Aquat Sci (2011) 73:317–330 DOI 10.1007/s00027-010-0179-y

Aquatic Sciences

RESEARCH ARTICLE

Low carbon dioxide partial pressure in a productive subtropical lake Binhe Gu • Claire L. Schelske • Michael F. Coveney

Received: 1 February 2010 / Accepted: 3 December 2010 / Published online: 22 December 2010 Ó Springer Basel AG 2010

Abstract The purposes of this study were to assess if Lake Apopka (FL, USA) was autotrophic or heterotrophic based on the partial pressure of dissolved carbon dioxide (pCO2) in the surface water and to evaluate factors that influence the long-term changes in pCO2. Monthly average pH, alkalinity and other limnological variables collected between 1987 and 2006 were used to estimate dissolved inorganic carbon (DIC), pCO2 and CO2 flux between surface water and atmosphere. Results indicated that average pCO2 in the surface water was 196 latm, well below the atmospheric pCO2. Direct measurements of DIC concentration on three sampling dates in 2009 also supported pCO2 undersaturation in Lake Apopka. Supersaturation in CO2 occurred in this lake in only 13% of the samples from the 20-year record. The surface-water pCO2 was inversely related to Chl a concentrations. Average annual CO2 flux was 28.2 g C m-2 year-1 from the atmosphere to the lake water and correlated significantly with Chl a concentration, indicating that biological carbon sequestration led to the low dissolved CO2 concentration. Low pCO2 and high invasion rates of atmospheric CO2 in Lake Apopka indicated persistent autotrophy. High rates of nutrient loading Present Address: B. Gu (&) Everglades Division, South Florida Water Management District, 3301 Gun Club Road, West Palm Beach, FL 33406, USA e-mail: [email protected] C. L. Schelske Department of Geological Sciences and Land Use and Environmental Change Institute, University of Florida, Gainesville, FL 32611, USA M. F. Coveney Department of Water Resources, St. Johns River Water Management District, 4049 Reid Street, Palatka, FL 32178, USA

and primary production, a high buffering capacity, a lack of allochthonous loading of organic matter, and the dominance of a planktivorous–benthivorous fish food web have supported long-term net autotrophy in this shallow subtropical eutrophic lake. Our results also showed that lake restoration by the means of nutrient reduction resulted in significantly lower total phosphorus (TP) and Chl a concentrations, and higher pCO2. Keywords Autotrophy  CO2 flux  pCO2  pH  Restoration  Subtropical lake  Trophic state

Introduction Aquatic metabolism is central to the study of the sources, sinks and balance of carbon in lakes. Previous research has shown that the majority of lakes are heterotrophic and net sources of CO2 to atmosphere (Kling et al. 1991; Cole et al. 1994; Duarte and Prairie 2005). In contrast, a small number of lakes are autotrophic and net sinks for CO2 (Peng and Broecker 1980; Kling et al. 1992; Cole et al. 1994). The only determinant for autotrophy and heterotrophy is the ratio of gross primary production (GPP) to respiration (R). In autotrophic lakes, GPP: R ratio is greater than 1.0 and the partial pressure of CO2 (pCO2) is lower than that of atmospheric CO2. On the other hand, GPP: R ratio in heterotrophic lakes is less than 1.0 and the partial pressure of CO2 (pCO2) is higher than that of atmospheric CO2. Primary production is affected by nutrients (Elser et al. 1990; Smith et al. 2006), light availability and thermal cycle (Wetzel 2001), and food web structure (Proulx et al. 1996; Schindler et al. 1997; Cole et al. 2000) while ecosystem respiration may be strongly subsidized by

123

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allochthonous loading of organic matter (Pace et al. 2004; Carpenter et al. 2005; Duarte and Prairie 2005). There is a continuing debate on the factors that control pCO2 in the surface water of lakes. Water temperature plays important roles in microbial metabolism and gas solubility. However, Sobek et al. (2005) found, in an analysis of a global data base of more than 4,000 lakes, that pCO2 is independent of water temperature. The changes in pCO2 are instead attributed to changes in export of dissolved organic carbon (DOC) to aquatic systems. Recently, Marotta et al. (2009) expanded previous data sets in over 100 tropical lakes, revealing a global positive relationship between temperature and pCO2 in lake waters. They also found a trend of decreasing pCO2 along the latitudinal gradient. By contrast, calculated dissolved free CO2 from the surface water of 30 lakes around the world displayed a negative relationship with water temperature and latitude (Gu et al. 2011), consistent with an early report of latitudinal trend of sea surface CO2 which is partially attributed to increases in gas solubility as latitude increases (Rau et al. 1989). Lake trophic state is one of the most important factors controlling pCO2 in aquatic ecosystems. In fact, the increases in sea-surface pCO2 along latitude are also attributed to decreases in primary productivity (Rau et al. 1989). There is consistent evidence that unproductive aquatic systems support higher respiration rates than productive aquatic ecosystems and tend to be net heterotrophic whereas productive systems are more likely to be net sinks for CO2 (Duarte and Agustı´ 1998). It is not clear if the reduction of nutrients in polluted lakes which often leads to lower primary production will also change the surface water pCO2. Recent research indicates that some ecosystem traits may not be directly reversible due to the shifts of ecosystem baseline (Duarte et al. 2009). However, Marotta et al. (2010) recently showed that pCO2 in different lakes responded differently to eutrophication. A clear-water lake became a net CO2 sink, and a humic lake remained supersaturated with CO2 after receiving nutrient addition. Primary production displays strong seasonality in some aquatic ecosystems, especially in the temperate and arctic regions. Many lakes, including heterotrophic lakes, may be net sinks for CO2 during periods of high primary production. They fail to maintain net autotrophy because respiration during the majority of the seasonal cycle exceeds annual production (Dillon and Molot 1997; Nieveen et al. 1998; Casper et al. 2000). In contrast, primary production in subtropical and tropical lakes is less likely to be affected by seasonal changes in temperature and light. Primary production and respiration also follow a diurnal cycle with carbon fixation restricted to the day-light hours and respiration occurring over the entire 24-h cycle (Schelske et al. 2003, 2006). A lake could be net

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autotrophic during the day but could be net heterotrophic due to high night-time respiration. Productive lakes often exhibit a strong diurnal change in phytoplankton photosynthesis (Schindler and Fee 1973; Cole et al. 2000). Unproductive lakes with high loadings of terrestrial organic carbon display persistent heterotrophy characterized by supersaturation in CO2, both seasonally and diurnally (Kling et al. 1991, 1992; Cole et al. 1994). Many lakes are heterotrophic because of both low productivity and high DOC concentration (Cole et al. 2000). Although DOC derived from the terrestrial system is likely refractory, it still supports a portion of the aquatic respiration and often results in net heterotrophy. GPP is typically controlled by nutrients, particularly by total phosphorus (TP) in freshwaters. However, high GPP is not the sole factor that determines if a lake is autotrophic or heterotrophic. This is because respiration may be controlled by DOC concentration (Hanson et al. 2006) and may exceed GPP even in eutrophic lakes (Cole et al. 2000; Staehr et al. 2010). Therefore, a productive lake is autotrophic only when allochthonous loading of DOC is sufficiently low (Cole et al. 2000). Recently, there has been debate whether Lake Apopka, a hypereutrophic lake in Florida, is autotrophic or heterotrophic (Bachmann et al. 2000, 2006; Schelske et al. 2003, 2006). These discussions were based on changes in dissolved oxygen (DO) in light–dark bottle incubations and diurnal oxygen profiles with time scales from several days to one year. Here we present an analysis of monthly limnological data collected in Lake Apopka from 1987 to 2006. We calculated the concentrations of DIC, dissolved free CO2, pCO2 and air–water CO2 flux. The purposes of this analysis were to (1) determine whether Lake Apopka is an autotrophic or a heterotrophic system using an independent approach from the previous studies and (2) discuss the potential impacts of the changes in nutrients, primary productivity and DOC on pCO2. We hypothesized that high nutrient loading resulting in persistently high phytoplankton production and low external DOC loading are major factors leading to the long-term net autotrophy in Lake Apopka. We also hypothesized that reduction in TP loading that reduces primary production will also lead to increases in pCO2 in the surface water.

Methods Study site Lake Apopka is a large (125 km2) and shallow (mean depth = 1.6 m) lake located in central Florida (28°370 N, 81°380 W). The major sources of water were direct precipitation on the lake surface, an underwater spring,

Low pCO2 in a subtropical lake

backpumping from adjacent vegetable farms, and groundwater seepage. The major losses of water are from discharge through the Apopka-Beauclair Canal and evaporation. Hydraulic retention time in Lake Apopka is about 2.5 years (Coveney et al. 2005). Lake Apopka is situated in a transitional zone between temperate and subtropical regions. Prior to 1947, Lake Apopka was a macrophytedominated system with clear water and a large recreational fishery (Schelske and Brezonik 1992). Due to high nutrient loading from agriculture practices and land-use changes in the watershed, Lake Apopka has since become eutrophic (Schelske and Brezonik 1992; Lowe et al. 2001; Coveney et al. 2005; Schelske et al. 2010). The biological communities experienced two major shifts since 1947: the shift of the plant community from submerged aquatic macrophytes to phytoplankton and the shift of the dominant fish community from a piscivore (Micropterus salmoides) to a planktivore–benthivore (Dorosomma cepedianum). The lake has since supported intensive phytoplankton blooms dominated by nanophytoplankton (Carrick et al. 1993). Since 1993, several measures have been taken to reduce TP loading to Lake Apopka and have resulted in a steady reduction in concentrations of TP and Chl a, and increases in water clarity (Coveney et al. 2005). Sample collection and analysis Two data sets which include long-term measurements and diurnal measurements were provided by the St. Johns River Water Management District (SJRWMD). Water samples were collected at 0.5 m below the surface at least twice a month from 1987 to 2006. The majority of field measurements were taken between the hours of 09:00 and 12:00 local time. Sample collection and most analyses were described in Coveney et al. (2005). Additional analyses included DO, pH, and water temperature by calibrated probes; water color by comparison of filtered sample with glass disks; and alkalinity by titration to pH 4.5. In addition, diurnal data for pH, alkalinity, and water temperature at various water depths at a central lake station were collected on four occasions between 1990 and 1997 (pH and temperature by Hydrolab Surveyor or YSI 6000). The accuracy of pH measurement is ±0.1 unit under normal conditions, especially for measurement of water (APHA 1998). Both Hydrolab and YSI give ±0.2 pH unit as the accuracy of their current electrodes made for use in field sondes with precision on the order of 0.01–0.02. Field procedures included pre and post-calibration with a range of buffers, and these measurements should meet the general accuracy standards. Limnological variables including Chl a, alkalinity, color, DO, pH, TP, total nitrogen (TN), major dissolved ions, total organic carbon (TOC), DOC and water

319

temperature from 3 to 10 sites were pooled to calculate monthly averages. Data for Chl a concentration were available from 1989 to 2006 only. Six missing data points were filled using the average of two adjacent values. An outlier of pH (6.0) in September, 1987 was deleted and replaced by the average of two adjacent pH values. DOC data for the period between October 1993 and September 1998 were not collected. Predicted values based on the regression equation between TOC and DOC (y = 0.60x ? 9.04, R2 = 0.42, n = 178, p \ 0.0001) were used to fill the data gap. Wind speed was recorded at an observation tower situated above the open water of the lake. In addition, individual field readings of pH and DO from all stations and sampling dates were used to analyze the changes of these variables with time. We tested the relationship between DIC concentrations estimated from alkalinity, pH and water temperature and DIC measured directly in samples from Lake Apopka on three different dates between March and June 2009. Water temperature, pH, and alkalinity were determined as described above. Split water samples were taken to the Department of Oceanography, Florida State University to determine DIC concentrations using a Hewlett Packard 5890 Series II Gas Chromatograph (GC). The GC was equipped with a Chromopack (Raritan, NJ, USA) Poroplot Q column (27.5 m long with a 2.5 m particle trap) and was operated at an oven temperature of 45°C. Data analyses Concentrations of dissolved free CO2 (CO2(aq), g C L-1) were calculated using the following equation (Plummer and Busenburg 1982): ½CO2ðaqÞ  ¼

CT ½Hþ 2 ð½Hþ 2 þ Ka1 ½Hþ  þ Ka1 Ka2 Þ 0

0

0

ð1Þ

(CT) is total DIC concentration calculated from the relationship between alkalinity, pH and temperature Wetzel and Likens (1991). The concentration of DIC is very sensitive to changes in pH which is a function of biological activities in aquatic systems. Values for pH measured from surface water immediately before sunrise typically are affected by respiration during the night and should represent minima for the preceding 24 h (sunrise to sunrise). Since nearly all pH values in this study were recorded after sunrise and likely were elevated due to photosynthesis, we estimated pH at sunrise based on the duration between sunrise and pH measurement, and the rate of daytime pH change. We obtained daily sunrise data for the Orlando area (http://www.timeanddate.com), which were then converted to monthly averages. We used pH measurements from two diurnal profiles of the

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B. Gu et al.

PCO2 ¼

where KH is a dissolution constant of CO2 corrected for water temperature.

G

A

pH

8.0 190

-1

H

Alk (mg CaCO3 L )

B

160 130 100 70

I

14 12 10 8 6

D

J

110

0.2

-1

Molar TN:TP ratio

0.4

DO (mg L )

C

90

80

70 50

20

30

K

E 150

8

100

6

50

4

F

200

-1

50

L 300

150

200

100

100

50

0

0 88 90 92 94 96 98 00 02 04 06 88 90 92 94 96 98 00 02 04 06

Year

123

10.0 9.0

-1 -1

TSS (mg L )

ð2Þ

TN (mg L )

110

½CO2ðaqÞ  KH

Year

-1 TP ( µgL )

Water temp (°C) Secchi depth (m)

0.6

Color (Pt unit)

-1

estimated sunrise values should approximate the diurnal pH minimum. All pH values cited or used for the estimates of DIC chemistry were the sunrise minima unless noted. The uncorrected field pH is termed daytime pH. [H?] is the hydrogen ion concentration derived from pH. 0 0 Ka1 and Ka2 are the first and second dissolution constants (Ka1 and Ka2) corrected for ambient water temperature and ionic strength using the Gu¨ntelberg approximation (Stumm and Morgan 1981). Partial pressure (atm) of dissolved CO2 (pCO2) in the surface water was calculated using Henry’s law:

12 10 8 6 4 2 30 25 20 15 10

Chl a (µg L )

Fig. 1 Variations in monthly averages of 12 selected physical and chemical variables in the surface water of Lake Apopka from 1987 to 2006. pH values were diurnal minima (see ‘‘Methods’’ section for explanation)

Wind speed (m s )

multiparameter sonde readings from 1990 and 1996, and diurnal data reported by Biedermann (1980) and Reddy (1981), daytime data reported by Gu and Schelske (1996) during a light–dark bottle incubation, and daytime probe readings from 1987 to 1997, to correct pH for the time of collection. We chose to use the pH data from 1987 to 1997 because lake trophic state indicated by Chl a was relatively constant during this time period as compared to the recent period (1998–2007) of lower trophic state (Fig. 1). All of the pH values and the associated measurement times were used in regression analyses. These analyses showed an average increase or decrease of 0.03 pH unit h-1 for the day and night, respectively (Table 1). We multiplied the time difference between pH measurement and sunrise by the 0.03 pH unit h-1 to obtain the net gain in pH and then subtracted the net gain from the measured pH. These

Low pCO2 in a subtropical lake Table 1 Rate of change in pH (units per h) for the day and night periods in Lake Apopka

N number of measurements. All pH readings were taken at water depth B 0.9 m. All regressions are significant (p \ 0.05) a

Daytime measurements, N = 20 for nighttime

321

Date

Day

Night

N

Type of data

Source of data

1977

0.03

-0.03

13

Diurnal

Biedermann (1980)

1979–1980

0.06

-0.04

16a

Diurnal

Reddy (1981)

1990

0.01

-0.02

13

Diurnal

This study

1994

0.05

-0.05

9

Light–dark bottle

Gu and Schelske (1996)

1996

0.04

-0.02

24

Diurnal

This study

590

Daytime

This study

1987–1997

0.03

NA

Average

0.03

-0.03

Flux of CO2 (FCO2 ; g C m-2 d-1) between atmosphere and the lake water was estimated using the following equation:    
FCO2 ¼ aK600 CO2ðatmÞ  CO2ðaqÞ ð3Þ where a is the chemical enhancement factor of the air– water transfer of CO2 calculated following the procedure of Wanninkhof and Knox (1996). K600 is a piston velocity (m d-1) for the Schmidt number of 600 estimated as a function of wind speed (Cole et al. 1998). [CO2(atm)] is the atmospheric CO2 concentration estimated from the Henry’s Law using the annual average atmospheric CO2 partial pressure (360 latm) during the study period (http://www. esrl.noaa.gov/gmd/ccgg/trends). Statistical analyses All statistical analyses were performed using SAS Jump (Version 7, SAS Institute, Cary, NC, USA). Comparisons were conducted using analysis of variance (ANOVA). Correlation and regression analyses were conducted on logtransformed data except dates and pH. Statistics were considered significant at p \ 0.05.

Results Variations in selected limnological variables The limnological variables in Lake Apopka reflected the characteristics of a eutrophic lake in the subtropical region (Fig. 1, Table 2). This is evident by high water temperature (Fig. 1b), and high concentrations of TP (Fig. 1l), TN (Fig. 1k) and Chl a (Fig. 1e). Over the 20 year period of record, TP, TN, pH (Fig. 1g), color (Fig. 1d), Chl a (Fig. 1e), and TSS (Fig. 1f) decreased while Secchi depth (Fig. 1c) and molar TN:TP ratio (Fig. 1j) increased significantly (Table 3) as a result of P reduction (Coveney et al., 2005). There was no apparent trend for other variables including water temperature (Fig. 1b), alkalinity (Fig. 1h) and DO (Fig. 1i). Lake Apopka had a strong buffering capacity with high alkalinity. The majority of daytime pH

Table 2 Variations in monthly physical, chemical variables related to carbon equilibrium and flux in Lake Apopka between 1987 and 2006 Variable Wind speed (m s-1)

Min

Max

Mean

SD

0.0

10.3

3.8

Temperature (°C)

10.5

32.0

23.1

5.1

Alkalinity (mg CaCO3 L-1) pH

71.5 7.55

159.1 10.03

115.0 8.98

15.8 0.34

DIC (mg L-1)

4.07

14.68

37.11

25.95

CO2aq (mg L-1)

0.00

1.23

0.07

pCO2 (latm)

8

CO2 flux (g C m-2 mo-1)

-44.3

3304 10.6

196

0.9

0.11 323

2.35

0.45

Water temperature and alkalinity are daytime measurements; wind speed is diurnal weighted; inorganic carbon species are calculated using sunrise pH. See ‘‘Methods’’ section for more explanations

measurements were taken between the hours of 09:00 and 12:00 local time. Only 8% of the pH values were lower than 8.55 (see discussion below). Monthly daytime pH varied from 7.44 to 10.13 with an average of 9.08, while the calculated sunrise pH minima averaged 8.98 with a range from 7.55 to 10.03 (Fig. 1g). This indicates that Lake Apopka maintained a high early morning pH after overnight respiration and a 0.11 unit increase between sunrise and midmorning. Other studies performed in Lake Apopka between 1970s and 1990s also recorded high daytime pH (Biedermann 1980; Reddy 1981; Carrick et al. 1993; Gu et al. 2004). Concentrations, partial pressure and air–water flux The DIC concentration in Lake Apopka averaged 25.95 mg L-1 (Table 2) and fluctuated over the study period (Fig. 2a). It was typically high during the colder months and decreased as water temperature increased (Fig. 2g). Dissolved CO2 was a small fraction (0.3%) of the DIC pool due to high pH and high phytoplankton production (Fig. 2b) and its seasonality resembles that of DIC (Fig. 2h). DIC concentration was negatively correlated with water temperature and Chl a concentration (Table 3). Total organic C (Fig. 2c) and DOC concentrations (Fig. 2d) averaged 31.4 and 30.0 mg L-1, respectively,

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B. Gu et al.

Table 3 Correlation coefficients of date and selected limnological variables in Lake Apopka between 1987 and 2006 Date WSP

WSP

Temp

Secchi TSS

Chl a

Color

DO

TP

TN

TN:TP Alk

DOC

DIC

pCO2

-0.04

Temp

0.00 -0.19

Secchi

0.53 -0.04 -0.16

TSS

-0.43

0.07

0.12 -0.70

Chl a

-0.54

0.10

0.19 -0.76

pH

-0.13 -0.03

0.19 -0.28

0.19

0.26

Color

-0.40

0.07 -0.19

0.12

0.30

DO

0.09

TP

-0.68

TN

-0.45

TN:TP Alk DOC

pH

0.65 0.01 -0.05

DIC

0.07

0.05 -0.54

0.23

0.13

0.03 -0.70

0.16

0.08 -0.69

0.05 0.03 0.07 -0.44 0.01

0.45 0.08

0.10 -0.42

0.77

-0.19 -0.22

0.21 0.07 -0.11

0.79

0.83

0.11

0.38 -0.17

0.75

0.74

0.11

0.31 -0.21

-0.52 -0.59 -0.09 -0.28 -0.19 -0.25 -0.35 0.02 0.49

0.41

0.83

0.05 -0.78 -0.31 0.14 -0.05 0.06

0.16 -0.03 -0.07

0.35

0.05

0.08 -0.45

0.14

-0.22 -0.29 -0.54 -0.05

pCO2

0.13

0.02

0.26

-0.21 -0.28 -0.99 -0.20 -0.09 -0.12 -0.10

CO.2flux

0.06 -0.01

0.17

0.04 -0.12

0.05

0.13

0.74

0.00

0.12 -0.08

0.18

0.47 -0.08 0.02

0.03 -0.07 -0.02

0.19 0.12 0.08

-0.04 0.97 -0.08 0.45 -0.15 -0.20

0.64

0.12 -0.32 -0.71

All data were log-transformed except pH, TK:TP and CO; flux. Values C 0.13 (N = 240 months except Chl a = 219 months) are significant at p \ 0.05 WSP, wind speed; Temp, water temperature; Alk, alkalinity

with the majority (90%) of TOC in the form of DOC. DOC concentration was significantly correlated with TP, TN and Chl a (Table 3). Average surface water pCO2 was 196 latm in Lake Apopka between 1987 and 2006 which was considerably lower than atmospheric pCO2 (Fig. 2e). Variation in pCO2 was large, ranging from well below saturation (8 latm) to highly saturated (3,304 latm). However, approximately 87% of the samples were undersaturated in CO2. The high pCO2 in 1987 resulted from two low pH measurements that are problematic because closely related variables such as water temperature, wind speed and alkalinity measured at the same time, fell within the normal ranges (Fig. 1). Supersaturation of CO2 typically occurred in the cold months especially between December and March although sporadic CO2 outgassing also took place in other months (Fig. 2k). On an annual basis, Lake Apopka was undersaturated in CO2 for the majority of the two decades with an exception of 1987. pCO2 showed a trend of slight increase over the 20 year record and was not significantly correlated with water temperature, TP, molar TN:TP ratio or DO concentration, but increased significantly as DOC and Chl a decreased (Table 3). The rate of CO2 flux from atmosphere to the surface water in Lake Apopka was elevated with an average chemical enhancement factor of 1.38 due to the strong ion strength. Monthly CO2 flux ranged from -47.3 to 10.6 g C m-2 with an average of 2.35 g C m-2 or annual average of 28.2 g C m-2. CO2 flux showed marked

123

seasonal variations without a pattern of temporal change (Fig. 2f). In contrast to pCO2, high rates of CO2 invasion were found in early winter (October and November) and early summer (May and June) with moderate water temperatures during both periods (Fig. 2l). Months (January and August) with the lowest and the highest water temperature were accompanied with the lowest CO2 invasion rates. Corresponding to the high pCO2 in the cold months, high CO2 evasions were largely found between December and March. CO2 flux increased significantly as Chl a increased (Table 3). On an annual basis, average CO2 flux was 28.2 g C m-2 year-1 with a net loss of CO2 in 1987 (Table 2). Concentrations of DIC measured directly and calculated from water temperature, pH and alkalinity from 2009 were similar (paired t-test, p = 0.54) with the measured DIC concentration 1.1 mg L-1 lower than the calculated DIC concentration (Table 4). pCO2 based on measured and calculated DIC was undersaturated relative to the atmospheric pCO2 (average = 387 latm). The highest pCO2 was found on March 4, 2009 with a low water temperature (12.6°C) which is similar to the lowest long-term monthly average temperature (Table 2). Diurnal measurements Diurnal measurements of pH were made on four separate occasions for 3–8 days in 1990, 1996 and 1997 (Fig. 3). Biedermann (1980) also reported diurnal pH (Fig. 3a),

Low pCO2 in a subtropical lake

323

0.4 0.3

0.6

0.2

0.4

0.1

0.2

0.0

70 60 50 40 30 20 50

C

I

D

J

70 60 50 40 30 20 50

40

40

30

30

20

20

-1

DIC (mg L )

K

3000

600

2000

400

1000

200 0

0

L

-1

F

10

-2

-1

H

CO2 (mg L )

B

0.8

-1

15

TOC (mg L )

20

15

-1

25

20

DOC (mg L )

30

pCO2 (µatm)

pCO2 (µatm)

35

25

E

CO2 flux (g C m mo )

40

0

10 5

-10

0

-20

-5

-30

-10

-40

-15 87

89

91

93

95

97

99

01

03

05 07 J F M A M J J A S O N D

Year

-1

-1

CO2 (mg L ) -1

TOC (mg L )

G

30

1.0

DOC (mg L )

-1

A

35

-2

-1

DIC (mg L )

40

CO2 flux (g C m mo )

Fig. 2 Time-series and monthly box plots of DIC, CO2, TOC, DOC, pCO2 and CO2 flux in the surface water of Lake Apopka from 1987 to 2006

Month

Table 4 Carbon dioxide partial pressure based on direct measurement of DIC (pCO2m) and relationship between alkalinity, pH and water temperature (pCO2c), and selected environmental variables from a central lake station in 2009 Depth (cm)

pH (SU)

Temp (°C)

Alkalinity (mg CaCO3 L-1)

DICm (mg L-1)

DICc (mg L-1)

pCO2m (latm)

pCO2c (latm)

3/4/2009

0

8.65

12.61

150

25.78

36.00

269

375

3/18/2009

0

9.04

21.67

139

24.99

31.97

137

175

3/18/2009

25

9.06

21.72

140

24.02

32.20

125

168

3/18/2009

50

9.06

21.78

140

23.93

32.20

125

168

3/18/2009

75

9.06

21.77

139

22.84

31.97

119

167

6/17/2009

5

9.09

29.34

107

29.79

24.61

185

153

6/17/2009

35

9.12

29.36

108

28.58

24.84

165

144

6/17/2009

65

9.11

29.26

109

28.82

25.07

170

148

6/17/2009

105

8.97

28.79

109

36.27

25.07

296

205

Date

123

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B. Gu et al.

Fig. 3 Diurnal changes in pH and pCO2 at various water depths in Lake Apopka between 1977 and 1997. These data represent 1–8 days of pH readings and estimated pCO2 at 1–4 h intervals with a few exceptions. The top left panel was redrawn from Biedermann (1980)

9.30

A

F

Sep 1977

200

9.20 150

9.10 9.00

100

8.90 0.5 m Feb 1990

G

B 8.90

600

8.80 400

8.70 8.60

200 0.5 m

H

May 1996 150

pH

9.30 100 9.10 50

8.90

pCO2 ( µatm)

C

0.9 m

I

D

Aug-Sep 1996

9.50 60 9.40 40

9.30 9.20

E

9.10

1.7 m Feb 1997

J

20

150 9.00 100

8.90 1.6 m

8.80 0

2

water temperature and other variables for Lake Apopka in September 1977. All of the pH readings were above 8.6 and generally fluctuated between 9.0 and 9.5 and between 8.8 and 9.0 for day and night, respectively. The May 1996 dataset with hourly YSI sonde readings for five consecutive days represents the typical diurnal change in pH for the surface water (Fig. 3c), with pH maxima and minima at 17:00 and 06:00 h local time, respectively. Two diurnal measurements near the lake bottom for the warm and cold periods also showed consistently high pH (Fig. 3d, e). Reddy (1981) made pH measurements every 3 h for a 24-h cycle in four different months between 1979 and 1980 and found that pH ranged from 7.50 to 9.05. The average rate of diurnal change in pH using the above data was

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0.03 unit h-1 (Table 1). This translated to a total increase or decrease of 0.19 pH unit starting from pH at sunrise and estimating pH about 6 h later or earlier for midday and midnight values, respectively. Diurnal measurements of alkalinity and water temperature were also made during February 1990, and were used, along with the pH readings, to calculate pCO2 for the surface water. For February 1990, we calculated an average pCO2 of 224 latm at night, which was slightly higher than the daytime pCO2 (Fig. 3g), but was undersaturated with reference to atmospheric pCO2. We did not have diurnal alkalinity data for other datasets and used the monthly daytime average alkalinity and water temperature to calculate the diurnal changes in pCO2. Because of the high pH and consequently

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reaches supersaturation when DIC concentration is greater than 54.6 mg L-1 (Fig. 4b) while the maximum monthly DIC concentration from Lake Apopka was 37.1 mg L-1 (Table 2). The response of pCO2 to the changes in pH is the most sensitive and dramatic (Fig. 4c). For a change of 0.1 unit of pH, there was a corresponding change of over 20% in pCO2. Surface water pCO2 was supersaturated when pH declined to 8.55 while water temperature and DIC were kept constant at the long-term averages. Comparisons of pre-restoration and restoration period Reduction in P loading to Lake Apopka began in 1993, and changes in TP concentrations in the lake were evident by 1995 (Coveney et al. 2005). When data are divided into a pre-restoration (1987–1994) and a restoration period (1995–2006), several key variables relevant to carbon dynamics display significant changes (Fig. 5). TP concentration was reduced by 64 lg L-1 (ANOVA, F = 8.62, p = 0.004) which resulted in a reduction of Chl a concentration by 34 lg L-1 (ANOVA, F = 33.28, p \ 0.001) and pH by 0.2 unit (ANOVA, F = 8.62, p = 0.004). Changes in DO (9.5 vs. 9.6 mg L-1) and DOC concentration (28.7 vs. 27.6 mg L-1) between the two periods are not significant. The average pCO2 in the restoration period was 35 latm greater than the previous period which is also significant (ANOVA, F = 4.73, p = 0.032).

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Fig. 4 Sensitivity analysis of pCO2 responses to changes in water temperature (a), DIC concentration (b) and pH (c). Dashed lines represent atmospheric pCO2 at 360 latm. Solid lines represent the predicted surface-water pCO2 using long-term averages of two abovementioned variables and the measured range of the third variable. Solid dots are the calculated surface-water pCO2 using the measured data. Five calculated values ([1,000 latm) were not plotted

low CO2 concentrations, diurnal pCO2 in the other datasets was always below 200 latm (Figs. 3f, h, 4j), considerably undersaturated compared to the atmospheric pCO2. Sensitivity analysis of pCO2 Water temperature, pH and DIC are the three primary parameters used to estimate pCO2 in this study. We conducted a sensitivity analysis to test the magnitude of changes of pCO2 in response to the variation in each parameter. This was done by varying one parameter within the data range while keeping other two parameters constant at their respective long-term averages. As expected, responses of pCO2 to the changes of these parameters are different. pCO2 never reaches supersaturation at the range of water temperature in Lake Apopka (Fig. 4a) and only

Discussion Accuracies of pH measurement and DIC calculation The most sensitive factor affecting the estimates of DIC concentration is pH. Both Hydrolab and YSI give ±0.2 pH unit as the accuracy of their current electrodes. For the worst case scenario, if all of our field pH measurements were reduced by 0.2 unit, the average morning pH becomes 8.78, which is still 0.23 unit higher than the threshold pH (8.55) which would yield pCO2 saturation under average alkalinity and water temperature. In fact, several studies reported that in situ pH readings were typically lower than the actual values in lakes with high DOC (Herczeg and Hesslein 1984; Herczeg 1987; Stauffer 1990). Therefore, our estimates of DIC and CO2 concentrations and pCO2 based on the field pH are very likely overestimates, and our CO2 flux rates are underestimates. Total DIC concentration has been estimated from the relationship with alkalinity and pH which were in turn used to calculate the aqueous pCO2 (Kling et al.1992; Cole et al. 1994; Maberly 1996). The accuracy of DIC data is critical to assess the metabolic status of the aquatic system in concern. Quay et al. (1986) found only 1% difference in

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Fig. 5 Comparisons of pCO2 and several environmental variables between the prerestoration (1987–1995) and the restoration (1996–2006) period. Five extreme high values were not included. Boxes with different letters are significantly different (p \ 0.05)

B. Gu et al.

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DIC concentration between manometric measurements and calculations from alkalinity and pH. Raymond et al. (1997) reported a close relationship between the estimated and measured DIC values (R2 = 0.60). We compared the monthly DIC measurements using the coulometer method (Gu et al. 2004) with those derived from the alkalinity and pH in 1994 and also found a similar relationship (R2 = 0.63). Data from the 2009 comparison showed on average a small difference between the measured and calculated DIC concentrations (Table 4). These findings provide strong evidence that the calculated DIC concentration based on alkalinity and relevant parameters can be used in the absence of DIC data from direct measurements. Magnitude and seasonal variations in pCO2 and air–water CO2 exchange Unlike many freshwater lakes in the world, Lake Apopka had a consistently high flux of CO2 from the atmosphere to the surface water, high pH and low pCO2. Our results suggest that Lake Apopka is autotrophic and productive.

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Schindler and Fee (1973) found that net primary production was equal to the CO2 invasion rate during summer stratification. Our estimate of net atmospheric CO2 influx (20-year average = 28.2 g C m-2 year-1) might contribute strongly to the net accumulation of organic matter observed in the sediment of Lake Apopka (Schelske 2006). Peng and Broecker (1980) reported even higher CO2 invasion rate (72–456 g C m-2 year-1) for three landlocked alkaline lakes. CO2 fixation by the intensive phytoplankton blooms is the major mechanism leading to low pCO2, and high pH and CO2 invasion in lakes (Schindler and Fee 1973; Emerson 1975; Maberly 1996). Herczeg (1987) reported that summer phytoplankton uptake decreased pCO2 to as low as 20 latm in a softwater lake because atmospheric invasion failed to keep pace with phytoplankton uptake. The lowest pCO2 we estimated for Lake Apopka was 8 latm, indicating virtually no CO2 presence in the surface water. Many productive lakes exhibit a period of CO2 invasion when primary production is high and a period of CO2 evasion when production is low, which frequently leads to a net loss of CO2 when the

Low pCO2 in a subtropical lake

whole year’s primary production and respiration are taken into account (Maberly 1996; Cole et al. 2000). Supersaturation of CO2 in Lake Apopka was episodic, and with the exception of 1987, the lake acted as a net sink of CO2 during the entire 20-year study period. The high concentrations of nutrients and water temperature supported sustained phytoplankton blooms year round. A previous study indicated that the lake still maintained a positive net ecosystem production in January when water temperature was the lowest (Schelske et al. 2003). Diurnal changes in pH and pCO2 Biological carbon fixation and respiration exhibit a strong diurnal cycle in many eutrophic lakes (Talling 1976; Maberly 1996). Cessation of photosynthesis at night may lead to a substantial buildup of CO2 due to continuing respiration, resulting in supersaturation of CO2 and decline in pH (Maberly 1996; Cole et al. 2000). However, in some productive lakes, the addition of CO2 due to nighttime respiration may not be sufficient to cause significant decline in pH and CO2 supersaturation. Several lines of evidence provide strong support for high nighttime pH and consequently low pCO2 in Lake Apopka. First, high midmorning pH is the result of high photosynthesis and relatively low respiration of the previous day and night. The average pH corrected for carbon fixation between sunrise and the time of measurement is 8.98 which represented the minimum pH over a 24-h cycle. Our sensitivity analysis indicates that pH must drop below 8.55 before pCO2 reached supersaturation under the average water temperature and DIC concentration. Monthly daytime values revealed average pH B 8.55 for 18 of the 240 sampling months, or 7.5%. However, pH in Lake Apopka could have been above 8.55 during the day but below 8.55 at night, causing a net loss of CO2. We applied the nighttime rate of decline in pH (Table 1) to each monthly average daytime pH to estimate the percentage of the diurnal period with pH B 8.55. These estimates reveal that Lake Apopka was supersaturated in CO2 13% of time, or about 45 days/year of net loss of CO2. This calculation has several important implications for the metabolic state and DIC dynamics in Lake Apopka: (1) This calculation confirms the daytime estimates indicating that Lake Apopka acted as a net CO2 sink. (2) Lake Apopka possessed an alkalinity buffering system highly resilient to changes in pH and CO2. (3) The rate of respiration was not high enough to cause prolonged seasonal and diurnal CO2 supersaturation and significant decline in pH. Second, our four independent diurnal measurements from this study and two from the literature (Biedermann 1980; Reddy 1981) which represented nearly 600 readings and time scales from 2 to 8 days displayed consistently

327

high nighttime pH and small changes on a diurnal cycle in Lake Apopka. None of these measurements was below 8.55, and the pCO2 only reached supersaturation once as a result of a questionable midnight pH reading. Two diurnal datasets were collected at water depths of 1.6–1.7 m and still showed remarkably high pH from 8.9 to 9.2. Reddy (1981) reported that average pH was above 8.61 in 3 of 4 months in 1979 and 1980. The small difference in pH between the day and night time is also supported by the average day and night pH estimated from the sunrise minimum pH (8.98) and the rate of diurnal change in pH (0.03 unit h-1) presented in Table 1. This calculation results in an average day and night pH of 9.16 and 8.80, respectively. The above findings are consistent with previous studies of diurnal pH variations in productive lakes. For example, Schindler and Fee (1973) reported a slight diurnal change of 0.1–0.2 pH unit and no substantial increase in pCO2 at a pH of 10 in fertilized Lake 227 during the summer period. Bonoff et al. (1996) showed consistent pH at 9.0 over a diurnal cycle during an algal bloom in a productive reservoir. Finally, the small nighttime respiration as compared to the high daytime photosynthesis also is supported by the high mid-morning DO concentration (Fig. 1i) and supersaturation in the surface water of Lake Apopka. Schelske et al. (2006) calculated average percent DO saturation using surface water temperature and daytime DO measurements on 704 samples in Lake Apopka for the period of 1985–1997 and found that percent saturation ranged from 0 to 180% with only 10 measurements less than 60% saturation. They also reported over 100% average percent saturation from Winkler titration and DO probe measurements (n = 33) from a study conducted in the early 1990s. We expanded the calculation by Schelske et al. (2006) using a larger data set from 1987 to 2006 (n = 1,287 measurements). This period yielded an average midmorning saturation of 111%; only 25 and 8% of the data were below 100 and 90% saturation, respectively. Limited measurements taken between 18:00 and 06:00 h local time also showed close to or above saturation (data not shown). Using the morning DO and sampling time from the stable productivity period (1987–1997), we obtained a rate of change in percent saturation of 5.6% h-1 (R2 = 0.11, p \ 0.01, n = 726). In a 24-h period we expect to find the minimum DO concentration at sunrise. We extrapolated morning values to mean sunrise (06:20 h) to estimate the mean sunrise (minimum) DO saturation. That average minimum value, 89%, was close to saturation despite overnight respiration. These calculations substantiated the long-term, mid-morning and sunrise minimum pH patterns and the diurnal pH profiles, demonstrating low respiration relative to the high production and small pH changes in the nighttime.

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Controls of pCO2 in Lake Apopka Several factors including water temperature, nutrients, primary productivity and DOC concentration have been used to explain variations of pCO2 in lakes. Findings for the relationship between water temperature and pCO2 in the literature are variable and contradictory (Sobek et al. 2005; Marotta et al. 2009). Our data displayed a weak and negative relationship between water temperature and pCO2 which could be attributed to increases in gas solubility and decreases in CO2 uptake at low temperature (Rau et al. 1989; Gu 2011). This contrasts to an among lake analysis of water temperature and pCO2 by Marotta et al. (2009). This disparity is probably due to different controlling mechanisms on a single autotrophic lake (gas solubility and algal growth rate) and multiple lakes (allochthonous input of organic matter). High nutrient concentrations, especially high TP, often drive higher primary production that in turn reduces surface-water pCO2 in lakes (Duarte and Agustı´ 1998; Cole et al. 2000). This is also demonstrated in our analysis showing negative relationships between annual pCO2 and Chl a concentration in Lake Apopka. This finding supports our hypothesis that high nutrient loading results in low pCO2 in Lake Apopka. During the restoration period (1995–2006), the average TP and Chl a concentrations decreased dramatically and pH also declined by 0.1 unit. As the result of reduced primary production, the average pCO2 increased by 35 latm over the previous period (Fig. 5). This suggests that oligotrophication process leads to reduced CO2 assimilation and increased pCO2 in Lake Apopka. Allochthonous loading of organic matter has been attributed to cause high pCO2 in lakes. pCO2 typically increases as DOC concentration increases in lakes (Hanson et al. 2006; Sobek et al. 2005). Several studies conducted in temperate lake regions suggest that a lake is heterotrophic when DOC exceeds *6 mg L-1 (Carignan et al. 2000; Prairie et al. 2002; Hanson et al. 2006). However, with the minimum DOC concentration of *18 mg L-1 (average = 28 mg L-1), the pCO2 in Lake Apopka averaged 196 latm and only exceeded the atmospheric pressure 13% of the time over 20 years. This does not support a conclusion that low pCO2 is attributed to low DOC concentration in Lake Apopka. Our data displayed a negative relationship between DOC concentration and pCO2 (Table 3). This suggests that pCO2 in Lake Apopka was not controlled by DOC, but by primary production which was probably sufficiently high so that GPP exceeded R (Schelske et al. 2006). In addition, the DOC in Lake Apopka was largely derived from autochthonous production which is supported by a close correlation between Chl a and DOC concentration (Table 3), and not by

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allochthonous loading which is the case for many heterotophic lakes. Gu et al. (2004) showed that external loading of TOC to Lake Apopka consisted of less than 10% of the total loading, supporting our hypothesis that the low pCO2 in Lake Apopka was partly attributable to low external DOC loading. Controls of autotrophy in Lake Apopka This study revealed low pCO2 and high flux of atmospheric CO2 to Lake Apopka. We demonstrated that Lake Apopka with high pH, alkalinity and Chl a is an autotrophic ecosystem with higher assimilation of CO2 than respiration. In light of the fact that recent studies indicate that many lakes in the world are heterotrophic and a net source of CO2 to atmosphere, Lake Apopka must be different from many other systems in maintaining its balance between primary production and respiration. Lake Apopka is a productive system with intensive phytoplankton blooms (Coveney et al. 2005) and primary production (Schelske et al. 2003). Lake Apopka differs from lakes in arctic and temperate regions where there is often a period of extremely low water temperature and ice-cover. The thermal regime of Lake Apopka is well buffered by the warm Florida subtropical climate which allows high photosynthetic rates all the year long. There are also two important characteristics of an autotrophic lake: relatively small allochthonous loading of organic matter and a food web dominated by planktivorous fishes (Cole et al. 2000). Current studies on Lake Apopka carbon budget show that carbon loading from the watershed was minimal and balanced by the outflow (Gu et al. 2004; Bachmann et al. 2006). Aquatic food web structure is an important factor in regulating primary production (Proulx et al. 1996; Cole et al. 2000; Pace et al. 2004). First, in planktivore-dominated lakes, large-bodied herbivorous zooplankton are eliminated, resulting in higher primary production and net sinks for atmospheric CO2 (Schindler et al. 1997; Cole et al. 2000). Large-bodied crustaceans are generally scarce in subtropical lakes (Crisman and Beaver 1990) and predated by gizzard shad which has formed a large population since the dieoff of submerged macrophytes in Lake Apopka in the late 1940 s (Schelske and Brezonik, 1992). A previous study estimated that zooplankton supported about 40% of the growth of gizzard shad in Lake Apopka (Gu et al. 1996). Second, herbivorous fishes play a key role in promoting nutrient cycling in eutrophic lakes (Vanni 2002). Gizzard shad are omnivorous filter feeders moving between the pelagic and the benthic habitats. Gizzard shad also ingest large amounts of algae and detritus, which contributes to increases in nutrient concentration because the undigested foods, when released back to the lake, are recycled rapidly (Vanni

Low pCO2 in a subtropical lake

2002). Furthermore, gizzard shad often disturb sediments and therefore increase nutrient fluxes to the water column (Schaus et al. 1997; Schaus and Vanni 2000). Many studies have found that stocking planktivorous fishes will not slow the process of lake eutrophication, but intensify algal blooms (e.g., Laws and Weisburd 1990; Vanni 2002). The recent increases in pCO2 in Lake Apopka can be attributed to lower Chl a from nutrient load reduction and possibly from intensive gizzard shad harvesting (Coveney et al. 2005). The important role of gizzard shad in nutrient cycling in Lake Apopka has been studied recently in detail (Schaus et al. 2010).

Conclusions We used DIC values calculated from alkalinity, pH and water temperature in Lake Apopka, Florida between 1987 and 2006 to estimate pCO2 and air–water CO2 exchange. Results from this study show (1) there is a close relationship between measured and calculated DIC concentrations, and (2) the calculated DIC concentration is generally higher than the measured DIC concentration. Hence the use of the calculated DIC might result in overestimating pCO2 in the surface water of Lake Apopka. However, our results showed that Lake Apopka is undersaturated with CO2 and has high invasion rates of atmospheric CO2. This finding leads us to conclude that Lake Apopka is an autotrophic system with a net flux of CO2 to its surface water. The high rates of carbon fixation and strong buffering capacity in Lake Apopka were greater than community respiration, providing a net sink for CO2. Compared to the majority of lakes in the world, Lake Apopka is different because of its warm climate, high nutrient concentrations, high pH, high ionic strength, insignificant loadings of organic matter from the watershed, and a planktivore–benthivore-dominated food web which favors high primary production through accelerated nutrient recycling. These characteristics are consistent with a productive autotrophic system with a low partial pressure and a high invasion rate of CO2. On-going ecosystem restoration via nutrient reduction has significantly reduced TP and Chl a concentrations, and increased pCO2 in Lake Apopka. However, so far this measure did not lead to significant decreases in DOC and DO concentrations. Acknowledgments This study was partly supported by the Carl S. Swisher Endowment, University of Florida Foundation, Inc. We thank Jonathan Cole for comments on an early draft of this manuscript and Thomas Dreschel for editorial suggestions. Jim Peterson coordinated sampling for direct DIC measurements, and Jeff Chanton performed the analyses. We also want to thank an anonymous reviewer for constructive comments.

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