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Lake and Reservoir Management 21(1):39-48, 2005© Copyright by the North American Lake Management Society 2005

Temporal Coherence in Limnological Features of Two Southwestern Reservoirs

Thomas H. Chrzanowski and James P. GroverDepartment of Biology

PO Box 19498 The University of Texas at Arlington

Arlington, TX 76019

AbstractChrzanowski, T.H. and J.P. Grover. 2005. Temporal coherence in limnological features of two southwestern reservoirs. Lake and Reserv. Manage. 21(1):39-48.

Properties of aquatic ecosystems have recently been considered in a landscape context where lakes in a geographic area are examined to identify common and long-term behavior patterns for one or more variables. Identifying such temporally coherent features should permit generalizations about lake behavior in specific regions and therefore, predictive models based upon such information should have broad applicability within a regional landscape. We considered the temporal coherence of a number of physical, chemical, and biological features of two southwestern reservoirs that differed in age, watersheds, and trophic status to identify common landscape-level predictors of behavior. We found synchronous behavior (temporal coherence) associated with particulate nutrient dynamics (organic carbon, nitrogen and phosphate (PP)), dissolved factors that force plankton dynamics (total dissolved phosphate, reactive phosphate and reactive silicate (SRSi), and with nutrient ratios used as indices of nutrient limitation in the plankton (TDN:TDP, C:P, and N:P). Algal parameters related to biomass (chlorophyll and Simpson’s diversity index) did not vary coherently but algal genus richness and bacterial abundance did. Temperature was identified as a forcing function explaining synchronous variability in all cases except SRSi, PP, C:P, N:P, bacteria, and richness. The two systems, although managed for different purposes, behaved similarly with respect to several commonly measured limnological features, most notably, those involving phosphorus. We conclude that it may be possible to use such analysis to establish reference conditions for reservoirs in a given geographic region.

Key Words: reservoirs, synchrony, nutrients, models.

Fundamental to the concept of ecosystem management is the ability to predict patterns and trends in ecosystem properties. Recently, properties of aquatic ecosystems have been con-sidered in a landscape context where a collection of lakes in a geographic area is examined for common or synchronous fluctuations in one or more variables (Magnuson et al. 1990, Rusak et al. 1999, Baines et al. 2000, Kling et al. 2000, Pace and Cole 2002). Such temporal coherence among lakes with respect to physical, nutrient, or biological dynamics would imply that ecosystem behavior responds similarly to various extrinsic and/or intrinsic forcing functions. The implication is that predictive models based on temporally coherent features would have broad applicability within a landscape. Much of the work on temporal coherence stems from the long-term data sets available for north temperate glacial lakes and addresses year-to-year variability. The annual time frame of these studies permit synchronous changes in lakes to be considered in the context of long-term forcing-functions, as global climate change.

Reservoirs share many features with natural lakes. However,

they also have features that separate them from natural lakes (Gloss et al. 1980, Hoyer and Jones 1983, Kimmel 1983, Groeger and Kimmel 1984, Kimmel and Groeger 1984, Lind 2002) and these differences present some interesting chal-lenges to predictive limnology. Reservoirs are, in comparison to lakes of the north temperate zone, short-lived and created to serve a variety of needs associated with water supply, flood control, power generation, and recreation. Their construc-tion and differences among watersheds in which they are built create reservoir-to-reservoir variation in age, shoreline development, depth, basin characteristics, and water reten-tion times. Management practices can also create variance among reservoirs and within years for many hydrologic and biologic factors. Thus, it seems particularly appropriate to ask if reservoir-to-reservoir variability is great enough to preclude the identification of landscape-level predictors of ecosystem behavior similar to those used to characterize lakes.

Lind (2000) recently discussed some of the similarities and the differences between reservoirs and natural lakes and pointed out some of the sources of variability among and

Chrzanowski and Grover

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within reservoirs. In a series of papers, we compared various aspects of plankton dynamics in two southwestern reservoirs differing in morphometry, age, drainage basins and trophic status. Despite differences in trophic status and hydrology between the reservoirs there was remarkable similarity in patterns of bacterioplankton substrate utilization (Grover and Chrzanowski 2000), to the timing and in strength of responses of the plankton to nutrient additions (Chrzanowski and Grover 2001a), and to the response of plankton to the balance between light availability and nutrient availability (Chrzanowski and Grover 2001b). These combined studies, suggested that it might be possible to identify landscape-level predictors for reservoirs similar to those described for north-temperate lakes. Given the duration of the study pre-sented here (2.5 years), we expected synchronous behavior to result from seasonal forcing, especially by temperature. Using regression, we explored whether seasonal temperature variation explains synchronous dynamics, and found that it does for several variables. However, we also found several variables that displayed synchronous behavior that was not explained by temperature forcing, and which might result from landscape level synchrony in factors such as rainfall, or storm frequency.

MethodsJoe Pool Lake (JPL) and Eagle Mountain Lake (EML) are two of over 50 large reservoirs located in the Dallas-Fort Worth metropolitan area (north Texas, United States). Several of these systems have been subjects of previous studies of nutrient and plankton dynamics (Sterner 1994, Sterner and Grover 1998, Grover et al. 1999, Grover and Chrzanowski 2000, Chrzanowski and Grover 2001a, b, Grover and Chr-zanowski 2004). JPL (32°38’ N, 97°0’ W) is a bifurcated reservoir that has a NE-SW orientation and was impounded in 1986. The reservoir is mesotrophic with a surface area of 3620 ha, mean depth of 7.2 m and water residence time of 2.5 yr. EML (32°52’ N, 97°30’ W) has a N-S orientation, was filled in 1932 and is eutrophic with surface area of 3653 ha, mean depth of 6.1 m, and water residence time of 0.5 yr. The two reservoirs are approximately 55 km apart, are in different watersheds, and are not hydrologically linked. In both reservoirs water temperatures vary from ~ 7° in winter to >30° C in summer. The water column was mixed during cool weather and, due to high wind velocities during the second quarter of each year, neither reservoir displayed persistent thermal stratification during warm seasons. During warm weather, short-lived surface layers formed 4 - 8 m deep, that were 0.5 - 1° warmer than underlying water but these did not persist over weeks. During warm weather, both reservoirs also had reduced dissolved oxygen concentrations in a layer about 2 m above the sediments.

SamplingEach reservoir was sampled between March 1998 and Oc-tober 2000 at a single station near its deepest part. Depth of the sampling position was between 10-12 m in EML and 12-16 m in JPL. Samples were collected about every 2 weeks when water temperature was >16° and monthly at other times. Photosynthetically active radiation was measured with a Li-Cor model LI-185B coupled to a spherical sensor. Incident radiation was measured by shrouding the sensor to eliminate reflected light. Below-surface light was measured at 1 m intervals and the diffuse attenuation coefficient was estimated from the regression of ln(irradiance) against depth. Secchi depth was determined with a 20 cm disk. Depth pro-files of temperature and dissolved oxygen were taken (YSI model 33) and from these, the depth of the surface mixed layer was determined. Samples were taken with a 6 L Van Dorn bottle at discrete depths near top, bottom, and middle of the mixed layer, screened through 153-µm Nitex, and combined in 20-L polyethylene carboys to create a pooled mixed layer sample (PML). Three such pooled samples were taken during each sampling thereby creating true triplicate samples. Publicly available databases maintained by man-agement agencies were used for hydrological and wind data, with the latter derived from monitoring stations within 15 km of each reservoir.

AnalyticalIn the laboratory, subsamples were removed from each PML and preserved in formaldehyde (2% final concentration) for enumeration of bacteria (epifluorescence microscopy using DAPI as the fluorochrome, Porter and Feig 1980). Aliquots were collected on filters (Whatman GF/F), immersed in saturated MgCO3 (1 mL), and frozen for later determina-tion of chlorophyll a (CHL) concentration (overnight freeze-thaw extraction in 90% acetone without grinding, Glover and Morris 1979). Aliquots (50 mL) from each PML were preserved with Lugol’s iodine and formalin for identification and enumeration of algae. Larger aliquots (500 mL) were also sub-sampled. Particulate matter was collected on filters (Whatman GF/F) frozen or immediately dried for later determinations of particulate nutrients. Dis-solved nutrients contained in filtrates were frozen for later analyses (see below). Particulate organic carbon (POC) and nitrogen (PN) were determined using a Perkin-Elmer CHN analyzer. Dissolved organic carbon (DOC) was determined from CO2 released from oxidation with persulfate. Various forms of phosphate (particulate (PP), total dissolved (TDP) and soluble reactive (SRP)), soluble reactive silica (SRSi), and nitrogen (total dissolved (TDN), ammonium, nitrate, and nitrite) were determined by conventional colorimetric methods (Strickland and Parsons 1972). Nitrate was reduced by shaking with spongy cadmium (Jones 1984) and analyzed as nitrite with final concentrations corrected for reduction


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efficiency and ambient nitrite concentrations (Wetzel and Likens 1991). Total phosphate (TP) was taken as the sum of PP and TDP. Total nitrogen (TN) was taken as the sum of PN and TDN. Algae were identified morphologically and enumerated with an inverted microscope (Margalef 1969) with 200-400 individuals counted for dominant taxa and smaller numbers counted for less common taxa (Grover and Chrzanowski 2004). For all samples collected in 1998, 10-30 individuals of common taxa were measured with an ocular micrometer and biovolume calculated (Margalef 1969). For less common or rare taxa, biovolumes were estimated from occasional measurements, or from literature values. From biovolumes and cell densities of each taxon, biomass was calculated using the conversion of 0.2 pg C µm-3 (Rocha and Duncan 1985) and from this, the total and relative biomasses of each taxon were calculated. Phytoplankton diversity, as the Simpson Index, was computed based on biomass (Interlandi and Kilham 2001) and richness was determined at genus level (Grover and Chrzanowski 2004).

Temporal coherenceSamples were collected on a schedule based upon water temperature. However, disruptions of the sampling sched-ules created situations where one reservoir may have been sampled more frequently. Consequently, some data were eliminated to create data sets having sampling dates as close as possible (46-49 observations for most parameters, depend-ing on occasional missing values). Samples were eliminated purely to minimize time between sample collection and not on any analytical feature of the data itself; 84% of samples were collected from both reservoirs within 8 days, and 16% within 14 days (Table 1). Temporal coherence was assessed from the Pearson product-moment correlation coefficient (r) calculated from simple regression of a variable of one reservoir against its counterpart from the other reservoir. No attempts were made to extract ‘outliers’ from the data. The correlation coefficient and a probability limit of p < 0.05 were used simply to identify variables having similar fluctuations and that may subsequently be used to explore landscape-level forcing-functions. Each reservoir was considered an independent variable and lines fitting statistically significant correlations shown in Fig. 2 are plotted as the geometric mean regression line (Ricker 1973).

Results and DiscussionBoth EML and JPL are located in urban settings and neither reservoir serves as a source of cooling water for power gen-eration. EML is approximately 50 years older than JPL and managed as a water supply reservoir whereas JPL is managed as a flood control reservoir.

Physical conditionsLarge seasonal and inter-annual variations in hydrodynamic features of the reservoirs were observed (Fig. 1). Rainfall and inflows (data not shown) to each reservoir tended to be higher in spring and autumn than in winter and summer; however, a regional drought from the last quarter of 1999 to the end of the study period drove variations in reservoir level that were not strictly seasonal (Grover and Chrzanowski 2004). Water level at EML fell continually through the study and especially rapidly during the third quarter of 2000. Water level in JPL did not decline continually (Fig. 1). In both reservoirs, temperature varied seasonally from ~7 (winter) to ~30° C (late summer) and seasonal temperature shifts were not accompanied by persistent thermal stratification.

VariablesSimple descriptive statistics for the suite of variables mea-sured in each reservoir are presented in Table 2. JPL, the younger of the two systems, is less turbid than EML and has a deeper average mixing depth. As might be expected from turbidity indices (attenuation coefficient, Secchi depth), EML has higher concentrations of particulate nutrients, CHL, and bacteria than does JPL. Dissolved nutrients, with few excep-tions, reveal similar differences between the reservoirs.

The relationship between TN and TP fit into the hyperbolic relationship described by Guildford and Hecky (2000) for a wide variety of systems. Seston C:P and N:P ratios sug-gest that phytoplankton in EML range between P-sufficient to moderately P-deficient whereas phytoplankton in JPL is extremely P-deficient.

Temporal CoherenceTemporal coherence is commonly assessed through corre-lation analysis of annual or seasonal averages of a variable monitored over many years in a collection of lakes (Rusak et al. 1999, Pace and Cole 2002). Since sampling frequen-cies may vary over years, annual or seasonal averages offer

Table 1.-Days separating sample collection at Joe Pool and Eagle Mountain Lakes.

Days between Percent of sampling Frequency total samples

4 3 6 5 4 8 6 6 12 7 22 45 8 6 12 9 2 4 12 1 2 14 5 10


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an expedient means of creating paired data sets appropriate for correlation analysis. Our data set spans approximately 2.5 years and, while this time span is short in comparison to some studies of temporal coherence, our sampling protocol allowed us to create a finer-scaled paired-data set and capture much of the seasonal and within-year variance inherent in these reservoirs.

When considered in overview, there was considerable scatter in the data when a variable from one reservoir was plotted against the corresponding variable from the other (Fig. 2).

As was found for other systems (Kratz et al. 1988) there was considerably less scatter when purely physical features were considered (as temperature, Fig. 2) and greater scatter when chemical features (as total dissolved nitrogen, Fig. 2), or features that integrated physical, chemical, and biological features (as Secchi depth, attenuation, or mixing depth; Table 3, Fig. 2) were considered.

Despite generally high between-reservoir variability with respect to many measures, we identified several measures that demonstrated synchronous behavior. For the most part,

Figure 1.-Variability in some physical forcing functions in Eagle Mountain and Joe Pool Lakes. Wind speed is indicated by the lines without symbols.


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Table 2.-Descriptive statistics for the suite of variables measured at Joe Pool and Eagle Mountain Lakes.

Eagle Mountain Joe Pool

Form Identity Mean Range N Mean Range N

Particulates Organic carbon (µM) 195 68-282 50 121 62-203 50 Nitrogen (µM) 16 6-33 50 6.0 1-12 50 Phosphorus (µM) 1.0 0.4-2.0 50 0.4 0.1-0.8 50Dissolved Organic carbon (µM) 558 258-4923 50 297 198-509 50 Total nitrogen (µM) 38 24-55 49 36 24-63 51 Ammonium (µM) 2.8 0.4-8.9 50 2.2 0.5-6.6 50 Nitrate (µM) 0.5 0.06-2.2 50 0.3 0.01-0.9 50 Nitrite (µM) 3.2 0.2-12.4 50 7.5 0.2-27.3 50 Total phosphorus (µM) 0.8 0.4-2.0 50 0.3 0.1-0.9 51 Reactive phosphorus (µM) 0.4 0.1-1.2 50 0.1 0.03-0.4 51 Reactive silicate (µM) 70 13-123 49 46 16-101 51Nutrient ratios TN:TP (molar) 31 15-43 50 69 33-120 51 TDN:TDP (molar) 56 15-112 49 129 57-453 51Seston ratios C:P (molar) 204 51-405 50 478 79-1330 49 N:P (molar) 16 4-28 50 23 3-57 49Microbial Bacteria (109 cells L-1) 3.9 1.8-7.9 47 2.9 1.4-5.7 51 Chlorophyll a (µg L-1) 20 3-41 48 7 3-25 50 Algal diversity (Simpson) 8.85 2.57-14.09 49 8.46 2.51-17.43 49 Algal richness 34.17 21.67-44.67 49 30.43 14.00-43.67 49Physical Temperature (C) 23 7.3-29.8 49 23 10.3-30.4 51 Secchi depth (m) 1 0.6-1.7 46 1.3 0.1-2.5 44 Attenuation (m-1) 1.2 2.4-0.8 50 0.8 1.7-0.6 51 Zmix (m) 8 3-13 50 12 4-17 51

Table 3.-Temporal coherence (r) between Joe Pool and Eagle Mountain Lakes. p = probability, N = number of samples, NS = not significant.

Form Identity r p N

Particulates Organic carbon (µM) 0.39 <0.007 48 Nitrogen (µM) 0.37 <0.010 48 Phosphorus (µM) 0.42 <0.003 48Dissolved Organic carbon (µM) 0.08 NS 49 Total nitrogen (µM) 0.06 NS 48 Ammonium (µM) 0.01 NS 48 Nitrate (µM) 0.01 NS 49 Nitrite (µM) 0.06 NS 49 Total phosphorus (µM) 0.31 <0.03 49 Reactive phosphorus (µM) 0.35 <0.015 49 Reactive silicate (µM) 0.74 <0.001 48Nutrient ratios TN:TP 0.24 NS 47 TDN:TDP 0.45 <0.002 47Seston ratios C:P 0.53 <0.001 47 N:P 0.54 <0.001 47Microbial Bacteria (109 cells L-1) 0.48 <0.001 46 Chlorophyll a (µg L-1) 0.14 NS 46 Algal diversity (Simpson) 0.28 NS 49 Algal richness 0.59 <0.001 49Physical Temperature (C) 0.97 <0.001 48 Secchi depth (m) 0.18 NS 40 Attenuation 0.26 NS 49 Zmix (m) 0.22 NS 49


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Figure 2.-Correlation plots for examples of particulate, dissolved, microbial components of Eagle Mountain and Joe Pool lakes. Lines represent the geometric mean regression for significant correlations.

and perhaps not surprisingly, the measures showing synchro-nous behavior are those normally seasonal and associated with particulate plankton dynamics (POC, PN, and PP) or dissolved factors that force plankton dynamics (TP, SRP and SRSi ). Similarly, nutrient ratios customarily used as indices of nutrient limitation in the plankton (TDN:TDP, C:P, and N:P) were found to be coherent.

We examined two indices related to algal biomass, CHL and biomass-based Simpson’s diversity index, neither of which varied coherently. This result is consistent with the findings of several studies of temporal coherence (Magnuson et al. 1990, Kratz et al. 1998, and Baines et al. 2000) and is not surprising since these indices should reflect system-specific forcing functions; JPL is less turbid, has a deeper average mixing depth and lower nutrient concentrations than does EML. Despite the lack of synchrony relative to indices of algal biomass, the systems were coherent with respect to richness, which resulted from high overlap among taxa com-mon in both reservoirs and similar seasonal occurrence. In contrast to our findings with phytoplankton, variability in bacterioplankton abundance was temporally coherent. This result was surprising in light of the lack of synchrony in phy-toplankton biomass and our previous work where we found that phytoplankton and bacterioplankton growth responds in remarkably similar ways to nutrient additions (Chrzanowski and Grover 2001a). The temporal coherence between the two reservoirs for bacteria tends to confirm previous speculation that bacteria were less constrained by nutrients than were the phytoplankton. Further, this may suggest that studies of temporal coherence may find greater usefulness when descriptive data (concentrations, abundances) are examined rather than process data (integrative rate functions).

Can coherence be explained?Perhaps the most notable feature of reservoirs in the cen-tral and southwestern US is the water temperature. These systems rarely, if ever, develop any ice cover and surface waters may approach 35°C during summer months. In deep systems, thermoclines may be very sharp and stratification long-lived (Chrzanowski, unpublished). In shallow systems, thermoclines are poorly defined and stratification short-lived or non-existent. There is a strong seasonal component associ-ated with many of the variables considered in this study and this seasonal signature mimics that of temperature. Many of the variables analyzed were positively correlated with temperature (Table 4). These results imply that synchronous behavior between reservoirs for some variables may be simply driven by strong temperature forcing (and perhaps unresponsive to differing management practices). Conse-quently, we re-examined all cases of coherence between reservoirs where there was also a significant correlation between the coherent variable and temperature in at least one of the reservoirs. Coherence was re-evaluated after using linear regression to remove the effect of temperature on the temporal distribution of the variables. Thus, our measure of coherence stems from a correlation of the residuals from a regression of a given variable against temperature for EML against the corresponding residuals for JPL. Synchronous behavior in the absence of significant correlations between a variable and temperature implies that temperature was not a primary function forcing synchrony.

Comparison of Tables 3 and 4 suggest that two variables, SRSi and TDP, are driven by factors other than tempera-ture. However, TDP requires some additional consideration (see below). Eight variables showing synchronous behavior (Table 3) also had significant correlation with temperature in at least one reservoir: POC, PN, PP, SRP, seston C:P, ses-ton N:P, bacteria, and algal richness (Table 4). For three of


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these variables, POC, PN, and SRP, synchrony was reduced to insignificance (p>0.05) after using regression to remove temperature effects (Table 5). Biological kinetics that might directly depend on temperature strongly influence these three variables, although influences of other factors correlated to temperature cannot be ruled out.

TDP appears to be driven by factors other than temperature. However, a major component of TDP, SRP, was strongly correlated to temperature and temperature effects accounted for SRP synchrony (above). Therefore, it seems reasonable to conclude that temperature forcing also explained coher-ence in TDP.

For another five variables correlated to temperature in at least one reservoir, PP, seston C:P, seston N:P, bacteria, and algal richness (Table 4), synchrony between reservoirs remained

significant (p<0.05) even after removing temperature effects (compare Tables 3 and 5).

Such synchrony independent of temperature implicates landscape-scale drivers that are not strongly related to tem-perature, such as rainfall, storm frequency and sediment re-suspension (Fig. 1). Bacterioplankton in another north Texas reservoir exhibited higher heterotrophic activity in response to storm events (Hubbard and Chrzanowski 1986). Given the strong impact of bacteria on the phosphorus cycle (Cotner and Biddanda 2000), such changes in bacterial activity could produce changes in indices of microbial phosphorus status such as PP, seston C:P and seston N:P.

Two other variables were synchronous but did not have significant correlations with temperature in either reservoir: SRSi, and TDN:TDP (Tables 3 and 4). Like PP, seston

Table 4.-Correlations (r) with temperature for the suite of variables measured at Joe Pool and Eagle Mountain Lakes. p = probability, N = number of samples, NS = not significant.

Eagle Mountain Joe Pool

Form Identity r p N r p N

Particulates Organic carbon (µM) 0.45 <0.002 47 0.49 <0.001 48 Nitrogen (µM) 0.47 <0.001 47 0.64 <0.001 48 Phosphorus (µM) 0.14 NS 47 0.41 <0.004 48Dissolved Organic carbon (µM) 0.23 NS 48 0.01 NS 49 Total nitrogen (µM) -0.46 <0.002 47 -0.46 <0.001 47 Ammonium (µM) -0.37 <0.02 47 -0.22 NS 48 Nitrate (µM) -0.18 NS 48 -0.52 <0.001 49 Nitrite (µM) -0.63 <0.001 48 -0.58 <0.001 49 Total phosphorus (µM) 0.05 NS 48 0.26 NS 49 Reactive phosphorus (µM) 0.38 <0.008 48 0.49 <0.001 48 Reactive silicate (µM) 0.06 NS 47 0.04 NS 47Nutrient ratios TN:TP 0.04 NS 46 0.23 NS 46 TDN:TDP -0.08 NS 46 -0.06 NS 47Seston ratios C:P 0.34 <0.03 46 0.41 <0.005 46 N:P 0.54 <0.001 46 0.58 <0.001 46Microbial Bacteria (109 cells L-1) 0.46 <0.002 45 0.15 NS 46 Chlorophyll a (µg L-1) 0.43 <0.004 45 0.44 <0.003 46 Algal diversity (Simpson) 0.34 <0.02 48 0.64 <0.001 49 Algal richness 0.46 <0.001 48 0.83 <0.001 49

Table 5.-Temporal coherence (r) between Joe Pool and Eagle Mountain Lakes following removal of the effects of temperature. p = probability, N = number of samples, NS = not significant.

Form Identity r p N

Particulates Organic carbon (µM) 0.10 NS 47 Nitrogen (µM) 0.06 NS 47 Phosphorus (µM) 0.35 <0.02 47Dissolved Reactive phosphorus (µM) 0.14 NS 48Seston ratios C:P 0.42 <0.004 46 N:P 0.28 <0.05 46Microbial Bacteria (109 cells L-1) 0.46 <0.002 45 Algal richness 0.36 <0.02 48


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C:P and seston N:P, landscape-scale factors unrelated to temperature might drive synchrony in SRSi and TDN:TDP. Coherence for SRSi resulted from a large increases in con-centration over the course of the study, with especially high concentrations during the dry winter of 1999-2000 and the dry summer of 2000 (Grover and Chrzanowski 2004).

Reservoirs have often been considered as individually unique; separated by age, basin characteristic, morphology and a host of other features. However, it appears, from this comparison of two very different reservoirs (see Methods) that the systems behave similarly with respect to several commonly measured limnological features, most notably, those involving phosphorus. The time frame over which these reservoirs were compared is short (about 2.5 yrs) and tends to place more emphasis on week-to-week variability than on the year-to-year variability common in other studies of temporal coherence. Emphasis on shorter-term variability seems appropriate for managed systems such as reservoirs but the long-term perspective, typically lacking in reservoir studies (Lind 2000), should not be ignored. Our intent was to discover if reservoirs might have features similar to those of north temperate lake systems that permit landscape level predictions of ecosystem behavior. The results of these analyses of two very different reservoirs encourage longer duration comparisons encompassing reservoirs having a greater range in limnological characteristics (for example, inclusion of deep clear-water systems) and purposes (for example, those used in conjunction with power generation, which may magnify temperature effects). Further, and per-haps more intriguing, the data suggest that it may be possible to establish reference conditions (behavior) for reservoirs in a given geographic area and identify systems that do not behave coherently or are becoming impacted and moving outside of coherent norms.

AcknowledgmentsWe thank K. Frangioso, J. Hardwick, M. Hurt, K. Penne-baker, B. Smith, M.A. Stout, R. Miller, K. Vinson, and R. Curry for their technical assistance. Hydrological data were provided by the Tarrant Regional Water District (TRWD) and the U.S. Army Corps of Engineers. We thank M. Ernst (TRWD) and G. Clingenpeel (Trinity River Authority) for discussions of reservoir dynamics, and P.N. Grover for advice on wind data. US Environmental Protection Agency grant R825868 to JPG and THC supported this work. Although the research described in this article has been funded by the US EPA, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency, and no official endorsement should be implied.

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