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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.

iii | NSW Office of Water, August 2012

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.........................................................................................................................


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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. ................................................................................................


v | NSW Office of Water, August 2012

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. .........................................................................................................................


vi | NSW Office of Water, August 2012

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


iii | NSW Office of Water, August 2012

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.


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,

1 | NSW Office of Water, August 2012


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



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



(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



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

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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


Ph

yco

cyan

in (

RF

U)

Chlorophyll-a data logging, Merbein, 10 February 2009

0

0.2

0.4

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


Ch

loro

ph

yll-

a (R

FU

)

Phycocyanin data logging, downstream Yarrawonga, 19 February 2009

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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


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


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.



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


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


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


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


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


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


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




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.


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.



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.



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.)



0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9


-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




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


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.




0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8


-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


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


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


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


-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


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


-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


-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).



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


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



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.



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



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.



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%)


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%)



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



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%)


issatschenkoi (12.7%)


Upper and Edward 66.8 Anabaena circinalis (19.5%)

Anabaena pla(20.0%


nktonica )

Upper and Sunraysia 49.3 Microcystis flos-aquae (28.7%)


Rhabdoderma sp. (14.1%)

Mid and Edward 73.0 No taxa decreased in abundance by more than

10%



Mid and Sunraysia 55.1 Microcystis flos-aquae (18.4%)

Rhabdoderma sp. (12.1%)




Sunraysia and Edward 49.1 No taxa decreased in bundance by more than

10% a

An lis



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



-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



-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



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.



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


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


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


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



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


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


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.



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.



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.



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


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.




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



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



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



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



-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).



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.



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


Water temperature

0.13 +ve 66 P = 0.003


Water temperature

0.18 +ve 40 P = 0.006


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


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.



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


Aphanizomenon sp(p). cell volume

0.22 +ve 54 P = 0.000


Aphanocapsa sp(p). cell volume

0.11 +ve 62 P = 0.009


Aphanizomenon sp(p). cell volume

0.33 +ve 29 P = 0.001


Aphanocapsa sp(p). cell volume

0.19 +ve 139 P = 0.000


Cyanodictyon sp(p). cell volume

0.41 +ve 35 P = 0.000


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



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



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).



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



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



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.



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



“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



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-



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



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



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



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



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,



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61

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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

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Bu Me

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r

Fo

rt C

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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

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m

wn

s

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on

on a

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in aa

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Name

-2

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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



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


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



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




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



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



Merbein

Curlwaa 0.5 4 56

Fo e rt Courag -0.7195 -0.8563 -0.8523

Lock 8

Mathoura

Moulamein

Kyalite 0.5466



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



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



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



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.

evaluation of a ysi fluorometer to determine cyanobacterial … · 2015. 3. 19. · evaluation of a...

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