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Observations of two reservoirs during a drought in central Texas, USA: Strategies for detecting harmful algal blooms
Ecological Indicators 104 (2019) 588–593
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Observations of two reservoirs during a drought in central Texas, USA: Strategies for detecting harmful algal blooms
T
Tatiana E. Gámeza,b, , Lisa Bentonb, Schonna R. Manningc ⁎
a
Department of Biology, Texas State University, San Marcos, TX, USA Lower Colorado River Authority, Austin, TX, USA c Department of Molecular Biosciences, University of Texas at Austin, TX, USA b
ARTICLE INFO
ABSTRACT
Keywords: Eutrophication Drought Chlorophyll-a Harmful Algal Blooms Akaike Information Criterion Carlson’s Trophic State Index
Problems associated with eutrophication are increasing in freshwater reservoirs worldwide due to many factors, the majority of which are attributed directly to human development. Eutrophic bodies of water are much more susceptible to algal blooms, including potentially harmful species, as well as associated taste-and-odor compounds and toxic secondary metabolites. To assess the likelihood of eutrophication and harmful blooms, two reservoirs in the Highland Lakes of central Texas, which are located within the same drainage basin and climate zone, were observed for the duration of an extended drought period lasting from 2010 to 2015. This brief investigation examined a suite of physiochemical characteristics to determine which environmental factors had the most significant impacts on the eutrophication of a reservoir coupled with the abundance of microalgae. Standard water quality parameters that have been hypothesized to influence eutrophication were measured and compared with chlorophyll-a fluorescence as a proxy for phytoplankton abundance. Samples were taken every other month and each reservoir responded differently to the drought – Lake Lyndon B. Johnson (LBJ) became eutrophic while Lake Travis remained mesotrophic, according to Carlson’s Trophic State Index. Linear models using corrected Akaike Information Criterion analyses indicated that the primary indicators for microalgae in Lake LBJ were organic and inorganic nitrogen, but the indicators for Lake Travis were rainfall, chloride, sulfate, and conductivity. Preserved phytoplankton samples were analyzed from both reservoirs across seasons in 2013 and 2014 to supplement the models. Cyanobacteria were a significant factor in both lakes – samples indicated that the diazotroph and harmful genus, Aphanizomenon, was the most abundant cyanobacterium in Lake LBJ, while Limnothrix was dominant in Lake Travis. Although diazotrophic, it was hypothesized that Aphanizomenon used free nitrogen when available rather than fixing atmospheric nitrogen, thus allowing for rapid establishment and leaving less free nitrogen for plankton downstream. This study indicated that an increase in inorganic and organic nitrogen following a period of drought could amplify the potential for harmful blooms of Aphanizomenon in the upper Highland Lakes of central Texas.
1. Introduction Eutrophication is a concerning phenomenon magnified by human pollution that has been shown to result in an overall reduction in water quality (Anderson et al., 2002, Grafton et al., 2012, Woodhouse et al., 2016). To reduce the harmful impacts of eutrophication, humans must reduce the rate at which they are releasing pollutants. However, a rapid, significant reduction in pollution is not feasible due to human population growth and energy demands, making solutions for freshwater eutrophication-fueled predicaments challenging to arrive at, al-
beit not impossible (Wang and Wang, 2009, Cunha et al., 2012, Guo et al., 2018). The most detrimental impacts of eutrophication in freshwater systems are attributed to HABs1 (Paerl and Huisman, 2009, O’Neil et al., 2012). The influence of certain nutrients creates surges in the phytoplankton community that can yield undesirable growth of nuisance phytoplankton and toxin production (Mohlin et al., 2011, Li et al., 2017). Mitigation strategies tend to be specific to the individual species since microalgae may react differently to certain environmental factors and the availability of specific nutrients (Umphres et al., 2012, Seger
Corresponding author. E-mail address: [email protected] (T.E. Gámez). 1 Harmful Algal Blooms ⁎
https://doi.org/10.1016/j.ecolind.2019.05.022 Received 11 January 2019; Received in revised form 7 May 2019; Accepted 9 May 2019 Available online 21 May 2019 1470-160X/ Published by Elsevier Ltd.
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et al., 2015, Yang et al., 2016, Joo et al., 2017). Monitoring phytoplankton can be costly and time-consuming, and the identification of undesirable species can pose a significant challenge. In addition, it is difficult to predict how certain ecosystems will react to major climatic changes, such as an extended drought period. For many monitoring authorities, it is in their best interest to determine if further analyses are required for a given water body based on simple, rapid, and inexpensive metrics. Chl-a2 is a pigment present in all phytoplankton and it is commonly used as a direct measurement of phytoplankton biomass in a body of water (Boyer et al., 2008). However, this metric cannot differentiate between the different types of microalgae, therefore additional microscopic and genetic identification is required (e.g., molecular barcoding). Reservoirs experiencing lower than average inflows during drought times may exhibit an increase in trophic status due to decreased dilution in the system (Li et al., 2015). Conductivity and salinity can also be higher as a result of drought (Sprague, 2005). Even subtle changes in water quality may result in anomalies in the phytoplankton community due to the presence of certain species that are capable of thriving in drought conditions (Drerup and Vadeboncoeur, 2016). Nutrient cycling in lakes may also be altered due to reduced inflow and increased stratification, resulting in distinct shifts in the phytoplankton community and offset nutrient ratios (Kalff, 2002, Mosley, 2014). Bloom-forming cyanobacteria may often compose an overwhelming majority of the phytoplankton community (Paerl and Huisman, 2008). Cyanobacteria, commonly referred to as blue-green algae, can be responsible for both taste-and-odor problems and toxin production (Watson et al., 2016, Waters, 2016); and they are the primary culprits in the majority of freshwater phytoplankton blooms occurring in subtropical climates, such as Texas (Deng et al., 2014). Independent of drought-induced chemical anomalies, cyanobacteria will thrive under nutrient-dense conditions and can outcompete many other groups of phytoplankton (Huszar et al., 2006, Schindler, 2012). This has resulted in the establishment of regular monitoring programs across the United States (Schaeffer, 2018).
Fig. 1. The Highland Lakes of Central Texas span 65 miles from Lake Buchanan to Lake Austin, meandering more than 140 miles through the hill country. This illustration shows the approximate location of each lake and dams.
combination of factors influenced the response variable, chl-a, a method that has not been previously used to assess the potential for HABs. Preserved phytoplankton samples were analyzed for 2013 and 2014; community assembly and key taxa were noted for the phytoplankton communities as complementary data. 2. Methods
1.1. Background
2.1. Study sites
An intense drought period persisted in central Texas from Fall 2010 to Fall 2015 and this event was documented by the Lower Colorado River Authority, LCRA (2017). Using Carlson’s TSI3 (Carlson, 1977), most reservoirs in the Highland Lakes of central Texas (Fig. 1) increased in terms of their trophic status as reported by the Texas Commission for Environmental Quality, TCEQ (2017). Carlson’s TSI is a nutrient-based index that is widely implemented throughout the United States (Environmental Protection Agency, 1979). While other methods can be used to evaluate trophic states, they measure nutrients separately or they take into account parameters that were not available for this particular study. Lake Lyndon B. Johnson (LBJ), which is directly upstream of Lake Travis, experienced an increase in its TSI during the drought years; however, Lake Travis did not. Due to their close proximity and differences in chemical composition, these two reservoirs were selected for analysis in this study. Lake Travis is the larger and historically more oligotrophic reservoir compared to the other Highland Lakes (TCEQ, 2017), and it has also experienced significant increases in nearby human development (LCRA, 2017). This study examined nutrients from Lake LBJ and Lake Travis, and applied AICc4 models to determine the primary factors affecting changes in the TSI and microalgae of each reservoir. In this analysis, AICc compared additive linear models to statistically describe how a
Lake Travis and Lake LBJ are both impoundments of the Colorado River. They are about 90 km apart, with Lake LBJ flowing into another reservoir – Lake Marble Falls – before flowing into Lake Travis. Lake LBJ is located about 72.42 km northwest of Austin, Texas, with a maximum depth of 27 m and specific coordinates of 30.56 °N, 98.35 °W. This reservoir is 2531.7 ha; it was created in 1950 and it has an average residence time of approximately 3 months (LCRA, 2017). Lake Travis is located at 30.42 °N, 97.91 °W and is 7660.3 ha with a maximum depth of 64 m. The residence time in Lake Travis is variable because it is used for flood control and municipal water supplies, but it is approximately 9.5 months. Lake LBJ contains more macrophytes than Lake Travis due to its relatively shallow coves and silty-gravelly sediments found throughout most of the lake. In contrast, Lake Travis is very deep with rocky shorelines and it is not conducive to significant macrophyte growth in most parts of the lake. When analyzing the physicochemical parameters in both reservoirs during the course of this study, these characteristics were taken into consideration. 2.2. Analysis of field samples The LCRA routinely collects water samples every other month from lakes LBJ and Travis starting in February and ending in December. This study used their water chemistry data from 2010 to 2016 to evaluate the drought and transition period ( ± 6 months), since it is often difficult to assign strict dates during prolonged periods of drought. Water quality analyses were performed at the LCRA Environmental
2
Chlorophyll-a Trophic State Index 4 Corrected Akaike Information Criterion 3
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TKN6, and TP7:
Laboratory Services, Austin, TX, in the same month that they were collected. Water samples were analyzed for common nutrients (e.g., nitrate, nitrite, ammonium, phosphate) using ion chromatography-mass spectrometry according to TCEQ (2012). Rainfall, alkalinity, pH, turbidity, conductivity, and temperature data were collected for the two reservoirs as standard water quality measurements. The measurements for average rainfall per month were acquired from LCRA’s Hydromet gages (http://hydromet.lcra.org). Other parameters were either measured or collected at each sampling site simultaneously with corresponding chl-a measurements and phytoplankton samples. These sampling sites were close to the center of each reservoir and the established monitoring sites for the LCRA that corresponded to routine water quality measurements. Conductivity, temperature and pH data were collected with an EXO1 multiparameter 4-port water sonde calibrated within 24 h of the sample collection event. Measurements were taken from the sonde suspended 0.3 m below the surface. Chl-a was measured using fluorometric analysis and samples were preserved at 4 °C before filtration (Arar and Collins, 1997).
Y = β0 + β1Rainfall + ε Y = β0 + β1Rainfall + β2Temperature + β3Month + ε Y = β0 + β1Temperature + β2Month + ε Y = β0 + β1Rainfall+ β2Conductivity + β3Sulfate + β4Chloride + ε Y = β0 + β1Conductivity + β2Sulfate + β3Chloride + ε Y = β0 + β1Sulfate + β2Chloride + ε Y = β0 + β1TIN+ β2TKN + β3TP + ε Y = β0 + β1TIN + β2TKN + ε Y = β0 + β1TP + ε 3. Results 3.1. Assessment of water quality Chl-a, conductivity, TKN, and TP are shown for both reservoirs (Fig. 2), where samples were taken every other month, concurrently with the phytoplankton samples (Figs. 3 and 4). Alkalinity, pH, turbidity, and temperature were also considered, but there were no major differences between reservoirs, suggesting that these factors did not significantly influence the microalgal community. Between 2010 and 2016, mean chl-a values in Lake Travis were 10 µg/L, and they were 14 µg/L in Lake LBJ. A reservoir is considered eutrophic if the mean TSI chl-a value, which is calculated by 9.81ln (Chla) + 30.6, exceeds 45 μg/L. If the value should exceed 55 μg/L, then the reservoir is considered hypereutrophic (TCEQ, 2011). While chl-a measurements did approach concentrations of 35 µg/L in Lake LBJ, these values indicated that phytoplankton presence and abundance was seasonal. Chl-a values peaked in the late summer through the early fall of each year. Both lakes had increases in phytoplankton abundance each year of the study, with the exception of samples from 2015 where Lake Travis had very little phytoplankton by comparison. Overall, Lake Travis had a higher average conductivity than Lake LBJ. Lake Travis also had much steadier conductivity values in comparison to Lake LBJ. Considerable decreases in conductivity were observed for Lake LBJ in years 2012, 2014, and 2015. Between 2010 and 2016, TKN increased and total suspended solids have decreased in Lake LBJ. Despite the obvious increase of TKN in Lake Travis in 2015, there was no corresponding increase in phytoplankton. In fact, there were very few phytoplankton observed for that year when compared to Lake LBJ. Similar to nitrogen species, phosphorus, measured as phosphate, oscillated throughout each year. However, there is no apparent positive correlation with phytoplankton abundance during these nutrient surges. Aside from these observations, significant differences in phytoplankton abundance among physicochemical parameters were not noted.
2.3. Phytoplankton sampling and community analysis Phytoplankton samples were collected simultaneously with all other data in this experiment according to the protocols described in LCRA (2014). Samples (125-mL) were maintained in glass bottles, preserved with 2% Lugol’s solution, and refrigerated at 4 °C before analysis. The preserved field samples were settled for 24 h and 25 mL were decanted. Samples were settled in a 100-mL Utermöhl-style Combined Plate Chamber (HydroBios, catalog #435025). Cellular dimensions were estimated to calculate biovolume using cellSens (Olympus) on an Olympus IX73 inverted microscope at 400X total magnification. Cellular density was estimated according to Utermöhl (1931), where cells were counted in 10 frames and the conversion factor was adjusted for a sample that was originally 25-mL. The relative abundance of each major group was determined by calculating the total cellular biovolume per phylum for each sample. 2.4. AICc model selection The AICc method was chosen for the unbiased selection to assess the most appropriate linear model for a reservoir given a set of parameters (Hurvich and Tsai, 1989). Although there are other statistical methods for model selection that may yield similar results, AICc is considered more appropriate for smaller datasets (Cavanaugh, 2000). This analysis was performed using the MuMIn package in R (http://www.r-project. org/), which is used specifically for model selection (Burnham and Anderson, 2002). Additive linear models, for which AICc was originally designed (Cavanaugh, 2000), were created using a combination of physiochemical characteristics according to the influence they were estimated to have on the response variable, chl-a (Y), based on assumptions for increased eutrophication in a water system. Although AICc still functions with different sets of statistical models, the additive linear models were the most efficient in showing the best fit for a suite of physicochemical parameters. Further statistical parameters (i.e., Pvalues) could be evaluated to determine how well the model truly represented the data. The same models were used to compare data from both Lake Travis and Lake LBJ, and to determine if similar environmental factors were influencing chl-a content using AICc. Since this methodology does not yield information regarding the fit of the selected model, the models were run individually in R using the linear model function for analysis of statistical parameters. The following additive linear models considered the amount of rainfall, temperature, the month in which the sample was collected, conductivity, as well as TIN5,
3.2. Statistical comparison using AICc The impact of nitrogen on chl-a levels was the most significant for Lake LBJ (Table 1). Lake Travis had a different optimal model, where conductivity, including chloride and sulfate, and rainfall were the most impactful on the chl-a values during the drought period. The abundance of TP was not a significant predictor in either model. The optimal model for Lake LBJ suggested inorganic and organic nitrogen had the strongest influence on the variation in chl-a for the study period. In this model, the r2-value was 0.55, and the P-value was 2.595e-07. As for Lake Travis, the model suggested conductivity, chloride, sulfate, and rainfall had the greatest influence on chl-a values with an r2-value of 0.6038 and P-value of 6.856e-07 (Table 2). 6
5
7
Total Inorganic Nitrogen 590
Total Kjeldahl Nitrogen Total Phosphorus
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Fig. 3. Phytoplankton biovolume was calculated in μm3 for each sample. Biovolume was calculated by multiplying cells/mL for a given genera by the approximate volume of the average cell for that genus per sample.
total biovolume of phytoplankton is shown for both lakes for each collection period (Fig. 3). The major groups of microalgae for each reservoir are shown in Fig. 4, where percentage is shown as a function of biovolume. While phytoplankton biovolume and the community composition are most similar in August 2013, overall, Lake LBJ had significantly higher phytoplankton biovolume than Lake Travis, yearto-year. While dinoflagellates and green algae were abundant in the cooler months, cyanobacteria were most abundant during the summer months, with the exception of samples from August 2014 where both lakes transitioned to containing more diatoms. Up to 80% of the phytoplankton community in LBJ were classified as cyanobacteria in samples from 2013, excluding April. In April 2013, dinoflagellates and chlorophytes comprised the majority of the phytoplankton community, whereas the abundance of cyanobacteria was around 5%, with Dolichospermum being the most abundant cyanobacterium. In June and August of 2013, Aphanizomenon contributed significantly to the calculated cyanobacterial biovolume, contributing greater than 80% of the phytoplankton community throughout 2013, excluding April where it was still detected yet not dominant. This pattern declined in October 2013, when cyanobacterial biovolume decreased to 32% of the total community. At this time, Cylindrospermopsis, a non-diazotrophic filament, became the dominant cyanobacterium. By August 2014, cyanobacterial community biovolume remained around 30%, but Aphanizomenon was still the most abundant cyanobacterium in the phytoplankton community. In Lake Travis, diatoms were the most abundant phytoplankton group in April 2013, followed by dinoflagellates, which corresponded to higher-than-usual dinoflagellate populations in Lake LBJ during that sampling period. Cyanobacteria were dominant in the summer months (June and August) and chlorophytes were most abundant later in the year (October). The abundance of cyanobacteria was attributed to Limnothrix, a non-diazotrophic filamentous cyanobacterium that is not currently acknowledged as being associated with HABs. Diazotrophic species were noted in the community, including cells of Dolichospermum and Aphanizomenon, but they did not make up a significant percentage of the biovolume. Dinoflagellates and chlorophytes were co-dominant during April, August, and October, whereas in June, only chlorophytes were dominant. Cyanobacteria comprised greater than 40% of the phytoplankton community for the entirety of 2013 with Limnothrix being the most abundant genus.
Fig. 2. Water quality measurements for Lakes LBJ (black line) and Lake Travis (gray line), including chl-a, conductivity, Kjeldahl nitrogen, and phosphorus, shown as a time series. Samples were taken concurrently with phytoplankton data during the first week of every other month, starting with February each year.
4. Discussion Many parameters can promote the abundance of microalgae during droughts, including rain events, nutrient availability, temperature, and conductivity, which were tested in this study. An absence of rainfall was hypothesized to have an additive weight on potential micronutrient levels as well as conductivity since these factors are normally present in
3.3. Cyanobacterial abundance Fixed samples were evaluated from 2013 and 2014 to identify the type and abundance of different major groups of phytoplankton. The 591
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Fig. 4. Relative abundance of major phytoplankton groups that were identified from samples. Values were obtained from phytoplankton biovolume calculations. “Others” included primarily cryptophytes and euglenoids.
significant influence on the abundance of chl-a in Lake Travis between 2010 and 2016 (p < 0.05), further reiterating the statistical significance of each parameter in the model. Given the AICc data, nitrogen was not shown to influence the chl-a values in Lake Travis but it was the most appropriate explanation for Lake LBJ’s increase in chl-a during the examination period (Table 1). Chl-a serves as the simplest model for microalgal productivity (Shamshirband et al., 2019), but it cannot be used as a proxy for the identification of harmful and nuisance species. Since many diazotrophic cyanobacteria can yield HABs, it is important to identify potential risk and begin considering mitigation strategies. This study demonstrates that multiple chemical parameters, when combined, can be used to assess if further action should be required to monitor the phytoplankton community. When using additive linear models in R, monitoring programs have the ability to project which reservoir systems may be at a higher risk for HABs, resulting in the ability to more accurately predict harmful bloom events. Eutrophication and diazotrophic cyanobacterial community dominance are typically strongly correlated (Paerl and Otten, 2015). Therefore, based on the results of this study, Lake LBJ should be monitored for cyanobacterial HABs, especially during drought years, given the high prevalence of Aphanizomenon in the system and the presumed influence of nitrogen on its abundance. An increase in nutrient loading, primarily nitrogen, coupled with the duration and frequency of droughts could favor the growth of HABs. This warrants further studies on the algal community in Lake LBJ to help document how the phytoplankton community will change over time. Ideally, this will help to predict shifts towards cyanobacterial dominance and potentially HAB-forming conditions. The diazotrophic filamentous Aphanizomenon was scarce in Lake Travis, but it was notably abundant in Lake LBJ. Limnothrix was dominant within the cyanobacterial community of Lake Travis, but it was not dominant in LBJ. This was presumably due to elevated conductivity and decreased rainfall that resulted in a favorable environment for cyanobacterial growth, but a lack of nutrients supporting the dominance of diazotrophs. Thus, it is not unreasonable to postulate that reservoirs in the upper Highland Lakes may serve as a catchment system, with HAB genera failing to thrive further downstream in Lake Travis at high abundances due to unsuitable nutrient levels or other unfavorable conditions.
Table 1 AICc Values for Lake LBJ. Model
Df1
Log Likelihood
Delta
TIN + TKN TIN + TKN + TP Temperature + Month Rainfall + Temperature + Month Sulfate + Chloride Cond + Sulfate + Chloride Cond + Sulfate + Chloride + Rainfall TP Rainfall
4 5 4 5 4 5 6 3 3
−124.22 −124.22 −124.49 −128.37 −133.87 −132.93 −132.81 −138.02 −140.58
0.00 2.60 8.54 10.91 19.31 20.02 22.54 25.14 30.26
1 degrees of freedom; Conductivity (Cond). Table 2 AICc Values for Lake Travis. Model
Df1
Log Likelihood
Delta
Cond + Sulfate + Chloride + Rainfall Cond + Sulfate + Chloride Sulfate + Chloride Rainfall + Temperature + Month TIN + TKN TIN + TKN + TP Temperature + Month Rainfall TP
6 5 4 5 4 5 4 3 3
−126.74 −129.88 −131.64 −138.71 −140.52 −140.43 −141.96 −143.72 −145.62
0.00 3.53 4.44 21.20 22.20 24.63 25.08 26.15 29.95
1 degrees of freedom; Conductivity (Cond).
higher levels in eutrophic freshwater systems that are influenced by droughts (Koba Yashi et al., 1990, Stets et al., 2018). However, these factors may impact chl-a levels independently due to extended periods of stratification (Kerimoglu and Rinke, 2013). TIN, TKN, and TP were hypothesized to have an additive influence on chl-a or act independently since these elements are required for phytoplankton growth, and they are normally significant factors in eutrophication events (Carpenter et al., 1998, Dzialowski et al., 2005). Nitrogen was considered separately from phosphorus primarily due to the association with diazotrophic cyanobacteria, which are commonly responsible for freshwater HABs (Beversdorf et al., 2013). Using the AICc method for additive linear models, both Lake LBJ and Lake Travis had different optimum models from one another. For Lake LBJ, the lowest delta value for the “TIN + TKN” model suggested organic and inorganic nitrogen had the highest influence on chl-a in Lake LBJ. While the delta values of 0.00–2.00 are competing models, values approaching 0.00 are considered the best fit. The second most appropriate model for Lake LBJ shows an additive influence of TP on TKN and TIN; however, this model was not truly competitive with a delta value of 2.60. Conductivity, chloride, sulfate, and rainfall all had a
5. Conclusions The results of this short study suggest that Aphanizomenon in Lake LBJ utilized free nitrogen in the water system in addition to its ability to fix nitrogen, likely promoting its dominance. This provides a foundation for future studies to examine the influence of different nitrogen species on the growth of Aphanizomenon, in culture and in mesocosm studies, since little is known about the role of nitrogen on cyanobacterial 592
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success (Dolman et al., 2012). Additionally, we present a relatively inexpensive framework for determining if further action is required for local freshwater monitoring programs in terms of cyanobacterial blooms, utilizing statistical modeling in R and basic water quality parameters that are readily available via an online, publicly-accessible database. Nutrient data were correlated with microalgal counts of limited available, preserved samples to rationalize the linear models selected using the AICc method. The impacts of this study could have been substantially improved if there were more microalgae samples for analysis. Ideally, each water quality sample would have had a corresponding microalgal community assessment. Additionally, more frequent samples could have strengthened the dataset. Nonetheless, this study provides a preliminary evaluation of the phytoplankton community along with a statistical assessment of available parameters to determine the likeliness of HAB formation.
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A 4700-year history of cyanobacteria toxin production in a shallow subtropical lake. Ecosystems 19, 426–436. https://doi.org/10.1007/s10021-0159943-0. Watson, S.B., Monis, P., Baker, P., Giglio, S., 2016. Biochemistry and genetics of taste- and odor-producing cyanobacteria. Harmful Algae 54, 112–127. https://doi.org/10. 1016/j.hal.2015.11.009. Woodhouse, J.N., Kinsela, A.S., Collins, R.N., Bowling, L.C., Honeyman, G.L., Holliday, J.K., Neilan, B.A., 2016. Microbial communities reflect temporal changes in cyanobacterial composition in a shallow ephemeral freshwater lake. ISME J. 10, 1337–1351. https://doi.org/10.1038/ismej.2015.218. Yang, J., Lv, H., Yang, J., Liu, L., Yu, X., Chen, H., 2016. Decline in water level boosts cyanobacteria dominance in subtropical reservoirs. Sci. Total Environ. 557–558, 445–452. https://doi.org/10.1016/j.scitotenv.2016.03.094.
Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Acknowledgements The authors are grateful to the LCRA for access to the data used for this analysis. References Anderson, D.M., Glibert, P.M., Burkholder, J.M., 2002. Harmful algal blooms and eutrophication: nutrient sources, composition, and consequences. Estuaries 25 (4B), 704–726. Arar, E.J., Collins, G.B., 1997. Method 445.0 In Vitro Determination of Chlorophyll a and Pheophytin a in Marine and Freshwater Algae by Fluorescence. U. S. Environmental Protection Agency, Washington DC. Beversdorf, L.J., Miller, T.R., McMahon, K.D., 2013. The role of nitrogen fixation in cyanobacterial bloom toxicity in a temperate, eutrophic lake. PLOS 8 (2). https://doi. org/10.1371/journal.pone.0056103. Boyer, J.N., Kelble, C.R., Ortner, P.B., Rudnick, D.T., 2008. Phytoplankton bloom status: Chlorophyll a biomass as an indicator of water quality condition in the southern estuaries of Florida, USA. Ecol. Indicat. https://doi.org/10.1016/j.ecolind.2008.11. 013. Burnham, K.P., Anderson, D.R., 2002. Model Selection and Multimodel Inference: A Practical Information-theoretic Approach, 2nd ed. Springer-Verlag, New York. Carlson, R.E., 1977. A trophic state index for lakes. Limnol. Oceanogr. 22 361-269. Carpenter, S.R., Caraco, N.F., Correll, D.L., Howarth, R.W., Sharpley, A.N., Smith, V.H., 1998. Nonpoint pollution of surface waters with phosphorus and nitrogen. Ecol. Appl. 8 (3), 559–568. https://doi.org/10.1890/1051-0761(1998) 008[0559:NPOSWW]2. 0.CO;2. Cavanaugh, J.E., 2000. Unifying the derivations for the Akaike and Corrected Akaike Information Criteria. Stat. Probabil. Lett 33(2). 10.1016/S0167-7152(96)00128-9. Cunha, D.G.F., Ogura, A.P., Calijuri, M.C., 2012. Nutrient reference concentrations and trophic state boundaries in subtropical reservoirs. Wat. Sci. Tech. 65 (8), 1461–1467. https://doi.org/10.2166/wst.2012.035. Deng, J., Qin, B., Paerl, H.W., Zhang, Y., Ma, J., Chen, Y., 2014. Earlier and warmer springs increase cyanobacterial (Microcystis spp.) blooms in subtropical Lake Taihu. China. Freshwat. Biol. 59, 1076–1085. https://doi.org/10.1111/fwb.12330. Dolman, A.M., Rücker, J., Pick, F.R., Fastner, J., Rohrlack, T., Mischke, U., Wiedner, C., 2012. Cyanobacteria and cyanotoxins: the influence of nitrogen versus phosphorus. PLoS One 7 (6). https://doi.org/10.1371/journal.pone.0038757. Drerup, S., Vadeboncoeur, Y., 2016. Elevated specific conductance enhances productivity and biomass of periphytic cyanobacteria from Lake Tahoe and Lake Tanganyika. Phycologia 55 (3), 295–298. https://doi.org/10.2216/15-100.1. Dzialowski, A.R., Wang, S.H., Lim, N.C., Spotts, W.W., Huggins, D.G., 2005. Nutrient limitation of phytoplankton growth in central plains reservoirs. USA. J. Plankton R. 27 (6), 587–595. https://doi.org/10.1093/plankt/fbi034. Environmental Protection Agency, 1979. Lake and Reservoir Classification Systems. United States EPA 600/3-79-074, Research and Development. https://nepis.epa.gov/ Exe/ZyPURL.cgi?Dockey=9101QXT0.TXT. (accessed 28 February 2019). Grafton, R.Q., Pittock, J., Davis, R., Williams, J., Fu, G., Warburton, M., Udall, B., McKenzie, R., Yu, X., Che, N., Connell, D., Jiang, Q., Kompas, T., Lynch, A., Norris, R., Possingham, H., Quiggin, J., 2012. Global insights into water resources, climate change and governance. Nat. Clim. Change 3, 315–321. Guo, J., Zhang, C., Zheng, G., Xue, J., Zhang, L., 2018. The establishment of seasonspecific eutrophication assessment standards for a water-supply reservoir located in Northeast China based on chlorophyll-a levels. Ecol. Indicat. 85, 11–20. https://doi. org/10.1016/j.ecolind.2017.09.056. Hurvich, C.M., Tsai, C.L., 1989. Regression and time series model selection in small samples. Biometrika 76 (2), 297–307.
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Observations of two reservoirs during a drought in central Texas, USA: Strategies for detecting harmful algal blooms
T
Tatiana E. Gámeza,b, , Lisa Bentonb, Schonna R. Manningc ⁎
a
Department of Biology, Texas State University, San Marcos, TX, USA Lower Colorado River Authority, Austin, TX, USA c Department of Molecular Biosciences, University of Texas at Austin, TX, USA b
ARTICLE INFO
ABSTRACT
Keywords: Eutrophication Drought Chlorophyll-a Harmful Algal Blooms Akaike Information Criterion Carlson’s Trophic State Index
Problems associated with eutrophication are increasing in freshwater reservoirs worldwide due to many factors, the majority of which are attributed directly to human development. Eutrophic bodies of water are much more susceptible to algal blooms, including potentially harmful species, as well as associated taste-and-odor compounds and toxic secondary metabolites. To assess the likelihood of eutrophication and harmful blooms, two reservoirs in the Highland Lakes of central Texas, which are located within the same drainage basin and climate zone, were observed for the duration of an extended drought period lasting from 2010 to 2015. This brief investigation examined a suite of physiochemical characteristics to determine which environmental factors had the most significant impacts on the eutrophication of a reservoir coupled with the abundance of microalgae. Standard water quality parameters that have been hypothesized to influence eutrophication were measured and compared with chlorophyll-a fluorescence as a proxy for phytoplankton abundance. Samples were taken every other month and each reservoir responded differently to the drought – Lake Lyndon B. Johnson (LBJ) became eutrophic while Lake Travis remained mesotrophic, according to Carlson’s Trophic State Index. Linear models using corrected Akaike Information Criterion analyses indicated that the primary indicators for microalgae in Lake LBJ were organic and inorganic nitrogen, but the indicators for Lake Travis were rainfall, chloride, sulfate, and conductivity. Preserved phytoplankton samples were analyzed from both reservoirs across seasons in 2013 and 2014 to supplement the models. Cyanobacteria were a significant factor in both lakes – samples indicated that the diazotroph and harmful genus, Aphanizomenon, was the most abundant cyanobacterium in Lake LBJ, while Limnothrix was dominant in Lake Travis. Although diazotrophic, it was hypothesized that Aphanizomenon used free nitrogen when available rather than fixing atmospheric nitrogen, thus allowing for rapid establishment and leaving less free nitrogen for plankton downstream. This study indicated that an increase in inorganic and organic nitrogen following a period of drought could amplify the potential for harmful blooms of Aphanizomenon in the upper Highland Lakes of central Texas.
1. Introduction Eutrophication is a concerning phenomenon magnified by human pollution that has been shown to result in an overall reduction in water quality (Anderson et al., 2002, Grafton et al., 2012, Woodhouse et al., 2016). To reduce the harmful impacts of eutrophication, humans must reduce the rate at which they are releasing pollutants. However, a rapid, significant reduction in pollution is not feasible due to human population growth and energy demands, making solutions for freshwater eutrophication-fueled predicaments challenging to arrive at, al-
beit not impossible (Wang and Wang, 2009, Cunha et al., 2012, Guo et al., 2018). The most detrimental impacts of eutrophication in freshwater systems are attributed to HABs1 (Paerl and Huisman, 2009, O’Neil et al., 2012). The influence of certain nutrients creates surges in the phytoplankton community that can yield undesirable growth of nuisance phytoplankton and toxin production (Mohlin et al., 2011, Li et al., 2017). Mitigation strategies tend to be specific to the individual species since microalgae may react differently to certain environmental factors and the availability of specific nutrients (Umphres et al., 2012, Seger
Corresponding author. E-mail address: [email protected] (T.E. Gámez). 1 Harmful Algal Blooms ⁎
https://doi.org/10.1016/j.ecolind.2019.05.022 Received 11 January 2019; Received in revised form 7 May 2019; Accepted 9 May 2019 Available online 21 May 2019 1470-160X/ Published by Elsevier Ltd.
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et al., 2015, Yang et al., 2016, Joo et al., 2017). Monitoring phytoplankton can be costly and time-consuming, and the identification of undesirable species can pose a significant challenge. In addition, it is difficult to predict how certain ecosystems will react to major climatic changes, such as an extended drought period. For many monitoring authorities, it is in their best interest to determine if further analyses are required for a given water body based on simple, rapid, and inexpensive metrics. Chl-a2 is a pigment present in all phytoplankton and it is commonly used as a direct measurement of phytoplankton biomass in a body of water (Boyer et al., 2008). However, this metric cannot differentiate between the different types of microalgae, therefore additional microscopic and genetic identification is required (e.g., molecular barcoding). Reservoirs experiencing lower than average inflows during drought times may exhibit an increase in trophic status due to decreased dilution in the system (Li et al., 2015). Conductivity and salinity can also be higher as a result of drought (Sprague, 2005). Even subtle changes in water quality may result in anomalies in the phytoplankton community due to the presence of certain species that are capable of thriving in drought conditions (Drerup and Vadeboncoeur, 2016). Nutrient cycling in lakes may also be altered due to reduced inflow and increased stratification, resulting in distinct shifts in the phytoplankton community and offset nutrient ratios (Kalff, 2002, Mosley, 2014). Bloom-forming cyanobacteria may often compose an overwhelming majority of the phytoplankton community (Paerl and Huisman, 2008). Cyanobacteria, commonly referred to as blue-green algae, can be responsible for both taste-and-odor problems and toxin production (Watson et al., 2016, Waters, 2016); and they are the primary culprits in the majority of freshwater phytoplankton blooms occurring in subtropical climates, such as Texas (Deng et al., 2014). Independent of drought-induced chemical anomalies, cyanobacteria will thrive under nutrient-dense conditions and can outcompete many other groups of phytoplankton (Huszar et al., 2006, Schindler, 2012). This has resulted in the establishment of regular monitoring programs across the United States (Schaeffer, 2018).
Fig. 1. The Highland Lakes of Central Texas span 65 miles from Lake Buchanan to Lake Austin, meandering more than 140 miles through the hill country. This illustration shows the approximate location of each lake and dams.
combination of factors influenced the response variable, chl-a, a method that has not been previously used to assess the potential for HABs. Preserved phytoplankton samples were analyzed for 2013 and 2014; community assembly and key taxa were noted for the phytoplankton communities as complementary data. 2. Methods
1.1. Background
2.1. Study sites
An intense drought period persisted in central Texas from Fall 2010 to Fall 2015 and this event was documented by the Lower Colorado River Authority, LCRA (2017). Using Carlson’s TSI3 (Carlson, 1977), most reservoirs in the Highland Lakes of central Texas (Fig. 1) increased in terms of their trophic status as reported by the Texas Commission for Environmental Quality, TCEQ (2017). Carlson’s TSI is a nutrient-based index that is widely implemented throughout the United States (Environmental Protection Agency, 1979). While other methods can be used to evaluate trophic states, they measure nutrients separately or they take into account parameters that were not available for this particular study. Lake Lyndon B. Johnson (LBJ), which is directly upstream of Lake Travis, experienced an increase in its TSI during the drought years; however, Lake Travis did not. Due to their close proximity and differences in chemical composition, these two reservoirs were selected for analysis in this study. Lake Travis is the larger and historically more oligotrophic reservoir compared to the other Highland Lakes (TCEQ, 2017), and it has also experienced significant increases in nearby human development (LCRA, 2017). This study examined nutrients from Lake LBJ and Lake Travis, and applied AICc4 models to determine the primary factors affecting changes in the TSI and microalgae of each reservoir. In this analysis, AICc compared additive linear models to statistically describe how a
Lake Travis and Lake LBJ are both impoundments of the Colorado River. They are about 90 km apart, with Lake LBJ flowing into another reservoir – Lake Marble Falls – before flowing into Lake Travis. Lake LBJ is located about 72.42 km northwest of Austin, Texas, with a maximum depth of 27 m and specific coordinates of 30.56 °N, 98.35 °W. This reservoir is 2531.7 ha; it was created in 1950 and it has an average residence time of approximately 3 months (LCRA, 2017). Lake Travis is located at 30.42 °N, 97.91 °W and is 7660.3 ha with a maximum depth of 64 m. The residence time in Lake Travis is variable because it is used for flood control and municipal water supplies, but it is approximately 9.5 months. Lake LBJ contains more macrophytes than Lake Travis due to its relatively shallow coves and silty-gravelly sediments found throughout most of the lake. In contrast, Lake Travis is very deep with rocky shorelines and it is not conducive to significant macrophyte growth in most parts of the lake. When analyzing the physicochemical parameters in both reservoirs during the course of this study, these characteristics were taken into consideration. 2.2. Analysis of field samples The LCRA routinely collects water samples every other month from lakes LBJ and Travis starting in February and ending in December. This study used their water chemistry data from 2010 to 2016 to evaluate the drought and transition period ( ± 6 months), since it is often difficult to assign strict dates during prolonged periods of drought. Water quality analyses were performed at the LCRA Environmental
2
Chlorophyll-a Trophic State Index 4 Corrected Akaike Information Criterion 3
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TKN6, and TP7:
Laboratory Services, Austin, TX, in the same month that they were collected. Water samples were analyzed for common nutrients (e.g., nitrate, nitrite, ammonium, phosphate) using ion chromatography-mass spectrometry according to TCEQ (2012). Rainfall, alkalinity, pH, turbidity, conductivity, and temperature data were collected for the two reservoirs as standard water quality measurements. The measurements for average rainfall per month were acquired from LCRA’s Hydromet gages (http://hydromet.lcra.org). Other parameters were either measured or collected at each sampling site simultaneously with corresponding chl-a measurements and phytoplankton samples. These sampling sites were close to the center of each reservoir and the established monitoring sites for the LCRA that corresponded to routine water quality measurements. Conductivity, temperature and pH data were collected with an EXO1 multiparameter 4-port water sonde calibrated within 24 h of the sample collection event. Measurements were taken from the sonde suspended 0.3 m below the surface. Chl-a was measured using fluorometric analysis and samples were preserved at 4 °C before filtration (Arar and Collins, 1997).
Y = β0 + β1Rainfall + ε Y = β0 + β1Rainfall + β2Temperature + β3Month + ε Y = β0 + β1Temperature + β2Month + ε Y = β0 + β1Rainfall+ β2Conductivity + β3Sulfate + β4Chloride + ε Y = β0 + β1Conductivity + β2Sulfate + β3Chloride + ε Y = β0 + β1Sulfate + β2Chloride + ε Y = β0 + β1TIN+ β2TKN + β3TP + ε Y = β0 + β1TIN + β2TKN + ε Y = β0 + β1TP + ε 3. Results 3.1. Assessment of water quality Chl-a, conductivity, TKN, and TP are shown for both reservoirs (Fig. 2), where samples were taken every other month, concurrently with the phytoplankton samples (Figs. 3 and 4). Alkalinity, pH, turbidity, and temperature were also considered, but there were no major differences between reservoirs, suggesting that these factors did not significantly influence the microalgal community. Between 2010 and 2016, mean chl-a values in Lake Travis were 10 µg/L, and they were 14 µg/L in Lake LBJ. A reservoir is considered eutrophic if the mean TSI chl-a value, which is calculated by 9.81ln (Chla) + 30.6, exceeds 45 μg/L. If the value should exceed 55 μg/L, then the reservoir is considered hypereutrophic (TCEQ, 2011). While chl-a measurements did approach concentrations of 35 µg/L in Lake LBJ, these values indicated that phytoplankton presence and abundance was seasonal. Chl-a values peaked in the late summer through the early fall of each year. Both lakes had increases in phytoplankton abundance each year of the study, with the exception of samples from 2015 where Lake Travis had very little phytoplankton by comparison. Overall, Lake Travis had a higher average conductivity than Lake LBJ. Lake Travis also had much steadier conductivity values in comparison to Lake LBJ. Considerable decreases in conductivity were observed for Lake LBJ in years 2012, 2014, and 2015. Between 2010 and 2016, TKN increased and total suspended solids have decreased in Lake LBJ. Despite the obvious increase of TKN in Lake Travis in 2015, there was no corresponding increase in phytoplankton. In fact, there were very few phytoplankton observed for that year when compared to Lake LBJ. Similar to nitrogen species, phosphorus, measured as phosphate, oscillated throughout each year. However, there is no apparent positive correlation with phytoplankton abundance during these nutrient surges. Aside from these observations, significant differences in phytoplankton abundance among physicochemical parameters were not noted.
2.3. Phytoplankton sampling and community analysis Phytoplankton samples were collected simultaneously with all other data in this experiment according to the protocols described in LCRA (2014). Samples (125-mL) were maintained in glass bottles, preserved with 2% Lugol’s solution, and refrigerated at 4 °C before analysis. The preserved field samples were settled for 24 h and 25 mL were decanted. Samples were settled in a 100-mL Utermöhl-style Combined Plate Chamber (HydroBios, catalog #435025). Cellular dimensions were estimated to calculate biovolume using cellSens (Olympus) on an Olympus IX73 inverted microscope at 400X total magnification. Cellular density was estimated according to Utermöhl (1931), where cells were counted in 10 frames and the conversion factor was adjusted for a sample that was originally 25-mL. The relative abundance of each major group was determined by calculating the total cellular biovolume per phylum for each sample. 2.4. AICc model selection The AICc method was chosen for the unbiased selection to assess the most appropriate linear model for a reservoir given a set of parameters (Hurvich and Tsai, 1989). Although there are other statistical methods for model selection that may yield similar results, AICc is considered more appropriate for smaller datasets (Cavanaugh, 2000). This analysis was performed using the MuMIn package in R (http://www.r-project. org/), which is used specifically for model selection (Burnham and Anderson, 2002). Additive linear models, for which AICc was originally designed (Cavanaugh, 2000), were created using a combination of physiochemical characteristics according to the influence they were estimated to have on the response variable, chl-a (Y), based on assumptions for increased eutrophication in a water system. Although AICc still functions with different sets of statistical models, the additive linear models were the most efficient in showing the best fit for a suite of physicochemical parameters. Further statistical parameters (i.e., Pvalues) could be evaluated to determine how well the model truly represented the data. The same models were used to compare data from both Lake Travis and Lake LBJ, and to determine if similar environmental factors were influencing chl-a content using AICc. Since this methodology does not yield information regarding the fit of the selected model, the models were run individually in R using the linear model function for analysis of statistical parameters. The following additive linear models considered the amount of rainfall, temperature, the month in which the sample was collected, conductivity, as well as TIN5,
3.2. Statistical comparison using AICc The impact of nitrogen on chl-a levels was the most significant for Lake LBJ (Table 1). Lake Travis had a different optimal model, where conductivity, including chloride and sulfate, and rainfall were the most impactful on the chl-a values during the drought period. The abundance of TP was not a significant predictor in either model. The optimal model for Lake LBJ suggested inorganic and organic nitrogen had the strongest influence on the variation in chl-a for the study period. In this model, the r2-value was 0.55, and the P-value was 2.595e-07. As for Lake Travis, the model suggested conductivity, chloride, sulfate, and rainfall had the greatest influence on chl-a values with an r2-value of 0.6038 and P-value of 6.856e-07 (Table 2). 6
5
7
Total Inorganic Nitrogen 590
Total Kjeldahl Nitrogen Total Phosphorus
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Fig. 3. Phytoplankton biovolume was calculated in μm3 for each sample. Biovolume was calculated by multiplying cells/mL for a given genera by the approximate volume of the average cell for that genus per sample.
total biovolume of phytoplankton is shown for both lakes for each collection period (Fig. 3). The major groups of microalgae for each reservoir are shown in Fig. 4, where percentage is shown as a function of biovolume. While phytoplankton biovolume and the community composition are most similar in August 2013, overall, Lake LBJ had significantly higher phytoplankton biovolume than Lake Travis, yearto-year. While dinoflagellates and green algae were abundant in the cooler months, cyanobacteria were most abundant during the summer months, with the exception of samples from August 2014 where both lakes transitioned to containing more diatoms. Up to 80% of the phytoplankton community in LBJ were classified as cyanobacteria in samples from 2013, excluding April. In April 2013, dinoflagellates and chlorophytes comprised the majority of the phytoplankton community, whereas the abundance of cyanobacteria was around 5%, with Dolichospermum being the most abundant cyanobacterium. In June and August of 2013, Aphanizomenon contributed significantly to the calculated cyanobacterial biovolume, contributing greater than 80% of the phytoplankton community throughout 2013, excluding April where it was still detected yet not dominant. This pattern declined in October 2013, when cyanobacterial biovolume decreased to 32% of the total community. At this time, Cylindrospermopsis, a non-diazotrophic filament, became the dominant cyanobacterium. By August 2014, cyanobacterial community biovolume remained around 30%, but Aphanizomenon was still the most abundant cyanobacterium in the phytoplankton community. In Lake Travis, diatoms were the most abundant phytoplankton group in April 2013, followed by dinoflagellates, which corresponded to higher-than-usual dinoflagellate populations in Lake LBJ during that sampling period. Cyanobacteria were dominant in the summer months (June and August) and chlorophytes were most abundant later in the year (October). The abundance of cyanobacteria was attributed to Limnothrix, a non-diazotrophic filamentous cyanobacterium that is not currently acknowledged as being associated with HABs. Diazotrophic species were noted in the community, including cells of Dolichospermum and Aphanizomenon, but they did not make up a significant percentage of the biovolume. Dinoflagellates and chlorophytes were co-dominant during April, August, and October, whereas in June, only chlorophytes were dominant. Cyanobacteria comprised greater than 40% of the phytoplankton community for the entirety of 2013 with Limnothrix being the most abundant genus.
Fig. 2. Water quality measurements for Lakes LBJ (black line) and Lake Travis (gray line), including chl-a, conductivity, Kjeldahl nitrogen, and phosphorus, shown as a time series. Samples were taken concurrently with phytoplankton data during the first week of every other month, starting with February each year.
4. Discussion Many parameters can promote the abundance of microalgae during droughts, including rain events, nutrient availability, temperature, and conductivity, which were tested in this study. An absence of rainfall was hypothesized to have an additive weight on potential micronutrient levels as well as conductivity since these factors are normally present in
3.3. Cyanobacterial abundance Fixed samples were evaluated from 2013 and 2014 to identify the type and abundance of different major groups of phytoplankton. The 591
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Fig. 4. Relative abundance of major phytoplankton groups that were identified from samples. Values were obtained from phytoplankton biovolume calculations. “Others” included primarily cryptophytes and euglenoids.
significant influence on the abundance of chl-a in Lake Travis between 2010 and 2016 (p < 0.05), further reiterating the statistical significance of each parameter in the model. Given the AICc data, nitrogen was not shown to influence the chl-a values in Lake Travis but it was the most appropriate explanation for Lake LBJ’s increase in chl-a during the examination period (Table 1). Chl-a serves as the simplest model for microalgal productivity (Shamshirband et al., 2019), but it cannot be used as a proxy for the identification of harmful and nuisance species. Since many diazotrophic cyanobacteria can yield HABs, it is important to identify potential risk and begin considering mitigation strategies. This study demonstrates that multiple chemical parameters, when combined, can be used to assess if further action should be required to monitor the phytoplankton community. When using additive linear models in R, monitoring programs have the ability to project which reservoir systems may be at a higher risk for HABs, resulting in the ability to more accurately predict harmful bloom events. Eutrophication and diazotrophic cyanobacterial community dominance are typically strongly correlated (Paerl and Otten, 2015). Therefore, based on the results of this study, Lake LBJ should be monitored for cyanobacterial HABs, especially during drought years, given the high prevalence of Aphanizomenon in the system and the presumed influence of nitrogen on its abundance. An increase in nutrient loading, primarily nitrogen, coupled with the duration and frequency of droughts could favor the growth of HABs. This warrants further studies on the algal community in Lake LBJ to help document how the phytoplankton community will change over time. Ideally, this will help to predict shifts towards cyanobacterial dominance and potentially HAB-forming conditions. The diazotrophic filamentous Aphanizomenon was scarce in Lake Travis, but it was notably abundant in Lake LBJ. Limnothrix was dominant within the cyanobacterial community of Lake Travis, but it was not dominant in LBJ. This was presumably due to elevated conductivity and decreased rainfall that resulted in a favorable environment for cyanobacterial growth, but a lack of nutrients supporting the dominance of diazotrophs. Thus, it is not unreasonable to postulate that reservoirs in the upper Highland Lakes may serve as a catchment system, with HAB genera failing to thrive further downstream in Lake Travis at high abundances due to unsuitable nutrient levels or other unfavorable conditions.
Table 1 AICc Values for Lake LBJ. Model
Df1
Log Likelihood
Delta
TIN + TKN TIN + TKN + TP Temperature + Month Rainfall + Temperature + Month Sulfate + Chloride Cond + Sulfate + Chloride Cond + Sulfate + Chloride + Rainfall TP Rainfall
4 5 4 5 4 5 6 3 3
−124.22 −124.22 −124.49 −128.37 −133.87 −132.93 −132.81 −138.02 −140.58
0.00 2.60 8.54 10.91 19.31 20.02 22.54 25.14 30.26
1 degrees of freedom; Conductivity (Cond). Table 2 AICc Values for Lake Travis. Model
Df1
Log Likelihood
Delta
Cond + Sulfate + Chloride + Rainfall Cond + Sulfate + Chloride Sulfate + Chloride Rainfall + Temperature + Month TIN + TKN TIN + TKN + TP Temperature + Month Rainfall TP
6 5 4 5 4 5 4 3 3
−126.74 −129.88 −131.64 −138.71 −140.52 −140.43 −141.96 −143.72 −145.62
0.00 3.53 4.44 21.20 22.20 24.63 25.08 26.15 29.95
1 degrees of freedom; Conductivity (Cond).
higher levels in eutrophic freshwater systems that are influenced by droughts (Koba Yashi et al., 1990, Stets et al., 2018). However, these factors may impact chl-a levels independently due to extended periods of stratification (Kerimoglu and Rinke, 2013). TIN, TKN, and TP were hypothesized to have an additive influence on chl-a or act independently since these elements are required for phytoplankton growth, and they are normally significant factors in eutrophication events (Carpenter et al., 1998, Dzialowski et al., 2005). Nitrogen was considered separately from phosphorus primarily due to the association with diazotrophic cyanobacteria, which are commonly responsible for freshwater HABs (Beversdorf et al., 2013). Using the AICc method for additive linear models, both Lake LBJ and Lake Travis had different optimum models from one another. For Lake LBJ, the lowest delta value for the “TIN + TKN” model suggested organic and inorganic nitrogen had the highest influence on chl-a in Lake LBJ. While the delta values of 0.00–2.00 are competing models, values approaching 0.00 are considered the best fit. The second most appropriate model for Lake LBJ shows an additive influence of TP on TKN and TIN; however, this model was not truly competitive with a delta value of 2.60. Conductivity, chloride, sulfate, and rainfall all had a
5. Conclusions The results of this short study suggest that Aphanizomenon in Lake LBJ utilized free nitrogen in the water system in addition to its ability to fix nitrogen, likely promoting its dominance. This provides a foundation for future studies to examine the influence of different nitrogen species on the growth of Aphanizomenon, in culture and in mesocosm studies, since little is known about the role of nitrogen on cyanobacterial 592
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success (Dolman et al., 2012). Additionally, we present a relatively inexpensive framework for determining if further action is required for local freshwater monitoring programs in terms of cyanobacterial blooms, utilizing statistical modeling in R and basic water quality parameters that are readily available via an online, publicly-accessible database. Nutrient data were correlated with microalgal counts of limited available, preserved samples to rationalize the linear models selected using the AICc method. The impacts of this study could have been substantially improved if there were more microalgae samples for analysis. Ideally, each water quality sample would have had a corresponding microalgal community assessment. Additionally, more frequent samples could have strengthened the dataset. Nonetheless, this study provides a preliminary evaluation of the phytoplankton community along with a statistical assessment of available parameters to determine the likeliness of HAB formation.
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