Introduction
Permafrost degradation is one of the most prominent responses of Arctic
landscapes to accelerated warming. Many factors can influence the thaw rate
of permafrost (Zhang et al., 2005; Jorgenson et al., 2010), but permafrost
thaw is very sensitive to even small changes in air and ground temperatures
(Hinkel and Nelson, 2003; Zhang et al., 2005; Romanovsky et al., 2007; White et
al., 2007). Permafrost is expected to continue to degrade in response to
climate warming (Jorgensen et al., 2001, 2006; Lawrence and Slater, 2005; Frey and McClelland, 2009), with some models predicting that in areas of
continuous permafrost, near-surface permafrost extent will decrease by
26–90 % (Lawrence and Slater, 2005; Anisimov and Reneva, 2006) and soil
active-layer depth will deepen by 30–100 % (Stendel and Christensen, 2002;
Zhang et al., 2008) over the next century. Such changes are likely to alter
biogeochemical cycling in soils and the aquatic systems that receive
material from soils.
The soil active-layer controls much of the tundra landscape's hydrologic and
biogeochemical activity (Hinzman et al., 1991; Waelbroeck et al., 1997; Zhang
et al., 2005; Schuur et al., 2008; Frey and McClelland, 2009), which in turn affects
groundwater and nutrient inputs to Arctic aquatic ecosystems (Hobbie et al., 1999; Zhang et al., 2005; White et al., 2007). Degradation of permafrost
typically increases phosphorus (P) export to surface waters (Hobbie et al.,
1999, Frey and McClelland, 2009), while changes in inorganic nitrogen (N) and
dissolved organic carbon (DOC) are less consistent. For example, with
permafrost thaw, watershed DOC export in the Yukon, Alaska, and central
Siberia decreased (Carey, 2003; Kawahigashi et al., 2004; Striegl et al., 2005;
McClelland et al., 2007), whereas it increased in west Siberia (Frey and
Smith,
2005).
These water chemistry changes are important for the ecology of Arctic lakes
because they alter nutrient and energy availability to phytoplankton and
heterotrophic microbes. Hobbie et al. (1999) demonstrated that permafrost
thaw in northern Alaska contributed 30 % of inflowing phosphate and
nitrate into Toolik Lake. Long-term experimental manipulation of another
lake at that study site demonstrated that sustained P inputs increased
primary production and increased phytoplankton biomass (Hobbie et al., 1999).
It was therefore speculated that P inputs from permafrost degradation would
increase lake eutrophication. However, another experimental study on Alaskan
Arctic lakes indicated that N subsidies may be more important than P in
stimulating phytoplankton productivity (Levine and Whalen, 2001). Despite
these few studies, the nature and magnitude of permafrost degradation
effects on Arctic lakes remain largely uncharacterized and largely focused
on phytoplankton production.
Heterotrophic bacteria are key to aquatic biogeochemical reactions and
transformations, and they should be susceptible to changes in DOC and
nutrient input to lakes in Arctic systems (Cotner and Biddanda, 2002;
Villar-Argaiz et al., 2002; Crump et al., 2003). Due to microbial
mineralization of C, Arctic lakes can release significant amounts of
greenhouse gases, such as CO2 and CH4 (Kling et al., 1992). In
Arctic lakes, the source of DOM can shift seasonally from the landscape
(allochthonous DOM) to in-lake production (autochthonous) (Whalen and
Cornwell, 1985). This can shift microbial community structure and production
rates (Crump et al., 2003). In aquatic systems receiving nutrient subsidies,
nutrient limitation of bacteria should relax. This would increase the rate
by which heterotrophic bacteria consume organic matter for growth and
respiration, resulting in increased CO2 production in oxygenic
environments, or CH4 in anoxic ones (del Giorgio and Cole, 1998; Smith
and Prairie, 2004). This has important implications for Arctic lakes that may
receive nutrient subsidies through permafrost degradation. For instance,
microbial growth increased in lake and pond waters of the high Canadian
Arctic that received experimental P subsidies, indicating microbial P
limitation (Granéli et al., 2004). Alternatively, in lakes that receive fewer
hydrological inputs of nutrients and DOM due to increased soil active-layer
depth and catchment microbial activity, the size of DOM and nutrient pools
would decrease. Such a decrease would initiate a simplification of lake
microbial food web structure (Hobbie et al., 2000). Further investigating
heterotrophic microbial activity in Arctic lakes at present is important to
understanding future thaw-driven changes in nutrient inputs.
One way to assess the nutrient demands of microbial communities is to
measure activities of extracellular enzymes (EEAs), used by microbes to
cleave complex organic molecules into smaller compounds that can be
assimilated. Relative activities of enzymes associated with C, N, and P
acquisition can be used to infer nutrient limitation following resource
allocation models (Sinsabaugh et al., 2008). Enzymes of interest in EEA
studies commonly include β-1,4-glucosidase (BG), which degrades
cellulose and β-1,4-glucans to glucose for C acquisition (Ljungdahl
and Eriksson 1985; Sinsabaugh et al., 2008); β-1,4-N-acetylglucosaminidase (NAG)
and leucine aminopeptidase (LAP), which acquire N from chitin and
polypeptides, respectively (Sinsabaugh and Foreman, 2001; Sinsabaugh et al.,
2008); and phosphatase (AP), which degrades phosphomonoesters to obtain P
(Turner et al., 2002; Sinsabaugh et al., 2008). These enzymes are catalysts
for terminal reactions in which organic matter is converted to monomer
nutrients (Sinsabaugh et al., 2008). As such, their activity reflects total
microbial demand for C (via BG), N (via NAG + LAP), and P (via AP). Recent
work has established the use of EEAs as a method to infer microbial nutrient
limitation (Sinsabaugh et al., 2008; Moorhead et al., 2013; Hill et al., 2014),
making EEA assays an extremely valuable tool for evaluating changing
nutrient concentrations in aquatic ecosystems.
We examined microbial nutrient limitation patterns, via EEA analysis, and
water chemistry in lakes spanning a range of nutrient availability in the
continuous permafrost landscape of southwest Greenland. While the Alaskan,
Siberian, and Canadian Arctic have shown consistent increases in air
temperatures and active-layer thickening since the mid-1970s (Blunden and
Arndt, 2014), recent and abrupt (> 10 ∘C) warming in
western Greenland (Hanna et al., 2012) coincides with deepening permafrost
active layer only since the mid-1990s (Christiansen et al., 2010). As a
result, these relatively recent changes in Greenland provide a unique
situation in which we could examine patterns in microbe–nutrient
relationships in a landscape with relatively low permafrost loss, providing
a baseline from which to gauge future change. We measured EEA during the
summer of 2013 in 24 lakes situated around Kangerlussuaq, southwest
Greenland. We hypothesized that most lakes would be P-limited based on
previous findings with phytoplankton experiments in this area (Brutemark et
al., 2006), but that patterns in microbial enzyme allocation toward C, N, and
P would track variation in lake water DOC, dissolved inorganic N (DIN), and
total P (TP) availability.
Map of study site, with the 24 study lakes surrounding
Kangerlussuaq, Greenland, indicated by dots.
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Methods
Study site
The region around Kangerlussuaq, Greenland (67∘00′ N,
50∘41′ W, Fig. 1), contains more than 20 000 lakes (Anderson et
al., 2001) and is underlain by continuous permafrost estimated to be 300 m
thick (Nielson, 2010; Harper et al., 2011). The climate of this region is low
Arctic continental with a mean summer temperature of 10.2 ∘C. In
western Greenland, annual air temperature has increased by 3 ∘C
and annual melting degree days by 100 % when comparing 2007–2012 to
1979–2000 (Mayewski et al., 2014). The region is semiarid, receiving
approximately 150 mm of precipitation per year and even less at the ice
sheet margin. Lakes in this study ranged in surface area from 0.02 to 0.8 km2
and in maximum depth from 9 to 36 m. Most of these lakes are
oligotrophic, with low nutrient concentrations characteristic of lakes in
this region (Anderson et al., 2001; Perren et al., 2009). Lakes were first
sampled in June shortly following ice-off. At that time, about half of the
study lakes were weakly stratified. For those that were not, the
“hypolimnion” sample depth was the estimated limit of the euphotic zone
determined as twice the measured Secchi depth. Lakes were sampled again in
July, during the period of stable thermal stratification for all lakes.
Environmental parameters
Physical and chemical variables of the lakes were measured to determine
their relationship to microbial EEAs within the epilimnia of the study
lakes. Temperature and pH were measured at the point of greatest lake depth
using a submersible HydroLab Datasonde 5a. Epilimnetic and hypolimnetic
water samples were collected with a van Dorn bottle. Water samples for
measurement of dissolved nutrients and DOC were filtered through Whatman
GF/F filters that were pre-rinsed with DI water. Samples for total nutrients
were unfiltered. All samples were collected into acid-washed bottles and
kept refrigerated until analysis. Dissolved inorganic (NH4+,
NO3-, and PO43-) and total nutrient (TN and TP)
concentrations were analyzed on a Lachat QuickChem 8500 flow injection
analyzer. Nitrate was measured with the cadmium reduction method,
NH4+ with the phenate method, and PO43- with the
ascorbic acid method (APHA, 2000). TN and TP were determined by measurement
of NO3- and PO43- on unfiltered water samples following
digestion with persulfate (APHA, 2000). Quantification limits on all
nutrients were 2 µg L-1 except for TN, which was 10 µg L-1.
For statistical analyses, nutrient values below the 2 µg L-1 quantification limit were replaced with 1. Dissolved inorganic
nitrogen : TP (DIN : TP) ratios were calculated, with DIN determined by the
addition of NH4+ and NO3-. The DIN : TP ratio is a useful
metric for inferring nutrient limitation, moving from N to P limitation with
an increase from 1.5 to 3.4 (Bergström, 2010). DOC was analyzed with an OI
Analytical Aurora 1030D TOC analyzer using wet chemical oxidation.
EEA analysis
Water samples for EEA analysis were collected in the same way as total
nutrient samples (i.e., not filtered). Due to the remote location of the
lakes, samples from June were stored frozen (-20 ∘C) for 60 days
and samples from July were refrigerated for 30 days and then frozen for 30 days before analysis.
Though the analysis of fresh samples is considered
preferable due to the uncertainty of whether freezing introduces bias into
results, it is common for freshwater EEA studies to freeze samples owing to
logistical constraints (e.g., Simon et al., 2009; Clinton et al., 2010;
Freimann et al., 2013; Parr et al., 2015). We are assuming that if freezing
had any effect it was similar across systems. EEA samples were thawed,
processed, and analyzed with a Thermo Electron Corporation Fluoroskan Ascent
FL fluorescence spectrophotometer using fluorescent-labeled substrates
following published methods (Sinsabaugh and Foreman, 2001; Findlay et al., 2003).
Fluorescent substrates were used to measure activity of BG
(4-MUB-β-D-glucoside), NAG (4-MUB-N-acetyl-β-D-glucosaminide),
LAP (L-Leucine-7-AMC), and AP (4-MUB-phosphate). Briefly, 200 µL
sub-samples from each lake sample were added in triplicate to 96 well assay
plates and combined with 50 µL of substrate for a final saturated
substrate concentration of 200 µM and assayed at 25 ∘C.
Controls for substrate and sample fluorescence and quenching were included.
Pilot assays were used to ensure substrate concentrations saturated enzyme
kinetics such that kinetic rates were equal to Vmax, and readings were
made during linear increases in fluorescence. Throughout the analysis, steps
were taken to standardize and optimize the procedure following the
suggestions of German et al. (2011).
An example of vector plot analysis for a hypothetical lake sampled
in June and July. The 1:1 line is drawn in dashes and separates zones of P
imitation (above) from N limitation (below). Vectors for each data point are
drawn in arrows. Their angles indicate microbial nutrient limitation, such
that the positive angle value with respect to the 1:1 line in June indicates
P limitation, while the negative one in July indicates a shift to N
limitation. The lengths of the vectors are also indicative of microbial C
acquisition efforts, which in this example are greater in July than in June.
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Data analysis
Microbial nutrient limitation was inferred from activity of individual
enzymes and from ratios of multiple enzymes. Across ecosystems, nutrient
acquisition effort as measured by BG, NAG + LAP, and AP is typically close
to 1 : 1 : 1 based on global empirical evidence and following stoichiometric and
metabolic theories (Sinsabaugh et al., 2008, 2009). Departures from these
values are indicative of differential microbial nutrient demand as microbes
invest resources in enzymes to acquire limiting nutrients. The degree of C
limitation can be inferred from ratios of C to nutrient-acquiring enzymes
(BG : NAG + LAP and BG : AP; Sinsabaugh et al., 2008, 2009). Further, the
stoichiometric ratios BG : NAG + LAP and BG : AP can be considered in concert to
gauge degree of microbial N or P limitation (Moorhead et al., 2013; Hill et
al., 2014). This can be done by plotting BG : NAG + LAP vs. BG : AP and measuring
deviation from the 1 : 1 line, which indicates equal nutrient acquisition
effort (Sinsabaugh et al., 2008, 2009; Moorhead et al., 2013; Hill et al., 2014). On these plots the distance from the origin to a
data point forms a vector. Deviation of the vector angle from the 1:1
(45∘) line indicates increasing P (higher angles) or N (lower
angles) limitation (Moorhead et al., 2013; Hill et al., 2014). Figure 2
displays hypothetical data from a lake in June and July plotted onto a
vector plot with the 1:1 line drawn in dashes. The vectors from which angles
are calculated are shown as arrows from the origin to the individual data
points. In June, the vector angle is positive with respect to the 1:1 line
(> 45∘), indicating P limitation in this lake. However,
in July nutrient limitation shifts from P to N, as indicated by the negative
angle with respect to the 1:1 line (< 45∘). We quantified
vector angles for our samples and display the data as degrees of deviation
from the 1:1 line such that positive values indicate P limitation and
negative ones indicate N limitation (Fig. 5). Lastly, the distance of data
points from the origin indicates microbial investment in C acquisition
relative to that of N and P, such that C demand is indicated by larger
vector lengths (Moorhead et al., 2013, Hill et al., 2014).
Epilimnetic physical and chemical data across the 24 lakes in June
and July 2013. Significant changes (p ≤ 0.05) between June and July
means are indicated by asterisk (*). Depth was not measured in July. SE: standard error.
June
July
Mean
SE
Range
Mean
SE
Range
Depth (m)
19.7
1.67
8–36
–
–
–
pH
6.6
0.08
5.8–7.2
7.0*
0.07
6.4–7.7
Temperature (∘C)
7.8
0.40
5.3–11.6
13.5*
0.35
9.1–17.4
DIN (µg L-1)
6
1.2
2–25
12*
1.1
4–22
TN (µg L-1)
467
47
178–1042
554*
61
197–1132
TP (µg L-1)
4
0.7
< 2–12
5
0.7
< 2–11
DOC (mg L-1)
12
1.9
4–35
13*
2.2
4–40
DIN : TP
2.1
0.37
0.17–6.0
5.4*
1.4
0.55–22
To test whether water quality parameters, nutrient concentrations, or
enzyme-related activities differed between months (June vs. July) or lake
strata (epilimnia vs. hypolimnia), two-tailed, paired t tests were used. To
determine whether certain factors, such as nutrient concentrations or
ratios, were related to enzyme activities, simple least-squares linear
regression was used with a level of significance of p= 0.05. All
statistical analyses were completed using R (version 3.1.2).
Results
Water quality parameters
Several physical and chemical parameters varied from June to July in lake
epilimnia (Table 1; Table S1 in the Supplement). Surface water temperatures
increased between June and July, on average from 7.8 to
13.5 ∘C (p < 0.01). pH was relatively consistent across
lakes (5.8–7.7) but on average increased from 6.6 in June to 7.0 in July
(p < 0.01). DOC ranged 10-fold from 4 to 40 mg L-1 across study
lakes, and it slightly increased, on average, over the summer (12.1 to 13.4 mg L-1, p < 0.01). DIN ranged from 2 to 25 µg L-1, and
increased from June to July (6 to 12 µg L-1, p < 0.01),
as did TN (467 to 554 µg L-1, p < 0.01, range from 178
to 1132 µg L-1). TP ranged from < 2 to 12 µg L-1 and did not appreciably increase from June to July (4 to 5 µg L-1, p= 0.68).
PO43- was below quantification limits at
all times. DIN : TP ranged from 0.17 to 22 and increased from June to July
(2.1 to 5.4, p < 0.01). TN was tightly related to DOC in both months
(June; R2= 0.83, p < 0.01; July R2= 0.77 p < 0.01). There were no relationships between TP and DOC, or DIN : TP and DOC.
Seasonal and spatial patterns in enzyme activities
Activity of all enzymes increased from June to July in both the epilimnia (p values ≤ 0.03) and hypolimnia (p values ≤ 0.02; Fig. 3).
Averaged between lake strata, BG activity increased 73 %, NAG + LAP
79 %, and AP 70 % from June to July. Averaged across lakes, absolute
activities of single enzymes differed between epilimnetic and hypolimnetic
samples. For BG, epilimnetic activities were 1.7 times higher than those of
hypolimnia in June (p= 0.01) and 1.3 times higher in July (p= 0.42). For
NAG + LAP, epilimnetic activities were 1.1 and 1.3 times higher than those of
hypolimnia in June and July (p < 0.01 and p= 0.01, respectively).
There were no differences in AP activities between strata in either month (p values > 0.05).
Absolute enzyme activities for (a) β-1,4-glucosidase (BG),
(b) β-1,4-N-acetylglucosaminidase and leucine aminopeptidase (NAG + LAP),
and (c) alkaline phosphatase (AP) across epilimnia and hypolimnia of study
lakes, from June to July. Error bars are standard deviation. Units of
activity are µmol mL-1 h-1.
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Ratios of C to nutrient-acquiring enzyme activity varied by lake strata and
time (Fig. 4). Hypolimnetic BG : AP was consistently lower than epilimnetic
BG : AP, though the difference was not significant in July (June p= 0.01,
July p= 0.09). In June, epilimnetic BG : NAG + LAP was greater than that of
the hypolimnia (p= 0.03), but in July there was no difference (p= 0.72). There were no seasonal differences in BG : AP or BG : NAG + LAP within
the same stratum (p values < 0.05). BG : NAG + LAP was greater than
BG : AP in epilimnia and hypolimnia in June (p values < 0.01) but not
in July (p values > 0.05). These data suggest microbes were
investing more in P and less in N acquisition across lakes in June but not
July.
EEA ratios of BG relative to those of (a) NAG + LAP and (b) AP
across lake strata for June and July. Error bars are standard deviation.
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Nutrient Limitation and C acquisition as indicated by mean vector
angle and length for both months. Significant changes (p ≤ 0.05)
between June and July means are indicated by asterisk (*). SE: standard
error.
June
July
Mean
SE
Range
Mean
SE
Range
Epilimnia
Vector angle (∘)
16
2.82
-8 to 41
4*
5.52
-32 to 41
Vector length
8.9
1.75
1.6–40.2
9.9
2.98
0.2– 58.4
Hypolimnia
Vector angle (∘)
20
3.08
-6 to 45
15*
4.93
-30 to 43
Vector length
4.7
1.10
0.2–24.8
7.3
2.69
0.2–62.8
Scatterplot of microbial enzyme ratios (BG : NAG + LAP vs. BG :
AP) about the 1:1 line. Included are C : P and C : N acquisition data of lake
epilimnia (circles) and hypolimnia (triangles) from June (gray) to July
(black). Dotted line indicates 1:1 (45∘) line. Vector angles
(indicative of nutrient limitation) are calculated from these plotted data
points, as deviation from the 1:1 line.
![]()
Vector angles were mostly positive, indicating consistent microbial P
limitation across all lakes in both time periods (Fig. 5). In the
epilimnia, the angle magnitude was about 3 times lower in July than in June,
suggesting relaxed P limitation later in the year (p < 0.01, Table 2).
This was less obvious in the hypolimnia where June and July data were
much more similar (p= 0.04). Angles in the hypolimnia were 1.3 times greater
than those of the epilimnia in June and 3.6 times greater in July (p values < 0.01). On the vector plots, distance of data points from the
origin did not change between June and July within the same strata (Table 2). This indicates that microbial investment in C acquisition did not
appreciably vary with respect to N- and P-acquiring enzymes. In June, the
investment in microbial C acquisition was greater in the epilimnia compared
to the hypolimnia (mean vector length 8.9 vs. 4.7, p= 0.02). In July,
however, this trend was no longer significant (p= 0.54), indicating
distributed C acquisition of similar magnitude across lake strata in late
summer.
Relationships between water chemistry and enzyme activities in lake
epilimnia
Due to sampling constraints, DOC was measured in epilimnetic water only.
There was a strong, linear increase in epilimnetic AP activity with
increasing DOC concentration in June and July (R2= 0.73 and 0.84,
respectively; p values < 0.01; Fig. 6). BG activity was not
related to DOC in June or July. Likewise, the activities of the N-acquiring
enzymes, NAG + LAP, were unrelated to DOC in both seasons. None of the
absolute EEAs were related to epilimnetic DIN : TP. When considered as enzyme
ratios, epilimnetic investment in C : P acquisition (BG : AP) decreased with DOC
concentration in June (R2= 0.24, p= 0.01) and was unrelated to
DOC in July. C : N acquisition (BG : NAG + LAP) was unrelated to DOC in both
months.
Response of (a) AP, (b) BG, and (c) BG : AP to DOC in lake epilimnia.
Data from June are indicated by gray triangles; data from July are indicated
by black circles. Only significant relationships are displayed.
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The magnitude of epilimnetic microbial P limitation, described by vector
angles, increased with rising DOC concentration in both months (June and
July R2= 0.67 and 0.57, respectively; p values < 0.01; Fig. 6).
There were no relationships between vector angles and DIN : TP (data not
shown). Likewise, there were no statistically significant relationships
between vector length (i.e., C limitation) and water chemistry (data not
shown).
Discussion
Our results reveal a pattern of microbial P limitation across these
southwest Greenland Arctic lakes. Vector analysis indicated more severe P
limitation in June compared to July, despite DIN : TP increasing in July. The
overall pattern of P limitation of bacterioplankton and phytoplankton is
consistent with previous research of plankton alkaline phosphatase activity
in two lakes in the same study region, which also suggested P limitation of
plankton communities (Brutemark et al., 2006). P limitation can be a factor
that controls lake algal and microbial productivity and trophic status. In
the high Canadian Arctic, for instance, P subsidies to lake and pond water
caused increased microbial growth, indicating P as the primary limiting
nutrient (Granéli et al., 2004). This was supported by another study
completed across 20 lakes in Quebec, Canada, which demonstrated that TP, and
not DOC, controlled microbial growth rates, respiration rates, and growth
efficiency (Smith and Prairie, 2004). Further, P availability controlled the
fate of DOC, such that in oligotrophic, low-P concentration environments
DOC was mostly used for respiration (converted to CO2), rather than
being incorporated into biomass.
Various measures of enzyme activity indicated a positive relationship
between P limitation and DOC, contrary to our expectation that DIN : TP might
be a stronger predictor. Here, we quantitatively measured and reported it as
DOC, but for discussion, DOM is a more appropriate term as it includes
organic N and P as well as carbon. DOM is a broad group of organic compounds
with varying lability depending on the source, chemical structure, and N and
P content (Mineau et al., 2013; Parr et al., 2015). In these lakes, DOM
positively co-varied with TN but had no relationship with TP. Collectively,
these enzyme and water chemistry data suggest that the DOM in these lakes
may provide a readily available source of N, while higher DOM concentrations
are associated with enzyme-mediated microbial P acquisition.
DOM can contain distinct nutrient pools available for microbial consumption
when conditions become stoichiometrically favorable. For instance, in a
study of N-limited humic lakes in northern Sweden, DOM-associated P was used
by bacterioplankton and phytoplankton when N was added into the experimental
systems (Jansson et al., 2001). Furthermore, in the same study the authors
showed that bacterioplankton production was strongly controlled by DOC, such
that bacterioplanktonic production in water containing 15–20 mg L-1 of
DOC could not be stimulated by further nutrient addition. However, DOM is not
consistently a source of P in all lakes. Phosphorus amendments, in addition
to simulated sunlight, were important in stimulating microbial degradation
of DOC in an experiment using water from a southern Sweden humic lake,
suggesting P limitation (Kragh et al., 2008). Another study of humic lakes
located in southern Sweden demonstrated that P alone was not sufficient for
stimulating microbial respiration and production; a source of labile C
(glucose) was also required (Vidal et al., 2011). Together, these studies
demonstrate that the interactions between bacteria and DOM are complex – DOM
can either behave as a source of bioavailable material, providing bacteria
with energy and certain nutrients, or remain a recalcitrant, unavailable
pool of organic compounds.
Vector angles in response to (a) DOC and (b) epilimnetic DIN : TP.
Data from June are indicated by gray triangles; data from July are indicated
by black circles. Dotted line indicates the boundary between P limitation
(positive values) and N limitation (negative values). Only significant
relationships are displayed.
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DOM remained an important factor in determining microbial P demand and
limitation from June to July, suggesting DOM is the dominant source of
nutrients available for microbial degradation and use. Lakes with low DOM
appeared to have more seasonality of nutrient limitation than those with
higher concentrations; as can be inferred from vector angles in Fig. 7a,
only low-DOM lakes switched from P to N limitation in July, while higher-DOM
lakes remained consistently P-limited. If the DOM pool is representative of
N availability, it follows that N limitation would be more likely in low-
than in high-DOM lakes. A seasonal shift in the type of DOM pool in low-DOM
lakes could also be contributing to seasonal differences in DOM-related
nutrient dynamics. Crump et al. (2003) investigated bacterioplankton
community dynamics in relation to DOM in Toolik Lake, Alaska. In spring DOM
was flushed from the landscape into an inlet stream and was labile due to
extended soil and plant leaching, freeze–thaw processing, and microbial cell
lysis. Moreover, this DOC was transported across the surface of the frozen
tundra rather than the subsurface soils. The quality of this DOC then
decreased as leaching of organic material decreased and the active layer
deepened, allowing microbial degradation of DOM during transport.
In some areas of the Arctic, discharge-normalized DOC export to Arctic
surface waters has decreased in recent decades (Striegl et al., 2005), with
permafrost thaw and soil active-layer deepening contributing to this trend
(Carey 2003; Kawahigashi et al., 2004; Striegl et al., 2005, 2007). If lake water DOM concentrations and quality are also declining, it is
likely that nutrient subsidy and limitation patterns will also change. Our
data suggest that lakes receiving less DOC may become less P-limited and
move towards N limitation, since DOM is being suggested as an N source.
Control of microbial production of enzymes and nutrient limitation may shift
from organic matter to inorganic nutrients that are primarily flushed into
the lakes during snowmelt and ice-off. Conversely, if DOC input into lakes
were to increase due to increased terrestrial production within catchments,
lakes would be predicted to become universally P-limited. Changes in
nutrient sources and concentrations will affect microbial and autotrophic
productivity (Smith and Prairie, 2004; Elser et al., 2007). It is likely
that, as the amount of lake DOM changes, DOM composition and lability will shift
as well. Bacterial community structure has been shown to change in
correspondence with DOM quality in Arctic lakes, as some bacteria prefer
more labile compounds while other species are adapted to utilizing
recalcitrant forms (Crump et al., 2003). In this study, the seasonal source
and quality of the DOM pool might have been inferred by the inclusion of
oxidative enzymes, such as phenol oxidase and peroxidase, which are
responsible for degrading terrestrially derived compounds such as phenols
and aromatics, respectively (Sinsabaugh et al., 2008). Though BG is assumed
to broadly represent C acquisition activity, oxidative enzyme activity may
be an important metric in future studies.
Bacteria are of primary importance to freshwater ecosystems, as they
transfer energy and nutrients often contained in organic matter to higher
trophic levels (Azam et al., 1983), yet relatively little research on
microbial ecology has been conducted in Arctic lakes. It is therefore
important to consider microbial responses to factors that will be changing
in the near future (such as active-layer depth and DOM concentrations) in
order to understand ecological effects and directions of future change. We
found that microbes in southwest Greenland lakes are generally P-limited
and that the strength of microbial P limitation decreased by mid-summer.
Further, DOM was very important in determining microbial nutrient demands
and limitation due to its potential as an N source. Lakes within the
permafrost landscape of this study region are likely to experience shifts in
nutrient limitation patterns as aquatic–terrestrial linkages potentially
weaken (due to active-layer increase) and DOM inputs decline. This study
establishes current microbial nutrient limitation patterns that will allow
us to assess response to future changes in this region.