Limnol. Oceanogr., 55(1), 2010, 30–42
2010, by the American Society of Limnology and Oceanography, Inc.
E
Long-term patterns of dissolved organic carbon in lakes across eastern Canada:
Evidence of a pronounced climate effect
Jan Zhang,a Jeff Hudson,a,* Richard Neal,a Jeff Sereda,a Thomas Clair,b Michael Turner,c
Dean Jeffries,d Peter Dillon,e Lewis Molot,f Keith Somers,g and Ray Hessleinc
a Department
of Biology, University of Saskatchewan, Saskatoon, Saskatchewan, Canada
Science and Technology Br, Environment Canada, Sackville, New Brunswick, Canada
c Environment Canada, National Freshwater Institute, Kenora, Ontario, Canada
d Environment Canada, National Water Research Institute, Burlington, Ontario, Canada
e Environmental and Resource Science, Trent University, Peterborough, Ontario, Canada
f Faculty of Environmental Studies, York University, Toronto, Ontario, Canada
g Ontario Ministry of the Environment, Dorset Environmental Science Centre, Ontario, Canada
b Water
Abstract
We analyzed the 21-yr dynamics of dissolved organic carbon (DOC) in 55 lakes during ice-free periods in five
regions across eastern Canada in relation to total solar radiation (TSR), precipitation, air temperature, sulfate
deposition (SO4), Southern Oscillation Index (SOI), North Atlantic Oscillation, and Pacific Decadal Oscillation
(PDO). A synchronous pattern in DOC was found among lakes within each region; however, a synchronous
pattern in DOC was not found among regions, except for Kejimkujik and Yarmouth. Long-term trends of
increasing or decreasing DOC concentration were not evident except at the Experimental Lakes Area (ELA),
where an increase in DOC correlated with a decrease in summer TSR and an increase in summer precipitation.
Annual mean temperature increased at the Nova Scotia and Turkey Lakes Watershed regions (TLW) over the
study period, but there was no corresponding change in DOC. TSR and precipitation were important explanatory
variables across all regions, except for the TLW. Summer TSR, or annual TSR, had a negative relationship, while
summer precipitation had a positive relationship with the temporal DOC pattern in all regions except TLW. TSR
and precipitation explained 78%, 49%, and 84% of the variation in the long-term DOC patterns at Dorset, ELA,
and Nova Scotia (NS) regions, respectively. In contrast, the long-term pattern in DOC at TLW was only weakly
related to SOI and PDO.
Dissolved organic carbon (DOC) has multiple effects on
the physical, chemical, and biological processes that occur
in lakes. For example, colored DOC selectively attenuates
solar radiation (Bukaveckas and Robbins-Forbes 2000).
This in turn affects the thermal environment and mixing
depth of water bodies (Snucins and Gunn 2000). DOC
strongly attenuates ultraviolet radiation (UVR; Morris et
al. 1995) and has been shown to reduce harmful UVR
damage to phytoplankton (Moeller 1994) and zooplankton
(Rautio and Korhola 2002; Molot et al. 2004). DOC also
binds many metals and nutrients, thereby influencing their
bioavailability (Perdue 1998). Furthermore, DOC is
composed of many compounds that can be used by
microorganisms for energy (Anesio et al. 2005). Therefore,
changes in DOC concentration can significantly affect
multiple abiotic and biotic processes in lakes.
There are, in turn, many local and regional factors that
affect the concentration and patterns of DOC in lakes.
DOC originates mainly from terrestrial systems and from
in-lake processes (Aitkenhead-Peterson et al. 2003; Lennon
2004). The quality and quantity exported from terrestrial
systems is affected by watershed properties, such as the
proportion of wetlands (Dillon and Molot 1997), vegetation type (Sobek et al. 2007), land use (Findlay et al. 2001),
runoff (Dillon and Molot 2005) and geology, morphology,
and geography of the surrounding area (Moore 1998).
Furthermore, within-lake characteristics also affect DOC.
For example, lake acidity and Fe concentration affect the
rate of DOC photodecomposition (Anesio and Graneli
2003; Molot et al. 2005).
Despite the growing concern over the effect of a
changing climate on ecosystems, the long-term influences
of regional and global factors on lake DOC are poorly
understood. According to a handful of recent studies there
is evidence that regional variables (e.g., precipitation, total
solar radiation, sulfate deposition, and temperature) are
affecting long-term patterns in DOC in water bodies. For
example, models have been developed with climate-related
variables (e.g., temperature and precipitation) and catchment variables to describe the dynamics of DOC in surface
waters (Correll et al. 2001; Futter et al. 2007). Other
climate-related studies have reported a long-term increase
in DOC concentrations in European and North American
surface waters (Evans et al. 2005; Monteith et al. 2007).
Such increases are believed to be caused by a greater
mobilization of DOC from soils to surface waters as
watersheds become less acidic from declining rates of
deposition of atmospheric sulfate. Although it is prudent
that the above studies have considered climate variables in
their analyses, a broader set of regional variables is
necessary to more fully understand the effect of climate
on the long-term dynamics of DOC (Posh et al. 2008).
* Corresponding author: [email protected]
30
DOC long-term patterns in Canadian lakes
31
Hudson et al. (2003) included both regional and globalscale variables to examine the long-term pattern in DOC in
eight lakes at Dorset (Ontario, Canada). Of the large
number of regional and global variables considered, only
photosynthetically active radiation (PAR) and precipitation were found to be strongly related to the long-term
pattern of DOC in their lakes. Although short-term studies
(e.g., days) had previously documented that solar radiation
(both UV and visible) degraded DOC (e.g., Gennings et al.
2001; Molot et al. 2005), this was the first study to provide
evidence that a negative long-term effect was present.
However, the generality of their result to other regions
beyond the Dorset lakes is not clear and further testing was
thought to be warranted.
Our present study looks at the generality of the Hudson
et al. (2003) results by examining the long-term pattern of
DOC at additional regions across eastern Canada. This
analysis also includes a larger set of regional and global
independent variables. In addition, we use a more objective
analysis for variable selection (Akaike’s Information
Criteria) in combination with multiple linear regression
analysis (MLR).
Study regions
The study lakes were initially grouped into five regions
across eastern Canada (Fig. 1). Three of the five regions
(Dorset, the Experimental Lakes Area [ELA] and Turkey
Lakes Watershed [TLW]) are in Ontario, while the other
two regions (Kejimkujik and Yarmouth) are in Nova
Scotia. The two Nova Scotia regions were later combined
into one region, because the patterns in DOC and climate
variables were found to be synchronous. Only lakes in
which ice-free whole-lake DOC concentrations were
measured for $ 20 yr were used. A total of 55 lakes were
selected from the five regions across eastern Canada.
Dorset—The Dorset study region is located in the boreal
ecozone near the southern boundary of the Precambrian
Shield, ,150 km northeast of Toronto. The eight lakes
within this region were in the Muskoka District and
Haliburton County in south-central Ontario. The catchments were primarily forested and underlain by Precambrian metamorphic plutonic and volcanic silicate bedrock.
Some cottage development is found in the catchments
(Dillon and Evans 2001). The eight lakes are oligotrophic
with low DOC concentrations (from 1.8 mg L21 to
5.1 mg L21). The surface area (Ao) of the lakes ranges
from 0.21 km2 to 0.94 km2 (Fig. 2), and the maximum
depth (Zmax) ranges from 5.8 m to 38.0 m. The mean
surface area to mean maximum depth ratio (Ao : Zmax) is
0.024.
Experimental Lakes Area (ELA)—The ELA region is
located within the Precambrian Shield in the northwestern
region of Ontario. The region is remote and removed from
most regional anthropogenic disturbances. Most of the
watersheds are uninhabited, with a few subjected to
seasonal recreational uses. These watersheds are typically
characterized by thin, poorly developed soils, and a
Fig. 1. Location of the five study sites across eastern
Canada. Note that Yarmouth and Kejimkujik study sites are
situated near each other in Nova Scotia.
dominant vegetation cover of black spruce (Picea mariana)
and jack pine (Pinus banksiana) forests (Brunskill and
Schindler 1971), which were partly subjected to forest fires
in 1974 and 1980 (Schindler et al. 1997). Half of the
catchment area of one of the study lakes (Lake 239) was
subjected to forest fires. Four reference lakes (i.e., those
that had not been manipulated) were available for analysis.
The surface area (Ao) of the lakes ranges from 0.26 km2 to
0.54 km2 (Fig. 2), and the mean maximum depth (Zmax)
ranges from 13 m to 30 m. The mean surface area to
maximum depth ratio (Ao : Zmax) is 0.017. DOC concentrations of the four lakes ranged from 3.0 mg L21 to
6.7 mg L21 (Fig. 2).
Turkey Lakes Watershed (TLW)—The TLW is located
in the Precambrian Shield in the Algoma District of central
Ontario, ,60 km north of Sault Ste. Marie. The TLW
region is a completely forested basin, 10.50 km2 in area,
containing a chain of five lakes. The watershed is
completely underlain by sparingly soluble silicate bedrock
(greenstones and granites) and is overlain by thin and
discontinuous glacial till. The surface area (Ao) of the lakes
ranges from 0.06 km2 to 0.52 km2 (Fig. 2), and the
maximum depth (Zmax) ranges from 4.5 m to 37.0 m.
The mean surface area to mean maximum depth ratio
(A : Zmax) is 0.013. DOC concentration in the five lakes
ranged from 3.6 mg L21 to 4.8 mg L21 (Fig. 2).
Kejimkujik and Yarmouth (NS)—The study lakes in
southwestern Nova Scotia are located in the Kejimkujik
and Yarmouth areas. Kejimkujik National Park is located
on the Southern Upland of Nova Scotia, which is an area
underlain by slates and granite. Much of the area is covered
32
Zhang et al.
with fens and bogs. Forests in the watersheds consist of
mixed coniferous and deciduous trees (Clair and Sayer
1997). Yarmouth is located on the Gulf of Maine in
southwestern Nova Scotia. Both Kejimkujik and Yarmouth areas are undeveloped. We used data from 27 lakes
in Kejimkujik and 11 lakes in the Yarmouth area. The
surface area (Ao) of the lakes ranges from 0.04 km2 to
6.85 km2 in Kejimkujik and 0.15 km2 to 1.00 km2 in
Yarmouth, and the maximum depth (Zmax) ranges from
0.7 m to 19.2 m in Kejimkujik and 1.5 m to 12.4 m in
Yarmouth, respectively (Fig. 2). The mean surface area to
mean maximum depth ratio (Ao : Zmax) is 0.22 in
Kejimkujik, and 0.16 in Yarmouth, respectively, indicating
that these lakes are shallower than lakes from the other
regions. DOC concentrations ranged from 2.1 mg L21 to
15.8 mg L21 at Kejmkujik and from 2.7 mg L21 to
16.2 mg L21 at Yarmouth (Fig. 2).
Methods
Fig. 2. Summary of the median chemical (DOC and pH) and
physical (lake area, maximum depth, and mean lake depth)
characteristics of the lakes at each of the five study sites (Dorset,
ELA, TLW, Kejimkujik, and Yarmouth). Each box-plot contains
the median, first and third quartile, the minimum, and
maximum value.
Variables—Whole-lake ice-free DOC concentrations
(mg L21) were measured on 5–24 occasions/yr from May
to October at Dorset, ELA, and TLW. DOC concentrations from Kejimkujik and Yarmouth consisted of one
measurement at spring and one at autumn turnover each
year. In cases where DOC concentration was measured
separately for different thermal layers, whole-lake DOC
concentration was calculated by adding up the total mass
of DOC in each layer and dividing by lake volume. The
methods of measuring DOC concentrations are described
by Hudson et al. (2003) for Dorset; by Stainton et al. (1977)
for ELA; by Clair et al. (2008) for Kejimkujik and
Yarmouth, and by American Public Health Association
(2005; Technique 5310C and D) for TLW.
Seven explanatory variables were used to predict DOC.
Four were regional variables that included monthly total
precipitation (PPTN, mm), daily mean air temperature (T,
uC), daily total solar radiation (TSR, kJ m22), and monthly
total sulfate deposition (SO4, mmol m22). They were
measured at meteorological field stations in each region.
However, at Kejimkujik and Yarmouth, TSR was obtained
from a nearby station in Kentville, Nova Scotia; and at
ELA, TSR was not available and was calculated from PAR
by dividing PAR by 0.457 (Rao 1984). In addition, PAR
was only available from May to October of each year at
ELA. The remaining three variables were global in scale
and included the Southern Oscillation Index (SOI), North
Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO). Standardized data for these variables were
obtained from the National Oceanic and Atmospheric
Administration (http://www.cpc.ncep.noaa.gov/data/, and
http://www.arctic.noaa.gov/data.html). Each explanatory
variable was analyzed in relation to whole-lake ice-free
DOC over various temporal periods: 1 month, 2 months, 4
months, 6 months, and annually within the year of DOC
measurement and also from the previous year for a total of
83 different time periods for each explanatory variable.
Synchronous patterns among lakes and regions—The
long-term patterns of DOC in the study lakes of a region
DOC long-term patterns in Canadian lakes
33
Table 1. Summary of mean ice-free (May to Oct) dissolved organic carbon (DOC), mean daily total solar radiation (TSR), mean
total precipitation (PPTN), daily mean air temperature (T), and SO4 for all regions during the study period. The coefficients of variation
(C.V.%) are given in parentheses. Kejimkujik and Yarmouth regions were later combined into region NS. SO4 deposition was not
available for the Yarmouth region. All locations are in eastern Canada. Data are for the period 1982–2002, except for Dorset where they
are for the period 1978–1998.
Variable
L21)
Mean ice-free DOC (mg
TSR (May–Oct; kJ m22 d21)
PPTN (May–Oct total, mm)
PPTN (annual total, mm)
T (daily mean, uC)
SO4 (annual mean total, mmol m22)
Dorset
ELA
TLW
Kejimkujik
Yarmouth
NS
3.4(6.5)
538(5.0)
522(17.0)
1004(10.4)
4.7(17.6)
28.0(23.6)
5.0(7.1)
533(7.5)
516(21.8)
704(20.4)
2.9(38.5)
8.4(23.5)
4.2(10.6)
525(4.1)
671(15.8)
1224(11.5)
4.5(27.6)
26.9(22.2)
7.3(19.8)
493(6.4)
571(17.3)
1332(11.3)
6.8(13.2)
14.8(20.1)
5.7(16.5)
493(6.4)
555(20.6)
1251(11.7)
7.1(8.1)
—
6.6(17.9)
493(6.4)
563(18.2)
1292(10.6)
6.9(10.3)
14.8(20.1)
were analyzed for temporal coherence, which was estimated
by using Pearson’s correlation coefficient (Brien et al.
1984). If the temporal patterns were synchronous for the
study lakes at a region, then a mean DOC pattern was used
to represent the temporal variation. Then we analyzed these
mean patterns to determine if they were temporally
coherent across study regions. This second analysis was
also repeated to determine if the regional variables (e.g.,
climate variables) were also synchronous across study
regions. This latter analysis was conducted to determine if
some regions could be combined into one region.
The relationship between DOC and regional variables—
The relationships between the long-term pattern in DOC
and the regional and global variables were examined using
MLR analysis. Akaike’s Information Criterion (AIC) was
used to select the best model, because it is considered
superior to traditional methods of model selection,
particularly when many independent variables are being
considered (Anderson et al. 2000). Because our sample size
(i.e., number of years in our time series) was relatively small
in relation to the number of explanatory variables and time
periods being considered, we calculated AICc, which is the
second-order AIC that accounts for additional parameters,
particularly when the ratio of sample size (n) to explanatory
variables (k) is small, (i.e., n : k , ,40; Anderson et al.
2000; Burnham and Anderson 2002). The number of
models to be evaluated for each site is astronomic (seven
variables in combination with 83 time periods, or 783) and
so for practical reasons we reduced the number of
variables, mainly time periods, in the following way. First,
we analyzed each of the seven variables independently for
all 83 time periods and determined their correlation with
DOC for each site. We then selected between 5 and 15 time
periods for each variable for each site that had the highest
correlations with DOC for further analysis. Then we
applied MLR for different combinations of time periods
and variables for each site and used AIC to progressively
reduce the number of variables, and selected the top five
models with the lowest AIC values for each region for
further analysis (Burnham and Anderson 2002). The
relative goodness-of-fit of these models for each region
was determined by calculating their Akaike Weight (wi)
and evidence ratio to determine if a single model was
considerably better than the others for a region, or whether
alternative models (competing models) were possible
(Anderson et al. 2000). Only these ‘best’ models and
competing models (if present) are described in this paper.
Results
Regional characteristics—The eight Dorset lakes had the
lowest average mean concentration of ice-free DOC
(3.4 mg L21) and coefficient of variation (C.V. 5 6.5%)
of all regions (Table 1). The average annual mean
concentration of DOC in the four lakes at ELA and the
five lakes at TLW were 5.0 mg L21 (C.V. 5 7.1%) and
4.2 mg L21 (C.V. 5 10.6%), respectively. Lakes at the
Kejimkujik (27 lakes) and Yarmouth regions (11 lakes)
had greater average annual mean DOC concentrations
(7.3 mg L21 and 5.7 mg L21, respectively), and greater
variability in DOC (C.V. 5 19.8% and 16.5%, respectively)
than the other regions (Table 1). The lakes at Kejimkujik
and Yarmouth regions were the most acidic, and those at
ELA the least acidic (Fig. 2).
During the study period, ELA received the least amount
of precipitation. Conversely, Kejimkujik and Yarmouth
received the most precipitation and the least amount of
solar radiation (Table 1). SO4 deposition was greatest at
the Dorset and TLW regions (28 mmol m22 yr21 and
27 mmol m22 yr21, respectively). ELA was the coolest
region with the greatest period of ice-cover, while NS
(Kejimkujik and Yarmouth) was the warmest region with
the shortest period of ice-cover (Table 1).
Synchronicity of patterns in DOC and regional variables—
With one exception at Kejimkujik, the DOC patterns of all
lakes in each region were temporally coherent (p , 0.05
[Fig. 3; Table 2]). Therefore, the lakes of each region
(Fig. 3A) were merged into a single average DOC pattern
(Fig. 3B). However, one lake (Upper Silver) at the Kejimkujik region was not temporally coherent with the other
lakes and was excluded from further analysis.
The temporal patterns in DOC and regional variables
(except for temperature) across regions were not temporally
coherent, except for those between Kejimkujik and Yarmouth regions (Table 3). TSR data for Kejimkujik and
Yarmouth regions was obtained from the same weather
station and, therefore, temporal coherence in TSR between
these two regions could not be analyzed. These two nearby
34
Zhang et al.
Fig. 3. (A) Long-term patterns in DOC of all lakes within a region were temporally coherent (Pearson’s correlation coefficient r, p #
0.05), except Upper Silver Lake (Kejimkujik), which was removed from further analyses. (B) Therefore, the lakes at each region could be
combined and represented by a single average DOC pattern. DOC patterns between Kejimkujik and Yarmouth were temporally coherent
(r 5 0.90); therefore, these two regions were combined into one region (NS region). However, DOC patterns across the remaining regions
were not temporally coherent, and could not be combined. Therefore, each region was represented by its own DOC pattern. DOC
concentrations were Z-scored to correct for lake-specific differences in DOC concentrations (i.e., mean annual ice-free DOC
concentrations for each lake were standardized to Z-scores by subtracting the 21-yr mean and dividing by the SD).
DOC long-term patterns in Canadian lakes
Table 2. Average and range of Pearson correlation coefficients for the temporal coherence analyses of the long-term
pattern in DOC for the period 1982–2002, except for Dorset which
is for the period 1978–1998. Temporal variation in the DOC
pattern of each lake was highly correlated to the average DOC
pattern in each region. Upper Silver lake was not temporally
coherent (r 5 0.37) with the other 26 lakes from Kejimkujik and
was excluded from further analysis. All sites are in eastern
Canada.
Pearson correlation (r)
Area
No. of
lakes
Mean
Range
Dorset
ELA
TLW
Kejimkujik
Yarmouth
8
4
5
26
11
0.78
0.77
0.85
0.79
0.77
0.60–0.96
0.55–0.89
0.75–0.94
0.52–0.96
0.49–0.92
regions were combined into one and referred to as the NS
region (Fig. 3). This resulted in the five study regions being
reduced to four study regions: Dorset, ELA, TLW and NS.
The average DOC patterns (i.e., one per region) were used
in subsequent analyses.
Trends in DOC and regional variables—Interestingly, all
regions appeared to have a cyclic pattern to their DOC
(Fig. 3B), where study periods started with elevated DOC
concentration, which then declined, and then increased,
and then declined again near the end of the study period,
except for ELA, where DOC concentration still appeared
to be increasing at the end of the study period. These
patterns were not completely synchronous across regions
(i.e., the Dorset lake’s pattern appeared more advanced in
time [Fig. 3B]). As a result, only ELA demonstrated an
increase in its average ice-free DOC pattern (p 5 0.015)
over the study period (Fig. 4). Concomitantly at ELA,
precipitation also increased (p 5 0.012), while TSR
decreased (p 5 0.001). Temperature increased at TLW (p
5 0.015) and NS (p 5 0.04) regions (Fig. 4), but a
concurrent trend in DOC, precipitation, and TSR was not
evident at either region during the study period. SO4
deposition declined at all regions (p , 0.0003). SO4
declined more rapidly at Dorset (r2 5 0.85, slope 5
22.1 mmol m22 y21, p , 0.0001) and TLW (r2 5 0.6,
slope 5 21.5 mmol m22 y21, p , 0.0001).
Single region models—The relationship between the
average yearly ice-free DOC pattern (Fig. 3B) and the
independent regional and global variables (i.e., TSR,
PPTN, T, SO4, SOI, PDO and NAO) was analyzed with
MLR and AIC. The best model(s) for each region is listed
in Table 4.
Dorset model—A single model was selected for Dorset
(Table 4), which contained three variables that explained
78% of the variation in the long-term pattern of DOC.
DOC was negatively correlated with summer total solar
radiation (TSR, Jun to Aug), and positively correlated with
summer precipitation (PPTN, May to Oct) and winter TSR
35
Table 3. Results of temporal coherence analyses on the longterm pattern of variables among regions for the period 1982–1998:
DOC (ice-free), total solar radiation (TSR, May–Oct),
precipitation (PPTN, annual), and temperature (T, annual). NS
represents the combined regions of Kejimkujik and Yarmouth. All
sites are in eastern Canada.
Study
area
DOC
ELA
TLW
Yarmouth
Kejimkujik
NS
Pearson correlation coefficient (r)
Dorset
0.23
20.08
20.13
20.23
20.21
ELA
TLW
Yarmouth
1
20.02
0.33
0.27
0.30
—
1
0.11
0.32
0.26
—
—
—
0.90**
—
TSR
ELA
TLW
NS
0.05
0.26
0.21
1
0.22
20.36*
1
20.33
—
—
—
Precipitation
ELA
TLW
Yarmouth
Kejimkujik
NS
0.22
0.35*
0.16
0.27
0.24
1
0.24
0.04
0.03
0.04
1
20.01
0.28
0.15
—
—
—
0.74**
—
Temperature
ELA
TLW
Yarmouth
Kejimkujik
NS
0.50**
0.42**
0.74**
0.66**
0.71**
1
0.54**
0.55**
0.55**
0.56**
1
0.59**
0.68**
0.66**
—
—
—
0.91**
—
* Correlation is significant at the 0.05 level; ** Correlation is significant
at the 0.01 level
(previous Dec to current Feb [e.g., Dec 2001 to Feb 2002];
Table 4; Fig. 5). Both temperature (T) and sulfate deposition (SO4) were selected as minor variables in some of the
top five models for this region, but not in the best model.
Experimental Lakes Area (ELA) model—Two possible
models were selected for ELA (Table 4). The best model
had two variables (summer precipitation [PPTN] from Jul
to Aug and total solar radiation [TSR] from May to Oct)
that explained 49% of the variation in the long-term
pattern of DOC. The alternative competing model only
used precipitation (Jul to Aug), which explained 41% of the
variation in DOC. DOC was negatively correlated with
summer TSR (from May to Oct) and positively correlated
with precipitation (from Jul to Aug [Table 4; Fig. 6]).
Interestingly, precipitation during the ice-free period (May
to Oct) was positively correlated to DOC at ELA as well,
but the best model showed that DOC concentration was
more sensitive to the changes in precipitation during the
period of July to August. Temperature was also selected in
some of the top models for this region, but not in the best
model or the competing model.
Nova Scotia model—Two possible models were also
selected for this region (Table 4). The best model contained
36
Zhang et al.
two variables, TSR (annual mean) and precipitation
(PPTN, May to Oct), which explained 84% of the variation
in the long-term pattern in DOC. The alternative model
added a third variable, the mean temperature (T) from
February to May, which only increased the explained
variation from 84% to 85% (Table 4). DOC was negatively
correlated with TSR and positively correlated with summer
precipitation (Table 4; Fig. 7). PDO and SOI were also
selected in some of the top five models for this region, but
not in the best model or the competing model.
TLW model—Unlike the previous three sites, TSR and
precipitation were not selected in the TLW model. Instead,
the mean SOI from the previous year (1-yr lag, Jan to Oct)
and the mean PDO of the same year (Sep to Oct) were
selected: these two explanatory variables explained 39% of
the variation in the long-term pattern in DOC (20% by SOI
and 19% by PDO). Both variables were negatively
correlated with DOC at TLW (Table 4; Fig. 8).
Discussion
Lake characteristics—Mean annual ice-free DOC concentration was greatest at the Nova Scotia region (Fig. 2;
Table 1). This may reflect the differences in climate and
catchments between the Nova Scotia and the Ontario
regions. For example, DOC concentration in lakes is
directly related to the proportion of wetlands in a
catchment (Dillon and Molot 1997), and the Nova Scotia
study lakes do contain a greater proportion of wetlands in
their catchments than the other study regions (Clair et al.
1994).
Fig. 4. Significant trends (p , 0.05) between DOC and
regional variables over the study period. At ELA the (A) DOC
concentration and (B) total precipitation (May to Oct) have
increased. However, (C) total solar radiation (May to Oct) at ELA
has decreased. Annual mean air temperature (D and E) has
increased at NS and TLW regions.
Synchronicity of patterns in DOC and in the independent
variables—A synchronous DOC pattern was found within
the lakes of each of the five regions over the 21-yr period
(Fig. 3B), indicating that an average DOC pattern could be
used to represent the temporal variation of DOC for all
study lakes in each region. However, in the large-scale
inter-region comparison, only two of the five regions were
synchronous in DOC: Kejimkujik and Yarmouth (Fig. 3B;
Table 3). Regional climate variables were also synchronous
between Kejimkujik and Yarmouth (Table 3). Because the
two regions are only about 80 km apart from each other
(Fig. 1), this might indicate that the temporal variation in
DOC responded to a set of common variables within the
overall area. In contrast, the temporal variations in DOC
were not correlated between Dorset, ELA, TLW, and NS,
nor were their regional variables, except for temperature.
This lack of correlation probably reflects the large distance
between these regions, where regional climates (e.g.,
precipitation) range considerably. This outcome further
supports the emerging understanding that regional variables, particularly precipitation (Pace and Cole 2002) and
TSR, are strong drivers of DOC patterns in northeast
North American lakes (Hudson et al. 2003).
Trends in DOC—Additional years of data are required
to determine if the cyclic pattern observed in Fig. 3B for the
four regions will continue beyond the years studied. For
DOC long-term patterns in Canadian lakes
37
Table 4. Explanatory variables (regional and global) that best describe the variation in the long-term pattern in ice-free DOC in each
region. Models were developed from multiple linear regression (MLR) analyses and Akaike’s Information Criterion (AIC). Only the best
models (evidence ratio [ER] 5 1) and competing models (2.7 . ER .1) are presented. The Akaike weight (wi) indicates which model is
the best of the top five models for each region (total weight of five models 5 1). Variation in the DOC pattern at Dorset, ELA, and NS
regions was best described by only two regional variables: total solar radiation (TSR) and precipitation (PPTN). Little of the variation at
the TLW region could be accounted for by regional or global variables. MLR and AIC analyses are for the period 1982–2002, except for
Dorset which is for the period 1978–1998.
Region
Model
Explanatory variable
Coefficient
Variance
explained (%)
r2
wi
ER
Dorset
1
ELA
1
NS
2
1
TSR (previous Dec–Feb)
PPTN (May–Oct)
TSR (Jun–Aug)
PPTN (Jul–Aug)
TSR (May–Oct)
PPTN (Jul–Aug)
PPTN (May–Oct)
TSR (annual mean)
PPTN (May–Oct)
TSR (annual mean)
T (Feb–May)
SOI (previous Jan–Oct)
PDO (Sept–Oct)
0.60
0.51
20.41
0.54
20.30
0.64
0.79
20.64
0.75
20.57
0.12
20.49
20.47
31
26
21
30
19
41
47
37
46
32
7
20
19
0.78***
—
—
0.49**
—
0.41**
0.84***
—
0.85***
—
—
0.39*
—
0.63
—
—
0.39
—
0.28
0.48
—
0.25
—
—
0.39
—
1
—
—
1
—
1.43
1
—
1.96
—
—
1
—
2
TLW
1
* Correlation is significant at p,0.01 level; ** Correlation is significant at p,0.001 level; *** Correlation is significant at p,0.00001 level.
example, the most recent decline in DOC at three of the
four sites may only represent short-term variability and not
a long-term trend. However, if the cyclic pattern continues,
explanations for the DOC patterns in northeast North
America may require further evaluation and more general
causal factors may have to be considered (e.g., long-term
climate factors).
General relationships between DOC, TSR, precipitation,
and other regional variables—The models developed for the
four regions (Table 4) suggest that the Turkey Lakes
system was very different from the other three regions, and
so this region will be discussed separately later. There were
many similarities in the models developed for Dorset, ELA,
and NS. The concentration of DOC was positively
correlated with summer precipitation and negatively
correlated with summer TSR (or annual TSR in NS) in
these regions (Table 4). It should be noted that these
correlations were independent of the concentration and
variability of DOC (e.g., these factors were the lowest at
Dorset and the greatest in Nova Scotia [Table 1]), and were
also independent of the regional variables (e.g., ELA had
the least precipitation, and NS had the greatest precipitation [Table 1]). The results are also consistent with the
long-term trends observed at the ELA region, which was
the only region where DOC showed a significant increase (p
5 0.015) during the study period, with a concomitant
increase in summer precipitation (p 5 0.012), and decrease
in summer TSR (p 5 0.001; Fig. 4). Similar correlations
were found in an earlier period (from 1972 to 1990) under
opposite conditions (Schindler et al. 1996 and 1997); that is,
DOC declined when summer precipitation decreased, and
summer solar radiation increased. This observation,
together with the lack of long-term trends in DOC,
precipitation and TSR at other regions, leads us to
conclude that TSR and precipitation are likely the main
causal factors responsible for the varying DOC concentrations in study lakes of these three regions. Summer
precipitation probably increases DOC by exporting it to
lakes from surrounding watershed (Dillon and Molot 1997;
Correll et al. 2001) and that summer TSR decreases DOC
by photochemical processes (Köhler et al. 2002; Molot et
al. 2005).
The negative correlation between DOC and TSR has
been found in many short-term studies. Through photochemical processes, solar radiation, particularly UV, has
been shown to cause reductions in DOC of ,20–60% over
the course of days (11–70 d [Köhler et al. 2002; Molot et al.
2005]). Hudson et al. (2003) compared the long-term DOC
pattern with regional and global-scale variables and
determined that TSR had a negative correlation with the
long-term DOC pattern in eight lakes in Dorset, central
Ontario. Our results from multiple regions add further
evidence to support and extend this negative relationship
between TSR and DOC.
Precipitation was another important variable explaining
changes in the long-term DOC pattern and is a complex
variable that influences DOC through indirect mechanisms
(Hudson et al. 2003). An increase in regional precipitation
is expected to cause a greater loss of organic carbon from
terrestrial systems (Clair et al. 1994) and a reduction in
precipitation is, thus, expected to cause a decline in the
export of DOC from terrestrial to aquatic systems
(Schindler et al. 1997) because DOC mass export and
runoff are tightly coupled (Dillon and Molot 2005).
Increases in regional precipitation would, therefore, be
expected to increase the export of DOC; however, an
increase in precipitation may not always correspond with
an increase in the concentration of DOC in lakes, in fact,
heavy precipitation events may actually dilute lake DOC
concentrations, particularly if such events occur when
DOC is not readily available from terrestrial or wetland
38
Zhang et al.
Fig. 5. The three regional variables that best describe the
variation in the long-term pattern of DOC at Dorset. (A) Mean
summer daily total solar radiation (TSR, Jun to Aug) was
negatively correlated with the long-term pattern in DOC. (B)
Mean winter daily total solar radiation (TSR, previous Dec to
Feb) was positively correlated with the long-term pattern in DOC.
habitats (e.g., in early spring when soils or peat are still
frozen; Hudson et al. 2003). However, the strong positive
relationships between the long-term pattern in DOC
concentration and precipitation (Table 4) occur during
the ice-free period (e.g., May to Oct), when DOC may be
more readily available. Rapid increases in DOC concentrations in streams and lakes of forested catchments have
been associated with summer precipitation elsewhere (e.g.,
Hinton et al. 1997; Köhler et al. 2008), particularly after the
upper layers of peat in a watershed have been oxidized to
produce labile DOC (Dillon and Molot 1997; Freeman et
al. 2001; Eimers et al. 2008).
Winter TSR was a strong positive predictor variable at
Dorset (Table 4). The positive relationship between DOC
and winter TSR at Dorset is difficult to explain. Hudson et
al. (2003) addressed the negative correlation of DOC to
winter precipitation (also see Köhler et al. 2008). A strong
inverse relationship (p 5 0.008) between winter TSR and
winter precipitation is present at the Dorset region. Winters
with greater precipitation and less TSR may result in the
accumulation of more snow on the landscape, which melts
in the spring. The excess snow then increases spring
flooding and, thus, increases DOC exports. Although
spring flooding exports large loads of DOC, it also results
in the dilution of DOC concentrations in the tributaries
that are entering the Dorset lakes (see fig. 5 in Hudson et
al. [2003]) and, thus, dilutes lake DOC concentrations.
Temperature was synchronous across all regions, but
DOC was not (Table 3; Fig. 3B). This demonstrates that
the effect of temperature on the long-term pattern in DOC
was weak compared to TSR and PPTN; otherwise, we
would expect DOC to be synchronous across regions where
temperature is synchronous. Hongve et al. (2004) also
found that variation in DOC was not correlated with
temperature. However, other studies have found a correlation between temperature and DOC. For example, rising
temperatures associated with drought conditions resulted
in reduced DOC loading to lakes (Dillon and Molot 1997).
In contrast, other studies have reported an increase in DOC
export from greater decomposition rates occurring at
higher temperatures (Freeman et al. 2001). However, these
studies did not include solar radiation in their analyses, and
temperature may become a less significant correlate when
solar radiation is considered.
Besides precipitation, TSR, and temperature, other
regional and global variables were not correlated or were
only weakly correlated with the long-term DOC patterns.
SO4 deposition was not selected in any of the best or
competing models in these three regions despite reductions
in the rate of SO4 deposition at all sites, nor did we observe
an increase in DOC over our study period (accept for ELA)
as documented elsewhere for European and North American surface waters (Evans et al. 2005; Monteith et al.
2007). Such increases in DOC in North America may be a
consequence of a decline in acid-enhanced photo-oxidation
r
(C) Total summer precipitation (PPTN, May to Oct) was
positively correlated with the long-term pattern in DOC.
DOC long-term patterns in Canadian lakes
Fig. 6. The two regional variables that best describe the
variation in the long-term pattern of DOC at ELA. (A) Mean
summer daily total solar radiation (TSR, May to Oct) was
negatively correlated with DOC pattern, and (B) total summer
precipitation (PPTN, Jul to Aug) was positively correlated with
the long-term pattern in DOC.
rates occurring via the photo-Fenton pathway (Gennings et
al. 2001; Kohler et al. 2002; Molot et al. 2005), or from
increased mobilization of DOC from watershed soils that are
becoming less acidic from reductions in SO4 deposition
(Evans et al. 2005; Monteith et al. 2007), or a combination of
both. Although we did not observe a strong relationship
between SO4 deposition and DOC, TSR explained more of
the variation in DOC in lakes of lower pH (e.g., in NS with a
mean pH 5.5, TSR explained 38% of variation in DOC; in
ELA with a mean potential hydrogen [pH] 7.0, TSR
explained 19% [Fig. 2; Table 4]). Therefore, although not
seen in our analyses, a reverse acidification process may also
be involved in explaining our long-term patterns in DOC.
If a reverse acidification effect is present, why did our
analyses not detect it? Most importantly, DOC concentra-
39
Fig. 7. The two regional variables that best describe the
variation in the long-term pattern of DOC at NS. (A) Annual
mean daily total solar radiation (TSR) was negatively correlated
with DOC pattern, and (B) total summer precipitation (PPTN,
May to Oct) was positively correlated with the long-term pattern
in DOC.
tions were not increasing in our lakes (except for ELA).
Additional studies from Dorset and Kejimkujik have also
not found evidence for an increase in DOC concentrations
in our study lakes (Clair et al. 2008; Keller et al. 2008).
However, our analyses may not have captured the full
effect of a decline in SO4 deposition on lake DOC patterns
because such changes may take decades to materialize in
lakes (Dillon et al. 1996). Nonetheless, we concur with
Roulet and Moore (2006) that a more complex set of
variables, in addition to sulfate deposition, may be
affecting the long-term patterns in DOC in different
regions. For example, a series of recent studies (including
this one) provide alternative explanations or models that
40
Zhang et al.
Fig. 8. The two regional variables that best describe the
variation in the long-term pattern of DOC at TLW. (A) Mean SOI
(previous Jan to Oct), and (B) PDO (Sep to Oct) were negatively
correlated with the long-term pattern of DOC at TLW.
describe long-term patterns in DOC (Eimers et al. 2008;
Keller et al. 2008; Clair et al. 2008; Hruška et al. 2009).
Our results and those of others (listed immediately
above) indicate that long-term changes in DOC may be
related to a complex set of regional variables. For example,
are increases in DOC a result of a reduction in sulfate
deposition, or a reduction in solar radiation, or an increase
in precipitation, or a combination of all three in addition to
other regional variables? The interplay between regional
variables and lake acidity becomes even more complex
when we consider climate warming. Temperate lakes and
streams are experiencing longer ice-free winters. Longer icefree periods will expose lake DOC to greater amounts of
solar radiation, and possibly result in greater photochemical losses of DOC.
Exception of TLW model—The model for TLW
was different from those of the other three regions. It
did not contain any current year variables. The five
study lakes at TLW are all connected hydrologically in a
series and, therefore, they are not fully independent of
each other and, therefore, the temporal coherence
found among these lakes may be a result of local factors
instead of regional factors. In addition, the water residence
times are very short (x̄ 5 0.6 yr) in the TLW lakes; DOC
may not reside for a sufficient amount of time in these lakes
to be strongly affected by TSR and other internal
processes.
Although SOI was in the best model for the TLW region,
these global-scale indices were not strongly correlated with
any of the long-term DOC patterns. These global indices
describe worldwide weather patterns and may not always
be good indicators of regional climate (Stenseth at el. 2003).
For example, we found considerable differences in the
climate patterns at each region (except temperature) in a
relatively small area of the globe (eastern Canada). As
found elsewhere (Hudson et al. 2003; Stenseth at el. 2003),
long-term DOC patterns are better correlated to the climate
of a region (i.e., precipitation).
We have highlighted the potential importance of
regional factors on the long-term DOC patterns in lakes
across eastern Canada. Future research may consider the
factors that make some lake regions more sensitive to
regional factors than others. For example, why was the
variation in DOC at region NS better explained by regional
factors than at ELA? We suspect that the long ice-free
period and shallow lake morphometry (i.e., x̄ Ao : Zmax 5
0.22 for Kejimkujik and 0.16 for Yarmouth; Fig. 2) may
render these NS lakes more sensitive to regional factors.
We could not address this question quantitatively with our
small sample size (i.e., four regions with four climate
regimes), but such an analysis would be possible with a
larger number of regions (e.g., n . 10) that included a
larger gradient in regional variables. In addition, such
studies would be more complete if they also included local
factors (i.e., catchment properties) in their analyses because
local factors often modify a lake’s response to regional
factors. We also encourage further monitoring of lake
water chemistry to identify any patterns (e.g., cycles) that
may extend beyond the 21-yr data set presented here.
Finally, it may now be prudent to test the hypotheses
generated by correlational studies (as here) with watershed
manipulations to better isolate the factors (local, regional
and global) that affect long-term lake DOC patterns of
different regions (e.g., differing in climate, soil and
vegetation).
Acknowledgments
We thank S. Kasian, D. Guss, J. Findeis, and I. Dennis for
technical assistance and data management. We also thank two
anonymous reviewers for their helpful comments that improved
the manuscript. We are grateful for long-term data from the
Dorset Environmental Science Centre, the Freshwater Institute, the National Water Research Institute (Ontario), and
Environment Canada (Nova Scotia region). This work was
financially supported by grants to J.J.H. from N.S.E.R.C.
(Canada).
DOC long-term patterns in Canadian lakes
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Received: 24 July 2008
Accepted: 11 August 2009
Amended: 21 July 2009