Introduction
Nutrient regulation of freshwater plankton productivity is central to the
response of river and lake ecosystems to changes in nutrient loading that
result from land use and climate change. By controlling phytoplankton
primary production (PP) and bacterioplankton secondary production (BP),
phosphorus (P) and nitrogen (N) are the two key macronutrients shaping
aquatic ecosystems, with consequences for food web structure, biodiversity,
and biogeochemical cycles (Jones, 1998). In addition to these
nutrients, the supply of dissolved organic carbon (DOC) has strong effects
on ecosystem functioning by fuelling BP and bacterial-based heterotrophic
food chains (Dillon and Molot, 2005; Karlsson et al., 2012; Tranvik,
1998). While nutrient availability can be influenced by
internal lake processes, the regulation of PP and BP in the majority of
lakes worldwide is constrained by loading of inorganic and organic resources
from the surrounding terrestrial landscape (Wetzel, 2001). In
brown-water boreal lakes, nutrients bound to dissolved organic matter (DOM)
(e.g. humic substances) often dominate inputs (Jansson, 1998). In
such systems, terrestrial nutrient support of BP is of particular ecological
and biogeochemical importance, as heterotrophic processes often greatly
exceed autotrophy (Jansson et al., 2000).
While the importance of nutrient availability at the ecosystem level is
evident, characterisations of the actual proportion of terrestrially derived
resources that can be readily used by aquatic microorganisms are difficult
and attempts to characterise it are rare. A variable fraction of C, N, and P of terrestrial
origin is chemically bound in organic molecules that are typically too large
to be directly taken up by microbes (Battin et al., 2008). The nature of
the covalent bonds and the structure of organic compounds that hold N and P
also differentially influence the bioavailability and turnover of associated
nutrients (Vitousek et al., 2002). Such
complexity makes it difficult to predict the potential for bacterial usage
of these resources in an ecologically meaningful way (Bronk et al.,
2007; Berggren et al., 2015; Helton et al., 2015). It is generally thought
that the major fraction of DOC originating from terrestrial soils is
recalcitrant, yet bioavailability estimates from different lakes suggest
that a variable proportion of DOC can be used by bacteria (e.g. 6–14 %;
Tranvik, 1988). For dissolved organic nitrogen (DON), a summary of
published assays suggests that anywhere from 2 to 75 % of the organic N pool
may be bioavailable (Pellerin et al., 2006), with a range of
19–28 % reported for boreal streams during base flow
(Stepanauskas et al., 1999). Similarly, while less studied,
P bioavailability appears to be highly variable over space and time
(Muscarella et al., 2014). For example, it has been shown
that seasonal concentrations of bioavailable P ranged from 1 to 14 µgPL-1
in boreal headwater streams, representing from < 5 % to
nearly 50 % of the total P pool (Jansson et al., 2012). Most
studies on nutrient availability conducted in humic-rich waters have
neglected this variability in bioavailability, focusing on either total
inputs (i.e. total N or total P) or on the turnover of specific fractions
assumed to be bioavailable (e.g. dissolved inorganic nitrogen, DIN;
molybdate reactive phosphorus, MRP). However, inorganic fractions may
constitute only a small part of the total nutrient pools and can
underestimate resource bioavailability in organic-rich waters with large
pools of labile DON or dissolved organic phosphorus (DOP;
Seitzinger et al., 2002).
These pitfalls of assuming resource availability to bacterioplankton from
total pools or inorganic fractions have prompted the suggestion that
standardised bioavailability assays (growth bioassays) should be
incorporated into the analytical toolbox of aquatic researchers
(Lewis Jr., 2011). Bioavailability represents an operationally
defined resource, typically measured in assays in which a bacterial inoculum is
added to a sterile-filtered water sample and the bacterial biomass is
allowed to grow during a standardised incubation period at a determined
temperature. The growth response is used to quantify the resource that was
consumed during the incubation, which is a measure of bioavailability
(sensu Berggren et al., 2015).Unfortunately results from the few
different studies addressing bioavailable resource shares for
bacterioplankton are difficult to compare since different methodological
approaches are used (Berggren et al., 2015). For instance, studies of DOC
bioavailability have used methods that differ in terms of incubation length,
temperature, as well as the choices of inoculum and inorganic growth media
(del Giorgio and Davis, 2003). Similarly, as different techniques
and assumptions have been applied to assess nutrient availability, results
for N and P differ among studies and are generally not comparable as they
often reflect variation in experimental factors rather than in the intrinsic
molecular properties of the nutrients themselves. Thus, a standard and
comparable method that can tackle the bioavailability of multiple elements
to bacterioplankton is missing.
Most previous attempts to measure nutrient bioavailability of multiple elements
have been performed over very long timescales (most data
from 100-day incubations; see data review by Lonborg and Anton
Alvarez-Salgado, 2012) and do not represent the pool that is immediately
available for consumption. These long-term assays have not been based on
growth, but on changes in bulk nutrient concentrations in solution
(Lonborg and Anton Alvarez-Salgado, 2012). However, during long
incubation periods various factors can interfere with the uptake of
bioavailable resources, such as the dynamics of viruses and the development
of toxic conditions that may arise from repeated bacterial regeneration of
resources (Cho et al., 1996). To move the nutrient
stoichiometry field forward, a promising option is to measure the uptake of
nutrients through growth bioassays conducted at shorter timescales, in which
the incubation length is reduced to a minimum and sufficient time for
bacteria to take up most of the readily bioavailable pool (sensu Berggren
et al., 2015). Such bioassays can increase our understanding of the direct
controls on bacterial metabolism by bioavailable nutrient pools. Although
growth bioassays have previously been applied to calculate bioavailability
of single elements (Stepanauskas et al., 2000, 2002; Jansson et al.,
2012), no efforts to date have quantified the
bioavailability of more than two elements simultaneously so that the
relative availability of multiple resources can be directly compared. In a
recent review on bioavailability (Berggren et al., 2015), it was
additionally suggested that nutrient bioavailability (as a fraction of the
total pool) tends to increase from C to N and N to P in DOM-rich systems.
While this hypothesis is generally consistent with our understanding of
resource use in soils (Vitousek et al., 2002), it remains to be
systematically tested in surface waters.
Descriptive lake data and concentrations of total dissolved
nitrogen (TDN), dissolved inorganic nitrogen (DIN), total
phosphorus (TP), phosphate (PO4-P), and dissolved organic carbon (DOC)
given as minimum and maximum values observed during the experimental period.
Variables
Övre Björntjärnen
Lillsjöliden
Struptjärnen
Stortjärnen
Location (latitude [N],
64∘7′23.53′′ N,
63∘50′41.71′′ N,
64∘1′22.62′′ N,
64∘15′42.11′′ N,
longitude [E])
18∘46′43.04′′ E
18∘36′59.62′′ E
19∘29′21.18′′ E
19∘45′44.73′′ E
Lake surface area (ha)
4.8
0.8
3.1
3.9
Maximal depth (m)
9.5
5.2
5.8
6.7
Total catchment area (ha)
284
25
79
82
Wetland coverage (%)
16
2
4
12
Forest coverage (%)
84
98
96
88
DOC (mg L-1)
18–29
13–19
19–25
19–27
TDN (µgL-1)
376–502
336–501
360–521
355–598
DIN (µgL-1)
5–35
10–40
3–43
4–35
TP (µgL-1)
8–25
4–15
8–25
7–15
PO4-P (µg L-1)
1–8
0–4
0–3
0–2
In this study, we designed bioassays with the purpose of rapidly exhausting
the pools of readily available organic C, N, and P that are accessible to
bacterioplankton in DOM-rich lakes. The bioassays were designed such that
most of the nutrients were used within 3 days, although we measured the
cumulative nutrient use up to 7 days. We first calibrated our method by
detecting the response (leucine incorporation) of nutrient-starved bacteria
to known added amounts of bioavailable resources. We then validated this
bacterial response through comparison with common methods to detect
bioavailability: lability incubations for DOC bioavailability (del
Giorgio and Cole, 1998), cell production bioassays with N-starved bacteria
for N bioavailability (Stepanauskas et al., 2000) and measuring P
content in bacterial growth cultures harvested on filters (Jansson et
al., 2012). Specifically, by using this new bacterioplankton growth bioassay,
our study addresses the following questions. (1) How does the relative total
bioavailability in DOM-rich surface waters differ between the elements, i.e.
bioavailable dissolved organic carbon (BDOC) out of total DOC, bioavailable
dissolved nitrogen (BDN) out of total N, and bioavailable dissolved
phosphorus (BDP) out of total P, and do these proportions vary
seasonally? (2) Are the organic bioavailable N and P pools larger than the
corresponding inorganic pools? (3) By how much do total C : N, C : P and N : P
ratios exceed bioavailable C : N, C : P and N : P? This was tested by performing
bacterial growth bioassays on four boreal lakes in northern Sweden with high
DOM concentrations. In addition, we applied a simplified version of our new
method to assess broad patterns in nutrient bioavailability across a larger
cross-regional scale and climate gradient that comprises seven river systems
with variable DOM concentrations.
Methods
Study area and sampling
We studied four lakes in northern boreal Sweden: Övre
Björntjärnen, Lillsjöliden, Struptjärnen and
Stortjärnen. All lakes are unproductive brown-water systems of similar
size and morphology (Table 1). Lake catchments are dominated by coniferous
forest (Scots Pine, Pinus sylvestris; Norway spruce,
Picea abies); and wetlands (mires) in different proportions. The
lakes are closely co-located (maximum distance 75 km) and influenced by
similar climatic conditions. Average annual temperature, precipitation, and
run-off in this area are approximately 1.8 ∘C, 614, and 311 mm
respectively (from 1981 to 2010; Laudon et al., 2013). Lake surface ice
coverage extends from November to May; stratification occurs during late
May/early June and mixing occurs after mid-September.
Leucine incorporation rates over the incubation time for a blank
incubation and five spikes of C (spike 1=330, spike 2=660, spike
3=1000, spike 4=1330, and spike 5=1500 µg C L-1), N (spike
1=105, spike 2=133, spike 3=205, spike 4=305 and spike 5=405 µgNL-1)
and P (blank, spike 1=15.5, spike 2=18.8, spike
3=20.5, spike 4=30.5, and spike 5=40.5 µgPL-1).
![]()
In addition to these lakes, we also sampled the outlet of seven Swedish
rivers (Lyckebeån, Helge å, Nyköpingsån, Motala Ström,
Torne älv, Töre älv, Öre älv) that drain into the Baltic
Sea. River catchments are located between latitudes 55 and
65∘ N, falling along a 1300 km north–south gradient, spanning a
range of drainage areas of 440–34 441 km2, and with DOC concentrations
from 5.6 to 23 mg L-1. These rivers drain very different terrestrial
environments from mountains, forests, and wetlands in the north to
catchments with a significant fraction of agricultural land and urban
development in the south (Sponseller et al., 2014). In addition, these
systems are influenced by different climates, from subarctic in the north
to temperate in the south. From north to south, average temperature,
precipitation, and discharge respectively span from 1 to 8 ∘C,
631 to 824 mm, and 34 to 450 m3 s-1 (for 1999–2013; Swedish Meteorological and
Hydrological Institute, SMHI).
Lake samples (2 L) were collected from 0.5 m depth on seven dates from
September 2012 to September 2014 (Table 2). Samples were stored in acid-washed 2 L high-density polyethylene bottles or 4 L low-density polyethylene
containers (Thermo Scientific) in the dark at approximately 1 ∘C
until arrival at the laboratory. River sampling was conducted once at the
outlet of each river between June and July 2013 at 0.3 m depth, in the middle
of the river or 7 m from the shore.
Determination of bioavailable C, N, and P
To determine concentrations of BDOC, BDN, and BDP we conducted growth
bioassays in which the limitation of either C, N, or P was strongly induced by
adding different combinations of bacterial growth media. Our growth
bioassays were designed so that resource use efficiency was at its maximum
and bacterial production would occur mostly within 3 days from the
beginning of the experiment. The bacterial response to those bioassays was
measured by leucine incorporation (Kirchman et al., 1985).
The amount of leucine incorporated in each bioassay was then converted into
concentrations of bioavailable resource based on experimentally determined
standard growth curves (see detailed description below).
Bioassays were prepared immediately after or at latest within 1 to 2
weeks after sampling. To ensure proper conservation of the samples prior to
the experiment, they were immediately filtered (Whatman GF/F) and stored in
a climate-controlled chamber at a temperature close to 1 ∘C. At
the initiation of the experiment, 500 mL of each lake and river water sample
was again filtered at 0.2 µm (suporCap 100, Gelman Sciences) and
placed in a 1000 mL Erlenmeyer flask. All bioassay samples were then
inoculated with a standard bacterial community 2 % (v/v) to ensure that
differences in bacterial community composition did not influence resource
bioavailability measurements (Martinez et al., 1996). The standard bacterial
community consisted of a mixture of fresh unfiltered water from the
epilimnion and inlet of the lakes sampled at one occasion, which was
maintained in the fridge at 4 ∘C between experiments. The
water was amended 5 % (v/v) with a modified (excluding C, N, and P)
bacterial medium (“L16”; Lindström, 1991) rich in
micro-nutrients, trace metals, and vitamins required for bacterial growth.
The sample was then divided into three subvolumes to which strong
limitation of either C, N or P was induced by adding appropriate
combinations of nutrients. C limitation was induced by adding N as
NH4NO3 (final concentration 2000 µgNL-1) and P as
Na2HPO4 (200 µgPL-1). N-limiting conditions were
created by adding C as C6H12O6 (20 000 µg C L-1)
and P as Na2HPO4 (200 µgPL-1). P-limiting conditions
were created by adding C as C6H12O6 (20 000 µgCL-1)
and N as NH4NO3 (2000 µgNL-1). Samples
were then transferred into 1.5 mL Eppendorf tubes that were incubated in the
dark at the standard temperature of 20 ∘C, which is the most
broadly applied temperature in bioavailability assessments of the literature
(del Giorgio and Davis, 2003). For each bioassay incubation,
leucine incorporation was measured at six time points (after 0, 1, 2, 2, 3,
and 7 days) on five replicate samples each time. The inoculum added to our
sample water represents an unknown addition of bioavailable C, N, and P. To
ensure that the amount of resource added through inoculation was
insignificant, we analysed five control bioassay replicates in which the
only source of C, N, or P was the amount of resource contained in the
inoculum and thus the lake sample was replaced by Mili-Q water. All such
control bioassays resulted in low amounts of leucine uptake (Fig. 1), which
was then used to correct our estimates of resource bioavailability through
subtraction (see Supplement Table S2).
To create standard curves for bacterial growth per unit limiting nutrient,
sampled lake water from September 2012 was used to perform a bioassay
following the approach described above but with varying concentration of
target elements. For example, to a subvolume that was induced to be
C-limited, C6H12O6 was added to final concentrations of
330, 660, 1000, 1330, and 1500 µgCL-1 respectively. The
response to each concentration was measured on triplicate samples and was
used to construct the standard curve. The same procedure was applied to
produce standard curves for N and P limited assays. NH4NO3 was
added to concentrations of 105, 133, 205, 305, 405 µgNL-1, and
Na2HPO4 was added to concentrations of 15.5, 18.8, 20.5, 30.5,
40.5 µgPL-1 (see Supplement Table S1). Standard
curves for the rivers were based on the same method but bacterial responses
to each concentration were recorded once.
Integrated (cumulative) amounts of leucine incorporated by bacteria during
lake or river bioassays over 7 days were converted to concentrations of
bioavailable element based on the slopes of the standard growth curves of
either rivers or lakes, which describe how much leucine was incorporated per
unit of bioavailable limiting element. For this conversion, the amount of
incorporated leucine (given in nmol of leucine L-1 for 7 days)
during each bioassay was divided by the slope of the standard growth curve
(nmol of leucine L-1 per mg of bioavailable nutrient L-1 for 7
days). The resulting quotient represents the total amount of bioavailable
nutrient taken up by bacterioplankton (mg L-1 for 7 days; see
Supplement Table S3).
Leucine incorporation
Measurements of protein synthesis were done using the method described by
Smith and Azam (1992) and modified by Karlsson et al. (2002).
Accordingly, 3H-leucine was added to sample water in Eppendorf tubes
(specific activity varied between 60.5 and 115.8 Ci mmol-1, Perkin Elmer)
with a final concentration of 30–100 nmol L-1. Additions of
3H-leucine were dependent on bacterial activity tests performed prior
to the experiments in which different concentrations of 3H-leucine
identified the isotope saturation levels. Triplicate measurements were taken
after 24, 48 (we obtained six replicates at this time point), 72, 96
and 168 h. Leucine incorporation into protein was determined by incubation
for 1 h in the dark at 20 ∘C and incubations were terminated with
trichloroacetic acid (TCA) additions of 5 % (w/v). A bacterial pellet was
formed by centrifugation for 10 min at 14 000 rpm. The bacterial pellet was
rinsed with 5 % TCA. After the addition of 1.2 mL of scintillation cocktail
(PerkinElmer), radioactivity was measured on a Wallac WinSpectral 1414
Scintillation counter (PerkinElmer). The incorporation of 3H-leucine was
calculated using an intracellular dilution factor of 2 (Smith and
Azam, 1992). Leucine incorporation measurements were integrated for the six
time points and summed into a single value that represented the total amount
of leucine incorporated for the 7-day period. Lastly, at time point 96 h,
an extra vial was collected and used as a blank, pretreated with TCA 5 % (w/v),
followed by the addition of leucine at a final concentration of 30 nmol L-1.
Validation
We validated the bacterial leucine uptake response to added amounts of BDOC,
BDN, and BDP (i.e. the slope of the standard curves) by relating the measured
leucine uptake to alternative estimates of bioavailable resources obtained
with independent methods. An alternative estimate of BDOC was obtained from
measuring bacterial respiration (BR) during a lability incubation, which has
been often applied in previous studies (del Giorgio and Cole,
1998; Jansson et al., 2000). The BR was determined by assessing decreases in
dissolved oxygen concentrations in water samples from lakes (n=13) and
rivers (n=8). Sample water was prepared in parallel with, and in the same
way as, the C bioassays described above. Volumes of 0.5 L were added to
glass incubation bottles (in duplicate) which had sensor spots affixed to
the inside surface. Oxygen concentrations were measured in the dark every 5 min
for up to 7 days with a FIBOX 3 (PreSens) that took optical readings
from the outside of bioassay bottles. Estimates of BR were calculated from
the averaged consumption of dissolved oxygen from the duplicate bottles by
assuming a respiratory quotient of 1, which is a conservative value for
unproductive lakes (Berggren et al., 2012).
Bioavailable N was assessed using an alternative method described by
Stepanauskas et al. (2000) by counting the cells produced in growth
bioassays with N-starved bacteria. For this test, two aliquots of 30 mL were
used for bioassays and one of them was amended with N-NH4NO3 to
a final concentration of 0.405 mg N L-1. Both incubations were
performed at 20 ∘C degrees in the dark. Bacterial biomass was
determined at the start of the incubation (t=0) and after 3 days
(t=3) when the bacterial growth had peaked (Fig. 1). Bacterial samples
were fixed with 3 % (v/v) glutaraldehyde and kept at 5 ∘C
until analysis. Analyses of bacterial cells were conducted on a flow
cytometer (FACScan, Becton Dickinson) on samples stained with SYTO 13 and
run with the addition of beads as an internal standard according to
del Giorgio et al. (1996), using CellQuest Pro software.
Bacterial cells were distinguished based on green fluorescence intensity and
side scatter signals. Total bacterial abundance was calculated as the sum of
the populations that were distinguished in the cytograms. The N content per
bacterial cell was determined by dividing the amount of N added to the
amended aliquot by the difference in bacterial abundance between the
N-amended and the unamended aliquot. To obtain BDN, the calculated average N
content per cell was multiplied by the number of bacterial cells that were
produced in the bioassay without addition. We validated our estimates of
leucine incorporation per unit of bioavailable P by comparing it with the
corresponding ratio in a completely independent boreal data set (Jansson et
al., 2012). These independent data come from a freshwater study with
near-identical bioassay conditions to those in our P bioassays, with the major
difference being that Jansson et al. (2012) used larger incubation volumes
(> 700 mL) than we did when incubating in 1.5 mL Eppendorf tubes.
Moreover, bioavailable P in the validation data was not assessed from
bacterial growth data, but instead measured as P accumulation in bacterial
cells harvested on filters. This is possible for P because standard TP
methods provide high analytical precision at the microgram level (molybdenum
blue method) that can resolve small changes in P concentration. Thus, we
extracted the raw data from Jansson et al. (2012), in which both
cumulative leucine incorporation and bioavailable P were quantified during
the incubation of water from two northern Swedish streams sampled on six dates
from late April to late October 2010.
Analytical methods and calculations
Lake water chemistry was analysed at the department of Ecology and
Environmental Science at Umeå University. Sample water for determination
of DOC and TDN was filtered through a pre-ignited (400 ∘C, 3 h)
acid-rinsed Whatman GF/F filters. The filtered water was acidified with 1.2 M HCl
and analysed for DOC using a HACH-IL 550 TOC-TN. Filtered sample was
analysed for TDN also using a HACH-IL 550 TOC-TN, while determination of
nitrate (NO3-) and ammonium (NH4+) was done
according to the International Organization for Standardisation (ISO)
13395-1996. Concentration of phosphate (PO4-P, assumed to be
represented by soluble reactive P) was determined from filtrates (GF/F) of
water samples using the molybdate blue method (Murphy and Riley, 1962)
and total phosphorus (TP) determined after oxidative hydrolysis with
potassium persulfate (ISO 15861-1).
River DOC samples were filtered through a Whatman GF/F filter into a
pre-acid-washed 40 mL amber borosilicate vial, filled to the brim and
tightly closed with silicon septa screw caps. Samples were kept cold in the
fridge until analysis which took place at the G.G. Hatch Stable Isotope
Laboratory, University of Ottawa. River samples for determination of (total
dissolved nitrogen) TDN, NO3-, NH4+, TP, and
PO4-P were frozen until analysis at the Evolutionary Biology Center,
Uppsala University, following standard methods.
Our results provided estimates of total bioavailable resource pools. To
calculate shares of bioavailable DON (BDON) and bioavailable DOP (BDOP), we
subtracted the inorganic pools of DIN (NO3-, NH4+), and
PO4-P from the respective total bioavailable pools. Nutrient ratios
were calculated in molar. We further calculated inorganic nutrient ratios of
DIN to PO4-P (DIN : PO4-P).
(a) Bioavailable dissolved organic carbon, (b) bioavailable total
nitrogen, and (c) bioavailable total phosphorus on seven sampling dates
(columns). Values show means of five analytical replicates and standard
deviations are provided within parentheses. Shared index letters within rows
identify dates significantly different from each other (p < 0.05)
which were determined by the Kruskal-Wallis h and Dunn's post-hoc test.
Lake
Sep 2012
Oct 2012
Jul 2013
Jun 2014
Jul 2014
Aug 2014
Sep 2014
(a) BDOC, µgCL-1
Övre Björntjärnen
273a (143)
371bcd (64)
420aefg (60)
248be (39)
243cf (22)
216dg (21)
Lillsjöliden
471 (435)
552ab (338)
334cde (49)
176ac (36)
205d (7)
215 (17)
176be (36)
Struptjärnen
361a (327)
432b (93)
692acd (85)
337e (27)
178c (21)
107bde (6)
Stortjärnen
319a (210)
428b (228)
283c (49)
301d (35)
213e (15)
104abcdf (8)
406ef (130)
(b) BDN, µgNL-1
Övre Björntjärnen
209ab (13)
74c (13)
61a (6)
84d (14)
73e (5)
23bcde (1)
Lillsjöliden
287abc (10)
232def (24)
111gh (7)
33adg (10)
64be (5)
89a (6)
51cfh (3)
Struptjärnen
259abc (6)
107ad (28)
220e (14)
273dfg 28
60bf (6)
37ceg (2)
Stortjärnen
188ab (15)
206cef (18)
82ac (6)
67be (5)
84f (5)
28acfg (3)
119g (38)
(c) BDP, µgPL-1
Övre Björntjärnen
9abc (1)
5ad (0)
5be (0)
9def (1)
7g (0)
3cfg (0)
Lillsjöliden
3ab (0)
3c (0)
2def (0)
2cgh (0)
10acd (2)
7beg (1)
6fh (0)
Struptjärnen
6ab (1)
6c (0)
9ad (1)
16bce (2)
7e (1)
4de (0)
Stortjärnen
0ab (0)
0cdef (0)
1gh (0)
10acg (2)
12bdh (2)
6e (0)
5f (2)
Statistical analyses
Standard curves were fit by linear regressions using JMP 10 (SAS).
Differences between the slopes of standard curves for each nutrient across
lakes were tested by one-way analysis of variance (ANOVA, p < 0.05)
in SPSS 22.0 (SPSS Inc., Chicago, IL, USA). Since there
were no statistical differences between the slopes for the four lakes for
each resource (ANOVA, p>0.44, n=20),
slopes were averaged for each nutrient across lakes. Differences between results
of bioavailable resources across lakes and for each lake across time
were tested using the Kruskal-Wallis H test and Dunn's post-hoc test
(p<0.05) in SPSS. Differences between total and
bioavailable resource ratios for the lakes were tested with dependent
t tests (p = 0.05) in SPSS. Previous work suggests that at higher
DOM concentrations there is a greater discrepancy between bioavailable and
total DOM fractions (Berggren et al., 2015). We therefore pooled the
seven different rivers into two categories according to their DOC
concentrations (3.7–23.0 mg C L-1); this resulted in an ensemble of
three rivers which had DOC concentrations higher than 10 mg C L-1
(rivers>10mgCL-1) and four rivers that had DOC
concentrations lower than 10 mg C L-1 (rivers<10mgCL-1).
Differences between total and bioavailable river nutrient
ratios for the two groups were tested with dependent t tests (p = 0.05)
in SPSS.
Results
The rate of leucine incorporation increased over time in most bioassays
until day 2 (t=2), before gradually decreasing until day 7 (t=7;
Fig. 1). In the bioassays that were performed with resource additions, the
accumulated leucine incorporation over the 7-day period was proportional to
the concentrations of bioavailable resource added (Fig. 2). The results
rendered an average linear relationship describing amounts of leucine
incorporated per bioavailable C, N, and P (Fig. 2).
Measurements of leucine incorporation in relation to additions of
bioavailable C (as C6H12O6), N (NH4NO3), and
P (Na2HPO4). Regression equations for all points pooled together:
bioavailable C=784x+384 (R2=0.74, p < 0.0001;
n = 20); bioavailable N=2667x+159
(R2=0.75, p < 0.0001, n = 20);
bioavailable P=67575x-110 (R2=0.80, p < 0.0001,
n = 20). Note that each individual regression
line in the figure has a better fit than the regression line for all observations merged.
![]()
Bioavailable resource concentration spanned from 104 to 692 µgCL-1,
23 to 287 µgNL-1, and 0 to 16 µgPL-1 (Table 2).
Concentrations of BDOC did not differ among lakes (ANOVA, p > 0.61,
n = 130). By contrast, the four lakes did
vary in average BDN and BDP (ANOVA, p < 0.05, n = 130).
Lake Struptjärnen had on average the highest BDN and BDP
concentrations (159 µgNL-1 ± 111 SE and 8 µgPL-1 ± 4 SE)
and lake Lillsjöliden had the lowest values (124 µgNL-1 ± 97 SE
and 5 µgPL-1 ± 3 SE).
There was a significant difference in bioavailable resource concentrations
over time across the lakes (ANOVA, p < 0.05, n = 30–35;
Table 2). In general, concentrations of BDOC across the lakes were
highest in October 2012 (mean 356 µgCL-1 ± 84 SE) and
lowest in August 2014 (mean 185 µgCL-1 ± 59 SE), with a
33 % difference in BDOC between maximum and minimum values during the
studied period. Concentrations of BDN tended to be high in September 2012
(mean of 236 µgNL-1 ± 45 SE), lowest in September
2014 (mean of 58 µgNL-1 ± 42 SE) and were 85 % higher
at its maximum compared to its minimum concentration. Concentrations of BDP were
the highest in July 2014 (mean of 12 µgPL-1 ± 3 SE),
lowest in October 2012 (mean of 4 µgPL-1 ± 3.6 SE) and
varied approximately 83 % throughout the studied period. There was no
correlation between total and bioavailable element concentrations. Average
fractions of bioavailable resources relative to the total pool were lowest
for C, highest for P, and intermediate for N (Fig. 3). Organic forms were
the major source of bioavailable resources for bacterioplankton, and
represented 80 % (±13 SE) of the bioavailable N pool and 61 %
(±46 SE) of the bioavailable P pool (Fig. 3). The contribution of
inorganic fractions was therefore relatively more important for overall P
than N bioavailability.
Molar nutrient ratios calculated for the total pool of nutrients were
significantly higher than ratios calculated on the basis of the bioavailable
fraction (dependent t test, p < 0.05, n = 26;
Fig. 4). For example, the average ratio of total C : N was 55 (±9 SE)
and was ca. 13 times higher than the C : N bioavailable ratio which averaged 4
(±3 SE). Similarly, average C : P total ratio was 4774 (±2135 SE)
and was 12 times significantly higher than the average bioavailable C : P
ratio 369 (±915 SE). However, there were no significant differences
(dependent t test, p > 0.474, n = 26) between
total N : P ratios (average of 145 ± 386 SE) and bioavailable N : P
ratios (average of 89 ± 44 SE), or between bioavailable N : P ratios
and the DIN : PO4-P ratio (mean of 29 ± 19 SE; dependent t test,
p > 0.134, n = 26).
Proportion of organic non-bioavailable, organic bioavailable and
inorganic nutrient shares of dissolved organic carbon (DOC), total dissolved
nitrogen (TDN) and total phosphorus (TP) for all lakes and all sampling
occasions (n=26).
![]()
Bioavailable (bio) and total (tot) ratios (molar) of carbon to
nitrogen (C : N) and carbon to phosphorus (C : P) for all lakes and all sampling
dates (n=26). Ratios of N : P are shown for total, bioavailable, and
inorganic (inorg) fractions. Different letters stand for significant
differences (dependent t test; p < 0.05; n = 26)
among ratios. Data shown as box plots and includes mean as diamonds.
![]()
The amounts of leucine incorporated per unit of bioavailable resource in our
growth bioassays (as determined by the slopes in Fig. 2) were validated by
extracting the same ratio from the experiments performed using alternative
bioassay methods (Fig. 5). The alternative bioassay methods were based on
(1) inferring BDOC from bacterial respiration, (2) calculating BDN from cell
yields, and (3) analysing BDP directly on the bacterial biomass (see
methods). The growth responses (leucine incorporation) in our growth
bioassays overlapped with the growth responses obtained from experiments
using the alternative methods. However, on average the growth response was
slightly higher in our bioassays compared to the alternative bioassays
(Fig. 5).
Log-scale box plots of incubation show leucine amounts per unit of
bioavailable nutrient measured with validation methods: bacterial
respiration (C), cytometry (N), and harvesting of cells in filters (P).
Diamonds are average values for the validation methods and filled squares
are average slope values for standard curves (same values as slopes in Fig. 2).
![]()
For rivers, DOC appeared as the least bioavailable resource (in relation to
the total pool) for both groups, rivers>10mgCL-1
and rivers<10mgCL-1 (Table 3). In contrast, the
BDN share was the most bioavailable with approximately half of the TN pool
being bioavailable. Total nutrient ratios of C : N and C : P were
statistically significantly higher (approximately 26- and 5-fold
respectively) than the respective bioavailable resource ratios for
rivers>10mgCL-1 (dependent t test, p < 0.05,
n = 4). We found no differences between total N : P ratio
and bioavailable N : P ratios, nor between each of these and DIN : PO4-P
ratios for both rivers>10mgCL-1 (dependent t test,
p > 0.07, n = 4) and
rivers<10mgCL-1 (dependent t test, p > 0.10,
n = 3).
Resource bioavailability in relation to the total resource pool,
shown as percent bioavailable dissolved organic carbon (BDOC), bioavailable
dissolved nitrogen (BDN) and bioavailable dissolved phosphorus (BDP). The
data are divided into two groups which show average results for rivers with
more than 10 mg C L-1 (rivers>10mgCL-1; n = 3)
and rivers with less than 10 mg C L-1 (rivers<10mgCL-1;
n = 4). Average element ratios of carbon to nitrogen
(C : N), carbon to phosphorus (C : P), nitrogen to phosphorus (N : P) are
calculated in molar for total (tot) and bioavailable resource fractions
(bio). Ratios of dissolved inorganic nitrogen to phosphate (DIN : PO4-P)
are also provided. Standard deviations are given within
parentheses.
Variable
rivers>10mgCL-1
rivers<10mgCL-1
BDOC (%)
2 (1)
3 (2)
BDN (%)
48 (16)
36 (20)
BDP (%)
20 (12)
31 (45)
C : N (bio)
1 (1)
2 (1)
C : N (total)
26 (5)
24 (13)
C : P (bio)
319 (287)
523 (795)
C : P (total)
1722 (378)
920 (93)
N : P (bio)
294 (353)
240 (251)
N : P (tot)
70 (27)
46 (21)
DIN: PO4-P
88 (68)
2 (2)
Discussion
Resource bioavailability as a driver of ecological patterns
Results from this study underscore the ineffectiveness of total nutrient
fractions as predictors of bioavailability in boreal freshwater ecosystems.
In these aquatic systems that have high absolute concentrations of DOC, C bioavailability was lowest relative to N and P. This study not only
reveals the likely control that C has on boreal heterotrophic aquatic
metabolism but also suggests that possible changes in C loading of the
boreal water systems in the future may impact aquatic productivity and the
turnover of nutrients. Northern catchments are thought to be particularly
sensitive to ongoing climate change (Tetzlaff et al., 2013) and this
refined understanding of bioavailable resource stoichiometry may be
essential to forecast and mitigate aquatic ecosystem responses to these and
other anthropogenic pressures at high latitudes.
Bioavailable concentrations of DOC, TDN, and TDP in lakes
Our estimates, which reflect the resource pool readily available to
bacterioplankton at any point in time, supported our expectations by showing
that nutrient bioavailability (as percentage of the total pool), increased
from BDOC to BDN and from BDN to BDP. The observed differences in N and P
bioavailability match the overall trend reported for aquatic ecosystems in
the literature (Berggren et al., 2015) and are generally consistent with our
understanding of how these elements are bound to organic matter. Organic N
tends to form covalent bonds directly to C and may be physically and
chemically protected within complex, organic compounds that are resistant to
decay (Schulten and Schnitzer, 1997). Liberating this N is linked to organic
matter depolymerisation and C mineralisation (Schimel and Bennett, 2004),
requiring multiple exo-enzymatic steps that are energetically expensive
(Sinsabaugh and Follstad, 2011). By contrast, organic P is more often
associated with ester bonds (C-O-P) that can be cleaved in a single
enzymatic step independent of C mineralisation (McGill and Cole, 1981). In
addition, other forms of inorganic P (e.g. orthophosphate) may be only
loosely bound and exchanging with iron-humic complexes (Jones, 1998). These
binding properties are thought to govern differences in the relative rates
of N and P cycling in soils (Vitousek et al., 2002) and our results suggest
that the same factors may shape the relative bioavailability of these
resources in freshwater environments as well.
The method we describe here generated simultaneous bioavailability estimates
for C, N, and P that were comparable to those from single-element bioassays
reported elsewhere. Absolute concentrations of BDOC (100–690 µgCL-1)
were within the range of reported values for cedar bog wetlands
(12–408 µgCL-1; Wiegner and Seitzinger, 2004) and were at
the lower end of values reported for rivers (108–180 µgCL-1;
Wiegner et al., 2006). Concentrations of BDN (30–320 µgNL-1)
were in agreement with bioavailable N concentrations reported for
cedar bog wetlands (0–322 µgNL-1; Wiegner and Seitzinger,
2004). BDP (0–16 µgPL-1) was comparable to values from a
recent study on headwater streams during low flow
(1–14 µgPL-1; Jansson et al., 2012). In addition, organic forms dominated the
total bioavailable N and P pool (80 and 61 %) in our
four lakes, and 27 and 36 % of these organic pools were bioavailable for
N and P
respectively. These results are in line with previous estimates and show
that a large fraction of DON is available to bacterioplankton in diverse
limnetic systems, e.g. in Baltic Sea rivers (30 %; Stepanauskas et
al., 2002), in eastern US rivers (23 %; Wiegner et al., 2006)
and in cedar bog wetland streams (33 %; Wiegner and Seitzinger,
2004). Published estimates of the share of BDOP (bioavailable dissolved
organic phosphorus) relative to the total DOP pool varied from 33 to 60 %
in Baltic Sea brackish waters (Nausch and Nausch, 2007).
Thus, our results agree with the results from previous studies and together
they emphasise the importance of organic nutrient fractions in systems rich
in organic matter as well as the capacity of bacterioplankton to take up organic
compounds.
Concentrations of BDOC, BDN and BDP varied seasonally in all lakes during
the study period (Table 2). Major differences in BDOC were observed between
midsummer, when concentrations were lowest, and the end of the summer, when
concentrations were high. Previous experimental work on boreal and arctic
rivers has also shown minimal concentrations of BDOC during the summer
season (Wickland et al., 2012). In addition, concentrations of BDOC
tended to follow bulk DOC concentrations in boreal freshwater systems as
suggested in Søndergaard and Middelboe (1995). By contrast, patterns
of BDP concentrations opposed those of BDOC (Table 2): specifically, BDP
peaked in midsummer (July) and declined in the autumn. It has been shown
elsewhere that bioavailable P concentrations in boreal streams can be 2–10
times higher during summer than during autumn (Jansson et al.,
2012). This may be due to higher soil temperatures during summer which
promote soil C metabolism and result in a higher export of P from soils to
surface waters compared to that of C (Jansson et al., 2012).
Our results also supported the prediction that the bioavailable ratios
of C : N and C : P would be considerably lower than their counterparts based on total
pools. A major implication of these differences is that ratios based on
total pools grossly overestimate actual C availability. When such
differences are large, the elemental ratios based on total pools can lead to
incorrect predictions of resource limitation (Berggren et al., 2015). For
example, in a recent study of two temperate estuaries, total resource
stoichiometry predicted P limitation of bacterioplankton, while experimental
evidence showed that C was the element constraining bacterial growth during
base flow (Hitchcock and Mitrovic, 2013). Average DIN : PO4-P ratios
and particularly total TN : TP were however, closer to the
average ratio of bioavailable TN : TP. Due to the high C recalcitrance,
nutrient limitation predictions based on the ratio of total resource pools
may be inadequate when C is included in the ratio, but seem more promising when
based on N and P.
Our results further show that while the median bulk stoichiometric ratio
(3651C : 71N : 1P; Fig. 4) was 1–2 orders of magnitude higher than that expected
from the Redfield ratio (106C : 16N : 1P; Anderson, 1995; Redfield, 1958), the
median C : N : P of bioavailable resources (144C : 29N : 1P) was surprisingly
comparable yet slightly above Redfield values (Fig. 4). There was, however,
a wide variability in the bioavailable ratios among samples collected over
space and time. Such variance is consistent with another study that
evaluated bacterial stoichiometry across a large number of lakes and showed
that, while elemental stoichiometry varied among lakes in response to
intrinsic and extrinsic factors, the overall mean ratio tended to converge
with Redfield (Cotner et al., 2010).
Broad-scale riverine BDOC, BDN and BDP patterns
Broad-scale patterns of nutrient bioavailability at the river mouths did not
differ between rivers<10mgCL-1 and rivers>10mgCL-1.
Similar to what was observed in the lakes, DOC was the most
recalcitrant nutrient considered. However, in contrast to our results from
the lakes, TDN was the most bioavailable resource observed in the river
mouths (Table 3). Although previous studies suggest that temperature
differences across catchments can influence C : N ratios in streams and
rivers through effects on terrestrial ecosystem properties (e.g. vegetation
type) and soil development (Sponseller et al., 2014), our results show a
similar bioavailable resource stoichiometry at the outlet of all these
rivers. Organic forms of N were a major source of bioavailability and
dominated TDN, in agreement with estimates from other studies (Wiegner et
al., 2006; Seitzinger and Sanders, 1997; Stepanauskas et al., 2002).
Significant differences between total and bioavailable C : N and C : P
ratios occurred only in rivers>10mgCL-1. However,
neither rivers>10mgCL-1 nor rivers<10mgCL-1
showed differences between total N : P, bioavailable N : P and
DIN : PO43- ratios. These results indicate that, similar to the patterns
observed in lakes, the use of bulk resource ratios misrepresents resource
bioavailability and limitation when (1) C is part of the nutrient ratio and
(2) there is a high concentration of DOC in the water.
While Swedish rivers have substantial water renewal along watercourses from
the Scandes to the Baltic Sea (Muller et al., 2013), at such
broad scales several environmental factors may modify element
bioavailability through modification and differential uptake and
remineralisation of C, N, and P. For example, bacterial processing
(Creed et al., 2015), photodegradation
(Bushaw et al., 1996) and reactive oxygen
(Gao and Zepp, 1998) may influence organic matter degradation and
changes in bioavailability over the long timescales encompassed by large
river systems. In this regard, it is interesting to note that our estimates
of short-term macro-nutrient bioavailability were similar for lakes and
rivers, which suggest that possible differences in long-term macronutrient
bioavailability across these very different sites did not seem to impact on
the results determined under our specific laboratory conditions. The general
pattern that we found across all sites was a relatively low bioavailability
of C relative to that of N and P. This may suggest that C is more important
as limiting factor for bacterial metabolism than previously thought.
However, while our results directly provide information on the maximal pools of
bioavailable macronutrients that can be readily consumed, the true
exploitation of these resources in nature is dependent on other (extrinsic)
factors such as micro-element limitation, element co-limitation, and grazing
pressures. Thus, based on our result alone it is not possible to determine
whether or not the in situ bacterial metabolism was limited by a specific
macronutrient, although it appears more likely that C would be limiting than
N or P.
Measuring bioavailability of C, N, and P with leucine
incorporation
The linear relationships obtained from standard growth curves relating
leucine incorporation to bioavailable resource concentrations showed that
incorporation over a 7-day period was significantly and positively related
to the amount of resource added. The fact that these relationships were not
statistically different between the lakes suggests that leucine incorporation
was driven by the added resources rather than other factors that could have
affected the experiment. For example, variations in lake pH could have
impacted the amount of resources taken up in the bioassays (del Giorgio
and Davis 2003; Li et al., 2012). Due to the lack of replication of standard river
curves, we could not test whether or not the standard curves of
individual rivers were also similar to those of lakes. However, we suspect
that physical and chemical water sample properties may differentially
influence leucine uptake in different systems. Our blank bioassays further
confirmed the dependency between leucine incorporation and limiting resource
concentration by showing that virtually no leucine incorporation occurred
when the limiting resource was lacking in the growth media.
We used the leucine incorporation method as a proxy for bacterial growth and
related it to bioavailable resource concentrations based on the premise that
this process measures the rate of bacterial protein synthesis
(Kirchman et al., 1985). Because proteins are large
macromolecules within bacterial cells (approximately half of bacterial dry
weight), they represent a substantial fraction of the resource uptake and
its consequent conversion into biomass. Also, to carry out protein
synthesis, bacteria use both C and N; nitrogenous compounds are taken up
from the growth medium to build proteins with energy obtained from C
substrates. Phosphorus is also used in the process as it is crucial for
controlling the adenosine triphosphate–adenosine diphosphate cycle, which
provides energy for the intracellular molecular synthesis. Due to the
critical role that these three elements play within protein synthesis, our
results represent an unequivocal relationship between resource availability
and the amount of protein synthesised. We measured resource bioavailability
over a time period of 7 days and the major part of the resource pool was
exhausted within 3 days (Fig. 1). In the context of bioavailability
assessments, 7 days is a relatively short period and repeated bacterial
regeneration of resources was avoided in this way (Cho et
al., 1996). Although there may have been some resource recycling, our
bioavailability estimates are automatically corrected for this artefact as
these were calculated based on standard curves for leucine incorporation per
absolute unit of added bioavailable resource, constructed for the exact same
time period.
The design of our experiment could lead to possible sources of errors in
estimates. For example, reference assays (standard curves) were performed on
one occasion and used to interpret actual nutrient bioavailability on other
occasions. This means that if BGE varied during the studied period it could
result in differences in the amounts of leucine incorporated. We dealt with
this possible shortcoming by designing our bioassays such that resource use
efficiency would be maximised (by strongly inducing resource
limitation; Jansson et al., 2006) and thus, possible variations in resource
use efficiency most likely did not play a substantial role on rates of
leucine uptake (Fig. 5). In addition, the fact that glucose was used as
the reference source of C and energy in the calibration could lead to an
overestimation of the standard C growth curves and possibly result in
conservative estimates of bioavailable C. For example, glucose additions
could have supported the part of the community with the fastest growth and
therefore results may not compare to results from a community that was
instead exposed to a natural substrate. Nonetheless, when comparing the
amount of leucine incorporated by our standard bacterial community per unit
of bioavailable glucose with amounts of leucine incorporated per unit of
natural bioavailable substrate (Fig. 5), we show that, on average, our
growth response was only slightly higher than the growth response in
experiments based on alternative bioassay methods (Fig. 5). Thus, our
resource bioavailable estimates presented here are most likely conservative
but realistic.