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Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers
www.water.nsw.gov.au
Publisher
NSW Department of Primary Industries, a division of NSW Department of Trade and Investment, Regional Infrastructure and Services.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers
First published August 2012
ISBN 978 1 74256 326 8
© State of New South Wales through Department of Trade and Investment, Regional Infrastructure and Services 2012.
This publication is copyright. You may download, display, print and reproduce this material in an unaltered form only (retaining this notice) for your personal use or for non-commercial use within your organisation. To copy, adapt, publish, distribute or commercialise any of this publication you will need to seek permission from the Department of Trade and Investment, Regional Infrastructure and Services.
Disclaimer The information contained in this publication is based on knowledge and understanding at the time of writing (July 2012). However, because of advances in knowledge, users are reminded of the need to ensure that information on which they rely is up to date and to check the currency of the information with the appropriate officer of the Department of Primary Industries or the user’s independent advisor.
Publication number 11433
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
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Contents
Executive Summary................................................................................................................................ ii
1. Introduction........................................................................................................................................ 1
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2. Methods............................................................................................................................................. 2
3. Results...............................................................................................................................................
3.1. Data logging. ..........................................................................................................................
3.2. Initial comparison of field phycocyanin measurements and total cyanobacterial
biovolume measurements...................................................................................................
3.3. Analysis of the phycocyanin data........................................................................................... 9
3.4. Variation in phycocyanin response between sampling sites................................................
3.5. Analysis of the Chlorophyll-a data.......................................................................................
3.6. Within-species variation in cell size.....................................................................................
5. Discussion .......................................................................................................................................
5.1 In-situ fluorometry .............................................................................................................
5.2. Use of Chlorophyll-a fluorometry for cyanobacterial bloom management........................
5.3. Temporal and spatial variability in cell size.......................................................................
6. Conclusions.................................................................................................................................
7. Recommendations ......................................................................................................................
8. References ..................................................................................................................................
Appendix A – Box Plots .......................................................................................................................
Appendix B – Summaries of Site by Site Correlation Analyses ..........................................................
Appendix C – Summaries of Site by Site Linear Regression Analyses ..............................................
Appendix D – Matrix of Site by Site Comparisons of Linear Regressions using Homogeneity of Slope Analysis.........................................................................................................................
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
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Tables
Table 1. Comparisons of the adjusted r2 values from linear regression between log (x +
1) phycocyanin and log (x + 1) biovolume and the adjusted r2 values obtained for the multiple regressions using the water quality attributes shown and log (x = 1) biovolume. Also presented are the partial η2 values obtained from the
multiple regressions. The partial η2 values that are highlighted indicate a statistically significant (at the 5% level or greater) of the regression coefficients for these water quality attributes. It was not possible to calculate adjusted r2
values for the multiple regressions for Merbein and Curlwaa (the “unadjusted” r2 values were very low). .................................................................................................... 17
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Table 2. Table of PERMANOVA pairwise comparison results comparing community
composition at sites in different sections of the Murray and Edward Rivers. Statistically significance Permational P-values - * p<0.05, ** p<0.01, *** p<0.001, NS = not significant..........................................................................................
Table 3. Average within-group similarities and the major taxa contributing to this within-group similarity in each section of the Murray and Edward Rivers by more than 10%. ................................................................................................................................
Table 4. Average between-group similarities and the major taxa contributing to the between-group differences in sections of the Murray and Edward Rivers by more than 10%................................................................................................................
Table 5. Summary statistics for cell volumes calculated for taxa measured from the Murray and Darling River sampling sites. .......................................................................
Table 6. Summary statistics for cell width/diameter measurements for taxa from the
Murray and Darling River sampling sites. .......................................................................
Table 7. Summary statistics for cell length measurements for taxa from the Murray and Darling River sampling sites. ..........................................................................................
Table 8. Summary statistics for cell length to width ratio for taxa from the Murray and Darling River sampling sites. ..........................................................................................
Table 9. Statistically significant correlations between the cell volume of various taxa of
cyanobacteria and physico-chemical attributes measured in the Murray and Darling Rivers..................................................................................................................
Table 10. Statistically significant correlations between the cell volume of various taxa of
cyanobacteria and other biological variables measured in the Murray and Darling Rivers..................................................................................................................
Figures
Figure 1. Location of the major sampling sites sampled for this study. ...........................................
Figure 2. Examples of logged data for chlorophyll-a and phycocyanin for various locations
and dates on occasions when logged data remained relatively stable throughout the measurement periods. ................................................................................................
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Figure 3. Examples where logged data displayed considerable variability during the logging period, due to either an initial equilibration of the probe when measurements were first commenced, or due to logging stopping and starting
again either side of a probe cleaning period..................................................................... 6
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Figure 4. Raw data of field measured phycocyanin plotted against total cyanobacterial biovolume measured in the laboratory..............................................................................
Figure 5. Raw data of field measured phycocyanin plotted against total cyanobacterial biovolume calculated using tables of standard cell sizes. ................................................
Figure 6. Scatterplot comparing biovolumes calculated using actual laboratory cell size
measurements and those calculated using standard cell size tables...............................
Figure 7. Plots of phycocyanin against turbidity, showing how phycocyanin measurements increase in response to turbidity once turbidity exceeds 50 NTU;
and of cyanobacterial biovolume against turbidity, showing usually low biovolume was present when turbidity exceeded 50 NTU................................................
Figure 8. Relationship between phycocyanin measured using the YSI equipment and
cyanobacterial biovolume (calculated using standard cell size tables) for waters with a turbidity of 50 NTU or less. ...................................................................................
Figure 9. Testing the robustness of the analysis between phycocyanin measured in-situ
with the YSI instrument and cyanobacterial biovolume estimated from standard cell size tables. The graph on the left shows the relationship using an 18 week subset of the data set (20 weeks in all), while the graph on the right shows the
biovolumes estimated using the relationship developed from the 18 week subset for the other two weeks plotted against the biovolumes for these weeks estimated from standard cell size tables. Solid line indicates the relationship
between the two methods of biovolume estimation fitted through the plotted data, while the dashed line shows the expected 1 to 1 relationship...............................
Figure 10. Relationship between log turbidity and log phycocyanin (x + 1) when turbidity
exceeds 50 NTU, and the correction curve determined from this to remove false positive phycocyanin measurements in highly turbid water. All data had total cyanobacterial biovolumes of less than 0.50 mm3 L-1, so the measurement of
phycocyanin contributed by cyanobacteria would be minimal........................................
Figure 11. Scatterplots of phycocyanin against Chlorophyll-a, electrical conductivity, pH, water temperature and dissolved oxygen. ......................................................................
Figure 12. Linear regression analysis of log phycocyanin (x + 1) against log biovolume (x + 1) calculated from tables for representative sites along the Murray River, for Gulpa Creek at Mathoura and for Darling River at Ellerslie............................................
Figure 13. A comparison of the linear regression results obtained for sites along the Murray River and the Gulpa Creek/Edward River/Wakool River system, comparing phycocyanin measured in-situ using the YSI instrument with total
cyanobacterial biovolume (calculated using standard cell size tables). Data where the turbidity exceeded 50 NTU were removed prior to these regression analyses. .........................................................................................................................
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
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Figure 14. Variation in adjusted r2 for the 22 site by site linear regression analyses of phycocyanin against total cyanobacteria biovolume (both log transformed (x + 1)) with mean cyanobacterial biovolume and mean phycocyanin measured for
each site.......................................................................................................................... 16
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Figure 15. nMDS ordination summarising the difference in cyanobacterial community composition in different sections of the Murray River (Upper Murray, Mid Murray
and Sunraysia) and in the Gulpa Creek/Edward River/Wakool River distributary branch. ............................................................................................................................
Figure 16. Relationships between chlorophyll-a measured with the YSI instrument (as μg L-
1) and total cyanobacterial biovolume estimated using standard cell size tables and from laboratory measurements................................................................................
Figure 17. Log chlorophyll-a (x + 1) measurements compared against other water quality
attributes measured simultaneously with the same instrument, including turbidity (all measurements and those of less than 50 NTU), electrical conductivity, pH, water temperature and dissolved oxygen concentrations. .............................................
Figure 18. Temporal variation in cell volume, given as the mean and ±1 standard error of the mean, for major taxa that occurred in the Murray and Darling Rivers during the study period ..............................................................................................................
Figure 19. Spatial variation in cell volume, given as the mean and ±1 standard error of the mean, for major taxa that occurred in the Murray and Darling Rivers during the study period.....................................................................................................................
Figure 20. Temporal variation in cell width, given as the mean and ±1 standard error of the mean, for major taxa that occurred in the Murray and Darling Rivers during the study period.....................................................................................................................
Figure 21. Spatial variation in cell width, given as the mean and ±1 standard error of the mean, for major taxa that occurred in the Murray and Darling Rivers during the study period.....................................................................................................................
Figure 22. Temporal variation in cell length, given as the mean and ±1 standard error of the mean, for major taxa that occurred in the Murray and Darling Rivers during the study period ..............................................................................................................
Figure 23. Spatial variation in cell length, given as the mean and ±1 standard error of the mean, for major taxa that occurred in the Murray and Darling Rivers during the study period.....................................................................................................................
Figure 24. Temporal variation in cell length to width ratio for major taxa that occurred in the Murray and Darling Rivers during the study period. .......................................................
Figure 25. Spatial variation in cell length to width ratio for major taxa that occurred in the
Murray and Darling Rivers during the study period. .......................................................
Figure 26. Relationship between variation in cell volume of various taxa of cyanobacteria and water temperature....................................................................................................
Figure 27. Examples of relationships where there were significant positive or negative relationships between the cell size of various cyanobacteria taxa and other biological variables..........................................................................................................
Executive Summary
A Yellow Springs Instruments (YSI) water quality sonde fitted with probes for the detection of
phycocyanin and chlorophyll-a by in-situ fluorometry was trialled over the spring-summer-autumn period of 2008-09 at 27 sites along the Murray and Lower Darling Rivers, NSW. In freshwater, phycocyanin is a pigment specific to cyanobacteria (blue-green algae). This project examined
whether the in-situ quantification of phycocyanin by fluorometry could be used to determine the abundance of cyanobacteria present (as biovolume), and use this measurement of abundance for cyanobacterial bloom management by comparing the results with management guidelines for
recreational water use and an Alert Levels Framework for raw water sources for potable supply.
The study found a strong positive relationship between phycocyanin measured in-situ and total cyanobacterial biovolume determined by laboratory measurements from phytoplankton samples
collected from the same water in the same container as the in-situ measurements were made. Despite the good correlation between phycocyanin and total cyanobacterial biovolume, it was found that the use of in-situ phycocyanin fluorometry was not effective in turbid water above 50 NTU. Water
with a turbidity greater than 50 NTU often gave false positive readings for phycocyanin, that is, a high phycocyanin reading when there was very little cyanobacterial biovolume present. Hence all data where the turbidity of the water was greater than 50 NTU had to be excluded from further analyses in
this study.
However, there was a considerable amount of variance within these data, much of it most likely caused by error in sub-sampling for counting, and error in counting and estimating cyanobacterial
biovolumes in the laboratory. A smaller amount of the variability may also be attributed to error involved in the phycocyanin measurement, including equilibration and cleaning cycle problems associated with the probe that led to slight variability in the phycocyanin signal during the 5 minute
data logging period by the YSI at some sites and sampling occasions. Other small errors can be attributed to slight differences in fluoresence properties of phycocyanin in different species; to cyanobacterial abundance often being close to the lower limit of quantification of the instrument; to
prior light saturation; to possible other water quality factors such as ambient nutrient availability that affect cyanobacterial growth; and to the age, growth stage and physiology of the cyanobacteria present. All these factors can cause phycocyanin fluoresence to vary over time.
Low cyanobacterial presence, even in non-turbid water, also led to considerable variance within the data. As a result, the measurement of cyanobacterial abundance when total biovolume is less than 0.4 mm3 L-1 is likely to be subject to the greatest error. This biovolume marks the threshold of the
Amber alert level for the management of water for recreational purposes (National Health and Medical Research Council 2008), and is below the High alert level for raw water sourced for potable supply (Newcombe et al 2010). Difficulty in measuring cyanobacteria at low abundance is not really a
management issue, as these levels of cyanobacteria do not pose public health threats. Being able to determine either an Amber or Red alert for recreation, or a High alert for raw water for potable supply makes in-situ fluorometry of phycocyanin a potentially valuable tool for cyanobacterial bloom
management, as it has the potential to provide an almost instantaneous measure of cyanobacterial presence, compared to the delays of several to many days that may occur with tradition cyanobacterial monitoring methods that rely on collecting water samples and their transport to and
analysis at a laboratory.
One further finding of the study was that the relationship between phycocyanin measured in-situ by fluorometry and total cyanobacterial biovolume may vary spatially between sites along the Murray
River. This is partly caused by differences in cyanobacterial abundance between sites, with sites along the upper Murray River from Albury to Tocumwal having a higher abundance of cyanobacteria for much of the study period than sites along the Murray River in Sunraysia. The species composition
of the cyanobacterial communities in different sections of the river was also found to vary from
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
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upstream to downstream. Both factors, total abundance and species composition, may have led to different relationships being measured between phycocyanin and total cyanobacterial biovolume in different sections of the river. These spatial differences may need to be taken into account if in-situ
phycocyanin fluorometry is to be broadly adopted as a cyanobacterial bloom management tool across NSW. However a single linear relationship may still also be appropriate, as the error caused by site to site variance in fluorometry is likely to be less than the error involved in biovolume determination using
current laboratory practices and standard cell size tables.
There was no relationship established between total cyanobacterial biovolume and chlorophyll-a measured by in-situ fluorometry. This is probably because of the contribution to the chlorophyll-a
signal by phytoplankton other than cyanobacteria (eukaryotic species of algae), which also contain considerable amounts of chlorophyll-a, and to the fact that chlorophyll-a contained within cyanobacterial cells is poorly fluorescent.
One further facet examined as part of this study was the spatial and temporal variation in the size of the cells of commonly occurring cyanobacterial species in the Murray River. The sizes of the cells of these species were measured by microscopy and image enhancement throughout the study,
principally for comparison with the in-situ fluorometry results, but these data have also been useful for other purposes. Some temporal variation in cell size was observed, with cell size (especially cell width and cell volume) in a number of species decreasing during summer and autumn. However few spatial
patterns in the cell sizes were observed. Few of the environmental attributes that were also measured during the study were found to be major factors influencing cell size change, although some weak positive correlations were found between both cell width and cell volume and water temperature for
several species. The average cell volumes calculated for Anabaena circinalis, Anabaena planktonica, Anabaena aphanizomenioides and Microcystis flos-aquae in this study were all found to be considerably less than the cell sizes for these species published in commonly used standard cell size
tables. These species contributed the bulk of the total cyanobacterial biovolume in the Murray River during this study, so use of published standard cell sizes would lead to overestimations of total biovolume, and at times lead to Red alerts being declared for recreational water use when actual
biovolume may still be under the alert threshold. However this may be acceptable as it provides an extra margin of safety for algal bloom management.
The main recommendations of the study are that in-situ phycocyanin fluorometry should be adopted
as a cyanobacterial bloom management tool for NSW, provided that the equipment used has a comparable performance to the YSI, and that the method not be used in turbid water. The data collected may be used by Regional Algal Coordinating Committees during periods of Amber or Red
alerts for rapid management actions, but will require follow up confirmation through the laboratory analysis of samples. Although likely to provide better estimates of total cyanobacteria biovolume, the measurement of cell sizes of major taxa on a sample by sample basis is time consuming and costly
and is therefore not recommended. The published standard cell sizes are currently the only viable option for use in making total cyanobacterial biovolume estimates, as such their continued use is recommended. However consideration needs to be given to the development of standard cell size
tables for common species of cyanobacteria that are applicable specifically for NSW, or even for different regions of the state should average cell size for these species vary regionally.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
1. Introduction
Each year cyanobacterial (blue-green algae) blooms cause multiple water quality problems across
New South Wales. The blooms pose a major hazard to the environment, to livestock and to public health, as a number of the most commonly occurring species have the ability to produce potent toxins, and all species have components of their cell walls that act as contact irritants.
The traditional method of monitoring inland freshwaters for cyanobacterial presence and bloom formation is through the collection of water samples that are sent to a laboratory for analysis. Analysis includes the identification of any cyanobacterial taxa present to genus or species level, and an
enumeration of the number of cells per taxa present in one millilitre of water (cells mL-1). These data, either as cell counts or converted to a biovolume value (cubic millimetres per litre – mm3 L-1) are then compared with guideline values for the various water uses, including for raw (untreated) drinking
water, stock watering and recreation. If the recreational Red alert guidelines (National Health and Medical Research Council 2008) are exceeded, media releases are issued to warn the public not to use the water for any purpose. If only the alert levels framework for raw waters used for potable
supply (Newcombe et al 2010) or the livestock watering guidelines are exceeded (NSW State Algal Advisory Group 2007), only the relevant water utilities, landholders and other stakeholders are advised.
One problem with this method of monitoring cyanobacteria is that there are often delays of several days or longer between sample collection and the analytical data becoming available for management use. One factor affecting this includes the time taken between the sample being collected and its
arrival at the analytical laboratory. Because of the long distances from the laboratory and remoteness of many sampling locations in regional areas of NSW, especially in the Murray Darling Basin, this transportation time may be considerable. Another factor is the ability of the laboratory to analyse the
samples promptly, and there may be delays if the samples end up queued behind others awaiting analysis because the laboratory is already operating at its full capacity. The longer the delays between sample collection and the data becoming available, the less relevant and useful these data
are for management purposes.
Cyanobacterial cells contain a number of different pigments to trap light energy for use in photosynthesis, the main ones in freshwater species being chlorophyll-a and phycocyanin.
Chlorophyll-a is a green coloured pigment, while phycocyanin is a bluish coloured pigment. These pigments trap light of various wavelengths from the visible spectrum, but some is subsequently retransmitted at a different wavelength, a feature known as fluoresence. The amount of retransmitted
light can provide a measure of the amount the pigment present. Phycocyanin absorbs light in the orange and red wavelengths (550 to 630 nm) with maximum excitation at 620 nm, and has a fluoresence emission peak of around 650 to 660 nm (Lee et al 1995, Beutler et al 2003, Gregor and
Maršálek 2005, Gregor et al 2007, Seppälä et al 2007, Bastien et al 2011). Chlorophyll–a absorbs light predominantly from the blue wavelengths (maximum excitation at 440 nm) and has a fluoresence peak at 675 to 685 nm (Lee et al 1995, Gregor and Maršálek 2005).
The use of in-situ fluorometry to detect phycocyanin has been proposed as a means of providing rapid information on the abundance of cyanobacteria present in freshwater bodies so as to enable more rapid management responses. Phycocyanin is a pigment found only in cyanobacteria in freshwater
phytoplankton communities, is highly fluorescent and readily measurable, and its peak fluorescence signal differs from that of chlorophyll-a (Gregor and Maršálek 2005), although some overlap may occur at wavelengths above 660 nm (Seppälä et al 2007). The measurement of phycocyanin fluoresence is
therefore a highly sensitive indicator of the concentration of cyanobacteria in raw water (Izydorczyk et al 2005). Brient et al (2008) found a strong linear correlation between phycocyanin concentration measured with a TriOS microFlu-blue sensor and cyanobacterial presence measured as cells mL-1,
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Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
even when there was a very heterogeneous mix of taxa present. They found a slightly better correlation between phycocyanin content and estimated biovolume. Likewise, Leboulanger et al (2002) found good correlation between fluoresence measured with a bbe-Moldaenke FluoroProbe with
cell counts of the potentially toxic cyanobacterium Planktothrix rubescens in an almost monospecific bloom situation in a French lake. A number of other studies (Gregor and Maršálek 2005, Gregor et al 2005, Gregor et al 2007, Izydorczyk et al 2005, Izydorczyk et al 2009) have also demonstrated the
effectiveness of in-situ fluorometry within reservoirs and on-line in inflows to water treatment plants using a range of different instruments to determine cyanobacterial abundance. Seppälä et al (2007) found it a useful tool for monitoring filamentous cyanobacterial blooms in the Baltic Sea. Recently,
McQuaid et al (2011) found a significant positive relationship between phycocyanin fluoresence measured with a Yellow Springs Instruments water quality multi-probe fitted with an in vivo phycocyanin fluoresence probe and cyanobacterial biovolume in Missisquoi Bay, Quebec, Canada.
This project aimed at determining the potential for in-situ fluorometric measurements of phycocyanin and chlorophyll-a to be used as a rapid means of determining total cyanobacterial biovolume present in a water body and as a tool for a more rapid management response to blooms.
2. Methods
The study took place in the southern Murray Darling River system, a major river system separating NSW and Victoria that enters the sea in South Australia (Figure 1) over a period of six months.
A Yellow Springs Instruments (YSI) 6600 V2 water quality sonde was purchased in October 2008,
which contained sensors for measuring the following water quality attributes:- temperature, dissolved oxygen, specific conductivity, depth, pH, turbidity, chlorophyll-a and phycocyanin. The chlorophyll-a and phycocyanin sensors were the YSI 6025 Chlorophyll sensor and the YSI 6131 Phycocyanin Blue-
Green Algae sensor. This was the same make and model of instrument used by McQuaid et al (2011) in their study in Quebec.
Run-of-the-river sampling commenced in mid November 2008 and proceeded on an approximately
weekly basis until late May 2009, although several weeks were missed in December 2008 and in March 2009. In all, 20 one week long sampling runs were made, with sampling undertaken at up to 27 sites each week. The sites were located at major towns and other points along the Murray River from
Albury downstream to Lock 8 (just upstream of the border with South Australia), on Gulpa Creek, the Edward River and the Wakool River between Mathoura and Kyalite, and on the lower Darling River between Menindee and Wentworth (Figure 1). Some sites on both the lower Murrumbidgee and lower
Lachlan Rivers were also initially sampled, but later dropped because inclusion of these sites meant excessive time taken to complete the sampling runs each week.
Eight 1 litre water samples were collected at each sampling site on each visit from different locations
along the river bank and combined together in a bucket. The YSI sonde was then inserted in the bucket of water for measurements to be made. This was done to reduce the amount of variability that would have otherwise occurred in the data had the sonde been placed directly into the river with the
water flowing past. A 250 mL sample of the pooled water in the bucket was also collected for laboratory analysis, meaning that the field data and laboratory data were collected from the identical water source, making them directly comparable. This method was similar to that used by Gregor and
Maršálek (2005) and Gregor et al (2007) who undertook their fluorometry measurements with a bbe Fluoroprobe immersed in water in a 20 litre barrel.
Measurements from the YSI sonde were recorded every 10 seconds. The sonde was left in the
bucket and measurements collected for a period of 3 to 5 minutes, sometimes longer. These data were later audited to remove any outliers and any early data recorded for each site that indicated that the sonde had not fully equilibrated to the water in the bucket at the commencement of each
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Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
Figure 1. Location of the major sampling sites sampled for this study.
measuring period. This was done for each variable separately. The mean value for each of the water quality attributes measured in the field was then calculated from the audited data.
There was no means of calibrating the YSI instrument prior to its use in the field, so the
manufacturers’ default calibrations of the instrument made prior to the instrument’s purchase were used instead. Calibrations of the instrument to cell counts of Microcystis aeruginosa are of only limited use in New South Wales, as few cyanobacterial blooms are dominated by this species. In addition,
with remote field use of this instrument, it is not possible to culture this cyanobacterium in remote field offices as laboratory facilities are unavailable, and the instrument calibration can never be checked. The most appropriate data to collect is that provided by the instrument as Relative Fluoresence Units
(RFU). Again with no standards of phycocyanin or of chlorophyll-a available in these field locations, it was not possible to check the instrument for drift in its manufacturer’s RFU calibration. However laboratory tests of an identical YSI instrument by Bastien et al (2011) have shown very little drift
occurs with the manufacturer’s default calibration.
The 250 mL water sample from the bucket was preserved in Lugols iodine solution and forwarded to the Office of Water laboratory in Wolli Creek (Sydney) for analysis. Cyanobacterial taxa were
identified and counted to genus or species level of taxonomy as appropriate. Those identified to species level were generally the potentially toxic taxa, and other representatives of the genera Anabaena (recently renamed Dolichospermum), Microcystis, Cylindrospermopsis, and Cuspidothrix
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Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
(Aphanizomenon). Counting was undertaken to at least ± 20% precision using a Lund cell and compound microscope (Hötzel and Croome 1999).
Cell size measurements were also made on commonly occurring taxa using a compound microscope
fitted with a high resolution digital camera and the images captured were measured using image enhancement software, as described in Hawkins et al (2005). The equipment was initially calibrated using blue dyed polystyrene monodispersed latex microspheres of 1.0 micron and 6.0 micron mean
diameters (Polysciences Inc., Warrington, PA, USA). The length, width or diameter of usually more than 20 randomly selected cells (except in samples with a very low density of cells) were measured for each species in each sample, depending on the density of the taxon within that sample, and the mean
cell dimensions were obtained from these measurements. Cellular volumes were then calculated using these mean cell sizes using the formula for the most appropriate geometric shape following Hillebrand et al (1999).
Total cyanobacterial biovolumes were calculated for each sample by multiplying the cell count for each taxa by the measured cellular volume for that taxa, and by summing these across all taxa. Published cellular volumes, the “Biovolume Calculator”, (Victorian Department of Human Services 2007) were
used for the less commonly occurring taxa where no sample-specific cell size measurements could be made.
Data were examined visually after plotting using Microsoft Excel. Because of the positively skewed
distribution of the data, all data were logarithmically transformed before detailed statistical analysis was conducted. Because some of the raw biovolume data had values of zero, all data were transformed as log X + c, where c is a constant (c = 1). Box plots to provide visual summaries of the
data, correlation analysis and regression analyses to examine relationships between the fluorometric measurements and the biovolume measurements, and homogeneity of slopes analyses to examine if there were different relationships between the fluorometric measurements and biovolume on a site by
site basis were undertaken using STATISTICA v9.
To examine any variation in cyanobacterial community composition between sites along the Murray and Gulpa Creek/Edward River/Wakool River system, the data for each cyanobacterial taxa were log
transformed as above, and an average of the community composition obtained across all sampling dates for each site. Rare taxa and those that contributed <10% to the total cyanobacterial biovolume were excluded. A S17 Bray Curtis similarity matrix between the sites was then computed, from which
a non-metric multi-dimensional scaling (nMDS) ordination was plotted using PRIMER v6 (Clarke and Gorley, 2006). Permutational Multivariate Analysis of Variance (PERMANOVA) to test for significance between the communities was undertaken using PERMANOVA+ (Anderson et al, 2008). SIMPER
(Clarke and Gorley, 2006) was used to distinguish the main taxa causing the main between-site differences in the cyanobacterial communities.
3. Results
3.1. Data logging.
The YSI sonde and sensors were placed in a bucket with a sample of water from each sampling site and left to log over a period of 4 to 5 minutes and sometimes longer. Measurements were taken every
10 seconds. Although the data collected over the logging period generally displayed only small variability, especially for phycocyanin and chlorophyll-a (Figure 2), on some occasions some problems associated with the data collection were observed.
Firstly, the instrument occasionally required an equilibration time for the measurements of phycocyanin and chlorophyll-a to stabilise once the data logging commenced. This occurred in about 10% of measurements. Examples are shown in Figure 3(a) for chlorophyll-a measurements at
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Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
Merbein and phycocyanin measurements for Tocumwal (Figure 3b), both in November 2008. In both examples, the initial data recorded had low values, but values tended to increase during the first minute of logging. In most cases the readings from the YSI stabilised and became more constant
within a minute or less of logging commencing, but these data required audit prior to further use. A point after which the recordings appeared to become stable was arbitrarily selected, and all data logged prior to this point was rejected. An average value of the “stabilised” data could then be
calculated for comparison with the laboratory results. Data for many of the other water quality attributes such as water temperature, turbidity, dissolved oxygen and especially pH also indicated that these particular probes also needed some time to equilibrate before reliable data could be collected.
A second problem was that the probes are equipped with self cleaning devices that wipe the optical sensors and remove any external material that may have accumulated on them. The YSI probe stops recording data while the cleaning cycle takes place, and recommences following its completion. Often
the data collected immediately before the cleaning cycle was either higher than or lower than the data collected after the cleaning cycle (Figure 3c – f). In most instances however sufficient data were
Chlorophyll-a data logging, Cobram, 27 November 2008
0
0.5
1
1.5
2
2.5
3
10:55:12 10:56:38 10:58:05 10:59:31 11:00:58 11:02:24 11:03:50 11:05:17
Time (hour/minute/second)
Ch
loro
ph
yll-
a (R
FU
)
Phycocyanin data logging, Buronga, 25 November 2008
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
16:28:34 16:29:17 16:30:00 16:30:43 16:31:26 16:32:10 16:32:53 16:33:36 16:34:19 16:35:02 16:35:46
Time (hour/minute/second)
Ph
yco
cyan
in (
RF
U)
Chlorophyll-a data logging, Merbein, 10 February 2009
0
0.2
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0.6
0.8
1
1.2
1.4
12:37:26 12:40:19 12:43:12 12:46:05 12:48:58 12:51:50 12:54:43
Time (hour/minute/second)
Ch
loro
ph
yll-
a (R
FU
)
Phycocyanin data logging, downstream Yarrawonga, 19 February 2009
0
0.1
0.2
0.3
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0.7
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12:15:50 12:17:17 12:18:43 12:20:10 12:21:36 12:23:02 12:24:29 12:25:55 12:27:22 12:28:48
Time (hours/minutes/seconds)
Ph
yco
cyan
in (
RF
U)
Chlorophyll-a data logging, Tocumwal, 28 May 2009
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
10:48:00 10:49:26 10:50:53 10:52:19 10:53:46 10:55:12 10:56:38 10:58:05 10:59:31
Time (hour/minute/second)
Ch
loro
ph
yll-
a (R
FU
)
Phycocyanin data logging, Albury, 28 May 2009
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
16:37:55 16:39:22 16:40:48 16:42:14 16:43:41 16:45:07 16:46:34 16:48:00 16:49:26 16:50:53 16:52:19
Time (hour/minute/second)
Ph
yco
cyan
in (
RF
U)
Figure 2. Examples of logged data for chlorophyll-a and phycocyanin for various locations and dates on occasions when logged data remained relatively stable throughout the measurement periods.
5 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
collected both before and after the cleaning interruption to provide an average value for the entire logging period.
A summary of the data collected using the YSI instrument each sampling run after auditing and
averaging, and the biovolumes calculated using standard cell size tables from the laboratory cell counts, is provided as box plots in Appendix A.
Chlorophyll-a data logging, Merbein, 25 November 2008
0
0.2
0.4
0.6
0.8
1
1.2
13:52:19 13:55:12 13:58:05 14:00:58 14:03:50 14:06:43 14:09:36 14:12:29
Time (hour/minute/second)
Ch
loro
ph
yll-
a (R
FU
)
Ch
A
Phycocyanin data logging, Tocumwal, 27 November 2008
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
9:44:38 9:46:05 9:47:31 9:48:58 9:50:24 9:51:50 9:53:17 9:54:43 9:56:10 9:57:36 9:59:02
Time (hour/minute/second)
Ph
yco
cyan
in (
RF
U)
B
Chlorophyll-a data logging, Corowa, 12 February 2009
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
14:22:34 14:24:00 14:25:26 14:26:53 14:28:19 14:29:46 14:31:12 14:32:38 14:34:05 14:35:31 14:36:58
Time (hours/minutes/seconds)
Ch
loro
ph
yll-
a (R
FU
)
C
Phycocyanin data logging, Corowa, 12 February 2009
1.5
1.7
1.9
2.1
2.3
2.5
2.7
2.9
14:22:34 14:24:00 14:25:26 14:26:53 14:28:19 14:29:46 14:31:12 14:32:38 14:34:05 14:35:31 14:36:58
Time (hours/minutes/seconds)
Ph
yc
oc
ya
nin
(R
FU
)
D
Chlorophyll-a data logging, Albury, 28 May 2009
0
0.5
1
1.5
2
2.5
3
16:37:55 16:39:22 16:40:48 16:42:14 16:43:41 16:45:07 16:46:34 16:48:00 16:49:26 16:50:53 16:52:19
Time (hour/minute/second)
Ch
loro
ph
yll-
a (R
FU
)
E
Phycocyanin data logging, Howlong, 28 May 2009
0.3
0.4
0.5
0.6
0.7
0.8
0.9
15:38:53 15:41:46 15:44:38 15:47:31 15:50:24 15:53:17 15:56:10 15:59:02
Time (hour/minute/second)
Ph
yc
oc
ya
nin
(R
FU
)
F
Figure 3. Examples where logged data displayed considerable variability during the logging period, due to either an initial equilibration of the probe when measurements were first commenced (a, b), or due to logging stopping and starting again either side of a probe cleaning period (c,d,e,f).
3.2. Initial comparison of field phycocyanin measurements and total cyanobacterial biovolume measurements.
The mean values of the phycocyanin measurements from the YSI instrument after data checking and
audit, measured as Relative Fluoresence Units (RFU), were plotted against total cyanobacterial biovolume and are shown in Figures 4 and 5. Two plots are shown, because laboratory cell size measurements were not made on all samples collected during the study. Samples from only 12 of the
20 weeks and part of a 13th actually had laboratory cell size measurements undertaken on them. Figure 4 shows the comparisons between field measured phycocyanin and the laboratory measured biovolumes for these 12 and a bit weeks, while Figure 5 shows the comparisons between the field
6 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
7 | NSW Office of Water, August 2012
measured phycocyanin and biovolumes calculated from standard cell size tables (“Biovolume Calculator” - Victorian Department of Human Services, 2007) for the full 20 weeks of data available. Figure 6 compares the laboratory measured biovolumes with the biovolumes calculated from the
standard cell size tables for the 12 and a bit weeks when both were available. While correlation analysis indicates there is a good general agreement between the two measures of total cyanobacterial biovolume (r2 = 0.81, n = 333, p <0.05), those estimated using the standard cell size
tables were on average 1.5 to 1.6 times larger than the value of the biovolumes calculated using actual laboratory cell size measurements.
The mean YSI phycocyanin measurements plotted against the total cyanobacterial biovolumes (Figs 4
and 5) show the following:-
The positively skewed nature of the data distribution, with the majority of the data points falling below a total cyanobacterial biovolume of 1 mm3 L-1 and a phycocyanin value of 1.5 RFU.
There are much fewer data in the total cyanobacterial biovolume range of 2 to 6 mm3 L-1, although the bloom in the Murray River during autumn 2009 (Al-Tebrineh et al 2012) provided some points for this area of the plot.
False positives of up to almost 4 RFU for phycocyanin, despite total cyanobacterial biovolume being negligible in these samples. Interference from suspended particulate matter at the more turbid sites is suspected to have caused this.
Considerable variation is shown within the data, typical of biological and environmental measurements. Nevertheless, a general trend of increasing phycocyanin fluoresence with increasing total cyanobacterial biovolume is indicated.
0 1 2 3 4 5 6 7 8 9
Phycocyanin (RFU)
0
1
2
3
4
5
6
Labo
rato
ry M
easu
red
Tot
al C
yano
bact
eria
l Bio
volu
me
(mm
3 L-1
)
Figure 4. Means of field measured phycocyanin plotted against total cyanobacterial biovolume measured in the laboratory.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
0 1 2 3 4 5 6 7 8 9
Phycocyanin (RFU)
0
2
4
6
8
10
12
14
16
Tot
al C
yano
bact
eria
Bio
volu
me
Cal
cula
ted
usin
g S
tand
ard
Cel
l Siz
e
Tab
les
(mm
3 L-1
)
Figure 5. Means of field measured phycocyanin plotted against total cyanobacterial biovolume calculated using tables of standard cell sizes.
-1 0 1 2 3 4 5 6
Biovolume estimated from standard cell size tables (mm3 L-1)
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
Bio
volu
me
calc
ulat
ed fr
om la
bora
tory
mea
sure
men
ts (
mm
3 L-1
)
Figure 6. Scatterplot comparing biovolumes calculated using actual laboratory cell size measurements and those calculated using standard cell size tables. Also shown is the line of best fit and the 95% confidence limits.
8 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
3.3. Analysis of the phycocyanin data.
All data were logarithmically transformed due to the positively skewed distribution of the data. Some of the raw biovolume data had values of zero, so all data were transformed as log X + c, where c is a
constant (c = 1). Laboratory measurements were not made on the full 20 weeks of samples collected, to ensure consistency the assessments reported here are made using the biovolumes estimated from standard cell size tables. Similar results were found when using the laboratory measured
cyanobacterial biovolume data.
Environmental factors, especially turbidity, were suspected of influencing the phycocyanin measurements, and the effects of these were investigated before further analysis of relationships
between phycocyanin and cyanobacterial biovolume. Highly turbid water was considered likely to cause false positive phycocyanin recordings, that is high phycocyanin values being recorded when very little cyanobacterial biovolume was actually present. These false positives of up to 3.7 RFU are
shown along the x axes of Figures 4 and 5.
Plotting turbidity against phycocyanin (Figure 7a) indicated that phycocyanin values tended to increase with increasing turbidity when turbidity exceeded 50 NTU. Likewise, cyanobacterial
biovolume was always low when turbidity exceeded 50 NTU (Figure 7b). The relationship between Log phycocyanin (x + 1) and log cyanobacterial biovolume (x + 1) in waters with turbidity values of 50 NTU or less is shown in Figure 8. The correlation coefficient (r2) between the two attributes was 0.55
(n = 415, p <0.05). A slightly better fit between the two attributes was obtained when all data from waters with a turbidity of greater than 10 NTU were excluded (r2 = 0.71, n = 170, p <0.05), but this is considered to be an impractical restriction because it would limit the usefulness of the YSI instrument
for cyanobacterial assessment to very few locations across NSW (many have turbidity in excess of 10 NTU). Additionally, progressively excluding data for waters with turbidities of 40 NTU, 30 NTU and 20 NTU provided correlations between phycocyanin and cyanobacterial biovolume that were little
different from that obtained for 50 NTU. This turbidity was therefore determined to be the most practical cut off point, above which phycocyanin measurements made using the YSI equipment may be subject to false positive errors due to non-cyanobacterial material suspended within the water
column. It also enables the use of the YSI fluorometric equipment over a much wider range of water bodies across NSW.
-50 0 50 100 150 200 250 300 350 400
Turbidity (NTU)
-1
0
1
2
3
4
5
6
7
8
9
Phy
cocy
anin
(R
FU
)
A
-50 0 50 100 150 200 250 300 350 400
Turbidity (NTU)
-2
0
2
4
6
8
10
12
14
16
Bio
volu
me
(fro
m t
able
s) (
mm
3 L-1)
B
Figure 7. Plots of (a) phycocyanin against turbidity, showing how phycocyanin measurements increase in response to turbidity once turbidity exceeds 50 NTU; and of (b) cyanobacterial biovolume against turbidity, showing usually low biovolume was present when turbidity exceeded 50 NTU. . Also show is the line of best fit and the 95% confidence limits.
9 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
The robustness of the relationship between phycocyanin measured in-situ with the YSI instrument and biovolume (calculated using tables) was tested by undertaking another regression using an 18 week sub-set of the data (weeks 1 to 4, 6 to 14 and 16 to 20 inclusive) and testing this new regression
against the two trial weeks (weeks 5 and 15) not included in the regression analysis (Figure 9). The results of the regressions using only the 18 weeks of data and that using the full 20 weeks of data were similar (Figures 8 and 9a). The in-situ phycocyanin data for weeks 5 and 15 (week 15 occurred
during the 2009 Murray River cyanobacterial bloom) were then converted to biovolumes using the regression equation developed for the 18 week subset of data and compared to biovolumes calculated using standard cell size tables (Figure 9b). This indicates a slight overestimation of biovolume at very
low cyanobacterial presence (< 0.50 mm2 L-1) and an underestimation of biovolume at larger cyanobacterial presence (> 1.0 mm3 L-1) (Fig 9b).
The relationship between phycocyanin measured by the YSI and turbidity in all waters where the
turbidity exceeded 50 NTU is shown in Figure 10a. The total cyanobacterial biovolumes in all samples for which these data were obtained were all less than 0.50 mm3 L-1 (and in many cases close to zero), so that the actual concentration of phycocyanin present at the time of the measurements would have
been minimal. The two attributes display a strong positive correlation (r2 = 0.70, P = 0.00, n = 111) which may allow the development of a correction factor to account for the false positive effect of highly turbid waters on phycocyanin measurements using the YSI instrument. Using the regression obtained
(Log Phycocyanin (x + 1) = -0.376 + 0.3959(Log turbidity)), the predicted false positive phycocyanin values for turbidities between 50 NTU and 400 NTU have been calculated (Figure 10b). Subtracting the false positive phycocyanin value predicted for the measured turbidity from the measured
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Log Phycocyanin (x + 1) (RFU)
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Log
BV
(T
able
s) (
x +
1)
(mm
3 L-1
)
Figure 8. Relationship between field measured phycocyanin measured using the YSI equipment and cyanobacterial biovolume (calculated using standard cell size tables) for waters with a turbidity of 50 NTU or less. (Data for the full 20 weeks of the study.)
10 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Log Phycocyanin (x + 1) (RFU)
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2Lo
g B
iovo
lum
e (f
rom
tab
les)
(x
+ 1
) (m
m3 L
-1)
A
0 1 2 3 4 5 6
Biovolume (from tables) (mm3 l-1)
0
1
2
3
4
5
6
Bio
volu
me
(fro
m r
egre
ssio
n) (
mm
3 L-1)
B
Figure 9. Testing the robustness of the analysis between phycocyanin measured in-situ with the YSI instrument and cyanobacterial biovolume estimated from standard cell size tables. Figure 9a shows the relationship using an 18 week subset of the full 20 week data set, while Figure 9b shows the biovolumes for the other two weeks estimated using the relationship developed from the 18 week subset plotted against the biovolumes for these weeks estimated from standard cell size tables. Solid line indicates the relationship between the two methods of biovolume estimation fitted through the plotted data, while the dashed line shows the expected 1 to 1 relationship.
1.7 1.8 1.9 2.0 2.1 2.2 2.3 2.4 2.5 2.6
Log Turbidity (NTU)
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
Log
Phy
cocy
anin
(x
+ 1
) (R
FU
)
A
0 50 100 150 200 250 300 350 400 450
Turbidity (NTU)
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
2.8
3.0
3.2
3.4
3.6
3.8
Phy
cocy
anin
(R
FU
)
B
Figure 10. Relationship between log turbidity and log phycocyanin (x + 1) when turbidity exceeds 50 NTU (a), and the correction curve determined from this to remove false positive phycocyanin measurements in highly turbid water (b). All data had total cyanobacterial biovolumes of less than 0.50 mm3 L-1, so the measurement of phycocyanin contributed by cyanobacteria would be minimal.
phycocyanin value (in RFU) may provide an estimate of the phycocyanin concentration due to any cyanobacteria actually present. This however may only be applicable for these rivers. Different rivers
with different types of clays and particles in suspension may cause different results.
The potential effects of several other water quality attributes measured by the YSI equipment on the phycocyanin measurements were also considered, especially that of chlorophyll-a, using correlation
analysis. While a scatter plot of phycocyanin against chlorophyll-a (Figure 11a) showed a general trend of both attributes increasing together, as would be expected as cyanobacteria contain both pigments, there was also much variability. Chlorophyll-a containing eukaryotic phytoplankton would
also have been present when the measurements were made, and contributed to the chlorophyll-a signal but not to the phycocyanin signal. There was a weak positive correlation found between the
11 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
12 | NSW Office of Water, August 2012
0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6
Log Chl-a (ug/L) (x + 1)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Log
Phy
cocy
anin
(x
+ 1
) (R
FU
)
A
1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2 3.4 3.6 3.8
Log Electrical conductivity (uS cm-1)
0.0
0.2
0.4
0.6
0.8
1.0
Log
Phy
cocy
anin
(x
+ 1
) (R
FU
)
B
5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
pH
0.0
0.2
0.4
0.6
0.8
1.0
Log
Phy
cocy
anin
(x
+ 1
) (R
FU
)
C
10 12 14 16 18 20 22 24 26 28 30 32 34
Water temperature (oC)
0.0
0.2
0.4
0.6
0.8
1.0
Log
Phy
cocy
anin
(x
+ 1
) (R
FU
)
D
3 4 5 6 7 8 9 10 11 12 13
Dissolved oxygen (mg L-1)
0.0
0.2
0.4
0.6
0.8
1.0
Log
Phy
cocy
anin
(x
+ 1
) (R
FU
)
E
Figure 11. Scatterplots of phycocyanin against (a) Chlorophyll-a, (b) electrical conductivity, (c) pH, (d) water temperature and (e) dissolved oxygen.
two sets of measurements (r2 = 0.075, n = 420, p <0.05). Similar weak positive trends were also shown between phycocyanin and electrical conductivity (r2 = 0.05, n = 539, p <0.05) and pH (r2 = 0.04, n = 513, p <0.05) despite much variability within these data (Figure 9). Water temperature (r2 = 0.00,
n = 539, p = 0.69) and dissolved oxygen concentrations (r2 = 0.00, n = 539, p = 0.79) do not appear to affect the phycocyanin measurements (Fig 11 b – e).
3.4. Variation in phycocyanin response between sampling sites.
Relationships between phycocyanin and total cyanobacterial biovolume were also examined on a site by site basis because cyanobacterial abundance, water quality and competition with eukaryotic
phytoplankton would differ temporally and spatially along the river, and this may also influence the phycocyanin measurements. This included both correlation analyses (Appendix B), linear regression
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
analysis (Appendix C), multiple regression analysis and multivariate analytical techniques including nMDS, Permanova and SIMPER.
The results of correlation analysis between biovolume, phycocyanin and chlorophyll-a and the various
other water quality attributes measured conducted on a site by site basis are presented in Appendix B. Briefly, phycocyanin and biovolume were significantly positively correlated at all sites except Merbein and Curlwaa. Turbidity tended to be positively correlated with both phycocyanin and biovolume
between Mulwala and Picnic Point (these sites however tended to have the lowest turbidity – see Appendix A) and also at Mathoura. Electrical conductivity was significantly negatively correlated with both phycocyanin and biovolume between Mulwala and Picnic Point, and at Mathoura (electrical
conductivity was also lowest at these sites – Appendix A). Other correlations appear to occur randomly with little pattern, including correlations between chlorophyll-a and the other attributes.
Examples of linear regression analyses between log (x + 1) biovolume (from tables) (the dependent
variable) and log (x + 1) phycocyanin measured by the YSI equipment (the predictor variable) for representative sites are provided in Figure 12, and a summary of the correlation coefficients and the regression slopes and intercepts for all sites is provided in Appendix C. Most of the sites, especially
those with low turbidity, displayed highly significant relationships between the two attributes.
The effect of very high turbidity and very low total cyanobacterial biovolume is exemplified in the plot for the Darling River at Ellerslie (Figure 12h). The phycocyanin measurement increased as turbidity
increased, whereas the total cyanobacterial biovolume decreased, possibly due to a decrease in the amount of light available for photosynthesis as turbidity increased. The effect of the high turbidity led to false positive phycocyanin measurements. All other sites on the Darling River, and at Lake Victoria,
showed a similar response, and were excluded from further analysis. Data where a turbidity of >50 NTU had also been measured were also excluded from the analyses for other sites where this occurred, and regression and correlation analysis then repeated for these sites. A further comparison
between the regression slopes for all remaining sites (all sites on the Murray River and Edward River system) is provided in Figure 13.
The regression equations of these 22 sites were compared with each other using a homogeneity of
slopes model (Statistica V9), which indicated significant differences between some of the individual regression equations for the sites (F = 2.2, P = 0.002*, d.f. = 21). Because the regression slopes for Merbein and Curlwaa were considerably different visually from those of the other 20 sites (Figure 13),
these two sites were also excluded and the homogeneity of slopes analysis repeated. However this still indicated some significant differences between the regression slopes for some sites (F = 1.8, P = 0.027*, d.f. = 19).
Pair-wise comparisons were then undertaken between each of the 22 sites, the results of which are provided in a matrix (Appendix D) which gives the F value and the probability below it for each pair-wise comparison. Differences in regression slopes for the different sites which were significant have
been shaded. In summary:
The regression equations for all sites between Albury and Tocumwal were not significantly different from each other.
The regression equations for all sites along the Murray River from Picnic Point to Mt Dispersion and along Gulpa Creek/Edward River/Wakool River were not significantly different from each other.
The regression equations for Picnic Point, Moama, Barham and Moulamein were often (but not always) significantly different from the regression equations of the sites along the Murray River from Albury to Tocumwal.
13 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
14 | NSW Office of Water, August 2012
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Log Phycocyanin (x + 1) (RFU)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9Lo
g B
iovo
lum
e (t
able
s) (
x +
1)
(mm
3 L-1)
A
0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65
Log Phycocyanin (x + 1) (RFU)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Log
biov
olum
e (t
able
s) (
x +
1)
(mm
3 L-1)
B
0.20 0.22 0.24 0.26 0.28 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50
Log Phycocyanin (x + 1) (RFU)
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Log
biov
olum
e (t
able
s) (
x +
1)
(mm
3 L-1)
C
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Log Phycocyanin (x + 1) (RFU)
0.0
0.2
0.4
0.6
0.8
1.0
1.2
Log
biov
olum
e (t
able
s) (
x +
1)
(mm
3 L-1)
D
0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45
Log Phycocyanin (x + 1) (RFU)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
Log
biov
olum
e (t
able
s) (
x +
1)
(mm
3 L-1)
E
0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40
Log Phycocyanin (x + 1) (RFU)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
0.24
0.26
Log
BV
(ta
bles
) (x
+ 1
) (m
m3 L
-1)
F
0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50
Log Phycocyanin (x + 1) (RFU)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
Log
BV
(ta
bles
) (x
+ 1
) (m
m3 L
-1)
G
0.20 0.25 0.30 0.35 0.40 0.45 0.50 0.55 0.60 0.65
Log Phycocyanin (x + 1) (RFU)
-0.02
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
0.20
0.22
Log
BV
(ta
bles
) (x
+ 1
) (m
m3 L
-1)
H
Figure 12. Linear regression analysis of log phycocyanin (x + 1) against log biovolume (x + 1) calculated from tables for representative sites along the Murray River (a) Albury, (b) Tocumwal, (c) Barham, (d) Tooleybuc, (e) Buronga, (f) Curlwaa; for Gulpa Creek at Mathoura (g) and for Darling River at Ellerslie (h).
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
15 | NSW Office of Water, August 2012
Regression slopes
Mulwala Yarrawonga
-0.3
-0.1
0.1
0.3
0.5
0.7
0.9
-0.1 0.1 0.3 0.5 0.7 0.9 1.1
Log Phycocyanin (x + 1) (RFU)
Lo
g B
iovo
lum
e (t
ab
les)
(x
+ 1
) (m
m3 L
-1)
AlburyCorowa
CobramTocumwal
Picnic Point
Moama
Barha Murray Downs
Koraleigh
TooleybucEuston
Mt Dispersion
Buronga
Merbein
Curlwaa
eFort CouragLock 8
Mathoura
Moulamein
Kyalite
Figure 13. A comparison of the linear regression results obtained for sites along the Murray River and the Gulpa Creek/Edward River/Wakool River system, comparing phycocyanin measured in-situ using the YSI instrument with total cyanobacterial biovolume (calculated using standard cell size tables). Data where the turbidity exceeded 50 NTU were removed prior to these regression analyses.
Thalm
Th or the sites in the Sunraysia section of the Murray River (Buronga to Lock 8) were often (but not always) significantly different to each other.
ein and Kyalite). F
k 8). F = 6.86, P = 0.000*, d.f. = 4.
d.f. = 1.
y have also fitted into the Mid
would have fitted alysis it was
s described above could be the sample size at each
e regression equations for Merbein and Curlwaa were significantly different from those for ost all sites upstream of them, but not from each other.
e regression equations f
Based on these results, the sites were grouped according to location, and the homogeneity of slopes
tested for the sites in each group:
Upper Murray sites (Albury to Tocumwal inclusive). F = 1.00, P = 0.410, d.f. = 5
Mid Murray and Edward (Picnic Point to Mt Dispersion, Mathoura, Moulam
= 0.60, P = 0.820, d.f. = 10
Sunraysia (Buronga to Loc
Sunraysia (excluding Merbein and Curlwaa). F = 4.42, P = 0.018*, d.f. = 2
The Upper Murray and the Mid Murray/Edward groupings were then compared with each other: F = 4.42, P = 0.018*,
Some sites, such as Mulwala, Yarrawonga, Cobram and Tocumwal ma
Murray/Edward grouping as well as into the Upper Murray grouping, while Burongainto both the Mid Murray/Edward and Sunraysia groupings. However for subsequent andecided to leave them in their original groupings.
One potential limitation of the regression analyselocation. There were either 19 or 20 measurements made at most sites, although slightly fewer at Sunraysia sites (15 to 19 measurements per site), and Edward River sites (16 to 19, but only 11 at
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
16 | NSW Office of Water, August 2012
Moulamein). The lower number of measurements at some sites would reduce the statistical power ofsome of these regression analyses.
rial
with the means of the phycocyanin and biovolume
ct on aps
nd log transformed where appropriate. The adjusted r2 for these 2
ue
al
There are several possible factors that may lead to the variability in the linear regression results
between sites. The first of these is the abundance of cyanobacteria, measured as total cyanobactebiovolume that occurred at each site during the study period. The goodness of fit (r2 values) for the regressions between phycocyanin and total cyanobacterial biovolume (both log transformed (x + 1))
for each of the 22 sites are comparedmeasurements for these sites in Figure 14. Where there is a high mean biovolume (Figure 14a) or mean phycocyanin concentration (Figure 14b), there is also a high r2 value. In contrast, where there is
a low mean biovolume or mean phycocyanin concentration there is a large range of r2 values. r2 was especially low at Merbein and Curlwaa (Table 1). In other words, there is more variability in the relationship between phycocyanin measured in-situ and total cyanobacterial biovolume when
cyanobacterial presence is low. This could be due to the error involved in laboratory biovolume determinations, but it may also be due in part to a possible lower sensitivity of the YSI instrument to low phycocyanin concentrations.
Whether the other water quality attributes measured as part of this study were also having an effethe site by site relationships between total cyanobacterial biovolume and phycocyanin (as perhsuggested by the site by site correlation analysis, Appendix B) was also examined using multiple
regression analysis. The data were tested for normality using normality probability plots prior to the regression analyses being done, amultiple regression analyses were compared with the adjusted r for the linear regression analyses of
log (x + 1) biovolume (from tables) against log (x + 1) phycocyanin (Table 1). There were only small increases in the r2 values for the linear and multiple regressions at many sites, indicating that the additional water quality attributes (temperature, dissolved oxygen, pH, chlorophyll-a, turbidity and
electrical conductivity) provided little additional explanatory power to that of phycocyanin when determining total cyanobacterial biovolume at these sites. In some instances (Corowa, Moama, Buronga and Mathoura) the multiple regression adjusted r2 was actually slightly less than the linear
regression adjusted r2; while at Picnic Point and at Mt. Dispersion the adjusted r2 for the multiple regressions were considerably lower than the adjusted r2 for the linear regression, reducing the valof phycocyanin as a predictor of total cyanobacterial biovolume in these situations. This is an
indication that the extra predictors in these multiple regressions were providing little or no additionpredictive value to that of phycocyanin. In comparison, the adjusted r2 values for the multiple
Mean biovolume ~ adjusted r2
1.2
1.4
1.6
1.8
m3 L
-1)
0
0.2
0.4
0.6
0.8
1
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
adjusted r squared
Mea
n b
iovo
lum
e (m
A Mean Phycocyanin ~ adjusted r2
1.4
1.6
1.8
2
(R
FU
)
0
0.2
0.4
0.6
0.8
1
1.2
-0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
adjusted r squared
Mea
n P
hyc
ocy
anin
B
Figure 14. Variation in adjusted r2 for the 22 site by site linear regression analyses of phycocyanin against total cyanobacteria biovolume (both log transformed (x + 1)) with (a) mean cyanobacterial biovolume and (b) mean phycocyanin measured for each site.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
17 | NSW Office of Water, August 2012
Table 1. Comparisons of the adjusted r2 values from linear regression between log (x + 1) phycocand log (x + 1) biovolume and the adjusted r2 values obtained for the multiple regresusing the water quality attributes shown and log (x + 1) biovolume. Also prese
2 2
yanin sions
nted are the partial η values obtained from the multiple regressions. Only the partial η values that made a statistically significant (at the 5% level or greater) contribution to the regression coefficients are shown. It was not possible to calculate adjusted r2 values for the multiple regressions for
2
Site
Merbein and Curlwaa (the “unadjusted” r values were very low). There were no significant partial η2 for electrical conductivity.
Adjusted r2 (linear
regression)
Adjusted r2 (multiple
regression)
Partial η2
Temp
Partial η2 DO
Partial η2 pH
Partial η2 Log PCY
Partial η2 Log Chl-a
Partial η2 Log Turb
Albury .548 0.84 0.87
C orowa 0.80 0.75 .661
Mulwala 0.71 0.76
D/S Yarra a
0.80 0.81 .675 wong
Cobram 0.74 0.81 .555
T .386 .395 .579 ocumwal 0.79 0.83
Picnic int Po 0.53 0.33
Moama 0.69 0.65 .710
Barham 0.65 0.83 .399 .431 .452 .699
Murray Downs
0.35 0.50 .595 .3 4 2
Koral .375 eigh 0.73 0.83 .799
Tooleybuc 0.64 0.70 .749
Euston 0.74 0.75 .733
Mt ioDispers n
0.51 0.21 .407
Buronga 0.46 0.42 .620
Merbei -0.05 n xxx
Curl a wa 0.06 xxx
F 0.79 0.86 ort Courage
Lock 8 0.66 0.71 .655
Mathoura 0.50 0.45 .309
M .9 .8 1 oulamein 0.77 0.95 30 9
Kyalite 0.61 0.75 .788 .
reg urray Do s, Moulamein and Kyalite were consi ly larger (0.14
0.18) than the ad r2 values for the linear regressions of phycocyanin against biovolume (both log transformed (x + 1)), while those for Koraleigh and Tooleybuc ere also reater (by 0.10 and 0.08
spectively). All six sites lie within the combined Mid Murray/Edward grouping of sites.
2
the total non-error variation. The regression coefficient obtained for log (x + 1) phycocyanin was also
ressions for Barham, M wn derab to
justed
w gre
Partial eta-squared (partial η ) values obtained from the multiple regression analyses (Table 1) indicated that a large proportion of the variation accounted for by these multiple regression analyses was attributable to log (x + 1) phycocyanin, after other water quality factors had been excluded from
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
18 | NSW Office of Water, August 2012
statistically significant (to the 5% level of significance) in many of the multiple regression analyses conducted, exceptions being at Mulwala, Picnic Point, Merbein, Curlwaa and Fort Courage. Why log
oint
pH s
hat were present but contributed
(x + 1) phycocyanin was not significant in the multiple regression analyses for Mulwala, Picnic P
and Fort Courage is not known, because it was significant in the linear regression analyses for these sites (Appendix B). The low concentrations and variability in both biovolume and phycocyanin concentrations at Merbein and Curlwaa, as discussed above, would be a contributing factor for the
non-significance of log (x + 1) phycocyanin in the multiple regression analyses for these sites. Generally the partial η2 values for the other water quality attributes used in the multiple regressions were small and indicated little contribution to the variation explained by the regressions (in keeping
with the results of the correlation analyses, Appendix B), and the regression coefficients for these attributes were mostly non-significant. While some of these water quality attributes may contribute significantly to the variation explained by the multiple regressions at a few sites, notably turbidity at
Murray Downs and Moulamein, chlorophyll-a at Koraleigh, and temperature, dissolved oxygen and at Tocumwal and Barham, overall the multiple regression analyses do little to explain whether variouwater quality attributes have an influence on the site by site relationships between phycocyanin and
total cyanobacterial biovolume in different parts of the Murray River.
Another important factor that may help account for the site by site variations in the regression analyses are changes in cyanobacterial community composition between sites. Non-metric multi-
dimensional scaling (nMDS) was undertaken using PRIMER v6 (Clarke and Gorley 2006) to examine the variation in cyanobacterial community composition between sites. The data were log transformed (x + 1) prior to analysis, and then an average of the community composition obtained from the data
across all dates that each site was sampled. The rare taxa or those t
Transform: Log(X+1)Resemblance: S17 Bray Curtis similarity
AreaUpperMidSunraysiaEdward
Albury
Corowa
Mulwala
YarrawongaCobram
TocumwalPicnic Point
Moama
Barham
Murray DownsKoraleigh
Tooleybuc
Euston
Mt Dispersion
BurongaMerbein
Curlwaa
Fort Courage
Lock 8
MathouraMoulamein
Kyalite2D Stress: 0.12
Figure 15. nMDS ordination summarising the difference in cyanobacterial community composition in different sections of the Murray River (Upper Murray, Mid Murray and Sunraysia) and in the Gulpa Creek/Edward River/Wakool River distributary branch.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
19 | NSW Office of Water, August 2012
very little to the total cyanobacterial biovolume (to less than 10% of the variance) were then excluded. A S17 Bray Curtis similarity matrix between the sites was then computed, from which the nMDS ordination was plotted using PRIMER v9.
The ordination (Figure 15) indicated that the sites separated out on a similar basis to the regression analyses and Homogeneity of Slopes analysis, that is, sites in the Upper Murray section separate from those of the Mid Murray/Edward section, while the sites in Sunraysia form a third more dispersed
grouping in the bottom left of the ordination. Analysis of the data using PERMANOVA (Permutational Multivariate Analysis of Variance) using PERMANOVA+ (Anderson et al 2008) gave a Pseudo-F value of 6.852, which revealed a significant difference (Permutational P-value <0.001, 9925 unique
permutations, total permutations = 9999) between the cyanobacterial communities within the four different sections of the study area (the Edward River sites were included as a separate group). A pairwise comparison of these groupings also undertaken using PERMANOVA is provided in Table 2.
Table 2. Table of PERMANOVA pairwise comparison results comparing community composition at sites in different sections of the Murray and Edward Rivers. Statistically significance Permutational P-values - * p<0.05, ** p<0.01, *** p<0.001, NS = not significant. Total permutations = 9999.
Sections of the river Unique permutations d.f. Pseudo-t values and significance
Upper and Mid 2899 12 2.6777***
Upper and Sunraysia 462 9 3.2635**
Upper and Edward 84 7 2.2494*
Mid and Sunraysia 1285 11 2.7182**
Mid an 1.2659 NS d Edward 165 9
Sunraysia and Edward 56 6 2.6836*
Table 3. Average within-group similarities and the major taxa contributing to this within-group similarity by more than 10% in each section of the Murray and Edward Rivers.
Grouping of sites Average within-group similarity Major contributors per group (with percent contribution in
brackets)
Upper 75.1 Anabaena circinalis (40.0%
Microcystis flos-aquae (26.6%)
)
Mid 72.1 Anabaena circinalis (22.0%)
Cuspidothrix issatschenkoi (13.6%)
Aphanocapsa sp. (12.1%)
Anabaena planktonica (11.6%)
Microcystis flos-aquae (11.4%)
Sunraysia 65.8 Rhabdoderma sp. (26.3%)
Anabaena circinalis (21.6%)
Aphanocapsa sp. (14.4%)
Edward 78.5 Microcys
Anabaena plan .4%)
Anabaena circi
Cuspidothrix issatschenk 1.3%)
Aphanocapsa
tis flos-aquae (27.4%
ktonica (13
nalis (11.9%)
oi (1
sp. (10.8%)
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
20 | NSW Office of Water, August 2012
This cant differences anobacterial community structure between t ection of the Murray with the Mid section an ith Sunraysia, as well a etween the Mid section of the Murray and Sunraysia. The communities in the Edward River sites were still significantly different (at
sections of the riv at sites in the M urray Riv two
Mid/Edward grouping
Finally a SIMPER (Similarity Percentages – spec ntributions) analysi g PRIMER v9 (Clark and Gorley, 2006). The average within-group similar
contribute to this are presented in Table 3. Anab s was the mUpper Murray grouping of sites, whereas the Mid and Edward groupingmix of taxa, and Rhabdoderma sp. was a taxa that occurred commonly
The similarities in community composition between the groupings and the producing changes between the groups is shown in Table 4. The cyanobecame increasingly less similar with distance downstream from the Up
Anabaena d Microcystis flos-aquae g lly decreasing in abdownstream, while Anabaena planktonica and Cuspidothrix issatschenkoi the Mid Murray and Edward Rivers, and Rhabdoderma sp. was a prominent part
community in Sunraysia. The greatest similarity was found between the Msections of the study area, further supporting the n into a s
3.5. Analysis of the Chlorophyll-a data.
Chlorophyll-a measured by the YSI equipment is unsuitable as a predicyanobacterial biovolume present. Relationships between chlorophyll-a an
ements
electrical conductivity, pH, water temperature and dissolved oxygen
d
This
rial species tems in NSW. These measurements also
ariability of within-species cell size in
indicated signifi in cy he Upper sd w s b
around a 2% level of significance) from the communities at sites in both the Upper and Sunraysia
er. However the Edward River communities were similar to the communitiesid section of the M er, further supporting joining the groups into a single
ies co s was also performed usinities and the major taxa that
aena circinali ajor taxa present in the s were comprised of a larger in the Sunraysia grouping.
major changes in taxa bacterial communities per Murray to Sunraysia, with
circinalis an enera undance with distance increased in abundance in
of the cyanobacterial
id Murray and Edward ir amalgamatio ingle section.
ctor of the amount of total d both laboratory
measured biovolume and that calculated from standard tables are shown in Figure 16a and 16b
respectively. The correlation coefficient (r2) between chlorophyll-a measured by the YSI and laboratory measured biovolume was 0.048 (n = 253, p = 0.000), while that for biovolume calculated using standard tables was 0.015 (N = 420, p = 0.012). Logarithmic transformation of these data (x +
1) made only minor improvements to the correlations.
Chlorophyll-a measurements made in-situ using the YSI instrument were compared with other water quality attributes such as turbidity (both all turbidity measurements and when turbidity measur
were 50 NTU or less), concentrations measured simultaneously with the same instrument (Figure 17). The highest chlorophyll-a concentrations were recorded in waters with low turbidity (< 20 NTU); however like
phycocyanin, chlorophyll-a concentrations measured with the YSI instrument also tended to increase with increasing turbidity above 50 NTU (Figure 17a, b). This also suggests that highly turbid waters may cause a false positive signal for chlorophyll-a, although phytoplankton cells themselves can lea
to increased turbidity measurements. The increased chlorophyll-a in these waters could also be due to chlorophyll-a containing eukaryotic algal cells, which were not measured in this investigation.second alternative is less likely however, as generally chlorophyll-a measurements tended to increase
at the same time that phycocyanin measurements increased in these turbid waters, although the correlation between the two was low (r2 = 0.17, P = 0.000, n = 118).
3.6. Within-species variation in cell size.
One part of this study involved the measurement of the cell sizes of a range of cyanobactethat occur commonly within the Murray and Darling River sys
provided an opportunity to assess the spatial and temporal v
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
21 | NSW Office of Water, August 2012
Table 4. Average between-group similarities and the major taxa contributing to the between-groupdifferences by more than 10% in sections of the Murray and Edward Rivers.
Groupings compared Average between-group similarity
Taxa that decreased in abundance from the
first group to the second (and percentage
decrease)
Taxa that increasabundance fr
first group to the second (and percenta
increase)
ed in om the
ge
Upper and Mid 63.7 Microcystis flos-aquae Cuspidothrix (17.5%)
Anabaena circinalis (16.9%)
issatschenkoi (12.7%)
Anabaena planktonica (11.7%)
Upper and Edward 66.8 Anabaena circinalis (19.5%)
Anabaena pla(20.0%
Cuspidothrix issatschenkoi (10.3%)
nktonica )
Upper and Sunraysia 49.3 Microcystis flos-aquae (28.7%)
Anabaena circinalis (23.8%)
Rhabdoderma sp. (14.1%)
Mid and Edward 73.0 No taxa decreased in abundance by more than
10%
Microcystis flos-aquae (21.4%)
Anabaena planktonica (12.8%)
Mid and Sunraysia 55.1 Microcystis flos-aquae (18.4%)
Rhabdoderma sp. (12.1%)
Anabaena circinalis (14.0%)
Cuspidothrix issatschenkoi (11.4%)
Anabaena planktonica (10.2%)
Sunraysia and Edward 49.1 No taxa decreased in bundance by more than
10% a
An lis
Microcystis flos-aquae (28.0%)
Anabaena planktonica (14.5%)
abaena circina(12.8%)
these taxa. Although cell size meas ents were ma ifferenmeasurements for many of these were limited to only a few s occasions (1 to 2throughout the study period. The most commonly occurring species that were m
measured included Anabaena circinalis (167 samples), Anabaena planktonica (6An des (42 s Aphanizo Cusissatschenkoi) (144 samples), Aphanocapsa sp(p). (possibly sa ince
samples), Chroococcus sp(p). (68 samples), Cyanodict les), Cylindrospermopsis raciborskii (20 samples) and Microcystis flos-aquae (134 sample
Summary statistics of the cell volumes calculated for resented i , while
the cell width, cell length and cell length to width ratio for the 9 most commonly oabove are presented in Tables 6, 7 and 8 respectively. The very broad range in cell vocal ear measurem for some tax 00 μm
μm3, is greater than expected for a natural range in cell size e species and is p
urem de on a total of 23 dampling
t taxa, 0 times)
ost frequently
7 samples), abaena aphanizomenioi amples), menon sp(p). (possibly
mostly Aphanocappidothrix
rta) (257
yon sp(p). (70 samps).
all taxa measured are p n Table 5
ccurring taxa listed lumes
culated from the lin ents a, from greater than 1
in a singl
3 to less than 10
robably
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
22 | NSW Office of Water, August 2012
-5 0 5 10 15 20 25 30 35
Chlorophyll-a (ug L-1)
-2
0
2
4
6
8
10
Tot
al C
yano
bact
eria
l Bio
volu
me
(fro
m t
able
s) (
mm
3 L-1)
A
-5 0 5 10 15 20 25 30 35
Chlorophyll-a (ug L-1)
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.
3.
4.
4.5
0
5
0
Labo
rato
ry m
easu
red
Tot
al C
yano
bact
eria
l Bio
volu
me
(mm
3 L-1) B
irect
cope
chome width) was measured
ll ny of
ation cell volume in those species that were identified to species level, especially the Anabaena species.
The temporal variations in the cell volume of the nine most commonly occurring taxa are shown in Figure 18 and the spatial variations in Figure 19. Likewise the temporal and spatial variations in cell width of these species are shown in Figures 20 and 21 respectively; the temporal and spatial
variations in cell length (for those species that are longer than wide) are shown in Figures 22 and 23 respectively; and the temporal and spatial variations in the length to width ratio are shown in Figures 24 and 25 respectively.
A general trend towards smaller cell sizes in a number of the taxa examined as summer and autumn progressed is represented in Figures 18, 19 and 20. The trends were less marked in the linear measurements (cell width and length), but these were still sufficient to cause a considerable decrease
in cell volume once converted to a volumetric measurement. For example, the typical cell width of Anabaena circinalis decreased from around 7 microns in the spring of 2008 to around 5 microns in
April and May 2009, resulting in an approximate halving of the average cell volume of the species.
s and Aphanizom r cell sized s
Cyanodictyon sp(p)., Microcystis flos-aquae) showed little or no decrease in average size during this
lume
Figure 16. Relationships between chlorophyll-a measured with the YSI instrument and total cyanobacterial biovolume estimated using standard cell size tables and from laboratorymeasurements. The data here are reported as μg L-1 rather than as RFU, as there is a dlinear relationship between the two measures.
indicative of the difficulties and large inherent error in measuring the sizes of cells under a micros
(Hawkins et al 2005). This may be especially so when measuring the sizes of the cells of small colonial species, or with filamentous species such as Cylindrospermopsis raciborskii that have indistinct lateral cell walls. For Anabaena circinalis, only cell diameter (tri
on most occasions, and as a result these cells had to be assumed to be completely spherical when calculating their cell volume, rather than slightly flattened oblate spheres as is their most common ceshape. This would lead to a slight overestimation of cell volume in this species. In addition ma
the taxa were only identified to genus level, and the measurements may therefore be of a range of different sized species within that genus, although this does not account to some of the large variin
Likewise the cell sizes (widths, lengths and volumes) of A. planktonica, A. aphanizomenioideenon sp. also decreased over the same time frame. In comparison, cell size of the smallepecies from the Order Chroococcales (Aphanocapsa sp(p)., Chroococcus sp(p).,
period. Variability in the cell length to width ratio (Figure 24) was always considerable throughout the study period and unlikely to have had a major effect in the temporal variation shown in the cell vo
measurements.
Figures 19, 21 and 23 indicate that the changes in cell size were mainly a temporal phenomenon, rather than a spatial phenomenon. Both larger sized and smaller sized Anabaena circinalis cells
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
23 | NSW Office of Water, August 2012
-1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
Log Turbidity (NTU)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Log
Chl
-a (
x +
1)
(RF
U)
A
-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8
Log Turbidity (NTU)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Log
Chl
-a (
x +
1)
(RF
U)
B
1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8 3.0 3.2
Log EC (uS cm-1)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Log
Chl
-a (
x +
1)
(RF
U)
C
5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0 9.5 10.0
pH
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Log
Chl
-a (
x +
1)
(RF
U)
D
10 12 14 16 18 20 22 24 26 28 30 32 34
Water Temperature (oC)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Log
Chl
- 1
)a
(x +
(R
FU
)
E
3 4 5 6 7 8 9 10 11 12 13
Dissolved Oxygen (mg L-1)
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
Log
Chl
-a (
x +
1)
(RF
U)
F
Figure 17. Log chlorophyll-a (x + 1) measurements compared against other water quality attributes measured simultaneously with the same instrument, including (a) turbidity (all measurements) (b) turbidity of less than 50 NTU), (c) electrical conductivity, (d) pH, (e) water temperature and (f) dissolved oxygen concentrations.
occurred at one sampling occasion or another at virtually all sites where the cell size of this species was measured, and a similar pattern is displayed by A. planktonica, Aphanizomenon sp.,
Aphanocapsa and most of the other taxa. There is a slight indication that A. aphanizomenioides may have had a smaller cell size in the Upper Murray section of the study area compared to the Mid Murray, Sunraysia and Edward River sections (and at Pooncarie on the Darling River); while
Microcystis flos-aquae may have had a slightly smaller cell size in the Edward River and in the Sunraysia section of the Murray River. However the variation in the cell sizes of these species at most sites is considerable, so these possible spatial patterns may simply be due to this variation, and to a
lower occurrence of these species in samples from these downstream sections of the rivers. Certainly
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
24 | NSW Office of Water, August 2012
Table 5. Summary statistics for cell volumes calculated for taxa measured from the Murray and Darling River sampling sites.
Taxa Minimum (μm3)
10th %ile (μm3)
Median
(μm3)
Mean
(μm3)
Standard error
(μm3)
90th %ile (μm3)
Maximum (μm3)
Number of
samples
Anabaena circinalis 9.4 41.3 92.2 109.3 4.99 201.9 282.9 167
Anabaena planktonica
59.1 83.6 190.6 206.3 13.74 357.7 550.9 67
Anabaena aphanizomenioides
6.67 10.2 29.0 34.6 4.52 61.1 142.3 42
Anabaena torulosa 60.1 60.1 1
Anabaena sp(p). 5.89 10.6 38.5 93.1 22.8 265.4 345.0 21
Anabaenopsis sp(p).
78.3 85.5 112.6 126.0 26.5 177.4 299.8 4
Aphanizomenon sp(p).
21.8 32.0 60.1 71.9 3.42 122.4 234.7 144
Aphanocapsa sp(p).
0.80 1.27 2.11 2.35 0.06 3.57 7.39 257
Aphanothecae sp(p).
1.74 2.46 5.03 6.22 1.11 12.6 13.5 13
Chroococcus sp(p). 1.58 2.05 3.81 5.06 0.42 12.05 15.0 68
Coelomoron sp(p). 18.3 20.4 29.0 44.9 21.5 75.7 87.4 3
Coelosphaerium sp(p).
1.43 1.63 11.4 22.9 9.23 70.9 114.6 15
Cyanodyction sp(p).
1.29 1.93 3.94 4.89 0.42 8.05 16.2 50
Cylindrospermopsis raciborskii
14.1 33.3 55.6 53.9 4.48 83.6 88.5 20
Merismopedia sp(p)
1.01 1.13 2.70 4.40 1.27 7.44 24.2 18
Microcystis flos-aquae
2.20 4.36 10.37 10.54 0.42 16.4 26.9 134
Microcystis sp(p) 5.69 5.74 5.94 5.94 0.25 6.14 6.19 2
Oscillatoria sp(p) 45.8 54.7 90.3 90.3 44.5 125.9 134.8 2
Phormidium sp. 7.81 7.81 1
Planktolyngbya sp. 34.6 34.6 1
Pseudanasp(p
baena ).
16.8 22.9 44.5 49.8 5.96 81.9 107.8 17
Rhabdoderma sp(p)
8.82 11.7 15.1 18.5 2.59 30.0 31.1 10
Romeria sp(p). 8.93 13.1 64.1 70.1 12.9 138.4 145.2 15
A. aphanizomenioides was less common in the Murray River downstream of Euston, as was Microcystis flos-aquae (Figures 19, 21, 23 and 25). The figures also show few species were commonly recorded in samples from the Darling River during the sampling period, and that
Cylindrospermopsis raciborskii occurred mainly in the upper sections of the Murray River and then only in autumn 2009.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
25 | NSW Office of Water, August 2012
Table 6. Summary statistics for cell width (or diameter) measurements for taxa from the Murray and Darling River sampling sites.
) ian
m) Me(μm)
th
(μma(
Minimum(μm
10th %ile (μm)
Med (μ
an 90 %ile )
M ximum μm)
N
Anabaena circinalis 2.359 4.136 5.462 5.532 7.206 7.709 169
Anabaena planktonica
6 04 .874 679 8.192 9.744 4.45 5.1 6 6. 67
Anabaena aph ides
2.123 2.476 3.506 3.459 4.486 5.428 42 anizomenio
Aphanizomenon (sp)p*
2.372 3.065 3.765 3.853 4.790 5.844 144
Aphanocapsa sp(p) 1 03 .545 1.570 1.851 2.232 259 1.10 1.3 1
C 7 75 880 2.036 2.645 4.758 71 hroococcus sp(p) 1.42 1.5 1.
Cyan sp(p) 1.187 1.319 1.742 1.740 2.143 2.633 50 odictyon
Cylindrospermopsis raciborskii
6 27 .824 3.721 4.197 5.043 2.86 3.1 3 21
Microcystis flosaquae
-
2 99 .615 2.567 3.066 3.622 135 1.65 1.9 2
*M pid oi
y stat r cell length remen r taxa from the Mu y and ing Rivg si
mum(μm)
%ile(μm)
edia(μm)
Mea(μm)
90th (μm)
Maximum (μm)
ost likely to be Cus othrix issatschenk
Table 7. Summar istics fo measu ts fo rra Darl er samplin tes.
Mini 10th M n n %ile N
A alis 212 .353 7.413 7.281 8.194 8.235 nabaena circin 5. 6 8
Anabaena planktonica 153 .942 7.605 7.465 9.016 105. 5 .686 62
Anabaena 2.500 2.923 4.070 4.139 5.545 6.718 42 aphanizomenioides
Aphan on (sp)p* 3.384 3.966 4.996 5.130 6.462 8.580 144 izomen
Aphanocapsa sp(p)
Chroococcus sp(p)
Cyanodictyon sp(p) 1.391 1.586 2.085 2.119 2.606 3.271 48
Cylindrospermopsis raciborskii
3.683 3.887 4.797 4.804 5.535 7.438 21
Microcystis flos-aquae
* uspid oi
H temp nd s varia the cell sizes o e ma a is d
enviro al conditions that the cells are experiencing, and how much of it is measurement error is difficult to determine. The laboratory staff undertaking easu nts a atly ncedyanobacterial taxonomy, so the misidentification of taxa is unlikely to be a major cause of the
ariation. However the Anabaena species in many samples often consisted only of vegetative cells, his may erational
ment
nder this working environment. Possibly the trend towards smaller cell sizes in
Most likely to be C othrix issatschenk
ow much of the
nment
oral a patial tion in f thes in tax ue to
the m reme re gre experie in c
vand had no heterocysts or akinetes to aid in the identification of the trichomes to species, so thave contributed some identification error. It should also be noted that the laboratory is an op
laboratory with a high turnover of samples (1000s) each summer, producing data for managepurposes, and it is not a laboratory dedicated to taxonomic research, so some misidentification of trichomes is possible u
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
26 | NSW Office of Water, August 2012
Table 8. Summary s tio for ta from vesampling s
m ile n Maximum
tatistics for cites.
ell length to width ra xa the Murray and Darling Ri r
Minimu 10th % Media Mean 90th %ile N
An nalis 1.11 1.12 1.14 8 abaena circi 1.09 1.09 1.11
Ana onica 1.07 1.08 1.11 1.11 1.13 1.16 62 baena plankt
Anabaena aphanizomenioides
1.10 1.14 1.17 1.28 1.39 42 1.19
A )p* 1.32 1.43 2.13 144 phanizomenon (sp 1.19 1.23 1.33
Aphanocapsa sp(p)
Chroococcus sp(p)
Cyanodictyon sp(p) 1.14 1.17 1.22 1.22 1.27 1.33 48
Cylindrospermopsis raciborskii
1.19 1.22 1.31 1.29 1.33 1.48 21
Microcystis flos -aquae
*M pidothri
measuring making more accurate measurements as the study progressed. However this is also unlikely, as s c cia bae a w throughout the study, inclu hen sized ere p in oth mples res 18 and
2
e etwe ple c on an ell si sure eing made amples b reser th Lu ine s . Sh or o anges
ce ge between sample collection and measurement (Hawkins et al 2005) may also ion
In lysis using correla analysi was undertaken between the environmental
(p ttributes water tem rature, di , electric l conductivity, pH and turbidity that were measu itu, mor ical a s (ce , cell , cell l th to width ratio, only occu ng taxa (present in 20 or more
sa TISTI . Thi investi ether of these environ l uting the intras ecific temp ral and sp ial variation in cell size of the
solved oxygen and pH measurements were those at the
me the samples were collected, and do not take diurnal changes into account. Biological factors that ll (i.e.
ost likely to be Cus x issatschenkoi
autumn could be due to other operator error, in particular with staff becoming more familiar with the
equipment and mall sized
ding wells espe large
lly of Anacells w
na taxresent
ere measured in sampleser sa (Figu
0 for example).
There was usually som delay b en sam ollecti d the c ze mea ments b, with the s
ll size during stora
eing p ved wi gols iod olution rinkage ther ch in
contribute to the variat .
itial exploratory ana tion s
hysico-chemical) a pe ssolved oxygen ared in-s and the pholog ttribute ll width length eng
cell volume
mples) using STA
) of several of t
CA v9
he most
s was to
comm
gate wh
rri
any mentaattributes were contrib to p o atcyanobacteria taxa. The temperature, dis
timay lead to differences in cell sizes through greater competition for available resources per cepossibly smaller cell sizes when there is greater competition for resources) were also considered,
including phycocyanin and total cyanobacterial biovolume measurements (competition amongst cyanobacteria), chlorophyll-a (competition with the entire phytoplankton community, including eukaryotic phytoplankton as well as cyanobacteria), and within-species competition (species cell
counts). Phycocyanin and chlorophyll-a data where the turbidity exceeded 50 NTU were excluded. Size selective predation by zooplankton could not be tested as there were no zooplankton data.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
27 | NSW Office of Water, August 2012
Temporal Variation in Anabaena circinalis (cell volume)
0
50
100
150
200
250
11/2
00
300
350
11/
8
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
01/2
009
02/2
009
17/0
2/20
9
20/ 3/
09
3/03
/200
9
17/0
3/20
0
/03/
2009
14/0
4/20
09
28
2009
Date
Cel
lula
r B
iovo
lum
e (μ
m3 )
9
31/0
4/20
09
12/0
5/20
09
26/0
5/
Temporal variation in Anabaena planktonica (cell volume)
0
100
200
300
400
500
600
700
11/1
1/20
08
25
2008
6/01
/200
9
20/0
1/2
/11/
2008
9/12
/200
8
23/1
2/00
9
3/02
/200
9
17/0
2/20
0
3/03
/200
9
17/0
3/20
09
31/2
009
12/0
5/20
09
26/0
5/9
/03/
2009
14/0
4/20
09
28/0
420
0
Date
Cel
lula
r (μ
m3 )
9
Bio
volu
me
Temporal variation in Anabaena aphaniz oides (cell vo
20
omeni lume)
0
2008
2008
2008
2008
2009
2009
20
40
60
Cel
lula
80
100
120
140
160
180
11/1
1/
25/1
1/9/
12/
23/1
2/6/
01/
20/0
1/3/
02/
17/0
2/3/
03/
17/0
3/
31/0
3/
14/0
4/
28/0
4/
12/0
5/
26/0
5/
Date
020
9 020
0920
0920
0920
09 920
0920
09
r B
iovo
lum
e (μ
m3 )
920
0
Temporal variation in Aphanizomenon sp(p). (cell volume)
0
50
100
150
200
250
300
350
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/
Date
Cel
lula
r B
iovo
lum
e (μ
m3 )
2009
2009
Temporal variation in Aphanocapsa sp(p). (cell volume)
0
2
4
6
8
10
08
Cel
lula
t B
iovo
lum
e (μ
m3 )
12
2008
2008
2008
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
2009
11/1
1/20
25/1
1/9/
12/
23/1
2/6/
01/
20/0
1/3/
02/
17/0
2/3/
03/
17/0
3/
31/0
3/
14/0
4/
28/0
4/
12/0
5/
26/0
5/
Date
Temporal variability in Chroococcus sp(p). (cell volume)
0
5
10
15
20
25
30
Ce
llula
r B
iovo
lum
e (μ
m3)
35
2008 00
820
0820
0820
0920
0920
0920
0920
0920
0920
0920
0920
0920
0920
09
11/1
1/
25/1
1/2
9/12
/
23/1
2/6/
01/
20/0
1/3/
02/
17/0
2/3/
03/
17/0
3/
31/0
3/
14/0
4/
28/0
4/
12/0
5/
26/0
5/
Date
Temporal variation in Cyanodictyon sp(p). (cell volume)
18
20
0
2
4
6
8
10
12
14
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Ce
llula
r B
iovo
lum
e (μ
m
163)
Temporal variation in Cylindrospermopsis racaborskii (cell volume)
0
50
100
150
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
200
250
09
3)
Date
Ce
llula
r B
iovo
lum
e (μ
m
Temporal variation in Microcystis flos-aquae (cell volume)
0
5
10
15
20
25
30
35
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
lula
r B
iovo
lum
e (μ
m3)
Figure 18. Temporal variation in cell volume, given as the mean ±1 standard error, for nine major taxa that occurred in the Murray and Darling Rivers during the study period.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
28 | NSW Office of Water, August 2012
Spatial variation in Anabaena circinalis (cell volume)
0
50
100
150
200
250
300
350
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Cu
rlw
aa
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Ell
ers
lie
Ta
pio
Location
Cel
l v
olu
me
(C
ub
ic m
icro
ns)
Spatial variation in Anabaena planktonica (cell volume)
0
100
200
300
400
500
600
700
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Cu
rlw
aa
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Ell
ers
lie
Ta
pio
Location
Cel
l v
olu
me
(c
ub
ic m
icro
ns)
Spatial variation in Anabaena aphanizomenonioides (cell volume)
0
20
40
60
80
100
120
140
160
180
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Cu
rlw
aa
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Ell
ers
lie
Ta
pio
Location
Cel
l v
olu
me
(c
ub
ic m
icro
ns)
Spatial variation in Aphanizomenon sp(p). (cell volume)
0
50
100
150
200
250
300
350
Lo
cati
on
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Me
rbei
n
Cu
rlw
aa
Fo
rt C
ou
rag
e
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Ell
ers
lie
Ta
pio
Location
Cel
l v
olu
me
(cu
bic
mic
ron
s)
Spatial variation in Aphanocapsa sp(p). (cell volume)
0
2
4
6
8
10
12
Lo
cati
on
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Fo
rt C
ou
rag
e
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Bu
rtu
nd
y
Ell
ers
lie
Ca
rath
oo
l
Location
Cel
l v
olu
me
(c
ub
ic m
icro
ns)
Spatial variation in Chroococcus sp(p). (cell volume)
0
5
10
15
20
25
30
35
Lo
cati
on
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Fo
rt C
ou
rag
e
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Bu
rtu
nd
y
Ell
ers
lie
Ca
rath
oo
l
Location
Cel
l v
olu
me
(c
ub
ic m
icro
ns)
Spatial variation in Cyanodictyon sp(p). (cell volume)
0
2
4
6
8
10
12
14
16
18
20
Lo
cati
on
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Fo
rt C
ou
rag
e
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Bu
rtu
nd
y
Ell
ers
lie
Ca
rath
oo
l
Location
Ce
ll v
olu
me
(cu
bic
mic
ron
s)
Spatial variation in Cylindrospermopsis racaborskii (cell volume)
0
50
100
150
200
250
Lo
cati
on
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Cu
rlw
aa
Fo
rt C
ou
rag
e
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Ell
ers
lie
Ta
pio
Location
Cel
l v
olu
me
(c
ub
ic m
icro
ns)
Spatial variation in Microcystis flos-aquae (cell volume)
0
5
10
15
20
25
30
35
Lo
cati
on
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Me
rbei
n
Fo
rt C
ou
rag
e
Lo
ck
8
La
ke V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
ali
te
To
larn
o
Po
on
car
ie
Bu
rtu
nd
y
Ell
ers
lie
Ca
rath
oo
l
Location
Cel
l v
olu
me
(c
ub
ic m
icro
ns)
Figure 19. Spatial variation in cell volume, given as the mean ±1 standard error, for nine major taxa that occurred in the Murray and Darling Rivers during the study period.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
29 | NSW Office of Water, August 2012
Temporal variation in Anabaena circinalis (cell width)
0
1
2
3
4
5
6
7
8
9
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l w
idth
(m
icro
ns
)
Temporal variation in Anabaena planktonica (cell width)
0
2
4
6
8
10
12
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l w
idth
(m
icro
ns
)
Temporal variation in Anabaena aphanizomenioides (cell width)
0
1
2
3
4
5
6
7
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l w
idth
(m
icro
ns
)
Temporal variation in Aphanizomenon sp(p) (cell width)
0
1
2
3
4
5
6
7
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
DateC
ell
wid
th (
mic
ron
s)
Temporal variation in Aphanocapsa sp(p) (cell diameter)
0.0
0.5
1.0
1.5
2.0
2.5
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l d
iam
eter
(m
icro
ns
)
Temporal variation in Chroococcus sp(p) (cell diameter)
0
1
2
3
4
5
6
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l d
iam
eter
(m
icro
ns
)
Temporal variation in Cyanodictyon sp(p) (cell width)
0
0.5
1
1.5
2
2.5
3
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l wid
th (
mic
ron
)
Temporal variation in Cylindrospermopsis racaborskii (cell width)
0
1
2
3
4
5
6
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l w
idth
(m
icro
ns
)
Temporal variation in Microcystis flos-aquae (cell diameter)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l dia
met
er (
mic
ron
s)
Figure 20. r, for nine major taxa that occurred in the Murray and Darling Rivers during the study period. Temporal variation in cell width, given as the mean ±1 standard erro
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
Spatial variation in Anabaena circinalis (cell width)
0
1
2
3
4
5
6
7
8
9A
lbu
ry
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
Location
Cel
l w
idth
(m
icro
ns)
Spatial variation in Anabaena planktonica (cell width)
0
2
4
6
8
10
12
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
Location
Cel
l w
idth
(m
icro
ns)
Spatial variation in Anabaena aphaniizomenioides (cell width)
0
1
2
3
4
5
6
7
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
Location
Cel
l w
idth
(m
icro
ns)
Spatial variation in Aphanizomenon sp(p) (cell width)
0
1
2
3
4
5
6
7
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
LocationC
ell
wid
th (
mic
ron
s)
Spatial variation in Aphanocapsa sp(p) (cell diameter)
0.0
0.5
1.0
1.5
2.0
2.5
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
Location
Cel
l d
iam
eter
(m
icro
ns)
Spatial variation in Chroococcus sp(p) (cell diameter)
0
1
2
3
4
5
6
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
Location
Cel
l d
iam
ete
r (M
icro
ns
)
Spatial variation in Cyanodictyon sp(p) (cell width)
0
0.5
1
1.5
2
2.5
3
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
alit
e
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Ce
ll w
idth
(m
icro
ns)
Spatial variation in Cylindrospermopsis racaborskii (cell width)
0
1
2
3
4
5
6
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
am
a
Bar
ha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Ky
alit
e
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Ce
ll w
idth
(m
icro
ns)
Spatial variation in Microcystis flos-aquae (cell diameter)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cu
mw
al
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
yb
uc
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Ma
tho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Ta
pio
Hil
lsto
n
Location
Cel
l d
iam
ete
r (m
icro
ns)
Figure 21. Spatial variation in cell width, given as the mean ±1 standard error, for nine major taxa that occurred in the Murray and Darling Rivers during the study period.
30 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
31 | NSW Office of Water, August 2012
Temporal variation in Anabaena circinalis (cell length)
0
1
2
3
4
5
6
7
8
9
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l le
ng
th (
mic
ron
s)
Temporal variation in Anabaena planktonica (cell length)
0.00000
2.00000
4.00000
6.00000
8.00000
10.00000
12.00000
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l len
gh
t (m
icro
ns)
Temporal variation in Anabaena aphanzomenioides (cell length)
0
1
2
3
4
5
6
7
8
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l le
ng
ht
(Mic
ron
s)
Temporal variation in Aphanizomenon sp(p) (cell length)
0
1
2
3
4
5
6
7
8
9
10
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l le
ng
ht
(Mic
ron
s)
Temporal variation in Cyanodictyon sp(p) (cell length)
0
0.5
1
1.5
2
2.5
3
3.5
4
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l le
ng
th (
Mic
ron
s)
Temportal variation in Cylindrospermopsis racaborskii (cell length)
0
1
2
3
4
5
6
7
8
9
11/1
1/20
08
25/1
1/20
08
9/12
/200
8
23/1
2/20
08
6/01
/200
9
20/0
1/20
09
3/02
/200
9
17/0
2/20
09
3/03
/200
9
17/0
3/20
09
31/0
3/20
09
14/0
4/20
09
28/0
4/20
09
12/0
5/20
09
26/0
5/20
09
Date
Cel
l L
eng
th (
Mic
ron
s)
Figure 22. Temporal variation in cell length, given as the mean ±1 standard error, for six major taxa that occurred in the Murray and Darling Rivers during the study period.
The analyses indicated weak but significant (at the 5% level of significance) correlations between the following morphological and physico-chemical attributes:-
Water temperature – weak significant positive correlations with the cell widths of A. circinalis, A planktonica, A. aphanizomenioides, Aphanizomenon sp(p)., Aphanocapsa sp(p)., and Cyanodictyon sp(p).; the cell lengths of A. planktonica, A. aphanizomenioides,
Aphanizomenon sp(p)., and Cyanodictyon sp(p).; the cell length to width ratio of A. aphanizomenioides; and the cell volumes A. circinalis, A. planktonica, A. aphanizomenioides, Aphanizomenon sp(p)., Aphanocapsa sp(p)., and Cyanodictyon sp(p). A weak significant
negative correlation was also between Chroococcus sp(p). cell volume and water temperature. The statistical significance of these correlations may in part be due to the high number of observations (N) used for many of the correlations (Quinn and Keough, 2002). The
n this analysis, and may also influence the results. However seasonal changes in water
temperature would be greater than the diurnal variation and have a greater effect on temporal changes in cell size.
results of the correlations between cell volume and water temperature are given in Table 9 and shown in Figure 26. Diurnal variation in water temperature was not taken into account i
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
32 | NSW Office of Water, August 2012
Spatial variation in Anabaena circinalis (cell length)
0
1
2
3
4
5
6
7
8
9
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Cel
l len
gh
t (M
icro
ns)
Spatial variation in Anabaena planktonica (cell lenght)
0
2
4
6
8
10
12
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Cel
l le
ng
th (
Mic
ron
s)
Spatial variation in Anabaena aphanizomenioides (cell length)
0
1
2
3
4
5
6
7
8
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Cel
l le
ng
th (
Mic
ron
s)
Spatial variation in Aphanizomenon sp(p) (cell length)
0
1
2
3
4
5
6
7
8
9
10
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Cel
l len
gh
t (M
icro
ns)
Spatial variation in Cyanodictyon sp(p) (cell length)
0
0.5
1
1.5
2
2.5
3
3.5
4
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Cel
l le
ng
th (
Mic
ron
s)
Spatial variation in Cylindrospermopsis racaborskii (cell length)
0
1
2
3
4
5
6
7
8
9
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Pic
nic
Po
int
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ers
ion
Bu
ron
ga
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Bu
rtu
nd
y
Tap
io
Hil
lsto
n
Location
Cel
l le
ng
th (
Mic
ron
s)
Figure 23. for six major taxa that occurred in the Murray and Darling Rivers during the study period.
Dissolved oxygen – weak significant negative correlations were recorded with the cell
of A. xygen, while
ated with
riod
nd cell length of Cyanodictyon sp(p). were positively correlated.
Spatial variation in cell length, given as the mean ±1 standard error,
diameter of Aphanocapsa sp(p). and the cell length of A. planktonica, while a significant
positive correlation was recorded with Chroococcus sp(p). cell diameter. The cell volumeplanktonica and Aphanocapsa sp(p). was negatively correlated with dissolved othe cell volume of Chroococcus sp(p). and Cyanodictyon sp(p). was positively correl
dissolved oxygen. Diurnal variation in dissolved oxygen was not accounted for in this analysis, and although concentrations were mostly high at all sites throughout the study pe(see box plot in Appendix A) this may increase the error in this analysis. An actual
mechanism of how dissolved oxygen could affect the size of cyanobacterial cells cannot be proposed (unless through a physiological response in photosynthesis or respiration), and the significant correlations found above may be by chance.
Electrical conductivity – both the cell width and cell volume of A. circinalis were weakly negatively correlated with electrical conductivity, as was the cell diameter of M. flos-aquae; while both the cell width a
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
33 | NSW Office of Water, August 2012
Temporal variation in Anabaena circinalis (Length/width ratio)
1.08
1.09
1.10
1.11
1.12
1.13
1.14
1.15
8/11/2008 28/12/2008 16/02/2009 7/04/2009 27/05/2009
Date
Len
gth
:Wid
th r
atio
Temporal variation in Anabaena planktonica (Length/width ratio)
1.06
1.07
1.08
1.09
1.10
1.11
1.12
1.13
1.14
1.15
1.16
1.17
8/11/2008 28/12/2008 16/02/2009 7/04/2009 27/05/2009
Date
Le
ng
th:w
idth
ra
tio
Temporal variation in Anabaena aphanizomenioides (Length/width ratio)
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
8/11/2008 28/12/2008 16/02/2009 7/04/2009 27/05/2009
Date
Le
ng
th:W
idth
ra
tio
Temporal variation in Aphanizomenon sp(p) (Length/width ratio)
1.00
1.10
1.20
1.30
1.40
1.50
1.60
1.70
8/11/2008 28/12/2008 16/02/2009 7/04/2009 27/05/2009
Date
Le
ng
th:w
idth
ra
tio
Temporal variation in Cyanodictyon sp(p) (Length/width ratio)
1.10
1.15
1.20
1.25
1.30
1.35
8/11/2008 28/12/2008 16/02/2009 7/04/2009 27/05/2009
Date
Le
ng
th:w
idth
ra
tio
Temporal variation in Cylindrospermopsis racaborskii (Length/width ratio)
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
8/11/2008 28/12/2008 16/02/2009 7/04/2009 27/05/2009
Date
Le
ng
th:w
idth
ra
tio
Figure 24. Temporal variation in cell length to width ratio for six major taxa that occurred in the Murrayand Darling Rivers during the study period.
es A.
hysio-
ell volume and biological variables, nted in
were all negatively correlated with phycocyanin concentration; while both the cell diameter and cell volume of M. flos-aquae were also negatively correlated. The cell volume of Chroococcus
sp(p). was positively correlated.
Turbidity – the cell width and cell length of both A. planktonica and A. aphanizomenioidwere positively correlated with turbidity, although surprisingly their cell volumes were not.circinalis cell length was negatively correlated, although this is based on very few
measurements (n = 7).
pH – there were no significant correlations between the morphological attributes of any of the cyanobacterial taxa and pH. Diurnal variation in pH was not accounted for in this study.
The significant correlations between the cell volume of certain cyanobacterial taxa and the pchemical attributes are also listed in Table 9, although there is no apparent pattern to these. The correlations were mostly weak.
There were few significant correlations between cyanobacterial cand few patterns in those that were significant. The correlations involving cell volume are preseTable 10. The significant correlations, which were mostly weak, were as follows:-
Phycocyanin – the cell width, the cell length and the cell volume of A. aphanizomenioides
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
34 | NSW Office of Water, August 2012
Spatial variation in Anabaena circinalis (Length/width ratio)
1.06
1.07
1.08
1.09
1.10
1.11
1.12
1.13
1.14
1.15
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Mer
bei
n
Cu
rlw
aa
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Elle
rsli
e
Tap
io
Location
len
gth
:wid
th r
atio
Spatial variation in Anabaena planktonica (Length/width ratio)
1.02
1.04
1.06
1.08
1.10
1.12
1.14
1.16
1.18
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Mer
bei
n
Cu
rlw
aa
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Elle
rsli
e
Tap
io
Location
Le
ng
th:w
idth
ra
tio
Spatial variation in Anabaena aphanizomenioides (Length/width ratio)
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Mer
bei
n
Cu
rlw
aa
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Elle
rsli
e
Tap
io
Location
Len
gth
:wid
th r
atio
Spatial variation in Aphanizomenon sp(p) (Length/width ratio)
1.00
1.10
1.20
1.30
1.40
1.50
1.60
1.70
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Mer
bei
n
Cu
rlw
aa
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Elle
rsli
e
Tap
io
Location
Len
gth
:wid
th r
atio
Spatial variation in Cyanodictyon sp(p) (Length/width ratio)
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Mer
bei
n
Cu
rlw
aa
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Elle
rsli
e
Tap
io
Location
Len
gth
:wid
th r
ati
o
Spatial variation in Cylindrospermopsis racaborskii (Length/width ratio)
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
1.45
1.50
Alb
ury
Ho
wlo
ng
Co
row
a
Mu
lwal
a
Yar
raw
on
ga
Co
bra
m
To
cum
wal
Mo
ama
Bar
ham
Mu
rray
Do
wn
s
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mo
un
t D
isp
ersi
on
Bu
ron
ga
Mer
bei
n
Cu
rlw
aa
Lo
ck 8
Lak
e V
icto
ria
Mat
ho
ura
Mo
ula
mei
n
Kya
lite
To
larn
o
Po
on
cari
e
Elle
rsli
e
Tap
io
Location
Len
gth
:wid
th r
atio
Figure 25. r six major taxa that occurred in the Murray and Darling Rivers during the study period.
volume – both the cell diameter and the cell volume of M. flos-aquae
he not show any correlations with the cell counts for these taxa.
species during
Spatial variation in cell length to width ratio fo
Chlorophyll-a – a significant positive correlation was found between Chroococcus sp(p). cell volume and chlorophyll-a.
Total cyanobacterial biowere negatively correlated with total cyanobacterial biovolume. Chroococcus sp(p). cell volume was positively correlated.
Cell counts – both the cell diameter and the cell volume of M. flos-aquae were negatively correlated with the cell count of M. flos-aquae. Changes in the morphological features of tother taxa examined did
M. flos-aquae was the only taxa where the cell size (diameter and therefore volume) tended to decrease with increasing total cyanobacterial biovolume, increasing phycocyanin concentrations, and increasing cell counts of M. flos-aquae, indicating possible smaller cell sizes in this
bloom conditions when competition for resources between cells may be greatest. In contrast, cell volume of Chroococcus sp(p). tended to increase with increasing total cyanobacterial biovolume and
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
35 | NSW Office of Water, August 2012
-50 0 50 100 150 200 250 300
Cell Size A. circinalis (um3)
10
12
14
16
18
20
22
24
26
28
30
Wat
er T
empe
ratu
re (
o C)
A
0 100 200 300 400 500 600
Cell Size A. planktonica (um3)
12
14
16
18
20
22
24
26
28
30
32
Wat
er T
empe
ratu
re (
o C)
B
0 20 40 60 80 100 120 140 160
Cell Size A.aphanizomenioides (um3)
12
14
16
18
20
22
24
26
28
30
Wat
er T
empe
ratu
re (
o C)
C
0 20 40 60 80 100 120 140 160 180 200 220 240 260
Cell size Aphanizomenon sp(p). (um3)
12
14
16
18
20
22
24
26
28
30
32
Wat
er T
empe
ratu
re (
o C)
D
0 1 2 3 4 5 6 7 8
Cell Size Aphanocapsa sp(p). (um3)
10
14
16
18
20
22
24
26
28
30
32
12
Wat
er T
empe
ratu
re (
o C)
E
0 2 4 6 8 10 12 14 16
Cell Size Chroococcus sp(p). (um3)
12
14
16
18
20
22
24
26
28
30
Wat
er T
empe
ratu
re (
o C)
F
0 2 4 6 8 10 12 14 16 18
Cell size Cyanodictyon sp(p). (um3)
12
14
16
18
20
22
24
26
28
30
32
Wat
er T
empe
ratu
re (
o C)
G
Figure 26. Relationship between variation in cell volume of various taxa of cyanobacteria and water temperature. (A) Anabaena circinalis, (B) Anabaena planktonica, (C) Anabaena aphanizomenioides, (D) Aphanizomenon sp(p)., (E) Aphanocapsa sp(p)., (F) Chroococcus sp(p), (G) Cyanodicyon sp(p).
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
36 | NSW Office of Water, August 2012
0 20 40 60 80 100 120 140 160
Cell size A. aphanizomenioides (um3)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Phy
cocy
anin
(R
FU
)
0 2 4 6 8 10 12 14 16
Cell size Chroococcus sp(p). (um3)
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.6
Tot
al C
yano
bact
eria
l Bio
volu
me
(mm
3 L-1)
0 2 4 6 8 10 12 14 16
Cell size Chroococcus sp(p). (um3)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
Phy
cocy
anin
(R
FU
)
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Cell size M. flos-aquae (mm3 L-1)
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
Tot
al C
yano
bact
eria
l Bio
volu
me
(mm
3 L-1)
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Cell size M. flos-aquae (um3)
0
1
2
3
4
5
Phy
cocy
anin
(R
FU
)
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28
Cell size M. flos-aquae (um3)
-20000
0
20000
40000
60000
80000
1E5
1.2E5
1.4E5
1.6E5
1.8E5
Cel
l Cou
nt M
. flo
s-aq
uae
(cel
ls m
L-1)
-50 0 50 100 150 200 250 300
Cell size A. circinalis (um3)
0
20
40
60
80
100
120
140
160
180
200
220
240
260
Cel
l siz
e A
phan
izom
enon
sp(
p).
(um
3 )
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300
Cell size A. circinalis (um3)
0
2
4
6
8
10
12
14
16
18
20
22
24
26
28
Cel
l siz
e M
. flo
s-aq
uae
(um
3 )
Figure 27. Examples of relationships where there were significant positive or negative relationships between the cell size of various cyanobacteria taxa and other biological variables.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
37 | NSW Office of Water, August 2012
Table 9. Statistically significant correlations between the cell volume of various taxa of cyanobacteria and physico-chemical attributes measured in the Murray and Darling Rivers.
Taxa (cell volume)
Physico-chemical attribute
r2 Direction Number of observations
(N)
Probability
Anabaena circinalis
Water temperature
0.06 +ve 164 P = 0.002
Anabaena planktonica
Water temperature
0.13 +ve 66 P = 0.003
Anabaena aphanizomenioides
Water temperature
0.18 +ve 40 P = 0.006
Aphanizomenon sp(p).
Water temperature
0.05 +ve 142 P = 0.007
Aphanocapsa sp(p).
Water temperature
0.15 +ve 254 P = 0.000
Chroococcus sp(p).
Water temperature
0.14 -ve 66 P = 0.002
Cyanodictyon sp(p).
Water temperature
0.13 +ve 50 P = 0.009
Anabaena circinalis
Electrical conductivity
0.05 -ve 164 P = 0.005
Anabaena planktonica
Dissolved oxygen
0.06 -ve 66 P = 0.045
Aphanocapsa sp(p).
Dissolved oxygen
0.04 -ve 254 P = 0.001
Chroococcus sp(p).
Dissolved oxygen
0.21 +ve 66 P = 0.000
Cyanodictyon sp(p).
Dissolved oxygen
0.18 +ve 50 P = 0.002
with increasing chlorophyll-a and phycocyanin c trations. However Chroococcus was generally
only a minor component of the cyanobacterial community, generally contributing less than 5% of the total cyanobacterial biomass on most sampling occasions, so these indications of it having larger sized cells during bloom conditions may be circumstantial. In particular, all the correlations (Table 10)
were weak. Examples of these interactions are shown in Figure 27.
Finally, the cell widths (or diameters) and the cell volumes of the various cyanobacterial taxa examined tended mainly to increase in unison, with the cell widths and cell volumes of Anabaena
circinalis in particular being weakly but significantly positively correlated with the respective cell widths and cell volumes of the various other taxa. The cell widths and cell volumes of Anabaena planktonica, Aphanizomenon sp(p). and Aphanocapsa sp(p). also had a number of significant but weak positive
correlations with the respective cell widths and cell volumes of several other taxa. The correlations for cell volume are shown in Table 10. Possibly the factor or factors influencing within-species cell size
oncen
have similar effects across a range of taxa.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
38 | NSW Office of Water, August 2012
Table 10. Statistically significant correlations between the cell volume of various taxa of cyanobacteria
Taxa (cell e ns
Probability
and other biological variables measured in the Murray and Darling Rivers.
Biological r2 Direction Number of volume) variabl observatio
(N)
Anabaena apha
0.13 -ve P = 0.027 nizomenioides
Phycocyanin 39
Microcystis flos- n 0.10 -ve 128 P = 0.000 aquae
Phycocyani
Chroococcus sp(p).
Phycocyanin 0.18 +ve 64 P = 0.001
Chroococcus sp(p).
Chlorophyll-a 0.18 +ve 64 P = 0.001
Chroococcus cy terial
0.07 +ve 68 P = 0.024 sp(p).
Total anobacbiovolume
M l al
08 e 3 icrocystis flos-aquae
Totacyanobacteri
biovolume
0. -v 13 P = 0.001
Microcystis flos s-aqu
-aquae
Microcystis floae cell volume
0.06 -ve 130 P = 0.005
Anabaena circinalis
Anabaena pla cell
0.08 +ve 55 P = 0.039 nktonicavolume
Anabaena circinalis
Anabaena hanizomeni
cell voluap oides
me
ve 0.24 + 37 P = 0.002
Anabaena ircinalisc
A non sp(p). cell size
37 e 4 phanizome 0. +v 10 P = 0.001
Anabaena ircinalisc
sa sp(p). cell size
21 e 4 Aphanocap 0. +v 15 P = 0.000
Anabaena circinalis
Microcystis flos-aquae cell volume
0.12 +ve 109 P = 0.000
Anabaena planktonica
Aphanizomenon sp(p). cell volume
0.22 +ve 54 P = 0.000
Anabaena planktonica
Aphanocapsa sp(p). cell volume
0.11 +ve 62 P = 0.009
Anabaena aphanizomenioides
Aphanizomenon sp(p). cell volume
0.33 +ve 29 P = 0.001
Aphanizomenon sp(p).
Aphanocapsa sp(p). cell volume
0.19 +ve 139 P = 0.000
Aphanizomenon sp(p).
Cyanodictyon sp(p). cell volume
0.41 +ve 35 P = 0.000
Aphanizomenon sp(p).
Microcystis flos-aquae cell volume
0.13 +ve 77 P = 0.002
Aphanocapsa sp(p).
Cyanodictyon sp(p). cell volume
0.29 +ve 49 P = 0.000
Aphanocapsa Microcystis flos-sp(p). aquae cell volume
0.10 +ve 127 P = 0.000
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
39 | NSW Office of Water, August 2012
5. Discussion
5.1 itu fluorometry
The re in-si YSI 660 water quality sonde to measure cyanobarray rling Rivers show that this method has co erable po
th ion of rial b present, which can them nagem rposes des sufficieco tecting prese n the Ambe rt range and ove for a
mana t response under the National Health and Medical Research Council (2008) recreational wa ideline High” al el for raw waters sourced f otable supaccording to the Alert Levels Framework of Newcombe et al (2010). Ahn et al (2007) proposed an
alert levels framework based on laboratory m ments o cocyanin co ntration foreserv hile Brien gested the use of in-situ measurements of phycocyanin to determine equivalent cy al cell counts corresponding to management alert level thresholds
in rczyk et a 9) proposed lerts Level ework for a r treatmbased on the results o monitoring of the influent raw water stream coming from a reservoi oppo W study reported he re the
o ass itu flu etry to test compliance agai stablisheguidelin urements ma using in-situ metry mrapi obacte ooms, it is recommended th ese field
measurements be confirm llow up laboratory analysis, especially the identification and enu f the cyan axa prese rough estab d microsco ethods. The use of in-si etry a g tool followed up by laboratory analysis has been also
recommended by a num r authors (Gregor and Maršálek, 2005; Gregor et al 2005; Izyd l 2009 led microscopy is not always necessary (Gregor et al
Des nt lation be n phycocya easured w e YSI flu
and obacteri this study, there was still much variability in the data obt(Fig ch of t uld aris the difficu accurately rmining tcyan l biovo wkins et al (2005) have discussed the error involved in
biov rmina mpling r, counting ka with the aLug ative elapsed before cell size measurements, and in making the cell size ents th converting these to a cell volume using t ost appr
geo e. C associated with large colonial or filamentous aggregations may also r (Izyd 9). Standard cell sizes were used in thi dy as no
e ments made on them, and using only the laboratory measured
s ve m g too much of the field data. The use of dard cell ld also b rce of e tory measurements indicated considerable spatial and temporal variability in the cell si er of tax yanobacte he use of s ard cell s
to ove ate the a otal cyanobacterial biovolume present (see Fig 6), with the d n th ndard cell sizes varying stantly as w -species chang tially an ghout the study period.
Err the sureme would also add to the variability seen in the data, althoug bly less than that cause olume deteSome variabil was ield me nt of phycocyanin when lo ata every 10
econds (Figs 2 and 3), but this can be reduced by logging sufficient data over a sufficient time period (every 10 seconds for 5 minutes is suggested) and then taking the average of these data. Other error
herent in the fluorometric measurement of phycocyanin, especially in the field, has been extensively
discussed by other authors. These include:-
In-s
sults of the presence in the Mu
tu trials of the and lower Da
0 V2 cterial tential for nsid
e rapid estimatanagement tool for bloom
total cyanobacte response ma
iovolume ent pu
n be utilised arovi
s a rapid nt . The method p
nfidence in de
gemen
cyanobacterial nce i r ale ab
ter quality gu s, and at the “ ert lev or p ply
easure f phy nce r Korean oirs, w t et al (2008) sug
anobacteri
France. Izydo l (200f fluorescence
an A Fram wate ent plant
r in Poland. This h
bjective was to
owever is the
ess the use of in-s
site of the NS
orom
re, whe
nst e d es. While initial fi
d management reeld meas
sponses to cyan
ed by fo
de rial b
fluoro ay be useful tat th
o initiating based
meration otu fluorom
obacteria ts an initial screenin
ber of othe
nt th lishe py m
orczyk et a ), or where detai 2005).
pite the significa
total cyan
positive corre
al biovolume in
twee nin m ith th orometer
ained ure 8). Muobacteria
his variation wolume present. Ha
e from lty in dete he total
olume deteols preserv
tion, including saand the time then
erro error, cell shrin ge ddition of
measurem
metric shap
emselves and
ounting problems
he m opriate
cause errosamples could hav
orczyk et al 200 cell size measure
s stu t all field
amples would hae a sou
eant excludinrror, as the labora
stan sizes wou
zes of a numb
ctual amount of t
a of c ria. T tand izes tended
restimifference betwee
ed spae actual and sta
d temporally throu con ithin cell size
or involved in fluorometric mea nts h this is likely to be
ityconsidera
observed in the fd by the error in biov rmination.
asureme gging d
s
in
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
40 | NSW Office of Water, August 2012
Growth stage, agepigment content of
and physiology of the cells (Beutler et al 2002, Gregor et al 2005). The cyanobacterial cells may vary over time (Seppälä et al 2007). Gregor et al
r phycocyanin fluorescence may occur in older cells, while Lee et
cterial cells had lower phycocyanin content when growth rates
n us of
ent
light yanin in
sed to
nin
rge
e
at
und some correlation between
(2007) considered that highe
al (1995) found that cyanobawere high.
The actual peak wavelength of the phycocyanin emission spectrum may vary slightly between
different species of cyanobacteria (Beutler et al 2002, 2003; Gregor et al 2007), depending opigment content of phycobilisomes (the phycocyanin containing photosynthetic apparatcyanobacteria) (Seppälä et al 2007). Other factors that may lead to differences in
fluorescence measured in different species include differences in phycocyanin content and dispersion per cell (Lee et al 1995, Izydorczyk et al 2005), and difference in cell or filamgeometry (Lee et al 1995). Brient et al (2008) however found good correlations between
phycocyanin and cell count despite species of different cell sizes being present.
The level of light saturation. High light intensity may cause photoinhibition and reduce the fluorescence yield of the phycocyanin in cyanobacteria exposed close to the surface
(Leboulanger et al 2002). Seppälä et al (2007) found higher phycocyanin content when intensity was low, and Gregor et al (2007) found a higher fluorescence yield of phycocsamples from deep within a reservoir compared to shallow samples which were expo
more light. Izydorczyk et al (2005) suggested that low light intensity may stimulate phycocyanin production in cyanobacteria. Brient et al (2008) considered that the phycocyasignal measured in their study was not affected by natural light.
Nutrients, especially nitrogen. Phycocyanin is a protein, and low nitrogen concentrations may stimulate its degradation within cyanobacteria (Izydorczyk et al 2005)
Aggregation of cells into colonies and filaments (Lee et al 1995, Gregor and Maršálek 2005,
Gregor et al 2005). Large colonies and aggregations of tangled filaments may prevent the excitation wavelengths from reaching the inner most cells within the aggregations, while fluoresced light may be scattered or reabsorbed by cells within the colony leading to
underestimations of the amount of phycocyanin present. Seppälä et al (2007) found that lacolonies produced noisier fluorescence signals.
Dense blooms of cyanobacteria (Leboulanger et al 2002, Beutler et al 2002, Gregor and
Maršálek 2005, Gregor et al 2005) have been shown to lead than lower than expected phycocyanin fluorescence yield due to shading effects and fluorescence reabsorption.
Small false positive measurements of phycocyanin may occur when cyanobacterial presenc
is low and the phytoplankton community is dominated by a high eukaryotic species presence. Low cyanobacterial presence can also increase the variability in the phycocyanin measurements made. (Izydorczyk et al 2005, Gregor and Maršálek 2005, Gregor et al 2005,
Gregor et al 2007, Seppälä et al 2007, Brient et al 2008, McQuaid et al 2011).
Although Seppälä et al (2007) found little interference in their study, they have suggested thoverlap of the emission spectra of phycocyanin and chlorophyll-a may occur causing slight
error in phycocyanin measurements. Ahn et al (2007) fophycocyanin and chlorophyll-a when cyanobacterial presence was low, but the effect decreased when cyanobacteria contributed a greater proportion of total phytoplankton
population. Brient et al (2008) also found a slight phycocyanin yield due to eukaryotic algae, but this was small compared to the yield from cyanobacteria.
Other factors that have been suggested to affect phycocyanin content in cyanobacteria
include carbon dioxide concentrations, salinity and the availability of iron (Beutler et al 2003).
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
41 | NSW Office of Water, August 2012
The extent to which any of these factors may have influenced the fluorometry results obtained usingthe YSI in the Murray and Darling Rivers cannot be ascertained, although it is possible that several may have added to the variance displayed by the in-situ phycocyanin measurements. A range of
species were present with different patterns of dominance in different parts of the river, so slight variations in the emis
sion spectra between species may have resulted. The study commenced when
ded
utions by extracellular
in fluorescence
ence
en the study commenced would have
decomposed, and the low cyanobacterial presence throughout the study indicates that it never han
The site by site variation found in the relationship between phycocyanin fluorescence and total
rray, but
the Murray
zyk et
e upper 007)
uring the Murray River bloom
cyanobacteria presence at most sites along the rivers was low, but concluded soon after a major
bloom, although it is unlikely that the maximum biovolumes measured (Al-Tebrineh et al 2012) were high enough to impact severely through selfshading or reabsorption of fluoresced light. Although colonial and filamentous species dominated, on most occasions colony size would have been small,
given the generally low total biovolumes observed. The measurements were all made on the river bank in a shaded bucket, so if there was an impact on the measurements of phycocyanin due to photoinhibition, this would have occurred equally at all sites throughout the data collection. Any
impact of eukaryotic algae is not known, but is not likely to be great, as total chlorophyll-a concentrations measured by the YSI rarely exceeded 10 μg L-1, and this value was only exceeduring the cyanobacterial bloom of April and May 2009.
One water quality factor that did have a probable effect on phycocyanin fluorescence was turbidity, causing false positive measurements when turbidity was in excess of 50 NTU. Beutler et al (2002) and Brient et al (2008) tested the effect of bentonite and a sieved soil mix, respectively, on a bbe
Fluoroprobe and a TriOS instrument, respectively. Both found small reductions in fluorometric yield, and neither reported any false positive signal. Seppälä et al (2007) reported that phycocyanin fluorescence was sometimes, but not always, positively related to turbidity, but in this study the
increased turbidity was due to the increased cyanobacterial biomass. Contribphycocyanin have been reported by Bastien et al (2011), especially when there were high densities of the genus Anabaena present. Brient et al (2008) also reported high phycocyan
measurements that were not correlated with cyanobacterial cell counts, which they attributed to eitherthe cyanobacteria being missed in the microscopic analysis of samples, or due to the death and celllyses of the cyanobacteria prior to phycocyanin measurement being made. In this study, the pres
of extracellular phycocyanin was not likely to have had a great impact as cyanobacterial biovolume was very low throughout the study in the turbid sites of the Darling River. Any cyanobacteria and extracellular phycocyanin that may have been present wh
returned. The false positives for phycocyanin are therefore most probably from the high turbidity tfrom extracellular phycocyanin.
cyanobacterial biovolume along the Murray River during this study was possibly caused by changes incyanobacterial species composition and abundance in different parts of the river. Anabaena circinalis
and Microcystis flos-aquae were the more dominant species in the upper section of the Muthese declined in their relative contributions to the cyanobacterial community with distance downstream. There was also greater cyanobacterial abundance in the upper section of
River compared to Sunraysia, especially during the bloom period towards the end of the study period. Factors that may have contributed to the site by site variation include variation in the peak emission signal for phycocyanin from species to species (Beutler et al 2002, 2003; Lee et al 1995, Izydorc
al 2005, Gregor et al 2007) and the larger sized filamentous and colonial species present in thpart of the river (Lee et al 1995, Gregor and Maršálek 2005, Gregor et al 2005). Gregor et al (2reported better correlation between phycocyanin fluorescence and biomass at higher concentrations
of cyanobacteria compared to low concentrations. It is unlikely, even dperiod, that cyanobacterial presence at these sites was so dense to cause self shading and fluorescence problems. Seppälä et al (2007) found differences in regression slopes of phycocyanin
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
42 | NSW Office of Water, August 2012
fluorescence against cyanobacterial biomass on different cruises across the Baltic Sea, which they attributed to changes in cell pigment content over time.
A further factor in the site by site variation may be the contribution of picoplanktonic cyanobacteria
the phycocyanin fluorescence signal. Picoplanktonic species were common at all sites during the Murray River study, contributing a higher proportion of the total cyanobacterial biovolume at the Sunraysia sites where total biovolume was usually lowest. Gregor et al (2005) found that
picoplanktonic cyanobacterial presence could be detected using in-situ phycocyanin fluorometry in Czech reservoirs; while Pemberton et al (2007) found that they were not well detected and were underreported by fluorescence in Lake Ontario. Both authors were using the same equipment (bbe
Fluoroprobes). Seppälä et al (2007) also reported that the contribution of picoplanktonic cyanobacteria in the Baltic Sea to phycocyanin fluorescence was low.
Gregor et al 2005 have suggested that dense concentrations of cyanobacteria where chloro
concentrations exceed 50 to 60 μg L-1 may present difficulty in quantification using in-situ fluorometrdue to self shading effects and reabsorbance of fluoresce
to
phyll-a
y, d wavelengths. In the Murray River study,
-1 re
ested
p
total chlorophyll-a concentrations as measured in-situ by the YSI, never exceeded 30 μg L and we
frequently less than 10 μg L-1, so this was never an issue. However there was considerable noise inthe phycocyanin signal throughout the biovolume range, and especially when cyanobacterial biovolume was below 0.4 mm3 L-1 (Figure 8, log (0.4 + 1) = 0.146) or 0.6 mm3 L-1 (Figure 8, log (0.6 +
1) = 0.204). These two values coincide with the Amber alert level in the recreational guidelines (National Health and Medical Research Council 2008) and the High alert level in the Alert LevelsFramework for raw water used for potable supply (Newcombe et al 2010), respectively. Bastien et al
(2011) determined a minimum detection limit of 1500 cells mL-1 of Microcystis aeruginosa (equivalent to a biovolume of 0.057 mm3 L-1 (log(x + 1) = 0.020)) and a minimum quantification limit of 5,000 cells mL-1 of M. aeruginosa (equivalent to a biovolume of 0.19 mm3 L-1 (log(x + 1) = 0.076)) for identical YSI
phycocyanin fluorometric equipment in laboratory studies. McQuaid et al (2011) calculated a detection limit of 0.2 RFU (log(x + 1) = 0.079) and a limit of quantification of 0.7 RFU (log(x + 1) = 0.230) for YSI phycocyanin equipment in field studies. Using bbe Fluoroprobe equipment, Izydorczyk
et al (2005) suggested a cyanobacterial biovolume greater than 0.2 mm3 L-1 as adequate to obtain afluorescence signal. The quantification limits of Bastien et al (2011) and McQuaid et al (2011) are lower than the Amber alert for recreation and the High alert for raw water for potable supply sugg
from the Murray River data. Because a lot of the data collected during the Murray River study had low cyanobacterial presence and were close to the quantification limits, this may also add to the variability displayed by these data.
One problem encountered in this study was that of calibrating the YSI instrument. Calibration methods using laboratory grown cultures of Microcystis aeruginosa have been described by Bastien et al (2011), however such calibrations are not possible in remote field locations. Calibration using
commercially available phycocyanin can also be performed, but again there is a problem in making uaccurate standards at remote field locations. Brient et al (2008) has also noted the difficulty in calibrating phycocyanin fluorometry probes due to the lack of pure phycocyanin for use as a standard,
and Seppälä et al (2007) note that the lack of a practical laboratory based analytical method for phycocyanin determination due to low extraction efficiency also complicates the interpretation of phycocyanin data measured by field based fluorometry. However Seppälä et al (2007) used solid
secondary standards to check the stability of their Turner Designs phycocyanin and chlorophyll-a fluorescence probes. For this study, there was therefore a need to use the calibration of the instrument as it was when received from the manufacturer. Bastien et al (2011) found excellent
stability in the calibration of their YSI instrument over the entire period of time (2 years) of their evaluation. Because we used a brand new YSI instrument identical to that of Basiten et al (2011) forour study, we assume that there would also have been very little drift from the factory calibration of
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
43 | NSW Office of Water, August 2012
this instrument, but would recommend the use of solid standards to enable calibration checks to be undertaken in remote areas in future.
5.2. Use of Chlorophyll-a fluorometry for cyanobacterial bloom management.
There was very poor correlation between cyanobacterial biovolume and chlorophyll-a conc
measured by in-situ fluorometry with the YSI equipment in this study, indicating that chlorophyll-a fluorometry is not an appropriate means of estimating cyanobacterial presence in the Murray RiveThere are a number of possible reasons for this poor result, the first most likely to be the presence of
eukaryotic species of phytoplankton, all of which contain chlorophyll-a, being present and to the fluorometric signal but not to cyanobacterial biovolume estimation
entrations
r.
contributing s. The amount of additional
n
yotes,
yk et
r
re 07) alis
er
eukaryotic phytoplankton biovolume that was present in the Murray River at the time of the field
measurements was not determined. Secondly, fluorometric detection of chlorophyll-a in cyanobacteriais impeded by the structure of their photosynthetic apparatus, as most chlorophyll-a is contained withitheir Photosystem 1, and this contributes little to fluorescence (Beutler et al 2003, Seppälä et al 2007).
Seppälä et al (2007) also found that chlorophyll-a was not suitable for detecting cyanobacterial distributions in the Baltic Sea as it was masked by high chlorophyll-a fluorescence from eukarand found a poor fit between laboratory measured chlorophyll-a and fluorescence measured
chlorophyll-a when a lot of cyanobacteria were present in total phytoplankton biomass. Lzydorczal (2009) also found fluorescence measurements of chlorophyll-a to be lower than laboratory measurements.
5.3. Temporal and spatial variability in cell size.
Hawkins et al (2005) have discussed the analytical error caused by sampling, counting and measuring cell size when estimating the total cyanobacterial biovolume of samples. Further error would occu
due to temporal and spatial variation in within-species cell size if using standard cell sizes instead, andthis may account for some of the site by site variability shown in the data from this study. Some sites may at times have predominantly smaller or larger sized cells than other sites and these may also
differ in size from the published cell sizes, leading to differences in the regression slopes determined between phycocyanin and biovolume at different sites. Preservation of the samples with Lugols iodine solution may also have caused changes in cell size from the live condition (Hawkins et al 2005),
producing further variation.
The linear dimensions of several taxa measured in this study from the Murray and Darling River were compared with published data for other locations, mainly from nearby locations in South Australia,
including the River Murray. Baker (1991) reported average cell breadth (width) for Anabaena circinalis from several sites ranging between 6.76 and 7.59 microns, with minimum and maximum cell breadth from the 5 sites reported ranging from 5.8 to 8.5 microns. The width of A. circinalis cells reported for
our study was generally smaller than this, with a mean of 5.53 microns (Table 6). Baker (1991) also reported the length of the cells to be generally shorter than their breadth. In our study, the length of the A. circinalis cells was infrequently measured, so that the cells had to be assumed to be near
spherical. This would lead to a slight overestimation of cell volume compared to if the volumes wecalculated on cells which were slightly flattened oblate spheres. Interestingly Zapomĕlová et al (20provide cell width (8.4 to 12.1 microns) and length (5.0 to 11.2 microns) measurements for A. circin
measured in the Czech Republic, which are generally larger than any of the Australian measurements. The cell length and width measurements of A. circinalis provided in taxonomic descriptions (Geitl1932 and Komárek 1995, cited by Zapomĕlová et al 2007) are also generally larger than the
Australian measurements.
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
44 | NSW Office of Water, August 2012
Baker (1991) also presents data for the cell length and breadth of A. aphanizomenioides and A. planktonica (A. solitaria f. planktonica). Baker’s description of these species also indicates that the
an those reported by Baker (1991), although the range in cell lengths reported similar to those reported by him. However our cells were generally reported as
within ). x
rts the cell diameters of two Chroococcalean species, Microcystis aeruginosa f. flos-
s flos-aquae and reggor and Fabbro, 2001).
,
ese species were all considerably smaller than those provided by the
cells of these species are mostly shorter than they are wide, whereas our measurements indicated
usually slightly the opposite. The cell widths measured for our sites along the Murray and DarlingRivers were smaller thfor both species were
being longer than wide, whereas Baker (1991) indicated that the opposite is true for both species. Whether these differences are due to actual size differences, misidentification and measurement errors or cell size and shape contortions due to the Lugols iodine preservative (Hawkins et al 2005)
are not known.
Baker (1991) also reports morphological data for two other species of Nostacales, Aphanizomenon issatschenkoi (now known as Cuspidothrix issatschenkoi – Rajaniemi et al (2005))) and
Cylindrospermopsis raciborskii. Although Aphanizomenon was only reported to genus level of taxonomy in this study, C. issatschenkoi is very common in the Murray River and most of the Aphanizomenon sp(p). reported here is likely to be comprised of this species. The cell widths of
Aphanizomenon sp(p). and Cylindrospermopsis raciborskii measured in this study (Table 6) are the ranges reported for Cuspidothrix issatshenkoi and Cylindrospermopsis raciborskii by Baker (1991The cell lengths for Aphanizomenon sp(p). were shorter (Table 7) than those reported for Cuspidothri
issatshenkoi by Baker (1991), although the cell lengths for Cylindrospermopsis raciborskii were similar.
Baker (1992) repo
aquae and Microcystis incerta. These have now been renamed MicrocystiAphanocapsa incerta respectively (Komárek and Anagnostidis, 1999; McGAgain, in this study of cyanobacterial cell size from the Murray and lower Darling Rivers, Aphanocapsa
was only reported to genus level of taxonomy, but Aphanocapsa incerta is considered to be one of the most commonly occurring species of this genus in these water bodies, so it is assumed that the measurements of Aphanocapsa sp(p) mostly comprise measurements of Aphanocapsa incerta. The
range and mean measures of cell diameter in M. flos-aquae in this study were slightly less than the values reported for M. aeruginosa f. flos-aquae by Baker (1992), and also for the range in cell diameter reported for this species from a Spanish reservoir (Sanchis et al 2004). However the range
and mean value reported in our study for Aphanocapsa sp(p) were similar to those reported for M. incerta by Baker (1992).
Although some species measured in this study had smaller mean linear dimensions than those
reported elsewhere (Baker 1991, 1992), the mean linear dimensions of other species were similar to those reported by Baker (1991, 1992). In addition, the equipment had been calibrated prior to measurements using 1 micron and 6 micron mean diameter microspheres. This suggests that factors
other than systematic mismeasurement may be responsible for the smaller linear cell size measurements, such as large morphological heterogeneity within a species (Zapomĕlová et al 2008b2010) or cell shrinkage due to preservative (Hawkins et al 2005).
The mean cell volumes determined for a number of commonly occurring species of cyanobacteriafrom the Murray and Darling Rivers in this study differ considerably from mean cell volumes publishedelsewhere, such as in the “Biovolume Calculator” (Victorian Department of Human Services, 2007).
This “Calculator” provides standard cell volumes for many species determined from specimens collected from a range of south-eastern Australian locations by the Australian Water Quality Centre. Because of the smaller linear cell measurements obtained in this study for Anabaena circinalis, A.
planktonica, A. aphanizomenioides, A. planktonica and Microcystis flos-aquae, the mean cell biovolumes calculated for th
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
45 | NSW Office of Water, August 2012
“Biovolume Calculator”, although the cell volume for Cylindrospermopsis raciborskii in our study wlarger.
The effect of only small differences in mean linear measurements made on cells have increasingly
greater effects on the volumetric measurements calculated as cell size increases. Even differences incell diameter of 1 to 2 microns can have marked differences on calculated cell volume in species with cell diameters greater than 5 microns. For example, the average cell volume calculated for Anabaena
circinalis in this study was 109 μm3 (with a 10th to 90th percentile range of range of 41 to 202 μm3 – Table 5). In comparison, the cell volumes calculated (for oblate sphere shaped cells) for this speciefrom the average cell width and length data provided by Baker (1991) for five locations in the Murray
Darling Basin an
as
s
d Adelaide (Murtho, Swan Reach, Myponga Reservoir, Hope Valley Reservoir,
of the
s for
ile
ples
ide variations in vegetative cell morphology have been reported include for Microcystis
al
’Farrell et al (2007) reported that cyanobacteria of the same species can r
ons cell
e more elongated at low phosphorus concentrations. Different nitrogen sources have been shown to cause variations in the vegetative cell length of Cylindrospermopsis raciborskii, with
Chaffey Reservoir) ranged between 119 and 191 μm3, and for mean data for the Czech Republic (Zapomĕlová et al 2007) which gave a cell volume of 436 μm3. Likewise using the middle
minimum-maximum cell width and length ranges of Geitler (1932) (which was 8.0 to 14.0 micronboth width and length, cited in Zapomĕlová et al 2007) gives a cell volume of 697 μm3. For Microcystis flos-aquae, the average cell volume from our study was 10.5 μm3 (10th to 90th percent
range of range of 4 to 16 μm3 – Table 5), whereas the cell volumes calculated from the mean diameter measurements of Baker (1992) for samples from Lake Makoan (Victoria), Lake Mulwala (Victoria /NSW) and Mt Bold Reservoir (South Australia) were 19.7, 35.3 and 31.3 μm3 respectively. Data from
a eutrophic Spanish reservoir (Sanchis et al 2004) gave a range of 14.1 to 47.7 μm3. Other examwhere waeruginosa from East African lakes (Haande et al 2008) and for Cylindrospermopsis raciborskii from
lakes in Uruguay (Vidal and Kruk 2008).
A range of environmental and growth conditions may lead to morphological variations within species of cyanobacteria (Lyra et al 2001), the most notable being changes in morphology between wild and
cultured specimens of the same species. For example, change in cell size, colony morphology and loss of the colonial aggregation of cells occurs in Microcystis species while in culture (Sanchis et2004), while some species of Anabaena that have coiled trichomes in wild populations can form
straight trichomes when grown in culture (Zapomĕlová et al 2008a).
Possible environmental factors that could influence the cell size of planktonic cyanobacterial species in wild populations include
Light limitation. Odisplay a high morphological variation under different underwater light conditions. Largesized cells of Cylindrospermopsis raciborskii and Planktothrix agardhii grew in mesocosms
under high light intensity, while sites with acute light constraints presented small unicellular, thin filamentous or small tabular colonial cyanobacteria. Zapomĕlová et al (2010) reported that decreased light intensity led to decreased cell size in an Anabaena flos-aquae strain,
although most Anabaena strains did not exhibit any responses in cell morphology to light intensity.
Temperature. Cell volume increases in Microcystis flos-aquae and Snowella have been
shown to be positively correlated with temperature (Morabito et al 2007). Zapomĕlová et al (2008a, 2010) found larger cells at higher temperatures in strains of Anabaena circinalis. Aktan et al (2009) reported the largest cell sizes in Microcystis aeruginosa occurred in late
summer when water temperatures were warmest.
Nutrient availability. Zapomĕlová et al (2008a, 2010) reported that phosphorus concentratihad significant effects on the vegetative cell dimensions of 6 strains of Anabaena, with
length and width generally decreasing as phosphorus concentrations increased. Cells wer
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
46 | NSW Office of Water, August 2012
ammonia in particular causing cell elongation (Saker and Neilan 2001). In comparison Zapomĕlová et al (2008a) found nitrogen had no effect on cell morphology in strains of
om. also
as
wska
007) reported a large range of morphological variability,
n
d a easurements and cell volumes
anodictyon sp(p). with water temperature. The cell size of A. circinalis has
d
.
g ould be a result of temperature, as dissolved oxygen saturation concentrations
ns
not observed in this study. In contrast
Anabaena, due possibly to their ability to fix atmospheric nitrogen.
Salinity. Changes in cell size and shape in response to salinity changes have been reported by Garcia-Pichel et al (1998), although this study was confined to unicellular coccoid marine orhalotolerant species that did not grow in freshwater.
Seasonal or life cycle stage. Alster et al (2010) reported considerable morphological plasticitywithin Cylindrospermopsis raciborskii, both between populations from different locations and even within a single population. The diameter of filaments was greatest earliest in the bloom
after new trichomes had emerged from akinetes, and again in autumn at the end of the bloSmallest diameters occurred at the peak of the bloom in late summer. Moore et al (2004) suggested morphological plasticity in C. raciborskii due to different stages of its life cycle.
Grazing pressures from zooplankton. A remarkable morphological transformation wreported in the cyanobacterium Cyanobium sp. in the presence of a protistan grazer (Jezberová and Komárková, 2007). The species, which is usually present as singular cells in
the absence of the grazer, aggregated into microcolonies and produced helical structures or “spinae” extending outwards from their surfaces when the grazer was present. However cell size did not change much whether the grazer was present or absent. Phormidium sp. has
also been reported to change colony shape as a defense against a ciliate grazer (Fiałkoand Pajdak-Stós, 2002), although any variation in cell size was not reported.
Genetic diversity. Haande et al (2
especially in cell diameter, in 24 strains of Microcystis aeruginosa from lakes in East Africa, with at least 4 different genotypes present. They also found some correspondence betweethe morphotypes and the genotypes of the strains.
The examination of whether any environmental factors were causing the variation in cell size of the most commonly occurring taxa from the Murray and Darling Rivers observed in this study indicateweak but statistically significant positive correlation of the linear cell m
of several taxa, including A. circinalis, A. planktonica, A. aphanizomenioides, Aphanizomenon sp(p)., Aphanocapsa sp(p). and Cybeen reported to increase as water temperature increases (Zapomĕlová et al, 2008a, 2010). Morabito
et al (2007) reported a similar effect for Microcystis flos-aquae, but this was not indicated for this species in our study. Water temperature varies diurnally in the field depending on the time of day anthe weather, and this may have some effect on the responses of the cyanobacterial species with
respect to their cell size. However seasonal variation in water temperature is likely to be greater thanmost diurnal variation, and this study looked at cell size changes across a period of almost 6 monthsCell size changes due to temperature would therefore be more likely to be due to seasonal effects
than diurnal variation. Decreases in some cell size measurements in some species with increasindissolved oxygen cdecrease as water temperature rises. Most of the taxa that indicated cell size changes due to
dissolved oxygen also displayed an inverse response to temperature. Increasing turbidity would leadto a reduction in the amount of underwater light available to the cyanobacterial cells at some locatioalong the Murray and Darling River, and could result in a decrease in the cell size of some species
(O’Farrell et al 2007, Zapomĕlová et al, 2010), but this washowever, the linear dimensions of both A. planktonica and A. aphanizomenioides were found to increase as turbidity increased. Increasing salinity (measured as electrical conductivity) could have
caused decreases in some of the cell size measurements of A. circinalis and M. flos-aquae, but the range in salinity in the Murray River is very small compared to the salinity changes reported to have caused changes in cyanobacterial cell size and shape by Garcia-Pichel et al (1998). Microcystis flos-
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
47 | NSW Office of Water, August 2012
aquae may have smaller sized cells when large amounts of this or other species of cyanobacterpresent, possibly due to within-species or between-species competition for resources during bloperiods. Perhaps cell division leads to smaller cells when resources such a
ia are om
s nutrients are limited and
environmental variable does not
t
y
enioides in
s
gation of the cells of Anabaena species where cell length is greater than width has been reported by Zapomĕlová et al (2008a, 2010) as a
shed
hed
mic
need to be shared amongst a large population, although Zapomĕlová et al (2008a, 2010) consider cells of some Anabaena species grow larger under phosphorus limitation as they do not divide. Morabito et al (2007) reported that many species of cyanobacteria show variability in cell morphology
across a season, but the overwhelming controlling role of a single always emerge. In this study of cyanobacteria from the Murray River, cell volume changes occurred in several taxa which were correlated with environmental factors, which also supports the hypothesis of
environmental influences on cell size.
Nutrients, and in particular phosphorus, have been indicated as a factor influencing the size of some species of cyanobacteria, especially species of Anabaena (Zapomĕlová et al, 2008a, 2010). There
were too few nutrient data available for the Murray River during this study to examine whether this was a cause of the smaller cell sizes and variability measured in the commonly found cyanobacterialspecies in the river, especially Anabaena. However, when measured at five locations, nutrien
concentrations generally indicated upper mesotrophic to low eutrophic conditions (median total nitrogen = 360 μg L-1, median total phosphorus = 29 μg L-1). If increasing phosphorus concentrationsinduce a decrease in the cell size of Anabaena species (Zapomĕlová et al, 2008a, 2010), the
reasonably low phosphorus concentrations present in much of the Murray River for much of the studperiod should lead to larger sized cells, not the smaller sized cells observed. However the vegetative cell shape reported by Baker (1991) for A. circinalis, A. planktonica and A. aphanizom
Australia was most commonly (but not always) a slightly compressed oblate sphere shape, as it wafor A. circinalis from the Czech Republic (Zapomĕlová et al, 2007). In comparison, in this study, cell measurements for these three Anabaena species indicated slightly elongated, prolate sphere shaped
cells, with a length to width ratio slightly greater the 1.0. Elon
response to low phosphorus concentrations, so possibly the mesotrophic to low eutrophic total
phosphorus concentrations in the Murray River lead to elongated Anabaena cells in the species commonly occurring there.
It is of concern that this study found large variations in the linear cell measurements and especially in
the cell volumes calculated for many of the taxa. One possible reason for this is the misidentification of some taxa, especially where very large variations (much bigger or much smaller) from the publidimensions for these taxa (e.g. Baker 1991, 1992) were reported. Excluding these outliers in the top
and bottom 10 percentile ranges excluded obvious errors and brought the data closer to the publisdescriptions, but the range in the remaining cell measurements (the 10th to 90th percentile range) were often still smaller than the published range. The project was carried out in an operational laboratory
(the Water Environmental Laboratory of the NSW Office of Water) whose function is to provide, as rapidly as possible, cyanobacterial identification, count and biovolume data for management use, especially during cyanobacterial bloom periods. It is not a research laboratory dedicated to taxono
research into cyanobacteria. During this study a major bloom occurred in the Murray River (Al-Tebrineh et al 2012), resulting in a high demand for up-to-date information, and the need to process many samples per day. With this pressure, it is likely that some species may be misidentified in some
samples.
Baker (1991) reported a range of different coiled species of Anabaena that co-existed in the NSW section of the Murray River, with cell diameters ranging from 4.0 μm (A. flos-aquae) to 12 μm (A.
crassa). The only coiled species of Anabaena reported in this current study was A. circinalis, suggesting that possibly any other coiled species of Anabaena may have been misidentified as A. circinalis, and leading to the broad range of cell size for A. circinalis reported in this study. However
full sequencing of the 16S ribosomal RNA gene on several samples collected from both the 2009 and
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
48 | NSW Office of Water, August 2012
2010 Murray River bloom by the University of New South Wales matched almost exactly the sequences of other A. circinalis deposited in the GenBank database (http://www.ncbi.nlm.nhi.gov/Genbank) (Brett Neilan, UNSW, pers. comm.). Adding to the problem of
identification was that many samples contained, for the Nostocales, trichomes that contained only vegetative cells, and no akinetes or heterocysts to aid in the identification of these trichomes to species. A combination of morphological features, not just cell size but also coil dimensions and
akinete features are also required for a reliable identification of closely related taxa (Peter Baker, personal communication).
A number of authors have reported large variations in the morphological features of a number of
cyanobacteria isolated from field samples – for example Haande et al (2007) for Microcystis
le
ons
ps
g the
oo can vary in response to growing conditions, especially nutrients
or
sent,
stimates may lead at times to the declaration of a Red alert for recreational water use lic
aining ntly
e
aeruginosa, Alster et al (2010) and Vidal and Kruk (2008) for Cylindrospermopsis raciborskii, and Zapomĕlová et al (2007, 2008a,b, 2010) for Anabaena species. Problems in the identification of
Microcystis aeruginosa in field samples have arisen due to variations in response to environmental changes (Haande et al 2007), and due to morphological variations that frequently occur in definaband recognizable phenotypes (Sanchis et al 2004). Traditional cyanobacterial taxonomy uses
morphological characteristics to establish differences between individuals within natural populatiand separate them into groups. Such groupings are now sometimes referred to as “morphospecies” rather than species (Sanchis et al, 2004; Zapomĕlová et al, 2007, 2008a, 2010). Large overla
between different morphospecies of Anabaena have been reported by Zapomĕlová et al, (2007, 2008a, 2010), with traditional morphospecies not distinctly defined by their morphological characteristics. No reliable vegetative morphological criteria exist to distinguish between some
species (Zapomĕlová et al 2008a), with Zapomĕlová et al (2007) reporting most coiled Anabaena morphospecies formed a morphological continuum rather than representing distinct species. There exists a potential for a single Anabaena strain to span the total variability of all relevant morphospecies
or morphospecies complexes (Zapomĕlová et al 2010). Zapomĕlová et al (2007) also report that genetic methods such as 16S rRNA PCA do not distinguish well between Anabaena species (morphospecies), showing them to be highly similar. The extent of coiling of trichomes, includin
symmetry of the coil, has also been questioned as a reliable taxonomic feature to distinguish Anabaena species, as this t(Zapomĕlová et al, 2007, 2008a, b). Such variability within the genus Anabaena in particular must
pose considerable problems in the identification of the different morphospecies in laboratories such asthe NSW Office of Water’s Water Environmental Laboratory, and invariably leads to many similar morphospecies being identified together as a single taxon. This may also account for the wide
variability reported in the cell sizes of the Anabaena species in particular in this study.
The large variations found in the cellular volumes of many of the commonly occurring cyanobacterial taxa from the Murray River does however raise concerns about the utility of using standard cell sizes
to calculate total cyanobacterial biovolumes, especially as the average cellular volumes calculated fthe main Anabaena and Microcystis species in this study were considerably smaller than those supplied in the “Biovolume Calculator” (Victorian Department of Human Services, 2007). Use of the
“Biovolume Calculator” would considerably overestimate the total cyanobacterial biovolume preas indicated in Figure 6 where biovolumes calculated from actual laboratory cell size measurements were compared with biovolumes for the same samples estimated using the “Biovolume Calculator”.
Such overe(National Health and Medical Research Council, 2008) leading to restriction in water use by the pubwhen in fact a Red alert may not be applicable.
Unfortunately undertaking cell size measurements is time consuming and requires specialist trand equipment, and undertaking them on all major taxa in all samples is simply not feasible. Currethe only means to obtain quick total cyanobacterial biovolume estimates for every sample is to use th
“Biovolume Calculator”. However it may be feasible to produce lists of standard cell sizes for
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
49 | NSW Office of Water, August 2012
particular water bodies such as the Murray River, where these are known to differ markedly frvalues provided in the “Biovolume Calculator”. Alternatively any overbiovolume through use of the “Biovolume Calculator” may be seen as an additional safety factor for
the management of algal blooms when issuing Red alerts.
6. Conclusions
Phycocyanin fluorometry w
om the estimation of total cyanobacterial
ould be a useful tool for the in-situ measurement of cyanobacterial
n
rial
or
f
rial
ance
he
ze.
iovolume because the actual cell sizes of some major species occurring in NSW are actually smaller than the published standard cell size. This
abundance (as biovolume) which can be used for management purposes (comparisons against
guidelines). The main conclusions from the study are:
In-situ fluorometry is best undertaken in waters where the turbidity is less than 50 NTU.
There needs to be a sufficient cyanobacterial abundance (biovolume) present to provide a
adequate phycocyanin signal (at least > 0.4 mm3 L-1 – the Amber alert threshold).
The reduced ability to determine cyanobacterial presence below the Amber alert level
because of the high variance in phycocyanin and biovolume measurements is of low consequence, as this does not represent a cyanobacterial management problem anyway.
The relationship between phycocyanin measured by the YSI instrument and cyanobacte
biovolume may not be the same for all of NSW, but may vary slightly from river to river, even between different parts of the same river.
The variance between different rivers and sites may also be less of a consequence – itdepends on how the data are to be used – especially if only for the rapid determination o
alert levels for management purposes. (Laboratory measurement of biovolume is also subject to considerable variance and error).
Chlorophyll-a measurements by fluorometry do not provide good estimates of cyanobactepresence, probably because of considerable chlorophyll-a amounts also contributed by
eukaryotic algae present.
In-situ fluorometry should provide an initial warning of increasing cyanobacterial abund
and the likely need for management actions. Sampling and laboratory based analysis of tcyanobacteria present will still be required to confirm the in-situ assessments, and to identify
the species responsible, especially if they are known potentially toxic species.
Conclusions arising from the investigation of the spatial and temporal variance in cell size are:
The cell size of the major cyanobacterial taxa that occur in the Murray River may vary over time, especially in response to water temperature. Other environmental factors that were
measured during this study had little effect on cell size. Nutrients, especially phosphorus, were seldom measured and when they were, concentrations were generally in the mesotrophic to low eutrophic range. Low phosphorus concentrations may affect cell si
The misidentification of some species, especially coiled Anabaena species, may have led to
the broader range of cell sizes reported in this study than actually recorded in the literature. Misidentifications are difficult to avoid when a full range of morphometric features, includingthe presence of akinetes, are lacking to aid more positive identifications.
The use of standard cell size tables to calculate total cyanobacterial biovolumes for algal
samples is likely to overestimate this b
may lead at times to Red alerts being declared when the actual total cyanobacterial
biovolume is still below the Red alert threshold. However this would add an extra margin of safety in the management of cyanobacterial blooms, given the large amount of error already
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
50 | NSW Office of Water, August 2012
inherent in the sampling and laboratory analysis of cyanobacterial samples, and conversions of cell counts to biovolume.
The measurement of actual cell sizes of the major taxa occurring in every sample is currenimpractical, given the large amount of additional ti
tly me required to do these measurements.
While such measurements may improve the accuracy of biovolume estimations compared to tables, it also needs to be realised that there remains a large ng these cell size measurements with the technology available.
d by the “Biovolume
ycocyanin fluorescence data every 10 to 30
ous
struction and Standard Operating Protocol or
al
s quickly as possible after measurement so that it can be
ith the actual ults
nce will
I,
obacterial taxa to
an improved understanding of regional heterogeneity in cell sizes. Such work will rely
the use of standard cell sizemargin for error in just maki
Due to time and cost constraints, the use of standard cell size tables for biovolume
estimations remains the most practical method, although locally derived cell size tableswould be more applicable for NSW compared to those provideCalculator” if they can be developed.
7. Recommendations
The following recommendations can be made resulting from the findings of this study:
The use of phycocyanin fluorometry to detect cyanobacterial blooms should be adopted in
inland waters across NSW where blooms are likely to occur, and where the turbidity of thesewaters is generally less than 50 NTU.
Data collection should be based on collecting ph
seconds over a period of 5 minutes at each site, auditing out any outlying data or erronedata due to data sonde equilibration or probe cleaning, and the average phycocyanin measurement, in RFU, calculated.
The Office of Water should develop a Work In
procedure for the fluoroprobes that incorporates these recommendations
The results from this in-situ monitoring need to be supplied to coordinators of the Region
Algal Coordinating Committees aassessed and used for cyanobacterial bloom management, including the preliminary release
of Red alerts if the fluorometric results indicate high cyanobacterial concentrations likely to be in excess of the Red alert thresholds.
The in-situ assessment of cyanobacterial abundance needs to be followed up wcollection of algal samples for confirmation by laboratory analysis where the in-situ res
indicate an increasing abundance, or a biovolume in excess of Red alert thresholds.
The newly purchased Hydrolab Data Sonde 5s which are equipped with Turner Designs
fluorometric probes for phycocyanin and for chlorophyll-a should be employed on a state wide basis to collect in-situ data for cyanobacterial management, but their performa
need to be assessed to see if they also present similar problems to the in-situ use of the YSincluding giving false positives in turbid water, and variable results when cyanobacterial abundance is low.
Because there are currently no other viable alternatives, the “Biovolume Calculator” should
continue to be used in NSW as the basis of converting cell counts of cyanbiovolume estimates.
Consideration needs to be given to developing locally derived standard cell size tables for the major cyanobacterial taxa occurring in NSW, to eventually replace the use of the
“Biovolume Calculator”.
The best approach to achieving locally relevant standard cell size tables may be to continue
building a database of cell volumes from different parts of Australia over time that will allow
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
51 | NSW Office of Water, August 2012
critically on the accurate identification of the taxa, and the measurement of their cell size by experienced phycologists. The use of digitised images will assist the delivery of accurate cell size measurements,
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
52 | NSW Office of Water, August 2012
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cyanobacterial bloom, using phycocyanin as a level determinant. The Journal of Microbiology, 45, 98 – 104.
Aktan, Y., Luglié, A., and Sechi, N. (2009). Morphological plasticity of dominant species in response to nutrients dynamics in Bidighinzu Reservoir of Sardinia, Italy. Turkish Journal of Fisheries and Aquatic Sciences, 9, 137 – 144.
Alster, A., Kaplan-Levy, R.N., Sukenik, A., and Zohary, T. (2010). Morphology and phylogeny of a non-toxic invasive Cylindrospermopsis raciborskii from a Mediterranean Lake. Hydrobiologia, 639, 115 – 128.
Al-Tebrineh, J., Merrick, C., Humpage, A., Bowling, L., and Neilan, B.A. (2012). Community composition, toxigenicity and environmental conditions during a cyanobacterial bloom occurring along 1,100 kilometers of the Murray River. Applied and Environmental Microbiology, 78, 263 – 272.
Anderson, M.J., Gorley, R.N., and Clarke, K.R. (2008). PERMANOVA+ for PRIMER: Guide to software and statistical methods. (PRIMER-E, Plymouth, UK.)
Baker, P. (1991). Identification of common noxious cyanobacteria Part I – Nostocales. Research Report No. 29. (Urban Water Research Association of Australia, Melbourne).
Baker, P. (1992). Identification of common noxious cyanobacteria Part II – Chroococcales, Oscillatoriales. Research Report No. 46. (Urban Water Research Association of Australia, Melbourne).
Bastian, C., Cardin, R., Veilleux, É., Deblois, C., Warren, A., and Laurion, I. (2011). Performance evaluation of phycocyanin probes for the monitoring of cyanobacteria. Journal of Environmental Monitoring, 13, 110 – 118.
Beutler, M., Wiltshire, K.H., Meyer, B., Moldaenke, C., Lüring, C., Meyerhöfer, M., Hansen, U.-P., and Dau, H. (2002). A fluorometric method for the differentiation of algal populations in vivo and in-situ. Photosynthesis Research, 72, 39 – 53.
Beutler, M., Wiltshire, K.H., Arp, M., Kruse, J., Reineke, C., Moldaenke, C., and Hansen U.-P. (2003). A reduced model of the fluorescence from the cyanobacterial photosynthetic apparatus designed for the in-situ detection of cyanobacteria. Biochimica et Biophysica Acta, 1604, 33 – 46.
Brient, L., Lengronne, M., Bertrand, E., Rolland, D., Sipel, A., Steinmann, D., Baudin, I., Legeas, M., Le Rouzic, B., and Bormans, M. (2008). A phycocyanin probe as a tool for monitoring cyanobacteria in freshwater bodies. Journal of Environmental Monitoring, 10, 248 – 255.
Clarke, K.R., and Gorley, R.N. (2006). PRIMER v6: User manual/tutorial. (PRIMER-E, Plymouth, UK).
Fiałkowska, E., and Pajdak-Stós, A. (2002). Dependence of cyanobacteria defense mode on grazer pressure. Aquatic Microbial Ecology, 27, 149 – 157.
Garcia-Pichel, F., Nübel, U., and Muyzer, G. (1998). The phylogeny of unicellular, extremely halotolerant cyanobacteria. Archives of Microbiology, 169, 469 – 482.
Gregor, J., and Maršálek, B. (2005). A simple in vivo fluorescence method for the selective detection and quantification of freshwater cyanobacteria and eukaryotic algae. Acta Hydrochimica et Hydrobiologica, 33, 142 – 148.
Gregor, J., Geriš, R., Maršálek, B., Heteša, J., and Marvan, P. (2005). In-situ quantification of phytoplankton in reservoirs using a submersible spectrofluorometer. Hydrobiologia, 548, 141 – 151.
Gregor, J., Maršálek, B., and Śípková, H. (2007). Detection and estimation of potentially toxic cyanobacteria in raw water at the drinking water treatment plant by in vivo fluorescence method. Water Research, 41, 228 – 234.
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Haande, S., Ballot, A., Rohrlack, T.Microcystis aeruginosa is
, Fastner, J., Wiedner, C., and Edvardsen, B. (2007). Diversity of olates (Chroococcales, Cyanobacteria) from East-African water
Hawkins, iovolume ’s Iodine. Harmful Algae, 4, 1033 – 1043.
Hötzel, G., and Cro n methods manual for Australian freshwaters.
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Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
55 | NSW Office of Water, August 2012
Appendix A – Box Plots
Box Plot of Biovolume (tables) grouped by NameA
Co
Mu
Ya
rra
w
Co
To
cu
Pic
nic M
o
Ba
Mu
rra
y D
o
Ko
ra
To
ol Eu
Mt D
isp
e
Bu Me
Cu
r
Fo
rt C
L
Ma
t
Mo
ul
lb ey st rs
i
ron
g
r lw
ou
ra o ho
u
am
ein
Kya
lite
ury
row
a
lwa
la
on
ga
bra
m
mw
al
Po
int
am
a
rha
m
wn
s
leig
h
bu
c
on
on a
be
in aa
ge
ck 8 ra
Name
-2
0
2Bio
v
4
6
8
10
olu
mb
les)
e (
ta
Box Plot of Phycocyanin (RFU) grouped by Name
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wa
l
Pic
nic
Po
int
Mo
am
a
Ba
rha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mt D
isp
ers
ion
Bu
ron
ga
Me
rbe
in
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Ma
tho
ura
Mo
ula
me
in
Kya
lite
Name
0
1
2
3
5
6
Ph
yco
nin
(R
FU 4)
cya
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
56 | NSW Office of Water, August 2012
Box Plot of Chl-a (RFU) grouped by Name
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wa
l
Pic
nic
Po
int
Mo
am
a
Ba
rha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mt D
isp
ers
ion
Bu
ron
ga
Me
rbe
in
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Ma
tho
ura
Mo
ula
me
in
Kya
lite
Name
-1
0
1
2
3
4
5
6
7
Ch
l-a
(R
FU
)8
Box Plot of Temp grouped by Name
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wa
l
Pic
nic
Po
int
Mo
am
a
Ba
rha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mt D
isp
ers
ion
Bu
ron
ga
Me
rbe
in
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Ma
tho
ura
Mo
ula
me
in
Kya
lite
Name
10
12
14
16
18
20
22
24
26
28
30
32
Te
mp
34
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
Box Plot of DO (mg/L) grouped by Name
Alb
ury
Co
row
a
Mul
wal
a
Yar
raw
on
ga
Cob
ram
Toc
umw
al
Pic
nic
Poi
nt
Moa
ma
Bar
ham
Mu
rra
y D
ow
ns
Kor
alei
gh
Too
leyb
uc
Eus
ton
Mt D
ispe
rsio
n
Bur
onga
Mer
bein
Cu
rlwa
a
Fo
rt C
our
ag
e
Lock
8
Ma
thou
ra
Mou
lam
ein
Kya
lite
Name
5
6
7
8
9
10
11
12
DO
(m
g/L)
Box Plot of EC grouped by Name
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wa
l
Pic
nic
Po
int
Mo
am
a
Ba
rha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mt D
isp
ers
ion
Bu
ron
ga
Me
rbe
in
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Ma
tho
ura
Mo
ula
me
in
Kya
lite
Name
0
50
100
150
200
250
300
350
400
450
EC
57 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
Box Plot of Turbidity grouped by Name
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wa
l
Pic
nic
Po
int
Mo
am
a
Ba
rha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mt D
isp
ers
ion
Bu
ron
ga
Me
rbe
in
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Ma
tho
ura
Mo
ula
me
in
Kya
lite
Name
-5
0
5
10
15
20
25
30
35
40
45
50T
urb
idity
Box Plot of pH grouped by Name
Alb
ury
Co
row
a
Mu
lwa
la
Ya
rra
wo
ng
a
Co
bra
m
To
cum
wa
l
Pic
nic
Po
int
Mo
am
a
Ba
rha
m
Mu
rra
y D
ow
ns
Ko
rale
igh
To
ole
ybu
c
Eu
sto
n
Mt D
isp
ers
ion
Bu
ron
ga
Me
rbe
in
Cu
rlw
aa
Fo
rt C
ou
rag
e
Lo
ck 8
Ma
tho
ura
Mo
ula
me
in
Kya
lite
Name
6.6
6.8
7.0
7.2
7.4
7.6
7.8
8.0
8.2
8.4
8.6
8.8
9.0
9.2
9.4
9.6
pH
58 | NSW Office of Water, August 2012
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
59 | NSW Office of Water, August 2012
Appendix B – Summaries of Site by Site Correlation Analyses
Summary of significant correlations with log (x + 1) Biovolume (from tables). These are values of R rather than r2, to enable significant negative correlations to also be shown (shaded). Temp = water temperature, DO = dissolved oxygen, PCY= phycocyanin, CHL = Chlorophyll-a, Turb = turbidity, EC = electrical conductivity.
Temp DO pH Log (X + 1) PCY
Log (X + 1) CHL
Log Turb Log EC
Albury 0.7179 0.9184
Corowa 0.6409 0.9023
Mulwala 0.6116 0.8592 0.5956 0.5781 -0.6028
Yarrawonga 0.9203 0.4705 -0.5992
Cobram 0.9300 0.6118 -0.5249
Tocumwal 0.8950 0.558 -0.6446
Picnic Point 0.7340 0.5372 -0.5779
Moama 0.8325 0.4923
Barham 0.7143 0.8097
Murray Downs
0.6148
Koraleigh 0.8520
Tooleybuc 0.8077
Euston 0.8656 -0.5395
Mt Dispersion
0.7201
Buronga 0.7262
Merbein
Curlwaa
Fort Courage
0.6587 -0.5542 0.5424 0.8976 -0.8563
Lock 8 0.6797 0.6231 0.8286
Mathoura 0.7276 0.4649 -0.6143
Moulamein 0.8892 0.7142
Kyalite 0.7992
Summary of significant correlations with Log (x + 1) Phycocyanin. These are values of R rather than r2, to enable significant negative correlations to also be shown (shaded). Temp = water temperature, DO = dissolved oxygen, BV = total cyanobacterial biovolume, CHL = Chlorophyll-a, Turb = turbidity, EC = electrical conductivity.
Temp DO pH Log (x + 1) BV (from
tables)
Log (x + 1) CHL
Log Turb Log EC
Albury 0.8103 0.9184 0.5914 0.4907
Corowa 0.7423 0.9023
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
60 | NSW Office of Water, August 2012
Mulwala 0.6403 0.8592 0.6356 -0.6456
Yarrawonga 0.9203 0.4666 -0.6572
Cobram 0.9300 0.6028 -0.6057
Tocumwal 0.8950 0.5126 -0.6660
Picnic Point 0.7340 0.5618 -0.6638
Moama 0.8325
Barham 0.5285 0.8097
Murra Downs y 0.6148
Koraleigh 0 0.852
Tooleybuc 0.8077
Euston 0.8656
Mt Dispersion 0.7201 0.6117
Buronga 0.5540 0.7262 -0.5118
Merbein
Curlwaa 0. 64 55
Fort Courage 0.7251 -0.7441 0.6192 0.8976 -0.8523
Lock 8 0.7791 -0.5813 0.5863 0.8286 0.6033
Mathoura 0.7276 -0.7605
Moulamein -0.6103 0.8 68 0.6810 0.8892 0
Kyalite -0.4985 -.5250 0.7992
Summary of signifi ant correlat ns with Log (x + 1) Ch ll-a. These are values of R rather than r2, to enable signifi nt negative correlations to also be (sha mp = water temperat re, DO = dissolved oxygen, BV = total cyanobacterial biovolume, PCY = phycocyanin, Turb = turbidity, EC
conductivity.
mp H 1) BV (from
ta les)
Log (x + 1) PCY
Log Turb Log
c io lorophyca shown ded). Te u
= electrical
Te DO p Log (x +
b
EC
Albury -0.4965 0.4907 0.7467 0.6175 0.5914
Corowa -0.7213 0.7809 0.5158 0.6734
Mulwala 7 0.6356 0.686 0.5978 0.5956
Yarrawonga
Cobram 0.5319
Tocumwal 0.6208
Picnic Point
Moama
Barham
Murray Downs
Kora igh le
Tooleybuc
Euston -0.5395
Mt Dispersion 0.6117 -0.5766
Buronga -0.6533 0 0.554 -0.4997
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
61 | NSW Office of Water, August 2012
Merbein
Curlwaa 0.5 4 56
Fo e rt Courag -0.7195 -0.8563 -0.8523
Lock 8
Mathoura
Moulamein
Kyalite 0.5466
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
62 | NSW Office of Water, August 2012
Appendix C – Summaries of Site by Site Linear Regression Analyses
Tab sults of linear regression analyses f r log (x + 1) phycocyanin against log (x + 1) biovolume
(fr correlatio coefficients (R and r2), number of samples (N), the probability (P) and the sl and intercept for the regression equation:
(log biovolume (from tables)) = a + b (( g x + 1) phycocyanin).
he shaded locations are where some measurements had a turbidity of >50 NTU and these have been deleted from the regression.
Location R r2 N P Slope (b) Intercept (a)
Mean Turbidity
le of re o
om tables), including n
ope
(x + 1) lo
T
Albury 0.92 0.85 19 0.000 1.067 -0.1161 5.41
Corowa 0.90 0.81 20 0.000 1.0213 -0.146 6.70
Mulwala 0.85 0.73 19 0.000 1.349 -0.1279 3.23
D/S Yarrawonga 0.90 0.81 20 0.000 1.2815 -0.1714 5.87
Cobram 0.87 0.75 19 0.000 1.3184 -0.1367 5.18
Tocumwal 0.90 0.80 20 0.000 1.2923 -0.1266 5.55
Picnic Point 0.74 0.55 20 0.000 1.7736 -0.2768 7.55
Moama (all data) 0.85 0.73 20 0.000 1.8755 -0.3642 16.16
Moama (<50 NTU) 0.84 0.71 19 0.000 1.945 -0.3862 12.48
Barham 0.82 0.66 20 0.000 2.1257 -0.4887 12.27
Murray Downs 0.62 0.39 20 0.003 1.273 -0.2153 19.15
Koraleigh 0.86 0.74 20 0.000 1.8619 -0.4037 22.34
Tooleybuc 0.81 0.66 20 0.000 1.425 -0.2826 22.69
Euston 0.87 0.75 17 0.000 1.5458 -0.3035 15.52
Mount Dispersion 0.73 0.54 17 0.001 1.5743 -0.2236 14.09
Buronga 0.70 0.49 17 0.002 1.1258 -0.1408 11.86
Merbein 0.08 0.01 19 0.75 0.0879 0.1012 12.00
Curlwaa 0.35 0.12 19 0.14 0.3125 0.0094 16.20
Curlwaa (<50 NTU) 0.33 0.11 18 0.17 0.3767 -0.008 11.32
Fort Courage (all data) 0.71 0.50 18 0.001 0.8718 -0.1969 33.11
Fort Courage (<50 NTU)
0.90 0.81 15 0.000 1.1544 -0.2571 17.69
Lock 8 (all data) 0.70 0.49 17 0.002 1.9732 -0.4397 31.03
Lock 8 (<50 NTU) 0.83 0.68 15 0.000 2.3783 -0.5223 18.59
Lake Victoria (all data) -0.20 0.04 18 0.43 -0.1288 0.0908 170
Lake Victoria (<50 NTU)
0 All >100 NTU
Mathoura 0.73 0.53 19 0.000 1.5481 -0.2341 13.07
Moulamein (all data) 0.86 0.73 16 0.000 1.8567 -0.4431 41.42
Moulamein (<50 NTU) 0.89 0.79 11 0.000 1.6844 -0.3447 30.55
Kyalite (all data) 0.80 0.64 19 0.000 1.5257 -0.3317 34.23
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
63 | NSW Office of Water, August 2012
Kyalite (<50 NTU) 0.80 0.64 16 0.000 1.5037 -0.3156 30.63
Tolarno (all data) -0.34 0.12 17 0.18 -0.0787 0.0519 182
Tolarno (<50 NTU) 0 All >100 NTU
Pooncarie (all data) 0.08 0.01 18 0.74 0.1348 0.014 137
Pooncarie (<50 NTU) 0.96 0.93 3 0.18 2.0325 -0.4528 36.98
Burtundy (all data) 0.72 0.51 18 0.001 1.356 -0.5638 138
Burtundy (<50NTU) 0 Only 1 point <50
NTU
Ellersl a) -0.47 0.22 17 0.06 ie (all dat -0.2072 0.1213 134
Ellerslie (<50NTU) 0 Only 1 point <50
NTU
Tapio (all data) - -0.33 0.11 18 0.18 0.0911 0.0622 111
Tapi U) o (<50 NT -0.96 0.92 3 0.18 -1.4177 0.5904 39.2
All Darling River combi U) ned (<50 NT
0.69 0.48 8 0.06 1.6272 -0.4122 34.2
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
64 | NSW Office of Water, August 2012
Appendix D – Matrix of Site by Site Comparisons of Li r res s Homogeneity of Slope Analysis.
The F value (upper value) and probability (lower value) are shown for each pair-wise comparison. u (Al re along the Murray River, with the 3 Edward River sites (Mathoura, Moulamein, Kyalite) listed at o ate p ris
a significant difference in regression slope was found. Site abbreviations are explained below t Cor Mul Yar Cob Toc PP Moa Bar M
Down Kor Tool Eus M
Dis Bur Mer Cur FC L8 Math Moul Kya
nea
the end. The sha
he table.
Re
Sites are listed from
g
ded b
si
pstrxes indi
on
eamc
us
buryair-
in
) to wise
g
dow co
nstmpa
am (ons
Loc wh
k 8)ere
Alb 0.1 .777
1.7 .206
1.4 .246
1.5 .234
1.5 .231
4.14 .049
8.8 .005
9.73 .004
.35
.557 9.20 .004
2.0 .163
3.70 .062
1.56 .220 .849
.004 7.87 .008
4.13 .050 .684
.20 9.74 .004 .152
2.14 5.10 .033 .134
2.37
Cor 2.10 .154
2.00 .171
2.00 .169
2.00 .168
4.57 .039
8.00 .005
9.70 .004
.51
.480 9.80 .003
2.55 .119
3.89 .057
1.53 .225 4
.10
.75 3.939 .020
2.85 .101 8
.32
.578.99 .005
2.45 .127
5.12 .032
2..1
79 05
Mul 0.10 .785
.01
.911 .10 .821
1.07 .308
2.71 .109
3.66 .064
.03
.854 2.50 .123
.06
.811 .38 .541
.20
.657 .34 .563
8.49 .006
4.99 .032
.45
.506 4.10 .052
.257
.616 .910 .349
.194
.663
Yar .00 .875
.00
.960 1.75 .194
4.13 .050
5.20 .029
.00
.982 4.10 .051
.27
.606 .90 .356
.40
.530 .655 .20 9.11
.005 5.24 .028 .618
.25 5.52 .025 .458 .206 .479
.56 1.68 .51
Cob .000 .914
1.29 .263
2.82 .102
4.04 .052
.01
.910 3.00 .094
.12
.726 .52 .475
.25
.619 .25
7 .617.94 .008
4.55 .040
.33 2 .57
4.29 .047
.36 1.11 .302
.30 .555 .590
Toc 1.65 .207
4.10 .051
5.20 .029
.00
.959 3.90 .057
.22
.641 .80 .366
.41
.526 .25 .622
10.10 .003
5.98 .020
.30
.575 5.81 .022
.52
.477 1.66 .208
.45
.506
PP .12 .730
.43
.515 .88 .355
.04
.846 .62 .435
.25
.620 .09 .770
1 7
1.5.22
8.24 .007
5.34 .027
4 4
.82
.374 .03 .860
2.0.16
.10
.667 .3.5
1 81
Moa .514 .430
1.05 .313
.01
.914 .97 .330
.52
.478 .2 .659
2.51 3 .12
12.99 .001
8.96 .005
3.63 6
1.01 .324
.72 .39 .537
1.02 .06 .402 .321
Bar 2.57 .118
.36
.555 2.40 .130
1.93 .175
.93
.343 4.51 .041
16.14 .000
11.94 .001
6.37 .017
.18
.670 .264 .92 .345 .208
1.29 1.65
M Down
1.72 .199
.12
.734 .37 .550
.21
.653 .08
.779 4 67 .1.049
2.28 .140
.08 . . .2
.784 2.8- .104
28 .599
68 .416
3 37 .6
Evaluation of a YSI fluorometer to determine cyanobacterial presence in the Murray and Lower Darling Rivers.
65 | NSW Office of Water, August 2012
Kor 1.48 .78 .28 3.06 .231 .383 .601 .090
14.18 .001
9.68 .004
4.59 .040 .353 .42
.89 .53 2
.20
.656 .83 .370
Tool .11 .06 .46 .744 .802 .503
6.959 .012 .052 .439 .113 .776
.41
.528 .04 .839
4.04 .61 2.67 .08
Eus .00 .951
1.27 .268
13.16 .001
9.11 .005
1.96 .172
2.97 .095
.00
.996 .15 .701
.01
.916
M Dis . 84.367
9.17 .005
6.99 .013
1.18
1.87 3
.00 .05 .01 .288 .18 .967 .829 .909
Bur 5.569 .025
3.20 .01 .083 .930
5.72 .024
.74 1.84 .65
.396 .188 .427
Mer .441 .511
9.66 .004
18.40 .000
7.224 .011
13.17 .001
7.184 .012
Cur 6.21 .019
15.31 .001
4.50 .041
9.50 .005
4.36 .045
FC 8.15 .008
.97
.333 2.91 .102
.88
.356
L8 77
1. .194
1.65 .212
2.02 .166
Math 8
.0 .775
.01
.925
Moul 7
.1 .688
Alb = Albury, Cor = Corowa, Mul = Mulwala Main ftake, = immediately downstream of wonPi ic Po t, Mo = Moama, Bar = Ba ham, Dow u ow Ko or h, l = y s = Euston, M Di o isp n, Bga, Mer = Merbein, Cur = Curlwaa, FC = Fort Cour 8 k h t y al
Channel at the ou Yar Yarra ga Weir, Cob = Cobram, Toc = Tocumwal, PP = Buron
cn in a r M n = M rray D ns, r = K aleig Too Toole buc, EuK
s = M unt D ersio ur = age, L = Loc 8, Mat = Ma houra, Moul = Moulamein, a = Ky ite.