Heterotrophic marine bacteria utilize organic carbon for growth and biomass synthesis. Thus, their physiological variability is key to the balance
between the production and consumption of organic matter and ultimately particle export in the ocean. Here we investigate a potential link between
bacterial traits and ecosystem functions in the rapidly warming West Antarctic Peninsula (WAP) region based on a bacteria-oriented ecosystem
model. Using a data assimilation scheme, we utilize the observations of bacterial groups with different physiological traits to constrain the
group-specific bacterial ecosystem functions in the model. We then examine the association of the modeled bacterial and other key ecosystem
functions with eight recurrent modes representative of different bacterial taxonomic traits. Both taxonomic and physiological traits reflect the
variability in bacterial carbon demand, net primary production, and particle sinking flux. Numerical experiments under perturbed climate conditions
demonstrate a potential shift from low nucleic acid bacteria to high nucleic acid bacteria-dominated communities in the coastal WAP. Our study
suggests that bacterial diversity via different taxonomic and physiological traits can guide the modeling of the polar marine ecosystem functions
under climate change.
Introduction
Microbes regulate many key ecosystem functions in the marine food web. Unicellular primary producers fix organic carbon (i.e., an ecosystem function
termed primary production), while heterotrophic marine bacteria and archaea (hereafter bacteria) utilize the fixed organic carbon for growth and
biomass synthesis (i.e., an ecosystem function termed bacterial production, or BP; Azam et al., 1983). Thus, the variability in the abundance and
activity of bacteria is central to understanding the balance between production and consumption of organic matter and ultimately particle export in
the ocean. In flow cytometric analyses, bacteria cluster into two groups of cells with different nucleic acid content, including high nucleic acid
(HNA) and low nucleic acid (LNA) cells (Bouvier et al., 2007; Gasol et al., 1999). These two groups are suggested to represent lineages (Schattenhofer
et al., 2011; Vila-Costa et al., 2012) or physiological states (Bowman et al., 2017), in which HNA cells are generally larger in both cell and genome
size compared to LNA cells (Bouvier et al., 2007; Calvo-Díaz and Morán, 2006). The significance of HNA versus LNA cells in determining
distinct ecosystem states and functions has been investigated, but much is still unknown. In a recent study along the West Antarctic Peninsula (WAP),
the high dimensionality of the bacterial community structure data was reduced via emergent self-organizing maps and subdivided into a small number of
bacterial modes associated with specific taxonomic and functional traits (Bowman et al., 2017). Bowman et al. (2017) demonstrated that a combination
of taxonomy, physiological structure (i.e., HNA and LNA cells), and abundance of bacterial communities explained up to 73 % of the variance in
bulk BP. Their findings imply that physiological and taxonomic traits of bacteria may inform a predictive ecosystem model to further explore
ecologically important questions including the following: would these bacterial traits reflect other important ecosystem functions such as the net primary
production and particle sinking flux? If so, what would be the potential mechanisms and how will the relationship between bacterial traits and
ecosystem functions be impacted by climate change?
The WAP is a rapidly warming marine ecosystem, with resulting changes in physical, ecological, and biogeochemical processes (Clarke et al., 2009; Cook
et al., 2005; Ducklow et al., 2007; King, 1994; Meredith and King, 2005; Stammerjohn et al., 2008; Vaughan et al., 2003; Vaughan, 2006; Whitehouse
et al., 2008). Routine monitoring through the Palmer Long-Term Ecological Research project (Palmer LTER; since 1991) has revealed climate-driven
variations in seasonal phytoplankton accumulation (Saba et al., 2014; Schofield et al., 2017), bacterial dynamics (Bowman and Ducklow, 2015; Ducklow
et al., 2012a; Kim and Ducklow, 2016; Luria et al., 2017, 2014), nutrient drawdown (Kim et al.,
2016), and micro- and macrozooplankton dynamics (Garzio and Steinberg, 2013; Steinberg et al., 2015; Thibodeau et al., 2019). The wealth of Palmer
LTER observations has enabled the construction of a numerical marine ecosystem model for the coastal WAP region (i.e., the WAP-1D-VAR v1.0 model; Kim
et al., 2021) by adapting the regional test-bed models of other ocean basins (Friedrichs, 2001; Friedrichs et al., 2006, 2007; Luo et al., 2010,
2012). The WAP-1D-VAR v1.0 model is compared against roughly bi-weekly time-series observations over the growth season (October–March) near Palmer
Station (64.77∘ S, 64.05∘ W; the mean depth ∼ 65 m, the bottom depth ∼ 75 m) that record seasonal
variations in ecological processes modulated by variations in surface light, mixed layer depth, and surface sea-ice cover. The WAP-1D-VAR v1.0 model
utilizes a data assimilation scheme to minimize the misfits between model results and observational data via a variational adjoint method (Lawson
et al., 1995) that assimilates the available Palmer LTER data to objectively adjust the model parameters. Serving as a mechanistic model, assimilation
of the Palmer LTER observations enables the model to constrain poorly measured bacterial processes (e.g., respiration, viral and grazing mortality,
growth efficiency, carbon demand, and utilization of dissolved organic matter with varying lability) and to predict microbial system states in
changing environments. Yet, incorporating molecular observations into a numerical ecosystem model is a challenge because of the difference in how
levels of biological organization are treated in observations and models (Hellweger, 2020), as well as the high dimensionality of microbial molecular
observations. One argument is that molecular-level changes in microbial dynamics may not directly translate into a clear picture of changes in
community structure or resulting changes in bulk ecosystem functions.
In this study, we explore a potential link between bacterial traits and ecosystem functions in the warming coastal WAP using a bacteria-oriented
ecosystem model modified from the WAP-1D-VAR v1.0 model (Kim et al., 2021). The bacterial traits examined in this study include physiological and
taxonomic traits. For physiological traits, our model explicitly simulates the time-evolving dynamics of two ubiquitous bacterial groups with
differing nucleic acid contents, the HNA group and the LNA group, by directly assimilating the group-specific carbon biomass observations estimated by
flow cytometry. For taxonomic traits, taxonomic modes derived from bacterial 16S rRNA gene sequence data (Bowman et al., 2017) are compared to final
model results at the corresponding time points with the assumption that bacterial taxonomy would provide information about bacterial ecosystem
processes and structures. Our study indirectly incorporates bacterial molecular information into ecosystem-level dynamics, in contrast to genome-scale
or gene-centric models predicting the time-evolving dynamics of microbial molecular processes.
Model structure. The model is forced by five different physical forcings, denoted as a horizontal row across the top of the schematic. As the ecosystem component, heterotrophic bacteria are divided into two groups of differing physiological states, including high nucleic acid (HNA) and low nucleic acid (LNA) bacterial compartments. The flows between the prognostic state variables with the name of the numbered flows in the legend only represent these two bacterial compartments.
Material and methodsBacteria-oriented ecosystem model
The model is originally derived and modified from the one-dimensional (1-D) variational data assimilation planktonic ecosystem model for the coastal
WAP region called the WAP-1D-VAR v1.0 model (Kim et al., 2021). The WAP-1D-VAR v1.0 model tracks C, N, and P of its state variables with flexible
stoichiometry. For our study, we modify the original model's single bacterial compartment into HNA and LNA bacterial compartments and only discuss
their C stocks and rates, as well as other state variables that remain the same as the WAP-1D-VAR v1.0 model. The state variables in this modified
model include HNA and LNA bacteria, diatoms, cryptophytes, microzooplankton, krill, labile dissolved organic carbon (LDOC), semi-labile DOC (SDOC),
ammonium (NH4), nitrate (NO3), phosphate (PO4), and particulate (carbon) detritus (Fig. 1). Refractory DOC (RDOC) and
higher trophic levels are implicit to serve as model closure terms (i.e., they are source or sink terms of other explicit state variables, and their
time derivatives are not calculated in the model). The model is forced by mixed layer depth (MLD), photosynthetically active radiation (PAR) at the
ocean surface, surface sea-ice concentration, water-column temperature, and eddy diffusivity (Fig. S1 in the Supplement) using a constant time step of
1 h and a second-order Runge–Kutta scheme (Texts S1 and S2 in the Supplement). The model allows both LDOC and SDOC
as substrate sources for bacteria, and it is the nutrient quota of bacteria that allows the lability of SDOC to vary. In contrast to LDOC pool that is
entirely available for bacteria, a parameter controlling the lability of SDOC (rSDOC, Tables S2–S6 in the Supplement) regulates the size of the
portion of SDOC that can be made available for bacterial uptake. Bacterial C growth is determined by their cellular nutrient quota, as well as available LDOC
and SDOC concentrations (Kim et al., 2021).
Initial values of bacterial model parameters. Different initial values are assigned to the model parameters of high nucleic acid (HNA) and low nucleic acid (LNA) bacterial groups to simulate their distinct physiological processes and trophic interactions.
ParameterDefinitionHNALNAkDOCDOC half-saturation concentration for bacterial uptake, mmolCm-30.50.2μMaximum bacterial growth rate, d-12.01.0bRParameter control for bacterial active respiration versus production, (mmolCm-3d-1)-10.080.2remiBacterial nutrient regeneration rate, d-18.02.0exREFRBacterial RDOC excretion rate, d-10.040.01fSBacterial selection strength on SDOC0.10.7rBBacterial basal respiration rate, d-10.040.01rminABacterial minimum active respiration rate, d-10.080.04rmaxABacterial maximum active respiration rate, d-10.40.1mortBacterial mortality rate, d-10.20.01gBacterial half-saturation concentration in microzooplankton grazing, mmolCm-30.550.55
The time derivative of C biomass for each bacterial group is determined as follows:
1dCHNAdt=GHNA,LDOCC+GHNA,SDOCC-RHNAC-EHNA,RDOCC-EHNA,SDOCC-GZHNAC-MHNAC,2dCLNAdt=GLNA,LDOCC+GLNA,SDOCC-RLNAC-ELNA,RDOCC-ELNA,SDOCC-GZLNAC-MLNAC,
where C is bacterial C biomass, GLDOCC is LDOC consumption, GSDOCC is SDOC consumption,
RC is respiration, ERDOCC is RDOC excretion, ESDOCC is SDOC excretion,
GZC is the amount of C biomass grazed by microzooplankton, and MC is mortality caused by viral attack
(unit: mmolCm-3 for C and mmolCm-3d-1 for the rest of the terms). The sum of the first three terms on the right-hand side
in Eqs. (1) and (2) is defined as BP for each bacterial group (i.e., BPHNA=GHNA,LDOCC+GHNA,SDOCC-RHNAC, BPLNA=GLNA,LDOCC+GLNA,SDOCC-RLNAC). Both CHNA and CHNA are constrained by the
group-specific C biomass data estimated via flow cytometry, while only bulk BP (i.e., BP=BPHNA+BPLNA), not the
group-specific BP, is constrained by the observations due to the lack of the group-specific BP data at the study site. With the initial parameter
values distinct to each bacterial group (Table 1), the model incorporates the observations of CHNA, CLNA, BP, and other state
and rate variables to its built-in data assimilation scheme (Sect. 2.2) that optimizes the parameters and calculates the resulting C stock and flows
of each bacterial group (Eqs. 1 and 2). In other words, the partitioning of BP into HNA and LNA groups is purely determined by parameter optimization
using the information about other ecosystem observations.
Modeling framework. The modeling framework in this study consists of the mechanistic part as the processes associated with the bacteria-oriented ecosystem model (Fig. 1) and the data-driven part that represents how bacterial taxonomic modes are associated with the modeled bacterial and other key ecosystem functions. A variational adjoint method is employed for the parameter optimization and data assimilation processes (adapted from Glover et al., 2011). Gradient: the sensitivity of the total cost function with respect to the model parameter from optimization. Optimized model results are interpreted as a function of bacterial taxonomic and physiological traits.
Modeling framework
The modeling framework consists of the mechanistic and data-driven parts (Fig. 2). The mechanistic part represents prognostic, time-evolving
microbial processes in the model (Fig. 1, Sect. 2.1) with its built-in data assimilation scheme via a variational adjoint method (Lawson et al.,
1995). The data assimilation scheme minimizes the misfits between observations (i.e., assimilated data; Sect. 2.3) and model results by objectively
optimizing a subset of model parameters (Friedrichs, 2001; Spitz et al., 2001; Ward et al., 2010; details in Text S3 in the Supplement). The
data-driven part represents the pairing of the optimized model results from the mechanistic part and the bacterial taxonomic “modes” derived from
16S rRNA gene sequence abundance data (Bowman et al., 2017). We call this latter part data-driven because the high dimensionality of the bacterial
community structure data is reduced to a single taxonomic mode using an unsupervised machine learning algorithm called Kohonen's self-organizing maps
(Kohonen, 2001). Each taxonomic mode (mode hereafter) represents its specific taxonomic traits and is expressed as a single categorical variable
without linear progression in between. For example, mode 1 is not necessarily closer to mode 3 than it is to mode 7. Modes are not necessarily
correlated to the physiological traits of bacteria (i.e., HNA and LNA C biomass) despite being derived from the same samples. In other words, the
taxonomic and physiological traits are independent of each other.
We select the nearshore Palmer LTER Station B (64.77∘ S, 64.05∘ W; ∼ 65 m) in the coastal WAP as the modeling site. The
Station B datasets consist of roughly bi-weekly physical, chemical, and biological profiles collected via a profiling conductivity–temperature–density (CTD) rosette. Flow cytometric data for HNA and LNA C biomass and 16S rRNA gene amplicon data for taxonomic modes come from Arthur Harbour Station B
at 10 m depth (situated 1 km from Station B) or Palmer Station seawater intake at 6 m depth (Bowman et al., 2017). Three
upper-ocean depth levels, 0, 10, and 20 m (with the layer thickness of 2, 16, and 4 m, respectively), are modeled for four consecutive
growth seasons, including November 2010–March 2011 (2010–11 hereafter), 2011–12, 2012–13, and 2013–14. However, only the results from
10 m are presented in detail because of the availability of the bacterial trait data at that depth. Despite the advantage of simulating the
full water-column layers, it would be best to exclude the depth levels without bacterial trait observations, yet to include an adequate number of
depth levels to simulate seasonal MLD and light impacts on bacterial dynamics. Thus, we choose to model three layers, that is, 0, 10, and
20 m, in a 1-D (vertical) water column.
The 1-D modeling of the coastal WAP region is justifiable given that the WAP shows relatively weak net advection compared with the Antarctic
Circumpolar Current (ACC) or the subpolar gyres (Meredith et al., 2008, 2013). In addition, the CTD observations at Palmer Station do not show abrupt
changes in physical and biogeochemical tracers as a result of lateral advection, with fairly homogeneous temperature and salinity distributions for
the years and depths modeled in our study (Kim and Ducklow, 2016). There is a 6-month sampling gap in the austral autumn and winter months, so we
optimize the model each year separately only for the austral spring to summer months. This results in each year possessing its own uniquely optimized
parameter set that drives the minimized model–observation misfits for the given year. We also optimize the model for the climatological year, referred
to as the climatological model, constructed by averaging 4-year observations (2010–11 to 2013–14; Text S4 in the Supplement). We do not average the
whole Palmer LTER multi-decadal period (since 1991) because of the lack of HNA and LNA C biomass data except for those 4 years. Other modeling aspects
(e.g., model initialization, spin-up, and bottom boundary conditions) are detailed in Text S4 and Kim et al. (2021).
Assimilated data
We assimilate the Palmer LTER observations from 0, 10, and 20 m that correspond to the compartments and flows in the model, including
NO3, PO4, phytoplankton taxonomic-specific chlorophyll (Chl) for diatoms and cryptophytes (Schofield et al., 2017),
microzooplankton C biomass (Garzio et al., 2013), bulk primary production (PP), bulk BP, HNA bacterial C biomass, LNA bacterial C biomass, SDOC,
particulate organic carbon (POC), and particulate organic nitrogen (PON). Though not available in 2011–12, because of the importance in constraining
the group-specific phytoplankton dynamics, the 4-year climatological value of the group-specific Chl is assimilated for 2011–12. NO3 is not
assimilated in 2010–11, while POC, PON, and SDOC are not assimilated in 2012–13 and 2013–14 because of the lack of observations in those
years. Krill C biomass is not assimilated due to the strong patchiness of their distribution with many zero values that may hinder proper model
optimization, while microzooplankton C biomass (2010–11) from a single year's measurement is assimilated for all 4 model years to at least provide
constraints on the parameter values for phytoplankton grazing. The model–observation misfits for microzooplankton are not examined because of the
discrepancy in the timing and location of those data assimilated compared to our study.
SDOC is calculated by subtracting the background (RDOC) concentration (40.0 mmolCm-3) from climatological total DOC
concentration. POC (PON) is assimilated to represent the model detrital pool, but its measurements contain living biomass from bottle filter
experiments. Climatological observations show that living phytoplankton and bacterial biomass account for 26 % of total POC and 29 % of total
PON, so these fractions are used to exclude living biomass from the bulk particulate material pool. When converting Chl to phytoplankton C biomass,
the maximum Chl / N ratio is used along with the reference (Redfield) C/N ratio of 0.15. BP (mmolCm-3d-1) is derived
from 3H-leucine incorporation rate (pmoll-1h-1) data using the conversion factor of 1.5 kgCmol-1 leucine
incorporated (Ducklow, 2000). The bacterial group-specific C biomass (mmolCm-3) is estimated from bacterial abundance measured by flow
cytometry (i.e., bulk bacterial C biomass multiplied by the fraction of each physiological group, fHNA or fLNA, with
the conversion factor of 10 fgC cell-1; Fukuda et al., 1998).
Cost function and portability index
The total cost function is calculated to represent the misfit between observations and model results as follows:
J=∑m=1M1Nm∑n=1Nma^m,n-am,nσm2,
where m and n represent assimilated data types and data points, respectively, M and Nm are the total number of assimilated data types and
data points for data type m, respectively, σm is the target error for data type m, am,n is observations, and a^m,n is
model output. Hereafter, we present the total cost function as the total cost function normalized by M (J′=J/M) and normalized costs of
individual data types (Jm′=Jm/M) as the model–observation misfit equivalent to a reduced Chi-square estimate of the model
goodness of fit (i.e., J′=1 as a good fit from optimization, J′≫ 1 as a poor fit due to underestimation of the error
variance or the fit not fully capturing the data, and J′≪ 1 as an overfitting of the data, fitting the noise, or overestimation of
the error variance). The base-10 logarithm of Chl and PP is used in Eq. (3) to account for high productivity of the WAP waters and the approximate
log-normal distribution of those data types (Campbell, 1995; Glover et al., 2018). The target error σm is calculated for each data type m
as follows:
σm=am,n‾⋅CVm,
where am,n‾ is the climatological mean of the observations, and CVm is the adjusted coefficient of variation (CV) of the
observations of each data type over 0, 10, and 20 m (due to observational error and seasonal and interannual
variations). CVm values for the 4 modeled years in our study are higher than those across every measured depth within the mixed layer for
an extended year period in the original WAP-1D-VAR v1.0 model (2002–03 to 2011–12; Kim et al., 2021) and are therefore reduced to the levels in the
mixed layer to avoid an overestimated target error of each data type (Text S5 in the Supplement). The rationale behind using the adjusted CV in the
target error calculation is based on Luo et al. (2010), in which all properties should be completely mixed in the mixed layer, a perfect measurement
without significant errors should generate similar values at every measured depth within the mixed layer, and the average CV of all depth profiles can
be used as CV in the target error calculation. The standard deviation is used as target errors of the log-converted data types. The CV of the
log-converted data type is estimated as the average of ± 1 standard deviation in log space converted back into normal space (Doney et al., 2003;
Glover et al., 2018).
We compute the portability index (Friedrichs et al., 2007) to evaluate the broader applicability of the optimized model parameter set for each year in
predicting the dynamics of the other year as follows:
Portability index=Jc′/Jx′,
where Jx′ is the normalized cross-validation total cost function when a model parameter set optimized for a given year is used
to simulate another year, and Jc′ is the normalized total cost function of the climatological model. A portability index value
close to 1 indicates a more portable model or a system that is not particularly sensitive to year-to-year variations in optimized parameters, while
an index value ≪ 1 indicates a less portable model or a system that is sensitive to year-to-year variations in optimized parameters.
Uncertainty analysis
The uncertainties of the optimized parameters are estimated using a finite difference approximation of the complete Hessian matrix during the
iterative data assimilation process (i.e., the second derivatives of the cost function with respect to the model parameters). When computed at the
minimum of the cost function value, the square root of a diagonal element in the inversed Hessian matrix represents the logarithm of the relative
uncertainty of the corresponding optimized parameter. The absolute uncertainty of the optimized parameter is calculated as pf⋅e±σf, where pf is the value of the optimized parameter, and
σf is its relative uncertainty. We denote an optimized parameter with σf larger than 50 % as an “optimized” parameter, while an optimized parameter with σf
smaller than 50 % is denoted as a “constrained” parameter (Text S3, Tables S2–S6). We then conduct Monte Carlo experiments to examine the
impact of the uncertainties of the constrained parameters on the modeled fields. The Monte Carlo experiments consist of (1) creating an ensemble of
parameter sets (N=1000) by randomly sampling values within the uncertainty ranges of the constrained parameters and (2) then performing a model
simulation using each parameter set. All uncertainty estimates are calculated following standard error propagation rules and presented herein as
± 1 standard deviation.
Data types, observed means, coefficient of variation, target errors, and costs before and after optimization. The observed mean (a‾), coefficient of variation (CV), and target error (σ) of each assimilated data type used for calculating the normalized cost function (unitless; Eq. 3) before (J0′) and after optimization (Jf′). Data type unit: mmolNm-3 for nitrate (NO3); mmolPm-3 phosphate (PO4); mgm-3 for diatom chlorophyll (ChlDA) and cryptophyte chlorophyll (ChlCR); mmolCm-3 for HNA and LNA bacterial biomass, SDOC, and POC; mmolNm-3 for PON; and mmolCm-3d-1 for primary production (PP) and bacterial production (BP). NO3 was not assimilated in 2010–11, while SDOC, POC, and PON were not assimilated in 2012–13 and 2013–14 (denoted as ”–” in the table).
Data typesa‾CVσ2010–11 modelparameter set2011–12 modelparameter set2012–13 modelparameter set2013–14 modelparameter setClimatological model parameter setJ0′Jf′J0′Jf′J0′Jf′J0′Jf′J0′Jf′NO319.700.040.76––8.045.2311.748.8827.8213.5210.419.62PO41.310.030.049.207.0886.2621.0341.416.642.707.0545.7610.47log10(ChlDA)0.160.080.0912.945.696.559.4912.1912.6610.577.766.578.52log10(ChlCR)-0.900.100.108.756.4111.047.3310.028.379.928.2311.106.95log10(PP)1.320.210.214.504.714.512.699.816.269.867.617.193.83HNA biomass0.210.080.0220.392.080.150.2024.868.4936.3410.2823.7810.87LNA biomass0.330.080.024.263.06673.7321.05860.141.9910.656.15590.299.27BP0.110.160.023.543.8316.720.6524.2013.235.655.0512.823.50SDOC10.520.202.133.883.961.381.42––––2.762.68POC11.240.130.7812.0312.6833.3923.19––––39.2316.26PON2.400.120.4348.4448.2642.7726.19––––43.8627.30Total cost 127.9497.77884.53118.46994.3866.51113.5165.65793.77109.29ResultsModel skill assessment
The iterative optimization procedure reduced by 24 %–93 % the misfits between observations and model results for each year and for the
climatological year compared to those obtained using the initial parameter values (Table 2). The optimized parameter sets satisfied the pre-set
convergence criteria, including the local minima achieved by the total costs, low gradients of the total costs with respect to each optimized
parameter, and positive eigenvalues of the Hessian matrix (details in Kim et al., 2021). The total costs were reduced by optimizing only a subset of
the parameters: five–seven constrained and three–six optimized parameters (Tables S2–S6). The optimized parameters in common across all years
were αDA (the initial slope of photosynthesis versus irradiance curve of diatoms,
molC(gChla)-1d-1(Wm-2)-1), μHNA (the maximum HNA bacterial growth rate, d-1),
μLNA (the maximum LNA bacterial growth rate, d-1), and gCR (the half-saturation density of cryptophytes in
microzooplankton grazing, mmolCm-3). gHNA (the half-saturation density of HNA bacteria in microzooplankton grazing,
mmolCm-3), gMZ (the half-saturation density of microzooplankton in krill grazing, mmolCm-3), and
μKR (the maximum krill growth rate, d-1) were the next most frequently optimized, at least for 4 years out of a total of 5 modeled
years including the climatological year.
Model skill assessment. A Taylor diagram using a polar-coordinate system summarizing the model–observational correspondence for each model stock and flow for individual annual simulations for the 4 modeled years together (2010–11 to 2013–14; a) and for the climatological year (b). The angular coordinate denotes the Pearson correlation coefficient (r), the distance from the origin denotes the standard deviation normalized by the standard deviation of the observations, and the distance from point (1,0), marked as REF on the x axis, describes the centered (bias removed) root-mean-square difference (RMSD) between model results and observations.
Because of this study's focus on the modeled bacterial and other ecosystem functions as a function of bacterial traits (Sect. 3.3) rather than of
year (Figs. S2–S5 in the Supplement), we combined the observations and model results from all 4 years together for model skill
assessment. According to the Taylor diagrams, model skills were overall similar among the 4 study years (Fig. 3a) and the climatological year
(Fig. 3b). Three core variables in this study, including HNA biomass, LNA biomass, and BP, had better model–observation agreements than other data
types, with relatively high correlations, a low centered (bias removed) root-mean-square difference (RMSD), and the normalized standard deviation closer
to 1. These variables also had better fits to the 4-year seasonal cycles of the observations than other data types (Fig. S7 in the
Supplement). However, the model skill for HNA biomass slightly degraded in the climatological model (Fig. 3b), with the insignificant correlation
(p=0.61 versus r=0.53 and p=0.003 in Fig. 3a), lower normalized standard deviation, and higher RMSD than the 4 years together
(Fig. 3a). After optimization, the models tended to underestimate PP with relatively larger errors than for other data types (Fig. S7), while its
temporal and spatial (depth) variability was captured well as shown by high correlations (Fig. 3). By contrast, there were slight positive model
biases for POC and PON (Fig. S7), and their variability was not well captured as shown by their negative correlations (Fig. 3).
Cross-validation cost and portability index. Jc′ is the normalized optimized cost from the climatological model (equivalent to Jf′ under the climatological model parameter set in Table 2), and Jx′ is the normalized cross-validation cost (Eq. 5), in which, for example, Jx,2011–12′ under the row “2010–11 model parameter set” in Table 3 indicates the normalized cross-validation cost from simulating the 2010–11 model parameter set against 2011–12.
Data typesJc′2010–11 model parameter set 2011–12 model parameter set 2012–13 model parameter set 2013–14 model parameter set Jx,2011–12′Jx,2012–13′Jx,2013–14′Jx,2010–11′Jx,2012–13′Jx,2013–14′Jx,2010–11′Jx,2011–12′Jx,2013–14′Jx,2010–11′Jx,2011–12′Jx,2012–13′NO39.624.8810.3530.82NA10.6131.96NA4.6820.32NA6.5410.26PO410.4724.365.421.469.155.470.888.3328.542.707.5637.7410.70log10(ChlDA)8.528.6613.938.457.4213.928.675.957.377.915.647.3412.45log10(ChlCR)6.9512.0017.3819.628.478.449.5010.227.248.999.507.167.54log10(PP)3.831.868.4510.716.0810.3812.943.871.708.184.682.285.88HNA biomass10.8722.9311.5712.5726.9025.8643.086.119.4416.012.7527.7114.95LNA biomass9.2722.544.6416.394.6017.147.5712.9128.9827.437.0325.4028.47BP3.503.3914.145.486.7616.3210.023.801.665.863.023.2113.69SDOC2.681.40NANA3.90NANA3.531.85NA3.472.46NAPOC16.2623.70NANA12.02NANA12.6321.51NA14.3520.23NAPON27.3026.04NANA47.53NANA47.9227.48NA49.5829.47NATotal cost109.29151.7785.86105.51132.85108.15124.62115.27140.4797.39107.57169.54103.93Portability index 0.68 ± 0.08 0.61 ± 0.12 0.76 ± 0.11 0.73 ± 0.17
NA: not available.
Cross-validation cost analyses showed the increased model–observation misfits when a set of parameters optimized for a given year was applied to simulate
another year's dynamics (Tables 2 and 3), suggesting that each year was best modeled using its own unique set of the optimized parameters. The
magnitude of increase in the cost function varied by year pair, with the average portability index values indicating that the optimized model
parameters for 2012–13 was most portable (0.76 ± 0.11), followed by those for 2013–14 (0.73 ± 0.17), 2010–11 (0.68 ± 0.08), and
2011–12 (0.61 ± 0.12; Table 3), though the differences were not always significantly different among the years.
Seasonal progression of modeled HNA and LNA bacterial carbon stocks and rates and key ecosystem functions across years. Seasonal patterns of HNA and LNA bacterial carbon stocks and flows, NPP and POC sinking flux at 10 m depth over the growth season (November–March) for each of the 4 simulation years (a), and coefficient of variation (the Monte-Carlo-derived standard deviation divided by each data point from Fig. 4a) from 1000 Monte Carlo experiments (b).
Annual mean carbon stocks and flows. Carbon stocks (mmolCm-3) and flows (mmolCm-3d-1), particle sinking flux (mmolCm-2d-1), and other stocks (e.g., nutrients, mmolm-3) averaged over the growth season in each year are denoted as the numbers on the first row, while the numbers in the second row or in the parentheses are the standard deviation propagated from averaging over the growth season and the Monte-Carlo-derived uncertainties. Numbers around the arrows represent intercompartmental flows and do not necessarily balance to zero due to the build-up or loss in a compartment over the growth season. The magnitude of the N and P flows, as well as the flows smaller than 0.01 mmolCm-3d-1, are omitted. RDOC and higher levels are implicit.
Bacterial carbon stocks and flows
C stocks and flows for each bacterial group represented significant seasonal and interannual variability (Figs. 4a and S8 in the Supplement). Across
years, HNA bacteria had significantly higher seasonal maximum values than their LNA counterparts when normalized by the group-specific biomass. These
so-called cell-specific, seasonal maximum rates of the HNA group ranged from 0.10 ± 0.004 to 0.59 ± 0.24 d-1,
0.03 ± 0.001 to 0.18 ± 0.12 d-1, 0.07 ± 0.003 to 0.18 ± 0.08 d-1, 0.05 ± 0.002 to
0.57 ± 0.26 d-1, and 0.07 ± 0.03 to 0.36 ± 0.17 d-1 for LDOC uptake, SDOC uptake, respiration, BP, and grazing
rates, respectively (Fig. 4). For the LNA group, the maximum cell-specific rates ranged from 0.01 ± 0.002 to 0.12 ± 0.02 d-1,
0.004 ± 0.002 to 0.03 ± 0.01 d-1, 0.01 ± 0.001 to 0.02 ± 0.002 d-1, 0.01 ± 0.003 to
0.13 ± 0.02 d-1, and 0.02 ± 0.0004 to 0.17 ± 0.03 d-1 for LDOC uptake, SDOC uptake, respiration, BP, and
grazing rates, respectively (Fig. 4). For each year, C stocks and flows averaged over the growth season (Fig. 5) and those normalized by NPP
(net primary production; normalized by NPP in 1 d for C stocks; Fig. S9 in the Supplement) summarized an annual snapshot of the group-specific bacterial dynamics. The
annual mean LNA biomass was ∼ 17 times larger than that of HNA biomass in 2011–12 (Fig. 5b), in contrast to relatively similar average biomass
values of both groups in other years (Fig. 5a, c, and d). Bacterial carbon demand (BCD; i.e., BCD = BP + bacterial respiration; blue arrows
in Fig. 5) was mostly supported by LDOC (67 %–81 %) for both bacterial groups.
The rest of the modeled C stocks and flows fell into one of the following categories: (1) the variable for which a single year's values were
assimilated (i.e., microzooplankton C biomass), (2) the variables for which observational values for the given year were assimilated (i.e., nutrients,
POC or detritus, and SDOC), and (3) the variables that were not assimilated at all (i.e., krill C biomass, LDOC, NH4, and particle sinking
flux). Compared to bacterial variables, there was little interannual variability in the average microzooplankton C biomass (Fig. 5). Even in the years
when NO3, POC, and SDOC were not assimilated, their values were modeled similarly to those modeled in other assimilated years
(Fig. 5). Modeled LDOC and NH4 were also within the reasonable ranges of their typically small values (< 1 µM).
Bacterial physiological and taxonomic association with modeled ecosystem functions. The results from linear regression of the key modeled ecosystem functions on a categorical predictor of the observed mode (a–c) and on the observed fraction of HNA cells (d–f). Regression statistics: (a) number of observations (N)=43, error degrees of freedom (df)=35, root-mean-square error (RMSE) = 0.68, r2= 0.39, adjusted r2= 0.27, F statistic value = 3.22, p value = 0.01; (b)N=43, df = 35, RMSE = 2.88, r2= 0.48, adjusted r2= 0.37, F statistic value = 4.55, p value = 0.001; (c)N=43, df = 35, RMSE = 0.03, r2= 0.81, adjusted r2= 0.77, F statistic value = 20.7, p value < 0.001; (d)N=43, df = 41, RMSE = 0.65, r2= 0.36, adjusted r2= 0.34, F statistic value = 22.8, p value < 0.001; (e)N=43, df = 41, RMSE = 2.57, r2= 0.51, adjusted r2= 0.50, F statistic value = 43.0, p value < 0.001; (f)N=43, df = 41, RMSE = 0.04, r2= 0.57, adjusted r2= 0.56, F statistic value = 53.5, p value < 0.001.
Bacterial physiological and taxonomic association with ecosystem functions
Each mode was dominated by unique bacterial taxa, thereby representing taxonomic traits (Fig. S10 in the Supplement). Candidatus Pelagibacter
was the most abundant in mode 6 (Fig. S10c), Dokdonia sp. MED134 in mode 7 (Fig. S10d), Candidatus Thioglobus singularis PS1 in mode 1
(Fig. S10e), Owenweeksia hongkongensis DSM 17368 in mode 2 (Fig. S10f), Rhodobacteraceae in mode 5 (Fig. S10g), and Planktomarina temperata RCA23 in mode 4 (Fig. S10h). To explore a potential link between the bacterial taxonomic traits and the key ecosystem functions, we first
extracted the modeled NPP, POC sinking flux, and BCD from the ecosystem model (i.e., the “final optimized output” in Fig. 2) at the time the
bacterial samples and depth (10 m) were placed into a single mode derived from the observations. We then performed a linear regression with the
mode as a factor, in which the mode is a categorical predictor with eight modes rather than an ordinal or continuous variable (i.e., equivalent to a one-way
ANOVA with eight different categories). 27 %, 37 %, and 77 % of the total variance in the modeled NPP, POC sinking flux, and BCD were
explained by the bacterial taxonomic mode (Fig. 6a–c). In particular, modes 3, 5, and 7 were associated with 2–3 times higher NPP, POC sinking flux,
and BCD compared to when mode 4 dominated (two-sample t test with unequal sample size, p=0.02 for NPP and p< 0.001 for POC sinking flux
and BCD) or to when mode 6 dominated (p=0.03 for NPP, p=0.003 for POC sinking flux, and p< 0.001 for BCD).
The observed mode was also positively correlated to the observed fHNA (r2= 0.52, p< 0.001; not shown). Thus, we
examined a potential link between the bacterial physiological traits and the key ecosystem functions as described above, using a linear regression
with the observed fHNA as a predictor and the modeled ecosystem functions as dependent variables. The observed fHNA
was positively correlated to the modeled NPP (r2= 0.34, p< 0.001; Fig. 6d) and to a stronger extent to the modeled POC sinking
flux (r2= 0.50, p< 0.001; Fig. 6e) and to the modeled BCD (r2= 0.56, p< 0.001; Fig. 6f). The stepwise addition of
one predictor variable to the other predictor variable (i.e., fHNA adding to mode or vice versa) did not improve the model performance
(not shown). These results suggest a clear link between the modeled ecosystem functions and the bacterial taxonomic (modes) and physiological
(fHNA) trait observations.
Climate change experiments. Seasonal progression of the modeled HNA and LNA bacterial carbon stocks and rates and key ecosystem functions under observed physical forcing and climate change experiments (a) and the percent change in the corresponding variable compared to observed fields in the second and third row of each panel, with the first row of each panel as zero to represent base states (b). For example, the percent anomaly of HNA biomass in (b)= (HNA biomass under +1 ∘C and -10 % - HNA biomass under observed forcing) ⋅ 100/HNA biomass under observed forcing.
Climate change experiments
We explored the responses of the modeled bacterial dynamics and other ecosystem functions (Sects. 3.2 and 3.3) to changing climates along the WAP
(Fig. 7). Due to the varying portability of the optimized parameter sets among the 4 study years, we used the optimized parameter set for the
climatological year (Table S6) to simulate an overall WAP system response under perturbed ocean temperatures (i.e., +0.5 ∘C and
+1.0 ∘C relative to observed temperatures) and sea-ice forcing fields (i.e., 5 % and 10 % loss of sea-ice concentrations
relative to observed sea-ice concentrations). These experiments were conducted under each perturbed condition separately (i.e., warming alone in
Fig. S11 in the Supplement versus melting alone in Fig. S12 in the Supplement), as well as simultaneously (i.e., climate change; Fig. 7). We only
analyzed the results from the climate change experiments, given that despite different impacts of each forcing change (i.e., the impact of warming on
rate processes versus the impact of melting on light and photosynthesis but not MLD in our model), climate change would cause simultaneous changes in
sea ice and water temperature along the WAP.
The climate change experiments resulted in a combination of changes in overall bacterial C stocks and rates, as well as the key ecosystem functions,
and shifts in their seasonal timing (Fig. 7a) compared to the base state (the first row as the base state in Fig. 7a and b, while the second and
third rows as anomalies under perturbed conditions in Fig. 7b). HNA bacterial C stock and rates responded more strongly to the perturbed climate
conditions compared to their LNA counterparts. Under combined warming and melting (+1.0 ∘C and -10 %) conditions, there were the
maximum increases in the HNA C stock and rates by 19 %–35 % (29 ± 89 % for biomass, 22 ± 67 % for LDOC uptake,
35 ± 111 % for SDOC uptake, 26 ± 79 % for respiration, 25 ± 78 % for BP, 29 ± 89 % for viral mortality,
19 ± 26 % for grazing, and 29 ± 89 % for RDOC excretion) compared to the maximum increases in the LNA C stock and rates by
3 %–15 % (3 ± 2 % for biomass, 6 ± 11 % for LDOC uptake, 15 ± 27 % for SDOC uptake, 8 ± 3 % for
respiration, 7 ± 6 % for BP, 3 ± 2 % for viral mortality, 7 ± 18 % for grazing, and 3 ± 2 % for RDOC
excretion). In contrast to bacterial C stocks and rates that increased consistently throughout the growth season, microzooplankton grazing rates
showed seasonally mixed responses for both HNA and LNA cases, with the maximum decreases of 8 ± 32 % for HNA bacteria and of
4 ± 32 % for LNA bacteria. Similarly, there were the maximum increases in NPP and POC sinking flux by 14 ± 15 % and
3 ± 22 % and the maximum decreases in NPP and POC sinking flux by 4 ± 11 % and 3 ± 13 %, respectively. SDOC exhibited
the maximum increase by 2 ± 1 % early in the season but became depleted strongly as the season progressed. LDOC decreased consistently in
response to the perturbed conditions, with the maximum decrease by 10 ± 43 %.
DiscussionModel skill assessment
Despite the important role that heterotrophic marine bacteria play in the ocean carbon cycle, the vast majority of mechanistic biogeochemical models
neither include them as a model state variable nor explicitly simulate their physiological processes. Most models parameterize the bacterial
remineralization of sinking organic matter with depth by fitting the power law functions or other similarly derived empirical approaches (Buesseler
et al., 2020; Cael and Bisson, 2018). Cellular
functions, taxa, and functional gene expression of other prokaryotes, such as cyanobacteria (Hellweger, 2010; Martín-Figueroa et al., 2000;
Miller et al., 2013), or a diverse suite of microbial functional groups (Coles et al., 2017; Dutkiewicz et al., 2020) have been modeled so
far. However, our study is the first to explicitly model heterotrophic bacterial groups of different physiological traits and to link their
relationship with the key ecosystem functions.
Only a subset of the parameters was optimized in our model to simulate microbial and ecological patterns for each year, consistent with other data
assimilation modeling studies (Friedrichs, 2001; Friedrichs et al., 2006, 2007; Luo et al., 2010, 2012). In general, optimization of this class of
marine ecosystem model requires adjustment of a small number of independent parameters to achieve well-posed model solutions because of the highly
cross-correlated nature of the parameters in the inherently nonlinear model equations (Fennel et al., 2001; Harmon and Challenor, 1997; Matear, 1996;
Prunet et al., 1996a, b). In
our study, most of the constrained parameters were directly associated with the bacterial processes, and there were overall better model–observation
fits for the bacterial data types compared to other data types. These results provide confidence in the simulated bacterial C stocks and rates.
Optimization also sheds light on major unknown parameters in bacterial grazing processes involving gHNA and gLNA (the
half-saturation densities of HNA and LNA bacteria in microzooplankton grazing, respectively). Microzooplankton grazing of the given bacterial group is
simulated using a Holling type 2 density-dependent grazing function with a preferential prey selection on diatoms, cryptophytes, and the other
bacterial group, in which a single microzooplankton maximum grazing rate is implemented for both bacterial groups for model simplicity purposes
(Tables S2–S6 in the Supplement; Kim et al., 2021). Thus, it is the half-saturation density that determines the degree of preferential grazing by microzooplankton on
the given bacterial group, the change in which may ultimately depend on the group-specific C biomass. Due to the lack of a priori knowledge on the
relative magnitude of gHNA and gLNA, we assigned an identical initial parameter value (Table 1) to let the data assimilation
scheme determine the values that best fit the overall observations. Compared to gLNA, smaller optimized gHNA values
(Tables S2–S6) reflect preferential grazing of HNA cells by microzooplankton, consistent with previous speculations that grazers selectively remove
larger and more active bacterial cells (del Giorgio et al., 1996; Gonzalez et al., 1990; Sherr et al., 1992), so HNA
bacteria (Garzio et al., 2013). Together with the higher mean cell-specific grazing rates for HNA bacteria (Sect. 3.2), our results suggest
preferential grazing of HNA cells by microzooplankton.
The portability index in our study reflects the extent to which a single model framework represented by its distinctly optimized parameters in the
same model equations captures the observed variability in different years, given variable environmental forcing and the accompanying shift in plankton
ecosystem structure. The model parameter set optimized for 2012–13 was the most portable, while the model parameter set optimized for 2011–12 was the
least portable (Table 3), in which the most (n=7 out of total 11) and the least numbers (n=5 out of total 11) of parameters were constrained,
respectively (Tables S3 and S4). The other two years exhibited intermediate levels of model portability, with similar portability index values
characterized by the same number of the constrained parameters (n=6 out of total 10 for 2010–11 and n=6 out of total 12 for 2013–14; Tables S2
and S5). In other words, it is the number of the constrained parameters that matters the most in driving high model portability, suggesting a connection
between overfitting and the portability of the optimized parameter sets. Also, varying degrees of portability across the 4 study years rendered it
difficult to choose one particular year's model solution to perform the climate change experiments (Sects. 3.4 and 4.4), consistent with the
characteristics of the original WAP-1D-VAR v1.0 model. Instead, better model skill was found by utilizing the parameters from assimilating the
climatological observations (i.e., the climatological model).
Bacterial carbon stocks and flows
The fact that cell-specific BP, respiration, and SDOC uptake rates of HNA bacteria were significantly higher than those of LNA bacteria (Sect. 3.2) is
mainly because of the way the parameter optimization was conducted (Text S3). The higher initial parameter values assigned for HNA bacterial growth,
RDOC excretion, mortality, and respiration rates (Table 1) might drive not only their faster cell-specific growth rates but also their higher DOC
uptake rates to coexist with their LNA counterparts when the loss rates were relatively large for HNA bacteria. Though driven by the model
assumptions, the important aspect of these results lies in the fact that the model can leverage such assumptions to examine the implications for the
WAP food-web dynamics and biogeochemistry. As with phylogenetic groups (Fuchs et al., 2000; Teira et al., 2009; Yokokawa et al., 2004), cell-specific
bacterial growth rates are expected to differ among distinct bacterial physiological groups, but there are limited studies focusing on group-specific
cell activities (Gasol et al., 1999; del Giorgio et al., 1996; Günter et al., 2008; Longnecker et al., 2005;
Moràn et al., 2011). Moràn et al. (2011) showed that HNA bacteria greatly outgrew LNA bacteria in Waquoit Bay estuary, with a cell-specific
growth rate of up to 2.26 d-1 for HNA cells versus < 0.5 d-1 for LNA cells. Other studies have demonstrated that HNA bacteria
might depend on phytoplankton substrates more than LNA bacteria (Li et al., 1995; Morán et al., 2007; Scharek and Latasa, 2007). The hypothesis
that WAP bacteria might rely on SDOC when limited by LDOC availability has received indirect support previously (Ducklow et al., 2011; Kim and
Ducklow, 2016; Luria et al., 2017), providing the basis for bacterial SDOC utilization in our model formulation.
The model also captured the rest of the ecosystem variables fairly well. The modeled nutrient stocks were above the detection limits, indicating no
evidence of macronutrient limitations at the study site. The WAP typically exhibits strong interannual variability in physical forcing and ecological
and biogeochemical processes (Ducklow et al., 2007), but the lack of strong interannual variability in the modeled microzooplankton C biomass is due
to assimilating their climatological observations. One exception is krill C biomass that was modeled 3–8 times larger than the maximum value from
the available field measurement in 2017–2018 (0.57 mmolCm-3; not shown). It should be noted that there were inconsistences in the
nature of the assimilated data types, including a single-year observation of microzooplankton C biomass (versus each year-specific observation of
other variables) and two unassimilated data types (e.g., krill C biomass). Also, there can be compensating errors in krill grazing rate and metabolism
values given that krill are mobile laterally. These observational limitations make it challenging to construct a complete bacterial C budget without
significant uncertainties. A more complete assimilation of zooplankton data should be the next effort to improve the model fits and minimize
uncertainties in the bacterial variables. Another source of uncertainty in our study is that the model forcing does not seem to have sufficient
information to capture small-scale and high-frequency sources of variability (e.g., local circulation and tidal flow near Palmer Station), resulting in
relatively low standard deviation values of the modeled bacterial and ecosystem variables than those of the observations (e.g., Figs. 3 and S2). By
contrast, our model adequately captures seasonal variations in modeled ecosystem dynamics likely because such high frequency processes do not
strongly rectify in the seasonal cycles in the WAP ecosystem.
Bacterial physiological and taxonomic association with ecosystem functions
The positive associations of the observed fHNA with the modeled NPP and POC sinking flux suggest a relatively strong resource control
on these actively growing HNA cells compared to slow-growing LNA cells. This is consistent with previous studies showing increased HNA growth rates
in response to enhanced phytoplankton-derived organic substrate (Morán et al., 2011) and more
abundant HNA cells in areas or periods in which bacterial assemblages were predominantly controlled by resources rather than grazing (Morán et al.,
2007). It has been hypothesized that due to minimal inputs of terrestrial organic matter, bacteria must ultimately rely on in situ NPP as an organic
matter source in the WAP (Ducklow et al., 2012a), supporting the importance of resource control on these actively growing bacterial populations.
In our study, modes 3, 5, and 7, characterized by copiotrophic taxa with large genomes and more 16S rRNA gene copies (Bowman et al., 2017), were
associated with high values of the modeled NPP, POC sinking flux, and BCD, while modes 4 and 6, characterized by taxa associated with more
oligotrophic conditions, were associated with low values of the modeled NPP, POC sinking flux, and BCD. Dokdonia sp. MED134, a common
bacterial species of the modes associated with high NPP, POC sinking flux, and BCD, is a proteorhodopsin-containing marine flavobacterium that grows
faster with light (Gómez-Consarnau et al., 2007; Kimura et al., 2011) and in conditions under which resources are abundant (Gómez-Consarnau
et al., 2007). Given the coastal WAP being primarily light-limited (Ducklow et al., 2012b), the correspondence of D. Dokdonia MED134 to high
values of the modeled NPP suggests light-enhanced growth rates and cell yields from sufficient irradiance. By contrast, mode 4, dominated by
Planktomarina temperata RCA23, is a slow-growing bacterium that specializes in using complex organic substrates (Giebel et al.,
2013). These attributes are consistent with the high occurrence of mode 4 during the periods of low values of the modeled NPP and POC sinking
flux. Candidatus Pelagibacter, abundant in mode 6, is generally known as an oligotrophic specialist with a low DOC requirement but often
appears during Antarctic phytoplankton blooms (Delmont et al., 2014; Luria et al., 2014), the characteristics of which support its occurrence during
the periods of high values of the modeled NPP. In summary, our study provides a novel numerical framework combining the dynamics of different
ecosystem functions and microbial physiology and taxonomy. Certain modes represent distinct WAP ecosystem states, and the mode–state associations are
reasonably explained from microbial perspectives. However, we did not investigate a seasonal succession and development in mode itself or the mode
association of the key WAP ecosystem states. Future investigations should focus on including a few dominant or seasonally distinct modes in the data
assimilation process in order to fully resolve the seasonality of the mode–ecosystem state associations along the WAP.
Climate change experiments
The WAP has experienced significant atmospheric and ocean warming and resulting changes in marine ecological processes, and further climate change is
projected for the next several decades. The magnitudes of the perturbations used in the climate change experiments (+0.5 ∘C and +1.0 ∘C
compared to observed temperature fields and -5 % and -10 % compared to observed sea-ice fields) are within the range of the long-term changes
in temperature and sea-ice duration along the WAP continental shelf. The temperature of the ACC water that has direct access to the WAP shelf has
shown a large increase after the 1980s, equivalent to a uniform warming of the upper 300 m layer by 0.7 ∘C (Ducklow et al.,
2012b). The trend in the annual ice season duration is -1.5 dyr-1 over 1979–80 to 2017–18 field season (Henley et al., 2019). The
degree of melting (5 %–10 %) chosen for the climate change experiments is translated into the shortening of the ice season duration by
1–3 d (not shown), falling within the range of the trend in Henley et al. (2019).
Under combined warming and melting conditions, we expected that increased NPP and phytoplankton accumulations early in the season would result in a
significant build-up of all DOC pools. However, this was the case only for SDOC, and bacteria were soon LDOC-limited due to their preferential LDOC
uptake for their primary C source. Nonetheless, the growth of bacteria and increased bacterial rates under LDOC limitation were still possible because
bacteria depended on SDOC to meet the rest of their C demand, resulting in the strong depletion of the SDOC pool later in the season (Fig. 7b). In other
words, bacteria were more likely resource-limited, in particular by the labile DOC pool, and SDOC subsequently played an increasingly important
role. This change was particularly important in HNA bacteria, as shown by the relatively large increase in HNA bacterial C demand via SDOC compared to
LNA bacteria. Temperature is often regarded as a major factor regulating physiological rates by changing the rate of enzymatic reactions (Kirchman
et al., 2009; White et al., 1991). In our study, the modeled C stock and rates of HNA bacteria increased under the warming alone conditions (Fig. S11)
but equally or more than under the melting alone conditions (i.e., increased photosynthesis and resource availability; Fig. S12). This suggests that
temperature per se is not necessarily a more important limiting factor for bacterial growth, at least for HNA bacteria, than resource availability
(Ducklow et al., 2012a), and warming may rather enhance HNA bacterial utilization of the already
increased organic matter from the increased phytoplankton productivity. Also, future climate may impact the (re)distribution of bacterial taxonomic
groups, with a potential shift to more abundant HNA cells in the WAP bacterial communities owing to their preferential SDOC utilization.
The major limitation of our climate change experiments is the short duration of the simulations. An ideal set of climate change simulations should be
performed for longer-term periods, as well as continuously across many years. However, our study could not accommodate these requirements because of
the limited observations and existing data gaps in each year. Despite these challenges, we were able to validate the capacity of the climatological
model to partly reproduce the already observed, climate-driven trends of some ecosystem variables along the WAP. Under each year's forcing fields, the
climatological model parameter set reproduced the interannual variability fairly well compared to the observed interannual variability, except for
only a few cases (e.g., overestimated BP and HNA biomass in 2011–12, underestimated PP in 2012–13 and 2013–14; Table S7). The period of 2011–12 was
characterized by a negative temperature anomaly (-0.13 ± 0.83 ∘C versus 0.03 ± 0.84 ∘C for the 4-year
climatology) and a positive sea-ice anomaly (24 ± 38 % versus 21 ± 29 % for the 4-year climatology), with lower temperature and
higher sea-ice concentrations than the other 3 years (all p< 0.05, two-sample t test). This coldest year had the lowest values of BP,
HNA biomass, and PP observations (Table S7), consistent with increases in the modeled BP, HNA biomass, and PP under the combined warming and melting
conditions. A combination of low HNA biomass, low PP, and low POC flux was also modeled in 2011–12, being largely responsible for driving the positive
association of the observed fHNA with the modeled NPP and POC sinking across years (Sect. 4.3). Sea ice did not retreat until
mid-December in 2011–12 (Fig. S1), and as a result of subsequently low light levels PP was modeled to be low. The low modeled PP drove both low HNA
biomass and low particle sinking flux, reinforcing the strong resource control on these fast-growing bacterial populations and the conventional “high
PP-high export” paradigm along the WAP.
Finally, our climate change simulations share similar results with those performed using the WAP-1D-VAR v1.0 model with one bacterial compartment (Kim
et al., 2021). In the original WAP-1D-VAR v1.0 model, combined warming and reduced sea-ice conditions also increased NPP, net community production,
POC sinking flux, bulk bacterial productivity and biomass, and SDOC, in contrast to LDOC that was strongly limited early in the season. This potential
shift to a more productive and efficient export system state is partially in agreement with the speculations suggested by previous studies that
warming may induce more recycling-favorable and microbial-dominated food webs (Moline et al., 2004; Sailley et al., 2013). Despite the increased
productivity and plankton accumulations, LDOC may become strongly depleted, and, therefore, bacteria may need to depend more on SDOC to meet a
significant part of their C demand (i.e., an increasingly important role of SDOC for bulk bacterial communities). Most of these results convey the
same story as our experiments, thereby adding confidence to the results of the climate change experiments in our study. Yet, it should be noted that
the increased complexity of bacterial dynamics in our study's bacteria-oriented model adds two important contributions to the original WAP ecosystem
model including (1) the dominance of HNA bacteria over LNA bacteria in the warming WAP waters and (2) bacterial taxonomic (i.e., mode) and
physiological (i.e., fHNA) traits being a significant indicator of the key WAP ecosystem functions.
Conclusions
Heterotrophic microbial diversity has seldom been considered in detail in the formulation and analysis of marine pelagic ecosystem models, reflecting
in part the lack of suitable field data for model evaluation. Utilizing genomic products to prescribe the taxonomic aspects of bacterial dynamics, our
study demonstrates the association of bacterial abundance with different physiological states, bacterial community structure, and key ecosystem
functions. The modeling approach in our study enables the observations in different bacterial populations to constrain the group-specific processes
and model parameters that have been poorly understood. These include the partitioning of BP specific to HNA and LNA groups, the partitioning of the
bacterial uptake of DOC pools with different lability, and the half-saturation density of each bacterial group in microzooplankton grazing. The model
also serves as an effective numerical platform to explore the WAP microbial response to changing climate conditions, in which ocean warming and melting
sea ice would induce a potential shift to the dominance of HNA bacteria in more productive waters due to their increasing dependence on SDOC.
Code availability
The Tangent linear and Adjoint Model Compiler (TAPENADE) used to construct an adjoint model is available online (https://team.inria.fr/ecuador/en/tapenade/, Inria at Sophia Antipolis, 2021). The original version of the model used in this study (WAP-1D-VAR model v1.0) is available from the project website: https://zenodo.org/record/5041139 (Kim et al., 2021) under the Creative Commons Attribution 4.0 International license. The exact version of the model used to produce the results, input data, and scripts to run the model and produce the plots for all the simulations presented in this article is available upon request.
Data availability
Complete Palmer LTER time-series data used for data assimilation are available online (http://pal.lternet.edu/data, PAL-LTER, 2021). Surface downward solar radiation flux data used for physical forcing of the model simulations can be found at the National Centers for Environmental Prediction website (https://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.surface.html, NOAA-ESRL, 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/bg-19-117-2022-supplement.
Author contributions
HHK designed the study, performed the model simulations, and wrote the manuscript. JSB provided the observational data and helped with data analyses and interpretation. HWD, OMS, and DKS provided observational data. YWL contributed the model simulations. SCD supervised the study and significantly revised the manuscript.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This study leverages the wealth of marine biogeochemical data collected by the Palmer LTER program along the WAP, and the authors thank the scientists, students, technicians, station support and logistical staff, and ship captains, officers, and crew involved. This research was supported, in part, by the US National Science Foundation Office of Polar Programs through award NSF PLR-1440435 and the US National Aeronautics and Space Administration Ocean Biology and Biogeochemistry Program through award NASA NNX14AL86G. Hyewon Heather Kim was also supported by the Investment in Science Fund and the Reuben F. and Elizabeth B. Richards Endowed Fund from Woods Hole Oceanographic Institution.
Financial support
This research has been supported by the NASA (grant no. NNX14AL86G) and the NSF (grant no. PLR-1440435).
Review statement
This paper was edited by Marilaure Grégoire and reviewed by three anonymous referees.
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