Abstract
Harmful algae blooms have become an increasing concern in context to the safety of water resources around the globe; however, little is known about the dynamics and specific causes of such blooms in the prairie ecozone in North America. The aim of this study was to research the nitrogen (N) and phosphorous (P) content and nutrient limitation (defined as N and P limitation) of growth of cyanobacteria in a northern prairies reservoir (Lake Diefenbaker [LD], SK, Canada). A combination of concentration balance analysis for N and P, controlled bioassays with the natural consortium of phytoplankton or defined monocultures of cyanobacteria, and satellite imagery was applied to address this aim. The current trophic status of Lake Diefenbaker is one of moderate eutrophication. Primary production in the lake is P-limited, and N did not represent a limiting factor for algal production. There was no significant increase in TP concentrations between the upper and lower portions of the reservoir, indicating that most of the phosphorus in LD comes from upstream sites in Alberta. Anabaena circinalis, a species that has the potential to seriously degrade lake ecosystems, was identified as the predominant cyanobacteria in LD. Together with the fact that total P (TP) influxes into the reservoir primarily originate from upstream sources, these results suggest the need for remedial measures in the upstream reach of the South Saskatchewan River. Satellite imaging represented a promising approach in support of monitoring for potential algal blooms in LD; however, due to limited sensitivity and issues associated with atmosphere interference this methodology should only be used in combination with in situ water quality monitoring. In summary, while this study indicated that Lake Diefenbaker is potentially at risk from blooms with cyanobacteria (some of which such as Anabena sp. that can produce toxins) during late summer and fall, development of clear causal relationships and risk assessment strategies is currently limited due to lack of monitoring data and programs.
Introduction
Intensification of agricultural and industrial activities and increases in population over the past century have led to significant eutrophication of surface waters around the world, including in Canada [1] . One concern in context with such eutrophication is development of harmful cyanobacteria blooms and associated toxins, which represent a threat to drinking water sources such as lowland rivers, lakes, and reservoirs [2, 3] . Water bodies in the prairie ecozone of Canada are particularly at risk to such algal blooms because of phosphorus-rich soils, intense agricultural use and more intense solar irradiances [4] . One aquatic prairie ecosystem of concern in this context is the South Saskatchewan River (SSR), which flows across southern Alberta and then into southern Saskatchewan has been identified as one of Canada's most threatened rivers [5] . Approximately 67% of the catchment area of the SSR is used for agriculture, which results in significant influxes of nutrients and pesticides [6] . In addition, most of the volume of water in the SSR originates in the Rocky Mountains and is formed by the Oldman, Bow and Red Deer Rivers, with relatively less water originating on the prairies [7] . Lake Diefenbaker (51˚1'53"N, 106˚50'9"W) is a multipurpose reservoir on the SSR, which is formed by the construction of Gardiner Dam and Qu'Appelle River Dam across the SSR and Qu'Appelle Rivers, respectively. The lake is 225 km long with approximately 800 km of shoreline and a surface area that reaches approximately 500 km 2 with a mean depth of approximately 33 m and a maximum depth of 66 m. Lake Diefenbaker provides water for domestic irrigation and municipal use for approximately 75% of the Saskatchewan population of more than one million people, and allows for regulation of flow of the South Saskatchewan and Qu'Appelle rivers, while providing hydro-electric power [8] . Approximately 75% of the drainage to Lake Diefenbaker is in Alberta [9] . The inflowing rivers also receive irrigation return flows and municipal and industrial effluents. In Alberta, upstream of Lake Diefenbaker, the watershed contains urbanized areas, including Calgary, Red Deer, Medicine Hat, and Lethbridge, intense agricultural lands and feedlots. In fact, it is estimated that over 80% of the population living in the South Saskatchewan River basin are in Alberta [10] . As a result, significant amounts of nutrients, primarily nitrogen (N) and phosphorous (P), and contaminants are released into the river, which can affect water quality. Riparian and in-lake activities represent additional sources of nutrients to Lake Diefenbaker from domestic sewage of small communities that use lagoons for treating the sewage, agriculture, and fish farming.
Introduction
Even though Lake Diefenbaker is a critical water resource for a large portion of the Western Prairie Region of Canada there is little information on its limnology and in particular the status and trends of nutrients, specifically N and P. While Lake Diefenbaker is representative of other reservoirs in similar eco-regions, it has unique characteristics and properties. It is characterized by some embayments that are deeper than 20 m and an annual fluctuation of water level that is approximately 6 m. A thermocline forms at approximately 25 m, but this is infrequent and dependent on discharge of the SSR. Stratification does not occur throughout the lake and there is little water exchange between embayments and the main channel. While the current status of concentrations of N and P in Lake Diefenbaker is comparable to that reported in other studies to support blooms of cyanobacteria [11] [12] [13] , due to the lack of continuous monitoring programs and research efforts, no information on historic blooms with one exception are available for this reservoir. This exception was the report of a bloom of cyanobacteria that occurred in the southern and western parts of the reservoir during fall 2007 [14] . P and N are commonly considered as the limiting nutriaents for phytoplankton production, and particularly availability of P has been hypothesized as the critical factor in contest with freshwater cyanobacteria blooms [15] [16] [17] . However, a number of studies that investigated the correlation between N and P and cyanobacteria or phytoplankton abundance have not always found clear relationships between these two nutrients and algal biomass. In fact, algal growth and abundance also depends on the availability of additional micronutrients, particularly Fe but also trace amounts of Mo, Mn and Cu [18] [19] [20] . The relevance of Fe becomes obvious if one considers that the two most energy-demanding systems in the cell, photosynthetic carbon reduction and nitrogen reduction, are heavily dependent on Fe-containing compounds. Particularly, Fe serves a crucial role in electron transfer reactions and in enzymes. In addition to Fe, other trace elements such as Cu and Mn also represent essential micronutrients at very low concentrations. However, they become toxic, particularly to cyanobacteria, at greater concentrations (i.e. in the micromolar range) [18] . Finally, a number of other environmental factors such as altered temperature and irradiance (e.g. as a consequence of climate change) and hydrological processes have been hypothesized to significantly influence phytoplankton/cyanobacteria blooms in freshwater and marine environments [17, 21] .
Introduction
The goal of this study was to elucidate the nutrient (N, P and other micronutrients including Fe and Mn) content and nutrient limitation of growth of primary production, particularly of cyanobacteria, in Lake Diefenbaker, as the primary local source of water, but also as a model for reservoirs and lakes with comparable hydro-ecology in the western prairies of Canada. Information was collected to conduct a rudimentary concentration balance for nitrogen and phosphorus. Specifically, the question of relative contributions from upstream and in the vicinity of the lake was addressed. In addition, to determine the limiting factors (N and/or P), bioassays were conducted with either the natural consortium of phytoplankton or defined monocultures of cyanobacteria, including Anabaena sp. Satellite imagery was investigated to determine its potential use to quantify the distribution of chlorophylls in Lake Diefenbaker to determine the extent of blooms of algae and cyanobacteria in space and time. Furthermore, models and assessment tools, based on basic water quality parameters, N and P loads, and satellite imaging, were developed to aid in prediction and assessment of algal blooms in prairie lakes. mand (COD), total iron (Fe 3+ ), manganese (Mn 2+ ), and algal abundance of algae. Quantifications of the various parameters were made according to standard methods (Supplemental Materials, Table S1 ). Method detection limits (MDLs) were established for all analytes by use of reagent water (blank) fortified at a concentration of 3 -5 times the estimated instrument detection limits (or MDLs). The ionfortified reagent water samples were used for determination of MDLs as follows: 20 μg 2 NO  /L (parts per billion; ppb), 24 μg /L, 24 μg 3 /L, and phosphate 60 μg
Introduction
PO  /L. Any sample containing the above concentrations of ions went through the entire analytical process with 7 replicates done in three separate days. Concentrations of ions were reported as the mean and standard deviation (SD).
Introduction
October 2008. Sampling was conducted between 10 am and 6 pm based on weather conditions [22] . Except for collections at the outflow, downstream of Gardner Dam (site 7), where only surface water was collected, the sampling site was located by GPS and 3 stratified water samples were collected according to depth as follows: 1) Surface: water surface at a depth of 0.5 m; 2) Mid-depth; and 3) Bottom: 0.5 m above the bottom. A total of 81 samples were collected for nutrient analyses during the surveys.
Introduction
Parameters measured directly in the field included temperature, depth, pH, conductivity (Cond.), turbidity, chlorophyll a (Chl-a), total dissolved solids (TDS), salinity, and dissolved oxygen (DO) by means of a Hydrolab Data Sonde DS5 (OTT, Chesterfield, UK). Additional samples of water were collected, maintained at 4˚C, and immediately shipped to the laboratory for analysis of nutrients as well as organic and inorganic constituents (Supplemental Materials, Table S1 ).
Introduction
Chl-a concentrations were quantified both in samples collected in the field and during the controlled bioassays following the spectrophotometric methods described by Lorenzen [23] and Jeffrey & Humphrey [24] with modifications as described in US-EPA Method 445.0 [25] . Specifically, we used a set of narrow bandpass excitation and emission filters (Turner Designs, Sunnyvale, CA; excitation filter 436FS10; emission filter 680FS10) that nearly eliminate the spectral interference caused by the presence of Pheo-a and Chl-b, and which has been previously validated and accepted by the US-EPA (EPA 1997). Briefly, phytoplankton were concentrated by filtering at low vacuum through a 0.7 μm glass fiber filter, and pigments were extracted from phytoplankton in to
Quantification of Nutrients and Inorganic and Organic Constituents
Upon arrival at the laboratory (wet chemistry and analytical lab of the Toxicology Centre, University of Saskatchewan) water samples were immediately analyzed for alkalinity, hardness, nitrate ( 90% acetone with the aid of a mechanical tissue grinder and allowed to steep for a minimum of 2 h. The filter slurry was then centrifuged at 675 × g for 15 min and an aliquot of the supernatant was transferred to a glass cuvette. Fluorescence was measured before and after acidification to 0.003 N HCl with 0.1 N HCl using a Turner Designs Model 10 Series fluorometer.
Bioassays
Bioassays were conducted in the laboratory to determine the limiting factors for growth of cyanobacteria. Bioassays were performed with three different waters from Lake Diefenbaker. The experimental design for bioassays was a 5 × 5 full factorial design with 3 replicates to test for the effects of N (as KNO 3 ) and P (KH 2 PO 4 ) individually and in combination. Five concentrations of both N (0.00, 0.25, 0.50, 1.00 and 2.00 mg N/L) and P (0.00, 0.0125, 0.025, 0.05 and 0.10 mg P/L) were tested. Concentration ranges were such that they bracketed average concentrations of nitrite, 3 and 2 ( 4 PO NO  NO   ) of 0.0164, 1.013 and 0.0124 mg/L, respectively, that were measured in lake water during the field portion of the studies. One treatment was the control to which no N or P was added. Thus, there were a total of 75 flasks (25 treatment combinations with 3 replicates each). The five concentrations of N and P were selected in combination for nutrient addition (Supplemental Materials, Table S2 ). Bioassays were conducted either with consortia of plankton or with filtered water F and flasks were inoculated with either a single species of cyanobacteria or a consortium of cells isolated from Lake Diefenbaker water. Qualitative microscopical analysis revealed five algal species in surface and mid water of Lake Diefenbaker sampled on September 29 and 30, 2008 (Figure 2) . At site 4, for which a cyanobacteria bloom was reported in October 2007, the dominant algae species was Anabena circinalis in surface water. Furthermore, Woronichinia naegeliana at this location and in surface and middle layer lake water at site 5. None of the other sites (1, 2, 3, 6 and 7) contained these species. Furthermore, three unidentified species occurred at most of the locations. While no quantitative assessments of algal abundance at the laboratory were made due to lack of appropriate sample preservation, the qualitative assessment indicated that Anabaena sp. was the dominant algal species.
Bioassays
In the first study with a natural consortium of phytoplankton from Lake Diefenbaker, a volume of 150 mL surface water was collected at site 4 and placed into 250 mL Erlenmeyer flasks. Phytoplankton was identified and enumerated initially and concentrations of chlorophyll a were determined at the beginning of the experiment and then every few days until the assay was terminated. Cultures were incubated for 14 d at 26˚C, under 2000-lux lighting on a 12:12 light:dark cycle. Flasks were shaken by hand twice a day. Three replicate samples and blanks were taken as the quality control.
Bioassays
In the second study with the species of Anabaena sp., a volume of 150 mL filtered and autoclaved lake surface water collected at site 5 was placed into 250 ml Erlenmeyer flasks. At the beginning of the experiment the number of Anabaena sp. cells was determined and the concentration of chl-a was determined. An initial cell density of 1.0 × 10 4 per ml Anabaena sp. was used and N and P were added to each flask. The culture and experimental conditions were the same as those of the first bioassay.
Satellite Imaging of Cyanobacteria Blooms
Satellite images of Lake Diefenbaker were obtained and analyzed to determine the overall spatial distribution of algal and/or cyanobacteria biomass, as measured by spectral reflection of chlorophylls. A total of five scenes of MODIS (http://modis.gsfc.nasa.gov/; Supplemental Materials, Table S3 -S6 and Figure S1 ) matched with in situ measurements of chlorophyll were collected. Therefore, further analyses of the available image data were conducted to develop predictive relationships between spectral radiance imaging and distributions of concentrations chlorophyll within Lake Diefenbaker.
Satellite Imaging of Cyanobacteria Blooms
Because the spatial resolution within MODIS is only km, the algorithm to relate concentrations of chlorophyll from spectral radiance within MODIS was not applied Alternatively, MODIS bands 1 & 2 with spatial resolution of 250 m were used. These bands cannot be used to specifically predict concentrations of chlorophyll, but rather give an indication of Vegetation Index (VI). Thus, the VI was used as a surrogate and has been applied to indirectly estimate the spatial distribution of algal biomass. Two VIs were applied in this study, first the Normalized Difference Vegetation Index (NDVI) [36] , which is defined as the normalized difference of brightness values indicating relative distribution and activity from the satellite image (Equation 1).
Satellite Imaging of Cyanobacteria Blooms
where ρ nir and ρ red represent the spectral reflectance measurements acquired in the near-infrared and visible (red) regions, respectively. MODIS NDVI was processed as described in the following steps (Equation 2). 1) Use band 1 and 2 having 250 m spatial resolution for detecting green algae;
Satellite Imaging of Cyanobacteria Blooms
2) Reflectance data was of limited use owing to over exclusion for the atmospheric correction;
Satellite Imaging of Cyanobacteria Blooms
3) Use radiance data instead of the reflectance data.
Satellite Imaging of Cyanobacteria Blooms
The second index applied was the Transformed Vege- tation Index (TVI), which enhanced values of the NDVI for the elimination of negative values (Equation 3) .
Statistical Analysis
Statistical analyses were conducted by use of STATIS-TICA 6 (StatSoft Inc., Tulsa, OK, USA), SigmaStat 3.0 (SPSS Inc., Chicago, IL, USA) and SPSS 11.5 (SPSS Inc.). Prior to the use of parametric statistical procedures, normality was evaluated by the Shapiro-Wilks test and ated using Levene's test. All parameters met the assump-
Statistical Analysis
the assumption of homogeneity of variance was evalu-studies fulfilled all assumptions tions of homogeneity of variance test (Supplemental Materials, Table S7 ), and thus, measures of productivity, including Chl-a and number of cells, in factorial assays of nutrient (N and P) limitation were analyzed by threeway ANOVA with Student-Newman-Keuls test (SNK). Values for parameters measured in the field were investigated by two-way ANOVA (location and depth). Relationships between the Chl-a, and limiting factors were explored using multiple correlations, and stepwise regression analyses [26] . Data obtained during the field for the calculation of linear regressions, and thus, were not transformed. Chl-a data from the two laboratory experipl es among most water f time, depth and sam-ments were log-transformed to approximate normal distribution prior to conduct of linear regression analyses. Data for site 1 -6 (collected from July to October 2008) were analyzed by use of three-way ANOVA with saming site, sampling depth, and sampling date as the class variables, followed by SNK pair-wise multiple comparesons, or by the non-parametric Kruskal-Wallis test followed by the Mann-Whitney U test, as appropriate. Parameters, including hardness, COD, DO, pH, Chl-a, and depth passed the normality test and equal variance test using a three-way ANOVA followed by application of the SNK test (SigmaStat 3.0). The remaining 16 parameters (dependent variables) that did not meet the assumptions for parametric analyses were analyzed by the median test of variances (STATISTICA 6).
Results and Discussion

Water Quality Analysis
There were significant differenc quality parameters as a function o pling location (Tables 1 and 2). There were statistically significant differences among sampling time-points for hardness, COD, pH, NO 2 , NO 3 . Estimation o of Chl-a Chl-a was selected as an index of phytoplankton biomass and/or production [27, 28] . Concentrations of Chl-a in lake water are summarized to show a monthly variation of algal biomass in Lake Diefenbaker during June to Oct 2008. In general, concentrations of Chl-a increased from June to October (Figures 3(a)-(c) ). Concentrations of Chl-a reached a maximum of 5.31 μg Chl-a/L in October at site 4. This is approximately 6-times greater than Chl-a measured in June (0.95 μg Chl-a/L), which indicates a significant increase in phytoplankton biomass. In general, Chl-a concentrations measured in surface water from Lake Diefenbaker were comparable to those reported in other lakes (0.22-7.09 μg Chl-a/L) in Canada [29] [30] [31] [32] . However, compared to other freshwater systems in which massive cyanobacteria blooms occurred (the associated Chl-a concentrations were more than 100-times greater than those recorded during this study; up to 830 μg Chl-a/L; [21] ), Chl-a concentrations in Lake Diefenbaker measured during the 2008 sampling season were low.
Water Quality Analysis
Based on monthly concentrations of Chl-a in surface water, there was a general decreasing concentrat and the more downstream locations. In September and October, there was no such decreasing trend. In these months, the greatest concentrations of Chl-a were observed at sites 2 and 4 (Figures 3(a)-(c) ). Previous reports indicated that blooms of phytoplankton are often associated with intensity of solar radiation over time [33, 34] or other weather condition such as wind [35] . While no specific data on intensity of solar irradiation or cloud cover was available for the sampling region in 2008, few days (=3) with precipitation in October 2008 would indicate relatively great light intensities prior to the peak of Chl-a in later October (http://climate.weatherof-fice.gc.ca/ climateData/). Furthermore, the prevailing winds in October 2008 were out of the West and North-West, which would favor accumulation of surface alga in the South-East arm of the reservoir (http://climate.Weatheroffice.gc.ca/ climateData/). This, together with the poor water exchange between this arm and the flowing portion of the reservoir, is hypothesized to represent contributing factors to the maximum Chl-a concentrations observed at sampling site 4 in October, which is located in the Qu'-Appelle arm of the reservoir. 
Satellite Image Analysis

Chl a Concentrations in NearBottom Water
July August
September October

Chl a [g/L]

Chl-a
Chl-a (c)
Figure 3. Monthly average concentrations of Chl-a [g/L] in 3 stratified water layers (Surface [A], MidDepth [B], and Near-Bottom [C]) at sites 1-7 in Lake Diefenbaker during June to Oct, 2008. Bars represent mean values. Error bars represent 1 × SD.
ured by NDVI (Figures 4 and 5 Table S3 and Figure S1 ) and concentrations of chlorophyll were calculated. NDVI values were negative because of the relatively small concentrations of chloro-cided with the approximate day of the cyanbacteria bloom. In general, NDVI measured at Sites 1 and 2 and at Site 7 exhibited less seasonality than that of Sites in the more downstream region of the reservoir. With exception of Site 2, this may be attributed to the greater flow rates in these areas that do not promote primary production similar to lake-type habitats. Table S4 ). This is in accordance with earlier studies that that found correlations between NDVI and surface cyanobacteria concentrations [36, 37] . The overall distribution of algal biomass along the river from upstream to downstream locations could be clearly identified by satellite image analysis.
Figure 3. Monthly average concentrations of Chl-a [g/L] in 3 stratified water layers (Surface [A], MidDepth [B], and Near-Bottom [C]) at sites 1-7 in Lake Diefenbaker during June to Oct, 2008. Bars represent mean values. Error bars represent 1 × SD.
The results of the image analyses indicated that either the NDVI or TVI indexes of MODIS represent promising tools to aid in explaining algal biomass distribution in surface waters such as Lake Diefenbaker [38] . Analysis of satellite images was useful for remote monitoring of the general conditions and spatial distributions of algal biomass in Lake Diefenbaker. However, the limited sources of available satellite image due to atmosphere interferences and relatively narrow range of chlorophyll concentrations in the lake represent potential limitations in the application of satellite imaging to estimate algal biomass distribution. Overall, the current data set and our experience suggests that it will be possible to use satellite image processing to examine and estimate algal distribution at a relatively large scale, but the resolution of concentrations by this method will need to be linked to supplemental water sampling. In particular, to enable accurate determination of the types of algal cells present and their absolute and relative densities will require parallel collection of field data. In addition, satellite imagery will be more useful for those species such as cyanobacteria that tend to float on or near the surface and will be of negligible use for quan-tifying chlorophyll at deeper .
Relationships between Water Quality, Nutrient
Status and Chl-a Relationships between Chl-a and 20 other parameters in lake water were investigated by use of multiple regression analysis. In addition, to determining relationships between and among these parameters, models to predict algal productivity were developed (Supplemental Materials, Tables S8-S10). Chl-a is an integrating variable for a relatively complex set of conditions that can influence phytoplankton biomass and production in lake water. Dynamics and content of Chl-a in bodies of water are restricted and controlled by several types of physical and chemical factors as well as biological factors. Conversely, these factors are changed in response to phytoplankton biomass as represented by Chl-a [39] . The correlation coefficient hat resulted from the multiple regression analysis comparing Chl-a with water quality parameters was 0.867 (r 2 = 0.752 N = 81). Because the concentration of Chl-a could be used to indicate phytoplankton biomass, the linear regression (model 1) showed that Chl-a concentration was closely associated with TP, and which is in accordance with other studies that reported strong dependence of algal biomass, as represented by Chl-a, on TP in both cold and warm freshwater lakes [16, 40] . Total phosphorus was found to be the limiting factor affecting primary production of phytoplankton in Lake Diefenbaker. If predictions of future loadings of TP can be estimated then the resulting change in algal biomass can be predicted.
Relationships between Water Quality, Nutrient
Furthermore, nutrient indexes based on concentrations of nitrite, nitrate, SO 4 , PO 4 , Cl -, TN, TP, Mn, and Fe were selected and their relationships with Chl-a determined by use of multivariate, stepwise regression analyses (Supplemental Materials, Table S8 ). The resulting linear regression model is defined by (Equation 4 ).
Relationships between Water Quality, Nutrient
[Chl-a] = 1.813 + 80. 44 [TP] (4) where: concentration of chlorophyll-a (Chl-a) is the dependent variable and concentration of total phosphorus ([TP]) is the independent variable expressed as mg P/L. The correlation coefficient (between Chl-a and TP in the water was 0.427) r 2 = 0.182, N = 81, p < 0.001. Based on this analysis, concentration of TP was significantly associated with Chl-a and the coefficient of determination of the standardization partial regression between these two variables was 0.392, which was greater than for any other parameters. Thus, using this type of multivariate analysis to investigate the accessory factors and micronutrients in Lake Diefenbaker, it was found that the primary limiting factor for algal production was TP, as is the case in many other freshwater system in temperate regions [16, 40] . In addition, the micro-nutri nts, Mn and mental studies in Lake Erie [20] reported Fe as a co-limiting factor of phytoplankton with Fe supplementation resulting in a synergistic stimulating effect on Chl-a in combination with P and N supplementation. Interestingly, depths.
Relationships between Water Quality, Nutrient
Fe were important cofactors in algal production. Experi- . 190 in this study a significant and negative correlation was observed between Fe concentrations in Lake Diefenbaker water and Chl-a concentrations (r = -0.482; r 2 = 0.232; N = 81; p < 0.001). Fe concentrations in Lake Diefenbaker water were approximately 10-to 100-times greater than those reported to stimulate phytoplankton growth as described by North et al. [20] , and thus, Fe is unlikely to have represented a limiting factor in Lake Diefenbaker. However, to date no information on optimum Fe concentrations or Fe-P-N ratios in freshwater systems are available, indicating the need for further studies to elucidate these interrelationships. None of the measures of nitroi tio of ors, determined based on concentrations of be the limiting nutrient and algal growth increased at conce g P/L (Figure 7(a) ).
Relationships between Water Quality, Nutrient
lim ot increase sig ena sp. an gen were significant predictors of Chl-a.
Identification of Limiting Factors for Cyanobacteria Growth
L mnologists and lake managers have developed a general consensus about freshwater lake responses to nutriaent additions. Essentially, at an ambient TP of greater than approximately 0.01 mg/L and or TN of about 0.15 mg/L it is likely that there will be blooms of cyanobacteria during the growing season [41] . The thresholds of concentrations of nutrients associated with eutrophication of lakes are a TP of 0.02 mg P/L and TN of 0.2 mg N/L [42] [43] [44] . According to the "Redfield" hypothesis [39, 45, 46] the typical molecular formula of algal cells is (CH 2 O) 106 (NH 3 ) 16 (H 3 PO 4 ), which is equivalent to a ra critical nitrogen/phosphorus concentrations of 16:1. On a mass basis this is equivalent to a nitrogen/phosphorus ratio 7.2:1. From this theoretical perspective, if the ratio of nitrogen/phosphorus was less than the critical value, nitrogen would be the factor limiting phytoplankton growth, while if the ratio was greater than this value, phosphorus would be the limiting factor. Average concentration of TN and TP in Lake Diefenbaker were 0.423 and 0.015 mg/L, respectively. Thus, the mean ratio of TN/TP in Lake Diefenbaker was 42:1 (min-max = 16:1 -440:1; Figure 6 ). Since the ratio is greater than the critical value, TP is currently the limiting nutrient factor in Lake Diefenbaker. Furthermore, based on the abundance of other micronutrients such as Fe as discussed in the previous section it is unlikely that P-N-Fe co-limitation affects biomass of phytoplankton in general, and biomass of cyanobacteria specifically. Based on this and a current relative classification scheme, Lake Diefenbaker would be classified as moderately eutrophic, and it can be concluded that excessive nutrients and phosphorus in particular would be the most likely cause of the prevalence of cyanobacteria (i.e. A. circinalis).
Identification of Limiting Factors for Cyanobacteria Growth
As a confirmation of the determination of putative limiting fact nutrients and theoretical relationships, two nutrient addition bioassays were conducted. Phosphorus was found to ntrations greater than 0.025 m After two weeks of exposure, concentrations of Chl-a in lake water doubled at concentrations of 0.025 mg P/L, regardless of N concentration. However, concentrations of Chl-a slightly increased as N increased when P concentrations were greater than 0.05 mg P/L (Figure 7(a) ). Thus, if concentrations of P increased, Chl-a (phytoplankton biomass) would only increase to a certain point until N became limiting. However, since some cyanobacteria genera have the ability to fix nitrogen [47] , more detailed and longer-term studies with different consortia of phytoplankton would be needed to develop a more robust predictive relationship.
Identification of Limiting Factors for Cyanobacteria Growth
The relationship between Chl-a and nutrient indexes TN and TP after 2 weeks for phytoplankton from Lake Diefenbaker cultured in water from the lake was analyzed by use of stepwise, multivariate, regression analysis. The resulting linear regression model is defined by (Equation 5):
Identification of Limiting Factors for Cyanobacteria Growth
Ln[Chl-a] = 6.788 + 1.076 Ln[TP] (5) where: Chl-a is the dependent variable and TP is the independent variable expressed as mg/L. The coefficient for the correlation between concentrations of Chl-a and TP was 0.632 (r 2 = 0.399, N = 25). TP was significantly associated with Chl-a. Thus, in Lake Diefenbaker the primary limiting factor for algal production was TP, and TN was not a significant predictor of Chl-a. In the second bioassay, which investigated N and P itations of growth of the single cyanobacteria that caused the bloom in Lake Diefenbaker, Anabaena sp. (the dominant species observed in the cyanobacteria bloom in 2007), after two weeks of exposure, Chl-a doubled at concentrations of 0.025 mg P/L or greater, regardless of the concentration of TN. However, Chl-a did n nificantly as a function of the concentration of TN (Figure 7(b) ). This result is similar to that observed in the bioassay that used lake water containing a consortium of algal species. This experiment confirmed that P was the limiting nutrient and algal growth increased at concentrations greater than 0.025 mg P/L.
Identification of Limiting Factors for Cyanobacteria Growth
The relationship between Chl-a from Anaba d TN and TP after two weeks cultured in the second bioassay were analyzed by use of stepwise, multivariate, regression analysis (Supplemental Materials, Table S9 ). 
Identification of Limiting Factors for Cyanobacteria Growth
where: Chl-a is the dependent variable and TP is the independent variable expressed as mg/L. The coefficient for the correlation between Chl-a and TP in water was 0.748 (r 2 = 0.559, N = 25). The TP concentration was also significantly associated with Chl-a, which showed anada, as a poten-s in P-influxes represent a potential risk factor in context trast, nitrogen did not represent a limiting factor for algal 
TN/TP Ratio

Conclusion
This study characterized N, P and micronutrient concentrations (Fe, Mn) during the summer/fall season in 2008 in Lake Diefenbaker, Saskatchewan, C e study. tial predictor of cyanobacteria blooms. Overall, the current trophic status of Lake Diefenbaker is one of moderate eutrophication. It could be demonstrated that primary production in Lake Diefenbaker is P-limited, and that increase with the occurrence of such algal blooms. In con- production. In addition, other factors such as wind patterns, hydrodynamics and the resulting temperature patterns as well as micronutrients such as Mn and Fe also are likely to contribute to the occurrence of algal blooms in the reservoir. A. circinalis, a common nuisance species that has the potential to seriously degrade the Lake eco-sysneeds to be reduced to anobacteria blooms. It was demonno significant increase in TP con-centrations between the upper and lower portions of the reservoir, indicating that most of the phosphorus in Lake Diefenbaker comes from upstream sites in Alberta, resulting in the need for remedial measures in the upstream reach of the South Saskatchewa ver. In addition to the investigation of relationships between algal tems, was identified as the predominant cyanobacteria in Lake Diefenbaker. Therefore, it is important to continu-production and water quality in Lake Diefenbaker, the use of satellite imaging techniques as a predictive tool to ously monitor for this species. Because increases in algal productivity were directly proportional to increases in TP, the loading of TP to the reservoir prevent future cy strated that there was n Ri identify algal blooms was explored in this study. While in general a promising approach, due to limited sensitiveity and issues associated with atmosphere interference this methodology should only be used in combination with parallel in situ water quality monitoring. It repre-sents, a potentially useful adjunct tool to inform sampling in context with water quality assessment studies. In summary, while this study indicated that Lake Diefenbaker is potentially at risk in context with cyanobacteria blooms during late summer and fall, development of clear causal relationships and risk assessment strategies is currently limited due to lack of historic monitoring data and ongoing programs. ples were shipped to the Laboratory as soon as possible after collection; All samples were preserved at 4˚C sealed in a freezer storage bag 
Acknowledgements


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