Inter- and Intra-Annual Chemical Variability During the ice-Free ...

INTER- AND INTRA-ANNUAL CHEMICAL VARIABILITY DURING THE ICE-FREE SEASON IN LAKES WITH DIFFERENT FLUSHING RATES AND ACID DEPOSITION HISTORIES SHELLEY E. ARNOTT1∗ , PETER J. DILLON2 , KEITH SOMERS3 and BILL KELLER4 1 Cooperative Freshwater Ecology Unit, Laurentian University, Ramsey Lake Road, Sudbury, ON, Canada, P3E 2C6, ∗ current address: Biology Department, Queen’s University, Kingston, ON,

Canada, K7L 3N6; 2 Environmental and Resource Studies, Trent University, Peterborough, ON, Canada, K9J 7B8; 3 Ministry of the Environment, Dorset Environmental Science Centre, P.O. Box 39, Dorset, ON,

Canada, P0A 1E0; 4 Ministry of the Environment, Cooperative Freshwater Ecology Unit, Laurentian University,

Ramsey Lake Road, Sudbury, ON, Canada, P3E 2C6 (∗ author for correspondence: [email protected])

Abstract. Quantifying chemical variability in different lake types is important for the assessment of both chemical and biological responses to environmental change. For monitoring programs that emphasize a large number of lakes at the expense of frequent samples, high variability may influence how representative single samples are of the average conditions of individual lakes. Intensive temporal data from long-term research sites provide a unique opportunity to assess chemical variability in lakes with different characteristics. We compared the intra- and inter-annual variability of four acidification related variables (Gran alkalinity, pH, sulphate concentration, and total base cation concentration) in four lakes with different flushing rates and acid deposition histories. Variability was highest in lakes with high flushing rates and was not influenced by historic acid deposition in our study lakes. This has implications for the amount of effort required in monitoring programs. Lakes with high flushing rates will require more frequent sampling intervals than lakes with low flushing rates. Consideration of specific lake types should be included in the design of monitoring programs. Keywords: acid deposition, acidification, flushing rates, long-term data, variability, water chemistry

1. Introduction Extensive long-term and spatial data are often collected in lake ecosystems as a means of detecting environmental change. Two contrasting approaches have been employed in many monitoring programs; 1.temporally intensive sample collection from a small number of lakes and 2. extensive spatial surveys where a small number of samples are taken from many lakes in a region. Because of limited resources there is often a trade-off between the temporal and spatial extent of the monitoring programme. Studies with high temporal resolution are generally restricted to a small number of lakes, whereas large regional surveys are generally Environmental Monitoring and Assessment 88: 21–37, 2003. © 2003 Kluwer Academic Publishers. Printed in the Netherlands.

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SHELLEY E. ARNOTT ET AL.

limited to a small number of samples per year. For example, there are several long-term research sites that have been monitoring six to eight lakes for more than two decades (The Experimental Lakes Area, the North Temperate Lakes – Long-term Ecological Research site, the Dorset Environmental Science Centre, and the Cooperative Freshwater Ecology Unit) and there are several monitoring programs designed to look at regional stressors across many lakes where samples are taken infrequently throughout the year (Keller et al., 2001a, b; Clair et al., 1995; http://www.epa.gov/emap/index.html). Given these temporal and spatial limitations in monitoring programme design, it is important to quantify a) how representative the few temporally intensive sites are of other sites in the region, and b) the temporal variability of sites in the infrequently-sampled spatial survey. In this paper we use long-term data from four intensively studied lakes to assess temporal variability of four acidification-related chemical parameters. Quantifying and understanding factors influencing variability are important to the assessment of resource status. High temporal variability within a given site will mean that single samples may not adequately represent the character of the site and may complicate the detection of environmental stress. In some cases, this variability results from the responsiveness of lakes to environmental drivers, such as climate events. For example, periodic increases in sulphate concentrations in lakes and streams have been associated with El Nino events (Dillon et al., 1997; Dillon and Evans, 2001). Some variability may be associated with seasonal changes in nutrient concentrations and plankton succession during the stratified summer period (Sommer, 1986). Other sources of variability include episodic seasonal events such as spring acidification (Wigington Jr. et al., 1996, Gunn and Keller, 1986). The degree of within-lake chemical variability may differ substantially among lakes. While many lake characteristics may influence chemical variability, here we focus on examining two factors, water residence time and sulphur deposition history. Fast flushing lakes have large watersheds relative to the volume of the lake. This ratio also influences other lake characteristics, such as dissolved organic carbon (DOC) concentration, phosphorus, and lake morphometry, that play important roles in regulating chemical reactions within the lake (Keller et al., 2001a; W. Keller unpublished data). We hypothesized that lakes having short water residence times (and associated physical and chemical characteristics) would have more chemical variation, both inter-and intra-annually. Historic sulphur deposition in lakes and catchments may also influence chemical variability. One source of inter-annual variation may reflect long-term recovery trajectories, which have been documented in many Sudbury lakes (Keller et al., 1992). Another possible source of variation in acidification-related variables is related to the oxidation and release of formerly reduced sulphur from catchments following periods of drought (Yan et al., 1996; Dillon and Evans, 2001). These weather related re-acidification events will certainly depend on the catchment characteristics, i.e., the extent of reducing zones (e.g., wetlands, exposed littoral sedi-

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CHEMICAL VARIABILITY IN LAKES

ments, dried stream beds), but it is less certain how important deposition history is to the phenomenon. In this study, we examined with-year and among-year variability in four chemical parameters; pH, alkalinity, total base cations, and sulfate concentration. We contrasted the magnitude of chemical variability in lakes with fast and slow flushing rates and high and low historic acid deposition. Specifically, our objectives were to (1) quantify inter- and intra-annual variability, (2) determine if inter-annual variability resulted from temporal trends, (3) determine if the intra-annual variability resulted from repeatable seasonal fluctuations, (4) determine if lakes have similar patterns of variability, and (5) calculate the number of samples required to adequately estimate an annual mean.

2. Methods 2.1. S TUDY SITES We analyzed chemical variability in four study lakes that have been monitored for over two decades. Blue Chalk and Heney lakes are located in south-central Ontario, near Dorset, while Swan and Clearwater lakes are located in northeastern Ontario near Sudbury. Some physical and chemical characteristics are given in Table 1. TABLE I Some physical and chemical characteristics of the study lakes from 1982 to 1991. Numbers in parentheses are standard deviations from the mean of annual means. Lake

Latitude Longitude Area (ha) Depth (m) DOC Mean Max. (mg/L)

TP (µg/L)

Flushing Rate (yr)

Blue Chalk Heney Clearwater Swan

45◦ 11 45◦ 08 46◦ 22 46◦ 21

6.0(0.5) 6.6 (0.6) 3.3 (0.7) 7.5 (4.7)

5.8 1.6 3.2 0.6

78◦ 56 79◦ 06 81◦ 03 81◦ 03

52.4 21.4 75.9 5.8

8.5 3.3 8.4 2.8

23.0 5.8 21.5 8.8

1.8 (.05) 2.8 (0.3) 0.5 (0.2) 1.7 (0.7)

The lakes differ in their histories of acid deposition and their hydrologic characteristics. Historically, the Sudbury lakes had high levels of acid deposition due to local metal smelters, and both Clearwater and Swan lakes were probably very acidic (pH ∼ 4) for decades. Since the 1970s, smelter sulphur emissions have been reduced by an order of magnitude (Keller, 1992). As a result, many of the lakes in the area have started to recover chemically, including Clearwater and Swan lakes (LaZerte and Dillon, 1984; Dillon et al., 1986; Keller et al., 1992, 1999). The Dorset lakes are largely beyond the influence of the Sudbury smelters (Scheider et al., 1981; Dillon et al., 1988), although precipitation is acidic in southern Ontario.

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Based on inferences from diatom sedimentation (Hall and Smol, 1996), we know that Heney Lake is more acidic now than it was 100 years ago, although the pH change from 1978-present was not uni-directional (Dillon and Evans, 2001). No significant decline in pH has been observed or inferred in Blue Chalk Lake, but geochemical models (MAGIC) suggest that alkalinity may have declined by 20–40% over the past 50 years (Dillon and Larsen, unpublished studies). The lakes also differ in water replenishment times. Swan and Heney lakes have short replenishment times (flushing times of approximately 0.6 and 1.6 years, respectively; Yan et al., 1996; Dillon and Evans, 2001) whereas Clearwater and Blue Chalk have slower flushing times (approximately 3.2 and 5.8 years, respectively; Bodo and Dillon, 1994). Flushing times are dependent on run-off and therefore affected by climatic variability. However, the relative differences in flushing times among the study lakes remain relatively consistent irrespective of changes in precipitation. 2.2. S AMPLING AND ANALYTICAL TECHNIQUES Samples for chemical analyses were collected six to 16 times per year during the ice-free season. Samples were collected from the mid-point of each 1 m stratum for Swan Lake, and at the mid-point of each 2 m stratum for Clearwater, Heney, and Blue Chalk lakes. For Clearwater, Swan, and Heney lakes, the samples from each stratum were combined in proportion to that stratum’s volume, a sampling strategy that resulted in a single whole-lake volume-weighted sample. For Blue Chalk Lake, volume-weighted samples for each thermal layer (epi-, meta- and hypolimnion) were prepared in an analogous way, and the resulting concentration data combined, to give a volume-weighted value. Analytical methods are described in detail in Ontario Ministry of the Environment (1983). 2.3. DATA ANALYSES Our analyses focussed on four acidification-related variables; pH, sulphate, Gran alkalinity, and total base cation (TBC) concentrations. Based on data availability, we chose the 10-year period from 1982 to 1991 so that all data could be standardized to six evenly-spaced monthly samples per year during the ice-free season, which was the maximum sampling frequency common to all lakes. pH was converted to hydrogen ion concentration for all analyses. Base cation concentration was calculated as the sum of calcium, magnesium, and potassium in equivalence units. Sodium was excluded from TBC because the concentration in some lakes (particularly Clearwater Lake) was influenced by road salt application. Differences in variability were assessed using Levene’s test (Levene, 1960; Schultz, 1985). Variability was a measure of the mean deviation on each sample


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date from the median value. Each variate, X in a set of 1, m samples, was converted to standardized median absolute deviations sMAD = |Xi − M(X)| M(X), where M(X) is the median. We standardized the variation to permit comparisons among lakes and variables. Data were transformed (sqrt(sMAD + 0.001)) to normalize residuals and the distribution of variables. Analysis of variance (ANOVA) was applied to the transformed sMAD to test for differences in variation among lakes. Inter-annual variation was calculated using the standardized absolute deviations of the annual means (based on six samples) from the long-term median. Trends were not statistically removed; therefore, we expected high variation in lakes with directional changes in chemistry. Intra-annual variation was based on the standardized absolute deviation of each of the six monthly samples from the annual mean. Mean absolute deviations calculated for each year were then averaged to calculate mean intra-annual variation. Temporal trends in each variable were assessed using the non-parametric MannKendall test, corrected for auto-correlation. We used annual means for each variable to eliminate variation resulting from seasonality. The Mann-Kendall trend test is commonly used to assess short time series. This statistic is based on rank order of time series and detects monotonic trends. Trends do not have to be linear, but they can only be detected if, overall they proceed in one direction. The test statistic S, is the sum of the difference between all pairs of time series points. The variance of S was estimated as var(S) = n(n − 1)(2n + 5)/18, where n is the number of observations. Because our time series data were autocorrelated, a correction factor was applied to the variance estimate of the test statistic, S because the variance of S is under-estimated when data are positively autocorrelated (Hamed and Rao, 1998). The significance of trends was assessed by comparing the standardized test statistic, Z = S/[var(S)]0.5 with the standard normal variate at P = 0.05. Within-year variability may arise from irregular fluctuations in variables or from repeatable seasonal patterns. To determine if there were similar seasonal patterns in chemistry from year to year (i.e., coherence among years), we calculated the mean Pearson correlation coefficient for all possible combinations of pairs of years for each lake (Magnuson et al., 1990). A significant positive correlation indicates that variables increased and decreased in a synchronous manner from year to year, suggesting a consistent seasonal pattern. A lack of correlation suggests that stochastic processes, such as weather events may drive seasonal variation in variables. We examined regional coherence by comparing the Pearson correlation coefficient between all pairs of lakes for each variable. A positive correlation indicates that lakes were behaving in a similar way over the study period for a given variable. We assessed the implication of seasonal variability on sampling protocols by calculating the number of samples required to estimate a seasonal and annual mean with a 90% certainty. We arbitrarily chose a 90% certainty as a standard and are

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Figure 1. Mean annual values for alkalinity, sulphate concentration, pH, and total base cations (not including Na). Blue Chalk Lake , Clearwater Lake , Heney Lake , and Swan Lake ×.

not implying that this level of certainty should always be achieved. The number of samples required was estimated as CV 2 n = 200 ∗ r where CV is the coefficient of variation and r is the desired relative error (Krebs, 1989). Because alkalinity had both positive and negative values, we added 40 to each measurement so all values were positive.

3. Results The study lakes span a broad range in the four water chemistry variables considered here (Figure 1), but they are typical of many of the thousands of lakes on the Canadian Shield (assessed by the Ontario Ministry of Environment, in McQueen et al., 2001). Calcium concentrations of all four lakes are lower than the mean value (94 to 292 ueq/L vs. 319 ueq/L), but span the median (175 ueq/L) from a regional survey of several thousand lakes on the Canadian Shield. Three of the four lakes have sulphate concentrations higher than the mean (132 to 351 ueq/L vs. 133 ueq/L) and median (136 ueq/L) found in the regional survey. Alkalinity and pH are lower in the study lakes (except pH in Blue Chalk which was 6.7) than both the mean (pH 4.7 to 5.9 vs. 6.7; alkalinity −23 to 85 ueq/L vs. 384 ueq/L) and median


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Figure 2. Standarized median absolute deviation (sMAD) and S.E. among years for each of the four study lakes. The light bars represent the low acid deposition lakes (Dorset lakes), the dark bars represent the high acid deposition lakes (Sudbury lakes). Letters indicate similarities among lakes (Tukey HSD, P < 0.05).

(pH 6.7; alkalinity 138 ueq/L) of the lakes in the regional survey. For Swan and Clearwater lakes, low pH and alkalinity and high sulphate concentrations indicate the presence of strong mineral acidity of anthropogenic origin. There were significant differences in inter-annual variability among the four lakes (Figure 2). More variation occurred in the two fast-flushing lakes. In particular, Swan Lake, a fast-flushing, historically acidified lake had the highest variation among the four lakes in each of the four variables. Variation in both sulphate and TBC was significantly higher in Swan Lake than in the other lakes. Swan also had significantly higher variation in alkalinity than the other lakes with the exception of Heney Lake, which is also fast flushing, but with lower acid deposition (Tukey HSD, P < 0.05). Historical acid deposition did not appear to be an important determinant of within-year or among-year variation. Of the four variables we investigated, alkalinity and H+ concentration were more variable among years than sulphate and TBC concentration in all lakes (Table II). Alkalinity was two to ten times more variable than sulphate and TBC concentration, whereas H+ concentration was up to six times more variable. Within-year variation also tended to be higher for the fast flushing lakes, particularly for alkalinity and H+ concentration (Figure 3). Alkalinity was highly variable within each year in Heney and Swan lakes, having, on average, a 68% sMAD (variation). In contrast, the sMAD value for alkalinity for Blue Chalk and Clearwater lakes was only 6%. For all lakes, average TBC and sulphate variation was lower than 6% and there was little difference among the four lakes.

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TABLE II ANOVA results for comparison of inter- and intra-annual variability among variables. There was no significant difference (P < 0.05) in MADs among variables, except in the cases indicated by >, which indicates that MADs for the variable to the left is significantly greater than MADs for those to the right. For example, for inter-annual variability in Blue Chalk Lake, there were two groups with statistically similar MADs; (1) pH and alkalinity (alk), and (2) alkalinity, sulphate (SO4 ), and total base cations (TBC). Lake

F

P

Tukey HSD

Inter-annual variability Blue Chalk Clearwater Heney Swan

4.77 3.71 8.02 3.50

0.01 0.02 0.00 0.02

Intra-annual variability Blue Chalk Clearwater Heney Swan

25.72 13.78 27.64 12.67

SO4 , TBC pH > Alk, SO4 , TBC Alk, pH > SO4 , TBC Alk > pH > SO4 , TBC Alk, pH > SO4 , TBC

The variation in variables within each year was similar to the variation among years (t-test, P > 0.05). The exceptions were sulphate and TBC in Swan Lake where inter-annual variability exceeded intra-annual variability (Figure 4). This difference was driven by an extreme re-acidification event in 1988 that re-mobilized historically deposited sulphur and increased base cation concentrations (Yan et al., 1996). Despite reductions in sulphur emissions and subsequent improvement in water quality of many Sudbury-area lakes (Keller et al., 1999), trends were only detected for pH, alkalinity and sulphate concentrations in Clearwater Lake and for TBC in Swan Lake. Alkalinity and pH in Clearwater Lake increased through time whereas sulphate decreased (Table III). In the early 1980s, Swan Lake showed signs of improved water quality, but recovery was set back with a dramatic re-acidification event in 1988 (Keller et al., 1992; Yan et al., 1996). Blue Chalk Lake did not exhibit directional trends related to reductions in acid deposition because there was no indication that it had acidified within the past two decades. Previous studies found that Heney Lake water chemistry has responded to decreased sulphate deposition, but reductions in sulphate concentrations in the water column have been interrupted by the effects of El Nino events (Dillon and Evans, 2001).


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Figure 3. Standardized median absolute deviation (sMAD) and S.E. within years for each of the four study lakes. The light bars represent the low acid deposition lakes (Dorset lakes), the dark bars represent the high acid deposition lakes (Sudbury lakes). Letters indicate similarities among lakes (Tukey HSD, P < 0.05).

Figure 4. Inter-and intra-annual standardized median absolute deviation (sMAD) and S.E. for each lake and variable. Inter-annual variation is represented by the light bars and intra-annual variation is represented by the dark bars.

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Figure 5. Annual values for August temperature and oxygen concentration measured 1 m off the bottom at the deepest location of the lake. Blue Chalk Lake , Clearwater Lake , Heney Lake , and Swan Lake ×. Note that oxygen data for Clearwater Lake was unavailable.

None of the variables exhibited similar seasonal patterns during the study period (Table IV). For H+ concentration, the mean correlation coefficient among yearpairs ranged from 0.37 for Blue Chalk to −0.04 for Clearwater Lake, indicating that seasonal patterns were not consistent across years for any of the study lakes. Among-lake correlations were low and not statistically significant for all lake-pairs except three. Sulphate concentrations in Blue Chalk and Clearwater lakes were significantly correlated through time (r = 0.82). Total base cations in Swan Lake were correlated with Blue Chalk (r = 0.66) and Clearwater (r = 0.67) lakes. Our results suggest that lakes may require varying sampling efforts, depending on the variable being measured. For example, we calculated that to achieve a 10% error in estimating the mean H+ concentration for one year, we would need more

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TABLE III Mann-Kendall trend test on mean annual values for each of the acid-related variables. Blue Chalk Lake = BC, Clearwater Lake = CW, Heney Lake = HN, Swan Lake = SW. S is the sum of the difference between all pairs of time series points. Z is the standardized test statistic (see text). Variable

Lake

Alkalinity

BC CW HN SW

PH

S

Z

p-level

19 37 −1 −9

1.41 2.74 −0.07 −0.67

0.08 0.003 0.47 0.25

BC CW HN SW

9 35 3 −3

0.67 2.59 0.22 −0.22

0.25 0.005 0.41 0.41

Sulphate

BC CW HN SW

−19 −25 −21 13

−1.41 −1.85 −1.55 0.96

0.08 0.03 0.06 0.17

Base Cations

BC CW HN SW

11 7 −9 23

0.81 0.52 −0.67 1.70

0.21 0.30 0.25 0.04

samples for the fast flushing lakes (91 samples for Swan Lake, 34 for Heney Lake) than the slow flushing lakes (only four samples for Clearwater and15 samples for Blue Chalk) (Table V). For alkalinity, however, the two Sudbury lakes would require more intensive sampling (99 samples for Swan and 39 for Clearwater) than the two Dorset lakes (two for Blue Chalk and six for Heney). Sulphate concentration and TBC were much less variable in each of the lakes, requiring one to three samples per year to achieve an error less than 10% of the mean. The pattern of required samples would be similar for a multiple year sampling program except that, in general, the number of yearly samples required to achieve a 10% error in the mean was considerably higher for all variables in Swan Lake, including sulphate and TBC.

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TABLE IV Mean Pearson correlation among years for each variable. None of the correlations were statistically significant at P < 0.05. N = 10.

Alkalinity PH SO4 TBC

Blue Chalk

Clearwater

Heney

Swan

0.64 0.37 0.01 0.06

−0.01 −0.04 0.11 −0.02

0.29 0.31 0.00 0.16

0.00 0.06 −0.02 −0.01

TABLE V Calculated number of samples required to attain a mean estimate with a 10% error.

ALK SO4 TBC PH

Intra-annual Sampling BC CW HN

SW

Inter-annual Sampling BC CW HN

SW

2 2 2 15

99 3 2 91

2 1 1 9

48 40 22 322

39 2 1 4

6 2 2 34

43 3 2 30

7 4 2 95

4. Discussion Large temporal variability in acid-related variables has been observed in other regions (e.g., Driscoll and Van Dreason 1993; Psenner, 1988). Hakanson (1992) calculated the relative standard deviation (CV) within years for several chemical variables in 75 Swedish lakes from 1986 to 1989. Variation ranged from 2% for pH to 20–40% for alkalinity. Similarly, seasonal (intra-annual) variation in acid-related variables (pH, alkalinity, sulphate, Ca and Mg) was relatively large at three longterm monitoring sites in Ontario (Clair et al., 1995) and the Adirondacks (Driscoll and Van Dreason, 1993). There are many factors that influence variation in acidification-related water chemistry, including reduced sulphur deposition, declines in atmospheric base cations (Hedin et al., 1994), base cation depletion of catchments (Kirchner and Lydersen, 1995), drought-induced changes in groundwater flow (Webster et al., 1990), and re-mobilization of sulphur from catchments during drought (Dillon and LaZerte, 1992; Yan et al., 1996). In addition, there are a number of within-lake processes that generate changes in alkalinity, which in turn influences other aspects of water chemistry. These include microbial reduction of sulphate and nitrate in the sediments (Schindler, 1986; Cook et al., 1986), base cation exchange (Carignan, 1985)


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and redox reactions in sediments. Nitrification or uptake of amonium can be important consumers of alkalinity in the water column (Schindler et al., 1986). The relative importance of each of these sources of variability is determined by local geology, the extent of lake and watershed acidification (Schindler, 1988), the watershed area to lake volume ratio (Shaffer and Church, 1989), nutrient availablity, and the extent of wetlands, streambeds, and other reducing zones in the catchment (Devito et al., 1999). In our study lakes, variation in acidification-related water chemistry is influenced by reduction of sulphur deposition, factors controlling terrestrial run-off, and drought-induced remobilization of sulphur in the catchment and littoral areas; i.e., processes external to the lakes. Precipitation and temperature influence mineral weathering and cation exchange in the catchment, which determine the amount of ions transported to the lakes in run-off. Internal alkalinity generation, such as the reduction of sulphate by microbial processes (Kelly, 1988) is certainly occurring in the lakes, but for the fast-flushing lakes with relatively large catchments (Heney and Swan lakes) terrestrial processes are probably more influential in determining variation in lake chemistry. As a result, between year and within year variation in precipitation and air temperature are probably the most important determinants of water chemistry variability in our study lakes. This is most evident in both Swan and Heney lakes where sulphate concentrations and pH are strongly influenced by El Nino-induced droughts (Yan et al., 1996; Dillon et al., 1997). Flushing rate determines the response that a particular lake will have to changes in precipitation, and, to a lesser extent, temperature. Lakes with high flushing rates have large watershed area to lake volume ratios and therefore alkalinity generation is dominated by watershed processes (Shaffer and Church, 1989). The amount of precipitation and temperature determines the rate of mineral weathering and cation exchange, which contribute to variability in lake chemistry. In addition, a rapidly flushing lake will respond to changes in precipitation more readily than a slowly flushing lake, which tends to integrate the contributions of chemical species from catchment-based processes over a longer period of time, thereby dampening oscillations in lake chemistry. In addition to these factors, large changes in water chemistry in Swan and Heney lakes also result from the oxidation of reduced sulphur in the littoral sediments and surrounding wetlands during drought periods. This is probably one of the most important contributors to variation in these lakes during our study period. To a lesser extent, internal alkalinity generation probably contributes to chemical variability in our study lakes. The declines in sulphate concentration observed in Clearwater, Swan, and Heney lakes (note that only the Clearwater Lake trend was statistically significant) were most likely the result of reduced sulphur deposition, combined with within-lake alkalinity generation in both the littoral zone and the hypolimnion (Cook et al., 1986). Internal alkalinity generation via sulphate reduction is microbially mediated, requiring anoxic conditions. Therefore, interannual changes in temperature and oxygen concentration can influence alkalinity

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generation. However, this probably is not an important source of chemical variation in our study lakes because there was no statistically significant difference in the inter-annual variation of August bottom water temperatures or oxygen concentration (ANOVA; P = 0.23 for temperature, P = 0.08 for oxygen), although temperature variation in Swan Lake was high (sMAD = 25%) Our findings indicate that lakes with large within year variation also have large variation among years. Although they are acting at different time scales, similar mechanisms may be acting to influence both types of variability. Precipitation and temperature, which vary both seasonally and annually, determine catchment run-off, and therefore the loading of nutrients and other chemical ions into the lake, influencing both external and internal alkalinity generation. These processes influence acid-base chemistry and therefore have the potential to influence interand intra-annual variability. The intra-annual variation that we observed in our study lakes, based on monthly, ice-free season sampling, was probably the result of unpredictable weather events throughout the year. Unlike in other studies (Stoddard and Kellogg, 1993; Driscoll and Van Dreason, 1993), we did not detect repeatable seasonal patterns in any of the chemical variables, suggesting that variability was not the result of spring minima in pH associated with snow-melt, nor was it attributable to predictable seasonal changes in primary production (photosynthesis). This does not suggest that it doesn’t occur. Our samples were collected after ice-out and we therefore, may have missed under-ice spring minima in pH (Gunn and Keller, 1986). Studies conducted in northern Wisconsin have indicated that there is a high amount of synchrony in chemistry among lakes (Magnuson et al., 1990; Baines et al., 2000), likely because of regional climate patterns. Similar climate patterns occur between Dorset and Sudbury (Arnott, unpublished analyses), but for most variables we did not observe significant correlation in chemistry among lakes (i.e., these lakes did not behave synchronously). This does not imply that chemical variability is not influenced by regional climate patterns. Studies indicate that El Nino events may be controlling changes in pH and sulphate in Swan Lake and sulphate in Heney and Blue Chalk lakes (Yan et al., 1996; Dillon et al., 1997; Dillon and Evans, 2001). Although the four study lakes experienced similar temperature and precipitation patterns during our study period, individual lake responses were mediated by flushing rates, water table levels, and the amount of reducing areas (e.g., wetlands, streambeds) in the catchment. One implication of substantial chemical variability is the influence it has on the sampling intensity necessary to estimate variables within pre-defined criteria. In our study, sulphate concentration and total base cation concentration were relatively stable and both the annual and inter-annual mean could be accurately estimated with 1 to 4 samples. The acidic, fast-flushing lake, Swan Lake, was this exception to this, requiring 22 to 40 annual samples to accurately estimate the longterm mean. Similarly, the most samples were required in Swan Lake to estimate the annual and long-term pH and alkalinity. This results from the extreme responsive-


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ness of Swan Lake to variation in weather (Yan et al., 1996). pH in Heney Lake was also highly variable, necessitating a large number of samples to accurately estimate the mean, whereas Blue Chalk and Clearwater required considerably less samples (Table V). This suggests that more frequent samples should be taken to estimate pH in fast flushing lakes. For alkalinity, however, it was the lakes with historic acid deposition which were more variable, requiring more intensive sampling. This general result should be taken into account when designing sampling protocols for water chemistry. This study demonstrates that there is substantial inter- and intra-year variability in key acidification-related variables. Given the potential importance of fluctuations in chemistry on biological communities (e.g., Arnott et al., 2001), assessments of chemical variability, not just average conditions, may permit better evaluation of biological responses related to lake acidification and recovery. The degree of chemical variability is related to general lake characteristics, including flushing time. Therefore, consideration of specific lake types should be included in the design of monitoring programs to ensure that sampling frequency is adequate to permit the effective assessment of both average and extreme conditions. Acknowledgements This work was supported by the Ontario Ministries of the Environment and Natural Resources, by an NSERC Industrially-Oriented Research Grant with INCO and Falconbridge as partners, and by grants from Ontario Power Generation Inc. and NSERC. We appreciate the assistance of Martyn Futter, Joe Findeis, and Jocelyne Heneberry. Norm Yan provided thoughtful discussions and advice. Comments and suggestions by Kathy Webster and Thomas Clair improved the manuscript. References Arnott, S. E., Yan, N. D., Keller, W. and Nicholls, K.: 2001, ‘The influence of drought-induced acidification on the recovery of plankton in Swan Lake’, Ecol. Appl., 11, 747–763. Baines, S. B., Webster, K. E., Kratz, T. K., Carpenter, S. R. and Magnuson, J. J.: 2000, ‘Synchronous behavior of temperature, calcium, and chlorophyll in lakes of Northern Wisconsin’, Ecology 81, 815–825. Bodo, B. A. and Dillon, P. J.: 1994, ‘De-acidification trends in Clearwater Lake near Sudbury, Ontario 1973–1992’, Stochastic and Statistical Methods in Hydrology and Environmental Engineering 3, 285–298. Carignan, R.: 1985, ‘Quantitative importance of alkalinity flux from the sediments of acid lakes’, Nature 317, 158–160. Clair, T. A., Dillon, P. J., Ion, J., Jeffries, D. S., Papineau, M. and Vet, R. J.: 1995, ‘Regional precipitation and surface water chemistry trends in southeastern Canada (1983–1991)’. Can. J. Fish. Aquat. Sci. 52, 197–212. Cook, R. B., Kelly, C. A., Schindler, D. W. and Turner, M. A.: 1986, ‘Mechanisms of hydrogen ion neutralization in an experimentally acidified lake’, Limnol. Oceanogr. 31, 134–148.

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