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HomeMy WebLinkAbout3338; Agua Hedionda & Calavera Creek Dredging; Calavera Lake Water Chemistry, Diversity, Structure, Toxin;LOMA LINDA UNIVERSIIY --A - -- School of Science anclTechnology Calavera Lake and aloal tax4ns I' ' , + !,- : Marine Research Group of Lorna Linda University Tracey Magrann, Ph.D., Biology candidate (4th year) Stephen G. Dunbar, Ph.D., LLU, (Marine Biology) Martha Sutula, Ph.D., SCCWRP (Aquatic Ecology) Danilo Boskovic, Ph.D., LLU, (Biochemistry) William Hayes, Ph.D., LLU, (Statistics) Paul Buchheim, Ph.D., LLU (Lirnnology) Introduction Lake Calavera is a 520 acre-ft, man-made reservoir in Carlsbad, along the North San Diego coastline. It is owned by the Carlsbad Municipal Water District. The lake is a popular recreation site for shore fishing and small non-motorized boating. Preserve Calavera (PreserveCa1avera.org) is a non-profit organization of residents and users of the Calavera open space. Their goals include the creation of a regional nature preserve to: preserve and protect the open space around Mt Calavera and Lake Calavera as a regional nature preserve promote the use of these areas by a responsible public support education and restoration programs minimize the adverse effects of development on this natural environment. Lake Calavera was selected by this study for evaluation of phytoplankton biodiversity and algal toxin production due to its ecological importance to plants, fish, birds, small mammals, small reptiles, and humans. Background (Water Chemistry and Algal Assays) Three important water chemistry measurements are nitrogen, phosphorous, and alkalinity. The former are the two main nutrients for algal growth, and the latter affects the buffering capacity (ability to resist changes in pH) of a pond. Nitrogen Total Nitrogen (TN) represents the total nitrogen found in the water sample. Nitrogen can be further subdivided intohissolved nitrogen and particulate (solid) nitrogen (PN). ~otal Dissolved Nitrogen (TDN) can be further subdivided int&~issolved Organic Nitrogen @ON) and Dissolved Inorganic Nitrogen (DIN). The latter can be Wer subdivided into nitrate (NO3), nitrite (NO2), and ammonium (Nh). - Nitrogen is generally the growth limiting nutrient in estuaries and coastal waters (Wetzel2001). Dissolved Organic Nitrogen (DON) is found in the cells of all living things and is a component of proteins, peptides, and amino acids. When plants and animals die, proteins (which contain DON) are broken down by bacteria to Dissolved Inorganic Nitrogen (DIN) in the form of ammonium (NH4), which is then broken down by other bacteria to form nitrite (NOz), which is rapidly converted to nitrate (NO3). Nitrates can then be used for growth by plants and algae. Therefore, ponds with dead leaves, insects, or other decaying organic material will have high values of these forms of nitrogen. PN DON DIN i- NO3 I I NO2 NH4 Excess nitrates have several deleterious effects on birds, including inhibition of thyroid hormone (Fidanci, Yavuz et al. 20 lo), impaired growth (Grizzle, Armbrust et al. 1997), liver and kidney damage, and immune dysfunction (Atefa, Abo-Noragea et al. 1991). High concentrations of nitrate can produce "brown blood disease" in fish. Nitrite enters the bloodstream through the gills and turns the blood a chocolate-brown color. The blood cannot transport oxygen effectively, and the fish suffocate, despite adequate oxygen concentration in the water.(MSU 1998). The major sources of nitrogen in aquatic ecosystems include human and animal waste, fertilizers, fossil fuels, and cleaning products. Ammonia and organic forms of nitrogen are largely removed by wastewater treatment plants. However, these processes result in an increase in nitrate discharge, so the total nitrogen content does not change. Therefore, concerns about fish toxicity have decreased, but the potential for eutrophication has not changed (Mueller and Helsel 1999). Phosphorus Total Phosphorus (TP) represents the total phosphorous found in the water sample. Phosphorous can be further subdivided into dissolved phosphorous and particulate (solid) phosphorous (PP). Total Dissolved Phosphorous (TDP) can be further subdivided into Dissolved Organic Phosphorous POP) and Dissolved Inorganic Phosphorous (DIP). The latter can be further subdivided into phosphate (PO4). The process of nutrient loading into a water body is called eutrophication. Excessive nutrients lead to excessive production of algae, called an algal bloom. When the algae die, much of the oxygen in the water is consumed in the decay process, which may lead to the death of fish and other aquatic organisms. A site with excessive eutrophication is one that is at risk for algal blooms and fish kills. Limiting phosphorous and nitrogen inhibits algal growth. Each subcategory of nutrients is measured separately to ascertain the source of the excessive nutrient and evaluate its impact on the ecosystem. According to Wetzel(2001), phosphorus is often the growth limiting nutrient in fieshwater lakes and rivers because it occurs in the least amount relative to the needs of plants. Phosphorous in natural waters is usually found in the form of phosphate which is dissolved in the water (Wetzel2001). Every organism contains a large amount of phosphorous. Therefore, ponds with dead leaves, insects, or other decaying material will have a high PP value. As the particulate material completely erodes, the phosphorous dissolves in the water. Sites with high PP can be filtered to remove the solid organic material. This will prevent the phosphorous fiom being released into its dissolved form, which is then available for algae to utilize. Dissolved phosphorous is either organic or inorganic. Dissolved organic phosphate (DOP) is bound to plant or animal tissue, usually in the form of body waste (feces) and food residues. Dissolved inorganic phosphate is not associated with living tissues, and may occur from detergents, pesticides, or fertilizers that wash into the lake. Once the phosphorous is dissolved in the water, it can no longer be mechanically filtered out. However, chemicals, such as ferric (iron) chloride, can be added to the water to bind the phosphorous, causing it to precipitate out of solution. This means that the dissolved phosphorous become solid and heavy, and sinks to the bottom of the water, where it can then be filtered out by mechanical means. The resulting mass is a non-toxic, iron-phosphate "cake" that can be disposed of in landfill. One system that utilizes this technique is called Blue Pro. In May, 2009, a pilot study was performed on Mason Lake (Irvine, CA) using this technology. It was found to reduce algal cells by 94% and decreased POs 96% (Magrann, Dunbar et al. 2009). Alkalinitv Alkalinity is a reflection of the buffering capacity of water. It does not refer to pH, but instead refers to the ability of water to resist changes in pH. Alkalinity is the amount of fiee OH- ions in the water. These free OH- ions bind to free H+ ions (forming H20), which dilutes the pond, and changes the pH. The free H+ ions often originate from rain which precipitates within clouds containing acid products from pollution. When a site has a small amount of fiee OH' ions (low alkalinity), the buffering capacity can be rapidly overwhelmed during an acid rain, and the pH of the entire pond can become acidic. This shift in pH can kill sensitive aquatic organisms, including beneficial algae that contribute to the food web. Sites with high alkalinity might be at risk for eutrophication. Alkalinity reflects the bicarbonate concentration, which is an important source of inorganic carbon in photosynthetic organisms (Vestergaard and Sand-Jensen 2000). Alkalinity of natural water is determined by its bedrock and sediments. Alkalinity is higher in the presence of rocks with carbonate (~03'') and bicarbonate (HCOd, such as limestone (Water- research.net). Alkalinity is also increased by evaporation which concentrates these compounds. Conversely, areas rich in granites and sandstones may have low alkalinity and therefore poor buffering capacity (Water-research.net). Alkalinity may decrease over time through bacterial action which produces acidic compounds that combine with and reduce the alkalinity components. For protection of aquatic life the alkalinity should range from 50 to 150 mg/L CaC03. (Wurts and Durborow 1992). Alkalinity not only helps regulate the pH of a water body, but also the metal content. Wurts and Durborow (1 992) state that bicarbonate and carbonate ions in water can remove toxic metals (such as lead, arsenic, and cadmium) by precipitating the metals out of solution. Algal Assays There are two categories of algae: macroalgae (seaweeds) and microalgae. The latter is further divided into two types: periphyton (those that cling to surfaces), and phytoplankton (those that float in water). There are five main phyla of phytoplankton. The first phylum, Bacillariophyta, is known as the diatoms. They consist of fiustules (shells) made of silica. This makes them difficult for fish to digest, but they contribute to the ecological health of a lake because their decomposition leads to silicates in the sediment (Paasche 1973). Schleiske and Stoermer (1972) suggest that shifts can occur from a diatom-dominated community to a cyanobacteria community if the silicate levels decrease. The second phylum, Chlorophyta, is known as green algae. The third phylum, Euglenophyta, is known as euglena. Both of these phyla contain large amounts of lipids and chlorophyll, and are important nutrient sources for non-predatory fish (Kunne, Pistorius et al. 1998). In the current study, those sites containing a variety of green algae had the highest diversity and evenness values. The fourth phylum, Pyrrophyta, is known as dinoflagellates. "Red tides" are algal blooms, usually caused by from this group of organisms. The situation is dangerous to marine organisms because the dinoflagellates deplete the oxygen from the water (Whitford and Schumacher 1984). The fifth phylum, Cyanophyta, is known as the blue-green algae. They are also called cyanobacteria, since they contain lipopolysaccharide cell walls, typical of bacteria. However, they are photosynthetic by their chloroplasts, so they are similar to algae. They are often referred to as nuisance algae because they out-compete the healthier green alga as phosphate levels increase, and they can produce toxins from their lipopolysaccharide cell walls (Falconer 2004). Three such toxins that were found in this study were microcystin, cylindrospermopsin, and anatoxin-a. Microcystin and cylindrospermopsin are protein phosphatase inhibitors, which cause liver toxicity and promote tumor formation in fish, birds, and humans (any organism with a liver). Anatoxin-a is a neurotoxin which causes convulsions and death in fish, birds, and humans (any organism with skeletal muscles). Microcystin is the toxin produced by the cyanobacteria genus Microcystis. Cylindrospermopsin is produced by Cylindrospermopsis, and anatoxin-a can be produced by several cyanobacteria. Richness and Evenness are statistical measures of biodiversity in an ecosystem. Richness refers to the number of individuals in the site, and evenness refers to the relative abundance or proportion of individuals in the site. A site with healthy biodiversity is one that contains a large variety of organisms. A site with normal richness will have a Shannon Index of 1-4 (Irvine and Murphy 2009). A site with ideal evenness will have a Pielou's Index of loo%, indicating an even distribution of each genus within the site. Methods In this study, 66 water samples were collected in 42 lakes, ponds, bays, and lagoons from Santa Barbara to San Diego along the Pacific Flyway. The first series was collected from 30 sites during early summer (June-July) 2009. The series was repeated in late summer (August-September) 2009, but without six sites which were no longer accessible, and with the addition of 12 locations. Sites were selected based on their variance in nutrient levels and for their ecological importance to wildlife and human recreational activities. All water samples were analyzed for water chemistry values, phytoplankton composition, biodiversity, and toxin content. Funding permitted toxin analysis on 40 of the sites; all were from the late summer series. Measurements of visibility (secchi depth), air and water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), and total dissolved solids (TDS) were obtained at the point and approximate time of water sample collection. Samples were analyzed at the Southern California Coastal Waters Research Project (SCCWRP) for alkalinity (Alk) and chlorophyll-a (Chl-a). Algal assays were performed at Loma Linda University for cell counts and identification to genus. Samples were analyzed at the University of Georgia Stable Isotope Laboratory for total dissolved phosphate (TDP), total dissolved nitrogen (TDN), total phosphorus (TP), total nitrogen (TP), and dissolved organic carbon (DOC). Samples were analyzed at the University of California Santa Barbara Marine Science Institute Analytical Laboratory for ammonium (NH4), phosphate (PO4), nitrite (NOz), and nitrate + nitrite (NO3 + NOz). From those results, values were calculated for dissolved organic phosphorous (DOP), particulate phosphorous (PP), nitrate (NO3), dissolved inorganic nitrogen (DIN), and dissolved organic nitrogen (DON). Samples were also sent to the University of New York, MERHAB Laboratory for algal toxin analysis by protein phosphatase inhibition assay (PPIA) and liquid chromatography/mass spectrometry (LCIMS). Calavera Lake was sampled on July 9 and September 14,2009. Toxin analysis on Calavera Lake was performed on the sample obtained September 14,2009. Results: Toxin Analysis Three algal toxins were found in Calavera Lake: microcystin (0.08 pg.~-'), cylindrospermopsin (0.040 pg.~-'), and anatoxin-a (0.234 pg~-'). All three toxin levels were below drinking water limits. The World Health Organization recommends microcystin and cylindrospermopsin levels be kept below 1.0 pg.~-l, and anatoxin-a below 3.0 pg~-' (WHO 2001). Results: Water Chemistry Data for Calavera Lake are provided in the attached Excel spreadsheet. Results: Algal Assays The overall distribution of phyla throughout the study sites were Pyrrhophyta (0. I%), Bacillariophyta (2%), Euglenophyta (2%), Chlorophyta (4%), and Cyanophyta (92%). This is illustrated in Fig. I. Fig. 1. Distribution of phyla throughout the study sites N Bacillariophyta 2% Euglenophyta 2% Chlorophyta 4% Cyanophyta 92% Pyrrophyta 4% -. Phyla in all sites The distribution of phyla in the July 9'h sample from Calavera Lake was Chlorophyta (I%), Bacillariophyta (3%), Euglenophyta (3%), Pyrrhophyta (6%), and Cyanophyta (86%). In the September 14'~ samples, the phyla distribution was Bacillariophyta (I%), Pyrrhophyta (I%), Chlorophyta (7%), Euglenophyta (1 8%), and Cyanophyta (73%). In both Calavera Lake samples, there were eight genera present. In the July 9'h sample, the total cells/ml~' were 86,069. The genera, in order of predominance, were as follows: Microcystis (Cyanophyta) 73,809 cells/ml-' Dinoflagellates (Pyrrophyta) 5504 cellslml-' Euglena (Euglenophyta) 2502 cells/ml-' Navicula (Bacillariophyta) 125 1 cells/ml-' Nitzchia (Bacillariophyta) 100 1 cells/ml~' Cyclotella (Bacillariophyta) 75 1 cells/mf' Desmidium (Chlorophyta) 75 1 cellslml-' Anabaena (Cyanophyta) 500 cells/ml~' In the September 14'~ sample, the total cells/ml-' were 34,528. The genera, in order of predominance, were as follows: Microcystis (Cyanophyta) 25,020 cells/ml-' Euglena (Euglenophyta) 6 130 cells/ml-' Pandorina (Chlorophyta) 2002 cellslml-' Dinoflagellates (Pyrrophyta) 375 cells/ml~' Cymbella (Bacillariophyta) 375 cells/ml-' Dictyosphaerium (Chlorophyta) 250 cells/ml~' Cylindrospermopsis (Cyanophyta) 250 cellslml~' Golenkinia radiata (Chlorophyta) 125 cellslml-' Analysis: Water Chemistry vs. Toxins (all sites) Most variables were normalized with log transformation, but microcystin required square root transformation, and NO3 + NOz, NK, DIN, and EC required reciprocal square root transformation. Two variables (NO;! and TDS) would not normalize with any transformation. Following transformations, significant univariate predictors of (1) Cyanophyta, and (2) microcystin were placed in two separate multivariable models (ANOVA) to determine which predictors were still significant while adjusting for the presence of the other variables. In both multivariable models, none of the predictors were significant. The lack of significance with multivariable analysis may be due to the presence of high correlations (redundancy) between the predictor variables. Univariate linear regression analyses were then performed to ascertain the presence of significant predictors for (I) Cyanophyta, (2) microcystin (with the presence of outliers from Andree Clark and Mason Lake). The following analyses are shown in Tables I and 2, with significant predictors in bold. Table 1. Univariate Analysis for predictors of Cyanophyta Predictor Variable P PP 0.0211 DON 0.0223 TN 0.0365 TDN 0.0466 TP 0.0974 TDP 0.1 152 Alk 0.1586 NO2 0.2404 EC 0.3 110 NH4 0.4006 TDS 0.4578 DO 0.5305 DOP 0.5624 Po4 0.7 174 DIN 0.8 165 NO3 + NOz 0.841 1 Table 2. Univariate Analysis for predictors of Microcystin Predictor Variable P C hlorop hyll-a 0.0008 Total Cells 0.0060 DON 0.0188 TN 0.0218 PP 0.0377 TP 0.0498 TDN 0.0691 TDP 0.0756 Alk 0.1 138 TDS 0.3361 No2 0.3420 NO3 + NOz 0.3594 EC 0.3881 DOP 0.4019 DOC 0.5310 Po4 0.633 1 Nfb 0.7724 DIN 0.9352 DO 0.9869 Simificant predictors for Cvanovhvta were as follows: PP (p = 0.021 1) Average for all sites PP = 6 pM/L Average for toxic sites PP = I I pM/L Calavera Lake PP = 0.82 pM/L (July) 2.06 pM/L (Sept) DON (p = 0.0223) Average for all sites DON = 124 pM/L Average for toxic sites DON = 296 pM/L Calavera Lake DON = 29.02 pM/L (July) 56.58 pM/L (Sept) TN (p = 0.0365) Average for all sites TN = 241 pM/L Average for toxic sites TN = 470 pM/L Calavera Lake TN = 35.44 pM/L (July) 88.63 pM/L (Sept) TDN (p = 0.0466) Average for all sites TDN = 173 pM/L Average for toxic sites TDN = 306 pM/L Calavera Lake TDN = 3 1.17 pM/L (July) 64.98 pM/L (Sept) Significant predictors for microcystin were as follows: DON (p = 0.01 88) TN (p = 0.0218) PP (p = 0.0377) TP (p = 0.0498) Average for all sites TP = 16 pM/L Average for toxic sites TP = 27 pM/L Calavera Lake TP = 1.20 pM/L (July) 4.76 pM/L (Sept) Total ~ellslml~' (p = 0.0060) Average for all sites Total ~ellslml-' = 206,040 Average for toxic sites Total ~ells/ml~' = 36 1,09 1 Calavera Lake Total ~ells/ml~' = 86,069 (July) 34,528 (Sept) Chl-a (p = 0.0008) Average for all sites Chl-a = 39 pglml Average for toxic sites Chl-a = 174 pglml Calavera Lake Chl-a = 9 pglml (July) 12 pglml (Sept) Since the dependent variable of interest in this study was microcystin, and its presence in high levels registered as outliers, the toxin data was fh-ther dichotomized into a nominal variable of two levels. Since the World Health Organization (WHO) limits microcystins in drinking water to I pg-~-', those sites with microcystin levels >I .O were labeled as "Toxic" whereas Microcystin levels <I .0 were labeled as "Non-Toxic". Using odds ratios, the most significant finding was Alk (OR = 3.345). This means that, for every unit increase in alkalinity, it greater than 3 times more likely that microcystin will be present in toxic levels. Other significant odds ratio findings were as follows: PP (OR = 2.673), NI& (OR = 2.43 I), DON (OR = 2.400), DOP (OR = 1.964), and TDN (OR = 1.851). All Odds Ratios included the null value of OR=1 .O, and are at 95% confidence. The results of this analysis are listed in a Table 3, with significant impactors in bold. Table 4 shows these values found in Calavera Lake. Since alkalinity was the most noteworthy variable, a logistic regression graph of alkalinity vs. microcystin is shown in Fig. 2. Fig. 2: Alkalinity vs. Microcystin Alkalinity mg/l CaCO, Table 3. Microcystin Odds Ratio 95% confidence limits Dichotomized (Toxic >1; Non-Toxic 4) Predictor Variable OR Alk 3.345 PP NH4 DON DOP TDN TDP TN TP DIN NO3 + NO2 PO4 TDS DO NO2 Table 4. Water Chemistry factors affecting toxin production Water Average Average Calavera Calavera Chemistry of all of toxic Lake Lake Factors sites sites (July) (Sept) Alk mg/L CaC03 183 193 152 38 PP~M/L 6 I I 0.82 2.06 NH4 pm 8 8 0.48 7.68 DON pM/L 124 296 29.02 56.58 DOP pM/L 4 8 0.00 1.45 TDN pM/L 173 3 06 31.17 64.98 Analysis: Water Chemistry vs. Phytoplankton Diversity and Community Structure Frequency tables of phytoplankton diversity were prepared. Table 5 shows the study sites in San Diego, sorted by number of genera. The cyanobacterium, Microcystis, was the predominant genus found in 96% of the study sites, with Euglena and Attheya comprising the remainder. The second most predominant genus was Euglena in 29% of the sites, with 2 1 other genera comprising the remainder. The third most predominant genus in the study sites was a complex of 24 genera, diffisely distributed among Bacillariophyta (diatoms), Euglenophyta (Euglenoids), Chlorophyta (green algae), and Cyanophyta (blue-green algae). The five most numerous genera in the study sites were Microc stis (Cyanophyta; over 12 million cells/mL,-I), Euglena (Euglenophyta; 1.9 Y million cells/mL- ), Merismopedia (Cyanophyta; 1.7 million cells/mL,"), Chlamydomonas (Chlorophyta; 0.16 million cells/m~-I), and Nitzchia (Bacillariophyta; 0.13 million cells/m~-I). The least numerous genera were Spirogyra and Melosira (Chlorophyta; 125 cellslm~~~). Table 5. Study sites in San Diego sorted by genera predominance Total Most 2nd most 3" most Number of predominant predominant predominant Site Name genera genera genera genera San Marcos Lake, July 18 Microcystis Navicula Sphoerocystis San Marcos Lake, Sept 13 Microcystis Nitzschia Cylindrospermopsis Buena Vista Lagoon, Sept 13 Euglena Microcystis Palmellococcus Buena Vista Lagoon, July 10 Microcystis Palmellococcus ChQmydomonos Calavera Lake, Sept 8 Microcystis Euglena Pandorina Calavera Lake, July 8 Microcystis Dinoflagellates Euglena San Mateo Lagoon, July 7 Microcystis Desmidium Navicula The higher the number of genera that is present in each site, the more biodiversity there is. The average number of genera in all sites was 9, the average number of genera in toxic sites was 6, and the number of genera in both samples from Calavera Lake was 8. The number of genera throughout the sites ranged from 1-20. The Shannon Index (below) was used to calculate richness (H') of genera and phyla of each site. H' values for richness of phyla throughout the study sites ranged from 0- 1.3 1. Average H' values of richness of phyla throughout the study sites = 0.40. Average H' values of richness of phyla in toxic sites =. 0.18 In Calavera Lake, H' for phyla = 0.39 (July) and 0.77 (Sept). H' values for richness of genera throughout the study sites ranged from 0-2.03. Average H' values for richness of genera throughout the study sites = 0.61 Average H' values for richness of genera in toxic sites = 0.21 In Calavera Lake, H' for genera = 0.64 (July) and 0.90 (Sept) To understand the impact of Microcystis upon algal richness, formulas were then computed without the numbers for Microcystis. H' values for richness of phyla throughout the study sites (without Microcystis) ranged from 0-1.29. Average H' values for richness throughout the study sites (without Microcystis) = 0.87. Average H' values for richness of phyla (without Microcystis) in toxic sites = 0.55 In Calavera Lake, H' for phyla without Microcystis = 0.93 (July) and 1 .OO (Sept). When the formulas were computed without the numbers for Microcystis, H' values for richness of genera throughout the study sites (without Microcystis) ranged from 0-2.54. Average H' values for richness of genera throughout the study sites (without Microcystis) = 1.48. Average H' values for richness of genera (without Microcystis) in toxic sites = 1.04 In Calavera Lake, H' for genera without Microcystis =' 1.19 (July) and 1.59 (Sept). Pielou's Index (below) was used to calculate evenness (J') of genera and phyla of each site. J' values for evenness of phyla throughout the study sites ranged from 0-100%. Average J' values for evenness of phyla throughout the study sites - 29%. Average J' values for evenness of phyla in toxic sites = 14%. In Calavera Lake, J' for phyla = 28% (July) and 48% (Sept). J' values for evenness of genera throughout the study sites ranged from 0-88%. Average J' values for evenness of genera throughout the study sites = 28%. Average J' values for evenness of genera in toxic sites = 12%. In Calavera Lake, J' for genera = 3 1% (July) and 43% (Sept). To understand the impact of Microcystis upon algal evenness, formulas were then computed without the numbers for Microcystis. J' values for evenness of phyla throughout the study sites (without Microcystis) ranged from 0-100% Average J' values for evenness of phyla throughout the study sites (without Microcystis) = 66%. In Calavera Lake, J' for phyla without Microcystis = 67% (July) and 62% (Sept). J' values for evenness of genera throughout the study sites (without Microcystis) ranged from 0-91% Average J' values for evenness of genera throughout the study sites (without Microcystis) = 69%. Average J' values for evenness of genera in toxic sites (without Microcystis) = 61%. In Calavera Lake, J' for genera without Microcystis = 57% (July) and 77% (Sept). Richness (H') and evenness (J') of genera and phyla, with and without Microcystis is displayed in Table 6. Table 6. Richness (Hy) and evenness (J') of genera and phyla in San Diego sites, with and without Microcystis, sorted by richness of genera J H ' No No # J H' Micro- Micro- Gen- Gen- Gen- cystis cystis Lake Name era era era Genera Genera Ave, aii sites 9 28% 0.61 69% 1.48 J H' No No Micro- Micro- # J H ' cystis cystis Phyla Phyla Phyla Phyla Phyla 3 29% 0.40 66% 0.87 Ave, toxic sites 6 12% 0.21 61% 1.04 1 2 14% 0.18 41% 0.55 Buena Vista I Lagoon, Sept 13 74% 1.91 83% 2.12 1 4 94% 1.31 84% 1.16 Buena Vista Lagoon, July 10 49% 1.14 79% 1.82 4 65% 0.90 81% 1.13 San Marcos Lake, Juiy 18 32% 0.94 90% 2.15 13 69% 0.76 100% 1.29 San Marcos Lake, Sept 13 35% 0.90 60% 1.71 4 48% 0.67 91% 1.26 Calavera Lake, Sept 8 43% 0.90 77% 1.59 15 48% 0.77 62% 1.00 Calavera Lake, July 8 31% 0.64 57% 4 28% 0.39 67% 0.93 San Mateo Lagoon, Juiy 7 27% 0.53 53% 1.02 1 3 43% 0.47 76% 0.84 Linear regression analysis was used to evaluate the effects of water chemistry factors on diversity. The following three nutrients were found to lower diversity throughout the study sites: Total Dissolved Phosphorous (R2 = 0.101), phosphate (R2 = 0.0867), and Total Phosphorous (R~ = 0.0776). Table 7 lists the average values of these factors throughout the study sites, and the values at Calavera Lake. Fig. 3 illustrates the decline of diversiy (H') with increasing phosphate (PO4). Table 7. Factors affecting diversity Water Average Average Calavera Calavera Chemistry of all of toxic Lake Lake Factors sites sites (July) (Sept) TDP pM/L 10 17 0.37 2.70 Po4 pm 7 8 0.65 1.26 Nh ~m 8 8 0.48 7.68 TP pM/L 16 27. 1.20 4.76 Pig. 3. Decline of diversity with increase in POs Diversity H1 - PO4 28 37 Site number Discussion Water Chemistry, Cvano~hvta. Microcvstis, and microcvstin toxins Compared with the other study sites, Calavera Lake contained very low levels of Cyanophyta, Microcystis, and all toxin levels were below drinking water limits. The lake was also low in those water chemistry factors that predict negative impacts. Throughout the study sites, the average number of Cyanophyta was 190,2 12 ~ellslml-', while Calavera Lake contained 73,809 (July) 25,270 (Sept), almost all from Microcystis. We found four main predictors for these nuisance algae (PP, DON, TN, TDN). However, Calavera Lake had very low levels of these factors. Microcystis is one genus of Cyanophyta, and is a colonial algae. Therefore, when this species is present, it occurs in large numbers. The average number of Microcystis throughout the study sites was 186,566 ~ellslml~'. However, Calavera Lake . contained only 73,809 ~ellslml~~ (July) and 25,020 cellslml-' (Sept). The presence of Microcystis does not imply that toxins will be present. We found the same four factors that predict the presence of Cyanophyta (PP, DON, TN, TDN) also predicted the presence of microcystin toxins, in addition to three other factors: TP, Chl-a, and Total Cells. Both samples from Calavera Lake had low levels of these factors as well. All of the sites in this study contained microcystin toxins, yet only five sites were in excess of drinking water limits. Six water chemistry factors were found to predict whether or not toxins are likely to be above acceptable levels: Alk, PP, Nh, DON, DOP, and TDN. Three of these factors (PP, DON, TDN) have already been discussed in relation to predicting the presence of Cyanophyta and microcystin. However, the other three factors (Ak, NH4, DOP) are unique for predicting toxin levels that are excessive. Both samples from Calavera Lake were very low in all of these factors, with the exception of the September sample contained NH4 levels (7.68 pM/L) only slightly lower than the average for the toxic sites (8 pM/L). Phvtovlankton comvosition, biodiversity, richness, and evenness Compared to the other 66 sites, number of genera at Calavera Lake ranked 37 (July) and 35 (Sept). Compared to the other sites, genera evenness (J') at Calavera Lake ranked 14 (Sept) and 26 (July). The number of genera in Calavera Lake was one less than average, and one more than the average of the toxic sites. Richness (H') of genera at Calavera Lake in July was almost the same as the average of all sites. In September, it was higher (0.90) than average (0.61), and much higher than average for the toxic sites (0.21). If Microcystis was filtered out of the lake, the richness of genera is estimated to rise to 1.59 (July) and 1.19 (Sept), which is within normal limits. Evenness (J') of genera at Calavera Lake in July was 3 1%, almost the same as the average (28%) of all sites. In September, it rose to 43%, which is much higher than the 12% average for the toxic sites. If Microcystis was filtered out of the lake, the evenness of genera is estimated to rise to 57% in July and 77% in September. The four water chemistry factors affecting diversity (TDP, PO4, NH4, TP) were lower than average in Calavera Lake, and much lower than the average of the toxic sites. Recommendations We found that Calavera Lake ranked 18' (Sept) and 26th (July) out of the 66 sites in biodiversity quality. Because the nutrients in Calavera Lake are not excessive, the site has good biodiversity and low toxin levels. We found only two areas of concern with this site. The ammonium concentration is borderline high during late summer, and one of the predominate algal organisms is a dinoflagellate, which can cause a "red tide" algal bloom. This condition is not toxic, but it can deplete the oxygen in the lake, causing fish to suffocate. Because of these findings, we recommend the following: Monitor N& levels during the hottest time of the year. Concentrations greater than 8 pM/L may produce conditions favoring microcystin production in excess of drinking water limits. Monitor the levels of dinoflagellates, especially in the early summer. Excessive numbers might precipitate a "red tide" algal bloom. If Microcystis or dinoflagellate densities increase, consider removal of colonies, phosphorous (TP, PP), and dissolved phosphorous (TDP, DOP, PO4) by the use of ionized sand filtration infused with alum and ferric chloride (Magrann, Dunbar et al. 2009).