doi.org/10.1093/biomet/52.3-4.591.
Sharp
,
JH
.
1974
.
Improved analysis for “particulate” organic carbon and nitrogen from seawater
.
Limnology and Oceanography
19
(
6
):
984
989
. DOI: https://doi.org/10.4319/lo.1974.19.6.0984.
Shen
,
Y
,
Benner
,
R.
2019
.
Molecular properties are a primary control on the microbial utilization of dissolved organic matter in the ocean
.
Limnology and Oceanography
:
lno.11369
. DOI: http://dx.doi.org/10.1002/lno.11369.
Sherry
,
ND
,
Boyd
,
PW
,
Sugimoto
,
K
,
Harrison
,
PJ.
1999
.
Seasonal and spatial patterns of heterotrophic bacterial production, respiration, and biomass in the subarctic NE Pacific
.
Deep Sea Research Part II: Topical Studies in Oceanography
46
(
11–12
):
2557
2578
. DOI: http://dx.doi.org/10.1016/S0967-0645(99)00076-4.
Siegel
,
DA
,
Buesseler
,
KO
,
Behrenfeld
,
MJ
,
Benitez-Nelson
,
CR
,
Boss
,
E
,
Brzezinski
,
MA
,
Burd
,
A
,
Carlson
,
CA
,
D’Asaro
,
EA
,
Doney
,
SC
,
Perry
,
MJ
,
Stanley
,
RHR
,
Steinberg
,
DK.
2016
.
Prediction of the export and fate of global ocean net primary production: The EXPORTS science plan
.
Frontiers in Marine Science
3
(
March
):
1
10
. DOI: http://dx.doi.org/10.3389/fmars.2016.00022.
Siegel
,
DA
,
Carlson
,
CA
,
Hansell
,
DA
,
Nelson
,
NB
,
Graff
,
JR
,
Stephens
,
BM
.
2019
.
EXPORTS
.
SeaWiFS Bio-Optical Archive and Storage System (SeaBASS), NASA
.
Available at
http://dx.doi.org/10.5067/SeaBASS/EXPORTS/DATA001.
Accessed November 11, 2019
.
Siegel
,
DA
,
Cetinić
,
I
,
Graff
,
JR
,
Lee
,
CM
,
Nelson
,
N
,
Perry
,
MJ
,
Ramos
,
IS
,
Steinberg
,
DK
,
Buesseler
,
K
,
Hamme
,
R
,
Fassbender
,
AJ
,
Nicholson
,
D
,
Omand
,
MM
,
Robert
,
M
,
Thompson
,
A
,
Amaral
,
V
,
Behrenfeld
,
M
,
Benitez-Nelson
,
C
,
Bisson
,
K
,
Boss
,
E
,
Boyd
,
PW
,
Brzezinski
,
M
,
Buck
,
K
,
Burd
,
A
,
Burns
,
S
,
Caprara
,
S
,
Carlson
,
C
,
Cassar
,
N
,
Close
,
H
,
D’Asaro
,
E
,
Durkin
,
C
,
Erickson
,
Z
,
Estapa
,
ML
,
Fields
,
E
,
Fox
,
J
,
Freeman
,
S
,
Gifford
,
S
,
Gong
,
W
,
Gray
,
D
,
Guidi
,
L
,
Haëntjens
,
N
,
Halsey
,
K
,
Huot
,
Y
,
Hansell
,
D
,
Jenkins
,
B
,
Karp-Boss
,
L
,
Kramer
,
S
,
Lam
,
P
,
Lee
,
JM
,
Maas
,
A
,
Marchal
,
O
,
Marchetti
,
A
,
McDonnell
,
A
,
McNair
,
H
,
Menden-Deuer
,
S
,
Morison
,
F
,
Niebergall
,
AK
,
Passow
,
U
,
Popp
,
B
,
Potvin
,
G
,
Resplandy
,
L
,
Roca-Martí
,
M
,
Roesler
,
C
,
Rynearson
,
T
,
Traylor
,
S
,
Santoro
,
A
,
Seraphin
,
KD
,
Sosik
,
HM
,
Stamieszkin
,
K
,
Stephens
,
B
,
Tang
,
W
,
Van Mooy
,
B
,
Xiong
,
Y
,
Zhang
,
X
.
2021
.
An operational overview of the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Northeast Pacific field deployment
.
Elementa: Science of the Anthropocene
9
(
1
). DOI: http://dx.doi.org/10.1525/elementa.2020.00107.
Smith
,
D
,
Azam
,
F
.
1992
.
A simple, economical method for measuring bacterial protein synthesis rates in seawater using
.
Marine Microbial Food Webs
6
(
2
):
107
114
.
Stamatakis
,
A
.
2014
.
RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies
.
Bioinformatics
30
(
9
):
1312
1313
. DOI: http://dx.doi.org/10.1093/bioinformatics/btu033.
Stephens
,
BM
,
Opalk
,
K
,
Petras
,
D
,
Liu
,
S
,
Comstock
,
J
,
Aluwihare
,
LI
,
Hansell
,
DA
,
Carlson
,
CA
.
2020
.
Organic matter composition at Ocean Station Papa affects its bioavailability, bacterioplankton growth efficiency and the responding taxa
.
Frontiers in Marine Science
7
:
1077
. DOI: http://dx.doi.org/10.3389/fmars.2020.590273.
Strickland
,
J
,
Parsons
,
T
.
1972
.
A practical handbook of seawater analysis. Fisheries Research Board of Canada Bulletin 167
.
The Quarterly Review of Biology
44
(
3
):
327
327
. DOI: https://doi.org/10.1086/406210.
Teira
,
E
,
Hernando-Morales
,
V
,
Fernández
,
A
,
Martínez-García
,
S
,
Álvarez-Salgado
,
X
,
Bode
,
A
,
Varela
,
M
.
2015
.
Local differences in phytoplankton-bacterioplankton coupling in the coastal upwelling off Galicia (NW Spain)
.
Marine Ecology Progress Series
528
:
53
69
. DOI: http://dx.doi.org/10.3354/meps11228.
Tilstone
,
GH
,
Smyth
,
TJ
,
Gowen
,
RJ
,
Martinez-Vicente
,
V
,
Groom
,
SB
.
2005
.
Inherent optical properties of the Irish Sea and their effect on satellite primary production algorithms
.
Journal of Plankton Research
27
(
11
):
1127
1148
. DOI: http://dx.doi.org/10.1093/plankt/fbi075.
Tortell
,
PD
,
Maldonado
,
MT
,
Price
,
NM
.
1996
.
The role of heterotrophic bacteria in iron-limited ocean ecosystems
.
Nature
383
(
6598
):
330
332
. DOI: http://dx.doi.org/10.1038/383330a0.
Traving
,
SJ
,
Kellogg
,
CTE
,
Ross
,
T
,
McLaughlin
,
R
,
Kieft
,
B
,
Ho
,
GY
,
Peña
,
A
,
Krzywinski
,
M
,
Robert
,
M
,
Hallam
,
SJ
.
2021
.
Prokaryotic responses to a warm temperature anomaly in northeast subarctic Pacific waters
.
Communications Biology
4
(
1
):
1
12
. DOI: http://dx.doi.org/10.1038/s42003-021-02731-9.
Vergin
,
KL
,
Done
,
B
,
Carlson
,
CA
,
Giovannoni
,
SJ
.
2013
.
Spatiotemporal distributions of rare bacterioplankton populations indicate adaptive strategies in the oligotrophic ocean
.
Aquatic Microbial Ecology
71
(
1
):
1
13
. DOI: http://dx.doi.org/10.3354/ame01661.
Wear
,
EK
,
Wilbanks
,
EG
,
Nelson
,
CE
,
Carlson
,
CA
.
2018
.
Primer selection impacts specific population abundances but not community dynamics in a monthly time-series 16S rRNA gene amplicon analysis of coastal marine bacterioplankton
.
Environmental Microbiology
20
(
8
):
2709
2726
.
Available at
https://ami-journals.onlinelibrary.wiley.com/doi/10.1111/1462-2920.14091.
Webb
,
WL
,
Newton
,
M
,
Starr
,
D
.
1974
.
Carbon dioxide exchange of Alnus rubra—A mathematical model
.
Oecologia
17
(
4
):
281
291
. DOI: http://dx.doi.org/10.1007/BF00345747.
Welschmeyer
,
NA
,
Strom
,
S
,
Goericke
,
R
,
DiTullio
,
G
,
Belvin
,
M
,
Petersen
,
W
.
1993
.
Primary production in the subarctic Pacific Ocean: Project SUPER
.
Progress in Oceanography
. DOI: http://dx.doi.org/10.1016/0079-6611(93)90010-B.
Werdell
,
PJ
,
Bailey
,
S
,
Fargion
,
G
,
Pietras
,
C
,
Knobelspiesse
,
K
,
Feidman
,
G
,
Mcclain
,
C
.
2003
.
Unique data repository facilitates ocean color satellite validation
.
Eos
84
(
38
):
377
. DOI: http://dx.doi.org/10.1029/2003EO380001.
Wetz
,
MS
,
Wheeler
,
PA
.
2003
.
Production and partitioning of organic matter during simulated phytoplankton blooms
.
Limnology and Oceanography
48
(
5
):
1808
1817
. DOI: http://dx.doi.org/10.4319/lo.2003.48.5.1808.
Willis
,
AD
.
2019
.
Rarefaction, alpha diversity, and statistics
.
Frontiers in Microbiology
10
(
OCT
):
2407
. DOI: http://dx.doi.org/10.3389/fmicb.2019.02407.
Yentsch
,
CS
,
Menzel
,
DW
.
1963
.
A method for the determination of phytoplankton chlorophyll and phaeophytin by fluorescence
.
Deep Sea Research and Oceanographic Abstracts
10
(
3
):
221
231
. DOI: http://dx.doi.org/10.1016/0011-7471(63)90358-9.

How to cite this article: Stephens, BM, Fox, J, Liu, S, Halsey, KH, Nicholson, DP, Traylor, S, Carlson, CA. 2023. Influence of amino acids on bacterioplankton production, biomass and community composition at Ocean Station Papa in the subarctic Pacific. Elementa: Science of the Anthropocene 11(1). DOI: https://doi.org/10.1525/elementa.2022.00095

Domain Editor-in-Chief: Jody W. Deming, University of Washington, Seattle, WA, USA

Associate Editor: Jeff Bowman, University of California San Diego Scripps Institution of Oceanography, La Jolla, CA, USA

Knowledge Domain: Ocean Science

Part of an Elementa Special Feature: Accomplishments from the EXport Processes in the Ocean from RemoTe Sensing (EXPORTS) Field Campaign to the Northeast Pacific Ocean

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

Supplementary data